4,329 Matching Annotations
  1. Oct 2020
    1. Curso_HDCICAFCUNAM

      Curso Habilidades digitales y competencias informacionales para ciencias 2020

      FACULTAD DE CIENCIAS, UNAM

      Layla Michán

    1. But first, what would motivate any young person today to pull the plug? Well maybe they should consider this for a moment. Who most wants you to stay on the grid? The advertisers. Your boss. Human Resources. The advertisers. Your parents (irony of ironies – once they distrusted it, now they need to tag you electronically, share your Facebook photos and message you to death). The advertisers. The government. Your local authority. Your school. Advertisers.

      Going of the grid hurts "The man" in 70's parlance.

    1. How do you manage information flows? If anyone is using a personal wiki-style long term information tool I’d love to hear from you!

      I've got a handful of interesting things bookmarked here: https://boffosocko.com/tag/wikis/ which includes a rabbit hole of a request similar to your own.

    1. In short to add wiki-style functionality to my blog, the only functionality that is really needed is that 1) I myself have a edit button on static items, 2) the ability to categorise and tag those items, and 3) keep those items outside of the blog posting stream on the front page, and outside of the RSS feed. WordPress pages fit that description, when I’m logged in, and after adding a plugin to allow categories and tags on pages. So a page based section it is, or rather, will be over time.

      I like the idea of this and the overall structure. It reminds me a bit of Wikity which may provide this functionality plus a bit more. I really need to spin up a version and play around with it to see if it will give me what I'm looking for in terms of a blog linked with wiki-like functionality.

    1. It isn't rocket science, but as Jon indicates, it's incredibly powerful.

      I use my personal website with several levels of taxonomy for tagging and categorizing a variety of things for later search and research.

      Much like the example of the Public Radio International producer, I've created what I call a "faux-cast" because I tag everything I listen to online and save it to my website including the appropriate <audio> link to the.mp3 file so that anyone who wants to follow the feed of my listens can have a playlist of all the podcast and internet-related audio I'm listening to.

      A visual version of my "listened to" tags can be found at https://boffosocko.com/kind/listen/ with the RSS feed at https://boffosocko.com/kind/listen/feed/

    1. appreciate your help

      I think that a major part of improving the issue of abuse and providing consent is building in notifications so that website owners will at least be aware that their site is being marked up, highlighted, annotated, and commented on in other locations or by other platforms. Then the site owner at least has the knowledge of what's happening and can then be potentially provided with information and tools to allow/disallow such interactions, particularly if they can block individual bad actors, but still support positive additions, thought, and communication. Ideally this blocking wouldn't occur site wide, which many may be tempted to do now as a knee-jerk reaction to recent events, but would be fine grained enough to filter out the worst offenders.

      Toward the end of notifications to site owners, it would be great if any annotating activity would trigger trackbacks, pingbacks, or the relatively newer and better webmention protocol of the WW3C out of the http://IndieWebCamp.com movement. Then site owners would at least have notifications about what is happening on their site that might otherwise be invisible to them.

      Perhaps there's a way to further implement filters or tools (a la Akismet on platforms like WordPress) that allow site users to mark materials as spam, abusive, or other so that they are then potentially moved from "public" facing to "private" so that the original highlighter can still see their notes, but that the platform isn't allowing the person's own website to act as a platform to give reach to bad actors.

      Further some site owners might appreciate graded filters (G, PG, PG-13, R, X) so that users or even parents can filter what they're willing to see. Consider also annotations on narrative forms that might be posted as spoilers--how can these be guarded against? (Possibly with CSS and a spoiler tag?) Options can be built into the platform itself as well as allowing server-side options for truly hard cases.

      My coding skills are rustier than I wish they were, but I'm available to help/consult if needed.

    1. When I received Chris’s comment, my first response was that I should delete my post or at least the incorrect part of it. It’s embarrassing to have your incorrect understandings available for public view. But I decided to leave the post as is but put in a disclaimer so that others would not be misled by my misunderstandings. This experience reminded me that learning makes us vulnerable. Admitting that you don’t know something is hard and being corrected is even harder. Chris was incredibly gentle in his correction. It makes me think about how I respond to my students’ work. Am I as gentle with their work as Chris was to mine? Could I be more gentle? How often have I graded my students’ work and only focused on what they did wrong? Or forgotten that feeling of vulnerability when you don’t know something, when you put your work out for others to judge? This experience has also reminded me that it’s important that we as teachers regularly put ourselves into situations in which we authentically grapple with not knowing something. We should regularly share our less than fully formed understandings with others for feedback. It helps us remember that even confident learners can struggle with being vulnerable. And we need to keep in mind that many of our students are not confident learners.

      I'm reminded here of the broad idea that many bloggers write about sooner or later of their website being a "thought space" or place to contemplate out in the open. More often than not, even if they don't have an audience to interact with, their writings become a way of thinking out loud, clarifying things for themselves, self-evolving, or putting themselves out there for potential public reactions (good, bad, or indifferent).

      While writing things out loud to no audience can be helpful and useful on an individual level, it's often even more helpful to have some sort of productive and constructive feedback. While a handful of likes or positive seeming responses can be useful, I always prefer the ones that make me think more broadly, deeply, or force me to consider other pieces I hadn't envisioned before. To me this is the real value of these open and often very public thought spaces.

      For those interested in the general idea, I've been bookmarking/tagging things around the idea of thought spaces I've read on my own website. Hopefully this collection helps others better understand the spectrum of these ideas for themselves.

      With respect to the vulnerability piece, I'm reminded of an episode of <cite>The Human Current</cite> I listened to a few weeks back. There was an excellent section that touched on building up trust with students or even a class when it comes to providing feedback and criticism. Having a bank of trust makes it easier to give feedback as well as to receive it. Here's a link to the audio portion and a copy of the relevant text.

    1. The Task Annotation Project in Science (TAPS) provides K-12 educators with annotated assessment tasks, aligned to the Next Generation Science Standards, that help guide teachers in more equitably monitoring their students’ learning.37 Osmosis is a repository of open educational resources (OER) created to crowdsource the future of medical education.38 Undergraduate and graduate medical students have access to thousands of digital resources, and they have also used annotation - through comments, feedback forms, and ratings - to improve the quality of these learning materials.39 The National Science Digital Library (NSDL), created in 2000, is an archive of open access teaching and learning resources for learners of all ages across science, technology, engineering, and mathematics disciplines.40 Annotation has been used to tag the NSDL’s resources and improve information accessibility, support student interaction with multimedia content through a digital notebook, and educators have annotated NSDL resources to design online learning activities for their students.41 And research about the digital annotation tool Perusall.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.5) !important; }3Troy Hicks, Nate Angell, Jeremy Dean, often used in conjunction with science textbooks, has shown that college students’ pre-reading and annotation practices can subsequently improve exam performance.
  2. link-springer-com.uaccess.univie.ac.at link-springer-com.uaccess.univie.ac.at
    1. ites of heightened, future-oriented public debate aboutpossible futures

      tag

    2. ites of heightened, future-oriented public debate aboutpossible futures

      tag

    1. Author Response

      Reviewer #1:

      Hutchings et al. report an updated cryo-electron tomography study of the yeast COP-II coat assembled around model membranes. The improved overall resolution and additional compositional states enabled the authors to identify new domains and interfaces--including what the authors hypothesize is a previously overlooked structural role for the SEC31 C-Terminal Domain (CTD). By perturbing a subset of these new features with mutants, the authors uncover some functional consequences pertaining to the flexibility or stability of COP-II assemblies.

      Overall, the structural and functional work appears reliable, but certain questions and comments should be addressed prior to publication. However, this reviewer failed to appreciate the conceptual advance that warrants publication in a general biology journal like eLIFE. Rather, this study provides a valuable refinement of our understanding of COP-II that I believe is better suited to a more specialized, structure-focused journal.

      We agree that in our original submission our description of the experimental setup, indeed similar to previous work, did not fully capture the novel findings of this paper. Rather than being simply a higher resolution structure of the COPII coat, in fact we have discovered new interactions in the COPII assembly network, and we have probed their functional roles, significantly changing our understanding of the mechanisms of COPII-mediated membrane curvature. In the revised submission we have included additional genetic data that further illuminate this mechanism, and have rewritten the text to better communicate the novel aspects of our work.

      Our combination of structural, functional and genetic analyses goes beyond refining our textbook understanding of the COPII coat as a simple ‘adaptor and cage’, but rather it provides a completely new picture of how dynamic regulation of assembly and disassembly of a complex network leads to membrane remodelling.

      These new insights have important implications for how coat assembly provides structural force to bend a membrane but is still able to adapt to distinct morphologies. These questions are at the forefront of protein secretion, where there is debate about how different types of carriers might be generated that can accommodate cargoes of different size.

      Major Comments: 1) The authors belabor what this reviewer thinks is an unimportant comparison between the yeast reconstruction of the outer coat vertex with prior work on the human outer coat vertex. Considering the modest resolution of both the yeast and human reconstructions, the transformative changes in cryo-EM camera technology since the publication of the human complex, and the differences in sample preparation (inclusion of the membrane, cylindrical versus spherical assemblies, presence of inner coat components), I did not find this comparison informative. The speculations about a changing interface over evolutionary time are unwarranted and would require a detailed comparison of co-evolutionary changes at this interface. The simpler explanation is that this is a flexible vertex, observed at low resolution in both studies, plus the samples are very different.

      We do agree that our proposal that the vertex interface changes over evolutionary time is speculative and we have removed this discussion. We agree that a co-evolutionary analysis will be enlightening here, but is beyond the scope of the current work.

      We respectfully disagree with the reviewer’s interpretation that the difference between the two vertices is due to low resolution. The interfaces are clearly different, and the resolutions of the reconstructions are sufficient to state this. The reviewer’s suggestion that the difference in vertex orientation might be simply attributable to differences in sample, such as inclusion of the membrane, cylindrical versus spherical morphology, or presence of inner coat components were ruled out in our original submission: we resolved yeast vertices on spherical vesicles (in addition to those on tubes) and on membrane-less cages. These analyses clearly showed that neither the presence of a membrane, nor the change in geometry (tubular vs. spherical) affect vertex interactions. These experiments are presented in Supplementary Fig 4 (Supplementary Fig. 3 in the original version). Similarly, we discount that differences might be due to the presence or absence of inner coat components, since membrane-less cages were previously solved in both conditions and are no different in terms of their vertex structure (Stagg et al. Nature 2006 and Cell 2008).

      We believe it is important to report on the differences between the two vertex structures. Nevertheless, we have shifted our emphasis on the functional aspects of vertex formation and moved the comparison between the two vertices to the supplement.

      2) As one of the major take home messages of the paper, the presentation and discussion of the modeling and assignment of the SEC31-CTD could be clarified. First, it isn't clear from the figures or the movies if the connectivity makes sense. Where is the C-terminal end of the alpha-solenoid compared to this new domain? Can the authors plausibly account for the connectivity in terms of primary sequence? Please also include a side-by-side comparison of the SRA1 structure and the CTD homology model, along with some explanation of the quality of the model as measured by Modeller. Finally, even if the new density is the CTD, it isn't clear from the structure how this sub-stoichiometric and apparently flexible interaction enhances stability. Hence, when the authors wrote "when the [CTD] truncated form was the sole copy of Sec31 in yeast, cells were not viable, indicating that the novel interaction we detect is essential for COPII coat function." Maybe, but could this statement be a leap to far? Is it the putative interaction essential, or is the CTD itself essential for reasons that remain to be fully determined?

      The CTD is separated from the C-terminus of the alpha solenoid domain by an extended domain (~350 amino acids) that is predicted to be disordered, and contains the PPP motifs and catalytic fragment that contact the inner coat. This is depicted in cartoon form in Figures 3A and 7, and discussed at length in the text. This arrangement explains why no connectivity is seen, or expected. We could highlight the C-terminus of the alpha-solenoid domain to emphasize where the disordered region should emerge from the rod, but connectivity of the disordered domain to the CTD could arise from multiple positions, including from an adjacent rod.

      The reviewer’s point about the essentiality of the CTD being independent of its interaction with the Sec31 rod, is an important one. The basis for our model that the CTD enhances stability or rigidity of the coat is the yeast phenotype of Sec31-deltaCTD, which resembles that of a sec13 null. Both mutants are lethal, but rescued by deletion of emp24, which leads to more easily deformable membranes (Čopič et al. Science 2012). We agree that even if this model is true, the interaction of the CTD with Sec31 that our new structure reveals is not proven to drive rigidity or essentiality. We have tempered this hypothesis and added alternative possibilities to the discussion.

      We have included the SRA1 structure in Supplementary Fig 5, as requested, and the model z-score in the Methods. The Z-score, as calculated by the proSA-web server is -6.07 (see figure below, black dot), and falls in line with experimentally determined structures including that of the template (PDB 2mgx, z-score = -5.38).

      img

      3) Are extra rods discussed in Fig. 4 are a curiosity of unclear functional significance? This reviewer is concerned that these extra rods could be an in vitro stoichiometry problem, rather than a functional property of COP-II.

      This is an important point, that, as we state in the paper, cannot be answered at the moment: the resolution is too low to identify the residues involved in the interaction. Therefore we are hampered in our ability to assess the physiological importance of this interaction. We still believe the ‘extra’ rods are an important observation, as they clearly show that another mode of outer coat interaction, different from what was reported before, is possible.

      The concern that interactions visualised in vitro might not be physiologically relevant is broadly applicable to structural biology approaches. However, our experimental approach uses samples that result from active membrane remodelling under near-physiological conditions, and we therefore expect these to be less prone to artefacts than most in vitro reconstitution approaches, where proteins are used at high concentrations and in high salt buffer conditions.

      4) The clashsccore for the PDB is quite high--and I am dubious about the reliability of refining sidechain positions with maps at this resolution. In addition to the Ramchandran stats, I would like to see the Ramachandran plot as well as, for any residue-level claims, the density surrounding the modeled side chain (e.g. S742).

      The clashscore is 13.2, which, according to molprobity, is in the 57th percentile for all structures and in the 97th for structures of similar resolutions. We would argue therefore that the clashscore is rather low. In fact, the model was refined from crystal structures previously obtained by other groups, which had worse clashscore (17), despite being at higher resolution. Our refinement has therefore improved the clashscore. During refinement we have chosen restraint levels appropriate to the resolution of our map (Afonine et al., Acta Cryst D 2018)

      The Ramachandran plot is copied here and could be included in a supplemental figure if required. We make only one residue-level claim (S742), the density for which is indeed not visible at our resolution. We claim that S742 is close to the Sec23-23 interface, and do not propose any specific interactions. Nevertheless we have removed reference to S742 from the manuscript. We included this specific information because of the potential importance of this residue as a site of phosphorylation, thereby putting this interface in broader context for the general eLife reader.

      img

      Minor Comments:

      1) The authors wrote "To assess the relative positioning of the two coat layers, we analysed the localisation of inner coat subunits with respect to each outer coat vertex: for each aligned vertex particle, we superimposed the positions of all inner coat particles at close range, obtaining the average distribution of neighbouring inner coat subunits. From this 'neighbour plot' we did not detect any pattern, indicating random relative positions. This is consistent with a flexible linkage between the two layers that allows adaptation of the two lattices to different curvatures (Supplementary Fig 1E)." I do not understand this claim, since the pattern both looks far from random and the interactions depend on molecular interactions that are not random. Please clarify.

      We apologize for the confusion: the pattern of each of the two coats are not random. Our sentence refers to the positions of inner and outer coats relative to each other. The two lattices have different parameters and the two layers are linked by flexible linkers (the 350 amino acids referred to above). We have now clarified the sentence.

      2) Related to major point #1, the author wrote "We manually picked vertices and performed carefully controlled alignments." I do now know what it means to carefully control alignments, and fear this suggests human model bias.

      We used different starting references for the alignments, with the precise aim to avoid model bias. For both vesicle and cage vertex datasets, we have aligned the subtomograms against either the vertex obtained from tubules, or the vertex from previously published membrane-less cages. In all cases, we retrieved a structure that resembles the one on tubules, suggesting that the vertex arrangement we observe isn’t simply the result of reference bias. This procedure is depicted in Supplementary Fig 4 (Supplementary Fig. 3 in the original manuscript), but we have now clarified it also in the methods section.

      3) Why do some experiments use EDTA? I may be confused, but I was surprised to see the budding reaction employed 1mM GMPPNP, and 2.5mM EDTA (but no Magnesium?). Also, for the budding reaction, please replace or expand upon the "the 10% GUV (v/v)" with a mass or molar lipid-to-protein ratio.

      We regret the confusion. As stated in the methods, all our budding reactions are performed in the presence of EDTA and Magnesium, which is present in the buffer (at 1.2 mM). The reason is to facilitate nucleotide exchange, as reported and validated in Bacia et al., Scientific Reports 2011.

      Lipids in GUV preparations are difficult to quantify. We report the stock concentrations used, but in each preparation the amount of dry lipid that forms GUVs might be different, as is the concentration of GUVs after hydration. However since we analyse reactions where COPII proteins have bound and remodelled individual GUVs, we do not believe the protein/lipid ratio influences our structures.

      4) Please cite the AnchorMap procedure.

      We cite the SerialEM software, and are not aware of other citations specifically for the anchor map procedure.

      5) Please edit for typos (focussing, functionl, others)

      Done

      Reviewer #2:

      The manuscript describes new cryo-EM, biochemistry, and genetic data on the structure and function of the COPII coat. Several new discoveries are reported including the discovery of an extra density near the dimerization region of Sec13/31, and "extra rods" of Sec13/31 that also bind near the dimerization region. Additionally, they showed new interactions between the Sec31 C-terminal unstructured region and Sec23 that appear to bridge multiple Sec23 molecules. Finally, they increased the resolution of the Sec23/24 region of their structure compared to their previous studies and were able to resolve a previously unresolved L-loop in Sec23 that makes contact with Sar1. Most of their structural observations were nicely backed up with biochemical and genetic experiments which give confidence in their structural observations. Overall the paper is well-written and the conclusions justified.

      However, this is the third iteration of structure determination of the COPII coat on membrane with essentially the same preparation and methods. Each time, there has been an incremental increase in resolution and new discoveries, but the impact of the present study is deemed to be modest. The science is good, but it may be more appropriate for a more specialized journal. Areas of specific concern are described below.

      As described above, we respectfully disagree with this interpretation of the advance made by the current work. This work improves on previous work in many aspects. The resolution of the outer coat increases from over 40A to 10-12A, allowing visualisation of features that were not previously resolved, including a novel vertex arrangement, the Sec31 CTD, and the outer coat ‘extra rods’. An improved map of the inner coat also allows us to resolve the Sec23 ‘L-loop’. We would argue that these are not just extra details, but correspond to a suite of novel interactions that expand our understanding of the complex COPII assembly network. Moreover, we include biochemical and genetic experiments that not only back up our structural observations but bring new insights into COPII function. As pointed out in response to reviewer 1, we believe our work contributes a significant conceptual advance, and have modified the manuscript to convey this more effectively.

      1) The abstract is vague and should be re-written with a better description of the work.

      We have modified the abstract to specifically outline what we have done and the major new discoveries of this paper.

      2) Line 166 - "Surprisingly, this mutant was capable of tubulating GUVs". This experiment gets to one of the fundamental unknown questions in COPII vesiculation. It is not clear what components are driving the membrane remodeling and at what stages during vesicle formation. Isn't it possible that the tubulation activity the authors observe in vitro is not being driven at all by Sec13/31 but rather Sec23/24-Sar1? Their Sec31ΔCTD data supports this idea because it lacks a clear ordered outer coat despite making tubules. An interesting experiment would be to see if tubules form in the absence of all of Sec13/31 except the disordered domain of Sec31 that the authors suggest crosslinks adjacent Sec23/24s.

      This is an astute observation, and we agree with the reviewer that the source of membrane deformation is not fully understood. We favour the model that budding is driven significantly by the Sec23-24 array. To further support this, we have performed a new experiment, where we expressed Sec31ΔN in yeast cells lacking Emp24, which have more deformable membranes and are tolerant to the otherwise lethal deletion of Sec13. While Sec31ΔN in a wild type background did not support cell viability, this was rescued in a Δemp24 yeast strain, strongly supporting the hypothesis that a major contributor to membrane remodelling is the inner coat, with the outer coat becoming necessary to overcome membrane bending resistance that ensues from the presence of cargo. We now include these results in Figure 1.

      However, we must also take into account the results presented in Fig. 6, where we show that weakening the Sec23-24 interface still leads to budding, but only if Sec13-31 is fully functional, and that in this case budding leads to connected pseudo-spherical vesicles rather than tubes. When Sec13-31 assembly is also impaired, tubes appear unstructured. We believe this strongly supports our conclusions that both inner and outer coat interactions are fundamental for membrane remodelling, and it is the interplay between the two that determines membrane morphology (i.e. tubes vs. spheres).

      To dissect the roles of inner and outer coats even further, we have done the experiment that the reviewer suggests: we expressed Sec31768-1114, but the protein was not well-behaved and co-purified with chaperones. We believe the disordered domain aggregates when not scaffolded by the structured elements of the rod. Nonetheless, we used this fragment in a budding reaction, and could not see any budding. We did not include this experiment as it was inconclusive: the lack of functionality of the purified Sec31 fragment could be attributed to the inability of the disordered region to bind its inner coat partner in the absence of the scaffolding Sec13-31 rod. As an alternative approach, we have used a version of Sec31 that lacks the CTD, and harbours a His tag at the N-terminus (known from previous studies to partially disrupt vertex assembly). We think this construct is more likely to be near native, since both modifications on their own lead to functional protein. We could detect no tubulation with this construct by negative stain, while both control constructs (Sec31ΔCTD and Nhis-Sec31) gave tubulation. This suggests that the cross-linking function of Sec31 is not sufficient to tubulate GUV membranes, but some degree of functional outer coat organisation (either mediated by N- or C-terminal interactions) is needed. It is also possible that the lack of outer coat organisation might lead to less efficient recruitment to the inner coat and cross-linking activity. We have added this new observation to the manuscript.

      3) Line 191 - "Inspecting cryo-tomograms of these tubules revealed no lozenge pattern for the outer 192 coat" - this phrasing is vague. The reviewer thinks that what they mean is that there is a lack of order for the Sec13/31 layer. Please clarify.

      The reviewer is correct, we have changed the sentence.

      4) Line 198 - "unambiguously confirming this density corresponds to 199 the CTD." This only confirms that it is the CTD if that were the only change and the Sec13/31 lattice still formed. Another possibility is that it is density from other Sec13/31 that only appears when the lattice is formed such as the "extra rods". One possibility is that the density is from the extra rods. The reviewer agrees that their interpretation is indeed the most likely, but it is not unambiguous. The authors should consider cross-linking mass spectrometry.

      We have removed the word ‘unambiguously’, and changed to ‘confirming that this density most likely corresponds to the CTD’. Nonetheless, we believe that our interpretation is correct: the extra rods bind to a different position, and themselves also show the CTD appendage. In this experiment, the lack of the CTD was the only biochemical change.

      5) In the Sec31ΔCTD section, the authors should comment on why ΔCTD is so deleterious to oligomer organization in yeast when cages form so abundantly in preparations of human Sec13/31 ΔC (Paraan et al 2018).

      We have added a comment to address this. “Interestingly, human Sec31 proteins lacking the CTD assemble in cages, indicating that either the vertex is more stable for human proteins and sufficient for assembly, or that the CTD is important in the context of membrane budding but not for cage formation in high salt conditions.”

      6) The data is good for the existence of the "extra rods", but significance and importance of them is not clear. How can these extra densities be distinguished from packing artifacts due to imperfections in the helical symmetry.

      Please also see our response to point 3 from reviewer 1. Regarding the specific concern that artefacts might be a consequence of imperfection in the helical symmetry, we would argue such imperfections are indeed expected in physiological conditions, and to a much higher extent. For this reason interactions seen in the context of helical imperfections are likely to be relevant. In fact, in normal GTP hydrolysis conditions, we expect long tubes would not be able to form, and the outer coat to be present on a wide range of continuously changing membrane curvatures. We think that the ability of the coat to form many interactions when the symmetry is imperfect might be exactly what confers the coat its flexibility and adaptability.

      7) Figure 5 is very hard to interpret and should be redone. Panels B and C are particularly hard to interpret.

      We have made a new figure where we think clarity is improved.

      8) The features present in Sec23/24 structure do not reflect the reported resolution of 4.7 Å. It seems that the resolution is overestimated.

      We report an average resolution of 4.6 Å. In most of our map we can clearly distinguish beta strands, follow the twist of alpha helices and see bulky side chains. These features typically become visible at 4.5-5A resolution. We agree that some areas are worse than 4.6 Å, as typically expected for such a flexible assembly, but we believe that the average resolution value reported is accurate. We obtained the same resolution estimate using different software including relion, phenix and dynamo, so that is really the best value we can provide. To further convince ourselves that we have the resolution we claim, we sampled EM maps from the EMDB with the same stated resolution (we just took the 7 most recent ones which had an associated atomic model), and visualised their features at arbitrary positions. For both beta strands and alpha helices, we do not feel our map looks any worse than the others we have examined. We include a figure here.

      img

      9) Lines 315/316 - "We have combined cryo-tomography with biochemical and genetic assays to obtain a complete picture of the assembled COPII coat at unprecedented resolution (Fig. 7)"

      10) Figure 7. is a schematic model/picture the authors should reference a different figure or rephrase the sentence.

      We now refer to Fig 7 in a more appropriate place.

      Reviewer #3:

      The manuscript by Hutchings et al. describes several previously uncharacterised molecular interactions in the coats of COP-II vesicles by using a reconstituted coats of yeast COPI-II. They have improved the resolution of the inner coat to 4.7A by tomography and subtomogram averaging, revealing detailed interactions, including those made by the so-called L-loop not observed before. Analysis of the outer layer also led to new interesting discoveries. The sec 31 CTD was assigned in the map by comparing the WT and deletion mutant STA-generated density maps. It seems to stabilise the COP-II coats and further evidence from yeast deletion mutants and microsome budding reconstitution experiments suggests that this stabilisation is required in vitro. Furthermore, COP-II rods that cover the membrane tubules in right-handed manner revealed sometimes an extra rod, which is not part of the canonical lattice, bound to them. The binding mode of these extra rods (which I refer to here a Y-shape) is different from the canonical two-fold symmetric vertex (X-shape). When the same binding mode is utilized on both sides of the extra rod (Y-Y) the rod seems to simply insert in the canonical lattice. However, when the Y-binding mode is utilized on one side of the rod and the X-binding mode on the other side, this leads to bridging different lattices together. This potentially contributes to increased flexibility in the outer coat, which maybe be required to adopt different membrane curvatures and shapes with different cargos. These observations build a picture where stabilising elements in both COP-II layers contribute to functional cargo transport. The paper makes significant novel findings that are described well. Technically the paper is excellent and the figures nicely support the text. I have only minor suggestions that I think would improve the text and figure.

      We thank the reviewer for helpful suggestions which we agree improve the manuscript.

      Minor Comments:

      L 108: "We collected .... tomograms". While the meaning is clear to a specialist, this may sound somewhat odd to a generic reader. Perhaps you could say "We acquired cryo-EM data of COP-II induced tubules as tilt series that were subsequently used to reconstruct 3D tomograms of the tubules."

      We have changed this as suggested

      L 114: "we developed an unbiased, localisation-based approach". What is the part that was developed here? It seems that the inner layer particle coordinates where simply shifted to get starting points in the outer layer. Developing an approach sounds more substantial than this. Also, it's unclear what is unbiased about this approach. The whole point is that it's biased to certain regions (which is a good thing as it incorporates prior knowledge on the location of the structures).

      We have modified the sentence to “To target the sparser outer coat lattice for STA, we used the refined coordinates of the inner coat to locate the outer coat tetrameric vertices”, and explain the approach in detail in the methods.

      L 124: "The outer coat vertex was refined to a resolution of approximately ~12 A, revealing unprecedented detail of the molecular interactions between Sec31 molecules (Supplementary Fig 2A)". The map alone does not reveal molecular interactions; the main understanding comes from fitting of X-ray structures to the low-resolution map. Also "unprecedented detail" itself is somewhat problematic as the map of Noble et al (2013) of the Sec31 vertex is also at nominal resolution of 12 A. Furthermore, Supplementary Fig 2A does not reveal this "unprecedented detail", it shows the resolution estimation by FSC. To clarify, these points you could say: "Fitting of the Sec31 atomic model to our reconstruction vertex at 12-A resolution (Supplementary Fig 2A) revealed the molecular interactions between different copies of Sec31 in the membrane-assembled coat.

      We have changed the sentence as suggested.

      L 150: Can the authors exclude the possibility that the difference is due to differences in data processing? E.g. how the maps amplitudes have been adjusted?

      Yes, we can exclude this scenario by measuring distances between vertices in the right and left handed direction. These measurements are only compatible with our vertex arrangement, and cannot be explained by the big deviation from 4-fold symmetry seen in the membrane-less cage vertices.

      L 172: "that wrap tubules either in a left- or right-handed manner". Don't they do always both on each tubule? Now this sentence could be interpreted to mean that some tubules have a left-handed coat and some a right-handed coat.

      We have changed this sentence to clarify. “Outer coat vertices are connected by Sec13-31 rods that wrap tubules both in a left- and right-handed manner.”

      L276: "The difference map" hasn't been introduced earlier but is referred to here as if it has been.

      We now introduce the difference map.

      L299: Can "Secondary structure predictions" denote a protein region "highly prone to protein binding"?

      Yes, this is done through DISOPRED3, a feature include in the PSIPRED server we used for our predictions. The reference is: Jones D.T., Cozzetto D. DISOPRED3: precise disordered region predictions with annotated protein-binding activity Bioinformatics. 2015; 31:857–863. We have now added this reference to the manuscript.

      L316: It's true that the detail in the map of the inner coat is unprecedented and the model presented in Figure 7 is partially based on that. But here "unprecedented resolution" sounds strange as this sentence refers to a schematic model and not a map.

      We have changed this by moving the reference to Fig 7 to a more appropriate place

      L325: "have 'compacted' during evolution" -> remove. It's enough to say it's more compact in humans and less compact in yeast as there could have been different adaptations in different organisms at this interface.

      We have changed as requested. See also our response to reviewer 1, point 1.

      L327: What's exactly meant by "sequence diversity or variability at this density".

      We have now clarified: “Since multiple charge clusters in yeast Sec31 may contribute to this interaction interface (Stancheva et al., 2020), the low resolution could be explained by the fact that the density is an average of different sequences.”

      L606-607: The description of this custom data processing approach is difficult to follow. Why is in-plane flip needed and how is it used here?

      Initially particles are picked ignoring tube directionality (as this cannot be assessed easily from the tomograms due to the pseudo-twofold symmetry of the Sec23/24/Sar1 trimer). So the in plane rotation of inner coat subunit could be near 0 or 180°. For each tube, both angles are sampled (in-plane flip). Most tubes result in the majority of particles being assigned one of the two orientations (which is then assumed as the tube directionality). Particles that do not conform are removed, and rare tubes where directionality cannot be determined are also removed. We have re-written the description to clarify these points: “Initial alignments were conducted on a tube-by-tube basis using the Dynamo in-plane flip setting to search in-plane rotation angles 180° apart. This allowed to assign directionality to each tube, and particles that were not conforming to it were discarded by using the Dynamo dtgrep_direction command in custom MATLAB scripts”

      L627: "Z" here refers to the coordinate system of aligned particles not that of the original tomogram. Perhaps just say "shifted 8 pixels further away from the membrane".

      Changed as requested.

      L642-643: How can the "left-handed" and "right-handed" rods be separated here? These terms refer to the long-range organisation of the rods in the lattice it's not clear how they were separated in the early alignments.

      They are separated by picking only one subset using the dynamo sub-boxing feature. This extracts boxes from the tomogram which are in set positions and orientation relative to the average of previously aligned subtomograms. From the average vertex structure, we sub-box rods at 4 different positions that correspond to the centre of the rods, and the 2-fold symmetric pairs are combined into the same dataset. We have clarified this in the text: “The refined positions of vertices were used to extract two distinct datasets of left and right-handed rods respectively using the dynamo sub-boxing feature.”

      Figure 2B. It's difficult to see the difference between dark and light pink colours.

      We have changed colours to enhance the difference.

      Figure 3C. These panels report the relative frequency of neighbouring vertices at each position; "intensity" does not seem to be the right measure for this. You could say that the colour bar indicates the "relative frequency of neighbouring vertices at each position" and add detail how the values were scaled between 0 and 1. The same applies to SFigure 1E.

      Changed as requested.

      Figure 4. The COP-II rods themselves are relatively straight, and they are not left-handed or right-handed. Here, more accurate would be "architecture of COPII rods organised in a left-handed manner". (In the text the authors may of course define and then use this shorter expression if they so wish.) Panel 4B top panel could have the title "left-handed" and the lower panel should have the title "right-handed" (for consistency and clarity).

      We have now defined left- and right-handed rods in the text, and have changed the figure and panel titles as requested.

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      Reply to the reviewers

      Response to Reviewers and Revision Plan

      We thank all three reviewers for their time and their comments on our manuscript.

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

      Here Ryan et al. have used localization analysis following induced rapid relocalization of endogenous proteins to investigate the composition and recruitment hierarchy of a clathrin-TACC3-based spindle complex that is important for microtubule organization and stability.

      The authors generate different HeLa cell lines, each with one of four complex members (TACC3, CLTA, chTOG and GTSE1) endogenously tagged with FKBP-GFP via Cas9-mediated editing. This tag allows rapid recruitment to the mitochondria upon rapamycin addition ("knocksideways"). They ultimately quantify each of the 4 components' localization to the spindle following knocksideways of each component using fluorescently-tagged transfected constructs. The authors' interpretation of the results of this analysis are summarized in the last model figure, in which a core MT-binding complex of clathrin and TACC3 recruit the ancillary components GTSE1 and chTOG. In addition, the authors investigate the contribution of individual clathrin-binding LIDL motifs in GTSE1 to the recruitment of clathrin and GTSE1 to spindles. Their findings here largely agree with and confirm a recent report regarding the contribution of these motifs to GTSE1 recruitment to the spindle. They further analyzed GTSE1 fragments for interphase and mitotic microtubule localization, and identified a second region of GTSE1 required (but not sufficient) for spindle localization. Finally, the authors report that PIK3C2A is not part of this complex, contradicting (correcting) a previously published study.

      **Major comments:**

      1.The chTOG-FKBP-GFP cell line the authors generate has only a small fraction of chTOG tagged, and thus should not be used for any conclusions about protein localization dependency on chTOG. Because they were unable to construct a HeLa cell line with all copies tagged, the authors expect that the homozygous knock-in of chTOG-FKBP-GFP is lethal, and thus their experience is appropriate to report. However, the authors should not use this cell line alone to make statements about chTOG dependency. They would have to use similar localization analysis, but after another method to disrupt chTOG (as a second-best approach), such as RNAi. In fact, they have reported this in a previous publication (Booth et al 2011). However, the result was different. There, loss of chTOG resulted in reduced clathrin on spindles, suggesting it may stabilize or help recruit the complex. Alternatively, they could remove their chTOG data, but this would compromise the "comprehensive" nature of the work.

      The referee is correct. The point here is to show the results we had using this approach for all four proteins under study. For this reason, we do not want to remove this data and prefer to show our results “warts-and-all”. We feel that the shortcomings of our approach are honestly presented and discussed in the manuscript. While only a fraction of chTOG was tagged, we should expect some co-removal after its induced mislocalization. Since we saw no change, we concluded that chTOG is auxiliary.

