In your initial tag line you have "education" and I like that better. It is just a bit more broad.

Author Response

Reviewer #1:

Major comments:

1) The title and the conclusion that SON and SRRM2 form nuclear speckles are not supported by the data. The data show that SON and SRRM2 are necessary for nuclear speckle formation. They do not rule out that another factor is necessary, such as SRRM1, which interacts with SRRM2 and itself harbors an intrinsically-disordered domain. That is, the authors have not shown that SON and SRRM2 are also sufficient for nuclear speckle formation. Such a test is necessary to draw the strong conclusion the authors make, and precedence for such a test has been established in the study of Cajal bodies. Specifically, central factors to Cajal body formation were shown to nucleate Cajal body formation at a specific site in chromatin when such central factors were localized to that site. The authors either need to perform such a sufficiency experiment or moderate their conclusions (and title).

2) In principle, in the immunofluorescence studies, the disappearance of mAb SC35 signal on depletion of SRRM2 does not alone prove that SRRM2 is what is visualized by the mAb SC35 in such assays. Given that this paper seeks to establish rigorously that mAb SC35 marks nuclear speckles by recognition of SRRM2, given that SRSF7 is recognized by the antibody on blots, and given that SRSF2 has been traditionally presumed the target of mAb SC35 in nuclear speckles, the rigor of this study demands that SRFS7 and SRSF2 be visualized in cells in the presence of an SRRM2 truncation to rule out that either SRSF7 or SRSF2 phenocopy SRRM2 in this assay.

This is a valid concern and we have thought of the same principal that is if any strongly speckle-associated intrinsically disordered domain containing protein, such as SRRM1 or RBM25, two proteins that are also frequently used as NS markes, would have a similar impact on NS formation as SRRM2 has. To this end, we performed a co-depletion of SON and SRRM1 (shown in Supplementary Figure 10) in a cell line that has a TagGFP2 inserted into SRRM2 gene locus. As it can be seen from the imaging presented in this figure for 4 individual cells (but also more generally on 10 independent field imaged, (data not shown)) we did not score a reduction in the GFP intensity, or dissolution of the spherical bodies as is the case in SON-SRRM2 co-depleted cells. We observed the nuclear speckles have the round-up morphology, that is seen upon SON-KD, but are not dissolved shown with PNN staining and SRRM2-TagGFP signals. Moreover, we performed a co-depletion of RBM25 (another strongly NS-associated protein also used as a NS-marker) and SON which did not result in the dissolution of nuclear speckles (Supplementary Figure 10). Therefore, we have reached to the conclusion that SON and SRRM2 form nuclear speckles with the contribution of SON being more important for the formation and titled our study accordingly.

Traditionally, because of the Fu & Maniatis 1992 paper, as pointed out by the reviewer, it is assumed that SC-35 recognizes SRSF2 in immunofluorescence experiments and potentially multiple SR-proteins in immunoblots. The former point, to the best of our knowledge, has never really been proven in any type of rigorous experiment. Fu lab. has generated SRSF2 K/O mice, but never provided an immunofluorescence image that shows that SC-35 signal disappears in K/O cells.

Just to summarize our line of reasoning here:

1) We do an unbiased IP-MS experiment, which shows that SRRM2 is the top candidate protein, at least an order of magnitude away from any other protein in the dataset by any measure. This strongly suggest that SRRM2 is the primary target of this antibody, although doesn’t prove it due to technical reasons i.e. no input normalization, some proteins produce more ‘mass-specable’ peptides than others, and larger proteins tend to produce more peptides.

2) We carry out a biased screen of 12 SR-proteins and find that SRSF7 is strongly recognized by mAb SC-35

3) We do IP-western blotting experiments, which correct for input and are not affected by relative ‘mass-specable’ peptide issues or protein sizes, which reveal a strong enrichment of SRRM2 (>10% of input), some enrichment for SRSF7 (~2% of input) and no enrichment for SRSF2, SRSF1 or other proteins that we have tested.

4) Since the “35kDa” protein is so engrained with the history of this antibody and our results were most consistent with the idea that this protein is SRSF7 rather than anything else, we insert a degron tag to SRSF7. If the hypothesis is true, then we expect a shift of the SC-35 band, concomitant to the shift in SRSF7, which is indeed the case. This is not proof that SC-35 doesn’t recognize any other protein but it does provide very strong evidence (combined with the other two experiments) that the 35kDa band detected by SC-35 in immunoblots is in fact SRSF7.

5) We then show, by TagGFP2 insertion into the SRRM2 locus, that SC-35 mAb can recognize SRRM2 specifically on immunoblots, and furthermore truncations beyond a certain point completely eliminates this signal. We also show later that siRNA mediated KD of SRRM2 also leads to the elimination of the signal from immunoblots (Supplementary Figure 9).

6) Combining the results so far, we address the issue of immunofluorescence, i.e. which protein or proteins are responsible for this signal. We think there are two possible scenarios that could both be true based on the presented evidence so far:

a. This signal is mainly, if not entirely, originates from SRRM2. b. The signal is a combination of SRRM2, SRSF7 and/or other SR-proteins that the SC-35 might be cross-reacting.

7) We then take advantage of our cell lines with SRRM2 truncations. These truncated SRRM2 version are not recognized by SC-35 mAb on immunoblots, therefore it is reasonable to suspect that they will not be recognized by SC-35 mAb in immunofluorescence as well.

8) If scenario (b) is correct and nuclear speckles are still intact in these cells (which we show that they are indeed intact, judged by SON, RBM25 and SRRM1 stainings Fig. 3A-B), then we would expect either no change in SC-35 signal, or a somewhat reduced signal. We see a complete loss of signal.

9) Being extra careful with this result, we also mix the control cell line and SRRM2-truncated cells and image them side-by-side to address any issues related to imaging settings etc. There is no detectable SC-35 signal in truncated cells.

10) We also show that the 35kDa band is still unchanged in SRRM2 truncated cells (Figure 2E), showing that SRSF7 itself is not affected in these cells.

These results, combined together, show that SC-35 signal in immunofluorescence originates from SRRM2, and any other signal potentially contributed by other proteins are below the detection of immunofluorescence microscopy.

Reviewer #2:

This study reports important evidence that the widely-used SC-35 antibody primarily recognizes SRRM2 rather than the assumed SRSF2. The manuscript provides several lines of evidence supporting this conclusion, and the work has broad impact on the field of nuclear structure and function as this antibody is the most common marker for the major nuclear component, nuclear speckles.

The one concern with the manuscript is the interpretation of some of the previous literature and understanding in the field.

First, since the 1990s it has been widely known that the SC-35 mAb has very limited specificity for denatured proteins and was not suitable for immunoblots (see abcam page for ab11826). Indeed, the assumption has always been that it recognizes a folded epitope. Therefore, the use of western blots to conclude anything about the specificity of this antibody is inappropriate.

Secondly, it has also been previously documented that this antibody has cross-reactivity with SRSF7 (i.e. 9G8; Lynch and Maniatis Genes Dev 1996).

Third, most SR proteins are not abundantly observed in tryptic MS due to high cleavage of RS domains. This is particularly true of SRSF2, which has a highly "pure" RS domain (i.e. all RS repeats) that encompasses almost half of the total protein. SRRM2, on the other hand, has much more complex and degenerate RS domains that encompass a much smaller percentage of the total protein. SRRM2 is also 10x the size of SRSF2. Thus, given equal molar amounts of SRSF2 and SRRM2, one would expect at least 20x the number of peptides and much more complete coverage of SRRM2 vs. SRSF2. Therefore, while the subsequent immunoblot in Figure 1C is compelling evidence that SRRM2 is precipitated with the SC-35 antibody, while SRSF2 is not, the IP-MS data alone is not strong proof that the SC35 mAb primarily recognizes SRRM2 rather than SRSF2. The text should be revised accordingly.

Finally, the abstract implies that the demonstration of SON as a central component of speckles is new ("elusive core"). As appropriately referenced in the text, this is not the case, rather SON is often used as a marker for nuclear speckles, and SON has long been considered to be part of the core of speckles, as knock-down has been documented by several groups to disrupt speckles. The wording in the abstract should therefore be more parsimonious.

With all due respect to all previous researchers that have used mAb SC35 and published their results, we think that the specificity issue has become unnecessarily convoluted due to the initial inaccurate characterization. Abcam’s recommendations highlight the issue in an interesting way. In the old marketing images, abcam shows a single band in a total lysate prepared from HEK293 cells: https://www.abcam.com/ps/products/11/ab11826/reviews/images/ab11826_49518.jpg

However, producing such an image, in our experience as we have also reported in the manuscript, is only possible under non-ideal western-blotting conditions i.e. when the transfer is not adequate to reveal proteins with large molecular weights. Intriguingly, a customer (not us) complains about an improper WB result obtained with this antibody (with a 2-star rating):

https://www.abcam.com/sc35-antibody-sc-35-nuclear-speckle-marker-ab11826/reviews/68414?productWallTab=ShowAll

It looks like an unexplainable high-molecular smear without the information that we provide in our manuscript, but in light of it, it’s clear that protein stained here is SRRM2.

In our experience the antibody works perfectly fine for western blotting, and very specifically and robustly reveals SRRM2 at ~300kDa, as long as the immunoblotting conditions are optimized for large proteins. We also show that bulk of the signal around 35kDa originates from SRSF7, however as indicated by the other reviewer’s comments, and also previous research, the antibody probably cross-reacts with other proteins as well with varying degree.

In this sense, the antibody can be used for immunoblotting, but pretty much any result obtained from such an experiment must be verified with an independent antibody or independent methods, which we did in this manuscript.

The SC35 mAb is actually suitable for western blotting if the gel running and transfer conditions are carefully performed to have SRRM2: a) enter the gel and b) transferred properly to the membrane. Under conditions where SRRM2 is just not entering the gel (due to high percentage gels, or gels with too much bis-acrylamide), or doesn’t get transferred to a membrane (non-ideal buffer conditions, protein stuck in stacking part and cut away etc.), we have seen the unspecific bands, but we had to use the most sensitive detection reagents at hand to see those, so they are rather weak. We have provided a detailed explanation to what these conditions are in the methods section of our manuscript, but briefly: running the gel slowly allowing the protein to enter in the gel and transferring overnight with CAPS buffer were key to get the western blot working. As we have shown in Figure 2C and 2E, the majority of signal detected comes from SRRM2. The unspecific binding of SC35 mAb could only be scored if the above-mentioned conditions were not met.

We believe what made matters historically worse has been the use Mg++ precipitation that enriches many SR proteins, but actually completely depletes SRRM2 (Blencowe et al. 1994 DOI: 10.1083/jcb.127.3.593, Figure 5, https://pubmed.ncbi.nlm.nih.gov/7962048/ ). When we’re sure that SRRM2 is in the gel though, it just shines as a single band. So in conclusion, SC-35 is reasonably specific to SRRM2, especially in immunofluorescence, but it certainly cross-reacts with other SR-proteins, especially when SRRM2 is missing for technical or biochemical reasons.

We will update in the manuscript for the corresponding section by citing earlier studies reporting the specificity issues of mAb SC35.

We absolutely agree that IP-MS data alone is not enough to conclude that SC-35 recognizes SRRM2, or whether it is the primary target or not. The overwhelming amount of SRRM2 peptides detected, in addition to the overwhelming amount of total peptide counts from SRRM2 does strongly suggest that it is the case, which we then followed up by IP-western blotting which controls for relative input, and the various experiments shown in later figures.

We have looked at our MS results and found out that:

SRSF2 was detected with 4 unique peptides with an MS/MS count of 5 and a sequence coverage of 29% (intensity 3E+07), whereas SRRM2 was detected with 227 unique peptides with an MS/MS count of 3317 and a sequence coverage of 61.9% (intensity 2E+11).

These numbers show a 6600 times higher intensity for SRRM2 (not normalized). As the identification and abundance of different peptides/proteins can by dramatically different in MS, it is indeed correct that one should be careful with such comparisons. The only way would be to use peptide standards for both proteins and record standard curves, then a real quantitative comparison would give the true numbers. Hence, we will revise the wording of that section.

Finally, as the reviewer has pointed out, we have not shown that speckles can be reformed by introducing ectopically expressed SON/SRRM2 into cells which now appear not to have nuclear speckles. This would indeed be the formal proof showing that SON/SRRM2 are not just necessary but also sufficient to form nuclear speckles. Such an experiment is quite challenging due to the length of these proteins and difficulty in establishing conditions where one can express these proteins, but not overexpress them which leads to round-up speckles (as shown and discussed by Belmonte lab). Therefore, we will change the title to “SON and SRRM2 are essential for the formation of nuclear speckles” to better reflect our conclusions.

We really did try to be clear and just about the previous literature around SON. Indeed, it is clear that SON is a crucial part of NS, likely the most important component for the integrity of speckles. However, in all of these previous studies, RNAi-mediated depletion of SON, without exception, leaves behind spherical bodies that are strongly stained with mAb SC35, that also harbor other NS-markers (which we also show). This is of course not new, as we also appropriately cited previous work, however being able to dissolve these “left-over” speckles by co-depletion of SRRM2, and perhaps more importantly by deletion of the SRRM2’s C-terminal region is indeed novel.

In essence, our results show that in the absence of SON, as shown by previous work as well, NS-associated proteins are still able to organize themselves into nuclear bodies, indicating that either all other SR-proteins without the need of another organizer clump together, or another factor (or factors) is still acting as an organizer. When we remove the C-terminus of SRRM2, which we show is the primary target of SC-35, which strongly stains these left-over nuclear bodies in the absence of SON, then deplete SON, all NS markers that we could find become diffuse, indicating that nuclear speckles no longer exist, or become too small to be detected or classified as “nuclear bodies”. Co-depletion of SON and SRRM2 leads to the same phenotype, but co-depletion of SON and SRRM1 (or RBM25) doesn’t, leaving behind spherical nuclear speckles that harbor SRRM2 which are no different than SON KD cells.

Reviewer #3:

Nuclear speckles in the last several years have attracted significant attention for their association with transcriptionally active chromosome regions (after largely being ignored by most for the previous 20 years). Overwhelmingly, a single monoclonal antibody has been used as a marker for nuclear speckles for several decades.

This manuscript now argues convincingly that the main target that is recognized by this monoclonal antibody is not SRSF2 (SC35) as long thought, but rather SRRM2. The authors thus clarify a vast literature, while also focusing attention on the very large protein SRRM2 that in many ways resembles another nuclear speckle protein, SON. Both have huge IDRs and unusual RS repeats, while SON has been proposed to act as a scaffold for many SR-containing proteins, which is likely also true for SRRM2, by extension. Moreover, the manuscript provides a convincing explanation for why the target of this antibody was previously misidentified, by showing a lesser cross-reaction with SRSF7, of similar MW to SC35.

Finally, the manuscript suggests that SON and SRRM2 together help nucleate nuclear speckles, as a double KD, or a SON KD in a background of a truncated SRRM2, leads to loss of nuclear speckle-like staining of other proteins normally enriched in nuclear speckles (RBM25, SRRM1, PNN). The authors go on to suggest that this double KD approach will now provide an important means of disrupting nuclear speckles to aid in functional studies.

Interestingly, some of the results of this manuscript actually are already confirmed or consistent with previous literature. For example, a cited paper describes changes in Hi-C compartmentalization patterns after "elimination" of nuclear speckles- actually, they performed a SRRM2 KD and showed loss of SC35 staining, which is now explained as simply due to the KD that they performed. More recently, a new proteomics study of nuclear speckles (Dopie et al, JCB, 2020: https://doi.org/10.1083/jcb.201910207) reported both SON and SRRM2 as the two most highly enriched nuclear speckle proteins, with enrichment scores similar to each other but more than twice that of all other speckle proteins. Moreover, this same paper also did a SRRM2 KD and observed loss of anti-SC35 staining but not SON staining.

Overall, I found this manuscript of significant interest for people in the nuclear cell biology field and technically thorough and well done. I just had one issue and one point to make in my main comments, plus some minor points.

1) The evidence that nuclear speckles are nucleated by SON and SRRM2 is based on the dispersion of staining of nuclear speckle proteins RMB25, SRRM1, and PNN. However, an alternative explanation is that some other protein(s) nucleates nuclear speckles, while these other nuclear speckle proteins bind to SON and SRRM2, and are therefore enriched in nuclear speckles. To eliminate this concern, the authors could show that SON and/or SRRM2 do not bind to these proteins- for instance using co-IP or other methods. Of course, it could be that such binding or scaffolding of nuclear speckle proteins is how they form nuclear speckles. But just one protein that is not bound by SON and SRRM2 but still stains nuclear speckles after the double KD would be inconsistent with their hypothesis. Therefore, if they do find that all these proteins bind SON and/or SRRM2 they could simply discuss this as a scaffolding mechanism but qualify their conclusion based on the alternative explanation described above.

2) In our lab we have not been comfortable using the kinase manipulations, discussed in this paper, to eliminate nuclear speckles for experimental purposes because the cells appear very sick after these manipulations. For other reasons, we also tried a double SON and SRRM2 KD. Our experience is that the cells after this double KD were also not very normal. If the authors are suggesting the SON and SRRM2 double KD as an experimental tool to disrupt nuclear speckles in order to access nuclear speckle function, then it would be valuable for them to indicate cell toxicity, etc. Many SR-protein KDs for example do not allow selection of stable cells. What about this double KD?

The first point of Reviewer #3 has been addressed above in response to the Reviewer #2.

We have stated that our work identifying SON and SRRM2 as the elusive core of nuclear speckles paves the way to study the nuclear speckles under physiological conditions. Here, we have used the cells 24 hours after transfection (~18 hours of knock-down) as the primary reason being that SON-KD caused a mitotic arrest if the cells were kept longer in culture. This was reported earlier in Sharma et al MBC 2010. There was no additional severity in the phenotype when the SON-KD was combined with SRRM2-KD, therefore we believe the arrest phenotype we scored is mainly due to depletion SON. In this sense, double-depletion of SON and SRRM2 can be used to study the effects of loss of NS (transcription, post-transcriptional, topological), but certainly within a time-frame of around 24 hours in cells that haven’t gone through mitosis. We will clarify this statement in the revised manuscript to avoid any misunderstanding as pointed by the reviewer. Faster depletion strategies, and/or a system where cells are mitotically arrested would be required to observe long term effects more reliably.

DOI: 10.1016/j.celrep.2020.108101

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What is this?

.h1 test

this is a .tag-test

twitter protesters forget to translate text about votings in Iran and were inconsiderate to the fact that they speak farsi there. they are protesting without actually aiming to create change.

western journalists took matter into their own hands without truly knowing what was happening from people inside of Iran. they took what they knew from things they collected from twitter and put it into their own hands. this is where i strongly disagree with social media activism. you may not need to be face to face but you do need to speak directly to people instead of making assumptions.

Die Verkorperung hier ist sehr stark und ich finde das sehr interessant, dass Auslander eine Person, die aus dem Keller kommt, mit die Stunden in Exil. Ich glaube, dass die Stunded durch einen Tag sich andern. Was meint ihr, was die Wirkung von die Verkorperung hier ist?

Die Exilerfahrung wird hier wie eine Haftstrafe beschreibt. Was ist die Wirkung von diesem Vergleich?

Wow thats a large price tag.

DEF

DOI: 10.1186/s13064-020-00146-6

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Curator comments: Anti-HA EPITOPE TAG (RABBIT) Antibody DyLight 549 Conjugated - 600-442-384 Rockland Cat# 600-442-384

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What is this?

DOI: 10.3390/cancers12071989

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Curator comments: HA tag antibody [HA.C5] Abcam Cat# ab18181

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What is this?

DOI: 10.1111/bph.15023

Resource: (Cell Signaling Technology Cat# 2276, RRID:AB_331783)

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SciCrunch record: RRID:AB_331783

Curator comments: Mouse Anti-Myc-Tag Monoclonal Antibody, Unconjugated, Clone 9B11 Cell Signaling Technology Cat# 2276

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DOI: 10.1016/j.chembiol.2020.03.004

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DOI: 10.1101/gad.332395.119

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SciCrunch record: RRID:AB_10691311

Curator comments: HA-Tag (6E2) Mouse mAb antibody Cell Signaling Technology Cat# 2367

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What is this?

DOI: 10.1101/gad.332395.119

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Curator comments: Rabbit Anti-HA-Tag Monoclonal Antibody, Unconjugated, Clone C29F4 Cell Signaling Technology Cat# 3724

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DOI: 10.2337/db19-0508

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What is this?

DOI: 10.2337/db19-0508

Resource: (Sigma-Aldrich Cat# F1804, RRID:AB_262044)

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Curator comments: Monoclonal ANTI-FLAG® M2 antibody Sigma-Aldrich Cat# F1804

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What is this?

DOI: 10.2337/db19-0508

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Curator comments: Anti-Myc Tag, clone 4A6 antibody Millipore Cat# 05-724

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DOI: 10.3233/CBM-190993

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What is this?

DOI: 10.3233/CBM-190993

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DOI: 10.1016/j.cell.2020.04.031

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What is this?

DOI: 10.1038/s41586-020-1947-z

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What is this?

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

__*Review 1 Summary:

__In this manuscript, Borah et al showed that Heh2, a component of INM, can be co-purified with a specific subset of nucleoporins. They also found that disrupting interactions between Heh2 and NPC causes NPC clustering. Lastly, they showed that the knockout of Nup133, which does not physically interact with Heh2, causes the dissociation of Heh2 from NPCs. These findings led the authors to propose that Heh2 acts as a sensor of NPC assembly state. *

__Reviewer 1 major comment 1:__ The authors claimed that Heh2 acts as a sensor of NPC assembly state, as evidenced by their finding that Heh2 fails to bind with NPCs in nup133 Δ cells (Fig2, Fig 5). However, there is a possibility that the association between Heh2 and NPCs is merely affected by the clustering of the NPCs (as the authors discussed) but not related to the structural integrity of NPC.

• *

Our Response: We agree that this is a possibility, however, we ask the reviewer to also consider that we artificially cluster NPCs using the anchor away system (Figure 3C) and this does not affect Heh2’s association with NPCs. Thus, clustering per se is insufficient to disrupt Heh2 binding to NPCs. We will also make changes in the text to make this point.

• *

Reviewer 1 major comment 2: In addition, their data showing that the Heh2-NPCs association is not easily disrupted by knocking out the individual components of the IRC (Fig. 5A and 5D), also disfavor the idea that Heh2 could sense NPC assembly state.

Our Response: There are three considerations here. The first is that as this is the first evidence of any kind of “NPC assembly state” sensor, it is difficult to make any assumptions as to what specifically such a sensor would be monitoring. i.e. perhaps sensing only the ORC is what is functionally important. Second, for obvious reasons, we only tested non-essential IRC nups so by definition there is inherent functional redundancy that maintains NPC function and thus there may be no need to “sense” anything in the absence of these IRC nups. Further (and last), the IRC is essential for NPC assembly. Thus, without an IRC there is no NPC assembly state to sense.

Reviewer 1 major comment 3: Since some nup knockout strains, other than nup133 Δ, are also known to show the NPC clustering (ex. nup159 (Gorsch JCB 1995) and nup120 (Aitchison JCB 1995; Heath JCB 1995)), it will be worth trying to monitor the localization of Heh2 and its interaction with nucleoporins (by Heh2-TAP) using these strains. While Nup159 is a member of the cytoplasmic complex, Nup120 is an ORC nucleoporin. Thus, biochemical and phenotypical analysis using these mutant cells will be useful to clarify if the striking phenotypes the authors found are specific to nup133 knockout strain (or ORC Nup knockouts) or could be commonly observed in the strains that show NPC clustering. Another interesting point is that Nup159 shows strong interaction with Heh2, even in nup133Δ cells. As the authors mentioned, Nup159-Heh2 interaction may not be sufficient for Heh2-NPC association, but it could be important for NPC clustering.

Our Response: These are excellent points and we agree that there is a need to more thoroughly explore how NPC clustering driven by abrogating the function of other nups impacts Heh2’s association with NPCs. Thus, in a revised manuscript, we would examine Heh2’s association with NPCs in several additional genetic backgrounds where NPCs cluster.

Reviewer 1 major comment 4: Figure 4C: Is it known that rapamycin treatment in this strain did not affect the protein levels of nucleoporins? Otherwise, the authors should confirm this by western blotting (at least some of them).

Our Response: This is a good point and we will directly address this with Western blotting of some nups.

Reviewer 1 major comment 5: Figure 5: The authors mentioned (line 256-257) that "in all cases the punctate, NPC-like distribution of Heh2-GFP was retained (Fig 5D)". However, nup107 KO strain seems to show more diminished punctate staining as compared with other strains. To clarify this, the authors should express mCherry tagged Nup as in Fig. 2 or Fig. 3.

Our Response: Yes, we agree and in fact this observation is consistent with the fact that there is an ER-pool of Heh2 observed in this strain and we observe loss of nup interactions in the affinity purification. We will include a more thorough quantification of this in a revised manuscript and more directly address this in the text.

**Minor comments:**

Reviewer 1 minor comment 1: Figure 4A and 4B: The authors should show Scatter plot as in Fig. 2 and Fig. 3.

• *

We will include this in a revised manuscript.

Reviewer 1 minor comment 2: Figure 5C: Explanations of the arrowheads is missing in the figure legend.

Thank you for pointing this out, it will be fixed in a revised manuscript.

Reviewer 1 minor comment 3: Figure 6: Is there any information as to where Heh2 (316-663) is localized in the cell?

As this truncation lacks INM targeting sequences, it is found throughout the cortical ER. The determinants of Heh2 targeting (including truncations) has been extensively evaluated in King et al. 2006, Meinema et al., 2011 and Rempel et al. 2020. We will make this clearer in the revised manuscript.

Reviewer 1 minor comment 4: Figure 6B: Nucleoporins should be marked with color circles as in Fig. 1 and Fig. 5.

This will be done.

Reviewer 2

Borah et al. present a biochemical and cell biological examination of the inner nuclear membrane (INM) protein Heh2 and its putative interactions with the nuclear pore complex (NPC). The potential conceptual advance of this study is that Heh2 interacts with the NPC, while mutations believed to trigger NPC mis-assembly are shown to abolish interaction with Heh2, leading to the hypothesis that Heh2 is a sensor for NPC assembly states within the (INM). The conclusions would undoubtably be of broad interest to the nucleocytoplasmic transport field, but the evidence provided thus far is insufficient to build confidence and consequently this manuscript is premature for publication.

Our Response: We thank the reviewer for recognizing the potential for a significant conceptual advance for the field but object to the notion that the work is “premature for publication”. This is a highly subjective statement that does not seem to meet the mission or purpose of the Review Commons platform. While it is possible that some of the conclusions drawn in our manuscript might not be fully supported by the data in its current form, there is a substantial body of work here that is certainly publishable.

Reviewer 2 major comment 1: The TAP-tag Heh1/Heh2 pulldowns are the most significant experiment presented, and on face value provide compelling evidence that Heh2 interacts with the NPC. It is stated that mass spectroscopy (MS) was used to confirm the identities of the labeled bands yet there is no methods section, nor any MS data reported in the manuscript. Given the large number of unspecified proteins observed in these gels, and the single-step pulldown methodology used, knowledge of the contaminants present may aid in elucidating how Heh2 pulls down NPC components. Consequently, within the supplementary materials, the authors must indicate which regions of the gel were excised for MS analysis and provide a table listing all of the proteins that were detected for each sample, including the number of unique/expected peptides observed. Our Response: This was a major oversight on our part and a revised manuscript will contain all relevant details with regards to the MS analysis including a more detailed description of the excised bands and the quantification of spectra derived from these bands.

Reviewer 2 major comment 2a: The representative micrographs provided across Figures 2, 3, 4, 5 and 6 are very noisy. Particularly in the case of the mCherry labeled nucleoporins, this is both unusual and unfortunate given this is used to infer colocalization of Heh2 with the NPC.

Our Response: These micrographs are not unusual and are in fact of respectable quality. We agree that the apparent “noise” is unfortunate, but this is simply a reality of the yeast system. We remind the reviewer that there are only ~100 to ~200 NPCs per budding yeast nucleus, which is an order of magnitude smaller than a typical mammalian cell nucleus. Further, the copy number of yeast nups per NPC is half of the mammalian cell NPC. Further, budding yeast are spherical with a cell wall that is extremely effective at scattering light; they are also highly autofluorescent (particularly in the red channel). Lastly, unlike in mammalian cells, budding yeast NPCs are mobile on the nuclear envelope. Thus, co-localization is challenging (particularly with the long exposures required to obtain good images). This is why clustering of NPCs driven by nup133**∆ cells has provided one of the key assays in the field to assess whether a given protein associates with NPCs at the level of light microscopy.

Reviewer 2 major comment 2b: As a result it is unclear whether this experiment can be used to differentiate between NPC colocalization vs. nuclear envelope colocalization.

Our Response: The reviewer is correct. Co-localization between Heh2-GFP and any Nup-mCherry is insufficient to assess NPC association in WT cells. In fact, as we point out in Figure 3B, at best one can expect a correlation of r = 0.48 for two well established nups. Thus, to further support the conclusion that Heh2 associates with NPCs, we established the Nsp1-FRB NPC clustering assay (Figure 3).

