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The Pretty Reckless - And So It Went [feat. Tom Morello] (Official Lyric Video)

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Jan 11, 2021

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The Pretty Reckless - And So It Went [feat. Tom Morello] (Official Lyric Video) From the upcoming album 'Death By Rock And Roll' | Available February 12, 2021: http://found.ee/dbrr Subscribe to The Pretty Reckless on YouTube: https://found.ee/tpr_subscribeyt Photo by: Rob Fenn Pre-order/Pre-Save the album 'Death By Rock And Roll': iTunes: http://found.ee/dbrr_it Apple Music: http://found.ee/dbrr_am Spotify: http://found.ee/dbrr_sp Amazon Music: http://found.ee/dbrr_amzm Amazon: http://found.ee/dbrr_amz YouTube Music: http://found.ee/dbrr_ytm Stay connected with The Pretty Reckless Website: https://deathbyrockandroll.com/ Facebook: https://found.ee/tpr_facebook Twitter: https://found.ee/tpr_twitter Instagram: https://found.ee/tpr_instagram LYRICS And so it went the children lost their minds Begging for forgiveness was such a waste of time And the bullets start to fly And the bough's about to break When you hear them cry It's too much for me to take The world does not belong to you You are not the king I am not the fool They said the world does not belong to you It don't belong to you It belongs to me And, so it went the children lost their minds Crawling over bodies of those who gave their lives And the fists begin to throw And the fire starts to blaze Don't you think they know They're the fucking human race The world does not belong to you You are not the king, I am not the fool They said the world does not belong to you It don't belong to you It belongs to Everyone is crying out, I can hear them scream With all these eyes upon us but no one seems to see That you and me are just the same as god meant it to be But you're much too close to me. You're much too close to me So it went The children lost their minds Nowhere to run, nowhere to hide And the wind begins to howl And the wolf is at your door You have so much of everything But still you wanted more They said the world does not belong to you You are not the king, I am not the fool They said the world does not belong to you It don't belong to you It belongs to me #ThePrettyReckless #DeathByRockAndRoll

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802 Comments

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Adam Dobrin

1 second ago

VERBATIM: "stop hurting people, stop hurting people. if you do not believe people should be hurt" ... ((ishing)) ~either disable the attackers or leave the area~

1

REPLY

Adam Dobrin

1 second ago

sometimes words have two meanings. sometimes "kill" means "excorcized the demons" @LOUDON @SALEM

REPLY

CANCELREPLY

Raziel Moreno

7 months ago

I'm so damn happy she chose to rock instead of staying as an actress.

426

REPLY

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sonya fuks

7 months ago

How is it possible they just keep on getting better and better with each album?!?!

341

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Graziano D'Ovidio

7 months ago

I love her voice. So much.

652

REPLY

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Natsumi Ikari

7 months ago

Most of people know Tom Morello from RATM of Prophets of Rage, but don't forget he was also the guitarist of Audioslave, whose singer was Chris Cornell. Must be emotional for Taylor to record this song him. I'm sure Chris is proud of them both.

631

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Matstrr Salazar

7 months ago

This is why TPR is one of my all time favorite bands. They release bad-ass singles and albums

322

REPLY

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Mike Whicker

7 months ago

The entire death by rock n roll album is first on my list of reasons why 2021 will not stink as much as last year did!

180

REPLY

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Mackenzie Dinkins

5 months ago

A good theme for this year's Elimination Chamber event.

25

REPLY

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FDCAnselmo

7 months ago

The Anthem of Youth... Fuck me what a tune.

159

REPLY

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norangutan

7 months ago

I love how obviously this song draws inspiration from Audioslave's sound, and in a way it's paying homage to chris cornell especially with the "you have so much of everything still you wanted more" line - which is almost the same as a line from Audioslave's 'What you are'

165

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Shiteyanyo

7 months ago

Tom Morello?! Each single they release for this album just keeps getting better and better

55

REPLY

View reply

Lau pin

7 months ago

I fell in love with every song they released

90

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🕸️Haley Munster🕷️

7 months ago

I really hope this album has more Going to Hell vibes 🥺🥺🥺🖤

201

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M K

7 months ago

Great tune and a good example for why I think TPR is among the best of the current rock bands. They always have catchy riffs and melodies however, especially in the bridge, you never know which turn the song is gonna take. Yet it always fits the song and never sounds out of place.

15

REPLY

Mike B

7 months ago

Underrated band

154

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🕸️Haley Munster🕷️

7 months ago

THIS is the Pretty Reckless I love !! 💙

45

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pedrogoularth

7 months ago

This is SO fucking good! 💖 🇧🇷

31

REPLY

Nick Brick

7 months ago

Kicking the year off with a banger. Looking forward to the album!

19

REPLY

Jo Mill Hyde

6 months ago

The fucking power in her voice and this song

49

REPLY

View reply

HT82 Smash

6 months ago

WWE main roster stepping up their game with these theme songs. Elimination Chamber 2021 everyone

17

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Hide

7 months ago

The addition of the children's choir killed and buried me. 😔🤘🏾

80

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Lynne Kelly is correct here that we need better delineations of the words we're using here.

To some of us, we're taking historical methods and expanding them into larger super sets based on our personal experiences. I've read enough of Kelly's work and her personal experiences on her website (and that of many others) that I better understand the shorthand she uses when she describes pieces.

Even in the literature throughout the middle ages and the Renaissance we see this same sort of picking and choosing of methods in descriptions of various texts. Some will choose to focus on one or two keys, which seemed to work for them, but they'd leave out the others which means that subsequent generations would miss out on the lost bits and pieces.

Having a larger superset of methods to choose from as well as encouraging further explorations is certainly desired.

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

Learn more at Review Commons

Reply to the reviewers

Reviewer #1

This manuscript by Gulyrutlu and co-workers addresses the role of CUG expanded repeat RNA associated with DM1 in regulating the formation of higher order RNP assemblies such as stress granules and P-bodies in the cell. The authors used lens epithelial cells (hLECs) derived from a DM1 patient

We used cell lines from several patients and age-matched controls to avoid effects of individual cell-line variation. We will make sure that this is clear in the text.

or a HeLa cell inducible model of DM1 to investigate whether expression of the CUG repeat-associated protein MBNL1 and CUGBP1 affected the formation and dispersal of stress granules and P-bodies. The authors show that MBNL1 and CUGBP1 are components of SGs and PBs in hLECs and HeLa cells. In cells expressing the CUG repeat, there are minor alterations in the dispersal of stress granules as well as in the formation of P-bodies.

The alterations in the formation and dispersal of stress granules are not minor. For example, in the HeLa cell model, stress granules take more than twice as long to form in cell expressing the CUGexp repeats associated with DM1 and disperse in half the time. These data are already in the results section, but we will highlight them in a revision and have included an additional representation of the data to the figure, using graphs of ‘proportion of cells with stress granules’ against time. The changes we see are as large, or larger, then results published elsewhere (see appendix below)

MBNL1 could affect the formation and dispersal of SGs independent of the CUG repeat.

In fact, we present data in HeLa cells with MBNL1 almost completely removed by shRNA revealing that this has a much smaller effect on stress granules than does the expression of CUGexp RNA. This is an important point, as it is widely assumed that most of the cellular defects in DM1 are caused by the ‘sequestration’ of MBNL1 in the CUGexp foci. Since only . This is not what our data show. In the hexanediol experiments, both cell lines over-express MBNL1 in similar amounts. The difference between them is that one cell line expresses a DMPK1 mini-gene with a CUG expansion and the other expresses a mini-gene without the expansion. Again, our results show that the alteration to P-body responses to 1,6-hexanediol can be attributed to the presence of the CUGexp RNA, rather than altered levels of MBNL1. We will revise the results and discussion to further emphasise this point.

Finally, in HeLa cells, overexpression of MBNL1 can reduce the dispersal of P-bodies upon 1,6-hexanediol treatment.

This is not what our data show. In the hexanediol experiments, both cell lines over-express MBNL1 in similar amounts. The difference between them is that one cell line expresses a DMPK1 mini-gene with a CUG expansion and the other expresses a mini-gene without the expansion. Again, our results show that the alteration to P-body responses to 1,6-hexanediol can be attributed to the presence of the CUGexp RNA, rather than altered levels of MBNL1. We will revise the results and discussion to further emphasise this point.

Major comments:

One limitation of the work is that the perturbations seen with stress granules or P-bodies are all relatively small, and no evidence for a functional consequence on gene expression is demonstrated. Specifically, the authors observe only minor alterations in the formation or disaggregation of PBs and SGs in these DM1 models. Further, some of the effects observed are independent of the CUG repeat expression, suggesting that MBNL1 and CUGBP1 might have independent roles in modulating some properties of SG and PB formation or dispersal.

As above, the changes we see in SG formation and dispersal are not small. There are already numerous studies of the effects of DM1 on gene expression and mRNA splicing. This is not what we set out to study: we are interested in perturbations to the organisation of cellular structures associated with the expression of the CUGexp repeat RNA characteristic of DM1. We do show some data relating specifically to the proteins MBNL1 and CUGBP1 in the paper. shRNA resulting in almost complete loss of these proteins has much smaller effects that the expression of CUGexp RNA, suggesting that the major part of the effects caused by expression of the CUGexp RNA is not mediated through changes in MBNL1 or CUGBP1 levels. MBNL1 and CUGBP1 levels may well contribute to alterations in SG dynamics, but our data suggest that they are minor contributors. This is an advance in our current knowledge

1. The authors could investigate whether the CUG repeat RNA itself is localized to SGs or PBs in their models, and whether the presence of the repeat RNA is absolutely necessary for regulating the dynamics of SG or PB formation.

We have now done this. The CUG repeat RNA is not localised in stress granules or PBs to a detectable extent. This suggests that the effect we see on these structures by expression of the expanded RNA occurs despite the absence of the RNA from the structures. This is similar to the effects of the ALS-associated paraspeckle protein FUS, which can affect the integrity of nuclear LLPS structures (gems) despite not co-localising with them https://doi.org/10.1016/j.celrep.2012.08.025 We have added these data to the manuscript, as part of draft figure 8, and will add text emphasising this as an additional example of disease-causing macromolecules affecting the structure of LLPS domains in which they are not found.

1. The authors use 1,6-hexanediol to suggests that PBs and SGs in HeLa cells show behavior analogous to LLPS. However, the use of 1,6,-hexanediol to establish an assembly as a LLPS is a relatively limited analysis (despite its widespread use in the field), since this compound can affect the formation of multiple cellular substructures that are not always LLPS (for example, see Wheeler et al, 2016, eLife).

We are aware of and have cited this publication. Our comments about LLPS structures are measured, as there is still controversy about how to definitively identify them in cells. SGs and PBs have, however, previously been widely published to be formed by LLPS. The rapid exchange of SG and PB components during FRAP and the ability of SGs to both fuse and bud (seen in our supplementary movies) are also supportive of these structures behaving as LLPS structures in our models. Wheeler et showed that, in yeast, the nuclear pore complex and some cytoskeletal structures were affected by 1,6-hexanediol but membrane-bound structures such as the ER and mitochondria were not. The disruption of the nuclear pore complex is not unexpected, since phase separation is involved in cargo shuttling through the NPC (reviewed in https://doi.org/10.1016/j.devcel.2020.06.033). We will revise our discussion to make it more clear that we are not relying only on the use of 1,6-hexanediol to define SGs and PBs as LLPS structures but also on other aspects of their dynamic behaviour and on extensive prior literature.

Significance

This study would be of interest to the field if the impact of the DM! repeat RNAs on PB and SG were more substantial...

As above, the effects we see on SG formation and loss are substantial. Tissue types affected in DM1 are prone to stress, particularly the lens of the eye, so alterations to cellular response to stress associated with the presence of CUG repeats are of key importance to understanding the cellular pathology of DM1.

...and if some functional consequences were demonstrated.

In terms of function, we show altered responses to stress caused by the expression of CUGexp RNA and probably mediated through alterations in the propensity of LLPS cytoplasmic structures (SGs and PBs) to form and be resolved. Additionally, we can now show that SGs in HeLa cells expressing CUGexp RNA contain less total polyA RNA than is seen in controls, and that ‘docking’ events between SGs and PBs are compromised in cells with CUGexp RNA. These docking events are proposed to mediate transfer of RNA from SGs to PBs (reviewed in https://doi.org/10.1007/978-1-4614-5107-5_12). These new data demonstrate functional impairment of SGs and PBs associated with DM1. We have included this as an additional draft figure 8.

The lack of a strong effect on SG or PB formation in the DM1 models, along with the CUG repeat-independent effect of MBNL1 on the formation and dispersal of these complexes, argues that MBNL1/CUGBP1 may not significantly affect the formation or dispersal of SGs and PBs.

We are actually not arguing that MBNL1 and CUGBP1 are the main effectors in the changes we see to SGs and PBs, but that the CUGexp RNA is the key player, so are a little confused by this comment.

Reviewer #2:

In the current study, the authors compared the dynamics of P-bodies (PBs) and stress granules (SGs) between control and several DM1 cell lines. They found that MBNL1 and CUGBP1, two CUG repeat RNA-binding proteins that are primarily nuclear, could also co-localize with PBs in the cytoplasm and re-localize to SGs under stress. Small differences were observed in SG assembly and disassembly dynamics between control and DM1 HLECs, between HeLa cells expressing either CTG12 or CTG960, and between HeLa cells with and without shRNAs targeting CUGBP1 or MBNL1.

As detailed above, the alterations in SG assembly and disassembly in cells expressing CUGexp RNA are not small, in contrast to those in cells will lowered expression of MBNL1 and CUGBP1, which are much smaller suggesting that the changes caused by CUGexp RNA largely do not result from loss of MBNL1 (or CUGBP1). We have inserted additional graphs of ‘proportion of cells with stress granules’ against time' and will modify the text to emphasise both of these points.

Overall, the experiments were clearly described and the results properly presented. However, critical controls, as detailed below, are missing in multiple analyses. The mechanisms underlying these apparent differences are also unknown.

We do not consider that any ‘critical controls’ are missing, but can supply all of the additional analysis of our data that the reviewer requests below. We can also now provide additional mechanistic insight and will add an additional figure showing lowered amount of polyA RNA in stress granules in cells expressing CUGexp RNA and compromised docking events between stress granules and P-bodies, suggesting impaired communication between them.

Major concerns:

1. Throughout the study, the authors compared MBNL1 and CUGBP1 association with PBs and SGs without considering the potential differences in their cytoplasmic abundance between control and DM1 cell lines, which seems to be case for MBNL1 abundance in CTG960-expressing HeLa cells (Fig. 3). Provided that PBs and SGs exchange components with the cytosol at an equilibrium, if the cytoplasmic abundance of, for example, MBNL1 is decreased in DM1, one would expect the equilibrium being shifted resulting in less MBNL1 associated with PB/SG. Therefore, before measuring the association or the assembly/disassembly kinetics of PB and SG, the authors should first test whether MBNL1 and CUGBP1 abundance may be different between control and DM cell lines.

There is, in fact, no difference in the relative cytoplasmic abundance of GFP-MBNL1 between CTG12 and CTG960- expressing HeLa cells. Each has approximately a 50/50 split between nucleus and cytoplasm, with <3% of nuclear GFP-MBNL1 found in nuclear CUGexp foci when they are present. We have added a graph demonstrating this to the supplementary data. The abundance of total endogenous MBNL1 is also not altered in DM1 patient-derived lens cell lines compared to controls, as shown by semi-quantitative western blot analysis, which we have also added to the supplementary data. However, if the expression of CUGexp RNA did cause a major loss of cytoplasmic MBNL1, this change would be reflective of the situation seen in DM1 and would not invalidate our results or conclusions.

The same caveat applies to MBNL1/CUGBP1 knockdown experiments, where knocking down one may change the abundance of the other.

To carry out FRAP experiments or live cell analysis of SG formation and loss, it is necessary to over-express a tagged version of the protein being studied. For the knockdown experiments shown in figure 6, therefore, when we knocked down MBNL1, CUGBP1 was present in excess as a GFP-tagged protein and when we knocked down CUGBP1, MBNL1 was present in excess as a GFP-tagged protein. Thus, any effects of the knockdowns on expression of the endogenous proteins being analysed would be highly unlikely to influence the results.

1. Similarly, the authors did not consider the possibility that changes in SG/PB dynamics may be due to changes in the abundance/availability of essential SG/PB components such as GE1 and G3BP1.

From our immunofluorescence experiments, there was certainly no obvious reduction in GE1 or TIA1 abundance (we did not assess G3BP1). We have quantitative proteomic analysis (unpublished) from a similar pair of cell lines, expressing CUGexp RNA alongside GFP rather than GFP-MBNL1. This shows no change in GE1 or G3BP1, so we would not expect to see any here either. We can easily carry out a quantitative western blot analysis to confirm and will add this to the supplementary data

1. Most of the observed differences between control and DM cell lines were modest, leaving one wonder whether it could be simply due to cell line-to-cell line variability. Whenever possible, the authors should present results for each individual lines. For example, in Fig.2, 3 DM1 lines and 2 control lines were used. Was the difference in SG disassembly (Fig. 2B) observed in each of the 3 lines?

Some of the alterations were modest and there is cell line-to-cell line variability in the lens cell lines. This is why we pooled the data: on average, DM1 cells disperse their SGs more quickly than control cell lines do on average. This is not an unusual way to present data from patient cell lines of diverse genetic background. We have added data for stress granule loss in the individual cell lines to the supplementary data. These data show a consistent trend towards quicker dispersal of stress granules in patient cell lines. The variability between the patient lens cell lines was also the primary reason for us to develop the inducible system in HeLa cells, on a fixed genetic background, as explained in the manuscript.

Minor points:

1. Western blot in Fig. 3 shows two protein products from both endogenous and overexpressed MBNL1. Please explain.

Many of the commercially available anti-MBNL1 antibodies show this double-band in some cell lines as evidenced in numerous publications and on manufacturers’ websites (for example https://abclonal.com/catalog-antibodies/MBNL1RabbitmAb/A5149, https://www.ptglab.com/products/MBNL1-Antibody-66837-1-Ig.htm). We haven’t analysed the two bands in detail, but assume this to be a result of a post translational modification of some sort. Since GFP-MBNL1 and endogenous MBNL1 show the same thing, we do not consider it to be a major concern. We do mention the double-band as ‘characteristic’ in the figure legend for figure 3 so are not seeking to conceal anything here.

1. No data were shown to substantiate the statement that "MBNL1 localises to CUGexp foci and CUGBP1 does not" (page 6).

This has been published many times and is shown in figure 1A. However, we will add in a citation for this and have added an additional supplementary figure showing the lack of co-localisation in the foci from figure 1A more clearly together with separate data confirming that MBNL1 and CUGexp RNA do not co-localise with CUGBP1 in the nuclei of line HeLa_CTG960_GFPMBNL1.

1. The y-axis of Fig. 4D should not go beyond 1.

We will trim the axis. There are no data points above 1.0, just the indicator of statistical significance

Significance:

The nature of the current study is highly descriptive with little mechanistic insights.

Our work is not descriptive, as we observed a change in stress granules in patient cells, which we could then replicate (and enhance) in a novel inducible model of DM1 designed to abrogate the unavoidable variation in patient-derived cell lines. We also now have additional mechanistic insights (see above) and have added an additional figure (draft figure 8) detailing these.

For the subtle differences observed between control and DM1 cell lines, it remains unclear whether it may be due to cell line-to-cell line variation (see above).

We cannot completely rule out an influence of cell line-to-cell line variation in the patient-derived lens cell lines (see above), though we think this unlikely as we saw the same effect repeated and amplified in the inducible HeLa-derived cell model, which was designed to minimise this concern. Furthermore, for stress granule loss, we see a larger effect in the HeLa cell model after 72hrs of induction than after 24hrs (figure 5C). This argues strongly that the effects seen are due to the expression of CUGexp RNA and we will emphasise this point more strongly.

Some difference appear to be specific to one model but not the others (e.g., SG formation is slower in HeLa-CTG960 cells but not in DM1 HLECs).

Even for the observations that seem consistent between models, the current results yielded little novel biological insights into whether and how these subtle differences in PB/SG dynamics may relate to DM1 pathogenesis. Collectively, these weaknesses render the current study incremental at best.

The key biological insight the results provide is that the presence of the CUGexp repeat RNA results in defects in LLPS structures that are largely separable from any sequestration of MBNL1 in nuclear foci. With many researchers attributing the cellular defects in DM1 simply to the loss of MBNL1 by sequestration into nuclear foci, both this separation of altered stress response from MBNL1 levels and the involvement of altered LLPS formation (evidenced by the changes in PB behaviour on 1,6-hexanediol treatment) are novel biological insights into the cellular pathology of DM1. Additionally, our results shift the emphasis from nuclear effects to those seen in the cytoplasm.

In terms of specific DM1 pathogenesis, the eye lens is subject to constant repeated stress and is subject to continued growth throughout the life span, relying on lens epithelial cells as a stem cell pool. Epithelial cells are also vital to the homeostatic regulation of ions, growth factor and nutrient flow from the aqueous humor to the underlying fibre cells. Any alterations in the response of lens epithelial cells, in particular, to stress is highly relevant to the pathology of cataract seen in DM1. We will revise our discussion to emphasise these key points more strongly.

Reviewer #3

The manuscript entiled "Phase-separated stress granules and processing bodies are compromised in Myotonic Dystrophy Type 1" by Gulyurtlu et al., characterizes the composition and ydnamics of stress granules and P-bodies in two Myotonic Dystrophy Type 1 (DM1) cell models, human lens epithelial cells from DM1 patients and age-matched controls and HeLa_CTG12_GFPMBNL1 and HeLa_CTG960GFPMBNL1 cell lines. The manuscript is somewhat descriptive with lack of functional data and some discrepancies. For example, in the discussion section, the authors conclude that "MBNL1 appears to be absent from P-bodies in cells with CUGexp foci in their nuclei. This observation suggests that the role of MBNL1 in P-bodies may be disrupted by the presence of CUGexp RNA." Figure 4A shows that "P-bodies in the DM1 model line, HeLa_CTG960GFPMBNL1 do not contain detectable amounts of GFPMBNL1". However, Figure 4E shows similar levels of total cellular MBNL1 per PB between the control CTG12 and mutated CTG960 lines.

There is no discrepancy here. The reviewer has misinterpreted our data. PBs in the HeLa CTG960 cell line do not contain detectable amounts of GFP-MBNL1 under normal growth conditions, as shown in figure 4A. The data shown in figure 4E concern arsenite-treated cells, where some PBs in the CTG960 line do contain detectable levels of GFP-MBNL1, but significantly less than in the control CTG12 cells. We will reword these sections to make sure this is clear.

Most importantly, in Figure S3 the authors show that CUGexp foci are present in 1-2 % of the cells. The claim appears to be too strong for the data presented in the manuscript.

This is not what is shown in figure S3. The reviewer has misinterpreted our data. Figure S3 shows that in cells from line CTG960, only 1-2% of the total nuclear GFP-MBNL1 signal is found in the CUGexp foci, despite the intensity of the signal within them. Virtually all of the cells from the CTG960 cell line contain CUGexp foci (>95%). We will add a statement to this effect into the results section. We would not have continued working with a cell line in which only 1-2% of cells showed the DM1 phenotype of nuclear CUGexp RNA foci.

Although the findings are interesting and of potential impact for a better understanding of the implications of RNA-protein condensate dynamics in the pathogenesis of DM1, the work presented here is still descriptive and preliminary in my opinion. In summary, the conclusions are not so convincing and additional experiments are essential to support the authors claims.

This reviewer seems to have misinterpreted several of our data sets, including the specific points above, leading to the assertion that our conclusions are not convincing.

Several months of works will be required to consolidate data and reorganize and ameliorate the manuscript, including the way data are presented and quantified.

We already have data with which to address the majority of the queries posed, so should be able to make the adjustments relatively quickly.

Specific comments:

"On removal of stress, clearance of stress granules is mediated largely by a form of autophagy." This statement is not correct since the majority of stress granules disassemble and are not targeted to autophagy; in healthy cells only 5 % (or less) of the total SGs tend to persist in presence of autophagy or lysosome inhibitors, while the vast majority disassembles. Please rephrase carefully.

The degree and manner of dispersal of stress granules in healthy cells on removal of stress is not well understood, but is known to differ depending on the type and duration of the stress (DOI: 10.1126/science.abj2400). We do not yet know how this may be altered in DM1, however, compromised autophagy is implicated in cataract formation, which is of relevance to our study. We will re-phrase this section of the discussion carefully to reflect the complex situation.

Figure 1: RNA-protein complexes have heterogeneous composition. In HLECs, do all PBs colocalize with MBNL1 and CUGBP1 or only a fraction of them?

We do not routinely see PBs without MBNL1 or CUGBP1 in the HLECs, in contrast to the situation in the HeLa CTG960 line. We have data available in order to quantify this and will add the numbers to the text of the results.

Figure 2: Stress granules and P-Bodies are known to touch each-other, a process referred to as a "kissing event". The authors have studied the mobility of GFP-MBNL1 inside these two types of assemblies. It would be important also to quantify the "kissing" events. Is this altered in DM1 cells?

We couldn’t find reference in the literature to ‘kissing events’ between SGs and PBs, but found several references to ‘docking’ events. We have noticed such interactions between PBs and SGs in our models. We are currently quantifying this and our first experiments (one in the HeLa cell model and one comparing one of the patient-derived lens cell lines to a control) suggest that there is a change in the frequency and/or size of such interactions in the HeLa CTG960 cell line compared to the CTG12 control and in the DM1 lens cell line derived to control. If this holds true in our repeat experiments (currently in progress), this would also provide the mechanistic insight requested by reviewer 2. We have included this, together with our data showing a decrease in total polyA RNA in stress granules in HeLa cells expressing CUGexp RNA, as an additional draft figure 8.

Figure 3: In HeLa cells overexpressing CTG960_GFPMBNL1, beside the accumulation of one bright CUGexp puncta, several intranuclear GFPMBNL1 protein foci are visible. This subcellular distribution is different from the one observed in the control HeLaCTG12 GFPMBNL1. Can the author describe what these intranuclear GFPMBNL1 protein foci are?

In most cells expressing CUGexp RNA, several nuclear foci form (usually one large and several smaller) and all of them contain MBNL1 (or GFP-MBNL1 in the HeLa_CTG960_GFPMBNL1 cell line). Figure S3 shows object identification using MBNL1 in this cell line, with two clear foci detected as the reviewer points out. We have added an additional panel to supplementary figure 1 to confirm that the additional foci are also CUGexp RNA foci and will clarify in the text of the results that there is not a single focus of CUGexp RNA in each nucleus.

Is GFPMBNL1 accumulating at the level of splicing speckles? Or paraspeckles? Or other types on intranuclear condensates such as e.g. PML nuclear bodies? The different intranuclear distribution of GFPMBNL1 should be better characterized.

The sub-nuclear distribution of MBNL1 is, indeed, very complex. MBNL1 also sometimes co-localises to splicing speckles/interchromatin granule clusters as we have previously reported in lens epithelial cell lines (DOI: 10.1042/BJ20130870 ) . The details of differences in the nuclear distribution of MBNL1, beyond its accumulation in CUGexp RNA foci, in DM1 cells compared to controls is the subject of another manuscript we have in preparation but are beyond the scope of the current study.

Moreover, the % of cells expressing CTG960_GFPMBNL1 and forming intranuclear CUGexp foci is only mentioned in the discussion (Figure S3); for clarity it should be reported in the main text when describing Figure 3.

The number of cells forming nuclear CUGexp foci on expression of CTG960_GFP-MBNL1 is >95% and we will add this to the text of the results section.

"Figure S2: Quantitation of GFPMBNL1 in P-bodies in HeLa cell model of DM1." The authors report in the legend "Some, but not all, of these P-bodies contain detectable amounts of GFPMBNL1". However, the figure only shows a representative image of cells without quantification. Quantitation should be provided.

We have data available to provide this simple quantitation. Approximately 38% of PBs in arsenite-treated cells from line HeLa_CTG960_GFPMBNL1 contain detectable levels of GFPMBNL1 using a manually-assigned cut-off intensity. We will add this to the relevant figure legend (now figure S5). However, this method of analysis requires an intensity to be manually set above which GFP-MBNL1 signal is considered ‘detectable’. This is hugely subjective, and in our opinion, the automatically generated quantitative comparison of “% total cellular MBNL1 per P-body” as shown in figure 4E is a more experimentally robust way to demonstrate a small loss of MBNL1 from P-bodies in cells from line HeLa_CTG960_GFPMBNL1 treated with arsenite compared to the relevant control.

The authors report "a subtle change in stress granule architecture associated with the presence of CUGexp RNA". This statement is not supported by experimental data and should be omitted.

We will qualify this statement to make it clear that we are referring to a subtle alteration in the co-localisation between CUGBP1 and MBNL1 specifically in the SGs, as our experimental data shown in figure 4D clearly support that, showing a statistically significant increase in the Pearson’s co-efficient of cololcalisation between MBNL1 and CUGBP1 in cell containing CUGexp RNA compared to the relevant control (0.90+/-0.05 for CTG960; 0.87+/-0.07 for CTG12).

Figure 4. MBNL1 and CUGBP1 co-localise in P-bodies. What is the % of colocalization?

We’re not sure exactly what is being requested here or what biological question the reviewer is asking us to address. MBNL1 and CUGBP1 co-localise in virtually all PBs (except in the HeLa CTG960 line where MBNL1 is undetectable in PBs under normal growth conditions). Figure 4E shows that, in cells with PBs upregulated by sodium arsenite, the mean amount of total cellular MBNL1 per PB is 0.1%, so it will be similar in cells grown under normal conditions as the PB sizes are similar and they appear to be of similar brightness by immunofluorescence. Again, this would be straightforward to quantify with our existing data if this is, indeed what the reviewer is requesting, but we question the biological significance. We would be reluctant to derive a Pearson’s co-efficient for the degree of co-localisation between CUGBP1 and MBNL1 in P-bodies as the structures are too small in size for this to be meaningful within the limits of imaging capabilities. We could, however, provide this if this is a specific request.

Figure 5: "Treatment with sodium arsenite was then carried out under time-lapse microscopy, with Z-stacks of images taken every 4 minutes until stress granule formation was clearly seen (Fig.5A). This revealed a pronounced delay in formation of stress granules in cells containing CUGexp foci (HeLa CTG960 GFPMBNL1, 36 min +/- 12) compared to those without (HeLa CTG12 GFPMBNL1, 15 min +/- 2) (Fig.5B)." Data representation in Figure 5 is unclear and the pronounced delay in stress granule formation is not appreciated. Since the authors performed a live imaging taking pictures every 4 minutes, it would me more informative to plot the data and show the assembly and disassembly kinetics over time for both control and CTG960_ GFPMBNL1 cell lines (similar to what shown in e.g. Gwon et al., Science 2021, Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner, Figure 2G).

The bar graph in figure 5B shows that cells from the CTG960 line take more than twice as long to form SGs compared to controls and are lost in half the time, with the precise numbers given in the text. A simple bar graph seemed the clearest way to present this. However, we have plotted our existing data in a similar manner similar to that in the cited reference and added this to figure 5. These graphs clearly show that the differences we see are at least as great as in other published literature, including the reference given by the reviewer (see below).

Concerning Figure 1, the authors report no difference in the kinetic of stress granule formation in HLECs. However, they only report data after 45 and 60 min of arsenite treatment; at these time-points the assembly step is maximal. Thus, for consistency, the authors should include earlier time-points to the analysis of stress granule assembly also in HLECs, similar to what done in HeLa cells in Figure 5.

The assembly step is not ‘maximal’ in these cell lines after 45 minutes. Figure 2A clearly shows that only ~30% of cells have SGs after 45 minutes of treatment, compared with 100% of cells after 90 minutes shown in figure 2B. We have additional data at 10, 20 and 30 minutes all showing no significant differences. We had omitted them to keep the graph simple, but have now included them as a graph of ‘% of cells with stress granules against time’ in figure 2.

"Having established that MBNL1 and CUGBP1 co-localise closely in stress granules": the authors investigated the colocalization of each of these two proteins with stress granule markers but they did not verify whether MBNL1 and CUGBP1 co-localise.

In figure 1B we show that endogenous CUGBP1 and endogenous MBNL1 both co-localise with the stress granule marker TIA1 in stress granules in lens epithelial cells. It would, therefore, be highly unlikely that CUGBP1 and MBNL1 would not co-localise with each other in stress granules. We have also previously verified that GFPMBNL1 behaves identically to its endogenous counterpart (Coleman et al, 2014). Furthermore, in figure 4C and 4D, we show close co-localisation between endogenous CUGBP1 and GFPMBNL1 in stress granules in our HeLa cell model, using high-resolution AiryScan microscopy for which we provide detailed quantitation.

This aspect should be addressed experimentally since the authors also conclude that "a complex relationship between MBNL1 and CUGBP1 in stress granules" exists. Thus, the authors need to assess the colocalization of GFPMBNL1 with endogenous CUGBP1 in stress granules and the one of GFPCUGBP1 with endogenous MBNL1.

The complex relationship we propose is based on the effects of CUGBP1 or MBNL1 knockdown on the dynamic behaviours of each other by FRAP assay and not solely on their co-localisation, although we have already analysed their co-localisation in detail as above.

Figure 6: Please add antibody labeling to microscopy panels A and B.

Certainly, this was an accidental omission and has been added

Moreover, specify is the numbers refer to minutes in panel F. The data representation is also unclear - see comment above, Figure 5.

As stated in the figure legend and on the graph axes, these numbers have been normalised to the mean time taken for SG formation/loss in the control CTG12 cell line (set at 100%). The precise numbers in minutes for mean and SD are given in the text. We have added additional graphs of ‘% of cells with stress granules against time’ to this figure, with the values in minutes given to clarify the exact time-scale.

Figure 7: was 1,6-hexanediol added in presence of arsenite? Or was arsenite removed?

Arsenite was not removed (neither was Doxycycline) as we wanted to examine the effect of 1,6-hexanediol on SGs and PBs without the added complication of the effects of stress removal. We will clarify this point in the methods/results.

Aberrant persistent stress granules have been implicated in age-related (Mateju et al., 2017) and neurodegenerative diseases (Protter and Parker, 2016), such as ALS and FTD (Jain et al., 2016; Markmiller et al., 2018; Zhang et al., 2018). These are proposed to result from increased liquid-to-solid phase transitions within the stress granules (Mateju et al., 2017)." The authors should better define what are aberrant stress granules (e.g. see Ganassi et al., 2016; Turakhiya et al., 2018, PMID: 29804830).*

We will expand on this subject in the discussion

"Persistent stress granules have long been associated with degenerative conditions, notably ALS (Li et al., 2013)". I suggest updating the reference adding a more recent one.

We selected this 2013 review to emphasise that there is a long history of association of persistent stress granules with degenerative conditions. We will add in an additional, more recent review.

Significance

The work is descriptive; thus, in this form I do not consider that it is strongly advancing the field.

Having noted alterations to stress granule disassembly in lens epithelial cells from DM1 patients, we went on to develop a novel inducible model in which we replicated and enhanced these effects by expressing the large CUGexp RNA that causes DM1 as part of a DMPK mini-gene mimicking the genetic mutation seen in DM1 patients. This is not purely descriptive. Furthermore, we are now in a position to add an additional figure showing two pieces of evidence for functional defects in stress granules associated with CUGexp RNA expression 1) reduced accumulation of total PolyA RNA in stress granules indicating compromised function and 2) compromised ‘docking’ events between stress granules and P-bodies, a process proposed to be integral to the function of both structures.

Author Response:

Reviewer #1:

The authors demonstrate deficits in perceptual tests related to fine-time perception in non-speech and speech sounds in a group of patients with stroke aphasia compared to a control group without a lesion. A subgroup of patients with deficits in spectrotemporal processing at a fine timescale have lesions mapped to the posterior STS, MTG and adjacent white matter. The area associated with deficits in spectrotemporal analysis with a fine timescale is then used as a seed for probabilistic fibre tractography based on diffusion MR. These results show connectivity of the functionally defined seed region with a number of areas including the cerebellum.

The work is carefully done and I think interesting in demonstrating the cerebellar connections of the functionally defined region associated with deficits in fine temporal analysis that might be a basis for event representation at this temporal level.

We appreciate the referee's evaluation and constructive feedback.

Reviewer #2:

Based on consideration of supportive evidence in the literature, the authors propose that a cerebellar-temporal lobe functional network plays a key role in auditory temporal processing. The precise parsing of temporal information is critical to understanding dynamic auditory processing and thus is an interesting area of study. Better understanding of how the cerebellum and temporal lobe may interact to achieve such parsing of the dynamic signal in a generative/predictive internal model is of clear interest to a broad readership. This idea is put to the test by first having individuals with lesions in the posterior portion of left temporal lobe perform speech perception and timing tasks and comparing performance with 12 healthy controls to establish the role of this region in tasks reliant on intact fine temporal processing. Typically, a lesion model will be helpful when a dissociation between structure and function can be demonstrated, and preferably this would be a double dissociation. Here, while lesions to auditory regions of the left temporal lobe are associated with impoverished performance on speech and temporal order tasks relative to a healthy control group, performance on comparably difficult auditory tasks that do not require good temporal discrimination is not tested to determine if there is such a dissociation. Given the extensive discussion of hypothesized different time sensitivities of right and left auditory cortices in the Introduction, patients with right homologous lesions might also have served as an interesting control and could have supported a double dissociation. In a second step to their study, a seed region was generated based on comparison of the lesion loci for the half of the patients who performed most poorly on the behavioral tasks to the other half, and this was used to explore anatomical tract connectivity of the seed region to the rest of the brain in the neuroimaging data from the healthy controls, with a focus on connections with the cerebellum. This approach to establishing that "temporo-cerebellar connectivity underlies timing constraints in audition" is unfortunately just not that convincing. The data are interesting, but taken alone they simply do not support such a conclusion. In the data, there is no clear functional link established or even hinted at between the temporal lobe and the cerebellum.

We appreciate the referee's evaluation and constructive feedback. We address the raised concerns point by point below. We appreciate the concerns regarding our methodological choice and our interpretation of a functional link between the temporal lobe and the cerebellum. It certainly is more reasonable to derive a functional interpretation based on disconnection measured directly in patients’ DTI. However, if unavailable, indirect measures of disconnection can also be used to establish a functional link between a lesioned region and the networks associated with it. The rationale behind this is that it reflects an indirect estimation of the effect of a lesion on structural brain networks. To make this approach clearer, we have revised the manuscript accordingly. See revised manuscript pages 6 and 12:

[...] Assessing connectivity in healthy participants based on lesion information is a relatively new method that measures structural disconnection in networks associated with given anatomical regions (Foulon et al., 2018). This allows for the indirect estimation of the lesion effect on structural brain networks. In this regard, it was shown that behavioral deficits can be explained similarly by local brain damage and indirectly measured disconnection (Salvalaggio et al., 2020). [...]

[...] We next used the respective areas as seed regions for probabilistic fiber tractography in a healthy age-matched sample to visualize the underlying common connectivity pattern (see Methods). Thus, we indirectly explored the association between posterior superior temporal disconnection and processing of sound at short timescales. [...]

We also changed the abstract and conclusion accordingly. See pages 2 and 15 of the revised manuscript.

[...] Here we tested whether temporo-cerebellar disconnection is associated with the processing of sound at short timescales. [...]

[...] The evidence we describe (i) shows that lesion-related deficits in spectrotemporal analysis occur in posterior temporal regions connected to the cerebellum [...].

Reviewer #3:

Stockert et al. investigate the cortico-cerebellar network underpinning rapid temporal auditory analysis. This study uses a well-defined group of stroke participants with mostly circumscribed lesions to the left posterior superior temporal lobe to motivate probabilistic tractography from cortical regions associated with verbal and non-verbal rapid auditory temporal analysis. Lesion-symptom mapping identifies a specific region of the posterior superior temporal sulcus and underlying white matter as statistically associated with impairment in rapid auditory temporal analysis. Tractography results demonstrate that these regions have high structural connectivity to wider regions of the left hemisphere cortical language network and ipsilateral and contralateral connectivity to postero-lateral cerebellum and dentate nucleus. It is interpreted that this cortico-cerebellar network is crucial to developing representations of fine auditory temporal structure.

The conclusions of the paper are an interpretation which is based on integrating previous neuropsychology with the current tractography results and based on well-defined models in the motor domain. Such conclusions are not unreasonable but there is no direct (associative) evidence linking this network to the cognitive function of interest.

Strengths:

The paper integrates neuropsychology and neuroimaging methodologies to build a coherent picture which is more than the sum of its parts. The stroke group has well-defined and selected lesions which enable testing of the hypotheses put forward by the authors. The behavioural measures are sensitive and suitable to identify impairments in the behaviours of interest. There has been a detailed analysis of the behavioural speech perception data in the stroke group which largely, although perhaps not entirely, conforms to the asymmetric temporal sampling hypothesis. The lesion-symptom mapping approach is suitable for the nature of the population (small group with similar lesion distributions) and has allowed neuropsychologically guided tractography in the neurotypical population. This has clearly illustrated the complexity of the structural connectivity of the posterior superior temporal sulcus and underlying regions.

Weaknesses:

The selective nature of the stroke population - relatively small, chronic lesions - has resulted in only mild impairments for a small number of participants (6/12 participants). At the group level there is no difference between the stroke and neurotypical population on speech perception measures - group statistics do not reach one tailed significance. This reduces the certainty with which the regions identified are associated with the behaviour or interest. However, the results do conform to previous neuropsychology and lesion studies and it is likely that this lack of effect is due to low statistical power.

Please refer to our response to the next point.

All the stroke participants have a similar lesion distribution, and this makes lesion-symptom mapping challenging. For example, lesion data do not give an indication of the functional integrity of perilesional regions which can be reduced, even at the chronic stage, therefore the superior temporal sulcus may not be functioning effectively, even in the proportion of the group without lesions to this area. Lesion symptom mapping is more robust with a wider distribution of lesions and the inclusion of participants with lesions remote from the area of interest. Having said that, the behavioural measures appear sensitive enough to identify mild impairments and the authors, for good reason, wished to reduce the extension of lesion into primary auditory regions. As above, given the limited sample and homogeneous lesion, the lesion symptom mapping approach is reasonable.

We agree that the small number of patients is a possible limitation to the study and add this point to the limitations section. See revised manuscript page 21.

[...] First, the study population is relatively small and lesion symptom mapping is typically applied to larger populations with wider lesion distribution. Although careful selection of circumscribed lesions has the advantage of highlighting behavioral differences without confounding other deficits (e.g., primary auditory processing), it is possible that additional regions are involved in processing of sound at short timescales. However, tractography based on healthy participants makes it possible to indirectly obtain information (i.e., structural disconnection) about brain regions contributing to the investigated function. In addition, it is likely that the small number of patients might hamper the ability to detect statistically significant differences between the behavior of controls and patients. Nevertheless, we are confident that the current results align with the fact that the posterior superior temporal cortex contributes to the processing of sound at short timescales, as indicated by previous neuropsychological evidence and lesion studies (Boemio et al., 2005; Chedru, Bastard, and Efron, 1978; Efron, 1963; Robson, Grube, Lambon Ralph, Griffiths, & Sage, 2013; Swisher & Hirsh, 1972). Further studies should however test larger populations to replicate and extend this finding. [...]

The authors suggest that the behavioural results conform to the asymmetric temporal sampling hypothesis in that only place of articulation discrimination impairments in the stroke group can be (just about) detected, whereas there were no significant stroke-neurotypical differences in other phonetic contrasts. It is not clear that the VOT differences associated with plosive voicing changes and the cues associated with place changes happen over fundamentally different time-scales and, therefore, it is important to further justify the interpretation of the data. In the future it will be helpful to have this level of analysis applied to individuals with lesions to the wider speech perception network to draw conclusions about the specificity of the impairment to these regions - for example, impairments in phoneme discrimination have been associated with frontal lobe lesions.

It appears that voicing contrasts in which shorter and longer voice onset times result in the perception of a voiced or voiceless plosive (for example [t] and [d]) are encoded in both the temporal envelope and fine structure (Rosen 1992) of the speech signal that occur in time windows of 20-500 ms and <2 ms, respectively. In words an additional cue is the closure time, which can be further used to discriminate between voiced and voiceless plosives. However, place of articulation contrasts are exclusively encoded in the temporal fine structure (i.e., very quick transitions of the frequency spectrum, formant transitions). Even though for all contrasts shorter timescale information plays a role, somewhat redundant encoding is present for voice contrasts. Ultimately, place of articulation contrasts seem to be the most difficult to discriminate. In Figure 2D it is apparent that despite highest error rates for the place of articulation contrasts, several patients also showed impaired discrimination for voicing contrast when compared to healthy controls. We do agree with the referee that it would be interesting to also extend this level of analysis to individuals with lesions in the wider speech perception network in future work.

The tractography results reveal a complex pattern of structural connectivity, including other regions associated with speech perception. The authors have a theoretical motivation to focus on the importance of the temporo-cerebellar pathway but there is no correlation evidence to link auditory temporal analysis to the integrity of this pathway in the neurotypical population. The non-verbal measures appear to be sufficiently sensitive for this type of analysis. This lack of association with behaviour makes it hard to draw conclusions about the functional role of this network.

We appreciate the referee’s concerns about our interpretation of the functional link between the temporal lobe and the cerebellum regarding auditory temporal analysis. It certainly is more reasonable to derive a functional interpretation based on disconnection measured directly in patients DTI. However, if unavailable, indirect measures of disconnection can also be used to establish a functional link between a lesioned region and the networks associated with it. The rationale behind this is that it reflects an indirect estimation of the effect of a lesion on structural brain networks. To make this approach clearer, we have modified the manuscript as such. See revised manuscript pages 6 and 12:

[...] Assessing connectivity in healthy participants based on lesion information is a relatively new method that measures structural disconnection in networks associated with given anatomical regions (Foulon et al., 2018). This allows for the indirect estimation of the effect of a lesion on structural brain networks. In this regard, it has been shown that behavioral deficits are explained to a similar extent by both the local damage and indirectly measured disconnection (Salvalaggio et al., 2020). [...]

[...] We next used the respective areas as seed regions for probabilistic fiber tractography in a healthy age-matched sample to visualize the underlying common connectivity pattern (see Methods). Thus, we indirectly explored the association between posterior superior temporal disconnection and processing of sound at short timescales. [...]

We also changed the abstract and conclusion accordingly. See revised manuscript pages 2 and 15.

[...] Here we tested whether temporo-cerebellar disconnection is associated with processing of sound at short timescale. [...]

[...] The evidence we describe (i) shows that lesion-related deficits in spectrotemporal analysis occur in posterior temporal regions connected to the cerebellum [...].

I understand that Disney may not have openly queer characters and I understand people want more representation. However, I think it is weird to compare the sisters to anything "couple" related since they are sisters and that has nothing to do with being Queer. So I don't exactly understand how someone could take that into an account and have a theory that Frozen is a queer movie.

Author Response:

Reviewer #1:

The manuscript by Takahashi et al describes the interaction between MLL fusion proteins with HBO1 and its role in leukemogenesis. Myeloid progenitor transformation assays using various MLL fusion proteins reveal that MLL fusion proteins requires the TRX2 domain of MLL for effective leukemic transformation. IP-MS identifies HBO1 as a bona fide binding partner of the MLL TRX2 domain. ChIP-seq experiments show genome-wide colocalization of HBO1 complex with MLL-ENL and the WT MLL in MLL-fusion leukemia cells and MLL WT cells, respectively. ChIP-qPCR in MLL-deficient cells suggest that recruitment of HBO1 to MLL target genes (such as MYC and CDKN2C) depends on MLL. Truncation analysis of the ELL part of the MLL-ELL fusion reveal that MLL-ELL transformation activity requires OHD domain-mediated recruitment of AF4 and EAF1. Furthermore, co-IP and ChIP experiments with various fragments show that AF4 and EAF1 form two distinct SL1/MED26-containing complexes and likely the AEP/SL1/MED26 complex is competent for transactivation. Series of transformation assays suggest that MLL-ELL transforms hematopoietic progenitors via association with AEP, but not other ELL-associated proteins. Finally, the authors also show that NUP98-HBO1 fusion transforms myeloid progenitors through interaction with MLL. Overall, this is a quite comprehensive study demonstrating that various MLL fusions and NUP98 fusions transform hematopoietic progenitors via HBO1-MLL interaction, which suggests that targeting their interaction might be s new therapeutic approach.

We appreciate the comments and inputs from the reviewers.

Reviewer #2:

In this manuscript, the authors identified an interesting interaction of MLL (a methyltransferase) with an HBO1-JADE complex (an acetyltransferase) and investigated the functional impact in leukemogenesis by fusion proteins containing MLL or HBO1. The data is clear and the connection between MLL and HBO1 is unexpected. The manuscript is also well organized and relatively easy to follow.

Comments:

1) The functional relevance of the interaction between MLL and HBO1 is still correlative. It would be important to know whether there are any results directly about the impact of the loss of the HBO1 complex on the function of MLL.

We performed a sgRNA-dropout assay which showed that HBO1 is critically required for the survival of leukemia cell lines, as depicted in Figure 2F and Figure 2-figure supplement 3.

2) It is important to show the source and specificity of the antibodies that were used for ChIP of the HBO1 complex.

The details of the antibodies are provided in Key Resource Table.

3) It might be interesting to check whether other JADE proteins and also BRD1 (another partner of HBO1) are involved.

We agree that it would be very interesting to examine the involvement of other JADE/BRPF family proteins in the future because they share the ING4/5 subunits and BRD1 plays an important role in hematopoiesis (1). This can be addressed in future studies.

4) The acronym TRX2 may be confusing as some might think that it is thioredoxin.

As advised, we have changed this to THD2 (TRX homology domain 2).

Reviewer #3:

This paper starts with a series of bone marrow transformation assays comparing MLL fusions and domain-deletion mutants thereof to define the minimal elements for robust leukemic transformation and surveying growth and attendant common fusion targets HoxA9, Meis1 in colony replanting assays. Here they discover that a region of the MLL-N portion just upstream of the well-studied CXXC domain, termed in their previous work the "TRX2 domain" is important for the transformation capacity for several different MLL-fusions (and more minimal chimeras of key modules). A small region of the MLL-N protein encompassing the TRX2 domain and the CXXC module are subjected to complex purification, it is clear from comparison to number of controls that the TRX2 domain is an important mediator of association, perhaps indirect, with the HBO complex. Drop out experiments confirm that HBO1 knockout is lethal to MLL-rearranged leukemia, nicely confirming recent work (Ay et al., MacPherson et al.).

ChIP-seq experiments in an ALL with MLL-ENL fusion, and then more extensively in a kidney cancer cell line indicate overlap with some of the HBO complex subunits and MLL, however this does not establish recruitment at these sites. ChIP-qPCR at a few MLL-fusion target genes with MLL depletion supports the recruitment hypothesis somewhat although mixed and modest effect sizes indicate that alternate pathways for HBO1 recruitment are involved, and could also be explained as reduced deposition of marks known to recruit HBO1, rather than direct recruitment. Sadly, the real potential strength of this work goes unrealized, as the recruitment of HBO1 mechanism remains tantalizingly out of reach. More experiments in this space could conclusively establish the molecular mechanism of a seemingly biomedically important recruitment paradigm, and thereby have much more impact.

As the reviewer pointed out, MLL is not the only element that recruits the HBO1 complex to the target chromatin. MLL is known to deposit H3K4me marks, and the HBO1 complex is known to recognize these marks via ING4/5 subunits. We performed a ChIP-qPCR analysis of H3K4me3 in MLL-knockout cells. At the MYC promoter, the H3K4me3 marks were substantially decreased (Figure 3F). Moreover, recruitment of HBO1 was not recovered by transient expression of an MLL mutant containing THD2, indicating that the presence of H3K4me3 marks is a prerequisite for HBO1 recruitment. In accordance with this, ING5-histone interaction is required for the stable association of MLL with the HBO1 complex (Figure 8A-C). Thus, a more appropriate molecular mechanism would be the cooperative recruitment of the HBO1 complex by ING4/5-mediated chromatin association and MLL-mediated association. Because of the multiple contacts involved in this molecular network, it is not easy to pinpoint the direct contacts as desired, but our biochemical analyses indicate that PHF16 and ING4/5 offer relatively strong binding surfaces (Figure 8A-C). The ING domain of ING5 is the most likely direct binding surface identified thus far.

At this point the paper shifts to a seemingly distinct line of inquiry, which is not closely related to the HBO1-TRX2 story to the first three figures. The new direction examines the ELL fusion partner in some detail using similar fusion protein chimeras, but a portion of Figure 4, is largely confirmatory of previously established findings about the critical regions of ELL for transformation and its AF4/EAF1 partners, adding only that portions of the MLL fusion protein are dispensable, provided that they are replaced with the PWWP of LEDGF. It is a little bit of a Frankenstein's monster experiment, and does not add much new to the field. Further experiments define potentially two distinct complexes that have already been characterized being recruited by ELL, although there is overlap here again with their previous studies, and the results are a little hard to interpret.

A portion of Figure 4 was confirmatory to previous results. We have moved this to figure supplements in the revised manuscript (Figure 4-figure supplement 1B,C). The main topic of this paper is the role of the HBO1 complex in MLL-mediated transactivation pathways. The structure/function analysis of MLL fusion proteins demonstrated that MLL-ELL is highly dependent on the HBO1-mediated function in leukemic transformation (Figures 1 and 2). Hence, it was important to clarify the mechanism of gene activation by MLL-ELL in this study to understand why HBO1 association is required for MLL-ELL-mediated transformation. Because MLL-ELL associates with AEP similarly to major MLL fusions such as MLL-AF4 and MLL-ENL, it was speculated that MLL-ELL also activates its target genes via AEP. However, ELL associates with EAF family proteins and MLL-EAF also has transforming ability (3). Thus, EAF1-mediated functions could be more important for MLL-ELL-mediated transformation rather than AEP-mediated functions. To clarify the mechanism of MLL-ELL-mediated transformation, we generated a point mutant that selectively impaired ELL-EAF interaction and demonstrated that EAF1-association is dispensable for MLL-ELL-mediated transformation (Figure 6), thereby indicating that MLL-ELL transforms via AEP-mediated functions, which demands HBO1-mediated functions. We also showed that the presence of THD2 enhances ELL-AEP association to further suggest that one of the roles of the HBO1 complex is to enhance the association of ELL with AEP (Figure 6E). These findings are not reinterpretations of our prior results and are relevant to the main topic of this paper. We believe this part adds new information to the field, and therefore we have included it in the revised manuscript.

The authors create structure-guided separation of function mutants in the ELL domain that binds both AEP and SL1, permitting them to specifically disrupt EAF1 interactions but not AF4. Further experiments solidify this interpretation, and find that this mutant shows no deficits in hematopoietic progenitor transformation or primary leukemia lethality, although there appears to be some effect upon reimplantation.

The last figure in the paper tackles the seemingly unrelated Nup98-HBO1 fusion, a rare patient mutation-they demonstrate a requirement for MLL for viability of hematopoietic progenitors transformed by this fusion, connecting back to the TRX2 interaction, and show that menin inhibitors slow growth.

Strengths:

The identification of the TRX2 region of the MLL-N protein as the major point of contact (perhaps not direct), to the HBO1 complex adds mechanistic depth to the really important recent discovery (confirmed in this work) the MLL-fusion leukemias rely on HBO1 function. This lab has published a number of technically similar types of papers defining minimal regions of MLL and distinct interacting partners by chimeric fusions, with bone marrow transformation assays, mouse model engrafting studies, IP's, ChIP etc. In my view they are very much under cited, likely because they are similarly so challenging to read.

Thank you for your pointed feedback. We will try our best to make the necessary improvements so that our papers are widely read and cited.

The mixture of Co-IP biochemistry, bone marrow transformation assays, and ChIP, to define interactions, minimal requirements for transformation, and their chromatin consequences for a host of different MLL-fusions and HBO1-fusions has the potential to define the key interfaces underlying recruitment.

Weaknesses:

The mechanistic inquiry stops short of really defining the critical MLL-HBO1 complex interface. Defining the point of contact on the HBO1 side (even which subunit) and determining whether it is direct, or bridged by some, as yet unidentified factor, as well as conclusively demonstrating that this is the mechanism of HBO1 recruitment remain the major shortcomings.

To address this criticism, we further investigated the mechanism of complex formation by MLL and the HBO1 complex. As we demonstrated in Figure 8A-C, the association appears to be mediated by multiple contacts mainly through PHF16 and ING4/5. Because this association needs an intact PHD finger of ING5, it likely occurs depending on the context where ING4/5 is bound to histone H3K4me2/3. The ING domain of ING5 was also required for the association, indicating that this portion may contains a point of direct contact. We speculate that HBO1 recruitment is mediated primarily by ING4/5-H3K4me3 interaction and MLL reinforces its chromatin association.

And the follow-on figures apart from the last one, appear disconnected from this portion of the story and distract from it.

We depicted a revised model incorporating the above-mentioned aspect in Figure 8D of the revised manuscript.

The complex nomenclature and density/organization/logic of the presentation of experiments makes this paper difficult to read. Absence of sufficient grounding in the broader literature much beyond their own lab's work further compounds the problem.

We changed some of the nomenclature and density/organization/logic of the presentation of the experiments to improve the readability.

There is a lot of overlap, particularly in parts of figure 1 and figure 4 with previously published results. So perhaps re-organizing the display of data, and the organization of presentation, putting confirmatory work in the supplementary figures, would improve accessibility.

We moved some portions of Figure 4 to figure supplement. The data for MLL-AF10 and MLL-ENL were retained in the Figure 1 as important references.

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Reply to the reviewers

Reviewer #1:

This paper puts together a nice set of data showing that a specific gene called Resf1 when deleted effects the ability of ESCs to self-renew and proceed to germline fates. I believe the data are sound and that they provide the evidence needed for the authors to make their conclusions.While I think the data are presented well and the manuscript is well-written, the "modest" functional results suggest this work would be more suited for a specialized journal.

We thank Reviewer 1 for their supportive comments.

• *

Reviewer #2:

1. In the presence of LIF, there is no difference between Resf1 knockout mESCs and WT mESCs except the expression of Esrrb, Nanog and Pou5f1. What about other genes? RNA-seq is needed to distinguish the two cell lines.

Fukuda et al. have shown that deletion of Resf1 leads to misregulation of ~1000 genes (adj. p-value 2) in presence of LIF. This highlights large differences between transcriptomes of Resf1 KO and WT cells that occur despite only a marginal difference in self-renewal efficiency between Resf1 KO cells and WT in the presence of LIF. It is therefore questionable whether the time and resources required to perform the requested RNA-seq would produce data that could unambiguously identify the potential causative effector difference downstream of Resf1.

As an alternative approach, we have reanalysed the Fukuda et al RNA-seq data. We find that Esrrb is significantly downregulated (in agreement with our Q-RT-PCRs), as are Klf4 and LifR (FDR 1.5). However, our meta-analysis of the Fukuda et al data did not show Pou5f1 and Nanog to be differentially expressed (FDR 1.5). This is in line with the lower level of downregulation of Pou5f1 and Nanog, compared to Esrrb in our Q-RT-PCR data. Notably, our gene expression analyses were performed in 5 biological replicates, whereas Fukuda et al. performed RNA-seq in two biological replicates. We can include the meta-analysis of the Fukuda et al data in our submission. As the change in ESC self-renewal that we see at low LIF concentrations could result from a decrease in Lifr expression, we will verify the change in expression of Lifr by Q-RT-PCR. Importantly, we will do this in a way that discriminates between expression of the transmembrane Lifr and soluble LifR, since the latter acts antagonistically (PMID: 9396734, Chambers, BJ, 1997).

1. The authors showed Resf1 is not required for Nanog function, so how does Resf1 regulate the expression of pluripotency genes? Through epigenetic modifications or signaling pathways? The authors should design experiments to explain the detailed mechanisms.

The strength of the immunoblot signal for RESF1 is low, even when Resf1 is expressed episomally. Therefore, although we could try to co-immunoprecipitate with the Resf1-v5 cell line and endogenous Nanog, the expression level of RESF1 may mean this effort is unsuccessful. Given the fact that the result will not affect the conclusions of our study, we do not think this effort is justifiable.

1. The authors showed that Resf1 interacts with Nanog, but they used forced expressed proteins. Does the endogenous Resf1 interacts with endogenous Nanog? Do they bind to some same DNA sequences?

This are important questions to answer. However, many more experiments would be required to reach firm conclusions. The reviewer is right to say that the mechanisms by which Resf1 affects pluripotency are unknown and remain to be answered in future. We therefore propose to improve the text discussing similarities in pluripotency phenotype between deletions of Trim28, SETDB1, YTHDC1 and RESF1. As deletion of RESF1 partner SETDB1 or other proteins involved in repression of retrotransposons lead to downregulation of pluripotency genes and in some cases collapse of ESCs (e.g. PMID: 19884255, Bilodeau et al. 2009; PMID: 19884257, Yuan et al. 2009), we hypothesise that the RESF1 phenotype may be explained by affecting SETDB1 chromatin binding and therefore repression of SETDB1 targets. The mild phenotype of RESF1 KO indicates that RESF1 would not be an essential component of this repressor complex but rather “a modulatory protein”.

It is also worth noting that the meta-analysis of the RNA-seq data from Fukuda et al. suggests that Resf1-null ESCs may express reduced levels of LifR mRNA, and this is something we plan to investigate.

1. In figure 5C, some Resf1 positive cells showed Nanog negative. Are these Nanog negative cells pluripotent?

Nanog-null ESCs are pluripotent (PMID: 18097409, Chambers et al., 2007). In addition, NANOG-negative cells in FCS/LIF cultures can retain pluripotency. Our purpose in this figure was therefore not to say whether NANOG-negative:RESF1-positive cells are pluripotent but to draw attention to the broader expression of RESF1 in FCS/LIF compared to NANOG. Such broader expression has also been noted for other heterogeneously expressed factors (PMID: 31582397, Pantier et al. 2019).

1. In figure 6A, the naïve mESCs are induced to EpiLCs. Is the transition efficiency of Resf1 knockout cells the same with WT mESCs? The finally obtained PGCLCs should be identified.

We show that the key TFs of EpiLC state are expressed similarly in WT and Resf1 KO cells (Supplementary figure 4) and we have data showing that WT and Resf1KO EpiLCs have a similar morphology. Together this suggests an efficient transition to an EpiLC state. Our analysis has identified expression of Blimp1/Ap2g/Prdm14 in Resf1-null cultures. Compared to wild-type cells these levels are reduced up to 3-fold. As this is from an unsorted population and the number of SSEA1/CD61-positive cells is decreased around 2x, this suggests that the PGCLC population formed by Resf1-null cells is reduced in proportion but is otherwise normal.

We will add photographs of EpiLC colonies formed by Resf1 KO and WT cells.

1. in figure 5c, the scale bar is missing.

We will add missing scale bars in the figure 5C.

Reviewer #3:

1. What was less clear was an explanation of why colonies 4 and 24 were chosen. Were there other colonies with the desired expression? Was this amount of expression repeated in replicative experiments with approximately 2 colonies only available to be selected?

Approximately 30 colonies were selected for analysis. Of these, only 2 had deletion of both Resf1 alleles. We will make this point clearer in the text.

1. Figure 1C, 5C and S2B with microscopic images should include a scale bar.

Missing scalebars in the Figure 1C will be added. Unfortunately the microscopy setup used to collect the images in Figures 5C and S2B did not allow scalebars to be added at the time of imaging and these cannot be added retrospectively. However, we do not think that inclusion of scalebars, even were it possible would affect the conclusions of our manuscript.

1. Figure 1E needs a better explanation of the significance, "less clear cut" is not adequate. Reporting statistics, or lack of significance, on the graph would help.

We will update the manuscript and the Figure 1E to include results of a statistical analysis (Wilcoxon-rank sum test) comparing formation of AP+ colonies between Resf1 KO and WT cells at different LIF concentrations. These results show that both Resf1 KO cell lines have lower median number of AP+ colonies than WT cells at LIF concentrations 0 and 1 (p.adj. *

1. It's translatability to medicine, although perhaps that is not the intention, is somewhat lacking. Is there a naturally occurring situation where LIF is absent that would require this pathway to be used? These were mouse ESC's, perhaps this study could incorporate information about relevant translation to a human condition to aid in the significance. This manuscript suggests a mechanistic evaluation by which self-renewal can occur other than the canonical pathway, which is interesting and can inform the field.

Our results suggest that RESF1 directly or indirectly supports self-renewal of ESCs. Interestingly, Human cell atlas identified RESF1 expression as a negative predictor of survival of renal cancer and was found to be expressed in testis cancer cells and other cancer tissues. Therefore, RESF1 could promote self-renewal of cancer cells similarly to ESCs. However, this is speculative and needs further studies. As this is both outside of the scope of this manuscript and our expertise, we do not think it prudent for us to pursue this line of inquiry. However, we agree that further studies could evaluate RESF1 function in human tissues, especially pluripotent cells and germ cells. As we show that RESF1 deletion leads to reduced induction of PGCLCs and previous studies showed infertility of Resf1 KO mice, investigating link between human fertility and RESF1 could have implications in reproductive medicine.

We will improve our discussion to highlight the possible significance of RESF1 function in human fertility.