      The “second best” approach suggested (RNAi of chTOG) is problematic for two reasons. First, chTOG RNAi results in gross changes to spindle structure (multipolar spindles) and it is difficult to pick apart differences in protein partner localization that result from loss of chTOG from those resulting from changes in spindle structure. Second, the paper is about induced mislocalization as a method for determining protein complexes once a normal spindle has formed. So, removing chTOG prior to mitosis is not comparable. If we get the same or different result, does it confirm or conflict with the data we have? Nonetheless, given the discrepancy with our earlier work, we should investigate this further.

      To address this concern, we will stain endogenous clathrin, TACC3 and GTSE1 following chTOG RNAi and measure their relative levels at the spindle.

      Making the chTOG-FKBP-GFP cell line was difficult. As described in the paper, we only recovered heterozygous clones despite repeated attempts. Since submission, we have been made aware of a HCT116 chTOG-FKBP-GFP cell line that is reported to be homozygously tagged (Cherry et al. 2019 doi: 10.1002/glia.23628).

      A note about this cell line has been added to the paper (Results section, final sentence of 1st paragraph).

      2.The authors initially analyze complex member localization after knocksideways experiments by antibody staining, which has the advantage of analyzing endogenous proteins (versus the later transfected fluorescent constructs). Setting aside potential artefacts from fixation, this would seem to be a better method for controlled analysis to take advantage of their setup (short of generating stable cell lines with second proteins endogenously tagged in a second color - a huge undertaking). The authors conclude that antibody specificity problems confounded their analysis and explained unusual results. However, I think is worth investing a little more effort to sort this out, rather than bringing doubt to the whole data set. Verifying and then using another antibody for chTOG localization would be informative. Of course, the negative control should not be their chTOG-FKBP-GFP line, as it does not relocalize most of chTOG.

      In the case of GTSE1, an alternative explanation to antibody specificity issues would be that the GTSE1-FKBP-GFP cell line is not in fact homozygously tagged. Given the low expression levels on the western provided, and the detection of GTSE1 on the spindle in the induced GTSE1-FKBP-GFP cell line (but not TACC3-FKBP-GFP), it seems plausible that an untagged copy remains. If there are multiple copies of GTSE1 in Hela cells, one untagged copy could represent a small fraction of total GTSE1. This should thus be ruled out. GTSE1 clones should be analyzed with more protein extracts loaded - dilutions of the extracts can determine the sensitivity of the blot to lower protein levels. In addition, sequencing of genomic DNA can reveal a small percentage with different reads.

      We used a two-pronged approach for assessing relocalization of protein partners (staining vs transfected constructs). The staining approach is superior since endogenous proteins are examined, but it is limited by antibody specificity. The transfection approach overcomes this limitation but is in turn limited by effects of overexpression and tagging. Together the two approaches allow us, and anyone employing this method, to get a picture of protein complexes. We didn’t want to create the impression that one or other approach is confounded, but the referee is correct that this analysis would benefit from further work.

      Specifically, to address these concerns:

      • We will verify and use alternative chTOG antibodies to try to improve this dataset.
      • We will test the possibility that an untagged allele of GTSE1 remains. We will use western blotting and a summary of our genomic analysis will be added to the paper.

        3.There is a lot of data contained in the small graphs summarizing quantification of localization in Figs 3 and 4. They would be more accessible to the reader if they were larger and/or an "example" of the chart with labels was present explaining it (essentially what is in the figure legends). Furthermore, there is no statistical test applied to this data that I see. This is needed. How do authors determine whether there is an "effect"?

      Our aim was to compress a lot of information into a small space, while still showing some example primary data. All reviewers raised the same concern which tells us that we went too far towards “data visualization”.

      To address this point, we will rework these figures.

      **Minor issues:**

      1.The GTSE1 constructs used for mutation and localization analysis are 720 amino acids long. A recent study analyzing similar mutations uses a 739 amino acid construct (Rondelet et al 2020). The latter is the predominant transcript in NCBI and Ensembl databases. It appears the construct used by the authors omits the first 19 a.a.. I do not think using the truncated transcript affects conclusions of the manuscript, but it could generate confusion when identifying residues based on a.a.#s of mutant constructs (Fig 6). This should be somehow clarified.

      We were aware of the longer transcript but were using the 720 residue form since it is the canonical sequence in Uniprot (https://www.uniprot.org/uniprot/Q9NYZ3). We did not know that the 739 form is the predominant transcript. We agree this is unlikely to affect our work but that the numbering may cause confusion.

      We have added a note to the Methods (Molecular Biology section) to accurately describe what we and Rondelet et al. have used.

      2.The labeling of constructs in Fig 6C/D is confusing, and appears shifted by eye at places. Please relabel this more clearly.

      Apologies for the error.

      We have relabeled Figure 6C,D and also made a similar alteration to Figure 5C.

      The recommended new experimental data (Analysis complex member levels on spindles after full perturbation of spindle chTOG; new chTOG antibody stainings in the FKBP lines; reanalysis of GTSE1 DNA/protein in GTSE1-FKBP line) should only require a new antibody/siRNA, plus a few weeks time to repeat the analyses already in the paper with new reagents.

      Reviewer #1 (Significance (Required)):

      While multiple individual components of this complex have been previously characterized, the structure and nature of the complex formation and its recruitment to microtubules/spindles remains a complex problem that has yet to be solved.

      Overall this study represents a comprehensive localization-dependency analysis of the Clathrin-TACC3 based spindle complex using a consistent methodology. Although several of the conclusions of the findings echo previous reports, some of the previous literature is contradictory within itself as well as with the conclusions here. Analyzing all components with a single, rapid-perturbation technique thus has great value to present a clear data set, given that the experimental setup conditions and analysis are solid (a goal to which the majority of comments refer).

      Beyond the complex localization/recruitment analysis, two novel findings of this study that emerge are:

      a)GTSE1 contains a second, separate protein region, distinct from the clathrin-binding motifs that is required for its localization to the spindle, and most likely a microtubule-interaction site. This suggests that GTSE1 recruitment to the spindle is more complex than previously reported.

      b)PI3KC2A, which has been reported previously to be a stabilizing member of this complex, is in fact not a member, nor localizes to spindles, nor displays a mitotic defect after loss. This is important conclusion to be made as it would correct the literature, and avoid future confusion.

      --

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

      In this paper, the authors investigate the nature of interactions between members of the TACC3-chTOG-clathrin-GTSE1 complex on the mitotic spindle. By using a series of HeLa cell lines that they have created by CRISPR/Cas9 editing to enable spatial manipulation (knocksideways) of either TACC3, chTOG, clathrin and GTSE1, they show that on spindle microtubules TACC3 and clathrin represent core complex members whereas chTOG and GTSE1 bind to them respectively but not to each other. Additionally, the authors find that the protein PIK3C2A, which has been implicated in this complex previously is in fact not a component of this complex in mitotic cells. The main advance of the paper in my opinion is the endogenous tagging of the proteins for knocksideways experiments since former experiments depended on RNAi silencing and expression of tagged proteins from plasmids, which introduced issues of protein silencing efficiency and plasmid overexpression problems. This approach seems to alleviate these problems, except in the case of chTOG which seems to be lethal in its homozygous variant.

      **Major comments:**

      I find the key conclusions regarding the localization of the components of the complex convincing. There are some issues regarding the specificity of antibodies in immunostaining experiments (Fig 3.) and the influence of mCherry-TACC3 expression on distorted localization of the complex prior to knocksideways. However, I think the general conclusion about which complex components (clathrin and TACC3) influence the localization of the other proteins in the complex (chTOG and GTSE1) stands. One thing that I miss from the paper is the data on the consequences on the spindle shape and morphology after knocksideways. I have noticed on images in both Figure 3 and Figure 4 that in some cases distribution of the signal seems to influence quite a bit the spindle morphology. Also, In Figure 3 I have noticed what seems to me a quite big variation in spindle size in tubulin signal in both untreated and rapamycin cells. Since authors have many of these images already, I believe it would be realistic, not costly and of additional value for the paper to provide more data on the consequences of the knocksideways experiments. Change of spindle size, tubulin intensity and DNA/kinetochore misalignment upon knocksideways would be helpful to appreciate more the findings of the paper. More so since the authors on more than one occasion find their motivation in the field of cancer research and spindle stability relation to it. Some data connection to this motivation would be of value. Experiments seem reproducible.

      The focus of the paper is on using the knocksideways methodology to understand a protein complex during mitosis, rather than looking at its function. We are not keen to do new experiments that are not part of the central message of the paper. However, the Reviewer is correct that we do already have a dataset that can be mined in the manner described.

      To address this point, we will analyze spindle size parameters and also the intensity of tubulin. Our analysis will be limited to the short timeframe of our experiments, but it should reveal or refute any changes in spindle structure that may result from loss of complex members.

      **Minor comments:**

      I have some problems with the clarity of Figure 3 and 4. For Figure 3. In Figure 3 plots on the right are a bit small and not easy to read. Some reorganization of the figure might be beneficial. In Figure 4 plots to the right are also too small to be clear. Also, I miss the number of cells (n) I can't see the number of individual arrows because of the size of graphs.

      Our aim was to compress a lot of information into a small space, while still showing some example primary data. All reviewers raised the same concern which tells us that we went too far towards “data visualization”.

      To address this point, we will rework these figures.

      Reviewer #2 (Significance (Required)):

      I find that the biggest significance of the paper is in the creation of new tools (cell lines) to study the localization of proteins TACC3, chTOG, clathrin and GTSE1. Cell lines where endogenous proteins can be delocalized rapidly will be of value for scientist working not only in mitosis but such as in the case of clathrin research, vesicle formation and trafficking or p53-dependent apoptosis in the case of GTSE1. In the field of mitosis it will surely help and speed up the research concerning the role of these proteins in spindle assembly and stability.

      Field of expertise: mitotic spindle

      --

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

      **Summary:**

      This papers analyses the chTog/TACC3/clathrin/GTSE1 complex that crosslinks and stabilises microtubule bundles in the mitotic spindle. The authors have developed an elegant knock sideways approach to specifically analyse the effects of removing individual components of the complex from the spindle and study the effect this has on the other interactors. They report, based on these assays that the core of the complex is formed by TACC3 and Clathrin while GTSE1 and chTog are auxiliary interactors. They also refute previous evidence that this complex also incorporates PIK3C2A. Overall, this is an interesting study that distinguishes itself predominantly by its methodology. However, some of the reported results need more thorough analysis to allow convincing conclusions.

      **Major comments:**

      1)The knockside way method is the main highlight if this paper. Unlike previous studies by the PI, this time endogenous genes are tagged which is a key advance and allows much better interpretation of the results. I am not sure why the authors have chosen HeLa cells as their model here, given the messed up genome of these cells. A non-transformed cell line would have been preferable, but as a proof of principle study, I think HeLa are acceptable, and I wouldn't expect the authors to repeat all the experiment in another system.

      Figure 1,2 and S1 are describing and validating this approach in some detail, but this will require some more work.

      The authors state that gene targeting was validated using a combination of PCR, sequencing, Western blotting, but show only the results for westerns. PCR analysis that demonstrates homozygous or heterozygous gene targeting should be shown here.

      Another issue is the penetrance of the phenotypes induced by Rapamycin. The authors show nice data of the system working in individual cells but do not give us an idea if this happens in all cells. The localisation of the individual tagged genes should be quantified (ideally with line plots) in 50 randomly chosen mitotic cells with 3 repeats before and after rapamycin treatment. Moreover, the analysis of mitotic duration (Figure S1D) should be extended to include a plus Rapamycin cohort and this should be moved in the main Figure.

      If the system works only in a small proportion of cells, this should be clearly stated. I don't think this would prevent publication, but it is an important piece of information that is missing.

      The Reviewer raises two issues here.

      • PCR analysis should be shown. This issue was also partly raised by Reviewer 1. A summary of our PCR analysis was actually included in Table 1, since the analysis we did is pretty unwieldy. We agree though that presenting our evidence for homozygosity of the cell lines would be useful. To address this point, we will add more detail of the PCR and sequencing work done to validate these cell lines.
      • Does knocksideways happen in all cells? The answer to this depends on the transient expression of MitoTrap and sufficient application of rapamycin. We agree that this will be a useful piece of information to add to the manuscript. A related issue is whether knocksideways of complex members affects mitotic progression. We have established through other experiments that rapamycin application to wild-type cells alters mitotic progression, although application of Rapalog does not have this effect. Our plan to address these points is 1) to analyze the efficacy of knocksideways that readers can expect to achieve using these, or similar cells, and 2) analyze mitotic duration in rapalog-treated cells expressing a rapalog sensitive MitoTrap.

        2)Apart from a simple quantification of mitotic duration, I believe a more detailed mitotic phenotype analysis for each knock-side way gene, especially the homozygous targeted clones, should be included. This can involve more high-resolution live cell imaging of mitotic progression with SiR-DNA and GFP-tubulin, using the dark mitotrap.

      We don’t agree that such an analysis should be included. The focus of this paper is on using the knocksideways methodology to understand a protein complex during mitosis, and not looking at its function. There are several papers on the mitotic phenotypes of these genes probed using RNAi in different cellular systems (examples for chTOG: 10.1101/gad.245603; TACC3/clathrin: 10.1038/emboj.2011.15, 10.1242/jcs.075911, 10.1083/jcb.200911091, 10.1083/jcb.200911120; GTSE1: 10.1083/jcb.201606081). Moreover, our 2013 paper used knocksideways (with RNAi and overexpression) and has a detailed analysis of mitotic progression, microtubule stability, checkpoint activity and kinetochore motions (Cheeseman et al., 2013 doi: 10.1242/jcs.124834).

      New experiments that are not part of the central message of the paper and are unlikely to give new insight are not the best use of our revision efforts for this paper (especially during the pandemic). Having said this, Reviewer 2’s suggestion to use our existing dataset to investigate mitotic phenotypes, will largely answer Reviewer 3’s request.

      We will analyze spindle size parameters and also the intensity of tubulin. Our analysis will be limited to the short timeframe of our experiments, but it should reveal or refute any changes in spindle structure that result from the loss of complex members.

      3)Overall, the quantitative analysis in Figure 3 ,4 and 7 is not good enough and sometimes doesn't fully support the conclusions. In Figure 3,4 a convoluted way of demonstrating the change in localisation is shown and this panel is so small that is almost impossible to read. Also, there is no statistical analysis, and the sample size seems very small . At least 25 cells should be analysed here in 3 repeats. I would suggest to unify the quantification in the MS and use the line plots shown in Figure 5 and 6 and compare each protein before and after rapamycin addition. This is much easier to read and more convincing. The images of the cells panels can be moved to a supplement as they contain very little information. This would generate space to expand the size and depth of the quantitative analysis. Instead of Anova tests, I would recommend using a simple t-test comparing each condition to its relevant control since this is the only relevant comparison in the experiment. Statistical significance should be calculated for each experiment with sufficient sample size. It would also be better to show the individual data points from the three repeats in different colours so that the reproducibility between repeat can be judged.

      This type of statistical analysis should be uniformly done throughout the MS and also extended to Figure 7.

      The referee raises several issues here with our data presentation and statistical analysis.

      • Our aim in Figures 3 and 4 was to compress a lot of information into a small space, while still showing some example primary data. All reviewers raised the same concern about these figures which tells us that we went too far towards “data visualization”. To address this point, we will rework Figures 3 and 4 to provide more clear data presentation.
      • The Reviewer’s comments about statistical analysis however are not sound. First, it is incorrect to state that simple t-tests can be applied (this is a form of p-hacking). Correction for multiple testing must be done on these datasets. Second, the reviewer arbitrarily states numbers for cells and experimental repeats without considering the effect size or it seems, understanding the structure of the data that we have collected. Sample sizes are small but they are taken from many independent replicates. Third, and related to the previous point, the fixed and live cell data are structured differently which means that a uniform data presentation is not possible. The live data has a paired design and each cell is an independent replicate (with replicates done over several trials). The fixed data is unpaired and we have taken measures from several experiments (independent replicates). The point about applying statistical tests to the data is also made by Reviewer 1 and we will use appropriate tests (NHST or estimation statistics) as we re-work the figures.

        Reviewer #3 (Significance (Required)):

      In my opinion, the most interesting aspect of the MS is the methodology. Based on this, publication is justified and will be of interest to a wider audience. That is why a more detailed analysis of the penetrance of this manipulation across the cell population will be critical.

      The application of this method to analyse the composition of the TACC3/Clathrin complex on the spindle is the main biological advance, and the novel information is rather limited but not unimportant.

      Overall, if these results can be properly quantified I would recommend publication.

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      Referee #1

      Evidence, reproducibility and clarity

      Here Ryan et al. have used localization analysis following induced rapid relocalization of endogenous proteins to investigate the composition and recruitment hierarchy of a clathrin-TACC3-based spindle complex that is important for microtubule organization and stability. The authors generate different HeLa cell lines, each with one of four complex members (TACC3, CLTA, chTOG and GTSE1) endogenously tagged with FKBP-GFP via Cas9-mediated editing. This tag allows rapid recruitment to the mitochondria upon rapamycin addition ("knocksideways"). They ultimately quantify each of the 4 components' localization to the spindle following knocksideways of each component using fluorescently-tagged transfected constructs. The authors' interpretation of the results of this analysis are summarized in the last model figure, in which a core MT-binding complex of clathrin and TACC3 recruit the ancillary components GTSE1 and chTOG. In addition, the authors investigate the contribution of individual clathrin-binding LIDL motifs in GTSE1 to the recruitment of clathrin and GTSE1 to spindles. Their findings here largely agree with and confirm a recent report regarding the contribution of these motifs to GTSE1 recruitment to the spindle. They further analyzed GTSE1 fragments for interphase and mitotic microtubule localization, and identified a second region of GTSE1 required (but not sufficient) for spindle localization. Finally, the authors report that PIK3C2A is not part of this complex, contradicting (correcting) a previously published study.

      Major comments:

      1.The chTOG-FKBP-GFP cell line the authors generate has only a small fraction of chTOG tagged, and thus should not be used for any conclusions about protein localization dependency on chTOG. Because they were unable to construct a HeLa cell line with all copies tagged, the authors expect that the homozygous knock-in of chTOG-FKBP-GFP is lethal, and thus their experience is appropriate to report. However, the authors should not use this cell line alone to make statements about chTOG dependency. They would have to use similar localization analysis, but after another method to disrupt chTOG (as a second-best approach), such as RNAi. In fact, they have reported this in a previous publication (Booth et al 2011). However, the result was different. There, loss of chTOG resulted in reduced clathrin on spindles, suggesting it may stabilize or help recruit the complex. Alternatively, they could remove their chTOG data, but this would compromise the "comprehensive" nature of the work.

      2.The authors initially analyze complex member localization after knocksideways experiments by antibody staining, which has the advantage of analyzing endogenous proteins (versus the later transfected fluorescent constructs). Setting aside potential artefacts from fixation, this would seem to be a better method for controlled analysis to take advantage of their setup (short of generating stable cell lines with second proteins endogenously tagged in a second color - a huge undertaking). The authors conclude that antibody specificity problems confounded their analysis and explained unusual results. However, I think is worth investing a little more effort to sort this out, rather than bringing doubt to the whole data set. Verifying and then using another antibody for chTOG localization would be informative. Of course, the negative control should not be their chTOG-FKBP-GFP line, as it does not relocalize most of chTOG.

      In the case of GTSE1, an alternative explanation to antibody specificity issues would be that the GTSE1-FKBP-GFP cell line is not in fact homozygously tagged. Given the low expression levels on the western provided, and the detection of GTSE1 on the spindle in the induced GTSE1-FKBP-GFP cell line (but not TACC3-FKBP-GFP), it seems plausible that an untagged copy remains. If there are multiple copies of GTSE1 in Hela cells, one untagged copy could represent a small fraction of total GTSE1. This should thus be ruled out. GTSE1 clones should be analyzed with more protein extracts loaded - dilutions of the extracts can determine the sensitivity of the blot to lower protein levels. In addition, sequencing of genomic DNA can reveal a small percentage with different reads.

      3.There is a lot of data contained in the small graphs summarizing quantification of localization in Figs 3 and 4. They would be more accessible to the reader if they were larger and/or an "example" of the chart with labels was present explaining it (essentially what is in the figure legends). Furthermore, there is no statistical test applied to this data that I see. This is needed. How do authors determine whether there is an "effect"?

      Minor issues:

      1.The GTSE1 constructs used for mutation and localization analysis are 720 amino acids long. A recent study analyzing similar mutations uses a 739 amino acid construct (Rondelet et al 2020). The latter is the predominant transcript in NCBI and Ensembl databases. It appears the construct used by the authors omits the first 19 a.a.. I do not think using the truncated transcript affects conclusions of the manuscript, but it could generate confusion when identifying residues based on a.a.#s of mutant constructs (Fig 6). This should be somehow clarified.

      2.The labeling of constructs in Fig 6C/D is confusing, and appears shifted by eye at places. Please relabel this more clearly.

      The recommended new experimental data (Analysis complex member levels on spindles after full perturbation of spindle chTOG; new chTOG antibody stainings in the FKBP lines; reanalysis of GTSE1 DNA/protein in GTSE1-FKBP line) should only require a new antibody/siRNA, plus a few weeks time to repeat the analyses already in the paper with new reagents.

      Significance

      While multiple individual components of this complex have been previously characterized, the structure and nature of the complex formation and its recruitment to microtubules/spindles remains a complex problem that has yet to be solved.

      Overall this study represents a comprehensive localization-dependency analysis of the Clathrin-TACC3 based spindle complex using a consistent methodology. Although several of the conclusions of the findings echo previous reports, some of the previous literature is contradictory within itself as well as with the conclusions here. Analyzing all components with a single, rapid-perturbation technique thus has great value to present a clear data set, given that the experimental setup conditions and analysis are solid (a goal to which the majority of comments refer).

      Beyond the complex localization/recruitment analysis, two novel findings of this study that emerge are:

      a)GTSE1 contains a second, separate protein region, distinct from the clathrin-binding motifs that is required for its localization to the spindle, and most likely a microtubule-interaction site. This suggests that GTSE1 recruitment to the spindle is more complex than previously reported.

      b)PI3KC2A, which has been reported previously to be a stabilizing member of this complex, is in fact not a member, nor localizes to spindles, nor displays a mitotic defect after loss. This is important conclusion to be made as it would correct the literature, and avoid future confusion.

    1. The general justification for appropriating the tag has been that, in addition to killing Black people, White supremacy also continues to kill and harm a lot of non-Black people of color as well.

      This year we have really highlighted the tragic deaths of many black people. We call people out for their racist behaviors and views. We are getting closer to uncovering the corruption some people have and cancelling them for having them. In this case I feel cancel culture would be legitimate because of how these people are looking down upon groups of people.

    1. Congress appears to have missed a key point in its questioning last week. It’s clear that fake news and outright lies are, in fact, a small portion of the total content on any of the big tech platforms. But what matters are the routes that these companies provide to unreliable sources of information. You don’t have to silence Julian Assange or some random Twitter account that’s set up to look like a real news outfit, but you also don’t have to inject them into a legitimate news discussion.

      For something to pop up on the top "news" section, I think these developers should have to fact check and read through the information. They could also add a tag such as potentially untruthful which would help stop spread the misinformation..

