Review coordinated via ASAPbio’s crowd preprint review

This review was completed by Ruchika Bajaj.

This study has developed a tool to characterize small molecule modulators of RNA-protein binding events. Please see below a few points which may help strengthen the manuscript.

• The term “temporal” is used multiple times in the paper, to facilitate clarity for readers from different disciplines, it may be useful to provide some further explanation or context for the term.
• Introduction section, “independent datasets have failed to reach consensus”, please provide some brief explanation about those independent datasets mentioned.
• Introduction section, last paragraph “We apply TRIBE ID to profile cytoplasmic G3BP1-RNA interactions …” - further explanation of these three processes linked together would be helpful.
• Figure 1, please provide some further explanation for the difference between TRIBE and TRIBE-ID. Since the dimerization is forced by rapamycin, a control experiment to explain artifact binding would be helpful.
• In the section, “Rapamycin-mediated dimerization of G3BP1-FRB and FKBP-ADAR”, recommend adding some clarification about the goal of this experiment, which could be understanding either native processes or in a rapamycin-dependent manner.
• In section, “G3BP1 TRIBE analysis with human and Drosophila ADAR2 catalytic domains” - suggest commenting on the reasoning for ideal ADAR to possess characteristics like “high editing activity when dimerized or fused to G3BP1”. Are these characteristics important to increase signal/noise ratio in the assay? Also, an explanation of T375G mutation and control experiments with wild type ADAR for any inhibition effect for Figure 2 would be helpful.
• In the section, “Temporally controlled G3BP1-RNA interaction analysis with TRIBE-ID”, please clarify whether the experiment described in Figure 3 provides information about the time of interaction between RNA and G3BP1.
• A paragraph describing any limitations and other possible applications of this tool on other systems would add to the manuscript.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Sree Rama Chaitanya Sridhara and Sara El Zahed. Review synthesized by Ruchika Bajaj.

This study has developed a novel one-step methodology for the incorporation of membrane proteins from cells to lipid Salipro nanoparticles for structure-function studies using surface plasmon resonance (SPR) and single-particle cryoelectron microscopy (cryo-EM), which is a profound technology in the field of membrane protein structural biology. We raise some points that may strengthen the manuscript below:

• Main section, 4th paragraph “resuspended in digitoxin-containing buffer”- Does the sentence mean that membrane proteins were solubilized by detergent before reconstitution into salipro particles? Are salipro and digitoxin added at the same step? If this is the case, it is unclear how one can distinguish between the step wise solubilization and reconstitution or direct reconstitution into salipro particles. Further discussion on the mechanism of reconstitution would be helpful. In the same paragraph, the fragment “to increase membrane fluidity and render lipids” raises the question of whether the concentration of digitonin was optimized to balance the increase in membrane fluidity but not rendering the solubilization of membrane proteins.
• Main section, 4th paragraph, “the formation of saponin-containing mPANX1-GFP particles was assessed by analytical size exclusion chromatography using fluorescence detector” - It is assumed that fluorescence is detected from GFP. As the construct expressed is PANX1-GFP, GFP fluorescence signal will be received from reconstituted as well as not reconstituted PANX1. Is saponin specific signal being used as a signal for measuring the reconstitution of PANX1-GFP? In the same paragraph, “PreScission protease for on-column cleavage” is mentioned. Is GFP still intact in the expressed PANX-1 or is it cleaved? A diagram of these procedures showing the various steps will be helpful for readers.
• Main section, 4th paragraph “SDS-PAGE revealed the formation of pure and homogeneous Salipro-mPANX1 nanoparticles”- However, extra bands are present above the major band in Figure 1E, can some comment be provided on this point. Possible explanations for the additional bands could be post translational modifications or degradation of mPANX1.
• Methodology section, “membrane protein reconstitution screening using fluorescence-detection size exclusion chromatography (FSEC)” - The amount of salipro is given in ug. A comment on the ratio of protein to salipro particles would be important to decide the concentration of salipro with respect to the mass of the cell pellet.
• Figure 1G: The molecular weight of Salipro-mPANX1 particles is mentioned to be approximately 466kD. mPANX1 weighs about 48kD and heptamer will be 336kDa. A discussion on comparison of experimental and actual molecular weight would be interesting.
• hPANX1 was expressed in sf9 insect cells. A description regarding trials of expression of this construct in expi293 cells would be informative.
• Supplemental Figure 1B: The gel is overloaded and shows multiple bands for hPANX1, recommend selecting an alternative image for hPANX.
• Paragraph 6A phrase, “challenged with bezoylbenzoyl-ATP(bzATP), spironolactone and cabenoxolone” - Please explain the meaning of ‘challenged’ here.
• Supplementary Figure 2: Paragraph 6 mentions “binding constant could not be determined”. Please provide an explanation for this. Is it about the saturation phase not being approachable because of the feasibility of the binding experiment at higher concentration of cabenoxolone?
• The last summary sentence in Paragraph 6 is not clear, recommend rephrasing it.
• Figure 2A shows that Salipro particles have His tag. This suggests that an additional step of affinity purification with His tag could have been used to distinguish or separate reconstituted and un-reconstituted PANX1.
• Supplementary figure 4: Please explain whether the datasets for samples in the presence and absence of fluorinated lipids were combined together.
• Paragraph 8, “intracellular helices were not well resolved” - Please comment on a possible explanation. Does the Salipro scaffold contribute to the resolution? Please mention any future possibilities regarding improving the resolution by modifying the salipro scaffold or alternative scaffold. In the same paragraph, rmsd is mentioned at promoter level, please comment on how this value changes at heptamer level and why is it important to report the rmdd value to appreciate the direct reconstitution methodology.
• Last paragraph 10, “future membrane protein research” - Please comment on the utility of this methodology on prokaryotic membrane proteins, bacterial outer or inner membrane proteins or eukaryotic membrane proteins. Some more examples of reconstitution with the same method will support the applicability of this methodology on diverse kinds of membrane proteins. A discussion section comparing this methodology to other methods would also be useful for readers.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj and Sanjay Kumar Sukumar. Review synthesized by Iratxe Puebla.