Reviewer 2 major comment 2c: The authors should include negative controls for an alternative NE membrane protein that doesn't bind the NPC, which would be expected to exhibit a reduced level of colocalization with NPC proteins when compared to Heh2. For example, Heh1 would be a suitable, given the clear-cut negative pulldown data and its prior usage as a negative control in Figure 4.

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Our Response: This is included in Figure 3D.

Reviewer 2 major comment 3a. Figure 2. The rim staining for the Nup82-mCherry in the WT background is unusually punctate, bringing into question the viability of the cells imaged.

Our Response: As the middle cell in the panel is undergoing cell division, these cells are clearly viable. All our imaging is performed on mid-log phase cultures.

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Reviewer 2 major comment 3b. Why has ScNup82, a cytoplasmic filament component, been selected for colocalization experiments when Heh2 is proposed to interact with the inner ring complex?

Our Response: The resolution of a conventional light microscope is, at best, 200 nm in x, y. As NPCs are 100 nm in diameter, even two NPCs side-by-side cannot be resolved. The IRC is tens of nm away from the cytoplasmic filaments thus any nup is relevant for a co-localization analysis with a light microscope.

Reviewer 2 major comment 3c: Additionally, the experiments shown in panels A and C are not directly comparable, ScNup82 is an asymmetric cytoplasmic nucleoporin, while SpNup107 is located in the Y-shaped Nup84 nucleoporin complex and present on both faces of the NPC. This experiment should be repeated with scNup84 to match panel C, additionally a viability dot spot assay and western blot analysis of the labeled proteins should be conducted.

Our response: These are in fact directly comparable within the limits of resolution of light microscopy as described above. Viability assays are not required here as both nups are essential and perturbation to their function would lead to inviability.

Reviewer 2 major comment 4: Figure 3, the authors use yeast strains where proteins are tagged with FRB and FKBP12 domains, which dimerize upon the addition of rapamycin inducing NPC clusters. The authors then observe the effect this has on Heh2 NPC colocalization. However, Rapamycin may also have an effect independent from the induced dimerization event. Negative controls should be performed in strains lacking the FRB and FKBP12 tagged proteins to demonstrate that Rapamycin doesn't modify Heh2 localization independently of NPC clustering.

Our response: This is a good point and important control that we performed in prior studies, see Colombi et al., JCB, 2013. We will be more explicit in describing that this control has been done.

Reviewer 2 major comment 5: Figure 4. The authors provide a qualitative description of the colocalization presented, while in all other instances they calculate a Pearson correlation coefficient. This is significant because Heh2 appears to be evenly distributed within the NE of the DMSO control (panel B). Given the presented hypothesis isn't colocalization expected with Nup192? As a minimum, a Pearson correlation coefficient analysis should be conducted and added to Figure 4.

Our response: This will be included in a revised manuscript.

Reviewer 2 major comment 6: Figure 4. Pom152-mCherry localizes at both the NE and strongly within the cytoplasm, which is unexpected given typical rim staining phenotypes observed previously for both Pom152-YFP and Pom152-GFP strains (Katta, ..., Jaspersen et al., Genetics (2015) & Upla, ..., Fernandez-Martinez et al., Structure (2017), respectively). Given the unusually weak rim staining observed throughout, viability assays of the strains listed in Table S1 and protein expression analysis of the tagged nucleoporins via western blot is necessary.

Our response: This is not localization in the cytoplasm but is in fact autofluorescence from the yeast vacuole. We regret we were not more explicit in describing this and we will make the manuscript more accessible for the non yeast expert. In order to perform the Western blot analysis for all strains requested by the reviewer would require a battery of antibodies to the endogenous proteins to directly assess how tagging influences nup levels, which we do not have (nor does anyone else that we are aware of). This is also not standard practice in the field as it is an onerous and unnecessary burden.

Reviewer 2 major comment 7:* Figure 5A. The TAP-tagged pulldowns from ∆Pom152 and ∆Nup133 strains appear to be from a different round of experiments than the previous deletion strains presented. Interestingly, there appears to be an additional band at approximately 250 kDa in both cases that is not present in any other experiments. This band could be a contaminant observed due to different experimental conditions, or a protein that exclusively binds to Heh2 in the ∆Pom152 and ∆Nup133 background. Either way the authors should identify this protein with MS to address this ambiguity.

*

Our response: We will include negative controls for these specific experiments to show that this is a non specific band.

Reviewer 2 major comment 8: Figure 6B. Please label the nucleoporin bands in the TAP-tagged pulldowns.

Our response: This will be done.

Reviewer 2 major comment 9: Figure 6D. Please specify Heh2-GFP clustering in the y-axis.

Our response: As this represents both Heh2-GFP and heh2-1-570-GFP, we will keep it as is to avoid confusion.

Reviewer 2 major comment 10: *Under the results section titled 'Heh2 binds to specific nups in evolutionarily distant yeasts', the authors state that spHeh2 co-purifies with "several specific species". The meaning is unclear, this sentence should be rephrased and the specific species clearly described. **

*

Our response: Ok.

Reviewer 2 major comment 11: Under the results section titled 'Heh2 fails to interact with NPCs lacking Nup133', the authors refer to a Pearson correlation coefficient of -0.03 as a clear anticorrelation. Instead state there was no correlation.

Our response: Ok.

Reviewer 2 major comment 12: In the discussion, the authors state that "clustering itself may sterically preclude an interaction with Heh2". The text should be expanded to explain this in more detail, it is not clear from the presented data why this would occur.

Our response: Ok.

Reviewer 2 comment on significance: the manuscript is premature for publication.

Our Response: Such a statement has no relevance to this form of review as a decision as to whether a study is premature for publication should be made by journal editors, not reviewers. We would argue quite strongly that we have definitively shown that Heh2 binds to NPCs, that it does so in multiple evolutionarily distant yeasts and that this binding is functionally relevant. For example, we can specifically disrupt the association of Heh2 with NPCs with a specific domain deletion and observe a loss of function phenotype (e.g. NPC clustering). What all three reviewers agree on is that the concept of a “NPC assembly state sensor” needs additional data to be fully supported, although we note that this reviewer did not provide any suggestions for how we might achieve this goal. We further note that we added the qualifier “may” into the title of the work. Thus, we will therefore perform additional experiments as outlined in comments to Reviewer 1 to support this conclusion in order to introduce this as a new concept in the field.

Reviewer Comment from Cross Commenting: It seems to me that all reviewers agree that the manuscript is premature for publication. The data thus far do not support the conclusion that Heh2 may be an NPC assembly sensor nor does it provide any mechanistic insight. Reading the comments of the other two reviewers makes me more negative, as it is care that the paper also lacks scientific rigor. The manuscript is a great starting point for a rigorous dissection but I do not see this paper to be a candidate for a broad impact journal.

Our Response: The statement that this manuscript is premature for publication is an opinion and does not seem to reflect the sentiment of the other reviewers. It is also confounding that this reviewer suggests that this work lacks rigor. With the exception of the omission of the MS analysis (our fault), the data are of high quality and rigorously quantified. Our assertion of rigor and data quality is based on our collective team’s many decades-long history of publishing and reviewing papers at the highest levels in this field. Questions as to the quality of the data as stated by this reviewer (and only this reviewer) in fact address limitations of light microscopy and the yeast system more generally in this one respect.

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Reviewer 3

Reviewer 3 Summary part a*: This is quite an interesting manuscript that explores the relationship between an INM protein, Heh2, and NPCs. It represents an extension of earlier work performed by this group in which it was shown that the HEH2 gene shares genetic interactions with the genes encoding various nucleoporins. Heh2 belongs to an intriguing family of conserved proteins that includes its orthologue, Heh1, as well as human MAN1 (LEMD3) and LEMD2, among others. Each of these proteins contains two transmembrane domains with the N- and C-terminal regions extending in to the nucleoplasm. The two TM domains are separated by a short lumenal loop.

In this study, the authors show that a population of Heh2 is associated with Nups of the NPC inner ring complex. This was demonstrated initially in pulldown experiments. The authors go on to show that when NPCs are caused to aggregate, by physical tethering employing an FKBP/FRP system in combination with Rapamycin, Heh2, but not Heh1, colocalizes with the NPC clusters. *

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Our Response: Thank you to the reviewer for recognizing the value of this work.

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Reviewer 3 Summary_b. Although not stated explicitly in the manuscript, this would imply that there is a population of Heh2 that resides in the NPC membrane domain, with the remainder in the INM. As an idle question, is there any evidence for a similar localization of MAN1 or LEMD2 in mammals? I am guessing probably not.

Our Response: We regret this was not made more clear but the idea that there is a pool of Heh2 at the POM and a pool at the INM is an important conclusion of the work and was stated in the results - we’ll re-emphasize in the revised discussion. As to whether MAN1 or LEMD2 has a similar NPC association, we hypothesize that MAN1 but not LEMD2 will indeed interact with NPCs in mammalian cells. This is based on considering that we show that both the budding and fission yeast orthologues of MAN1 share this association so unless it was lost in evolution, this is a likely outcome of future studies.

Reviewer 3 Significance statement a: The complications arise when the authors show that an alternative method of NPC aggregation (although they did this first), involving Nup133 deletion, results in failure of Heh2 to co-aggregate. In other words, Nup133 is required for the association of Heh2 with NPCs. The issue here is that there is no evidence for an interaction between Heh2 and Nup133, and furthermore that loss of Nup133 (a Y complex component of the outer ring complex) leaves the inner ring complex intact.

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Our Response: We tested the nup133Δ background first as this is the standard approach for assessing NPC-association of a given protein so we felt this would be logical for a reader in the field. Further, while the disruption of Heh2’s binding by loss of Nup133 may be a complication, we prefer to see it as an opportunity for discovery. As described in our manuscript, we have chosen to interpret this result in the context of a new biological function/concept with Heh2 being a novel “NPC assembly state” sensor. While one could argue that we have not fully met this bar yet, we will perform additional experiments as outlined in our response to reviewer 1 to help support this compelling conclusion.

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Reviewer 3 Signfiicance statement b: What is clear, however, is that Heh2 seems to be required to inhibit NPC aggregation since Heh2 deficient cells exhibit NPC clusters. The association between Heh2 and IRC Nups resides in the C-terminal nucleoplasmic winged helix domain. The N-terminal domain, in contrast confers INM localization.

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Our Response: We agree.__*

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Reviewer 3 Signfiicance statement c I must admit, I am in two minds about this manuscript. The data clearly show that Heh2 is associated with IRC components and I agree with the authors that this protein may well have a role in NPC assembly quality control perhaps in the guise of a chaperone. However, I find it hard to come up with a convincing model for the effects of Nup133. On the one hand, one could make an argument that the data presented here is too preliminary and fails to provide a complete story. On the other hand, it does provide an intriguing foundation for future studies and I do feel positively disposed towards it. In short, I have no fundamental complaints about the science, I am just uncertain as to whether the study is ready for publication.

Our Response: This statement nicely articulates the challenge with this manuscript as there are some solid findings (that Heh2 binds specifically to NPCs etc.) but also a provocative finding (that loss of Nup133 breaks Heh2’s interaction with NPCs despite not physically interacting). Thus, there is a decision to be made about whether there is value in introducing a novel concept to the field once additional data is provided in a revised manuscript.

Reviewer 3 Cross commenting: I have no fundamental disagreements with either of the other two reviewers. The comment from Reviewer#2 summarises this quite neatly. While I have fewer concerns about the quality of the data as presented, I think we all agree that at best the study is preliminary. What the authors need to do is to construct a coherent model that will account for the observations described here and then to design experiments that will test this model. I'm not suggesting that they must have a complete story, but they do need to go beyond what is in the current manuscript.

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Our Response: We appreciate that the reviewer does not have any questions about the quality of our data, but we argue that we have in fact presented the most coherent interpretation of the data as it currently stands. As described above, we intend to attempt to solidify this model by performing experiments suggested by reviewer 1.

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The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). Though this is not stated in the MS

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Referee #2

Evidence, reproducibility and clarity

Borah et al. present a biochemical and cell biological examination of the inner nuclear membrane (INM) protein Heh2 and its putative interactions with the nuclear pore complex (NPC). The potential conceptual advance of this study is that Heh2 interacts with the NPC, while mutations believed to trigger NPC mis-assembly are shown to abolish interaction with Heh2, leading to the hypothesis that Heh2 is a sensor for NPC assembly states within the (INM). The conclusions would undoubtably be of broad interest to the nucleocytoplasmic transport field, but the evidence provided thus far is insufficient to build confidence and consequently this manuscript is premature for publication.

Specific comments:

(1)The TAP-tag Heh1/Heh2 pulldowns are the most significant experiment presented, and on face value provide compelling evidence that Heh2 interacts with the NPC. It is stated that mass spectroscopy (MS) was used to confirm the identities of the labeled bands yet there is no methods section, nor any MS data reported in the manuscript. Given the large number of unspecified proteins observed in these gels, and the single-step pulldown methodology used, knowledge of the contaminants present may aid in elucidating how Heh2 pulls down NPC components. Consequently, within the supplementary materials, the authors must indicate which regions of the gel were excised for MS analysis and provide a table listing all of the proteins that were detected for each sample, including the number of unique/expected peptides observed.

(2)The representative micrographs provided across Figures 2, 3, 4, 5 and 6 are very noisy. Particularly in the case of the mCherry labeled nucleoporins, this is both unusual and unfortunate given this is used to infer colocalization of Heh2 with the NPC. As a result it is unclear whether this experiment can be used to differentiate between NPC colocalization vs. nuclear envelope colocalization. The authors should include negative controls for an alternative NE membrane protein that doesn't bind the NPC, which would be expected to exhibit a reduced level of colocalization with NPC proteins when compared to Heh2. For example, Heh1 would be a suitable, given the clear-cut negative pulldown data and its prior usage as a negative control in Figure 4.

(3)Figure 2. The rim staining for the Nup82-mCherry in the WT background is unusually punctate, bringing into question the viability of the cells imaged. Why has ScNup82, a cytoplasmic filament component, been selected for colocalization experiments when Heh2 is proposed to interact with the inner ring complex? Additionally, the experiments shown in panels A and C are not directly comparable, ScNup82 is an asymmetric cytoplasmic nucleoporin, while SpNup107 is located in the Y-shaped Nup84 nucleoporin complex and present on both faces of the NPC. This experiment should be repeated with scNup84 to match panel C, additionally a viability dot spot assay and western blot analysis of the labeled proteins should be conducted.

(4)Figure 3, the authors use yeast strains where proteins are tagged with FRB and FKBP12 domains, which dimerize upon the addition of rapamycin inducing NPC clusters. The authors then observe the effect this has on Heh2 NPC colocalization. However, Rapamycin may also have an effect independent from the induced dimerization event. Negative controls should be performed in strains lacking the FRB and FKBP12 tagged proteins to demonstrate that Rapamycin doesn't modify Heh2 localization independently of NPC clustering.

(5)Figure 4. The authors provide a qualitative description of the colocalization presented, while in all other instances they calculate a Pearson correlation coefficient. This is significant because Heh2 appears to be evenly distributed within the NE of the DMSO control (panel B). Given the presented hypothesis isn't colocalization expected with Nup192? As a minimum, a Pearson correlation coefficient analysis should be conducted and added to Figure 4.

(6)Figure 4. Pom152-mCherry localizes at both the NE and strongly within the cytoplasm, which is unexpected given typical rim staining phenotypes observed previously for both Pom152-YFP and Pom152-GFP strains (Katta, ..., Jaspersen et al., Genetics (2015) & Upla, ..., Fernandez-Martinez et al., Structure (2017), respectively). Given the unusually weak rim staining observed throughout, viability assays of the strains listed in Table S1 and protein expression analysis of the tagged nucleoporins via western blot is necessary.

(7)Figure 5A. The TAP-tagged pulldowns from ∆Pom152 and ∆Nup133 strains appear to be from a different round of experiments than the previous deletion strains presented. Interestingly, there appears to be an additional band at approximately 250 kDa in both cases that is not present in any other experiments. This band could be a contaminant observed due to different experimental conditions, or a protein that exclusively binds to Heh2 in the ∆Pom152 and ∆Nup133 background. Either way the authors should identify this protein with MS to address this ambiguity.

(8)Figure 6B. Please label the nucleoporin bands in the TAP-tagged pulldowns.

(9)Figure 6D. Please specify Heh2-GFP clustering in the y-axis.

(10)Under the results section titled 'Heh2 binds to specific nups in evolutionarily distant yeasts', the authors state that spHeh2 co-purifies with "several specific species". The meaning is unclear, this sentence should be rephrased and the specific species clearly described.

(11)Under the results section titled 'Heh2 fails to interact with NPCs lacking Nup133', the authors refer to a Pearson correlation coefficient of -0.03 as a clear anticorrelation. Instead state there was no correlation.

(12)In the discussion, the authors state that "clustering itself may sterically preclude an interaction with Heh2". The text should be expanded to explain this in more detail, it is not clear from the presented data why this would occur.

Significance

the manuscript is premature for publication.

REFEREES CROSS COMMENTING

It seems to me that all reviewers agree that the manuscript is premature for publication. The data thus far do not support the conclusion that Heh2 may be an NPC assembly sensor nor does it provide any mechanistic insight. Reading the comments of the other two reviewers makes me more negative, as it is care that the paper also lacks scientific rigor. The manuscript is a great starting point for a rigorous dissection but I do not see this paper to be a candidate for a broad impact journal.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): The manuscript by Huh et al. reports that oxidative stress causes fragmentation of a specific tyrosine pre-tRNA, leading to two parallel outcomes. First, the fragmentation depletes the mature tRNA, causing translational repression of genes that are disproportionally rich in tyrosine codon. These genes are enriched for those involved in electron transport chain, cell cycle and growth. Second, the fragmentation generates tRNA fragments (tRFs) that bind to two known RNA binding proteins. Finally, the authors identify a nuclease that is needed for efficient formation of tyrosine tRFs. Comment 1: Th­­­­e authors should include a short diagram indicating the various known steps of pre-tRNA fragmentation (perhaps as a supplement) for general readers.

Response: We thank the reviewer for their suggestion. Pre-tRNA fragmentation is still an unknown field but an initial introduction is best seen from pre-tRNA processing where there is a cleavage event for pre-tRNAs with an intron. This is a complex subject but a recent review from Hopper and Nostramo has done an excellent job in in describing the current field in yeast and vertebrate species (Hopper and Nostramo, Front. Genet., 2019). We have added this citation and new text in the manuscript about pre-tRNA processing for general readers to follow up on. We feel that a supplementary figure might be a bit too brief in describing the knowns and unknowns of pre-tRNA processing and fragmentation.

Comment 2: I find the enrichment for mitochondrial electron transport chain (ETC) curious. The ETC includes several oxidoreductases, which may be rich in tyrosine as it is a common amino acid used in electron transfer. The depletion of the tyrosine tRNA from among many tRNAs under oxidative stress may not be incidental but related to an attempt by the cell to decrease oxygen consumption to avoid further oxidative damage. The authors could further mine their data to corroborate this hypothesis. For example, are the ETC genes among the targets of the RNA binding proteins targeted by tyrosine tRFs? This could potentially connect the effects of mature tRNA depletion and tRFs.

Response: We thank the reviewer for this very interesting comment and insight, which had not occurred to us. The relationship between this response and oxidoreductase regulation could be a factor in both the tRNA and tRF modulations seen in our cells. Interestingly, we find that many oxidoreductases genes (such as the NDUF family) are bound by hnRNPA1 by CLIP. In new data, we have done stability experiments with the tRF (new Fig 7E-F) to show the regulon of hnRNPA1 is modulated with overexpression and LNA against the tRF, revealing that this tRNA fragmentation response modulates expression of certain oxidoreductase genes. However, we do not see clear and significant differences for ETC genes in particular. As hnRNPA1 is known to act as both a promoter and destabilizer of genes depending on context, it is likely that further and more detailed work will be needed to parse this hypothesis out in future studies.

Comment 3: In figure 4A, the authors should provide the tyrosine codon content of the overlap genes and show how much it differs from a randomly selected sample.

Response: We have identified an error in our manuscript where the overlap actually identifies 109 proteins rather than the 102 reported in the original manuscript. We apologize for this oversight. As for the overlap proteins, we plotted the downstream proteins detected in the proteome by mass spectrometry based off on Tyr-codon content. As explained in the text, the targets we tested were chosen for having higher than median levels of Tyr-codon, as seen in the histogram, and for showing some of the greatest reduction after Tyr tRNA-GUA depletion (Fig S4A). The other proteins found in the overlap will fall in a similar pattern along the histogram.

Comment 4: Fig.6F, lower panel: the model should show pre-tRNA, as opposed to mature tRNA, because it is the former that is fragmented.

Response: We apologize for the confusion. The model in Fig 7F was supposed to denote the pre-tRNA with the trailer and leader sequences intact initially, then lost with processing to mature tRNA. To make it clearer, we have now labeled the first species as “Pre-tRNA.”

Reviewer #1 (Significance (Required)): This study is comprehensive and novel, and includes several orthogonal and complementary approaches to provide convincing evidence for the conclusions. The main discovery is significant because it presents an important advance in post-transcriptional control of gene expression. The process of tRF formation was previously thought not to affect the levels of mature tRNA. This study changes that understanding by describing for the first time the depletion of a specific mature tRNA as its precursor form is fragmented to generate tRFs. Finally, the authors identify DIS3L2 as a nuclease involved in fragmentation. This is also an important finding as the only other suspected nuclease, albeit with contradictory evidence, is angiogenin. Collectively, the findings of this study would be of interest to a broad group of scientists. I only have a few minor comments and suggestions (see above).

Response: We thank the reviewer for their very positive and insightful comments and feedback.

REFEREES CROSS-COMMENTING I have the following comments on other reviewers' critiques. Regarding the concern that the disappearance of the pre-tRNA could be a transcriptional response (reviewer 2), I think that the appearance of tRFs makes this scenario unlikely. If pre-tRNA levels decreased due to transcriptional repression, wouldn't one expect that both tRNA and the tRF levels diminish concomitantly? Reviewer 3 raises the issue of cross hybridization in Northern blots. The authors indicate that they "could not detect the other tyrosyl tRNA (tRNA Tyr AUA) in MCF10A cells by northern blot..." (page 6). Also, they gel extracted tRFs and sequenced them (figure S6B), directly identifying the fragments. I think these findings mitigate the concern of cross hybridization and clearly identify the nature of tRFs. Finally, I think that the codon-dependent reporter experiment (figure 5D) addresses many issues surrounding codon dependent vs indirect effects. In that experiment, the authors mutate 5 tyrosine codons of a reporter gene and demonstrate that the encoded protein is less susceptible to repression in response to oxidative stress.

Response: We thank the reviewer for their tremendous insights. We are in agreement regarding the three points in the cross-comments.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): This very interesting study from Sohail Tavazoie's lab describes the consequences of oxidative stress on the tRNA pool in human epithelial cell lines. As previously described, the authors observed that tRNA fragments were generated upon exposure of cells to ROS. In addition, the authors made the novel observation that specific mature tRNAs were also depleted under these conditions. In particular, the authors focused on tyrosyl tRNA-GUA, which was decreased ~50% after 24 hours of ROS exposure, an effect attributable to a decrease in the pre-tRNA pool. Depletion of tyrosyl tRNA resulted in reduced translation of specific mRNAs that are enriched in tyr codons and likely contributed to the anti-proliferative effects of ROS exposure. In addition, the authors demonstrated that the tRFs produced from tyr tRNA-GUA can interact with specific RNA binding proteins (SSB and hnRNPA1). The major contribution of this paper is the novel finding that stress-induced tRNA fragmentation can result in a measurable reduction of specific mature tRNAs, leading to a selective reduction in translation of mRNAs that are enriched for the corresponding codons. Previously, studies of tRNA fragmentation largely focused on the functions of the tRFs themselves and it was generally believed that the mature tRNA pool was not impacted sufficiently to reduce translation. The findings reported here therefore add a new dimension to our understanding of the cellular consequences of stress-induced tRNA cleavage. Overall, the data are of high quality, the experiments are convincing, and the conclusions are well supported. I have the following suggestions that would further strengthen the study and bolster the conclusions. Comment 1: The authors have not formally demonstrated that the reduction in pre-tRNA in H2O2-treated cells is a consequence of pre-tRNA cleavage. It is possible that reduced transcription contributes to this effect. Pulse-chase experiments with nucleotides such as EU would provide a tractable approach to demonstrate that a labelled pool of pre-tRNA is rapidly depleted upon H2O2 treatment, which would further support their model. Since the response occurs rapidly (within 1 hour), it would be feasible to monitor the rate of pre-tRNA depletion during this time period in control vs. H2O2-treated cells.

Response: We thank the reviewer for their suggestion and agree that testing for a transcriptional effect using a pulse-chase experiment would further support these findings. We are grateful to both reviewer 1 and reviewer 2 in the cross-comments for recognizing that the tRNA repression response we see is too rapid to be a transcriptional response and that the fact that this tRNA depletion response occurs concomitantly with the tRF generation supports our model that this is a pre-tRNA fragmentation response. It would be of interest for future studies to also examine the impact of cellular stress on tRNA transcription.

Comment 2: To what extent is the growth arrest that results from H2O2 treatment attributable to tyr tRNA-GUA depletion (Fig. 3A)? Since the reduction in tRNA levels is only partial (~50%), it should be feasible to restore tRNA levels by overexpression (strategy used in Fig. 3E, S3B) and determine whether this measurably rescues growth in H2O2-treated cells.

Response: We thank the reviewer for their suggestion. Originally, we had also thought of this experiment and attempted to test this hypothesis. Upon experimentation, we ran into technical challenges that prevented us from drawing any conclusions. The problems were that we were unable to develop a cell line that stably overexpressed the Tyr tRNA-GUA and had to settle for a transient overexpression that only lasted for a couple of days (Fig S3B). For transient transfection, we used Lipofectamine 3000 (Invitrogen) that has associated cell toxicities and requires a control RNA transfection in lipofectamine. In addition, H2O2 in itself is a stress. The simultaneous occurrence of these two stresses led to a combination of cell death and cell growth for the control and experimental group. Given the high variability, we were unable to draw any conclusions on cell growth with this combination. We hope to identify a way to stably overexpress Tyr tRNA-GUA in the future to address this hypothesis.

Comment 3: Knockdown of YARS/tyr tRNA-GUA resulted in reduced expression of EPCAM, SCD, and USP3 at both the protein and mRNA levels (Fig. 4C-D, S4C). In contrast, H2O2-exposure reduced the abundance of these proteins without affecting mRNA levels (Fig. 5A-B, S5A). The authors should comment on this apparent discrepancy. Perhaps translational stalling induces No-Go decay, but it is unclear why this response would not also be triggered by ROS.

Response: We would like to clarify that out of the three genes in Fig. S5A, only EPCAM mRNA levels were significantly reduced with H2O2-exposure while no changes were observed in the mRNA levels of USP3 or SCD. It is difficult to ascertain the reason for EPCAM mRNA reduction but one hypothesis is due to timing and steady state levels. Levels of mRNAs seen with knockdown of YARS or tRNA represent steady state levels where mRNA decay and transcriptional changes can be easily seen. Following H2O2, the data is collected at 24 hours, which may be before mRNA effects can be fully appreciated. We have edited the text to clarify the uncertainty involved. We agree with the reviewer’s insightful comment and find these differences to be interesting and will consider them in future studies to better understand the interplay between translation and mRNA levels in the context of tRNA depletion.

Comment 4: In addition to the analyses of ribosome profiling in Fig. 5E-F, it might also be helpful to show a metagene analysis of ribosome occupancy centered upon UAC/UAU codons (for an example, see Figure 2 of Schuller et al., Mol Cell, 2017). This has previously been used as an effective way to visualize ribosome stalling at specific codons. Additionally, do the authors see a global correlation between tyrosine codon density and reduced translational efficiency in tRNA knockdown cells?

Response: We thank the reviewer for their important suggestion. We have expanded the analysis to look at codon usage scatterplots across all codons for shTyr and shControl replicates (Fig S5D). The 5 most changed codons are labeled with UAC, a codon for the tyrosine amino acid, being the most affected (red arrow). Consistent with our model, a tyrosine codon, when at the ribosome A-site, is most affected with depletion of the corresponding tRNA. The text has also been edited to reflect our new analysis providing further evidence that ribosomal stalling could occur upon depletion of this tRNA. The gray outline around the regression line represents the 95% confidence interval.

Fig S5D

As seen in Fig 5F, a significant overlap was noted for genes with the lowest translational efficiency and tyrosine enrichment. We did further analysis to test if a direct and linear relationship exists between tyrosine codon density and reduced translational efficiency on the global scale (i.e. does more stalling occur with more tyrosine codons on a global scale). We again see that a reduced translational efficiency is significantly correlated with tyrosine codon enrichment (above median parameters) in the tRNA knockdown ribosome profiling data. However, our analysis on a direct relationship between codon density and translational efficiency is inconclusive. This analysis is limited given the sequencing depth and number of experimental replicates available and we lack the statistical power to draw strong conclusions. To prevent overstating our claims, we have omitted any conclusions regarding this second analysis.