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Author Response:

Reviewer #1 Public Review:

Nakayama and colleagues report a unique screening concept utilizing conserved mechanisms between zebrafish gastrulation and cancer metastasis for identification of potential anti-metastatic drugs. They screen 1280 FDA-approved drugs using the gastrulation as a marker, and identify Pizotifen as an epiboly interrupting drug. Then they find that pharmacologic and genetic inhibition of HTR2C, a target of Pizotifen, suppresses metastatic progression in a zebrafish and mouse model through inhibition of epithelial to mesenchymal transition (EMT) via Wnt-signaling.

Their work is of interest and has the potential to appeal to a broad audience. However, additional experiments are needed to further substantiate their concept that human cancer metastasis mimic/recapitulate zebrafish gastrulation in terms of conserved mechanism, as well as to confirm the validity of their screening method regarding to the effects of global toxicity.

Major concerns:

The first major concern I have is the appropriateness to think the gastrulation as a parameter/index of cancer metastasis. While they cherry-picked some genes that they are known to be involved in both gastrulation and cancer metastasis, more broad analysis should probably be necessary to conclude so. For examples, the authors can analyze comprehensive RNA-seq data set to see if the pathways/networks are similar between gastrulation (zebrafish embryo development data set) and cancer metastasis (benign/primary tumors vs metastasis tumors in TCGA).

The conservation of embryonic EMT and tumor metastasis EMT has long been well recognized. Now we cited some of these published references (Nieto et al., 2016; Thiery et al., 2009; Yang and Weinberg, 2008). In Table 1, we compiled 50 genes based on published literature to provide further and strong evidence to support this conservation. Knockdown of these genes in Xenopus or zebrafish induced gastrulation defects; conversely, overexpression of these genes conferred metastatic potential on cancer cells and knockdown of these genes suppressed metastasis. Although this point is not really an objective of this study, we believe that the evidence for the conservation is sufficiently convincing to provide the basis for our study. Further RNA-seq comparison of zebrafish embryonic EMT and human tumor metastasis should be beyond the scope of the current study. Generally, the transcriptomic data for zebrafish embryo development at the epiboly/gastrulation stage are based on the whole embryos which include all other activities and are not specific to EMT; thus, it may not be a proper comparison with tumor metastasis data to search for more evidence.

The second concern is about the Pizotifen's effects on cancer metastasis. Since the Pizotifen suppresses gastrulation, it might have some harmful effect on the organogenesis/development of day2 embryos that they used in zebrafish transplantation model. And if so, cancer metastasis can be suppressed indirectly. The authors could examine if Pizotifen could have some side effects on day2 embryos. The drug also has some cell viability suppressive effects in vivo so as the pics in Fig.2D looks like, and it would be good if this possibility was excluded.

We had not observed any abnormality in development of Tg (kdrl;GFP) and WT zebrafish at day 2 when these fish were treated with 5µM Pizotifen. However, more than 20µM Pizotifen treatment affected approximately 10% of these fish. The affected fish show shorter tail rather than that of vehicle-treated zebrafish. In xenograft experiments, zebrafish embryos at day 2 were treated with 5µM Pizotifen. The concentration of Pizotifen did not affected development of day2 embryos.

Futhermore, we demonstrated Pizotifen did not affect primary tumor growth in a mice model of metastasis using 4T1 cells by two different experimental methods. One is that tumor measurement revealed that the sizes of the primary tumors in Pizotifen-treated mice were equal to those in the vehicle-treated mice at the time of resection on day 10 post inoculation. The other is that IF-staining showed the percentage of Ki67 positive cells in the resected primary tumors of Pizotifen-treated mice were the same as those of vehicle-treated mice (Figure 3A and B). Therefore, we conclude that Pizotifen suppress metastasis without affecting cell viability in vivo.

Finally, the mechanistic parts would need more confirmation and rescue experiments. Transplanted cells can be sorted after the treatment and the expression changes of EMT markers can be examined to see if the phenomenon happens in vivo as well. All main results can be rescued to see if the effect of Pizotifen against EMT happens through HTR2C-Wnt axis.

Figure 5C showed that 4T1 primary tumors from Pizotifen-treated mice has elevated E95 cadherin expression compared with tumors from vehicle-treated mice. Furthermore, Figure 5C also demonstrated that β-catenin accumulated in the nucleus, and phospho-GSKβ and Zeb1 expression were decreased in 4T1 primary tumors from Pizotifen-treated mice compared with vehicle-treated mice. Loss of E-cadherin plays an essential role in promoting EMT-mediated metastasis since loss of E-cadherin itself is enough to promote metastasis. In contrast, overexpression of mesenchymal markers: vimentin and N-cadherin is not sufficient to induce metastasis. Based on our data from Figure 5C and the accumulated evidences, we conclude that Pizotifen restored epithelial properties to metastatic cells through a decrease of transcriptional activity of β-catenin in vivo

Cultivating an Abundance Mindset.

During all proceeding steps and activites, remember that "Through effort and experience can the brain change." This will help to explain the motivation behind the need for discomfort if change is to be made.

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Reply to the reviewers

First of all, we would like to thank the each of the expert reviewers for their effort in evaluating our study. We are confident that we can positively address each of the issues and queries raised by the reviewers.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** This study investigates the functions of the tricalbin proteins in S. cerevisiae, which are homologs extended synaptotagmins in mammals. It suggests the tricalbins modulate plasma membrane phospholipid composition and are particularly important for surviving shift to elevated temperature. The tricalbins are proposed to directly or indirectly promote phosphatidylserine transport from the ER to the plasma membrane during heat stress. They also promote the localization of the kinase Pkh1 to the plasma membrane during heat stress.

**Major comments:**

General Response: We thank the reviewer for the comments and opinions. While they are starkly different from the other two reviewers’, they have caused us to consider how we can add additional support for our conclusions and to consider alternative possibilities.

1. To determine whether the tricalbins and other tethering proteins play a role in phospholipid homeostasis, lipid distribution is measured using biosensors (FLARES) and phospholipid levels are determined by mass spec. The experiments are well done but say little about what role the tricalbins or other tethering proteins play in homeostasis. There are no measurements of lipid transport or rates of phospholipid production, degradation or modification. It is reasonable to propose the tricalbins transport lipids, since other proteins with SMP domains do, but this study does not present any evidence that they do.

Response: We thank the reviewer for stating that the quantitative microscopy and lipidomics experiments in our study are well done. However, we strongly disagree that the results do not provide new information on the roles of the tricalbins or other tethering proteins in membrane lipid homeostasis. In fact, the lipidomics results definitively show that Ist2 and Scs2/22 control phosphatidylserine (PS) levels. In contrast, loss of the tricalbins does not significantly affect PS levels. We will provide a new figure to make this even more clear to expert and non-expert readers.

While the tricalbins do not regulate PS levels, the data clearly show that the tricalbins control PS acyl chain saturation and cellular distribution. A PS reporter is reduced at the plasma membrane (PM) upon loss of the tricalbins and we have now confirmed increased localization at the endoplasmic reticulum (ER). These new results will be included in a revised manuscript. As the lipid desaturase Ole1 is localized in the ER, the lipidomics are consistent with increased ER residency of PS species. Thus, the data indicate that while the tricalbins do not regulate PS levels, they control either delivery of PS from the ER to the PM, and/or they may control the organization and stability of PS at the PM which would be a novel finding on its own.

The reviewer must be aware that there are currently no in vivo cell assays that directly (only) measure lipid transport. These experiments are subject to several factors including lipid metabolism, anterograde transport rates, bilayer organization, lipid accessibility, and retrograde transport rates. While our findings clearly show that the Tcb proteins do not control PS levels, we agree that there are alternative possibilities to explain the changes in PS distribution. In our revised manuscript, we will perform additional experiments to distinguish between these possibilities. New experiments will include mutant forms of Tcb3 bearing substitutions in the SMP domain. We will also examine whether the Tcb proteins control PS organization, availability/accessibility, and stability at the PM (also see Reviewer #3, comment 2). This latter possibility may reveal a novel concept regarding Tcb/E-Syt protein function that goes beyond their proposed conventional role as lipid transfer proteins. Based on the outcome of these experiments, we shall adjust the final cartoon model and conclusions in the discussion accordingly.

The study convincingly demonstrates that there are fewer Pkh1 puncta formed after temperature shift in cells lacking tricalbins than in wild-type cells (Fig. 6 C,D). However, there is no demonstration that this change in localization alters Pkh1 function or signaling.

Response: Regulation of Pkh1 by lipids is outside the scope of our current study that is focused on providing new understanding of Tcb protein function. However, the decrease in heat-induced Pkh1 puncta may provide insight into the PM integrity defects in cells lacking Tcb1/2/3 (as Pkh1 is required for PM integrity). To test whether Pkh1 function is compromised in the tcb1/2/3 mutant cells, we can test whether constitutively active Ypk1 (which acts downstream of Pkh1) rescues the PM integrity defects in tcb1/2/3 mutant cells.

There is no demonstration that association of tricalbins and Skh1 (Fig. 4) has any functional significance or affects phosphoinositide metabolism.

Response: We thank the reviewer for raising this issue. If the association of Tcb3 and Sfk1 has functional significance, then one would expect that loss of the proteins should phenocopy one another. Deletion of the Sfk1 cytoplasmic domain necessary for co-localization with Tcb3 should also phenocopy loss of Tcb3. This is exactly what we find. Localization of the PS probe is decreased at 42oC upon loss of Sfk1 or truncation of the Sfk1 cytoplasmic tail, similar to cells lacking Tcb3. Furthermore, we find that Tcb3 regulates sterol homeostasis at the PM (using the D4H probe), as has been recently reported for Sfk1 (Kishimoto et al, 2021). Thus, Tcb3 and Sfk1 not only co-localize, but they also share common functions in PM lipid organization. These new results will be presented in our revised manuscript.

The reviewer also inquired about potential roles for Tcb3 and Sfk1 in phosphoinositide lipid homeostasis, as Sfk1 has reportedly been implicated in heat-induced PI(4,5)P2 synthesis. However, while we find clear roles for Sfk1 and the tricalbins in PS and sterol homeostasis, we did not find a requirement for Sfk1 or the tricalbins in PI(4,5)P2 homeostasis upon heat stress conditions. These findings will be included in our revised manuscript. Importantly, our results indicate that the tricalbins and Sfk1 primarily control PS and sterol homeostasis at the PM, and may regulate phosphoinositides indirectly, and thus provide new insight into the key role of these proteins.

The study proposes the tricalbins directly or indirectly promote phosphatidylserine transport after temperature shift, but transport has not measured and other possibilities are not ruled out.

Response: While the Tcb3 SMP domain has been shown to transfer phospholipids in vitro (Qian et al., 2021), we agree that a role in PS transfer in vivo should be examined in more detail and that other possible roles in PS homeostasis should also be considered (also see responses to Reviewer #3, comment 2).

Upon heat shock, we not only observe a decrease in relative levels of the PS reporter at the PM in the tcb1/2/3 mutant cells (as shown in our original manuscript), but also a corresponding increase in relative levels of the PS probe at the ER and vacuole membrane (also see response to Reviewer #3, comment 5). This could reflect impaired delivery of PS from the ER to the PM and reduced stability of PS at the PM (i.e. increased internalization of PS into the cell).

In our original manuscript, we showed that deletion of the SMP domain (the lipid transfer domain), phenocopies deletion of the full-length Tcb3 protein in terms of PS distribution and PM integrity following heat shock. To more rigorously test whether lipid transport activity of the SMP domain is responsible for these phenotypes, we will generate amino acid substitutions within the SMP domain of Tcb3 that maintains its overall structure but impairs its ability to transport lipids (by targeting conserved key residues identified in Saheki et al., 2016). We will then assess whether SMP-mediated lipid transfer is necessary for PS homeostasis and PM integrity under heat stress.

We also agree that other possibilities should be examined. First, to rule out a defect in PS production upon heat stress, we are performing new mass spectrometry lipidomics experiments to measure levels of individual phospholipid species in the tcb1/2/3 mutant and wild type cells after heat stress.

Second, we have considered whether the Tcb proteins control phospholipid bilayer distribution (e.g. flip and flop). However, cells lacking the Tcbs are not hypersensitive to duramycin (Omnus et al. 2016) and thus phosphatidylethanolamine exposure on the extracellular leaflet is not increased. Moreover, cells lacking the Tcbs (and Scs2/22 and Ist2) are not impaired in the uptake of exogenous NBD-labelled phospholipids (and thus flip across the PM bilayer is not impaired). Possibly, there may be increased lipid ‘flop’ in the mutant cells at high temperature. We can test whether there is increased phospholipid exposure in the extracellular leaflet at high temperature, but our results thus far indicate accumulation of PS on internal membrane compartments (the ER and vacuole membrane).

Another potentially exciting possibility is that the tricalbin proteins bind and stabilize PS within the cytosolic leaflet of the PM and prevent its internalization by endocytosis or non-vesicular transfer. This mechanism would be completely independent of lipid transport to the PM and would constitute new mechanistic insight into Tcb function. We will test whether PS (and sterol) becomes more accessible (less stable or reduced sequestered pools at the PM) and internalized into the cell, upon removal of the tricalbin proteins. For example, we will monitor PS distribution in cells where endocytosis is blocked with latrunculin A.

As mentioned, there currently no cellular lipid transport assay that directly (only) measure anterograde transport. However, if the Tcb3 SMP domain mutants are impaired in PS homeostasis and PM integrity, then we can consider monitoring PS transfer in vivo. By performing the experiments outlined here, we will have thoroughly characterised the roles of the tricalbin proteins in PS homeostasis at the PM. Moreover, the new findings may even reveal novel roles that are independent of transport.

Reviewer #1 (Significance (Required)): While this study is likely to be of interest to those studying the tricalbins or phospholipid homeostasis, it is incremental and provides little conceptual advance on what is already known about the tricalbins and extended synaptotagmins. They have already been implicated in lipid homeostasis in the plasma membrane and this study provides no new mechanistic insight into how this occurs. Similarly, it has already been shown that the tricalbins play a role in maintaining cell integrity during heat stress and there is little new insight into what role the tricalbins play. Perhaps the most notable part of the study is the idea that tricalbins are necessary for phosphatidylserine transport during stress, but considerable additional work is necessary to make a strong case for this claim.

Response: We strongly disagree with the reviewer’s opinions. Indeed, Reviewer #2 found our study “novel and detailed” and Reviewer #3 found the results in our study to be “highly valuable” and “interesting”.

In contrast to the reviewer’s claims, there are certainly novel findings in our study. Foremost, this is the first study that demonstrates a role of the tricalbins in PS homeostasis. Previous studies have implicated E-Syt family members in diacylglycerol and phosphoinositide regulation. Our results indicate that the tricalbins and Sfk1 primarily control PS and sterol homeostasis at the PM, not phosphoinositides, and thus provide new insight into the key role of these proteins. Second, while a previous study by Collado et al reported a role of the tricalbins in PM integrity upon heat stress, this work did not provide mechanistic insight into this process. We performed the PM integrity assays for the Collado et al study (as co-authors). Our current study now shows that Tcb function is needed for PS homeostasis and Pkh1 recruitment at the PM upon heat stress; both factors are needed for PM integrity under these conditions. As such, our current study does provide new insight into roles of the Tcbs in PM integrity. Finally, we are exploring roles of the Tcb proteins in PS homeostasis that go beyond their proposed functions as lipid transfer proteins. We are convinced that our study will provide novel and deep mechanistic understanding of the Tcb/E-Syt protein family.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): The study examines the roles of tricalbins in signalling in yeast. By using several mutations they are able to evaluate whether the interactions are caused by tethering the PM and ER or by other mechanisms. Particualrly powerful is their use of cryo-EM tomography and shot-gun mass spectrometery. They look at all the major lipids of the ER and PM and consider their interconversions. They identify large changes in lipid species with one double bond and with two, particularly for PS.

Reviewer #2 (Significance (Required)): There is little information about membrane physical properties and how it changes as a result of changes in lipid molecular species. Nevertheless, the information provided is novel and detailed. The topic of ER-PM contact sites is new and evolving and this paper advances our understanding of the yeast system considerably. It also looks at protein-protein interactions by fluorescence methods and studies the consequences of heat shock.

Response: We are pleased that the reviewer concluded that our study on the Tcb proteins “advances our understanding of the yeast system considerably” and found our use of lipidomics to be “powerful”.

This reviewer only had only one critique; there “is little information about membrane physical properties and how it changes as a result of changes in lipid molecular species”. In our revised manuscript, we will provide new data showing changes in levels of sterol (ergosterol) accessibility/availability at the PM in cells lacking the tricalbin proteins. Sterol lipids exist in distinct pools in the PM bilayer (extracellular vs. cytoplasmic leaflet, accessible vs. sequestered) that control the biophysical and mechanical properties of the PM (packing order, permeability, etc.). Moreover, PS and sterol lipids are proposed to undergo mutual associations whereby PS controls sterol accessibility (the ‘umbrella’ model) and sterol in turn stabilizes PS in the cytoplasmic leaflet of the PM. Our findings demonstrate that the primary function of the Tcb proteins is PS and sterol organization in the PM, providing new mechanistic insight into regulatory mechanisms for membrane homeostasis. We will attempt to further characterize changes in PM mechano-chemical and biophysical properties to further understand how changes in membrane lipid composition affect membrane integrity.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): The Tcb/ESyt proteins play important role in contact site formation and non-vesicular lipid transport. However, their exact functions remain highly controversial. This study by Stefan and colleagues revealed a new role for the yeast Tcb proteins, especially Tcb3, in regulating plasma membrane phosphatidylserine, as well as PIP2. These results are highly valuable to people working on membrane contact sites and lipid trafficking. Overall, the results are fairly convincing and interesting. There are however some concerns and suggestions:

Response: We are pleased that the reviewer stated that our study has “revealed a new role for the yeast Tcb proteins” and that the findings are “fairly convincing and interesting”. We also thank the reviewer for providing constructive criticisms and helpful suggestions.

1. Fig. 4, for Tcb3 and Sfk1 interaction, what about Tcb1/2, which would be good controls for the specificity of the interaction.

Response: We agree that it would be useful to determine if the Tcb3-Sfk1 interaction is specific. We will perform additional BiFC experiments to address whether Tcb1 and Tcb2 associate with Sfk1. Our previous work suggested that Tcb3 forms heterodimers with Tcb1 and Tcb2 necessary for PM integrity (Collado et al., 2019). However, the functional association between Sfk1 and Tcb3 may be specific to Tcb3, and we will test this possibility. It would be interesting to identify a function specific to an individual Tcb protein.

A central question is whether Tcb3 transfers PS by itself through the SMP domain or requires other lipid transfer proteins. One possibility is that the transfer is mediated by Osh6 and Osh7. Did the expression level of Osh6/7 change between the delta tether and the ist2scs2/22 null strain? Under normal and stressful conditions?

Response: We agree that it is important to address whether Tcb3 transfers PS via its SMP domain (an issue also raised by Reviewer #1) or whether this activity is carried out by another lipid transfer protein, such as Osh6 and Osh7. We have considered several alternative possibilities, as well as designed and performed new experiments, as described below (two key experiments are in italics).

To rigorously test whether SMP domain-mediated lipid transport activity is required for PS homeostasis at the PM under heat stress, we will generate amino acid substitutions within the Tcb3 SMP domain that maintain its overall structure but impair its ability to transport lipids (targeting conserved key residues described in Saheki et al., 2016 & Bian et al., 2018). We will then assess whether SMP-mediated lipid transfer is necessary for PS homeostasis and PM integrity under heat stress.

As suggested by the reviewer (and discussed in our original manuscript), the tricalbins might serve as scaffolds for PS transfer proteins, including the Osh6 and Osh7 proteins, under stress conditions. Osh6 and Osh7 are recruited to ER-PM contacts through interactions with the Ist2 tether protein where they mediate PS delivery to the PM under non-stress conditions (D’Ambrosio et al., 2020). However, strong lines of evidence suggest that Ist2, Osh6, and Osh7 are not required for PS homeostasis at the PM under stress conditions. First, loss of Ist2 has no impact on the PS probe under heat stress conditions (this result will be included in the revised manuscript). Therefore, the Ist2-Osh6/7 interaction is not required for PS homeostasis upon heat stress. Ist2 is not required for PM integrity upon heat stress either (Omnus et al., 2016).

These results do not rule out the possibility that the tricalbins serve as scaffolds for other PS transfer proteins, such as Osh6 and Osh7, under stress conditions. In this scenario, a switch between Osh6/7 tethering proteins may occur: from Ist2 under normal growth conditions to the Tcb proteins during stress conditions. However, our findings also suggest that Osh6 and Osh7 function is impaired upon stress conditions, including heat and nutrient starvation. Notably, Osh6 and Osh7 become mis-localized from the PM under upon heat stress (we can provide this data in the revised manuscript). The mechanisms for Osh6/7 attenuation upon stress conditions is outside the scope of our current study, but our preliminary results suggest that changes in cytoplasmic pH and ion homeostasis are involved; this will be a focus of a future study. This is also in line with results from a previous study (Omnus et al., 2020) that showed Osh3 forms intracellular aggregates in response to heat stress. The activity of the Osh proteins and other lipid transfer proteins, in general, may be impaired upon stress conditions (see below).

To directly determine whether Osh6 and Osh7 are required for PS homeostasis at the PM under heat stress conditions, we will monitor the distribution of the PS probe in cells lacking the Osh6 and Osh7 (osh6 osh7 double mutant cells) upon heat stress. This is a key experiment that will directly address the reviewer’s concerns.

As suggested by the reviewer, we can also examine the expression and localization of GFP-tagged Osh6 and/or Osh7 in tcb1/2/3, scs2/22 ist2, and ‘delta tether’ mutant cells. However, we have not observed any changes in other Osh proteins, including Osh2 and Osh3, in the ‘delta tether’ mutant strain.

Finally, we have considered yet another possibility. PS transfer to the PM may be generally attenuated under heat stress conditions and a key role of the Tcb proteins may be to bind and stabilize PS at the PM. In other words, the Tcb proteins may act as a ‘buttress’ to stabilize and maintain pre-existing pools of PS at the PM under stress conditions. Consistent with this idea, the Tcb3 C2 domains are required for PS homeostasis and PM integrity upon heat stress. If the Tcb3 SMP domain mutants are not impaired in PS homeostasis or PM integrity, then this alternative mechanism may be very relevant to PM quality control and organization in response to membrane stress. To address this possibility, we will address whether PS is internalized or removed (extracted) from the PM upon heat stress in cells lacking the Tcb proteins. This may uncover a novel role of the Tcb proteins that is independent of SMP domain-mediated lipid transfer.

Figure 5A. It is not obvious that the intensity of C2 decreases in tcb null cells at 42 degree. Perhaps there is more internal staining.

Response: We thank the reviewer for pointing out this issue. We think Figures 5A and 5B together convincingly show increased cytoplasmic localization of the PS reporter in the tcb1/2/3 mutant cells upon heat stress. More importantly, we thank the reviewer for pointing out the increased localization at internal membrane compartments. We realized it would be important to identify the PS-containing membrane compartments in the tcb1/2/3 mutant cells upon heat stress. We have now confirmed that the PS probe localizes at the ER and vacuole membrane (to be included in the revised manuscript). The example in Figure 5A also shows accumulation of the PS probe at both the nuclear ER and vacuole membrane. Thus, whilst wild type cells show little PS reporter localization at the ER or vacuole membrane, loss of the tricalbin proteins leads to an increase in ER and vacuole membrane PS probe localization after heat shock. Accumulation at the ER may reflect impaired PS delivery from the ER to the PM, and possibly rerouting to the vacuole membrane. Alternatively, as discussed above, vacuole membrane localization may be due increased PS removal from the PM and delivery to the vacuole membrane in tcb1/2/3 cells upon heat stress.

It is important to determine the primary function of Tcb3 since both defects in both PIP2 and PS were observed. If the change in PIP2 is due to a lack of PS, can overexpressing Osh6/7 rescue the PIP2 defect in the tetherless mutant?

Response: We agree that is important to determine whether the primary function of the Tcb proteins is regulation of PS or PI(4,5)P2 homeostasis. Our new findings definitively indicate that their primary function is PS regulation, not PI(4,5)P2 regulation. A clear effect in the distribution of the PS reporter was observed following heat shock in the tcb1/2/3 mutant cells. In contrast, there is no difference in the localization of the PI(4,5)P2 reporter in tcb1/2/3 cells compared to wild type after heat shock (also see response to reviewer #1, comment 3). In addition, cells lacking the Tcb proteins were not impaired in heat-induced PI(4,5)P2 synthesis, as assessed by metabolic labelling and HPLC analysis. These findings will be included in our revised manuscript, as they indicate that the Tcb proteins primarily control PS at the PM, not phosphoinositides, and thus provide new insight into the main role of these proteins.

Both PS and sterol are required for the proper recruitment of type I PIP5K to the PM (Nishimura et al., 2019). Therefore, defects in PS distribution could be responsible for the PI(4,5)P2 effects observed in Figure 2. However, overexpression of Osh7 in the ‘delta tether’ mutant did not significantly rescue localization of the PI(4,5)P2 reporter (included in the revision plans). Sterol organization is also perturbed in the ‘tether’ mutant cells (Quon et al., 2018), and this may explain why Osh7 expression did not rescue. Accordingly, Osh2/3/4 (sterol transfer proteins) rescue the PI(4,5)P2 effects.

The detection of PS by LactC2 has been well-established. However, an alternative approach would be to use the 2XPH in permeabilized cells. See PMID: 33929485 for some detailed discussions on the techniques. It is not a requirement for the authors to adopt the 2XPH.

Response: We thank the reviewer for suggesting another technique to confirm the results of the LactC2 domain as a PS probe. In this study, we have primarily used a genetically encoded LactC2 probe to observe PS distribution within live, intact cells. Whilst this approach was sufficient to identify accumulation of PS on cytosolic membrane leaflets of the ER and vacuole (see above), the addition of an exogenous probe to permeabilized cells may allow the detection of PS on luminal and extracellular membrane leaflets. Therefore, we plan to repeat our heat shock experiments using permeabilized cells and a purified tagged form of the LactC2 protein. This may allow for improved imaging of intracellular PS localisation and bilayer distribution. However, these experiments are technically challenging, and fixation and permeabilization conditions have not yet been optimized for yeast cell experiments. It is not yet known whether we will be able to optimize these protocols in a reasonable amount of time while completing revisions to the manuscript.

**Minor:**

1. the discussion seems to be a bit long

Response: We will shorten the discussion and modify our final conclusions based on the results from the new experiments.

Reviewer #3 (Significance (Required)): These proteins are highly important in cell biology/contact sites. The redundancy made it difficult to pinpoint their function. Previous studies have had a number of models. The current study proposed a new function of these proteins, i.e. PS transfer, and this is very interesting and valuable. There will be a good audience for this work. I specialize in lipid storage and trafficking, lipid droplets, cholesterol and phosphatidylserine.

Response: We are pleased that this expert reviewer found our study to be “very interesting and valuable”.

Author Response:

Reviewer #1:

The paper by -Blackwell et al. develops the ideas developed in the influential paper by Dash et al. (2017) which defined a similarity matrix for CDR3s TCRdist which is based on a weighted combination of local and global similarity measurements. In this paper they use the metric to develop the idea of a meta-clonotype, a set of similar TCRs which enrich for TCRs directed at the same antigen. They demonstrate that these meta-clonotypes show greater publicity than individual clonotypes, and show evidence of HLA-restriction. The authors speculate that the metaclonotype may be a useful biomarker. They provide open-access software tools for defining meta-clonotypes in antigen enriched repertoires.

The major findings are: (1) Meta-clonotypes are more public than clonotypes, a result which seems not unexpected, given that meta-clonotypes include many different sequences; (2) Meta-clonotypes show evidence of HLA restriction, again predicted given the well-established fact that specific antigens can be recognised by sets of similar TCRs.

The concept of a metaclonotype is an interesting one which could have widespread use in analysis of TCR repertoire. However, the impact could be much greater, by sharpening the focus of the paper, and adding detail and clairty to the idea of teh clonotype. In particular, while the introduction correctly points out that prediction of SARS-Cov_2 clinical outcome, or better understanding of the role of coronavirus prior exposure in determining outcome are important unanswered questions, this paper does not address these questions.

Thank you for your careful review of the manuscript. We have submitted a major revision with greater focus on the definition of a TCR meta-clonotype. We have removed from the introduction much of the background about SARS-CoV-2 and potential implications for the pandemic. In its place we’ve added greater detail about meta-clonotypes, how they can be defined from antigen-enriched TCR data, and how they can be used to analyze bulk TCR sequencing data.

A substantial portion of the paper is devoted to analysing data obtained using the MIRA assay (Klinger et al PLoS One 10 :e0141561) to define SARS-COV-2 responses, and it is not always clear whether the objective is to evaluate the accuracy of this data set, or to test the power of the meta-clonotype approach.

Our objective with the analyses of the IMMUNEcode dataset (Nolan et al. 2020) dataset, using MIRA method from Klinger et al. 2015, is to demonstrate that TCR meta-clonotypes can be defined from antigen-enriched TCR data and that they can be used to identify and quantify antigen-specific TCRs in bulk sequenced data. Furthermore, the analysis provides evidence that meta-clonotypes have greater publicity than individual clonotypes, thus increasing sensitivity of detection for antigen-specific TCRs. Using bulk repertoires from COVID-19 patients we then demonstrated that population-level analysis can be made possible using meta-clonotypes and provided supporting evidence that the antigen specificity of the centroid TCR is retained. These analyses and their interpretation is further revised on lines 319-348. We think that in the process of evaluating meta-clonotypes, our analysis also shows that the publicly released data contains valuable information about SARS-CoV-2 TCR specificities; however, we have not systematically attempted to verify the validity of the dataset. In the current revision these objectives are made clear in the revised Introduction section.

Reviewer #2:

Summary of main aims: The main aim of this paper is to build a framework for TCR meta-clonotypes for finding similar TCRs across individuals (or different repertoires). The majority of the investigations performed in this work have the objective of showing the data properties of meta-clonotypes as well as the metaclonotypes' usefulness for the analysis of antigen-specific TCR data and disease-labeled immune repertoire data.

Major strengths: Building meta clonotypes is a possible path towards a better coverage of immune repertoire biology as well as inter-individual repertoire comparison. TCRdist3 is an efficient method for building meta-clonotypes that enables the study of the specific characteristics of meta-clonotypes. So, far clusters of similar sequences have not been investigated in depth. The author team is making a significant step forward in this direction by characterizing meta clonotypes in differentially antigen-specific-clone-enriched repertoires and by relating the results to generation probability, HLA, sex and immune status.

Major weaknesses: Although the authors show a significant amount of data, I am not sure if these data convey sufficient intuition about the characteristics and behavior of meta-clonotypes. The authors seem too focused on relating meta-clonotypes to immune status instead of focusing on the specific biological characteristics of metaclonotypes.

We agree and have shifted the focus of the manuscript away from the results of the application and towards providing greater detail about the characteristics and behaviors of meta-clonotypes; for example, we’ve removed much of the background about SARS-CoV-2 and the COVID-19 cohorts and we’ve added details about how the meta-clonotype radius can be optimized. We’ve also reframed the data analysis section to emphasize that the results demonstrate how meta-clonotypes carry important antigen-specific signals above and beyond individual clonotypes; this makes the results valuable beyond the application to SARS-CoV-2. For example, while demonstrating HLA restriction occurs in SARS-CoV-2 specific T cell responses in COVID-19 patients is not a surprising finding, it provides evidence that meta-clonotypes enable quantification of an antigen-specific and HLA-restricted T cell response from a bulk single-chain TCR repertoire. We use this example analysis to compare the strength of this signal in individual clonotypes, meta-clonotypes with radius alone and meta-clonotypes with a motif constraint. The revision of lines 98-100 and lines 461-470 provide clarity about this motivation for the analysis and interpretation of the results.

Furthermore, we have added a section to the Results that demonstrates how meta-clonotypes and tcrdist3 enable analyses that can provide biological insights about the biochemical properties that may confer antigen specificity (lines 383-418, Figure 10). Since meta-clonotypes define groups of sequences, we can use CDR3 logo plots to dissect how positions and amino acid properties in the CDR3 define the group. In the revision we demonstrate a “background-adjusted” logo plot, that is able to emphasize amino acids that define the meta-clonotype, yet are uncommon among TCRs using the same V and J genes. Visualizing the results in this way can generate hypotheses that can be experimentally validated about the amino acids that are essential for antigen recognition.

Furthermore, the authors fail to convincingly show that the background repertoires chosen for meta clonotypes are robust and to what extent meta-clonotypes are sensitive to changes in the background repertoire.

We agree that it is important to understand how the creation of meta-clonotypes, and specifically optimization of the radius, depends on the background repertoire. Therefore, we conducted sensitivity analyses varying the size (25K to 1M) and makeup (synthetic OLGA vs. cord blood vs. a blend) of the background (lines 259-294). We also empirically demonstrate the value of over-sampling background TCRs with matching V and J genes. We show that using a background of 200,000 TCRs was sufficient for reducing the bias and variability in selecting a meta-clonotype radius, compared to a reference set of 2 M background TCRs; this is important because while it is tractable to use large backgrounds for a small number of meta-clonotypes, for larger studies or analyses confined to a laptop, the smaller background set is sufficient; we also show that this can largely be attributed to the gain in efficiency that comes with using a background that includes synthetic OLGA TCRs with VJ-gene frequencies that match the TCRs included in the meta-clonotype. However, we note that “Ultimately, the best choice for the background may depend on the question being asked and the data that is available, with factors including donor HLA, age, potential antigen exposures, and other factors that may shape the repertoire.” Our goal with tcrdist3 was to make it easy for the user to customize the background to the scientific question.

The authors also do not convincingly differentiate themselves from previous approaches that have used network analysis and generation probability in order to find clusters of similar sequences (very much conceptually similar to the approach taken here).

We agree it’s important to communicate how meta-clonotypes differ from existing TCR analysis approaches. There are several important distinctions with the existing methods that use networks and generation probability, namely TCRNET and ALICE. We have highlighted these differences in the Discussion (lines 483-497), quoting:

“The meta-clonotype approach also differs from methods, such as TCRNET (Ritvo et al., 2018) and ALICE (Pogorelyy et al., 2019), that seek to identify TCRs sharing antigen-specificity within bulk repertoires. These methods were developed to identify TCR nodes in a network with an enriched number of edges compared to the expected number of edges in a background (TCRNET) or derived from a probability model (ALICE). Similarly, another recent method attempts to find antigen-associated sequences in bulk repertoires using a two-stage agglomerative clustering of a k-mer based representation of CDRs, first within and then across bulk repertoires (Yohannes et al., 2021). Our framework is designed for a different task than these algorithms. Specifically, we sought to construct definitions of TCR groupings among already antigen-associated TCRs, which would have high sensitivity and specificity for finding similar TCRs in bulk repertoires. This is an important distinction because the existing network-enrichment methods would simply find that most or all of the TCR groupings among a set of antigen-associated sequences were statistically enriched compared with their frequency in antigen-naïve repertoires. By contrast, a flexible meta-clonotype radius permits the definition of the largest possible group of antigen-associated TCRs with the constraint that the likelihood of finding a TCR within the radius in an antigen-naïve background is equally low across all meta-clonotypes.”

Finally, the authors do not provide detailed descriptions of how the comparison of meta-clonotypes across repertoires is handled as well as potential sequence redundancies across meta-clonotypes (in potentially different individuals). I believe that all of the perceived shortcomings are readily addressable in a revision.

You are quite right that many meta-clonotypes are overlapping in that a single TCR might conform to more than one meta-clonotype definition. Thus, in the application of meta-clonotypes to the COVID-19 dataset we tested each meta-clonotype individually for an association with the predicted HLA- restricting genotype. Depending on the context, if a summary across meta-clonotypes is required (e.g. finding the overall abundance of conformant TCRs in a repertoire) it may be appropriate to use meta- clonotypes to identify conformant sequences, but then tally them based on actual abundance (i.e. no double counting). In a prediction context, it may be desirable to have overlapping meta-clonotype features, and in fact many machine learning algorithms excel in this regime. With tcrdist3 we have incorporated a “join” functionality that allows for relational database-style joining of meta-clonotypes with a TCR repertoire; this makes it relatively easy to eliminate or keep redundancies, depending on the context. We have added a sentence to the Discussion pointing out that there is overlap among meta-clonotypes that needs to be considered in their application and we provide a link to an example of how to use the join functionality on https://tcrdist3.readthedocs.io/en/latest/join.html#step-by-step-example.

All in all, this manuscript is an important steps towards a better understanding of immune receptor biology. tcrdist3 is an evolution of a previously published method (tcrdist) that is here used to build meta-clonotypes. After reading the paper, it remains slightly unclear (addressable in a revision) as to how useful they are for understanding repertoire biology as well as how to use them in practice in terms of robustness and sensitivity.

Thank you for your constructive comments, and I hope we’ve addressed these issues around biological interpretability and application in the revision.

Reviewer #3:

Mayer-Blackwell et al introduce a new framework for leveraging antigen-annotated T cell receptor (TCR) sequencing data to search for similar TCRs in bulk repertoire data, which potentially recognise the same antigen peptide. They introduce the notion of meta-clonotype, a T cell receptor (TCR) feature consisting of a main TCR sequence ("centroid") and a distance radius around it (+/- a CDR3 motif), with distance measured according to their previously published TCRdist method (Dash et al, 2017). The meta-clonotypes benefit from increased publicity over exact clonotype matching, and enhance the ability to find potentially relevant TCRs in repertoires from unrelated individuals, which are usually highly diverse, predominantly private, and subject to sampling constraints. The idea of meta-clonotypes is very interesting, and will provide a very useful tool in future repertoire analyses. For example, public databases of annotated TCRs (e.g. VDJdb) can be used to derive the set of meta-clonotypes for a variety of antigens, which can in turn be searched for in bulk repertoire data to identify e.g. memory to previous antigen exposure, immune status etc.

The tool for performing the analysis, tcrdist3, is open-source, well-documented with instructions and examples, and the statistical analysis has been well-thought out. It is also useful to have the comparison to the current alternative of k-mer based TCR distance (i.e. GLIPH2), and the added flexibility for the user to define the precise distance metric to be used in the tcrdist3 tool.

The authors then apply their method to analyse TCR beta sequences from COVID-19 datasets that have been publicly released by Adaptive Biotechnologies through the immuneRACE project. They use the MIRA set, the peptide-enriched set, to identify the meta-clonotypes, and then search for these in an independent cohort of COVID-19 bulk repertoires from 694 individuals. The authors find that a large proportion of the meta-clonotypes were more abundant in patients expressing the relevant restricting HLA allele, and suggest this could potentially lead to the development of disease biomarkers. The set of sars-cov-2 related meta-clonotypes is a useful resource in itself, as researchers generating other COVID-19 TCR datasets will be able to utilize this set of meta-clonotypes to search and potentially stratify patients in their own generated data.

There are a few areas were further detail / examples would strengthen the paper's claims, in particular in the application of the tcrdist3 method to the COVID-19 data.

1) Bulk TCR data from repertoires with past antigen exposure are likely to contain varying sizes of clones due to the proliferation of responding T cells and a remaining memory population. Due to the sharp drop in size between a TCR sequencing sample and the entire repertoire, clones above a particular size relative to the sample size are highly unlikely to have been sampled by chance, and identifying significantly/meaningfully expanded clonotypes in a sample is often used to identify a potentially antigen-recognising set of TCRs. The authors demonstrate the detection of meta-clonotypes in the repertoire sets, but it is somewhat unclear how the abundance of a clonotype conforming to a particular meta-clonotype is addressed. For example, there may be rationale for treating the following cases differently: meta-clonotype A is instantiated by (i) a unique clonotype with abundance 1; (ii) a single clonotype with abundance 1000; (iii) 100 different clonotypes (i.e. a "dense neighbourhood" around this meta-clonotype). If used to develop biomarkers, perhaps some degree of granularity in how the frequency/occurrence of meta-clonotypes is calculated would be helpful here.

Thanks for this helpful suggestion. We agree that the scenarios you outlined above, which differ in the level of clonal breadth (i.e. number of unique clones), may have great immunological relevance. Though we have not specifically assessed the clinical or immunological relevance of clonal breadth vs. clonal frequency, we have noted in the revision (lines 514-519) that there are multiple ways of counting meta-clonotype conformant sequences and multiple ways of aggregating counts across meta-clonotypes, for example without double counting clones that may be conformant to multiple meta-clonotypes. We have also added a documentation page about how to tabulate abundance or breadth of conforming clones: https://tcrdist3.readthedocs.io/en/latest/join.html

2) The authors focus their analysis on detecting meta-clonotypes from MIRA sets with strong evidence of HLA-restriction. They report 59.7% of these meta-clonotypes were more abundant in patients expressing the corresponding HLA allele. This means that over 40% of meta-clonotypes with strong HLA restriction were more abundant in repertoires with other HLA types. This point could be further elucidated by comparing results with the control repertoires from the COVID-19 set, from MIRA sets with low evidence of HLA restriction, or combining the sets of low and high evidence of HLA restriction (i.e. HLA agnostic results).

We’d like to clarify that the results do not imply that 40% of meta-clonotypes were more abundant in participants lacking the restricting HLA allele; rather, these meta-clonotypes did not have a significant association with presence or absence of the HLA genotype. In the discussion we highlight several of the possible explanations for this including that meta-clonotypes were too rarely detected in the population (lines 470-478). The volcano plots in Figure 6A and 7A show that there are very few if any HLA associations of the opposite sign (i.e. meta-clonotypes more abundant in patients without the restricting HLA allele). In fact, at the chosen significance threshold (FDR <0.01), 0 of 1831 predicted HLA-associated meta-clonotype were significantly negatively associated with the predicted HLA.

3) The MIRA55 set is used as an illustrative example throughout the manuscript, which familiarises the reader with this dataset as they are reading the paper. However, the claims made by the paper about MIRA sets / strong HLA evidence MIRA sets could be strengthened by providing an indication of how measured characteristics of the MIRA55 set compare to the other sets being assessed.

This is a good point and we have tried to provide as much information about MIRA55 and the other MIRA sets to help establish that MIRA55 is a representative set. Characteristics of the other MIRA sets appear in Supporting Table S6, including:

• Input number of clonotypes (AA exact)
• Number of non-redundant, public meta-clonotypes
• Clonotypes spanned by at least one meta-clonotype
• Span (% of clonotypes conforming to a meta-clonotype definition)
• Number of public enhanced sequences that match an identified TCRβ

As well as summary statistics for other meta-clonotype properties:

• Pgen
• Radius (TCRdist units)
• TRBV-CDR length
• Number of MIRA subjects contributing at least 1 sequence

Furthermore, Table S7 provides the strength of evidence for HLA-restriction for each meta-clonotype, which is then summarized by MIRA set visually in Figure 4.

Based on these criteria, we think MIRA 55 is a reasonably representative set to focus on.

4) There is some discussion throughout the manuscript about using the sars-cov-2 meta-clonotypes to identify differing clinical outcomes such as disease severity. Perhaps the dataset does not have sufficient power to allow for such sub-analysis, but a method of using meta-clonotypes to differentiate between patients based on the occurrence of meta-clonotypes in their repertoire is not provided [e.g. the number of observed clonotypes, the density distribution around clonotypes etc.)

That is true. With this manuscript we have tried to focus on establishing the methodology, evaluating the strength of the antigen-specific signal and demonstrating its potential applications; we have tried to make these goals more explicit throughout. Specifically in the revision we note that: “Much like any biomarker study, to establish a TCR-based predictor of a particular outcome, the features must be measured among a sufficiently large cohort of individuals, with a sufficient mix of outcomes.” At this time the publicly available ImmuneRACE data lack negative controls and sufficient clinical details to allow for building a predictor of SARS-CoV-2 infection or disease severity.

Author Response:

Reviewer #1 (Public Review):

This paper aims to address the question of whether the rotational dynamics in motor cortex may be due to sensory feedback signals rather than to recurrent connections and autonomous dynamics as is typically assumed. This is indeed a question of importance in neural control of movement.

The authors employ both analyses of motor cortical data and simulation analyses where a neural network is trained to perform a motor task. For the simulations, the authors use a neural network model of a brain performing arm control tasks. Importantly, in addition to the task goals, the brain also receives delayed sensory feedback from the muscle activity and kinematics of the simulated arm. The brain is modeled either using a stack of two recurrent neural networks (RNN) or using two non-recurrent neural network layers to investigate the importance of autonomous recurrent dynamics. The authors use this framework to simulate the brain performing two tasks: 1) posture perturbation task, where the arm is perturbed by external loads and has to return to original posture, and 2) delayed center-out reach task. In both tasks, the authors apply jPCA to units of the trained network, simulated muscle activity, and simulated kinematics and investigate their rotational dynamics. They find that when using an RNN in the brain model, both the RNN layers and kinematics show rotational dynamics but the muscle activity does not. Interestingly, these conclusions for both tasks also hold when networks without recurrent connections are used instead of the RNNs. Also importantly, the rotational dynamics also exist in the sensory feedback signals about the limb state (e.g. joint position, velocity). These results suggest that recurrent dynamics are not necessary for the emergence of rotational dynamics in population activity, rather sensory feedback can also achieve the same.

The authors perform similar jPCA analyses on monkey motor cortical (MC) or somatosensory cortical activity during the same two tasks and find largely consistent results. As with simulations, neural population activity and kinematics show rotational dynamics but muscle activity, which is explored only in the posture task, does not. Importantly, population activity in both motor and somatosensory cortices shows rotational dynamics. This observation is more consistent with the view that rotational dynamics emerge due to inter-region communications and processing of sensory feedback and planning, rather than autonomous dynamics within the motor cortex.

The approach of the paper is interesting and valuable and the questions being addressed are very important to the field. To further improve the paper and the analyses, there are several major comments that should be addressed to fully support the conclusions and clarify the results:

Major:

1) In the Methods, the authors explain how they model a non-recurrent network as follows: "We also examined networks where we removed the recurrent connections from each layer by effectively setting Whh, Woo to zero for the entire simulation and optimization (NO-REC networks)". However, if this is the only modification, it still leaves recurrent elements in the network. For example, if we set W_{hh} to zero, equation 2 will be:

h_{t+1} = (1-a) h_t + a tanh(W_{sh} * s_t + b_h)

where a is a constant scalar (seems to be equal to 0.5). This is indeed still a recurrent neural network since h_{t+1} depends on ht. If their explanation in the Methods is accurate, then the current approach restricts the recurrent dynamics to be a specific linear dynamic (i.e. "h{t+1} = (1-a) ht + …") but does not fully remove them. The second layer is also similar (equation 3) and will still have recurrent linear dynamics even if W{oo} is set to 0. To be able to describe networks as non-recurrent, the first terms in equations 2 and 3 (that is (1-a)h_t and (1-a)o_t) should also be set to 0. This is critical as an important argument in the paper is that non-recurrent networks can also produce rotational dynamics, so the networks supporting that argument must be fully non-recurrent. Perhaps the authors have already done this but just didn't explain it in the Methods, in which case they should clarify the Methods. However, if the current Method description is accurate, they should rerun their NO-REC simulations by also setting the fixed linear recurrent components (that is (1-a)h_t and (1-a)*o_t) to zero as explained above to have a truly non-recurrent model.

We thank the reviewer for raising this important concern. We have re-simulated the NO-REC network while removing the dynamics related to the leaky-integration component. This removal did not impact the network’s ability to perform the tasks and yielded virtually identical neural dynamics (see Figure 8). Throughout the Results we have updated the figures for the NO-REC network to the network without the leak-integration component.

2) Assuming my comment in 1 is addressed and the results stay similar, the authors show in simulations that even without recurrent dynamics (referred to as the NO-REC case), rotational dynamics are observed in the simulated brain during both tasks (Figure 8). This result is used to suggest that the sensory feedback is what causes the rotational dynamics in the brain model in this case. However, I think to fully demonstrate the role of feedback, additional simulations are also needed where the sensory feedback is removed from the brain model. In other words, what would happen if recurrent and non-recurrent brain models are trained to perform the tasks but are not provided with the sensory feedback (only receive task goals)? One would expect the recurrent model to still be able to perform the task and autonomously produce similar rotational dynamics (as has been shown in prior work), but the non-recurrent model to fail in doing the task well and in showing rotational dynamics. I think adding such simulations without the feedback signals would really strengthen the paper and help its message.

We apologize if the network architecture was not clear. In the case of the NO-REC network the only way they can generate the time-varying signals needed for the tasks is through sensory feedback. The network simply will not work without recurrent AND sensory feedback. For the posture task there are no additional inputs since it only receives sensory feedback. For the reaching task the task-goal input is static and the GO cue turns off on a timescale considerably shorter (~20ms) than the reach duration. Thus, the REC network would always perform better than the NO-REC network when sensory feedback was removed as the NO-REC network cannot generate any dynamics. We have now included in the Results the following statement. "Note, by removing the recurrent connections these networks can only generate time-varying outputs by exploiting the time-varying sensory inputs from the limb." (line 345-347).

We have also now included simulations to highlight how REC networks that receive sensory feedback are able to generalize better to scenarios with increased motor noise than REC networks where sensory feedback is either completely removed (reaching task) or only provided at the beginning of the trial (posture task) (Figure S8). Thus, sensory feedback makes REC networks more robust in less predictable scenarios.

We agree that this could be an interesting manipulation and have now included manipulations of the sensory feedback delays. We considered three separate delays, 0ms, 50ms and 100ms and found that there was a dependence on the rotational frequency of the top jPC plane with greater delays resulting in a general reduction in frequency (see now Supplementary Figure 10). There was less effect of delay on fit qualities to the constrained and unconstrained dynamical system. This has been added to the Results section (line 423-446).

We simulated this scenario and found the answer to be rather complex and we have added these results to the supplementary material. The network's behavioural performance in the perturbation posture task is similar to the previous networks with joint-based feedback. However, the dynamics in the output layer are not the same with a clear reduction in how well the dynamics are described as rotational (Figure S11A-B).

Oddly, rotational dynamics could still be observed in the input layer dynamics (data now shown) and the kinematic signals when they were converted to a cartesian reference frame (Figure S11D-E). Furthermore, rotational dynamics could emerge in the output layer if we used a different initialization method for the network weights. We initialized weights from a uniform distribution bound from ±1/√N, where N is the number of units. In contrast, previous studies have initialized network weights using a Gaussian distribution with standard deviation equal to g/√N where g is constant larger than 1. This alternative initialization scheme encourages strong intrinsic dynamics often needed for autonomous RNN models (Sussillo et al., 2015). We found networks initialized with this method and trained on the perturbation posture task exhibited stronger rotational dynamics with fits to the constrained and unconstrained dynamical systems of 0.5 and 0.88, respectively (Figure S11C-D). When examining the reaching task, we found similar results (Figure S11F-K). When initialized with a uniform distribution, fit quality for the constrained and unconstrained dynamical systems were 0.4 and 0.77, respectively (Figure S11F-G), which were smaller than for the joint-based feedback (Figure 7B, constrained R2=0.7, unconstrained R2=0.83). Qualitatively, the dynamics were different when the network was initialized with a Gaussian distribution (Figure S11H), however fit qualities were comparable between the two initialization methods (Figure S11 I). There was also a noticeable reduction in the fit quality for the kinematic signals particularly for the constrained dynamical system (Figure S11K, constrained R2=0.36, unconstrained R2=0.77). These findings have been added to the Results

3) A measure of how well each trained network is able to perform the task should be provided. For example, is the non-recurrent network able to perform the tasks as accurately as the recurrent models? The authors could use an appropriate measure, for example average displacement in the posture task and time-to-target in the center-out task, to objectively quantify task performance for each network. Another performance measure could be the first term of the loss in equation 5. Also, plots of example trials that show the task performance should be provided for the non-recurrent networks (for example by adding to Figure 8), similar to how they are shown for the recurrent models in Figures 2 and 6.

We have now presented and quantified the NO-REC network behavioural performance. Kinematics for the NO-REC network are shown in Figure S7A-C and E-G which are comparable to the REC network. Furthermore, quantifying the maximum displacement during the posture task yielded no obvious differences between the NO-REC and REC networks (Figure S7D). For the reaching task, the time-to-target was noticeably more variable and tended to be slower for the NO-REC network (Figure S7H). These observations have been added to the Results.

4) An important observation is that rotational dynamics also exist in the sensory signals about the limb state. This may imply that the task structure that dictates the limb state and thus the associated sensory feedback may play an important role in the rotations without the recurrent connections. While the present study will be a valuable addition regardless of what the answer is, this is an important point to address: What is the role of the task structure in producing rotational dynamics? In both the posture task and the center-out task, the task instruction instructs subjects to return to the initial movement 'state' by the end of the trial: in the posture task the simulated arm needs to return to the original posture upon disturbance, and in the center out task the arm needs to start from zero velocity and settle at the target with zero velocity. Is this structure what's causing the rotational dynamics? This is an important question both for this paper and for the field and the authors have a great simulation setup to explore it. For example, what happens if the task instructions u* instruct the arm to follow a random trajectory continuously, instead of stopping at some targets? With a simulated tracking task like this, one could eliminate obvious cases of return-to-original-state from the task. Would the network still produce rotational dynamics? Of course, I don't expect the authors to collect experimental monkey data for such new tasks, rather to just change the task instructions in their numerical simulations to explore the dependence of observed rotational dynamics on the task structure. I think this will help the message of the paper and can be very useful for the field.

We agree that a tracking task would be an interesting manipulation and have simulated this with the REC and NO-REC networks (Figure 9). Here, we trained up the network to reach from the starting position and track a target moving radially at a constant velocity for the rest of the trial (1.2seconds). Thus, the network has to move the limb at a constant velocity. We found there was a consistent reduction in how well the network’s dynamics (constrained R2=0.13, unconstrained R2=0.3) were described as rotational when compared to the previous reaching task (Figure 7, constrained R2=0.7, unconstrained R2=0.83). Also, note that this reduction in rotational dynamics remained even when we initialized the network weights using a Gaussian distribution (see Essential revision 2.3). These simulations have been added to the Results section.

5) It would be beneficial if the authors could elaborate in the discussion on intuitive explanations of why sensory feedback can produce rotational dynamics even with no internal recurrent dynamics in the brain model. To me, it seems like sensory feedback is providing a path for recurrence to exist in the overall brain-arm system, so the non-recurrent neural networks can learn to exploit that path to effectively implement some recurrent dynamics. Some intuitive explanations like this will be helpful for readers.

The main reason why rotational dynamics emerge in sensory feedback is due to the phase offset between the joint position and velocity as changes first occur in the velocity followed by position (see pendulum example Pandarinath et al., 2018a also DeWolf et al., 2016; Susilaradeya et al., 2019). This phase offset is maintained across reach directions and gives rise to the orderly rotational dynamics observed in the kinematic signals (DeWolf et al., 2016; Pandarinath et al., 2018a; Susilaradeya et al., 2019; Vyas et al., 2020). Furthermore, the tracking task disrupted this phase relationship and thus the rotational dynamics were substantively reduced in the network models. This text has been added to the Discussion (lines 519-526).

6) One main result in data from non-human primates is that there exist rotations also in the somatosensory cortex not just in motor cortex. A more thorough discussion of prior work on rotational dynamics or lack thereof across brain regions and behavioral tasks is important to add here. For example, besides the works cited by the authors, there are other works such as (Kao et al., 2015; Gao et al., 2016; Remington et al., 2018; Stavisky et al., 2019; Aoi et al., 2020; Sani et al., 2021) that discuss or show rotational dynamics in various brain regions and behavioral tasks and should be cited and discussed.

We have cited the above papers and included in the Discussion the following paragraph (lines 537-549) “Importantly, findings of rotational dynamics in cortical circuits are not trivial. Activity in the supplementary motor area does not exhibit rotational dynamics during reaching (Lara et al., 2018). The hand area of MC also does not exhibit rotational dynamics during grasping-only behaviour (Suresh et al., 2020), though it does exhibit rotational dynamics during reach-to-grasp (Abbaspourazad et al., 2021; Rouse and Schieber, 2018) which may reflect the reaching component of the behaviour. More broadly there is a growing body of work characterizing cortical neural dynamics across different behavioural tasks which have revealed rotational (Abbaspourazad et al., 2021; Aoi et al., 2020; Libby and Buschman, 2021; Remington et al., 2018; Sohn et al., 2019; Stavisky et al., 2019), helical (Russo et al., 2020), stationary (Machens et al., 2010), and ramping dynamics (Finkelstein et al., 2021; Kaufman et al., 2016; Machens et al., 2010) and these dynamics appear to support various classes of computations. Thus, finding rotational dynamics across the fronto-parietal circuit in our study is not trivial."

7) The authors state that "In contrast, rotational dynamics appear to be absent in… MC activity during grasping driven by sensory inputs (Suresh et al., 2020)." There are other papers that study dynamics during reach-grasps and still finds rotational dynamics and modes (Abbaspourazad et al., 2021; Vaidya et al., 2015) and should be cited and discussed. The recent paper on naturalistic reach-grasps (Abbaspourazad et al., 2021) also argues for the involvement of a large-scale network in these movements, which further supports the authors' interpretation that "This interpretation of motor control emphasizes that the objective of the motor system is to attain the behavioural goal and this requires feedback processed by a distributed network." A discussion of this point made in this recent paper in the intro/discussion is important. Finally, there is a recent paper that argues for the input-driven nature of motor cortex (Sauerbrei et al., 2020) and is cited/discussed by the authors but briefly and mainly in the discussion. I think given the relevance of this recent paper to the core message here, it should also be briefly discussed in the introduction to better set up the work.

We agree with the reviewer that there are discrepancies between the motor cortical dynamics reported by Suresh et al. 2020 and Abbaspourazad et al., 2021 during grasping tasks. This difference may reflect differences in task as in Suresh et al. 2020 the monkeys grasped objects whereas in Abbaspourazad et al., 2021 monkeys had to reach and grasp objects. Thus, rotations may reflect the reaching component of the behaviour. This has been elaborated on in the Discussion which now reads (lines 539-542) “The hand area of MC also does not exhibit rotational dynamics during grasping-only behaviour (Suresh et al., 2020), though it does exhibit rotational dynamics during reach-to-grasp (Abbaspourazad et al., 2021; Rouse and Schieber, 2018; Vaidya et al., 2015) which may reflect the reaching component of the behaviour.”.

We have also briefly mentioned the findings by Sauerbrei et al. 2020 in the Introduction which now reads (line 79-81) “Lastly a recent study demonstrates that motor cortical dynamics are driven by inputs coming from motor thalamus (Sauerbrei et al., 2020)."

Minor:

1) The Methods are clear and comprehensive, but just to make understanding of the simulation setup easier, it would help to have a diagram of the computation graph for the recurrent and non-recurrent networks that shows their number of units, activations/nonlinearities, RNN cell type, etc., added as supplementary figure.

We agree that this is useful and have added it to Figure 1

2) Again, to help more clearly convey the simulations, it would help to show the task goals (x*) that are inputs to the simulated brain for example trials in each task (for example added to Figures 2 and 6).

We agree that this is useful and have added it to Figure 1

3) Similar to how VAF is shown on top of all plots of jPC planes, it would be helpful to have the rotation frequency for each jPC plane noted next to it. Currently it is difficult to find the jPC frequency associated with each plot from the text.

We agree and have added it to the appropriate figures

4) I am a bit surprised by how different the null distributions are for modeling muscle activity (Figure 3F) and kinematics (Figure 3H). The null distribution is simply the R2 for a constrained or unconstrained dynamic model fit to a subsampled version of the neural activity. The only difference between the null distributions in Figure 3F and Figure 3H seems to be the downsampled dimension, which for muscle activity is 6 and for kinematics is 4 (per equation 1). Any insight will be welcome as to why down sampling the population activity to 4 (Figure 3H) results in so much worse R2 compared with down sampling it to 6 (Figure 3F)?

We thank the reviewer for raising this concern. Originally, we had applied PCA to reduce the dimensionality of the kinematic signals from 4 dimensions to 2, and the muscle signals from 6 to 4. We realize now that to be more conservative in our significance testing, we should use the full dimensionality of the kinematic and muscle signals. As such, we have changed the figures throughout to reflect this.

References:

Abbaspourazad, H., Choudhury, M., Wong, Y.T., Pesaran, B., Shanechi, M.M., 2021. Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior. Nature Communications 12, 607. https://doi.org/10.1038/s41467-020-20197-x

Aoi, M.C., Mante, V., Pillow, J.W., 2020. Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making. Nature Neuroscience 1-11. https://doi.org/10.1038/s41593-020-0696-5

Gao, Y., Archer, E.W., Paninski, L., Cunningham, J.P., 2016. Linear dynamical neural population models through nonlinear embeddings, in: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (Eds.), Advances in Neural Information Processing Systems 29. Curran Associates, Inc., pp. 163-171.

Kao, J.C., Nuyujukian, P., Ryu, S.I., Churchland, M.M., Cunningham, J.P., Shenoy, K.V., 2015. Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nature Communications 6, 7759. https://doi.org/10.1038/ncomms8759

Remington, E.D., Narain, D., Hosseini, E.A., Jazayeri, M., 2018. Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics. Neuron 98, 1005-1019.e5. https://doi.org/10.1016/j.neuron.2018.05.020

Sani, O.G., Abbaspourazad, H., Wong, Y.T., Pesaran, B., Shanechi, M.M., 2021. Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification. Nature Neuroscience 24, 140-149. https://doi.org/10.1038/s41593-020-00733-0

Stavisky, S.D., Willett, F.R., Wilson, G.H., Murphy, B.A., Rezaii, P., Avansino, D.T., Memberg, W.D., Miller, J.P., Kirsch, R.F., Hochberg, L.R., Ajiboye, A.B., Druckmann, S., Shenoy, K.V., Henderson, J.M., 2019. Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. eLife 8, e46015. https://doi.org/10.7554/eLife.46015

Vaidya, M., Kording, K., Saleh, M., Takahashi, K., Hatsopoulos, N.G., 2015. Neural coordination during reach-to-grasp. Journal of Neurophysiology 114, 1827-1836. https://doi.org/10.1152/jn.00349.2015

Reviewer #1 (Public Review):

This paper aims to address the question of whether the rotational dynamics in motor cortex may be due to sensory feedback signals rather than to recurrent connections and autonomous dynamics as is typically assumed. This is indeed a question of importance in neural control of movement.

The authors employ both analyses of motor cortical data and simulation analyses where a neural network is trained to perform a motor task. For the simulations, the authors use a neural network model of a brain performing arm control tasks. Importantly, in addition to the task goals, the brain also receives delayed sensory feedback from the muscle activity and kinematics of the simulated arm. The brain is modeled either using a stack of two recurrent neural networks (RNN) or using two non-recurrent neural network layers to investigate the importance of autonomous recurrent dynamics. The authors use this framework to simulate the brain performing two tasks: 1) posture perturbation task, where the arm is perturbed by external loads and has to return to original posture, and 2) delayed center-out reach task. In both tasks, the authors apply jPCA to units of the trained network, simulated muscle activity, and simulated kinematics and investigate their rotational dynamics. They find that when using an RNN in the brain model, both the RNN layers and kinematics show rotational dynamics but the muscle activity does not. Interestingly, these conclusions for both tasks also hold when networks without recurrent connections are used instead of the RNNs. Also importantly, the rotational dynamics also exist in the sensory feedback signals about the limb state (e.g. joint position, velocity). These results suggest that recurrent dynamics are not necessary for the emergence of rotational dynamics in population activity, rather sensory feedback can also achieve the same.

The authors perform similar jPCA analyses on monkey motor cortical (MC) or somatosensory cortical activity during the same two tasks and find largely consistent results. As with simulations, neural population activity and kinematics show rotational dynamics but muscle activity, which is explored only in the posture task, does not. Importantly, population activity in both motor and somatosensory cortices shows rotational dynamics. This observation is more consistent with the view that rotational dynamics emerge due to inter-region communications and processing of sensory feedback and planning, rather than autonomous dynamics within the motor cortex.

The approach of the paper is interesting and valuable and the questions being addressed are very important to the field. To further improve the paper and the analyses, there are several major comments that should be addressed to fully support the conclusions and clarify the results:

Major:

1) In the Methods, the authors explain how they model a non-recurrent network as follows: "We also examined networks where we removed the recurrent connections from each layer by effectively setting Whh, Woo to zero for the entire simulation and optimization (NO-REC networks)". However, if this is the only modification, it still leaves recurrent elements in the network. For example, if we set W_{hh} to zero, equation 2 will be:

h_{t+1} = (1-a) * h_t + a * tanh(W_{sh} * s_t + b_h)

where a is a constant scalar (seems to be equal to 0.5). This is indeed still a recurrent neural network since h_{t+1} depends on h_t. If their explanation in the Methods is accurate, then the current approach restricts the recurrent dynamics to be a specific linear dynamic (i.e. "h_{t+1} = (1-a) * h_t + ...") but does not fully remove them. The second layer is also similar (equation 3) and will still have recurrent linear dynamics even if W_{oo} is set to 0. To be able to describe networks as non-recurrent, the first terms in equations 2 and 3 (that is (1-a)*h_t and (1-a)*o_t) should also be set to 0. This is critical as an important argument in the paper is that non-recurrent networks can also produce rotational dynamics, so the networks supporting that argument must be fully non-recurrent. Perhaps the authors have already done this but just didn't explain it in the Methods, in which case they should clarify the Methods. However, if the current Method description is accurate, they should rerun their NO-REC simulations by also setting the fixed linear recurrent components (that is (1-a)*h_t and (1-a)*o_t) to zero as explained above to have a truly non-recurrent model.