The preprint studied the conformational changes upon binding of the Akt protein kinase to the Akt active site inhibitor A-443654 and the Akt allosteric inhibitor MK-2206, under three states of Akt: inactive monophosphorylated, partially active tris-phosphorylated, and fully activated, tris-phosphorylated bound to PIP3 membranes. The MK-2206 resulted in allosteric conformational changes in all states and restricted membrane binding through sequestration of the PH domain. The A-443654 inhibitor led to allosteric conformational changes in the monophosphorylated and phosphorylated states, with increased protection in the PH domain upon membrane binding. The results can assist the design of Akt-targeted therapeutics.

The reviewers had a few minor comments about the paper:

• It could be helpful to include a short explanation early in the text about the use of HDX-MS, how it works and why it is useful for exploring conformational changes.
• Figure 2A+B provide a nice representation of the HDX exchange data.
• Results ‘3 seconds at 1°C, which is referred to as 0.3 sec in all graphs and the source data)’ - This may be a bit confusing for someone who wants to look at the data in the figures independently. Consider an alternative way of representation or providing some further clarification in the figure legend.
• Results ‘Decreases in exchange in the kinase domain were similar to those observed in the absence of membranes, occurring in regions encompassing the αC helix, the ATP binding pocket, as well as changes covering the activation loop and C-lobe:PH interface’ - Please clarify whether the comparison here relates to the data in Fig. 3A/C vs Fig 4A/C.
• Results ‘There were multiple regions of significantly decreased deuterium exchange in the kinase and PH domains (Fig. 2B, 2D, 2E).’ - This section mainly focuses on conformational change upon the addition of MK-2206 allosteric inhibitor binding. Figure 2F appears to be the most relevant for the comparison. It is suggested to provide additional combination of data with ATP analogs to understand the coordination of ATP and inhibitor during the inhibition step in the cycle.
• Results ‘Both experiments were carried out under saturating concentrations of inhibitor binding, so this difference reflects intrinsic conformational differences.’ - Saturating concentrations implies that most of the population will be in the same conformation. Please comment on the association between saturating concentrations and intrinsic conformational differences.
• Discussion - There do not appear to be many structures available for different conformational states of Akt. The study has mapped hdx data on available structures, however, it'd be good to see correlation of conformational changes by HDX with conformational changes in structure.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Gary McDowell, Sree Rama Chaitanya Sridhara. Review synthesized by Iratxe Puebla.