Comment 5: MINOR: On pg. 4, the authors state that tRF-tyrGUA is the most highly induced tRF, but Fig. S1B appears to show stronger induction of tRF-LeuTAA.

Response: The reviewer is correct in that the data from Fig S1B shows Leu-tRFs with higher induction. Our text was meant to suggest we focused on tRF-TyrGUA due to higher band intensity seen on northern blot validation. We have edited the text in the manuscript to clarify this.

Reviewer #2 (Significance (Required)): The major advance provided by this work is the demonstration that stress-induced tRNA cleavage can reduce the abundance of the mature tRNA pool sufficiently to impact translation. Moreover, the effect on mature tRNAs is selective, resulting in the reduced translation of a specific set of mRNAs under these conditions. These findings reveal previously unknown consequences of oxidative stress on gene expression and will be of interest to scientists working on cellular stress responses and post-transcriptional regulation.

Response: We thank the reviewer for the kind comments and feedback.

REFEREES CROSS-COMMENTING Regarding the concern that the disappearance of the pre-tRNA could be a transcriptional response (reviewer 2), I think that the appearance of tRFs makes this scenario unlikely. If pre-tRNA levels decreased due to transcriptional repression, wouldn't one expect that both tRNA and the tRF levels diminish concomitantly? Here is what I was thinking: The generation of tRFs does not generally result in reduction in levels of the mature tRNAs. So you can imagine a scenario where oxidative stress causes tRF generation from the mature tyr tRNA (which does not impact its steady-state levels), as is the case for other tRNAs. At the same time, decreased transcription would reduce the pre-tRNA pool, leading to a delayed reduction in mature tRNA, as observed. However, looking back at the data, I see that after only 5 min of H2O2 treatment, the authors observed reduced pre-tRNA and increased tRFs (Fig. 2A). This seems very fast for a transcriptional response, which would presumably require some kind of signal transduction. In addition, when you consider the amount of tRFs produced in Fig. S2C, it is hard to imagine that this would not impact the mature tRNA pool if they were derived from there. So I agree that the transcriptional scenario seems unlikely. Nevertheless, I think that looking at pre-tRNA degradation directly with the pulse-chase strategy would strengthen their story, so I would like to give the authors this suggestion. However, I am fine with listing this as an optional experiment which would enhance the paper but should not be essential for publication.

Response: We thank the reviewer for these insightful comments. As mentioned above, five minutes is likely too rapid for a transcriptional response to be the main effect of H2O2 on Tyr-tRNA GUA. Moreover, the concomitant appearance of the tRF at this time-point makes tRNA fragmentation the most parsimonious and likely explanation rather than transcriptional repression, which would not cause a tRNA fragment to occur concurrently. Moreover, extraction and sequencing of the tRF shows it likely derives from the pre-tRNA as a 5’ leader sequence is present. We appreciate the reviewer’s suggestion and scholarly willingness to reassess their own hypothesis.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): The major findings in this manuscript are: 1.) Oxidative stress in human cells causes a decrease in tyrosine tRNA levels and accumulation of tyrosine tRNA fragments; 2.) The depletion of tyrosyl-tRNA synthetase or tyrosine tRNAs in human cells results in altered translation of certain genes and reduced cell growth and 3.) hnRNPA1 and SSB/La can bind tyrosine tRNA fragments. There is also preliminary evidence that the DIS3L2 endonuclease contributes to the appearance of tyrosine tRNA fragments upon oxidative stress. Based upon these results, the Authors conclude that tyrosine tRNA depletion is part of a conserved stress-response pathway to regulate translation in a codon-based manner. **Major comments:** Comment 1: There is a considerable amount of data in this paper and the experiments are performed in a generally rigorous manner. Sufficient details are provided for reproducing the findings and all results have been provided to appropriate databases (RNA-Seq and ribosome profiling).

Response: We thank the reviewer for the positive comments and feedback.

Comment 2: The manuscript uses a probe against the 5' half of Tyrosine tRNA for Northern blotting. However, tRNA probes can be prone to cross-hybridization, especially with some tRNA isoacceptors being similar in sequence. Thus, the blots in Figure 2 and Supplemental Figures should be probed with an oligonucleotide against the 3' half of tRNA-Tyr. This will confirm the pre- and mature tRNA-Tyr bands detected with the 5' probe. Moreover, this will determine whether 3' tRNA-Tyr fragments accumulate.

Response: We agree that the reviewer is correct in suggesting that the 3’ tRNA-Tyr might also accumulate. However, we disagree that any accumulation of the 3’ tRF might be relevant in our particular model for multiple reasons. As supported by reviewer 1’s cross-comments, cross-hybridization between isoacceptors (GUA vs AUA) would be unlikely as Tyr-AUA could not even be detected by the initial 5’ tRF probe. Additionally, the sequences for Tyr-GUA are different with no nucleotide alignment from Tyr-AUA. Furthermore, the extraction and sequencing of the 5’ tRF (Fig S6B) confirms the 5’ leader sequence unique to the pre-tRNA (also noted by reviewer 1). While the 3’ half of many Tyr-GUA are similar, we find selective binding of our RNA binding proteins only to the 5’ tRF. The 3’ tRF may play some role in binding to other proteins in cell regulatory pathways but such experiments would be outside the scope of this study.

Comment 3: The analysis of the proteomic and ribosome profiling experiments seem rather limited, or based upon what was presented in this manuscript. If additional analyses were performed, then they should be included as well, even if they yielded negative results. For example, the manuscript identifies 102 proteins that decrease after tRNA-Tyr depletion and YARS-depletion with a certain threshold of Tyr codon content. We realize the Authors were trying to find potential genes that are modulated under all three conditions. However, this does not provide information whether there is a relationship between a certain codon such as Tyr and protein abundance if only binning into two categories representing below and above a certain codon content. The Authors should plot the abundance change of each detected protein versus each codon and determine the correlation coefficient. This analysis is important for substantiating the conclusion of a codon-based system of specifically modulating transcripts enriched for certain codons. Otherwise, how could changes in tRNA-Tyr levels modulate codon-dependent gene expression if two different transcripts with the same Tyr codon content exhibit differences in translation? Moreover, this analysis should be performed with all the other codons as well.

Response: We have identified an error in our manuscript where the overlap identified 109 proteins and not 102 as reported previously. We apologize for this oversight. While the reviewer is correct in that identifying codon dependent changes for all 3500+ proteins detected would offer greater insight, our study was specifically focused on tyrosine as we observed this tRNA to become depleted and our experimental system modulated this specific tRNA. As for the second point on Tyr tRNA level effects on translation, we felt that the most rigorous course would be to assess causality rather than an association for this tRNA and its codon in regulating a target gene. The only way to do this is to perform mutagenesis and reporter studies. Our codon dependent reporter clearly shows a direct effect on translation in a tyrosine-codon dependent manner. As for translational regulation for two different transcripts with the same Tyr codon content, it is unclear the molecular mechanisms that could dictate these differences. The reviewer has already brought up possibilities in the next comment regarding Tyr codons in 5’ or 3’ ends or consecutive Tyr codons. These are all interesting hypotheses that others in the field have devoted entire publications to try and understand how and why codon interactions and localizations impact translation (see Gamble et al., Cell 2016, Kunec and Osterreider, Cell Reports 2016, Gobet et al., PNAS 2020). While these further analyses would be interesting, our current experimental data would be insufficient to properly address these questions. We have focused on a specific tRNA, its fragment, and demonstrated direct effects of the tRNA on the codon-dependent translation of a specific growth-regulating target gene and the tRNA fragment on the modulation of the activity of the RNA binding protein it binds to with respect to its regulon. We believe that these findings individually reveal causal roles for this tRNA and tRF in downstream gene regulation and collectively reveal a previously unappreciated post-transcriptional response. We hope the reviewer agrees with us regarding the already deep extent of the studies and that further such analyses beyond this tRNA are outside the scope and focus of this current study.

Comment 4: The Authors should provide the specific parameters used to calculate the median abundance of Tyr codons in a protein and the list of proteins containing higher than median abundance of Tyr codon content. Moreover, the complete list of 102 candidate genes should also be provided. This will allow one to determine what percentage of these Tyr-enriched proteins exhibited a decrease in levels. Moreover, is there anything special about these Tyr codon-enriched transcripts where they are affected at the level of translation but not the other Tyr-codon enriched transcripts? For example, are these transcripts enriched at the 5' or 3' ends for Tyr codons? Do these transcripts exhibit multiple consecutive Tyr codons? This deeper analysis would enrich the findings in this manuscript.

Response: For the proteins identified in the mass spectrometry and overlap listed in Fig 4A, Tyr codon abundance was calculated by dividing the number of Tyr amino acids present by the total number of amino acids for each protein. For genes with different isoforms possible, the principal isoform, using ENSEMBL, was used for calculations. We are also happy to provide the entire list of proteins. Additionally, please see above response to comment 3. We wish to emphasize that the goal of identification of these proteins was to identify downstream targets of this response for functional studies, which we have done. We have identified downstream genes that become modulated by this response and that regulate cell growth, consistent with the phenotype of the tRNA. We then demonstrated a direct causal tRNA-dependent codon-based response with a specific target gene using mutagenesis.

While we agree that the additional analysis the reviewer is requesting to determine what constitutes heightened translational sensitivity to this response is interesting, we believe this is a challenging question for future studies. It is possible that enrichment at 5’ or 3’ or concentration of tyrosine codons could cause increased sensitivity. Ideally, one would have information on a larger set of proteins so that such challenging questions could be better statistically bolstered. Ultimately, the requested experiments that go beyond our current work would require further analyses and experiments to allow firm conclusions to be drawn. As the other reviewers state and this reviewer agrees, we have uncovered the initial discovery regarding this tRNA fragmentation response and provided mechanistic characterization. Future studies, which are beyond the scope of the current work will undoubtedly further characterize features of this response.

Comment 5: The ribosome profiling results are condensed into two panels of Figure 5E and 5F. We recommend the ribosome profiling experiment be expanded into its own figure with more extensive analysis and comparison beyond just looking at tRNA-Tyr. This could reveal insight into other codons that are impacted coordinately with Tyr codons and perhaps strengthen their conclusion. As an example of a more thorough analysis of ribosome profiling and proteomics, we point the Authors to this recent paper: Lyu et al. 2020 PLoS Genetics, https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008836

Response: We thank the reviewer for their suggestion. We have expanded the analysis to look at codon usage scatterplots across all codons for shTyr and shControl replicates (Fig S5D). The 5 most changed codons are labeled with UAC, a codon for the tyrosine amino acid, being the most affected (red arrow). Consistent with our model, a tyrosine codon, when at the ribosome A-site, is most affected with depletion of the corresponding tRNA. The text has also been edited to reflect our new analysis providing further evidence that ribosomal stalling might occur with depletion of a given tRNA. The gray outline around the regression line represents the 95% confidence interval.

Fig S5D

Comment 6: Moreover, one would expect that the mRNAs encoding USP3, EPCAM and SCD would exhibit increased ribosome occupancy. Thus, the authors should at least provide relative ribosome occupancy information on these transcripts to provide evidence that the decrease in protein levels is indeed linked to ribosome pausing or stalling.

Response: We would like to emphasize that resolution of ribosomal profiling data at the codon level for specific genes requires a high number of reads and replicates to draw accurate conclusions. There is an inherent level of stochasticity when mapping RPFs to specific genes and as a result, our analysis revolved around Tyr-enriched vs Tyr-low populations as this analysis was appropriate for our sequencing depth and number of replicates. To be able to conclusively make claims regarding ribosome pausing or stalling for specific genes, we would likely need further experimentation than can be currently done. However, we are currently conducting the requested bioinformatic analysis and have promising preliminary transcript-level data supporting our model.

Comment 7: The results with hnRNPA1 and SSB/La are extremely preliminary and simply show binding of tRNA fragments but no biological relevance. We realize that the Authors attempted to see if Tyr-tRNA fragments impacted RNA Pol III RNA but found no effect. A potential experiment would be to perform HITS-CLIP on H2O2-treated cells to see if stress-induced tRNA fragments bind to SSB/La or hnRNPA1. In this case, at least the Authors would link the oxidative stress results found in Figure 1 and 2 with La/SSB and hnRNPA1.

Response: We agree with the reviewer that a tRF function was not established in the manuscript. As a result, we have recently completed experiments looking at mRNA stability of the hnRNPA1 regulon in the context of overexpressing the tRF as well as using LNA to inhibit this Tyr-tRF (Fig 7E-F). Our data shows, in an hnRNPA1-dependent manner, that its regulon can be functionally regulated by Tyr-tRF. With tRF overexpression and RNAi-mediated depletion of hnRNPA1, a right shift in transcript stability is seen. Importantly, when we do the converse experiment with tRF inhibition in the same RNAi-mediated reduction of hnRNPA1, we see a left shift. These complementary experiments provide data that the Tyr-tRF has a functional role when bound to hnRNPA1 by modulating the regulon of hnRNPA1 and expand the scope of this manuscript and extend the pathway defined downstream of this tRNA fragmentation event.

Fig 7E-F

Comment 8: The manuscript concludes that "Tyrosyl tRNA-GUA fragments are generated in a DIS3L2-dependent manner" based upon data in Supplemental Figure S7. However, there is still a substantial amount of tyrosine tRNA fragments in both worms and human cells depleted of DIS3L2. Thus, DIS3L could play a role in the formation of Tyrosine tRNA fragments but it is too strong a claim to say that tRNA fragments are "dependent" upon DIS3L2. We suggest that the Authors soften their conclusions.

Response: While there are certainly tRFs still apparent with DIS3L2 depletion (Fig S7F-I), we note significant impairment of tRF induction with DIS3L2 knockdown/knockout with multiple different methods in C. elegans and human cells. This data supports our conclusion that tRF generation is dependent on DIS3L2 as this ribonuclease is necessary to elicit the full Tyr-tRF response. We do not make claims that Tyr-tRFs are solely or completely dependent on DIS3L2. There must be other RNases involved given the data highlighted by the reviewer. To this point, we have added clarifying text that DIS3L2 depletion does not completely eliminate the tRF induction.

Comment 9: Moreover, what is the level of DIS3L2 depletion in the worm and human cell lines? The Authors should provide the immunoblot of DIS3L2 that was described in the Materials and Methods.

Response: An immunoblot of DIS3L2 depletion in human cells has now been added as a supplementary figure (Fig S7I). Depletion in C. elegans was confirmed through sequencing of a mutation, as is standard in the field. The wild-type PCR product is 1nt longer (859 bp) than the mutant product (858 bp) with CTC to TAG nonsynonymous mutation preceding a single nucleotide deletion.

Wild-type disl-2: GTTGAAGCCGCAGGGC[CTC]ACTCAGACAGCTACAGG

disl-2 (syb1033): GTTGAAGCCGCAGGGC[TAG]-CTCAGACAGCTACAGG

Fig S7I

Comment 10: The key conclusions of "a tRNA-regulated growth suppressive oxidative stress response pathway" and an "underlying adaptive codon-based gene regulatory logic inherent to the genetic code" are overstated. This is because of the major caveat that knockdown of tyrosine-tRNA or tyrosyl-tRNA synthetase are likely to trigger numerous indirect effects. While the authors validate that three proteins are expressed at lower levels under all three conditions (H2O2, tRNA-Tyr and YARS), they might overlap in some manner but not necessarily define a coordinated response. Thus, a glaring gap in this paper is a clear, mechanistic link between H2O2-induced changes in translation versus the changes in expression when either tRNA-Tyr or YARS is depleted. Thus, it is too preliminary to conclude that tRNA depletion is part of a "pathway" and "regulatory logic" when it could all be pleiotropic effects. At the very least, the authors should discuss the possibility of indirect effects to provide a more nuanced discussion of the results obtained using two different cell systems and oxidative stress.

Response: We thank the reviewer for the feedback. While we agree that indirect effects may exist, we do not make any claims that our pathway is the only one required to have translation effects. The text for Fig 4A already acknowledges the pleiotropic effects of tRNA depletion. Our data shows that H2O2 stress leads to a depletion of Tyr tRNA-GUA and that depletion of this tRNA through multiple complementary methods has a codon-dependent effect on protein expression. We hope the reviewer agrees that the reduction of a specific target gene in a tyrosine codon-dependent manner (demonstrated by mutagenesis) and the binding of the tRF directly to an RBP and the modulation of the regulon of this RBP by this tRF (demonstrated by gain- and loss-of-function studies) demonstrates a direct role of this response on specific downstream target genes rather than pleiotropy. This is in keeping with the cross-comments of reviewer 1, where Fig 5D shows a direct Tyr codon link between H2O2 and downstream effects. As a result, we feel that our conclusions of a pathway (not the only pathway) are valid. However, the conclusion of a “regulatory logic” might not be interpreted in the same way by all readers and we have thus changed the text to reflect a more nuanced position.

**Minor comments:** Comment 11: Tyrosyl-tRNAs refers to the aminoacylated form of tRNA. We recommend that all instances of tyrosyl-tRNA be changed to tyrosine tRNA or tRNA-Tyr which is more generic and provides no indication as to the aminoacylation status of a tRNA.

Response: We thank the reviewer for their correction. We have changed all instances of “tyrosyl” to “tyrosine” in the text.

Comment 12: In Figure 5C, the promoter is drawn as T7, which is a bacteriophage promoter. While the plasmid used in this manuscript (psiCHECK2) does contain a T7 promoter, mammalian gene expression is driven from the SV40 promoter. Thus, the relevant label in Figure 5C should be "SV40 promoter". Moreover, additional details should be provided on how the construct was made (such as sequence information etc.).

Response: We thank the reviewer for their correction. We have changed the promoter text in the figure. In the methods for the construct, we have included which USP3 was used and would be happy to include further information if requested.

Comment 13: Please provide original blots for each of the replicates in: Figure 4C, n=4 Figure 4A, n=9 Figure 4D, n=3 Figure 5D, n=3

Response: There appears to be an unintentional mislabeling of the requested blots by the reviewer. The original blots for Fig 4C, Fig 5A, Fig 5D, and Fig 6D have been made available in a separate file for reviewers.

Reviewer #3 (Significance (Required)): This manuscript provides evidence that specific tRNAs are depleted upon oxidative stress as part a conserved stress-response pathway in humans (and worms) to regulate translation in a codon-based manner. Unfortunately, the manuscript attempts to tie together results from different conditions and systems without providing any definitive links that suggest a "pathway" involved in the oxidative stress response. The findings in this paper provide a useful starting point but fall short of being a major advance due to the lack of a clear mechanism. However, there are intriguing results in this manuscript based upon the cell lines depleted of tRNA-Tyr or tyrosine synthetase that could interest researchers in the field of tRNA biology.

Response: We thank the reviewer for the positive comments regarding our demonstration of a conserved stress response, acknowledging the intriguing nature of our findings that will be a starting point for future studies and that our work will be of interest to researchers in the field of tRNA biology. We hope that the very positive comments of reviewer 1 and 2, the cross-comments of reviewer 1 in response to reviewer 3’s comments regarding the specificity of this response, and our inclusion for reviewer 3 of additional data on the function of the tRF in regulating the activity of the hnRNPA1 RNA binding protein defining a post-transcriptional pathway and additional corroborating requested codon-level computational analyses provide compelling support that that our findings indeed represent a major advance for the field.

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What is this?

Hello world

Eigentlich:

Y = L Y/L,

wobei Y/L als Arbeitsproduktivität bezeichnet wird, die angibt, wieviele Outputeinheiten pro Arbeitseinheit (Stunde, Tag, Woche oder Jahr) produziert werden. Wenn Y/L = 1, wenn also eine/r Beschäftigte/r pro Zeiteinheit genau eine Outputeinheit produziert, dann ist Y = L.

Gleichung dann auch in eine gesonderte Zeile und nummerieren

new line

Styling

Author Response

Summary:

A strength of the work was that the mathematical modeling of re-replication captured variability in origin firing and supported a mechanism that might explain copy number variation observed in many eukaryotes. However, concern was expressed regarding the influence of assumptions made in developing the model on the outcomes and the moderate correlations between simulations and experimental data. Further explanation of the questions being investigated, the validity and nature of assumptions that were used to develop the simulations, and details explaining how these assumptions were built into the modeling were considered important. Some attempt to align the modeling outcomes with known re-replication hotspots would also improve the study. Some of the parameters used for modeling were concerning, including the use of a 16C ploidy cutoff without adequate justification. Reviewers also made suggestions for improving the experimental validation tests. Reviewers also noted places in the manuscript that require additional clarification. Overall, some concerns were raised regarding the experimental methods, and the impact of the insights gained.

We would like to thank eLife for this Preprint Review service.

In this manuscript, we present for the first time a model of DNA rereplication, which permits us to analyse how the process evolves at the single-cell level, across a complete genome, over time. This analysis revealed a pronounced heterogeneity at the single cell level, resulting in increased copies of different genomic loci in different cells, and highlighted rereplication as a powerful mechanism for genome plasticity within an evolving population. We would like to thank the reviewers for their critical appraisal of our work and the editor for his summary of the reviews. The points raised were overall easy to address, and we have done so in a revised version of the manuscript, where we have also clarified points which were unclear to the reviewers. Importantly, we have clarified that: there are currently no available methods for studying rereplication dynamics experimentally at the single cell level across the genome, and it is exactly this analysis that our manuscript offers; model assumptions were either standard and previously validated experimentally for DNA replication or subjected to sensitivity analysis with key findings shown to be robust to model assumptions; there was no arbitrary cut-off point in the rereplication process, which was analysed over time - an advantage of our approach. Data were depicted early in the process (2C) and late in the process (16C) but findings were robust across the process; fission yeast cells can be experimentally induced to rereplicate to different extents (from 2C to 16C or even 32C) and our model permits us to capture the process as it evolves at any ploidy; correlations between experimental and simulated data were highly significant and robust to model assumptions.

We would like to thank the reviewers for their comments, which we believe have helped us improve our manuscript and clarify points of possible misunderstanding. A point-by-point response follows.

Reviewer #1:

The authors develop and analyse a mathematical model of DNA rereplication in situations, where re-firing of origins during replication is not suppressed. Using the experimentally measured position and relative strength of origins in yeast, the authors simulate DNA copy number profiles in individual cells. They show that the developed model can mostly recapitulate the experimentally measured DNA copy number profile along the genome, but that the simulated profiles are highly variable. The fact that increasing copy number of an origin will facilitate its preferential amplification essentially constitutes a self-reinforcing feedback loop and might be the mechanism that leads to overamplification of some genomic regions. In addition different regions compete for a limiting factor, and thereby repress each others' over-amplification. While the model generates some interesting hypotheses it is unclear in the current version of the manuscript, to what extent they arise from specific model assumptions. The authors do not clearly formulate the scientific questions asked, they do not discuss the model assumptions and their validity and they do not adequately describe how model results depend on those assumptions. Taken together, the scientific process is insufficiently documented in this manuscript, making it difficult to judge whether the conclusions are actually supported by the data.

The manuscript has been modified to further clarify the underlying questions and model assumptions. We would like to point out that the model was presented in detail in the supplementary material of the original manuscript, which included all model assumptions. In addition, model parameters used for the base-case model were systematically varied, the outcome was presented in a separate paragraph (“Sensitivity Analysis” in Results), and findings were shown to be robust to model assumptions. These points are presented in detail below.

1) It is not clear what questions the authors want to address with their model. Do they want to understand how the experimentally observed copy number differences between regions arise? The introduction should elaborate more on the open questions in the field and explain why they should be addressed with a mathematical model.

With this work our goal is to elucidate the fundamental mechanisms and properties underlying DNA re-replication. Specifically, we aim to investigate how re-replication evolves over time along the genome, and how it may lead to different number of copies of different loci at the single-cell level and result in genetic heterogeneity within a population. Given the large number of origins along the genome and the stochasticity of origin firing (Demczuk et al., 2012; Kaykov and Nurse, 2015; Patel et al., 2006), it is unclear how re-replication would evolve along the genome in each individual cell in a re-replicating population and how local properties and genome-wide effects would shape its progression and the resulting increases in the number of copies of specific loci. As no experimental method exists that can analyze DNA re-replication at the single-cell level over time along the genome, we designed a mathematical model that is able to track the firing and refiring of origins and the evolution of the resulting forks along a complete genome over time, and in this way capture the complex stochastic hybrid dynamics of DNA re-replication. Since existing methods to analyze DNA re-replication in vivo only provide static, population-level snapshots (Kiang et al., 2010; Menzel et al., 2020; Mickle et al., 2007), we believe that our in silico model, which is the first modeling framework of DNA re-replication, is an important contribution in the field.

In the revised version of our manuscript, we have modified the introduction to explain these points in more detail.

2) One of the main messages of the paper is that the amplification profiles are highly variable across single cells, because that was found in the described simulations. This behavior does however likely depend on specific choices that were made in the simulations, e.g. that the probabilities of the origin state transitions are exponentially distributed. These assumptions should at least be discussed, or better experimentally validated.

Modeling choices and assumptions are presented in detail in the Supplementary material of the manuscript, and were made to accurately capture the dynamics of origin firing, which is known to be stochastic, as established by many studies in fission yeast (Bechhoefer and Rhind, 2012; Patel et al., 2006; Rhind et al., 2010) and the continuous movement of forks along the DNA. Specifically, the choice of the exponential distribution used for assigning a firing time to each origin has already been discussed and validated in our previous work on normal DNA replication (Lygeros et al., 2008). Indeed, as shown in Figure 2 of (Lygeros et al., 2008), our model was able to accurately reconstruct experimental data derived by single molecule DNA combing experiments (Patel et al., 2006).

The use of the exponential distribution for transition firing times is standard in stochastic processes in general, including what are known as Piecewise Deterministic Markov Processes (PDMP), the class where the models considered in the paper belong. There are good mathematical reasons for this, for example the "memoryless" property that makes the resulting stochastic process Markov, a basic requirement for the model to be well-posed [M. H. A. Davis, "Markov models and optimization", Monographs on Statistics and Applied Probability, vol. 49, Chapman & Hall, London, 1993]. Practically, assuming an exponential distribution can be quite general, because the rate (the probability with which a transition "fires" per unit time) is allowed to depend on the state of the system, both the discrete state (in our case, the state of individual origins) and the continuous state (in our case, the progress of individual replication forks). It can be shown that one can exploit this dependence to write seemingly more general processes (that at first sight do not have exponential firing times) as PDMP (with exponential firing times) by appropriately defining a state for the system [M. H. A. Davis, "Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models", Journal of the Royal Statistical Society. Series B (Methodological), Vol. 46, No. 3 (1984), pp. 353-388]. In the manuscript this feature is exploited in what we call the LF model, where the rate of the exponential firing time of each origin (probability of firing per unit time) depends on the state of the system (specifically, the number of PreR origins), as discussed in the section on Sensitivity Analysis. We have further clarified these in the revised manuscript.

3) The authors aim at testing their prediction that rereplication is highly variable across cells. To this end they use the LacO/LacI system to estimate locus copy number. The locus intensity is indeed highly variable across cells. However, the Dapi quantification suggests that only a subset of cells actually undergo rereplication under the experimental conditions used (Fig. 4C). Therefore the analysis should atleast be limited to those cells. It would be even better, if a second locus could be labelled in another color to show that rereplication of two loci is anti-correlated as predicted by the model.

Under the experimental conditions employed (ectopic expression of a mutant version of the licensing factor Cdc18, stably integrated in the genome under a regulatable promoter), the vast majority of cells undergo rereplication but to relatively low levels, resulting in cells with a DNA content of 2C-8C. Though the DNA content of several cells indeed appears similar to the DNA content of normal G2 phase cells, the vast majority (>90%) of cells undergo rereplication, as manifested by the appearance of DNA damage and, eventually, loss of viability. We have chosen this experimental set-up (medium levels of rereplication) as it allows induction of rereplication in practically all cells in the population, without the abnormal nuclear and cellular morphology which accompanies a pronounced increase in DNA content (ie 16C), and would make single-cell imaging more prone to artifacts. Fission yeast cells can be induced to undergo rereplication to various extents, by regulated expression of different versions of Cdc18 to different levels and/or co-expression of Cdt1. We have now explained this more extensively in the revised manuscript and thank the reviewer for identifying a point which may not have been clear in the first version of the manuscript.

Concerning the possibility of studying two loci at the same time, we have indeed tried to tag a second region with TetR/TetO, however the signal-to-noise ratio and thus reproducible detection of the TetR focus was suboptimal under rereplication conditions. We therefore did not proceed further with this approach.

4) What does "signal ratio" in Fig. 2 mean? And why are the peaks much higher in the simulations? Would the signal ratio between simulation and experiment correspond better, if an earlier time point in the simulation was selected?

The definition of signal ratios is given in Results: DNA re-replication at the population level: “Specifically, we computed in silico mean amplification profiles across the genome, referred to as signal ratios in (Kiang et al., 2010), by averaging the number of copies for each origin location and normalizing it to the genome mean in 100 simulations. In these profiles, peaks above 1 correspond to highly re-replicated regions, and valleys below 1 correspond to regions that are under-replicated with respect to the mean.”