2) Assuming my comment in 1 is addressed and the results stay similar, the authors show in simulations that even without recurrent dynamics (referred to as the NO-REC case), rotational dynamics are observed in the simulated brain during both tasks (Figure 8). This result is used to suggest that the sensory feedback is what causes the rotational dynamics in the brain model in this case. However, I think to fully demonstrate the role of feedback, additional simulations are also needed where the sensory feedback is removed from the brain model. In other words, what would happen if recurrent and non-recurrent brain models are trained to perform the tasks but are not provided with the sensory feedback (only receive task goals)? One would expect the recurrent model to still be able to perform the task and autonomously produce similar rotational dynamics (as has been shown in prior work), but the non-recurrent model to fail in doing the task well and in showing rotational dynamics. I think adding such simulations without the feedback signals would really strengthen the paper and help its message.

3) A measure of how well each trained network is able to perform the task should be provided. For example, is the non-recurrent network able to perform the tasks as accurately as the recurrent models? The authors could use an appropriate measure, for example average displacement in the posture task and time-to-target in the center-out task, to objectively quantify task performance for each network. Another performance measure could be the first term of the loss in equation 5. Also, plots of example trials that show the task performance should be provided for the non-recurrent networks (for example by adding to Figure 8), similar to how they are shown for the recurrent models in Figures 2 and 6.

4) An important observation is that rotational dynamics also exist in the sensory signals about the limb state. This may imply that the task structure that dictates the limb state and thus the associated sensory feedback may play an important role in the rotations without the recurrent connections. While the present study will be a valuable addition regardless of what the answer is, this is an important point to address: What is the role of the task structure in producing rotational dynamics? In both the posture task and the center-out task, the task instruction instructs subjects to return to the initial movement 'state' by the end of the trial: in the posture task the simulated arm needs to return to the original posture upon disturbance, and in the center out task the arm needs to start from zero velocity and settle at the target with zero velocity. Is this structure what's causing the rotational dynamics? This is an important question both for this paper and for the field and the authors have a great simulation setup to explore it. For example, what happens if the task instructions u* instruct the arm to follow a random trajectory continuously, instead of stopping at some targets? With a simulated tracking task like this, one could eliminate obvious cases of return-to-original-state from the task. Would the network still produce rotational dynamics? Of course, I don't expect the authors to collect experimental monkey data for such new tasks, rather to just change the task instructions in their numerical simulations to explore the dependence of observed rotational dynamics on the task structure. I think this will help the message of the paper and can be very useful for the field.

5) It would be beneficial if the authors could elaborate in the discussion on intuitive explanations of why sensory feedback can produce rotational dynamics even with no internal recurrent dynamics in the brain model. To me, it seems like sensory feedback is providing a path for recurrence to exist in the overall brain-arm system, so the non-recurrent neural networks can learn to exploit that path to effectively implement some recurrent dynamics. Some intuitive explanations like this will be helpful for readers.

6) One main result in data from non-human primates is that there exist rotations also in the somatosensory cortex not just in motor cortex. A more thorough discussion of prior work on rotational dynamics or lack thereof across brain regions and behavioral tasks is important to add here. For example, besides the works cited by the authors, there are other works such as (Kao et al., 2015; Gao et al., 2016; Remington et al., 2018; Stavisky et al., 2019; Aoi et al., 2020; Sani et al., 2021) that discuss or show rotational dynamics in various brain regions and behavioral tasks and should be cited and discussed.

7) The authors state that "In contrast, rotational dynamics appear to be absent in... MC activity during grasping driven by sensory inputs (Suresh et al., 2020)." There are other papers that study dynamics during reach-grasps and still finds rotational dynamics and modes (Abbaspourazad et al., 2021; Vaidya et al., 2015) and should be cited and discussed. The recent paper on naturalistic reach-grasps (Abbaspourazad et al., 2021) also argues for the involvement of a large-scale network in these movements, which further supports the authors' interpretation that "This interpretation of motor control emphasizes that the objective of the motor system is to attain the behavioural goal and this requires feedback processed by a distributed network." A discussion of this point made in this recent paper in the intro/discussion is important. Finally, there is a recent paper that argues for the input-driven nature of motor cortex (Sauerbrei et al., 2020) and is cited/discussed by the authors but briefly and mainly in the discussion. I think given the relevance of this recent paper to the core message here, it should also be briefly discussed in the introduction to better set up the work.

Minor:

1) The Methods are clear and comprehensive, but just to make understanding of the simulation setup easier, it would help to have a diagram of the computation graph for the recurrent and non-recurrent networks that shows their number of units, activations/nonlinearities, RNN cell type, etc., added as supplementary figure.

2) Again, to help more clearly convey the simulations, it would help to show the task goals (x*) that are inputs to the simulated brain for example trials in each task (for example added to Figures 2 and 6).

3) Similar to how VAF is shown on top of all plots of jPC planes, it would be helpful to have the rotation frequency for each jPC plane noted next to it. Currently it is difficult to find the jPC frequency associated with each plot from the text.

4) I am a bit surprised by how different the null distributions are for modeling muscle activity (Figure 3F) and kinematics (Figure 3H). The null distribution is simply the R2 for a constrained or unconstrained dynamic model fit to a subsampled version of the neural activity. The only difference between the null distributions in Figure 3F and Figure 3H seems to be the downsampled dimension, which for muscle activity is 6 and for kinematics is 4 (per equation 1). Any insight will be welcome as to why down sampling the population activity to 4 (Figure 3H) results in so much worse R2 compared with down sampling it to 6 (Figure 3F)?

References:

Abbaspourazad, H., Choudhury, M., Wong, Y.T., Pesaran, B., Shanechi, M.M., 2021. Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior. Nature Communications 12, 607. https://doi.org/10.1038/s41467-020-20197-x

Aoi, M.C., Mante, V., Pillow, J.W., 2020. Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making. Nature Neuroscience 1-11. https://doi.org/10.1038/s41593-020-0696-5

Gao, Y., Archer, E.W., Paninski, L., Cunningham, J.P., 2016. Linear dynamical neural population models through nonlinear embeddings, in: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (Eds.), Advances in Neural Information Processing Systems 29. Curran Associates, Inc., pp. 163-171.

Kao, J.C., Nuyujukian, P., Ryu, S.I., Churchland, M.M., Cunningham, J.P., Shenoy, K.V., 2015. Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nature Communications 6, 7759. https://doi.org/10.1038/ncomms8759

Remington, E.D., Narain, D., Hosseini, E.A., Jazayeri, M., 2018. Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics. Neuron 98, 1005-1019.e5. https://doi.org/10.1016/j.neuron.2018.05.020

Sani, O.G., Abbaspourazad, H., Wong, Y.T., Pesaran, B., Shanechi, M.M., 2021. Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification. Nature Neuroscience 24, 140-149. https://doi.org/10.1038/s41593-020-00733-0

Stavisky, S.D., Willett, F.R., Wilson, G.H., Murphy, B.A., Rezaii, P., Avansino, D.T., Memberg, W.D., Miller, J.P., Kirsch, R.F., Hochberg, L.R., Ajiboye, A.B., Druckmann, S., Shenoy, K.V., Henderson, J.M., 2019. Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. eLife 8, e46015. https://doi.org/10.7554/eLife.46015

Vaidya, M., Kording, K., Saleh, M., Takahashi, K., Hatsopoulos, N.G., 2015. Neural coordination during reach-to-grasp. Journal of Neurophysiology 114, 1827-1836. https://doi.org/10.1152/jn.00349.2015

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

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Reply to the reviewers

Manuscript number: RC-2021-00831

Corresponding author(s): Lu, Gan

Reviewer comments are in regular font. Our rebuttal is in bolded font. Experiments that we plan for the full revision are preceded with “FULL:”. In the revision files, the changes are highlighted in yellow.

General Statements

We thank the reviewers for their detailed feedback. There are two major concerns. First, the manuscript lacks functional analysis of the meiotic triple helices (MTHs). Second, the manuscript makes claims about the properties of synaptonemal complexes (SCs) and MTHs that are inadequately supported. In order to address the first concern, we would need extensive experiments to first identify and then perturb the genes associated with the MTH. Such experiments are beyond the scope of this manuscript and are the focus of future studies. We will address the second concern with mostly text revisions. We will also improve some of the imaging analysis with new experiments that can be done in a few months’ time.

2. Description of the planned revisions

We will acquire new cryo-ET data of pachytene-arrested cell cryolamellae using our new K3-GIF camera. These new data have higher signal-to-noise ratios and allow us to generate a higher-resolution subtomogram average of the MTHs. The achievable resolution will depend on the conformational homogeneity of the MTH segments and on the number of cryotomograms we can capture. If we are able to achieve a subnanometer-resolution reconstruction, we will narrow down the possible identities of the subunits. Even if we cannot achieve subnanometer resolutions, the new data will allow us to test if ladder-like densities were missed in our lower-resolution older data, thereby improving our understanding of the SC’s structure. We will also perform subtomogram analysis of purified ribosomes as a control to strengthen our handedness determination.

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

The Reviewers’ original comments are reproduced in regular font. Line numbers refer to the preliminary revision.

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

Ma and coworkers report studies of budding yeast undergoing meiosis by cryo-ET. They fail to detect structures interpretable as synaptonemal complex, and instead detect feather-like bundles of what appears to be a triple helix. These structures do not appear to be related to the synaptonemal complex, as spo11 mutants that do not initiate recombination, red1 mutants that lack axial elements, and zip1 mutants that lack the central element of the SC still make these bundles. These structures are absent from cells treated with latrunculin A, which depolymerizes actin filaments, but expected structures are not visible in light microscopy of cells treated with two different F-actin-staining reagents. However, it should be noted that another study (Takagi et al, 2021, bioRxiv) did detect actin associated with these structures by immunogold labeling. The structures are also reversibly dissolved in 7% hexanediol.

This part of the paper's findings is well supported by data and is certainly of interest, although interest is somewhat limited by the unknown nature of these structures-what they contain, let alone their function, remains to be determined-in fact, it is not even determined whether or not they are made of protein. However, as an initial report of a previously unknown phenomenon, the paper is of some value.

__Thank you for raising the issue of whether MTHs are composed of protein or not. Aside from the proteins, the only other materials capable of forming large bundles of linear polymers are polysaccharides and DNA. Yeast polysaccharides are found in the cell wall, so they are unlikely to be a candidate for the MTHs. In the nucleus, DNA is abundant. While we favor that MTHs are composed of protein, we cannot rule out that the MTHs are non-chromatin DNA-protein complexes. Depending on the resolution of future subtomogram averages, we may get a better idea of the MTH’s composition.__

There is, unfortunately, a second aspect of the paper that cannot be supported. Although it is clear that synaptonemal complex is present in the cells examined (by standard cytological methods) the authors cannot detect structures consistent with SC in their cryo-ET images. Unfortunately, authors then extrapolate from their inability to detect SC to conclusions about SC, such as that it is not crystalline, and even go so far as to suggest that their failure to detect SC invalidates two models for crossover interference, and that the ladder-like structure reported for SC in many organisms using many difference approaches may be a fixation artifact. Authors show remember that the absence of evidence is not evidence for absence; the speculation described above should be removed from both the abstract and discussion.

We have removed the speculation about crossover interference and limited the scope of our discussion on SCs to budding yeast only.

The differences between traditional EM and cryo-EM are not trivial. In the introduction, we added more details to explain the differences in both the sample preparation and contrast generation:

Lines 79-87: “Meiotic nuclei have been studied for decades by traditional EM (Fawcett, 1956; Moses, 1956), but not by cryo-ET. Cryo-ET can reveal 3-D nanoscale structural details of cellular structures in a life-like state because the samples are kept unfixed, unstained, and frozen-hydrated during all stages of sample preparation and imaging (Ng and Gan, 2020). The densities seen in cryo-ET data come from electron scattering of the biological macromolecules. In comparison, the densities seen in traditional EM from electron scattering of heavy metals such as uranium, tungsten, and osmium, which have adhered to a subset of biological macromolecules that were not extracted in earlier steps.”

We have also removed the term ‘artifact’ from lines 201-202:

Original: “The ladder model is based on images of traditional EM samples, which are vulnerable to fixation and staining artifacts.”

Revised: “The ladder model is based on images of traditional EM samples.”

Reviewer #1 (Significance (Required)):

This paper reports a previously unreported structure in the nuclei of yeast cells undergoing meiosis. The composition and function of this structure remain to be determined. This considerably limits the significance of the paper.

**Referee Cross-commenting**

I agree with the concerns of the other reviewers. I also agree with reviewer 3 that to raise the significance of the paper would require much work. But I think that the raw observation is of value, albeit in a journal of record. So I would stick with my recommendation of text changes, keeping in mind that there may not be a suitable journal in LSA's portfolio.

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

This work describes helical filamentous structures observed in budding yeast nuclei that were cryosectioned and imaged using cryo-electron tomography (cryoET).

The goal of this work seems to have been to conduct an ultrastructural analysis of the synaptonemal complex (SC), a meiosis-specific protein structure that holds chromosomes together during meiosis and is thought to regulate meiotic recombination. In conventional TEM images of fixed, embedded, and stained sections obtained from pachytene nuclei, SCs usually appear as long, thin, transversely striated structures. At pachytene, SCs extend along the full length of a thin (100-nm) gap between paired chromosomes (typically 1-6 µm long in yeast cells). Surprisingly, the authors did not observe SCs, perhaps because these structures do not produce much contrast in cryo-EM images. Instead, they observe abundant triple helical structures in the nucleoplasm, which they designate as "meiotic triple helices" (MTHs). The authors report that these triple helices assemble at the same time as but independently of the SC. They publised a preprint in which they indicated that these structures were somehow related to SCs, but this the revised version reports that they appear independently of SCs. They further report that treatment of cells with the F-actin depolymerizing drug Latrunculin-A (LatA) resulted in a lack of detectable triple helical structures in the nucleus, suggesting that these structures may be a form of actin or, alternatively, that they may require F-actin for their assembly.

While the work is technically mostly sound, its significance is unclear because the reported structures have no known function. Many, if not most proteins will form helical structures if their concentration rises above a threshold defined by their binding affinity for themselves (see https://doi.org/10.1186/1741-7007-11-119 and references therein), so this may simply be an example of an abundant nuclear protein that polymerizes to form helical filaments under the conditions that trigger yeast sporulation.

Thank you for raising the interesting possibility that MTHs form helices because their subunits have exceeded a critical concentration threshold. In the revised text, we discuss the possibility that the MTH is simply a protein that is highly expressed in meiotic cells and polymerize either due to exceeding a critical concentration, or having undergone a biochemical change like a post-translational modification:

Lines 408-415: “Note it is possible that the MTHs may not be directly involved in meiosis, but are instead a protein that is at a sufficient concentration or has the right biochemical modifications to form helices in pachytene because it is known that many proteins can form a helix under the right conditions {Crane, 1950; Pauling, 1953; Theriot, 2013}. These MTHs also have lateral interactions that allow them to pack with crystalline density. Their sensitivity to 1,6-hexanediol suggests that the polymerization both within and between MTHs are based on hydrophobic interactions. Further work will be needed to determine the identity of the MTH’s subunits and their potential function.”

The authors perform cryosectioning and cryoEM on yeast cells undergoing meiosis to show that the assembly and disassembly of MTHs follow a similar time course as that of the SC. The observation of these triple-helical filaments in meiotic nuclei has also been reported in another study (https://www.biorxiv.org/content/10.1101/778100v2.full.pdf+html), which proposed, based on immuno-EM labeling, that they may be actin cables. This study reports that the structures are not detected using phalloidin or Lifeact-mCherry. However, treatment with LatA did eliminate detection of MTH structures, suggesting that they may be comprised of actin.

In my view, there are a number of issues that should be addressed before publication. Many of these relate to the presentation of the findings. Detailed comments below:

1. The presentation of the work is very confusing. The authors clearly expected to observe SC structures, but did not. They conclude that MTHs are not SCs, since they do not depend on molecular components required for SC assembly. They should describe their findings in a more straightforward way rather than veering from introducing the SC to describing the MTHs.

We have restructured the manuscript to tone down the discussion about the SC. However, we have to start with the SC because it is the most iconic feature of pachytene cells and a major organizer of chromatin in meiosis. Furthermore, its presence, as indicated by Zip1-GFP signals, was key to establishing that our cells were indeed arrested in pachytene. It would have been confusing to then overlook the absence of structural features as conspicuous and prevalent as what was expected of SCs. The other sections did have room for improvement. In the sections below, we describe the changes point by point.

Similarly, the discussion section on "recombination and chromosome segregation" seems inappropriate and irrelevant, since no data are presented in this study regarding the functions of the MTHs, and there is no reason to think that they contribute to crossover interference, chromosome segregation, or other aspects of meiosis. Additionally, most of the ideas presented in this section seem very muddled. I recommend deleting this section.

We have deleted the section entitled "Recombination and chromosome segregation".

Throughout the text, we have also changed the term “meiosis-specific” to “meiosis-related” when describing MTHs. Doing so allows for the possibility that MTHs might just form as a consequence of being expressed to a high enough concentration as discussed above.

Along the same lines as comment #1: The title should be changed - absence of evidence for "ladders" is not evidence of absence. Prior work using TEM and superresolution fluorescence microscopy has clearly shown that ladder-like SC structures exist in pachytene nuclei of budding yeast and many other organisms, although they apparently cannot be visualized using the methods described here.

We have changed the title to be less forceful, yet report what we see and don’t see by cryo-ET:

“Cryo-ET detects bundled triple helices but not ladders in meiotic budding yeast”

The authors should clarify whether cryo-sectioning was performed through the full volume of pachytene nuclei.

This comment refers to our attempt at serial cryosectioning, as shown in Fig. S8. We have revised the text in lines 332-334 to reflect the estimated volume covered:

“We successfully reconstructed six sequential sections from one ndt80Δ cell (Figure S8), which represents approximately one third of a nucleus (assuming a spherical shape).”

We also changed Fig. S8’s title so that it doesn’t sound like we reconstructed an entire nucleus:

“MTH bundles are extensive throughout the cell nucleus.” → “MTH bundles are extensive.”

It is not clear from the manuscript which camera/microscope configuration was used to acquire the cryoET data that were used for sub-tomogram averaging. The authors state in the methods that Falcon II and K3-GIF was used for projection images, but it's not clear if this applies to all images. These technical details should be clarified.

We have added a new column to Table S4 that reports the camera used for all the projection imaging and tomography experiments. In the original MS, all of the subtomogram averaging was done using Falcon II data.

FULL: In the full revision, we plan to incorporate new subtomogram averages of MTHs in situ, using K3-GIF data of cryolamellae.

The analysis of the handedness of the helices seems to be questionable as the resolution of the reconstructions for 80S are also quite low. I am uncertain whether this can be used to state with confidence that the MTH are right-handed.

FULL: In the full revision, we will use purified 70S ribosomes, imaged on the same K3-GIF camera and using the same software workflow as for the new subtomogram averaging of MTHs in situ. We expect higher resolution for both ribosomes and MTHs, which will make the handedness assignment unambiguous.

The authors claim that treatment with Latrunculin-A (LatA) leads to disappearance of MTHs. However, they support this with projection images of cells treated with LatA. The projection images are of poor quality and the vitrification in these cells (as well as the DMSO treated cells) do not look appropriate. They should present data for LatA-treated cells and DMSO-treated controls obtained using the same approach and ideally imaged in parallel with untreated cells. They should also quantify the number of sections and cells imaged for all conditions.

Once we realized that the MTH bundles were visible in projections, we chose to report detections of MTH bundles by projection imaging instead of the costly tilt series. The apparent poor quality and questionable vitrification comes from the fact that the projection images show the cryosection’s crevasses and knife marks, which reside on opposite cryosection surfaces. These sectioning artifacts are computationally excluded from tomographic slices. The following line was added to the figure caption to explain this:

“These image features are not devitrification artifacts; they are absent from the tomographic slices in other figures because they can be computationally excluded.”

The quantification of the MTHs in Lat-A vs control cells are in Table 2. We have now added these numbers to both the text and the figure caption.

The similarities between the MTHs and SCs - that both are present in meiotic nuclei and sensitive to hexanediol - seem unlikely to be functioanlly relevant. Again, I think the presentation suffers from being focused on the SC which was not seen, rather than on the MTHs.

We have toned down the discussion on SCs throughout the manuscript. We have retained the motivation for using 1,6-hexanediol to probe the MTHs physico-chemical properties and the fact the previous work on SCs provided motivation for this perturbation experiment. However, we removed the comparison of their relative sensitivities to 1,6-hexanediol (see reply to point #10 below).

The absence of 100-nm-wide zones containing nucleosomes is again not evidence for lack of SCs. SCs are ribbon-like structures - they are about 100 nm in one dimension but the thickness has not been characterized reliably; even if SCs do exclude nucleosomes (which is not certain) the excluded volume might be much smaller than the authors imagine.

We did not argue for “lack of SCs”; these structures clearly exist in our cells given the fluorescent linear structures seen in Zip1-GFP expressing cells. We only say that the textbook portrayal of SCs needs revision, though we should have restricted our statement to yeast. In the literature and textbooks: whenever the SC’s central element is drawn, it is depicted without internal nucleosomes and being densely packed with SC proteins.

Does Lifeact-mCherry enter the nucleus? This information is important in interpreting the failure to detect MTHs using this probe.

While Lifeact-mCherry is small enough to passively diffuse through the nuclear pore, our data cannot rule out that this molecule is excluded from the nucleus. We added the following sentence as a caveat:

Lines 244-245 “Note that we cannot rule out that Lifeact-mCherry is excluded from the nucleus.”

The sensitivity of the MTH structures to 1,6-hexanediol treatment is potentially interesting, but it does not reveal anything about their structure or function, only that their assembly likely depends in part on hydrophobic interactions. Caution should be used in interpreting these findings.

We have toned down the discussion about the meaning of the MTH bundles’ 1,6-hexanediol sensitivity by removing this line from the original Results:

“MTH bundles are therefore sensitive to a slightly higher concentration of 1,6-hexanediol than SCs are and reform when 1,6-hexanediol is removed.”

We have also added the following line to more clearly say what sensitivity to 1,6-hexanediol means:

Lines 412-414: “Their sensitivity to 1,6-hexanediol suggests that the polymerization both within and between MTHs are based on hydrophobic interactions”

The figure legends and/or Methods sections should clarify what is represented in each figure, and how the data were acquired. In particular, cryotomographic slices of varying dimensions (6nm, 10 nm or 12 nm or 70 nm) are mentioned in the captions of several figures (2-6, and S1, S2, S3). However, is often unclear whether these represent physical or computational sections.

“Tomographic slice” refers to a rendering of a computationally extracted slice from a reconstructed tomogram. To make it clearer, we have added the term “computational” to describe the tomographic slices in each figure caption.

Page 23 has a supplemental figure but no captions. Is this the same as Fig. S8?

Yes, this is a copy of Fig. S8 that appeared due to MS Word’s jumping-figure bugs. We will manually edit the PDF in the future revision.

I do not find the model figure (Figure 7) to be helpful. Additionally, the failure to detect SCs and the presence of MTHs do not warrant a "revised model of the meiotic yeast nucleus."

We now call panel A and B the “Traditional EM” and the “Cryo-EM” models, respectively. The figure therefore reports the large nuclear bodies seen by the two methods and no longer implies correctness.

We also changed the related sentence in the Introduction:

Original: “Our work strongly suggests that current models of pachytene nuclear cytology need revision.”

Revised: “Our analysis shows that MTHs coexist with SCs, which have an unknown cryo-ET structure.”

The absence of MTHs in haploid cells induced to undergo meiosis should perhaps be studied in more detail. Even SCs are present in haploid meiotic cells, so the absence of these structures may be informative as to their function. Haploid cells should also be stained for SCs and imaged by immunofluorescence to verify that they are in meiosis.

The haploid strain that was treated with sporulation media cannot enter meiosis. Haploid cells that are capable of entering meiosis need to be disomic for chromosome III, with each copy having a different mating type at the Mat locus. We believe that the construction and studies of such strains would be more meaningful after we identify the MTH’s subunits and determine its function in diploid cells.

The yeast strain is SK1, not SK-1.

Thank you. This mistake is corrected.

What are "self-pressurized-frozen samples" (p.2)?

Self-pressurized-frozen samples are generated by an alternative to the conventional machine-based high-pressure-freezing method. We have added more details in the new lines 135-141:

“Self-pressurized freezing is a simpler and lower-cost alternative to conventional high-pressure freezing, which requires a dedicated machine that consumes large amounts of cryogen. In the self-pressurized freezing method, the sample is sealed in a metal tube and rapidly cooled in liquid ethane. The material in direct contact with the metal cools first and expands by forming crystalline ice, which exerts pressure on the material in the center of the tube (Leunissen and Yi, 2009; Yakovlev and Downing, 2011; Han et al., 2012).”

Reviewer #2 (Significance (Required)):

The observation of MTHs is novel but (as stated above) of unclear significance, given that their molecular identity and function are unknown.

This work may be of interest to the meiosis field, with the caveats described above that the functional relevance is currently unclear.

This review was co-written by referees with expertise in meiosis, chromosome organization, SC structure and function, and cryoEM.

**Referee Cross-commenting**

The reviews are strikingly concordant so I don't think much needs to be added.

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

The authors report the observation of filamentous structures (further termed meiotic triple helices MTH) in the nucleoplasm during meiosis in yeast cells. Those structures are visualized using Cryo-ET. While the authors initially seem to assume an association of those structures with synaptonemal complexes, they discover that those structures rely on filamentous actin and are not affiliated with synaptonemal complexes after all.

While I think that the observation of those MTH by Cryo-ET is interesting, the overall structure of the paper and presentation of the data are not very well done. As the authors find throughout their experiments that the MTHs are not associated with synaptonemal complexes the strong focus in the first figures on synaptonemal complexes as well as the title of the paper are very mis-leading. The authors try to initially make the point that other labs have observed ladder like structures by transmission electron microscopy and want to make the claim that those observed structures might be an artifact of sample preparation, hence the title: Meiotic budding yeast assemble bundled triple helices but not ladders. However, at the end those structures seem unrelated to synaptonemal complexes.

In addition, several labs have reported the presence of nuclear actin in meiosis and mitosis and have even succeeded to show those structures by transmission electron microscopy, questioning the "artifact" argument.

Presumably, the Reviewer means intranuclear actin, as opposed to perinuclear (cytoplasmic) actin. If so, then we have only seen one paper, the one from the Takagi et at 2021 (bioRxiv) that has reported seeing structures associated with nuclear actin. Note however, that the revised Takagi bioRxiv paper is very careful in saying that the filament bundles contain actin, which is not the same as saying that the filaments are polymers of actin.

Our “artifact” argument – now removed – referred to SCs, not to nuclear actin.

In the revised manuscript, we use the term “intranuclear” to make it clear that we are referring to structures inside the nucleus. We also use the term F-actin, where appropriate, to refer to the best-studied actin polymer, which resembles a double helix. Doing so eliminates confusion about other forms of actin: G-actin, which cryo-ET cannot yet directly visualize due to its small size; and non-canonical actin polymers, for which there are no previous experimental X-ray or cryo-EM structures for comparisons.

The story line of the paper is weak and I think the authors would have been better of reporting their cryo-ET structures and making a better link to actin or determining what else they think might be a component of those structures. Immuno-EM (as actually shown in Reference 41) of actin would have been much more convincing. The authors could also use the power of Cryo-ET and the achievable higher resolution to describe those filaments in much more detail. In my opinion this would have been a much better and more exciting paper.

We disagree that “making a better link to actin” is the right approach because doing so presupposes that the structures are composed of actin, for which the present evidence is inadequate. We do agree that determining what the MTHs are (or are not) would be valuable.

FULL: Now that we have better access to a K3-GIF camera and a cryo-FIB-SEM, we will attempt higher-resolution subtomogram averaging analysis. If we are able to achieve subnanometer resolution, we will attempt to narrow down the fold of the MTH subunit. Note that this goal will require that the MTHs are conformationally homogenous and that we can image sufficient copies of the MTHs.

In summary: While I think the Cryo-ET images of those structures could be very exciting the paper unfortunately does not do a very good way in presenting this data and is at times misleading trying to proof or rather dis-proof a connection to synaptonemal complexes. Based on this I think that the paper can not be published in the current form and needs major revisions that would require a significant amount of time.

**Minor comments:**

Figure 1: The choice of timepoints is confusing and makes it hard to compare. While wild type is shown at 0,1,3,4,5,8h, the mutant is shown at 0,2,3,4,5,6,7h. It would be appropriate to select the same timpepoints for both conditions.

FULL: We will recollect fluorescence images of the mutant cells at the same time points as the wild type.

Figure 3 and 4 need a quantification of the number of observed MTHs, in particular as only selected regions of the nuclei are shown.

These images were taken from single cryosections instead of serial cryosections, which would have been too difficult to do for multiple conditions and multiple cells. Therefore, quantification would be obfuscated by the fact each cryosection samples a small fraction of the nuclear volume. We believe that reporting the number of cell cryosections that are MTH-positive (Table 2) is at present the best way to characterize their abundance and ability to polymerize. Once we are able to identify the MTH gene products, we will be able to perform GFP tagging experiments and thereby get a much better estimate of the polymer mass as a function of biochemical perturbations.

Fig 7. The data certainly does not support a "REVISED" model of the yeast nuclear organization.

We have changed the Figure title to “A cryo-ET model of the meiotic yeast nucleus.” We now also refer to panels A and B as “Traditional EM model” and “Cryo-ET model”, respectively.

Reviewer #3 (Significance (Required)):

Several publications have already shown the presence of actin in meiotic and mitotic nuclei and have even succeeded in observing those structures by transmission electron microscopy. Based on this it is not clear why the authors have not tried to put their work in context to all these observations and used their technology to obtain novel information on the structure, which might be helpful to identify which proteins compose the MTHs. Based on how the data is presented I do not think that this paper contributes anything new to the field.

Presumably, the reviewer means that F-actin has been imaged in yeast cells, because for actin to exist in nuclei in mitosis/meiosis, the organism would have to undergo closed mitosis/meiosis. Furthermore, for actin to be observable by transmission electron microscopy, it would have to be in the filamentous (F-actin) form. We could not find any publications that report transmission electron microscopy of F-actin in yeast cells. We therefore cannot relate our results to F-actin in the meiotic nucleus.

My field of expertise is meiosis and mitosis as well as imaging (light and electron microscopy).

**Referee Cross-commenting**

All reviewers seem to agree that the general observation of these structures is interesting but that there is a reduced significance as the function and identity of these structures remains unknown.

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

The main unanswered question is: what is the function of the MTH bundles? To address this question, we would first need to identify the gene products that are needed for MTH assembly. Next, we would need to do genetic perturbation experiments to actually determine the MTHs’ function. These experiments would constitute a complete study, which is better suited for a separate, future manuscript.

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

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Reply to the reviewers

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

Saha and colleagues investigated the functions of the long non-coding RNA (lncRNA) DRAIC in malignant glioma. They find that DRAIC expression decreases cell migration/invasion and tumorsphere/colony formation in vitro, and tumor growth in vivo using established cell lines. Mechanistically, DRAIC is known to inhibit NF-kB signaling and the authors demonstrate that DRAIC activates AMPK leading to repression of mTOR, which decreases protein synthesis and increases autophagy. This is a solid study highlighting a potentially interesting pathway of tumor growth and invasion in brain tumors.

Answer: We appreciate Reviewer 1 for the positive feedback of our study

Major Comments:

1) It is unclear whether the presented values (mean +/- SD) in the histograms refer to repeat measurements (in which case n = 1) or independent experiments (n>1). The number of replicate experiments is not stated in the methods or figure legends. This must be included.

Answer: We want to thank Reviewer 1 for pointing out this omission. We have now included this information in the Materials and methods and in figure legends section.

2) I don't think the immunoblot for p62 in Fig. 5C shows a convincing increase following DRAIC knockout, so the statement on p.8 should be revised.

Answer: We have revised the statement to say: Consistent with DRAIC decrease being associated with a decrease in autophagic flux, and despite a decrease in p62 mRNA, the level of P62 protein is increased in three of the DRAIC KO prostate cancer cells (Fig. 5C, KO1, KO2, KO4 compared to WT) and unchanged in the other two.

3) On p.8/Fig.5 the authors make a case that increased DRAIC levels increase lysosomal degradation of autophagosome core proteins LC3 II / p62 (resulting in decreased protein levels of both), while simultaneously increasing gene expression of LC3B and p62 (causing increased mRNA levels). The data for DRAIC overexpression fit this logic fairly well (even though I think more work is needed to fully support this claim), but I am finding it difficult to reconcile the DRAIC knockout data with this scenario - here, loss of DRAIC results in increased protein levels to decreased autophagy, but also decreased gene expression. To fully support this argument, rescue experiments would be needed using FoxO3a knockout/overexpression.

Answer: Note that the mRNA level is not always correlated with protein expression. This is particularly true for LC3 and p62, whose protein levels are significantly affected by the extent of fusion of autophagosomes and lysosomes and subsequent degradation in autophagolysosomes. Thus, although the mRNA of these genes is decreased in DRAIC KO cells (Fig. 5E), the proteins are increased (Fig. 5C) because of decrease of autophagic flux (and decrease of degradation in the autophagolysosomes).

The overexpression of FoxO3a in the DRAIC KO cells will not restore mRNA levels of LC3 or p62, because we show in Fig. 4H that FoxO3 phosphorylation by AMPK is suppressed by DRAIC KO. This phosphorylation is important for the induction of LC3 or p62 mRNA by FoxO3.

FoxO3 knockout or knockdown in DRAIC OE cells should decrease LC3B or p62 mRNA in Fig. 5D, but it is already known from the Literature that FoxO3a is necessary for inducing LC3B or p62 mRNA. Cell Metab. 2007 Dec;6(6):458-71. doi: 10.1016/j.cmet.2007.11.001.PMID: 18054315.

4) Similarly, the data supporting increased autophagy following DRAIC overexpression (Fig. 5F/G) are a bit weak and lack controls (is the LC3B-GFP overlapping with endogenous LC3B and autophagosomes? Was the transfection efficiency comparable? Is there fusion with lysosomes?). In the absence of stronger data, the authors should temper their claims that DRAIC increases autophagy.

Answer: LC3B of fusion protein LC3B-GFP is known to overlap with the p62 puncta (similar to endogenous LC3B). This result is in Fig. 4A of the citation that we have now added (Proc Natl Acad Sci U S A. 2016 Nov 22;113(47): E7490-E7499. doi: 10.1073/pnas.1615455113. Epub 2016 Oct 17)

To support our hypothesis that DRAIC OE induces more autophagy compared to empty vector, we used Bafilomycin A1 in Figure 5B to inhibit the autophagosome and lysosome fusion. We see the accumulation of more LC3B upon treatment with Bafilomycin A1 in the DRAIC OE cells (compared to EV containing U251 cells), consistent with the idea that autophagosome-lysosome fusion is increased by DRAIC OE.

5) No information is provided on animal numbers used in this study. How many mice were used per cohort? Were male and female mice used? Authors should follow ARRIVE guidelines in reporting animal experiments. The method for calculating tumor volume needs to be specified.

Answer: We have included the details about the animal study in methods section of our modified manuscript .

6) Student's T-test is inappropriate for comparisons of more than two groups (i.e. all experiments using DRAIC knockout cells) - for these experiments a Kruskal Wallis test or ANOVA should be used. Did the authors test for normal distribution of their data? This may affect statistical testing and should be taken into consideration.

Answer: We have now modified our statistical calculation and included in the statistical analysis section in our modified manuscript.

Minor Comments:

7) Authors mention that DRAIC expression is undetectable in immortalized astrocytes and GBM cancer stem cells (Fig. S1). What is the source of these cells and how were they cultured?

Answer: The immortalized astrocytes and GBM stem cells and their culture conditions is now described.

8) The immunoblot in Fig. 3D could be replaced with a slightly lower exposure to make the difference between WT and DRAIC KO more obvious.

Answer: We have now replaced the immunoblot with lower exposure.

9) Some immunoblots in Fig. 3 (panel E, p-S6K and S6K; panel H, actin) are not of the best quality and an effort should be made to replace them.

Answer: We have now replaced the immunoblot p-S6K as reviewer mentioned.

10) Why are different loading controls used in Fig. 3 (a-Tubulin v actin)?

Answer: We use multiple loading control to make sure that we are not underestimating or overestimating changes in the experimental protein because of unexpected changes in the loading controls.

11) Compared to other blot images in the same figure (e.g. Fig. 3E), the bands for p-mTOR and mTOR in Fig. 3F look compressed and should be shown appropriately sized.

Answer: We have modified the Figure as reviewer suggested.

12) The layout of Fig. 4 is somewhat confusing. I would suggest organizing this according to DRAIC overexpression in A172 and U373 cells versus DRAIC knockout in LNCaP cells. Each immunoblot should be clearly labelled with the corresponding cell line, and it should be clearly explained why p-FoxO3a was tested in U251 cells, rather than A172/U373 as in the rest of the figure.

Answer: We thank the reviewer for the constructive criticism. We have labeled all the cell lines in the Figure as reviewer suggested. We have now systematically alternated the prostate cancer cells (for KO) and the GBM cells (for OE), as we looked at each relevant marker. We have now included the western blot for p-FoxO3a from another glioblastoma cell line U373. Please find the modified Figure 4K for p-FoxO3a.

13) Labelling of immunoblot in Fig. 5B is confusing and should be improved.

Answer: We have modified the Fig. 5B to make the label clearer.

14) Changes in GLUT1 expression (Fig. 7A) should be validated on the protein level.

Answer: We have included the immunoblot for GLUT1 from DRAIC KO cells in Figure 7B. GLUT1 protein is increased upon DRAIC KO.

Reviewer #1 (Significance (Required)):

The authors describe a novel link between the lncRNA DRAIC and AMPK activation through inhibition of NF-kB-mediated regulation of GLUT1. This study extends their previous work on DRAIC inhibition of NF-kB in prostate cancer (Saha et al. Cancer Res 2020). There is one study describing DRAIC effects on growth and invasion in glioma cell lines (Li et al. Eur Rev Med Pharmacol Sci 2020), but the work presented by Saha and colleagues contains stronger experimental data and a more detailed and previously undescribed mechanism.

The current study presents a mechanistic advance that increases the understanding of tumor growth and protein synthesis in cancer cells. The data presented in the study are not supported by in vivo experiments (other than suppression of tumor growth by DRAIC overexpression), validation in human tissue and/or primary patient-derived human glioblastoma cells, or even substantial rescue experiments. This limits the influence of the work on the field. I'm also not sure how transferable findings from DRAIC knockout in prostate cancer cell lines are to glioma, although the results are mostly complementary to the data from glioma cell lines. This is particularly relevant to the proposed mechanism of GLUT1 regulation by NF-kB, as the bulk of experimental data in Figures 6 and 7 was generated in prostate cancer cell lines and is only poorly validated in glioma cells. The study results will be most relevant for researchers investigating cell signaling pathways and autophagy in cancer.

Answer: We like to thank reviewer for the positive comments on our study. The DRAIC KO experiments of Fig. 6 and 7 cannot be done in glioma cells, because as we show if Supp. Fig. S1, there are no glioma cells or GSC that express DRAIC to levels comparable to LnCaP. We have shown that GLUT1 mRNA decreases in the glioma cells when DRAIC is overexpressed (Supp. Fig. S4. We also show in Fig. 7G that AMP levels increase when DRAIC is overexpressed in glioma cells.__

__

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

In this manuscript, the authors describe DRAIC as a lncRNA downregulated in prostate cancer. They postulate that DRAIC expression surpasses invasion, migration and growth. Mechanistically, the authors show that DRAIC activates AMPK by suppressing NFkB target gene TOR and indirectly impacting translation and autophagy. Collectively the observation is interesting and robust. However, I have several technical requests, particularly regarding the mechanistic part of the paper.

Answer: We appreciate the positive feedback. We have addressed the reviewer’s concerns in our modified manuscript.

Major Comments:

1) The authors should rescue Ko phenotypes by over expressing DRAIC to consider potential off target effects.

Answer: DRAIC OE alone is sufficient to have exactly the opposite effect as DRAIC KO in protein translation (Fig. 3C-F), so DRAIC OE will rescue the effect of DRAIC KO. We make a similar argument for all the phenotypes, including mTOR, S6K and ULK1(S757) phosphorylation (Fig. 3G-J), AMPK and FoxO3a phosphorylation (Fig. 4B-C; J-L), autophagic flux (Fig. 5B, C) and effects on LC3B and p62 mRNAs (Fig. 5D, E). The same is true for our published phenotypes of DRAIC KO on invasion, migration and NF-kB activity (Saha, Cancer Research, 2020)

2) The blots showing TOR and ULK1 phosphorylation need to be repeated. This is an important part of the paper and I feel that these blots are hard to interpret. p-S6K typically run a bit higher in gels. there may be a technical problem.

Answer: We are not sure which specific blots the reviewer is referring to, and it is possible that the blots the other reviewers pointed to are the ones under question. We have changed those blots so that the results are clear.

3) GLUT1-related results are interesting, but the authors should provide genetic evidence that the effects are mediated by GLUT1. How do we know that glucose uptake is indeed upregulated upon knockout?

Answer: In Fig. 7 C-F we show that the effects of DRAIC KO on invasion, protein translation, AMP levels and AMPK activity are reversed by the GLUT1 inhibitor Bay-876. This is a cleaner result than using siRNA to knockdown GLUT1. siRNAs can have off-target activity and sometimes cannot decrease a protein sufficiently below the threshold necessary to see reversal of action.

Minor Comments:

4) The figures need to be updated. FOnts are all different, lots of unaligned graphs, quality of the blots are poor.

Answer: We have updated the Figures and changed fonts as reviewer mentioned.

Reviewer #2 (Significance (Required)):

The observation is interesting, but the mechanism is incompletely understood. This is a nice addition to the literature, even without the mechanism.

Answer: We want to thank the reviewer for the constructive criticism.

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

**Summary:**

Shaha and colleagues present a study demonstrating the tumor suppressive role of DRAIC, a long non-coding RNA transcript, through transmission of the signal from IKK/NF-kB to the AMPK/mTOR pathway via regulation of GLUT1 expression. The inhibition of mTOR by this pathway results in the reduction of protein translation, cellular invasion and activation of autophagy. Several diseases and models as well as multiple genetic and pharmacological manipulations were used to investigate the mechanisms at play. The manuscript is well written and the experiments are well designed. The conclusions are supported by the results. The following major and minor comments should be addressed:

Answer: We appreciate the reviewer for the positive comments on our study.

Major Comments:

1) In addition to reporting the effect of DRAIC overexpression on tumor volume, the authors should present survival studies with one or more models.__

Answer: __We thought of doing the survival study in our glioblastoma model but unfortunately, the tumor growth is very rapid (exceeding the size permitted by our IACUC in 2-3 weeks). The animal ethics welfare committee did not allow us to keep the mice for a longer time to perform the survival study.

2) Since the authors study metabolic energy sensor pathways, related to glycolysis, it would be important to perform some of the key experiments in physiological level of glucose: e.g., pmTOR, pAMPK, LC3-II expression level in DRAIC overexpressing and deficient cells.

Answer: The concentration of glucose in plasma is 1G/L, while that of the RPMI medium is 2G/L. We do not think we are too far from the physiological levels of glucose.

3) In addition to RT-PCR data, GLUT1 protein levels should be investigated in the different DRAIC expressing cells.

Answer: We have incorporated the GLUT1 protein expression data from DRAIC KO cells in Figure 7B and DRAIC overexpressing cells in supplementary Figure 4G-H. The blots from the same gels were split into different panels, the loading control GAPDH remain same in Figure 4K and Supplementary Figure S4H.

4) The effect of DRAIC on GLUT1 expression is also measured in condition of glucose saturation, which does not reflect disease state. The decrease of GLUT1 in response to DRAIC overexpression and the increased GLUT1 level in DRAIC deficient cells should be investigated in physiological levels of glucose.

Answer Same as above. We are near physiological levels of glucose.

__Minor Comments:

__

5) All the data are generated with established cell lines (e.g., U87) but more clinically relevant models, such as patient-derived primary cells like the ones used in Fig. S1, could be used to replicate some of the key findings.

Answer: As we showed in Fig. S1 that DRAIC is not expressed in glioma stem cells, and so knockout experiments are not possible. We believe that the knockout experiments are the most relevant to this paper because they do not run the risk of artefacts from overexpression of an RNA far beyond physiological levels.

6) Also please provide further details about the patient-derived cells from Fig. S1.

Answer: We have mentioned the details of the cell lines in our modified manuscript.

7) The statistical analysis section states that the number of measurements is indicated however I don't see the sample size of the experiments.

Answer: We have now incorporated the number the experiments in our modified text.

Reviewer #3 (Significance (Required)): The study reports a new model of regulation of tumor via long non-coding RNA. This article adds to the growing literature The topic and content of the article is relevant and significant to the field of tumor research but the significance and impact could be enhanced with the use of more physiologically relevant models and conditions as pointed in the major comments.

Answer: We want to thank the reviewer for the positive feedback on our study.

This is something that I've been thinking about a lot lately, particularly as a white academic. Students have things to say, but if we require them to conform to "Standard American English" (I'm in the US, so I'm using SAE as an example, but this would presumably apply elsewhere) and scholarly/disciplinary academic writing expectations/form, then a teacher's fixation on "proper grammar" or "academic voice" or "scholarly form" likely DO stifle the ideas and knowledges that students may have to share ("How many points off for not using an Oxford comma?" "What if I use 'informal language' or 'slang'?") As one student explained just before they were about to graduate, they had spent most of their academic career "whitewashing" their ideas and language, often writing multiple drafts, first in their own voice, then progressively more "academic," so they were never actually able to express what they really had to say...until a two faculty said "just write it the way you want. Here are some models to consider, but you don't have to conform to these either."

Author Response:

Reviewer #1 (Public Review):

[...] The authors do a great job of listing and evaluating possible explanations, one of which is simply that the strains carry multiple mutations of small effect. All but one of the successfully mapped variants consists of missense and nonsense mutations. I think it's important to note that this represents a particular range of the effect-size distribution of mutations affecting the YFP phenotype. We know from the authors' earlier work that there are lots of mutations that can affect gene expression in cis, and so the absence of trans-acting cis-regulatory variants here is parsimoniously interpreted as due to their small effects. In general, work in other systems (particularly human genetics) has shown that even molecular traits are often hugely polygenic, affected by thousands of variants of tiny but non-zero magnitude. With a forward screen of the sort performed here, it's difficult to know how much of the phenotypic variance is due to unmapped small-effect variants, but two lines of evidence suggest it may be a lot: first, the absence of mappable causal mutations in 36/82 mutants, and second, the differences between EMS mutant strains and their matched single-site mutants. The authors commendably report and discuss these issues but to my mind they neglect them in drawing inferences and generalizations from their findings.

We thank the reviewer for these encouraging comments and also appreciate the reviewer pointing out these concerns.

With respect to the overlap of the trans-regulatory mutations we mapped and previously identified eQTL, we agree the possibility of similar mapping biases in the two BSA-seq studies contributing to the overlap of trans-regulatory mutations and eQTL warrants further exploration. We interpret the reviewer’s comment to suggest that if some regions of the genome systematically showed lower sequencing read coverage (because of poor read mappability, PCR biases or any other reason), the power to detect trans-regulatory mutations and eQTL in these regions would be decreased compared to regions of the genome with higher coverage because the G-tests used to identify significant associations with expression in both studies are based on read counts. Consequently, variation in sequencing read coverage across the genome shared in this study and the prior study identifying eQTL, both of which used BSA-seq, could lead to the enrichment of transregulatory mutations in eQTL regions. Indeed, consistent patterns of read coverage across the S. cerevisiae genome have been observed in prior work.

To determine whether trans-regulatory mutations were enriched in regions of the genome with higher sequence read coverage, we compared read coverage between regions of the genome identified as having trans-regulatory mutation or non-regulatory mutations. The identification of variants classified as non-regulatory is expected to be less dependent on the depth of sequencing read coverage because this designation does not require a statistically significant G-test. We found that the mutations identified as trans-regulatory showed 120x coverage whereas mutations identified as non-regulatory showed only 100x coverage, consistent with greater power to detect associations with expression in regions of the genome with higher sequencing read coverage. However, eliminating this difference in read coverage by excluding non-regulatory mutations with lower sequence read coverage did not eliminate the observed enrichment of trans-regulatory mutations in regions previously shown to contain eQTL. Non-regulatory mutations with higher and lower sequencing read coverage were also equally likely to be found within eQTL regions, suggesting that similar variation in sequence read coverage across the genome between the two studies is unlikely to explain the observed overlap of trans-regulatory mutations and eQTL. These analyses are now included in a new Figure 7-figure supplement 1.

With respect to better incorporating biases in what we were able to map and considerations for extending findings from this work to other systems, we have tried to better address these issues in the revised discussion.

Reviewer #2 (Public Review):

Fabien Duveau et al. tried to characterize mutations in trans-regulation effects on expression of the TDH3 by using EMS mutants with TDH3 reporter in Saccharomyces cerevisiae. This work is an extension of works of Gruber et al. (2012) and Metzger et al. (2016) with specific mutation effect on TDH3 expression. They found that these trans-regulatory mutations that have effects on expression of TDH3 reporter were enriched in coding sequences of transcription factors. They found that the trans regulatory mutations with effect are associated with natural variants of trans within S. cerevisiae. In summary, the data is well described and supports their claims. The method of study could be used for study the mechanism how regulatory network works.

[...] Although the paper does have strengths in principle, some weaknesses of the paper would cause the quality of data presented. [...]

We thank the reviewer for taking the time to evaluate this work and have the following responses to the weaknesses noted:

1) The reviewer is correct that we focused this paper on trans-regulatory mutations because cis-regulatory mutations affecting TDH3 expression were previously characterized. Furthermore, long distance enhancers with cis-acting effects on expression have not been described in S. cerevisiae and the term promoter is commonly used to encompass both the basal (core) promoter (including a TATA box for some genes) as well as other upstream activating sequences (UAS) and upstream repressing sequences (URS). In other words, the cis-acting sequences for S. cerevisiae genes are confined to a particular region much more than in multicellular eukarlyotes. In fact, our prior work with TDH3 (Metzger et al. 2015) showed that 97% of cis-acting variation affecting TDH3 expression could be explained by sequence variation in the 678 bp region we define as its promoter. Consequently, all mutations outside of this region were considered to have transregulatory effects on TDH3 expression. In the revised version, we extended the discussion to specifically compare the structure of regulatory sequences in S. cerevisiae to other eukaryotic model systems.

2) In this study, a mutation is defined as trans-regulatory if it affects TDH3 expression and is not located in the TDH3 promoter, regardless of whether or not it also affects growth rate. In fact, mutations in RAP1 and GCR1 affect growth rate (Figure 5), but are clearly trans-regulators of TDH3 with well-established binding sites in the TDH3 promoter. In other words, we do not think that mutations should be discounted as having trans-regulatory effects because they also impact growth rate.

3) (A) Prior work examining the statistics of BSA-seq has shown that G-tests are most appropriate because they take into account the independent sampling from two bulk populations inherent to bulk-segregant analysis (Magwene et al. 2011 PLOS Computational Biology). (B) We are guessing that the reviewer is asking about multiple testing corrections rather than post-hoc tests, as we used a false discovery rate correction for multiple tests in Figure 2-supplement 5A. Although we did not use a multiple test correction for the BSA-seq data, we used a conservative significance threshold of 0.001 that was expected to result in a 3.5% false positive rate. Perhaps more importantly, we functionally validated the effects of 40 of the 41 associated mutations tested. (C) We may indeed have been overly optimistic about mapping power when choosing mutants to analyze with BSA-seq given that the 36 EMS mutants for which we failed to find a significant association between a mutation and fluorescence tended to have smaller effects on PTDH3-YFP expression than the EMS mutants for which we observed one or more associated sites (Figure 3-figure supplement 3). The reviewer’s comment also made us realize that our original sentence referring to mapping power had reported the effect size for estimated RNA levels rather than fluorescence. To avoid confusion, and because our anticipated mapping power does not affect the results of the study, we deleted this statement from the revised manuscript. Regardless of our anticipated mapping power, we were ultimately able to map mutations that affected fluorescence by as little as 1.6% relative to the wildtype strain.

4) The GO enrichment analysis was performed with widely used tools on www.pantherdb.org. The statistical significance of enrichment for each GO term was computed using Fisher’s exact tests that compared 1) the proportion of genes with non-regulatory mutations and 2) the proportion of genes with trans-regulatory mutations that corresponded to the tested GO term. Because the total number of genes identified in our study with trans-regulatory mutations (42 genes) was much lower than the total number of genes with non-regulatory mutations (1043 genes), it was possible to obtain strong and statistically significant enrichment (P < 0.05 in Fisher’s exact test) even if only a small number of genes corresponded to the GO term in both categories. Although we found a large number of enriched GO terms, these GO terms were not always independent from each other. For instance, GO:0009168 (purine ribonucleoside monophosphate biosynthetic process) and GO:0009167 (purine ribonucleoside monophosphate metabolic process) refers to the same biological process and contains the same genes. For this reason, even though we reported all enriched GO terms in Supplementary File 8, we only showed GO terms that were at the tips of different branches in the GO hierarchy on Figure 6 and we grouped GO terms in four main categories that together encompassed most genes with trans-regulatory mutations.

5) We agree with the reviewer that trans-regulatory mutations can affect either the function of a gene product (including the ability of a transcription factor to bind to DNA) as well as the abundance of that gene product, but we do not think this is a weakness of the study. In fact, we think one of the strengths of the study is that we have empirical data testing the relative frequency of these two types of possible changes, finding that mutations in coding regions (presumably more likely to affect the function of the gene product than its expression) are the primary source of changes in TDH3 expression greater than 1%.

6) The goal of the study was to characterize the effects of individual trans-regulatory mutations, thus we did not look at the combined effects of mutations in proteins that might work in a complex. We do, however, mention transcription factors working in a complex: "Transcription factors encoded by the TYE7 and GCR2 genes found to harbor trans-regulatory mutations affecting expression of PTDH3-YFP are known to regulate the expression of glycolytic genes (including TDH3) by forming a complex with transcription factors encoded by the RAP1 and GCR1 genes” (line 461). We think that looking at the combined effects of mutations that all impact the same complex of regulatory proteins is an interesting direction for future work.

Finally, we’d like to point out that the reviewer’s statement in their opening summary about mutations being enriched in the coding sequence of transcription factors is not quite correct: the mutants we mapped were enriched in coding sequences, and we found more mutations in transcription factors previously shown to regulate (directly or indirectly) expression of TDH3 than expected by chance, but trans-regulatory mutations were not significantly enriched in genes encoding transcription factors relative to non-regulatory mutations (as described in the manuscript).

Reviewer #3 (Public Review):

[...] The mutagenesis approach in yeast the authors used is very powerful, but it naturally has drawbacks. The regulatory landscape in yeast is arguably simpler compared to e.g. metazoa or plants, in that the cis-regulatory regions are predominantly closely linked to target genes, the genes in majority do not have introns and post-transcriptional regulation of mRNA through e.g. splicing is rare. These features distinguish the systems, as in animals and plants introns are a very prominent source of regulatory elements (close to half of all enhancers are intronic in many animals), and alternative splicing of e.g. transcription factors are known to play major roles in transcriptional regulation. Further, chromatin is a very important layer in metazoan and plant gene regulation. To benefit the general readership, it would be informative to further elaborate on the significance of the findings for researchers studying other organisms. In addition, it would help to clarify what aspects of the differences in the regulatory landscape the authors think are important to distinguish.

We thank the reviewer for their kind words and recognition of the novelty of this work. We have modified the introduction to try to clarify the relationship of this work to eQTL studies, which we hope addresses the reviewer’s first concern. Specifically, we’ve tried to clarify that the complex, polygenic nature of trans-regulatory variation segregating within a species is well established by prior eQTL studies. We also sought to clarify that our work (which maps single mutations from mutagenized strains rather than natural variation) provides complementary insight into the distribution of regulatory mutations within the genome and within a gene’s regulatory network. Revisions have also been made to try to clarify that the single mutations we mapped were from EMS-induced mutants containing only ~24 mutations per genome, which is more than 1000-fold less than the number of single nucleotide polymorphisms between two strains of S. cerevisiae. That is, this study was designed to identify single trans-regulatory mutations rather than to characterize the genetic architecture of naturally occurring trans-regulatory variation. Although we intentionally focused on characterizing properties of single mutations here, we agree with the reviewer that testing for epistatic interactions among trans-regulatory mutations will be an interesting avenue for future work, and have added this point to the revised discussion. We have also added text to the discussion describing some similarities and differences in gene regulation as among eukaryotes that should be considered when trying to generalize from this work.

Reviewer #1 (Public Review):

This paper aims to characterize mutations that act in trans to affect a single gene's expression in yeast. Trans-acting mutations potentially play an important role in variation and disease within species and in phenotypic evolution. The authors have previously described the mutational architecture and natural variation in the gene's cis-regulatory activity, creating a powerful experimental model for the causes of phenotypic variation. Trans-acting variation is much more challenging, because the mutational target space is the whole genome. The authors use a forward-genetic screen and bulked segregant analysis to identify 52 point mutations that affect their focal transgene's activity, and they identify an additional 17 by directly searching within a handful of candidate genes. The paper includes elegant validation using genome engineering to confirm the mapped variants are causal.

With this collection of trans-acting mutations in hand, the authors can compare their characteristics to a set of mutations that do not have detectable effects on the transgene. Overall, they conclude that trans-acting mutations are enriched for genes that are known to sit upstream of the focal gene in transcriptional cascades, but the majority of mutations are in other kinds of genes, outside the network of transcription factors.

This work is a valuable contribution to the authors' important experimental assault on the genetics of regulatory variation, a useful complement to their previous work on cis-regulatory mutations and polymorphisms. They provide evidence that experimentally defined regulatory networks have predictive value for the location of trans-acting mutations, and they reinforce the result (well established and widely accepted, but important to show in this kind of rigorous way) that trans-acting variation is distributed across a wide range of cellular and molecular functions. There are also some useful fine-grained results, such as the absence of mutations in a known regulator, RAP1, probably due to pleiotropic constraints, and an excess of mutations in iron homeostasis.

Because the dataset of trans-acting mutations is relatively modest in size (necessarily- it's a heroic effort to identify this many), many of the enrichments are also modest. In particular, the finding that mutations are enriched in eQTL regions holds for only two of three previous eQTL studies, and involves a slight elevation over the baseline that 66% of the genome is in eQTL regions. Because both the eQTL and the mutations were discovered by bulked-segregant analysis, biases in mappability will affect both similarly, and so I do not find the enrichment for overlapping hits to be completely persuasive.

This work is important in substantial measure because of its contribution to the larger yeast TDH3 model trait project, which is a landmark research program for understanding phenotypic variation and evolution. On its own, the results in this manuscript would be difficult to generalize to regulatory variation more broadly. There are narrow reasons for this (yeast has a distinctive compact CDS-dense genome; the focal transcript is YFP and so has no endogenous post-transcriptional regulation; only one class of mutations assayed), but the bigger reason is that the researchers are only able to discover mutations with effects above a particular size. Even among the 82 mutant strains they start with, some 36 strains have altered YFP levels but no successfully mapped causal variants. The authors do a great job of listing and evaluating possible explanations, one of which is simply that the strains carry multiple mutations of small effect. All but one of the successfully mapped variants consists of missense and nonsense mutations. I think it's important to note that this represents a particular range of the effect-size distribution of mutations affecting the YFP phenotype. We know from the authors' earlier work that there are lots of mutations that can affect gene expression in cis, and so the absence of trans-acting cis-regulatory variants here is parsimoniously interpreted as due to their small effects. In general, work in other systems (particularly human genetics) has shown that even molecular traits are often hugely polygenic, affected by thousands of variants of tiny but non-zero magnitude. With a forward screen of the sort performed here, it's difficult to know how much of the phenotypic variance is due to unmapped small-effect variants, but two lines of evidence suggest it may be a lot: first, the absence of mappable causal mutations in 36/82 mutants, and second, the differences between EMS mutant strains and their matched single-site mutants. The authors commendably report and discuss these issues but to my mind they neglect them in drawing inferences and generalizations from their findings.

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Referee #3

Evidence, reproducibility and clarity

Summary

Most laboratory research using T. b. brucei has made use of two strains, the monomorphic Lister 427 strain and the pleomorphic EATRO1125 strain. These strains have been passaged in vitro for decades in separate laboratories, thus selecting for populations that differ from both the original isolate and between laboratories.

In this study, Mulindwa et al. describe the isolation of two new T. b. brucei strains from cattle in Uganda, MAK65 and MAK98, which show differences in virulence and propensity for stumpy form differentiation in rodents. To assess the effect of culture adaptation on trypanosomes, the researchers compared the gene copy number of the MAK65 and MAK98 isolates before and after culture adaptation. The study found that isolates cultured for as little as a week already demonstrated a broader gene copy number distribution than those that were not culture adapted. Broader gene copy number distributions, compared to the non-culture-adapted isolates, were also observed for a number of routinely-passaged Lister 427 and EATRO1125 cultures. The researchers observed reproducible increases in copy number for certain genes, such as those encoding histones, HSP70 and PFR proteins. The study postulated that changes in gene copy number observed across the genome increased bias towards rapid proliferation and stress tolerance upon culture adaptation.

Major comments

I have some concerns about the method that was used for the copy-number calculations. Firstly, I can imagine that a non-uniform distribution of the reads across the genome for any of the datasets could influence the results. Was this checked? Secondly, in the methods section lines 394/395 it is said that the modal RPKM for each dataset was 'adjusted slightly' to get a symmetrical distribution. Upon checking the modal RPKMs and the adjusted values used for the calculations, the adjusted values appear to have been adjusted to different extents between the different datasets to fit the authors assumptions. Do the authors believe that this could perhaps account for some of the subtle gene copy number changes observed (as is discussed in lines 287-290)? Next, why were the reads aligned 20 times? I think in general the method needs to be explained far more clearly so that the audience can understand what you did.

Minor comments

Additional experiments:

Would it be possible to generate a phylogenetic tree comparing these new isolates with the Lister 427, TREU927 and EATRO1125 isolates in circulation to get an idea of how these strains may have evolved? I this could add to the strength of the manuscript.

Title: Since no other unicellular eukaryotes are mentioned throughout the text, I think a more appropriate title for this study might be 'Adaptation of Trypanosoma brucei brucei bloodstream forms to in vitro culture results in gene copy-number changes.'

Line 59: There have been more recent studies than the referenced paper (Cross et al., 2014) that quote far higher numbers of alternative VSG genes or pseudogenes in the Lister 427 strain. In Müller et al (2018, doi: 10.1038/s41586-018-0619-8) over 2500 VSGs are quoted and in Cosentino et al (2021, doi: 10.1101/2021.04.13.439624) 2872 VSGs are identified.

Line 109/110: The dates of isolation written here, Feb 1st and July 30th 2016, are different to what is written under the Date of Isolation column in Supplementary text 1, table 1- 25/5/2016 and 30/7/2017. Do these two dates represent different things?

Line 233: Should Figure 5 A, B, C and E (rather than A, B, D and E) be referenced here to show all the initial, not cultured results?

Line 270/271: PIP39 does not promote differentiation to stumpy forms, but instead contributes towards the efficient differentiation of stumpies to procyclic forms (Szoor et al, 2010, doi: 10.1101/gad.570310).

Discussion:

It should be discussed in the text that changes in gene copy number have also been observed upon Leishmania culture adaptation (see, for e.g., Gerald Späth's work). This will strengthen the authors' conclusions. Furthermore, similar observations of aneuploidy and triploidy in some T. brucei Lister 427 strains have recently been reported in Cosentino et al (2021, doi: 10.1101/2021.04.13.439624). This could also be referenced somewhere in the text.

Line 364: I think the Nijuru et al (2005, doi: 10.1007/s00436-004-1267-5) paper would be the correct reference here since it describes the use of the ITS-1 PCR. Reference 13 is actually referring to another paper, I cannot find Nijuru et al in the references list.

Line 379: PAD staining should be referenced to be more informative and allow reproducibility.

Figure 1, Line 580: How many cells were counted for determining the % PAD positivity?

Figure 1, Line 581: Scale bar should be included in D.

Supplement Text 1:

I found Supplement Text 1 a little confusing for two reasons. Firstly, I was never sure if the tables or references being referenced were from the main text or from the supplement text 1, perhaps this could be made a little clearer to aid the reader. Secondly, the names of the isolates switched between, for e.g., MAK 65 and Tb065. For simplicity it could help to try and stick to one naming system.

It might be worth adding a sentence about why Tb236B was not followed up. Was this because it could not easily be distinguished from MAK65 by microsatellite analysis?

S3 Figure: Legend describes red bars but there are none in the figure.

Table 2: In the legend for Sheet 2, the cut-off for the increase is missing.