The preprint studies the process for mitochondrial targeting of mitochondrial precursor proteins. Using a yeast model, experiments show that the cytosol transiently stores matrix-destined precursors in dedicated granules which the authors name MitoStores. The formation of MitoStores is controlled by the heat shock proteins Hsp42 and Hsp104, and suppresses the toxicity arising from non-imported accumulated mitochondrial precursor proteins.

The manuscript is clear and well-written. The reviewers raised a few comments and suggestions as outlined below:

The introduction was extremely clear and provides a good summary of the protein homeostasis dimension of the problem in question. However, there could be a clearer discussion of the processes of import, in particular with respect to the results discussing “clogging”. It is suggested to add a penultimate transitional paragraph in the introduction that facilitates this transition e.g. this could be expansion of the first paragraph in the Results section, moved into the introduction to provide more context about the cloggers, PACE, and the Rpn4-mediated proteasomal regulation.

Figure 2E and Figure S2 - Can some further explanation be provided about what data belongs to delta-rpn otr WT, or whether the associated fold change is reported - delta-rpn/WT.

Results ‘while the levels of most chaperones were unaffected or even reduced in Δrpn4 cells, the disaggregase Hsp104 and the small heat shock protein Hsp42 were considerably upregulated (Fig. 2F, G)’ - Suggest adding some further clarification as to why Hsp104 and Hsp42 are selected despite perturbations in other protein partners. Are there other proteins than proteosomes and chaperones which are significantly up- or down-regulated? STRING or cytoscape tools may help with the interactome analysis.

Figure 3

• Figure 3A - It seems Δrpn4 cells are bigger in size than control cells, suggest commenting on this point.
• Figure 3B ‘Hsp104-GFP was purified on nanotrap sepharose’ - Please clarify on which tag the purification was based.
• ‘grown at the indicated temperatures’ - Please clarify the rationale for using 30 or 40C.
• ‘SN, supernatant representing the non-bound fraction’ - Please report what is total, wash and elute etc.

Results ‘protein accumulated at similar levels as Hsp104-GFP in the yeast cytosol (Fig. S4B)’ - Please clarify whether the image reports qualitative or quantitative data, and how the levels of DHFR-GFP and Hsp104-GFP are compared based on S4B.

‘Owing to the striking acquisition of nuclear encoded mitochondrial proteins in these structures, we termed them MitoStores’- Suggest providing some discussion about the fraction of Hsp104 that is part of the MitoStores? Does a major portion of Hsp104 in the absence of Rpn4 form MitoStore structures?

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Pablo Ranea-Robles, Sree Rama Chaitanya Sridhara. Review synthesized by Iratxe Puebla.

In this preprint Munhoz et al. identify adiponectin as the main effector of the protective effects of sera from lean women and calorie-restricted rats on beta-cell integrity and glucose-stimulated insulin secretion. The study reports that sera from obese humans and rats impairs beta-cell integrity and insulin secretion in the absence of nutrient overload. This observation implies that changes in circulating factors between obese and lean individuals would explain the effects on beta-cell function. The levels of circulating adiponectin in rat sera and human plasma were consistent with the metabolic effects observed in beta-cells. Finally, adding adiponectin to islet cultures that were incubated with sera from obese individuals restored beta-cell integrity and glucose-stimulated insulin secretion. The data are reported in a clear way and the manuscript is well written. Data are consistent with a role of adiponectin in the observed protective effects, but some additional experiments are suggested to clarify this role.

Major comments

• The paper states that adiponectin is necessary to maintain islet function and integrity. According to the data reported, it is recommended to amend the conclusions to indicate that adiponectin is “sufficient”. A key experiment to demonstrate that adiponectin is necessary would be to deplete the sera of adiponectin and then evaluate the same parameters on beta-cells/islet primary culture. Adiponectin-receptor KO beta-cells would also help to clarify the role of adiponectin in the protective effects of sera. It may also be worth exploring if there were other hormones or other components beyond adiponectin which may have the similar increase in serum samples.
• In Figure 3A/3B, a picture of the corresponding Ponceau used for quantification should be shown next to the adiponectin blot. It would also be helpful to provide the full raw blots as supplementary files to allow for further evaluation, e.g. there seems to be a faint band in 3A above the predicted band which might be cropped in 3B, and there seems to be some difference in protein migration in different samples. Please show as a supplementary figure the full blot for adiponectin with all the samples shown in quantification.
• In the blot in Figure 3B there does not appear to be a clear difference between adiponectin levels in lean vs obese women, which would argue against adiponectin having a beneficial metabolic effect when treating beta-cells. It would be useful to provide some further comments on this possible discrepancy.
• Figure 4E compares different amounts of glucose with either FBS or no serum+adiponectin. Another condition with only no serum + vehicle for adiponectin should be included as a negative control, as shown in Figure 5.