    1. Why Are Finland’s Schools Successful? The country’s achievements in education have other nations, especially the United States, doing their homework <img src="https://thumbs-prod.si-cdn.com/thzZYTv2Evhq3x8iHdcaakihfVE=/800x600/filters:no_upscale()/https://public-media.si-cdn.com/filer/cd/ee/cdee1c82-f8e3-4de4-983e-8599d4485745/finland-smiles-wr.jpg" alt="Kirkkojarvi School" itemprop="image"> "This is what we do every day," says Kirkkojarvi Comprehensive School principal Kari Louhivuori, "prepare kids for life." (Stuart Conway) By LynNell Hancock Smithsonian Magazine | Subscribe September 2011 AddThis Sharing ButtonsShare to FacebookFacebookShare to TwitterTwitterShare to RedditReddit78Share to PinterestPinterest997Share to LinkedInLinkedInShare to FlipboardFlipboardShare to EmailEmailShare to PrintPrintShare to MoreAddThis934 It was the end of term at Kirkkojarvi Comprehensive School in Espoo, a sprawling suburb west of Helsinki, when Kari Louhivuori, a veteran teacher and the school’s principal, decided to try something extreme—by Finnish standards. One of his sixth-grade students, a Kosovo-Albanian boy, had drifted far off the learning grid, resisting his teacher’s best efforts. The school’s team of special educators—including a social worker, a nurse and a psychologist—convinced Louhivuori that laziness was not to blame. 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n=r.generatePrerollTag(e,t);r.monetization.onPrerollAdOpportunity(n)}),f()(this,"onSeekedWhileAdInProgress",function(){r.monetization.onMidrollAdOpportunity()});var i=t.getState;this.monetization=n,this.videoTimeSubscriber=new qi(t,this),this.videoSeekSubscriber=new zi(t,this),this.prerollScheduler=new Gi(t,this);var o=_i.adTagUrlTemplate(i());this.adTagGenerator=new Wi(o)},Yi=function(){function e(){Ai()(this,e)}return Vi()(e,null,[{key:"generateAdRequest",value:function(e,t,n){var r=new google.ima.AdsRequest;return r.adTagUrl=e,Fn()||r.setAdWillPlayMuted(t),r.vastLoadTimeout=n,r}}]),e}(),Zi=function(e){return function(t){t({type:"[MONETIZATION] change ad status",payload:e})}},Xi=function(e){return function(t){t({type:"[COMMON] set pending video status",payload:{pendingStatusObject:{type:e,value:""}}})}},Ji=function(e){return function(t){t({type:"[MONETIZATION] change loading ad status",payload:e})}},Qi=function(e){return function(t){t({type:"[MONETIZATION] update ad 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t=a.store,n=t.getState,r=t.dispatch,i=gn.volume(n());Bn()||gn.muted(n())?(e.setVolume(0),Qi(!0)(r)):(e.setVolume(gn.volume(n())),eo(i)(r),Qi(!1)(r))}),f()(this,"createIMAAdManager",function(t){a.IMAAdManager=t.getAdsManager(a.adVideoElement,e.getAdsRenderingSettings()),a.setAdVolume(a.IMAAdManager)}),f()(this,"registerToAdManagerEvents",function(){a.IMAAdManager.addEventListener(google.ima.AdErrorEvent.Type.AD_ERROR,a.onAdError),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.CONTENT_PAUSE_REQUESTED,a.onContentPauseRequested),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.CONTENT_RESUME_REQUESTED,a.onContentResumeRequested),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.STARTED,a.onAdStarted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.IMPRESSION,a.onAdImpression),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.SKIPPED,a.onAdSkipped),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.COMPLETE,a.onAdCompleted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.PAUSED,a.onAdPaused),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.RESUMED,a.onAdStarted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.AD_PROGRESS,a.onAdProgressChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.VOLUME_CHANGED,a.onVolumeChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.VOLUME_MUTED,a.onAdVolumeMutedChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.ALL_ADS_COMPLETED,a.onAdCompleted)}),f()(this,"onIMAAdsManagerLoaded",function(e){var t=a.store.dispatch;a.createIMAAdManager(e),a.registerToAdManagerEvents(),Zi("loaded")(t)}),f()(this,"onAdError",function(e){var t=a.store.dispatch;!function(e){return function(t){t({type:"[MONETIZATION] change ad error",payload:e})}}(e.getError().getMessage())(t),Ji(!1),a.continuePlayingContent()}),f()(this,"onAdImpression",function(e){var t=a.store.dispatch,n=!e.getAd().g.vpaid;a.setPodInfo(e),function(e){e({type:"[MONETIZATION] increase ad impression counter"})}(t),function(e){return function(t){t({type:"[MONETIZATION] update is vast ad",payload:e})}}(n)(t)}),f()(this,"onVolumeChanged",function(e){var t=a.store.dispatch;eo(e.target.getVolume())(t)}),f()(this,"onAdVolumeMutedChanged",function(e){var t=a.store.dispatch;0===e.target.getVolume()?Qi(!0)(t):Qi(!1)(t)}),f()(this,"continuePlayingContent",function(){var e=a.store,t=e.getState,n=e.dispatch,r=hn.videoTagStatus(t());Xi("idle"===r?"play":"resume")(n)}),f()(this,"stopPlayingContent",function(){var 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e=a.store.dispatch;Zi("skipped")(e)}),f()(this,"onResize",function(){Un(a.IMAAdManager)||(a.IMAAdManager.resize(a.videoPlayerElement.clientWidth,a.videoPlayerElement.clientHeight,google.ima.ViewMode.NORMAL),a.adContainerElement.style.height="".concat(a.videoPlayerElement.clientHeight,"px"))}),f()(this,"onAdProgressChanged",function(e){var t,n,r=a.store,i=r.dispatch,o=r.getState,s=e.getAdData().currentTime,u=e.getAdData().duration,c=_i.adDuration(o());(t=s,function(e){e({type:"[MONETIZATION] change ad current time",payload:t})})(i),c!==u&&(n=u,function(e){e({type:"[MONETIZATION] change ad duration",payload:n})})(i)}),f()(this,"onAnchorStatusChanged",function(){var e=a.store.getState;"processing"!==Pr(e())&&a.onResize()}),f()(this,"changeAdVolume",function(e){Un(a.IMAAdManager)||a.IMAAdManager.setVolume(e)}),f()(this,"changeAdMuted",function(e,t){Un(a.IMAAdManager)||(t?a.IMAAdManager.setVolume(0):a.IMAAdManager.setVolume(e))}),f()(this,"changeAdStatus",function(e){Un(a.IMAAdManager)||("playing"===e&&a.IMAAdManager.resume(),"paused"===e&&a.IMAAdManager.pause())});var s=t.getState;this.store=t,this.adVideoElement=r,this.videoPlayerElement=i,this.adContainerElement=n,this.adDisplayContainer=new google.ima.AdDisplayContainer(n,r),this.createAdLoader(s(),this.adDisplayContainer),this.adDisplayContainer.initialize(),this.anchorStatusStoreSubscriber=new ji(t,e.getAnchorDependencies,this.onAnchorStatusChanged.bind(this)),this.registerForWindowResize(),this.initMutationObserver(o)};f()(to,"getAdsRenderingSettings",function(){var e=new google.ima.AdsRenderingSettings;return 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e=s.store,t=e.dispatch,n=e.getState,r=_i.adStatus(n()),i=bi.continuePlayingWhileWaitingForAd(n());"loaded"===r?s.playAd(!0):"requested"===r&&(s.pendingMidrollAdPlay=!0,i||(Xi("pause")(t),Ji(!0)(t))),function(e){e({type:"[MONETIZATION] increase ad Opportunity counter"})}(t)}),f()(this,"onPrerollAdOpportunity",function(e){var t=s.store,n=t.getState,r=t.dispatch,i=Fi.loadingImaStatus(n());Un(s.adHandler)?"loading"!==i&&""!==i||(Ji(!0)(r),s.pendingPrerollAdPlay=!0,s.pendingPrerollAdTag=e):(s.pendingPrerollAdPlay=!0,Ji(!0)(r),s.adHandler.loadNewAd(e,"preroll"))}),f()(this,"onPreMidrollAdOpportunity",function(e,t){Un(s.adHandler)||(e.currentTime>=e.midrollTime&&(s.pendingMidrollAdPlay=!0),s.pendingMidrollNumber=e.midrollNumber,s.adHandler.loadNewAd(t,"midroll"))}),f()(this,"hasPendingAd",function(){return s.hasPendingMidrollAdPlay()||s.hasPendingPrerollAdPlay()}),f()(this,"onAdStatusChanged",function(e){var t=s.store.dispatch,n=_i.adStatus(e);"completed"===n&&Ji(!1)(t);var r=bi.continuePlayingWhileWaitingForAd(e),i=_i.loadingAd(e);"playing"!==n&&"error"!==n||r||!i||Ji(!1)(t),s.hasPendingAd()&&"loaded"===n?s.playAd(s.hasPendingMidrollAdPlay()):s.hasPendingAd()&&"error"===n?(Ji(!1),s.clearPendingMidroll(),s.clearPendingPreroll()):Hi(n)||(Ji(!1),function(e){e({type:"[MONETIZATION] clear ad data"})}(t))}),f()(this,"clearPendingMidroll",function(){s.pendingMidrollNumber=null,s.pendingMidrollAdPlay=!1}),f()(this,"clearPendingPreroll",function(){s.pendingPrerollAdPlay=!1,s.pendingPrerollAdTag=null}),f()(this,"onVideoTagStatusChanged",function(e){"complete"===hn.videoTagStatus(e)&&function(e){e({type:"[MONETIZATION] clear played midrolls"})}(s.store.dispatch)}),f()(this,"hasPendingMidrollAdPlay",function(){return s.pendingMidrollAdPlay}),f()(this,"hasPendingPrerollAdPlay",function(){return s.pendingPrerollAdPlay}),f()(this,"playAd",function(e){var t,n=s.store.dispatch,r=s.adHandler.playAd();e?((t=s.pendingMidrollNumber,function(e){e({type:"[MONETIZATION] add 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Ki(t,this),this.adStatusSubscriber=new ji(t,e.getAdStatusDependencies,this.onAdStatusChanged.bind(this)),this.videoTagStatusSubscriber=new ji(t,e.getVideoTagStatusDependencies,this.onVideoTagStatusChanged.bind(this)),e.canUseIMA(u())?this.adHandler=new to(t,r,i,o,a):this.imaLoadingStatusSubscriber=new ji(t,e.getIMALoadingStatusDependencies,this.onIMALoadingStatusChanged.bind(this)),this.pendingAdStatusStoreSubscriber=new ji(t,e.getPendingAdStatusDependencies,this.onPendingAdStatusChanged.bind(this)),this.adMutedStoreSubscriber=new ji(t,e.getAdMutedDependencies,this.onAdMutedChanged.bind(this)),this.adVolumeStoreSubscriber=new ji(t,e.getAdVolumeDependencies,this.onAdVolumeChanged.bind(this))};f()(no,"getAdStatusDependencies",function(e){return[_i.adStatus(e)]}),f()(no,"getVideoTagStatusDependencies",function(e){return[hn.videoTagStatus(e)]}),f()(no,"getIMALoadingStatusDependencies",function(e){return[Fi.loadingImaStatus(e)]}),f()(no,"canUseIMA",function(e){return"success"===Fi.loadingImaStatus(e)}),f()(no,"getPendingAdStatusDependencies",function(e){return[_i.pendingAdStatus(e)]}),f()(no,"getAdMutedDependencies",function(e){return[_i.adMuted(e)]}),f()(no,"getAdVolumeDependencies",function(e){return[_i.adVolume(e)]});var ro=function(e,t){!function(e,t){var n=document.getElementById(vn(t));B(b(Li,{store:e,playerId:t}),n)}(e,t);var n=function(e){var t=Ri(e);return document.getElementById(t)}(t),r=function(e){var t=Bn()?Di(e):En(e);return document.getElementById(t)}(t),i=function(e){var t=En(e);return document.getElementById(t)}(t),o=function(e){var t=bn(e);return document.getElementById(t)}(t);return new 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this.errorMessage=e,this}},{key:"setAdPodNumber",value:function(e){return this.adPodNumber=e,this}},{key:"setAdSlotNumber",value:function(e){return this.adSlotNumber=e,this}},{key:"build",value:function(){var e=[];return jn(this.position)||e.push("video current position=".concat(Hn(this.position),"sec")),jn(this.duration)||e.push("video duration time=".concat(Hn(this.duration),"sec")),jn(this.loadTime)||e.push("video load time=".concat(this.loadTime,"milliseconds")),jn(this.previousPosition)||e.push("previous position=".concat(Hn(this.previousPosition),"sec")),jn(this.adOrder)||e.push("ad order=".concat(this.adOrder)),jn(this.adType)||e.push("ad type=".concat(this.adType)),jn(this.adDuration)||e.push("ad duration=".concat(Hn(Number(this.adDuration)),"sec")),jn(this.adPodNumber)||e.push("pod number=".concat(this.adPodNumber)),jn(this.adSlotNumber)||e.push("slot number=".concat(this.adSlotNumber)),jn(this.errorMessage)||e.push("error 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The initial state may not be undefined, but can be null.')})}(n)}catch(s){o=s}return function(e,t){if(void 0===e&&(e={}),o)throw o;for(var r=!1,i={},s=0;s<a.length;s++){var u=a[s],c=n[u],l=e[u],d=c(l,t);if("undefined"===typeof d){var p=mt(u,t);throw new Error(p)}i[u]=d,r=r||d!==l}return(r=r||a.length!==Object.keys(e).length)?i:e}}({dependenciesLoadingStatus:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:da,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] update hls status":return la(la({},e),{},{loadingHLSStatus:t.payload});case"[CORE] update ima status":return la(la({},e),{},{loadingImaStatus:t.payload});default:return e}},playerData:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:ha,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":var n=t.payload;return fa({},function(e,t,n){var r=t.playback_method,i=t.player_id;return fa(fa({},e),{},{playbackMethod:Un(r)?e.playbackMethod:r,playerId:Un(i)?e.playerId:i,playerInstanceUniqId:n,playerMode:Fn()?"mobile":"desktop"})}(e,n.initiateParams,n.playerInstanceUniqId));case"[CORE] reset player data time params":return fa(fa({},e),{},{currentVideoTimeFragment:0,currentVideoBufferedTime:0,currentVideoDuration:0,currentVideoTime:0});case"[COMMON] set mute video":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{muted:t.payload})});case"[COMMON] set volume":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{volume:t.payload})});case"[COMMON] change selected settings category":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{selectedSettingsCategory:t.payload})});case"[COMMON] change settings speed":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{speed:t.payload})});case"[COMMON] change settings quality":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{quality:t.payload})});case"[COMMON] set fullscreen":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{fullscreen:fa(fa({},e.playerSettings.fullscreen),{},{isFullscreenOn:t.payload,pendingFullscreenRequest:""})})});case"[COMMON] set fullscreen request":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{fullscreen:fa(fa({},e.playerSettings.fullscreen),{},{pendingFullscreenRequest:t.payload})})});case"[COMMON] set pending video status":var r=t.payload.pendingStatusObject;return fa(fa({},e),{},{pendingVideoTagStatus:fa({},r)});case"[COMMON] set player mode":return fa(fa({},e),{},{playerMode:t.payload});case"[CORE] update video current fragment position":return fa(fa({},e),{},{currentVideoTimeFragment:t.payload});case"[CORE] update video current position":return fa(fa({},e),{},{currentVideoTime:t.payload});case"[CORE] update video current buffered time":return fa(fa({},e),{},{currentVideoBufferedTime:t.payload});case"[CORE] update video current duration":return fa(fa({},e),{},{currentVideoDuration:t.payload});case"[CORE] change video tag status":return fa(fa({},e),{},{videoTagStatus:t.payload});case"[CORE] update player visibility":return fa(fa({},e),{},{playerVisibility:t.payload});case"[CORE] update placeholder visibility":return fa(fa({},e),{},{playerPlaceholderVisibility:t.payload});case"[CORE] change loading player status":return fa(fa({},e),{},{loadingPlayer:t.payload});case"[COMMON] show black screen with loader":return fa(fa({},e),{},{loader:fa(fa({},e.loader),{},{showBlackScreen:t.payload})});case"[CORE] set player size":return fa(fa({},e),{},{playerSize:t.payload});case"[COMMON] set error message":return fa(fa({},e),{},{errorMessage:t.payload});default:return e}},brandingData:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:va,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return ga({},function(e,t){var n=t.powered_by_strip,r=t.brand_logo,i=t.brand_logo_click_url,o=t.brand_color;return ga(ga({},e),{},{showVoltaxLogo:Un(n)?e.showVoltaxLogo:n,brandingLogoSrc:Un(r)?e.brandingLogoSrc:r,brandingLogoUrl:Un(i)?e.brandingLogoUrl:i,brandingColor:Un(o)?e.brandingColor:o})}(e,t.payload.initiateParams));default:return e}},anchorOptions:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Oa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return ba({},function(e,t){var n=t.anchor_options;if(!Un(n)){var r=n.anchoring_appearance,i=n.can_close,o=n.closable_ad,a=n.close_after,s=n.continue_streaming,u=n.orientation,c=n.margins,l=n.sticky_below_class_name,d=n.width,p=Un(c)?e.margins:{top:Number.isInteger(c.top)?c.top:e.margins.top,bottom:Number.isInteger(c.bottom)?c.bottom:e.margins.bottom,left:Number.isInteger(c.left)?c.left:e.margins.left,right:Number.isInteger(c.right)?c.right:e.margins.right};return ba(ba({},e),{},{anchoringAppearance:r||e.anchoringAppearance,canClose:Un(i)?e.canClose:i,orientation:Un(u)?e.orientation:u,closableAd:Un(o)?e.closableAd:o,closeAfter:Un(a)?e.closeAfter:a,continueStreaming:Un(s)?e.continueStreaming:s,stickyBelowClassName:Un(l)?e.stickyBelowClassName:l,width:Un(d)?e.width:d,margins:p,anchorData:ba(ba({},e.anchorData),{},{anchorEnabled:!0})})}return e}(e,t.payload.initiateParams));case"[COMMON] set anchor enable":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorEnabled:t.payload})});case"[ANCHOR] update is anchor status":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorStatus:t.payload})});case"[COMMON] set anchor disabled by user":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorDisabledByUser:t.payload})});default:return e}},monetization:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:wa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Sa({},function(e,t){var n=t.monetization;if(Un(n))return e;var r=n.ad_tag,i=n.ad_type,o=n.vpaid_mode,a=n.ad_request_timeout,s=n.continue_content_play_while_waiting_for_ad,u=n.midrolls,c=u&&u.on&&u.on.sort(Wn),l=Un(s)?e.continuePlayingWhileWaitingForAd:s,d=c?c.indexOf(0):-1,p=-1!==d&&!l;return p&&(c=c.splice(d,1)),Sa(Sa({},e),{},{midrolls:Sa(Sa({},e.midrolls),{},{every:u&&u.every,on:c}),prerollEnabled:p,adRequestTimeout:Un(a)?e.adRequestTimeout:parseInt(a,10),vpaidMode:Un(o)?e.vpaidMode:o,continuePlayingWhileWaitingForAd:l,adsData:Sa(Sa({},e.adsData),{},{adType:Un(i)?e.adsData.adType:i,adTagUrlTemplate:Un(r)?e.adsData.adTagUrlTemplate:r})})}(e,t.payload.initiateParams));case"[COMMON] set new ad tag url template":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adTagUrlTemplate:t.payload})});case"[MONETIZATION] change ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adStatus:t.payload,adErrorMessage:null})});case"[MONETIZATION] change ad tag":var n=t.payload,r=n.adUnit,i=n.adTag;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{currentAdTag:i,adUnit:r})});case"[MONETIZATION] change pending ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{pendingAdStatus:t.payload})});case"[MONETIZATION] change ad error":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adStatus:"error",adErrorMessage:t.payload})});case"[MONETIZATION] increase ad impression counter":var o=e.adsData.adImpression;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adImpression:o+1})});case"[MONETIZATION] increase ad Opportunity counter":var a=e.adsData.adOpportunity;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOpportunity:a+1})});case"[MONETIZATION] add played midroll number":var s=e.adsData.playedMidrolls,u=In()(s);return u.push(t.payload),Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOrder:t.payload,playedMidrolls:u})});case"[MONETIZATION] clear played midrolls":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{playedMidrolls:[]})});case"[MONETIZATION] clear ad data":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOrder:0,currentAdTag:null,adDuration:0,adUnit:""})});case"[MONETIZATION] change ad duration":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adDuration:t.payload})});case"[MONETIZATION] update is vast ad":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{isVastAd:t.payload})});case"[MONETIZATION] change ad current time":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adCurrentTime:t.payload})});case"[MONETIZATION] update ad muted":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adMuted:t.payload})});case"[MONETIZATION] change ad volume":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adVolume:t.payload})});case"[MONETIZATION] change pod info":var c=t.payload,l=c.podNumber,d=c.slotNumber;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{podNumber:l,slotNumber:d})});case"[MONETIZATION] change loading ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{loadingAd:t.payload})});default:return e}},mediaData:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:ja,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Va({},function(e,t){var n=t.content_type,r=t.media_id,i=t.display_title;return Va(Va({},e),{},{mediaType:Un(n)?e.mediaType:n,mediaId:Un(r)?e.mediaId:r,videoData:Va(Va({},e.videoData),{},{showTitle:!!Un(i)||i})})}(e,t.payload.initiateParams));case"[CORE] load video request":return Va(Va({},e),{},{loadingMedia:!0});case"[CORE] load video request success":return Va(Va({},e),{},{loadingMedia:!1,videoList:t.payload});case"[CORE] set current video":var n=t.payload,r=n.index,i=n.videoData;return Va(Va({},e),{},{activeVideoIndex:r,videoData:i});case"[CORE] load video request error":return Va(Va({},e),{},{loadingMedia:!1,mediaLoadingError:t.payload});case"[COMMON] media request":var o=t.payload.mediaRequestObject;return Va(Va({},e),{},{mediaRequest:Va({},o)});default:return e}},semanticOptions:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Ba,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Fa({},function(e,t){var n=t.semantic_options;if(Un(n))return e;var r=n.minimum_date_factor,i=n.promoted_videos,o=n.scan_images_on_page,a=n.scanned_element,s=n.scanned_element_type,u=n.scoped_keywords,c=n.tags;return Fa(Fa({},e),{},{minimumDateFactor:Un(r)?e.minimumDateFactor:r,promotedVideos:Un(i)?e.promotedVideos:i,scanImagesOnPage:Un(o)?e.scanImagesOnPage:o,scannedElement:Un(a)?e.scannedElement:a,scannedElementType:Un(s)?e.scannedElementType:s,scopedKeywords:Un(u)?e.scopedKeywords:u,tags:Un(c)?e.tags:c})}(e,t.payload.initiateParams));default:return e}},userInteraction:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Wa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[USER INTERACTION] change user interaction":return qa(qa({},e),{},{userInteractionType:t.payload});default:return e}},splitView:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:$a,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Ga({},function(e,t){var n=t.anchor_options;if(!Un(n)){var r=n.split_view,i=n.split_view_ratio;return Ga(Ga({},e),{},{splitViewRatio:Un(r)||!r||Un(i)?e.splitViewRatio:i})}return e}(e,t.payload.initiateParams));default:return e}},discovery:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Za,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Ya({},function(e,t){var n=t.next_video;return Un(n)?e:Ya(Ya({},e),{},{nextVideo:Xa(n)})}(e,t.payload.initiateParams));case"[DISCOVERY] show up next":return Ya(Ya({},e),{},{showUpNext:t.payload});case"[DISCOVERY] show skippable content":return Ya(Ya({},e),{},{showSkippableContent:t.payload});default:return e}}}),Qa=[],es=!1,ts=function e(){return function(t){return function(n){if(es)return Qa.push(n),null;es=!0;var r=t(n);return es=!1,Qa.length>0&&e()(t)(Qa.shift()),r}}},ns=function(e){var t=[];if(function(e){return!Un(e)&&!Un(e.enable_redux_debugging)&&e.enable_redux_debugging}(e)){var n=window&&window.__REDUX_DEVTOOLS_EXTENSION__&&window.__REDUX_DEVTOOLS_EXTENSION__();"function"===typeof n&&t.push(n)}var r=Et.apply(void 0,[wt(ua,ts)].concat(t));return vt(Ja,r)},rs=function(){function e(t){Ai()(this,e),f()(this,"playerVisibilitySubscriber",void 0),f()(this,"videoTagStatusSubscriber",void 0),f()(this,"shouldPlayIfLazyplay",!0),f()(this,"shouldPlayIfAutoplayWhenViewable",!0),f()(this,"videoPausedByObserver",!1),this.store=t,this.playerVisibilitySubscriber=null,this.videoTagStatusSubscriber=null,this.playAccordingToPlaybackMethod()}return Vi()(e,[{key:"lazyplayHandler",value:function(e){hn.playerVisibility(e)>=.5&&(this.playVideo(),this.shouldPlayIfLazyplay=!1)}},{key:"autoplayWhenViewableHandler",value:function(e){hn.playerVisibility(e)>=.5?this.playVideo():this.pauseVideo()}},{key:"onPlayerVisibilityChanged",value:function(e){var t=hn.playbackMethod(e);"lazyplay"===t&&this.shouldPlayIfLazyplay&&this.lazyplayHandler(e),"autoplay_when_viewable"===t&&this.shouldPlayIfAutoplayWhenViewable&&this.autoplayWhenViewableHandler(e)}},{key:"onVideoTagStatusChanged",value:function(e){var t=hn.videoTagStatus(e);"paused"!==t||this.videoPausedByObserver||(this.shouldPlayIfAutoplayWhenViewable=!1),"playing"===t&&(this.shouldPlayIfAutoplayWhenViewable=!0,this.videoPausedByObserver=!1)}},{key:"initiatePlayerVisibilitySubscriber",value:function(){this.playerVisibilitySubscriber=new ji(this.store,e.getPlayerVisibilityDependencies,this.onPlayerVisibilityChanged.bind(this))}},{key:"initiateVideoTagStatusSubscriber",value:function(){this.videoTagStatusSubscriber=new ji(this.store,e.getVideoTagStatusDependencies,this.onVideoTagStatusChanged.bind(this))}},{key:"playVideo",value:function(){var e=this.store,t=e.dispatch,n=e.getState;"idle"===hn.videoTagStatus(n())?on("play")(t):on("resume")(t)}},{key:"pauseVideo",value:function(){var e=this.store,t=e.dispatch,n=e.getState;"paused"!==hn.videoTagStatus(n())&&(this.videoPausedByObserver=!0,on("pause")(t))}},{key:"playAccordingToPlaybackMethod",value:function(){var e=this.store,t=e.dispatch,n=(0,e.getState)();switch(hn.playbackMethod(n)){case"autoplay":this.playVideo();break;case"lazyplay":this.initiatePlayerVisibilitySubscriber();break;case"autoplay_when_viewable":this.initiatePlayerVisibilitySubscriber(),this.initiateVideoTagStatusSubscriber();break;case"none":an(!1)(t)}}}],[{key:"getPlayerVisibilityDependencies",value:function(e){return[hn.playerVisibility(e)]}},{key:"getVideoTagStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}}]),e}(),is=function(){function e(t,n,r,i){var o=this;Ai()(this,e),f()(this,"videoStatusSubscriber",void 0),f()(this,"videoListSubscriber",void 0),f()(this,"mediaRequestSubscriber",void 0),f()(this,"playerVisibilitySubscriber",void 0),f()(this,"playbackMethodManager",void 0),f()(this,"store",void 0),f()(this,"loadContent",function(e,t,n,r){o.loadMedia(t,n,r).then(function(){o.playbackMethodManager=new rs(e)})}),f()(this,"loadMedia",function(e,t,n){var r=o.store,i=r.dispatch,a=r.getState,s=Dn.showTitle(a());if("semantic"===e){var u=pn.semanticOptions(a());return Na(u,s,n)(i)}return ka(t,s,n)(i)}),this.store=t,this.videoStatusSubscriber=new ji(t,e.getVideoStatusDependencies,this.onVideoStatusChanged.bind(this)),this.videoListSubscriber=new ji(t,e.getVideoListDependencies,this.onVideoListChanged.bind(this)),this.mediaRequestSubscriber=new ji(t,e.getMediaRequestDependencies,this.onMediaRequestChanged.bind(this)),this.playerVisibilitySubscriber=null,this.loadContent(t,r,n,i)}return Vi()(e,null,[{key:"createInstance",value:function(t,n,r,i){return new e(t,n,r,i)}}]),Vi()(e,[{key:"playNextVideo",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e),r=Cn.activeVideoIndex(e)+1;n.length>1&&r>=n.length&&(r=0),r<n.length&&(!function(e){e({type:"[CORE] reset player data time params"})}(t),La(r,n[r])(t),on("play")(t))}},{key:"playPreviousVideo",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e),r=Cn.activeVideoIndex(e);if(r>0){var i=r-1;La(i,n[i])(t),on("play")(t)}}},{key:"onVideoStatusChanged",value:function(e){"complete"===hn.videoTagStatus(e)&&this.playNextVideo(e)}},{key:"onVideoListChanged",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e);!jn(n)&&n.length>0&&La(0,n[0])(t)}},{key:"onMediaRequestChanged",value:function(e){var t=Cn.mediaRequest(e);switch(t.type){case"playNewVideo":this.loadMedia("specific",t.value);break;case"playNextVideo":this.playNextVideo(e);break;case"playPreviousVideo":this.playPreviousVideo(e)}}}],[{key:"getVideoStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}},{key:"getVideoListDependencies",value:function(e){return[Cn.videoList(e)]}},{key:"getMediaRequestDependencies",value:function(e){return[Cn.mediaRequest(e)]}}]),e}(),os=function e(t){var n=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"onDependencyFailure",function(e,t){console.log("onDependencyFailure",e,t);var r=n.store,i=r.dispatch,o=r.getState;switch(e){case"ima":"blocked"!==Fi.loadingImaStatus(o())&&Qn("error")(i);break;case"hls":er("error")(i)}}),f()(this,"onDependencyReady",function(e){var t=n.store.dispatch;switch(e){case"ima":Qn("success")(t);break;case"hls":er("success")(t)}}),this.store=t},as=function(e){return function(t){t({type:"[COMMON] set fullscreen",payload:e})}},ss=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"videoTag",void 0),f()(this,"pendingFullscreenSubscriber",void 0),f()(this,"adStatusSubscriber",void 0),f()(this,"playerUniqId",void 0),f()(this,"onAdStatusChanged",function(e){var t=_i.adStatus(e),n=r.videoTag.webkitDisplayingFullscreen;"playing"===t&&Bn()&&n&&r.exitFullscreen(r.videoTag)}),f()(this,"isPlayerInFullscreen",function(){var e=document,t=Bn()?En(r.playerUniqId):bn(r.playerUniqId);return Un(e.fullscreenElement)?!Un(e.webkitFullscreenElement)&&0===e.webkitFullscreenElement.id.localeCompare(t):0===e.fullscreenElement.id.localeCompare(t)}),f()(this,"changePlayerWidth",function(e){r.videoTag.style.width=e?"100%":"auto"}),f()(this,"onFullscreenChanged",function(){var e=r.store.dispatch,t=r.isPlayerInFullscreen();r.changePlayerWidth(t),as(t)(e)}),f()(this,"onFullscreenChangedIos",function(){var e=r.store.dispatch,t=r.videoTag.webkitDisplayingFullscreen;t||on("resume")(e),r.changePlayerWidth(t),as(t)(e)}),f()(this,"onPendingFullscreenRequestChanged",function(e){var t=gn.pendingFullscreenRequest(e);"enter"===t?r.enterFullscreen(r.videoTag):"exit"===t&&r.exitFullscreen(r.videoTag)}),f()(this,"getFullScreenElement",function(e,t){var n=document.getElementById(bn(r.playerUniqId));return Bn()?t:e?document:n}),f()(this,"enterFullscreen",function(e){var t=r.getFullScreenElement(!1,e);Bn()?t.webkitEnterFullscreen():document.webkitExitFullscreen?t.webkitRequestFullscreen():document.webkitCancelFullScreen?t.webkitRequestFullScreen():document.mozCancelFullScreen?t.mozRequestFullScreen():document.msExitFullscreen&&t.msRequestFullscreen()}),f()(this,"exitFullscreen",function(e){var t=r.getFullScreenElement(!0,e);document.webkitExitFullscreen||Bn()?t.webkitExitFullscreen():document.webkitCancelFullScreen?t.webkitCancelFullScreen():document.mozCancelFullScreen?t.mozCancelFullScreen():document.msExitFullscreen&&t.msExitFullscreen()}),this.store=t,this.videoTag=document.getElementById(En(n)),this.playerUniqId=n,document.addEventListener("fullscreenchange",this.onFullscreenChanged.bind(this)),document.addEventListener("webkitfullscreenchange",this.onFullscreenChanged.bind(this)),Bn()&&(this.videoTag.addEventListener("webkitendfullscreen",this.onFullscreenChangedIos.bind(this)),this.videoTag.addEventListener("webkitbeginfullscreen",this.onFullscreenChangedIos.bind(this))),this.pendingFullscreenSubscriber=new ji(t,e.getPendingFullscreenDependencies,this.onPendingFullscreenRequestChanged.bind(this)),this.adStatusSubscriber=new ji(t,e.getAdStatusDependencies,this.onAdStatusChanged.bind(this))}return Vi()(e,null,[{key:"createInstance",value:function(t,n){return new e(t,n)}}]),Vi()(e,null,[{key:"getPendingFullscreenDependencies",value:function(e){return[gn.pendingFullscreenRequest(e)]}},{key:"getAdStatusDependencies",value:function(e){return[_i.adStatus(e)]}}]),e}();function us(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable})),n.push.apply(n,r)}return n}function cs(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?us(Object(n),!0).forEach(function(t){f()(e,t,n[t])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):us(Object(n)).forEach(function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))})}return e}var ls,ds=function(e){return function(e){return e&&window.monti.playerConfigs&&window.monti.playerConfigs[e]}(e)?function(e){return 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t=hn.pendingVideoTagStatus(e),n=Dn.sources(e),i=Fi.loadingHLSStatus(e),o="blocked"===Fi.loadingImaStatus(e);r.handlePendingVideoStatus(t,n,i,o)}),f()(this,"onVideoDataChanged",function(){r.newVideoDataLoaded=!0}),f()(this,"sendPrerollPlayRequest",function(){var e=r.store.dispatch;hs("playPreroll")(e)}),f()(this,"handlePlayRequest",function(e,t,n){var i=r.store.dispatch;if(e&&e.length>0){if(r.newVideoDataLoaded&&(r.loadVideoSource(r.videoTag,e,t),r.newVideoDataLoaded=!1,r.prerollEnabled&&!n))return void r.sendPrerollPlayRequest();r.videoTag.play().catch(function(e){return console.error("Error playing the video: ",e)})}else dn(Xn.VIDEO_ERROR)(i)}),f()(this,"handlePendingVideoStatus",function(e,t,n,i){switch(e.type){case"play":r.handlePlayRequest(t,n,i);break;case"resume":r.videoTag.play().catch(function(e){return console.error("Error resuming the video: ",e)});break;case"pause":r.videoTag.pause();break;case"replay":r.videoTag.currentTime=0,r.videoTag.play().catch(function(e){return console.error("Error replaying the video: ",e)});break;case"seekTo":r.videoTag.pause(),r.videoTag.currentTime=e.value}}),f()(this,"loadMp4Source",function(e,t,n){var r=Ra(t,ys);n.setAttribute("src",r),n.load()}),f()(this,"loadVideoSource",function(e,t,n){var i=r.store.dispatch,o=Ra(t,gs);switch(fs.suitableVideoSource(e,o,n)){case"mp4":r.loadMp4Source(n,t,e);break;case"m3u8 with hls":r.videoStreamingManager.hlsLibrarySetup(e,o,function(e){return un(e)(i)},function(e){return dn(e)(i)});break;case"m3u8 directly":fs.loadHlsVideoDirectly(e,o)}}),this.store=t;var i=t.getState;this.videoStreamingManager=new fs,this.videoTag=document.getElementById(En(n)),this.prerollEnabled=bi.prerollEnabled(i()),this.pendingVideoStatusSubscriber=new ji(t,e.getPendingVideoStatusDependencies,this.onPendingVideoStatusChanged.bind(this)),this.videoDataSubscriber=new ji(t,e.getVideoDataDependencies,this.onVideoDataChanged.bind(this)),this.hlsLoadingStatusSubscriber=new ji(t,e.getHLSLoadingStatusDependencies,this.onHlsLoadingStatusChanged.bind(this))}return Vi()(e,null,[{key:"createInstance",value:function(t,n){return new e(t,n)}}]),Vi()(e,null,[{key:"getHLSLoadingStatusDependencies",value:function(e){return[Fi.loadingHLSStatus(e)]}},{key:"getPendingVideoStatusDependencies",value:function(e){return[hn.pendingVideoTagStatus(e)]}},{key:"getVideoDataDependencies",value:function(e){return[Cn.videoData(e)]}}]),e}();function ms(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable})),n.push.apply(n,r)}return n}function bs(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?ms(Object(n),!0).forEach(function(t){f()(e,t,n[t])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):ms(Object(n)).forEach(function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))})}return e}var Os={READY_EVENT:"ready",PLAY_EVENT:"play",PAUSE_EVENT:"pause",TIME_EVENT:"time",SEEK_EVENT:"seek",COMPLETE_EVENT:"complete",VOLUME_EVENT:"volume",MUTE_EVENT:"mute"},_s=Object.values(Os),Ss={FULLSCREEN_EVENT:"fullscreen",ANCHOR_STATUS_EVENT:"anchorStatusChanged",ANCHOR_CLOSED_EVENT:"anchorClosed"},Es={AD_PLAY_EVENT:"adPlay",AD_PAUSE_EVENT:"adPause",AD_RESUME_EVENT:"adResume",AD_COMPLETE_EVENT:"adComplete",AD_TIME_EVENT:"adTime",AD_MUTE_EVENT:"adMute",AD_SKIPPED_EVENT:"adSkipped",AD_ERROR_EVENT:"adError",AD_BLOCK_EVENT:"adBlock",AD_REQUEST_EVENT:"adRequest",AD_OPPORTUNITY_EVENT:"adOpportunity",AD_IMPRESSION_EVENT:"adImpression"},ws=Object.values(Es),Ps=Object.values(bs(bs(bs({},Os),Es),Ss)),Ts=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"eventsCallbacksHandler",void 0),f()(this,"store",void 0),f()(this,"videoStatusSubscriber",void 0),f()(this,"videoMuteSubscriber",void 0),f()(this,"videoVolumeSubscriber",void 0),f()(this,"videoTimeFragmentSubscriber",void 0),f()(this,"videoListStoreSubscriber",void 0),f()(this,"previousVideoTagStatus",void 0),f()(this,"startSeekTime",0),f()(this,"canHandleReady",function(e,t,n){if(t===Os.READY_EVENT){var r=Cn.videoList(e);if(Array.isArray(r)&&r.length>0)return n(),!0}return!1}),f()(this,"canBeHandled",function(e,t){var n=r.store.getState;return r.canHandleReady(n(),e,t)}),f()(this,"reportSeekEnd",function(e){var t={position:hn.currentVideoTimeFragment(e),offset:r.startSeekTime};r.eventsCallbacksHandler.onEvent(Os.SEEK_EVENT,t)}),f()(this,"onMuteStateChanged",function(e){var t=gn.muted(e);r.eventsCallbacksHandler.onEvent(Os.MUTE_EVENT,{state:t})}),f()(this,"onVolumeChanged",function(e){var t=gn.muted(e),n=gn.volume(e);r.eventsCallbacksHandler.onEvent(Os.VOLUME_EVENT,{level:t?0:n})}),f()(this,"onVideoTimeFragmentChanged",function(e){var t=hn.currentVideoTimeFragment(e),n=hn.currentVideoDuration(e);r.eventsCallbacksHandler.onEvent(Os.TIME_EVENT,{duration:n,position:t})}),f()(this,"onVideoListChanged",function(){r.eventsCallbacksHandler.onEvent(Os.READY_EVENT)}),this.store=t,this.eventsCallbacksHandler=n,this.videoStatusSubscriber=new ji(t,e.getVideoStatusDependencies,this.onVideoStatusChanged.bind(this)),this.videoMuteSubscriber=new ji(t,e.getVideoMuteDependencies,this.onMuteStateChanged.bind(this)),this.videoVolumeSubscriber=new ji(t,e.getVolumeDependencies,this.onVolumeChanged.bind(this)),this.videoTimeFragmentSubscriber=new ji(t,e.getVideoTimeDependencies,this.onVideoTimeFragmentChanged.bind(this)),this.videoListStoreSubscriber=new ji(t,e.getVideoListDependencies,this.onVideoListChanged.bind(this)),this.previousVideoTagStatus=hn.videoTagStatus(t.getState())}return Vi()(e,[{key:"onVideoStatusChanged",value:function(e){var t=hn.videoTagStatus(e);switch("seeking"===this.previousVideoTagStatus&&this.reportSeekEnd(e),t){case"paused":this.eventsCallbacksHandler.onEvent(Os.PAUSE_EVENT);break;case"seeking":this.startSeekTime=hn.currentVideoTimeFragment(e);break;case"complete":this.eventsCallbacksHandler.onEvent(Os.COMPLETE_EVENT);break;case"playing":this.eventsCallbacksHandler.onEvent(Os.PLAY_EVENT)}this.previousVideoTagStatus=t}}],[{key:"getVideoStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}}]),e}();f()(Ts,"getVideoMuteDependencies",function(e){return[gn.muted(e)]}),f()(Ts,"getVolumeDependencies",function(e){return[gn.volume(e)]}),f()(Ts,"getVideoTimeDependencies",function(e){return[hn.currentVideoTimeFragment(e)]}),f()(Ts,"getVideoListDependencies",function(e){return[Cn.videoList(e)]}),f()(Ts,"isContentEvent",function(e){return _s.some(function(t){return t===e})});var As=function e(t,n){var r=this;Ai()(this,e),f()(this,"eventsCallbacksHandler",void 0),f()(this,"store",void 0),f()(this,"fullscreenSubscriber",void 0),f()(this,"anchorStatusSubscriber",void 0),f()(this,"anchorDisabledByUserSubscriber",void 0),f()(this,"onFullscreenChanged",function(e){var t=gn.isFullscreenOn(e);r.eventsCallbacksHandler.onEvent(Ss.FULLSCREEN_EVENT,{state:t})}),f()(this,"onAnchorStatusChanged",function(e){var t="active"===Pr(e)?"activated":"deactivated";r.eventsCallbacksHandler.onEvent(Ss.ANCHOR_STATUS_EVENT,{state:t})}),f()(this,"onAnchorDisabledByUser",function(e){if(wr(e)){var t=hn.currentVideoTimeFragment(e);r.eventsCallbacksHandler.onEvent(Ss.ANCHOR_CLOSED_EVENT,{position:t})}}),this.store=t,this.eventsCallbacksHandler=n,this.fullscreenSubscriber=new ji(t,e.getFullscreenDependencies,this.onFullscreenChanged.bind(this)),this.anchorStatusSubscriber=new ji(t,e.getAnchorStatusDependencies,this.onAnchorStatusChanged.bind(this)),this.anchorDisabledByUserSubscriber=new 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t=_i.adStatus(e),n=_i.currentAdTag(e);switch(t){case"requested":r.eventsCallbacksHandler.onEvent(Es.AD_REQUEST_EVENT,{tag:n});break;case"paused":r.eventsCallbacksHandler.onEvent(Es.AD_PAUSE_EVENT,{tag:n});break;case"completed":r.eventsCallbacksHandler.onEvent(Es.AD_COMPLETE_EVENT,{tag:n});break;case"skipped":r.eventsCallbacksHandler.onEvent(Es.AD_SKIPPED_EVENT,{tag:n});break;case"playing":"paused"===r.previousAdStatus?r.eventsCallbacksHandler.onEvent(Es.AD_RESUME_EVENT,{tag:n}):r.eventsCallbacksHandler.onEvent(Es.AD_PLAY_EVENT,{tag:n});break;case"error":var i=_i.adErrorMessage(e);r.eventsCallbacksHandler.onEvent(Es.AD_ERROR_EVENT,{tag:n,message:i})}r.previousAdStatus=t}),f()(this,"onAtTimeChanged",function(e){var t=_i.adCurrentTime(e),n=_i.currentAdTag(e),i=_i.adDuration(e);r.eventsCallbacksHandler.onEvent(Es.AD_TIME_EVENT,{position:t,tag:n,duration:i})}),f()(this,"onAdMuteChanged",function(e){var t=_i.adMuted(e);r.eventsCallbacksHandler.onEvent(Es.AD_MUTE_EVENT,{state:t})}),f()(this,"onAdProviderLoadingChanged",function(e){"blocked"===Fi.loadingImaStatus(e)&&r.eventsCallbacksHandler.onEvent(Es.AD_BLOCK_EVENT)}),f()(this,"onAdImpressionChanged",function(e){var t=_i.currentAdTag(e);r.eventsCallbacksHandler.onEvent(Es.AD_IMPRESSION_EVENT,{tag:t})}),f()(this,"onAdOpportunityChanged",function(e){var t=_i.currentAdTag(e);r.eventsCallbacksHandler.onEvent(Es.AD_OPPORTUNITY_EVENT,{tag:t})}),this.store=t,this.eventsCallbacksHandler=n,this.previousAdStatus=_i.adStatus(t.getState()),this.adStatusSubscriber=new ji(t,e.getAdStatusDependencies,this.onAdStatusChanged.bind(this)),this.adTimeSubscriber=new ji(t,e.getAdTimeDependencies,this.onAtTimeChanged.bind(this)),this.adMuteSubscriber=new ji(t,e.getAdMuteDependencies,this.onAdMuteChanged.bind(this)),this.adImpressionSubscriber=new ji(t,e.getAdImpressionDependencies,this.onAdImpressionChanged.bind(this)),this.adOpportunitySubscriber=new 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{"is_conflicting_with_other_jw_players":false,"programmatic_play_with_sound_on_desktop":false,"referrer_id":"af93e181-b289-0560-a2bf-808e93bb05bc","width":"100","comscore_publisher_id":"18120612","monetization":{"ad_type":"static_tag","continue_content_play_while_waiting_for_ad":false,"strategy":"on_player_load","ad_request_timeout":"10000","midrolls":{"on":[0]},"vpaid_mode":"ENABLED","ad_tag":"https://pubads.g.doubleclick.net/gampad/ads?sz=400x300|640x480|480x270|640x360&iu=/175840252/MMPlus/smithsonianmag/Video&impl=s&gdfp_req=1&env=vp&output=vast&unviewed_position_start=1&url=##REFERRER_URL_UNESC##&description_url=##DESCRIPTION_URL_UNESC##&correlator=##CACHEBUSTER##&cust_params=mm_midroll%3D##MIDROLL_ORDER##%26video_ID%3D##VIDEO_ID##"},"sponsorship":false,"player_identifier":"mplayer","recommendation_id":null,"brand_color":"#FF9900","powered_by_strip":true,"platform":"buffy","type":"video","config_name":"MM+ | Smithsonianmag | 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This 13-year-old, Besart Kabashi, received something akin to royal tutoring. “I took Besart on that year as my private student,” Louhivuori told me in his office, which boasted a Beatles “Yellow Submarine” poster on the wall and an electric guitar in the closet. When Besart was not studying science, geography and math, he was parked next to Louhivuori’s desk at the front of his class of 9- and 10-year- olds, cracking open books from a tall stack, slowly reading one, then another, then devouring them by the dozens. By the end of the year, the son of Kosovo war refugees had conquered his adopted country’s vowel-rich language and arrived at the realization that he could, in fact, learn. Years later, a 20-year-old Besart showed up at Kirkkojarvi’s Christmas party with a bottle of Cognac and a big grin. “You helped me,” he told his former teacher. Besart had opened his own car repair firm and a cleaning company. “No big fuss,” Louhivuori told me. “This is what we do every day, prepare kids for life.” This tale of a single rescued child hints at some of the reasons for the tiny Nordic nation’s staggering record of education success, a phenomenon that has inspired, baffled and even irked many of America’s parents and educators. Finnish schooling became an unlikely hot topic after the 2010 documentary film Waiting for “Superman” contrasted it with America’s troubled public schools. “Whatever it takes” is an attitude that drives not just Kirkkojarvi’s 30 teachers, but most of Finland’s 62,000 educators in 3,500 schools from Lapland to Turku—professionals selected from the top 10 percent of the nation’s graduates to earn a required master’s degree in education. Many schools are small enough so that teachers know every student. If one method fails, teachers consult with colleagues to try something else. They seem to relish the challenges. Nearly 30 percent of Finland’s children receive some kind of special help during their first nine years of school. The school where Louhivuori teaches served 240 first through ninth graders last year; and in contrast with Finland’s reputation for ethnic homogeneity, more than half of its 150 elementary-level students are immigrants—from Somalia, Iraq, Russia, Bangladesh, Estonia and Ethiopia, among other nations. “Children from wealthy families with lots of education can be taught by stupid teachers,” Louhivuori said, smiling. “We try to catch the weak students. It’s deep in our thinking.” Advertisement scroll for more The transformation of the Finns’ education system began some 40 years ago as the key propellent of the country’s economic recovery plan. Educators had little idea it was so successful until 2000, when the first results from the Programme for International Student Assessment (PISA), a standardized test given to 15-year-olds in more than 40 global venues, revealed Finnish youth to be the best young readers in the world. Three years later, they led in math. By 2006, Finland was first out of 57 countries (and a few cities) in science. In the 2009 PISA scores released last year, the nation came in second in science, third in reading and sixth in math among nearly half a million students worldwide. “I’m still surprised,” said Arjariita Heikkinen, principal of a Helsinki comprehensive school. “I didn’t realize we were that good.” In the United States, which has muddled along in the middle for the past decade, government officials have attempted to introduce marketplace competition into public schools. In recent years, a group of Wall Street financiers and philanthropists such as Bill Gates have put money behind private-sector ideas, such as vouchers, data-driven curriculum and charter schools, which have doubled in number in the past decade. President Obama, too, has apparently bet on compe­tition. His Race to the Top initiative invites states to compete for federal dollars using tests and other methods to measure teachers, a philosophy that would not fly in Finland. “I think, in fact, teachers would tear off their shirts,” said Timo Heikkinen, a Helsinki principal with 24 years of teaching experience. “If you only measure the statistics, you miss the human aspect.”