Significance

Though the T. b. brucei Lister 427 and EATRO1125 strains are used most commonly for laboratory-based research, they have been extensively passaged in vitro without characterisation of the changes that have occurred between them and the original isolate, or indeed, between laboratories.

Mulindwa et al. demonstrated that changes to gene copy number occur rapidly upon adaptation to culture of field isolates, and that different cultures of the same isolate can furthermore have different ploidies. This is an important advance and raises awareness a) that trypanosomes undergo changes upon laboratory adaptation, b) of the nature of some of these changes and c) that the changes can occur rapidly. Changes to gene copy number have also been shown to effect Leishmania donovani upon culture adaptation (Prieto Barja et al, 2017, doi: 10.1038/s41559-017-0361-x).

This study will be of interest to the trypanosome community in general, but particularly those who work on biology that we already know is impacted by prolonged in vitro passage-differentiation, virulence and antigenic variation.

Reviewer expertise: BSF differentiation, antigenic variation

Author Response:

Reviewer #1 (Public Review):

[...] Authors' rigorous experimental design (based on bacterial genetics and structural biology), solid biochemical assays (including photo-crosslinking, cysteine crosslinking, and Western blotting), and carefully drawn interpretation and conclusions are impressive. Finally, authors delineate the mechanisms of BepA activation and LptD biogenesis, which are supported by the current and previous studies by the authors and other research groups.

Thank you for the nice summarization and the positive evaluation of our study.

While this is overall a wonderful piece of work, this manuscript would be further improved by clarifying the following points:

1. Authors examined how mutations (Pro and Cys scanning) on the edge-strand of BepA affected degradation and maturation of LptD.

It was assumed that these mutations impact the structure of BepA only locally. However, a mutational effect can be propagated in an unexpected way affecting the structural integrity of other regions. Although authors tested that A106P retains proteolytic activity as shown by self-cleavage, a similar test (for example, in vitro experiments using a structureless substrate) may need to be extended to other mutations to support the conclusions.

Thank you for the suggestion. Unfortunately, we have not succeeded in reproducibly detecting the proteolytic activity of BepA with purified BepA even when an unstructured substrate (-casein) is used and the assay was conducted at an elevated temperature, possibly because the protases activity of isolated BepA is tightly repressed by the mechanism that included His-246-mediated regulation as described in our paper (please see Introduction). Although BepA mutants with a mutation of His-246 or a deletion of H9 loop (these mutations release the His-246-mediated repression) significantly degrade -casein, a combination of these mutations with the edge-strand mutations should make the interpretation of the results complicated. We thus think that the suggested experiments cannot be conducted soon.

Instead, we described the following points in the revised manuscript. Although we mentioned the self-cleavage activity of only the A106P mutant in the original manuscript, our results showed that the other edge-strand Pro mutants (other than F107P) exhibited significant self-cleavage activities as well (Figure 1-figure supplement 2B). In addition, the Pro mutants other than the A106P mutant degraded mis- or un-folded BamA at a detectable level (Figure 1-figure supplement 2A). Furthermore, all the Pro mutants accumulated at a level comparable to that of wild-type BepA. These observations together indicate that most of the Pro mutations specifically affected the edge-strand structure, but not drastically altered the active site or the protein's overall structures. We described the above points in the revised text (line 174 in p7 to line 181 in p8).

1. In the result (Line 159), authors report chaperone-like activity of BepA. Here, the term "chaperone-like" is rather obscure regarding whether this activity facilitates LptD maturation without proteolysis (i.e., via holdase activity), or involves proteolysis as a part of quality control mechanisms. In another experiment, authors show that the chaperone-like activity may not necessarily involve proteolysis. It would be good to describe a possible molecular principle of how the edge-stand binding to the substrate can lead to chaperone activity.

We suppose that the interaction of BepA (via the edge-strand) with an assembly intermediate of LptD on the BAM complex stabilizes the partially unfolded assembly intermediate of LptD on the BAM complex to help the association of LptE with LptD. This was explained in Discussion (lines 388–392 in p16) and the legend to Figure 5.

Reviewer #2 (Public Review):

The authors found that a conserved β-strand (edge-strand β2 of BepA) directly contacts with the N-terminal half of the β-barrel-forming domain of an immature LptD; the C-terminal region of the β-barrel-forming domain of the BepA-bound LptD intermediate interacts with a "seam" strand of BamA in the BAM complex. By combining crosslinking and mutational studies, they showed that the edge-strand of BepA may have both the proteolytic and the chaperone-like functions. Based on the authors' previous studies of BepA, they proposed a model that the edge-strand and His switch of BepA regulate BepA in LptD assembly and degradation.

Thank you for the nice summarization of our study.

Reviewer #3 (Public Review):

[...] By performing an impressive systematic cross linking analysis, combined with previous known findings, the authors are able to dissect the general architecture of how BepA interacts with beta-barrel substrates as they are being assembled by the Bam complex. The experiments presented are nicely executed and the data are of high quality. I am convinced that the edge strand of BepA interacts with LptD, likely as it is assembling on the Bam complex. It is also clear that this interaction is functionally important because mutations in this region that disrupt the BepA-LptD interaction interfere with LptD maturation and degradation. This suggests that the substrate binding to the protease domain of BepA is important for both its chaperone and proteolytic activity. The work is well executed and will be of value to others interested in the regulation of membrane protein folding and, more broadly, in the biogenesis of the bacterial cell envelope.

Thank you for the nice summarization and the positive evaluation of our study.

While the authors conclusively establish a link between this region of BepA and its function, the data do not explain the underlying mechanism of how BepA discriminates between substrates targeted for integration into the membrane and those targeted for destruction. The model proposed at the end incorporates the presence of the edge strand of BepA, but its role in the process remains vague. As mentioned in the discussion, the mechanisms that control the switch from chaperone to protease function in BepA is likely governed by the loops that gate access to the catalytic residues proximal to the edge strand. It is possible that the edge strand may just be reporting on substrate binding to the protease domain active site. While this may be important for substrate recognition, it does not mean that the edge strand-substrate interaction plays a deterministic role in subsequent protein triage during LptD assembly.

Our data demonstrated that the edge-strand of BepA directly binds a substrate. As pointed out by the reviewer, the involvement of the edge-strand in substrate binding has been known for other proteases. However, it was not known whether the substrate binding at the edge-strand contributes to the chaperone-like function; it was possible that the binding sites of a substrate on BepA during its proteolysis and its maturation are totally different as the chaperone-like activity of BepA is independent of its protease activity (it was conceivable, for example, that substrate binding during it maturation occurs on the surface of the C-terminal TPR domain that has been shown to interact with LptD）. Our results showed that the defective binding of a substrate (LptD) at the edge-strand impairs not only its proteolysis but also its normal maturation (assembly). Because the edge-strand-bound substrate would be directly presented to the proteolytic active site for its degradation, this binding step should be important for the determination of the fates of the bound substrate. Our results strongly suggest that the substrate binding by the edge-strand is a crucial common step required for the subsequent protein triage during the LptD assembly.

I think this is a super key consideration, and I think we all have to acknowledge the possibility that our findings for this project are only a part of the story, and may not be representative of the entire campus community. I'm really looking forward to seeing the final project and learning about your interpretation of the results.

Reviewer #3 (Public Review): 

Pancreatic beta cells in each islet of Langerhans act cooperatively to produce a coordinated response to blood glucose, which takes the form of roughly synchronous electrical oscillations that result in coherent pulses of insulin secretion. However, it has become more appreciated recently that the cells are heterogeneous and that there may subgroups of cells within the islet that contribute in different ways or control different aspects of the collective behavior. 

This paper addresses a subset of cells, termed first responders, that are the earliest to transition into activity when glucose is stepped up from a sub-stimulatory level. It is shown that the first responders determine the first transient phase of electrical activity, and implicitly secretion, that precedes the start of steady-state oscillations of the second phase. The first phase is of interest for the pathogenesis of diabetes because it is (or claimed to be) one of the first indications of the disease. The clinical data on this are actually ambiguous, but nonetheless the first phase is an important aspect of how secretion from islets is organized. This is clear from the existence of a subset of readily releasable insulin vesicles, but the electrical activity correlates that synergize with vesicle availability are less well understood. As such, the paper is an important contribution to both islet biology and potentially diabetology. 

The approach is to use a genetically encoded calcium indicator, GCamps, and confocal imaging to identify cells that show the earliest rise in calcium and then to verify that this property remains consistent when the glucose stimulus is repeated about an hour later. Further testing over 48 h shows the first responder slowly fading. This thus appears to be a matter of continuous variation of cell properties within the islet and moreover one that is persistent but decaying over long-enough periods of time, rather than a discrete sub-type of beta cell. This is confirmed by simulations using an islet model in which first responders emerge from random variation of properties. The text is a bit ambiguous about whether we should think of the glass as half full or half empty and could be clarified in this regard. 

The properties that matter are the density of KATP channels and gap junctional coupling which are both lower. This is also confirmed by simulations. The intriguing suggestion is made that there may be a reciprocal negative feedback relationship between these quantities that regulates their variation over time. This would be rather different from a persistent genetic difference and would be a good subject for future investigation. 

Interestingly and perhaps surprisingly, increased sensitivity to glucose is not a feature of the first responders. In contrast, other putative "leader" or "hub" cells that have been identified for the second phase of electrical activity are proposed to have increased glucose sensitivity. This and other features lead to the conclusion that the two types of leader cells are probably distinct. The much-discussed topic of hub cells provides interesting context and relevance to the paper, but its results stand by themselves and can be judged independent of hubs.

Author Response:

Reviewer #1 (Public Review):

Sokolsky et al. propose a new statistical model class for descriptive modeling of stimulus encoding in the spiking activity of neural populations. The main goals are to provide a model family that (G1) captures key activity statistics, such as spike count (noise) correlations, and their stimulus dependence, in potentially large neural populations, (G2) is relatively easy to fit, and (G3) when used as a forward encoder model for Bayesian decoders leads to efficient and accurate decoding. There are also three additional goals or claims: (C1) that this descriptive model family can serve to quantitatively test computational theories of probabilistic population coding against data, (C2) that the model can offer interpretable representations of information-limiting noise correlations, (C3) that the model can be extended to the case of temporal coding with dynamic stimuli and history dependence.

The starting point of their model is a finite mixture of independent Poisson distributions, which is then generalized and extended in two ways. Due to the "mixture", the model can account for correlations between neurons (see G1). As any mixture model, the model can be viewed in the language of latent variables, which (in this case) are discrete categorical variables corresponding to different mixture components. The two extensions of the model are based on realizing that the joint distribution (of the observed spike counts and the latent variables) is in the exponential family (EF), which opens the door to powerful classical results to be applied (e.g. towards G2-G3), and allows for the two extensions by: (E1) generalizing Poisson distributions in mixture components to Conway-Maxwell-Poisson distributions, and (E2) introducing stimulus dependence by allowing the natural parameters of the EF to depend on stimulus conditions. They call the resulting model a Conditional Poisson Mixture or CPM (although the "Poisson" in CPM really means Conway-Maxwell-Poisson). E1 is key for capturing under-dispersion, i.e. Fano Factors below 1. For the case of discrete set of stimulus conditions, they propose minimal, maximal versions of E2; depending on which natural parameters are stimulus dependent. In the case of a continuum of stimuli (they only consider 1D continuum of stimulus orientations, e.g. in V1 encoding) they also consider a model-based parametric version of the minimal E2 which gives rise to Von Mises orientation tuning curves.

Strengths:

-Proposing a new descriptive encoding model of spike responses that can account for sub-poissonian and correlated noise structure, and yet can be tractably fit and accurately decoded.

-Their experiments with simulated and real (macaque V1) data presented in Figs. 2-5 and Tables 1-2 provide good evidence that the model supports G1-3.

-Working out a concrete Expectation Maximization algorithm that allows efficient fits of the model to data.

-Exploiting the EP framework to provide a closed form expression for the model's Fisher Information for the minimal model class, a measure that plays a key role in theoretical studies of probabilistic population coding.

As such, the papers makes a valuable contribution to the arsenal of descriptive models used to describe stimulus encoding in neural population, including the structure and stimulus dependence of their higher-order statistics.

Thank you very much for your thorough, exact, and positive evaluation of our manuscript!

Weaknesses:

1) I found the title and abstract too vague, and not informative enough as to the concrete contributions of this paper. These parts should more concretely and clearly describe the proposed/developed model family and the particular contributions listed above.

We found your summary of the paper and model to be highly accurate, and we rewrote the abstract to summarize the key strengths as you’ve listed them. We found it difficult to develop a more exact title which wasn’t overlong, so we left it as is.

2) I was not convinced about claims C1 and C2 (which also contribute to the vagueness of abstract), but I think even without establishing these claims the more solid contributions of the paper are valuable. And while I can see how the model can be extended towards C3, there are no results pertaining to this in the current paper, nor even a concrete discussion of how the model may be extended in this direction.

2.1) Regarding C1, the claim is supposed to follow from the fact that the model's joint distribution is in the exponential family (EF), and that they have reasonably shown G1-G3 (in particular, that it captures noise correlations and its Bayesian inversion provides an accurate decoder). While I agree with the latter part, what puzzles me is that in the probabilistic population coding (PPC) theoretical models that claim can be quantitatively tested using their descriptive model are, as far as I remember/understand, the encoder itself is in EF. By contrast here the encoder is a mixture of EF's and as such is not itself in EF. Perhaps this distinction is not key to the claim - but if so, this has to be clearly explained, and more generally the exact connection between the descriptive encoder model here and the models used in the PPC literature should be better elaborated.

This claim was indeed poorly explained in our manuscript, and not self-evident. There is a deeper connection between our conditional models and PPCs, which we now make explicit in a new section of the manuscript (Constrained conditional mixtures support linear probabilistic population coding, line 364), which includes an equation (Equation 4) that shows their exact relationship.

2.2) Regarding C2, I do not see how their results in Fig 5 (and corresponding section) provide any evidence for this claim. As a theoretical neuroscientist, I take "interpretable" to mean with a mechanistic or computational (theoretical) interpretation. But, if anything, I think the example studied in Fig 5 provides a great example of the general point: that even when successful descriptive models accurately capture the statistics of data, they may nevertheless not reveal (or even hide or mis-identify) the mechanisms underlying the data. In this example's ground-truth model, the stimulus (orientation) is first corrupted by input noise and then an independent population of neurons with homogeneous tuning curves (and orientation-independent average population rate) responds to this corrupted version of the stimulus. That is a very simple AND mechanistic interpretation (which of course is not manifest to someonw only observing the raw stimulus and spiking data). The fit CPM, on the other hand, does not reveal the continuous input noise mechanism (and homogeneous population response) directly, but instead captures the resulting noise correlation structure by inferring a large (~20) number of mixture components, in each of which population response prefers a certain orientation. For a given stimulus orientation, the fluctuations between (3-4 relevant) mixture components then approximate the effect of input noise. This captures the generated data well, but misses the true mechanism and its simpler interpretation. Let me be clear that I don't take this as a fault of their descriptive model. This is a general phenomenon, despite which their descriptive model, like any expressive and tractible descriptive model, still can be a powerful tool for neural data analysis. I'm just not convinced about the claim.

This is a very fair point, and we’ve reformulated a few passages to emphasize that the model is primarily descriptive, at least in our applications in the paper (see new section title at like 393, the first corresponding paragraph).

2.3) Regarding C3, I think the authors can at least add a discussion of how the model can be extended in this direction (and as I'm sure they are aware, this can be done by generalizing the Von Mises version of the model, whereby the model I believe can be more generally thought of as a finite mixture of GLMs).

In Appendix 4 we detail the relationship between CPMs and GLMs. We also note here that, at least as far as we understand, CPMs are formally distinct from finite mixtures of GLMs — the easiest way to see this distinction is to note that the index probabilities of a CPM depend on the stimulus, whereas the equivalent index probabilities in a finite mixture of GLMs would not. We have also explained this in Appendix 4.

Reviewer #2 (Public Review):

Sokoloski, Aschner, and Coen-Cagli present a modeling approach for the joint activity of groups of neurons using a family of exponential models. The Conway-Maxwell (CoM) Poisson models extend the "standard" Poisson models, by incorporating dependencies between neurons.

They show the CoM models and their ability to capture mixture of Poisson distributions. Applied to V1 data from awake and anesthetized monkeys, they show it captures the Fano Factor values better than simple Poisson models, compare spike count variability and co-variability. Log-likelihood ratios in Table 1 show on-par or better performance of different variant of the CoM models, and the optimal number of parameters to use for maximizing the likelihood [balancing accuracy and overfitting] and are useful for decoding. Finally, they show how the latent variables of the model can help interpret the structure of population codes using simple simulated Poisson models over 200 neurons.

In summary, this new family of models offer a more accurate approach to the modeling and study of large populations, and so reflects the limited value of simple Poisson based models. Under some conditions it gives has higher likelihood than Poisson models and uses fewer parameters than ANN model.

However, the approach, presentation, and conclusions fall short on several issues that prevents a clear evaluation of the accuracy or benefits of this family of models. Key of them is the missing comparison to other statistical models.

1) Critically, the model is not evaluated against other commonly used models of the joint spiking patterns of large populations of neurons. For example: GLMs (e.g. Pillow et al Nature 2008), latent Gaussian models (e.g. Macke et al Neural Comp 2009), Restricted Boltzmann Machines (e.g. Gardella et al PNAS 2018), Ising models for large groups of neurons (e.g. Tkacik etal PNAS 2015, Meshulam et al Neuron 2017), and extensions to higher order terms (Tkacik et al J Stat Mech 2013), coarse grained versions (Meshulam et al Phys Rev Lett 2019), or Random Projections models (Maoz et al biorxiv 2018).

. Most of these models have been used to model comparable or even larger populations than the ones studied here, often with very high accuracy, measured by different statistics of the populations and detailed spiking patterns (see more below). Much of the benefit or usefulness of the new family of models hinges on its performance compared to these other models.

We agree very much with this point, and have done our best to address it by thoroughly comparing our model with a factor analysis encoding model in Appendices 1 and 2, and summarizing these results at appropriate points in the manuscript (lines 196–199 and 325–328). In particular, we visualized and compared the performance of factor analysis with our mixture models, and found that (i) factor analysis is better at capturing the first and second order statistics of the data, but (ii) when evaluated on held-out data, the performance gap more-or-less vanishes. Moreover, we found that an encoding model based on FA performs poorly as a Bayesian decoder, and we provided preliminary evidence that this is because our mixture models can capture higher-order statistics that FA cannot. We believe that these results have been very valuable to conveying the strengths and weaknesses of the mixture model approach.

We have also extended the introduction to explain the differences between other model families suggested by the reviewer and our approach, to explain how the different assumptions about the form of data make it difficult to compare them quantitatively (see lines 42–63). To wit, GLMs and latent Gaussian models are both models that critically depend on modelling spike trains, and not spike counts. On the other hand, Restricted Boltzmann machines, Ising models, and random projection models all assume binary, rather than counting spiking data. As such, any comparison would depend on coming up with methods for either (i) reshaping our datasets and comparing spike- train/binary spike-count likelihoods to trial-to-trial likelihoods, or (ii) extending our conditional mixture approach to temporal/binary data, both of which are beyond the scope of our paper. We instead used factor analysis because it has been applied widely to modelling trial-to-trial spike counts, and thus avoid further transformations that might reduce the validity of our comparisons.

2) As some of these models are exponential models, their relations to the family of the models suggested by the authors is relevant also in terms of the learned latent variables. Moreover, the number of parameters that are needed for these different models should be compared to the CoM and its variants.

In our comparisons with factor analysis we also compared number of latent states/dimensions required to achieve maximum performance. Overall FA was consistently the most efficient, at least when evaluated on the ability to capture second-order statistics, although our mixture models also performed quite well with modest numbers of parameters.

3) The analysis focuses on simple statistics of neural activity, like Fano Factors (Fig. 2) and visual comparisons rather than clear quantitative ones. More direct assessments of performance in terms of other spiking statistics for single neurons and small groups (e.g., correlations of different orders ) and direct comparison to individual spiking patterns (which would be practical for groups of up to 20 neurons) would be valuable

In the Appendix 2 we evaluated the ability of our mixtures to capture the empirical skewness and kurtosis of recorded neurons, and found that the CoM-based mixture performs quite well (r2 for the CoM-Based mixture was between 0.6 and 0.9). Because FA cannot capture these higher-order moments, we speculate that modelling these higher-order moments is critical for maximizing decoding performance. This adds another perspective on the strengths of our approach, and we appreciate the suggestion.

Reviewer #3 (Public Review):

The authors use multivariate mixtures of Poisson or Conway-Maxwell-Poisson distributions to model neural population activity. They derive an EM algorithm, a formula for Fisher information, and a Bayesian decoder for such models, and show it is competitive with other methods such as ANNs. The paper is clear and didactically written, and I learned a lot from reading it. Other than a few typos the math and analyses appear to be correct.

Thank you for the positive evaluation!

Nevertheless there are some ways the study could be further improved.

Most important, code for performing these analyses needs to be publicly released. The EM algorithm is complicated, involving a gradient optimization on each iteration - it is very unlikely people will rewrite this themselves, so unless the authors release well-packaged and well-documented code, their impact will be limited.

We very much agree, and we have done this. We provide a link to our gitlab page, where all relevant code can be downloaded, and installation instructions are provided (we indicate this in the manuscript at lines 799–803).

Second, it would be nice to extend the model to continuous latent factors. It seems likely that one or two latent factors could do the work of many mixture components, as well as increasing the interpretability of the models.

We certainly agree that in some cases continuous latent variables could be much more parsi- monious. However, to the best of our knowledge most of the expressions that we rely on would no longer be closed-form, and so the machinery of the model would require suitable approximations. Nevertheless, it’s an interesting possibility that we now address in the Discussion (lines 482–491).

Third, it would be interesting to see the models applied to more diverse types of population data (for example hippocampal place field recordings).

We certainly agree with the importance of applying our model to other datasets, and indeed the purpose of our manuscript is to offer a method that can be applied broadly, and our goal in making the code available publicly is to facilitate that. However, we have decided to maintain the focus of this manuscript on the method itself, and limit the application to one kind of data (V1), for which we also now provide more extensive analysis and quantification of the response statistics (Figure 2 C-D, Figure 3 G-H, Appendix 2), a study of the sample sizes required to fit the model (Appendix 3), and model-comparison (Appendix 1–2). Overall we feel that the paper is already quite long and dense even when limited to a single kind of data. We believe applications to multiple kinds of data would perhaps be better suited for a different study, focusing on the comparisons between them. In that regard, we are certainly open to future collaborations on large-scale recordings from various stimulus-driven brain areas.

Fourth, how does a user choose how many mixture components to add?

To clarify this, we’ve added a section in the methods (Strategies for choosing the CM form and latent structure), and in particular the number of mixture components.

Author Response:

Reviewer #2 (Public Review):

[...] 1) A weakness of the paper is the disruption of the complex during cryoEM grid preparation resulting in about half of the observed particles missing the membrane arm and likely also contributing to the disorder and biased orientation seen in the intact complexes. This leads to poor density in the membrane arm for all of the intact complex I structures presented and large variations in the local resolution of the membrane arm focused refinement.

Purified E. coli complex I has always been known to be labile in particularly at the junction of peripheral and membrane arms (https://pubmed.ncbi.nlm.nih.gov/12637579/).

Air-water interface likely plays a role in disrupting the complex in addition to other possible causes. Indeed, the dissociated arms, preferred particle orientation, and low protein concentration (~0.1 mg/ml) used to produce grids with high particle density all indicate that reconstituted complex I does interact with air-water interface. While disruption and denaturation of protein complexes on air-water interface has been well documented, (https://pubmed.ncbi.nlm.nih.gov/3043536/, https://pubmed.ncbi.nlm.nih.gov/30932812/ ), we are not aware of examples where air-water interfaces caused higher mobility of a complex or induced a stable conformation, different from the one in bulk solution. Therefore, we think that air-ware interface is neither the cause of the observed high arms mobility nor of their relative rotation.

Preferential orientation was observed in the cryo-EM studies of most complex I homologs (Gutiérrez-Fernández et al., 2020; Parey et al., 2019; Zhu et al., 2016) as well as of other proteins, suggesting that adsorption of complex I on air water interface is a common phenomenon. In this case it is not clear why relative movement of the arms observed in all the structurally characterized complex I homologs is not due to the air-water interface, but in the case of E. coli complex it is.

To provide additional support to our interpretation of the structural data we purified complex I in detergent LMNG, showed that it catalyzes redox reactions and solved its structure to resolution of 6.7 Å (Figure 6 and corresponding figure supplements). Because cryo-EM grids had to be prepared at a protein concentration of 2-3 mg/ml and the particles displayed nearly homogeneous distribution of orientations, we conclude that the interaction with the air-water interface was reduced. Still, the complex assumes a very similar, or even somewhat more uncoupled conformation and the relative mobility of the arms remained comparable to that in the nanodisc-reconstituted complex reconstructions. These data allow us to rule out the air-water interface and reconstitution of the protein into lipid nanodiscs as the possible causes of the high mobility and the unusual relative position of the arms.

The corresponding modifications were added to the manuscript on lines 372-382:

“To better understand the reasons for the observed uncoupled conformation and the missing density for HTMH1, we purified E. coli complex I in detergent LMNG, showed that it can catalyze redox reactions (Figure 6 - figure supplement 1) and solved its structure to resolution of 6.7 Å (Figure 6 - figure supplement 2). The detergent-solubilized complex also displays high relative mobility of the arms (Figure 6 - figure supplement 3) and has uncoupled conformation (Figure 6). Its peripheral arm is rotated even further away from the expected coupled state position than in the nanodisc-reconstituted structures. Both the cryo-EM sample preparation conditions and more homogeneous distribution of particle orientations indicate that interaction of the complex with air-water interface was significantly reduced when compared with the complex in nanodiscs. This allows us to conclude that neither air-water interface nor reconstitution into nanodiscs cause the uncoupled conformations.”

It is not very clear what referee means by “poor”, when referring to the focused density of the membrane arm. The density corresponds well to the reported resolution of 3.7 Å. Indeed, it is in a stark contrast with the quality of the density obtained for the peripheral arm at 2.1 Å resolution. Given high mobility of the membrane arm it had to be refined essentially independently of the peripheral arm which remains still challenging for a ~200 kDa membrane protein without water-soluble domains in lipid nanodiscs. The density is heterogeneous as clearly stated at the beginning of the section “Structure of membrane arm” from line 264:

“The model of complete membrane arm, including the previously missing subunit NuoH (Efremov and Sazanov, 2011), was built into the density map with local resolution better than 3.5 Å at the arm center and approximately 4.0 Å at its periphery (Figure 1A, Figure 1 - figure supplement 4).”

Finally, for most complex I homologs the resolution was gradually improved over several years, as reflected in multiple publications of essentially the same structures. In contrast, no high-resolution structure information was available for the intact E. coli complex I until now. Therefore, it would be unreasonable to expect the complete structure to be solved at resolution of 2 Å at once.

The resolution of the membrane domain in reconstructions of complete complex I is indeed lower due to high flexibility of the complex and the fact that refinement naturally focuses on more stable peripheral arm that does not have heterogeneous nanodisc around and that contains Fe-S clusters enhancing particle alignment power. Still, these conformations clearly resolve the interface between subunits albeit at lower resolution.

This fact was also clearly stated at the beginning of results section lines 102-106:

“Three conformations of the entire complex were reconstructed to average resolutions between 3.3 and 3.7 Å (Figure 1 - figure supplement 4) resolving the interface between the arms; however, due to high-residual mobility of the arms, the antiporter-like subunits were resolved at below 8 Å (Figure 1 - figure supplement 4).”

2) A weakness of the paper is the disorder of important functional regions of the complex, namely the NuoH TMH1, whose disorder is unique to these nanodisc E. coli structures, and the NuoA TMH1-TMH2 loop. As the NuoH TMH1 forms part of the entry to the quinone tunnel of the complex, its absence in the structure leads to concerns regarding the function of the nanodisc preparation. Its absence it curious as this suggests flexibility of the helix, as pointed out by the authors, but the authors also state that there is not enough room in the nanodisc to accommodate this helix (given the visible density for the lipid and membrane scaffold protein). These observations suggest denaturation or unfolding in this region of the complex as opposed to simple flexibility.

According to the usual definition of complex I activity our preparation in nanodiscs is active. We complemented our data with additional measurements and included NADH:DQ assays (see next point) that also indicate that our preparation is active. Additional 3D reconstruction of E. coli complex I that we obtained for protein solubilized in LMNG does resolve HTMH1 and its environment appears to be more similar to other detergent-solubilized structures of complex I homologues. At the same time, the helices around HTMH1 appear to be more tightly packed and more curved than in the nanodiscs which may reflect suppressed dynamics and distorted protein conformation. Most importantly, the overall conformation of the complex remains nearly the same and still corresponds to what we call the uncoupled conformation. That of course does not allow us to say where HTMH1 is positioned within the nanodisc, but it does enable us to conclude that the local changes in the vicinity of HTMH1 do not influence the global conformation of the complex.

The additional structure is not described on lines 383-388:

“The HTMH1 helix is resolved in the detergent-solubilized complex (Figure 6A). Its density is weaker than that of the surrounding helices and it is strongly bent (Figure 6B). Simultaneously, HAH1 takes the conformation resembling other complex I homologs while ATMH1 bends towards the arm core. The arrangement of helices in detergent-solubilized reconstruction appears to be more compact and more bent than in the lipid environment which may restrain the otherwise more flexible HTMH1.”

In the revised discussion the environment of HTMH1 is described more clearly on lines 426-433:

“The absence of HTMH1 density in nanodiscs, but not in detergent, is another unique feature of E. coli complex I. HTMH1 is exposed to the lipid environment and the width of the nanodisc next to HTMH1 is similar to other regions around the membrane arm (Movie 1). Moreover, homology modelled HHTM1 fits the empty space without steric clashes suggesting that HHTM1 is dynamic rather than displaced or unfolded. By comparing the detergent-solubilized and reconstituted complexes we can conclude that position and dynamics of this helix is neither the cause of the uncoupled conformation nor of the high relative mobility of the arms.”

Disorder of ATMH1-TMH2 loop is not unique to E. coli complex I but also observed in some conformations of ovine complex I PDB 6zkd, 6zke, 6zkf.

3) Unfortunately, the NADH:Q1 functional data do not fully address these concerns at Q1 is far more soluble that the native Q8 substrate of the complex. Although the Q1 activity is sensitive to the inhibitor Piericidin A, which clearly demonstrates that the Q1 reduction is occurring in the native quinone binding site as Piericidin A binds specifically at that site, this does not preclude the possibility of Q1 accessing this binding site via a different path. In fact, the structures indicate that given the flexibility in the connection between peripheral and membrane arms of the complex, the quinone binding site is likely open to the cytoplasm. This leads the authors themselves to conclude that the structures presented are likely disrupted/uncoupled states in which the energy converting mechanism of the complex is not likely possible.

To address the raised concern, we have measured the activity of complex I in nanodiscs with less soluble decylubiquinone (DQ) as well as its inhibition. Small amounts of LMNG was used to increase the DQ solubility. Our results have confirmed that E. coli complex I in nanodiscs is active and the NADH:DQ activity is sensitive to piericidin A (see the modified Figure 1-figure supplement 2). We have also remeasured the Q1 activity and its inhibition which showed lower values than previously, due to a flaw in the activity measurements reported in the original submission (qualitatively, the results remained unchanged). Moreover, we have observed a similar activity results with somewhat higher values for E. coli complex I in LMNG (Figure 6-figure supplement 1). These data demonstrate that in the reconstituted complex I quinone analogues can enter the Q-site through the membrane. It is worth noting that due to extremely low solubility of longer quinones, including native ones, they are not used for activity measurements in purified preparations.

Regarding the complex I conformation, we do think our reconstruction represents uncoupled state which is not able to pump protons (as states in the title). We have improved the clarity of this point throughout the manuscript including the discussion lines starting from the line 412.

“The high mobility of the interfacial regions and the relative rotation of the arms disrupts conserved interfacial interactions and exposes Q-cavity to the solvent (Figure 5A). This differentiates E. coli complex I from its structurally characterized homologs in which the Q-cavity is sealed from the solvent. Thus, we interpret the observed conformation as an uncoupled state.”

And from line 469: “We also observed the relative rotation of the membrane and peripheral arms disrupting the conserved interface and trapping the complex in an uncoupled conformation. Whether this conformation is biologically relevant or is a result of protein purification is to be clarified by further research.”

4) A weakness of the paper is the building of atomic models into regions of the map which do not contain sufficient detail to warrant atomic models. This is particularly the case for the intact models of complex I as well as the membrane arm focused maps and results in low map-model correlations (0.58-0.71). The models were clearly highly restrained during refinement, resulting in good geometry, as is necessary for low resolution regions. But being able to restrain the geometry is not sufficient for placing atoms into regions where the density is weak or absent. If additional information was used in building/constraining the model, such as the X-ray structure, the regions of the model that are biased towards the X-ray structure model needs to be made clearer. Also, in several places in the membrane arm map residues bulge out of the density (side chain and main chain) leading to possible frame shifts with respect to the match between subsequent residues in the model and the map (see NuoM Ile168 for example).

A large part of the membrane domain has been solved using X-ray crystallography to resolution of 3.0 Å which was used as a starting model for model building, therefore we don’t think there are register shifts in our model. We used standard setting for model refinement in phenix_refine. Our building and refinement procedure has been described in fine details in the original submission, see from line 674:

“For the membrane domain, the previously obtained E. coli model (PDB ID: 3RKO) was real-space-refined in PHENIX. The missing NuoH subunit was homology-modelled using the T. thermophilus structure (PDB ID: 4HEA) in Coot 0.9. The final model was obtained after several rounds of manual rebuilding and real-space refinement using standard parameters with Ramachandran restrains, secondary-structure restrains applied to the NuoL TMH9-13, without ADP restrains, and with the optimized nonbonded_weight parameter. To generate the model of the complete complex I, the separate peripheral and membrane arm structures were combined and the missing parts at the interface (Table 2) were built manually. As the density of NuoL and NuoM was very poor in all the resolved full conformations, these subunits were subjected to rigid-body refinement in PHENIX, whereas the others were subjected to real-space refinement with minimization_global, local_grid_search, morphing, and ADP refinement. Ramachandran, ADP, and secondary-structure restrains were used. After manual rebuilding in Coot, real-space refinement of the full complex was performed with standard parameters and restrains.”

To improve clarity, we added a following sentence to the Results section from line 116:

“Using the resulting maps, atomic models of the peripheral and membrane arms have been built. The entire E. coli complex I was modelled by fitting models of the arms and extending additionally resolved loops and termini. Due to limited resolution, the antiporter-like subunits were refined as rigid bodies.”

The model has been improved and side chains with absent density were truncated to C position.

The density for focused refinement density of the membrane fragment is relatively week, but of sufficient quality to allow building side chains for most of the map. It even visualizes lipid densities (not described in the manuscript). Such weaker densities are common for small membrane proteins. While fully usable for model building, they naturally result in lower model map FSC and consequently, in lower real-space correlation. In addition, real-space correlation is lower when the map is heterogeneous, and it strongly depends on the way the heterogeneous map has been filtered. Therefore, lower cross correlations do not necessarily mean that the model fit is poor. In our case they reflect weaker signal to noise of the density. Model-map FSCs (Figure 1 figure supplement 4) are more informative than a single number and show that model-map cross correlations remain above 0.5 for the complete resolution range for all models.

5) A weakness of the paper is that several specific claims are made about the positions of side chains but, when investigated, the density for those side chains is poorly resolved. An example of this is NuoH Lys274, which is in a low-resolution region of the map and although is fit as well as possible must be considered low confidence given the local resolution (nearby residues Phe277 and Phe282 have almost no side chain density for example).

At lower resolution, a presence of residues density strongly depends on their mobility. Well-ordered residues may have well-defined densities while others, even in the proximity, may have a poor density. In the case of Lys274, there is a clear density for the side chain, its position makes chemical sense, and it is hydrogen-bonded to the backbone oxygen of Gly258. In fact, if examined closely, this is also the only meaningful position for Lys274 side chain. At the same time, the conformations of Phe277 and Phe282 are not restrained by interactions with other residues in their vicinity which is likely why their densities are weaker.

6) A weakness of the paper is that the conformational changes seen between the membrane and peripheral arm of the complex in the different 3D classes are difficult to interpret. It is unclear if they are mechanistically significant or, perhaps more likely given the amount of broken complex observed, due to partial disruption of the complex before it completely breaks apart.

As we discussed above, the observed multiple conformations are not due to the complex disruption. It is not very clear what the reviewer means by ‘difficult to interpret’. Many conformations of the peripheral and membrane arms observed for the complex I homologues are likely not mechanistically meaningful per see, but rather reflect overall flexibility of such a large complex. Here, our goal was to describe our structural data as accurately as possible which resulted in several resolved conformations.

We do think they all represent the uncoupled complex I, in this respect they do not have different mechanistic meanings. However, they do permit us to understand how the arms move relative to each other and what degree of freedom exists between them.

7) A strength of the paper is the interesting and original mechanistic proposal put forward by the authors. But a weakness is that it is unclear how this proposal stems from the structural data presented. Also, the arguments presented are difficult to follow in their current form and warrant a more detailed discussion with the requisite thermodynamic treatment. This may warrant a more complete discussion in an appendix or unless the authors can more convincingly show how the data presented in the paper suggests their proposed mechanism perhaps a separate review article. Furthermore, the proposed mechanism, as presented would make a simple prediction that in the absence of NuoM and NuoL (or equivalent subunits in other species) complex I would not pump any net protons. Experiments that are relevant to this prediction have been done in E. coli (NuoL deletion) and Y. lipolytica (nb8m deletion that results in loss of both NuoM and NuoL subunits). See https://pubmed.ncbi.nlm.nih.gov/21417432/ and https://pubmed.ncbi.nlm.nih.gov/21886480/. In both cases the complex is still able to pump protons. The behavior of the NuoL deletion in E. coli is reconcilable with their proposed mechanism as NuoM is still present, however, the case of the nb8m deletion in Y. lipolytica is more difficult to reconcile with their proposed mechanism. The authors would need to address these experiments in order to include their proposed mechanism.

The description of the mechanism has been modified. It is very briefly outlined in the main text along with the Figure 7 and more detailed description, including thermodynamic considerations, is moved to the supplementary text. We have also explained more clearly how the model stems from the experimental data on line 435:

“The absence of a continuous proton-translocation pathway between the Q-site and subunit NuoN, as well as high flexibility of the peripheral arm interface are not consistent with the recently proposed coupling mechanisms relying on specific movements of the interfacial loops (Cabrera-Orefice et al., 2018; Kampjut and Sazanov, 2020). This led us to ask whether a coupling mechanism consistent with known complex I properties, but without the movements of interfacial loops is conceivable.”

Furthermore, we state that at this point this is a hypothetical mechanism.

Supplementary data describing mechanism in more details now also includes the discussion of both papers mentioned by the reviewer from line 1368.

“Experiments with engineering E. coli complex I lacking subunit NuoL and Y. lipolytica complex I lacking homologs of subunits NuoM and NuoL (Dröse et al., 2011; Steimle et al., 2011)(Dröse et al., 2011; Steimle et al., 2011) (correspond to n=2 and 1, respectively) both suggested that the engineered complexes were active and for both constructs stoichiometry was estimated as 2H+/2e-. While NuoL deletion experiments support our model, the NuoL/M deletion clearly contradicts it. Both experiments should be interpreted cautiously, however. Results of NuoL deletion for E. coli complex I were not reproducible (Verkhovskaya and Bloch, 2012). In the case of Y. lipolytica, the homologs of NuoL/M dissociated from the complex along with another 11 subunits upon deletion of supernumerary subunit NB8M located at the tip of NuoL (Zickermann et al., 2015). Since the proton-translocating modules were not deleted per se, the presence of contaminating amounts of assembled complex I in the preparations that generated observed proton pumping cannot be completely excluded. It is important to note that mutation of the conserved ionizable residues on the interface between NuoN and NuoM, i.e. ME144 (Torres-Bacete et al., 2007) or its counter ion NK395 (Amarneh and Vik, 2003), result in a completely inactive complex I suggesting that dissociation of subunits NuoL/M also should render complex I inactive (Verkhovskaya and Bloch, 2012).”

The main problem with these experiments that that they have never been reproduced by other laboratories and are not completely consistent with the mutagenesis data. Deletion of subunits may also result in distinct pumping behavior of the remaining subcomplex. For example, it was shown for the bovine complex I that it can translocate Na+ ions in the deactive state (https://pubmed.ncbi.nlm.nih.gov/22854968/).

Appraisal of whether the authors achieved their aims, and whether the results support their conclusions:

8) Overall, despite the many strengths of this paper detailed above it is unclear whether the authors achieved their goal of a structure of functional E. coli respiratory complex I reconstituted in lipid nano-discs. It appears that under the current grid preparation conditions that the complex is under excessive stress resulting in partial denaturation and partial-to-complete dissociation. Given the clear biophysical data presented on the intactness of the complex in solution, this disruption likely occurs during grid preparation and further optimization of grid conditions may resolve this issue. With the current maps more work needs to be done to improve the map-to-model correlation and to clearly indicate the regions in the models where this correlation is low.

Additional reconstruction of complex I solubilized in LMNG help us to exclude the interaction of the complex with water-air interface and its reconstitution into lipid nanodiscs as the causes of the relative subunit rotation and high flexibility between the arms. At this moment, whether the structure represents an artifact of purification or is a biologically-relevant state remains an open question. However, answering it goes beyond the current study and will require additional research. This is now explained in the discussion section.

Reviewer #2 (Public Review):

The authors performed six experiments examining the influence of beliefs regarding pain experience on behavioral and neural indices of empathy for pain and altruistic behavior. They demonstrate that manipulations that to reduce beliefs that individuals making painful expressions are actually in pain (e.g., revealing them to be actors, indicating that their treatment has been successful, etc.) attenuates subjective judgments of pain intensity, real monetary donations to these targets, and P2 amplitudes, and further, that regions involved in perspective-taking and emotion regulation are sensitive to representations of pain beliefs. While I think that the authors have done an admirable job in laying out the evidence for their argument across six well-devised experiments, I do think that the manuscript has some room for improvement. In particular, I hope that the authors can offer stronger grounding in the background literature and clarify some task and stimulus details.

1. In laying out their hypotheses, the authors write, "The current work tested the hypothesis that BOP provides a fundamental cognitive basis of empathy and altruistic behavior by modulating brain activity in response to others' pain. Specifically, we tested predictions that weakening BOP inhibits altruistic behavior by decreasing empathy and its underlying brain activity whereas enhancing BOP may produce opposite effects on empathy and altruistic behavior." While I'm a little dubious regarding the enhancement effects (see below), a supporting assumption here seems to be that at baseline, we expect that painful expressions reflect real pain experience. To that end, it might be helpful to ground some of the introduction in what we know about the perception of painful expressions (e.g., how rapidly/automatically is pain detected, do we preferentially attend to pain vs. other emotions, etc.).

2. For me, the key takeaway from this manuscript was that our assessment of and response to painful expressions is contextually-sensitive - specifically, to information reflecting whether or not targets are actually in pain. As the authors state it, "Our behavioral and neuroimaging results revealed critical functional roles of BOP in modulations of the perception-emotion-behavior reactivity by showing how BOP predicted and affected empathy/empathic brain activity and monetary donations. Our findings provide evidence that BOP constitutes a fundamental cognitive basis for empathy and altruistic behavior in humans." In other words, pain might be an incredibly socially salient signal, but it's still easily overridden from the top down provided relevant contextual information - you won't empathize with something that isn't there. While I think this hypothesis is well-supported by the data, it's also backed by a pretty healthy literature on contextual influences on pain judgments (including in clinical contexts) that I think the authors might want to consider referencing (here are just a few that come to mind: Craig et al., 2010; Twigg et al., 2015; Nicolardi et al., 2020; Martel et al., 2008; Riva et al., 2015; Hampton et al., 2018; Prkachin & Rocha, 2010; Cui et al., 2016).

3. I had a few questions regarding the stimuli the authors used across these experiments. First, just to confirm, these targets were posing (e.g., not experiencing) pain, correct? Second, the authors refer to counterbalancing assignment of these stimuli to condition within the various experiments. Was target gender balanced across groups in this counterbalancing scheme? (e.g., in Experiment 1, if 8 targets were revealed to be actors/actresses in Round 2, were 4 female and 4 male?) Third, were these stimuli selected at random from a larger set, or based on specific criteria (e.g., normed ratings of intensity, believability, specificity of expression, etc.?) If so, it would be helpful to provide these details for each experiment.

4. The nature of the charitable donation (particularly in Experiment 1) could be clarified. I couldn't tell if the same charity was being referenced in Rounds 1 and 2, and if there were multiple charities in Round 2 (one for the patients and one for the actors).

5. I'm also having a hard time understanding the authors' prediction that targets revealed to truly be patients in the 2nd round will be associated with enhanced BOP/altruism/etc. (as they state it: "By contrast, reconfirming patient identities enhanced the coupling between perceived pain expressions of faces and the painful emotional states of face owners and thus increased BOP.") They aren't in any additional pain than they were before, and at the outset of the task, there was no reason to believe that they weren't suffering from this painful condition - therefore I don't see why a second mention of their pain status should *increase* empathy/giving/etc. It seems likely that this is a contrast effect driven by the actor/actress targets. See the Recommendations for the Authors for specific suggestions regarding potential control experiments. (I'll note that the enhancement effect in Experiment 2 seems more sensible - here, the participant learns that treatment was ineffective, which may be painful in and of itself.)

6. I noted that in the Methods for Experiment 3, the authors stated "We recruited only male participants to exclude potential effects of gender difference in empathic neural responses." This approach continues through the rest of the studies. This raises a few questions. Are there gender differences in the first two studies (which recruited both male and female participants)? Moreover, are the authors not concerned about *target* gender effects? (Since, as far as I can tell, all studies use both male and female targets, which would mean that in Experiments 3 and on, half the targets are same-gender as the participants and the other half are other-gender.) Other work suggests that there are indeed effects of target gender on the recognition of painful expressions (Riva et al., 2011).

7. I was a little unclear on the motivation for Experiment 4. The authors state "If BOP rather than other processes was necessary for the modulation of empathic neural responses in Experiment 3, the same manipulation procedure to assign different face identities that do not change BOP should change the P2 amplitudes in response to pain expressions." What "other processes" are they referring to? As far as I could tell, the upshot of this study was just to demonstrate that differences in empathy for pain were not a mere consequence of assignment to social groups (e.g., the groups must have some relevance for pain experience). While the data are clear and as predicted, I'm not sure this was an alternate hypothesis that I would have suggested or that needs disconfirming.

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Reply to the reviewers

Reviewer 1

This paper proposes a noise-aware approach SCRaPL for modelling the associations of single cell multi-omic data. For gene expression, it uses Poisson-lognormal model. For DNAm data, it uses Binomial noise model which explicitly takes into account the average within the region. The Bayesian hierarchical framework employed by SCRaPL could achieve higher sensitivity and better robustness in identifying correlations, and also offer a template for the application of more complex analysis techniques to multi-omics data. The symbols of this paper are a little bit confusing, and I suggest authors to carefully check them.

We thank the reviewer for his/ her appreciation, and apologise for the confusion arising from the dense notation, which we will thoroughly revise.

1. The symbols used in this paper are messy. For example, "1" and "2" are subscripts in Eq.(2) but become superscripts in Figure 5. Besides, there are many symbols not explained such as mj, Hj, Ψ0, etc. Also, I don't know if x{j,i}^{(1)} , x{j,i}^{(2)} in Figure 5 are same with x{ij1} and x{ij2} in Eq.(3). There are many places mismatch, authors should check carefully.

1. Why the equations in Fig.5 are totally different with Section 4.2? For example, pj ∼Beta(αj ,βj ) in Fig.5 but ρj ∼ Beta−1,1 in Eq.(8).

We apologise for the notational confusion, this will be fully revised.

The paper involves a lot of hyper-parameters which doesn't demonstrate their selection. For example, c1, c2, d1, d2.

This is a good point. We will include a sensitivity analysis on the hyperparameters, justifying the choices on both simulated and real data.

In section4.8, I am confused about $ρ_j$ the experiment 2, 5, 8, 11. Why $ρ_j$ both represents ZI rate and correlation?

We apologise for the notational oversight, which will be rectified.

In Section 4.5, it is difficult to understand the sentence "for me threshold u". Besides, what is $r$ represent in Section 4.5?

We apologise for the confusing sentence. $r$ is the Pearson correlation coefficient, as explained at the start of 4.5

Why there is "(6a)Agreement between SCRaPL and Pearson" in Fig. 4?

This simply means that the panel shows a methylation/ expression scatterplot for a gene where estimation by SCRaPL and Pearson return both a significant association. We will expand the caption to explain further.

For Fig.1, I cannot see the text in the rectangle.

Apologies, we will improve the readability of the figures

I would like to see the efficiency analysis for SCRaPL.

As part of part of re-implementation in a more accessible programming language, we have preformed preliminary efficiency analysis for MCMC , demonstrating linear scalability. Results will appear in the revised manuscript.

Reviewer 2

The authors present a Bayesian model to determine noise-corrected correlation coefficients for gene expression (RNA) and DNA-methylation data at single-cell resolution. The authors present a series of simulation data and an example of matched multi-omics data, and compare their results with Pearson correlation. Noise modelling allows the model to determine gene-methylation correlation patterns more accurately. While the authors demonstrate a neat application on accurate quantification of correlation coefficients, I see a limited use of the model for the broader single-cell community. The authors may therefore improve their manuscript on several aspects.

We thank the reviewer for the encouraging words, and thank him/ her for the critical observations, which we have taken at heart, considerably broadening the scope of our paper to make it more attractive to a larger community.

- Abstract: please specify the omics layers that you are analyzing (RNA + DNA methylation) in the abstract

We acknowledge that, while SCRaPL is potentially general, in the first submission we focused only on RNA and DNA methylation. We have now decided to expand our analyses to include 10X data of simultaneous chromatin accessibility (ATAC-seq) and RNA.

- What is the benefit of using a Bayesian model formulation in this setting?

The benefit is twofold: a principled treatment of noise, and a quantification of the resulting uncertainty which allows for a meaningful way to compute Bayesian significance levels. We will expand the discussion of the relative merits of a Bayesian vs frequentist approach.

- Does it also apply to unmatched data?

In principle, given measurements with the same number of cells in all modalities, it is possible to apply SCRaPL. However, unless there is a natural pairing between different cells, the scaling of this approach will be quadratic in the number of cells, hence potentially expensive (although largely parallelizable). We will discuss this now, particularly in the light of applying SCRaPL in conjunction with other suites such as Seurat.

• Would SCRaPL allow for differential correlation testing?

At the moment, SCRaPL does not allow for differential correlation testing. Of course, one may run SCRaPL separately on two groups of cells and compare the resulting estimates, which would be informative. Nevertheless, extending SCRaPL to perform differential correlation testing (e.g. using Bayesian model selection) would be a non-trivial effort. We will add a comment on this issue to the discussion section.

• Figure 1: The graphical description of the model is rudimentary. I believe that the model description could profit from a graphical model representation of SCRaPL (as presented in figure 5).

We will redraw Fig. 1 and incorporate the graphical model from Fig 5.

- Simulated data: all experiments seem to have rather low cell numbers (max. 200) and genes (max. 300). Given that 10X Genomics is the most widely-used sequencing platform with approx. 10,000 cells and 3,000 (highly variable) genes per experiment, and given that the authors show a use-case with 9480 genes in 487 cells, it seems appropriate to extend the simulations and runtime estimates of the presented model to several thousands of cells and genes, respectively.

Thank you for this comment. The original simulation settings were designed with scMT data in mind, where indeed only a few hundred cells can be assayed at most. Partly because of this feedback, and also because of the request of implementing SCRaPL in a different language, we are working on a more scalable Tensorflow implementation which will be able to handle thousands of cells and genes in a matter of tens of minutes . The new simulated data will therefore extend into this regime with larger data sets.

- Figure 4: Please revise the figure legend as I did not understand the plotted results based on the description.

We will do so.

- Results section 2.5: Please formulate your whole argument about epigenetic regulators. I do not think that "For further information please refer to supplementary figure XYZ." Is an appropriate closing statement for a paragraph, nor does it motivate the reader to look at the supplementary figures (I did look at them and I do not see how they support the point made in the paragraph). Please elaborate and consider a "take home message" for the paragraph such that the reader is able to understand the benefit of SCRaPL without revisiting the original data publication.

Thank you for this pointer, we will take it on board in the full revision.

- Conclusion: The authors mention that SCRaPL would further offer a "template for the application of more complex analysis techniques (such as clustering, dimensionality reduction and network inference)". If that was the case, the authors should consider a comparison to other tools, which offer exactly that (e.g. Seurat's CCA or non-negative matrix factorization in LIGER). Further, the authors should set their work into context with tools like bindSC.

Thank you for the suggestion. As far as we can tell, all of these methods are thought for unmatched data, rather than multi-omics assays performed in the same cells. Having said that, it is in principle possible to “preprocess” data with SCRaPL and then feed to Seurat or other tools the latent means computed by SCRaPL. We will include an example of how this may be done in the revision.

- Implementation: Matlab is used in about 6% of the single-cell RNAseq tools (according to scrna-tools.org). To reach a larger scientific community, do the authors plan to provide an R or Python implementation of their model?

We are now implementing SCRaPL in Python using Tensorflow probability, hoping to achieve substantial speedups (see response to previous point).

Additional minor points about formatting by Reviewer 2 will all be addressed.

Reviewer 3

Maniatis et al propose a sound strategy to analyse single-cell multi-comic data sets. A key advance is to use bespoke error models for each of the omics data. These are integrated into a multivariate gaussian model. This method is a novel and, in my opinion, a valuable addition to the analyses of the growing multi-omics single-cell data sets.

We thank this reviewer for his/ her appreciation of our work.

- Authors make a convincing argument of the importance of principle methods and in particular to use noise models that tailored to the data at hand. To further support this, can authors elaborate on how results would be different from using commonly applied methods ? Eg those embedded in the Seurat, OSCA, and scanpy 'suites'? Authors compare to Pearson correlation-based methods but is not clear if that is the true state-of-the-art on those methods

As far as we know, volcano plots of p-value versus Pearson correlation are the most commonly employed approaches to assess correlations amongst different molecular modalities in single-cell multi-omics (see e.g. Argelaguet et al, Nature 2020). Seurat and other methods normally do not deal with single-cell multi-omics (i.e., multiple omics measured in the same cell), rather with multiple single-cell omics (different molecular modalities assayed in different cells). Nevertheless, it is possible to pre-apply SCRaPL to non-matched data and then use another suite; as an illustration, we will perform an analysis on scMT data using SCRaPL followed by Seurat.

- In the case study on mouse embryonic stem cells, authors excluded the chromatic accessibilty. Why not using it to more clearly show the value of the method?

We did use SCRaPL also on chromatin accessibility, however the signal was weaker and we did not include it in the manuscript, we will now present these results as supplementary material.

- It would also be great if authors would use a different single-cell multi-comic data sets, using other dat modalities, e.g. CITE-Seq data. If this not possible, at least they should elaborate on which omics SCRAPL can handle, what would be the noise models for different data types, etc.

We have started analysing a joint scATAC-scRNA- seq data set generated using the new 10X commercial platform, and will add the results of this analysis to the revised manuscript. We will also expand the description of the suitability for different data types.

*- As the authors acknowledge, computational burden is high, which presumably limits scalability. Are authors able to further explore this (scalability on Insilico data)? Or how complex is adopting the variational inference method suggested? I appreciate that the variational inference implementation might be out of the scope of this paper, though.

• It is a pity that the method is in Matlab. Nearly no-one in single-cell omics use Matlab. Our own lab is largely invested in this topic and we do not even have Matlab licenses. I strongly encourage authors to implement their method in e.g. R or python, ideally compatible with the broadly used 'suites' (Seurat, OSCA, and scanpy,...).*

We are addressed these two comments jointly by re-implementing SCRaPL in Tensorflow probability (Python based), which allow us to leverage powerful libraries for variational inference. We hope that this will lead to a substantial increase of scalability, providing the possibility of running on thousands of cells / genes in under one hour (results will appear in ).

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Reply to the reviewers

First of all, we would like to thank the editor and all reviewers for the effort to evaluate our paper in this difficult era of COVID-19.

Reviewer #1

(Significance): Overall, this manuscript is very clear and easy to follow. The manuscript could be improved by making the following changes:

We thank the reviewer for the favorable comment and will revise the manuscript according to the suggestions.

Reviewer #2

(Evidence, reproducibility and clarity): The use of genetics is particularly impressive but the lack of major discoveries dampens the enthusiasm. Additional efforts to mechanistically define wave initiation and wave propagation would significantly improve the impact of the manuscript. Moreover, some of the conclusions are not fully supported by the data and require further experimentation and/or analysis.

We admit that marked redundancy of function among the EGFR ligands and their essential roles in cell growth prevent us from obtaining very clear results. Considering the importance of EGFR ligands in biology, we believe, our observation will give invaluable suggestions to whom wishes to clarify the roles played by EGFR-family protein in other biological contexts.

(Significance): While it is known that ADAM17 is critical to process EGFR ligands, the specific or redundant roles of different ligands remains an open question. The authors find that all ADAM17 ligands contribute to ERK signaling waves but may have specific contributions to other phenotypes. This work would be of interest to the signaling dynamics, epithelial and developmental biology communities.

We thank the reviewer for the favorable comment.

Reviewer #3

(Evidence, reproducibility and clarity): Overall, this study is carried out with a high degree of rigor and technical excellence, with clear reporting of experimental details and replication. The writing and figures are very clear, and there are no obvious technical problems. However, there are some areas in which the strength and clarity of the conclusions could be strengthened by relatively simple experiments.

We thank the reviewer for the favorable comment. We have already performed some of the experiments suggested by the reviewer. As the reviewer might have anticipated, co-culture with the wild type MDCK cells helps mutant cells to survive. We believe we could propose a clearer model in the revised paper.

(Significance): This study definitively establishes the role of 4 EGFR ligands in the generation of ERK activity waves in MDCK cells. While other studies, including some from the senior author's lab, have strongly indicated that EGFR autocrine signaling is important for these waves, this study goes further in comparing the roles of these ligands using knockouts to unambiguously establish the autocrine factors involved. Others who use this common experimental system (MDCK) to study epithelial dynamics will find this study of great interest. A wider audience of those who work on EGFR-mediated signaling will also find the data quite fascinating as an example of the complex relationship between ERK activation and its downstream effects. The technical excellence of the paper will make it a must-read for those in these fields. However, there are some factors that limit the scope of the significance. MDCK cells are an important experimental model system but differ in substantial ways from other epithelial cells, particularly in the expression of EGFR ligands. Because different ligands such as amphiregulin dominate in other systems (as noted by the authors, and PMID 27405981), the ability to extrapolate from these findings to other cell types is somewhat limited. Also, the paper avoids addressing the major question of how ERK waves relate to collective migration rate. From the data presented it is clear that this relationship is complex; for example, bath application of the ligands restores a high migration rate but not ERK waves. Given this lack of a clear relationship it is an understandable decision to leave this question for future work; however this does limit the conclusions that can be drawn from the study.

We completely agree with the reviewer’s view. It is uncertain to what extent the observation with MDCK cells can be generalized to other cell types. We also admit that the conclusion is not very simple because EGFR signaling is required for various cellular functions including cell survival and migration. Even though the gene editing becomes so easy, it is still labor consuming work to knock out many genes in a single cell line with extensive characterization. We believe the data shown in our work will provide a basis for the understanding of EGFR ligands.

Reviewer #1

For Fig 1F, 3 individual experiments should be conducted to confirm results.

We will follow the reviewer’s suggestion and repeat the experiment.

For Fig 1G, could the authors please show the original western blot data in full rather than just the densitometry graphs?

We did not show just for the sake of brevity. We are happy to will include the images as a supplementary data.

The authors should explain the origin/phenotype of MDCK cells for those who are not familiar with the cell line.

We will modify the text according to the reviewer’s suggestion.

The authors should give a future outlook/direction for future experimentation to further confirm redundancy in EGF ligands in the propagation of ERK activation waves.

We will discuss on the redundancy in other cell types based on available NGS data.

Some mention of the use of biosensors in the abstract and introduction is recommended as this is a major part of the experimental work.

We will refer to the biosensors in the abstract and introduction.

Reviewer #2

There are conflicts with some of the conclusions made about ligands. dEGFR cells have basal ERK activity as high as WT which argues against EGF being responsible for basal ERK activity. Further, basal ERK activity was not rescued by restoration of EGF in the 4KO-EGF cells. The authors should address this discrepancy.

We agree that some new questions have arisen from our observations. The discrepancy of the phenotypes between dEGFR cells and dEGF cells is an example. We are currently establishing dEGF cell lines, in which different genomic sequences of the EGF gene were targeted. We have already started to develop these cell lines and will obtain them within a month. The result will provide some clues to answer the questions. However, even if we could not solve the question, we believe, it is worth reporting observations that could not be easily understood, because such questions are often leading to another discovery.

Besides the ones genetically disrupted in this work, other EGFR ligands seem to play functional roles given that dEGFR cells less migration and fewer ERK waves than 4KO cells. The authors could test if other ligands are upregulated in 4KO cells to compensate. On a similar note determining whether ADAM17 deficient cells are more similar to 4KO cells or dEGFR cells could provide some insight.

According to the reviewer’s suggestion, we will conduct qPCR of growth factors in mutant cell lines to see the expression levels of seven EGFR ligands might have changed significantly. At the same time, as the reviewer suggested, we will establish ADAM17 knockout cell lines and compare the phenotype with those of cell lines deficient from EGFR ligand genes.

• The authors propose that Nrg1 is responsible for ERK waves in QKO, 4KO, dEGFR, and 4KO-EGF cells but are limited in testing this due to Nrg1 being essential in 4KO cells. First, Nrg1 should have been deleted in TKO cells to confirm that it is only essential in the absence of the four EGFR ligands. Additionally, Nrg1 could be knocked out in 4KO-EGF cells to demonstrate the claim that EGF-induced ADAM17 cleavage of Nrg1 is responsible for ERK waves.*

We do not think the deletion of Nrg1 in the TKO cells will abolish the ERK activation waves because EREG in TKO cells could transmit the waves. To overcome the problem of cell growth, we will try to obtain 5KO cells by Cre-induced deletion of NRG1 in 5KO-loxP-NRG1 cells, wherein EGF is supplied exogenously. We already had preliminary data suggesting that co-culture with wild type MDCK cells helps 5KO cells grow.

The authors state that ERK activation waves are important for collective migration and seek to understand the roles of each EGFR ligand, but despite measuring migration and properties of ERK activity, there is very little analysis or commentary on the relationship between the two. The ability of HB-EGF to restore migration without ERK waves suggests that waves are not required per se. It is interesting to note that with restoration of ligands, migration is higher than WT but ERK activity is lower.

We refrained from spending much space about the essential role of ERK activation waves in collective cell migration, because several papers have already described this issue.

Probably, we should have spent more space to emphasize that the collective cell migration is comprised of at least two different phenomena. The migration of leader cells and the follower cells. The ERK activation waves are essential for the follower cells but not the leader cells. In 4KO cells, both the leader cell and follower cell migrations are impaired. We showed that GFs expression restore the leader cell migration, but not the follower cells. We will revise the text to include this issue.

It is suggested that the total amount of EGFR ligands may be the primary determinant of migration, but deletion of TGFα alone causes a significant decrease in migration comparable to the DKO cells. TGFα has the lowest expression of the four ligands studied but is the only ligand to have a significant impact on migration in the single knockout context, which disagrees with that conclusion.

Each EGFR ligand has different affinity to EGFR, which makes it difficult to link the mRNA levels directly to the effect of each EGFR ligand. We will modify the discussion to include this argument.

Other:

Fig. S3B needs clarification that the WT (black) and 4KO (green) did not receive a stimulus.

We will follow the reviewer’s advice.

Reviewer #3:

The experiments in Fig. 5 are undertaken with the purpose of assessing whether NRG acts as an additional ligand that mediates the residual ERK waves in 4KO/QKO cells. However, this question is never addressed in the NRG/4KO cells. While it might be challenging due to the proliferative defect, it seems important to attempt this experiment in some way; measuring the ERK waves for these cells would establish whether all of the critical autocrine factors have been identified. Can the proliferation be rescued by application of high amounts of growth factors?

This question is similar to a question raised by reviewer #2.

To overcome the problem of cell growth, we will try to obtain 5KO cells by Cre-induced deletion of NRG1 in 5KO-loxP-NRG1 cells, wherein EGF is supplied exogenously.

The bath exposure to EGFR ligands shown in Fig. S3A is an important experiment, but it is surprising that ERK signaling is not maintained under these conditions. Is this due to depletion of the added ligands, perhaps locally? Or is the intermittent nature of paracrine signaling needed to maintain ERK activity? These possibilities could be distinguished by checking whether the added EGF or the other ligands are depleted after several hours, or by restimulating with a new bolus of ligand after several hours.

We thank the reviewer for this invaluable suggestion. We will conduct the experiments suggested by the reviewer.