Minor comments

• Abstract - Please specify in which model (cell/islet culture) the effects are observed.
• Sex-specific differences - The findings in humans are really interesting. However, only male rats are reported in this manuscript. Would there be any difference between male and female CR-rats sera when applied to beta-cells? This experiment would be a great addition to the paper. If the experiment cannot be completed at this time, there should be a mention to this limitation in the discussion.
• Results ‘Fig. 1A shows that the animals on the CR diet gained significantly less weight over the course of 15 weeks, but did not lose mass’ - This text refers to mass, the figure legend says weight, the y axis title on the figure states body mass. Please clarify for consistency.
• Results ‘They were within the same age range (Table 1), but were clearly distinct in body mass indexes (BMI), which separated them into lean and obese groups: lean women (BMI 22 ± 0.9, Fig. 2B)’- Please clarify the reference to Figure 2B in this fragment as the figure does not report BMI.
• Results ‘these results show a clear modulatory effect of circulating blood factors on metabolic fluxes in β-cells, which are stimulated by factors present in samples from lean and female subjects.’ - It is interesting that this is only observed for females, does this suggest that there may be sex-related factors involved, instead of or in addition to diet status? Could some further comment be added as to why the effect may only be observed in females.
• Please mention in the abstract/discussion that the results are obtained from in vitro experiments using beta-cells and islet primary cultures.
• Conclusions: suggest specifying “in the blood of lean rats” in the fragment that states “... in the blood of lean animals”.

Methods

• Please report the method of euthanasia.
• ‘experiments were carried out in accordance with the A. C. Camargo Cancer Center Institutional Review Board under registration n°. 3117/21’ - Please clarify whether the study received ethical approval, or was exempt from this requirement at this setting.
• Please report what type of fetal bovine serum (FBS) was used (e.g., charcoal-stripped FBS) as well as the FBS catalog number.
• ‘sera from both groups were collected to be used on cultured INS-1E β-cells, under physiologically relevant conditions’- Please provide further clarification on the conditions applied.
• ‘adiponectin supplementation in the plasma from obese donors’- Please report how this was prepared.
• ‘Data were expressed as means ± standard error of the mean (SEM)’ - There is a concern about using SEM to illustrate the distribution of data points, please consider using SD.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Luciana Gallo, Claudia Molina Pelayo, Sónia Gomes Pereira, Asli Sadli. Review synthesized by Iratxe Puebla.

The preprint examines the meiotic recombination co-factor MND1 and its role in the repair of double-strand breaks (DSBs) in somatic cells. The paper reports that MND1 stimulates DNA repair through homologous recombination (HR) but is not involved in the response to replication-associated DSBs. MND1 localization to DSBs occurs through direct binding to RAD51-coated ssDNA. MND1 loss potentiates the G2 DNA damage checkpoint and the toxicity of IR-induced damage, opening avenues for therapeutic intervention, particularly in HR-proficient tumors.

The reviewers raised some minor comments and suggestions on the work:

Results ‘Therefore, we conclude that MND1-HOP2 are ubiquitously expressed proteins’ - we understand that the study looked at the transcript's expression level and not protein levels, consider revising this sentence.

Figure 1F - Due to the differences in intensity for the loading control, recommend quantifying the normalized level of MND1.

‘we used live-cell imaging of RPE1 cells’- Are these cells p53 KO? In Suppl. Figure 1K, RPE Delpta-p53 cells are used , but the HALO tag was introduced in the normal (WT) RPE cells. Could some clarification be provided for this difference, and report what's the level of MND1 and the effects of its loss in WT RPE cells?

‘Analysis of 53BP1 foci formation and resolution in asynchronously growing RPE1 cells revealed that MND1 depletion leads to slower repair and retention of DSBs after IR (Figure 2A, Suppl. Figure 2F&G)’ - While the quantification shown in Figure 2A is explicit, the foci in the raw images displayed in Suppl. Figure 2G appears to be more frequent in the siNT, especially in the last 2 time points. It may be worth making the images bigger and maybe clearer?