Indeed, as observed by the reviewer, simulated peaks appear overall sharper and higher than experimental peaks. This is expected, since simulated data show the actual number of copies generated, while experimental data are subject to background noise and represent averages of 3 probes and 2 independent experiments. We have clarified this in the Results.

Last, we chose to compare in silico and experimental profiles at a similar ploidy. Plotting in silico profiles of an earlier timepoint would indeed lead to visually more similar patterns in terms of peak intensity, but we believe this could be misleading for the readers.

5) From line 248 onwards, the authors compare different assumptions for polymerase speed and conclude that "0.5 kb/min is closer to experimental observations". It is unclear, however, which experimental observations they refer to and what was observed there. The same question arises when they compare the LF and UF models (line 275-277).

We have now clarified this point. Experimental observations show that under high levels of rereplication, DNA content reaches 16C four to six hours following accumulation of Cdc18 (Nishitani et al., 2000). Estimates for 0.5 kb/min and the LF model are therefore closer to experimental observations.

6) I find the description of cis- and trans-effects rather confusing. The authors should rather explain what happens in the model. Neighboring strong origins can amplify a weak origin and origins compete for factors. In line 475-476 for example, it should be clarified that the assumption of the LF model could lead to trans-effects, instead of presenting this as a general model prediction.

In the manuscript, we initially present what we observe in the Results section and then proceed to provide possible explanations in Discussion. We quote from the Discussion: “Such in trans negative regulation of distant origins could be explained by competition for the same limiting factor: high-level amplification of a given locus recruits high levels of the limiting factor, indirectly inhibiting firing of other genomic regions.” and “[…] in cis elements contribute to amplified copy numbers not only directly by passive re-replication, but also implicitly through increasing the firing activity of their neighbors”. To our understanding, these sentences are in complete agreement with the reviewer’s suggestions. Nonetheless, and to make this even more clear, we have modified the Discussion in our revised manuscript.

7) Throughout the manuscript, a clear distinction should be made between the firing activity of one origin molecule and the cumulative activity of multiple copies of an origin. For example, it should be clarified in line 435 that the cumulative activity of weak origins might increase if they are closed to a strong origin, because they get amplified, instead of just writing "increased firing activity of weak origins".

We have clarified this point in the revised manuscript.

8) One of the major conclusions of the manuscript is that rereplication is robust on the population level. It is not clear to me what the authors mean by that. The average amplification levels are probably determined by the origin efficiencies that are put into the model. What would robustness mean in this context?

As the reviewer points out, one of the important input parameters of the model are origin efficiencies. Since the model is stochastic however, origin efficiencies do not directly determine the amplification levels at a single-cell level. For example, in Figures 3A and Supplementary Figure S4, we show the outcome of 4 random simulations with identical underlying parameters, where it is clear that re-replication can lead to markedly different single-cell amplification levels. Indeed, genome-wide analysis across 100 simulations (Supplementary Figure S5) indicated that on the onset of re-replication, amplification levels are highly unpredictable (again, despite the fact that the input parameters are identical).

On the contrary, when analyzing amplification profiles at a population level (averaging across sets of 100 simulations), the most highly amplified regions appear to be highly reproducible. We agree with the reviewer that these population level profiles are strongly affected by the origin efficiencies, but they are not determined solely by them. For example, low efficiency origins can be highly amplified, or highly efficient origins can be suppressed (see discussion on in cis and in trans effects) depending on their neighborhood and system-wide effects, and the extend of these effects depends on the fork speed. Sensitivity analysis with respect to different model assumptions, or model parameters (see Results, section Sensitivity Analysis and Supplementary Figure S3) indicated that amplification profiles might appear sharper or flatter, but overall amplification hotspots were highly robust.

To summarize, in our conclusions (Discussion, section Emerging properties of re-replication) we highlight these properties (stochasticity vs. robustness) and elaborate further on how they emerge during the course of re-replication (onset vs. high re-replication) or depending on the level of analysis (single-cell vs. population level).

9) It would be helpful if, in Fig. 2 also the origins and their respective efficiencies could be shown to understand to what extent the signal ratio reflects these efficiencies.

We thank the reviewer for the useful suggestion, which we have incorporated in the revised manuscript.

10) The methods section should provide more detail.

We would like to point out that Supplementary Material, including a full mathematical description of the model is available on BioRxiv, which was also available at the time of the preprint review, (https://www.biorxiv.org/content/10.1101/2020.03.30.016576v1.supplementary-material ), and has also been uploaded as a separate document in our GitHub page: https://github.com/rapsoman/DNA_Rereplication

Reviewer #2:

Here, Rapsomaniki et al have modeled the process of DNA re-replication. The in silico analysis is an extension of their previous work describing normal DNA replication (Lygeros et al 2008). The authors show that there is a large amount of heterogeneity at the single cell level but when these heterogeneous signals are averaged across a population, the signal is robust. The authors support this with simulations and with experimental data, both at the single cell level and at the population level.

1) It is a bit concerning that simulations were carried out to a ploidy level of 16C. Has it been observed that the DNA content in any given cell can rise to 16 times the initial amount? Figure 3 (simulations) shows that certain chromosomal regions can reach 30x and 160x copies for 2C and 16C. However, Figure 4 (experiment) suggests that copy numbers should only be slightly more in re-replicating conditions, compared to normal replicating conditions. Additionally, in Figure 2, the simulated data seems to be consistently noisier than the experimental data. Taken together, this may suggest that the assumptions in the model do not adequately recapitulate the biological system.

Fission yeast cells undergo robust rereplication, and reach a ploidy up to 32C - see for example (Kiang et al., 2010; Mickle et al., 2007; Nishitani et al., 2000). 16C is therefore a usual ploidy for rereplicating fission yeast cells, observed under many experimental conditions. In addition, by manipulating the licensing factors over-expressed, different levels of ploidy can be experimentally achieved, ranging from 2C (the normal ploidy of a G2 cell, but with uneven replication) to 32C. In Figure 4, we have employed a truncated form of Cdc18 (d55P6-cdc18 (Baum et al., 1998)), which induces medium-level re-replication, as confirmed by FACS analysis in Supplementary Figure S6A. Under these conditions, the vast majority of the cells (>90%) undergo re-replication, albeit at medium to low levels. We have opted to use this strain to avoid artifacts due to disrupted nuclear morphology under high levels of re-replication We have now clarified this point in the revised manuscript. We would like to point out that in silico analysis is not carried out at 16C only but across different ploidies – it is actually a strength of our approach that we can follow the rereplication process as it evolves, at any ploidy, and we have shown that our conclusions are robust throughout. We show plots at the beginning of the process (2C) and towards the end (16C), at the single-cell and at the population level, to facilitate comparison.

Last, as also discussed in our response to reviewer 1, simulated data appear sharper, with higher peak values than experimental data (Figure 2). This is expected, since simulated data show the actual number of copies generated, while experimental data are subject to background noise and represent averages of 3 neighboring microarray probes and 2 independent experiments. We have clarified this in the revised manuscript.

2) This work currently is agnostic to the genes and sequences within the simulated genomes. The authors suggest that DNA re-replication can result in gene duplications. It might strengthen the manuscript if the authors are able to show that re-replication hotspots coincide with gene duplication events in S pombe. It should be relatively straightforward to overlap the hotspots found in this analysis with known gene duplication events in the literature.

We agree with the reviewer that comparing our predictions with known gene duplication events in S.pombe would be of interest. Unfortunately to our knowledge no such dataset for fission yeast exists in the literature. The most comprehensive datasets are the ones from (Kiang et al., 2010; Mickle et al., 2007), which analyse rereplicating cells, and which we have already exploited in our paper. We would like to point out that this manuscript aims to show how rereplication evolves genome-wide. Whether the additional copies generated can lead to gene duplication events is beyond the scope of the present manuscript.

3) The authors have nicely demonstrated that cis activation can be driven by the physical proximity of origins. The authors go on to describe trans suppression in which the activation of one origin suppresses the activation of a different origin. I would argue that this observation is simply the result of randomness in the model and stopping the simulations at fixed points.

One of the two origins will randomly re-replicate first and simply outpace the other. Stopping the simulations at 16C will simply prevent the lagging origin from catching up the first origin. There does not seem to be an inhibitory mechanism that acts between two origins.

This can be explained by the following equation: X + Y = constant Where X is the amount of origin 1 and Y is the amount of origin 2.

It is also possible that the two origins could start re-replicating at the same time. This would result in the data points observed for cluster 2 (Figure 6 BC)

We thank the reviewer for the positive comments. Indeed, as we elaborate in our Discussion, we believe that the mechanism behind the observed in trans effects is the competition for a factor that exists in a rate-limiting quantity (see also reply to point 6, reviewer 1 above), which is essentially the constant in his/her equation. Though less pronounced, such in-trans effects are also possible in the UF model, and could be due to the total DNA increase being dominated by certain origins, as suggested by the reviewer. We do not suggest anywhere in the manuscript that this inhibition is direct, but rather clearly state that it is an indirect effect.

Reviewer #3:

This manuscript by Rapsomaniki et al uses mathematical modeling to study the properties of DNA re-replication. They develop a model that shows some consistency with experimental data from S. pombe, and use it to conclude that re-replication is heterogeneous at the single-cell level.

The simulations have only moderate correlations with experimental data (0.5-0.6). Indeed, simulations and actual data (Figure 2) appear quite different. Despite the statistical significance of the overlap, the limited correspondence brings into question the usefulness of the model compared to directly generating new experimental data.

We would like to point out that the overlap between experimental and simulated data is highly significant. Firstly, the Spearman correlation coefficient between simulated and experimental genome-wide profiles is highly statistically significant (p values ranging from 7.310-12 to 3.610-41 for the three fission yeast chromosomes). Furthermore, 100.000 repetitions of random peak assignment resulted in only one case where 10 out of 22 peaks overlapped (median 2 out of 22 peaks overlapping), while comparing simulated and experimental data resulted in 14 out of 22 peaks overlapping. Simulations appear more sharp than experimental data, this is however expected as simulated data correspond to the actual number of copies generated, while experimental data are subject to background noise, have a signal-to-noise ratio that is limited by the experimental method employed and represent averages of 3 probes and 2 independent experiments (see Kiang et al., 2010 and also above). We have modified the manuscript to clarify this point. The reviewer suggests that the model is of limited use, because one could trivially generate new experimental data. We would like to point out that existing methods to analyze DNA re-replication in vivo only provide static, population-level snapshots (Kiang et al., 2010; Menzel et al., 2020; Mickle et al., 2007). To date no experimental method can generate single-cell, whole-genome, time-course measurements in re-replicating cells. Our model aims to fill this gap, and for this reason we believe in its usefulness.

Heterogeneity among single cells, which appears to be one of the main messages of this paper, is not necessarily a surprising finding, and may even arise from the nature of the simulation being stochastic and defined at the level of single origins. They validate this prediction experimentally at a single locus, providing little novel insight.

We would like to point out that it is the nature of replication in fission yeast which is stochastic, as experimentally shown (Patel et al., 2006), and defined at the level of single origins, and this is captured by the simulations. Heterogeneity amongst single rereplicating cells has not been previously shown or suggested in any organism, at least to the best of our knowledge. It is in our opinion a highly interesting observation, as it provides a powerful mechanism for generating a plethora of different genotypes within a population, from which phenotypic traits could be selected.

Overall, the insights here are limited and would need to await experimental validation and further empirical data. Given that experimental measurements of re-replication are now feasible genome-wide, the value of these simulations is limited.

Again, the reviewer seems unaware that no experimental method currently exists for analysing the dynamics of re-replication at a single-cell level genome-wide. We also feel obliged to point out that modeling and in silico analysis is in our opinion of great value for analysing complex biological processes, even when experimental methods are available. Though we are sure this is not what the reviewer really meant, his/her comment appears derogative to a complete field.

Fork speed is assumed based on limited data and assumptions regarding re-replication fork speed without empirical data.

As clearly stated in our manuscript (Results, section Modeling DNA re-replication across a complete genome), many studies have estimated fork speed in yeasts in normal DNA replication, with plausible values ranging from 0.5 kb/min to 3 kb/min (Duzdevich et al., 2015; Heichinger et al., 2006; Raghuraman et al., 2001; Sekedat et al., 2010; Yabuki et al., 2002). In our model, we set the base-case value as the lowest estimate (0.5 kb/min), but also explored the model’s sensitivity to this parameter by simulating the model for higher values (1 and 3 kb/min). This analysis indicated that estimates for 0.5 kb/min were closer to biological reality, a non-surprising finding given that fork speed is expected to be slower in re-replication that in normal replication.

Overall, the comments of reviewer 3 appear in our eyes more derogative than constructive and provide little specific criticism.

References

Baum, B., Nishitani, H., Yanow, S., and Nurse, P. (1998). Cdc18 transcription and proteolysis couple S phase to passage through mitosis. The EMBO Journal 17, 5689–5698.

Bechhoefer, J., and Rhind, N. (2012). Replication timing and its emergence from stochastic processes. Trends in Genetics 28, 374–381.

Duzdevich, D., Warner, M.D., Ticau, S., Ivica, N.A., Bell, S.P., and Greene, E.C. (2015). The dynamics of eukaryotic replication initiation: origin specificity, licensing, and firing at the singlemolecule level. Mol. Cell 58, 483–494.

Heichinger, C., Penkett, C.J., Bähler, J., and Nurse, P. (2006). Genome-wide characterization of fission yeast DNA replication origins. The EMBO Journal 25, 5171–5179.

Kiang, L., Heichinger, C., Watt, S., B\ähler, J., and Nurse, P. (2010). Specific replication origins promote DNA amplification in fission yeast. Journal of Cell Science 123, 3047–3051.

Lygeros, J., Koutroumpas, K., Dimopoulos, S., Legouras, I., Kouretas, P., Heichinger, C., Nurse, P., and Lygerou, Z. (2008). Stochastic hybrid modeling of DNA replication across a complete genome. Proceedings of the National Academy of Sciences 105, 12295–12300.

Menzel, J., Tatman, P., and Black, J.C. (2020). Isolation and analysis of rereplicated DNA by Rerep-Seq. Nucleic Acids Res 48, e58–e58.

Mickle, K.L., Oliva, A., Huberman, J.A., and Leatherwood, J. (2007). Checkpoint effects and telomere amplification during DNA re-replication in fission yeast. BMC Molecular Biology 8, 119.

Nishitani, H., Lygerou, Z., Nishimoto, T., and Nurse, P. (2000). The Cdt1 protein is required to license DNA for replication in fission yeast. Nature 404, 625–628.

Patel, P.K., Arcangioli, B., Baker, S.P., Bensimon, A., and Rhind, N. (2006). DNA Replication Origins Fire Stochastically in Fission Yeast. Mol. Biol. Cell 17, 308–316.

Raghuraman, M.K., Winzeler, E.A., Collingwood, D., Hunt, S., Wodicka, L., Conway, A., Lockhart, D.J., Davis, R.W., Brewer, B.J., and Fangman, W.L. (2001). Replication Dynamics of the Yeast Genome. Science 294, 115–121.

Rhind, N., Yang, S.C.-H., and Bechhoefer, J. (2010). Reconciling stochastic origin firing with defined replication timing. Chromosome Res 18, 35–43.

Sekedat, M.D., Fenyö, D., Rogers, R.S., Tackett, A.J., Aitchison, J.D., and Chait, B.T. (2010). GINS motion reveals replication fork progression is remarkably uniform throughout the yeast genome. Molecular Systems Biology 6, 353.

Yabuki, N., Terashima, H., and Kitada, K. (2002). Mapping of early firing origins on a replication profile of budding yeast. Genes to Cells 7, 781–789.

.productivity

DOI: 10.1016/j.molcel.2020.07.001

Resource: (Thermo Fisher Scientific Cat# R960-25, RRID:AB_2556564)

Curator: @Naa003

SciCrunch record: RRID:AB_2556564

Curator comments: V5 Tag Monoclonal Antibody Thermo Fisher Scientific Cat# R960-25

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What is this?

DOI: 10.7554/eLife.49894

Resource: (Thermo Fisher Scientific Cat# R960-25, RRID:AB_2556564)

Curator: @evieth

SciCrunch record: RRID:AB_2556564

Curator comments: Thermo Fisher Scientific Cat# R960-25, V5 Tag Monoclonal Antibody

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What is this?

I love Kynell's usage of these historical nuggets. The early stages of Engineering as a field struggling to carve out its own space are oddly similar to that of Tech Comm. Obviously the two fields are directly related historically but I would not be surprised if similar histories exist across most fields of study. This challenges my original belief that Tech Comm would be better off having its elements absorbed into Rhetoric or other related fields. It does not surpass it to the point where I believe the opposite but it does set a good foundation for a shift in my thinking.

DOI: 10.3390/cancers12061441

Resource: (Abcam Cat# ab32, RRID:AB_303599)

Curator: @Naa003

SciCrunch record: RRID:AB_303599

Curator comments: c-Myc antibody [9E10] - ChIP Grade Abcam Cat# ab32

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What is this?

Test

Gratton, C., Gagnon-St-Pierre, É., & Markovits, H. (2020). When forewarned is not forearmed: The paradoxical effect of single warnings attached to repeated fake news [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/h5cxp

This provides some reason to be agnostic as to whether vegans can obtain B12 naturally. Of course that shouldn't matter to any rational person (who'd just take a supplement). Note that I was searching for this info, and this is the first piece of evidence I found. Thus, I suspect there is more evidence since 1965. I'll try to tag updates with "nativeB12"

Voilà ! Aqui vamos a mais uma apresentação, mas agora com outros formalismos e curiosidades!

Sou Thays do Carmo, advogada, recém graduada em Direito pelo Centro Universitário de Brasília, e mestranda pela UnB na Linha de Pesquisa 2.

O tema objeto de minha dissertação de mestrado relaciona-se à pesquisa empírica em direito, busco encontrar padrões argumentativos típicos das decisões que estabelecem a modulação de efeitos no STF.

Assim, estou com grandes expectativas de aperfeiçoar minha estratégia de pesquisa para que os dados estejam cada fez mais organizados, sistematizados e coerentes.

What is oops?

DOI: 10.1016/j.celrep.2020.107974

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Curator: @Naa003

SciCrunch record: RRID:AB_1549585

Curator comments: Rabbit Anti-HA-Tag Monoclonal Antibody, Unconjugated, Clone C29F4 Cell Signaling Technology Cat# 3724

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What is this?

Author Response

Summary:

This is an interesting and creative paper implicating a differential mechanism of intracellular trafficking and subsequent signaling that is triggered by different dynorphins binding to the kappa opioid receptor. In principle, if the authors could explain the molecular basis for this phenomenon, the story would be of tremendous impact in the fields of opioid receptor signaling and trafficking. The reviewers noted a number of concerns that would require significant further work and clarification to support the authors' conclusions.

We are very happy that you and the reviewers found that the study could be of tremendous impact and describe the paper as “interesting and creative”, “novel and intriguing”, “fascinating and novel”, and feel that the study was “nicely conducted”. We appreciate the comments of the reviewers, and we are confident that we can address the comments as below.

Reviewer #1:

General assessment: In this manuscript the authors have assessed the different endocytic routes of KOR when activated by DynA or DynB. These are nicely conducted experiments that show interesting results, however the authors completely obviate the connection with their own work that highlights the different degradation mechanisms of these two peptides. As it stands it does not add to the field, and lacks a mechanistic explanation that could be explored given the authors’ expertise in these systems.

We thank the reviewer for the positive comments. We are happy that the reviewer felt that the experiments are nicely conducted, and that the results are interesting. However, we respectfully but strongly disagree with the comments that our study does not add to the field.

First, considering the extended and severe opioid epidemic, understanding the many ways in which the opioid peptide/receptor system is modulated is of high priority. Endogenous opioid peptides are highly relevant neuromodulators about which we know even less than opioid drugs. Why there are over 20 different endogenous opioid peptides but only three receptors, has been a question that has been unanswered for decades. We show that two highly related endogenous opioids, which initially activate KOR to similar levels but subsequently diverge in trafficking and endosomal signaling. We feel that this is a clear advance in the field of opioids and GPCRs.

Second, the idea that location-biased signaling can lead to different consequences for the same agonist is still a relatively new idea, and clearly a very important area of continuing research. Even for well-studied systems like the adrenergic receptor system, we know very little about the mechanisms or the relevance of differential signaling. Demonstrating that endogenous opioids take advantage of location bias to generate distinct signaling consequences is a clear indication that such differential trafficking and signaling is physiologically relevant. Considering that opioid receptor trafficking has been implicated in opioid signaling and tolerance (although again, the mechanisms are debated), showing that different endogenous opioids can regulate localization and trafficking of the same receptor is a key advance.

Numbered summary of substantive concerns:

1) The major conclusion of the study is that after endocytosis, DynA preferentially sorts KOR into the degradative pathway, while DynB sorts KOR into the recycling pathway and this has consequences in the duration of the active state of the receptor and its ability to signal. It is surprising that the authors do not investigate the connection between these results and previously published work that shows differences in the degradation of DynB vs DynA within endosomes. Indeed, the authors have previously shown that: i) ECE2 hydrolyzes DynB and not DynA (Mzhavia et al JBC 2003), ii) overexpression of ECE2 increases the rate of mu-opioid receptor recycling upon DynB stimulation (Gupta et al BJP 2015) and iii) inhibition of ECE2 decreases mu-opioid receptor recycling (Gupta et al BJP 2015). Considering this previous work, it is totally expected that the two ligands show distinct post-endocytic trafficking of KOR.

The reviewer cites data that the surface recovery rates of a different GPCR (MOR) is regulated by ECE2, and that ECE2 differentially processes Dyn A and B, to argue that it is expected that the two ligands will direct KOR to different subcellular localizations. While our results certainly could be one logical outcome of previous data, we disagree that it is a foregone conclusion.

Specific to the reviewer’s assessment of our previous work, we were never able to test DynA previously because traditional assays did not have the sensitivity to resolve DynA-mediated recycling or trafficking. This limitation precluded the key comparison, between DynA and DynB, necessary for addressing differences between these two physiologically relevant opioid peptides. Here we use advanced high-resolution imaging experiments to carefully address how DynA and DynB diverge in directing KOR trafficking and signaling.

More generally, we have known for over a decade that the rates of GPCR recycling can be regulated by signaling pathways without changing sorting, endosomal localization, or fates (e.g., PMID: 16604070, PMID: 27226565, PMID: 25801029, PMID: 24003153). Further, many recent studies have highlighted that the details of how GPCRs are regulated and how that affects their function diverges considerably between different receptors, even though the gross signaling characteristics are nearly identical. Therefore, it is becoming increasingly clear that we cannot apply our understanding of one GPCR too broadly to argue that we expect all GPCRs are regulated in the same manner.

We also appreciate the reviewer’s interest in the question of whether and how ECE2 regulates location-specific signaling, and we agree that it will be very exciting to study. This is particularly important since ECE2 is not ubiquitously expressed in every cell type in the brain and thus cells with no/low ECE2 expression should exhibit different profiles for recycling or location-based signaling by DynA and DynB compared to cells expressing moderate/high levels of ECE2.

Nevertheless, we disagree with the reviewer’s assumption that there is an obvious correlation. ECE2 sensitivity for opioid peptides was estimated using purified peptides and enzymes, and there is no evidence that the selectivity persists in vivo. In fact, most of the previous studies measured simply the sensitivity to overexpressed ECE2. Even within these constraints, the correlation is not obvious or direct. For example, we have found that BAM22 and BAM18, two peptides that activate opioid receptors, show much lower recycling of KOR than DynB (Gupta, Gomes and Devi, INRC 2019, manuscript in preparation) even though all three are ECE2 substrates (PMID: 12560336). Therefore, it is unlikely that ECE substrate sensitivity is the only difference between these peptides.

We will be happy to provide some insight on the question of ECE sensitivity and discuss possibilities, but we feel that a thorough characterization of how ECE regulates location-specific signaling, while interesting, is outside the scope of our study that demonstrates a physiological difference between two different endogenous opioids in neurons.

Most importantly, we respectfully feel that following up and demonstrating a logical conclusion is a strength, and should not be viewed as a negative. Clearly differentiating and establishing predicted outcomes is a critical part of advancing biology. Acknowledging and supporting this is especially important in these times where there is a clear effort and an opportunity to make academic publishing open and fair.

2) Similarly, the differences in ECE2 sensitivity can also explain the Nb39 results, with KOR activated by the ligand that is not hydrolysable (DynA) being able to remain in the active state (and signal) for longer than when activated with the hydrolyzable ligand (DynB).

As described in the response to #1, we agree that it is possible that the trafficking and signaling differences we see could correlate with ECE2 substrate sensitivity. Again, we feel that the focus of the manuscript is on signaling differences between endogenous opioids, and not on how ECE inhibition regulates location-specific signaling.

3) A simple experiment to address this obvious connection is to use an ECE2 inhibitor. One would expect that in the presence of this inhibitor DynB-activated KOR is retained intracellularly and remains active for longer.

We agree that ECE inhibitors are important tools to manipulate recycling. As mentioned above, we can provide some insight towards the correlation of ECE sensitivity and trafficking and discuss possibilities, but an in-depth characterization of how ECE proteases regulate GPCR location-specific signaling is not the focus of our study.

4) The authors state "this is the first example of different physiological agonists driving spatial localization and trafficking of a GPCR" in light of the above comment, previous work from Bunnett et al have shown how peptides with different endocytic enzyme sensitivity can indeed, localize GPCRs (e.g somatostatin receptor) in different compartments and elicit distinct signals (Padilla et al J Cell Biol 2007; Roosterman et al PNAS 2007; Zhao et al JBC 2013 to name a few).

We were quite taken aback by this comment. We take previously published work very seriously, and we try to be as fair as possible when we describe them. We will be happy to modify the sentence to match the current literature.

We carefully searched through the papers the reviewer pointed out for an example where two physiological agonists drive different spatial localization and signaling of the same receptor. But we could not find one. Padilla et al., 2007, show that the recycling of CLR, activated by the ECE1-sensitive CGRP, is sensitive to ECE inhibition, but that the recycling of angiotensin receptor or bradykinin receptor, whose ligands are not sensitive to ECE, is not. Similarly, Roosterman et al., 2007, focus on how NK1 receptor recycling is sensitive to ECE1 inhibition. To the best of our knowledge, neither paper shows that spatial localization or location-biased signaling of a given GPCR is regulated differentially by two different endogenous agonists.

The closest experiment we could find are in Fig 2, titled “Agonists induce endocytosis of SSTR2A in myenteric neurons” in Zhao et al JBC 2013. This figure shows that, when cells exposed to SST14 or the pro-peptide SST28 for 1 hour at 4˚C are followed at 37˚C and fixed, SSTR labeling at the plasma membrane and cytoplasm is similar at 30 min, but diverges after that. As far as we could figure out, receptor recycling, the precise endosomal distribution, or signaling were not tested in this manuscript.

Therefore, we respectfully submit that the manuscripts the reviewer points to, which describe how the recycling of a receptor that binds an ECE-sensitive peptide is sensitive to ECE inhibition, should not be conflated with our careful analysis of whether different endogenous opioids can drive different spatial localization and signaling fates of the same opioid receptor.

We would, however, be be happy to modify the sentence to state the impact of our work more precisely and to discuss the details on SSTR trafficking in the revised manuscript. If the reviewer would point us to specific examples that show that subcellular localization and spatially restricted signaling of a given GPCR is regulated differentially by two different endogenous agonists, we will be more than happy to include a discussion of that work.

5) Support for endosomal signalling falls a bit short. For example, if indeed KOR signals from endosomes, the authors should use an inhibitor of receptor internalization and assess Nb39 recruitment and KOR signalling.

We agree this experiment will support the conclusion, and we will be happy to provide this data.

Reviewer #2:

This manuscript demonstrates that two highly similar endogenous opioid agonists can give distinct opioid receptor trafficking and signaling fates. There are two key observations that are novel and intriguing: 1) two opioid peptides that are derived from the same precursor can distinctly modulate Kappa Opioid receptor (KOR) trafficking into two distinct pathways; Dynorphin A causes KOR trafficking to the late endosomes/lysosomes pathway whereas Dynorphin B promotes rapid recycling; 2) Dynorphin A activates Gi proteins on the late endosomes/lysosomes which leads to Gi-mediated cAMP inhibition from these compartments.

The idea that GPCRs can activate G proteins at the late endosome/lysosomal compartments is fascinating and novel, however, the data presented here does not fully support their model that Dynorphin A activated Gi proteins on the late endosomes/lysosomes.

We are very happy that the reviewer found our study fascinating and novel. We thank the reviewer for the comments, and we can address them as follows.

Main questions:

1) There is a mismatch with the timing of receptor colocalization experiment (Fig 3B and C, 20 min Dynorphin A/B treatment) and the cAMP assay (Fig 3H, 5 min treatment). There needs to be direct evidence that KOR is localized on the late endosomes/lysosomes at 5 minutes post agonist stimulation, i.e. at the time that cAMP levels are measured. It is important to demonstrate that the sustained signaling inhibition by DynA comes from the late endosomes/lysosomes as opposed to early endosomes. A colocalization experiment with 5 min DynA stimulation followed by a 25min washout would be necessary to support their model.