The connection between ERK activity and migration is somewhat confusing. It would be helpful to show the dose sensitivity of migration to a MEK or ERK inhibitor. Are other pathways downstream of EGFR such as PI3K involved in the autocrine-mediated migration? This could also be established with the appropriate inhibitors.

We should have spent more space to emphasize that the collective cell migration is comprised of at least two different phenomena. The migration of leader cells and the follower cells. The ERK activation waves are essential for the follower cells but not the leader cells. In 4KO cells, both the leader cell and follower cell migrations are impaired. We showed that GFs expression restore the leader cell migration, but not the follower cells. We will emphasize this issue in the revised manuscript.

Reviewer #1:

Line 47 in Abstract should read "Aiming for" not "Aiming at".

We have corrected the mistake as suggested.

Some in the field call fluorescence lifetime microscopy "FLIM", you could adopt the same wording in your manuscript to attract more readers.

We have included FLIM according to the reviewer’s suggestion.

Reviewer #1 :

Figure 1D, the images should be presented using the same scale for both the EKAREV and EKARrEV constructs so that they can be directly compared.

Because the basal FRET/CFP ratio is significantly different between EKAREV-NLS and EKARrEV-NLS, the changes during mitosis become unclear if we applied the same scale. This figure is prepared to show the reactivity to Cdk1 during mitosis; therefore, we believe the current scale is better for presentation.

The names QKO and 4KO are a bit confusing. Could the authors please change the naming of the knockout cells so that readers understand that QKO and 4KO are two separate cell types? Perhaps instead of 4KO use FKO for "full knockout" or something similar. The 5KO line might also need to be named something else if you change to FKO.

We have discussed this issue with the co-authors, but could not reach a better idea. Instead of changing the names, we will include a detailed explanation for each cell line.

Reviewer #2:

The interpretation of the RA-SOS coculture experiments is confusing. Based on the author's reasoning, I would expect ADAM17 shedding in the RA-SOS cells to trigger signaling at the interface of both WT and 4KO cells but the 4KO should be unable to propagate the wave farther away from the interface. This does not seem to be the case. Do RA-SOS ADAM17KO cells still trigger waves of ERK signaling in the WT cells? Do ADAM17KO cells behave as the 4KO cells in this coculture system?

Probably, the reviewer misunderstood the method. The GF-less 4KO cells were co-cultured with wild type cells harboring the RA-SOS system. We will describe more in detail to avoid misunderstanding.

Finally, the growth curve in Fig. 5B indicates that 5KO-loxP-NRG1-CreERT2 cells are viable for about two days after Cre induction. The authors could perform a confinement release assay of these cells 1-1.5 days after Cre induction to look for further reduction of ERK waves and migration to demonstrate the role of Nrg1.

This experiment may not be necessary. It is clear that NRG1 is required for the survival of 4KO cells. The reason why cells are still alive 1 to 2 days after 4-OHT application is simply because NRG1 protein is remaining. The interpretation of the results would be difficult during the phase of NRG1 reduction.

In Fig. 1G, the normalization of all WT pERK samples to 1 artificially lowers the variance to zero when performing the T-test.

For the comparison of immunoblotting data derived from independent experiments, the signals must be normalized to the control. We believe the use of pERK/ERK of the wild type cells as the control is reasonable for this experiment.

Author Response:

Reviewer #1 (Public Review):

I think that it is important for the authors to consider that for most (if not all) SARS-CoV-2 variants, increased transmissibility of the virus has not been directly demonstrated. While it is clear that numerous variants have emerged and will continue to emerge, the rapid upsurge of cases with a variant may be related to many factors (e.g. host susceptibility due to immunity or genetic factors, virus seeding events, predominant replication in particular age cohorts, ...) that cannot simply be captured as "transmissibility of the virus". Even for B.1.1.7 and D614G mutants, the direct evidence of increased transmissibility in humans is extremely limited if available at all. Most studies erroneously simply take the increasing occurrence of particular lineages or mutations in sequence databases as a measure of increased "transmissibility", which should be avoided, also in the present manuscript. Increased transmissibility can only be derived from field studies where transmission is measured directly.

We thank the reviewer for pointing out that this is a controversial area. We have adjusted the text throughout to accommodate the fact that the published evidence of increased transmissibility/infectivity is not definitive.

On several occasions in the manuscript (e.g. page 3, page 4 L58-59, page 9 in submitted version), the authors seem to suggest that changes that lead to increased "transmission" or binding affinity and changes that lead to immune escape are mutually exclusive. But the opposite might be true. Viruses may escape from antibody-mediated immunity by amino acid substitutions in linear or structural antibody-binding epitopes. However, viruses may also escape from antibody-mediated immunity through altered protein density on virion surfaces (e.g. less Spike) and/or altered affinity, making it harder for antibody to inhibit virus attachment. As an example, increased affinity may facilitate virus replication with less dense Spike protein, allowing more effective antibody escape. Lower affinity but more dense coverage of Spike may reduce accessibility of critical virus parts by antibodies. Several viruses are known to escape from antibody-mediated neutralization through changes in affinity/avidity.

We agree with this point and have modified the text to avoid implying that increased transmissibility and antibody escape are mutually exclusive.

In relation to the previous point, it is important that authors mention some limitations of the present work in the discussion. SARS-CoV-2 virion attachment to cells is not just a matter of spike protein binding and certainly not of a monomeric RBD. Escape from antibodies and effects on affinity are heavily influenced by the entire (trimeric) spike protein, including its N-terminal domains. Such components are not taken into account in the present experimental designs, and this should be discussed, as e.g. the NTD can be important in attachment and antibody-mediated neutralization.

We thank the reviewer for this suggestion. We have added an appropriate caveat to the Discussion.

The authors suggest that the pandemic virus as it spread across the globe initially did not have "optimized" affinity. However, in the first months of the pandemic, there was relatively limited variation in spike protein sequences. The major variants emerged only later and mostly in areas where population immunity was building up. Again, this begs the question whether natural selection is occurring as a consequence of receptor affinity or immune escape?

We thank the reviewer for making this point. However, we do not think it is that surprising that it took a few months for the first Spike variants to be detected, for the following reasons. Firstly, the number of infections would have been relatively low early in the pandemic and SARS-CoV-2 replicates with a comparatively low error rate for an RNA virus. Secondly, the introduction of strict non-pharmacological measures (social-distancing etc), which would have increased the selective pressure on the virus, was somewhat delayed. Thirdly, it would take some time for any variant that emerged by chance to expand sufficiently to be detected by sequencing. While there is evidence suggestive of broader immunity in populations were the Beta and Gamma variants emerged, which we cite, we are not aware of evidence of widespread immunity in populations where the D614G, S477N and Alpha variants first emerged.

Reviewer #2 (Public Review):

Barton and colleagues investigated the effect of common SARS-CoV-2 RBD mutations and two ACE2 mutations on the RBD/ACE2 interaction. They concluded that the N501Y, E484K and S477N increased receptor binding while the K417N/T had the opposite effect. Double and triple mutants were also included. The ACE2 mutations (that are rare in the human population) also increased binding to most RBD mutants. The study is well-performed and written clearly.

The primary conclusions of the manuscript were supported by the results. However, the interpretation was too speculative. In the abstract (lines 14-17), the authors suggest that the 501 and 477 mutations enhance transmission solely based on data on the RBD-ACE2 interaction. It is unknown whether increased affinity to ACE2 is beneficial for transmission. In addition, increased RBD affinity to ACE2 does not mean that the whole spike or virus particle also binds stronger to ACE2. Lastly, increasing ACE2 affinity does not necessarily increase binding to cells (for example S1A binding to sugars or spike abundance can also influence this).

We agree that it would be inappropriate to assume, based on our affinity/kinetic studies alone, that 501 and 477 enhance transmission. That is why the relevant sentence in the abstract starts with the phrase, “Taken together with other studies”. We summarises the evidence from these other studies in the Discussion. We acknowledge that we have not examined the effects of the mutations on binding of the whole Spike protein to ACE2 or viruses to cells, and have added a suitable caveats to the Discussion.

The overall impact on the field will be limited as there is substantial overlap with already published studies. The observation that the N501Y and E484K increase receptor binding while the K417N/T mutations decrease binding was already made prior by Laffeber et al (2021; J Mol Biol). Laffeber et al also investigated double and triple mutants and came to similar conclusions. Liu et al (2021) confirmed that the N501Y increases binding whereas the K417N/T have opposing effects (Liu et al., 2021 mAbs). The observation that the Y501N increases ACE2 affinity has been made by several groups (e.g. Liu et al 2021 Cell research; Starr et al 2020 Cell).

We thank the reviewer for highlighting these addition studies, two of which are very recent. We have now cited these studies.

Starr et all 2020 was a high throughput study in which the affinity measurements were semi-quantitative, and no kinetic analysis was performed. Liu et al (2021) and Laffeber et al (2021) were performed at 25 C and without rigorous controls for mass-transport and protein aggregation. Liu et al (2021) did not report kinetic measurements. Their results are broadly consistent with ours but their affinity and kinetic measurments are ~ 10 fold different. While we accept that some of the measurements of the effects of mutations have been made before, our measurements of affinity and especially kinetics are performed more rigorously than in previous studies and, for the first time, at a physiological temperature (37 C). Thus, the affinity and kinetic data that we have obtained for single and combinations variants are more definitive. As noted in our Discussion there is a wide variation in reported binding affinities and kinetics in previously published studies. We think the comprehensive data that we report here, the same robust method to measure binding properties of all these variants, adds significant value.

Reviewer #3 (Public Review):

[...] 1) The ACE2 receptor exists naturally as a dimeric form and the RBD is a component of the SARS-CoV-2 spike trimer. The assay format here was monomeric RBD binding against monomeric ACE2 throughout this study. While the measurements are indeed carefully executed and under more physiological conditions than many other reported studies, the authors should discuss potential avidity effects, the consequences of mutations on the accessibility of the RBD in VOC versus wildtype, and impact of other domains such as the NTD, in the context of their monomeric ACE2 measurements with isolated RBD here.

We thank the reviewer for raising this issue. We have added a section to the Discussion addressing these important points.

2) As shown in Figure S2, RBD WT, K417N, K417T, KN/EK, KT/EK, and S477N, the ~30kDa monomeric proteins were flanked by additional ~60kDa bands (which correspond to the smaller peaks to the left of the main peaks) some of which bleed through to the main fraction to different extents, whereas RBDs SA, UK1, UK2, BR, and E484K, do not seem to have as much or any of these extra species. Can the authors comment on whether these contaminants are RBD-dimers as observed before (Dai et al. 2020)? If yes, would such dimers affect the affinity and kinetics?

We thank the reviewer for pointing out these larger ~60 kDa bands in some RBD preps. We think that it is unlikely that these are RBD dimers as these are reducing gels. The strictly monophasic kinetics of all RBD preps, also argues against this being an RBD dimer. We have confirmed by densitometry that the larger band comprises less that 5% of the protein in all the preparations. This will have only a minor effect on estimated of RBD concentration. We have added this information to the Figure S2 legend.

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Reply to the reviewers

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

**Summary:**

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.

In this paper the authors used a targeted approach to identify rare mutations in a cohort of glioma patients. Using this approach they identified a recurrent mutation in the TOP2A gene encoding for Topoisomerase 2A, and suggest that this mutation creates a more effective protein, binding DNA strongly and maybe more enzymatically active. RNAseq analysis of TOP2A WT and TOP2A mut tumor samples suggest different transcription patterns and points to possible splicing defects. The most recurrent variant (E9448Q) is described in depth and some experimental information shows this variant might be a gain-of-function mutation.

**Major comments:**

• Are the key conclusions convincing? The validation of both the methodology and the presence of never described TOP2A variations in HGG is done quite successfully. Interesting evidence about relevance of the most frequent mutation is provided. However, besides having computational and biochemistry assays performed, lack of details about in vitro experiment statistics (no p-values are provided in figures 4 and 5, neither sample size, repetitions) weakens the conclusions claimed by the authors about the properties of the mutated topoisomerase. Ad. In the revised version we provided more details about in vitro experiments, including statistics when is applicable, sample size and a number of repetitions. In the fig. 4 we show the results of two repetitions (so we can’t calculate statistics) but I would like to stress that we tested independently two fragments of the protein and the results were similar, so our conclusion was justified. However, we do agree with the reviewer that a statistical analysis of those biochemical tests is required. We already started to produce a new batch of recombinant proteins and we will add repetitions to reinforce our claims. We will provide statistical analysis details once all experiments are performed. __

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Claims about E948Q variant function should be revised. Data is not presented in a convincing way, plus there is ambiguous language used from the results ("We conclude that the E9448Q TOP2A protein is functional, and MIGHT have a higher activity than the WT protein") to the rest of the paper where they strongly support the claims about the TOP2A activity. Ad. We will provide more data on biochemical features of the TOP2A variant to confirm the impact of the E948Q substitution on enzyme activities, which would allow more strong conclusions. This will present our results in more convincing way. A language of the manuscript has been critically revised and modified (see a version with tracked changes).

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. In line with the presented data in the paper, additional experiments that show catalytic changes of the E9448Q variation must be added. It is shown that there are differences in the DNA binding capacity by EMSA compared to the WT form, however, the DNA supercoil relaxation activities is not that different, at least the way the results are presented. The authors suggest that TOP2A mutation is a driver mutation but no validation in vitro of this claim is shown. Can this mutation alone or in combination with e.g. tumor suppressors transform normal cells to cancer cells? Do cell lines expressing this mutation (compared to parental TOP2A wt expressing cells) display increased transcription? Increased invasion? Ad. In the revised version we moderated our conclusions and we do not state that the mutated TOP2A is an oncogenic driver. We suggest this mutation (and possibly other TOP2A mutations, as we analyzed the impact of other variants on the TOP2A protein function) contribute to gliomagenesis. This conclusion is based not only on the changes in biochemical properties, but also on the observation of the impact of the mutation of transcription and patient survival. We expanded the analysis of TOP2A mutations and expression levels on TCGA datasets and those new results support our conclusions about a pathogenic nature of TOP2A overexpression and mutations (the supplementary fig.4). We believe in such situation, there is no rationale to make a classical oncogenic driver experiment.

Due to a rarity of the TOP2A mutations it is impossible to find a patient derived cell line with such defect. We attempted to overexpress TOP2A in glioma cells but apparently there is some autoregulation preventing overexpression of this protein is cells with endogenous TOP2A expression. Therefore, we can’t verify if cell lines expressing this variant (compared to parental TOP2A wt expressing cells) have increased transcription. Moreover, such experiments are costly and require more time investment for substantial experiments

I would like to stress that modeling some events in cell cultures is difficult and we found in GBMs the link between the mutated TOP2A and increased transcription along with decrease of splicing factors expression.

We have attempted to make CRISPR/Cas9 mediated knock-in in glioma cells but without success. This is a difficult and time consuming procedure. Although in principle, we agree on the rationale for such experiment, we think that the current data are consistent and convincing. If reviewers find it necessary we may attempt to create glioma cell lines with TOP2A knock-out and overexpression of the mutated TOP2A gene and study it functionally, but it would require more time.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. If the authors can complement the already presented in vitro experiments with additional ones supporting their hypothesis, this should be feasible. The authors can use patient derived glioma cells or glioma cell lines manipulated to express either the parental TOP2A wt enzyme or the identified mutated form. __Ad. Due to a rarity of the TOP2A mutations it is impossible to find a patient derived cell line with such defect. Our findings partly relied on frozen historical samples, so it is not possible to develop patient-derived cell lines. As mentioned above, we can create a TOP2A knock-out cell line and overexpress a wild type or mutated version but there is no certainty that TOP2A deficient cells would survive (this is an essential enzyme) and such manipulation would be feasible.__

• Are the data and the methods presented in such a way that they can be reproduced? Yes, the authors provide a quite detailed explanation of the methods implemented to reach each one of the results they are presenting.

• Are the experiments adequately replicated and statistical analysis adequate? No, there is no information about the statistical analysis or number of replicates in any of the in vitro experiments performed. This information should be added to the manuscript.

Ad. In the revised version the requested information was added where was possible and additional repetitions for biochemical experiments are currently in progress.

**Minor comments:**

• Specific experimental issues that are easily addressable.
• Are prior studies referenced appropriately? Yes, authors clearly address the state of the art regarding previous NGS methodologies and let us know the advantages and novelty of their approach.

• Are the text and figures clear and accurate? There are some discrepancies between the strength of the language used in different sections of the paper to refer the conclusions they can infer from the results they are showing. While they are all valid, authors should revise it. Ad. The text of the manuscript has been unified and revised.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? First of all, describe the statistical analysis used in every figure, include number of biological and technical replicates. I would also suggest to change the title or the scope of the discussion, there is too much focus on the TOP2A in the introduction, neglecting all the technical NGS work that actually lead to several new variants being described. This may be confusing when it collides with a conclusion that is heavily focused on the first half describing potential implications of at least another 3 proteins where genetic alterations were described. Given the fact there is not much experimental work that shows TOP2A mutations relevance in HGG or strong enough evidence of the variant's function I would suggest to change a bit the scope of the title. Ad. The description of the results and discussion have been revised to include additional data/discussion on technicalities and other finding not related to TOP2A. We performed additional computational analyses of TOP2A expression/mutations in the TCGA datasets. We believe that the planned experiments on genetically modified cell lines would provide additional support for our claims. We think that in the revised version a balance between landscape/NGS content and TOP2A content is well balanced.

Reviewer #1 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. The authors describe a methodology that proved to be sensitive and specific enough in order to allow them to detect rare genetic alterations in patient glioma samples. This information could be valuable to describe new driver mutations or infer in genetic pathway alterations that could be potential therapeutic targets. As the authors state at the beginning of the paper, given the poor therapeutical approaches existing for HGG currently, information of this kind could still be highly useful and provide a better outcome to a specific cohort of patients.

On a personal note, I think there is too much speculation about how TOP2A mutations could be interesting from a biological point of view (authors referred to evidence about implications of this mutation in other forms of cancer) but since no experimental validation is provided in glioma cells, it is difficult to conclude that this enzyme gain-of-function mutation could have a relevant role in HGG and thus make these variants a potential therapeutic target. There are no experiments conducted in glioma cells that express TOP2A variants, it would be interesting to see if it has an effect in the migratory/invasive phenotype like described in other cancer types or like it is suggested by analysis of the genetic pathways activated in the HGG patients samples harboring TOP2A mutation. In addition, there is no evidence of the TOP2A mutations possible role as a driver mutation, which is an interesting aspect that could be further explored from both a computational and an experimental approach.

Ad. As mentioned above, there is no glioma cells that express TOP2A variants and we are not convinced that such experiment will be feasible taking into account an essential role of TOP2A. We will attempt to perform experiments with CRISP/Cas9 knock-in cell lines and functional validation, but until now we did not accomplish knock-in in glioma cells. We will try to knock-out the endogenous TOP2A using CRISPR and express a TOP2A WT or E948Q variant from plasmids encoding these proteins, but we can’t predict if TOP2A KO cell would survive. If we manage to produce such cells, then we will investigate proliferation, migration and invasion of cells expressing TOP2A WT or mutated variant.

We do agree with the reviewer that our previous conclusions were too strong, and in the revised version we moderated our claims. We do not say that the mutated TOP2A is an oncogenic driver. We suggest this mutation (and possibly other TOP2A mutations, as we analyzed the impact of other variants on the TOP2A protein structure) contribute to gliomagenesis.

__Data on the Fig. 1A suggests that TOP2A has a mutational hotspot in the position E948Q in our dataset. In the revised version of the manuscript we have extended RNA-seq analysis of our datasets and TCGA PanCancer datasets to search for TOP2A mutations/ overexpression. We found that another computational prediction using CADD algorithm strongly confirms that TOP2A E948Q is in the top 1% of most deleterious variants in the human genome (CADD score >20). This results was added to Supplementary Table 2.__

• Place the work in the context of the existing literature (provide references, where appropriate). The quality of the paper is high and in line with other studies in the literature that perform genome and transcriptome analysis of tumor samples. It is only the experimental validation that is lacking data supporting the "in silico" findings. Ad. We would like to point that we provided the results of experimental, biochemical validation (2 assays) showing that the variant TOP2A proteins have different properties. The associations of transcriptional dysregulation in variant TOP2A bearing gliomas was not a in silico prediction but the result of the analysis of real tumor samples.

As stated above, we are ready to perform further biological validation if the editors find it necessary.

• State what audience might be interested in and influenced by the reported findings. Computational biologists are the right audience to target this paper. If additional experimental work further validating their initial bioinformatic findings is added to the manuscript then probably a wider population could be targeted.

Ad. As stated above, we are working now on providing more replicates of biochemical assays and we are ready to perform further biological validation if the editors find it necessary. I would like to stress that genome editing by knock-in is not always possible/feasible, and these type of experiments is time and money consuming.

• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Brain tumors, immunotherapy, cancer stem cells, tumor microenvironment, tumor heterogeneity. I do not have sufficient expertise to evaluate the bioinformatic analysis and software/programs used to analyze the NGS data.

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

By exon targeted resequencing of 664 genes frequently mutated in cancer, authors identify novel mutations associated to Glioma in a cohort of 182 Polish and Canadian samples. Most of these novel mutations have been identified as potential rare germline mutations, somatic mosaicism or loss-of-heterozygosity variants. Among them, authors focus on mutations associated to the TOP2A gene, which encodes one of the two Type II topoisomerases paralogs present in humans. By a limited number of in vitro experiments, authors conclude that TOP2A recurrent variant E948Q, displays increased binding to DNA and topoisomerase activity. Therefore, authors suggest that the TOP2A E948Q variant is a gain-of-function mutation.

**Major comments:**

• Authors show an interesting plethora of new exon mutations associated with High Grade Glioma. Nevertheless, the characterization of TOP2A E948Q variant, which is the main focus of the study, although very interesting and potentially clinically relevant, remains incomplete. Association of the TOP2A E948Q glioma variant with a gain-of-function mutation would require to improve the statistical power of the presented experiments (increase number of replicates). With the existing experimental evidence, the increased DNA binding and activity of the TOP2A E948Q variant should be considered as preliminary, especially in the case of 431-1193 aa fragment. I would consider mandatory to increase experimental replicates and to analyse statistical significance in the case of DNA binding experiments and DNA relaxation assays with the TOP2A 431-1193 aa fragment. A more detailed biochemical characterization should be performed. A titration of different amounts of protein should be included in these experiments, and at least two batches of purified proteins should be analysed. Decatenation assays should also be performed to characterize the activity of the mutant protein in more detail. Recapitulation of DNA binding and activity results with other TOP2A variants obtained in this study will significantly reinforce authors claims too. This improved biochemical characterization should not take longer than two months.

Ad. We would like to stress that while two replicates are presented, we were testing two forms of TOP2A proteins and the results were similar, confirming our conclusions. But we agree that additional replicates would strengthen our claims. Therefore, we are in the process of producing another batch of recombinant proteins to increase a number of replicates and calculate statistics for the biochemical assays (binding and relaxation assay). We will perform titration of different amounts of the protein using two batches of purified proteins.

The occurrence of other TOP2A variants is low (identified in only a single patient sample), therefore we will perform experimental validation only for E948Q. However, we performed additional computational analysis for other TOP2A variants showing the influence of the substitution on DNA binding by docking the DNA fragment into TOP2A binding pocket (Supplementary table 4).

• To increase the significance of the results, I would encourage authors to include experiments showing the functional impact of this TOP2A mutation in cells. The connection with transcriptomic alterations is merely correlative, and would be greatly strengthened by functional experiments in cellular models. To draw definitive conclusions regarding the changes in transcription, I would encourage authors to complement the results with experiments that point to the physiological impact of TOP2A variants within the cell. Overexpression of WT and E948Q variants in a cell model and transcriptomic analysis would be desirable, but validation in these experimental models of some of the target genes identified as deregulated in patients could suffice. These experiments could be accomplished in no more than 3-4 months.

Ad. We agree that the connection of the TOP2A mutation with transcriptomic alterations is correlative, and would be greatly strengthened by functional experiments in cellular models. If we develop a TOP2A E948Q knock-in cell line or TOP2A KO cell line with E948Q over-expression, we are planning to evaluate transcriptomic changes on selected genes by qPCR or whole transcriptome by RNAseq. We estimate that developing a stable CRISPR/Cas9 cell line may take up to 6 months.

We provided additional results showing that the connection of the TOP2A mutation with transcriptomic alterations may be due to different expression of splicing factors (Supplementary Fig. 6).

• Some of the methods are not presented with sufficient detail. Regarding the DNA and RNA sequencing experiments, I consider necessary to specify the DNA fragmentation method, reference for the indexed adapters and ligation and amplification procedures (ligase reference, number of PCR cycles, etc). It would be helpful to clarify or reference which are the "special oligonucleotide probes" that are mentioned. Finally, a reference for the "special beads" and final amplification number of cycles is needed. The sequence of primers used for TOP2A cloning and mutagenesis should be included. The reference for the "site mutagenesis kit" used is missing. When studying the survival rate of glioma patients depending of TOP2A expression levels, it should be clarified what is considered HIGH or LOW expression (i.e: which percentiles are used).

Ad. We expanded the description of methodological aspects of DNA and RNA sequencing experiments. This description was revised and more details are provided in the revised version. Regarding cloning and mutagenesis, we added a table with primer sequences (Supplementary Table 5). We did not use any kit for cloning and mutagenesis. Standard methods and primers with modified nucleotides were used.

__We have included information about the partitioned groups in the survival analyses in the figure 2 caption. “D - Kaplan-Meier overall survival curve for patients with high (> TOP2A mRNA median expression x 1.25) or low (- There is a major concern about how the experiments are replicated and about the statistical analysis, which is inexistent in some cases. Indeed, Figures 4 and 5 do not present any statistical analysis, it is therefore hard to draw any conclusion. In Figure 4b, the results for the 890-996 aa fragment looks qualitatively clear, but this is not the case for the 431-1193 aa fragment. More replicates and statistical analysis are mandatory, together with a protein titration. The replicates should be performed with at least two independent batches of protein purifications. The individual values of each experiment should be included in the graph to provide a better understanding of experimental variability. All this also applies to Figure 5.

Ad. We will increase a number of replicates for the binding and relaxation assay. We will perform a titration of different amounts of protein in these experiments using two batches of purified proteins.

**Minor comments:**

• The effect on transcription of co-occurrence of TOP2A mutations with other mutations could also be analysed with the already available data. Also, a more detailed analysis of genome-wide transcription could also be used to at least partially address the proposed hypotheses of increased transcriptional rate or splicing aberrations.

Ad. We don’t have enough samples with the TOP2A mutation to analyze the effect on transcription of co-occurrence of TOP2A with other mutations.

We addressed the hypothesis of increased transcriptional rate or splicing aberrations by performed additional analyses of RNA-seq data to confirm splicing aberrations. Indeed we found splicing machinery genes down-regulated in the E948Q TOP2A glioma samples (Supplementary Fig.6).

• There is no reference for the following argument "As the identified germline variants were exceptionally rare in the general population ... it is likely that these variants are pathogenic". I also find low number of references to support the suggested high frequency of altered genes in gliomas compare to other cancer types. I miss specific works relating TOP2 activity with transcriptional regulation.

Ad. The appropriate references are provided to back-up these statements.

• At several points in the text there are quantitative and comparative statements that should be backed up by the actual numbers (e.g. "The results of the targeted sequencing indicate a high frequency of altered genes", "The most altered gene was TP53, followed by IDH1...", "Other genes that were found to be frequently altered included KDM6B...", "These partial results combined with a low frequency of this variant in the Polish population suggest a somatic mutation"). The same thing applies to the co-occurrence of mutations, in which the percentage of co-occurrence and significance is not indicated. This lack of detail in the description is also observed in the description of the transcriptomic alterations in which no detail is provided regarding how many of the 105 analyzed samples correspond to low or high gliomas.

Ad. We apologize that the frequencies of mutated genes were not specified. This information is included in the main text of the revised version. We now provide a gnomAD frequency for all variants of interest, confirming the low frequency in the population (AF__ __

Regarding the total number of samples in the transcriptomic analysis, we provided an updated supplementary table covering also samples that were used for transcriptomic analyses (Supplementary Table 1).

• For TOP2A mutation analysis, sometimes is not clear when the analysis is done with the 9 mutated samples and when with the 4 recurrent TOP2A E948Q variants. For example, in figure 2b and 2c analysis are done with 9 samples while the figure 2e is based on the 4 E948Q variants. At least this is what I have deduced from the main text, it should be clarified in the figure legend).

Ad. This information has been included in the captions of Figure 2B, 2C and 2E and now we specify how many samples were used in each analysis.

• Fig1. In figure 1b it would be interesting to color-code patients by glioma grade. This would also apply to Figure S1a, S1c, 2a, S3 and S4. In figure 1D it would be very informative to distinguish mutations that passed the quality control or not with different colors.

Ad. Following reviewer’s suggestions, we have added this information, and oncoplot figures derived from the germline analysis have a distinct color for each glioma grade. In the figure 1D, all of the presented mutations have passed a quality control in terms of quality of sequencing. One additional criterion that was used for all genomic results (except some of the TOP2A variants) was a criterion of 20% variant penetration (20% of reads in the position had to come from the alternative allele). We corrected the description in the Supplementary Table to “passed 20% penetration criterion”. The rationale behind this criterion for TOP2A variants was a fact that for one of the E948Q samples it was ~13% and we didn’t want to lose this sample from the analysis due to rarity of the mutation.

• Fig2. In figure 2b and 2c the statistical significance of differences between TOP2A and the rest of genotypes should be included. Looking at Figures 2d and 2e it looks surprising how similar is the overall survival of HIGH TOP2A mRNA expression (500 days, fig 2d) with the overall survival of the TOP2A WT samples (400 days, fig 2e). Here a I would include a graph that summarizes the TOP2A mRNA expression levels of each group in fig 2d and 2e.

Ad. We agree that median overall survival is similar comparing patients with high TOP2A mRNA expression to TOP2A WT patients in our cohort. It is worth noting, however, that both datasets were produced using different library protocols, and the methodology is different, so it can’t be expected the levels to be equal. We think that adding two more graphs, as suggested, would add another layer of information to this section of the analysis. We have included two boxplots depicting TOP2A mRNA RPKMs, and it is clear now that the medians of High TOP2 mRNA and TOP2A mutant (E948Q) are more closely related, despite the fact that we only have a few patients with the mutation.

• Fig3. It would be interesting to include the same simulation for the rest of TOP2A mutations as supplementary figure.

Ad. We agree that the other TOP2A SNPs could potentially affect DNA binding. We focused on the recurrent mutation and did not analyze those occurring in a single patient. In the revised version we included predictions whether these variants could affect TOP2A DNA binding. For WT TOP2A and variants, we calculated the Gibbs free energy (ΔG). This information can be found in Supplementary Table 4. We have extended description in the Results section: “The TOP2A E948Q substitution may affect protein-DNA interactions”

• Fig4 and Fig5. Include statistical analysis and dots representing individual replicates.

Ad. For Fig 4 we have two replicates for two protein fragments, so we can’t present statistics now. As mentioned above we are preparing a new batch of proteins and will make more repetitions of EMSA and relaxation assays. For Fig 5. we have 3 replicates but despite a trend there is no statistical significance. We intent to make more replicates and a separate protein preparation. After including additional repetitions we will present the results as dots representing individual replicates.

• Fig6. In Figure 6d I would increase the size differences in the dots representing the gene counts, as it is not easily perceived with current parameters.

Ad. The dot size in Fig 6d did not reflect the true meaning. To make it easier to understand, we changed a plot type to a barplot, which now represents the number of differentially expressed genes involved in each pathway.

• FigS2. In figure S2B, it would be informative to establish which dots are significatively above or below the diagonal.

Ad. The purpose of this figure was to show which oncogenic signaling pathways from TCGA cohorts were affected in our cohort. The pathway's size is a variable that is used to normalize the calculation (shown in abscissa axis in S2B). RTK-RAS and NOTCH pathways contain hundreds of genes, whereas other pathways, such as the NRF2 oncogenic pathway, contains only a few. On the other hand, we counted how many genes in each pathway in our cohort were mutated (shown in ordinate axis, S2B). We used logarithms in both axes for visualization purposes, but this has no effect on the enrichment of these pathways, which is shown in the color-coded legend.

• FigS3. How were the samples shown selected from the total?

Ad. In this plot we show only somatic variants that were found in at least two different patients. We apologize that this information was missing, and we have added it to the figure's caption.

• FigS4. I would include a line with the TOP2A mutation to have an idea of how these mutations are distributed between groups.

Ad. Based on the feedback of the reviewer, this figure has been modified and improved. A new row has been added to the figure, displaying TOP2A mutations alongside other highly frequent mutations in other genes.

Reviewer #2 (Significance (Required)):

In this work authors have identified new mutations associated to gliomas by targeted exome sequencing using an important cohort of 182 samples. Among these new mutations epigenetic enzymes and modifiers are found. These results potentially increase the repertoire of putative molecular targets for future cancer therapies. Authors focus in mutations associated to TOP2A gene, that provides stronger DNA binding and DNA relaxation capacity in vitro. Although further characterization is needed, tumours harbouring this kind of mutations could show higher level of sensitivity to TOP2 drugs, providing potentially interesting clinical implications. Although the link between TOP2A expression and cancer prognosis is well established, the relevance of specific mutations in still largely unexplored.

On one hand this work brings novelties in the field of Glioma providing a series of putative new players in the development of this type of cancer. Audience interested in basic or clinical aspects of these tumours would be a good target for this work. On the other hand, this putative gain-of-function mutation of TOP2A represent an interesting aspect for the DNA topology and topoisomerases field. Although, as stated above a more detailed biochemical and functional characterization would be required to draw the attention of this audience-

Scientifically, I have experience in the DNA topology and topoisomerases field, 3D genome organization and gene regulation. I have no experience in Gliomas or any other clinical aspect of cancer, so it is difficult for me to properly establish the potential impact of the newly discovered mutations. Technically I have no capacity to critically evaluate the aspects related to the targeted exome sequencing and the suitability of the analysis performed for mutation identification.

**Referee Cross-commenting**

I fully agree with the comments of the other reviewer, which are perfectly aligned with my own regarding the preliminary nature of the conclusions about the biochemical and functional characterization of the TOP2A mutations.

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

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Referee #1

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2. In this paper the authors used a targeted approach to identify rare mutations in a cohort of glioma patients. Using this approach they identified a recurrent mutation in the TOP2A gene encoding for Topoisomerase 2A, and suggest that this mutation creates a more effective protein, binding DNA strongly and maybe more enzymatically active. RNAseq analysis of TOP2Awt and TOP2Amut tumor samples suggest different transcription patterns and points to possible splicing defects. The most recurrent variant (E9448Q) is described in depth and some experimental information shows this variant might be a gain-of-function mutation.

Major comments:

• Are the key conclusions convincing? The validation of both the methodology and the presence of never described TOP2A variations in HGG is done quite successfully. Interesting evidence about relevance of the most frequent mutation is provided. However, besides having computational and biochemistry assays performed, lack of details about in vitro experiment statistics (no p-values are provided in figures 4 and 5, neither sample size, repetitions) weakens the conclusions claimed by the authors about the properties of the mutated topoisomerase.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Claims about E948Q variant function should be revised. Data is not presented in a convincing way, plus there is ambiguous language used from the results ("We conclude that the E9448Q TOP2A protein is functional, and MIGHT have a higher activity than the WT protein") to the rest of the paper where they strongly support the claims about the TOP2A activity.

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. In line with the presented data in the paper, additional experiments that show catalytic changes of the E9448Q variation must be added. It is shown that there are differences in the DNA binding capacity by EMSA compared to the WT form, however, the DNA supercoil relaxation activities is not that different, at least the way the results are presented. The authors suggest that TOP2A mutation is a driver mutation but no validation in vitro of this claim is shown. Can this mutation alone or in combination with e.g. tumor suppressors transform normal cells to cancer cells? Do cell lines expressing this mutation (compared to parental TOP2A wt expressing cells) display increased transcription? Increased invasion?

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. If the authors can complement the already presented in vitro experiments with additional ones supporting their hypothesis, this should be feasible. The authors can use patient derived glioma cells or glioma cell lines manipulated to express either the parental TOP2A wt enzyme or the identified mutated form.

• Are the data and the methods presented in such a way that they can be reproduced? Yes, the authors provide a quite detailed explanation of the methods implemented to reach each one of the results they are presenting.

• Are the experiments adequately replicated and statistical analysis adequate? No, there is no information about the statistical analysis or number of replicates in any of the in vitro experiments performed. This information should be added to the manuscript.

Minor comments:

• Specific experimental issues that are easily addressable.

• Are prior studies referenced appropriately? Yes, authors clearly address the state of the art regarding previous NGS methodologies and let us know the advantages and novelty of their approach.

• Are the text and figures clear and accurate? There are some discrepancies between the strength of the language used in different sections of the paper to refer the conclusions they can infer from the results they are showing. While they are all valid, authors should revise it.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? First of all, describe the statistical analysis used in every figure, include number of biological and technical replicates. I would also suggest to change the title or the scope of the discussion, there is too much focus on the TOP2A in the introduction, neglecting all the technical NGS work that actually lead to several new variants being described. This may be confusing when it collides with a conclusion that is heavily focused on the first half describing potential implications of at least another 3 proteins where genetic alterations were described. Given the fact there is not much experimental work that shows TOP2A mutations relevance in HGG or strong enough evidence of the variant's function I would suggest to change a bit the scope of the title.

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. The authors describe a methodology that proved to be sensitive and specific enough in order to allow them to detect rare genetic alterations in patient glioma samples. This information could be valuable to describe new driver mutations or infer in genetic pathway alterations that could be potential therapeutic targets. As the authors state at the beginning of the paper, given the poor therapeutical approaches existing for HGG currently, information of this kind could still be highly useful and provide a better outcome to a specific cohort of patients.

On a personal note, I think there is too much speculation about how TOP2A mutations could be interesting from a biological point of view (authors referred to evidence about implications of this mutation in other forms of cancer) but since no experimental validation is provided in glioma cells, it is difficult to conclude that this enzyme gain-of-function mutation could have a relevant role in HGG and thus make these variants a potential therapeutic target. There are no experiments conducted in glioma cells that express TOP2A variants, it would be interesting to see if it has an effect in the migratory/invasive phenotype like described in other cancer types or like it is suggested by analysis of the genetic pathways activated in the HGG patients samples harboring TOP2A mutation. In addition, there is no evidence of the TOP2A mutations possible role as a driver mutation, which is an interesting aspect that could be further explored from both a computational and an experimental approach.

• Place the work in the context of the existing literature (provide references, where appropriate). The quality of the paper is high and in line with other studies in the literature that perform genome and transcriptome analysis of tumor samples. It is only the experimental validation that is lacking data supporting the "in silico" findings.

• State what audience might be interested in and influenced by the reported findings. Computational biologists are the right audience to target this paper. If additional experimental work further validating their initial bioinformatic findings is added to the manuscript then probably a wider population could be targeted.

• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Brain tumors, immunotherapy, cancer stem cells, tumor microenvironment, tumor heterogeneity. I do not have sufficient expertise to evaluate the bioinformatic analysis and software/programs used to analyze the NGS data.

SciScore for 10.1101/2021.07.21.21260691: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

However, this study also had several limitations. First, because we conducted a cross-sectional study, causality could not be determined. However, since it is theoretically unlikely that treatment interruption experienced by an individual will increase the COVID-19 infection rate in a region, we think it is likely that high regional infection rates cause treatment interruption. Second, we did not identify workers’ reasons for discontinuing treatment in this study. As discussed above, there are various possible causes of treatment interruption, which may vary by region. Third, we did not inquire about the diseases being treated. Treatment interruption may vary depending on the presence or absence of symptoms and the potential disadvantages of discontinuing treatment for a particular disease.

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

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Reply to the reviewers

We are grateful for the constructive and highly supportive reviews provided by our Reviewers. We especially appreciate the efforts they have made to provide suggestions on how to make our revised manuscript even more robust. We have incorporated many of these suggestions into the revised manuscript that will post to Biorxiv and will be submitted to an affiliate journal. We have provided point-by-point responses to each Reviewer below each item (starting with Response: …), along with any changes made in response to that comment/suggestion (starting with In our revised manuscript, …).

Finally, we agree with all Reviewers that this work should be of broad interest to the molecular biology, cell biology, and parasitology communities. Our discovery that Plasmodium and two related genera have taken the unorthodox approach of duplicating their NOT1 protein, and that Plasmodium has dedicated it for its unique transmission strategy, is a fascinating adaptation of the use of this core eukaryotic complex. We believe that those that focus on diverse aspects of RNA biology, including RNA preservation/decay, the maternal to zygotic transition, translational repression, and beyond will find this work to be of interest and relevant to their own research questions.

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

The manuscript „The Plasmodium NOT1-G paralogue acts as an essential nexus for sexual stage maturation and parasite transmission" investigates the two forms of NOT1 in rodent malaria parasites. The authors found out that the original NOT1 is crucial for gametocyte induction as well as transmission to the mosquito, they therefore renamed it NOT1-G. The paralogous proteins, on the other hand, appears to be crucial for intraerythrocytic growth, since it cannot be knocked out. The authors then investigated NOT1-G in more detail, using standard phenotyping assays. They found a slightly increased gametocytemia and a minor effect on transmission to the mosquito.

Response: In our submitted manuscript, we do focus on PyNOT1-G because of the exciting role it has for both sexes of gametocytes, which results in a complete defect in transmission to mosquitoes. Our investigations of what domains of PyNOT1-G focused on the most likely suspect: the putative tristetraprolin-binding domain (TTPbd). It was through deletion of this domain that we observed only a minor defect in the prevalence of infection of mosquitoes, indicating that the portion of PyNOT1-G that is required for transmission lies elsewhere (in part or in total). It is also important to correct Reviewer 1’s statement regarding the other (perhaps canonical) PyNOT1. To our surprise, PyNOT1 could be deleted, but resulted in a parasite that has an extreme fitness cost and a very slow growth phenotype. This is in stark contrast to other eukaryotes, where NOT1 is essential.

Reviewer #1 (Significance (Required)):

If the authors are able to provide convincing data that NOT1-G is indeed important for gametocyte induction and transmission to the mosquito, then the report would be of high significance for the malaria and molecular cell biology fields.

Response**: We have in fact shown this and more in the originally submitted manuscript, and thus we are grateful that Reviewer 1 considers this work to be of high significance in a broad readership (molecular and cell biology, parasitology). In our revised manuscript, we have added text throughout to make these results even more apparent and clear for the reader.

My expertise: molecular cell biology of gametocytes, translational regulation, parasite transmission

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

**Summary**

The manuscript by Hart et al. builds upon a fascinating finding presented in a previous manuscript by the same authors, in which they show that CCR4 seems to be able to associate with two members of the NOT1 family. In this work, the authors first re-annotate the two NOT1 paralogs in Plasmodium yoelii and then perform an in depth characterization of the role of NOT1-G during gametocytogenesis and early mosquito development. Using gene knockout and different genetic crosses, the authors show that NOT1-G is essential for male gametocyte development and leads to an arrest of development in zygotes arising from female gametocytes. Using RNA-seq the authors show that NOT1-G leads to lower transcript abundances, leading to the hypothesis that NOT1-G might be involved in preserving mRNAs in a larger RNA-binding complex. Lastly, the authors characterize a NOT1-G defining TPP domain and find that it is not essential for either male/female phenotype observed for the whole gene KO.

Response**: We appreciate the concise and accurate summary of these findings.

**Major comments:**

• Are the key conclusions convincing?

  The phenotypic characterization of NOT1-G during gametocytogenesis / early mosquito development is nicely presented and the experiments are well performed. Because a duplication of NOT1 with possibly opposing roles of the paralogs is a very unique feature with broad implication on RNA metabolism, it would have been great to see two select experiments on the molecular level adding evidence that 1) NOT1/NOT1-G are mutually exclusive in a complex with CCR4/CAF1 and 2) NOT1-G acts post-transcriptionally in an antagonistic way to NOT1 (i.e. as a mRNA 'stabilizer' as proposed by the authors).

Response**: We agree that inclusion of those two aspects would make for a more complete story about these two NOT1 paralogues.

First, we also think that it is highly likely that NOT1 and NOT1-G are mutually exclusive, as in other eukaryotes NOT1 acts as a scaffold protein upon which effector proteins bind and bridging interactions are made. In our original manuscript, we did not include a mention of our previous attempts to address this question through colocalization and proteomic approaches, as they were largely unsuccessful. Specifically, we generated rabbit polyclonal antisera to PyNOT1-G’s tristetraprolin-binding domain but it did not pass our rigorous quality control (e.g. too much staining persisted in pynot1-g- parasites). Using both asexual and sexual blood stage parasites, we also attempted immunoprecipitation (with and without chemical crosslinking) and proximal labeling approaches via BioID and TurboID but all approaches did not produce rigorous results and thus we did not report them in our original manuscript. However, this question of whether the two NOT1 paralogues were mutually exclusive in complexes was also taken up by the Bozdech Laboratory in their 2020 preprint (Liu et al.) where they were able to capture the P. falciparum NOT1-G and NOT1 proteins (called Not1.1 and Not1.2 in that work). While their proteomic evidence showed that they could capture these bait proteins and that the NOT1 paralogues were not in the same complex, these results should be taken with a grain of salt: all mass spectrometry-based proteomic approaches are limited in that an absence of evidence does not mean that the protein is not present/interacting. Moreover, these efforts only identified a few other proteins that were already known to interact with the CAF1/CCR4/NOT complex, but even so, they did not use statistically rigorous methods in an attempt to quantify these results. In our revised our manuscript, we have included additional text to describe our unsuccessful efforts to do these capture proteomics experiments, and we have expanded our discussion of the Liu et al findings that provide some evidence in support of a mutually exclusive complex.

Second, we also hypothesize that PyNOT1-G acts post-transcriptionally to affect mRNA abundance and translation. However, it is important to emphasize that NOT1 proteins typically act as scaffolds, with the recruited effector proteins acting to hasten the degradation and/or to preserve associated transcripts. We believe that studying these effector proteins is the next important effort to undertake. In fact, we hypothesized that these antagonistic effector proteins would be analogous to TTP and ELAV/HuR-family proteins as are found in other eukaryotes, and that the critical interaction with PyNOT1-G would be via its putative TTP-binding domain. It was for that reason that we interrogated the TTP-binding domain itself, and were surprised that its deletion did not phenocopy the complete gene deletion. Ongoing work will be focused on identifying these antagonistic effector proteins that likely are expressed in a stage-enriched manner, and to define how they interact with PyNOT1-G in order to direct specific mRNAs to their fates. Additionally, it would be very important and exciting to directly test if PyNOT1 and PyNOT1-G are functionally opposed. However, this would be exceptionally challenging to study from a technical standpoint. While we were able to delete the pynot1 gene after many repeated attempts, these parasites are very sickly and grow very slowly. Because of this, we believe that assessing direct versus indirect effects of PyNOT1 in these cells would not be feasible or robust. Given this, comparing functions between PyNOT1 and PyNOT1-G could not be done in a conclusive manner.** In our revised manuscript, we have expanded our descriptions of the mechanisms by which we believe PyNOT1-G and its complex affects mRNA fates. In particular, we have expanded our Discussion section to incorporate the results that indicate that the TTP-binding domain is not required for the essential functions of PyNOT1-G.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

  The authors describe the role of NOT1-G as 'preserving' mRNA. The lower abundance of many transcripts in the NOT1-G knockout suggest this, but experimental proof is not provided (see suggestions below). Maybe rephrase to 'putatively preserved/stabilized' or 'has a potentially stabilizing function'. The same is true for the mutually exclusive association of the two paralogs with CCR4/CAF1. The authors refer to a protein co-IP of CCR4 showing that CCR4 can interact with both NOT1 and NOT1-G, but a reciprocal experiment is lacking.

Response**: In our first publication on the deadenylase members of this complex, we also saw a similar effect on specific mRNAs when pyccr4-1 was deleted: the abundance of specific mRNAs went up in pyccr4-1- parasites. In that work and here in this manuscript, we have carefully decided to apply the word “preserved” to the fate of these mRNAs as it describes in a general way what is happening. In order to robustly state that mRNAs are stabilized by PyNOT1-G (directly or indirectly) would require additional experiments designed to test this (more description on this is provided on a response below). Second, as described above, we agree that doing a reciprocal IP for mass spectrometry-based proteomics would be ideal, we attempted four different approaches to do this to no avail. However, the composite proteomics data that is already available in the literature and via the Liu et al. preprint from the Bozdech Lab all indicate that these interactions occur, and perhaps that NOT1 and NOT1-G are mutually exclusive as expected. In our revised manuscript, we have provided further explanation in the Discussion for our use of the descriptor “preserve” instead of “stabilize”, and as noted above, and we have expanded our Discussion to more comprehensively define the interaction network depicted in Figure 7.

In both cases, the conclusions of the authors are very likely (e.g. downregulation of many genes as seen by RNA-seq), but the final experimental evidence is not provided and a network such as in Figure 7 is not fully supported. If the authors would like to maintain these statements, then they should be rephrased and made clear or the additional experimental evidence suggested below is necessary.

Response**: We hold that the published proteomic datasets do support such a network, with further support offered from the preliminary proteomic evidence from the Liu et al preprint. Therefore, we have not modified our manuscript beyond the additional text now provided in the Discussion as noted above.

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

  The essential claim that NOT1-G is important for gametocytogenesis and early mosquito development is well presented and fully supported by the experiments. As for the role of NOT1-G in 'preserving' mRNA, an mRNA half-life experiment would be necessary (or the text should be adjusted as mentioned above). In a short-term in vitro culture, pynot1-g- and WT parasites could be treated with ActD and abundances of select transcripts are measured by RT-qPCR.

Response**: We appreciate that Reviewer 2 considers the rigor of our experiments to be high. Regarding the use of the term “preserve” vs “stabilize”, we agree that to shift from our more general descriptor (preserve) to one that has specific connotations (stabilize) would require additional experimentation. To correctly and most robustly make the claim of stabilization would require work on par with that done by Painter et al. (PMID: 29985403) that uses a thiol-containing nucleotide (4-TU) along with a yeast-derived fusion enzyme (yFCU) to convert it for use by Plasmodium. Previously we have shown that an associated deadenylase (PyCCR4-1) also acted to preserve mRNAs, and moreover that deletion of its gene resulted in no discernable effect upon the poly(A) tail or 3’ UTR of an mRNA that is bound by this complex (p28).

While understanding mRNA stability is an exciting area of study, this 4-TU labeling experiment alone warranted a standalone, high impact publication for Painter et al. As this has not been adapted for any rodent-infectious Plasmodium species to date, and as adaptation of this labeling approach took several years for Dr. Painter while in the Llinas Laboratory (personal communication), we believe this work is beyond the scope of this study. Moreover, the additional information that it would provide to understand NOT1-g functions (preserve vs stabilize) would be incremental beyond the major storyline presented in this manuscript. In our revised manuscript, we have added text to ensure that our choice of “preserve” is well defined and explained.

To support the idea that NOT-1 and NOT1-G associate in a mutually exclusive way or to just show that they act in distinct complexes despite their similar expression patterns, an IFA with a double stained NOT1/NOT-1G cell line could be performed. Alternatively, the authors could perform a protein co-IP using the already existing NOT1/NOT1-G-GFP cell line and show that the proteins don't interact with each other or even have certain distinct interaction partners.

Response**: We agree, and these studies were attempted but were unsuccessful (described in our responses above). In our revised manuscript, we have included this information as noted above.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

  All necessary cell lines for a NOT1/NOT1-G co-IP and the ActD experiment are already present. The authors already present a ring to schizont in vitro culture (for ActD) and also have substantial experience in protein co-IP and proteomics.

  I am not sure about the cost for a proteomics experiment at the author's institute and I don't want to make a guess on time investment given the still on-going COVID situation.

Response**: We agree that these experiments would be interesting, and would be costly to do at a transcriptome-wide scale and would require substantial time to conduct. We believe that the 4-TU approach noted above is the most rigorous, but is well beyond the scope of this study as it has not yet been adapted to rodent-infectious malaria parasites. As noted above, we have attempted four different proteomics approaches to provide reciprocal evidence for the complex composition which were unsuccessful. In our revised manuscript, we have added text to ensure that our choice of “preserve” is well defined and explained, and have noted the unsuccessful reciprocal proteomics approaches.

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

  The MM section is well structured and presented and the supplemental material includes all data.

Response**: Thank you. We want to ensure that our work is clearly described and can be reproduced with the information reported.

• Are the experiments adequately replicated and statistical analysis adequate?

  There is hardly any test of significance presented in the main text of the manuscript (e.g. Figure 3B and 4A). Please show the individual data points for these graphs and make sure the n= and the statistical test is described in the figure legend. If you use the term significant in the text, then just add the p-value behind it. This is also true for the RNA-seq data: Genes are sorted by fold-changes, leaving it unclear if these changes are significant. These data are however presented in Table S1 and could be incorporated in the main text.

Response**: We agree. In our revised manuscript, we have incorporated additional details about the statistical tests used, p-values for noteworthy comparisons, and have included more panels for our comparative RNA-seq datasets (heatmap, PCA, MA plots). We have also made adjustments to our plots to make individual data points more readily observed, especially when error bars may block them (e.g. Figure 3B). And as in the original submission, all of the pertinent values, including fold changes, statistics and more are provided in our comprehensive supplementary files. We have structured the Supplementary Tables to flow from one tab to the next with the filtering/threshold applied noted both in the tab name and in the README tab that is found first among the tabs.

**Minor comments:**

• Specific experimental issues that are easily addressable.

  One idea that is also not discussed but could be added is for example that NOT1-G itself doesn't even have a stabilizing effect itself, but act as a decoy for other components of the CCR4/Caf1 complex, keeping them from associating with NOT1. In the NOT1-G knockout, the decrease in RNA abundance might then be just a result of an 'overactivity' of CCR4/Caf1/NOT1.

Response**: This hypothesis proposed by Reviewer 2, that PyNOT1-G is acting as a decoy or a binding partner sponge, is certainly feasible. For this scenario to be effective, PyNOT1-G would need to be in excess of PyNOT1 and/or would need to be able to bind to the critical effector protein(s) better than does PyNOT1. However, our microscopy data, along with the transcriptomic data presented here and previously published proteomic data would indicate that these two gene products are in approximately balanced proportions and are similarly localized. This does not exclude the possibility that PyNOT1-G could act as a sponge for relevant binding partners. In our revised manuscript, we have raised this possibility as an alternate explanation for the phenotype in the Discussion section.

• Are prior studies referenced appropriately?

  Throughout the manuscript, the authors should make clear what results come from which organism. Just as an example, the genome wide KO screens were performed in P. berghei and P. falciparum, CCR4/CAF1 experiments were performed in P. yoelii, whereas the original DDX6 work was done in P. berghei.

Response**: We agree. In our revised manuscript, we have added additional text to further clarify what data comes from which Plasmodium species.

• Are the text and figures clear and accurate?

  The Introduction is a bit long and partially turns into a minireview of eukaryotic RNA degradation. In the main text on page 13, the authors introduce a model for proteins involved in translational repression. This in not fully accurate, since for many of the proteins in this network, an effect on translation has actually not been shown. This includes NOT1-G characterized in the present work that most likely has an effect on mRNA stability, but for which a role in regulating translation is not presented.

Response**: We believe the length and content of this Introduction is appropriate to provide the context that some readers outside of the parasitology field will need to appreciate these findings. Regarding designations for these proteins as being related to translational repression, we think that the ample proteomic evidence tying them to translationally repressive complexes warrants this. In our revised manuscript, we have made it more clear that these proteins themselves have not been directly implicated in translational repression.

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

  Overall the RNA-seq is underrepresented and Figure 5 could easily be expanded by adding several panels that would help the future reader getting a better idea of the data:

1. Summary graphs such as PCA/MDS plots of the different replicates and MA-plots (all of which can be easily generated in DESeq2)
2. Heatmaps comparing the expression patterns of pynot1-g-, pbdozi-, pbcith-, pyalba4- highlighting some key gametocyte genes mentioned in the text

3. Alternatively to 2., a simple Venn Diagram would already be very informative

   An informative representation might also be to sort the differentially expressed genes as predominant male and/or female. The P. berghei data by Yeoh et al (PMID: 28923023) could be a starting point.

Response**: We agree. In our revised manuscript, we have expanded Figure 5 to include additional plots that speak the rigor of these datasets. Specifically, we have added a comprehensive heatmap and PCA plots, as well as MA plots as recommended. We have chosen not to include a Venn diagram for the overlap of affected mRNAs across these transgenic parasite lines, as we hold that this information is best provided in the text (high level observations) and the Supplement (details).

Reviewer #2 (Significance (Required)):

**Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.**

Technically this manuscript builds on standard methods of the field that are well executed. There is no direct clinical advancement, although one might argue that a unique adaptation of the parasite could always be a novel therapeutic target. Conceptually this is great advancement for the parasitology field as it is, providing additional evidence for the importance of post-transcriptional regulation for parasite transmission. With the two experiments suggested above and the additional evidence gained from it, this manuscript could also gain great interest to readers outside the field by clearly showing how alternative ways to regulate RNA stability evolved.

Response**: We are grateful for your careful review of our work and for the recommendations that you provided. We have incorporated many of them into the revised manuscript to make it even more rigorous and comprehensive. We also appreciate hearing that this work would be of great interest to a broader community. We feel that this is already the case, as the duplication of NOT1 and the dedication of one paralogue to an essential function is exciting and novel among eukaryotes.

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

The work builds on the early reports of the particular RNA metabolism in gametocytes performed in the groups of Andy Waters. Since then, the authors themselves have published a great set of manuscripts extending our knowledge of the proteins involved in gametocytogenesis and nicely place the current work into this framework.

Response**: We appreciate this positive feedback. This is a fascinating topic to study.

**State what audience might be interested in and influenced by the reported findings.**

The manuscript as it stands is particularly interesting for the parasitology and potentially the evolutionary biology field. For a broader readership for example in the RNA field, the possibly antagonistic roles and mutually exclusive association with CAF1/CCR4 are likely most interesting.

Response**: We agree that this should be interesting to readers beyond our own field, as the duplication and specialization of NOT1, and the finding that the “canonical” PyNOT1 can be deleted, are both of general interest to how eukaryotes have adapted and deployed a highly conserved and essential RNA metabolic complex.

**Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.**

**Expertise:**

RNA biology, Plasmodium falciparum, Bioinformatics

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

In this manuscript, the authors investigate the requirement of two possible Not1 paralogs for the development of asexual blood stages and for the sexual transmission stages of Plasmodium yoelii. While Not1 is critical for asexual blood stages, its putative paralog, Not1G is important for the development of sexual transmission stages. In the absence of Not1G, male gametes are not formed while female gametes are formed and can be fertilised by wt male gametes. However, the resulting zygote cannot develop further into ookinete. The in vitro genetic cross assay to show this is elegant! A transcriptomic analysis further indicates that the transcriptomes of Not1G deficient parasites are significantly different from their WT counterpart.

Response**: We are thrilled that you found our evidence and approaches to be rigorous and compelling. Thank you.

**Major comments:**

The discussion section is very nice and the authors describe well what is speculative and should be further confirmed by additional experiments. However, I did find this was not the case in the results section where the authors are proposing conclusions that are not supported by the results. I think the reading of this manuscript would be much more enjoyable if the authors only describe the results shown and move all the discussions to the dedicated section. Below are some examples. The data presented in this manuscript is not showing a nexus, this is a suggestion based on the results of other articles, the word should thus be removed from the title (and kept for a future review!). The last two sentences of the localisation section should be moved to the discussion because they do refer to results not shown in this manuscript. The last sentence of the second paragraph of the zygote development section should also be moved to the discussion. For the transcriptomic analysis there is also no formal comparison with transcriptomes of other previously analysed mutants: the results of the comparisons should either be shown or not discussed in the result section. Finally, the discussions mentioning interactors of the complex should be removed from the result section and moved to the discussion unless the results are formally analysed.

Response**: We again thank you for the complement. In our original manuscript, we opted to provide some limited interpretations and context within the Results section in order to help guide readers along our train-of-thought and line-of-experimentation. While a more traditional split of keeping essentially all discussion and interpretation for the Discussion is a tried-and-true approach, we prefer this more narrative method and have opted to keep these short sections in the Results section.

I would strongly suggest the author the better present and describe their transcriptomic results. There is only one volcano plot indicating the overall defect in mixed gametocytes in the main figure. Apart from this, the results are only described in the main text or in supplementary tables. It is therefore difficult to understand the subtilities of the analysis. For example, the authors frequently mention dysregulated genes, but without specifying whether it is up or down-regulated in the mutant. To address this issue, I would suggest the authors to better describe their results in the figures. They could show the GO term enrichment analysis they mention and show how they assign GO term or transcripts to male and female parasites. It would also be nice to discuss some of the results a bit more in details. For example, it is not surprising to see a reduction in transcripts that are under the control of AP2-O in retort-arrested ookinetes as the parasite do not reach this stage. It is thus highly speculative to specifically link this observation with ALBA4 without further detailed analysis. On the other hand, it is more surprising to see a decrease in ap2g transcripts, while the authors observe an increased gametocytaemia. Could the authors comment this observation? It may also be nice to better present the comparison between gametocytes and schizonts to possibly speculate on the early requirement of Not1G in committed schizonts.

Response**: We (and Reviewer 2) agree. In our revised manuscript, we have expanded Figure 5 to include additional plots that speak the rigor of these datasets. Specifically, we have added a heatmap, and PCA and MA plots as recommended. We have chosen not to include a Venn diagrams for the overlap of affected mRNAs across these transgenic parasite lines for the reasons stated above in our response to Reviewer 2. Similarly, we have opted to keep the specifics of the GO Term analyses in the Supplement as we believe these should always be taken with a grain of salt (especially high level GO Terms, as many choose to report). Finally, we have expanded our discussion on our observation that pyapiap2-g transcript levels are lower in the pynot1-g- line, despite seeing a slight increase in gametocytemia.

The conclusion regarding the similar localisation of Not1 and Not1G with other members of the CAF1/CCR4/NOT complex is not really convincing for two reasons. First, there is not colocalization shown and, second, the distribution is not very peculiar so it is difficult to draw any conclusion with this level of resolution. The presence of alpha-tubulin in the nucleus of male gametocytes is also very surprising as it is rather nucleus-excluded in both P. falciparum and P. berghei, could the authors comment this peculiar localisation?

Response**: We agree and disagree here. First, we agree that no colocalization data is presented here to place NOT1-G within the limit of resolution of fluorescence microscopy. What we can (and do) state is that these proteins are all localized to cytosolic puncta, which matches what is observed for essentially all other studied eukaryotes. In further support of this, our published, quantitative proteomic data indicates that the bioinformatically predictable members of the CAF1/CCR4/NOT complex do associate as anticipated. In the same vein, the micrographs presented were not captured by confocal microscopy, and thus the apparent localization of alpha tubulin “in” the nucleus is most likely attributed to being above and/or below the nucleus. Taken together, we do feel that the combined evidence is convincing. As we have already made all of these points in the original manuscript, we have not adjusted the revised manuscript further.

One of my major frustration when reading this manuscript was that the authors are not trying to discriminate between an early role of Not1G during gametocytogenesis or later in gametogenesis. The fact that the transcriptomes of gametocytes and schizonts seem to show similarities suggests that the phenotype observed during both male gametogenesis or ookinete development are probably linked to early knock-on defects during gametocytogenesis. Could the authors test whether male gametocytes replicate DNA or female activate translation? These are of course non-essential experiments as the authors are careful with their conclusions and mention possible defects during both gametocytogenesis or gametogenesis. Addressing this question may however add significant insights into the requirement for Not1G.

Response**: We are sorry for the frustration. We wrote the manuscript so as to state what we feel we could robustly say, and where we are drawn to speculate, we made that speculation clear. As Reviewer 3 notes, we have not attempted to discriminate between functions that PyNOT1-G may be playing in different stages or substages of development because we do not believe the experiments allow that discrimination. While we could investigate finer and finer aspects of possible defects in both male and female gametocyte development, the most impactful take home messages remain the same. We continue to address questions related to translational repression and its release, and anticipate that PyNOT1-G will play a substantial and essential role in this. As Reviewer 3 noted, we have already discussed these possibilities in the original manuscript, and thus have not added anything further about this in our revised manuscript.

**Minor comments:**

Please use page and line numbering for your next submissions! Please describe what "bioinformatics" was used. I would show the nice localisation in oocyst and sporozoite in the main section. The conclusions drawn from the genetic cross seem to come from a single biological replicate, if this is the case please indicate it clearly.

Response**: We apologize for these oversights. In our revised manuscript, we have provided page and line numbering, have expanded on what bioinformatic processes were done in the manuscript, and have made it more clear that the genetic crosses come from multiple biological replicates (biological triplicate for the transmission-based genetic cross, biological duplicate for the in vitro culture genetic cross). However, we have opted to retain the oocyst and sporozoite IFA data in the Supplement, as the rest of the story is focused on blood stage and early mosquito stage.

Reviewer #3 (Significance (Required)):

This manuscript highlights the requirement of a Not1 paralog in the transmission stages of a Plasmodium parasite. More specifically it describes a new player in the control of RNA biology during this process where our knowledge is scarce. It will be a valuable manuscript for molecular parasitologists interested in transmission or RNA biology.