      The facts show that America has it all wrong in putting to much emphasis on national "data-driven" competition. These approaches take away from the unique aspects of each child.

    2. t was the end of term at Kirkkojarvi Comprehensive School in Espoo, a sprawling suburb west of Helsinki, when Kari Louhivuori, a veteran teacher and the school’s principal, decided to try something extreme—by Finnish standards. One of his sixth-grade students, a Kosovo-Albanian boy, had drifted far off the learning grid, resisting his teacher’s best efforts. The school’s team of special educators—including a social worker, a nurse and a psychologist—convinced Louhivuori that laziness was not to blame. 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n=r.generatePrerollTag(e,t);r.monetization.onPrerollAdOpportunity(n)}),f()(this,"onSeekedWhileAdInProgress",function(){r.monetization.onMidrollAdOpportunity()});var i=t.getState;this.monetization=n,this.videoTimeSubscriber=new qi(t,this),this.videoSeekSubscriber=new zi(t,this),this.prerollScheduler=new Gi(t,this);var o=_i.adTagUrlTemplate(i());this.adTagGenerator=new Wi(o)},Yi=function(){function e(){Ai()(this,e)}return Vi()(e,null,[{key:"generateAdRequest",value:function(e,t,n){var r=new google.ima.AdsRequest;return r.adTagUrl=e,Fn()||r.setAdWillPlayMuted(t),r.vastLoadTimeout=n,r}}]),e}(),Zi=function(e){return function(t){t({type:"[MONETIZATION] change ad status",payload:e})}},Xi=function(e){return function(t){t({type:"[COMMON] set pending video status",payload:{pendingStatusObject:{type:e,value:""}}})}},Ji=function(e){return function(t){t({type:"[MONETIZATION] change loading ad status",payload:e})}},Qi=function(e){return function(t){t({type:"[MONETIZATION] update ad 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t=a.store,n=t.getState,r=t.dispatch,i=gn.volume(n());Bn()||gn.muted(n())?(e.setVolume(0),Qi(!0)(r)):(e.setVolume(gn.volume(n())),eo(i)(r),Qi(!1)(r))}),f()(this,"createIMAAdManager",function(t){a.IMAAdManager=t.getAdsManager(a.adVideoElement,e.getAdsRenderingSettings()),a.setAdVolume(a.IMAAdManager)}),f()(this,"registerToAdManagerEvents",function(){a.IMAAdManager.addEventListener(google.ima.AdErrorEvent.Type.AD_ERROR,a.onAdError),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.CONTENT_PAUSE_REQUESTED,a.onContentPauseRequested),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.CONTENT_RESUME_REQUESTED,a.onContentResumeRequested),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.STARTED,a.onAdStarted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.IMPRESSION,a.onAdImpression),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.SKIPPED,a.onAdSkipped),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.COMPLETE,a.onAdCompleted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.PAUSED,a.onAdPaused),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.RESUMED,a.onAdStarted),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.AD_PROGRESS,a.onAdProgressChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.VOLUME_CHANGED,a.onVolumeChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.VOLUME_MUTED,a.onAdVolumeMutedChanged),a.IMAAdManager.addEventListener(google.ima.AdEvent.Type.ALL_ADS_COMPLETED,a.onAdCompleted)}),f()(this,"onIMAAdsManagerLoaded",function(e){var t=a.store.dispatch;a.createIMAAdManager(e),a.registerToAdManagerEvents(),Zi("loaded")(t)}),f()(this,"onAdError",function(e){var t=a.store.dispatch;!function(e){return function(t){t({type:"[MONETIZATION] change ad error",payload:e})}}(e.getError().getMessage())(t),Ji(!1),a.continuePlayingContent()}),f()(this,"onAdImpression",function(e){var t=a.store.dispatch,n=!e.getAd().g.vpaid;a.setPodInfo(e),function(e){e({type:"[MONETIZATION] increase ad impression counter"})}(t),function(e){return function(t){t({type:"[MONETIZATION] update is vast ad",payload:e})}}(n)(t)}),f()(this,"onVolumeChanged",function(e){var t=a.store.dispatch;eo(e.target.getVolume())(t)}),f()(this,"onAdVolumeMutedChanged",function(e){var t=a.store.dispatch;0===e.target.getVolume()?Qi(!0)(t):Qi(!1)(t)}),f()(this,"continuePlayingContent",function(){var e=a.store,t=e.getState,n=e.dispatch,r=hn.videoTagStatus(t());Xi("idle"===r?"play":"resume")(n)}),f()(this,"stopPlayingContent",function(){var 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e=a.store.dispatch;Zi("skipped")(e)}),f()(this,"onResize",function(){Un(a.IMAAdManager)||(a.IMAAdManager.resize(a.videoPlayerElement.clientWidth,a.videoPlayerElement.clientHeight,google.ima.ViewMode.NORMAL),a.adContainerElement.style.height="".concat(a.videoPlayerElement.clientHeight,"px"))}),f()(this,"onAdProgressChanged",function(e){var t,n,r=a.store,i=r.dispatch,o=r.getState,s=e.getAdData().currentTime,u=e.getAdData().duration,c=_i.adDuration(o());(t=s,function(e){e({type:"[MONETIZATION] change ad current time",payload:t})})(i),c!==u&&(n=u,function(e){e({type:"[MONETIZATION] change ad duration",payload:n})})(i)}),f()(this,"onAnchorStatusChanged",function(){var e=a.store.getState;"processing"!==Pr(e())&&a.onResize()}),f()(this,"changeAdVolume",function(e){Un(a.IMAAdManager)||a.IMAAdManager.setVolume(e)}),f()(this,"changeAdMuted",function(e,t){Un(a.IMAAdManager)||(t?a.IMAAdManager.setVolume(0):a.IMAAdManager.setVolume(e))}),f()(this,"changeAdStatus",function(e){Un(a.IMAAdManager)||("playing"===e&&a.IMAAdManager.resume(),"paused"===e&&a.IMAAdManager.pause())});var s=t.getState;this.store=t,this.adVideoElement=r,this.videoPlayerElement=i,this.adContainerElement=n,this.adDisplayContainer=new google.ima.AdDisplayContainer(n,r),this.createAdLoader(s(),this.adDisplayContainer),this.adDisplayContainer.initialize(),this.anchorStatusStoreSubscriber=new ji(t,e.getAnchorDependencies,this.onAnchorStatusChanged.bind(this)),this.registerForWindowResize(),this.initMutationObserver(o)};f()(to,"getAdsRenderingSettings",function(){var e=new google.ima.AdsRenderingSettings;return 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e=s.store,t=e.dispatch,n=e.getState,r=_i.adStatus(n()),i=bi.continuePlayingWhileWaitingForAd(n());"loaded"===r?s.playAd(!0):"requested"===r&&(s.pendingMidrollAdPlay=!0,i||(Xi("pause")(t),Ji(!0)(t))),function(e){e({type:"[MONETIZATION] increase ad Opportunity counter"})}(t)}),f()(this,"onPrerollAdOpportunity",function(e){var t=s.store,n=t.getState,r=t.dispatch,i=Fi.loadingImaStatus(n());Un(s.adHandler)?"loading"!==i&&""!==i||(Ji(!0)(r),s.pendingPrerollAdPlay=!0,s.pendingPrerollAdTag=e):(s.pendingPrerollAdPlay=!0,Ji(!0)(r),s.adHandler.loadNewAd(e,"preroll"))}),f()(this,"onPreMidrollAdOpportunity",function(e,t){Un(s.adHandler)||(e.currentTime>=e.midrollTime&&(s.pendingMidrollAdPlay=!0),s.pendingMidrollNumber=e.midrollNumber,s.adHandler.loadNewAd(t,"midroll"))}),f()(this,"hasPendingAd",function(){return s.hasPendingMidrollAdPlay()||s.hasPendingPrerollAdPlay()}),f()(this,"onAdStatusChanged",function(e){var t=s.store.dispatch,n=_i.adStatus(e);"completed"===n&&Ji(!1)(t);var r=bi.continuePlayingWhileWaitingForAd(e),i=_i.loadingAd(e);"playing"!==n&&"error"!==n||r||!i||Ji(!1)(t),s.hasPendingAd()&&"loaded"===n?s.playAd(s.hasPendingMidrollAdPlay()):s.hasPendingAd()&&"error"===n?(Ji(!1),s.clearPendingMidroll(),s.clearPendingPreroll()):Hi(n)||(Ji(!1),function(e){e({type:"[MONETIZATION] clear ad data"})}(t))}),f()(this,"clearPendingMidroll",function(){s.pendingMidrollNumber=null,s.pendingMidrollAdPlay=!1}),f()(this,"clearPendingPreroll",function(){s.pendingPrerollAdPlay=!1,s.pendingPrerollAdTag=null}),f()(this,"onVideoTagStatusChanged",function(e){"complete"===hn.videoTagStatus(e)&&function(e){e({type:"[MONETIZATION] clear played midrolls"})}(s.store.dispatch)}),f()(this,"hasPendingMidrollAdPlay",function(){return s.pendingMidrollAdPlay}),f()(this,"hasPendingPrerollAdPlay",function(){return s.pendingPrerollAdPlay}),f()(this,"playAd",function(e){var t,n=s.store.dispatch,r=s.adHandler.playAd();e?((t=s.pendingMidrollNumber,function(e){e({type:"[MONETIZATION] add 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Ki(t,this),this.adStatusSubscriber=new ji(t,e.getAdStatusDependencies,this.onAdStatusChanged.bind(this)),this.videoTagStatusSubscriber=new ji(t,e.getVideoTagStatusDependencies,this.onVideoTagStatusChanged.bind(this)),e.canUseIMA(u())?this.adHandler=new to(t,r,i,o,a):this.imaLoadingStatusSubscriber=new ji(t,e.getIMALoadingStatusDependencies,this.onIMALoadingStatusChanged.bind(this)),this.pendingAdStatusStoreSubscriber=new ji(t,e.getPendingAdStatusDependencies,this.onPendingAdStatusChanged.bind(this)),this.adMutedStoreSubscriber=new ji(t,e.getAdMutedDependencies,this.onAdMutedChanged.bind(this)),this.adVolumeStoreSubscriber=new ji(t,e.getAdVolumeDependencies,this.onAdVolumeChanged.bind(this))};f()(no,"getAdStatusDependencies",function(e){return[_i.adStatus(e)]}),f()(no,"getVideoTagStatusDependencies",function(e){return[hn.videoTagStatus(e)]}),f()(no,"getIMALoadingStatusDependencies",function(e){return[Fi.loadingImaStatus(e)]}),f()(no,"canUseIMA",function(e){return"success"===Fi.loadingImaStatus(e)}),f()(no,"getPendingAdStatusDependencies",function(e){return[_i.pendingAdStatus(e)]}),f()(no,"getAdMutedDependencies",function(e){return[_i.adMuted(e)]}),f()(no,"getAdVolumeDependencies",function(e){return[_i.adVolume(e)]});var ro=function(e,t){!function(e,t){var n=document.getElementById(vn(t));B(b(Li,{store:e,playerId:t}),n)}(e,t);var n=function(e){var t=Ri(e);return document.getElementById(t)}(t),r=function(e){var t=Bn()?Di(e):En(e);return document.getElementById(t)}(t),i=function(e){var t=En(e);return document.getElementById(t)}(t),o=function(e){var t=bn(e);return document.getElementById(t)}(t);return new 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this.errorMessage=e,this}},{key:"setAdPodNumber",value:function(e){return this.adPodNumber=e,this}},{key:"setAdSlotNumber",value:function(e){return this.adSlotNumber=e,this}},{key:"build",value:function(){var e=[];return jn(this.position)||e.push("video current position=".concat(Hn(this.position),"sec")),jn(this.duration)||e.push("video duration time=".concat(Hn(this.duration),"sec")),jn(this.loadTime)||e.push("video load time=".concat(this.loadTime,"milliseconds")),jn(this.previousPosition)||e.push("previous position=".concat(Hn(this.previousPosition),"sec")),jn(this.adOrder)||e.push("ad order=".concat(this.adOrder)),jn(this.adType)||e.push("ad type=".concat(this.adType)),jn(this.adDuration)||e.push("ad duration=".concat(Hn(Number(this.adDuration)),"sec")),jn(this.adPodNumber)||e.push("pod number=".concat(this.adPodNumber)),jn(this.adSlotNumber)||e.push("slot number=".concat(this.adSlotNumber)),jn(this.errorMessage)||e.push("error 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The initial state may not be undefined, but can be null.')})}(n)}catch(s){o=s}return function(e,t){if(void 0===e&&(e={}),o)throw o;for(var r=!1,i={},s=0;s<a.length;s++){var u=a[s],c=n[u],l=e[u],d=c(l,t);if("undefined"===typeof d){var p=mt(u,t);throw new Error(p)}i[u]=d,r=r||d!==l}return(r=r||a.length!==Object.keys(e).length)?i:e}}({dependenciesLoadingStatus:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:da,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] update hls status":return la(la({},e),{},{loadingHLSStatus:t.payload});case"[CORE] update ima status":return la(la({},e),{},{loadingImaStatus:t.payload});default:return e}},playerData:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:ha,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":var n=t.payload;return fa({},function(e,t,n){var r=t.playback_method,i=t.player_id;return fa(fa({},e),{},{playbackMethod:Un(r)?e.playbackMethod:r,playerId:Un(i)?e.playerId:i,playerInstanceUniqId:n,playerMode:Fn()?"mobile":"desktop"})}(e,n.initiateParams,n.playerInstanceUniqId));case"[CORE] reset player data time params":return fa(fa({},e),{},{currentVideoTimeFragment:0,currentVideoBufferedTime:0,currentVideoDuration:0,currentVideoTime:0});case"[COMMON] set mute video":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{muted:t.payload})});case"[COMMON] set volume":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{volume:t.payload})});case"[COMMON] change selected settings category":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{selectedSettingsCategory:t.payload})});case"[COMMON] change settings speed":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{speed:t.payload})});case"[COMMON] change settings quality":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{quality:t.payload})});case"[COMMON] set fullscreen":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{fullscreen:fa(fa({},e.playerSettings.fullscreen),{},{isFullscreenOn:t.payload,pendingFullscreenRequest:""})})});case"[COMMON] set fullscreen request":return fa(fa({},e),{},{playerSettings:fa(fa({},e.playerSettings),{},{fullscreen:fa(fa({},e.playerSettings.fullscreen),{},{pendingFullscreenRequest:t.payload})})});case"[COMMON] set pending video status":var r=t.payload.pendingStatusObject;return fa(fa({},e),{},{pendingVideoTagStatus:fa({},r)});case"[COMMON] set player mode":return fa(fa({},e),{},{playerMode:t.payload});case"[CORE] update video current fragment position":return fa(fa({},e),{},{currentVideoTimeFragment:t.payload});case"[CORE] update video current position":return fa(fa({},e),{},{currentVideoTime:t.payload});case"[CORE] update video current buffered time":return fa(fa({},e),{},{currentVideoBufferedTime:t.payload});case"[CORE] update video current duration":return fa(fa({},e),{},{currentVideoDuration:t.payload});case"[CORE] change video tag status":return fa(fa({},e),{},{videoTagStatus:t.payload});case"[CORE] update player visibility":return fa(fa({},e),{},{playerVisibility:t.payload});case"[CORE] update placeholder visibility":return fa(fa({},e),{},{playerPlaceholderVisibility:t.payload});case"[CORE] change loading player status":return fa(fa({},e),{},{loadingPlayer:t.payload});case"[COMMON] show black screen with loader":return fa(fa({},e),{},{loader:fa(fa({},e.loader),{},{showBlackScreen:t.payload})});case"[CORE] set player size":return fa(fa({},e),{},{playerSize:t.payload});case"[COMMON] set error message":return fa(fa({},e),{},{errorMessage:t.payload});default:return e}},brandingData:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:va,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return ga({},function(e,t){var n=t.powered_by_strip,r=t.brand_logo,i=t.brand_logo_click_url,o=t.brand_color;return ga(ga({},e),{},{showVoltaxLogo:Un(n)?e.showVoltaxLogo:n,brandingLogoSrc:Un(r)?e.brandingLogoSrc:r,brandingLogoUrl:Un(i)?e.brandingLogoUrl:i,brandingColor:Un(o)?e.brandingColor:o})}(e,t.payload.initiateParams));default:return e}},anchorOptions:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Oa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return ba({},function(e,t){var n=t.anchor_options;if(!Un(n)){var r=n.anchoring_appearance,i=n.can_close,o=n.closable_ad,a=n.close_after,s=n.continue_streaming,u=n.orientation,c=n.margins,l=n.sticky_below_class_name,d=n.width,p=Un(c)?e.margins:{top:Number.isInteger(c.top)?c.top:e.margins.top,bottom:Number.isInteger(c.bottom)?c.bottom:e.margins.bottom,left:Number.isInteger(c.left)?c.left:e.margins.left,right:Number.isInteger(c.right)?c.right:e.margins.right};return ba(ba({},e),{},{anchoringAppearance:r||e.anchoringAppearance,canClose:Un(i)?e.canClose:i,orientation:Un(u)?e.orientation:u,closableAd:Un(o)?e.closableAd:o,closeAfter:Un(a)?e.closeAfter:a,continueStreaming:Un(s)?e.continueStreaming:s,stickyBelowClassName:Un(l)?e.stickyBelowClassName:l,width:Un(d)?e.width:d,margins:p,anchorData:ba(ba({},e.anchorData),{},{anchorEnabled:!0})})}return e}(e,t.payload.initiateParams));case"[COMMON] set anchor enable":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorEnabled:t.payload})});case"[ANCHOR] update is anchor status":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorStatus:t.payload})});case"[COMMON] set anchor disabled by user":return ba(ba({},e),{},{anchorData:ba(ba({},e.anchorData),{},{anchorDisabledByUser:t.payload})});default:return e}},monetization:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:wa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Sa({},function(e,t){var n=t.monetization;if(Un(n))return e;var r=n.ad_tag,i=n.ad_type,o=n.vpaid_mode,a=n.ad_request_timeout,s=n.continue_content_play_while_waiting_for_ad,u=n.midrolls,c=u&&u.on&&u.on.sort(Wn),l=Un(s)?e.continuePlayingWhileWaitingForAd:s,d=c?c.indexOf(0):-1,p=-1!==d&&!l;return p&&(c=c.splice(d,1)),Sa(Sa({},e),{},{midrolls:Sa(Sa({},e.midrolls),{},{every:u&&u.every,on:c}),prerollEnabled:p,adRequestTimeout:Un(a)?e.adRequestTimeout:parseInt(a,10),vpaidMode:Un(o)?e.vpaidMode:o,continuePlayingWhileWaitingForAd:l,adsData:Sa(Sa({},e.adsData),{},{adType:Un(i)?e.adsData.adType:i,adTagUrlTemplate:Un(r)?e.adsData.adTagUrlTemplate:r})})}(e,t.payload.initiateParams));case"[COMMON] set new ad tag url template":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adTagUrlTemplate:t.payload})});case"[MONETIZATION] change ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adStatus:t.payload,adErrorMessage:null})});case"[MONETIZATION] change ad tag":var n=t.payload,r=n.adUnit,i=n.adTag;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{currentAdTag:i,adUnit:r})});case"[MONETIZATION] change pending ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{pendingAdStatus:t.payload})});case"[MONETIZATION] change ad error":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adStatus:"error",adErrorMessage:t.payload})});case"[MONETIZATION] increase ad impression counter":var o=e.adsData.adImpression;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adImpression:o+1})});case"[MONETIZATION] increase ad Opportunity counter":var a=e.adsData.adOpportunity;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOpportunity:a+1})});case"[MONETIZATION] add played midroll number":var s=e.adsData.playedMidrolls,u=In()(s);return u.push(t.payload),Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOrder:t.payload,playedMidrolls:u})});case"[MONETIZATION] clear played midrolls":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{playedMidrolls:[]})});case"[MONETIZATION] clear ad data":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adOrder:0,currentAdTag:null,adDuration:0,adUnit:""})});case"[MONETIZATION] change ad duration":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adDuration:t.payload})});case"[MONETIZATION] update is vast ad":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{isVastAd:t.payload})});case"[MONETIZATION] change ad current time":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adCurrentTime:t.payload})});case"[MONETIZATION] update ad muted":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adMuted:t.payload})});case"[MONETIZATION] change ad volume":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{adVolume:t.payload})});case"[MONETIZATION] change pod info":var c=t.payload,l=c.podNumber,d=c.slotNumber;return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{podNumber:l,slotNumber:d})});case"[MONETIZATION] change loading ad status":return Sa(Sa({},e),{},{adsData:Sa(Sa({},e.adsData),{},{loadingAd:t.payload})});default:return e}},mediaData:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:ja,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Va({},function(e,t){var n=t.content_type,r=t.media_id,i=t.display_title;return Va(Va({},e),{},{mediaType:Un(n)?e.mediaType:n,mediaId:Un(r)?e.mediaId:r,videoData:Va(Va({},e.videoData),{},{showTitle:!!Un(i)||i})})}(e,t.payload.initiateParams));case"[CORE] load video request":return Va(Va({},e),{},{loadingMedia:!0});case"[CORE] load video request success":return Va(Va({},e),{},{loadingMedia:!1,videoList:t.payload});case"[CORE] set current video":var n=t.payload,r=n.index,i=n.videoData;return Va(Va({},e),{},{activeVideoIndex:r,videoData:i});case"[CORE] load video request error":return Va(Va({},e),{},{loadingMedia:!1,mediaLoadingError:t.payload});case"[COMMON] media request":var o=t.payload.mediaRequestObject;return Va(Va({},e),{},{mediaRequest:Va({},o)});default:return e}},semanticOptions:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Ba,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Fa({},function(e,t){var n=t.semantic_options;if(Un(n))return e;var r=n.minimum_date_factor,i=n.promoted_videos,o=n.scan_images_on_page,a=n.scanned_element,s=n.scanned_element_type,u=n.scoped_keywords,c=n.tags;return Fa(Fa({},e),{},{minimumDateFactor:Un(r)?e.minimumDateFactor:r,promotedVideos:Un(i)?e.promotedVideos:i,scanImagesOnPage:Un(o)?e.scanImagesOnPage:o,scannedElement:Un(a)?e.scannedElement:a,scannedElementType:Un(s)?e.scannedElementType:s,scopedKeywords:Un(u)?e.scopedKeywords:u,tags:Un(c)?e.tags:c})}(e,t.payload.initiateParams));default:return e}},userInteraction:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Wa,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[USER INTERACTION] change user interaction":return qa(qa({},e),{},{userInteractionType:t.payload});default:return e}},splitView:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:$a,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Ga({},function(e,t){var n=t.anchor_options;if(!Un(n)){var r=n.split_view,i=n.split_view_ratio;return Ga(Ga({},e),{},{splitViewRatio:Un(r)||!r||Un(i)?e.splitViewRatio:i})}return e}(e,t.payload.initiateParams));default:return e}},discovery:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Za,t=arguments.length>1?arguments[1]:void 0;switch(t.type){case"[CORE] initiate store":return Ya({},function(e,t){var n=t.next_video;return Un(n)?e:Ya(Ya({},e),{},{nextVideo:Xa(n)})}(e,t.payload.initiateParams));case"[DISCOVERY] show up next":return Ya(Ya({},e),{},{showUpNext:t.payload});case"[DISCOVERY] show skippable content":return Ya(Ya({},e),{},{showSkippableContent:t.payload});default:return e}}}),Qa=[],es=!1,ts=function e(){return function(t){return function(n){if(es)return Qa.push(n),null;es=!0;var r=t(n);return es=!1,Qa.length>0&&e()(t)(Qa.shift()),r}}},ns=function(e){var t=[];if(function(e){return!Un(e)&&!Un(e.enable_redux_debugging)&&e.enable_redux_debugging}(e)){var n=window&&window.__REDUX_DEVTOOLS_EXTENSION__&&window.__REDUX_DEVTOOLS_EXTENSION__();"function"===typeof n&&t.push(n)}var r=Et.apply(void 0,[wt(ua,ts)].concat(t));return vt(Ja,r)},rs=function(){function e(t){Ai()(this,e),f()(this,"playerVisibilitySubscriber",void 0),f()(this,"videoTagStatusSubscriber",void 0),f()(this,"shouldPlayIfLazyplay",!0),f()(this,"shouldPlayIfAutoplayWhenViewable",!0),f()(this,"videoPausedByObserver",!1),this.store=t,this.playerVisibilitySubscriber=null,this.videoTagStatusSubscriber=null,this.playAccordingToPlaybackMethod()}return Vi()(e,[{key:"lazyplayHandler",value:function(e){hn.playerVisibility(e)>=.5&&(this.playVideo(),this.shouldPlayIfLazyplay=!1)}},{key:"autoplayWhenViewableHandler",value:function(e){hn.playerVisibility(e)>=.5?this.playVideo():this.pauseVideo()}},{key:"onPlayerVisibilityChanged",value:function(e){var t=hn.playbackMethod(e);"lazyplay"===t&&this.shouldPlayIfLazyplay&&this.lazyplayHandler(e),"autoplay_when_viewable"===t&&this.shouldPlayIfAutoplayWhenViewable&&this.autoplayWhenViewableHandler(e)}},{key:"onVideoTagStatusChanged",value:function(e){var t=hn.videoTagStatus(e);"paused"!==t||this.videoPausedByObserver||(this.shouldPlayIfAutoplayWhenViewable=!1),"playing"===t&&(this.shouldPlayIfAutoplayWhenViewable=!0,this.videoPausedByObserver=!1)}},{key:"initiatePlayerVisibilitySubscriber",value:function(){this.playerVisibilitySubscriber=new ji(this.store,e.getPlayerVisibilityDependencies,this.onPlayerVisibilityChanged.bind(this))}},{key:"initiateVideoTagStatusSubscriber",value:function(){this.videoTagStatusSubscriber=new ji(this.store,e.getVideoTagStatusDependencies,this.onVideoTagStatusChanged.bind(this))}},{key:"playVideo",value:function(){var e=this.store,t=e.dispatch,n=e.getState;"idle"===hn.videoTagStatus(n())?on("play")(t):on("resume")(t)}},{key:"pauseVideo",value:function(){var e=this.store,t=e.dispatch,n=e.getState;"paused"!==hn.videoTagStatus(n())&&(this.videoPausedByObserver=!0,on("pause")(t))}},{key:"playAccordingToPlaybackMethod",value:function(){var e=this.store,t=e.dispatch,n=(0,e.getState)();switch(hn.playbackMethod(n)){case"autoplay":this.playVideo();break;case"lazyplay":this.initiatePlayerVisibilitySubscriber();break;case"autoplay_when_viewable":this.initiatePlayerVisibilitySubscriber(),this.initiateVideoTagStatusSubscriber();break;case"none":an(!1)(t)}}}],[{key:"getPlayerVisibilityDependencies",value:function(e){return[hn.playerVisibility(e)]}},{key:"getVideoTagStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}}]),e}(),is=function(){function e(t,n,r,i){var o=this;Ai()(this,e),f()(this,"videoStatusSubscriber",void 0),f()(this,"videoListSubscriber",void 0),f()(this,"mediaRequestSubscriber",void 0),f()(this,"playerVisibilitySubscriber",void 0),f()(this,"playbackMethodManager",void 0),f()(this,"store",void 0),f()(this,"loadContent",function(e,t,n,r){o.loadMedia(t,n,r).then(function(){o.playbackMethodManager=new rs(e)})}),f()(this,"loadMedia",function(e,t,n){var r=o.store,i=r.dispatch,a=r.getState,s=Dn.showTitle(a());if("semantic"===e){var u=pn.semanticOptions(a());return Na(u,s,n)(i)}return ka(t,s,n)(i)}),this.store=t,this.videoStatusSubscriber=new ji(t,e.getVideoStatusDependencies,this.onVideoStatusChanged.bind(this)),this.videoListSubscriber=new ji(t,e.getVideoListDependencies,this.onVideoListChanged.bind(this)),this.mediaRequestSubscriber=new ji(t,e.getMediaRequestDependencies,this.onMediaRequestChanged.bind(this)),this.playerVisibilitySubscriber=null,this.loadContent(t,r,n,i)}return Vi()(e,null,[{key:"createInstance",value:function(t,n,r,i){return new e(t,n,r,i)}}]),Vi()(e,[{key:"playNextVideo",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e),r=Cn.activeVideoIndex(e)+1;n.length>1&&r>=n.length&&(r=0),r<n.length&&(!function(e){e({type:"[CORE] reset player data time params"})}(t),La(r,n[r])(t),on("play")(t))}},{key:"playPreviousVideo",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e),r=Cn.activeVideoIndex(e);if(r>0){var i=r-1;La(i,n[i])(t),on("play")(t)}}},{key:"onVideoStatusChanged",value:function(e){"complete"===hn.videoTagStatus(e)&&this.playNextVideo(e)}},{key:"onVideoListChanged",value:function(e){var t=this.store.dispatch,n=Cn.videoList(e);!jn(n)&&n.length>0&&La(0,n[0])(t)}},{key:"onMediaRequestChanged",value:function(e){var t=Cn.mediaRequest(e);switch(t.type){case"playNewVideo":this.loadMedia("specific",t.value);break;case"playNextVideo":this.playNextVideo(e);break;case"playPreviousVideo":this.playPreviousVideo(e)}}}],[{key:"getVideoStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}},{key:"getVideoListDependencies",value:function(e){return[Cn.videoList(e)]}},{key:"getMediaRequestDependencies",value:function(e){return[Cn.mediaRequest(e)]}}]),e}(),os=function e(t){var n=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"onDependencyFailure",function(e,t){console.log("onDependencyFailure",e,t);var r=n.store,i=r.dispatch,o=r.getState;switch(e){case"ima":"blocked"!==Fi.loadingImaStatus(o())&&Qn("error")(i);break;case"hls":er("error")(i)}}),f()(this,"onDependencyReady",function(e){var t=n.store.dispatch;switch(e){case"ima":Qn("success")(t);break;case"hls":er("success")(t)}}),this.store=t},as=function(e){return function(t){t({type:"[COMMON] set fullscreen",payload:e})}},ss=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"videoTag",void 0),f()(this,"pendingFullscreenSubscriber",void 0),f()(this,"adStatusSubscriber",void 0),f()(this,"playerUniqId",void 0),f()(this,"onAdStatusChanged",function(e){var t=_i.adStatus(e),n=r.videoTag.webkitDisplayingFullscreen;"playing"===t&&Bn()&&n&&r.exitFullscreen(r.videoTag)}),f()(this,"isPlayerInFullscreen",function(){var e=document,t=Bn()?En(r.playerUniqId):bn(r.playerUniqId);return Un(e.fullscreenElement)?!Un(e.webkitFullscreenElement)&&0===e.webkitFullscreenElement.id.localeCompare(t):0===e.fullscreenElement.id.localeCompare(t)}),f()(this,"changePlayerWidth",function(e){r.videoTag.style.width=e?"100%":"auto"}),f()(this,"onFullscreenChanged",function(){var e=r.store.dispatch,t=r.isPlayerInFullscreen();r.changePlayerWidth(t),as(t)(e)}),f()(this,"onFullscreenChangedIos",function(){var e=r.store.dispatch,t=r.videoTag.webkitDisplayingFullscreen;t||on("resume")(e),r.changePlayerWidth(t),as(t)(e)}),f()(this,"onPendingFullscreenRequestChanged",function(e){var t=gn.pendingFullscreenRequest(e);"enter"===t?r.enterFullscreen(r.videoTag):"exit"===t&&r.exitFullscreen(r.videoTag)}),f()(this,"getFullScreenElement",function(e,t){var n=document.getElementById(bn(r.playerUniqId));return Bn()?t:e?document:n}),f()(this,"enterFullscreen",function(e){var t=r.getFullScreenElement(!1,e);Bn()?t.webkitEnterFullscreen():document.webkitExitFullscreen?t.webkitRequestFullscreen():document.webkitCancelFullScreen?t.webkitRequestFullScreen():document.mozCancelFullScreen?t.mozRequestFullScreen():document.msExitFullscreen&&t.msRequestFullscreen()}),f()(this,"exitFullscreen",function(e){var t=r.getFullScreenElement(!0,e);document.webkitExitFullscreen||Bn()?t.webkitExitFullscreen():document.webkitCancelFullScreen?t.webkitCancelFullScreen():document.mozCancelFullScreen?t.mozCancelFullScreen():document.msExitFullscreen&&t.msExitFullscreen()}),this.store=t,this.videoTag=document.getElementById(En(n)),this.playerUniqId=n,document.addEventListener("fullscreenchange",this.onFullscreenChanged.bind(this)),document.addEventListener("webkitfullscreenchange",this.onFullscreenChanged.bind(this)),Bn()&&(this.videoTag.addEventListener("webkitendfullscreen",this.onFullscreenChangedIos.bind(this)),this.videoTag.addEventListener("webkitbeginfullscreen",this.onFullscreenChangedIos.bind(this))),this.pendingFullscreenSubscriber=new ji(t,e.getPendingFullscreenDependencies,this.onPendingFullscreenRequestChanged.bind(this)),this.adStatusSubscriber=new ji(t,e.getAdStatusDependencies,this.onAdStatusChanged.bind(this))}return Vi()(e,null,[{key:"createInstance",value:function(t,n){return new e(t,n)}}]),Vi()(e,null,[{key:"getPendingFullscreenDependencies",value:function(e){return[gn.pendingFullscreenRequest(e)]}},{key:"getAdStatusDependencies",value:function(e){return[_i.adStatus(e)]}}]),e}();function us(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable})),n.push.apply(n,r)}return n}function cs(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?us(Object(n),!0).forEach(function(t){f()(e,t,n[t])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):us(Object(n)).forEach(function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))})}return e}var ls,ds=function(e){return function(e){return e&&window.monti.playerConfigs&&window.monti.playerConfigs[e]}(e)?function(e){return window.monti.playerConfigs[e]}(e):window.monti.playerConfigs?window.monti.playerConfigs&&window.monti.playerConfigs[Object.keys(window.monti.playerConfigs)[0]]:null},ps=function e(t){var n=this;Ai()(this,e),f()(this,"videoTag",void 0),f()(this,"isBufferError",void 0),f()(this,"hls",void 0),f()(this,"hlsSetup",function(e,t,r,i){n.initiateHls(e),n.loadHlsSource(e,t,r,i)}),f()(this,"detachMedia",function(){Un(n.hls)||(n.hls.detachMedia(),n.hls.destroy(),n.hls=null)}),f()(this,"initiateHls",function(e){n.hls=new e,n.hls.attachMedia(n.videoTag)}),f()(this,"loadHlsSource",function(e,t,r,i){n.hls.on(e.Events.MEDIA_ATTACHED,function(){n.hls.loadSource(t)}),n.hls.on(e.Events.ERROR,function(t,o){n.mapHlsToErrors(e,o,i),t.details===e.ErrorDetails.BUFFER_STALLED_ERROR&&(r(!0),n.isBufferError=!0)}),n.hls.on(e.Events.FRAG_BUFFERED,function(){n.isBufferError&&(r(!1),n.isBufferError=!1)})}),f()(this,"mapHlsToErrors",function(e,t,r){if(t.fatal)switch(t.type){case 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t=hn.pendingVideoTagStatus(e),n=Dn.sources(e),i=Fi.loadingHLSStatus(e),o="blocked"===Fi.loadingImaStatus(e);r.handlePendingVideoStatus(t,n,i,o)}),f()(this,"onVideoDataChanged",function(){r.newVideoDataLoaded=!0}),f()(this,"sendPrerollPlayRequest",function(){var e=r.store.dispatch;hs("playPreroll")(e)}),f()(this,"handlePlayRequest",function(e,t,n){var i=r.store.dispatch;if(e&&e.length>0){if(r.newVideoDataLoaded&&(r.loadVideoSource(r.videoTag,e,t),r.newVideoDataLoaded=!1,r.prerollEnabled&&!n))return void r.sendPrerollPlayRequest();r.videoTag.play().catch(function(e){return console.error("Error playing the video: ",e)})}else dn(Xn.VIDEO_ERROR)(i)}),f()(this,"handlePendingVideoStatus",function(e,t,n,i){switch(e.type){case"play":r.handlePlayRequest(t,n,i);break;case"resume":r.videoTag.play().catch(function(e){return console.error("Error resuming the video: ",e)});break;case"pause":r.videoTag.pause();break;case"replay":r.videoTag.currentTime=0,r.videoTag.play().catch(function(e){return console.error("Error replaying the video: ",e)});break;case"seekTo":r.videoTag.pause(),r.videoTag.currentTime=e.value}}),f()(this,"loadMp4Source",function(e,t,n){var r=Ra(t,ys);n.setAttribute("src",r),n.load()}),f()(this,"loadVideoSource",function(e,t,n){var i=r.store.dispatch,o=Ra(t,gs);switch(fs.suitableVideoSource(e,o,n)){case"mp4":r.loadMp4Source(n,t,e);break;case"m3u8 with hls":r.videoStreamingManager.hlsLibrarySetup(e,o,function(e){return un(e)(i)},function(e){return dn(e)(i)});break;case"m3u8 directly":fs.loadHlsVideoDirectly(e,o)}}),this.store=t;var i=t.getState;this.videoStreamingManager=new fs,this.videoTag=document.getElementById(En(n)),this.prerollEnabled=bi.prerollEnabled(i()),this.pendingVideoStatusSubscriber=new ji(t,e.getPendingVideoStatusDependencies,this.onPendingVideoStatusChanged.bind(this)),this.videoDataSubscriber=new ji(t,e.getVideoDataDependencies,this.onVideoDataChanged.bind(this)),this.hlsLoadingStatusSubscriber=new ji(t,e.getHLSLoadingStatusDependencies,this.onHlsLoadingStatusChanged.bind(this))}return Vi()(e,null,[{key:"createInstance",value:function(t,n){return new e(t,n)}}]),Vi()(e,null,[{key:"getHLSLoadingStatusDependencies",value:function(e){return[Fi.loadingHLSStatus(e)]}},{key:"getPendingVideoStatusDependencies",value:function(e){return[hn.pendingVideoTagStatus(e)]}},{key:"getVideoDataDependencies",value:function(e){return[Cn.videoData(e)]}}]),e}();function ms(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable})),n.push.apply(n,r)}return n}function bs(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?ms(Object(n),!0).forEach(function(t){f()(e,t,n[t])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):ms(Object(n)).forEach(function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))})}return e}var Os={READY_EVENT:"ready",PLAY_EVENT:"play",PAUSE_EVENT:"pause",TIME_EVENT:"time",SEEK_EVENT:"seek",COMPLETE_EVENT:"complete",VOLUME_EVENT:"volume",MUTE_EVENT:"mute"},_s=Object.values(Os),Ss={FULLSCREEN_EVENT:"fullscreen",ANCHOR_STATUS_EVENT:"anchorStatusChanged",ANCHOR_CLOSED_EVENT:"anchorClosed"},Es={AD_PLAY_EVENT:"adPlay",AD_PAUSE_EVENT:"adPause",AD_RESUME_EVENT:"adResume",AD_COMPLETE_EVENT:"adComplete",AD_TIME_EVENT:"adTime",AD_MUTE_EVENT:"adMute",AD_SKIPPED_EVENT:"adSkipped",AD_ERROR_EVENT:"adError",AD_BLOCK_EVENT:"adBlock",AD_REQUEST_EVENT:"adRequest",AD_OPPORTUNITY_EVENT:"adOpportunity",AD_IMPRESSION_EVENT:"adImpression"},ws=Object.values(Es),Ps=Object.values(bs(bs(bs({},Os),Es),Ss)),Ts=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"eventsCallbacksHandler",void 0),f()(this,"store",void 0),f()(this,"videoStatusSubscriber",void 0),f()(this,"videoMuteSubscriber",void 0),f()(this,"videoVolumeSubscriber",void 0),f()(this,"videoTimeFragmentSubscriber",void 0),f()(this,"videoListStoreSubscriber",void 0),f()(this,"previousVideoTagStatus",void 0),f()(this,"startSeekTime",0),f()(this,"canHandleReady",function(e,t,n){if(t===Os.READY_EVENT){var r=Cn.videoList(e);if(Array.isArray(r)&&r.length>0)return n(),!0}return!1}),f()(this,"canBeHandled",function(e,t){var n=r.store.getState;return r.canHandleReady(n(),e,t)}),f()(this,"reportSeekEnd",function(e){var t={position:hn.currentVideoTimeFragment(e),offset:r.startSeekTime};r.eventsCallbacksHandler.onEvent(Os.SEEK_EVENT,t)}),f()(this,"onMuteStateChanged",function(e){var t=gn.muted(e);r.eventsCallbacksHandler.onEvent(Os.MUTE_EVENT,{state:t})}),f()(this,"onVolumeChanged",function(e){var t=gn.muted(e),n=gn.volume(e);r.eventsCallbacksHandler.onEvent(Os.VOLUME_EVENT,{level:t?0:n})}),f()(this,"onVideoTimeFragmentChanged",function(e){var t=hn.currentVideoTimeFragment(e),n=hn.currentVideoDuration(e);r.eventsCallbacksHandler.onEvent(Os.TIME_EVENT,{duration:n,position:t})}),f()(this,"onVideoListChanged",function(){r.eventsCallbacksHandler.onEvent(Os.READY_EVENT)}),this.store=t,this.eventsCallbacksHandler=n,this.videoStatusSubscriber=new ji(t,e.getVideoStatusDependencies,this.onVideoStatusChanged.bind(this)),this.videoMuteSubscriber=new ji(t,e.getVideoMuteDependencies,this.onMuteStateChanged.bind(this)),this.videoVolumeSubscriber=new ji(t,e.getVolumeDependencies,this.onVolumeChanged.bind(this)),this.videoTimeFragmentSubscriber=new ji(t,e.getVideoTimeDependencies,this.onVideoTimeFragmentChanged.bind(this)),this.videoListStoreSubscriber=new ji(t,e.getVideoListDependencies,this.onVideoListChanged.bind(this)),this.previousVideoTagStatus=hn.videoTagStatus(t.getState())}return Vi()(e,[{key:"onVideoStatusChanged",value:function(e){var t=hn.videoTagStatus(e);switch("seeking"===this.previousVideoTagStatus&&this.reportSeekEnd(e),t){case"paused":this.eventsCallbacksHandler.onEvent(Os.PAUSE_EVENT);break;case"seeking":this.startSeekTime=hn.currentVideoTimeFragment(e);break;case"complete":this.eventsCallbacksHandler.onEvent(Os.COMPLETE_EVENT);break;case"playing":this.eventsCallbacksHandler.onEvent(Os.PLAY_EVENT)}this.previousVideoTagStatus=t}}],[{key:"getVideoStatusDependencies",value:function(e){return[hn.videoTagStatus(e)]}}]),e}();f()(Ts,"getVideoMuteDependencies",function(e){return[gn.muted(e)]}),f()(Ts,"getVolumeDependencies",function(e){return[gn.volume(e)]}),f()(Ts,"getVideoTimeDependencies",function(e){return[hn.currentVideoTimeFragment(e)]}),f()(Ts,"getVideoListDependencies",function(e){return[Cn.videoList(e)]}),f()(Ts,"isContentEvent",function(e){return _s.some(function(t){return t===e})});var As=function e(t,n){var r=this;Ai()(this,e),f()(this,"eventsCallbacksHandler",void 0),f()(this,"store",void 0),f()(this,"fullscreenSubscriber",void 0),f()(this,"anchorStatusSubscriber",void 0),f()(this,"anchorDisabledByUserSubscriber",void 0),f()(this,"onFullscreenChanged",function(e){var t=gn.isFullscreenOn(e);r.eventsCallbacksHandler.onEvent(Ss.FULLSCREEN_EVENT,{state:t})}),f()(this,"onAnchorStatusChanged",function(e){var t="active"===Pr(e)?"activated":"deactivated";r.eventsCallbacksHandler.onEvent(Ss.ANCHOR_STATUS_EVENT,{state:t})}),f()(this,"onAnchorDisabledByUser",function(e){if(wr(e)){var t=hn.currentVideoTimeFragment(e);r.eventsCallbacksHandler.onEvent(Ss.ANCHOR_CLOSED_EVENT,{position:t})}}),this.store=t,this.eventsCallbacksHandler=n,this.fullscreenSubscriber=new ji(t,e.getFullscreenDependencies,this.onFullscreenChanged.bind(this)),this.anchorStatusSubscriber=new ji(t,e.getAnchorStatusDependencies,this.onAnchorStatusChanged.bind(this)),this.anchorDisabledByUserSubscriber=new ji(t,e.getAnchorDisabledByUserDependencies,this.onAnchorDisabledByUser.bind(this))};f()(As,"getFullscreenDependencies",function(e){return[gn.isFullscreenOn(e)]}),f()(As,"getAnchorStatusDependencies",function(e){return[Pr(e)]}),f()(As,"getAnchorDisabledByUserDependencies",function(e){return[wr(e)]});var Cs=function(){function e(t,n){var r=this;Ai()(this,e),f()(this,"store",void 0),f()(this,"eventsCallbacksHandler",void 0),f()(this,"adStatusSubscriber",void 0),f()(this,"adImpressionSubscriber",void 0),f()(this,"adOpportunitySubscriber",void 0),f()(this,"adTimeSubscriber",void 0),f()(this,"adMuteSubscriber",void 0),f()(this,"adProviderLoadingStatusSubscriber",void 0),f()(this,"previousAdStatus",void 0),f()(this,"canBeHandled",function(e,t){var n=r.store.getState;switch(Fi.loadingImaStatus(n())){case"loading":return!1;case"success":case"error":return!0;case"blocked":return t(),!0;case"":default:return!1}}),f()(this,"onAdStatusChanged",function(e){var t=_i.adStatus(e),n=_i.currentAdTag(e);switch(t){case"requested":r.eventsCallbacksHandler.onEvent(Es.AD_REQUEST_EVENT,{tag:n});break;case"paused":r.eventsCallbacksHandler.onEvent(Es.AD_PAUSE_EVENT,{tag:n});break;case"completed":r.eventsCallbacksHandler.onEvent(Es.AD_COMPLETE_EVENT,{tag:n});break;case"skipped":r.eventsCallbacksHandler.onEvent(Es.AD_SKIPPED_EVENT,{tag:n});break;case"playing":"paused"===r.previousAdStatus?r.eventsCallbacksHandler.onEvent(Es.AD_RESUME_EVENT,{tag:n}):r.eventsCallbacksHandler.onEvent(Es.AD_PLAY_EVENT,{tag:n});break;case"error":var i=_i.adErrorMessage(e);r.eventsCallbacksHandler.onEvent(Es.AD_ERROR_EVENT,{tag:n,message:i})}r.previousAdStatus=t}),f()(this,"onAtTimeChanged",function(e){var t=_i.adCurrentTime(e),n=_i.currentAdTag(e),i=_i.adDuration(e);r.eventsCallbacksHandler.onEvent(Es.AD_TIME_EVENT,{position:t,tag:n,duration:i})}),f()(this,"onAdMuteChanged",function(e){var t=_i.adMuted(e);r.eventsCallbacksHandler.onEvent(Es.AD_MUTE_EVENT,{state:t})}),f()(this,"onAdProviderLoadingChanged",function(e){"blocked"===Fi.loadingImaStatus(e)&&r.eventsCallbacksHandler.onEvent(Es.AD_BLOCK_EVENT)}),f()(this,"onAdImpressionChanged",function(e){var t=_i.currentAdTag(e);r.eventsCallbacksHandler.onEvent(Es.AD_IMPRESSION_EVENT,{tag:t})}),f()(this,"onAdOpportunityChanged",function(e){var t=_i.currentAdTag(e);r.eventsCallbacksHandler.onEvent(Es.AD_OPPORTUNITY_EVENT,{tag:t})}),this.store=t,this.eventsCallbacksHandler=n,this.previousAdStatus=_i.adStatus(t.getState()),this.adStatusSubscriber=new ji(t,e.getAdStatusDependencies,this.onAdStatusChanged.bind(this)),this.adTimeSubscriber=new ji(t,e.getAdTimeDependencies,this.onAtTimeChanged.bind(this)),this.adMuteSubscriber=new ji(t,e.getAdMuteDependencies,this.onAdMuteChanged.bind(this)),this.adImpressionSubscriber=new ji(t,e.getAdImpressionDependencies,this.onAdImpressionChanged.bind(this)),this.adOpportunitySubscriber=new ji(t,e.getAdOpportunitySubscriberDependencies,this.onAdOpportunityChanged.bind(this)),this.adProviderLoadingStatusSubscriber=new ji(t,e.getAdProviderLoadingDependencies,this.onAdProviderLoadingChanged.bind(this))}return Vi()(e,null,[{key:"getAdStatusDependencies",value:function(e){return[_i.adStatus(e)]}},{key:"getAdTimeDependencies",value:function(e){return[_i.adCurrentTime(e)]}},{key:"getAdMuteDependencies",value:function(e){return[_i.adMuted(e)]}},{key:"getAdProviderLoadingDependencies",value:function(e){return[Fi.loadingImaStatus(e)]}}]),e}();f()(Cs,"isAdEvent",function(e){return ws.some(function(t){return t===e})}),f()(Cs,"getAdImpressionDependencies",function(e){return[_i.adImpression(e)]}),f()(Cs,"getAdOpportunitySubscriberDependencies",function(e){return[_i.adOpportunity(e)]});var Rs=function e(t){var n=this;Ai()(this,e),f()(this,"contentEvents",void 0),f()(this,"generalEvents",void 0),f()(this,"adEvents",void 0),f()(this,"subscribers",{}),f()(this,"onRegisterToEvent",function(e,t,r){n.isValidEvent(e)&&(Ts.isContentEvent(e)&&n.contentEvents.canBeHandled(e,t)||Cs.isAdEvent(e)&&n.adEvents.canBeHandled(e,t())||n.getEventSubscribersList(e).push({callback:t,once:r}))}),f()(this,"isValidEvent",function(e){return Ps.some(function(t){return t===e})}),f()(this,"getEventSubscribersList",function(e){return Array.isArray(n.subscribers[e])||(n.subscribers[e]=[]),n.subscribers[e]}),f()(this,"filterOutOnceCallbacks",function(e,t){n.subscribers[e]=t.filter(function(e){return!e.once})}),f()(this,"onEvent",function(e,t){var r=n.getEventSubscribersList(e);r.forEach(function(e){(0,e.callback)(t)}),n.filterOutOnceCallbacks(e,r)}),this.contentEvents=new Ts(t,this),this.generalEvents=new As(t,this),this.adEvents=new Cs(t,this)},Ds=function(){function e(t){Ai()(this,e),f()(this,"eventsHandler",void 0),this.eventsHandler=new Rs(t)}return Vi()(e,[{key:"on",value:function(e,t){this.eventsHandler.onRegisterToEvent(e,t,!1)}},{key:"once",value:function(e,t){this.eventsHandler.onRegisterToEvent(e,t,!0)}}]),e}();function Ms(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable})),n.push.apply(n,r)}return n}function Is(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?Ms(Object(n),!0).forEach(function(t){f()(e,t,n[t])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Ms(Object(n)).forEach(function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))})}return e}var ks=function(){var e=window.monti.dataset;return jn(e)?(e={players:{},preact:Is(Is({},r),i),store:{},plugins:{}},window.monti.dataset=e,e):e},Ns=function(e){var t=function(){var e=(new Date).getTime(),t=performance&&performance.now&&1e3*performance.now()||0;return"xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx".replace(/[xy]/g,function(n){var r=16*Math.random();return e>0?(r=(e+r)%16|0,e=Math.floor(e/16)):(r=(t+r)%16|0,t=Math.floor(t/16)),("x"===n?r:3&r|8).toString(16)})}(),n=function(e,t){var n=ns(e.dev_config),r=ks(),i=n.dispatch;return r.store[t]=n,function(e,t){return function(n){n({type:"[CORE] initiate store",payload:{initiateParams:e,playerInstanceUniqId:t}})}}(e,t)(i),n}(e,t);return function(e,t,n){B(b(Pi,{playerId:t,store:n,playerPosition:e}),e)}(e.player_pos,t,n),oa.getInstance().loadInternalPlugins(n,t,e),ss.createInstance(n,t),vs.createInstance(n,t),is.createInstance(n,e.media_id,e.content_type,e.dev_config),function(e){var t=e.dispatch;if(er(Ci.getInstance().getHLSLoadingStatus())(t),Qn(Ci.getInstance().getIMALoadingStatus())(t),!Ci.getInstance().isDependenciesReady()){var n=new os(e);Ci.getInstance().addDependenciesCallback(n)}}(n),function(e,t){ks().players[t]=new Ds(e)}(n,t),t},Ls=function(e){return console.log("player initiation start",e),new Promise(function(t,n){try{var r=function(e){var t=e.player_pos||document.currentScript.parentElement,n=e.media_id||e.content_id;return cs(cs({},e),{},{player_pos:t,media_id:n})}(function(e){var t=e.player_id,n=ds(t);return null===n?e:cs(cs({},e),n)}(e)),i=Ns(r);!function(e){var t=new CustomEvent("montiConfigLoaded",{detail:{playerKey:e}});window.dispatchEvent(t)}(i),t(i)}catch(o){console.error("Player initiation error",o),n(o)}})},xs=function(){return{initiate:Ls}};window.monti=xs,Ci.getInstance().loadExternalDependencies()}]); window.monti().initiate(Object.assign({player_pos: document.currentScript.parentElement}, {"is_conflicting_with_other_jw_players":false,"programmatic_play_with_sound_on_desktop":false,"referrer_id":"af93e181-b289-0560-a2bf-808e93bb05bc","width":"100","comscore_publisher_id":"18120612","monetization":{"ad_type":"static_tag","continue_content_play_while_waiting_for_ad":false,"strategy":"on_player_load","ad_request_timeout":"10000","midrolls":{"on":[0]},"vpaid_mode":"ENABLED","ad_tag":"https://pubads.g.doubleclick.net/gampad/ads?sz=400x300|640x480|480x270|640x360&iu=/175840252/MMPlus/smithsonianmag/Video&impl=s&gdfp_req=1&env=vp&output=vast&unviewed_position_start=1&url=##REFERRER_URL_UNESC##&description_url=##DESCRIPTION_URL_UNESC##&correlator=##CACHEBUSTER##&cust_params=mm_midroll%3D##MIDROLL_ORDER##%26video_ID%3D##VIDEO_ID##"},"sponsorship":false,"player_identifier":"mplayer","recommendation_id":null,"brand_color":"#FF9900","powered_by_strip":true,"platform":"buffy","type":"video","config_name":"MM+ | Smithsonianmag | Podding","player_id":"3v9g2u2f","playlist_id":"fSkmeWKF","playback_method":"autoplay","anchor_viewability_method":"none","player_version":"v4","playlist_type":"semantic","semantic_options":{"scan_images_on_page":true,"scanned_element":"","tags":"geogrophy,nature,animals,habitat,outdoors,science,history","minimum_date_factor":30,"scanned_element_type":"tag","scoped_keywords":"mentalfloss","promoted_videos":[]},"script_destination":"mm","publisher_contribution":"floor8","general_script_description":"","brand_logo":"","brand_logo_click_url":"","next_video":"none","uniq_key":"af93e181-b289-0560-a2bf-808e93bb05bc","content_id":"fSkmeWKF","content_type":"semantic"})); Finland has vastly improved in reading, math and science literacy over the past decade in large part because its teachers are trusted to do whatever it takes to turn young lives around. This 13-year-old, Besart Kabashi, received something akin to royal tutoring.