We agree that this is a good point, and we will be happy to perform the experiment suggested. In addition, we can also provide live cell imaging data, where we simultaneously localize the nanobody that recognizes active KOR with a lysosomal marker and KOR, to show that they colocalize after DynA treatment.

2) What percentage of KORs are proteolytically degraded in the late endosomes/lysosomes at 20 min DynA stimulation?

At 20 min, although some of the receptors reach the lysosome, it is unlikely that there is significant degradation. This is supported by our blots that show similar levels of KOR expression at 30 minutes, and loss of receptor levels at 2 hours. This is also roughly consistent with previous studies on GPCR degradation. We will include these details in the revised manuscript.

3) Given that KOR trafficking to the late endosomes and lysosomes is mediate by ubiquitination (as shown here PMID: 18212250), does mutation of these ubiquitination sites (3 lysine residues on KOR C-terminus) block its trafficking and the sustained signaling from the late endosomes/lysosomes?

The reviewer raises an interesting topic that has been a subject of considerable debate in the GPCR trafficking field. The mutation of the three lysine residues on the KOR C-terminus cause more residual KOR levels after 4 hours of Dyn A, suggesting that degradation/downregulation of KOR is reduced in these mutants, even though internalization is comparable. For some opioid receptors, although ubiquitination might be required for involution and entry into the intralumenal vesicles, lysosomal localization is arguably independent of ubiquitination. Ubiquitination and/or lysine residues that interact with Ub-transferases could also affect downstream signaling, especially in the endosomes, by some GPCRs. Therefore, we feel that interpretation of results from the lysine mutant receptors will not be straightforward. Nevertheless, we appreciate that this is an interesting point, and we will address this in the revised manuscript.

4) Is there any evidence for Gi protein localization on the late endosome/lysosomes?

This is another interesting point raised by the reviewer, as the majority of endosomal signaling data rely on Gs-coupled or Gq-coupled receptors. However, Gi-coupled GPCRs, such as the cannabinoid receptor or the related mu opioid receptor can exist in the active conformation in endosomes (e.g, PMID: 18267983, PMID: 29754753), and internalization is required for sustained cAMP inhibition for the Class B S1P receptor (PMID: 24638168). These provide indirect evidence that Gi proteins might be present and active on endosomes.

Unfortunately, directly testing whether Gi proteins are active on endosomes has been technically challenging, unlike with Gs proteins. The main limitation has been the lack of conformation-sensors for Gi proteins. We will be happy to discuss these points in the revised manuscript.

5) Additional functional readouts would also be helpful to support their model of Gi-mediated inhibition of cAMP response from late endosomes/lysosomes and not the plasma membrane or early endosomes. Perhaps mTOR activation (as authors have suggested in their discussion) could be used as a read out to show differences between DynA and B-mediated signaling?

We will be happy to test endosome-based mTOR signaling downstream of KOR to see if there is a difference between DynA and B. Since our data already suggest that the main impact might be on cAMP signaling, we will also discuss the implications to cAMP signaling.

Reviewer #3:

This is an interesting idea and creative paper implicating a differential mechanism of intracellular trafficking and subsequently signaling that is triggered by different dynorphins binding to the kappa opioid receptor. However, there are some questions for the authors:

We thank the reviewer for the comments that the paper is interesting and creative, and for the critique. We are confident that we can fully address them as follows.

1) My reading is that some dynorphins are extremely rapidly degraded in serum and with these experiments performed in 15% Horse/FCS there is concern that some of the differential results could be explained by differential degradation. One hypothesis could be a differential frequency of receptor activation over time of a fast recycling receptor population. Can the authors convince me that this difference in trafficking and subsequent signaling is an intrinsic property of the peptide and not an exhaustion of peptide (would be DynB) over the 30min assay?

We agree this is an important point, and we apologize for not specifically addressing this point. For the trafficking experiments, we directly compared results from experiments done with and without protease inhibitors. We saw no difference between the two conditions, possibly because we were using short time points, high enough concentrations, and dialyzed serum. We agree that it will be important to include these data in the revised manuscript. The signaling experiments, which required longer incubations, were performed in the presence of protease inhibitors, consistent with previous studies.

2) In Fig 2D, 2G and 2J at what time after addition peptides was this data obtained?

For measuring individual recycling events (2D and G), cells were treated with agonist for 5 minutes at 37°C. Receptor clustering was visualized using TIRF microscopy, and then a recycling movie was recorded at 10 Hz for 1 minute in TIRF. For 2J, we measured 2 time points, 30 min and 120 min after agonist addition. We apologize for not stating these details in the figure, and will be happy to do so.

3) In Fig 2F the divergence of internalized receptor only occurs from time 20-30 mins which was difficult for me to understand since DynA should result in lost surface receptor number. What confuses me is that in Fig2H the initial recycling induced by DynA17 is fast and slows down so I am wondering if a second hit is needed which feeds into my concern about peptide degradation in the media. Since released peptide would be pulsatile maybe in vivo DynA17 could act like DynB?

We realize that a better explanation is needed for the recycling experiment performed in 2F. The cells were imaged for a period of 2 minutes to collect baseline SpH fluorescence, which corresponds to the steady-state amount of KOR on the cell surface. After this period, cells were imaged for 15 min after DynA or DynB was added. In this period, because internalization is the predominant factor affecting surface levels, we see a loss in fluorescence as the receptors are internalized and SpH is quenched in the relatively acidic compartments. Because KOR internalization rates are not dramatically different between DynA and B, we do not expect the fluorescence traces to be different. The agonist was then washed out at this time (t=17), and cells were imaged in media containing antagonist. Because there is very little agonist-induced internalization after this point, the fluorescence change depends predominantly on reappearance of receptors via recycling. Therefore, if the main difference between DynA and DynB is in KOR recycling, we expect to see a divergence only in the late points of the trace.

We thank the reviewer for carefully viewing the traces in 2F and 2H. We understand the interpretation that there might be fast and slow components to DynA induced recycling. While it certainly is possible, we are not comfortable making a strong conclusion on that, based on the sensitivity of the assays used and the variability between cells.

As mentioned in point#1, it is unlikely, however that this divergence in recycling is due to significant degradation of DynA. Nevertheless, it is an important point to discuss in light of the new data we provide, and we will be happy to explain this in detail.

4) The assays seem to be done with a single concentration of peptide - 1µM. Do the authors have data to show that at lower (or higher) concentrations than 1µM result in the same trafficking patterns, albeit to a lesser or greater extent. Also, for the cAMP inhibition what concentration gives max inhibition? For a binding affinity of 0.01nM in the cells and with high expression, the 1micromolar concentration seems high.

We used the 1µM dose based on careful dose-response measurements for cAMP signaling. Part of the dose-response data has been published (PMID: 32393639). We will be happy to provide the extended data, and also provide a dose-response for trafficking. It is possible that the dose is what helps us mitigate potential degradation of the peptides.

5) In Fig 2H 100% of receptors appear to be recycled after DynB however 25% of kappa colocalize in Rab7 in 3C so do these Rb 7 co-localized receptors recycle?

It is certainly possible that some receptors from Rab7 endosomes can recycle. Current views are more aligned with overlapping populations of endosomes as labelled by biochemical markers, especially by trafficking components like Rabs. Therefore, our characterization likely describes a spread of receptor distributions across overlapping compartments. Moreover, the recycling of receptors in Fig 2H was quantitated using ELISA over 2 hours after agonist washout. The endosome colocalization in 3C was measured after 20 min of agonist treatment. As the reviewer would agree, it is difficult to directly compare data from these two experiments and draw definite conclusions.

That said, we certainly did not mean to imply that all of DynB-activated KOR is recycled and that DynA-activated KOR is degraded. Current data on trafficking support a more dynamic and flexible model for receptor sorting, where a fraction of the receptors is recycled while a fraction is degraded from each endosome. Our results are consistent with this model. We feel that, because the receptor populations undergo many rounds of rapid iterative sorting as the endosome matures, a larger fraction is recycled back to the surface in the case of DynB at a steady state, while a larger fraction stays behind in the case of DynA. Importantly, this difference in steady state localization is enough to cause a difference in endosomal receptor activation and cAMP signaling, suggesting that small differences in steady state localization can cause relevant changes in signaling.

We apologize for not making this important point clearer, and we will be happy to clarify this in the revised manuscript.

6) Could some of the signaling differences be explained by continued activation of receptors as a consequence of peptide processing in the endocytosed vesicle as opposed to different vesicles? I guess the continued signaling could also direct subsequent trafficking and this could be tested with a membrane permeable antagonist.

We thank the reviewer for raising this point. As we described in our response to reviewer#1, peptide processing by ECE proteases could contribute to the differences, but the data suggest that this is not a direct correlation or the main explanation for the differences we observe. We will be happy to provide data to address this aspect.

7) The impact statement "Co-released dynorphins, which signal similarly from the cell surface, can differentially localize GPCRs to specific subcellular compartments, and cause divergent receptor fates and distinct spatiotemporal patterns of signaling" could be misconstrued. If one of the pathways is dominant and blocks the other, then co-release may only have one signaling outcome. Have any dynorphin mix experiments been conducted? What might be anticipated?

We agree that the question of whether one peptide is dominant is an interesting one in the context of the paper, and we thank the reviewer for pointing this out. Assay sensitivity has remained a long-standing problem when trying these mixed experiments in the endogenous opioid system. We will be happy to try a dynorphin mix experiment with our state-of-the-art imaging assays. We will also revise the sentence to reduce ambiguity.

8) It looks like details for the ELISA measurements in the methods section was missing. Were the ELISA measurements done with untagged KOR or SpH-KOR? One might worry about the effects of the N-terminal SpH tag on KOR trafficking, and it would be nice if the fluorescence SpH-KOR data were supported by ELISA for untagged KOR. (At least some of the data is immunostaining of FLAG-KOR, which probably introduces only minimal perturbation)

We apologize for not including the details of the ELISA experiments. The ELISA experiments were performed essentially as described previously (PMID: 24990314; PMID: 24847082). Briefly, CHO-KOR cells or SpH-KOR cells (2x105) were seeded in complete growth media into each well of a 24 well poly-lysine coated plate. The following day cells were washed once in PBS, placed on ice and incubated with 1:1000 dilution (PBS containing 1% BSA) of either anti-Flag M1 mouse monoclonal antibody (for CHO-KOR cells), or anti-GFP rabbit polyclonal antibody (for SpH-KOR) for 1h at 4˚C. Cells were then gently washed twice with PBS and treated without or with 1mM peptides in either F-12 medium (for CHO-KOR cells) or F-12K(for SpH-KOR) containing protease inhibitor cocktail (Sigma) for 30 min at 37oC to induce receptor internalization. Cells were then washed and incubated in media without peptides for different time periods (5-120 min). Cells were chilled to 4˚C and briefly fixed with paraformaldehyde for 3 min. Cells were then incubated with 1:1000 dilution of either anti-mouse or anti-rabbit HRP-coupled secondary antibody. The substrate o-phenylenediamine (5 mg/10 ml in 0.15 M citrate buffer, pH 5, containing 20 ul of H2O2 ) was added to each well (100 ul) and reaction stopped after 10 min by addition of 50 ul 1N HCl. Absorbance at 490 nm was measured with a Bio-Rad ELISA reader. We will definitely correct this oversight and include these details in the revised manuscript.

The reviewer’s concern about the tag is a valid one, and one that we are very careful about. We have used three different tags to label the receptor, all on the N-terminus to reduce potential interference. The ELISA measurements were done using FLAG-tagged and HA-tagged KOR. The trafficking experiments were done with FLAG-tagged and SpH-tagged KOR. The results are consistent between all these experiments, suggesting that the difference we observe are not due to tagging. We will clarify these details in the revised manuscript.

9) Dynorphin A17 is a very sticky peptide and difficult to wash out. Since we don't have a dose response it may require only very doses to have full activation for cAMP inhibition. It would be nice to be able to discount this as a potential for prolonged activation after washout.

The reviewer brings up a good point. DynA is less sticky in media or solutions containing 150mM NaCl, but we realize that this is a concern that should be addressed. In our case, we picked the doses we used based on dose-response curves that we have performed for cAMP signaling for these peptides. We realize that it is important to explain the choice of our concentrations better, and we will be happy to do so in the revised manuscript.

Reviewer #3:

This is an interesting idea and creative paper implicating a differential mechanism of intracellular trafficking and subsequently signaling that is triggered by different dynorphins binding to the kappa opioid receptor. However, there are some questions for the authors:

1) My reading is that some dynorphins are extremely rapidly degraded in serum and with these experiments performed in 15% Horse/FCS there is concern that some of the differential results could be explained by differential degradation. One hypothesis could be a differential frequency of receptor activation over time of a fast recycling receptor population. Can the authors convince me that this difference in trafficking and subsequent signaling is an intrinsic property of the peptide and not an exhaustion of peptide (would be DynB) over the 30min assay?

2) In Fig 2D, 2G and 2J at what time after addition peptides was this data obtained?

3) In Fig 2F the divergence of internalized receptor only occurs from time 20-30 mins which was difficult for me to understand since DynA should result in lost surface receptor number. What confuses me is that in Fig2H the initial recycling induced by DynA17 is fast and slows down so I am wondering if a second hit is needed which feeds into my concern about peptide degradation in the media. Since released peptide would be pulsatile maybe in vivo DynA17 could act like DynB?

4) The assays seem to be done with a single concentration of peptide - 1µM. Do the authors have data to show that at lower (or higher) concentrations than 1µM result in the same trafficking patterns, albeit to a lesser or greater extent. Also, for the cAMP inhibition what concentration gives max inhibition? For a binding affinity of 0.01nM in the cells and with high expression, the 1micromolar concentration seems high.

5) In Fig 2H 100% of receptors appear to be recycled after DynB however 25% of kappa colocalize in Rab7 in 3C so do these Rb 7 co-localized receptors recycle?

6) Could some of the signaling differences be explained by continued activation of receptors as a consequence of peptide processing in the endocytosed vesicle as opposed to different vesicles? I guess the continued signaling could also direct subsequent trafficking and this could be tested with a membrane permeable antagonist.

7) The impact statement "Co-released dynorphins, which signal similarly from the cell surface, can differentially localize GPCRs to specific subcellular compartments, and cause divergent receptor fates and distinct spatiotemporal patterns of signaling" could be misconstrued. If one of the pathways is dominant and blocks the other, then co-release may only have one signaling outcome. Have any dynorphin mix experiments been conducted? What might be anticipated?

8) It looks like details for the ELISA measurements in the methods section was missing. Were the ELISA measurements done with untagged KOR or SpH-KOR? One might worry about the effects of the N-terminal SpH tag on KOR trafficking, and it would be nice if the fluorescence SpH-KOR data were supported by ELISA for untagged KOR. (At least some of the data is immunostaining of FLAG-KOR, which probably introduces only minimal perturbation)

9) Dynorphin A17 is a very sticky peptide and difficult to wash out. Since we don't have a dose response it may require only very doses to have full activation for cAMP inhibition. It would be nice to be able to discount this as a potential for prolonged activation after washout.

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Reply to the reviewers

Reviewer1

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

The manuscript is clearly written and the figures appropriate and informative. Some descriptions of data analyses are a little dense but reflect what would appear long hard efforts on the part of the authors to identify and control for possible sources of misinterpretation due to sensitivities of parameters in their fitness model. The authors efforts to retest interactions under non-competition conditions allay fears of most concerns that I would have. One problem though that I could not see explicitly addressed was that of potential effects of interactions between methotrexate and the other conditions and how this is controlled for. Specifically, I could be argued that the fact that a particular PPI is observed under a specific condition could have more to do with a synthetic effect of treatment of cells with a drug plus methotrexate. Is this controlled for and how? I raise this because in a chemical genetic screen for fitness it was shown that methotrexate is particularly promiscuous for drug-drug interactions (Hillenmeyer ME ,et al. Science 2008). I tried to think of how this works but couldn't come up with anything immediately. I'd appreciate if the authors would take a crack at resolving this issue. Otherwise I have no further concerns about the manuscript.

We thank the reviewer for the kind comments. We agree with the reviewer’s point that methotrexate could be interacting with drugs or other perturbagens, similar to how the chosen nitrogen source, carbon source, or other growth conditions may interact with a drug. However, the methotrexate concentration is held constant across all conditions, as is the rest of the media components such as the nitrogen and carbon source (with the exception of the raffinose perturbation). Any interactions with methotrexate, or other media components, is undetectable without systematically varying all components for all stressors. Therefore, we use the typical experimental design of measuring molecular variation from a reference, holding invariant media components (such as methotrexate, glucose, or vitamins) fixed between conditions. This is a general practice, and we describe that every condition contains methotrexate on page 3, line 10.

The library was grown under mild methotrexate selection in 9 environments for 12-18 generations in serial batch culture, diluting 1:8 every ~3 generations, with a bottleneck population size greater than 2 x 109 cells (Table S1).

We also list the full details of each environment in Table S1.

Reviewer #1 (Significance (Required)):

Lui et al expand on previous work from the Levy group to explore a massive in vivo protein interactome in the yeast S. cerevisiae. They achieve this by performing screens cross 9 growth conditions, which, with replication, results in a total of 44 million measurements. Interpreting their results based on a fitness model for pooled growth under methotrexate selection, they make the key observation that there is a vastly expanded pool of protein-protein interactions (PPI) that are found under only one or two condition compared to a more limited set of PPI that are found under a broad set of conditions (mutable versus immutable interactors). The authors show that this dichotomy suggests some important features of proteins and their PPIs that raise important questions about functionality and evolution of PPIs. Among these are that mutable PPIs are enriched for cross-compartmental, high disorder and higher rates of evolution and subcellular localization of proteins to chromatin, suggesting roles in gene regulation that are associated with cellular responses to new conditions. At the same time these interactions are not enriched for changes in abundance. These results are in contrast to those of immutable PPIs, which seem to form a core background noise, more determined by changes in abundance than what the authors interpret must be post-translational processes that may drive, for instance, changes in subcellular localization resulting in appearance of PPIs under specific conditions. The authors are also able to address a couple of key issues about protein interactomes, including the controversial Party-date Hub hypothesis of Vidal, in which they could now affirm support for this hypothesis based on their results and notably negative correlation of PPIs to protein abundance for mutable PPIs. Finally, they also addressed the problem of predicting the upper limit of PPIs in yeast, showing the remarkable results that it may be no more than about 2 times the number of proteins expressed by yeast. Such an upper limit is profoundly important to modelling cellular network complexity and, if it holds up, could define a general upper limit on organismal complexity.

This manuscript is a very important contribution to understanding dynamics of molecular networks in living cells and should be published with high priority.

Reviewer 2

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

Report on Liu et al. "A large accessory protein interactome is rewired across environments"

Liu et al. use a mDHFR-based, pooled barcode sequencing / competitive growth / mild methotrexate selection method to investigate changes of PPI abundance of 1.6 million protein pairs across different 9 growth conditions. Because most PPI screens aim to identify novel PPIs in standard growth conditions, the currently known yeast PPI network may be incomplete. The key concept is to define immutable" PPIs that are found in all conditions and "mutable" PPIs that are present in only some conditions.

The assay identified 13764 PPIs across the 9 conditions, using optimized fitness cut offs. Steady PPI i.e. across all environments, were identified in membrane compartments and cell division. Processes associated with the chromosome, transcription, protein translation, RNA processing and ribosome regulation were found to change between conditions. Mutable PPIs are form modules as topological analyses reveals.

Interestingly, a correlation on intrinsic disorder and PPI mutability was found and postulated as more flexible in the conformational context, while at the same time they are formed by less abundant proteins.

I appreciate the trick to use homodimerization as an abundance proxy to predict interaction between heterodimers (of proteins that homodimerize). This "mass-action kinetics model" explains the strength of 230 out of 1212 tested heterodimers.

A validation experiment of the glucose transporter network was performed and 90 "randomly chosen" PPIs that were present in the SD environment were tested in NaCl (osmotic stress) and Raffinose (low glucose) conditions through recording optical density growth trajectories. Hxt5 PPIs stayed similar in the tested conditions, supported by the current knowledge that Hxt5 is highly expressed in stationary phase and under salt stress. In Raffinose, Hxt7, previously reported to increase the mRNA expression, lost most PPIs indicating that other factors might influence Hxt7 PPIs.

**Points for consideration:**

*) A clear definition of mutable and immutable is missing, or could not be found e.g. at page 4 second paragraph.

We thank the reviewer for pointing this out. We have now added better definition of mutable and immutable on line 19 page 4:

We partitioned PPIs by the number of environments in which they were identified and defined PPIs at opposite ends of this spectrum as “mutable” PPIs (identified in only 1-3 environments) and “immutable” (identified in 8-9 environments).

*) Approximately half of the PPIs have been identified in one environment. Many of those mutable PPIs were detected in the 16{degree sign}C condition. Is there an explanation for the predominance of this specific environment? What are these PPIs about?

The reviewer is correct that ~40% of the PPIs identified in only one environment were found in the 16 ℃ environment. One reason for this could be technical: the positive predictive value (PPV) is the lowest amongst the conditions (16 ℃: 31.6%, mean: 57%, Table SM6). It must be noted, however, that PPVs are calculated using reference data that has generally been collected in standard growth conditions. So, it might be expected that the most divergent environment from standard growth conditions (resulting in the most differences in PPIs) would result in a lower PPV in our study even if the true frequency of false positives was equivalent across environments. We have attempted to be transparent about the quality of the data in each environment by reporting PPVs and other metrics in Table SM6. However, we suspect that the large number of PPIs unique to 16 ℃ is due in part to the fact that it causes the largest changes in the protein interactome, and believe that it should be included, even at the risk of lowering the overall quality of the data. The main reason for this is that this data is likely to contain valuable information about how the cell copes with this stress. For example, we find, but do not highlight in the manuscript, that 16 ℃-specific PPIs contain two major hubs (DID4: 285 PPIs involved in endocytosis and vacuolar trafficking, and DED1: 102 PPIs involved in translation), both of which are reported to be associated with cold adaptation in yeast (Hilliker et al., 2011; Isasa et al., 2015).

To assess whether the potentially higher false-positive rate in 16 ℃ could be impacting our conclusions related to PPI network organization and features of immutable and mutable PPIs, we repeated these analyses leaving out the 16 ℃ data and found that our main conclusions did not change. This new analysis is now presented in Figure S8 and described on page 5, line 10.

Finally, we used a pair of more conservative PPI calling procedures that either identified PPIs with a low rate of false positives across all environments (FPR

We have also added references to other panels in Figure S8 throughout the manuscript, where appropriate.

*) 50 % overall retest validation rate is fair and reflects a value comparable to other large-scale approaches. However what is the actual variation, e.g. between mutable PPIs and immutable or between condition. e.g. at 16{degree sign}C.

We validated 502 PPIs present in the SD environment and an additional 36 PPIs in the NaCl environment. As the reviewer suggests, we do indeed observe differences in the validation rate across mutability bins. This data is reported in Figures 3B and S6B, and we use this information to provide a confidence score for each PPI on page 5, line 4.

To better estimate how the number of PPIs changes with PPI mutability, we used these optical density assays to model the validation rate as a function of the mean PPiSeq fitness and the number of environments in which a PPI is detected. This accurate model (Spearman's r =0.98 between predicted and observed, see Methods) provided confidence scores (predicted validation rates) for each PPI (Table S5) and allowed us to adjust the true positive PPI estimate in each mutability bin. Using this more conservative estimate, we still found a preponderance of mutable PPIs (Figure S6E).

The validation rate in NaCl is similar to SD (39%, 14/36), suggesting that validation rates do not vary excessively across environments. Because validation experiments are time consuming (we performed 6 growth experiments per PPI), performing a similar scale of validations in all environments as in SD would be resource intensive. Insead, we report a number of metrics (true positive rate, false positive rate, positive predictive value) in Table SM6 using large positive and random reference sets. We believe these metrics are sufficient for readers to compare the quality of data across environments.

*) What is the R correlation cutoff for PPIs explained in the mass equilibrium model vs. not explained?

We do not use an R correlation cutoff to assess if a PPI is explained by the mass-action equilibrium model. We instead rely on ordinary least-squares regression as detailed in the methods on page 68, line 13.

...we used ordinary least-squares linear regression in R to fit a model of the geometric mean of the homodimer signals multiplied by a free constant and plus a free intercept. Significantly explained heterodimer PPIs were judged by a significant coefficient (FDR 0.05, single-test). This criteria was used to identify PPIs for which protein expression does or does not appear to play as significant of a role as other post-translational mechanisms.

The first criterion identifies a quantitative fit to the model of variation being related. The second criterion is used to filter out PPIs for which the relationship appears to be explained by more than just the homodimer signals. This approach is more stringent, but we believe this is the most appropriate statistical test to assess fit to this linear model.

*) 90 "randomly chosen" PPIs for validation. It needs to be demonstrated that these interaction are a random subset otherwise is could also mean cherry picked interactions.

We selected 90 of the 284 glucose transport-related PPIs for validation using the “sample” function in R (replace = FALSE). We have now included text that describes this on page 63, line 3 in the supplementary methods:

Diploids (PPIs) on each plate were randomly picked using the “sample” function in R (replace = FALSE) from PPIs that meet specific requirements.

*) Figure 4 provides interesting correlations with the goal to reveal properties of mutable and less mutable PPIs. PPIs detected in the PPIseq screen can partially be correlated to co-expression (4A) as well as co-localization. Does it make sense to correlate the co-expression across number of conditions? Are the expression correlation condition specific. In this graph it could be that expression correlation stems from condition 1 and 2 and the interaction takes place in 4 and 5 still leading to the same conclusion ... Is the picture of the co-expression correlation similar when you simply look at individual environments like in S4A?

We use co-expression mutual rank scores from the COXPRESdb v7.3 database (Obayashi et al., 2019). These mutual rank scores are derived from a broad set of 3593 environmental perturbations that are not limited to the environments we tested here. By using this data, we are asking if co-expression in general is correlated with mutability and report that it is in Figure 4A. We thank the reviewer for pointing out that this was not clear and have now added text to clarify that the co-expression analysis is derived from external data on page 6, line 7.

We first asked whether co-expression is indeed a predictor of PPI mutability and found that it is: co-expression mutual rank (which is inversely proportional to co-expression across thousands of microarray experiments) declined with PPI mutability (Figures 4A and S11) (Obayashi and Kinoshita, 2009; Obayashi et al., 2019).

The new figure S11 examines how the co-expression mutual rank changes with PPI mutability for PPIs identified in each environment, as the reviewer suggested. For each environment, we find the same general pattern as in Figure 4A (which considers PPIs from all environments).

*) Figure 4C: Interesting, how dependent are the various categories?

It is well known that many of these categories are correlated (e.g. mRNA expression level and protein abundance, and deletion fitness effect and genetic interaction degree). However, we believe it is most valuable to report the correlation of each category with PPI mutability independently in Figures 4C and S12, since similar correlations with related categories provide more confidence in our conclusions.

*) Figure 4 F: When binned in the number of environments in which the PPI was found, the distribution peaks at 6 environments and decreases with higher and lower number of environments. The description /explanation in the text clearly says something else.

We reported on page 7, line 15:

We next used logistic regression to determine what features may underlie a good or poor fit to the model (Figure S14C) and found that PPI mutability was the best predictor, with more mutable PPIs being less frequently explained (Figure 4F). Unexpectedly, mean protein abundance was the second best predictor, with high abundance predicting a poor fit to the model, particularly for less mutable PPIs (Figure S14D and S14E).

As the reviewer notes, Figure 4F shows that the percent of heterodimers explained by the model does appear to decrease for PPIs observed in the most environments. We suspect that the reviewer is correct that something more complicated is going on. One possibility is that extraordinarily stable PPIs (stable in all conditions) would have less quantitative variation in protein or PPI abundance across environments. If this is true, it would be statistically difficult to fit the mass action kinetics model for these PPIs (lower signal relative to noise), thereby resulting in the observed dip.

A second possibility is that multiple correlated factors are associated with contributing positively or negatively to a good fit, and the simplicity of Figure 4F or a Pearson correlation does not capture this interplay. This second possibility is why we used multivariate logistic regression (Figure S14C) to dissect the major contributing factors. In the text quote above, we report that high abundance is anti-correlated with a good fit to the model (S14D, S14E). Figure 4C shows that immutable PPIs tend to be formed from highly abundant proteins. One possible explanation is that highly abundant proteins saturate the binding sites of their binding partners, breaking from the assumptions of mass action kinetics model. We have now changed the word “limit” to “saturate” on page 7, line 22 to make this concept more explicit.

Taken together, these data suggest that mutable PPIs are subject to more post-translational regulation across environments and that high basal protein abundance may saturate the binding sites of their partners, limiting the ability of gene expression changes to regulate PPIs.

A third possibility is that the dip is simply due to noise. Given the complexity of the possible explanations and our uncertainty about which is more likely, we chose to leave this description out of the main text and focus on the major finding: that PPIs detected in more environments are generally associated with a better fit to the mass action kinetics model.