Response**: We agree and are grateful that our colleagues find this study to be a valuable addition in our efforts to understand how malaria parasites have adapted classic eukaryotic mechanisms to suit their purposes.

Our expertise is largely in molecular and cellular parasitology.

I think this is a framework that could use more emphasis. It’s one I am cueing into more after Caviola et al. (2021) included it in their review, “the psychology of (in)effective giving.”

People have a strong aversion to prioritizing some lives over others (see Tetlock et al., 2003, "Thinking the unthinkable: sacred values and taboo cognitions"). With limited resources, we of course do this all the time. But CBA makes it uncomfortably explicit. To prioritize some recipients as a result of CBA means to deprioritize others, which feels unfair. This is one explanation for why people prefer “distributed helping” when there are multiple possible recipients, even at the expense of helping more, since then at least no one is fully deprioritized (Caviola et al. 2020a, obstacle 5; Sharps & Schroder, 2019, “The Preference for Distributed Helping”). This could also be an explanation for Berman et al., 2018 finding that people prefer to prioritize investments rather than charities, since deprioritizing an investment isn’t nearly as aversive.

A moral aversion to (de)prioritization may also explain social judgments of people who donate effectively seeming “cold” (section 7.1). This is evidenced by the differences in instinctive judgments of “coldness” based on what is deprioritized. For example, deprioritizing investing in textbooks because it isn’t an effective intervention feels much different than deprioritizing investment in childhood cancer treatment because one could help more kids dying of malaria. People would likely make harsher judgments about someone doing the latter even though the reasoning is the same – it’s what is deprioritized that is different.

There might also be something else at play related to ‘CBA’ discomfort: choosing whom to help makes it clear to individuals that they can’t help everyone. It reminds people of all the suffering in the world that they can’t alleviate, whereas just choosing a neat charity only introduces one cause of suffering and then gives the donor the satisfaction that they have done something to alleviate it. I can imagine that CBAs role in revealing the reality of triage (1) makes people less inclined to engage in CBA and (2) less likely to donate a lot in accordance with CBA because there is less warm glow/ that one cause just isn’t as sexy anymore. (2) is related to the idea of Pseudoineffecay developed in (Slovic, 2007; Västfjäll et al., 2015). People are less inclined to help when they learn about others they can’t help.

A key idea that I think is relevant here is the “affect heuristic,” the importance of instinctive emotional cues of “goodness” or “badness” informing decisions (LINK). Deprioritization of emotional cause → instinctively violates moral value → aversion → less likely to engage with, worse social . Similarly, reminder of all the suffering in the world → feeling of sadness + helplessness → avoidant behavior. These oversimplified decision pathways can be overruled by rational, deliberate processing (see Tetlock, 2003 for discussion specific to sacred values) , but charitable giving is largely a system 1/emotional arena.

I feel this is one of the most important parts of documentation and observation. I feel we should give children the space to think about what they make to give them a sense of ownership and accomplishment. If we constantly share our ideas, the child may not see theirs as valid.

I think that awareness is key for being able to teach within a context we as adults may be familiar with. In this particular experience there was almost a system of checks and balances to make sure the students hypotheses and ideas stayed at the forefront of their research.

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We thank all four reviewers for their positive and constructive comments! We have carefully considered these comments and provided a point-by-point response below.

Reviewer #1 (Evidence, reproducibility and clarity):

This paper explores an interesting problem of SHP1/SHP2 preferences of inhibitory immunoreceptors. The author are quick to point out that many of their individual data points confirm published results at some level, but the power of the paper is in the parallel analysis of both PD1, which is strongly biased towards SHP2 and BTLA, which is biased towards SHP1. This gives them the opportunity to test the predictions of descriptive experiment by making simple mutated receptors with swapped ITIM or ITSM domains.

The work is very well done and generally the authors are quite careful and precise about the language used to describe results, in general.

The results are quite striking in that the find plenty of evidence for transient interaction of SHP1 with PD1 based on the biophysical measurements, but don't detect the interactions in pull down or in "in cell" microcluster recruitment experiments. In describing the pull-downs they discuss the issue of dissociation during washing potentially missing interactions that are taking place. I would prefer that the pull down is fine evidence for binding, but lack of pull down is not evidence for lack of binding. They should double check that this language is consistent. Also, unless something has changed in the microcluster binding experiments, this in situ recruitment of SHP2 to PD1 is only observed or a 2-3 minutes and then can't be detected, the situation for SHP2 becoming the same as it is for SHP1. If the kinetics are different in the cleaner systems that have now developed they should show this in a primary figure as this would be then different when what is reported previously.

We agree with the reviewer that pull down is evidence for binding. Indeed, in most, if not all of our assays, our results with pull down were consistent with those in the microcluster imaging. As suggested by the reviewer, we will check through the manuscript and ensure the language is accurate and consistent. In our recent study (Xu et al., JCB, 2020, PMID: 32437509), we conducted a side-by-side comparison of SHP2 and SHP1 recruitment kinetics to PD-1 in a similar system as the current study. Both microcluster imaging and co-IP assays showed that PD-1:SHP2 association lasted at least 10 minutes, whereas PD-1:SHP1 recruitment was nearly undetectable. The duration of PD- 1:SHP2 association was in good agreement with Takashi Saito’s finding in CD4+ mouse T cells (Yokosuka et al., JEM, 2012, PMID: 22641383). Regardless the somewhat different kinetics in different studies, SHP2 recruitment was transient, as pointed out by the reviewer. We believe that some other effectors contribute to PD-1 inhibitory signaling. In supportive of this notion, we recently found that PD-1 remains partially inhibitory in CD8+ T cells deficient in both SHP1 and SHP2 (Xu et al., JCB, 2020).

The gap in this study is lack of any functional analysis. The Jurkat model could be quite useful as they have a relatively clean system for asking if the transient binding of SHP1 to PD1 has any functional impact, which they have not yet followed through on. Does PD-1 recruited SHP2 have any impact on function after the 5 minutes? Furthermore, the authors need to keep in mind that mice deficient in SHP2 respond to anti-PD1 checkpoint therapies (Rota, G., Niogret, C., Dang, A. T., Barros, C. R., Fonta, N. P., Alfei, F., Morgado, L., Zehn, D., Birchmeier, W., Vivier, E., & Guarda, G. (2018). Shp-2 Is Dispensable for Establishing T Cell Exhaustion and for PD-1 Signaling In Vivo. Cell Rep, 23(1), 39-49. https://doi.org/10.1016/j.celrep.2018.03.026). This is an important issue to discuss in light the the very interesting binding analysis the authors have performed. But I think the functional analysis can be part of a future paper.

In our recent publication (Xu et al. JCB, 2020, PMID: 32437509), we found that deletion of SHP1 from Jurkat cells had little, if any effect on PD-1 mediated suppression of IL-2 production. As the reviewer alluded to, we did observe SHP2 dissociation from PD-1 after 10 minutes, so the question of whether and how PD-1:SHP2 complex influence T cell function in a longer term is a great one. We currently are pursuing a hypothesis that there is a SHP2-independent mechanism of PD-1 inhibitory function, and indeed, in our recent study (Xu et al. JCB, 2020, PMID: 32437509), we found that PD-1 retains its partial inhibitory function in SHP1/SHP2 double knockout murine primary T cells. These results are consistent with the in vivo data by Rota et al. cited by the reviewer. We will also briefly discuss this point in a revised manuscript.

I would suggest that the title be modified slightly from "SHP1/SHP2 discrimination" to "differential SHP1/SHP2 interaction" and leave discussion of discrimination until they have the functional data integrated over times that are relevant to T cell transcriptional regulation (1-2 hrs). The functional analysis can be in another paper, but it would be interesting to have a paragraph in the discussion raising the outstanding issues beyond stable binding detected by the pull-down and microcluster recruitment experiments- what are the implications for function. Could the transient interactions in the noise of the steady state and equilibrium measurements be functional?

We thank the reviewer for the suggestion, even though reviewer #3 felt that our current title is appropriate. We will be happy to change the title at the editors’ discretion.

I would summarise that the work is outstanding as biochemistry and biophysics and it should be published nearly as is. I'm suggesting minor revisions in that the changes are just to text, but I think this is important and somewhat nuanced aspect of the paper that will make it even more helpful to readers.

We appreciate the positive and insightful comments!

Reviewer #1 (Significance):

The authors generate a detailed descriptive data set about the component interaction of SHP1 and SHP2 SH2 domains with PD1 and BTLA intracellular domains. They then test hypotheses generated from the descriptive data set to better define the nature of the interactions and why PD1 recruits primarily SHP2, while BTLA mainly recruits SHP1. PD1 is a major driver or the cancer immunotherapy revolution and SHP2 is the major candidate for a signalling effector of PD1. This paper can become the reference paper for the specificity and engineering of this interaction, which will make it highly significant in a very active and still expanding field.

Referee Cross-commenting

I still feel that "discrimination" has a functional/activity connotation that is not addressed at all in this paper, but can be addressed. I'm happy to have the suggestion stand and let the authors decide. They need to live with it once its published. Another suggestion- the citations on regulation are mostly old. A good recent paper is Pádua, R. A. P., Sun, Y., Marko, I., Pitsawong, W., Stiller, J. B., Otten, R., & Kern, D. (2018). Mechanism of activating mutations and allosteric drug inhibition of the phosphatase SHP2. Nature Communications, 9(1),

1. https://doi.org/10.1038/s41467-018-06814-w .

We believe that some of the functional questions raised by this reviewer, including the SHP1 and SHP2 contribution in PD-1 signaling, was addressed in our recent publication (Xu et al., JCB, 2020). Using SHP1 KO and SHP2 KO T cells, we showed that PD-1 inhibitory function is contributed by SHP2, but very little if any by SHP1. Thus in the current study, we focus on the mechanism behind the striking SHP2 preference by PD-1. We thank this reviewer for suggesting this excellent reference. We will cite this reference in the revised manuscript.

Reviewer #2 (Evidence, reproducibility and clarity):

In this study, Xu and co-workers investigate the biophysical nature of the interaction between the structurally-related non-transmembrane PTPs Shp1 and Shp2 with the ITIM/ITSM-containing inhibitory receptors PD-1 and BTLA using cell-based, biochemical, biophysical and domain swapping assays. The primary aim being to better understand how these receptors discriminate between binding Shp1 and/or Shp2, and the orientation of Shp1 and Shp2 engagement. These are major unresolved questions in the field that the authors go some way to addressing in a methodical, rigorous, clear and concise manner. Findings are convincing, correlate well with previous findings and internally, and are complemented with excellent schematics, making it easy to comprehend.

Major comments

The authors focus primarily on binding affinities to explain differential binding of Shp1 and Shp2 by PD-1 and BTLA ITIMs and ITSMs, but this is only part of the story. Avidity, compartmentalization, stoichiometry of kinases, and relative abundance of Shp1 and Shp2 are also important aspects of the discriminatory mechanism that are not addressed. Competition assays would go some way to addressing the latter point and should be at least be considered and discussed.

We agree that various parameters mentioned by this reviewers, such as compartmentalization and relative expression levels would be a concern for purely cell-free assays such as SPR, however, we feel that our cell-based assays already integrate these parameters. This is also precisely the reason why we chose to examine the recruitments of Shp1/2 in a cellular context instead of a purely cell-free system.

Regarding the competition, we have confirmed our key results in both WT and SHP2 KO background, with or without the potential competition from endogenous SHP2, suggesting that competition might not be a dominant mechanism for the recruitment specificity we observed.

Similarly, authors do not address how distortion of the pY binding pocket of Shp1 and Shp2 nSH2 domains in the auto-inhibited conformation is released, allowing the domain to engage with phopho-ITIM/ITSM. Again, this should be at least discussed. Current binding studies do not address this issue.

We feel that the overall recruitment to the PD-1 microclusters as we observed in cells already integrate this auto-inhibition mechanism of Shp1 and Shp2, because we used full length proteins. We do agree with the reviewer that future studies are warranted to address the contributions of each mechanism, including auto-inhibition, concentration, competition, etc., to the overall recruitment. This might require careful and extensive biophysical analyses coupled with mathematical modeling.

Minor comments:

Phosphorylation should be indicated in schematic representations in Figures 3, 6 b, c.

We thank the reviewer for this advice, we will indicate phosphorylation in the revised figure 3.

Cellular and physiological significance should be further discussed, as well as broader implications of findings to other ITIM/ITSM-containing receptors in other lineages.

We will further discuss this as suggested.

Reviewer #2 (Significance)

Findings from this study advance our knowledge of how inhibitory checkpoint regulatory receptors discriminate between Shp1 and Shp2, which has important implications for understanding how the unique biochemical, cellular and physiological functions of these receptors and phosphatases are dictated. Indeed, findings lay the foundation for a universal mechanism, that may apply to all ITIM/ITSM receptors in other cell lineages, and perhaps novel ways of targeting these interactions therapeutically.

Compare to existing published knowledge

Although largely correlative with previous studies, findings from this study start to fill major gaps in our knowledge of these biochemical processes, in a highly rigorous, concise and clear manner. Findings from previous studies were more 'piecemeal', whereas this study consolidates and advances important nuances of these interactions. Moreover, it lays the foundation for further structural, physiological and therapeutic studies.

Audience

The immune receptor signaling community and beyond, including any lineage in which ITIM/ITSM-containing receptors play a major role in regulating cellular responses.

Your expertise

ITIM/ITSM-containing receptors, kinase-phosphatase molecular switches, cellular reactivity to extracellular matrix proteins

Referee Cross-commenting

Generally agree with reviewer's comments. Constructive overall and fair. Although I was thinking additional competition experiments, I do not think necessary. Over the top for this study. Hence, 1 month should suffice to revise accordingly.

We thank this reviewer for the excellent comments and understanding!

Reviewer #3 (Evidence, reproducibility and clarity):

Summary:

Inhibitory immune receptors containing ITIMs function through recruiting the phosphatases SHP-1 and SHP-2. SHP-1 and SHP-2 are remarkably similar yet have different roles in vivo. How can ITIM-containing immune receptors specifically recruit SHP-1 or SHP-2? In this paper, Xu et al ask how SHP-1 vs SHP-2 specificity is achieved. They use very thorough biochemical assays to measure the affinity of SHP-1 and SHP-2 for various ITIM/ITSMs and finally pin point some key amino acids that switch an ITIM/ITSM from SHP-2 to SHP-1 specificity. The in vitro biochemical assays are augmented by in cell assays that support their conclusions. Overall, this paper is an incredibly elegant and straight forward paper addressing how SHP-1/SHP-2 specificity is achieved.

Major Comments: none

Minor Comments:

• Could the western blots in Figure 1 be quantified as the western blots in other figures?

We will quantify the western blots in Figure 1 as suggested in the revised manuscript.

• The data that the y+1 reside is essential for SHP-1/2 specificity is very convincing. We are curious if the other residues of the ITIM/ITSM also contribute to this specificity, albeit less potently. The PD-1 G224A mutant is still less potent than the PD-1 BTLA ITIM swap, suggesting that while the y+1 position is most important, the other residues contribute some specificity. The authors also included data on a PD-1 variant with the BTLA ITIM A224G mutation (8f), which is slightly better at recruiting SHP-1 than the PD-1 ITIM. It may be worth mentioning this data in the text of the paper as well as displaying it in the figure.

The reviewer raised an excellent point, yes, our data does suggest that other pY-flanking residues within the ITIM also contribute to SHP1 binding. However, the pY+1 residue replacement produced the strongest effect as the reviewer noted. In the revised manuscript, we will acknowledge the potential contributions of other residues.

• A brief introduction to ITIM vs ITSM in the introduction of the paper may be helpful background for readers. For example, ITIM receptors are reasonably well known but how ITSM functionally differs is probably less well known.

We will rewrite the introduction about ITIM and ITSM for better clarity.

• Although not the major focus of the paper, broadening out this SHP-1/2 specificity to other immune receptors in the discussion is fascinating. (a) The authors find that a Valine, Leucine, or Isoleucine in place of the Alanine in y+1 is very close to equivalent, yet the A is highly conserved. The authors speculate that there may be an advantage to sub-maximal SHP-1 affinity because it is more easy to regulate. I think this is reasonable speculation but a little unsatisfying given the very small observed difference in SHP-1 binding. If the authors have additional thoughts, I would be interested to hear them. (b) The authors note that PD-1 is the only ITIM with a glycine in the Y+1 position. Are there other receptors that function primarily through SHP-2, and how might they achieve this specificity?

Response to a: Even though valine, leucine or isoleucine did not produce a striking enhancement in Shp1 recruitment over alanine, the differences were statistically significant. In fact, when we performed these point mutations at a BTLA ITIM background, valine, leucine or isoleucine markedly enhanced the SHP1 recruitment (see unpublished data below). We speculate that other pY-flanking residues in BTLA, as this reviewer alluded to above, creates an environment that amplifies the differences. The strong sensitivity on pY+1 residue, as observed in BTLA, might be true for other SHP1-recruiting receptors too. If they were to have leucine or isoleucine at the pY+1 position of ITIM, they may recruit too much SHP1 that presumably decreases the fitness/growth of the cells. We propose to show this unpublished data as a supplemental figure in the revised manuscript. We will also discuss the potential contributions of other pY-flanking residues as this reviewer suggested.

{{images cannot be rendered at this time in reply letters}}

Response to b: Among the several receptors that we tested, PD-1 is the only receptor that exhibited no recruitment of SHP1. The lack of SHP1 recruitment is also true for murine PD-1, which has a glutamate residue (charged) at Y+1 position. In addition, earlier work reported that PECAM1 also selectively recruits SHP2, but not SHP1. We have noted that PECAM1 contain a threonine (polar) at the pY+1 position of their ITIMs. Thus, their inability to recruit SHP1 is consistent with our model that a nonpolar residue at Y+1 position is required for strong SHP1 recruitment. We will discuss these points in the revised manuscript.

• Figure 9 b Val not Vla, Figure 3a - a legend for the color code may be nice (ie, 20-1000 nM) Thanks for catching this, we will fix the error in Figure 9b and provide the color code in Figure 3a in the revised manuscript.

Reviewer #3 (Significance):

Significance:

SHP-1 and SHP-2 play a critical role in regulating immune system function. In addition, the receptors recruiting these phosphatases (like PD-1) are important immunotherapy targets. Previously, the question of SHP-1/SHP-2 specificity has been primarily described for ITIM bearing receptors individually. Other studies have predicted consensus sequences for the tSH2 domains of SHP-1 or SHP-2, but not addressed the defining molecular characteristics of these consensus sites or how these could be combined on ITIM receptors to generate selectivity between these related phosphatases. This paper represents a significant step forward because it provides a unifying mechanism explaining how ITIM-bearing immune receptors specifically recruit SHP-1 or SHP-2. I expect this paper will be broadly interesting to biochemists, immunologists and cancer biologists.

Referee Cross-commenting

I generally think the other reviewers comments are reasonable and insightful. Together, they suggest no new experiments are necessary. As for the proposed title change, I prefer the authors title and find it to be justified given their data.

Reviewer #4 (Evidence, reproducibility and clarity):

In this manuscript, Xu and college performed an elaborate study to investigate the molecular basis of Shp1 and Shp2 discrimination by immune checkpoints PD-1 and BTLA. The paper is original, clear, and well written. I only have a few minor comments:

1. Please label the molecular weights to all the western blots/IPs results.

We will label the molecular weights to all the blots in the revised manuscript.

1. Please add scale bars to all the microscopy pictures.

We will add scale bars to all the microcopy images in the revised manuscript.

1. For the SPR data, please add the fitting curves.

We thank the reviewer for the suggestion. However, we did not use the fitting curve to calculate the Kd, we plotted the maximum response as a function of concentration to determine the Kd. This is another well accepted method for Kd calculation. In fact, some of the SPR curves fit poorly with the existing algorithm. Thus, showing the fitting curve might distract the readers.

Reviewer #4 (Significance):

The strength of this paper relies on the details they dissected by using a series of mutagenesis screening experiments, which should be interesting to cell biologists and cancer immunologists.

Referee Cross-commenting

I think the other reviewer's comments are insightful and constructive, the suggested experiments are necessary and will improve the paper.

We thank this reviewer for the positive comments!

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Referee #3

Evidence, reproducibility and clarity

Summary:

Inhibitory immune receptors containing ITIMs function through recruiting the phosphatases SHP-1 and SHP-2. SHP-1 and SHP-2 are remarkably similar yet have different roles in vivo. How can ITIM-containing immune receptors specifically recruit SHP-1 or SHP-2? In this paper, Xu et al ask how SHP-1 vs SHP-2 specificity is achieved. They use very thorough biochemical assays to measure the affinity of SHP-1 and SHP-2 for various ITIM/ITSMs and finally pin point some key amino acids that switch an ITIM/ITSM from SHP-2 to SHP-1 specificity. The in vitro biochemical assays are augmented by in cell assays that support their conclusions. Overall, this paper is an incredibly elegant and straight forward paper addressing how SHP-1/SHP-2 specificity is achieved.

Major Comments:

none

Minor Comments:

• Could the western blots in Figure 1 be quantified as the western blots in other figures?
• The data that the y+1 reside is essential for SHP-1/2 specificity is very convincing. We are curious if the other residues of the ITIM/ITSM also contribute to this specificity, albeit less potently. The PD-1 G224A mutant is still less potent than the PD-1 BTLA ITIM swap, suggesting that while the y+1 position is most important, the other residues contribute some specificity. The authors also included data on a PD-1 variant with the BTLA ITIM A224G mutation (8f), which is slightly better at recruiting SHP-1 than the PD-1 ITIM. It may be worth mentioning this data in the text of the paper as well as displaying it in the figure.
• A brief introduction to ITIM vs ITSM in the introduction of the paper may be helpful background for readers. For example, ITIM receptors are reasonably well known but how ITSM functionally differs is probably less well known.
• Although not the major focus of the paper, broadening out this SHP-1/2 specificity to other immune receptors in the discussion is fascinating. (a) The authors find that a Valine, Leucine, or Isoleucine in place of the Alanine in y+1 is very close to equivalent, yet the A is highly conserved. The authors speculate that there may be an advantage to sub-maximal SHP-1 affinity because it is more easy to regulate. I think this is reasonable speculation but a little unsatisfying given the very small observed difference in SHP-1 binding. If the authors have additional thoughts, I would be interested to hear them. (b) The authors note that PD-1 is the only ITIM with a glycine in the Y+1 position. Are there other receptors that function primarily through SHP-2, and how might they achieve this specificity?
• Figure 9 b Val not Vla, Figure 3a - a legend for the color code may be nice (ie, 20-1000 nM)

Significance

SHP-1 and SHP-2 play a critical role in regulating immune system function. In addition, the receptors recruiting these phosphatases (like PD-1) are important immunotherapy targets. Previously, the question of SHP-1/SHP-2 specificity has been primarily described for ITIM bearing receptors individually. Other studies have predicted consensus sequences for the tSH2 domains of SHP-1 or SHP-2, but not addressed the defining molecular characteristics of these consensus sites or how these could be combined on ITIM receptors to generate selectivity between these related phosphatases. This paper represents a significant step forward because it provides a unifying mechanism explaining how ITIM-bearing immune receptors specifically recruit SHP-1 or SHP-2. I expect this paper will be broadly interesting to biochemists, immunologists and cancer biologists.

Referee Cross-commenting

I generally think the other reviewers comments are reasonable and insightful. Together, they suggest no new experiments are necessary. As for the proposed title change, I prefer the authors title and find it to be justified given their data.

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Reply to the reviewers

Editor comments:

Thank you for sending your manuscript entitled "In situ imaging of bacterial membrane projections and associated protein complexes using electron cryo-tomography" to Review Commons. We have now completed the peer review of the manuscript. Please find the full set of reports below.

We thank the editors of Review Commons and all the reviewers for their insightful comments which helped us to improve our manuscript. We have now modified our manuscript based on the Reviewers’ comments and would like to ask you to consider our revised manuscript for publication.

Reviewer #1:

This manuscript by the Jensen lab surveys a plethora of bacterial outer-membrane projections captured over the years by in situ cryo-tomography under near-native conditions. The authors classify the different visualized structures, highlighting both similarities and differences among them. They further describe molecular complexes that are associated with these projections. The manuscript highlights the abundance of such understudied structures in nature, indicating the need to deepen our exploration into their biological functions and mechanisms of action.

We thank the reviewer for her/his insightful comments that allowed us to improve our manuscript.

The authors should state in the Abstract and Introduction that only diderm bacteria and outer- membrane extensions are included in the study.

Done. We have modified the title, the abstract and the introduction to explicitly highlight this point.

In the Introduction or Discussion the authors should mention the limits of the in situ cryo-tomography, such as the difficulty to observe regions in between neigbouring bacterial cells, and into the thick bacterial cell body.

Done. We have added the following to our revised manuscript:

“Currently, only electron cryo-tomography (cryo-ET) allows visualization of structures in a near-native state inside intact (frozen-hydrated) cells with macromolecular (~5 nm) resolution. However, this capability is limited to thin samples (few hundred nanometers thick, like individual bacterial cells of many species) while thicker samples like the central part of eukaryotic cells, thick bacterial cells, or clusters of bacterial cells are not amenable for direct cryo-ET imaging. Such thick samples can be rendered suitable for cryo-ET experiments by thinning them first using different methods including focused ion beam milling and cryosectioning [30]. Cryo-ET has already been invaluable in revealing the structures of several membrane extensions, including Shewanella oneidensis nanowires [6], Helicobacter pylori tubes [15], Delftia acidovorans nanopods [25], Vibrio vulnificus OMV chains [16], and more recently cell-cell bridges in the archaeon Haloferax volcanii [31].” (Lines 108-118)

Please provide a legend to Table S1 explaining the numbers (organelles?), how many cells were viewed? I think that at least part of it should be included in the main text. Also, there are examples of vesicles emanating from H. pylori. This information is missing from Table S1.

Done. We added a column to the table indicating the number of cells available for each species. We also added the information about the vesicles in H. pylori to the table. This table is now incorporated into the main text of the manuscript as Table 1.

Please provide an ordered list including all the strains (and IDs of the specific isolates) used in this study and their genotypes.

Done. We added Table S1 to the revised manuscript that contains this information. This table also includes relevant references to all the published papers where these strains were previously used.

The authors describe in detail the H. pylori tubes that seem to be flagellum-core independent. However, the authors found previously (ref 15) that during infection, these structures are dependent on CagA T4SS, and they visualized T4SS sub-complexes in proximity to the point of tube emanation. This should be described and discussed in the text. Also, please indicate if the "host-independent" tubes are similarly dependent on T4SS.

Done. We added the following to the revised manuscript:

“The scaffolded uniform tubes of H. pylori that we observed were formed in samples not incubated with eukaryotic cells, indicating that they can also form in their absence. However, the tubes we found had closed ends and no clear lateral ports, while some of the previously-reported tubes (formed in the presence of eukaryotic host cells) had open ends and prominent ports [15]. It is possible that such features are formed only when H. pylori are in the vicinity of host cells. Moreover, while it was previously hypothesized that the formation of membrane tubes in H. pylori (when they are in the vicinity of eukaryotic cells) is dependent on the cag T4SS [15], we could not identify any clear correlation between the emanation of membrane tubes and cag T4SS particles in our samples where H. pylori was not incubated with host cells. We also show that the tubes of H. pylori are CORE-independent, indicating that they are different from the CORE-dependent nanotubes described in other species.” (Lines 303-313)

Is there any difference in the frequency or length of the tubes in the mutants presented in Figure 4? The flgS mutant in the image exhibits a very short filament; is that typical?

We did not see any significant statistical difference in the number or lengths of the tubes in these different mutants. We added Table S2 to the revised manuscript which details the number of cells we visualized for each mutant and the number of the tubes seen there. In all these mutants the lengths of the tubes ranged between few tens to hundreds of nanometers. In addition, we added Fig. S2 to show more examples of these tubes in each of these mutants.

Minor points:

-Please check full bacterial names that are sometimes missing (e.g., lines 110-112).

Done.

-There is no reference to panel 2G. Please check the references to all panels.

Done. Please see lines 154 and 183 in the main text.

-Lines 181-184: There is no figure related to the formation of teardrop-like extensions from C. pinensis. Please review the text accordingly.

Done. Corrected.

-Line 235, not clear to what "as these" refers to.

Done. We modified the text as the following:

“As these MEs/MVs from S. oneidensis were purified” (Lines 246-247)

-Line 241, not clear what "a secretin-like complex" is, and no reference is provided.

Done. We modified the text as the following:

“In the third category, we observed a secretin-like complex in many tubes and vesicles of F. johnsoniae. Secretins are proteins that form a pore in the outer membrane and are associated with many secretion systems like type IV pili and type II secretion systems (T2SS) [39–41]” (Lines 252-254)

Reviewer #1 (Significance)

As described in this manuscript, even in model bacteria these structures are generated (e.g., Caulobacter forms the hardly studied nanopod extensions). The manuscript also provides visual categories of these structures, defining "extension types" that are likely to be used by the scientific community for years to come, similar to the initial pili classification during the 1960s-70s. It is a "descriptive study," in the positive sense of the term, as it significantly contributes to the field of bacteriology.

We thank the reviewer for her/his kind words and enthusiasm about our work. It is an honor to have our work compared to the seminal pili classification work done in the 1960s-70s by pioneers in the field of bacteriology.

Reviewer #2:

The manuscript "In situ imaging of bacterial membrane projections and associated protein complexes using electron cryo-tomography" by Kaplan et al., identifies and catalogues membrane extensions (MEs) and membrane vesicles (MVs) from 13 different species using cryo-electron tomography. Furthermore, they identify and discuss several protein complexes observed in these membrane projections.

The manuscript is beautifully written, interesting, and genuinely got this reviewer excited about the biology. I applaud the authors on their manuscript and have only minor comments and a few thoughts that the authors may wish to think on and discuss.

We thank the reviewer for her/his kind words and insightful comments that allowed us to improve our manuscript.

Some schematics throughout the introduction would be useful to readers new to the field/ outside the field who are not used to these different membrane structure features.

We thank the Reviewer for this suggestion. First, we made an extra figure with schematics showing the cell body and membrane tubes but that was rather redundant with Figure 8. For this reason, we added explicit labels to figure 1 highlighting the cell body and the tubes in these examples to help the reader following that figure and the subsequent ones. However, if the Reviewer has an explicit suggestion/view about the schematics then we would be very happy to do that.

The size of scale bars should be indicated on the figure panels themselves rather than in the figure legend to assist the reader.

Done.

In reference to lines 193-196 - what was the extracellular environment like in these micrographs? Were other cells present? Could it be the extracellular environment/surrounding cells that stimulate pearling? Have the authors considered this? Please discuss if relevant/insightful.

This is a good point. The cells were usually plunge-frozen in their standard growth media (except in H. pylori where the cells were resuspended in PBS and subsequently plunge-frozen). Yes, there are other cells present in the sample, however, usually, only one cell is present in the field of view of the tomogram as areas with multiple cells have thick ice and therefore not amenable for cryo-ET imaging. We added the following to the revised manuscript:

“As usually only one (or part of a) cell is present in the cryo-tomogram, we can’t exclude that differences in the extracellular environments, like the presence of a cluster of cells in the vicinity of the individual cells with pearling tubes, might play a role in this observation” (Lines 198-201).

"Randomly-located complexes" in this reviewers opinion should actually be described "seemingly randomly-located complexes" given there may be an organization present that is beyond the resolution limit of this study.

The is a good point. Indeed, we can’t exclude that these complexes have a preferred localization in specific lipid patches that we can’t detect in our cryo-tomograms. We added the following statement to the revised manuscript:

“These complexes, which were also found in the OM of intact cells, did not exhibit a preferred localization or regular arrangement within the tube at least within the fields of view provided by our cryo- tomograms (Fig. 5a & b).” (lines 227-230).

In reference to lines 287-292 - is it possible this has to do with lipid composition? Have the authors considered this? Please discuss if relevant/insightful.

Done. We added the following to the revised manuscript:

“In addition, differences in the lipid compositions among the various species investigated here might also play a role in the formation of these different forms of projections” (Lines 299-301).

Reviewer #2 (Significance ):

These results advance the field by shedding new light on bacterial membrane extension morphologies. The authors use a cryo-ET to catalogues membrane extensions and membrane vesicles which has not been done before.

This paper is likely to be of interest to structural biologists, biophysicist, membrane protein biologists, virologists and microbiologists.

This reviewer is a single-particle cryo-EM structural biologist with interest in membrane proteins._

We thank the reviewer for her/his enthusiasm about our work described here.

trails

Reviewer #3 (Public Review):

The biochemical and genetic characterization of BRCA2 has been an ongoing challenge in the DNA repair field as the protein is large, prone to degradation, and expressed at low levels in most cell types. While certain features of BRCA2 have been described previously including its ability to bind and load RAD51 onto resected DNA substrates, much remains to be discovered. In this study, the authors combine genetic studies in mouse ES cells with biochemical analysis to examine the spatial dynamics and molecular architecture of BRCA2. Notably, they utilize an innovative approach coupling endogenous tagging of mouse BRCA2 with a HALO tag to monitor BRCA2 movement within live cells by single particle tracking.

I applaud the authors for achieving a highly technical approach to epitope tagging both endogenous BRCA2 alleles in mouse ES cells and combining this strategy with a HALO tag providing additional utility for a variety of cell biological experiments. By analyzing the endogenous alleles, the authors' system provides physiological levels of protein expression as transcription will be driven by the endogenous promoter thus preserving stoichiometric protein interactions within the cell and avoiding artifacts caused by overexpression.

The authors determine the influence of the DNA binding domain (DBD) and c-terminal binding (CTD) on the dynamic activities of BRCA2. They begin by exposing cells containing 3 different deletion mutants ΔDBD, ΔCTD, and the double mutant ΔDBDΔCTD to four different types of DNA damage (IR, PARPi, MMC, and cisplatin). Notably, ΔDBD displays significant impairment in survival in response to all 4 types of DNA damage. The ΔCTD, in contrast, demonstrates less sensitivity to IR and Olaparib, however, complements as well as WT BRCA2 in response to crosslinking agents MMC and cisplatin. My only criticism in this aspect of the work is that it would have been informative to include a truncated BRCA2 (mimic of a patient pathogenic mutation) or null allele to compare to the survival of the ΔDBD and ΔCTD mutants. I realize that these alleles may be inviable but the authors should clearly state if that was indeed the case.

The authors then go on to demonstrate that the ΔDBD and ΔCTD mutants are recruited to sites of IR damage in a similar manner to WT BRCA2 based on number and intensity of foci. I think it would be informative if the authors provided statistical significance for the graphs depicting the quantitation of foci number and intensity as there do appear to be differences between the mutants and the WT protein. There appears to be a delay in the kinetics of recruitment, especially at the 2 hr timepoint, for the mutants compared to WT BRCA2, which could indicate a defect in the recognition of the DNA damage. Only at the 2 hr timepoint following IR are there less RAD51 foci, and of a lesser intensity, in the three deletion mutants compared to WT BRCA2. Another possibility is the results could be interpreted as a defect in RAD51 loading and/or stabilization of the nucleoprotein filament. While immunofluorescence imaging of DNA repair foci have become common practice to measure protein recruitment to damage, it is impossible to know exactly what is happening in these foci with any granularity.

Next, the authors measure BRCA2 movement in the mouse ES cells taking advantage of the HALO tag to track single particles. While technically and visually alluring, it is difficult to extract mechanistic insight from the results. DNA damage induces changes in diffusion leading to BRCA2 molecules with restricted mobility; the authors demonstrated this phenomenon in a prior publication. The deletion mutants appear to have little effect upon BRCA2 mobility.

Finally, the authors utilize scanning force microscopy to analyze binding of the purified human BRCA2 proteins to RAD51 and ssDNA. In the absence of RAD51/ssDNA binding, there is a notable shift in the deletion mutants from oligomeric forms to monomeric compared to full length WT BRCA2. Upon binding to RAD51, there is a dramatic change from multimeric to monomeric forms for the WT BRCA2 (~7% to 74%) with a slight suppression of these changes shown for the deletion mutants. While WT BRCA2 forms extended molecular assemblies upon binding ssDNA, not surprisingly, deletion of the DBD or CTD fail to demonstrate any significant changes in physical architecture. In both situations, the mutant proteins respond to RAD51 and ssDNA in a dampened manner likely due to altered or loss of binding. While the architectural effects of RAD51 and ssDNA binding to BRCA2 are measurable by SFM, it is difficult to reconcile these changes in shape and oligomerization to defects in response to DNA damage and at which specific steps in homologous recombination these physical forms would impact.

Strengths:

1. Generation of mouse ES cells with both endogenous alleles of BRCA2 containing the deletion mutations in addition to a HALO tag is an incredible technical breakthrough and will be a highly valuable reagent for genetic and cell biological studies of mouse BRCA2.<br> 2. The deletion mutants ablating either the DBD or the CTD, or both, is a great genetic approach to understanding the role of these key domains in BRCA2. The response of these mutants (versus WT BRCA2 as a benchmark) to various DNA damage (IR, PARPI, MMC, cisplatin) provides interesting information delineating the roles of these two important domains in BRCA2. For example, the ΔCTD mutant is significantly sensitive to IR and Olaparib, yet complements as well as WT BRCA2 in response to the crosslinking agents MMC and cisplatin.<br> 3. The BRCA2 protein is notoriously difficult to purify and yet the authors succeeded in purifying 4 different forms of the protein for biophysical analysis. While it is difficult to interpret the various forms of BRCA2 by SFM, there are clear differences in the architecture between WT and the three c-terminal mutants. These differences are highlighted upon binding to RAD51 or ssDNA.

Weaknesses:

1. While the separation-of-function result for the CTD deletion in response to crosslinking agents MMC and cisplatin is a novel and compelling result, it would have been informative to compare the survival results and gene targeting assay using a BRCA2 null or mimic of patient mutation (truncating mutation) to see how these 3 mutants stack up against a completely non-functioning BRCA2 allele. Likely, the BRCA2 null alleles are inviable but perhaps a conditional system or truncating allele similar to a patient germline mutation would give a window into response compared to the DBD and CTD deletion mutants.<br> 2. It's not clear in the manuscript what new information we are learning about the mechanisms of BRCA2 in the single particle tracking (SPT) data. The differences in mobility between the mutants and WT BRCA2 seem minimal, but more importantly, it is not immediately clear how these data help us understand the normal cellular functions of BRCA2. No doubt, the technology and innovation to track single particle proteins in the nuclei of cells is impressive, but the authors should clearly explain how we can gain mechanistic insight from the SPT data that is presented in this manuscript.

General Comments:

It is unclear how missing the c-terminal domain (CTD) or the DNA binding domain (DBD) of BRCA2 can be interpreted as having "roles beyond delivering strand exchange protein RAD51" unless a complete biochemical workup of the deletion mutants was performed to detect any alterations in DNA binding, stimulation of RAD51 dependent strand exchange, etc... While interesting and certainly an impressive technical feat, foci imaging and single particle tracking do not provide much information on mechanism (i.e. whether BRCA2 is binding DNA and loading/nucleating RAD51).

The interpretations in the discussion are not overstated, however, I somewhat disagree with the notion that the data, as presented, clarifies the role of BRCA2 beyond its canonical functions of RAD51 loading and nucleation on resected DNA substrates. I would have liked if the authors discussed the idea that it is surprising that mouse ES cells can tolerate complete loss of the DBD, CTD, and loss of both together. Questions that should be addressed in include some of the following: Are proliferation rates compromised compared to WT cells? Are they experiencing replication stress in the absence of any exogenous damage? Further, is there something unique about mouse ES cells that may differentiate BRCA2 behavior that would be expected in somatic human cells?

It is interesting to note that many years ago Ashworth and Taniguchi published back-to-back papers in Nature (2008) describing BRCA2 reversion alleles from in vitro screens of BRCA2 mutant cells selected in cisplatin or PARPi such that some of these reversions resulted in huge deletions of the entire DBD of BRCA2, and yet, they promoted resistance to PARPi. In this context, I would much appreciate if the authors commented on their findings that their constructed DBD deletion is not resistant to PARPi and if they offered some speculation as to why the reversions in those previous studies were.

Author Response:

Reviewer #3 (Public Review):

[...] I have only minor concerns regarding sources of error, particularly with respect to interpretation of the small effects the authors observe in many of their FRET experiments.

• Figure 2D shows rather small changes in ΔF/F-15 mV between fluorescent protein labels inserted at different positions in the ASIC sequence, particularly for the YFP constructs. As this metric is determined from the top and bottom asymptotes for the Boltzmann fits shown in Figure 2C, it would be useful to have some estimate as to the error associated with the fits at extreme values. Perhaps the authors could provide fits to their data (as in Figure 2C), including confidence intervals, or some similar estimate as to the size of the expected error compared to the effect size in Figure 2D.

Thank you for this point. We did use Boltzman’s fits to get the asymptotes for each cell and calculate a ΔF/F. However, we could also use a ‘fit free’ approach of simply taking the difference between fluorescence values measured at -180 mV and that at +120 mV, divided by that at -15 mV to normalize for each cell. This approach completely avoids any error associated with fitting the data or imposing any model at all. Using this approach results in slightly different ΔF/F values but the pattern of statistical significance is identical. This new analysis is included in Figure 2 figure supplement 4. It has also been corrected for multiple comparisons.

• Along those same lines, the authors use an interesting (and potentially generalizable) approach to reducing background from intracellular proteins in their experiments: co-transfecting their channels with empty plasmid DNA. What percentage of the remaining fluorescence signal is the result of intracellular background? How would that affect the data in Figure 2 and 3? Is the ΔF/Fnorm curve for YFP labeled positions in Figure 2-figure supplement 4 so flat because of contaminating background fluorescence?

This is a great question. We originally hoped that the CFP and YFP quenching data from different positions could be used to triangulate both a distance from the membrane and a value for background fluorescence assuming that CFP and YFP would yield similar background fluorescences. An analogous approach was used in Zachariassen et al. Proc Natl Acad Sci, 2016 where an equal background was assumed between conformational states within a recording. In the end, the YFP quenching appeared to have a greater background than CFP. We speculate that this may be because the YFP variant we used matures faster than the CFP (mVenus, 17.6 min verses mTurquiose2, 33.5 min; FPbase.org) and hence the YFP matures faster than the ‘new’ channels get to the plasma membrane. However, at present we are uncertain how much of the background fluorescence signal to confidently attribute to this intracellular FP issue.

• In Figure 3D, the FRET efficiency between CFP-cA1-cA1 and N YFP at a 1:15 ratio of the two plasmids is higher than the FRET efficiency between CFP and YFP in the same subunit, even though the authors conclude that fluorescent proteins on the same subunit show considerably more FRET than fluorescent proteins on neighboring subunits. Could this indicate that the N-termini of adjacent subunits are closer together than the N- and C-termini of a single subunit? If, on the other hand, this effect were entirely the result of crowding in the membrane why is FRET efficiency substantially lower when CFP-cA1-cA1 is co-expressed with C4 YFP? Wouldn't this construct produce a similar crowding effect?

We strongly suspect the N termini of adjacent subunits are closer to each other than N and C of single subunit simply because the N FPs would all be at the same ‘height’ or same depth with respect to the plasma membrane. Thus the measured FRET in this case primarily reflects distances in the x-y plane. This contrasts with the N and C FPs on the same or different subunits where both x-y distances and axial distances come into play.

• On page 23, the authors state that they detected no pH-dependent changes in FRET between their GFP tag on the N-terminus of ASIC1 and an RFP tag on the channel's C-terminus. However, Figure 4 shows a small, but significant change in fluorescence between pH 8 and pH 7.

We have corrected for multiple comparisons within a figure. As a result, this effect is no longer statistically significant (adjusted p value is 0.063).

• The interpretation of distances between various tagged position on ASIC and the plasma membrane in Figure 2 is based on using two different colored tags with two different distance dependences. However, the interpretation of the data from Figure 5 provided on page 25 is less clear. For example, the reduction in fluorescence from the N-terminal tag is interpreted as the tag moving closer to the plasma membrane. Without similar data from a YFP tag to verify, it seems equally likely that the reduction in fluorescence (at steady state) could result from a movement away from the plasma membrane.

This is a very good point. We tried to perform DPA quenching of YFP-containing constructs at pH 6.0, but the acidification resulted in proton-quenching of the YFP fluorescence (Figure 4). We didn’t feel confident in measuring DPA quenching with the concomitant loss of YFP fluorescence due to acidification. Therefore, we relied on the pH 8.0 CFP and YFP data as a starting point (Figure 2). Given the C1 insertion gives the greatest extent of CFP quenching, it is reasonable to place it around the top of the curve. The N position could then be on the left or right side of the hump or peak in the CFP distance curve. The N quenching is comparable to the C2 insertion quenching (Figure 2D, left) yet the N FP is ~ 16 amino acids from the pore-forming membrane helices while the C2 insertions is ~ 40 amino acids away. For reference, the C1 is ~ 24 amino acids. Thus we are reasonably confident the N insertion is on the left side of the hump or peak. A reduction in ΔF/F would indicate movement closer to the plasma membrane. While technically possible that the N position could move further away from the membrane, this would have to be a >25 Å movement. Given there are only 16 amino acids between the CFP and the beginning of TM1 of the channel, we do not think such a dramatic movement outward could occur.

interesting narrative shaping here.

scientifically, yes our understanding of our autonomic nervous system is super important to the healing the things that are wild and disturbing above — but jumping to the next part is where I get lost.

Agree a lot of our pain is from: "is the belief that we do not belong in this world." which I think we'll have to work on to fix the climate thing.

Author Response

Reviewer #1 (Public Review):

[...] My main technical concern lies in the choice of decomposition filter for SEP and alpha oscillations, and the conclusions the authors draw from that. Specifically, a CCA spatial filter is optimized here for the N20 component, which is then identically applied to isolate for alpha sources, with the logic being that this procedure extracts the alpha oscillation from the same sources (e.g., L359). I have no issues (or expertise) with using the CCA filter for the SEP, but if my understanding of the authors' intent is correct, then I don't agree with the logic that using the same filter isolate for alpha as well. The prestimulus alpha oscillation can have arbitrary source configurations that are different from the SEP sources, which may hypothetically have a different association with the behavioral responses when it's optimally isolated. In other words, just because one uses the same spatial filter, it does not imply that one is isolating alpha from the same source as the SEP, but rather simply projecting down to the same subspace - looking at a shadow on the same wall, if you will. To show that they are from the same sources, alpha should be isolated independently of the SEP (using CCA, ICA, or other methods), and compared against the SEP topology. If the topology is similar, then it would strengthen the authors' current claims, but ideally the same analyses (e.g., using the 1st and 5th quintile of alpha amplitude to partition the responses) is repeated using alpha derived from this procedure. Also, have the authors considered using individualized alpha filters given that alpha frequency vary across individuals? Why or why not?

Indeed, applying the same spatial filter to EEG signals with different spatial arrangements of the sources can lead to the extraction of neuronal activity which does not originate from the very same sources. We had chosen our approach, as it is well known that the generators of the early SEP components and the generators of the prominent somatosensory alpha rhythm co-reside at similar sites in the primary somatosensory cortex (e.g., Haegens et al., 2015). Therefore, we considered our approach appropriate to specifically focus on neural activity from the somatosensory region both in the frequency band of the SEP as well as of the alpha rhythm. Yet, we agree with the reviewer that it should be acknowledged that we may have missed or mixed-up effects of alpha activity from other sources by using this procedure (which might have led to different conclusions otherwise). In order to account for this, we repeated our analyses with an SEP-independent reconstruction of the oscillatory effects in source space (“whole brain analysis”). For this, we first reconstructed the sources of alpha activity using eLORETA and head models based on participant-specific MRI scans, and estimated the respective effects independently for all sources across the cortex using both linear-mixed effects models (LME) as well as a binning approach for the Signal Detection Theory (SDT) parameters sensitivity d’ and criterion c (consistent with the previous analyses in our manuscript). In the LME analyses, both the effects of pre-stimulus alpha activity on N20 amplitudes as well as on perceived stimulus intensity were strongest in the right primary somatosensory cortex – in accordance with the sources of the originally extracted tangential CCA component of the SEP (see Supplementary Figure 1 for Peer Review). Also, using the binning approach to examine the relation or pre-stimulus alpha activity with SDT parameter criterion c, the effects were most pronounced around the right somatosensory regions (Supplementary Figure 2 for Peer Review), yet these effects did not survive statistical correction for multiple comparisons (FDR-correction with p<.01). However, when performing the same binning analysis for our region of interest (ROI), the hand area in BA 3b of the right somatosensory cortex, a significant effect or pre-stimulus alpha on criterion c was indeed confirmed, t(31)=-2.951, p=.006, CI95%=[-.173, -.032]. Furthermore, in line with our previous CCA results, for sensitivity d’, neither the whole brain analysis nor the ROI analysis showed effects of pre-stimulus alpha amplitude, t(31)=0.633, p=.531, CI95%=[-.083, .157]. Taken together, the findings we report in our original manuscript for pre-stimulus alpha activity obtained with the spatial CCA filter can thus be replicated with a SEP-uninformed source reconstruction, both using LMEs for a “whole-brain analysis” as well as SDT analyses in a ROI-based approach. We therefore conclude that the relationships between pre-stimulus alpha activity, N20 potential of the SEP, and perceived stimulus intensity can indeed be attributed to neural activity from the same (or at least very similar) sources in the primary somatosensory cortex.

Addressing the question on filtering alpha activity in individualized frequency bands, we considered this option, too. However, the rather short length of our pre-stimulus window (-200 to -10 ms) constitutes a natural limit for the frequency resolution in the alpha range and slightly different filter ranges (adjusted with regards to the individual alpha peak frequency) are thus unlikely to lead to large differences in the estimation of pre-stimulus alpha amplitudes. Therefore, we refrained from using individualized frequency bands here and focused on the more generic approach using one common alpha band (8-13 Hz) for all participants, which should also facilitate direct comparisons with previous studies on pre-stimulus oscillatory effects.

In the same vein, both alpha and N20 amplitude relate to perceptual judgement, and to each other. I believe this is nicely accounted for in the multivariate analysis using the SEM, but the analysis that partitions the behavioral responses using the 20% and 80% are done separately, which means that different behavioral trials are used to compute the effect of N20 and alpha on sensitivity and criterion. While this is not necessarily an issue given that there IS a multivariate analysis, I would like to know how many of those trials overlap between the two analyses.

This is an interesting point indeed. We included both the binning analyses and the multivariate analyses in our manuscript as we believe they offer complimentary views on the data, and also allow a direct comparison to previous studies in the field (e.g., Iemi et al., 2017). In fact, the trial overlap between the extreme bins of the alpha and N20 data were rather small.

Since the expected trial overlap is 20% when partitioning the data into quintiles randomly, the effect-driven increments and reductions in trial overlap in our data appear to be rather small. However, they showed the expected directions: Larger alpha amplitudes were associated with more negative N20 amplitudes (and vice versa). Presumably, these small differences in trial overlap reflect the rather small effect sizes we also observed in the multivariate analyses. We have added this information to our revised manuscript in the following way to give the reader a better picture of the underlying data for the binning analyses (page 9, lines 137 ff.): “(Please note that this procedure resulted in a different trial selection as compared to the SDT analysis of pre-stimulus alpha activity. Please refer to Fig. 2—figure supplement 2 for further details on the trial overlap.)”

At multiple points, the authors comment that the covariation of N20 and alpha amplitude in the same direction is counterintuitive (e.g., L123-125), and it wasn't clear to me why that should be the case until much later on in the paper. My naive expectation (perhaps again being unfamiliar with the field) is that alpha amplitude SHOULD be positively correlated with SEP amplitude, due to the brain being in a general state of higher variability. It was explained later in the manuscript that lower alpha amplitude and higher SEP amplitude are associated with excitability, and hence should have the opposite directions. This could be explicitly stated earlier in the introduction, as well as the expected relationship between alpha amplitude and behavior.

Thank you for pointing out this unclarity. We have now made this rationale more explicit already at an early point in the introduction (page 3, lines 26 ff.): “According to the baseline sensory excitability model (BSEM; Samaha et al., 2020), higher alpha activity preceding a stimulus indicates a generally lower excitability level of the neural system, resulting in smaller stimulus-evoked responses, which are in turn associated with a lower detection rate of near-threshold stimuli but no changes in the discriminability of sensory stimuli (since neural noise and signal are assumed to be affected likewise).”

Furthermore, I have a concern with the interpretation here that's rooted in the same issue as the assumption that they are from the same sources: the authors' physiological interpretation makes sense if alpha and N20 originated from the same sources, but that is not necessarily the case. In fact, the population driving the alpha oscillation could hypothetically have a modulatory effect on the (separate) population that eventually encodes the sensory representation of the stimulus, in which case the explanation the authors provide would not be wrong per se, just not applicable. A comment on this would be appreciated in the revision.

Our extensive additional analyses suggest that the sources of behaviorally relevant alpha and N20 activity were located at very similar cortical sites. Nevertheless, this is not a proof that exactly the same neuronal populations were involved (for example, alpha and N20 effects could originate from different cortical layers). Therefore, we have added this potential limitation to our revised manuscript in the following way (page 19, lines 379 ff.): “Furthermore, with the present data, we cannot unambiguously conclude that the observed relation between pre-stimulus alpha activity and initial SEP indeed involved the very same neuronal populations – which may represent a limitation of the hypothesized mechanism. However, all approaches to localize these effects pointed to very similar cortical regions as discussed in the following section.”

In addition, given how closely related the investigation of these two quantities are in this specific study, I think it would be relevant to discuss the perspective that SEPs are potentially oscillation phase resets. Even though the SEP is extracted using an entirely different filter range, it could nevertheless be possible that when averaged over many trials, small alpha residues (or other low freq components) do have a contribution in the SEP. If the authors are motivated enough, a simulation study could be done to check this, but is not necessary from my point of view if there is an adequate discussion on this point.

Indeed, the phase reset mechanism may be a possible alternative explanation for relations between oscillations and later parts of the ERP. However, the N20 potential reflects the very first excitation of the cortex in response to a somatosensory stimulus and should therefore represent a textbook example of an additive response (EPSPs are added to ongoing background activity). Moreover, the N20 response should be over long before a possible phase reset in lower frequencies (such as alpha frequencies) would start to play a role (Hanslmayr et al., 2007; Sauseng et al., 2007). Nevertheless, we ran additional control analyses (including a simulation study) in order to exclude that some odd combination of phase-locking and filter residues led to the present findings: Please see Essential Revision #4 for details and how we included these considerations in our revised manuscript.

Reviewer #2 (Public Review):

[...] The main weaknesses of the manuscript becomes most apparent with respect to the stated impact that "The widespread belief that a larger brain response corresponds to a stronger percept of a stimulus may need to be revisited.". I am not really sure if there are many cognitive neuroscientists, that would actually subscribe to such a simplistic relationship between evoked responses and perception and that temporal differentiation (early vs late responses) and the biasing influence of prestimulus activity patterns are becoming increasingly recognized. So rather than actually changing a dominant paradigm, this work is an (excellent) contribution to a paradigm shift that is already taking place.

Thank you for this feedback. We agree that the paradigm shift away from simplistic assumptions about the relationship between variability of neural responses and perception is already taking place and that this is already being appreciated by many scientists in the field. Also, we agree that the present study contributes more evidence to this emerging notion rather than changing the whole field. However, we do think that particularly the observation of opposite amplitude modulations of initial somatosensory evoked responses associated with presented stimulus intensity on the one hand and pre-stimulus excitability state on the other, provides a novel perspective for our understanding of how fundamental features of sensory stimuli are processed at initial cortical levels. Following your suggestions to tone down claims about the controversiality as well as to avoid over-generalization, we have therefore adjusted the impact statement of this manuscript to: “Larger evoked responses during initial cortical processing may reflect states of lower excitability.”

Furthermore, we have adjusted similar statements throughout the manuscript accordingly.

Also it should be considered that with regards to the analysis approach using CCA, the claims are mainly restricted to BA3b: i.e. while I also think that this is a strength of the current study, one should refrain from overinterpreting the results in a very generalized manner. The authors do include some "thalamus" and "late" evoked response patterns as well, however that presentation of the results is somewhat changed now as compared to the N20 (e.g. using LMEs rather than comparison of extremes; not using SEMs). The readablity of results and especially the comparison of effects would profit from a more coherent approach.

We agree that our findings indeed have the specific focus on the N20 component and thus on its generators in BA3b. We did not intend to suggest that the effects we observed for this initial cortical response can be readily generalized to other (later) ERP components, too. However, we do believe (and hypothesize) that similar mechanisms may be in place for corresponding initial cortical responses in other sensory modalities, too – yet it is clear that we cannot test this generalization with the current study. To avoid misunderstandings of these interpretations and their limitations, we have further specified these aspects in the Discussion.

Regarding our analyses of the later SEP (i.e., N140 component) and thalamus-related activity (i.e., P15 component), we initially decided to use linear-mixed effects models as they are mathematically equivalent to the way the sub-equations of the structural equation model were constructed (Table 2 in the manuscript). Nevertheless, we have now additionally run binning analyses to make a direct comparison also with Signal Detection Theory (SDT) parameters possible: For the N140 component, there was a significant effect on criterion c, t(31)=-3.010, p=.005, but no effect on sensitivity d’, t(31)=0.246, p=.807. For the P15 component, no effects emerged either for criterion c or sensitivity d’, t(12)=1.201, p=.253, and t(12)=-0.201, p=.844, respectively. These findings correspond well to the previous LME analyses and may indeed further facilitate the comparison with the findings for the N20 potential and pre-stimulus alpha activity. Therefore, we have added these complimentary analyses to our manuscript in the following way:

Results: “In addition, the SDT analysis based on binning of the P15 amplitudes into quintiles neither suggested a relation with criterion c nor with sensitivity d’, t(12)=1.201, p=.253, and t(12)=-0.201, p=.844, respectively.” (page 14, lines 241 ff.)

“These findings were in line with a separate SDT analysis: N140 amplitudes were associated with an effect on criterion c, t(31)=-3.010, p=.005, but no effect on sensitivity d’ emerged, t(31)=0.246, p=.807.” (page 15, lines 263 ff.)

Discussion: “Crucially, our data are at the same time consistent with previous studies on somatosensory processing at later stages, where larger EEG potentials are typically associated with a stronger percept of a given stimulus (e.g., Al et al., 2020; Schröder et al., 2021; Schubert et al., 2006), as both our SDT and LME analyses of the N140 component showed.” (page 19, lines 367 ff.)

“Yet, neither our SDT analyses nor the LME models of the thalamus-related P15 component supported this notion.” (page 21, lines 414 ff.)

Methods (page 32, lines 681 ff.): “The effects of the EEG measures pre-stimulus alpha amplitude, N20 peak amplitude, P15 mean amplitude, and N140 mean amplitude on the SDT measures sensitivity d’ and criterion c were examined using a binning approach: […]”

I have some concerns whether the relationship between large alpha power and more negative N20s could be driven by more trivial factors rather than the model explanations the authors develop in the discussion. Concretely the question whether phase locking of large alpha power along with >30 Hz high pass filtering could produce a similar finding as shown e.g. in Figure 2c. This is an important issue, as prestimulus alpha influences the N20 amplitudes as well as the perceptual reports.

Indeed, potential phase-locking of alpha oscillations to stimulus onset and filter-related effects are important issues that could potentially offer an alternative explanation for the observed relationship between amplitudes of pre-stimulus alpha activity and the N20 potential of the SEP. Although such pre-stimulus alpha locking is rather unlikely in a paradigm with jittered stimulus onsets (in our case uniformly distributed between -50 ms and +50 ms; corresponding to a whole alpha cycle), we have run the following control analyses to fully exclude this possibility:

First, we analyzed whether pre-stimulus alpha phase values were distributed uniformly and whether these phase distributions differed between high and low alpha amplitudes as well as between high and low N20 amplitudes. The phase of pre-stimulus alpha activity was obtained from a Fast-Fourier transform in the pre-stimulus time window from -200 to -10 ms, applied to unfiltered, but otherwise identically pre-processed data as in the original manuscript (i.e., applying the spatial filter of the tangential CCA component). For the FFT, we used zero padding (extending the pre-stimulus data segments to 2048 data points each) in order to obtain an interpolated frequency resolution of around 3 Hz. The phase was extracted at the frequency 9.766 Hz (i.e., the closest available frequency to 10 Hz). As visible from Supplementary Figure 3 for Peer Review, pre-stimulus alpha phases were distributed uniformly across all five quintiles of both alpha and N20 amplitudes. This observation was confirmed by the Rayleigh test (testing for deviations from a uniform distribution; Berens, 2009): Neither in the concatenated phase data of all participants, z=1.130, p=.323, nor in single-participant analyses within every alpha amplitude or N20 amplitude bin, we found evidence for a non-uniform distribution of alpha phase, all p>.367 (after Bonferroni correction for multiple testing). Thus, there was no phase-locking of pre-stimulus alpha activity that could serve as a trivial alternative explanation of the relationship between pre-stimulus alpha amplitude and N20 amplitude.

Second, in order to examine whether the combination of our temporal filters (30 to 200 Hz band-pass for the SEP, and 8 to 13 Hz band-pass for alpha activity) could have led to the present findings, we additionally re-ran our analysis pipeline with simulated data: We mixed exemplary SEP responses with constant amplitudes (unfiltered; derived from within-participant averages), with simulated alpha band activity with randomized amplitude fluctuations, and pink noise, reflecting neural background activity as is typical for the human EEG. The SEP onsets were chosen according to our original experimental paradigm with inter-stimulus intervals of 1513 ms and a jitter of ±50 ms. Next, we filtered these mixed signals between 30 and 200 Hz in order to extract the single-trial SEPs, and estimated the pre-stimulus alpha amplitudes between -200 and -10 ms in the same way as was done in the original manuscript (i.e., by filtering the mixed signal between 8 and 13 Hz). This procedure was repeated for 32 generated data streams, containing 1000 SEPs each (corresponding to our empirical dataset of 32 participants). The resulting average SEPs did neither show a visually detectable difference between the five alpha amplitude quintiles nor indicated a random-slope linear-mixed-effects model any relation between pre-stimulus alpha amplitude and N20 amplitude on a single-trial level, βfixed=-.0005, t(255.16)=-.094, p=.925. Therefore, our findings cannot be explained by filter artifacts or residual activity leaking from the alpha frequency band to the frequency band of the N20 potential.

Third, we re-analyzed our empirical EEG data in time-frequency space to obtain a more detailed view of the effects of pre-stimulus alpha activity on N20 amplitudes. For this, we decomposed our pre-processed but unfiltered data with wavelet transformation (complex Morlet wavelets) and calculated linear-mixed effects models on the relation between signal amplitudes in the time-frequency domain and single-trial N20 amplitudes as obtained from our original analyses. As shown in Supplementary Figure 5 for Peer Review, the time-frequency representations of the effects on N20 amplitudes indeed indicated a specific role of the alpha band, with its effects (i.e., already 200 ms before stimulus and in the upper alpha frequency range) separated from the time- and frequency range of the N20 potential of the SEP (i.e., from ~20 ms after stimulus onwards and above ~20 Hz). In addition, we ran the same analysis for the behavioral effect (i.e., perceived stimulus intensity). Also here, pre-stimulus effects were predominantly visible in the alpha band. Of note, there were also strong effects in the beta band. These may be interesting to study further in future studies – in particular, whether they reflect independent physiological processes or rather harmonics of the alpha band. Furthermore, these time-frequency representations suggest that the studied pre-stimulus effects might have been even more pronounced if we had analyzed the data in pre-stimulus time windows from -300 to -10 ms. However, in order to avoid inflating effect sizes by post-hoc data digging (“p-hacking”), we prefer to keep the original, a priori chosen time window for the main analyses of the manuscript. Yet, these onsets of pre-stimulus effects at around -300 ms may be of interest for future work. Taken together, these time-frequency analyses further support the notion that the observed relation between pre-stimulus alpha activity and N20 amplitudes is not due to technical issues (such as filter leakage and phase-locking) but rather reflects genuine neurophysiological effects of alpha oscillations on SEPs.

We have added the time-frequency analysis, as well as the SEP simulation analysis as figure supplements to Figure 2 in our revised manuscript (page 8) since we believe that these control analyses comprehensively show that the observed effects were (a) specific to the alpha band and (b) not due to any data processing-related artifacts.

It is important to emphasize that the model develop is a post-hoc one, i.e. the authors do not develop already in the discussion various alternative scenario results based on different model predictions. Therefore there is no strong evidence in support of the specific one advanced in the discussion.

Thank you for raising this issue. Indeed, we cannot prove with the current findings that our proposed physiological model of the relation between alpha oscillations and the SEP is the correct model (or that it is at least the best one out of a selection of possible alternative models). To do so, future studies would be needed that can actually directly measure and/or manipulate differences in membrane potentials and trans-membrane currents. Rather, we aimed with the present study to associate a physiological meaning with the concept of excitability changes in the human EEG – offering a hypothesis that may be worthwhile to be studied (and either confirmed or rejected) in future studies. We have tried to make this motivation more explicit in the Discussion section (page 20, lines 384 ff.): “Also, we would like to emphasize that the presented mechanism reflects a hypothesized model, which shall be further supported or falsified with more targeted studies, for example, directly quantifying membrane potentials and trans-membrane currents in relation to different excitability states in somatosensation.”