      Kari Louhiuori, a principal at a Finnish school made a mostly unheaard of and uncanny decision to hold back an immigrant student from 6th grader named Besart because he hadnʻt felt that this young man was falling behind due to laziness but to a lack of comprehension. After a year of "royal tutoring," by allowing the boy to read at his own pace, it worked!

    1. Reviewer #3

      The work by Barros et al. looks at the role of the Ribosome Quality Control pathway (RQC) in regulating the expression of endogenous messages containing polybasic sequences. Using ribosome profiling and western blotting, the authors show that proteins containing various types of polybasic sequences are not targeted by the RQC. The authors argue that one of the few endogenous RQC substrate, RQC1, is not regulated via the canonical RQC pathway, but by a Ltn1p-dependent post transcriptional mechanism.

      The question of whether there are endogenous RQC substrates has previously been explored. With the exception of the few identified substrates, such as RQC1 (Brandman et al, 2012) and SDD1 (Matsuo et al., 2020), these studies largely concluded the RQC has a minimal regulatory role for endogenous messages, and is most likely protecting cells from damage and environmental stressors. This idea is further supported by the observation that the RQC is non-essential under standard growth condition, but becomes synthetic lethal with translation inhibitors (Kostova et al, 2017, Choe et al, 2016). The work by Barros et al. comes to the same conclusions, and therefore it is unclear how this work contributes to the already established role of the RQC.

      The authors also explore the regulation of RQC1 by the RQC and argue that this gene is regulated by Ltn1p in an RQC-independent way. However, mechanistic understanding of the proposed regulation is lacking, and the data are largely inconsistent with the previously published observations by Brandman et al, 2012.

      Major points:

      1) The authors use the dataset published by Pop et al., 2014 for their 27-29 nt no drug ribosome profiling analysis. However, these no-drug samples have been reported to exhibit surprising heterogeneity, and similarities with CHX-pretreated samples (see Hussmann et al., 2015 for detailed analysis). It is unclear how this heterogeneity can affect the analysis in the current manuscript, and whether the authors were aware of these caveats. Have the authors used independent datasets to confirm their observations? Have they excluded replicas that show CHX-like characteristics, such as A-site occupancy bias similar to CHX pretreated samples?

      2) It is not clear what the purpose of the analysis presented in Fig 2 is, and how it is different from the modeling in the Park and Subramaniam 2019 paper? Are the authors using these parameters (TE, Kozak score, etc.) to show adaptations that minimize ribosome collisions?

      3) Fig 3 - some of the selected examples (Dbp3, Yro2, Nop58) lack sufficient coverage in the region of interested highlighted in the right column for the short and/or long footprints. Since the data are insufficient to make conclusions about ribosome stalling and queuing, these examples should be excluded from the analysis.

      4) Fig 4:

      -Does ASC1 deletion cause frameshifting? Since the TAP-tag is C-terminal, it is possible that it is now out of frame, and therefore undetectable. Is it possible for the authors to introduce the tag on the N-terminus, and follow simultaneously the stalled nascent polypeptide (upon LTN1 deletion), and the full length protein?

      -Is the putative stalling site of Dbp3 too close to the stat codon to cause collisions?

      -Can the authors include a positive control, such as TAP-tagged Sdd1 to make sure their assay works and their strains and KOs behave as expected?

      5) Fig 5:

      -What is causing the inconsistency with the Brandman et al., 2012 data about RQC-dependent regulation of RQC1? In the original paper, Rqc1p has an N-terminal FLAG tag, so the authors primarily follow the stalled nascent polypeptide, whereas the current study focuses on the full length protein. Can the authors compare the same construct (FLAG-tagged Rqc1p) in their strains, so it is an "apples to apples" comparison?

      -Fig 5c bottom panel - the read coverage is too sparse to make a conclusion. This analysis should be removed.

      -5 d, e. The comparison between the GFP-12R-RFP stalling reporter and RQC2-TAP is not fair. The GFP construct reports on the fate of the stalled nascent polypeptide, whereas the RQC1-TAP looks at the full-length protein, and remains blind to the putative stalling product. Can the authors change the location of the tag, and repeat the experiment now looking at the stalled nascent polypeptide for RQC1? In addition, the signal in Fig. 5e look saturated. Is it possible that no effect is observed simply because the TAP signal is out of the dynamic range for the assay?

      Minor Comments:

      1) The introduction presents an overly simplistic view of ribosome stalling, arguing that stalling can be caused by polybasic stretches. We now know that stalling is much more complex, and there are many other factors, including the presence of non-optimal codon pairs, that cause ribosome collisions. Although the authors discuss these factors in their discussion, they should also be emphasized in the introductory paragraph.

    1. At

      Sentence structure. Most everything carries that long sentence structure. At least in the beginning parts. If it changes I will tag in the bottom when I get there!

  3. Sep 2020
    1. The FBI said it has stopped using the "Black Identity Extremist" tag and acknowledged that white supremacist violence is the biggest terrorist threat this country faces.

      When using the "Always Check" Approach, this headline generated many relevant Google searches, with multiple other media outlets covering this. Hence, The Root appears to be credible. I'm very surprised that it took a long time for the FBI to make this decision.

    1. The <output> tag represents the result of a calculation. Typically this element defines a region that will be used to display text output from some calculation.

      How <output> tag can be used in HTML5

    1. D&D is the superior mental health resource

      Useful tag with stories and experiences

    1. globals are assumed to have their field value on the window object and can be referenced inside the bundle by their field name globals: { name: 'Value', }, assumes that some other script tag or whatever establishes window.Value and the emitted umd bundle for example, calls the factory like factory(global.Value). So globals is just stuff to bring into the factory on the globals object. It doesn't even make it "global" inside the bundle. Basically, the resolver does not check the globals object during the loading process. The resolver needs to be told how to link these globals and that's what the external option is for. external: ['name'], Then you can reference it like import myName from 'name'; myName();
    1. Reviewer #1:

      Previous work has shown that the nuclear import of TyrRS is stimulated under stress and that nucleus-localized TyrRS functions through the transcriptional machinery to promote the expression of DNA damage response genes for cell protection. In this work, evidence is presented that nuclear TyrRS also inhibits bulk translation in a manner correlated with its association with several AARS-encoding genes and that for elongation factor eEF1A, and recruitment to these genes of HDACs. Mutation of the TyrRS NLS, whose function in nuclear localization provides for coupling between low tRNATyr binding and nuclear localization, was found to derepress bulk translation after prolonged oxidative stress by H2O2, without altering eIF2 phosphorylation levels or mTOR activation, and overexpression (o/e) of TyrRS can reduce protein synthesis, in a manner enhanced by the E196K mutation associated with Charcot-Marie-Tooth disease (CMT), shown previously to enhance TyrRS association with transcriptional co-repressors. ChIP-Seq of overexpressed V5-tagged TyrRS showed binding to only 17 sites, of which 15 are within gene coding sequences, among which four encode TyrRS, TrpRS, SerRS and GlyRS, and a fifth encodes elongation factor eEF1A. These results were confirmed by ChIP analysis of endogenous TyrRS, using the HisRS gene as negative control; and the occupancies were shown to increase on H2O2 treatment. The expression of these AARS/eEF1A gene transcripts was shown to be reduced by o/e of TyrRS, in a manner enhanced for at least some of them by the E196K CMT mutation; and the repression was shown to be eliminated by the NLS_mut for YARS expressed at native levels. Reductions in AARS/eEF1A protein expression were also observed on WT TyrRS o/e. Sequence analysis of the genes showing TyrRS binding by ChIP-seq led to identification of a motif that was shown to be required for binding to TyrRS in vitro in EMSA assays with either purified TyrRS or in extracts from cells overexpressing it, in a manner requiring the full-length TyrRS and not only the catalytic core of the enzyme. It was not shown however that eliminating this motif from any of the target genes attenuated their repression by nuclear-localized TyrRS. Mass spec analysis of affinity-purified, overexpressed TyrRS identified interacting proteins, and several of which were shown to be coimmunoprecipitated with endogenous TyrRS in non-stressed cells, including the transcription cofactors Trim28, HDAC1, and subunits of the NURD co-repressor/histone deacetylase complex. ChIP assays showed that overexpression of TyrRS lead to decreased levels of H3K27Ac, a histone mark of active transcription, and elevated occupancies HDAC1, TRIM28, or NURD subunit CHD4 in non-stressed cells at the AARS/eEF1A genes, with either TRIM28/HDAC1 or CHD4 being observed for all of the genes except the TyrRS gene that shows all three cofactors present. Based on these results, the authors conclude that increased nuclear localization of TyrRS on oxidative stress leads to increased binding of TyrRS to the AARS/eEF1A genes with attendant direct recruitment of either TRM28/HDAC1 or NURD, leading to transcriptional repression of these genes, which is responsible for the reduction in bulk protein synthesis observed after prolonged H2O2 treatment. They go on to provide evidence that cell survival in H2O2 is enhanced by nuclear association of TyrRS (dependent on the NLS), and that in its absence, conferred by the NLS_mut, apoptosis is increased. They also show that ROS increases by preventing TyrRS nuclear localization by the NLS_mut, and that this effect as well as decreased cell survival for this mutant in H2O2 can be rescued by the translation elongation inhibitor harringtonine.

      The results presented in this report provide some support for the main conclusions of the paper and the overall model presented in Fig. 4F. However, as detailed below, many of the main conclusions of the paper are based on correlations and lack direct experimental support, and a number of the experiments are not comprehensive enough with sufficient conditions and controls to establish that the effects observed can be attributed to enhanced nuclear localization of TyrRS in response to H2O2. Considering the statements in the abstract, the evidence is reasonably strong that nuclear localization of TyrRS leads to inhibition of global translation at a stage later than that of eIF2α/ATF4 and mTOR responses, and that excluding TyrRS from the nucleus increases apoptosis under prolonged oxidative stress (although even this last point requires better documentation). However, the evidence is inadequate in several respects to claim that TyrRS directly represses the transcription of translation-related genes by recruiting TRIM28 or NURD complex, and as claimed on p. 13 of the Discussion, that the repression of the four AARS genes and the gene for eEF1A accounts for the reduction in bulk protein synthesis on H2O2 treatment.

      Major issues:

      -Evidence is lacking that the binding of TyrRS to the AARS/eEF1A genes is functionally important for the repression of any of the 6 putative target genes upon increased nuclear localization of TyrRS conferred by the NLS_mut or in response to H2O2. This would require ChIP analysis of TyrRS binding to the target genes for WT vs. NLS_mut TyrRS in H2O2-treated cells; and CRISPR mutagenesis of the putative TryRS binding site in the genome and analysis of transcription in the presence and absence of H2O2 for at least one of the putative TyrRS target genes.

      -Evidence from ChIP analysis is lacking that TRIM28, HDAC1, or the NURD complex are recruited to the AARS/eEF1A genes at native levels of TyrRS in a manner dependent on the NLS and stimulated by H2O2, as the ChIP experiments involved only overexpressed WT TyrRS in non-stressed cells. It is also unclear whether H3K27Ac levels at the putative target genes decline at endogenous levels of TyrRS on treatment with H2O2. Similarly, evidence is lacking that the physical association of TyrRS with these co-repressors is dependent on the NLS and stimulated by H2O2, as the co-IP analysis was limited to endogenous WT TyrRS in non-stressed cells.

      -Evidence is lacking that the cofactors TRIM28, HDAC1, or CHD4 are required for the down-regulation of target gene transcription on H2O2 treatment, which would require knock-down or elimination of these factors by CRISPR accompanied by analysis of target gene transcription +/- H2O2.

      -Direct evidence is lacking from ChIP analysis of RNA Pol II that the transcription of the AARS/eEF1A genes is reduced on H2O2.

      -Evidence is lacking that the repression of bulk protein synthesis is actually mediated by the reduced expression of the 4 AARSs and eEF1A. The fact that the TyrRS-E196K mutation enhances repression of bulk translation and also repression of 3 of the 5 target genes does support the idea that the repression of the target genes is instrumental in reducing protein synthesis, but again, this is still a correlation. There is no evidence that the reduced expression of the AARSs is sufficient to reduce charging of the cognate tRNAs, or that the reduced expression of eEF1A decreases the rate of translation elongation in cells or cell extracts.

      -There is an important lack of information provided needed to evaluate the quality and significance of the ChIP-seq analysis of TyrRS binding to DNA. No details are provided concerning the ChIP-seq analysis of V5-tagged TyrRS to indicate how the TyrRS occupancy peaks were identified and distinguished above background signal from the cells expressing V5 tag alone, whether replicates were examined to provide statistical significance for the identified occupancy peaks, and the sequencing library depths. No genome browser views were provided to show the signals from the cells expressing V5-TyrRS vs V5 alone to demonstrate the quality and reproducibility of data from replicates. The supplementary table S1 describing these data was even omitted from the submission, and it's unclear whether these data are being deposited in GEO.

      -There is an important lack of information provided needed to evaluate the quality and significance of the mass-spec analysis of TyrRS interacting proteins. No details are provided about the statistical significance of the protein interactions identified by mass-spec analysis of the affinity-purified TyrRS; and a negative control for non-specific association seems not to have been included in the analysis. The supplementary table describing these data was even omitted from the submission.

      -It's unclear whether the motif described in Fig. 3A was found under the peaks of TyrRS occupancy in the various genes showing TyrRS binding in the ChIP-seq experiments, nor whether its occurrence is statistically significant. It was not indicated that the motif coincides with the peak ChIP-seq occupancies for TyrRS, and if not, how this could be explained.

      -Evidence is lacking that harringtonine treatment reduced bulk protein synthesis under the conditions where it suppressed the effects of the TryRS NLS mutation in elevating ROS and decreasing cell survival.

      -In general, the figure legends are poorly written in lacking important details about the nature of the TyrRS being examined in the experiment (tagged vs endogenous; overexpressed vs. native levels), and also whether oxidative stress was imposed in the experiment, and if so, the exact conditions for the treatment. Figure legends should contain all of the critical details needed to understand and evaluate the significance of the experimental results without having to search elsewhere in the paper for them.

      -It needs to be clarified whether the mini-TyrRS construct lacks the NLS, and the significance of its behavior as a negative control for the effects of overexpressing WT TyrRS.