*) Figure 6: I apologize, but for my taste this is not a final figure 6 for this study. Investigation of different environments increases the PPI network in yeast, yes, yet it is very well known that a saturation is reached after testing of several conditions, different methods and even screening repetition (sampling). It does not represent an important outcome. Move to suppl or remove.

We included Figure 6 to summarize and illustrate the path forward from this study. This is an explicit reference to impactful computational analyses done using earlier generations of data to assess the completeness of single-condition interaction networks (Hart et al., 2006; Sambourg and Thierry-Mieg, 2010). Here, we are extending PPI measurement of millions-scale networks across multiple environments, and are using this figure to extend these concepts to multi-condition screens. We agree that the property of saturation in sampling is well known, but it is surprising that we can quantitatively estimate convergence of this expanded condition-specific PPI set using only 9 conditions. Thus, we agree with Reviewer 1 that these are “remarkable results” and that the “upper limit is profoundly important to modelling cellular network complexity and, if it holds up, could define a general upper limit on organismal complexity.” We think this is an important advance of the paper, and this figure is useful to stimulate discussion and guide future work.

Reviewer #2 (Significance (Required)):

Liu et al. increase the current PPI network in yeast and offer a substantial dataset of novel PPIs seen in specific environments only. This resource can be used to further investigate the biological meaning of the PPI changes. The data set is compared to previous DHFR providing some sort of quality benchmarking. Mutable interactions are characterized well. Clearly a next step could be to start some "orthogonal" validation, i.e. beyond yeast growth under methotrexate treatment.

The reviewer makes a great point that we also discuss on page 9, line 33:

While we used reconstruction of C-terminal-attached mDHFR fragments as a reporter for PPI abundance, similar massively parallel assays could be constructed with different PCA reporters or tagging configurations to validate our observations and overcome false negatives that are specific to our reporter. Indeed, the recent development of “swap tag” libraries, where new markers can be inserted C- or N-terminal to most genes (Weill et al., 2018; Yofe et al., 2016), in combination with our iSeq double barcoder collection (Liu et al., 2019), makes extension of our approach eminently feasible.

Reviewer 3

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

**Summary**

The manuscript "A large accessory protein interactome is rewired across environments" by Liu et al. scales up a previously-described method (PPiSeq) to test a matrix of ~1.6 million protein pairs of direct protein-protein interactions in each of 9 different growth environments.

While the study found a small fraction of immutable PPIs that are relatively stable across environments, the vast majority were 'mutable' across environments. Surprisingly, PPIs detected only in one environment made up more than 60% of the map. In addition to a false positive fraction that can yield apparently-mutable interactions, retest experiments demonstrate (not surprisingly) that environment-specificity can sometimes be attributed to false-negatives. The study authors predict that the whole subnetwork within the space tested will contain 11K true interactions.

Much of environment-specific rewiring seemed to take place in an 'accessory module', which surrounds the core module made of mostly immutable PPIs. A number of interesting network clustering and functional enrichment analyses are performed to characterize the network overall and 'mutable' interactions in particular. The study report other global properties such as expression level, protein abundance and genetic interaction degree that differ between mutable and immutable PPIs. One of the interesting findings was evidence that many environmentally mutable PPI changes are regulated post-translationally. Finally, authors provide a case study about network rewiring related to glucose transport.

**Major issues**

-The results section should more prominently describe the dimensions of the matrix screen, both in terms of the set of protein pairs attempted and the set actually screened (I think this was 1741 x 1113 after filtering?). More importantly, the study should acknowledge in the introduction that this was NOT a random sample of protein pairs, but rather focused on pairs for which interaction had been previously observed in the baseline condition. This major bias has a potentially substantial impact on many of the downstream analyses. For example, any gene which was not expressed under the conditions of the original Tarrasov et al. study on which the screening space was based will not have been tested here. Thus, the study has systematically excluded interactions involving proteins with environment-dependent expression, except where they happened to be expressed in the single Tarrasov et al. environment. Heightened connectivity within the 'core module' may result from this bias, and if Tarrasov et al had screened in hydrogen peroxide (H2O2) instead of SD media, perhaps the network would have exhibited a code module in H2O2 decorated by less-densely connected accessory modules observed in other environments. The paper should clearly indicate which downstream analyses have special caveats in light of this design bias.

We have now added text the matrix dimensions of our study on page 3, line 3:

To generate a large PPiSeq library, all strains from the protein interactome (mDHFR-PCA) collection that were found to contain a protein likely to participate in at least one PPI (1742 X 1130 protein pairs), (Tarassov et al., 2008) were barcoded in duplicate using the double barcoder iSeq collection (Liu et al., 2019), and mated together in a single pool (Figure 1A). Double barcode sequencing revealed that the PPiSeq library contained 1.79 million protein pairs and 6.05 million double barcodes (92.3% and 78.1% of theoretical, respectively, 1741 X 1113 protein pairs), with each protein pair represented by an average of 3.4 unique double barcodes (Figure S1).

We agree with the reviewer that our selection of proteins from a previously identified set can introduce bias in our conclusions. Our research question was focused on how PPIs change across environments, and thus we chose to maximize our power to detect PPI changes by selecting a set of protein pairs that are enriched for PPIs. We have now added a discussion of the potential caveats of this choice to the discussion on page 9, line 4:

Results presented here and elsewhere (Huttlin et al., 2020) suggest that PPIs discovered under a single condition or cell type are a small subset of the full protein interactome emergent from a genome. We sampled nine diverse environments and found approximately 3-fold more interactions than in a single environment. However, the discovery of new PPIs began to saturate, indicating that most condition-specific PPIs can be captured in a limited number of conditions. Testing in many more conditions and with PPI assays orthogonal to PPiSeq will undoubtedly identify new PPIs, however a more important outcome could be the identification of coordinated network changes across conditions. Using a test set of ~1.6 million (of ~18 million) protein pairs across nine environments, we find that specific parts of the protein interactome are relatively stable (core modules) while others frequently change across environments (accessory modules). However, two important caveats of our study must be recognized before extrapolating these results to the entire protein interactome across all environment space. First, we tested for interactions between a biased set of proteins that have previously been found to participate in at least one PPI as measured by mDHFR-PCA under standard growth conditions (Tarassov et al., 2008). Thus, proteins that are not expressed under standard growth conditions are excluded from our study, as are PPIs that are not detectable by mDHFR-PCA or PPiSeq. It is possible that a comprehensive screen using multiple orthogonal PPI assays would alter our observations related to the relative dynamics of different regions of the protein interactome and the features of mutable and immutable PPIs. Second, we tested a limited number of environmental perturbations under similar growth conditions (batch liquid growth). It is possible that more extreme environmental shifts (e.g. growth as a colony, anaerobic growth, pseudohyphal growth) would introduce new accessory modules or alter the mutability of the PPIs we detect. Nevertheless, results presented here provide a new mechanistic view of how the cell changes in response to environmental challenges, building on the previous work that describes coordinated responses in the transcriptome (Brauer et al., 2007; Gasch et al., 2000) and proteome (Breker et al., 2013; Chong et al., 2015).

-Related to the previous issue, a quick look at the proteins tested (if I understood them correctly) showed that they were enriched for genes encoding the elongator holoenzyme complex, DNA-directed RNA polymerase I complex, membrane docking and actin binding proteins, among other functional enrichments. Genes related to DNA damage (endonuclease activity and transposition), were depleted. It was unclear whether the functional enrichment analyses described in the paper reported enrichments relative to what would be expected given the bias inherent to the tested space?

We did two functional enrichment analyses in this study: network density within Gene Ontology terms (related to Figure 2) and gene ontology enrichment of network communities (related to Figure 3). For both analyses, we performed comparisons to proteins included in PPiSeq library. This is described in the Supplementary Materials on page 63, line 35:

To estimate GO term enrichment in our PPI network, we constructed 1000 random networks by replacing each bait or prey protein that was involved in a PPI with a randomly chosen protein from all proteins in our screen. This randomization preserves the degree distribution of the network.

And on page 66, line 38:

The set of proteins used for enrichment comparison are proteins that are involved in at least one PPI as determined by PPiSeq.

-Re: data quality. To the study's great credit, they incorporated positive and random reference sets (PRS and RRS) into the screen. However, the results from this were concerning: Table SM6 shows that assay stringency was set such that between 1 and 3 out of 67 RRS pairs were detected. This specificity would be fine for an assay intended for retest or validate previous hits, where the prior probability of a true interaction is high, but in large-scale screening the prior probability of true interactions that are detectable by PCA is much lower, and a higher specificity is needed to avoid being overwhelmed by false positives. Consider this back of the envelope calculation: Let's say that the prior probability of true interaction is 1% as the authors' suggest (pg 49, section 6.5), and if PCA can optimistically detect 30% of these pairs, then the number of true interactions we might expect to see in an RRS of size 67 is 1% * 30% * 67 = 0.2 . This back of the envelope calculation suggests that a stringency allowing 1 hit in RRS will yield 80% [ (1 - 0.2) / 1 ] false positives, and a stringency allowing 3 hits in RRS will yield 93% [ (3 - 0.2) / 3] false positives. How do the authors reconcile these back of the envelope calculations from their PRS and RRS results with their estimates of precision?

We thank the reviewer for bringing up with this issue. We included positive and random reference sets (PRS:70 protein pairs, RRS:67 protein pairs) to benchmark our PPI calling (Yu et al., 2008). The PRS reference lists PPIs that have been validated by multiple independent studies and is therefore likely to represent true PPIs that are present in some subset of the environments we tested. For the PRS set, we found a rate of detection that is comparable to other studies (PPiSeq in SD: 28%, Y2H and yellow fluorescent protein-PCA: ~20%) (Yu et al., 2008). The RRS reference, developed ten years ago, is randomly chosen protein pairs for which there was no evidence of a PPI in the literature at the time (mostly in standard growth conditions). Given the relatively high rate of false negatives in PPI assays, this set may in fact contain some true PPIs that have yet to be discovered. We could detect PPIs for four RRS protein pairs in our study, when looking across all 9 environments. Three of these (Grs1_Pet10, Rck2_Csh1, and YDR492W_Rpd3) could be detected in multiple environments (9, 7, and 3, respectively), suggesting that their detection was not a statistical or experimental artifact of our bar-seq assay (see table below derived from Table S4). The remaining PPI detected in the RRS, was only detected in SD (standard growth conditions) but with a relatively high fitness (0.35), again suggesting its detection was not a statistical or experimental artifact. While we do acknowledge it is possible that these are indeed false positives due to erroneous interactions of chimeric DHFR-tagged versions of these proteins, the small size of the RRS combined with the fact that some of the protein pairs could be true PPIs, did not give us confidence that this rate (4 of 70) is representative of our true false positive rate. To determine a false positive rate that is less subject to biases stemming from sampling of small numbers, we instead generated 50 new, larger random reference sets, by sampling for each set ~ 60,000 protein pairs without a reported PPI in BioGRID. Using these new reference sets, we found that the putative false positive rate of our assay is generally lower than 0.3% across conditions for each of the 50 reference sets. We therefore used this more statistically robust measure of the false positive rate to estimate positive predictive values (PPV = 62%, TPR = 41% in SD). We detail these statistical methods in Section 6 of the supplementary methods and report all statistical metrics in Table SM6.

PPI

Environment_number

SD

H2O2

Hydroxyurea

Doxorubicin

Forskolin

Raffinose

NaCl

16℃

FK506

Rck2_Csh1

7

0.35

0.35

0

0.20

0.54

0.74

0

0.17

0.59

Grs1_Pet10

9

0.44

0.39

0.34

0.25

0.65

1.19

0.2

0.16

0.95

YDR492W_Rpd3

3

0

0.18

0

0

0

0

0

0.17

0.61

Mrps35_Bub3

1

0.35

0

0

0

0

0

0

0

0

Positive_control

9

1

0.8

0.73

0.62

1.4

2.44

0.4

0.28

1.8

Table. Mean fitness in each environment

-Methods for estimating precision and recall were not sufficiently well described to assess. Precision vs recall plots would be helpful to better understand this tradeoff as score thresholds were evaluated.

We describe in detail our approach to calling PPIs in section 6.6 of the supplementary methods, including Table SM6, and Figures SM3, SM4, SM6, and now Figure SM5. We identified positive PPIs using a dynamic threshold that considers the mean fitness and p-value in each environment. For each dynamic threshold, we estimated the precision and recall based on the reference sets (described supplementary methods in section 6.5). We then chose the threshold with the maximal Matthews correlation coefficient (MCC) to obtain the best balance between precision and recall. We have now added an additional plot (Figure SM5) that shows the precision and recall for the chosen dynamic threshold in each environment.

-Within the tested space, the Tarassov et al map and the current map could each be compared against a common 'bronze standard' (e.g. literature curated interactions), at least for the SD map, to have an idea about how the quality of the current map compares to that of the previous PCA map. Each could also be compared with the most recent large-scale Y2H study (Yu et al).

We thank the reviewer for this suggestion. We have now added a figure panel (Figure S4) that compares PPiSeq in SD (2 replicates) to mDHFR PCA (Tarassov et al., 2008), Y2H (Yu et al., 2008), and our newly constructed ‘bronze standard’ high-confidence positive reference set (PRS, supplementary method section 6.4).

• Experimental validation of the network was done by conventional PCA. However, it should be noted that this is a form of technical replication of the DHFR-based PCA assay, and not a truly independent validation. Other large-scale yeast interaction studies (e.g., Yu et al, Science 2008) have assessed a random subset of observed PPIs using an orthogonal approach, calibrated using PRS and RRS sets examined via the same orthogonal method, from which overall performance of the dataset could be determined.

We appreciate the reviewer’s perspective, since orthogonal validation experiments have been a critical tool to establish assay performance following early Y2H work. We know from careful work done previously that modern orthogonal assays have a low cross validation rate ((Yu et al., 2008) and that they tend to be enriched for PPIs in different cellular compartments (Jensen and Bork, 2008), indicating that high false negative rates are the likely explanation. High false negative rates have been confirmed here and elsewhere using positive reference sets (e.g. Y2H 80%, PCA 80%, PPiSeq 74% using the PRS in (Yu et al., 2008)). Therefore, the expectation is that PPiSeq, as with other assays, will have a low rate of validation using an orthogonal assay -- although we would not know if this rate is 10%, 30% or somewhere in between without performing the work. However, the exact number -- whether it be 10% or 30% -- has no practical impact on the main conclusions of this study (focused on network dynamics rather than network enumeration). Neither does that number speak to the confidence in our PPI calls, since a lower number may simply be due to less overlap in the sets of PPIs that are callable by PPiSeq and another assay. Our method uses bar-seq to extend an established mDHFR-PCA assay (Tarassov et al., 2008). The validations we performed were aimed at confirming that our sequencing, barcode counting, fitness estimation, and PPI calling protocols were not introducing excessive noise relative to mDHFR-PCA that resulted in a high number of PPI miscalls. Confirming this, we do indeed find a high rate of validation by lower throughput PCA (50-90%, Figure 3B). Finally, we do include independent tests of the quality of our data by comparing it to positive and random reference sets from literature curated data. We find that our assay performs extremely well (PPV > 61%, TPR > 41%) relative to other high-throughput assays.

-The Venn diagram in Figure 1G was not very informative in terms of assessing the quality of data. It looks like there is a relatively little overlap between PPIs identified in standard conditions (SD media) in the current study and those of the previous study using a very similar method. Is there any way to know how much of this disagreement can be attributed to each screen being sub-saturation (e.g. by comparing replica screens) and what fraction to systematic assay or environment differences?

We have now added a figure panel (Figure S4) that compares PPiSeq in SD (2 replicates) to mDHFR-PCA (Tarassov et al., 2008), Y2H (Yu et al., 2008), and our newly constructed ‘bronze standard’ high-confidence positive reference sets (PRS, supplementary methods section 6.4). We find that SD replicates have an overlap coefficient of 79% with each other, ~45% with mDHFR-PCA, ~45% the ‘bronze standard’ PRS, and ~13% with Y2H. Overlap coefficients between the SD replicates and mDHFR-PCA are much higher than those found between orthologous methods ((Yu et al., 2008), indicating that these two assays are identifying a similar set of PPIs. We do note that PPiSeq and mDHFR-PCA do screen for PPIs under different growth conditions (batch liquid growth vs. colonies on agar), so some fraction of the disagreement is due to environmental differences. PPIs that overlap between the two PPiSeq SD replicates are more likely to be found in mDHFR-PCA, PRS, and Y2H, indicating that PPIs identified in a single SD replicate are more likely to be false positives. However, we do find (a lower rate of) overlaps between PPIs identified in only one SD replicate and other methods, suggesting that a single PPiSeq replicate is not finding all discoverable PPIs.

-In Figure S5C, the environment-specificity rate of PPIs might be inflated due to the fact that authors only test for the absence of SD hits in other conditions, and the SD condition is the only condition that has been sampled twice during the screening. What would be the environment-specific verification rate if sample hits from each environment were tested in all environments? This seems important, as robustly detecting environment-specific PPIs is one of the key points of the study.

We use PPIs found in the SD environment to determine the environment-specificity because this provides the most conservative (highest) estimate of the number of PPIs found in other environments that were not detectable by our bar-seq assay. To identify PPIs in the SD environment, we pooled fitness estimates across the two replicates (~ 4 fitness estimates per replicate, ~ 8 total). The higher number of replicates results in a reduced rate of false positives (an erroneous fitness estimate has less impact on a PPI call), meaning that we are more confident that PPIs identified in SD are true positives. Because false positives in one environment (but not other environments) are likely to erroneously contribute to the environment-specificity rate, choosing the environment with the lowest rate of false positives (SD) should result in the lowest environment-specificity rate (highest estimate of PPIs found in other environments that were not detectable by our bar-seq assay).

**Minor issues**

-Re: "An interaction between the proteins reconstitutes mDHFR, providing resistance to the drug methotrexate and a growth advantage that is proportional to the PPI abundance" (pg 2). It may be more accurate to say "monotonically related" than "proportional" here. Fig 2 from the cited Freschi et al ref does suggests linearity with colony size over a wide range of inferred complex abundances, but non-linear at low complex abundance. Also note that Freschi measured colony area which is not linear with exponential growth rate nor with cell count.

We agree with the reviewer and have changed “proportional” to “monotonically related” on page 2, line 41.

-Re: "Using putatively positive and negative reference sets, we empirically determined a statistical threshold for each environment with the best balance of precision and recall (positive predictive value (PPV) > 61% in SD media, Methods, section 6)." (pg 3). Should state the recall at this PPV.

We agree with the reviewer and have added the recall (41%) in the main text (line 26, page3).

Using putatively positive and negative reference sets, we empirically determined a statistical threshold for each environment with the best balance of precision and recall (positive predictive value (PPV) > 61% and true positive rate > 41% in SD media, Methods, section 6).

-Authors could discuss the extent to which related methods (e.g. PMID: 28650476, PMID: 27107012, PMID: 29165646, PMID: 30217970) would be potentially suitable for screening in different environments.

We have now added a reference to a barcode-based Y2H study that examined interactions between yeast proteins to the introduction on page 2, line 2:

Yet, little is known about how PPI networks reorganize on a global scale or what drives these changes. One challenge is that commonly-used high-throughput PPI screening technologies are geared toward PPI identification (Gavin et al., 2002; Ito et al., 2001; Tarassov et al., 2008; Uetz et al., 2000; Yu et al., 2008, Yachie et al., 2016), not a quantitative analysis of relative PPI abundance that is necessary to determine if changes in the PPI network are occurring. The murine dihydrofolate reductase (mDHFR)‐based protein-fragment complementation assay (PCA) provides a viable path to characterize PPI abundance changes because it is a sensitive test for PPIs in the native cellular context and at native protein expression levels (Freschi et al., 2013; Remy and Michnick, 1999; Tarassov et al., 2008).

We have excluded the references to other barcode-based Y2H studies that reviewer mentions because they test heterologous proteins within yeast, and the effect of perturbations to yeast on these proteins would be difficult to interpret in the context of our questions. The yeast protein Y2H study, although a wonderful approach and paper, would also not be an appropriate method to examine how PPI networks change across environments because protein fusions are not expressed under their endogenous promoters and must be transported to, in many cases, a non-native compartment (cell nucleus) to be detected. Rather than explicitly discuss the caveats of this particular approach, we have instead chosen to discuss why we use PCA.

• the term "mutable" is certainly appropriate according to the dictionary definition of changeable. The authors may wish to consider though, that in a molecular biology context the term evokes changeability by mutation (a very interesting but distinct topic). Maybe another term (environment-dependent interactions or ePPIs?) would be clearer. Of course this is the authors' call.

We thank the reviewer for this suggestion, and have admittedly struggled with the terminology. For clarity of presentation, we strived to have a single word that describes the property of a PPI that is at the core of this manuscript -- how frequently a PPI is found across environments. However, the most descriptive words come with preloaded meanings in PPI research (e.g. transient, stable, dynamic), as does “mutable” with another research field. We are, quite frankly, open to suggestions from the reviewers or editors for a more appropriate word that does not raise similar objections.

-Some discussion is warranted about the phenomenon that a PPI that is unchanged in abundance could appear to change because of statistical significance thresholds that differ between screens. This would be a difficult question for any such study, and I don't think the authors need to solve it, but just to discuss.

We agree with the reviewer that significance thresholds could be impacting our interpretations and discuss this idea at length on page 4, line 23 of the Results. This section has been modified to include an additional analysis (excluding 16 ℃ data) in response to another reviewer’s comment:

Immutable PPIs were likely to have been previously reported by colony-based mDHFR-PCA or other methods, while the PPIs found in the fewest environments were not. One possible explanation for this observation is that previous PPI assays, which largely tested in standard laboratory growth conditions, and variations thereof, are biased toward identification of the least mutable PPIs. That is, since immutable PPIs are found in nearly all environments, they are more readily observed in just one. However, another possible explanation is that, in our assay, mutable PPIs are more likely to be false positives in environment(s) in which they are identified or false negatives in environments in which they are not identified. To investigate this second possibility, we first asked whether PPIs present in very few environments have lower fitnesses, as this might indicate that they are closer to our limit of detection. We found no such pattern: mean fitnesses were roughly consistent across PPIs found in 1 to 6 conditions, although they were elevated in PPIs found in 7-9 conditions (Figure S6A). To directly test the false-positive rate stemming from pooled growth and barcode sequencing, we validated randomly selected PPIs within each mutability bin by comparing their optical density growth trajectories against controls (Figures 3B). We found that mutable PPIs did indeed have lower validation rates in the environment in which they were identified, yet putative false positives were limited to ~50%, and, within a bin, do not differ between PPIs that have been previously identified and those that have been newly discovered by our assay (Figure S65B). We also note mutable PPIs might be more sensitive to environmental differences between our large pooled PPiSeq assays and clonal 96-well validation assays, indicating that differences in validation rates might be overstated. To test the false-negative rate, we assayed PPIs identified in only SD by PPiSeq across all other environments by optical density growth and found that PPIs can be assigned to additional environments (Figure S6C). However, the number of additional environments in which a PPI was detected was generally low (2.5 on average), and the interaction signal in other environments was generally weaker than in SD (Figure S6D). To better estimate how the number of PPIs changes with PPI mutability, we used these optical density assays to model the validation rate as a function of the mean PPiSeq fitness and the number of environments in which a PPI is detected. This accurate model (Spearman's r =0.98 between predicted and observed, see Methods) provided confidence scores (predicted validation rates) for each PPI (Table S5) and allowed us to adjust the true positive PPI estimate in each mutability bin. Using this more conservative estimate, we still found a preponderance of mutable PPIs (Figure S6E). Finally, we used a pair of more conservative PPI calling procedures that either identified PPIs with a low rate of false positives across all environments (FPR

We later examine major conclusions of our study using more conservative calling procedures, and find that they are consistent. On page 6, line 14:

Both the co-expression and co-localization patterns were also apparent in our higher confidence PPI sets (Figures S7B, and S7C, S8B, S8C ), indicating that they are not caused by different false positive rates between the mutability bins.

And on page 6, line 19:

We binned proteins by their PPI degree, and, within each bin, determined the correlation between the mutability score and another gene feature (Figure 4C and S12A, Table S8) (Costanzo et al., 2016; Finn et al., 2014; Gavin et al., 2006; Holstege et al., 1998; Krogan et al., 2006; Levy and Siegal, 2008; Myers et al., 2006; Newman et al., 2006; Östlund et al., 2010; Rice et al., 2000; Stark et al., 2011; Wapinski et al., 2007; Ward et al., 2004; Yang, 2007; Yu et al., 2008). These correlations were also calculated using our higher confidence PPI sets, confirming results from the full data set (Figures S7D and, S7E, S8D, S8E). We found that mutable hubs (> 15 PPIs) have more genetic interactions, in agreement with predictions from co-expression data (Bertin et al., 2007; Han et al., 2004), and that their deletion tends to cause larger fitness defects.

-More discussion would be helpful about the idea that immutability may to some extent favor interactions that PCA is better able to detect (possibly including membrane proteins?)

We agree with the reviewer and now added a discussion of this potential caveats to the discussion on page 9, line 4:

Results presented here and elsewhere (Huttlin et al., 2020) suggest that PPIs discovered under a single condition or cell type are a small subset of the full protein interactome emergent from a genome. We sampled nine diverse environments and found approximately 3-fold more interactions than in a single environment. However, the discovery of new PPIs began to saturate, indicating that most condition-specific PPIs can be captured in a limited number of conditions. Testing in many more conditions and with PPI assays orthogonal to PPiSeq will undoubtedly identify new PPIs, however a more important outcome could be the identification of coordinated network changes across conditions. Using a test set of ~1.6 million (of ~18 million) protein pairs across nine environments, we find that specific parts of the protein interactome are relatively stable (core modules) while others frequently change across environments (accessory modules). However, two important caveats of our study must be recognized before extrapolating these results to the entire protein interactome across all environment space. First, we tested for interactions between a biased set of proteins that have previously been found to participate in at least one PPI as measured by mDHFR-PCA under standard growth conditions (Tarassov et al., 2008). Thus, proteins that are not expressed under standard growth conditions are excluded from our study, as are PPIs that are not detectable by mDHFR-PCA or PPiSeq. It is possible that a comprehensive screen using multiple orthogonal PPI assays would alter our observations related to the relative dynamics of different regions of the protein interactome and the features of mutable and immutable PPIs. Second, we tested a limited number of environmental perturbations under similar growth conditions (batch liquid growth). It is possible that more extreme environmental shifts (e.g. growth as a colony, anaerobic growth, pseudohyphal growth) would introduce new accessory modules or alter the mutability of the PPIs we detect. Nevertheless, results presented here provide a new mechanistic view of how the cell changes in response to environmental challenges, building on the previous work that describes coordinated responses in the transcriptome (Brauer et al., 2007; Gasch et al., 2000) and proteome (Breker et al., 2013; Chong et al., 2015).

-Re: "As might be expected, we also found that mutable hubs, but not non-hubs, are more likely to participate in multiple protein complexes than less mutable proteins." (pg 6) This is a cool result. To what extent was this result driven by members of one or two complexes? If so, it would worth noting them.

We thank the reviewer for this question. We have now included Figue S13, which shows the number and size of protein complexes that underlie the finding that mutable hubs are more likely to participate in multiple protein complexes. We find that proteins in our screen that participate in multiple complexes are distributed over a wide range of complexes, indicating that this observation is not driven by one or two complexes. On page 6, line 34:

As might be expected, we also found that mutable hubs, but not non-hubs, are more likely to participate in multiple protein complexes than less mutable proteins (Figures S13A-C) (Costanzo et al., 2016).

-Re: "Borrowing a species richness estimator from ecology (Jari Oksanen et al., 2019), we estimate that there are ~10,840 true interactions within our search space across all environments, ~3-fold more than are detected in SD (note difference to Figure 3, which counts observed PPIs)." (pg 8) Should note that this only allows estimation of the number of interactions that are detectable by PCA methods. Previous work (Braun et al, 2019) showed that every known protein interaction assay (including PCA approaches) can only detect a fraction of bona fide interactions.

We agree with the reviewer and have modified the discussion to make this point explicit on page 9, line 4:

Results presented here and elsewhere (Huttlin et al., 2020) suggest that PPIs discovered under a single condition or cell type are a small subset of the full protein interactome emergent from a genome. We sampled nine diverse environments and found approximately 3-fold more interactions than in a single environment. However, the discovery of new PPIs began to saturate, indicating that most condition-specific PPIs can be captured in a limited number of conditions. Testing in many more conditions and with PPI assays orthogonal to PPiSeq will undoubtedly identify new PPIs, however a more important outcome could be the identification of coordinated network changes across conditions.

We continue in this paragraph to discuss the implications:

Using a test set of ~1.6 million (of ~18 million) protein pairs across nine environments, we find that specific parts of the protein interactome are relatively stable (core modules) while others frequently change across environments (accessory modules). However, two important caveats of our study must be recognized before extrapolating these results to the entire protein interactome across all environment space. First, we tested for interactions between a biased set of proteins that have previously been found to participate in at least one PPI as measured by mDHFR-PCA under standard growth conditions (Tarassov et al., 2008). Thus, proteins that are not expressed under standard growth conditions are excluded from our study, as are PPIs that are not detectable by mDHFR-PCA or PPiSeq. It is possible that a comprehensive screen using multiple orthogonal PPI assays would alter our observations related to the relative dynamics of different regions of the protein interactome and the features of mutable and immutable PPIs.