Author Response

Reviewer #1 (Public Review):

[...] The manuscript is excellently written and discusses the simulation results clearly and succinctly. The resolution of the simulations is very impressive and yields unprecedented insight into the effect of merozoite shape on alignment dynamics, which has important implications for how effectively the parasite can survive and multiply. The conclusions reached by the authors are certainly justified by the simulation data. In particular, the authors are careful not to draw conclusions beyond the limits of their study, and acknowledge other factors which may influence the merozoite shape, such as internal structural constraints and the energy of invasion following successful alignment.

We thank the reviewer for a thorough reading of our manuscript and the very positive judgement.

Regarding weaknesses of the manuscript, some of the explanations of the trends observed in the simulation data could be expanded slightly, to help gain a deeper understanding of the competition between adhesion and RBC deformability underlying the alignment dynamics. These are described in more detail below.

1. Line 114 and lines 120-129: The discussion here of the trends observed in Figure 1 (including why the LE shape has a larger energy compared to the OB shape despite having a smaller adhesion area) is somewhat vague and should be developed further. For example, currently there is only a video showing the egg-like shape and a second video comparing the LE shape to a spherical shape - it would be helpful to have a further video comparing the LE and OB shapes and the different RBC deformations they cause. Moreover, the explanation of the energy/mobility of each shape in terms of curvatures (e.g. the OB shape having "lower curvature at its flat side") could be made more precise. I would expect that the adhesion area depends on how close the principal curvatures of the merozoite surface are to being equal and opposite to the natural curvatures of the RBC, since this determines the bending energy associated with wrapping the merozoite and forming short bonds. This would explain why the spherical shape is most mobile (its principal curvatures are constant so there is no region where at least one is relatively small), and why alignment is most likely to occur in the dimple of the RBC where the membrane is naturally concave-outward. For a given adhesion area, the deformation energy should depend on the difference in principal curvatures in the contact region, with a larger difference causing more bending of the RBC membrane. This difference is larger for the LE shape, since one principal curvature remains large at each point on the surface, compared to the OB shape whose principal curvatures are both small on the 'flat side' where contact is most likely to occur.

We have expanded the discussion of these results to make it clearer. Furthermore, a new video was generated to visually see differences between different shapes.

1. Lines 175-176: Given that the ratio A_m/A_s (adhesion area to total surface area) plays a key role in the probability of alignment, the authors should be more quantitative at this point. How does the ratio A_m/A_s (as measured directly, or indirectly e.g. by the area under the probability distributions inside the alignment region in figures 3a,b) scale with the system parameters, such as the adhesion strength and the off-rate k_off? Can it be estimated from an energy balance between RBC bending/stretching and the average adhesion energy?

A change in A_m as a function of adhesion strength can be estimated analytically for a sphere, as was done in Hillringhaus et al. Biophys. J. 117:1202, 2019. For small deformations, there is essentially a competition of bending and adhesion energies, while for strong adhesion, stretching-elasticity contribution becomes important. We have included this theoretical result into the manuscript and discuss its implications.

1. Line 197-198 and Figure 4c: Why is the deformation energy associated with the OB shape much lower than all other shapes for values of k_off/k_on^{long} smaller than 2?

For k_off/k_on^{long} < 2, the magnitude of local curvature has a pronounced effect. For the OB shape, a large adhesion area is formed over the area with very low curvature, and close to the rim where the curvature is large, the adhesion strength may not be strong enough to induce membrane wrapping and deformation. For other shapes, the adhesion strength is large enough to lead to partial wrapping of the parasite by the membrane over moderate curvatures. As a result, the integrated deformation energy is significantly lower for the OB shape than for the other shapes in this regime of adhesion strengths. We have added this clarification to the manuscript.

1. Alignment requires that the distance between the merozoite apex and RBC membrane is very small, and the alignment criteria necessitate examining small changes in the apex angle \theta from \pi. Can the authors comment on how sensitive are the results to the numerical discretisation used?

The discretization length does affect the tightness of the alignment criteria. In our simulations, the average discretization length of the RBC membrane is about l0=0.2 m. The half circumference length of a parasite (corresponding to angle ) is R, which is equal to about 12 l0 for R=0.75 m, such that our angle resolution with respect to the parasite size is 0.1. Therefore, we use 0.2 for the alignment criteria, which is large enough to avoid strong discretization effects. Simulations with a finer discretization are possible, but they become very expensive computationally.

Reviewer #2 (Public Review):

[...] A major strength of the results is that it investigates an unstudied problem in malarial pathogenesis. The results pertaining to adhesion strength may be informative for preventing the organism from invading red blood cells. A primary weakness is that there is too little detail provided in the methods for this reviewer to adequate assess the computational method. Secondly, the results are somewhat inconclusive. While the egg-shape performs better than certain other shapes, there is no clear final understanding why this shape is preferred over the spherical or short ellipsoidal shapes. However, this possibly provides some clues as to why a certain malarial species does actively adopt a spherical shape during red blood cell binding and invasion.

We thank the reviewer for a positive judgment of our manuscript. We have significantly expanded the methods section, so it should contain now all necessary simulation details. We agree with the reviewer that the conclusions about shape advantages/disadvantages are equivocal to some extent, but this is exactly what our simulation data show. However, from our data it is clear that the two shapes (i.e. egg-like and sphere) stand out, and they also correspond to real examples of merozoite shapes. As the reviewer points out, we do discuss some clues for the importance of parasite shape in the alignment process.

Overall, the authors achieved their aims by quantitatively assessing the affect of parasite shape and adhesion strength on cell alignment, which is a proxy for invasion. The discussion at the end of the manuscript provides an accurate evaluation of the results that puts them into the context of invasion. While to some extent the results presented here are inconclusive, I do think that this paper achieves an important goal for its field. This is an understudied area pertinent to a major disease. This manuscript has the potential to bring questions of the biophysics of malarial invasion out to the broader community, specifically introducing these questions to biophysicists as well as microbiologists. Furthermore, the results naturally lead to new questions. If the spherical and egg shapes do not confer a strong advantage, then these specific shapes must also play a role in other processes. The authors do suggest some possibilities in the Discussion. That their remain interesting questions is a great spur for future work.

Thank you for emphasizing the importance of multidisciplinarity. We also hope that our work will ignite interest in different communities, as only a multidisciplinary effort can bring us much closer to understanding of parasite alignment and invasion, which clearly include a combination of different mechanical and biochemical processes.

Author Response

Reviewer #1 (Public Review):

[...] Their studies were complemented by transcriptomics and metabolomics and these results support the general conclusions that pollen contains diverse carbon sources which could be used in complementary ways by the different species, which have diverse metabolic capabilities encoded in their genomes.

Reply: We thank the reviewer for the positive assessment of our manuscript.

One of the points that was not completely explored in the paper is what happens in the simplified diet both in vitro and in the Bee gut. They propose in the discussion that in the presence of few and simple carbon sources (sugars) there is competition for nutrients and competitive exclusion is driving loss of some species. But this is not fully addressed in the paper.

Reply: All four species can colonize the gut individually and grow on their own in axenic cultures when providing the simple sugars or the pollen as the only carbon source. When cultured together, all four strains are stably maintained in the presence of pollen. However, three of the four strains steadily decrease in abundance in the simple sugars. These findings are, in our opinion, consistent with the consumer-resource model (more resources = more species that can coexist) and the competitive exclusion principle which predicts that if two or more strains compete for the same nutrients they will not be able to coexist. We have added a corresponding section on line 423-425.

The system they use (with 4 closely related bacterial species) is a simplified system. Therefore, it is not clear if the same general findings will hold in more complex systems. But the results supporting that nutrient complexity (in diet) and metabolic diversity (from the microbial side) are key factors to enable co-existence and persistence of complex microbiota communities are strong and likely generalizable. Although, it is possible that with other communities and other hosts other factors will also come into play. Nonetheless, the current study is important because it sets a good example for how these questions can be addressed to study more complex systems.

Reply: It is true that bacterial coexistence does not necessarily need to be dependent on the nutrient complexity and that in other communities the host, the structure of the environment, or cross-feeding activities may play a more important role. We have discussed this point in the revised manuscript starting on line 423 and on line 427.

Overall, the study described here is complete, and rigorous, except for a few points that still need to be addressed and clarified. Namely, it would be interesting to understand what drives exclusion of some members of the community in the simplified diet.

Reply: See our reply above.

Importantly, the current study opens the door for new studies (including in vitro studies) on the identification of network interactions that are important for Microbe-Microbe interactions that enable co-existence in other systems. Additionally, this study also highlights the importance of identifying the relevant nutritional (and metabolic) conditions for addressing those questions given the importance of the metabolic context in shaping microbe-microbe interactions.

Reply: Thank you. We agree.

Reviewer #2 (Public Review):

[...] Strengths: The use of community profiling, transcriptomics, and metabolomics adds depth, as does the comparison of defined culture conditions to the host environment. The main conclusions drawn by the authors is that the presence of pollen is necessary for gut species to coexist, and that the different species, although closely related, respond in distinct ways to nutrients in pollen and consume different profiles of nutrients from pollen.

Reply: We thank the reviewer for the positive feedback and the many valuable comments which helped us to further strengthen our manuscript.

Weaknesses: The main weakness I see with this work is the choice of in vitro comparison conditions. The strains are cultured either on pollen or sugar water, whereas in vivo bees are fed a diet of pollen and sugar water, or only sugar water. A direct comparison is possible between the strains grown on sugar water in vitro or in vivo, but I think that in several places, the authors may have to reconsider or modify their interpretations comparing in vitro culture on pollen/pollen extract with the in vivo growth of the community on pollen and sugar water. Because there is sugar in the bee diet, differences in assembly dynamics, transcription, or metabolite consumption between pollen-containing culture conditions and the bee gut might stem from the dietary intake of sugar, or from an aspect of the host environment.

Reply: We agree with the reviewer that the nutrient conditions that were used in vitro and in vivo are not identical and may have impacted the relative abundance of some of the community members, the transcriptional profiles, or the metabolite changes. Nevertheless, we believe that our experimental design is valid to test the main hypothesis of our study, i.e. a complex, pollen-based diet facilitates coexistence, while simple sugars lead to the dominance of a single strain independent of the environment (culture tube versus host). An important point to consider here is that bees will pre-digest the consumed pollen, and partially absorb dietary nutrients such as amino acids, glucose, and fructose, before they reach the bacteria in the hindgut. Consequently, the in vivo and in vitro conditions will never be the same even if we would have used the identical nutrients in our treatments. Also, pollen by itself contains glucose, fructose, and sucrose. So, although we have not added glucose to the in vitro pollen condition, this simple sugar was present in the corresponding condition. We have added a corresponding section in the discussion on line 402-422. This said, while we cannot recapitulate the exact same nutritional conditions in vitro, we still think that our main conclusions hold which is that we can recapitulate the pollen-dependent coexistence found in vivo.

Reviewer #3 (Public Review):

[...] Overall, the paper is strong and the arguments and conclusions put forth are well supported by the data. I only have a few suggestions:

Reply: We thank the reviewer for the positive evaluation of our manuscript.

1) The study focuses on one strain each of the 4 Firm-5 species; however, there is diversity within each species. This is only briefly mentioned in the paper at the very end, and I think the authors should address this a bit more directly. In particular, they have previously generated a large amount of genomic data from some of these other strains, so it is likely possible to infer or speculate, based on this data, whether they expect different strains within each species to utilize similar nutrients. Also, I'm wondering if the authors can comment on how their findings could extend to the related bumble bee gut microbiome. Such a discussion would help enhance the applicability and importance of this study.

Reply: We agree that the large amount of strain-level diversity within a given species is an important point to consider. However, we would like to not expand this point much further as it would require a relatively complex genomic analysis. Also, considering that many of the strain-specific transcriptional changes are in genes shared with the other species, I am not sure how much such an analysis would reveal. Anyways, we plan to compare the coexistence between strains from the same versus another lineages in a follow-up study.

As for the bumble bees, we currently do not know how many strains or species of Lactobacillus Firm5 can coexist in bumble bees. Therefore, we feel that a discussion extending to bumble bees would be too speculative. However, we included a sentence in the discussion which states that since pollen facilitates coexistence, it follows that dietary differences are likely to influence the diversity of Lactobacillus Firm5 and give the example of the Asian honey bee, which seems to only harbor one species of this phylotype. See line 479-488.

2) It is interesting that different species ended up dominating in the in vivo vs. in vitro simple sugar-based communities. What do the authors think may be behind this difference?

Reply: This is indeed an interesting point. We have not used the same sugars in vivo (sucrose) and in vitro (glucose). Moreover, the nutritional and physicochemical conditions in the hindgut are likely different from those found in a culture tube. We have mentioned that these are potential reasons for the observed differences in the relative abundance of different community members between in vivo and in vitro conditions on line 402-422 of the manuscript.

3) Since the observed coexistence of these gut microbes is largely due to nutritional niche partitioning, it would be helpful if the authors can comment on the natural variation of key pollen derived metabolites, and if/how we could expect ecological variation in the bee microbiome due to plant pollen availability based on biogeography and seasonality.

Reply: We agree and have included a corresponding sentence in the discussion on line 479. See also our reply to point 1.

4) The supplementary information is nicely documented and accessible, but I think it would be even more useful if genome-wide data for the RNA-seq results, not just for select genes, are made available. Furthermore, I suggest including descriptive titles and labels within the supplementary Excel files, as there are many separate sheets and it is not always clear what each one shows.

Reply: This has been included in the revised manuscript.

Identity, Mexican;

Time in the US, Immigration Status, Being secretive, Hiding/lying, In the shadows, Living undocumented; Reflections, The United States, US government and immigration; Feelings, Frustration; Time in the US, Jobs/employment/work, Documents, Driver's license, Social security card/ID

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Reply to the reviewers

We thank the reviewers for their thoughtful comments. We were delighted the reviewers found our results “compelling”, “striking”, “well presented”, “implications exciting”, “excellent results! really nice!”, “this microscopy is beautiful!” and “translational-dependence (of mRNA localization) in a transcript-specific way without perturbing translation globally”, which is a “complete surprise, and opens exciting doors to investigate how translation leads to mRNA organization and its connection to **tissue development” and “may represent a new pathway of mRNA transport”.

We also appreciated the comments regarding the “wide appeal”, “broad readership of readers”, and “broad interest” the reviewers gave to our manuscript regarding its impact, and also the comments of “well-written (and) well-cited”.

We can address all the concerns raised by the reviewers. In addition to textual changes, we will add the following to the Results section:

1. Additional quantitation of smFISH beyond Figure 2;
2. Addition of a negative (uniformly distributed) mRNA control and its quantitation;
3. Western blots for our ΔATG lines to determine what and how much protein is made.
4. Unbiased nuclear masking. Our specific responses are shown below, in blue.

Reviewer #1

**Major comments**

Fig. 1: Main and supplementary figures present smFISH signals for eight localized mRNAs, while in the results section authors describe that they analyzed twenty-five transcripts. Authors should explain the choice of transcripts presented in the paper.

We will include a panel in Fig. S1E to show every mRNA that we tested, and we will edit Table 1 to describe the observed subcellular localization.

We will edit the text, adding a few sentences to clarify, along the lines of: “O**ur survey revealed mRNAs with varying degrees of localization within epithelia that we divided into three classes: CeAJ/membrane localized, perinuclearly localized, and unlocalized (Fig. 1 and S1 and Table 1).” and “The rest of our tested mRNAs did not possess any evident subcellular localization at any of the analyzed embryonic stages/tissues and were not further investigated (Fig. S1E and Table 1).”

Moreover, smFISH signal of different localized mRNAs in epidermal cells was visualized at different stages (bean, comma or late comma), and authors did not comment what was the reason of such conditions. This may make transcripts localization results difficult to interpret, as further analysis showed that mRNA localization varied in a stage-specific manner.

We have clarified this point now in Figure legend 1: “Specific embryonic stages were selected for each transcript based on the highest degree of mRNA localization they exhibited.”

Did author used smFISH probes designed against endogenous mRNAs for all tested transcripts?

We did not. We clarify this point now in Materials and methods: “All probes were designed against the endogenous mRNA sequences except dlg-1 (some constructs), pkc-3, hmp-2, spc-1, let-805, and vab-10a, whose mRNA were detected with gfp probes in their corresponding transgenic lines (Table S2). An exception to this is Fig. S1A where we used probes against the endogenous dlg-1 mRNA.”.

Marking dlg-1 mRNA as dlg-1-gfp suggests that smFISH probe was specific for gfp transcript. Is it true? If yes, authors should compare localization of wild-type endogenous dlg-1 mRNA with that of the transcript encoding a fusion protein, to confirm that fusion does not affect mRNA localization.

Yes, in Fig. 1C we show smFISH for GFP (i.e., the tagged dlg-1 only). In Fig. S1A, we show smFISH against endogenous dlg-1. Tagged and endogenous dlg-1 mRNAs are both localized. We clarified this point in the main text: “Five of these transcripts were enriched at specific loci at or near the cell membrane: laterally and at the CeAJ for dlg-1 (Fig. 1C for endogenous/GFP CRISPR-tagged dlg-1::gfp mRNA and S1A for endogenous/non-tagged dlg-1 mRNA), (…)”. And in the Supplemental figure legend (Fig. S1A): “Endogenous/non-tagged dlg-1 mRNA shows CeAJ/membrane localization like its endogenous/GFP CRISPR-tagged counterpart.”

Fig. 2B: Authors conclude that at later stages of pharyngeal morphogenesis mRNA enrichment at the CeAJ decreased gradually in comparison to comma stage. Data do not show statistically significant decrease in ratio of localized mRNAs - for dlg-1: bean: 0.39{plus minus}0.09, comma: 0.29{plus minus}0.08, 1.5-fold: 0.30{plus minus}0.09; for ajm-1: bean: 0.36{plus minus}0.08, comma: 0.30{plus minus}0.05, 1.5-fold: 0.28{plus minus}0.09.

t-test (one-tailed) analysis revealed a significant difference between bean and comma stages for both dlg-1 and ajm-1 mRNAs. Statistical analysis and data will be provided.

Fig. 4: What was the difference between the first and the second __ΔATG transgenic line? Authors should analyze the size of the truncated DLG-1 protein that is expressed from the second Δ__ATG transgenic line that localizes to CeAJ. Knowing alternative ATGs and protein size may suggest domain composition of the truncated protein. This will allow to confront truncated protein localization with the results from.

We will perform a Western blot to determine the size and levels of proteins produced.

Fig. 5. Moreover, to prove that the localization of dlg-1 mRNA at the CeAJ is translation-dependent, additional experiment should be performed where transcripts localization will be analyzed in embryos treated with translation inhibitors such as cycloheximide (translation elongation inhibitor) and puromycin (that induces premature termination).

We believe this comment might refer to Fig. 4. If this is the case: drugs like cycloheximide and puromycin affect the translation of the whole transcriptome, whereas with our ΔATG experiment, we aimed to target the translation of one specific transcript and avoid secondary effects. Nevertheless, we understand Reviewer #1’s concern and will include a second experiment. In our hands, cycloheximide and puromycin have never worked in older embryos (it’s hard to get past the eggshell and into the embryo). Instead, we will use stress conditions, which induce a “ribosome drop-off” (Spriggs et al., 2010). Heat stress has been shown to decrease polysome occupancy (Arnold et al., 2014). We, therefore, have used heat-shock at 33°C for 30’, and the results are now shown in Fig. S4. These show the loss of RNA localization upon heat shock.

**Minor comments**

In the introduction section authors should emphasize the main goal and scientific significance of the paper.

We added this sentence to state the significance before summarizing the results: “To investigate the impact of mRNA localization during embryonic development, we conducted a single molecule fluorescence in situ hybridization (smFISH)-based survey (…)” and “Our data demonstrate that the dlg-1 UTRs are dispensable, whereas translation is required for localization, therefore providing an example of a translation-dependent mechanism for mRNA delivery in C. elegans.” To state the significance.

Fig 1A: It's hard to distinguish different colors on the schematics. Schematics presents intermediate filaments that are not included in the Table 1.

We modified Table 1 based on this and other reviewers’ comments.

Fig. 1C: dlg-1 transcript is marked as dlg-1-gfp on the left panel and dlg-1 on the right panel.

Corrected.

Fig. 2B: Axis labels and titles are not visible, larger font size should be used.

We will modify the graph (following Reviewer #2’s suggestion) and axes label and title sizes will be taken into account.

Fig. 5C: Enlarge the font size.

Will do.

Fig. S2: Embryonic stages should be marked on the figure for easier interpretation.

Added.

Reviewer #2

Major comments

Figure 2 requires a negative (or uniformly distributed) mRNA control for comparison. Figure 2C should be quantified. The plot quality should be improved, and appropriate statistical tests should be employed to strengthen the claimed findings.

We will add a negative control (jac-1 mRNA), and quantify Fig. 2C as well. Plots will be changed accordingly to the suggestion.

Most claims of perinuclear mRNA localization are difficult to see and not well supported visually or statistically. The usage of DAPI markers, membrane markers, 3D rendering, or a quantified metric would bolster this claim. Also, sax-7 is claimed to be perinuclear and elsewhere claimed to be uniform then used as a uniform control. Please explain or resolve these discrepancies more clearly.__

Regarding perinuclear mRNAs:

We are not trying to make a big statement out of these data as perinuclear (ER) localization of mRNAs coding for transmembrane/secreted proteins is well known. The aim of our study was to describe transcript localized at or in the proximity of the junction. However, we thought it was worth mentioning these examples of perinuclearly localized mRNAs (hmr-1, sax-7, and eat-20) for two reasons: scientific correctness – show accessory results that might be interesting for other scientists – and use as positive controls for our smFISH survey – these mRNAs were expected to localize perinuclearly for the reasons mentioned above. We will rewrite the text to make these points clearer.

Regarding sax-7 mRNA:

sax-7 mRNA localizes perinuclearly in sporadic instances (Fig S1C), but it is predominantly scattered throughout the cytoplasm (i.e., unlocalized). It presumably localizes perinuclearly in a translation-dependent manner as sax-7 codes for a transmembrane protein that would be targeted to the ER. We have described this ER-type of localization in the introduction and reiterated it partially in the first paragraph of the results. sax-7 UTRs are therefore presumably not responsible for subcellular localization, which would instead depend on a signal sequence. We will better clarify this point in the main text.

The major concern about the paper is the data display and interpretation of Figure 5C. I'm not comfortable with the approach the authors took of blurring out the nucleus. A more faithful practice would be to use an automated mask over DAPI staining or to quantify the entirety of the cell. If the entirety of the cell were quantified, one could still focus analysis on specific regions of relevance. The interpretations distinguishing membrane versus cytoplasmic localization (or mislocalization) are hard to differentiate in these images especially since they are lacking a membrane marker. The ability to make these distinctions forms the basis of Tocchini et al's two pathways of dlg-1 mRNA localization. These interpretations also heavily rely on how the image was processed through the different Z-stacks, and it's not clear to me how that was done. For example, the diffusion of mRNA in figure 5F and 5I are indistinguishable to my eye but are claimed to be different.

In the images, the nuclei have been blurred to allow the reader to focus on the cytoplasmic signal and not on the nuclear (transcriptional) signal as it is not meaningful for this study. In the quantitation, the nuclear signal has been unbiasedly and specifically removed from the analysis by cropping out the DNA signal from the other channels. The frontal plane views of the seam cells in Fig. 5 show maximum intensity projections (MIPs) of 3 Z-stacks (0.54 µm total) that each contain nuclei and, therefore, the transcriptional signal (schematics in Fig. 5B). We will clarify these points in the text.

Regarding cytoplasmic versus membrane-associated mRNAs, although we did not have a membrane marker, we relied on the brightness of the DLG-1::GFP signal to identify the cell borders (i.e., membranes) after over-exposure. This approach allowed us to discern apicobasal and apical sides for the intensity profile analyses. We will clarify this point as well in the text and, in parallel, we will try a different approach using transverse sections on top views to clarify our data.

To my eye, it seems that Figure 5 could be more faithfully interpreted to state that DGL-1 protein localization depends on the L27-SH3 domains. The Huk/Guk domains are dispensable for DLG-1 protein localization; however, through other studies, we know they are important for viability. In contrast, dlg-1 mRNA localization requires all domains of the protein (L27-Guk). It is exceptionally interesting to find a mutant condition in which the mRNA and protein localizations are uncoupled. It would be very interesting to explore in the discussion or by other means what the purpose of localized translation may be. Because, in this instance, proper mRNA localization and protein function are closely associated, it may suggest that DLG-1 needs to be translated locally to function properly.

We will rewrite the Results and Discussion to clarify our model. We agree that L27 and SH3 domains are critical, but we also detected effects of the HooK/GuK domains. We have refined our model to describe functions of the N and C termini for membrane or junctional localization.

The manuscript requires an improve materials & methods description of the quantification __procedures and statistics employed.__

We will add these points.

Minor & Major comments together - text

Summary statement: Is "adherent junction" supposed to be "adherens junction?"

Corrected.

Abstract: Sentence 1, I think they should add a caveat word to this sentence. Something like "...phenomenon that can facilitate sub-cellular protein targeting." In most instances this isn't very well characterized or known.

Corrected.

In the first paragraph, it might be good to mention that Moor et al also showed that mRNA localize to different regions to alter their level of translation (to concentrate them in high ribosome dense regions of the cell).

Added as follows: “For example, a global analysis of localized mRNAs in murine intestinal epithelia found that 30% of highly expressed transcripts were polarized and that their localization coincided with highly abundant regions in ribosomes **(Moor, 2017).”

There are some new studies of translation-dependent mRNA localization - that might be good to highlight - Li et al., Cell Reports (PMID: 33951426) 2021; Sepulveda et al., 2018 (PCM), Hirashima et al., 2018; Safieddine, et al 2021. Also, Hughes and Simmonds, 2019 reviews membrane associated mRNA localization in Drosophila. And a new review by Das et al (Nat Rev MCB) 2021 is also nice.

We will add them to the text.

Parker et al. did not show that the 3'UTR was dispensable for mRNA localization. They showed the 3'UTR was sufficient for mRNA localization.

Quoting from the paper Parker et al.: “3′UTRs of erm-1 and imb-2 were not sufficient to drive mRNA subcellular localization. Endogenous erm-1 and imb-2 mRNAs localize to the cell or nuclear peripheries, respectively, but mNeonGreen mRNA appended with erm-1 or imb-2 3′UTRs failed to recapitulate those patterns (Fig. 4A-D).” We will make this point clearer in the rewritten text.

In the second paragraph, the sentence about bean stages is missing one closing parenthesis.

Corrected.

Last paragraph: FISH is fluorescence, not fluorescent.

Corrected.

Both "subcellular" and "sub-cellular" are used.

Corrected.

Minor comments – Figures

Figure 1

o Figure 1A is confusing. It's not totally clear what the rectangles and circles signify. There are many acronyms within the figure. Which of the cell types depicted in the figure are shown here? For example, for the dorsal cells, which is the apical v. basal side?

We tried to simplify the cartoon for a general C. elegans epithelial cell. We followed schematics already shown in previous publications to maintain consistency. Acronyms and color-codes are listed in the corresponding figure legend and have been better clarified.

o Some of the colors are difficult to distinguish, particularly when printed out or for red/green colorblind readers. Is erm-1 meant to be a cytoskeletal associated or a basolateral polarity factor?

We understand the issue, but unfortunately, with 8 classes of factors, shades of gray might not solve the problem. We tried to circumvent the red-green issue changing red to dark grey. Furthermore, we added details about shapes to the figure legends. We will work to make the colors work better.

ERM-1 is a cytoskeletal-associated factor.

o The nomenclature for dlg-1 is inconsistent within "C".

Corrected.

o Please specify what the "cr" is in "cr.dlg-1:-gfp" in the legend.

Added.

Figure 2

o Can Figure 2C be quantified in a similar manner to 2A/2B?

Currently our script cannot do that, but we will try to optimize it to be able to quantify this type of images.

o 2B - please jitter the dots to better visualize them when they land on top of one another

Yes, we will.

o Please include a negative control example, a transcript that is not peripherally localized for comparison.

Yes, we will.

o There is no place in the text of the document where Fig 2C is referenced

Corrected (it was wrongly referred to as “2B”).

o I can't see any discernable ajm-1 localization in Fig 2A.

We added some arrowheads to point at specific examples and increased the intensities of the corresponding smFISH signal for better visualization.

o I can't see any dlg-1 pharyngeal localization in Fig2C.

We added some arrowheads to point at specific examples and increased the intensities of the corresponding smFISH signal for better visualization.

o More details on how the quantification was performed would be welcome. Particularly, in 2B, what is the distance from the membrane in which transcripts were called as membrane-associated? What statistics were used to test differences between groups?

We will add a full description of the script used as well as the statistic details.

Figure 3

o Totally optional but might be nice: can you make a better attempt to approximate the scale of the cartoon depiction?

The UTRs, especially the 5’ one, are much smaller than the dlg-1 gene sequence. A proper scaling of the cartoon to the actual sequences, would draw the attention away from the main subjects of this figure, the UTRs. Nevertheless, we made sure it is clear in the corresponding figure legend that the cartoon is not in scale: “The schematics are not in scale with the actual size of the corresponding sequences. UTR lengths: dlg-1 5’UTR: 61 nucleotides; sax-7 5’UTR: 63 nucleotides; dlg-1 3’UTR: 815 nucleotides; unc-54 3’UTR: 280 nucleotides.”

o The GFP as an asterisk illustration may be confusing for some readers. Could you add another rectangular box to depict the gfp coding sequence?

Corrected.

o This microscopy is beautiful!

Thanks Reviewer #2!

o Were introns removed? Is the endogenous copy still present?

All the transgenes were analyzed in a wild-type background, therefore, yes, the endogenous copy was still present. All the transgenes possessed introns. We will change the corresponding text as follows: “To test whether the localization of one of the identified localized mRNAs, dlg-1, relied on zip codes, we generated extrachromosomal transgenic lines carrying a dlg-1 gene whose sequence was fused to an in-frame GFP and to exogenous UTRs.”. In the figure “dlg-1 ORF” has been replaced with “dlg-1 gene”.

o The wording in the legend "CRISPR or transgenic" may be confusing as Cas9 genome editing is still a form of transgenesis.

We added “extrachromosomal” to clarify the nature of the mRNA.

o The authors state that the 5'-3'UTR construct produces perinuclear dlg-1 transcripts but in the absence of DAPI imaging, it's not clear that this is the case.

We could not find such a statement, but we tried to clarify the localization of these mRNAs in the text: “The mRNA localization patterns of the two UTR reporters were compared to the localization of dlg-1 transcripts from the CRISPR line (“wild-type”, Fig. 3A; Heppert et al., 2018), described in Fig. 2. Both reporter strains showed enrichment at the CeAJ and localization dynamics of their transcripts that were comparable to the wild-type cr.dlg-1 (Fig. 3B). These results indicate that the UTR sequences of dlg-1** mRNA are not required for its localization.”

o Which probe set was used? The gfp probe?

Yes, please see the main text: “Given that the transgenic constructs were expressed in a wild-type background, smFISH experiments were conducted with probes against GFP RNA sequences to focus on the transgenic dlg-1::GFP mRNAs (cr.dlg-1 and tg.dlg-1).”

o Here, sax-7 is used as a uniform control, but sax-7 is claimed in Fig S1B-D as being perinuclear. This is a bit confusing.

sax-7 mRNA localizes perinuclearly in sporadic instances (Fig S1C), but it is predominantly scattered throughout the cytoplasm (i.e., unlocalized). It presumably localizes perinuclearly in a translation-dependent manner as sax-7 codes for a transmembrane protein that would be targeted to the ER. We have described this ER-type of localization in the introduction and reiterated it partially in the first paragraph of the results. sax-7 UTRs are therefore presumably not responsible for any subcellular localization, which would instead rely on a signal sequence. We will better clarify this point in the main text.

Figure 4

o Excellent results! Really nice!

Thanks Reviewer #2!

o Fig 4A. The GFP depicted as a circle is strange.

We changed it into a rectangle.

o Fig 4A. Can you include the gene/protein name for easy skimming?

Added.

o Fig 4B. the color here is too faint and it is unclear what is being depicted. Overall, this part of the figure could be improved.

We are optimizing the coloring and simplifying the schematics.

o Were the introns removed?

No, the introns were maintained in this and in all our transgenic lines. We described our transgenic lines in the materials and methods section (now with more detail). What we depict in the scheme (Fig. 4A) is the mature RNA (now specified in the figure), therefore no introns depicted. We will also specify this in the main text.

Figure 5

o Fig 5A. can you add the gene/protein name

Added.

o Fig 5B. Can you make the example apicobasal (non-apical) mRNA more distinctive? If it had its own peak in the lower trace, the reader would more clearly understand that this mRNA will be excluded from apical measurements whereas it will be included in apicobasal measurements.

We actually wanted to show this specific example: a cytoplasmic mRNA and a junctional mRNA may seem close from the apicobasal analysis (partially overlapping peaks that Reviewer #2 mentioned). With the apical analysis, instead, we can show that these mRNAs are actually not close, and they belong to two different compartments (cytoplasm and junction). We would therefore like to keep the current scheme, while better clarifying this point in the corresponding figure legend.

o D' - I' The grey font is too light.

Noted. We will change it.

o D' - I' The inconsistent y-axis scaling makes it difficult to compare across these samples. Can you set them to the same maximum number?

The values are indeed quite different. We tried to use the same scale, but this would make some of the data unappreciable. The idea was to evaluate, within each graph, how mRNA and protein are localized relative to the junctional marker. We will make this clearer in the text.

o D' - I' The x-axis labels are formatted incorrectly

Corrected.

o The practice of masking out the nucleus appears to remove potentially important mRNAs that are not nuclear localized. This could really impact the findings and interpretation. Instead, consider an automated DAPI mask.

The masking on the images is not the same used for the analysis: in the images, a shaded circle has been drawn on the DNA channel and moved onto its corresponding location in the other channels or merges. For the analysis, the DNA signal has been specifically removed in the channel with the smFISH signal. Given that the analysis has been performed on maximum intensity projections of 3 Z-stacks, we believe we did not remove any non-nuclear mRNA. We will clarify this point in Materials and methods.

o I can't see what the authors are calling membrane diffuse versus cytoplasmic. This is making it hard for me to see their "two step" pathway to localization.

We will add in Fig. 5B-C an example of a membrane localized mRNA. Furthermore, we will add transverse sections of membrane and cytoplasm to make the date clearer to the reader.

o Can more details of the quantification be included? How were Z-sections selected, chosen for inclusion? Which Z-sections and how many were selected?

We will add the details to Materials and methods.

o Also, why do these measurements focus on what I think are the seam cells when Lockwood et al., 2008 show the entire epithelium that is much easier to see?

We are focusing on the seam cells at the bean stage as these are the cells and the embryonic stage where we see the highest localization of dlg-1 mRNA in the wild-type.

o Please name these constructs to correlate the text more explicitly to the figures.

Added.

o How many embryos were analyzed for each trace? How many embryos showed consistent patterns?

We will add the details of the analysis to Materials and methods.

o Why were these cells used for study here? Lockwood et al., 2008 use a larger field of epithelial cells for visualization.

As stated before: we are focusing on the seam cells at the bean stage as these are the cells and the embryonic stage where we see the highest localization of dlg-1 mRNA in the wild-type.

Figure 6

There are major discrepancies between what this figure is depicting graphically and what is described in the text. Again, I'm not comfortable making the "two step" claims this figure purports given the data shared in Figure 5.

We are planning to re-write the last part of the results to better clarify our two-step model. A two-step model had been previously suggested in McMahon et al., 2001, where they could show that DLG-1 and AJM-1 (referred to in that publication as JAM-1) are initially localized laterally and only later in development are then enriched apically. Our data agree with McMahon very well, so we used the earlier study as a start. We will cite and explain this paper in greater depth during the rewriting.

**Minor comments - Tables & Supplemental Figures**

Table 1

I think this table could be improved to more clearly illustrate which mRNAs were tested and what their mRNA localization patterns were (for example, gene name identifiers included, etc). Could the information that is depicted by gray shading instead be added as its own column? For example, have a column for "Observed mRNA localization"

We modified Table 1 based on these and the other reviewers’ comments.

Can you add distinct column names for the two columns that are labeled as "protein localization - group"

We modified Table 1 based on these and the other reviewers’ comments.

Can you also add which of these components are part of ASI v. ASII (as described in the introduction?)

A new table has been added with the factors belonging to the two adhesion systems (same color code as in Table 1).

Supplemental Figure 1

It is hard to see that some of these spots are perinuclear. More information (membrane marker, 3D rendering, improved metrics) is required to support this claim.

We are not trying to make a big statement out of these data as perinuclear localization for mRNAs coding for transmembrane/secreted proteins is well known. The aim of our study was to describe transcript localized at or in the proximity of the junction. We thought it was worth mentioning these examples of perinuclearly localized mRNAs (hmr-1, sax-7, and eat-20) for two reasons: scientific correctness – show accessory results that might be interesting for other scientists – and use as positive controls for our smFISH survey – these mRNAs were expected to have a somewhat perinuclear localization for the reasons mentioned above.

What do these images look like over the entire embryo, not just in the zoomed in section?

We added a column with the zoom-out embryos.

sax-7 localization in S4 looks similar but a different localization claim is made.

sax-7 mRNA can localize perinuclearly in sporadic instances (Fig S1C), but is predominantly scattered throughout the cytoplasm (i.e., unlocalized). It presumably localizes perinuclearly in a translation-dependent manner as sax-7 codes for a transmembrane protein that would be targeted to the ER. We have described this ER-type of localization in the introduction and reiterated it partially in the first paragraph of the results. sax-7 UTRs are therefore presumably not responsible for any subcellular localization, which would instead rely on a signal sequence. We will better clarify this point in the main text.

Supplemental Figure 2

Before adherens junctions even exist dlg-1 go to the membrane - this is really neat!

Thanks Reviewer #2!

Supplemental Figure 3

Technical question: If either 5 or 3 stack images are used, how does this work? Do they have different z-spacings? Or do they do 5-stack images represent a wider Z-space?

This is the sentence under question: “Maximum intensity projections of 5 (1.08 µm) (A) and 3 (0.54 µm) (B) Z-stacks”. The space between each Z-stack image is constant in all our imaging and its value is 270 nm. When we consider 5 planes, the distance from the 1st to the 5th is 4 x 270 nm = 1.08 µm, whereas for 3 planes will be 2 x 270 nm = 0.54 µm.

Supplemental Figure 4

Line #2 retains translation and keeps mRNA localization.

Totally optional, but consider showing both lines in the main figure to illustrate the two possibilities.

Noted.

Materials and methods - how did they created the ATG mutations? Is it an array? - why does one translate, and one doesn't?

We will clarify this point in Materials and methods: “dlg-1 deletion constructs ΔATG (SM2664 and SM2663) and ΔL27-PDZs (SM2641) were generated by overlap extension PCR using pML902 as a template.”.

We will perform a Western blot to clarify Reviewer #2’s last point. Currently we do not know what peptide is translated, but the comparison with our full-length control will probably shed some light on the issue.

Reviewer #3

Major comments

The smFISH results are striking and implications exciting. The conclusions made from the smFISH results reported in all Figures will be strengthened considerably by quantifying the mRNA localized to the defined specific subcellular regions. At the very least, localization to the cytoplasm versus the plasma membrane should be determined as performed in Figure 2B, but quantifying finer localization will enhance the conclusions made about regional localization (e.g. CeAJ versus plasma membrane mRNA localization in Figure 5). Inclusion of a non-localizing control in Figures 1-4 will enable statistical comparisons between mRNA localizing and non-localizing groups.

We will add more quantitation, statistics, and negative controls.

The script used for smFISH quantitation should be included in the methods or published in an accessible forum (Github, etc). Criteria for mRNA "dot" calling should be defined in the methods. All raw smFISH counts should also be reported.

We will add the full description of the script in Materials and methods, and we will provide the raw data in an additional supplementary table.

Figure 2: What is the localizing ratio of a non-localizing control mRNA (e.g. jac-1)? Including an unlocalized control with quantitation would strengthen the localization arguments presented.

Yes, we will add quantitation for an unlocalized mRNA.

Figure 5: Quantifying colocalization of mRNA and protein (+/- AJM-1) will strengthen the arguments made about mRNA/protein localization.

Yes, we will quantify Fig. S5 to have a full picture of the cells (the images in Fig. 5 represent only a portion of the cell).

Discussion of the CeAJ mRNA localization mechanism is warranted. Do the authors speculate that the newly translated protein drives localization during translation, similar in concept to SRP-mediated localization to the ER, or ribosome association is a trigger to permit a secondary factor to drive mRNA localization, or another model?

Unfortunately, this is hard to say at the moment as we do not have any data regarding where translation actually occurs. We will add a conjecture to the Discussion.

Minor comments

Please complete the following sentence: "We identified transcripts enriched at the CeAJ in a stage- and cell type-specific."

Corrected.

It would be helpful to provide reference(s) for the protein localization summary in Table 1.

Added.

Figure 2B: Did dlg-1 and ajm-1 localize at similar ratios? Appropriate statistics comparing the different ratios may be informative.

We will modify the graph (following Reviewer #2’s suggestion) and add the requested details.

Figure 2: In the paragraph that begins, "Morphogenesis of the digestive track," the text should refer to Figure 2C? If not, the text requires further clarification.

Corrected.

Figure 2: Reporting the smFISH localizing ratios of 8E and 16E will be informative.

We will add the information.

Please include citations when summarizing the nonsense-mediated decay NMD mechanism and AJM-1 identifying the CeAJ.

Added.

The sentence, "Embryos from our second __Δ__ATG transgenic line displayed a little GFP protein and some dlg-1::gfp mRNA," should refer to Figure S4.

Added.

An immunoblot of this reporter versus wild type may be informative regarding the approximate position of putative alternative start codon.

We will perform a Western blot to verify the size of the protein product produced.

Figure 5: N's and repetitions performed should be included for localization experiments.

Yes, we will add them here and in all the other quantifications we will add to the manuscript.

Please clarify that the "the mechanism of UTR-independent targeting is unknown in any species" refers to dlg-1 mRNA localization.

Added.

"Our findings suggest..." discussion paragraph should reference Figure 6.

Added.

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

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Referee #2

Evidence, reproducibility and clarity

Summary:

Tocchini et al. screened apical junction and cell membrane proteins for mRNA localization. They identified multiple proteins that are translated from localized mRNAs. Of these, dlg-1 (Discs large) mRNA localizes to cell cortices of dorsal epithelial cells, endoderm cells, and epidermal (seam) cells and is dependent on active translation for transport. The manuscript dissects the contributions of different DLG-1 protein domains to mRNA localization.

A major strength of the paper is the way it assesses translational-dependence in a transcript-specific way without perturbing translation globally. The authors cleverly combine mutations in ATG start sites with a knock down of the non-sense mediated decay pathway. This allows Tocchini et al to examine whether dlg-1 mRNA depends on active translation for localization, which it does. The authors observe an interesting finding, that the domains required for protein localization can be separated from those required for mRNA localization. Namely, mRNA localization (but not protein localization) requires C-terminal domains of the protein.

My major points of concern focus on the presentation and interpretation of Figure 5. In this figure, the blocking approach used seems confounding, the observations described by the authors are not visible, the quantification is confusing, and the interpretations seem like an over-reach. The

Major comments:

• Figure 2 requires a negative (or uniformly distributed) mRNA control for comparison. Figure 2C should be quantified. The plot quality should be improved, and appropriate statistical tests should be employed to strengthen the claimed findings.

• Most claims of perinuclear mRNA localization are difficult to see and not well supported visually or statistically. The usage of DAPI markers, membrane markers, 3D rendering, or a quantified metric would bolster this claim. Also, sax-7 is claimed to be perinuclear and elsewhere claimed to be uniform then used as a uniform control. Please explain or resolve these discrepancies more clearly.

• The major concern about the paper is the data display and interpretation of Figure 5C. I'm not comfortable with the approach the authors took of blurring out the nucleus. A more faithful practice would be to use an automated mask over DAPI staining or to quantify the entirety of the cell. If the entirety of the cell were quantified, one could still focus analysis on specific regions of relevance. The interpretations distinguishing membrane versus cytoplasmic localization (or mislocalization) are hard to differentiate in these images especially since they are lacking a membrane marker. The ability to make these distinctions forms the basis of Tocchini et al's two pathways of dlg-1 mRNA localization. These interpretations also heavily rely on how the image was processed through the different Z-stacks, and it's not clear to me how that was done. For example, the diffusion of mRNA in figure 5F and 5I are indistinguishable to my eye but are claimed to be different.

• To my eye, it seems that Figure 5 could be more faithfully interpreted to state that DGL-1 protein localization depends on the L27-SH3 domains. The Huk/Guk domains are dispensable for DLG-1 protein localization; however, through other studies, we know they are important for viability. In contrast, dlg-1 mRNA localization requires all domains of the protein (L27-Guk). It is exceptionally interesting to find a mutant condition in which the mRNA and protein localizations are uncoupled. It would be very interesting to explore in the discussion or by other means what the purpose of localized translation may be. Because, in this instance, proper mRNA localization and protein function are closely associated, it may suggest that DLG-1 needs to be translated locally to function properly.

• The manuscript requires an improve materials & methods description of the quantification procedures and statistics employed.

Minor & Major comments together:

Text

• Summary statement: Is "adherent junction" supposed to be "adherens junction?"

• Abstract: Sentence 1, I think they should add a caveat word to this sentence. Something like "...phenomenon that can facilitate sub-cellular protein targeting." In most instances this isn't very well characterized or known.

• In the first paragraph, it might be good to mention that Moor et al also showed that mRNA localize to different regions to alter their level of translation (to concentrate them in high ribosome dense regions of the cell).

• There are some new studies of translation-dependent mRNA localization - that might be good to highlight - Li et al., Cell Reports (PMID: 33951426) 2021; Sepulveda et al., 2018 (PCM), Hirashima et al., 2018; Safieddine, et al 2021. Also, Hughes and Simmonds, 2019 reviews membrane associated mRNA localization in Drosophila. And a new review by Das et al (Nat Rev MCB) 2021 is also nice.

• Parker et al. did not show that the 3'UTR was dispensable for mRNA localization. They showed the 3'UTR was sufficient for mRNA localization.

• In the second paragraph, the sentence about bean stages is missing one closing parenthesis.

• Last paragraph: FISH is fluorescence, not fluorescent.

• Both "subcellular" and "sub-cellular" are used. Minor comments - Figures

• Figure 1

o Figure 1A is confusing. It's not totally clear what the rectangles and circles signify. There are many acronyms within the figure. Which of the cell types depicted in the figure are shown here? For example, for the dorsal cells, which is the apical v. basal side? o Some of the colors are difficult to distinguish, particularly when printed out or for red/green colorblind readers. Is erm-1 meant to be a cytoskeletal associated or a basolateral polarity factor? o The nomenclature for dlg-1 is inconsistent within "C". o Please specify what the "cr" is in "cr.dlg-1:-gfp" in the legend.

• Figure 2

o Can Figure 2C be quantified in a similar manner to 2A/2B? o 2B - please jitter the dots to better visualize them when they land on top of one another o Please include a negative control example, a transcript that is not peripherally localized for comparison. o There is no place in the text of the document where Fig 2C is referenced o I can't see any discernable ajm-1 localization in Fig 2A. o I can't see any dlg-1 pharangeal localization in Fig2C. o More details on how the quantification was performed would be welcome. Particularly, in 2B, what is the distance from the membrane in which transcripts were called as membrane-associated? What statistics were used to test differences between groups?

• Figure 3

o Totally optional but might be nice: can you make a better attempt to approximate the scale of the cartoon depiction? o The GFP as an asterisk illustration may be confusing for some readers. Could you add another rectangular box to depict the gfp coding sequence? o This microscopy is beautiful! o Were introns removed? Is the endogenous copy still present? o The wording in the legend "CRISPR or transgenic" may be confusing as Cas9 genome editing is still a form of transgenesis. o The authors state that the 5'-3'UTR construct produces perinuclear dlg-1 transcripts but in the absence of DAPI imaging, it's not clear that this is the case. o Which probeset was used? The gfp probe? o Here, sax-7 is used as a uniform control, but sax-7 is claimed in Fig S1B-D as being perinuclear. This is a bit confusing.

• Figure 4

o Excellent results! Really nice! o Fig 4A. The GFP depicted as a circle is strange. o Fig 4A. Can you include the gene/protein name for easy skimming? o Fig 4B. the color here is too faint and it is unclear what is being depicted. Overall, this part of the figure could be improved. o Were the introns removed?

• Figure 5

o Fig 5A. can you add the gene/protein name o Fig 5B. Can you you make the example apicobasal (non-apical) mRNA more distinctive? If it had its own peak in the lower trace, the reader would more clearly understand that this mRNA will be excluded from apical measurements whereas it will be included in apicobasal measurements. o D' - I' The grey font is too light. o D' - I' The inconsistent y-axis scaling makes it difficult to compare across these samples. Can you set them to the same maximum number? o D' - I' The x-axis labels are formatted incorrectly o The practice of masking out the nucleus appears to remove potentially important mRNAs that are not nuclear localized. This could really impact the findings and interpretation. Instead, consider an automated DAPI mask. o I can't see what the authors are calling membrane diffuse versus cytoplasmic. This is making it hard for me to see their "two step" pathway to localization. o "F" looks the same as "I" to me, but the authors claim they represent different patterns and use these differences as the basis for their claim that X. o Can more details of the quantification be included? How were Z-sections selected, chosen for inclusion? Which Z-sections and how many were selected? o Also, why do these measurements focus on what I think are the seam cells when Lockwood et al., 2008 show the entire epithelium that is much easier to see? o Please name these constructs to correlate the text more explicitly to the figures. o How many embryos were analyzed for each trace? How many embryos showed consistent patterns? o Why were these cells used for study here? Lockwood et al., 2008 use a larger field of epithelial cells for visualization.

• Figure 6

o There are major discrepancies between what this figure is depicting graphically and what is described in the text. Again, I'm not comfortable making the "two step" claims this figure purports given the data shared in Figure 5.

Minor comments - Tables & Supplemental Figures

Table 1

• I think this table could be improved to more clearly illustrate which mRNAs were tested and what their mRNA localization patterns were (for example, gene name identifiers included, etc). Could the information that is depicted by gray shading instead be added as its own column? For example, have a column for "Observed mRNA localization"

• Can you add distinct column names for the two columns that are labeled as "protein localization - group"

• Can you also add which of these components are part of ASI v. ASII (as described in the introduction? Supplemental Figure 1

• It is hard to see that some of these spots are perinuclear. More information (membrane marker, 3D rendering, improved metrics) is required to support this claim.

• What do these images look like over the entire embryo, not just in the zoomed in section?

• sax-7 localization in S4 looks similar but a different localization claim is made.

Supplemental Figure 2

• Before adherens junctions even exist dlg-1 go to the membrane - this is really neat! Supplemental Figure 3

• Technical question: If either 5 or 3 stack images are used, how does this work? Do they have different z-spacings? Or do they do 5-stack images represent a wider Z-space?

Supplemental Figure 4

• Line #2 retains translation and keeps mRNA localization.

• Totally optional, but consider showing both lines in the main figure to illustrate the two possibilities.

• Materials and methods - how did they created the ATG mutations? Is it an array? - why does one translate, and one doesn't?

Significance

The authors discover that dlg-1, ajm-1, and hmr-1 mRNAs (among others) are locally translated, and this represents an important conceptual advance in the field as these are well studied proteins and important markers. This is the first study to illustrate translation-dependent mRNA localization in C. elegans, to my knowledge. The mechanisms transporting these mRNAs and their associated translational complexes to the membrane may represent a new pathway of mRNA transport and is therefore significant. The authors identify domains within DLG-1 responsible which is a nice advance. If they are unable to order the events of association as they claim in Figure 5 (and that I dispute), this doesn't detract from the impact of the paper.

Other high-profile studies have recently been published that echo how mRNA localization to membranes can be observed for transcripts that encode membrane-associated proteins (Choaib et al., Dev Cell, 2020; Li et al., Cell Reports, 2021 (PMID: 33951426); and Reviewed in Hughes & Simmonds, Front Gen, 2019). These recent findings underscore the impact of Tocchini et al.'s paper. Similar studies have identified mRNAs localizing through translation dependent mechanisms to a variety of different regions of the cell (Sepulveda et al., eLife, 2018; Hirashima et al., Sci Reports, 2018; Safieddine, et al., Nat Comm, 2021; and reviewed in Ryder et al., JCB 2020). Given the timely nature of these findings and the recent interest in these concepts, a broad readership of readers should be interested in this paper.

My field of expertise is in mRNA localization imaging and quantification. I feel sufficiently qualified to evaluate the manuscript on all its merits.

I think that technology has developed to the point that it is both a tool and an environment. When I use it to write a paper, it is a tool, but it becomes an environment when using it to interact with my classmates. Things like search engines are more ambiguous. They are a tool in how they help me achieve the goal of finding what I am looking for, but they immerse me into the environment created by websites and documents. Things like google street view are, without a doubt, in my mind, a tool and environment. They both help me find the place I was looking for and immerse me into the environment and visually experience it.

Author Response:

Reviewer #1:

By sequencing a large number of SARS-CoV-2 samples in duplicate and to high depth, the authors provide a detailed picture of the mutational processes that shape within-host diversity and go on to generate diversity at the global level.

1) Please add a description of the sequencing methods and how exactly the samples were replicated (two swaps? two RNA extractions? two RT-PCRs?). Have any limiting dilutions been done to quantify the relationship between RNA template input and CT values? Also, the read mapping/assembly pipeline needs to be described.

Limiting dilutions were not performed however the association between Ct and discordance between replicates was explored. Samples with Ct>=24 were found to have considerable discordance between replicates, likely resulting from a low number of input RNA molecules. This is described in the first section of the results and illustrated in Figure 1 - figure supplement 3.

We have now added additional sections to the methods to better describe the sequencing and mapping pipelines.

Sequencing: A single swab was taken for each sample. Two libraries were then generated from two aliquots of each sample with separate reverse transcription (RT), PCR amplification and library preparation steps in order to evaluate the quality and reproducibility of within-host variant calls. The ARTIC protocol v3 was used for library preparation (a full description of the protocol used available at dx.doi.org/10.17504/protocols.io.be3wjgpe).

Alignment and variant calling: Alignment was performed using the ARTIC Illumina nextflow pipeline available from https://github.com/connor-lab/ncov2019-artic-nf...

2) I find the way variants are reported rather unintuitive. Within-host variation is best characterized as minor variants relative to consensus (or first sample consensus when there are multiple samples). Reporting "Major Variants" along with minor variants conflates mutations accumulated prior to infection with diversity that arose within the host. The relative contributions of these two categories to the graphs in Fig 1 would for example be very different if this study was repeated now. Furthermore, it is unclear whether variants at 90% are reversions at 10% or within-host mutations at 90%. I'd suggest calling variants relative to the sample or patient consensus rather than relative to the reference sequence (as is the norm in most within-host sequencing studies of RNA viruses).

We are grateful for this comment and have tried to improve and clarify the reporting of variants to align with previous literature.

Our original classification intended to classify non-reference sites as fixed changes (VAF>95%) or within-host variants (which we called “minor variants”). While we chose 95% as a cutoff (which may have been confusing), the results are analogous with a 99% cutoff, as variants in this set essentially have VAF~100%, and nearly all are expected to have occurred in a previous host. Thus, the previous classification intended to cleanly separate inter-host (fixed) mutations from within-host mutations, to compare their patterns of selection and their mutation spectra.

Following the reviewer’s request, we have modified this classification to better align with other studies of RNA viruses by defining the majority allele at a site as the “consensus”. We note that the results remain largely similar, since the vast majority of within-host variants identified had a low VAFs (<<50%) with the majority/consensus allele most often corresponding to the reference (Wuhan) base.

When considering recurrent mutations we now discuss the number of times variants are observed at each location within a sample. This avoids the issue of how variants are polarised.

3) It is often unclear how numbers reported in the manuscript depend on various thresholds and parameters of the analysis pipeline. On page 2, for example, the median allele frequency will depend critically on the threshold used to call a variant, while the mean will depend on how variation is polarized. Why not report the mean of p(1-p) and show a cumulative histogram of iSNV frequencies on a log-log scale including. I think most of these analyses should be done without strict lower cut-offs or at least be done as a function of a cut-off. In contrast to analyses of cancer and bacteria, the mutation rates of the virus are on the same order of magnitude as errors introduced by RT-PCR and sequencing. Whether biological or technical variation dominates can be assessed straightforwardly, for example by plotting diversity at 1st, 2nd, and 3rd codon position as a function of the frequency threshold. See for example here:

https://academic.oup.com/view-large/figure/134188362/vez007f3.tif [academic.oup.com]

There are more sophisticated ways of doing this, but simpler is better in my mind.

It would be good to explore how estimates of the mean number of mutations per genome (0.72) depend on the cut-offs used. A more robust estimate might be 2\sum_i p_i(1-p_i) (where p_i is the iSNV frequency at site i) as a measure of the expected number of differences between two randomly chosen genomes. Ideally, the results of viral RNA produced of a plasmid would be subtracted from this.

The reviewer raises a number of important points that we have tried to address and clarify.

We think that the quality of our variant calls is supported by several lines of evidence, including: (1) the use of the ShearwaterML calling algorithm, which uses a base-specific overdispersed error model and calls mutations only when read support is statistically above background noise in other genomes, (2) we use two independent replicates from the RT step, (3) we provide several biological signals that cannot be expected to arise from errors, including the fact that the mutation spectra of low VAF iSNVs called in our study recapitulate that of consensus mutations and the clear signal of negative selection acting on iSNVs. We note that this dN/dS analysis is closely related to the suggestion by the reviewer of comparing the frequency of mutations at positions 1/2/3 of a codon.

To address this comment in the manuscript, we have amended the text to include these arguments and we provide two new supplementary figures: (1) a figure of the frequency of mutations at the three codon positions, as requested by the reviewer, and (2) the mutation spectra of low VAF iSNVs, demonstrating the quality of the mutation calls. Similar to the finding in Dyrak et al., (2019), and as expected from the dN/dS ratios, the distribution of variant sites is dominated by variants at the third position and not equally distributed as one might expect if errors were dominating the signal.

We have amended the relevant section of the text to read:

“To reliably detect within-host variants with the ARTIC protocol, we used ShearwaterML, an algorithm designed to detect variants at low allele frequencies. ShearwaterML uses a base-specific overdispersed error model and calls mutations only when read support is statistically above background noise in other genomes \cite{Gerstung2014-av,Martincorena2015-ef} (Methods). Two samples were excluded, as they had an unusually high number of low frequency variants unlikely to be of biological origin, leaving 1,179 samples for analysis, comprising 1,121 infected individuals of whom 49 had multiple samples. For all analyses we used only within-host variants that were statistically supported by both replicates (q-value<0.05 in at least one replicate and p-value<0.01 in the other, Methods). Within each sample, we classified variant calls as `consensus' if they were present in the majority of reads aligned to a position in the reference or as within-host variants otherwise. The allele frequency for each variant was taken as the frequency of the variant in the combined set of reads for both replicates.”

...

“The use of replicates and a base-specific statistical error model for calling within-host diversity reduces the risk of erroneous calls at low allele frequencies. We noticed a slight increase in the number of within-host diversity calls for samples with high Ct values, which may be caused by a small number of errors or by the amplification of rare alleles and that could inflate within-host diversity estimates (Figure 1 - figure supplement 3) \cite{McCrone2016-se}. However, the overall quality of the within-host mutation calls is supported by a number of biological signals. As described in the following sections, this includes the fact that the mutational spectrum of within-host mutations closely resembles that of consensus mutations and inter-host differences and the observation of a clear signal of negative selection from within-host mutations, as demonstrated by dN/dS and by an enrichment of within-host mutations at third codon positions \cite{Dyrdak2019-xk} (Figure 1 - figure supplement 4).”

Whilst we believe the remaining variant calls are reliable we acknowledge that how variants are polarised could impact some of the summary statistics reported. To help improve this we have amended Figure 1 to include a cumulative histogram of within-host variant frequencies on a log-log scale as suggested by the reviewer. We have also included estimates of the mean value of sqrt(p(1-p)) (indicating an estimate of the standard deviation of within-host variants assuming a Bernoulli distribution). We have also replaced the estimates of the mean number of mutations per genome with the expected number of differences between two randomly chosen genomes. The amended Figure 1C now displays a histogram of the expected number of differences between two genomes for each sample rather than the mean number of mutations.

4) This paper provides an important baseline characterization of within-host diversity, while the patterns themselves are not extremely surprising. It is thus important that the data are provided in a form that facilitates reuse. It would be helpful to provide intermediate analysis results in addition to the raw reads in the SRA and the shearwater calls. I would like to see simple csv tables with the number of times A,C,G,U,- was observed at every position in the genomes for every sample. This would greatly facilitate the reuse of the data.

We have now added raw count tables for each sample and each replicate to the GitHub repository. We have also archived this data using Zenodo to ensure it remains easily accessible.

Reviewer #2:

The paper by Tonkin-Hill and colleagues describes the analysis of intra-host variation across a large number of SARS-CoV-2 samples. The authors invested a lot of effort in replicate sequencing, allowing them to focus on more reliable data. They obtained several important insights regarding patterns of mutation and selection in this virus. Overall, this is an excellent paper that adds much novelty to our understanding of intra-host variation that develops during the time course of infection, its impact on transmission, and what we can or cannot learn on relationships between samples.

We are grateful to the reviewer for their positive comments.

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Reply to the Reviewers

We thank the Referees for their evaluation and their useful comments.

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

The MS from Bonaventure and colleagues used a CRISPR to identify novel IFN-induced antiviral effectors targeting HIV-1. One hit, the DEAD Box helicase DDX42, while not itself part of the IFN response, exerts a substantial inhibitory effect on HIV-1 replication when over expressed, and gives a several fold boost to viral replication when knocked down in cells. The effect of DDX42 KO or O/E is manifest at reverse transcription and PLA analysis suggests and interaction with incoming virions. Moreover, DDX42 appears to exert an inhibitory effect generally against retroviruses and retroelements, with evidence that it associates with viral/transposon RNA. The authors further show that DDX42 has antiviral against a range (but not all) RNA viruses, with very striking phenotypes seen especially with Zika, CHIKV and SARS CoV2, with DDX42 associating with dsRNA in infected cells. These data suggest DDX42 is a constitutively expressed a broad-spectrum inhibitor of a range of mammalian RNA viruses. The manuscript is very well written, the data is of good quality and clearly DDX42 is having a general effect on viral replication. The results are novel, important and potentially of wide interest. Where the MS is somewhat lacking is understanding whether DDX42 has direct antiviral activity or is globally affecting cellular RNA metabolism. Some important areas for the authors to consider are:

• DDX42 has a potential role in splicing and/or RNA metabolism so I think it would be important to see whether there is any clear global change in gene expression in knockout or knockdown cells cells vs control that might be suggestive of a generalized effect.

Responses

We thank the reviewer for this important question. Indeed, DDX42 didn’t impact the replication of 2 negative strand RNA viruses and this suggested that DDX42 didn’t have a global impact on the target cells, but we could not formally exclude a generalized effect. Therefore, we have performed RNA-seq analysis in order to evaluate the impact of DDX42 depletion (using 3 different siRNAs targeting DDX42 in comparison to a CTRL siRNA in U87-MG cells, and 2 different siRNA in comparison to a CTRL siRNA in A549-ACE2 cells, in samples obtained in 3 independent silencing experiments). The RNA-seq data (See Supplemental File 1 and Figure S5) showed that only 63 genes are commonly differentially expressed by the 3 siRNAs targeting DDX42 in U87-MG cells and only 23 of these genes were also found differentially expressed in A549-ACE2 cells depleted for DDX42. Importantly, the identity of these genes could not explain the observed antiviral phenotypes. These data are in favor of the absence of generalized effect on the target cells, which could have explained the antiviral phenotypes of the sensitive viruses.

• The HIV experiments in primary cells are only one round at present. Does the DDX42 knockdown enhance viral replication in multiround? Does it lead to more viral PAMPs for PRRs to induce IFN?

Responses

We agree with the reviewer that it would have been very informative to measure the impact of DDX42 knockdown in multiround infections in primary T cells. However, we tried several times to do this experiment (with primary T cells from several donors) and we were not successful: indeed, DDX42 KO appeared to slow down cell division, which could be taken into account for a short, one-cycle experiment (i.e. 24 h) 3 days post-Cas9/sgRNA electroporation by adjusting the number of cells at the time of infection. However, DDX42 KO appeared quite toxic in longer experiments, with cells stopping to grow.

The question regarding the generation of more viral PAMPs for PRRs to induce IFN is also very interesting. We know from published work (including ours) that primary T cells don’t normally produce IFN following HIV-1 infection (see for instance Bauby and Ward et al, mBio 2021). However, one can indeed hypothesize that as more viral DNAs are produced in the absence of DDX42, perhaps the primary T cells could detect them and produce IFN. To address this question in primary T cells, we would have needed to be able to perform multiround infections, which was not possible, as mentioned above. Moreover, we could not test this hypothesis in the cell lines that we used, such as U87-MG/CD4/CXCR4 cells, as they are unable to produce IFN following HIV-1 infection.