      -For the experiment in Fig. 5B, quantification of the fraction of caspase-3 or PARP cleaved from biological replicates is required.

      -The experiment in Supp. Fig. S4 lacks the results from cells untreated with H2O2 to ensure that these proteins were being induced by H2O2 in their hands.

    1. including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance<br> yes ma

    1. Your tooltip component will have to wrap your image with a span tag or something, it can’t just add events to its children. And if you are adding multiple actions to it you will have to wrap it multiple times.
      <Concern1> <Concern2> </Concern2> </Concern1>

      vs.

      <img use:concern1 use:concern2>

    1. “Who are you then?" "I am part of that power which eternally wills evil and eternally works good.”

    1. Anti‐Flag monoclonal

      DOI: 10.1096/fj.202001340R

      Resource: (Wako Cat# 018-22381, RRID:AB_10659453)

      Curator: @Naa003

      SciCrunch record: RRID:AB_10659453

      Curator comments: Anti-DYKDDDDK tag Monoclonal Antibody, Unconjugated, Clone 1E6 Wako Cat# 018-22381


      What is this?

    2. anti‐Myc‐tag

      DOI: 10.1096/fj.202001340R

      Resource: (MBL International Cat# 562, RRID:AB_591105)

      Curator: @Naa003

      SciCrunch record: RRID:AB_591105

      Curator comments: Rabbit Anti-Myc Tag Antibody, Unconjugated MBL International Cat# 562


      What is this?

    1. Ich würde das, was du hier diagnostizierst, als Epistemic Crisis bezeichnen. Ich nehme sie genauso wahr wie du, und ich bin auch darüber entsetzt. Das erste Warnsignal, das ich ernst genommen habe, war der Brexit, das zweite die Wahl von Trump. Beide habe ich vorher nicht erwartet, weil sie jenseits des Horizonts waren, in dem ich Entwicklungen erwartet habe. ich muss also auch an der Art und Weise zweifeln, in der ich politische Entwicklungen verstanden habe.—Später kam dann für mich der Aufstieg der Freiheitlichen hier in Österreich, bis hin zur Regierungsbeteiligung, und die rechtspopulistische Welle (wenn man es so nennen will) in Frankreich und Italien.

    1. Your solution is a very basic. The case above is more complex because using your solution you can't manipulate with fetched data outside of template and even outside {#await / } tag. So, if you need a read-only solution it's good but otherwise, it won't help you.
    1. I save the things I read online, too, in a digital research library. I’ve long used Evernote to clip the full text of articles I find and gather them in various digital notebooks, separated into categories for easy reference. I can full-text search everything that I've saved over the past decade, to find the citation really quickly. The combination of my physical library and my note-taking softwares act as a kind of external brain—in other words, my memory gets me to the original source of what I’ve read by searching my notebooks, Evernote, or Pinboard.Recently I’ve been migrating this clip-taking to Pinboard, inspired by a Superorganizers post. Pinboard is much like Evernote, but allows you to tag clipped articles into multiple categories. Pinboard automatically saves a full-text version of each page you clip, so you can search and reference the text even if the website is removed or the page is no longer available. 

      Pinboard, huh? I should take a look at this.

    1. The RFID tag - short for Radio-Frequency Identification - is ubiquitous in the modern economy.

      Bugs

    1. Say I want to style this javascript routing anchor tag on various pages (some may be buttons, plain links, images) it makes it incredibly difficult. Eg:
    1. learning

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

    1. 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.

    1. This package exposes an hyperscript compatible function: h(tag, properties, ...children) which returns a svelte component.
    1. Mouse anti-HA

      DOI: 10.1016/j.celrep.2020.108101

      Resource: AB_2864345

      Curator: @Naa003

      SciCrunch record: RRID:AB_2864345

      Curator comments: HA-Tag (26D11) Mouse Antibody Abmart Cat# M20003


      What is this?

    1. n the Iranian case, meanwhile, the people tweeting about the demonstrations were almost all in the West. “It is time toget Twitter’s role in the events in Iran right,” Golnaz Esfandiari wrote, this past summer, in Foreign Policy. “Simply put: There wasno Twitter Revolution inside Iran.” The cadre of prominent bloggers, like Andrew Sullivan, who championed the role of socialmedia in Iran, Esfandiari continued, misunderstood the situation. “Western journalists who couldn’t reach—or didn’t botherreaching?—people on the ground in Iran simply scrolled through the English-language tweets post with tag #iranelection,” shewrote. “Through it all, no one seemed to wonder why people trying to coordinate protests in Iran would be writing in anylanguage other than Farsi.”

      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.

    Annotators

    1. In the Iranian case, meanwhile, the people tweeting about the demonstrations were almost all in the West. “It is time to get Twitter’s role in the events in Iran right,” Golnaz Esfandiari wrote, this past summer, inForeign Policy.“Simply put: There was no Twitter Revolution inside Iran.” The cadre of prominent bloggers, like Andrew Sullivan, who championed the role of social media in Iran, Esfandiari continued, misunderstood the situation. “Western journalists whocouldn’t reach—or didn’t bother reaching?—people on the ground in Iran simply scrolled through the English-language tweets post with tag #iranelection,” she wrote. “Through it all, no one seemed to wonder why people trying to coordinate protests in Iran would be writing in any language other than Farsi.”

      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.

    Annotators

    1. Ein Tag im Exilwo die Stunden sich bückenum aus dem Keller ins Zimmer zu komme

      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?

    2. Ein Tag im ExilHaus ohne Türen und FensterAuf weißerTafel mit Kohle verzeichnetdie Zeit

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

    1. 「晨间记录」和「晚间思考」(Morning Journal & Evening Reflectin)这两个板块用于早晚的个人记录和总结。「输入(Input)」指我这一天做了什么、学了什么、了解了什么;「输出(Output)」则更关注产出,包括「地标(Landmark)」这样值得铭记的成就和阶段性成果;「个人观察记录」更多是跟我身心状态相关的记录。如果我工作在一个具体而较为宏观的任务上,我就会选择创建对应 Page 并跳转到其中去工作。等待任务完成再回到 Journal 中。此外,如果不生成新页面的话,我会尽量给某个记录添加相应的 Tag,以便索引。

      DEF

    1. rabbit anti-HA epitope tag, DyLight™ 549

      DOI: 10.1186/s13064-020-00146-6

      Resource: (Rockland Cat# 600-442-384, RRID:AB_1961543)

      Curator: @Naa003

      SciCrunch record: RRID:AB_1961543

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


      What is this?

    1. anti-HA

      DOI: 10.3390/cancers12071989

      Resource: (Abcam Cat# ab18181, RRID:AB_444303)

      Curator: @Naa003

      SciCrunch record: RRID:AB_444303

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


      What is this?

    1. anti‐Myc

      DOI: 10.1111/bph.15023

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

      Curator: @Naa003

      SciCrunch record: RRID:AB_331783

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


      What is this?

    1. HA-Tag (6E2) mouse mAb (Alexa Fluor 488 conjugate)

      DOI: 10.1016/j.chembiol.2020.03.004

      Resource: (Cell Signaling Technology Cat# 2350, RRID:AB_491023)

      Curator: @ethanbadger

      SciCrunch record: RRID:AB_491023


      What is this?

    1. 2367s

      DOI: 10.1101/gad.332395.119

      Resource: (Cell Signaling Technology Cat# 2367, RRID:AB_10691311)

      Curator: @Naa003

      SciCrunch record: RRID:AB_10691311

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


      What is this?

    2. HA

      DOI: 10.1101/gad.332395.119

      Resource: (Cell Signaling Technology Cat# 3724, RRID:AB_1549585)

      Curator: @Naa003

      SciCrunch record: RRID:AB_1549585

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


      What is this?

    1. HA

      DOI: 10.2337/db19-0508

      Resource: (Cell Signaling Technology Cat# 3724, RRID:AB_1549585)

      Curator: @Naa003

      SciCrunch record: RRID:AB_1549585

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


      What is this?

    2. FLAG-tag

      DOI: 10.2337/db19-0508

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

      Curator: @Naa003

      SciCrunch record: RRID:AB_262044

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


      What is this?

    3. MYC-ta

      DOI: 10.2337/db19-0508

      Resource: (Millipore Cat# 05-724, RRID:AB_309938)

      Curator: @Naa003

      SciCrunch record: RRID:AB_309938

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


      What is this?

    1. anti-HA tag

      DOI: 10.3233/CBM-190993

      Resource: (Abcam Cat# ab130275, RRID:AB_11156884)

      Curator: @Naa003

      SciCrunch record: RRID:AB_11156884

      Curator comments: HA tag antibody [16B12] Abcam Cat# ab130275


      What is this?

    2. anti-6 ××<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" alttext="\times" display="inline" id="S2.SS8.p2.m3"><mml:mo>×</mml:mo></mml:math> His tag

      DOI: 10.3233/CBM-190993

      Resource: (Abcam Cat# ab18184, RRID:AB_444306)

      Curator: @Naa003

      SciCrunch record: RRID:AB_444306

      Curator comments: 6X His tag® antibody [HIS.H8] Abcam Cat# ab18184


      What is this?

    1. HA-tag

      DOI: 10.3390/ijms21051773

      Resource: (Cell Signaling Technology Cat# 3724, RRID:AB_1549585)

      Curator: @Naa003

      SciCrunch record: RRID:AB_1549585

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


      What is this?

    1. anti-V5 antibody

      DOI: 10.1038/s41586-020-1947-z

      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


      What is this?

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

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer 1

      __*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.

      • *

      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.

      • *

      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.


      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. *

      • *

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

      • *

      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.

      • *

      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.

      • *

      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.

      • *

      Our Response: We agree.__*


      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.

      • *

      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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      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.

    1. RRID:AB_2556564

      DOI: 10.1016/j.celrep.2020.107944

      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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      DOI: 10.1016/j.cub.2019.11.058

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      Curator comments: Anti-HA.11 Epitope Tag antibody BioLegend Cat# 901515


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      DOI: 10.1126/scisignal.aax2364

      Resource: (Cell Signaling Technology Cat# 2272, RRID:AB_10692100)

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      Resource: AB_2532664

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      DOI: 10.1016/j.celrep.2020.108044

      Resource: (Cell Signaling Technology Cat# 3724, RRID:AB_1549585)

      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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      DOI: 10.1016/j.str.2020.04.023

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      DOI: 10.1016/j.redox.2020.101669

      Resource: (Sigma-Aldrich Cat# F3165, RRID:AB_259529)

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      Curator comments: Monoclonal ANTI-FLAG® M2 antibody Sigma-Aldrich Cat# F3165


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      DOI: 10.1016/j.redox.2020.101669

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

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      DOI: 10.1074/jbc.RA120.012835

      Resource: (Cell Signaling Technology Cat# 3724, RRID:AB_1549585)

      Curator: @Naa003

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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: 10.3390/cancers12061643

      Resource: (Cell Signaling Technology Cat# 3724, RRID:AB_1549585)

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      DOI: 10.1523/JNEUROSCI.1707-19.2020

      Resource: (Synaptic Systems Cat# 245 003, RRID:AB_10805758)

      Curator: @Naa003

      SciCrunch record: RRID:AB_10805758

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      Resource: (OriGene Cat# TA180128, RRID:AB_2622290)

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      DOI: 10.1128/JVI.00099-20

      Resource: (Sigma-Aldrich Cat# H3663, RRID:AB_262051)

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      DOI: 10.7554/eLife.55806

      Resource: (Thermo Fisher Scientific Cat# PA1-993, RRID:AB_561893)

      Curator: @Naa003

      SciCrunch record: RRID:AB_561893

      Curator comments: V5 Tag Polyclonal Antibody Thermo Fisher Scientific Cat# PA1-993


      What is this?

    2. anti-V5

      DOI: 10.7554/eLife.55806

      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


      What is this?

    3. anti-HA

      DOI: 10.7554/eLife.55806

      Resource: (BioLegend Cat# 901515, RRID:AB_2565334)

      Curator: @Naa003

      SciCrunch record: RRID:AB_2565334

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      DOI: 10.1038/s41467-020-16695-7

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      DOI: 10.1172/JCI128994

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      DOI: 10.1074/jbc.RA119.010472

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

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      What is this?

    1. Y=L

      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

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    1. 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.

    1. So how do you use inline tagging to add keywords and categories while you read? Simply highlight a passage and add a note beginning with a period (.) followed by a single word or abbreviation (with no spaces).

      .productivity

    1. V5 Antibody

      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


      What is this?

    1. RRID:AB_2556564

      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


      What is this?

  4. Aug 2020
    1. Technical Communication Quarterly 145educated per se. Second, the "tag" of vocationalism, once established,took hold and was difficult to excise. Early impressions often linger,and the perception of "training for a trade" was slow to disappear. Inorder to counter the often negative perceptions of the profession ofengineering, educators embarked on curricular revision as one meansto elevate the social status of engineers.

      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.

    1. Anti-Myc tag

      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


      What is this?

    1. 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

    2. The fight against misinformation on social media and the internet in general has gained tremendous attention in the recent years. One way of combatting this has been to attach warnings tags about verified content. In this paper, we report two studies that examine the potential effects of a single warning tag in a context where the gist of a False claim is often repeated without the tag, which, given the reality of the way that information is transmitted, can be said to be an ecologically realistic model. Study 1 showed that the placement of the tag makes no difference, while a simple tag produces higher levels of belief than a tag with explanatory details. Remarkably, the simple tag produces a large increase in belief in the False claim. Study 2 showed that enhancing the distinctive character of a tag by adding irrelevant information to it produces a relative increase in believability equivalent to that obtained by making the claim graphically more distinctive. However, repeating a simple tag more often reduces this effect. These results indicate that the effects of warning tags are a combination of adding to the distinctiveness of the memory trace of the False claim (which makes this more believable by increasing fluency) and the semantic content of the tag (which reduces belief).
    1. It appears therefore that the labeled vitamin was absorbed from the large intestine, perhaps by non-specific passive diffusion. Similarly, vitamin B12 synthesized in the large intestine by microbial flora could gain access to the tissues by such a mechanism. The possible role of intestinal vitamin B12 of microbial origin in rats, and perhaps in man, and factors affecting the utilization of this vitamin are discussed.

      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"

    1. Perfil

      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.

    1. RRID:AB_1549

      DOI: 10.1016/j.celrep.2020.107974

      Resource: (Cell Signaling Technology Cat# 3724, RRID:AB_1549585)

      Curator: @Naa003

      SciCrunch record: RRID:AB_1549585

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


      What is this?

    1. 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.

    2. 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.

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      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.

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      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.

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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.

    1. osterior pituitary (hypothalamus)

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

    1. „die Welt wird enger mit jedem Tag

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

    1. „die Welt wird enger mit jedem Tag

      hektische kommerzialisiere Welt

  5. icla2020.jonreeve.com icla2020.jonreeve.com
    1. young men

      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

    1. 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.

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

    1. This is especially true as infrastructure often carries a high price tag, paid by citizens

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

    1. Alors que l’identifiant est unique et isole, le tag permet de regrouper des fiches qui ne peuvent être liées directement, mais qui partagent le(s) même(s) sujet(s).

      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)
    2. tag

      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.

    1. 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.

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

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

    1. Curso Hypothesis DGBSDI

      Segunda parte del Taller, excelente herramienta

  6. Jul 2020
    1. 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.

    2. 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.

    1. Why was the container type inferred? Because we did not specify a height attribute for the amp-img tag. In HTML, reflow can be reduced by always specifying a fixed width and height for elements on a page. In AMP, you need to define the width and height for amp-img elements so that AMP can pre-determine the aspect ratio of the element.
    2. While stylesheets can be reworked relatively easily with AMP by inlining the CSS, the same is not true for JavaScript. The tag 'script' is disallowed except in specific forms. In general, scripts in AMP are only allowed if they follow two major requirements: All JavaScript must be asynchronous (i.e., include the async attribute in the script tag). The JavaScript is for the AMP library and for any AMP components on the page. This effectively rules out the use of all user-generated/third-party JavaScript in AMP except as noted below.
    3. The above errors can be resolved by simply adding the ⚡attribute to the <html> tag like so: <html ⚡ lang="en">
    4. The meta charset information must also be the first child of the <head> tag. The reason this tag must be first is to avoid re-interpreting content that was added before the meta charset tag.

      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.)

    1. The next step is to link the canonical article to the AMP page. This is achieved by including a <link rel="amphtml"> tag to the <head> section of the canonical article.
  7. learn-eu-central-1-prod-fleet01-xythos.s3.eu-central-1.amazonaws.com learn-eu-central-1-prod-fleet01-xythos.s3.eu-central-1.amazonaws.com
    1. FLAG tag

      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..

    1. “How heavenly; how simply heavenly!”

      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.

    1. So why does Qui-Gon keep letting Jar Jar tag along? It’s the same reason he butts heads with the Jedi council: his connection to the living Force. His compassion is greater than the rigid and, frankly, arrogant views of the Jedi.

      Great positive viewpoint about Jar Jar Binks

    1. 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.

    1. A # character always indicates a block opening tag. A / character always indicates a block closing tag. A : character, as in {:else}, indicates a block continuation tag.
    1. SubsecTimeA tag used to record fractions of seconds for the DateTime tag.Tag =37520 (9290.H)Type=ASCIICount=AnyDefault=none
    1. Author Response

      Reviewer #1:

      In this manuscript, Cobb and colleagues report on the biochemical and functional characterization of redox active ER proteins in the malaria parasite Plasmodium falciparum. They studied a protein called PfJ2, which contains HSP40 J and Trx domains and is homologous to human ERdj5. Using the TetR-PfDOZI aptamer system to tag PfJ2 and conditionally regulate its expression, they show that PfJ2 is localized in the parasite ER and is essential for parasite growth during the asexual blood stages. Using co-immunoprecipitation combined with mass spectrometry, they identify partner proteins of PfJ2 including other ER proteins such as PDI and BIP. Using a chemical biology approach based on DVSF crosslinker, they document the redox activity of PfJ2 and identify redox substrates of PfJ2, which include PDI8 and PDI11 protein disulfide isomerases. They further functionally characterized PDI8 and PDI11 using the glmS ribozyme for conditional knockdown. These experiments confirm that PDI8 and PDI11 are partners of PfJ2 and show that knockdown of PDI8 impairs parasite blood stage growth. Finally, the authors show that inhibitors of human PDI inhibit parasite growth (at best in the micromolar range) and block the redox activity of PfJ2 and parasite PDI.

      This is an interesting study combining genetic and chemical biology approaches to investigate an understudied compartment of the malaria parasite. The manuscript is clearly written and the work technically sound. In summary, this study illustrates that ER redox proteins in the malaria parasite perform similar functions as in other organisms. The main limitation of this study is that evidence showing that redox ER parasite proteins are druggable is rather weak. PfJ2 is very similar to human ERdj5 in terms of active redox site and function, and the authors used inhibitors that are active on human PDI. It thus remains uncertain whether an antimalarial strategy targeting such conserved pathways is achievable.

      RESPONSE: We thank the reviewer for their appreciation of our work. While PfJ2 shares some similarity to human ERdJ5, we disagree that they are functionally similar. Our data show that, unlike ERdJ5, PfJ2 substrates are primarily other redox chaperones. In terms of the redox active site, our data clearly identifies a pathway that is targeted by a small molecule inhibitor. There is a lot of precedence for targeting conserved pathways as an antimalarial strategy. For example, anti-translational and anti-proteasomal inhibitors are being widely studied for their potency as antimalarials (Baragana et al 2015 Nature; Li et al 2016 Nature; Wong et al 2017 Nat. Microbiol.; Kirkman et al 2018 PNAS; Stokes et al 2019 PLoS Path.), several proteases (with conserved active sites) are well known antimalarial targets (Sleebs et al 2014 PLoS Biol.; Nasamu et al 2017 Science; Pino et al 2017 Science; Favuzza et al 2020 Cell Host Microbe), and effective inhibitors targeting a parasite chaperone has been repurposed for antimalarial drug development (Lu et al 2020 PNAS). We thank the reviewer for recognizing that there is a long road ahead of us to develop a more specific inhibitor for PfJ2, however, that is beyond the scope of this study.

      In addition, a number of specific points should be addressed to improve the quality of the manuscript:

      Although PDI8 and PDI11 gene edition were performed in the PfJ2apt line, the authors did not attempt to knockdown both PfJ2 and PDI8/11 simultaneously (because PfJ2 is essential). Therefore, referring to "double conditional mutants" is misleading.

      RESPONSE: We are open to alternative ways to refer to these mutants. Since we have orthogonal systems for knockdown of two proteins, we refer to these as double conditional mutants.

      The authors should provide details on the parasite lysis conditions used for the co-IP experiments to identify interacting proteins (Table 1) and redox partners (figure 3). In their proteomic analysis, the authors considered proteins with a 5-fold increase in the specific versus control conditions. A more stringent analysis would retain only proteins identified exclusively in the modified J2apt line.

      RESPONSE: We will include this in a new version. We agree that a more stringent analysis would lead to fewer proteins being identified, however, it also runs the risk of missing real interactors. We chose to use a 5-fold cutoff based on previously published work (Boucher et al 2018 PLoS Biol; Florentin et al 2020 PNAS).

      In figure 6, the authors should probe the blots for a control protein that is not co-immunoprecipitated with PfJ2 or PDI8. In Supplementary fig 4, control untreated parasites should be analyzed in parallel to GlcN-treated parasites.

      RESPONSE: We will do this once our labs reopen after the pandemic.

      The partial reduction of protein levels (Fig S4) shows that the glmS system is not very efficient here, which might explain why there is no phenotype in the PDI11 mutant (Fig5B). This questions the conclusion that PDI11 is dispensable.

      RESPONSE: We agree and we state that “These results...suggest that PfPDI11 may be dispensable... conclusions are supported by a genome-wide essentiality screen performed in P. falciparum” (Lines 319-322). We will add more discussion to explain this result.

      Reviewer #2:

      The claim here is of having discovered a druggable cellular process in P. falciparum, one that opens the door to therapeutic intervention in the most deadly form of malaria.

      The study commences with a focus on what appears to be the Pf homologue of a eukaryotic protein disulphide isomerase, known to many as ERdJ5 and referred to here as PfJ2. Its cellular contingents were identified by cross-linking and pull down, it’s (predicted) thiol reactivity explored with agents that react with reduced thiols and it’s functional importance to parasite fitness (in the lab) explored by gene knockout. These experiments provide evidence that PfJ2 and it’s associated Pf PDIs engage in thiol redox chemistry in the ER of the parasite and that integrity of this biochemical process is important to viability of the parasite.

      Lacking all expertise in molecular parasitology, this reader is unable to judge the specific significance of these findings to the field nor indeed the extent to which these are hard-won discoveries.

      RESPONSE: We are gratified to note that the reviewer is cognizant of their limitations and their ability to judge the significance of this work.

      However, from the perspective of the fundamentals of ER redox chemistry the findings represent a modest advance, showing that what is true of yeast and mammals is also true of Apicomplexa. The important mystery related to the juxtaposition of a J-domain and thioredoxin domains in PfJ2, remains.

      The most important claim however is the one with translational potential, namely that one might be able to discover (electrophilic) compounds that, despite the monotony of shared chemical features of thiol chemistry, will nonetheless possess sufficient specificity towards this or that malarial protein to be converted one day to a useful drug. However, in regards to this important point the authors offer very little in the way of evidence how and if this might be achieved.

      RESPONSE: We disagree. The work does not reconfirm the ‘fundamentals of ER redox’ chemistry. There is no work, in any system, that has shown that PfJ2-like proteins act as reductases for PDIs. In fact, as we state in the paper, in other model systems, there is a lot of redundancy built in the ER redox systems and PfJ2-like proteins work with specific clients like SERCA pumps or LDL receptor. Thioredoxin domain proteins in the ER of other eukaryotes have not been shown to work with each other or other chaperones. Furthermore, our data actually does suggest a reason why the J-domain is juxtaposed to thioredoxin domains. It recruits BiP to the mixed disulfides formed by PDIs. This insight would not have been possible in other systems because of the redundant redox mechanisms. In terms of the translational aspect, this work identifies an essential, pathway and a starting point for developing better inhibitors. As the reviewer may be aware, once a starting drug-like molecule has been identified, one has to embark on a medicinal chemistry program to develop more potent inhibitor. However, this is beyond the scope of this manuscript.

      Therefore, the main conclusions to draw from this paper are that ER-localised thiol chemistry is also important in malaria parasites and that, assuming one were able to explore localised context-specific features of thiol reactivity in malarial proteins, it may one day be possible to develop anti-malarial drugs that exploit this as a mechanism of action. The generic nature of these considerations limits the significance of the conclusions one might draw from this paper.

      RESPONSE: We are disappointed that we were unable to satisfy the reviewer’s need for ‘a giant leap for mankind’ insights.

      Reviewer #3:

      This paper describes redox-active proteins in the ER of malaria parasites. The authors start out with PfJ2, a J- and Trx-domain containing protein. They find that it is an essential ER protein that interacts with other chaperone and Trx domain proteins. Using a crosslinker with specificity for redox-active cysteines they identify PfPDI8 and PfPDI11 as redox-partners that together may aid folding of other proteins in the secretory pathway. Finally the authors use inhibitors that act on human PDIs and show that they inhibit parasite growth, albeit at rather high concentrations. This may be fortunate as this suggests different specificities for host and parasite PDIs. However, it also means that from this work it is difficult to judge if the parasite PDIs can be specifically targeted.

      RESPONSE: We thank the reviewer for recognizing the important insights gained from this work. We agree that the specific inhibitor identified is not an ideal antimalarial. There is a lot of precedence in the field for antimalarial inhibitors that target conserved mechanisms such as protein translation (Baragana et al 2015 Nature; Wong et al 2017 Nat. Microbiol.), aspartic proteases (Sleebs et al 2014 PLoS Biol.; Nasamu et al 2017 Science; Pino et al 2017 Science; Favuzza et al 2020 Cell Host Microbe), the proteasome (Li et al 2016 Nature; Kirkman et al 2018 PNAS; Stokes et al 2019 PLoS Path.), the TRiC chaperone complex (Lu et al 2020 PNAS) etc. We are starting a medicinal chemistry program to identify more potent inhibitors of these redox chaperones. However, that is beyond the scope of this paper.

      This is an interesting paper and rightly emphasises that it addresses a much understudied process and organelle in the parasite. The DVSF-crosslinking and the knockdown cell lines are highlights (although the knockdown cell lines were not fully exploited). The paper covers a lot of ground. However, this comes at the cost of depth. The actual function of the studied proteins on folding of other proteins and on the state of the ER was not evaluated and it is also not clear if the human PDI inhibitors indeed target the parasite enzymes. The high concentrations of inhibitors needed to show an effect on DVSF-crosslinking might indicate a secondary effect due to loss of parasite viability. As a result it is not fully clear if the studied proteins are indeed critical for folding of relevant substrates and if this process is druggable. More work is needed to support the main conclusions of the paper.

      RESPONSE: We thank the reviewer for appreciating the diverse toolsets used here to gain important insights into the ER of malaria-causing parasites. Due to the short time-frame of the DVSF-crosslinking experiment (30 mins vs 48h life cycle), we are able to conclude that the effect of the drug is not secondary. A new version will clarify this.

      Major points:

      1) The authors describe conditional knockdown lines and find that PfJ2 and PfPDI8 are essential but these lines are not further exploited for functional studies. Did the knock downs have any effect on proteins they mention as potential substrates (Table 1)? Did it affect the state/morphology of the ER? Did knock down of PfPDI8 remove/shift one of the PfJ2 bands after DVSF-crosslinking, as would be expected? Is there an effect on BiP? A general folding problem in the ER with such a lethal phenotype might have profound effects on the morphology of the organelles receiving protein from the ER. What happens to other cellular markers after knock down of these proteins? Were the knock down cells analysed by EM? Was there an effect on protein export? As it stands the knock down data does not show a role of the complex in the folding of any type of substrate and the function in oxidative folding, as indicated in the title, remains tentative.

      RESPONSE: The morphology of the ER is difficult to address due the fact that in these lifecycle stages the ER is quite condensed. Further, the ER is not clearly identifiable via EM. The knockdown of PDI8 is not complete, therefore, it is not possible to perform the suggested experiment as we will always see the residual PDI8 crosslinked with PfJ2. We are not sure what or if there’s any effect on BiP upon knockdown of PfJ2. BiP does not crosslink with PfJ2 and its expression levels do not change. We are not sure what other effect the reviewer expects on BiP. The co-IP data show that BiP is part of a complex with PfJ2 and PDI8, this complex has not been previously observed in the ER of any organism. Since the parasites die during the trophozoite/early schizont stages, several of these organelles such as Rhoptries, micronemes etc probably do not form. Once the lab reopens after the pandemic, we will test for the presence of these organelles via immunofluorescence microscopy as well as EM. Similar experiments could show an effect on protein export. However, since we didn’t identify any exported proteins to be putative substrates of PfJ2 (despite the expectation that chaperones are sticky and bind everything), and therefore, any effect we observe is likely to be indirect. Given the published data establishing the function of PDIs as oxidative folding chaperones, their high degree of conservation, and in vitro characterization, we conclude that they function in oxidative folding. Furthermore, we show that PfJ2 regulates the function of Plasmodium PDIs as well as recruits BiP to the mixed disulfide complex. BiP is a highly conserved chaperone that has clear function in protein folding. Based on this and the data presented here, we conclude that PfJ2 functions as a regulator of oxidative folding in P. falciparum.

      2) While I like the idea to use established commercial drugs as novel potential antimalarials, those used here are specific for non-infectious human diseases and target the host which is not a desirable property. Considering this, their rather low activity against the parasite can be taken as a positive result. However, the low activity is less convincing to establish the folding pathway in the parasite ER as a drug target. Beside the issue that it is unclear if indeed oxidative folding is the essential function of the PfJ2 complex (see previous point), the data in Fig. 7 does not clearly establish that this function is targeted by the inhibitors used. The effect is only seen at concentrations of 5xIC50. It is possible that this severely reduced viability which could be a non-specific reason for the lack of DVSF-crosslinked products. This needs to be examined in more depth. For instance, is the crosslink still seen after equivalent treatment of cultures with 5xIC50 of other unrelated drugs? Were other, unrelated processes unaffected? What was the effect of exposure to the drug on the ER and parasite morphology? Was the appropriate parasite stage affected? Can it be tested how fast exposure to 5xIC50 of the drug kills the parasites (at least morphologically, but preferably also by more specific means)?

      RESPONSE: We agree that the drugs identified here are not ideal antimalarials but rather they are starting molecules for a larger medicinal chemistry program, that is beyond the scope of this manuscript. While we see significant loss of DVSF crosslinking (for PfJ2) even at the IC50, the relationship between protein activity inhibition and parasite death isn’t always linear. We are testing analogs of 16F16 to identify more potent inhibitors of these proteins. We thank the reviewer for the suggested experiments, and when the pandemic is no long limiting access to the lab, we will perform some of these.

      3) While generally sound, a few experiments would have benefitted from more controls. A reducing sample from the same parasites for Fig. S7 (loaded a couple of lanes away to avoid interference of the reducing agent) would have been nice for comparison to show specificity of the higher molecular weight adducts. Detection of a control protein not expected to co-purify (for instance a cytosolic protein or a membrane-bound protein to control for residual parasite material) would have been appropriate for the co-immunoprecipitations (e.g. Fig. 6A,D, Fig. S9).

      RESPONSE: We show that there are no non-specific bands for PDI11, because when we mutate the cysteines, we do not observe any cross-linking. We will include the control proteins for the co-IPs, they were not included for the sake of clarity.

    2. Reviewer #1:

      In this manuscript, Cobb and colleagues report on the biochemical and functional characterization of redox active ER proteins in the malaria parasite Plasmodium falciparum. They studied a protein called PfJ2, which contains HSP40 J and Trx domains and is homologous to human ERdj5. Using the TetR-PfDOZI aptamer system to tag PfJ2 and conditionally regulate its expression, they show that PfJ2 is localized in the parasite ER and is essential for parasite growth during the asexual blood stages. Using co-immunoprecipitation combined with mass spectrometry, they identify partner proteins of PfJ2 including other ER proteins such as PDI and BIP. Using a chemical biology approach based on DVSF crosslinker, they document the redox activity of PfJ2 and identify redox substrates of PfJ2, which include PDI8 and PDI11 protein disulfide isomerases. They further functionally characterized PDI8 and PDI11 using the glmS ribozyme for conditional knockdown. These experiments confirm that PDI8 and PDI11 are partners of PfJ2 and show that knockdown of PDI8 impairs parasite blood stage growth. Finally, the authors show that inhibitors of human PDI inhibit parasite growth (at best in the micromolar range) and block the redox activity of PfJ2 and parasite PDI.

      This is an interesting study combining genetic and chemical biology approaches to investigate an understudied compartment of the malaria parasite. The manuscript is clearly written and the work technically sound. In summary, this study illustrates that ER redox proteins in the malaria parasite perform similar functions as in other organisms. The main limitation of this study is that evidence showing that redox ER parasite proteins are druggable is rather weak. PfJ2 is very similar to human ERdj5 in terms of active redox site and function, and the authors used inhibitors that are active on human PDI. It thus remains uncertain whether an antimalarial strategy targeting such conserved pathways is achievable.

      In addition, a number of specific points should be addressed to improve the quality of the manuscript:

      Although PDI8 and PDI11 gene edition were performed in the PfJ2apt line, the authors did not attempt to knockdown both PfJ2 and PDI8/11 simultaneously (because PfJ2 is essential). Therefore, referring to "double conditional mutants" is misleading.

      The authors should provide details on the parasite lysis conditions used for the co-IP experiments to identify interacting proteins (Table 1) and redox partners (figure 3). In their proteomic analysis, the authors considered proteins with a 5-fold increase in the specific versus control conditions. A more stringent analysis would retain only proteins identified exclusively in the modified J2apt line.

      In figure 6, the authors should probe the blots for a control protein that is not co-immunoprecipitated with PfJ2 or PDI8.

      In Supplementary fig 4, control untreated parasites should be analyzed in parallel to GlcN-treated parasites.

      The partial reduction of protein levels (Fig S4) shows that the glmS system is not very efficient here, which might explain why there is no phenotype in the PDI11 mutant (Fig5B). This questions the conclusion that PDI11 is dispensable.

    1. SciScore for 10.1101/2020.07.16.20153437: (What is this?)

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

      Table 1: Rigor

      NIH rigor criteria are not applicable to paper type.