-Re: "This analysis shows that the number of PPIs present across all environments is much larger than the number observed in a single condition, but that it is feasible to discover most of these new PPIs by sampling a limited number of conditions." (pg 8). The main point is surely correct, but it is worth noting that extrapolation to the number of true interactions depends on the nine chosen environments being representative of all environments. The situation could change under more extreme, e.g., anaerobic, conditions.

We agree with the reviewer and make this point explicit, continuing from the paragraph quoted above on page 9, line 22:

Second, we tested a limited number of environmental perturbations under similar growth conditions (batch liquid growth). It is possible that more extreme environmental shifts (e.g. growth as a colony, anaerobic growth, pseudohyphal growth) would introduce new accessory modules or alter the mutability of the PPIs we detect. Nevertheless, results presented here provide a new mechanistic view of how the cell changes in response to environmental challenges, building on the previous work that describes coordinated responses in the transcriptome (Brauer et al., 2007; Gasch et al., 2000) and proteome (Breker et al., 2013; Chong et al., 2015).

-It stands to reason that proteins expressed in all conditions will yield less mutable interactions, if 'mutability' is primarily due to expression change at the transcriptional level. They should at least discuss that measuring mRNA levels could resolve questions about this. Could use Waern et al G3 2013 data (H202, SD, HU, NaCl) to predict the dynamic interactome purely by node removal, and see how conclusions would change

We agree with the reviewer that mRNA abundance could potentially be used as a proxy for protein abundance and have added this point on page 10, line 28:

Here we use homodimer abundance as a proxy for protein abundance. However, genome-wide mRNA abundance measures could be used as a proxy for protein abundance or protein abundance could be measured directly in the same pool (Levy et al., 2014) by, for example, attaching a full length mDHFR to each gene using “swap tag” libraries mentioned above (Weill et al., 2018; Yofe et al., 2016).

However, using mRNA abundance as a proxy for protein abundance in this study has several important caveats that would make interpretation difficult. First, mRNA and protein abundance correlate, but not perfectly (R2 = 0.45) (Lahtvee et al., 2017), and our findings suggest that post-translational regulation may be important to driving PPI changes. Second, mRNA abundance measures are for a single time point, while our PPI measures coarse grain over a growth cycle (lag, exponential growth, diauxic shift, saturation). Although we may be able to take multiple mRNA measures across the cycle, time delays between changes in mRNA and protein levels, combined with the fact that we do not know when a PPI is occurring or most prominent over the cycle, would pose a significant challenge to making any claims that PPI changes are driven by changes in protein abundance. We instead chose to focus on a subset of proteins (homodimers) where abundance measures can be coarse grained in the same way as PPI measures. In the above quote, we point to a potential method by which this can be done for all proteins. We also point to how a continuous culturing design could be used to better determine how protein (or mRNA proxy) abundance impacts PPI abundance on page 10, line 6:

Finally, our assays were performed across cycles of batch growth meaning that changes in PPI abundance across a growth cycle (e.g. lag, exponential growth, saturation) are coarse grained into one measurement. While this method potentially increases our chance of discovering a diverse set of PPIs, it might have an unpredictable impact on the relationship between fitness and PPI abundance (Li et al., 2018). To overcome these issues, strains containing natural or synthetic PPIs with known abundances and intracellular localizations could be spiked into cell pools to calibrate the relationship between fitness and PPI abundance in each environment. In addition, continuous culturing systems may be useful for refining precision of growth-based assays such as ours.

-The analysis showing that many interactions are likely due to post-translational modifications is very interesting, but caveats should be discussed. Where heterodimers do not fit the expression-level dependence model, some cases of non-fitting may simply be due to measurement error or non-linearity in the relationship between abundance and fitness.

We show the measurement error in Figures 1, S2, S3. While we agree with the reviewer that measurement error is a general caveat for all results reported, we do not feel that it is necessary to point to that fact in this particular case, which uses a logistic regression to report that PPI mutability was the best predictor of fit to the expression-level dependence model. We discuss the non-linearity caveat on page 9, line 41:

Our assay detected subtle fitness differences across environments (Fig S5B and S5C), which we used as a rough estimate for changes in relative PPI abundance. While it would be tempting to use fitness as a direct readout of absolute PPI abundance within a cell, non-linearities between fitness and PPI abundance may be common and PPI dependent. For example, the relative contribution of a reconstructed mDHFR molecule to fitness might diminish at high PPI abundances (saturation effects) and fitness differences between PPIs may be caused, in part, by differences in how accessible a reconstructed mDHFR molecule is to substrate. In addition, environmental shifts might impact cell growth rate, initiate a stress response, or result in other unpredictable cell effects that impact the selective pressure of methotrexate and thereby fitness (Figure S2 and S3).

-Line numbers would have been helpful to note more specific minor comments

We are sorry for this inconvenience. We have added line numbers in our revised manuscript.

-Sequence data should be shared via the Short-Read Archive.

The raw sequencing data have been uploaded to the Short-Read Archive. We mentioned it in the Data and Software Availability section on page 68, line 41.

Raw barcode sequencing data are available from the NIH Sequence Read Archive as accession PRJNA630095 (https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP259652).

Reviewer #3 (Significance (Required)):

Knowledge of protein-protein interactions (PPIs) provides a key window on biological mechanism, and unbiased screens have informed global principles underlying cellular organization. Several genome-scale screens for direct (binary) interactions between yeast proteins have been carried out, and while each has provided a wealth of new hypotheses, each has been sub-saturation. Therefore, even given multiple genome-scale screens our knowledge of yeast interactions remains incomplete. Different assays are better suited to find different interactions, and it is now clear that every assay evaluated thus far is only capable (even in a saturated screen) of detecting a minority of true interactions. More relevant to the current study, no binary interaction screen has been carried out at the scale of millions of protein pairs outside of a single 'baseline' condition.

The study by Liu et al is notable from a technology perspective in that it is one of several recombinant-barcode approaches have been developed to multiplex pairwise combinations of two barcoded libraries. Although other methods have been demonstrated at the scale of 1M protein pairs, this is the first study using such a technology at the scale of >1M pairs across multiple environments.

A limitation is that this study is not genome-scale, and the search space is biased towards proteins for which interactions were previously observed in a particular environment. This is perhaps understandable, as it made the study more tractable, but this does add caveats to many of the conclusions drawn. These would be acceptable if clearly described and discussed. There were also questions about data quality and assessment that would need to be addressed.

Assuming issues can be addressed, this is a timely study on an important topic, and will be of broad interest given the importance of protein interactions and the status of S. cerevisiae as a key testbed for systems biology.

*Reviewers' expertise:* Interaction assays, next-generation sequencing, computational genomics. Less able to assess evolutionary biology aspects.

References

Brauer, M.J., Huttenhower, C., Airoldi, E.M., Rosenstein, R., Matese, J.C., Gresham, D., Boer, V.M., Troyanskaya, O.G., and Botstein, D. (2007). Coordination of Growth Rate, Cell Cycle, Stress Response, and Metabolic Activity in Yeast. Mol. Biol. Cell 19, 352–367.

Breker, M., Gymrek, M., and Schuldiner, M. (2013). A novel single-cell screening platform reveals proteome plasticity during yeast stress responses. J. Cell Biol. 200, 839–850.

Chong, Y.T., Koh, J.L.Y., Friesen, H., Kaluarachchi Duffy, S., Cox, M.J., Moses, A., Moffat, J., Boone, C., and Andrews, B.J. (2015). Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis. Cell 161, 1413–1424.

Gasch, A.P., Spellman, P.T., Kao, C.M., Carmel-Harel, O., Eisen, M.B., Storz, G., Botstein, D., and Brown, P.O. (2000). Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes. Mol. Biol. Cell 11, 4241–4257.

Hart, G.T., Ramani, A.K., and Marcotte, E.M. (2006). How complete are current yeast and human protein-interaction networks? Genome Biol. 7, 120.

Hilliker, A., Gao, Z., Jankowsky, E., and Parker, R. (2011). The DEAD-box protein Ded1 modulates translation by the formation and resolution of an eIF4F-mRNA complex. Mol. Cell 43, 962–972.

Isasa, M., Suñer, C., Díaz, M., Puig-Sàrries, P., Zuin, A., Bichmann, A., Gygi, S.P., Rebollo, E., and Crosas, B. (2015). Cold Temperature Induces the Reprogramming of Proteolytic Pathways in Yeast. J. Biol. Chem. jbc.M115.698662.

Jensen, L.J., and Bork, P. (2008). Not Comparable, But Complementary. Science 322, 56–57.

Lahtvee, P.-J., Sánchez, B.J., Smialowska, A., Kasvandik, S., Elsemman, I.E., Gatto, F., and Nielsen, J. (2017). Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast. Cell Syst. 4, 495-504.e5.

Obayashi, T., Kagaya, Y., Aoki, Y., Tadaka, S., and Kinoshita, K. (2019). COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference. Nucleic Acids Res. 47, D55–D62.

Sambourg, L., and Thierry-Mieg, N. (2010). New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size. BMC Bioinformatics 11, 605.

Tarassov, K., Messier, V., Landry, C.R., Radinovic, S., Molina, M.M.S., Shames, I., Malitskaya, Y., Vogel, J., Bussey, H., and Michnick, S.W. (2008). An in Vivo Map of the Yeast Protein Interactome. Science 320, 1465–1470.

Yu, H., Braun, P., Yıldırım, M.A., Lemmens, I., Venkatesan, K., Sahalie, J., Hirozane-Kishikawa, T., Gebreab, F., Li, N., Simonis, N., et al. (2008). High-Quality Binary Protein Interaction Map of the Yeast Interactome Network. Science 322, 104–110.

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Reply to the reviewers

We thank the reviewers for their close reading and constructive comments on our manuscript. We believe that their insight has substantially strengthened our manuscript. Please find our response/revision plan for each comment below (in blue). Note, because of the substantial changes to the figures and the additional experiments that are we are undertaking, we have not initially revised the text. The proposed textual revisions will be included in the full revision.

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

The Katz lab has contributed greatly to the field of epigenetic reprogramming over the years, and this is

another excellent paper on the subject. I enjoyed reviewing this manuscript and don't have any major

comments/suggestions for improving it. The findings presented are novel and important, the results are clear

cut, and the writing is clear.

It's important to stress the novelty of the findings, which build upon previous studies from the same lab (upon

a shallow look one might think that some of the conclusions were described before, but this is not the case).

Despite the fact that this system has been studied in depth before, it remained unclear why and how

germline genes are bookmarked by H3K36 in the embryo, and it wasn't known why germline genes are not

expressed in the soma.

To study these questions Carpenter et al. examine multiple phenotypes (developmental aberrations,

sterility), that they combine with analysis of multiple genetic backgrounds, RNA-seq, CHIP-seq, single

molecule FISH, and fluorescent transgenes.

Previous observations from the Katz lab suggested that progeny derived from spr-5;met-2 double mutants

can develop abnormally. They show here that the progeny of these double mutants (unlike spr-5 and met-2

single mutants) develop severe and highly penetrate developmental delays, a Pvl phenotype, and sterility.

They show also that spr-5; met-2 maternal reprogramming prevents developmental delay by restricting

ectopic MES-4 bookmarking, and that developmental delay of spr-5;met-2 progeny is the result of ectopic

expression of MES-4 germline genes. The bottom line is that they shed light on how SPR-5, MET-2 and

MES-4 balance inter-generational inheritance of H3K4, H3K9, and H3K36 methylation, to allow correct

specification of germline and somatic cells. This is all very important and relevant also to other organisms.

**(very) Minor comments:**

-Since the word "heritable" is used in different contexts, it could be helpful to elaborate, perhaps in the

introduction, on the distinction between cellular memory and transgenerational inheritance.

We are happy to elaborate on this in the revised manuscript.

-It might be interesting in the Discussion to expand further about the links between heritable chromatin

marks and heritable small RNAs. The do hint that the result regarding the silencing of the somatic transgene

are especially intriguing.

We are happy to expand this in the revised manuscript.

Reviewer #1 (Significance (Required)):

This is an exciting paper which build upon years of important work in the Katz lab. The novelty of the paper

is in pinpointing the mechanisms that bookmark germline genes by H3K36 in the embryo, and explaining

why and how germline genes are prevented from being expressed in the soma.

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

Katz and colleagues examine the interaction between the methyltransferase MES-4 and spr-5; met-2 double

mutants. Their prior analysis (PNAS, 2014) showed the dramatic enhancement in sterility and development

for spr-5; met-2; this paper extends that finding by showing these effects depend on MES-4. The results are

interesting and the genetic interactions dramatic. The examination by RNAseq and ChIP helps move the

phenotypes into a more molecular analysis. The authors hypothesize that SPR-5 and MET-2 modify

chromatin of germline genes (MES-4 targets) in somatic cells, and this is required to silence germline genes

in the soma. A few issues need to be resolved to test these ideas and rule out others.

**Main comments:**

The authors' hypothesis is that SPR-5 and MET-2 act directly, to modify chromatin of germline genes (MES-

4 targets), but alternate hypothesis is that the key regulated genes are i) MES-4 itself and/or ii) known

regulators of germline gene expression e.g. the piwi pathway. Mis regulation of these factors in the soma

could be responsible for the phenotypes. Therefore, the authors should analyze expression (smFISH and

where possible protein stains) for MES-4 and PIWI components in the embryo and larvae of wildtype, double

and triple mutant strains. These experiments are essential and not difficult to perform.

In our RNA-seq analysis we see a small elevation of MES-4 itself (average 1.18 log2 fold change across 5 replicates). This does not seem likely to be solely driving such a dramatic phenotype. Nevertheless, it is possible that the small increase in expression of MES-4 itself could be contributing. To determine if MES-4 is being ectopically expressed in spr-5; met-2 double mutants, we have obtained a tag version of MES-4 from Dr. Susan Strome and will use this to examine the localization of MES-4 protein in spr-5; met-2 double mutants. We are definitely interested in the potential interaction between PIWI components and the histone modifying enzymes that we have explored in this study. However, since RNAi of MES-4 is sufficient to rescue the developmental delay of spr-5; met-2 mutants, we have chosen to focus on that interaction in this paper. In the future, we hope to examine the role of PIWI components in this system.

A second aspect of the hypothesis is that spr-5 and met-2 act before mes-4 and that while these genes are

maternally expressed, they act in the embryo. There really aren't data to support these ideas - the timing and

location of the factors' activities have not been pinned down. One way to begin to address this question

would be to perform smFISH on the target genes and on mes-4 in embryos and determine when and where

changes first appear. smFISH in embryos is critical - relying on L1 data is too late. If timing data cannot be

obtained, then I suggest that the authors back off of the timing ideas or at least explain the caveats.

Certainly, figure 8 should be simplified and timing removed. (note: Typical maternal effect tests probably

won't work because if the genes' RNAs are germline deposited, then a maternal effect test will reflect when

the RNA is expressed but not when the protein is active. A TS allele would be needed, and that may not be

available.)

To determine the timing of the ectopic expression of MES-4 targets, we have performed smFISH on two MES-4 targets in embryos. Thus far, these experiments show that MES-4 targets are ectopically expressed in the embryo, but only after the maternal to zygotic transition. This is consistent with our proposed model. A figure containing this data will be added to the revised manuscript. In addition, our model is predicated on the known embryonic protein localization of SPR-5 and MES-4. Maternal SPR-5 protein is present in the early embryo up to around the 8-cell stage, but absent in later embryos (Katz et al., 2009). In addition, in mice, the SPR-5 ortholog LSD1 is required maternally prior to the 2-cell stage (Wasson et al., 2016 and Ancelin et al., 2016). In contrast, MES-4 continues to be expressed in the embryo until later embryonic stages where it is concentrated into the germline precursors Z2 and Z3 (Fong et al., 2002). This is consistent with SPR-5 establishing a chromatin state that continues to be antagonized by MES-4. There is evidence that MET-2 is expressed both in early embryos and later embryos. However, since the phenotype of MET-2 so closely resembles the phenotype of SPR-5 (Kerr et al., 2014), we have included it in our model as working with SPR-5. Further experimentation will be required to substantiate the model, but we believe the model is consistent with all of the current data.

Writing/clarity:

-It would be helpful to include a table that lists the specific genes studied in the paper and how they behaved

in the different assays e.g. RNAseq 1, RNAseq 2, MES-4 target, ChIP. That way, readers will understand

each of the genes better.

We are happy to include a table in the revised manuscript.

-At the end of each experiment, it would be helpful to explain the conclusion and not wait until the

Discussion. For readers not in the field, the logic of the Results section is hard to follow.

This seems like a stylistic choice. Traditionally, papers did not include any conclusions in the results section, and it is our preference to keep our paper organized this way. However, if the reviewer would still like us to change this, we are happy to do so.

-The model is explained over three pages in the Discussion. It would be great to begin with a single

paragraph that summarizes the model/point of the paper simply and clearly.

The discussion in the revised manuscript will altered to include this.

**Specific comments:**

-Figure 1 has been published previously and should be moved to the supplement.

In our original paper (Kerr et al.) we reported in the text that spr-5; met-2 mutants have a developmental delay. However, we did not characterize this developmental delay. Nor did we include any images of the double mutants, except for one image of the adult germline phenotype. As a result, we believe that the inclusion of the developmental delay in the main body of this manuscript is warranted.

-Cite their prior paper for the vulval defects e.g. page 6 or show in supplement.

We are happy to include a citation of our previous paper for the vulval defects in the revised manuscript.

-The second RNAseq data should be shown in the Results since it is much stronger. The first RNAseq,

which is less robust, should be moved to supplement.

The revised manuscript will include this alteration.

-Figure 3 is very nice. Please explain why the RNAs were picked (+ the table, see comment above), and

please add here or in a new figure mes-4 and piwi pathway expression data in wildtype vs double/triple

mutants.

We performed RT-PCR on 9 MES-4 targets. These 9 targets were picked because they had the highest ectopic expression in spr-5; met-2 mutants and largest change in H3K36me3 in spr-5; met-2 mutants versus Wild Type. Amongst these 9 genes, we performed smFISH on htp-1 and cpb-1 because they are relatively well characterized as germline genes.

The revised manuscript will include added panels to supplemental figure 2 showing the expression of PIWI pathway components.

-Figure 3 here or later, please show if mes-4 RNAi removes somatic expression of target genes.

We are currently carrying out this experiment. Once it is completed, the data will hopefully be added to the paper.

-Is embryogenesis delayed?

Embryogenesis seems to be sped up in spr-5; met-2 mutants. A supplemental figure will be added to the revised manuscript showing this. It is unclear why embryogenesis is sped up. However, this confirms that the developmental delay is unique to the L1/L2 stages.

-Figure 4 since htp-1 smFISH is so dramatic, it would be helpful to include htp-1 in the lower panels.

htp-1 will be added to the lower panels in the revised manuscript.

-Figure 4, please add an extra 2 upper panels showing all the genes in N2 vs spr-5;met-2, for comparison to

the mes-4 cohort.

As a control, we will add panels showing a comparison to all germline genes, excluding MES-4 targets. This new data shows that germline genes that are not MES-4 targets do not have ectopic H3K36me3. This data, which further suggests that the phenomenon is confined to MES-4 targets, is consistent with our results showing that MES-4 RNAi is sufficient to suppress the developmental delay.

-Figure 6. Please show a control that met-1 RNAi is working.

We performed RT-PCR to try and confirm that met-1 RNAi was working. Despite controls repeating the MES-4 suppression and verifying that RNAi was working, we were unable to demonstrate that met-1 was knocked down. As a result, we will remove this result from the paper. Importantly, this does not affect the conclusion of the paper.

-To quantify histone marks more clearly, it would be wonderful to have a graph of the mean log across the

gene. showing the mean numbers would help clarify the degree of the effect. we had an image as an

example but it does not paste into the reviewer box. Instead, see figure 2 or figure 4

here: https://www.nature.com/articles/ng.322

We will attempt to include this analysis in the revised manuscript.

Reviewer #2 (Significance (Required)):

Katz and colleagues examine the interaction between the methyltransferase MES-4 and spr-5; met-2 double

mutants. Their prior analysis (PNAS, 2014) showed the dramatic enhancement in sterility and development

for spr-5; met-2; this paper extends that finding by showing these effects depend on MES-4. The results are

interesting and the genetic interactions dramatic. The examination by RNAseq and ChIP helps move the

phenotypes into a more molecular analysis.

This work will be of interest to people following transgenerational inheritance, generally in the C. elegans

field. People using other organisms may read it also, although some of the worm genetics may be

complicated. Some of the writing suggestions could make a difference.

I study C. elegans embryogenesis, chromatin and inheritance.

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

In the paper entitled "C. elegans establishes germline versus soma by balancing inherited histone

methylation" Carpenter BS et al examined a double mutant worm strain they had previously produced of the

H3K4me1/2 demethylase spr-5 and the predicted H3K9me1/me2 methylase met-2. These mutant worms

have a developmental delay that arises by the L2 larval stage. They performed an analysis of what genes

get misexpressed in these double mutants by performing RNAseq and compare this to datasets generated

from other labs on an H3K36me2/me3 methylase MES-4 where they see a high degree of overlap. They

validate the misexpression of some germline specific genes in the soma by in situ and validate that there is a

dysregulation of H3K36me3 in their double mutant worms. They further find that knocking down mes-4

reverts the developmental delay.

I think that the authors need to make more of an effort to be a bit more scholarly in terms of placing their

work in the context of the field as a whole and also need to add a few additional experiments as well as

reorganize a bit before this is ready for publication. Remember that the average reader is not necessarily an

expert in C. elegans or this particular field and you really want to try and make the manuscript as accessible

to everyone as possible.

**Major Points**

1)It would be good to see western blots or quantitative mass spec examining H3K36me3 in the WT and spr-

5;met-2 double mutant worms. I believe this was also previously reported by Greer EL et al Cell Rep 2014 in

the single spr-5 mutant worm so that work should be cited here in addition to the identification of JMJD-2 as

an enzyme involved in the inheritance of H3K4me2 phenotype.

The ectopic H3K36me3 is confined to a small set of MES-4 targets. We don’t even see ectopic H3K36me3 at non-MES-4 germline genes (see above). Therefore, we don’t expect to see any global differences in bulk H3K36me3. Greer et al reported that there are elevated H3K36me3 levels in spr-5 mutants. This discrepancy may be due to different stages (embryos, germline) present in their bulk preparation. Alternatively, the met-2 mutant may counteract the effect of the spr-5 mutation on H3K36me3. Regardless, we believe that the genome-wide ChIP-seq is more informative than bulk H3K36me3 levels.

We will add a citation for the Greer paper in the revised manuscript.

2)Missing from Fig.5 is mes-4 KD by itself. This is needed to determine whether these effects are specific to

the spr-5;met-2 double mutants or more general effects that KD of mes-4 would decrease the expression of

all these genes to a similar extent. Then statistics should be done to see if the decrease in the WT context is

the same or greater than the decrease in the double mutants.

The MES-4 targets are generally expressed only in the germline and defined by having mes-4 dependent H3K36me3. Knocking down mes-4 would be expected to prevent the expression of these genes in the germline, but this is difficult to test because mes-4 mutants basically don’t make a germline. Regardless, knocking down mes-4 by itself would only assess the role of MES-4 in germline transcription, not the ectopic expression that is being assayed in spr-5; met-2 mutants in Fig 5. Importantly, it remains possible that spr-5; met-2 mutants might also result in an increase in the expression of MES-4 targets in the germline. However, the experiments performed in this manuscript were conducted on L1 larvae, which do not have any germline expression, to eliminate this potential confounding contribution.

**Minor Points**

1)A greater attempt needs to be made to be more scholarly for citing previously published literature. This

includes work on the inheritance of H3K27 and H3K36 methylation in C. elegans and other species as well.

A few papers which seem germane to this story which should be cited in the intro are (Nottke AC et al PNAS

2011, Gaydos LJ et al Science 2014, Ost A et al Cell 2014, Greer EL et al Cell Rep 2014, Siklenka K et al

Science 2015, Tabuchi TM et al Nat Comm 2018, Kaneshiro KR et al Nat Comm 2019). This problem is not

restricted to the intro.

Although many of these excellent papers are broadly relevant to this current work, they are not necessarily directly relevant to this paper. For this reason, they were not originally cited. Nevertheless, we will attempt to cite these papers in the revised version when possible.

2)I think that the authors need to be a little less definitive with your language. Theories should be introduced

as possibilities rather than conclusions. Should remove "comprehensive" from intro as there are many other

methods which could be done to test this.

Throughout the manuscript, we have tried to be clear what the data suggests versus what is model based on the data. Nevertheless, to further clarify this, we are happy to remove “comprehensive” from the intro.

3)The authors should describe what PIE-1 is. Is this a transcription factor?

PIE-1 is a transcriptional inhibitor that is thought to block RNA polII elongation by mimicking the CTD of RNA polII and competing for phosphorylation. We are happy to add a reference to this function in the revised manuscript.

4)The language needs clarification about MES-4 germline genes and bookmark genes. Are these bound by

MES-4 or marked with K36me2/3?

The revised manuscript will be modified to make this definition more clear.

5)I think Fig S1 E+F should be in the main figure 1 so readers can see the extent of the phenotype.

The original single image of the spr-5; met-2 adult germline phenotype (including the protruding vulva) was included in our previous publication. In this manuscript, we have now quantified this phenotype, which is why it is included in the supplement here. However, because the original picture was included in our original publication, we prefer to leave it as supplemental.

6)For Fig S2 it would be good to do the same statistics that is done in Fig 2 and mention them in the text so

the readers can see that the overlap is statistically significant.

We are happy to include these statistics in the revised manuscript.

7)Fig S2.2 should be yellow blue rather than red green for the colorblind out there.

Thanks for pointing this out. We are happy to change the colors in the revised manuscript.

8)When saying "Many of these genes involved in these processes..." the authors need to include numbers

and statistics.

We will amend the revised text to make the definition of the MES-4 genes more clear.

9)Should use WT instead of N2 and specify what wildtype is in methods.

We will use WT instead of N2 in the revised manuscript.

10)Fig. 2A + B could be displayed in a single figure. And Fig 2D seems superfluous and could be combined

with 2C or alternatively it could be put in supplementary.

Figure 2A and 2B were purposely separated to make it clear how many of the overlapped changes are up versus down. In the revised manuscript, Figure

2D will be moved to the supplement.

11)Non-C. elegans experts won't understand what balancers are. An effort should be made to make this

accessible to all. Explaining when genes are heterozygous or homozygous mutants seems relevant

here.

The text of the revised manuscript will be amended to make it more accessible for non-C. elegans readers.

12)The GO categories (Fig. S2) should be in the main figure and need to be made to look more scientific

rather than copied and pasted from a program.

The GO categories were included to be comprehensive and do not contribute substantially to the main conclusion of the paper. This is why they are supplemental. In the revised manuscript, we will edit the GO results so that they look more scientific.

13)Fig. 7 seems a bit out of place. If the authors were to KD mes-4 and similarly show that the phenotype

reverts that would help justify its inclusion in this paper. Without it seems like a bit of an add on that belongs

elsewhere.

We believe that the somatic expression of a transgene in spr-5; met-2 mutants adds to our potential understanding of how this double mutant may lead to developmental delay. This is true, regardless of whether of whether the somatic transgene expression is mes-4 dependent or not.

Reviewer #3 (Significance (Required)):

I think this is an interesting and timely piece of work. A little more effort needs to be put in to make sure it is

accessible to the average reader and has sufficient inclusion of more of the large body of work on

inheritance of histone modifications. I think C. elegans researchers as well as people interested in

inheritance and the setup of the germline will be interested in this work.

REFEREES CROSS COMMENTING

I agree with Reviewer #2's comments on experiments to include or exclude alternative models. I also agree

about their statement about rewriting to make it more accessible to others who aren't experts in this

specialized portion of C. elegans research. All in all it seems like the experiments which are required by

reviewer #2 and myself as well as the rewriting should be quite feasible.

kit_kits

una_coleccion/diferente_software

Another Subpage and tag.

create a glossary note section/tag

CLARIFICATION: distinguish posterior pituitary vs. hypothalamus/what is meant by including both (see similar tag above)

Hiermit ist vielleicht gemeint, dass die Welt immer schneller wird.

hektische kommerzialisiere Welt

this was one of the common adjectives that I found doing my pos tag, I think its intresting seeing them here and how they are used to descrive and seperate people by simple binaries like young and old

SciScore for 10.1101/2020.08.12.247338: (What is this?)