• More could be made mechanistically of the lack of sensitivity of Flu and VSV to DDX42. In particular showing whether or not DDX42 interacts with the RNA of the insensitive virus, or whether DDX42/virus or dsRNA interactions by PLA occur with Flu would highlight the relevance of these observations to the antiviral mechanism.

Responses

This is an excellent remark. We have now performed RNA immunoprecipitation experiments using 2 viruses targeted by DDX42 (CHIKV and SARS-CoV-2) and 1 virus that is insensitive to DDX42 (IAV) (See New Figure 4J-L): whereas CHIKV and SARS-CoV-2 RNAs could be specifically pulled-down with DDX42 immunoprecipitation, this was not the case for IAV RNA. This strongly argues for a direct mechanism of action of DDX42 helicase on viral RNAs.

Reviewer #1 (Significance (Required)):

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__ The role of helicases in host defence are of wide interest and importance. This has the potential to be a very important study that deserves a wide audience. However in my opinion it needs some further mechanistic insight along the lines I have suggested.

Responses

As mentioned above, we have now added important data: First, DDX42 is able to interact with RNAs from targeted viruses (and not from an insensitive virus); Second, we have checked that DDX42 didn’t have a substantial impact on the cell transcriptome. Taken together, these data are clearly in favour of a direct mode of action of DDX42.

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

In this brief report, the authors use a CRISPR screening approach to identify cellular proteins that limit HIV infection. The screen itself is elegantly designed and most of the top hits are components of the interferon signaling pathway that would be expected to emerge from such a screen, thus providing confidence in the results. The authors followed up on DDX42 as a new hit identified in their screen and confirmed that targeting DDX42 with distinct guide RNAs resulted in increased HIV infection in at least 3 cell lines. Conversely, DDX42 overexpression inhibited infection. They also confirmed a role for DDX42 in inhibiting HIV infection in primary macrophages and CD4 T cells using siRNA and CRISPR KO strategies, respectively. They also demonstrate that DDX42 inhibits several other divergent lentiviruses as well as Chikungunya virus and SARS-CoV-2, but not influenza virus. These data convincingly show that DDX42 plays a role in inhibiting many lentivirus and positive sense RNA virus infections. Using PCR assays for reverse transcription products they conclude that DDX42 inhibits an early process in the HIV life cycle occurring after virus entry, though the statistical significance of these differences is not clear. They further use proximity ligation assays to suggest that DDX42 is in proximity to HIV-1 and SARS-CoV-2 replication complexes. Mechanistically, these data are largely unsatisfying as they do not provide specific insight into how DDX42 so broadly inhibits virus replication. Overall, the manuscript presents a significant advance, it also has some weaknesses as listed below.

1. Statistical analysis is not included in any of the figures.

Response

Statistical analyses have now been included.

Many of the figure legends do not state how many independent biological replicates the figures are based on.

Response

The number of biological replicates for each panel is stated at the very end of each figure legend.

Detailed mechanistic understanding of DDX42 effects on virus replication is not provided by the manuscript.

Response

As mentioned in response to Reviewer 1, we have now added data showing that DDX42 could interact with RNAs from targeted viruses but not from an insensitive virus, arguing for a direct antiviral mode of action of this Dead-Box helicase.

Reviewer #2 (Significance (Required)):

DDX42 is a new antiviral protein identified and confirmed in this manuscript. It was also identified as one of many hits in a genome wide CRISPR screen for cellular proteins that regulate SARS-CoV-2 infections, but was not followed up. Thus, the identification and confirmation of DDX42 antiviral activity is highly significant for both the HIV and SARS-CoV-2 fields. This high significance may compensate to some extent for the lack of mechanistic insight contained in this initial report.

**Referees Cross-commenting**

I find the comments of the other reviewers to be fair and reasonable, and I concur that the work is overall important and novel. It seems that reviewers generally agreed that some additional mechanistic insights would be desirable for publication in a high impact journal. Reviewer 1 makes some good suggestions in this regard. As for mouse experiments, I would reserve these for a follow up manuscript.

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

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__In this manuscript, Bonaventure et al report the results of a screen to identify cellular inhibitors of HIV-1 infection in IF treated cells. They identify DDX42 as such a factor though, unexpectedly, DDX42 did not turn out to be an ISG. Strikingly, DDX42 turns out to inhibit a wide range of retroviruses as well as retrotransposons and + sense, but not - sense, RNA viruses among which SARS-CoV2 turns out to be especially sensitive to DDX42, with siRNAs specific for SARS-CoV2 DDX42 increasing viral RNA expression by a startling 3 orders of magnitude, compared to only an 2-5 fold positive effect with HIV-1.

Response

We agree with the reviewer that DDX42’s impact on HIV-1 may appear as somewhat modest, however, it is highly reproducible across cell lines and primary cells and, more importantly, it is observed upon depletion of the endogenous protein (either by KO or silencing) in target cells that are highly permissive to viral replication, such as activated primary CD4+ T cells. We therefore believe that these findings, combined with the findings that other positive-strand RNA viruses are targeted, are of high interest.

Reviewer #3 (Significance (Required)):

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__I found this paper generally convincing and technically sound though the emphasis was odd and clearly driven more by the history of how this work was done than by the actual results obtained. Specifically, the emphasis is on HIV-1 yet the most interesting data are the dramatic effects seen with Chikungunya and SARS2. If I was writing this paper, I would delete figure 4 and focus this paper entirely on retroviruses and retrotransposons. In that form, I think it would be competitive at PLoS Pathogens or perhaps EMBO Journal. The RNA virus work shown in figure 4 could then be figure 1 of a new, high impact, paper looking at the mechanism of action of DDX42 as an inhibitor of + sense, but not - sense, viral gene expression. Though Wei et al do mention DDX42 in their SARS-CoV2 screening paper this is certainly not a major theme of that paper so I don't think that would be a problem.

Responses

We thank the reviewer for this comment. We had hesitated to present the manuscript as suggested by the reviewer (i.e. focusing only on HIV-1, retroviruses and retroelements) and prepare a second manuscript with the remaining data. We’ve finally decided against it, as we believe that showing a broad antiviral effect of DDX42 on +strand RNA viruses increases the impact of our findings.

On another note, a conditional DDX42 KO mouse has been generated by the Wellcome trust Sanger institute and it would greatly improve this manuscript if they could show an in vivo a result similar to figure 3F using MLV.

Responses

We thank the reviewer for this information. We completely agree that in vivo work would be a massive plus and we will be planning to explore this in the future, but not at this stage as it would require specific funding and resources.

Author Response:

Reviewer #1:

This study focuses on how the vmPFC supports delay discounting. The authors tested patients with vmPFC lesions (N=12) and healthy controls (N=41) on a delay discounting (DD) task with two additional conditions: (1) reward magnitude and (2) cues that should evoke episodic future thinking (EFT).

The authors replicate their previous finding that patients with vmPFC lesions show steeper DD, and report two novel findings: (1) DD in patients is insensitive to reward magnitude, suggesting that vmPFC is critical for reward magnitude to modulate DD; (2) vmPFC patients show normal effects of EFT cues on DD, such that all subjects discounted less in the presence of cues that promote episodic future thinking. These findings have important implications for how vmPFC contributes to delay discounting, as they suggest that vmPFC is not necessary for prospective thinking to affect the evaluation of future rewards.

1) A potential issue with the EFT finding is that it rests on accepting the null hypothesis of no group differences. However, there are reasons to assume this is not a trivial null result due to a lack of statistical power. Specifically, there is a significant effect of EFT within the vmPFC patient group and there is a significant group difference for the effect of reward magnitude. Assuming comparable power to detect effects of EFT and reward magnitude, it seems unlikely that the non-significant EFT effect is simply a lack of power. In any case, this caveat has to be considered when interpreting the effect.

We have added a discussion of this caveat on p. 10, which reads: “Before discussing this finding further, we note that it rests on accepting the null hypothesis of no group differences in the EFT effect on DD between vmPFC patients and controls. It is unlikely, however, that this null finding simply reflects a lack of statistical power, for example due to a small sample size. First, the null effect on group differences indeed reflects a significant within-participant effect, with greater regard for future amounts in the EFT compared to the Standard condition in vmPFC patients. Second, together with the preservation of the EFT effect, we found a significant reduction of the magnitude effect in the same vmPFC patient sample. Bayesian analyses confirmed greater evidence in favour of the null compared to the alternative hypothesis regarding group differences in the EFT effect on DD.”

2) It is somewhat surprising that the authors had such a strong prediction about the absence of group differences for the EFT effect. Based on previous work (Bertossi et al., 2016a, b), one could expect a smaller EFT effect in the VMPFC group. The authors appear to put much weight on the results by Ghosh et al. 2014, which suggest that vmPFC is critical for schema reinstatement. The rationale for this strong prediction is not very clear from the introduction.

We have now reframed our hypotheses as suggested by the reviewers and the editors. In the Introduction, we now make only the hypothesis of a reduced EFT effect on DD in vmPFC patients, which is based on previous evidence of an EFT impairment in vmPFC patients. We present the hypothesis that vmPFC is critical for schema instantiation only in the Discussion, as an explanation of the null finding on group differences on the EFT effect.

Thus, p. 5 now reads: “Concerning prospection, previous studies have observed an EFT effect on DD, such that people discount future rewards less steeply if cued to imagine personal future events during intertemporal choice (Peters and Büchel, 2010; Benoit et al., 2011). Considering that vmPFC is implicated in prospection (Schacter et al., 2012) and that vmPFC patients are impaired in EFT (Bertossi et al., 2016a,b; Bertossi et al., 2017), vmPFC patients' DD should remain steep even when EFT cues are provided, because patients may nevertheless fail to construct the vivid future events that might be needed to counteract DD. Thus, we predict a reduced EFT effect on DD in vmPFC patients compared to healthy controls.”

Reviewer #2:

Ciaramelli et al. address a timely and theoretically important issue with respect to the functional role of the vmPFC in decision-making more generally, and temporal discounting in particular. Strong points of the paper include 1) a theoretically important research question and 2) much-needed lesion data on two important behavioral effects in temporal discounting: the magnitude effect, and a modulation of discounting via episodic future thinking. Weaker points of the paper include 1) lack of clarity for a number of methodological issues (group comparisons & control group for the AI data, inconsistency analysis) and 2) many remaining open questions with respect to how vmPFC patients might have utilized the EFT cues, and whether different processes were at work compared to controls.

We thank the reviewer for this positive evaluation of the paper and address the reviewer’s comments below.

Major points:

1) The authors note that their interpretation of the preserved EFT effects in the vmPFC patients in terms of e.g. semantic processing remains speculative, but is supported by the finding of intact external details production following vmPFC damage in earlier studies. But was this also the case in the present data set? This remains unclear, because for the AI data, only z-scores relative to some earlier control group (Kwan et al. 2015) are reported (Table 1 and Supplement p. 30). Was this control group matched to the patients? And since the referenced Kwan et al. (2015) paper reports only on six patients (presumably the patients from the Canada site?) - what about the patients from the Italian site, which control group were their AI data compared to?

The Crovitz data of the Canadian patients are unpublished (the Kwan et al., 2015 paper is not about vmPFC patients, but about 6 MTL patients). We compared them to a sample of 18 age-matched healthy controls, a subset of those included in Kwan et al. (2015). The 4 Italian patients were part of the vmPFC sample tested on EFT (and episodic memory) in Bertossi et al. (2016). We compared their performance with that of the 11 healthy controls from the same study who were age-matched to the patients.

This is clarified on p. 17, which reads: “The results of the Italian patients (a subset of those included in Bertossi et al. 2016b) were contrasted with those of the 11 healthy controls from the same study (all males; Bertossi et al., 2016b) who were age-matched to the patients (vmPFC patients: M = 47.75, SD = 5.25; healthy controls: M = 41.63, SD = 11.89, t13 = -0.97, p = 0.34). The results of the Canadian patients (unpublished) were contrasted with those of 18 healthy controls (10 males; a subset of those included in Kwan et al., 2015) age-matched to the patients (vmPFC patients: M = 61.00, SD = 9.83; healthy controls: M = 67.94, SD = 13.57, t22 = 1.15, p = 0.26).”

2) Directly related to my previous point: The methods section states that external details were in the normal range in the vmPFC group (mean z-score for EFT = -.73) but from Table 1 we can see that 8/10 patients in fact exhibit a negative z-score. This suggests that a direct group comparison of the external details scores would very likely reveal a significant group difference. Generally, it would help to report to actual control data here, not just the z-scores, and report the respective group comparisons.

We now report the Crovitz data in Table 2 and have run two ANOVAs on internal and external details separately in vmPFC patients and controls tested in Italy and in Canada. As the two ANOVAs show, we confirm that both patient groups produced fewer internal (episodic) details but a similar number of external details during EFT (as well as episodic remembering) than healthy controls. Therefore, the previously reported EFT problems for internal (but not external) details in vmPFC patients also apply to the patients tested here.

P. 17 now reads: “As for the Italian sample, an ANOVA on the details produced with Group (vmPFC patients, healthy controls), Time (Past, Future), and Detail (internal, external) as factors showed a significant effect of Time (F1,13 = 14.66, p = 0.002, partial η2 = 0.53), such that all participants produced more details for past than future events (18.19 vs. 15.37). There were also significant effects of Group (F1,13 = 6.16, p = 0.02, partial η2 = 0.32) and Detail (F1,13 = 9.14, p = 0.009, partial η2 = 0.41), qualified by a Group x Detail interaction (F1,13 = 8.99, p = 0.01, partial η2 = 0.40). Post hoc Fisher tests showed that vmPFC patients produced fewer internal details (11.45 vs. 25.51; p = 0.004) but a similar number of external details than controls (11.39 vs. 11.96; p = 0.89). No other effect was significant (p > 0.31 in all cases). The same ANOVA on the Canadian sample revealed an effect of Group (F1,22 = 17.76, p = 0.0003, partial η2 =20.44), qualified by a significant Group x Detail interaction (F1,22 = 4.72, p = 0.04, partial η = 0.18), again indicating that vmPFC patients produced fewer internal details (10.63 vs. 31.78; p = 0.0003) but a similar number of external details than controls (16.79 vs. 25.65; p = 0.09). No other effect was significant (p > 0.32 in all cases).”

3) The description of the inconsistency analysis was somewhat unclear. The authors use the procedure suggested by Johnson & Bickel (2008), which makes sense, given the overall analytical approach that focuses on the analysis of indifference points. However, this procedure is based on a comparison of adjacent indifference points. In contrast, the authors are referring to the number of inconsistent choices - this is either a typo, or a different procedure. I think the former, because the reported absolute numbers (e.g. means around 1) and the single subject plots in the supplement appear to reflect the number of inconsistent ID points rather than choices. If this is the case, I disagree with the statement that the "mean number of inconsistent choices was very low" (p. 10) - as this probably reflects the mean number of inconsistent indifference points and not choices, about 1 out of 6 ID points was inconsistent in the vmPFC group, which is a lot.

We apologize for lack of clarity. Yes, we are referring to indifference points (as in our previous study; Sellitto et al., 2010), not single choices. Inconsistent preferences are defined as “data points in which the subjective value of a future outcome (amount = R) at a given delay (R2) was greater than that at the preceding delay (R1) by more than 10% of the amount of the future outcome (i.e., R2 > R1 + R/10, as in Sellitto et al., 2010).” To avoid confusion, we have now corrected the expression ‘inconsistent choice’ to ‘inconsistent preference’ throughout the paper, and have eliminated the claim about the low number of inconsistent choices in vmPFC patients.

4) The EFT cues are suggested to help vmPFC patients to "circumvent their initiation problems" (p. 12) but I am not sure I follow this logic. First, the AI procedure typically entails external cues as well, and here vmPFC patients showed impairments (Table 1, but see my point 1 above). Second, some of the cited papers (e.g. Verfaellie et al., 2019) also used specific event cues, and still observed reduced internal details production in vmPFC patients.

The AI (Crovitz) procedure uses external cues but typically these are words that are not particularly meaningful to the participants (indeed, they are the same for all participants). e.g., Imagine attending a Fourth of July cookout a few years from now; Verfaellie et al., 2019) but, again, these cues are the same for all participants. We used personalized cues, which were events that participants (1) had selected themselves, and (2) had already planned or found them plausible in their future, and therefore presumably were the most self-relevant and familiar to the participants, including patients. We think that these events may have been effective in activating self- and event- relevant schemata. We clarify this point on p. 11, which reads: “We propose, therefore, that subject-specific event cues, which were self-relevant and familiar to the participants because they had been selected by participants themselves, and were already planned or were plausible in their future, acted as external triggers of self- and situation-relevant schemata, helping to circumvent vmPFC patients’ EFT initiation problems. Their intact MTLs allowed them to construct episodic future events, which were then integrated into intertemporal choice, reducing DD.” As we note on p. 14, indeed, vmPFC patients are capable of imagining detailed experiences if they are guided to choose for themselves a specific moment from an extended future event to narrate in detail (Kurczek et al., 2015). Of course, we agree with the Reviewer’s point below that this interpretation is speculative at this point.

5) One shortcoming with the paper is that no data are available that could inform how vmPFC patients might have utilized the EFT cues, and whether the processes at work might have differed from those in controls. Many points mentioned in the discussion (self-referential processing, semantic processing, activation of schemata, self-initiation vs. external cueing etc.) thus necessarily remain conjecture.

We agree with the Reviewer, and we admit in several parts of the Discussion that this interpretation is speculative at this point. However, the interpretation that we offer seems the most plausible to us at this time, considering what we know about the role of the vmPFC (vs. the MTL) in event construction and the absence of the EFT effect on DD in MTL patients. We also propose an alternative interpretation, but the pattern of findings on the EFT effect on DD makes it less likely to us. On p. 12, we state, “An alternative interpretation of the DD modulation is that EFT cues simply shifted attention towards the future, or conferred a positive valence to it, as we encouraged positively valenced EFT. If so, however, one should consistently observe an EFT-induced benefit on DD also in patients with MTL lesions, but this is not the case (Kwan et al., 2015; Palombo et al., 2015).”

Reviewer #3:

In this manuscript, Ciaramelli et al. examined the decision-making behavior of 12 patients with vmPFC damage in a delay discounting task. The authors carried out two manipulations in this task: 1. They presented participants with small and large offers for both the immediate and delayed reward (magnitude manipulation), 2. They prefaced decisions with a cue prompting participants to vividly imagine an event in their future that was expected to occur at the same delay as the proposed larger offer (episodic future thinking (EFT) manipulation). Compared to age and education matched healthy controls, patients with vmPFC damage showed steeper discounting of delayed rewards, particularly when the amounts offered were large (reduced effect of magnitude). However, like controls, vmPFC damaged patients displayed shallower discounting of delayed rewards following the EFT manipulation.

The manuscript is clear and concise in its presentation of the results, while still providing a detailed description of the behavior of these patients. This paper is also a good example of how pooling participants from multiple institutions can increase statistical power in a study of patients with focal brain damage targeting a fairly specific cognitive question. The positive results of the study mostly replicate previous findings. While the null result for the EFT manipulation is novel, the finding is hard to interpret. The authors state that they predicted that the EFT manipulation would not change discounting behavior in vmPFC damaged patients a priori despite the deficits of these patients in EFT in previous papers, which are also replicated here. However, I do not know why the authors would design their task in such a way to test for a null result. It is also not clear if this null result is observed for the reason proposed by the authors (that the EFT cues externally activate this process), or if this result is null for some other reason that is not accounted for here. As the authors do not provide a direct test for their hypothesized rationale for predicting this null result, the findings are hard to interpret.

We agree with the reviewer’s and editor’s point that this paradigm does not allow testing whether subject-specific, personally relevant cues, such as those we used, are indeed effective in externally initiating EFT in vmPFC patients. Therefore, we concur that, for the sake of clarity, this is best presented only as speculative discussion of the preserved EFT effect on DD in vmPFC patients. In the Introduction, therefore, we now formulate only the hypothesis based on previous evidence of impaired EFT in vmPFC patients (e.g., Bertossi et al., 2016a,b, Verfaellie et al., 2019), which would lead to the prediction of a reduced EFT effect in vmPFC patients. We present the hypothesis that vmPFC is critical for schema instantiation only in the Discussion, as an explanation of the null finding on group differences on the EFT effect.

P. 5 now reads: “Concerning prospection, previous studies have observed an EFT effect on DD, such that people discount future rewards less steeply if cued to imagine personal future events during intertemporal choice (Peters and Büchel, 2010; Benoit et al., 2011). Considering that vmPFC is implicated in prospection (Schacter et al., 2012) and that vmPFC patients are impaired in EFT (Bertossi et al., 2016a,b; Bertossi et al., 2017), vmPFC patients' DD should remain steep even when EFT cues are provided, because patients may nevertheless fail to construct the vivid future events that might be needed to counteract DD. Thus, we predict a reduced EFT effect on DD in vmPFC patients compared to healthy controls.”

Overall, this manuscript makes a relatively modest contribution to our knowledge about the function of vmPFC during inter-temporal choice. It bolsters previous claims about how vmPFC damage impacts delay discounting and EFT, while not revealing new information about how vmPFC specifically contributes to the processes involved in these behaviors and why damage to this region impacts intertemporal choice in this way.

We concur with the reviewer that our findings confirm previous evidence that vmPFC is necessary for balanced DD and for EFT. However, we think that our finding of a complete abolishment of the magnitude effect together with a complete preservation of the EFT effect on DD in vmPFC patients configures a remarkable theoretical advancement on the role of vmPFC in intertemporal choice. Indeed, it shows that during intertemporal choice vmPFC is more prominently implicated in reward valuation than in prospection. This finding is important for current theories of intertemporal choice, and is surprising considering previous demonstrations of impaired EFT in vmPFC patients (a finding that was replicated in the current study), and therefore has important implications also for theories relating to the role of vmPFC in EFT. Finally, we note that the paper focuses on one important facet of impulsivity following damage to the vmPFC in humans: steep DD. Our findings, therefore, may inform the clinical management of impulsivity in patients with vmPFC damage or dysfunction, delineating the contextual manipulations that are or are not expected to push the reach of patients' choice into the future.

Author Response:

Reviewer #1:

This manuscript shows cell to cell variability in the relative levels of Sox2 and Brachyury (Bra) expression by individual cells within the region of the epiblast containing axial progenitors (the progenitor zone, PZ). Accordingly, some cells express high Bra and low Sox2 levels, others high Sox2 and low Bra and a third group expressing equivalent levels of both transcription factors. They then show that by experimentally promoting high Sox2 expression cells enter neural tube (NT) fates, whereas high Bra brings cells in the progenitor zone to enter the presomitic mesoderm (PSM). The authors then complement these experiments with evaluation of cell movements within the PZ, NT and PSM to show that cells in the NT are much less motile than those in the PZ and PSM. These data led the authors to propose a fundamental role for Sox2/Bra heterogeneity to maintain a pool of resident progenitors and that it is the high cell motility promoted by high Bra levels what pushes cells to join the PSM, whereas high Sox2 levels inhibit cell movement forcing cells to take NT fates. To validate their hypothesis, the authors generated a mathematical model to show that those expression and motility characteristics can indeed lead to axial extension generating NT and PSM derivatives in the proper positions, while keeping a PZ at the posterior end.

Some specific comments on the manuscript are specified below.

1) Although the description of cells within the PZ containing different Sox2 and Bra expression ratios is more explicit and quantitative in the present manuscript, this has already been previously reported by different methods including immunofluorescence (e.g., Wymeersch et al, 2016). Similarly, that breaking the Sox2/Bra balance towards high Sox2 or Bra is an essential step to bring the progenitors towards NT or PSM fates has also been previously shown in different ways. These observations are, therefore, not totally new. The novel contribution of this paper is the authors' interpretation that "heterogeneity among a population of progenitor cells is fundamental to maintain a pool of resident progenitors". In this work, however, this conclusion is only supported by their mathematical simulation, as the experiments described in this manuscript are not aimed at homogenizing Sox2/Bra expression levels in the progenitor cells (meaning keeping the double positive feature) but, instead, forcing the progenitors to express Sox2 or Bra alone, which permits evaluation of differentiation routes rather than how to maintain the resident progenitor pool. Interestingly, their alternative mathematical model in which the relative Sox2/Bra levels follow an anterior-posterior gradient (which is actually a feature observed in the embryo) was also successful in producing an extending embryo. This model was not favored by the authors (but see my comment below). According to this model, the progenitor zone could be maintained by a cell pool containing equivalent Sox2/Bra levels; when this balance is broken cells eventually enter NT or PSM routes. Therefore, while expression heterogeneity can be observed in the PZ, I am not sure that the work shown in this manuscript is conclusive enough to claim an essential role of such heterogeneity to maintain the progenitor pool.

We acknowledge that regional heterogeneity of Sox2 and Bra has been described in the PZ and we made sure that we cite the bibliography including Wymeersch et al, 2016 and Kawachi,2020. Although these papers described different levels of Sox2 and Bra in the PZ, they did not clearly reported and quantified the fact that direct neighboring cells have very different levels of Sox2 and Bra, therefore we believe that our description of a “random-like” pattern of heterogeneity constitutes a real novelty. In the same lines, we are aware of the several studies independently showing that gain or loss of-function of Sox2 or Bra can act on the progenitor decision to join either the NT or the PSM (these references are cited l.70, l.72). However, we believe that our study is the first to test systematically both overexpression and downregulation of Sox2 and Bra on progenitor distribution in the same biological system and to link Sox2/Bra functions to cellular motility.

Testing the requirements of spatial cell-to-cell heterogeneity to maintain a pool of progenitors is experimentally challenging and even if we were able to homogenize Sox2 and Bra expression, we would have to do it in all progenitors, which is not, so far, technically possible using bird embryo as a model system. We are well aware of these limitations and have toned down claims on the essential role of heterogeneity to maintain progenitor pool. In particular, we have changed the abstract (we removed the last sentence stating that heterogeneity is fundamental to maintain a pool of resident progenitors), as well as the end of the introduction (we removed “while progenitors expressing intermediate/equivalent levels of the two proteins tend to remain resident”). We have pondered our model in the discussion in saying by cell with comparable levels of Sox2 and Bra “could” remain resident (L.370)

To better apprehend the role of cell-to-cell spatial heterogeneity, we have developed a new mathematical model (Figure 5) which integrates both gradient and random heterogeneity in Sox2/Bra values within the PZ and thus fits better to our biological results. In the new version of the manuscript, we compared this model with a model in which the PZ is fully gradient-like and second one in which it is completely random. These comparisons allow us to describe better what properties random and patterned heterogeneities could bring to the system (Figure 6).

2) The other main novelty of this manuscript is the idea that differences in cell motility derived from their Sox2 or Bra contents are a major force driving the generation of NT and PSM from the progenitors in the PZ. While there are clear differences between cell motility in the NT and the other two regions, the differences between what is observed in the PSM and PZ is not that high (actually, from the data presented it is not clear that such differences actually exist). However, independently of motility differences, there is no experimental evidence demonstrating that the essential driver of the cell fate choices is motility itself. Differences in cell motility could be just one of the results of more fundamental (and causal) changes in cell characteristics triggered by Sox2 or Bra activity. Indeed, NT and PSM cells are different in many different ways, including adhesion properties, which are normally a major determinant of tissue morphogenesis. Cell motility could, therefore, be one of the factors but it is not clear that it plays the essential role proposed by the authors. (see also next comment).

Cell motility distributions in the PZ are slightly different from that of the PSM since slower cells were found in the PZ. We agree with the reviewer that this difference might be difficult to see because the average motilities between the two tissues are very similar (Figure 3 and Figure 3-figure Supplement 1). To reveal this difference more clearly we have used a reporter gene for Sox2 and analyze progenitor motility by time lapse imaging. We have specifically tracked GFP positive cells (reporter gene for Sox2) in the PZ and compared them to cells which are not expressing GFP. The result is that Sox2 high progenitors are globally slower than other progenitors clearly revealing heterogeneity in cell movements within the PZ and its relation to Sox2 expression (L.225-232, Figure 3-figure Supplement 1B, video 2).

We agree that there is no experimental evidence that motility itself is the driver of the cell fate choices. To test if the effect on cell motility is taking place downstream of differentiation events, we have analyzed the expression of markers for mesodermal and neural fate (Msgn1 and Pax6) 7hrs after overexpression of Sox2 and Bra. While Sox2 or Bra overexpression triggers changes on cell motility in this short time window, we did not observe any changes in Msgn1 and Pax6 expression (L.267-274, Figure 4-figure Supplement 2) arguing that the effect on motility is an early consequence of the Bra and Sox2 misexpression. Nevertheless, we are aware that this is not a strict demonstration that the effect on fate are coming from the differential motility only. We have therefore toned down our arguments and changed the title of the manuscript (“....guides destiny by controlling their motility “ has been replaced by “...guides motility and destiny”) .

The effects on cell motility we observe could be a consequence of Sox2 and Bra effect on adhesion as suggested by the reviewer, this is an interesting possibility that we cannot and don’t want to rule out. The effect on cell adhesion is taken into account in our model and we discuss this hypothesis in the new version of the manuscript (L. 456-459). Identifying the mechanisms underlying the effects of Sox2 and Bra on cell motility is an extremely interesting project we want to pursue but we consider that this aspect goes beyond the scope of the current manuscript.

3) The authors developed a mathematical model to confirm their hypothesis that Sox2/Bra expression diversity combined with different motility of cells with high, low or intermediate relative levels of Sox2 and Bra expression are the key to guarantee proper axial elongation from the PZ. I am, however, not sure that the model, the way it was designed, actually proves their point. In particular, because it introduces an additional variable that might actually be the essential parameter for the success of the mathematical model: physical boundaries between NT and PSM cells, meaning that cells with high Sox2 or high Bra are unable to mix. As I commented above, this variable reflects a key biological property of the two tissues involved, one epithelial and the other mesenchymal in nature, which might be more relevant that the motility of the cells themselves (e.g. by different cell adhesion properties). How would a model that does not include such physical barriers work? Conversely, how would a model work in which only physical barriers are applied, using similar starting conditions: a prefigured central neural tube (Sox2 high), flanked at both sides by PSM (Brachyury high) and with the PZ (variable Sox2/Bra levels) just posterior to the neural tube?

We agree that adhesion and non-mixing properties are essential to our models. Because it was not clear in the previous version, we have explained them in more details in the new version of the manuscript (l.295-300 and Appendix 1). To assess their roles, we have made two new simulations one without the regulation of non-mixing /adhesion properties and one without motility control by Sox2/Bra. Both simulations show strong defects in morphogenesis arguing that motility on its own is a key component of the system and that the non-mixing and adhesion properties are also important but not sufficient to drive morphogenesis (Figure 5F). Having the same non-mixing/adhesion and motility properties downstream of Sox2 and Bra in all our models allows us to isolate the phenomena we wish to study: the role of the distribution of cell -to cell heterogeneity in the PZ (Figure 6).

4) The authors generate two mathematical models, differing in whether they start with a random distribution of Sox2 and Bra expression throughout the PZ or with prefigured opposing Sox2 and Bra expression gradients, somehow resembling the image observed in the embryo. The two models generated structures resembling the elongating embryo, although with small differences in the extension process and the extension rate. After analyzing the behavior of those models, they concluded that the random model fits better with the expectations from the in vivo characteristics in the embryo. I am however not sure that I agree with the authors' interpretation. First, because the gradient model includes a natural characteristic observed in the embryo, which the random model does not. Second, because one of the deciding characteristics, namely the slower extension rate observed in the gradient model, does not necessarily make it worse than the random model, as it is not possible to properly determine which extension rate actually resembles more accurately axial extension in the embryo. Third, because the observation that in the gradient model the PZ undergoes fewer transient deformations and self-corrective behaviour is in my view an argument to favor, instead of to disfavor the gradient model, both because the final result is at least as good as the one obtained with the random model and it is actually not clear that in the embryo the PZ undergoes such clearly visible deformations and self-corrections during axial extension. In addition, the gradient model generates a "pure" PZ (just yellow cells) in the posterior end of the structure, while in the random model the PZ contains some islands of NT cells, which is not what is observed in the embryo. According to the last features, the gradient model seems better than the random model.

To answer the reviewer’s concern about similarity to the embryo, we have developed a new model that is clearly closer to the biological system because it integrates both the gradient and the random ratio distributions (new Figure 5). Interestingly, by comparing it to the two extreme models (random and gradient), we found that this more “natural” model combines the stability and fluidity brought by the gradient model and the random model, respectively. As pointed out by the reviewer, we found that graded distribution brings more stability to the system with a “purest” PZ. At the opposite, random distribution allows more tissue fluidity and cell rearrangements as well as tissue shape conservation (Figure 6). We want to thank the reviewer for his or her input; we think that the new model and the comparison with the two extreme cases allowed us to reveal more clearly properties that are specific to the two types of spatial distributions and therefore to point out what general morphogenetic properties could emerge from random- like heterogeneity in the embryo.

Reviewer #2:

In this manuscript, Romanos et al show firstly that there is extensive cell-to-cell heterogeneity in the relative levels of Sox2 and Bra in the region containing progenitors for neural and paraxial mesoderm, gradually resolving towards high Bra/low Sox2 in the mesoderm or high Sox2/low Bra in emerging neurectoderm. They then show that overexpression of Sox2/morpholino-based inhibition of Bra or vice versa lead cells to favour neurectoderm or mesoderm respectively. Next they show that cells expressing high Bra are more motile than those expressing Sox2, and show using mathematical modelling that these behaviours can explain many aspects of the eventual segregation of Sox2-high neurectoderm and Bra-high mesoderm.

This interesting and well-presented work leads to the elegant and novel hypothesis that random cell motility induced by Bra and inhibited by Sox2 are sufficient to explain the segregation of NMps towards mesoderm and neurectoderm respectively. The work will be of broad interest to developmental and mathematical biologists interested in the cell biological basis of self-organising cell behaviours. Nevertheless there are some concerns to address in order to solidify the claims in the manuscript.

1) The section where Sox2 and Bra levels are manipulated (line 152 onwards) is somewhat under-analysed. Results are presented as supporting a model where the two proteins mutually repress each other and lead to segregation of neural (high Sox2) and mesodermal (high Bra) cells. However the data presented does not unequivocally support the claims in the manuscript and would require further clarification.

In the new version of our manuscript, we give more details on the analysis of Sox2 Bra levels manipulations. In particular, we provide data showing the tissue localization of manipulated cells on transverse sections (L. 192, Figure 2-figure supplement 3). We have also studied the effects of Sox2 and Bra ovexpression on cell fate maturation in the PZ and provide some evidence that progenitors do not yet express differentiation markers as they acquire specific motile properties in response to Sox2 or Bra overexpression (L. 267-273, Figure 4-figure supplement 1). According to our results and to the literature, we revised the text by removing mentions to Sox2 and Bra mutual repression (L 171, L 386, L389).

2) The mathematical model may be an oversimplification of the role of these two genes in organising a balanced production of neurectoderm and mesoderm.

In the new version of our manuscript, we have made significant efforts to better explain how non- mixing properties are taken into consideration in our models and thus, hopefully, to avoid an impression of oversimplification. We would like to point out that simulations performed to evaluate the impact of non-mixing properties on the elongation process, indicate that adhesion and non- mixing properties alone cannot account for the morphogenetic events we modelled (new Figure 5F), thus reinforcing the view that regulation of cell motility is a key element in the system. Furthermore, we have designed a new mathematical model, which is closer to the biological system because it integrates both graded and random distribution of Sox2/ Bra values (as observed in vivo) (new Figure 5). As explained above in response to reviewer 1, comparison of this model with our previous models, based on either graded or random distribution of the Sox2/ Bra values, points out the importance of random like cell-to-cell heterogeneity in this morphogenetic process.

Reviewer #3:

The manuscript by Romanos and colleagues examines how Sox2 and Brachyury control the behavior and cell fate of neuro-mesodermal progenitors (NMPs) in avian embryos. Using immunohistochemistry, the authors showed that the cells residing in the progenitor zone (PZ) display high variability in Sox2/Bra expression. Manipulation on the levels of the two transcription factors affected NMPs' choice to stay or exit the PZ and their future tissue contributions. This motivated the authors to employ an agent-based computational model and additional functional experiments to explore the importance of Sox2/Bra for cellular motility. The results led the authors to propose that (i) heterogeneity in Sox2/Bra ratio is important for the spatial organization of the PZ and its derivatives and that (ii) Sox2/Bra determine the fate of progenitor cells by controlling cellular movements.

This is a technically sound report that combines single-cell analysis, in vivo functional experiments, and mathematical modeling to explore the link between cell motility and cell identity. While the model proposed by the authors is intriguing, I found that the study should provide evidence placing Sox2/Bra as primary regulators of cell motility in the context of the PZ. Given the extensively-studied role of these transcription factors in NMPs, it is challenging to decouple cellular behavior from cellular identity during tissue formation. The study would benefit from further demonstration that cell fate commitment is regulated by - and not a regulator of - cell migration of NMPs.

We have now tested the effect of Sox2 and Bra overexpression on cell identity. We show that, 7 hrs after electroporation (a time at which we observe an effect on cell movement), no modification of the expression of neural (Pax6) and mesodermal (Msgn1) maturating markers. These data thus indicate that the effect on cell motility happens without a major acceleration of the maturation program (Figure 4 figure supplement 2). However, as mentioned in response to Reviewer 1, these experiments are correlative and do not demonstrate that the effect of Sox2 and Bra on neural and mesodermal differentiation programs are going only thought cell motility, therefore we have accordingly toned down our arguments in the new version of our manuscript.

Strengths and Weaknesses:

• The idea that heterogeneity in cellular behaviors within a progenitor field may act as a driver of morphogenesis is interesting and nicely supported by the agent-based model.

We want to thank the reviewer for this comment. We believe that in the new version of the manuscript we go even further by developing a new model (Figure 5) which is closer to reality and by testing the influence of random versus gradient Sox2/Bra distribution on morphogenesis (Figure 6)

• One of the premises of the model (Fig 4) is that Sox2/Bra ratio determines how much cells move, but this is not clear from the in vivo experiments and seems speculative. A clear demonstration of correlation between Sox2/Bra ratio and cellular motility is necessary for proper support of the model.

The role of the Sox2 to Bra ratio on PZ cell motility is demonstrated in Figure 4. In the new version of the manuscript, these results are presented before the modelling section, we hope that it would help clarifying any doubt the reader can have on the fact that we do demonstrate clearly a role of Sox2 and Bra in controlling PZ cell motility in vivo.

• The authors found that manipulation in the levels of the TFs results in changes in NMP motility, but it is not clear if this the cause or a consequence of commitment to a neural or mesodermal fate. Could Bra-High cell moving more because they have been specified to a mesodermal fate? Conversely, Sox2-High cells might migrate less since they get incorporated into the neural tube. Establishing the timing of cell fate commitment is necessary to resolve this issue

We agree with the reviewer that it is an interesting issue; we have checked for expression of specification markers 7hrs after electroporation of Sox2 and Bra expression vectors, a time point at which electroporated cells did not yet leaved the PZ but have already changed their motility. In these conditions, overexpression of Sox2 and Bra had no discernable effect on expression of the neural marker Pax6 and on the PSM marker Msgn1, respectively (Figure 4 figure supplement 2).

• The study's impact and novelty depend on the demonstration that the primary function of Sox2/Bra in NMPs is to drive cell movement. This is not sufficiently explored in the study, and there are no proposed mechanisms for how Sox2/Bra modulate cellular behavior.

We do have shown that Sox2 and Bra act on progenitor motility in vivo (Figure 4). As a mechanism, we propose that Sox2 and Bra could act directly on motility or indirectly by regulating differential adhesion. Cell adhesion control by Sox2/Bra is part of our modeling assumptions and is therefore a hypothesis that will be the subject of future investigations in the lab. This hypothesis is part of the discussion in the new version of the manuscript (L.457).

Author Response:

Reviewer #1 (Public Review):

[...] Strengths:

1. The loss of ciliary GPR161 has a more robust phenotype in specific tissues (i.e., the limbs and face). As a result, the limb data (in Figure 6) and craniofacial data (in Figure 7) are well presented and clear. In these figures, the authors directly compare and highlight differences between primarily two genotypes (wt and Gpr161mut1/mut1 embryos) and quantify the changes (digit number and distance between nasal pits). Overall, these two figures support the existing GPR161 model, showcasing that a loss of ciliary GPR161 results in a tissue-specific loss of GLI3R (Figure 6D) and consequently the development of additional digits (Figure 6E) and craniofacial defects (Figure 7D and 7E).

Thank you.

Weaknesses:

1. There is no data in the paper showing that Gli3 repressor function is affected preferentially compared to Gli Activator function. In Figure 4C, Gli3 FL/R ratios are not different between wt/wt and mut/mut embryos. The data can be explained by the fact that the mutant Gpr161 is a partial loss of function allele and the resultant weaker phenotypes (compared to the full KO) show some tissue specificity. Linking this allele to a specific biochemical mechanism is not justified by the data.

We have now revised the title of the paper and the discussion emphasizing on these limitations. We have also added a new section in discussion on the limitations of our methods and other optogenetic/chemogenetic methods for generating cAMP in cilia. These limitations arise from the cilioplasm not being strictly restricted from the cytoplasm. Therefore, the second messengers cAMP and Ca2+ are freely diffusible between ciliary and extraciliary compartments (Delling et al., 2016; Truong et al., 2021). A paper published in Cell during revision of this study used optogenetic tools to show that ciliary, but not cytoplasmic, production of cAMP functions through PKA localized in cilia (Truong et al., 2021) to repress sonic hedgehog-mediated somite patterning in zebrafish (Wolff et al., 2003). We have also compared and discussed these results with our study. Our study highlights that the effects of ciliary loss of Gpr161 pools are tissue specific and dependent on the requirements of the tissues on GliR vs GliA in the morpho-phenotypic spectrum. Overall, our results using Gpr161mut1 allele are complementary to the optogenetic study by showing that lack of ciliary Gpr161 pools result in Hh hyperactivation phenotypes arising mainly from lack of GliR, in the limb buds, mid-face and intermediate neural tube.

1. The authors use an endpoint assay based on overexpression in 293T cells to claim that cAMP production is unaffected by the Gpr161mut allele. However, weak effects (very likely given the weak phenotypes) may not be evident this assay. We also do not know if the mutant allele is defective in some other biochemical function or in localization to other places in the cell. One way to address this is to measure ciliary and extraciliary cAMP in their knock-in cells. In Gpr161mut1/mut1 cells, is ciliary cAMP reduced to levels comparable to Gpr161ko/ko cells? Is extraciliary cAMP unchanged compared to WT cells? Or, is cAMP able to diffuse into the cilia from GPR161mut1 localized to vesicles at the ciliary base (Figure 1B)? Many of the conclusions made in the paper equate a loss of ciliary GPR161 to a loss of ciliary cAMP, but this loss of ciliary cAMP is not definitively shown in the paper.

As physiological ligands for Gpr161 are currently not known, we are unable to test extraciliary vs ciliary contribution of Gpr161 in cAMP production in a physiological context. Therefore, we resort to overexpression assays for constitutive cAMP production by Gpr161 and Gpr161mut1. Using these assays, we do not find a difference in constitutive activity among these variants.

As the cilioplasm is not strictly compartmentalized from the cytoplasm, the second messengers cAMP and Ca2+ are freely diffusible between ciliary and extraciliary compartments (Delling et al., 2016; Truong et al., 2021). Thus, in any approach for generating subcellular pools of cAMP, be it genetic, optogenetic or chemogenetic (Guo et al., 2019; Hansen et al., 2020; Truong et al., 2021), extraciliary cAMP could diffuse into ciliary compartments. A recent paper using optogenetic and chemogenetic tools for cAMP production inside cilia or in cytoplasm show that there is free access of cytoplasmic cAMP to intraciliary compartments but is unable to reach critical thresholds in activating PKA (Truong et al., 2021). Thus, we would assume that the extraciliary cAMP produced by extra copies of Gpr161mut1 could diffuse to cilia but is likely to be less effective in activating downstream effectors. In addition, the PKA regulatory subunit-AKAP complexes are fundamentally important in organizing and sustaining PKA catalytic subunit activation to organize localized substrate phosphorylation in restrictive nanodomains (Bock et al., 2020; Zhang et al., 2020). The dual functions of Gpr161 in Gs coupling and as an atypical AKAP (Bachmann et al., 2016) is likely to further restrict cAMP signaling in ciliary or extraciliary microdomains.

1. Compared to Figures 6 and 7, the data presented in Figures 3 and 5 are very confusing and difficult to interpret. On the one hand, this is understandable, the Gpr161mut/mut phenotypes are complex, and some tissues (like the developing spinal cord) are more resistant to change due to a loss of GliR. On the other hand, the data collected from the numerous genotypes analyzed could be easier to interpret by (i) providing a penetrance of the phenotypes and (ii) quantifying the phenotypes.

Thank you for all the suggestions. We have now carried out these quantifications or tabulations, which have considerably improved the presentation of the datasets (Table 2 and Figure 5-figure supplement 1). Some of these experiments required additional experimental animals (Table 1), and we have updated the text accordingly.

Below are a few examples of data that could be improved with quantifications:

— In Figure 3, the authors are trying to convey that the Gpr161mut allele is partially functional and produces a milder phenotype than the Gpr161ko allele. However, the Gpr161ko/ko, Gpr161mut/ko, and Gpr161mut/mut phenotypes showcased in the figure all look quite severe, and it is difficult to appreciate the differences in the defects fully. An accompanying table summarizing the phenotypes and their penetrance in the affected genotypes would help to convey this point.

We have added an accompanying Table 2 summarizing the phenotypes and penetrance for the respective genotypes, when present. Please note that rostral malformations such as exencephaly are similar between Gpr161 ko/ko and Gpr161 ko/mut1, whereas Gpr161 mut1/mut1 embryos have mid face widening. In the same line, Gpr161 ko/ko has no forelimbs, whereas Gpr161 ko/mut1 has smaller fore limb buds, whereas Gpr161 mut1/mut1 embryos have polydactyly.

— In Table 1, the authors note that the Gpr161mut1/mut1 mouse is embryonic lethal by e14.5, but the analysis in Table 1 appears to be incomplete. In the table titled "breeding between Gpr161 mut1/+ parents," the authors indicate that they only assessed one litter of e14.5 and e15.5 embryos. Oddly, the authors note that additional litters were collected, but the embryos were not genotyped because the embryos exhibited no phenotypes. The absence of phenotypes could be due to an absence of viable Gpr161mut1/mut1 embryos; however, the embryos need to be genotyped and a chi-square analysis conducted to verify this. Death can be a measure of phenotype severity, but I think it is important to surmise why the embryos are dying. It is unclear whether the embryos are dying due to the heart defects mentioned in the discussion. If the embryos are dying due to the heart defect, then it would be important to know whether the heart defects are more severe in the Gpr161ko/ko embryos.

Our apologies for the oversight. We have now analyzed additional timed pregnancies at E14.5, E14.75 and E15.5. We find that the embryonic lethality is seen fully by E14.75. Heart defects in Gpr161 ko/ko embryos are not apparent as they are E10.5 lethal. We do see apparent heart defect phenotypes in Gpr161 ko/mut1 vs Gpr161 mut1/mut1. These defects include pericardial effusion, outflow tract defects, A-V cushion abnormalities and smaller ventricles. These phenotypic descriptions are beyond the scope of the current paper. However, we have mentioned about pericardial effusion in the text and Table 2.

— In Figure 5, quantifying the progenitor domains would greatly assist in discerning differences between the various genotypes. For example, a quantification would help readers assess differences in NKX6.1 across the various genotypes.

We have now quantified the differences in Nkx6.1 across genotypes. The data is presented in Figure 5-figure supplement 1.

On an unrelated note, the PAX7 staining of the Gpr161mut1/ko spinal cord looks very strange because the line adjacent to the image does not accurately represent the dorsal-ventral patterning of PAX7 seen in the image. This image would need to be replaced.

Our apologies for the oversight. We have now revised this image.

Reviewer #2 (Public Review):

The premise of the entire study is predicated on GPR161mut1 failing to target to cilia and being WT in every other aspect. The Gs coupling of GPR161mut1 is examined. The ciliary localization ofGPR161mut1 is carefully assessed by conducting staining not just in WT cells but also in INPP5Ecells where GPR161 ciliary levels are known to be elevated. Another prediction is that GPR161mut1is found in an intermediate biosynthetic compartment. Some insights into the compartment whereGPR161mut1 is found would help interpret the phenotype of the GPR161mut1 animals. It would be important to know whether the GPR161mut1 mimics a pre-cilia targeted GPR161 (say at the plasma membrane) or whether it mimics a post-ciliary exit state (say recycling endosomes). In the past few years, work from the von Zastrow lab and others has shown that GPCRs keep activating their downstream partners after endocytosis from the plasma membrane. If GPR161mut1 were to mimic the post-ciliary exit state of GPR161, it may assume some of the signaling functions of ciliaryGPR161.

Thank you for all the suggestions. We have now examined and extensively discussed the plausible source of extraciliary Gpr161 in mediating Hh repression. We already showed that Gpr161 localizes to the periciliary recycling endosomal compartment where it localizes in addition to cilia (Mukhopadhyay et al., 2013) and could activate ACs and PKA in proximity to the centrosome. We now show that Gpr161mut1 also localizes to similar compartments (Figure 1-figure supplement 3). We propose that this compartment could promote Gpr161 activity outside cilia in the in vivo settings in GliR formation (please see model in Figure 8D).

We also compare our results with a recently published paper showing that ciliary, but not cytopasmic, production of cAMP functions through PKA localized in cilia to repress sonic hedgehog-mediated somite patterning in zebrafish (Truong et al., 2021). While this paper is an elegant demonstration of ciliary pools of cAMP in repressing Hh activity despite having no strict compartmentalization exclusively in cilia, it does not capture the roles of ciliary and extraciliary pools of Gpr161-mediated cAMP signaling in different tissues that we show are dependent on the requirements of the tissues on GliR vs GliA in the morpho-phenotypic spectrum.

A second point that the authors may wish to address is whether GPR161mut1 may fail to enrich in cilia because it is hyperactive and undergoes constitutive exit from cilia. The hypothesis here is thatGPR161mut1 couples to beta arrestin better than WT GPR161. Blocking GPR161mut1 exit via depletion of beta arrestin or BBSome is a simple way to test this hypothesis.

As advised by the reviewer, we have tested for Gpr161/Gpr161mut1 levels in cilia upon arrestin1/2 or BBSome loss. These experiments show that Gpr161mut1 is not present in cilia in arrestin1/2 (Arrb1/2) double ko MEFs (Figure 1-figure supplement 1) or upon RNAi of BBS4 (Figure 5-figure supplement 2). We previously also showed that knockdown of the 5’phosphpatase INPP5E that causes accumulation of Gpr161 in cilia does not show any accumulation of Gpr161mut1 in cilia. Based on all these experiments, we surmise that Gpr161mut1 does not transit through cilia.

Finally, it would be good to learn about the levels of expression of GPR161mut1 compared to WTGPR161 using immunoblotting. If GPR161mut1 were to be expressed at much higher levels than WTGPR161, it may compensate for its lack of ciliary localization by elevated total cellular activity.

We were unable to determine protein stability of the mutant receptor in the Gpr161mut1 embryos due to technical constraints in immunoblotting for endogenous levels. However, we note Gpr161mut1 in vesicles surrounding the base of cilia (Figure 1B) and constitutive cAMP signaling activity (Figure 1G, Figure supplements 1-3) in stable cell lines, suggesting that protein levels and activity of the mutant were comparable with wild type Gpr161. As suggested by the reviewer, we also tested LAP-tagged Gpr161mut1protein levels by tandem affinity purification and immunoblotting, with respect to LAP-tagged Gpr161wt in MEFs stably overexpressing these variants. We noted similar immunoblotting pattern from receptor glycosylation in both variants (Figure 2-figure supplement 2).

Reviewer #1 (Public Review):

The authors created a new GPR161 mutant mouse (Gpr161mut/mut) in which GPR161 does not localize to the primary cilium but is still cAMP signaling competent based on an over-expression assay in 293T cells. Through a detailed analysis of the Gpr161mut/mut mouse and its comparison to a previously generated Gpr161 knockout mouse (Gpr161ko/ko), the authors try to discriminate the ciliary and non-ciliary roles of GPR161. The current prevailing model is that GPR161 (localized to the primary cilium in the absence of Hh pathway activation) is constitutively active and elevates cAMP levels within the primary cilium. Elevated ciliary cAMP then activates ciliary (or ciliary adjacent) PKA, driving the processing of bifunctional GLI proteins into transcriptional repressors (GLIR). According to this model, the ciliary pool of GPR161 is critical for suppressing Hh signaling activity, and one would predict that the Gpr161mut/mut embryos would look identical to the Gpr161ko/ko embryos. However, this was not the case. Across multiple developmental tissues, the Gpr161mut/mut phenotype is less severe than the complete knockout, suggesting a role for non-ciliary GPR161 in suppressing Hh signaling activity. The observations made in this paper are interesting, but the data fails to make a clear distinction between the ciliary and non-ciliary roles of GPR161.

Strengths:

1. The loss of ciliary GPR161 has a more robust phenotype in specific tissues (i.e., the limbs and face). As a result, the limb data (in Figure 6) and craniofacial data (in Figure 7) are well presented and clear. In these figures, the authors directly compare and highlight differences between primarily two genotypes (wt and Gpr161mut1/mut1 embryos) and quantify the changes (digit number and distance between nasal pits). Overall, these two figures support the existing GPR161 model, showcasing that a loss of ciliary GPR161 results in a tissue-specific loss of GLI3R (Figure 6D) and consequently the development of additional digits (Figure 6E) and craniofacial defects (Figure 7D and 7E).

Weaknesses:

1. There is no data in the paper showing that Gli3 repressor function is affected preferentially compared to Gli Activator function. In Figure 4C, Gli3 FL/R ratios are not different between wt/wt and mut/mut embryos. The data can be explained by the fact that the mutant Gpr161 is a partial loss of function allele and the resultant weaker phenotypes (compared to the full KO) show some tissue specificity. Linking this allele to a specific biochemical mechanism is not justified by the data.

2. The authors use an endpoint assay based on overexpression in 293T cells to claim that cAMP production is unaffected by the Gpr161mut allele. However, weak effects (very likely given the weak phenotypes) may not be evident this assay. We also do not know if the mutant allele is defective in some other biochemical function or in localization to other places in the cell. One way to address this is to measure ciliary and extraciliary cAMP in their knock-in cells. In Gpr161mut1/mut1 cells, is ciliary cAMP reduced to levels comparable to Gpr161ko/ko cells? Is extraciliary cAMP unchanged compared to WT cells? Or, is cAMP able to diffuse into the cilia from GPR161mut1 localized to vesicles at the ciliary base (Figure 1B)? Many of the conclusions made in the paper equate a loss of ciliary GPR161 to a loss of ciliary cAMP, but this loss of ciliary cAMP is not definitively shown in the paper.

3. Compared to Figures 6 and 7, the data presented in Figures 3 and 5 are very confusing and difficult to interpret. On the one hand, this is understandable, the Gpr161mut/mut phenotypes are complex, and some tissues (like the developing spinal cord) are more resistant to change due to a loss of GliR. On the other hand, the data collected from the numerous genotypes analyzed could be easier to interpret by (i) providing a penetrance of the phenotypes and (ii) quantifying the phenotypes. Below are a few examples of data that could be improved with quantifications:

— In Figure 3, the authors are trying to convey that the Gpr161mut allele is partially functional and produces a milder phenotype than the Gpr161ko allele. However, the Gpr161ko/ko, Gpr161mut/ko, and Gpr161mut/mut phenotypes showcased in the figure all look quite severe, and it is difficult to appreciate the differences in the defects fully. An accompanying table summarizing the phenotypes and their penetrance in the affected genotypes would help to convey this point.

— In Table 1, the authors note that the Gpr161mut1/mut1 mouse is embryonic lethal by e14.5, but the analysis in Table 1 appears to be incomplete. In the table titled "breeding between Gpr161 mut1/+ parents," the authors indicate that they only assessed one litter of e14.5 and e15.5 embryos. Oddly, the authors note that additional litters were collected, but the embryos were not genotyped because the embryos exhibited no phenotypes. The absence of phenotypes could be due to an absence of viable Gpr161mut1/mut1 embryos; however, the embryos need to be genotyped and a chi-square analysis conducted to verify this. Death can be a measure of phenotype severity, but I think it is important to surmise why the embryos are dying. It is unclear whether the embryos are dying due to the heart defects mentioned in the discussion. If the embryos are dying due to the heart defect, then it would be important to know whether the heart defects are more severe in the Gpr161ko/ko embryos.

— In Figure 5, quantifying the progenitor domains would greatly assist in discerning differences between the various genotypes. For example, a quantification would help readers assess differences in NKX6.1 across the various genotypes. On an unrelated note, the PAX7 staining of the Gpr161mut1/ko spinal cord looks very strange because the line adjacent to the image does not accurately represent the dorsal-ventral patterning of PAX7 seen in the image. This image would need to be replaced.

While Franklin has a great suggestion for how the professorship should be setup, I think she makes a great point that, like men, women should be sought after to fill these positions. So, instead of waiting for someone to stop in to claim the position, they should instead seek out the brilliant minds to fill the position.

Reviewer #1 (Public Review):

1) The user manual and tutorial are well documented, although the actual code could do with more explicit documentation and comments throughout. The overall organisation of the code is also a bit messy.

2) My understanding is that this toolbox can take maps from BigBrain to MRI space and vice versa, but the maps that go in the direction BigBrain->MRI seem to be confined to those provided in the toolbox (essentially the density profiles). What if someone wants to do some different analysis on the BigBrain data (e.g. looking at cellular morphology) and wants that mapped onto MRI spaces? Does this tool allow for analyses that involve the raw BigBrain data? If so, then at what resolution and with what scripts? I think this tool will have much more impact if that was possible. Currently, it looks as though the 3 tutorial examples are basically the only thing that can be done (although I may be lacking imagination here).

3) An obvious caveat to bigbrain is that it is a single brain and we know there are sometimes substantial individual variations in e.g. areal definition. This is only slightly touched upon in the discussion. Might be worth commenting on this more. As I see it, there are multiple considerations. For example (i) Surface-to-Surface registration in the presence of morphological idiosyncracies: what parts of the brain can we "trust" and what parts are uncertain? (ii) MRI parcellations mapped onto BigBrain will vary in how accurately they may reflect the BigBrain areal boundaries: if histo boundaries do not correspond with MRI-derived ones, is that because BigBrain is slightly different or is it a genuine divergence between modalities? Of course addressing these questions is out of scope of this manuscript, but some discussion could be useful; I also think this toolbox may be useful for addressing this very concerns!

Worse still. Bush wrote As We May Think in 1939 We? Machines? Yet the Atlantic was quick to spin it with the subtitle "A TOP U.S, SCIENTIST FORESEES A POSSIBLE FUTURE WORLD IN WHICH MAN-MADE MACHINES WILL START TO THINK" We? Machines?

https://www.linkedin.com/in/gyuri-lajos/detail/recent-activity/

A fascinating take on "everyone is dying"

Time in the US, Homelife, Mexican traditions, Holidays, Spanish language, US traditions, Holidays

Return to Mexico, Jobs, Community, Opportunity, Family Relationships, Feelings, Dreams; Reflections, Mexico, The United States

This does not seem to be such a rigid definition to me. I think we use psychology in combination with closely related subjects, such as sociology, and it can become easy to mix the two. I think "a scientific description of the mind, of mental activity, or of mental products" seems like a reasonable definition for psychology.

Peer Reviewed and recommended by Peer Community in Evolutionary Biology

Recommendation<br> Separating adaptation from drift: A cautionary tale from a self-fertilizing plant<br> by Christoph Haag based on reviews by Jon Agren, Pierre Olivier Cheptou and Stefan Laurent.

In recent years many studies have documented shifts in phenology in response to climate change, be it in arrival times in migrating birds, budset in trees, adult emergence in butterflies, or flowering time in annual plants (Coen et al. 2018; Piao et al. 2019). While these changes are, in part, explained by phenotypic plasticity, more and more studies find that they involve also genetic changes, that is, they involve evolutionary change (e.g., Metz et al. 2020). Yet, evolutionary change may occur through genetic drift as well as selection. Therefore, in order to demonstrate adaptive evolutionary change in response to climate change, drift has to be excluded as an alternative explanation (Hansen et al. 2012). A new study by Gay et al. (2021) shows just how difficult this can be.

The authors investigated a recent evolutionary shift in flowering time by in a population an annual plant that reproduces predominantly by self-fertilization. The population has recently been subjected to increased temperatures and reduced rainfalls both of which are believed to select for earlier flowering times. They used a “resurrection” approach (Orsini et al. 2013; Weider et al. 2018): Genotypes from the past (resurrected from seeds) were compared alongside more recent genotypes (from more recently collected seeds) under identical conditions in the greenhouse. Using an experimental design that replicated genotypes, eliminated maternal effects, and controlled for microenvironmental variation, they found said genetic change in flowering times: Genotypes obtained from recently collected seeds flowered significantly (about 2 days) earlier than those obtained 22 generations before. However, neutral markers (microsatellites) also showed strong changes in allele frequencies across the 22 generations, suggesting that effective population size, Ne, was low (i.e., genetic drift was strong), which is typical for highly self-fertilizing populations. In addition, several multilocus genotypes were present at high frequencies and persisted over the 22 generations, almost as in clonal populations (e.g., Schaffner et al. 2019). The challenge was thus to evaluate whether the observed evolutionary change was the result of an adaptive response to selection or may be explained by drift alone.

Here, Gay et al. (2021) took a particularly careful and thorough approach. First, they carried out a selection gradient analysis, finding that earlier-flowering plants produced more seeds than later-flowering plants. This suggests that, under greenhouse conditions, there was indeed selection for earlier flowering times. Second, investigating other populations from the same region (all populations are located on the Mediterranean island of Corsica, France), they found that a concurrent shift to earlier flowering times occurred also in these populations. Under the hypothesis that the populations can be regarded as independent replicates of the evolutionary process, the observation of concurrent shifts rules out genetic drift (under drift, the direction of change is expected to be random).

The study may well have stopped here, concluding that there is good evidence for an adaptive response to selection for earlier flowering times in these self-fertilizing plants, at least under the hypothesis that selection gradients estimated in the greenhouse are relevant to field conditions. However, the authors went one step further. They used the change in the frequencies of the multilocus genotypes across the 22 generations as an estimate of realized fitness in the field and compared them to the phenotypic assays from the greenhouse. The results showed a tendency for high-fitness genotypes (positive frequency changes) to flower earlier and to produce more seeds than low-fitness genotypes. However, a simulation model showed that the observed correlations could be explained by drift alone, as long as Ne is lower than ca. 150 individuals. The findings were thus consistent with an adaptive evolutionary change in response to selection, but drift could only be excluded as the sole explanation if the effective population size was large enough.

The study did provide two estimates of Ne (19 and 136 individuals, based on individual microsatellite loci or multilocus genotypes, respectively), but both are problematic. First, frequency changes over time may be influenced by the presence of a seed bank or by immigration from a genetically dissimilar population, which may lead to an underestimation of Ne (Wang and Whitlock 2003). Indeed, the low effective size inferred from the allele frequency changes at microsatellite loci appears to be inconsistent with levels of genetic diversity found in the population. Moreover, high self-fertilization reduces effective recombination and therefore leads to non-independence among loci. This lowers the precision of the Ne estimates (due to a higher sampling variance) and may also violate the assumption of neutrality due to the possibility of selection (e.g., due to inbreeding depression) at linked loci, which may be anywhere in the genome in case of high degrees of self-fertilization.

There is thus no definite answer to the question of whether or not the observed changes in flowering time in this population were driven by selection. The study sets high standards for other, similar ones, in terms of thoroughness of the analyses and care in interpreting the findings. It also serves as a very instructive reminder to carefully check the assumptions when estimating neutral expectations, especially when working on species with complicated demographies or non-standard life cycles. Indeed the issues encountered here, in particular the difficulty of establishing neutral expectations in species with low effective recombination, may apply to many other species, including partially or fully asexual ones (Hartfield 2016). Furthermore, they may not be limited to estimating Ne but may also apply, for instance, to the establishment of neutral baselines for outlier analyses in genome scans (see e.g, Orsini et al. 2012).

References

Cohen JM, Lajeunesse MJ, Rohr JR (2018) A global synthesis of animal phenological responses to climate change. Nature Climate Change, 8, 224–228. https://doi.org/10.1038/s41558-018-0067-3

Gay L, Dhinaut J, Jullien M, Vitalis R, Navascués M, Ranwez V, Ronfort J (2021) Evolution of flowering time in a selfing annual plant: Roles of adaptation and genetic drift. bioRxiv, 2020.08.21.261230, ver. 4 recommended and peer-reviewed by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.08.21.261230

Hansen MM, Olivieri I, Waller DM, Nielsen EE (2012) Monitoring adaptive genetic responses to environmental change. Molecular Ecology, 21, 1311–1329. https://doi.org/10.1111/j.1365-294X.2011.05463.xISTEX

Hartfield M (2016) Evolutionary genetic consequences of facultative sex and outcrossing. Journal of Evolutionary Biology, 29, 5–22. https://doi.org/10.1111/jeb.12770