      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">Here, we utilize multiomics single-cell analysis to probe dynamic immune responses in patients with stable or progressive manifestations of COVID-19, and assess the effects of tocilizumab, an antiIL-6 receptor monoclonal antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>antiIL-6 receptor</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The signaling pathways driven by IL-1β, TNF-α, and IL-6 have been implicated in the pathogenesis of COVID-1910 and antibodies against IL-6 receptor have shown early promise9,11-13, including our own experience14; however, large-scale randomized trials are needed to adequately evaluate their efficacy.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>TNF-α</div> <div>suggested: None</div> </div>

            <div style="margin-bottom:8px">
              <div><b>antibodies against IL-6</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Here, we employed a single-cell multi-omics approach in order to study the dynamics of the innate and adaptive immune system responses in COVID-19, explore the molecular mechanisms that contribute to the progression of the diseases, and assess the effects of tocilizumab, a humanized anti-IL-6 receptor monoclonal antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-IL-6</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Tocilizumab effects differ across cell types and are associated with the levels of expression of IL6R and IL6ST Eight of ten COVID-19 patients in our study were treated with tocilizumab, an antiIL6 receptor (IL6R) antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>IL6ST</b></div>
              <div>suggested: (MBL International Cat# D023-3, <a href="https://scicrunch.org/resources/Any/search?q=AB_591799">AB_591799</a>)</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>antiIL6</b></div>
              <div>suggested: None</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>IL6R</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To better identify cellular multiplets and enable us to superload the cells onto the 10x platform, we used Cell Hashing technique and multiplexed 56 samples in each 10x reaction by using six hashing antibodies61.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>antibodies61</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibody-derived tag (ADT) and Hashtag oligonucleotide (HTO) sequencing libraries were generated.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Antibody-derived tag ( ADT )</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Following unsupervised clustering, annotation for CITE-seq cells was performed with both gene expression and antibody-derived counts (ADT) by using a manually curated marker gene list (Supp Table ST8).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>antibody-derived counts ( ADT</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">Yale Center for Genome Analysis/Keck Biotechnology Resource Laboratory, Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, CT, USA. 14. SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. 15</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Genome Analysis/Keck Biotechnology Resource Laboratory</b></div>
              <div>suggested: None</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>Biostatistics</b></div>
              <div>suggested: (BWH Biostatistics Center, <a href="https://scicrunch.org/resources/Any/search?q=SCR_009680">SCR_009680</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">D.A.H. has received research funding from Bristol-Myers Squibb, Novartis, Sanofi, and Genentech.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Genentech</b></div>
              <div>suggested: (Genentech, <a href="https://scicrunch.org/resources/Any/search?q=SCR_003997">SCR_003997</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Automated annotation using SingleR package22</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>SingleR</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Gene set enrichment analysis (GSEA) further demonstrated that dividing T cells in the progressive COVID-19 patients exhibited more terminally exhausted T cell signature and type 1 IFN response signature than those in stable patients (Fig 4J, Supp Table ST9).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Gene set enrichment analysis</b></div>
              <div>suggested: (Gene Set Enrichment Analysis, <a href="https://scicrunch.org/resources/Any/search?q=SCR_003199">SCR_003199</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.

    1. Imagine that instead of a dropdown containing the search results, you want a tag-like list of search results that always display:
    1. SciScore for 10.1101/2020.03.01.971499: (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">The study was approved by the Bioethical Committee of the Medical University of Silesia in Katowice , Poland ( approval no: KNW/0022/KB1/17/10 dated 16.02.2010) .</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">Due to the lack of KLK13 specific antibodies , we verified its presence based on RT-PCR ( Fig . 4A) .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>KLK13</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A mouse monoclonal anti-TMPRSS2 antibody ( clone P5H9-A3; 1:500 dilution; Sigma-Aldrich , Poland) , followed by incubation with a horseradish peroxidase-labeled anti-mouse IgG ( 65 ng/ml; Dako , Denmark ) diluted in 5 % skimmed milk / TBS-Tween ( 0.1 % ) .</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-TMPRSS2</div> <div>suggested: (Santa Cruz Biotechnology Cat# sc-101847, AB_2205599)</div> </div>

            <div style="margin-bottom:8px">
              <div><b>anti-mouse IgG</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A horseradish peroxidase-labeled anti-His tag antibody ( 1:25000 dilution; Sigma-Aldrich , Poland ) diluted in 5 % skimmed milk / TBS-Tween ( 0.1 % ) was used to detect the His-tagged HmuY proteins .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-His tag antibody</b></div>
              <div>suggested: None</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>anti-His tag</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">Subsequently , we transduced RD_ctrl , RD_KLK13 and RD_TMPRSS2 cells with HIV particles pseudotyped with HCoV-HKU1 S glycoprotein ( S-HKU1) , control VSV G protein ( VSV-G ) or lacking the fusion protein ( ΔEnv) .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>RD_TMPRSS2</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">This may be one of the factors limiting the HCoV-HKU1 replication in RD_KLK13 cells , as only minimal replication is observable.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>RD_KLK13</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">RD cells grown in 90 % confluency were infected with HCoV-HKU1 ( 108 RNA copies per ml ) in Dulbecco’s MEM ( Thermo Fisher Scientific , Poland</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>RD</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Lentivirus production and transduction 293T cells were seeded on 10 cm2 dishes , cultured for 24 h at 37°C with 5 % CO2 and transfected with psPAX , pMD2G and third transfer plasmid ( pWPI/KLK13 , pLKO.</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></table>
      


      Results from Barzooka: We also 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 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).


      About SciScore

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    1. 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

      We would like to thank Reviewer #1 and #2 for the evaluation of our research and comments to our manuscript. Their comments are highly appreciated and addressed as described below.

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

      **Summary:**

      *Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).*

      Here Ha et al. has further developed their Pumilio RNA tagging methodology for the isolation of UV-crosslinked proteins that are suggested to associate with Xist RNA in mouse embryonic stem cells (mESCs). Within this study the authors claim to have found the Lupus antigen RNA binding protein (La) as a novel Xist interacting partner that influences the efficacy of X-chromosome inactivation (XCI). The authors use a number of different techniques such as qPCR, fluorescent imaging, ATAC-SEQ and SHAPE to show aberration of XCI upon La shRNA knockdown. However, this study has significant flaws in the efficient isolation and validation of Xist associated proteins using their FLAG-out methodology. Furthermore, later experiments predominantly focus on cell death/survival assays, which is somewhat troubling given the essential roles La plays in processes such as cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation. I feel the authors need to robustly address the potential effects La knockdown may be having on their mESCs.

      Reviewer #1 did not fully understand the basic designs of the experimental systems (FLAG-out and iXist), and completely rejected these experimental systems. Reviewer #1 also ignored the majority of the functional analysis on the candidate protein, Ssb. These issues cannot be addressed by additional experiments

      **Major comments:**

      *-Are the key conclusions convincing?*

      My major concern is in their Xist RNA purification.

      First of all, I couldn't find any data on proving the enrichment of Xist RNA itself in their Pumilio pull-down experiment. It would have been useful to show Xist RNA enrichment before benzonase step. Secondly, it is hard to imagine the protocol would successfully isolated Xist RNA-protein complexes from the cell. An earlier report by Clemson et al., (J Cell Biol., 1996) has shown that majority of Xist RNA is still stuck in the nucleus after nuclear matrix prep protocol using detergent, which is not so different from the authors' protocol. Moreover, the authors used UV crosslink, which would have made even harder to purify Xist RNA without sonication. Thirdly, as the tag is located on 5' of Xist RNA, it is rather surprising to see that Spen is not detected in their pulldown. Spen is one of the main functional interactors with Xist, robustly detected by several previous reports. Similarly, other high-affinity binders of Xist such as hnRNP-K and Ciz1 were also lacking from this screen. Finally, the peptides found associated with FLAG-out Xist are extremely low in comparison with other data using glutaraldehyde or formaldehyde crosslinking. For example, HnRNP-M found in Chu et al 2015 has 1120 peptide counts in differentiated cells. The authors here use HnRNP-M as a baseline for specific interactions and show a total of 6 peptide counts in Xist expressing cells and 5 in i-Empty cells (Supplementary excel sheet 1). Similarly, the La protein of interest in this study has 8 counts in i-FLAG-Xist and 6 counts in i-Empty. I struggle to see how this result indicate specific Xist binding. Worryingly this is the starting rationale for the rest of their experiments, it is hard to therefore accept the rest of their conclusions either.

      We have detected Xist RNA after Pumilio pull-down, and added the data in the revised manuscript (Figure S1). The enrichment of Xist RNA by Pumilio pull-down is about 75-fold, comparable to the enrichment reported by Minajigi et al.

      Two out of three previous studies used similar protocols to prep cell lysates for co-IP, including UV cross-linking and detergent (McHugh et al. 2015 and Minajigi et al. 2015). The major difference between their protocols and ours is the co-IP step. They used antisense oligos to pull-down Xist RNA-protein complex, while we take advantage of the specific interaction between PUF and PBS to pull-down Xist RNA-protein complex. With the data in Figure S1, we are confident that our strategy is successful in isolating Xist RNA

      For systematic identification of Xist binding proteins, each method has its own strength and weakness. As we described in the introduction, only 4 proteins were commonly identified by all three studies to systematically identify Xist binding proteins. There is no doubt that our method also missed some authentic Xist binding proteins (false negative) and identified some false positive candidates. Thus, we have to be careful in balancing between the false negative and false positive calls. The reason that we applied the ranking gain to identify Xist binding protein candidates, is to minimize the false negative rate. Meanwhile, we compared our Xist binding protein candidate list with previous identified Xist-binding proteins to enhance the confidence in our candidate lists.

      Regardless the strength and weakness of our method, Ssb is also an Xist-binding protein identified by another study (Chu et al. 2015). More importantly, we have provided experimental validation to confirm Ssb’s involvement in XCI and extensive functional analysis to reveal the protein’s mechanistic role in XCI.

      The other key conclusion the authors make is from the use of numerous cell death/survival assays for both male and female cell lines. This is extremely troubling in the context of assessing their target protein La. La is involved in multiple RNA maturation events of rRNAs, tRNAs and other polIII transcripts. Furthermore, La has been implicated in binding to the mRNA for Cyclin D1 in both human cells and mouse fibroblasts (NIH/3T3 - male) which show a significant effect on cell proliferation upon siRNA knockdown https://www.nature.com/articles/onc2010425. This, along with the observation that La knock-out blastocysts fail to develop any mice or ES cell lines (male or female) show the effect observed in the authors results is most likely not X-linked cell death https://mcb.asm.org/content/mcb/26/4/1445.full.pdf. The authors need to show that their shRNA KD isn't affecting the proliferation and general fitness of their mESC lines.

      The cell death/survival assay was specially designed for analyzing the defect of XCI. The cell death of iXist ESCs upon adding Dox is due to the induction of Xist, which consequently initiates the silencing of the only X chromosome in male cells. Knockdown of genes involved in XCI compromises XCI, thus allowing cell survival. Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect slow growth and/or cell death of Ssb knockdown cells. Indeed, the result is consistent with our expectation (Figure 2C, without Dox). Nevertheless, more Ssb knockdown cells survive in the presence of Dox, compared with control cells (Figure 2C-E, with Dox), suggesting that Ssb plays an important role in XCI.

      *- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?*

      As discussed above, I feel the authors have not clearly demonstrated Xist specific protein enrichment and haven't proven X-linked cell death. Due to the lack of necessary control experiments as discussed below, I feel the notion that La is involved directly in XCI as an RNA chaperone is currently preliminary/speculative.

      The FLAG-out experiment just provided an initial point for the study. We have demonstrated the interaction between Xist and Ssb by RIP. And, Ssb knockdown antagonizes the lethal effect of induced XCI in male cells, allowing more cell to survive. This is contradictory to the diverse house-keeping functions of Ssb, which should lead to slow proliferation or cell death. Therefore, the data here (Figure 2C-E) should suggest a role of Ssb in XCI. In addition, we showed that knockdown of Ssb compromises the silencing of X-linked genes (Figure 2F, 2G, and 3E), the compaction of X chromosome (Figure 3D), Xist cloud formation (Figure 4), epigenetic modifications on Xi (Figure 5), Xist RNA folding (Figure 6F-I), and Xist RNA stability (Figure 7C and D). All these data indicate that Ssb is involved in XCI by regulating Xist RNA folding.

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

      I would suggest them to show RT-qPCR results of Xist RNA enrichment from the sample after flagIP before benzonase treatment.

      We have the data, and added it to Figure S1.

      Also, it would have been more convincing if their negative control construct (i-Empty) would contain 25 copies of PBSb RNA at least.

      This is a good alternative design of the negative control. Using i-Empty expressing 25 copies of PBSb RNA will allow us subtract the background causing by proteins binding to PBSb RNA. Yet, as discussed above, regardless how we improve the experimental setting, we cannot completely avoid the issue of false positive and false negative. Our goal of the FLAG-out experiment is to generate a list of Xist binding protein candidates, and their binding to Xist and their functions in XCI should be validated by additional experiments. With our current experimental setting, a list of Xist binding protein candidates has been generated, and we have validated the role of Ssb in XCI with subsequent experiments.

      In Fig1b, the total amount of proteins loaded on the gel is not equivalent between two lanes. The gel should show equivalent amounts of proteins on the gel. It looks like if the negative control sample had been loaded at the same amount as the one with Xist, the band pattern wouldn't be distinguishable between the two samples. Furthermore, as these samples were used in the following mass spectrometry screen it may suggest that the minimal increase in peptide counts observed in the iXist FLAG-out were due to an increased amount of sample being loaded? No controls are conducted to account for this.

      IP samples of i-Empty and i-FLAG-Xist were loaded in the gel in Figure 1b. It is expected that IP sample of i-FLAG-Xist should pull down more proteins than IP samples of i-Empty. The FLAG-PUFb bands (the strongest band in each lane) are about the same amount in two samples, indicating roughly equal amount of loading. After normalization of gel loading according to the FLAG-PUFb bands, the upper part of the i-FLAG-Xist lane showed some unique bands.

      For mass spectrometry analysis, the loading of two samples are independent, therefore, to compare the absolute amount of each protein between the two samples does not always provide valuable information. Yet, the relative amount of different proteins within one sample is not affected by the loading amount, thus, more informative. Therefore, we used the ranking information to estimate the relative amount of different proteins in each sample and used the ranking gain to further identify protein candidates.

      The authors quantify cell death in figures 2C - E. It seems clear that shSsb 1 and 2 have an effect on cell count even in the absence of Dox. The rescue effect seen upon Dox addition is minimal when compared to Empty + Dox 2D. The authors ∆A-iXist line with and without Ssb KD/Dox would be an informative control on whether the increase in cell survival that they see is X-linked.

      As the reviewer pointed out earlier, Ssb plays multiple roles in cellular processes. Inevitably, KD of Ssb leads to slow growth and/or cell death with or without Dox. Thus, it is less meaningful to compare the surviving cell counts in Figure 2D. Rather, the survival rate (Figure 2E) reflects the rescuing effect more precisely. Shown in Figure 2E, both shSsb 1 and 2 increase the survival rate significantly, compared with Empty control.

      Moreover, the data in Figure 3B and C demonstrated that Ssb KD compromises the survival of female differentiating cells, but not the survival of male differentiating cells, also indicating a role of Ssb in XCI. With these experiments, it should be sufficient to conclude that Ssb KD affects X-linked cell death/survival in both iXist male ESCs and WT female differentiating cells

      The qPCR results used to validate silencing defects show minor changes in expression and also don't show significant silencing of X-linked genes sufficient for cell death. Could this be because only ~ 50 - 60% of Male iXist cells seem to be expressing in the movies and that this will have an effect on the observed qPCR results? Furthermore, it seems counterintuitive that expression in the Empty male cells increases in 48h compared to 14h. Is this due to cell death and positive selection of cells less able to silence their X-chromosome? How would these data look in the female XX line? How would the data look in a ∆A-iXist line in the presence and absence of shSsb/Dox?

      First, high-quality live-cell imaging can only be carried out for 2 hours with 2-min time interval. The movies are meant to show the onset of Xist RNA signals. Therefore, they were taken one hour after Dox treatment (figure legend of Figure 4B-D). After overnight Dox treatment, Xist clouds can be seen in majority of cells.

      Second, in Fig. 2F-G, we did not include uninduced iXist male ESCs. Therefore, it is impossible to judge whether induction of Xist in this male ESC line results in Xist-dependent silencing at 14 and 48 hr. However, in our previous publication (Li et al., JMB, 2018, 430: 2734-2746), it has been shown that Gpc4, Hprt, Mecp2, G418, and TomatoRed are silenced (4- to 16-fold reduction) at 24 and 48 hours after Dox induction.

      Third, the qRT-PCR results in 14 h and in 48 h are not normalized to the same internal control. Thus, they are not directly comparable.

      Confusingly, the male line in Fig 3C shows a drop in live cell count at day 6 of differentiation? Surely given their previous results in Fig 2 the Ssb KD should increase cell viability with +Dox? Ssb KD seems to have an adverse effect on ES cells during extended differentiation protocols. In Figure S1 the authors show ~ 8 - 10% survival of male lines during differentiation. Could the recombination of the Xist sequence around the loxP sites enable the cells to outcompete the dead cells? How would iEmpty and ∆A-iXist cells compare here? Have the differentiated cells been tested for their expression of Xist? Additionally, how are there similar live cell counts for male vs female lines when ~90% of male cells die during differentiation? Were more cells plated at day 4? If so, this would bias the competition of male cell survival and therefore make the male line an inappropriate control.

      Given the essential role of La during development a control is needed to prove that this death is X-linked in the female 3F1 line. For example, an XO cell line retaining the Cast allele and shSsb expression could show the amount of death caused from shSsb alone independent of X-linked cell death.

      The reviewer completely misunderstood the experiment. The severe cell death specifically observed in female differentiating ESCs is a strong evidence showing Ssb is involved in XCI (Figure 3).

      The male ESCs in Figure 3C is a WT ESC line without the inducible Xist transgene, in which no XCI occurs upon differentiation. It is completely different from iXist male ESCs with Dox, in which forced Xist induction leads to XCI. Thus, the diverse functions of Ssb might contribute to the slight decrease in live cell count of wild type male cells at day 6 of differentiation.

      Figure S2 shows the differentiation of iXist male ESCs with or without Dox. As explained above, forced Xist induction silences the only X chromosome in male cells, resulting in cell death. In addition, XCI occurs more efficiently in differentiation condition (Figure S2) than in pluripotent status (Figure 2C)

      During differentiation, female ESCs silence one X chromosome, and the other X chromosome remains active. KD of Ssb compromises XCI, and two X chromosomes in some female differentiating cells maintain active, leading to cell death. The reviewer is correct that we need a control to rule out that the essential role of Ssb during development affects cell survival and death. An XO cell line can be used as a control. Similarly, a male cell line (XY) is also a good control. We already included a male cell line as a control in Figure 3B and 3C.

      If I understood correctly, the RNA FISH used dsDNA probes ("Sx9") against 40 kb of the X-inactivation centre (Xic). Surely Tsix or other Xic transcripts will also be visible? Can the authors use their RNA FISH to determine the XX or XO status of their cells? In Figure S5 a number of cells appear to show a single pinpoint of transcription. This could either be low levels of Xist transcripts or Xic transcription from an XO line in which the 129 chromosome is missing. It would be best to solely quantify cells which have two x chromosomes and if a significant amount of X chromosomes have been kicked out, this should be discussed and controlled for.

      This is a valid concern, but this concern can be adequately addressed with the available data in the manuscript.

      First, if the female Ssb KD cell line is an “XO” cell line, in which the X129 allele is “kicked out”, the RNA allelotyping results should show an absolute “silencing” of the X129 allele. However, in complete contrast to this notion, RNA allelotyping detected “more” RNA transcripts from X129, showing the chromosome-wide XCI defects (Figure 3D).

      Second, overexpression of Ssb in Ssb KD female cells restores the Xist clouds and the polycomb marks (Figure S8), suggesting that the Ssb KD female cells are XX, but not XO.

      Third, the severe cell death specifically occurred in female Ssb KD lines is also against the “XO” argument (Figure 3B&C).

      In Fig6, the authors generated a number of Ssb constructs for a rescue assay. However, these results complicate the matter and raise more questions than they address. It seems odd that the ∆RRM1 does not rescue based on comparison with their putative negative control, ∆NLS. However, the ∆RRM1 + 2 and ∆LAM do rescue the phenotype better than the full length Ssb? This makes no logical sense and highlights the inherent variation in cell viability these generated cell lines seem to show.

      Following on from this, figure S7 quantifies the GFP tag mRNA levels, depicting all ∆RRM mutants with expression below ~30%? How can ∆RRM1 or 2 be rescuing in this scenario? Have these lines been tested for their XX or XO status? The loss of an X chromosome would lead to a rescue of the cell death phenotype, which is a process known to occur in XX lines that have been cultured for extended periods of time. Could it also be that the cell lines derived are more or less sensitive to exogenous shRNA expression? Also, further validation is needed to assess the efficiency of KD in these lines as theoretically most of these constructs will be targeted by shRNA? What is the endogenous Ssb expression level in these lines? Where in the mRNA sequence are the shRNAs targeted to? Does this make sense on the relative expression levels of ∆RRM1/2 for example? Further testing of GFP expression could also be assessed by quantitative western blot of GFP or even visualised in their RNA FISH/IF samples (Figure S8), currently neither are shown. In addition, some kind of information of stability of each Ssb protein constructs has not been demonstrated.

      Our shRNA targets the LAM domain, so the expression of ∆LAM is not affected by the shRNA. The reviewer is correct that the detected GFP expression levels of ∆RRM1 and ∆RRM2 are too low to be conclusive. We have removed the data point of ∆RRM1 and ∆RRM2. Meanwhile, it is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed at similar levels. Ssb is a well known RNA chaperone/RNA helicase. Identifying Ssb is an Xist-binding protein already suggests the functional role of Ssb in XCI. The data of the plasmid rescue experiments further suggests that Ssb is involved in XCI as a RNA chaperone/RNA helicase.

      As for the Western blot and GFP fluorescence (IF), we have tried both. Neither of them detected GFP signal, reflecting the low expression level of these GFP fusion proteins. As the reviewers pointed out that the shSsb is not targeting the 5’ or 3’-UTR region, therefore, interfering the exogenous Ssb as well. This might be a reason for the low expression of these GFP fusion proteins.

      For the data shown in Figure 7A and B the authors quantify the % of cells with Xist signal. The authors have already shown a defect in Xist visualisation in Ssb KD. Surely it is plausible to assume a faster loss of Xist signal below background in weaker expressing cells. A more appropriate quantification would be the % loss of Xist signal per cell over time.

      With Figure 7C and D, the samples have been treated with actinomycin D which globally affects the transcription of cells even the PolIII associated genes Ssb is needed to mature. This treatment could have an added effect on cell mortality and function. Data confirming that actinomycin D doesn't affect the cells disproportionately is needed. The difference in half-life could be attributed to such a treatment.

      We agree with the reviewer that monitoring Xist signal loss per cell would be a better way to analyze the data. However, in Xist signal loss experiment, snapshot images were taken at four time points (1h, 2h, 3h and 4h). This is not a time-lapse imaging. High-quality time-lapse imaging can only be done within a 2-hour time period with 2-min time interval. Therefore, cell-tracking cannot be done in this experiment. In addition, even though Ssb KD slows down the formation of Xist cloud within the early phase (3 hours) of Xist induction (Figure 4), prolonged (overnight) Xist induction leads to Xist cloud formation in a significant fraction of Ssb KD cells, and the Xist cloud signals are about the same in WT and Ssb KD cells (Figure 7A, 0 h). Similarly, qRT-PCR also revealed that Xist RNA are at the same level in WT and Ssb KD cells (Figure 7C, 0 h). These data argue against that a faster loss of Xist signal in Ssb KD cells is due to weaker initial Xist signal.

      Actinomycin D was added at the last 11 hours of the experiment. During this period, no obvious adverse effects on cells were observed.

      In summarising the authors claim that La binds Xist to facilitate folding and appropriate spreading of Xist along the X-chromosome. No direct interaction has been shown, CLIP-seq data would resolve this, however I do understand this is a challenging technique. The authors have instead opted for RIP followed by qPCR (Figure S2). However, this process has a greater potential for non-specific recovery of RNAs via indirect binding. Furthermore, qPCR may also amplify the relative abundance of the RNA detected. As multiple nucleolar proteins came down in the mass spec screen and FLAG-Ssb is being over expressed, it is plausible to assume some transient Xist interactions may arise from nucleolar association at which La will be in high abundance. Positive and negative nuclear RNA controls (e.g. 7SK and U1 snRNA respectively) could be used so to determine the amount of non-specific Protein-RNA interactions in their RIP pull downs. Cytoplasmic actin is not an appropriate control as it is cytosolic.

      We have to clarify one point that the mass spec screen analyzed samples pulled down by FLAG-PUFb, but not FLAG-Ssb.

      We did not intend to distinguish whether Ssb directly binds Xist or is just associated with Xist. RIP followed by qPCR is sufficient to prove the association between Ssb and Xist RNA.

      We can include nuclear RNA as controls, if the reviewer regards RIP as a valid method to show protein and RNA association

      Other than this the authors may want to probe (via IF) for the presence of La accumulation on the X? Many other know factors such as Ciz1, hnrnpK and PRC1/2 complexes show clear accumulation on the X. If I understand correctly, there are many La antibodies on the market and endogenous levels on the X could be assessed. These antibodies may be useful in IP's and pull downs also.

      Many XCI factors play extensive roles in the cell and are not clearly enriched on Xi, including Spen (Moindrot et al. 2015). We have tried the immunostaining and did not detect Ssb’s enrichment on Xi. Ssb shows a general distribution in the nucleus without a clear enrichment on Xi (data not shown).

      *-Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.*

      The experiments suggested above are centrally focussed on the cell lines that are currently in the authors possession with maybe exceptions with the ∆A-iXist-shSsb line suggested. However, this should be reasonably quick to obtain given their previous work for this paper. Most experiments suggested will focus on the validation of karyotype, Xist expression, rescue construct expression, further RNA FISH classification and repeating more appropriate positive and negative controls for a number of experiments. In theory this can be obtained relatively simply and quickly from current resources. But with the sheer volume of further experiments that are required here, this may take a significant amount of time.

      One vital improvement needed is the replication of mass spec data and the validation of Xist specific recovery and protein enrichment. As it stands this manuscript seems to not have any replicates of the FLAG-out methodology and mass spec data. This is troubling given the poor recovery and specificity of the protein samples obtained. Repeating these experiments would be costly in time and also financially. As it stands, I feel this is essential to conclusively validate their target of interest.

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

      The data is presented relatively well, however, it would be beneficial if deailed methods were in the main text and not in a supplementary file. Similarly, more information about the process of differentiation and how cell death/survival was quantified and validated is needed.

      The reviewer rejected the basic design of the experimental system and ignored the majority of the functional analysis data. No additional experiment can address these issues

      We can include more information in the main text, regarding Ssb. However, there is limited space for the main text, various depending on the journals. Meanwhile, the current citation on Ssb is adequate to emphasize that Ssb is a versatile RNA binding protein involved in a variety of fundamental RNA processing events in the cell.

      *- Are the experiments adequately replicated and statistical analysis adequate?*

      In the most part yes, however there seems to be no replicates of the FLAG-out mass spec screen which is worrying given the minimal specificity observed in the current data.

      As we mentioned above, the FLAG-out experiment only serves as a starting point to generate a list of Xist binding protein candidates. Rather than repeating the FLAG-out experiment, we compared the result of FLAG-out to previously published lists of Xist binding protein candidates. More importantly, additional experiments are carried out to validate the Xist binding proteins identified by FLAG-out.

      **Minor comments:**

      *- Specific experimental issues that are easily addressable.*

      Unfortunately, the majority of experimental issues need to be addressed with more robust data which are highlighted above. However, some image analysis, quantification and classification can be amended relatively easily. For example, the live-cell imaging data should be quantified as loss of signal as discussed and RNA FISH should be used to classify XX positive cells and the XO cells can be discarded from analysis.

      We have addressed these issue in the previous sections of this rebuttal.

      *- Are prior studies referenced appropriately?*

      Most papers regarding Xist pull down and biology are discussed and referenced appropriately. However, the role in which La plays during development and its aberrant affects upon KD are seemingly downplayed. I would like to see more discussion of potential defects that could be caused due to globally altering cellular RNA folding.

      We have tried to cite key references about Ssb in development and RNA folding. Due to length limitation, we cannot cite all references in the topic. If necessary, we could discuss the possibility of indirect effect of Ssb KD on XCI through globally altering cellular RNA folding.

      *- Are the text and figures clear and accurate?*

      For the most part, lots of the figures are clear and accurate. Apart from these exceptions.

      1.The Y-axis of Figure 2D is confusing. What does 0.3 as a "sum of area" equate to? 30% of the area was ES cells? This doesn't look to be the case from Fig 2C. Also, how does the intensity of the signal compare? The area may not be a good quantification due to ES cells growing in colonies.

      We have revised the Y-axis labelling of Figure 2D to “sum of area cm2”. Thus, “0.3” means that the area of ESCs is 0.3 cm2. ALPP is highly expressed on ES cell surface. ALPP stain usually produce saturated stains on ES cell colonies. Thoroughly stained ES cell colonies, big and small, show similar signal intensity levels. To analyze the “total signal intensity” will be not much different from “sum of area”.

      2.In the Movies S1-7 there are boxes around certain cells and marked with "Figure 5a - c". This seems to be incorrect as figure 5 is currently the IF staining of polycomb marks. I assume this is in relation to Figure 4b-d?

      We have corrected the labelling mistakes.

      3.Similarly, in Movies S1-7, the intensities of Xist foci seem by eye to be similar. In the paper it is claimed that the Xist clouds that do form are lower in intensity. Are the Movies depicting the same range of pixel intensities? If not, this should be amended. Similarly, figure 7 seems to show relatively equivalent RNA signal at 0 h?

      All the images were collected using a fixed standard of the microscope and camera setting, and these movies depict the same range of pixel intensities. Movies S1-S3 are WT control, and Movies S4-S7 are Ssb KD cells. The Xist cloud signals are weaker in Movie S4-S7 (also quantified in Figure 4E). For the Xist cloud signal, not only the intensity, but also the area of Xist cloud, have to be taken into account.

      The 0 h in Figure 7 is after overnight Dox treatment, and different from the time point in Movies S1-7 (maximum 3 hour Dox treatment, figure legend of Figure 4B-D). The discrepancy can be explained by that knockdown of Ssb only slows down the formation of Xist clouds. After overnight forced expression, the Xist RNA still shows an accumulation in the cells. Figure 7 shows the forced accumulation of Xist RNA after prolonged Dox treatment disappears faster after Dox withdraw.

      4.In figure 4A the data is from female XX cells, this should be highlighted to limit confusion with the male iXist data shown below in 4B-E. It would also be helpful to have the male/female icons (as in figure 3B), for each figure that has images of cells. Currently Figure 4, 5, 7, S5 and S8 are lacking these icons.

      We have revised the labelling on Figure 3, 4, 5, 7 S6 and S9 (S5 and S8 before revision).

      5.No explanation of the Flag-Ssb expression is given for figure S2. Furthermore, is it really necessary to express Flag-Ssb? There are reasonably good antibodies out there for Ssb as this was how it was originally found in Systemic Lupus patients. Also, no data showing the amount of Ssb being overexpressed is shown. This may have big implication to the validity of the RIP-qPCR analysis.

      We could perform qRT-PCR to quantify the overexpression level of Flag-Ssb. If required, we could use Ssb antibody to do Western blot to show the amount of Flag-Ssb protein.

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

      Most of the data is presented reasonably well, but the robustness of the data somewhat retracts from their conclusions. I feel the certainty of their conclusion regarding Xist specific La binding and RNA chaperone activity is still presumptive and should be rewritten unless more robust data can confirm Xist interaction. I would also suggest deciding on the nomenclature for the protein of interest and use either La or Ssb, the continued use of both through the figures and text can get a little confusing to the reader.

      In the current literatures, Ssb seems to be commonly used as a gene name and La is used as a protein name. We have revised the manuscript to use one name “Ssb” to describe both the gene and the protein.

      Reviewer #1 (Significance (Required)):

      *- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.*

      It was a good trial to use PBSb-PUFb system to purify Xist RNA binding proteins, compared to previous reports had used anti-sense oligo purification using complementary sequence to Xist RNA sequences. But currently the purification still needs further validation and repeats to confirm its use. A potential complementary technique could be to isolate Xist directly by using biotinylated probes against the PBSb sequence.

      The authors further claim the identification of a novel Xist RNA chaperone (La/Ssb) which they say facilitates XCI progression. This would be a novel finding in the field; however, the data is currently not robust enough to support this

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

      This work has focused on the development of a milder methodology for purifying Xist RNA during XCI. Others have published similar methodologies predominantly focusing on purifying Xist RNA directly with biotinylated probes (McHugh et al. 2015; Minaji et al. 2015; and Chu et al. 2015). Although this method boasts a milder purification method, it seems to be low yielding in Xist specific proteins. Others have shown a more robust identification of bona fide Xist binding proteins which are currently missing in this manuscript. A recent preprint from the Plath lab has identified new factors involved in XCI during differentiation and their tethering/rescue experiments are far more convincing than the ones shown in this manuscript https://www.biorxiv.org/content/10.1101/2020.03.09.979369v1. The candidate protein Ha et al. have identified has multiple roles in developing cells and has shown to be important during mouse development. However, Ha et al do not robustly show that the knockdown of Ssb causes X-linked cell mortality. Alternatively, as would be presumed from Ssb's essential role in many housekeeping short non-coding RNAs, the cell death seems more ubiquitous upon shRNA KD. Therefore, the link the authors are making here are relatively weak.

      Ssb KD rescues cell death caused by forced induction of Xist in male ESCs. In addition, Ssb KD leads to cell death in differentiating female ESCs, while it has a negligible effect on cell death in differentiating male ESCs. These data clearly demonstrated X-linked cell survival/mortality by Ssb KD.

      Plath lab’s work is different from ours. In their manuscript, the authors report the observation of a protein condensation which is assembled by Xist but sustains in absence of Xist. TDP-43 (a.k.a. Tardbp) happens to be one protein factor involved in the protein condensation and also one candidate protein selected for further validation in our study. In our study, Tardbp KD did not rescue cell death caused by induced XCI in male cells. Thus, Tardbp is not further studied. In the manuscript, we have discussed the possibility that low efficiency of knockdown and redundancy might contribute to the failure in validation of Tardbp

      *- State what audience might be interested in and influenced by the reported findings.*

      The audience may be interested in the novel technique and the finding of a novel Xist binding protein.

      *- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.*

      RNA biochemistry and developmental biology

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

      **Summary:**

      This manuscript describes a novel "FLAG-out" system, where the authors sought to identify Xist RNA binding proteins. The authors focused on a specific protein found in their screen and also identified in several other screens for Xist RNA binding proteins, Ssb/La, and further characterize the role of this protein in XCI. This manuscript describes the loss of Ssb/La and suggest that it predominately impacts the canonical 'cloud' formation of Xist RNA on the X chromosome during XCI initiation. Further, they determine that loss of Ssb/La decreases Xist RNA half-life and alters folding of Xist RNA transcripts. Based on their findings, the authors propose that Ssb/La functions to directly bind and fold Xist RNA transcripts in a manner that stabilizes Xist RNA, allowing for proper 'cloud' formation and successful initiation of XCI.

      **Major comments:**

      The authors made an interesting findings that the SLE-relevant autoantigen Ssb/La stabilizes Xist RNA transcripts, and there is some evidence that this occurs by binding and maintaining proper folding of Xist RNA. Despite these intriguing observations, there are many parts of the manuscript that need to be addressed in order to support the authors main conclusions.

      The most troubling aspect of this manuscript is the persistent use of an artificial XCI system in male cells to draw strong conclusions about the function of Ssb in XCI. This issue is prevalent throughout the manuscript, and I question why the authors chose to perform most of their experiments in male cells when the same experiments can be (and have previously been by other groups) performed in female cells. Using male ESCs and then making conclusions for XCI, which is a female-specific process, is a major concern.

      In addition to iXist male ESC line, many experiments, such as cell death/survival (Figure 3B, C), allelotype (Figure 3E), Xist could formation (Figure 4A), H3K27me3 and H2AK119ub IF (Figure 5), were performed in female ESC. We chose to do SHAPE and Xist RNA stability assays in iXist male ESC line, because the onset of XCI is much more synchronized in this system. Moreover, in female cells, Xa causes additional layers of complication/noise in the ATAC-sequencing which may not be fully cleared up by data analysis. On the other hand, inducible Xist expression in male ESCs can be used as an experimental system to recapitulate the silencing step of XCI (Ha et al. 2018; Wutz et al. 2002).

      • Out of the 138 identified binding proteins, the authors chose to only validate three: Mybbp1a, Tardbp, and Ssb/La. The logic for choosing these candidates is weak, and the authors are only able to validate 1 out of 3 of these proteins.