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

Table 1: Rigor

<table><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Institutional Review Board Statement</td><td style="min-width:100px;border-bottom:1px solid lightgray">Mice were cared for and treated in accordance with the National Institutes of Health (NIH) guidelines for the care and use of laboratory animals (NIH Publication No. 85e23 Rev. 1985) as approved by the Animal Experimental Ethics Committee of TMMU (AMUWEC2020799).</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">The cracked membrane was obtained by centrifugation at 8,000 rpm for 10 min, washed with cold PBS containing protease inhibitors and sonicated with a Sonics (Newtown, CT, US) Vibra-Cell VCX-500 ultrasonic processor for 10 min at a power of 120 W.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A primary anti-ACE2 rabbit polyclonal antibody (1:1000, 10108-T26, Sino Biological, Beijing, CHN) and a goat anti-rabbit secondary antibody (1:1000, A0208, Beyotime) were employed to detect ACE2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-rabbit</div> <div>suggested: (GenWay Biotech Inc. Cat# GWB-A0208E, RRID:AB_10270952)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">β-actin determined by a mouse monoclonal antibody (AA128, Beyotime, 1:1000) was a reference.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>AA128</div> <div>suggested: (Beyotime Cat# AA128, RRID:AB_2861213)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A primary anti-His-tag mouse monoclonal antibody (1:1000, AF5060, Beyotime) and a goat anti-mouse secondary antibody (1:1000, A0216, Beyotime) were employed to detect SARS-CoV-2 S1.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-His-tag</div> <div>suggested: (AnaSpec; EGT Group Cat# 29673-1000, RRID:AB_11232932)</div> </div> <div style="margin-bottom:8px"> <div>anti-mouse</div> <div>suggested: (Beyotime Cat# A0216, RRID:AB_2860575)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A primary anti-ACE2 rabbit polyclonal antibody (1:200, 10108-T60, Sino Biological) and a goat anti-rabbit secondary antibody (1:500, A0516, Beyotime) were employed to stain ACE2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>ACE2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A primary anti-spike rabbit monoclonal antibody (1:100, 40150-R007, Sino Biological) and a goat anti-rabbit secondary antibody (Alexa Fluor 488, Invitrogen, Thermo Fisher Scientific) were used to stain S1.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-spike</div> <div>suggested: (Sino Biological Cat# 40150-R007, RRID:AB_2827979)</div> </div> </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Recombinant SARS-CoV-2 S1 (20 μg mL-1, 40591-V08H, Sino Biological) containing a His-tag preincubated with CMBNPs at 37 °C for 1 h were added to HK-2 cells and incubated for another 1 h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>HK-2</div> <div>suggested: RRID:CVCL_YE28)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Preparation of HEK-293T-hACE2 and HEK-293T NPs HEK-293T-hACE2 cells collected by trypsinization and centrifugation at 1500 rpm for 5 min were washed with cold sterile PBS and frozen at -80 °C and thawed at room temperature (repeated three times).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>HEK-293T</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>HEK-293T-hACE2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Toxicological Evaluation HUVECs obtained from the cell bank of CAS were seeded in a 96-well plate at a density of 5 × 103 cells/well.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>HUVECs</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The MS/MS database searches were conducted using SEQUEST search algorithm through Proteome Discoverer platform (version 1.4, Thermo Fisher Scientific).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Proteome Discoverer</div> <div>suggested: (Proteome Discoverer, RRID:SCR_014477)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Gene names of encoded proteins identified in the proteomics analysis were uploaded into the online STRING database (Version 11.0) (https://string-db.org) for Gene Ontology (GO) annotation.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>STRING</div> <div>suggested: (STRING, RRID:SCR_005223)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The experiment was conducted in triplicate and repeated twice, and the data were processed using FlowJo software (version 7.6.1).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>FlowJo</div> <div>suggested: (FlowJo, RRID:SCR_008520)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical Analysis The significant difference (P) between each group was calculated using SPSS 16.0 software and the LSD multiple-comparison test.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>SPSS</div> <div>suggested: (SPSS, RRID:SCR_002865)</div> </div> </td></tr></table>

---

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).

---

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

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.

---

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Here's a page note! You can see notes that others have added, and tag them so they are easier to find.

It's important to remember that it always comes back to citizens' tax dollars

Formulation un peu confuse.

• "isole" sonne bizarre. L'identifiant permet de distinguer de manière non ambigüe.
• on peut très bien avoir deux fiches avec le même tag et un lien entre elles, ce n'est pas mutuellement exclusif du tout
• une étiquette n'est pas forcément un mot-clé thématique, ça peut être fonctionnel et lié à des traitements (ex : #mes_publications)

Je mettrais plutôt étiquette ou mot-clé. On n'a pas tous la même opinion sur la loi Toubon… mais moi je préfère tout traduire.

SciScore for 10.1101/2020.08.01.20166553: (What is this?)

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

Table 1: Rigor

<table><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Institutional Review Board Statement</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consecutive out-patients diagnosed at the same 4 hospitals prior to March 15th and on a convenience sample of later days were approached for consent to collect serum and saliva at 412 weeks post onset of symptoms.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After washing 4 times, 10 µl of one of the following secondary antibodies (all from Jackson ImmunoResearch) diluted in 1% BLOTTO in PBS-T were added at the indicated concentrations followed by incubation for 2 hr at room temperature: Goat anti-human IgG Fcy – 035-129; 1:12,000 or 0.66 ng per well) or Goat anti-human IgA a chain - HRP (#109-035-127; 1:10,000 or 0.8 ng per well).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-human IgG</div> <div>suggested: (Jackson ImmunoResearch Labs Cat# 109-035-127, RRID:AB_2337587)</div> </div> <div style="margin-bottom:8px"> <div>anti-human IgA</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibodies used for the standard curves were: Human anti-spike S1 IgG (A02038, GenScript), anti-spike S1 IgM (A02046, GenScript) and Ab01680 anti-spike IgA (Ab01680-16, Absolute Antibody), all used at 0.5 to 10ng per well.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-spike S1 IgG</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>A02038</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>anti-spike S1 IgM</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>A02046</div> <div>suggested: (GenWay Biotech Inc. Cat# GWB-A02046, RRID:AB_10276779)</div> </div> <div style="margin-bottom:8px"> <div>anti-spike IgA</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-human Ig antibody (Southern Biotech, 2010-01) diluted 1:1000 in PBS was added to 96-well Nunc MaxiSorp™ plates (ThermoFisher, 44-2404-21).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Anti-human Ig antibody</div> <div>suggested: (SouthernBiotech Cat# 2010-01, RRID:AB_2687525)</div> </div> <div style="margin-bottom:8px"> <div>Anti-human Ig</div> <div>suggested: (SouthernBiotech Cat# 2010-01, RRID:AB_2687525)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HRP-conjugated secondary antibodies against IgA and IgG (goat anti-human IgA- and IgG-HRP, Southern Biotech, IgA: 2053-05, IgG: 2044-05) were added to the appropriate wells at 1:1000 in 2.5% BLOTTO and incubated for 1 hour at 37oC.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>HRP-conjugated secondary antibodies against IgA and IgG (goat anti-human IgA- and IgG-HRP, Southern Biotech, IgA: 2053-05, IgG: 2044-05)</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>HRP-conjugated secondary antibodies against IgA and IgG</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>goat anti-human IgA-</div> <div>suggested: (InvivoGen Cat# hrp-iga, RRID:AB_11124937)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Horseradish peroxidase (HRP)-conjugated Goat anti human-IgA and anti-IgG secondary antibodies (Southern Biotech, 2053-05 and 2044-05) were added to wells at dilutions of 1:2000 and 1:1000 in 2.5% BLOTTO, respectively, and incubated for 1 hour at 37oC.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Goat anti human-IgA</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>anti human-IgA</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>anti-IgG</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To stabilize the spike protein in a trimeric form, the cDNA was cloned in-frame with the human resistin cDNA (aa 23-108) containing a Cterminal FLAG-(His)6 tag (Cricetulus griseus codon bias, GenScript) into a modified cumateinducible pTT241 expression plasmid and transfected in CHO2353 cells (Stuible et al.,</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>CHO2353</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viral stock was expanded using Vero E6 as previously described such that stored aliquots of stock contain 2% FBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Vero E6</div> <div>suggested: RRID:CVCL_XD71)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Initial experiments were done with Triton X-100 (Sigma-Aldrich) serially diluted and applied to Vero-E6 cells in 96-well flat bottom plates to determine the minimum concentration required to prevent toxicity to cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Vero-E6</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antigen production – serum assays Spike trimer was expressed as follows: the SARS-CoV-2 spike sequence (aa 1-1208 from Genebank accession number MN908947 with the S1/S2 furin site (residues 682–685) mutated [RRAR->GGAS] and K986P / V987P stabilizing mutations was codon-optimized (Cricetulus griseus codon bias) and synthesized by Genscript.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Genebank</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A fourparameter logistic curve was used to determine the line of best fit for the standard curve, and sample Ig quantities were interpolated accordingly, using Prism Graphpad, Version 8.3.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Graphpad</div> <div>suggested: (GraphPad, RRID:SCR_000306)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All analysis was performed in SAS 9.4M6 (SAS Institute, Cary, NC).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>SAS Institute</div> <div>suggested: (Statistical Analysis System, RRID:SCR_008567)</div> </div> </td></tr></table>

---

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).

---

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

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.

---

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Concreto pero bastante entendible. Aunque quedan muchas dudas, creo que picándole aprenderemos..

Excelente taller, creo que sería muy útil otro para reforzar lo aprendido hoy. Gracis

Segunda parte del Taller, excelente herramienta

Gracias,

SciScore for 10.1101/2020.07.26.222026: (What is this?)

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

Table 1: Rigor

<table><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Institutional Review Board Statement</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">29 Antibodies and reagents Rabbit anti-DYKDDDDK Tag (D6W5B), rabbit anti-IRF3 (D83B9), rabbit anti-pIRF3 (4D46), rabbit anti-TBK1 (3031S), rabbit anti-pTBK1 (D52C2), and rabbit anti-TRAF3 were from Cell Signaling Technology (</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-DYKDDDDK</div> <div>suggested: (Cell Signaling Technology Cat# 14793, RRID:AB_2572291)</div> </div> <div style="margin-bottom:8px"> <div>anti-IRF3</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>anti-pIRF3 ( 4D46)</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>anti-TBK1</div> <div>suggested: (Cell Signaling Technology Cat# 14590, RRID:AB_2798527)</div> </div> <div style="margin-bottom:8px"> <div>anti-pTBK1</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>anti-TRAF3</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Alexa Fluor 488 goat anti-rabbit IgG secondary antibody, Alexa Fluor 568 goat anti-mouse IgG secondary antibody, Alexa Fluor 488 goat anti-mouse 10 / 55 IgG secondary antibody, and Alexa Fluor 568 goat anti-rabbit IgG secondary antibody were from Thermo Fisher Scientific (USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Alexa Fluor 488 goat anti-rabbit IgG secondary antibody</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>anti-mouse IgG</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>anti-mouse 10 / 55 IgG</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>anti-rabbit IgG</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Coimmunoprecipitation and immunoblotting For coimmunoprecipitation assays, HEK293T cells were collected 24 hours after transfection and lysed in lysis buffer [1.0% (v/v) NP-40, 50 mM Tris-HCl, pH 7.4, 50 mM EDTA, 0.15 M NaCl] supplemented with a protease inhibitor cocktail (Sigma, USA) and a phosphatase inhibitor cocktail (Sigma, USA) as described in our previous publications.30,31 After centrifugation for 10 min at 14,000 g, the supernatants were collected and incubated with the indicated 13 / 55 antibodies, followed by the addition of protein A/G beads (Santa Cruz, USA)</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>NP-40</div> <div>suggested: (Covance Cat# MMS-503P-100, RRID:AB_291448)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The expression plasmids of the SARS-CoV-2 M protein and plasmids expressing RIG-I or MDA-5 were cotransfected into HEK293T cell, 24 hours later, MAVS antibodies were used to perform coimmunoprecipitation.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>MAVS</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">9 / 55 Materials and methods Cell culture and transfection HEK293, HEK293T, HeLa, and Vero-E6 cells were cultured in Dulbecco's modified Eagle's medium (DMEM, Gibco, USA) with 10% heat-inactivated fetal bovine serum (FBS, Gibco, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Vero-E6</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">32,35 Briefly, approximately 0.5 x 105 HEK293T cells were seeded in 48-well plates and transfected 12 hours later with the luciferase reporter plasmid and the expression vector plasmids of RIG-I, RIG-IN, MDA-5, MAVS, TBK1, IKK , IRF3-5D, TRIF, and STING, alone or together with the plasmid ε expressing the SARS-CoV-2 M protein, as indicated in the experiments.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>MDA-5</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viruses and infection VSV-enhanced green fluorescent protein (eGFP) and SeV were used to infect HeLa, HEK293, or HEK293T cells as described in our previous publications.30-32 Briefly, before infection, prewarmed serum-free DMEM medium at 37°C was used to wash the target cells, after which the virus was diluted to the desired multiplicity of infection (MOI) in serum-free DMEM and incubated with the target cells for 1-2 hours.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>HeLa</div> <div>suggested: None</div> </div> <div style="margin-bottom:8px"> <div>HEK293</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">32 Briefly, Vero cells were seeded on 24-well plates.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>Vero</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In a ll cases,ava lue ofP<0.0 5was conside red tobestatisti callysi gnifican t.16/ 55R esultsT heSAR S-Co V-2Mprot ei ninhibitst ypeIandI IIIFNin ductio nbySeV andpol y(I: C)To exp lorewhethertheSA RS-Co V-2Mpr otein affec tstype IandI III FNpro duct ion,HEK2 93 Tcel ls e xpressingt heSARS-Co V-2M pr otei nwe reinfect edwi thSeVortran sfec tedwith adsRNAmi mic ,pol y(I:C ).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>HEK293T</div> <div>suggested: KCB Cat# KCB 200744YJ, RRID:CVCL_0063</div> </div> </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After removing the solid agarose-medium mix, the cells were stained with 0.05% crystal violet, and the plaques on the monolayer were then counted to calculate the virus titer. 15 / 55 Bioinformatics analysis The transmembrane motifs were predicted with the TMHMM server version 2.0 (http://www.cbs.dtu.dk/services/TMHMM/).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>http://www.cbs.dtu.dk/services/TMHMM/</div> <div>suggested: (TMHMM Server, RRID:SCR_014935)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For statistical analysis, two-tailed unpaired Student's t-tests were performed by GraphPad Prism 8.0 and Microsoft Excel.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>GraphPad Prism</div> <div>suggested: (GraphPad Prism, RRID:SCR_002798)</div> </div> <div style="margin-bottom:8px"> <div>Microsoft Excel</div> <div>suggested: (Microsoft Excel, RRID:SCR_016137)</div> </div> </td></tr></table>

---

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).

---

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

---

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

---

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

SciScore for 10.1101/2020.07.26.222026: (What is this?)

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

Table 1: Rigor

<table><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Institutional Review Board Statement</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">29 Antibodies and reagents Rabbit anti-DYKDDDDK Tag (D6W5B), rabbit anti-IRF3 (D83B9), rabbit anti-pIRF3 (4D46), rabbit anti-TBK1 (3031S), rabbit anti-pTBK1 (D52C2), and rabbit anti-TRAF3 were from Cell Signaling Technology (</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-DYKDDDDK</div> <div>suggested: (Cell Signaling Technology Cat# 14793, AB_2572291)</div> </div>

      <div style="margin-bottom:8px">
        <div><b>anti-IRF3</b></div>
        <div>suggested: None</div>
      </div>

      <div style="margin-bottom:8px">
        <div><b>anti-pIRF3 ( 4D46)</b></div>
        <div>suggested: None</div>
      </div>

      <div style="margin-bottom:8px">
        <div><b>anti-pTBK1</b></div>
        <div>suggested: None</div>
      </div>

      <div style="margin-bottom:8px">
        <div><b>anti-TRAF3</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Alexa Fluor 488 goat anti-rabbit IgG secondary antibody, Alexa Fluor 568 goat anti-mouse IgG secondary antibody, Alexa Fluor 488 goat anti-mouse 10 / 55 IgG secondary antibody, and Alexa Fluor 568 goat anti-rabbit IgG secondary antibody were from Thermo Fisher Scientific (USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Alexa Fluor 488 goat anti-rabbit IgG secondary antibody</b></div>
        <div>suggested: None</div>
      </div>

      <div style="margin-bottom:8px">
        <div><b>anti-mouse IgG</b></div>
        <div>suggested: None</div>
      </div>

      <div style="margin-bottom:8px">
        <div><b>anti-mouse 10 / 55 IgG</b></div>
        <div>suggested: None</div>
      </div>

      <div style="margin-bottom:8px">
        <div><b>anti-rabbit IgG</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Coimmunoprecipitation and immunoblotting For coimmunoprecipitation assays, HEK293T cells were collected 24 hours after transfection and lysed in lysis buffer [1.0% (v/v) NP-40, 50 mM Tris-HCl, pH 7.4, 50 mM EDTA, 0.15 M NaCl] supplemented with a protease inhibitor cocktail (Sigma, USA) and a phosphatase inhibitor cocktail (Sigma, USA) as described in our previous publications.30,31 After centrifugation for 10 min at 14,000 g, the supernatants were collected and incubated with the indicated 13 / 55 antibodies, followed by the addition of protein A/G beads (Santa Cruz, USA)</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>NP-40</b></div>
        <div>suggested: (Covance Cat# MMS-503P-100, <a href="https://scicrunch.org/resources/Any/search?q=AB_291448">AB_291448</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The expression plasmids of the SARS-CoV-2 M protein and plasmids expressing RIG-I or MDA-5 were cotransfected into HEK293T cell, 24 hours later, MAVS antibodies were used to perform coimmunoprecipitation.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>MAVS</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">When using the TBK1 antibody to perform endogenous coimmunoprecipitation, MAVS was detected in the TBK1 immunoprecipitates (Fig. 5c).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>TBK1</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Experimental Models: Cell Lines</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">12 The binding of the type I or III IFNs to their specific receptors, the type I IFN receptor (IFNAR) and the type III IFN receptor (IFNLR), respectively, triggers the activation of the receptor-associated Janus kinase 1 (JAK1)/tyrosine kinase 2 (TYK2), which stimulates the phosphorylation of STAT1 and STAT2.9,13 JAK2 also participates in type III IFN-induced STAT phosphorylation.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>STAT2.9,13 JAK2</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">This study reveals a previously undiscovered mechanism of SARS-CoV-2 in evading host antiviral immunity, which may partially explain the clinical features of impaired antiviral immunity in COVID-19 patients and provide insights into the viral pathogenicity and treatment. 9 / 55 Materials and methods Cell culture and transfection HEK293, HEK293T, HeLa, and Vero-E6 cells were cultured in Dulbecco's modified Eagle's medium (DMEM, Gibco, USA) with 10% heat-inactivated fetal bovine serum (FBS, Gibco, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Vero-E6</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viruses and infection VSV-enhanced green fluorescent protein (eGFP) and SeV were used to infect HeLa, HEK293, or HEK293T cells as described in our previous publications.30-32 Briefly, before infection, prewarmed serum-free DMEM medium at 37°C was used to wash the target cells, after which the virus was diluted to the desired multiplicity of infection (MOI) in serum-free DMEM and incubated with the target cells for 1-2 hours.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>HEK293</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">32 Briefly, Vero cells were seeded on 24-well plates.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Vero</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The results indicated that both SeV infection and poly (I:C) transfection strongly stimulated the expression of IFN- , IFN- 1, β λ ISG56, and CXCL10 in the control HEK293T cells (Fig. 1).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>HEK293T</b></div>
        <div>suggested: KCB Cat# KCB 200744YJ, <a href="https://scicrunch.org/resources/Any/search?q=CVCL_0063">CVCL_0063</a></div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">When the SARS-CoV-2 M protein was overexpressed, the binding between RIG-I and MAVS was reduced (Fig. 5a, lanes 2 compared to lane 3); however, in the same condition, the interaction between MDA-5 and MAVS was not affected (Fig 5b, lanes 2 compared to lane 3), indicating that the SARS-CoV-2 M protein impedes the complex formation of RIG-I and MAVS but has no effect on the interaction between MDA-5 and MAVS.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>MDA-5</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To address the effect of the SARS-CoV-2 M protein on virus-induced IRF3 phosphorylation, control HeLa cells and HeLa cells expressing the SARS-CoV-2 M protein were infected with SeV.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>HeLa</b></div>
        <div>suggested: CLS Cat# 300194/p772_HeLa, <a href="https://scicrunch.org/resources/Any/search?q=CVCL_0030">CVCL_0030</a></div>
      </div>
    </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Software and Algorithms</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After removing the solid agarose-medium mix, the cells were stained with 0.05% crystal violet, and the plaques on the monolayer were then counted to calculate the virus titer. 15 / 55 Bioinformatics analysis The transmembrane motifs were predicted with the TMHMM server version 2.0 (http://www.cbs.dtu.dk/services/TMHMM/).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>http://www.cbs.dtu.dk/services/TMHMM/</b></div>
        <div>suggested: (TMHMM Server, <a href="https://scicrunch.org/resources/Any/search?q=SCR_014935">SCR_014935</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For statistical analysis, two-tailed unpaired Student's t-tests were performed by GraphPad Prism 8.0 and Microsoft Excel.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>GraphPad Prism</b></div>
        <div>suggested: (GraphPad Prism, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002798">SCR_002798</a>)</div>
      </div>

      <div style="margin-bottom:8px">
        <div><b>Microsoft Excel</b></div>
        <div>suggested: (Microsoft Excel, <a href="https://scicrunch.org/resources/Any/search?q=SCR_016137">SCR_016137</a>)</div>
      </div>
    </td></tr></table>

Data from additional tools added to each annotation on a weekly basis.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

But what if another tag also specified that it had to be the first child "because ..."? Maybe that hasn't happened yet, but it could and then you'd have to decide which one truly was more important to put first? (Hopefully/probably it wouldn't even matter that much.)

This is a fairly hydrophilic sequence ((DYKDDDDK)) that a number of very specific antibodied that have been raised to it. Being so hydrophilic, it tends not to denature proteins to which it is fused (presumably hydrophobic peptides could interfere with proteins' folding - the "molten globule" - as the protein is synthesised from the ribosome..

This reminds me of what this text would look like with a POS tag on it. There are so many adjectives that reapeat themselves. This one is funny becuase it is a repition of words to describe something else.

Great positive viewpoint about Jar Jar Binks

SciScore for 10.1101/2020.07.21.214759: (What is this?)

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

Table 1: Rigor

<table><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Institutional Review Board Statement</td><td style="min-width:100px;border-bottom:1px solid lightgray">All plasma samples were obtained under protocols approved by Institutional Review Boards at both institutions.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr"><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">All cell lines have been tested negative for contamination with mycoplasma.</td></tr></table>

Table 2: Resources

<table><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</td></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Abstract Neutralizing antibodies elicited by prior infection or vaccination are likely to be key for future protection of individuals and populations against SARS-CoV-2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>SARS-CoV-2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Moreover, passively administered antibodies are among the most promising therapeutic and prophylactic anti-SARSCoV-2 agents.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-SARSCoV-2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Results Selection of SARS-CoV-2 S variants using a replication competent VSV/SARS-CoV-2 chimeric virus To select SARS-CoV-2 S variants that escape neutralization by antibodies, we used a recently described replication-competent chimeric virus based on vesicular stomatitis virus that encodes the SARS-CoV-2 spike (S) protein and green fluorescent protein (rVSV/SARS-CoV2/GFP) (</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>rVSV/SARS-CoV2/GFP</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibodies that constitute at last part of the neutralizing activity evident in COV-NY plasma appear to recognize an epitope that includes and K444 and V445.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>V445</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibody binding and ACE2 binding inhibition assay A conformationally stabilized (6P) version of the SARS-CoV-2 S protein(25), appended at its C-terminus with a trimerization domain, a GGSGGn spacer sequence, NanoLuc luciferase, Strep-tag, HRV 3C protease cleavage site and 8XHis (S-6P-NanoLuc) was expressed and purified from the supernatant of 293T Expi cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>ACE2</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Mutants thereof were also expressed and purifies following substitution of sequences encoding the RBD that originated from the unmodified Sexpression plasmids For antibody binding assays, 20ng, 40ng, or 80ng S-6P-NanoLuc (or mutants thereof) were mixed with 100ng of antibodies, C121, C135, or C144, \ diluted in LI-COR Intercept blocking buffer, in a total volume of 60μl/well in 96-well plate.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>C121</div> <div>suggested: (Leinco Technologies Cat# C121, AB_2828361)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Patent applications submitted by Rockefeller University are pending for anti SARS-CoV-2 antibodies (MCN, DR, inventors) and VSV/SARS-CoV-2 chimeric virus (PDB, TH FS and YW, inventors) A 10μg/ml C121, C135, C144 or plasma dilution 10μg/ml C121, C135, C144 or plasma dilution 1x106 IU rVSV/SARS-CoV-2/GFP p1 B p2 No Antibody Sequence, Isolate mutants by limiting dilution Plasma [5x initial] Sequence, Isolate mutants by limiting dilution Sequence Plasma [5x initial] p3 p4 10μg/ml C121 C Figure 1.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti SARS-CoV-2</div> <div>suggested: (Abcam Cat# ab273074, AB_2847846)</div> </div>

      <div style="margin-bottom:8px">
        <div><b>C135</b></div>
        <div>suggested: None</div>
      </div>

      <div style="margin-bottom:8px">
        <div><b>p3</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Selection of SARS-CoV-2 S mutations that confer antibody resistance. A. Outline of serial passage experiments with replication competent VSV derivatives encoding the SARS-CoV-2 S envelope glycoprotein and a GFP reporter (rVSV/SARS-CoV-2/GFP) in 293T/ACE2(B) cells in the presence of neutralizing antibodies or plasma. B. Representative images of 293T/ACE2(B) cells infected with 1x106 PFU of rVSV/SARS-CoV2/GFP in the presence or absence of 10μg/ml of the monoclonal antibody C121. C.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>SARS-CoV-2 S envelope glycoprotein</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">C144 passaged rVSV/SARS-CoV-2/GFP Populations Mutant purification by limiting dilution rVSV/SARS-CoV-2/GFP (E484K) rVSV/SARS-CoV-2/GFP (Q493R) Figure 3 - supplement 1 Example of plaque purification of individual viral mutants from populations passaged in the presence of antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>rVSV/SARS-CoV-2/GFP</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Amino acids whose substitution confers partial or complete (IC50 >10μg/ml) resistance to each monoclonal antibody in the HIV-pseudotype assays are indicated for C121 (red) C135 (green) and C144 (purple). E. Binding of S-NanoLuc fusion protein in relative light units (RLU) to 293T or 293T/ACE2cl.22 cells after preincubation in the absence or presence of C121, C135 and C144 monoclonal antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>C144</b></div>
        <div>suggested: (Leinco Technologies Cat# C144, <a href="https://scicrunch.org/resources/Any/search?q=AB_2828501">AB_2828501</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Experimental Models: Cell Lines</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines HEK-293T cells and derivatives were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) at 37oC and 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>HEK-293T</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 293T cells were transfected with pHIVNLGagPol, pCCNanoLuc2AEGFP and a WT or mutant SARS-CoV-2 expression plasmid (pSARS-CoV2Δ19) using polyethyleneimine.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>293T</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Amino acids whose substitution confers partial or complete (IC50 >10μg/ml) resistance to each monoclonal antibody in the HIV-pseudotype assays are indicated for C121 (red) C135 (green) and C144 (purple). E. Binding of S-NanoLuc fusion protein in relative light units (RLU) to 293T or 293T/ACE2cl.22 cells after preincubation in the absence or presence of C121, C135 and C144 monoclonal antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>293T/ACE2cl.22</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Software and Algorithms</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The PCR products were gel-purified and sequenced either using Sanger-sequencing or NGS as previously described (31).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>NGS</b></div>
        <div>suggested: (NGSadmix, <a href="https://scicrunch.org/resources/Any/search?q=SCR_003208">SCR_003208</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For analysis of NGS data, the raw paired-end reads were pre-processed to remove adapter sequences and trim low-quality reads (Phred quality score <20) using BBDuk.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Phred</b></div>
        <div>suggested: (Phred, <a href="https://scicrunch.org/resources/Any/search?q=SCR_001017">SCR_001017</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Information regarding RBD-specific variant frequencies, their corresponding P-values, and read depth were compiled using the Python programming language (version 3.7) running pandas (1.0.5), numpy (1.18.5), and matplotlib (3.2.2).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Python</b></div>
        <div>suggested: (IPython, <a href="https://scicrunch.org/resources/Any/search?q=SCR_001658">SCR_001658</a>)</div>
      </div>

      <div style="margin-bottom:8px">
        <div><b>matplotlib</b></div>
        <div>suggested: (MatPlotLib, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008624">SCR_008624</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The half maximal inhibitory concentrations for plasma (NT50), and monoclonal antibodies (IC50) was calculated using 4-parameter nonlinear regression curve fit to raw or normalized infectivity data (GraphPad Prism).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>GraphPad</b></div>
        <div>suggested: (GraphPad Prism, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002798">SCR_002798</a>)</div>
      </div>
    </td></tr></table>

Data from additional tools added to each annotation on a weekly basis.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.