      In theory, all candidate proteins in the list are possibly involved in XCI. There is no method which can help to make accurate prediction. We did not follow a clear-cut logic in selecting candidates for validation, but we do consider the candidate gene’s knockout phenotype, “early embryonic lethality”, as a phenotype consistent with a critical role of the candidate gene in XCI. Meanwhile, in the manuscript, we have discussed why we chose the three proteins for validation as the following:

      “……From the candidate proteins, we shortlisted three proteins for individual validation. Myb-binding protein 1A (Mybbp1a, Q7TPV4) and TAR DNA-binding protein 43 (Tardbp, Q921F2) were selected because they are known transcription repressors (11, 12). The Lupus autoantigen La (P32067, encoding-gene name: Ssb) was selected because systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a strikingly high female to male ratios of 9:1 (13). Moreover, its autoimmune antigen La is a ubiquitous and versatile RNA-binding protein and a known RNA chaperone (14). All the three selected candidates have also been identified as Xist-binding proteins in previous studies (2, 4). Moreover, the knockout of these three genes all lead to early embryonic death. Tardbp knockout causes embryonic lethality at the blastocyst implantation stage (15). Mybbp1a and Ssb knockout affect blastocyst formation (16, 17). Early embryonic lethality is a mutant phenotype consistent with a critical role of the mutated gene in XCI (1)** ……”

      We used cell death/survival assay to further validate the role of Xist binding protein candidates in XCI. This is a stringent assay. It requires not only that Xist binding protein candidates bind to Xist, but also that the candidates have to be functionally important in XCI.

      Indeed, it has been demonstrated by Plath lab (the BioRxix manuscript mentioned by reviewer 1) that Tardbp (also named TDP-43), together with other RBPs, bind to the E repeat of Xist to form a condensate and create an Xi-domain. Yet, Tardbp KD did not rescue cell death caused by forced XCI in male cells in our studies. Thus, only 1 out of 3 of these candidates is validated and further studied. In the manuscript, we also discussed that low efficiency of knockdown and redundancy might contribute to the failure in validation of Tardbp and Mybbp1a.

      • Use of the cell death assay is not strong enough to "confirm that La is involved in induced XCI" as stated by the authors. This is a huge overstatement.

      Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect less surviving Ssb knockdown cells. In contrast, more Ssb knockdown cells survives in the presence of Dox, suggesting that Ssb plays an important role in XCI. Considering the reviewer’s comment, we revised the sentence to “further suggest that Ssb is involved in induced XCI”.

      While the authors observed differences in X-linked gene expression after Ssb KD, they did not examine expression of these genes in after KD of either Mybbp1a or Tardbp. Are the changes observed in these genes specific to Ssb KD? Or could there still be alterations of X-linked gene expression in the non-validated KDs? This experiment should be performed and included in the manuscript, either within Fig 2 or in the supplemental. As well, inclusion of a well characterized positive control, for example Hnrnpu, as comparison to Ssb should be included.

      Mybbp1a and Tardbp were not validated by the cell death assay. Thus, compared with Ssb, Mybbp1a and Tardbp are less important for XCI functionally. We only focused on Ssb in the subsequent studies. Mybbp1a and Tardbp KD could be additional negative controls. Yet, we have used empty vector as a negative control. We do not need so many controls.

      As mentioned, Tardbp indeed binds to Xist RNA. It is very likely that Tardbp KD might alter some X-linked gene expression. This rules out Tardbp KD as a good negative control.

      If we do not see any effect of Ssb KD on X-linked gene expression, a positive control is absolutely required. However, we have detected that Ssb KD compromises the silencing of several X-linked gene. A positive control might not be essential.

      • The authors perform RIP to validate the interaction of Ssb with Xist, but this is performed in male ES cells with induced Xist RNA and with FLAG-tagged Ssb. Aside from these cells being male, in this system Xist RNA expression is much higher than would be found endogenously. RIP should have been done in female differentiated ESCs if there is in fact a role for XCI.

      • The authors need to include more details in the methods section to explain how the FLAG-Ssb is expressed in these cells, and why the authors chose to use a tagged contrast over endogenous Ssb. Due to these issues the result from this experiment is essentially meaningless and is not convincing of Ssb interaction with Xist RNA. There is no reason RIP cannot be performed in female cells, and the authors should repeat this experiment in the relevant experimental condition. As well, if a validated Ssb antibody exists the authors should perform RIP using the endogenous protein.

      If required, we could try to perform RIP and/or CLIP using Ssb antibody in female cells.

      The authors state in Fig 3A-C that the results of the cell death and differentiation experiments "...support a functional role of La in XCI". The authors state earlier that Ssb is a ubiquitous protein that is embryonic lethal (in both female and males). Based on this, the cell death results shown do not support a functional role of La in XCI as the Ssb KD could be having an indirect affect due to its other developmental functions. This manuscript lacks a direct functional link between Ssb and XCI; more data is necessary.

      Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect less surviving Ssb knockdown cells. In contrast, more Ssb knockdown cells survives in the presence of Dox, suggesting that Ssb plays an important role in XCI.

      For the data in Fig 3A-C, Ssb KD causes the death of female differentiating cells, but not male differentiating cells. Therefore, it rules out that the death of female cells is due to the general function of Ssb. Rather, the specific role of Ssb in XCI contributes to the female specific cell death.

      In Fig 3D, the authors perform ATAC-seq in inducible male ES cells. The authors claim that the extremely slight reduction in chromatin compaction of the Ssb KD compared to control iXist "directly connect La to the heterochromatinization of Xi, supporting a functional role of La in XCI". This is also an overstatement based on the minimal, and possibly indirect, change in compaction. The positive control i-detaA-Xist sample has significantly less compaction (and thus significantly higher compaction defect) than the Ssb KD again disputing the claim stated above. It is unclear why performing ATAC-seq is even necessary, as Ssb isn't stated to have a function in regulating chromatin architecture. In addition, why the authors performed ATAC-seq in the artificial male XCI system and not in the F1 female cells, and the N of the experiment is unclear. If the authors want to include the ATAC-seq in further revisions it should be repeated n=3 in the female system.

      The male induced XCI system provides a more synchronized onset of XCI. More importantly, in the male induced XCI system, only one X chromosome exists, avoiding the interference from the active X chromosome in female cells. If ATAC-seq was performed in female cells, only loci with SNPs can be distinguished. The sequencing reads from Xa will create additional layers of complication/noise which may not be cleared up fully by data analysis

      “i-delat-Xist” is a positive control to show the experimental system works. It is not justified to compare the chromatin accessibility of the mutant, which is only a Ssb “knockdown” mutant, and the control “i-delat-Xist”, in which the Repeat A is “deleted”. We admit that ATAC-Seq results did not reveal a drastic difference in chromatin accessibility between the wild type sample and the mutant sample. However, as what we discussed in the manuscript, clear difference can still be seen at the 14 h time point. This is shown clearly by the heatmap (Fig. 3E) and the sequencing coverage profile (Fig. S4A).

      • In Fig 6, the authors state in their methods that "The shRNA construct, which worked efficiently against Ssb, was not designed against the 3' UTR of the RNA. Therefore, the shRNA is against some of the rescue plasmid constructs. Nonetheless, transfecting the Ssb knockdown cells with the rescue plasmids should compensate the effect of Ssb knockdown and serve as a rescue assay to study the functional domains of La.". This is troubling and seems like a major experimental issue; the specific rescue constructs that may be impacted by this issue are not stated and should be explicitly mentioned. This becomes more confusing when examining the data from rescue experiments.

      We pointed out this issue in the original manuscript. We agree that the experiment was not perfectly designed. In the revision, we added in the information on the shRNA target site. Our shRNA targets the LAM domain, so the expression of ∆LAM is not affected by the shRNA. We agree that the detected GFP expression levels of ∆RRM1 and ∆RRM2 are too low to be conclusive. In the revision, we have removed the data point of ∆RRM1 and ∆RRM2. Meanwhile, it is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed at similar levels. Ssb is a well-known RNA chaperone/RNA helicase. Identifying Ssb is an Xist-binding protein already suggests the functional role of Ssb in XCI. The data of the plasmid rescue experiments further suggests that Ssb is involved in XCI as a RNA chaperone/RNA helicase.

      If it is necessary, we could redo this experiments using a shSsb targeting 3’-UTR or expressing GFP-Ssb immune to shSsb.

      In Figure S7, the expression of the rescue constructs deltaRRM1 and deltaRRM2 is extremely low, yet the authors observe a rescue of the cloud phenotype (fig 6D) from those constructs that reaches almost the level of full length Ssb. This is confusing, and the authors need to address this by performing a western blot to show the protein levels of these rescue constructs and discuss further how such a low level of expression can show a rescue phenotype. The results would also be stronger if the authors examined H3K27me3 and H2AK119ub1 enrichment since they observed decreased overlap of these marks with Xist RNA after Ssb KD. Finally, the authors state that "...all three RNA-binding domains are required for the functionality of La in XCI..." however I have trouble coming to this conclusion based on the above issues. As well, if the authors want to support direct function, they should repeat the RIP experiments with these rescues constructs to show that the domains capable of rescue can still bind to Xist RNA.

      Reviewer 1 raised similar concerns. In Figure 6C, the live cell counts of ∆RRM1 and ∆NLS are about the same. It might be due to the low expression level of ∆RRM1 (Figure S7). It is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed as similar levels. To make the data more straight forward, we removed the data point of ∆RRM1 and ∆RRM2, because of their low expression levels.

      As for the Western blot and GFP fluorescence (IF), we have tried both. Neither of them detected GFP signal, reflecting the low expression level of these GFP fusion proteins. The shSsb is not targeting the 5’ or 3’-UTR region, therefore interfering the exogenous Ssb as well. This might be a reason for the low expression of these GFP fusion proteins. If it is necessary, we could redo this experiments using a shSsb targeting 3’–UTR or expressing GFP-Ssb immune to shSsb.

      We deleted the sentence "all three RNA-binding domains are required for the functionality of La in XCI".

      **Minor comments:**

      The authors may want to consider better highlighting the strengths of their "FLAG-out" system. As written, is it difficult to tell how this system sets them apart from the previously published studies referenced in the text, especially as some of these studies used similar crosslinking conditions and cell types. Additionally, the logic and questions the authors pose in the introduction as to why they performed this project are too general and not very strong. For example, the authors mention how might protein machinery may assemble on Xist RNA, and how might Xist RNA may spread on the X chromosome. However neither of these topics are actually addressed in their experiments or discussion. These are interesting questions, but the authors should either discuss them further within the context of their results or take these questions out. It would also be helpful if the authors could better label Figure 4, as it is unclear in the figure itself that Fig 4A is in reference to female cells, but remaining panels are in male cells.

      The inducible XCI in male cells is a valid system to recapitulate the silencing step of XCI. It also provides unique advantages in many experiments, such as ATAC-seq. Meanwhile, we did perform extensive functional analysis on the endogenous XCI process using female cells. However, we do realize that presenting the data of induced XCI in male cells together with the data from female cells is confusing to many readers. We have revised the labelling on Figure 3, 4, 5, 7 S6 and S9 (S5 and S8 before revision).

      To understand “how the protein machinery is assembled by Xist” and “how Xist spreads along its host chromosome territory” are not specifically the initial aims of this study. We removed the sentences from the introduction section. However, we believe Ssb may provide clues for the future studies to fully address these questions, and we did provide the following thoughts in the discussion section:

      “……Secondly, as Ssb is able to utilize ATP to unwind RNA-RNA and RNA-DNA duplex, it may play a more active role in controlling the structural dynamics of Xist in living cells (14, 23). These structural dynamics may be important for recruiting proteins onto the RNA and spreading of the RNA along its host chromosome territory……”

      Reviewer #2 (Significance (Required)):

      I am not convinced the this manuscript, as written, has sufficient novelty. Ssb/La has been previously identified to be an Xist RNA binding protein with older/different approaches. However, there are some interesting observations in this manuscript. Major revisions are necessary.

      We agree with the reviewer that identification of Ssb as an Xist RNA binding protein is not novel. The novelty of our discovery lies in: 1) we developed a new method for isolating lincRNA associated proteins; 2) we confirmed that Ssb is an important player involved in XCI; 3) we showed that Ssb regulates the folding of Xist RNA, consequently the stability of Xist and the formation of Xist cloud.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      Here Ha et al. has further developed their Pumilio RNA tagging methodology for the isolation of UV-crosslinked proteins that are suggested to associate with Xist RNA in mouse embryonic stem cells (mESCs). Within this study the authors claim to have found the Lupus antigen RNA binding protein (La) as a novel Xist interacting partner that influences the efficacy of X-chromosome inactivation (XCI). The authors use a number of different techniques such as qPCR, fluorescent imaging, ATAC-SEQ and SHAPE to show aberration of XCI upon La shRNA knockdown. However, this study has significant flaws in the efficient isolation and validation of Xist associated proteins using their FLAG-out methodology. Furthermore, later experiments predominantly focus on cell death/survival assays, which is somewhat troubling given the essential roles La plays in processes such as cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation. I feel the authors need to robustly address the potential effects La knockdown may be having on their mESCs.

      Major comments:

      -Are the key conclusions convincing?

      My major concern is in their Xist RNA purification. First of all, I couldn't find any data on proving the enrichment of Xist RNA itself in their Pumilio pull-down experiment. It would have been useful to show Xist RNA enrichment before benzonase step. Secondly, it is hard to imagine the protocol would successfully isolated Xist RNA-protein complexes from the cell. An earlier report by Clemson et al., (J Cell Biol., 1996) has shown that majority of Xist RNA is still stuck in the nucleus after nuclear matrix prep protocol using detergent, which is not so different from the authors' protocol. Moreover, the authors used UV crosslink, which would have made even harder to purify Xist RNA without sonication. Thirdly, as the tag is located on 5' of Xist RNA, it is rather surprising to see that Spen is not detected in their pulldown. Spen is one of the main functional interactors with Xist, robustly detected by several previous reports. Similarly, other high-affinity binders of Xist such as hnRNP-K and Ciz1 were also lacking from this screen. Finally, the peptides found associated with FLAG-out Xist are extremely low in comparison with other data using glutaraldehyde or formaldehyde crosslinking. For example, HnRNP-M found in Chu et al 2015 has 1120 peptide counts in differentiated cells. The authors here use HnRNP-M as a baseline for specific interactions and show a total of 6 peptide counts in Xist expressing cells and 5 in i-Empty cells (Supplementary excel sheet 1). Similarly, the La protein of interest in this study has 8 counts in i-FLAG-Xist and 6 counts in i-Empty. I struggle to see how this result indicate specific Xist binding. Worryingly this is the starting rationale for the rest of their experiments, it is hard to therefore accept the rest of their conclusions either.

      The other key conclusion the authors make is from the use of numerous cell death/survival assays for both male and female cell lines. This is extremely troubling in the context of assessing their target protein La. La is involved in multiple RNA maturation events of rRNAs, tRNAs and other polIII transcripts. Furthermore, La has been implicated in binding to the mRNA for Cyclin D1 in both human cells and mouse fibroblasts (NIH/3T3 - male) which show a significant effect on cell proliferation upon siRNA knockdown https://www.nature.com/articles/onc2010425. This, along with the observation that La knock-out blastocysts fail to develop any mice or ES cell lines (male or female) show the effect observed in the authors results is most likely not X-linked cell death https://mcb.asm.org/content/mcb/26/4/1445.full.pdf. The authors need to show that their shRNA KD isn't affecting the proliferation and general fitness of their mESC lines.

      - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      As discussed above, I feel the authors have not clearly demonstrated Xist specific protein enrichment and haven't proven X-linked cell death. Due to the lack of necessary control experiments as discussed below, I feel the notion that La is involved directly in XCI as an RNA chaperone is currently preliminary/speculative.

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

      I would suggest them to show RT-qPCR results of Xist RNA enrichment from the sample after flagIP before benzonase treatment.

      Also, it would have been more convincing if their negative control construct (i-Empty) would contain 25 copies of PBSb RNA at least.

      In Fig1b, the total amount of proteins loaded on the gel is not equivalent between two lanes. The gel should show equivalent amounts of proteins on the gel. It looks like if the negative control sample had been loaded at the same amount as the one with Xist, the band pattern wouldn't be distinguishable between the two samples. Furthermore, as these samples were used in the following mass spectrometry screen it may suggest that the minimal increase in peptide counts observed in the iXist FLAG-out were due to an increased amount of sample being loaded? No controls are conducted to account for this.

      The authors quantify cell death in figures 2C - E. It seems clear that shSsb 1 and 2 have an effect on cell count even in the absence of Dox. The rescue effect seen upon Dox addition is minimal when compared to Empty + Dox 2D. The authors ∆A-iXist line with and without Ssb KD/Dox would be an informative control on whether the increase in cell survival that they see is X-linked.

      The qPCR results used to validate silencing defects show minor changes in expression and also don't show significant silencing of X-linked genes sufficient for cell death. Could this be because only ~ 50 - 60% of Male iXist cells seem to be expressing in the movies and that this will have an effect on the observed qPCR results? Furthermore, it seems counterintuitive that expression in the Empty male cells increases in 48h compared to 14h. Is this due to cell death and positive selection of cells less able to silence their X-chromosome? How would these data look in the female XX line? How would the data look in a ∆A-iXist line in the presence and absence of shSsb/Dox?

      Confusingly, the male line in Fig 3C shows a drop in live cell count at day 6 of differentiation? Surely given their previous results in Fig 2 the Ssb KD should increase cell viability with +Dox? Ssb KD seems to have an adverse effect on ES cells during extended differentiation protocols. In Figure S1 the authors show ~ 8 - 10% survival of male lines during differentiation. Could the recombination of the Xist sequence around the loxP sites enable the cells to outcompete the dead cells? How would iEmpty and ∆A-iXist cells compare here? Have the differentiated cells been tested for their expression of Xist? Additionally, how are there similar live cell counts for male vs female lines when ~90% of male cells die during differentiation? Were more cells plated at day 4? If so, this would bias the competition of male cell survival and therefore make the male line an inappropriate control. Given the essential role of La during development a control is needed to prove that this death is X-linked in the female 3F1 line. For example, an XO cell line retaining the Cast allele and shSsb expression could show the amount of death caused from shSsb alone independent of X-linked cell death.

      If I understood correctly, the RNA FISH used dsDNA probes ("Sx9") against 40 kb of the X-inactivation centre (Xic). Surely Tsix or other Xic transcripts will also be visible? Can the authors use their RNA FISH to determine the XX or XO status of their cells? In Figure S5 a number of cells appear to show a single pinpoint of transcription. This could either be low levels of Xist transcripts or Xic transcription from an XO line in which the 129 chromosome is missing. It would be best to solely quantify cells which have two x chromosomes and if a significant amount of X chromosomes have been kicked out, this should be discussed and controlled for.

      In Fig6, the authors generated a number of Ssb constructs for a rescue assay. However, these results complicate the matter and raise more questions than they address. It seems odd that the ∆RRM1 does not rescue based on comparison with their putative negative control, ∆NLS. However, the ∆RRM1 + 2 and ∆LAM do rescue the phenotype better than the full length Ssb? This makes no logical sense and highlights the inherent variation in cell viability these generated cell lines seem to show. Following on from this, figure S7 quantifies the GFP tag mRNA levels, depicting all ∆RRM mutants with expression below ~30%? How can ∆RRM1 or 2 be rescuing in this scenario? Have these lines been tested for their XX or XO status? The loss of an X chromosome would lead to a rescue of the cell death phenotype, which is a process known to occur in XX lines that have been cultured for extended periods of time. Could it also be that the cell lines derived are more or less sensitive to exogenous shRNA expression? Also, further validation is needed to assess the efficiency of KD in these lines as theoretically most of these constructs will be targeted by shRNA? What is the endogenous Ssb expression level in these lines? Where in the mRNA sequence are the shRNAs targeted to? Does this make sense on the relative expression levels of ∆RRM1/2 for example? Further testing of GFP expression could also be assessed by quantitative western blot of GFP or even visualised in their RNA FISH/IF samples (Figure S8), currently neither are shown. In addition, some kind of information of stability of each Ssb protein constructs has not been demonstrated.

      For the data shown in Figure 7A and B the authors quantify the % of cells with Xist signal. The authors have already shown a defect in Xist visualisation in Ssb KD. Surely it is plausible to assume a faster loss of Xist signal below background in weaker expressing cells. A more appropriate quantification would be the % loss of Xist signal per cell over time.

      With Figure 7C and D, the samples have been treated with actinomycin D which globally affects the transcription of cells even the PolIII associated genes Ssb is needed to mature. This treatment could have an added effect on cell mortality and function. Data confirming that actinomycin D doesn't affect the cells disproportionately is needed. The difference in half-life could be attributed to such a treatment.

      In summarising the authors claim that La binds Xist to facilitate folding and appropriate spreading of Xist along the X-chromosome. No direct interaction has been shown, CLIP-seq data would resolve this, however I do understand this is a challenging technique. The authors have instead opted for RIP followed by qPCR (Figure S2). However, this process has a greater potential for non-specific recovery of RNAs via indirect binding. Furthermore, qPCR may also amplify the relative abundance of the RNA detected. As multiple nucleolar proteins came down in the mass spec screen and FLAG-Ssb is being over expressed, it is plausible to assume some transient Xist interactions may arise from nucleolar association at which La will be in high abundance. Positive and negative nuclear RNA controls (e.g. 7SK and U1 snRNA respectively) could be used so to determine the amount of non-specific Protein-RNA interactions in their RIP pull downs. Cytoplasmic actin is not an appropriate control as it is cytosolic.

      Other than this the authors may want to probe (via IF) for the presence of La accumulation on the X? Many other know factors such as Ciz1, hnrnpK and PRC1/2 complexes show clear accumulation on the X. If I understand correctly, there are many La antibodies on the market and endogenous levels on the X could be assessed. These antibodies may be useful in IP's and pull downs also.

      -Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      The experiments suggested above are centrally focussed on the cell lines that are currently in the authors possession with maybe exceptions with the ∆A-iXist-shSsb line suggested. However, this should be reasonably quick to obtain given their previous work for this paper. Most experiments suggested will focus on the validation of karyotype, Xist expression, rescue construct expression, further RNA FISH classification and repeating more appropriate positive and negative controls for a number of experiments. In theory this can be obtained relatively simply and quickly from current resources. But with the sheer volume of further experiments that are required here, this may take a significant amount of time. One vital improvement needed is the replication of mass spec data and the validation of Xist specific recovery and protein enrichment. As it stands this manuscript seems to not have any replicates of the FLAG-out methodology and mass spec data. This is troubling given the poor recovery and specificity of the protein samples obtained. Repeating these experiments would be costly in time and also financially. As it stands, I feel this is essential to conclusively validate their target of interest.

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

      The data is presented relatively well, however, it would be beneficial if deailed methods were in the main text and not in a supplementary file. Similarly, more information about the process of differentiation and how cell death/survival was quantified and validated is needed.

      - Are the experiments adequately replicated and statistical analysis adequate?

      In the most part yes, however there seems to be no replicates of the FLAG-out mass spec screen which is worrying given the minimal specificity observed in the current data.

      Minor comments:

      - Specific experimental issues that are easily addressable.

      Unfortunately, the majority of experimental issues need to be addressed with more robust data which are highlighted above. However, some image analysis, quantification and classification can be amended relatively easily. For example, the live-cell imaging data should be quantified as loss of signal as discussed and RNA FISH should be used to classify XX positive cells and the XO cells can be discarded from analysis.

      - Are prior studies referenced appropriately?

      Most papers regarding Xist pull down and biology are discussed and referenced appropriately. However, the role in which La plays during development and its aberrant affects upon KD are seemingly downplayed. I would like to see more discussion of potential defects that could be caused due to globally altering cellular RNA folding.

      - Are the text and figures clear and accurate?

      For the most part, lots of the figures are clear and accurate. Apart from these exceptions.

      1.The Y-axis of Figure 2D is confusing. What does 0.3 as a "sum of area" equate to? 30% of the area was ES cells? This doesn't look to be the case from Fig 2C. Also, how does the intensity of the signal compare? The area may not be a good quantification due to ES cells growing in colonies.

      2.In the Movies S1-7 there are boxes around certain cells and marked with "Figure 5a - c". This seems to be incorrect as figure 5 is currently the IF staining of polycomb marks. I assume this is in relation to Figure 4b-d?

      3.Similarly, in Movies S1-7, the intensities of Xist foci seem by eye to be similar. In the paper it is claimed that the Xist clouds that do form are lower in intensity. Are the Movies depicting the same range of pixel intensities? If not, this should be amended. Similarly, figure 7 seems to show relatively equivalent RNA signal at 0 h?

      4.In figure 4A the data is from female XX cells, this should be highlighted to limit confusion with the male iXist data shown below in 4B-E. It would also be helpful to have the male/female icons (as in figure 3B), for each figure that has images of cells. Currently Figure 4, 5, 7, S5 and S8 are lacking these icons.

      5.No explanation of the Flag-Ssb expression is given for figure S2. Furthermore, is it really necessary to express Flag-Ssb? There are reasonably good antibodies out there for Ssb as this was how it was originally found in Systemic Lupus patients. Also, no data showing the amount of Ssb being overexpressed is shown. This may have big implication to the validity of the RIP-qPCR analysis.

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

      Most of the data is presented reasonably well, but the robustness of the data somewhat retracts from their conclusions. I feel the certainty of their conclusion regarding Xist specific La binding and RNA chaperone activity is still presumptive and should be rewritten unless more robust data can confirm Xist interaction. I would also suggest deciding on the nomenclature for the protein of interest and use either La or Ssb, the continued use of both through the figures and text can get a little confusing to the reader.

      Significance

      - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      It was a good trial to use PBSb-PUFb system to purify Xist RNA binding proteins, compared to previous reports had used anti-sense oligo purification using complementary sequence to Xist RNA sequences. But currently the purification still needs further validation and repeats to confirm its use. A potential complementary technique could be to isolate Xist directly by using biotinylated probes against the PBSb sequence. The authors further claim the identification of a novel Xist RNA chaperone (La/Ssb) which they say facilitates XCI progression. This would be a novel finding in the field; however, the data is currently not robust enough to support this.

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

      This work has focused on the development of a milder methodology for purifying Xist RNA during XCI. Others have published similar methodologies predominantly focusing on purifying Xist RNA directly with biotinylated probes (McHugh et al. Minaji et al and Chu et al.). Although this method boasts a milder purification method, it seems to be low yielding in Xist specific proteins. Others have shown a more robust identification of bona fide Xist binding proteins which are currently missing in this manuscript. A recent preprint from the Plath lab has identified new factors involved in XCI during differentiation and their tethering/rescue experiments are far more convincing than the ones shown in this manuscript https://www.biorxiv.org/content/10.1101/2020.03.09.979369v1. The candidate protein Ha et al have identified has multiple roles in developing cells and has shown to be important during mouse development. However, Ha et al do not robustly show that the knockdown of Ssb causes X-linked cell mortality. Alternatively, as would be presumed from Ssb's essential role in many housekeeping short non-coding RNAs, the cell death seems more ubiquitous upon shRNA KD. Therefore, the link the authors are making here are relatively weak.

      - State what audience might be interested in and influenced by the reported findings.

      The audience may be interested in the novel technique and the finding of a novel Xist binding protein.

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

      RNA biochemistry and developmental biology

    1. One way around this is simply linking to each SVG with an <img> tag, instead of embedding the actual SVG in the DOM. This way, the virtual DOM only needs to track one node per image, instead of hundreds for each SVG. Inline SVG [above] vs linked SVG. But in doing so we’ve crippled our ability to manipulate our SVGs. No longer can we add stroke, move shapes, remove nodes or change fill. In short, if you want :hover to change the fill color, you’re back in the stone age.
    1. You know the trade-off. Use the img tag to display an SVG, and you get clean markup — at the cost of styling the SVG using its properties like fill, stroke, SVG filters and more.
    1. This commit does not belong to any branch on this repository.

      How would I download this commit/changeset with a git client then?? Or is it simply the case that if someone ever deletes the source branch for a merge request and "orphans" those commits, that there is now no longer a way to download it via the usual git fetch methods and the only way now to view these commits is via the web interface?

      Idea: Create a permanent tag for every version of every pull request that gets pushed up. (Which maybe the already do internally to prevent it from being GC'd?)

      https://github.com/ruby/ruby/pull/1758

      Ana06 deleted the Ana06:array-diff branch on Apr 30, 2019

    1. Malcolm X

      Hello all! Please As you read, please record three (3) annotations. Here are few ways to use annotations:

      1. Record a question that this reading sparked in your mind (add the tag “raised a question”)
      2. Leave a simple question mark (?) in the margins next to a passage or sentence that you found confusing (no tag needed)
      3. Share the dictionary definition of an unfamiliar word (I recommend Oxford English Dictionary online) or your research on an unknown allusion (add the tag “gloss”)
      4. Share your knowledge of what was going on around the time that a text was written or published that would help us better understand what we’re reading (add the tag “historical context”)
      5. Puzzle out one difficult section by putting it into your own words (add the tag “In other words”)
      6. Respond to another participant's question or comment. Start a conversation!
    1. SciScore for 10.1101/2020.07.07.20148106: (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">Written informed consent obtained from all participants in this study and was approved by the following IRBs: 1 ) IRB# SUNY:269846 .</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">In this regard , it is interesting to note that a bruton tyrosine kinase ( BTK ) inhibitor , that targets Fc-receptor signaling in macrophages , is being tested in a randomized clinical trial 32 .</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">Subdividing the subjects by sex did not reveal any statistical difference in IgG levels at any of the disease stages , although hospitalized females in the non-ICU setting had significantly lower antibody levels than ICU/deceased patients , whereas the difference in males was not significant ( Fig . 2f) .</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">CoV-2 Spike protein or Nucleocapsid protein specific IgG antibodies at titers more than 1:100,000 were detectable in all PCR+ subjects (n=87) and were absent in the negative controls.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>CoV-2 Spike protein or Nucleocapsid protein specific IgG</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Other isotype antibodies (IgA, IgG1-4) were also detected.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>IgA, IgG1-4</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CoV-2 infection2-6 To predict protection against CoV-2, it is critical to understand the quantity, quality and duration of the antibody responses during different stages of COVID-19 and in the convalescent period.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>CoV-2</div> <div>suggested: (Abcam Cat# ab272504, AB_2847845)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In this assay, we immobilized biotinylated CoV-2 Spike protein receptor binding domain (RBD) or the Nucleoprotein (N) on streptavidin beads, to detect specific antibodies from patient plasma (Fig.</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>CoV-2 Spike protein receptor binding domain (RBD)</div> <div>suggested: None</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Different antibody isotypes were measured using anti-Ig (IgG, IgA, IgM) specific secondary antibodies conjugated to a fluorescent tag (Fig. 1a).</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div>anti-Ig ( IgG</div> <div>suggested: None</div> </div>

            <div style="margin-bottom:8px">
              <div><b>IgA , IgM</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Using either anti-SRBD antibody or soluble ACE2-Fc, we show very high sensitivity in detecting Spike protein binding, down to picogram ranges (Fig. 1b).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-SRBD</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Furthermore, Nucleocapsid protein-specific IgG levels and S-RBD specific IgA positively correlated with S-RBD IgG antibodies (Supplementary Fig. 1b, c).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>S-RBD IgG</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Notably, IgG1 subclass antibody levels were comparable to total IgG levels whereas the other subtypes were relatively lower (Fig. 2b).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>IgG1</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To evaluate membrane expression of Spike protein, cells were stained with recombinant soluble ACE2-Fc fusion protein followed by a secondary staining with an anti-Fc antibody (Fig 3a).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-Fc</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">ACE2 overexpression of ACE2-IRES-GFP or ACE2mKO2 was confirmed by staining with CoV-2 Spike-protein fused with mouse Fc (mFc) and antimFc secondary antibody (Supplementary Fig. 2a, b).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>antimFc</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Of note, there was a significant negative correlation between the number of days and the IgG or IgA to S-RBD, anti-nucleocapsid IgG or the NT50 values ( !" = -0.67) (Fig. 6d), suggesting a potential decline in antibody titers over time.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-nucleocapsid IgG</b></div>
              <div>suggested: (Imported from the IEDB Cat# 3E9, <a href="https://scicrunch.org/resources/Any/search?q=AB_2848062">AB_2848062</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Neutralization of the virus by antibodies (NAbs) is one of the goals to achieve protection against CoV-218.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>CoV-218</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">However, another study showed IgA antibodies, but not IgG, increased in severe patients28.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>IgA</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Although it will be important to follow the same individual subject convalescent plasma over time to better assess this finding, our data point towards a relatively short-lived antibody response to COVID-19.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>COVID-19</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">WT and ACE2 over-expressing HEK-293T were also stained with SARS-CoV-2 S1 protein, Mouse IgG2a Fc Tag (Acro Biosystems) followed with APC Goat anti-mouse IgG2a Fc Antibody (Invitrogen).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Mouse IgG2a</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-S-RBD antibody and ACE2-Fc was tested both at 5 µg/mL starting concentration and in additional 5-fold serial dilutions.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Anti-S-RBD</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudotype virus neutralization assay Three-fold serially diluted monoclonal antibodies including anti-SARS-CoV-2 Neutralizing human IgG1 Antibody from Acro Biosystems, NAb#3 (Fig 4D), Genscript clone ID 6D11F2, NAb#2 (Fig 4D) and Genscript clone ID 10G6H5, NAb#1 (Fig 4D)</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>anti-SARS-CoV-2</b></div>
              <div>suggested: (Abcam Cat# ab272854, <a href="https://scicrunch.org/resources/Any/search?q=AB_2847844">AB_2847844</a>)</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>human IgG1</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Percent infection obtained was normalized samples derived from cells infected with CoV-2 or SARS pseudotyped virus in the absence of plasma, ACE2-Fc or monoclonal antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>ACE2-Fc</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">In addition, we also developed SARS Spike protein pseudotyped lentivirus, which similarly infected 293-ACE2 cells at almost 100% efficiency at higher virus supernatant volumes (Fig. 3f)</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>293-ACE2</b></div>
              <div>suggested: <a href="https://scicrunch.org/resources/Any/search?q=CVCL_DR94">CVCL_DR94</a></div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK-293T cells (ATCC; mycoplasma-free low passage stock) were transfected with the expression plasmids using Lipofectamine 3000 (Invitrogen) according to the manufacturer’s protocol as previously described33.</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;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">Generating human ACE2 over-expressing cells Wildtype ACE2 sequence was obtained from Ensembl Gene Browser (Transcript ID: ENST00000252519.8) and codon optimized with SnapGene by removing restriction enzyme recognition sites necessary for subsequent molecular cloning steps, preserving the amino acid sequence.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>Ensembl Gene Browser</b></div>
              <div>suggested: None</div>
            </div>
      
            <div style="margin-bottom:8px">
              <div><b>SnapGene</b></div>
              <div>suggested: (SnapGene, <a href="https://scicrunch.org/resources/Any/search?q=SCR_015052">SCR_015052</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Flow cytometry data were analyzed using FlowJo (BD biosciences).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>FlowJo</b></div>
              <div>suggested: (FlowJo, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008520">SCR_008520</a>)</div>
            </div>
          </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses were performed using GraphPad Prism 8.0 software (GraphPad Software)</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>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><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Author contributions M.D., L.K. and D.U. conceived, designed the experiments. M.D., L.K., L.P., M.Y. and R.H. carried out the experiments. B.T.L. designed the clinical research study on UConn Healthcare workers and M.K. recruited participants and executed clinical protocols. R.G. and O.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
            <div style="margin-bottom:8px">
              <div><b>UConn Healthcare</b></div>
              <div>suggested: None</div>
            </div>
          </td></tr></table>
      

      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.

    1. As a result, search engines came to ignore the <meta> tag in favor of using complex algorithms to analyze the actual content of a webpage.

      this is why we can't have nice things.

      human, spams, selfishness

      metadata x PageRank == CV x trial period == the dialogue x subtext == what they say x what they do