‘our data show that the role of MND1 in DNA repair is most prominent in G2 phase cells and restricted to repair of two-ended DSBs’ - Can some further context be provided for the last part of this claim. Is this due to the different modes of action of the different drugs used? If so, it would be nice to clarify in the text which drugs induce the two-ended DSBs.

‘These data show that MND1 is recruited to sites of DSBs’ - The data shows that there is an increase in MND1 foci, but whether these are or not the sites of DSBs is not clear. Recommend co-staining with a known DSBs marker.

Methods

• Haploid genetic screen - Please describe how cells were fixed.
• Please detail if/what software was used for the Fisher’s exact test.
• ‘Cells were fixed after 7 days of growth in 80% methanol and stained with 0.2% crystal violet’ - Please report at which temperature and for how long the steps were completed, and provide a reference for the crystal violet reagent.
• ‘Membranes were blocked in 5% BSA’ - Please report the temperature and duration for this step.
• Please describe how the propidium iodide staining was performed.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Vasihvani Ananthanarayanan, Sam Lord, Rinalda Proko, Luciana Gallo, Sónia Gomes Pereira, Asli Sadli, Mugda Sathe, Parijat Sil. Review synthesized by Iratxe Puebla.

The preprint studies the molecular function of Arl15, a member of the Arf-like GTPases (Arls) group, which has been linked to magnesium homeostasis. The manuscript reports that Arl15 localizes in the Golgi and plasma membrane, including filopodia. The dissociation of Golgi or the expression of Arf1 dominant-negative mutant leads to a mislocalization of Arl15 to the cytosol. Knocking down Arl15 results in reduced filopodial number, altered focal adhesion kinase organization, and enhanced cargo uptake. Arl15 knockdown decreases cell migration and enhances cell spreading and adhesion strength. The findings point to a functional role for Arl15 in the Golgi.

General comments

• Figures 1,2, 3 - The images display one representative example, recommend providing quantification (e.g. PCC/Manders) across several biological replicates, as well as information on the type of images reported, single slice, max Z projection etc.
• For the bar plots, the paper reports the number of cells as well as the number of times the experiment was repeated, which is excellent. However, it is unclear whether the SEM error bars and p-values were calculated based on the number of repeats (correct) or based on the number of cells (incorrect). Can clarification be provided for this point See https://doi.org/10.1083/jcb.202001064 and https://doi.org/10.1371/journal.pbio.2005282.
• Throughout the paper there are several references to ‘data not shown’ - please report the data for those items.

Specific comments

Introduction, first paragraph - Suggest shortening the paragraph, particularly regarding the description of the different Arls and their relationship/correlation with all diseases.

‘These results show that similar to HeLa cells, Arl15-GFP localizes to PM along with filopodia and Golgi in all mammalian cell types’ - Suggest revising the fragment to ‘all the mammalian cell types tested in the study’, to avoid generalizing to every mammalian cell type.

‘the localization of Arl15-GFP to PM however remained unchanged as compared to DMSO treated cells (Fig. 2A).’ - Fig 2A only compares mCherry-UtrCH against Arl15-GFP. To support this claim, Arl15-GFP would need to be compared to WGA-AF, as in Figure 1, and their colocalization quantified to confirm that it remained unchanged.

‘We treated mCherry-UtrCH expressing HeLa:Arl15-GFP stable cells with a small molecular inhibitor of Rac1 (CAS 1177865-17-6) or Cdc42 (ML141)’ - Please report the concentration of both inhibitors.

‘Overall, these studies indicate that neither actin depolymerization nor the key regulatory molecules of filopodia/lamellipodia affect the localization of Arl15 to PM/Golgi.’ - The visualization reports Arl15-GFP v mCherry-UtrCH, to support the claim please check against WGA/GM130 as in Figure 1. Also, Figure 2c Arl15 for FAK inhibitor looks different from the DMSO control, recommend confirmation with WGA staining. Can also some explanation be provided for the fact that the Arl-15 in Figure 2A and 2C DMSO looks quite different from 2B and 2D despite the stable cell line with uniform expression?

‘which mislocalized Golgi pool of Arl15 without affecting its PM localization (Fig. 2D)’ - There does not seem to be a marked difference in Arl15-GFP's intracelluar localisation in cells with and without microtubules, and the PM signal appears slightly reduced in the Nocodazole-treated cells. Is it possible to please quantify the localisation?

Figure 2 -The quality of the images from panels B and D looks very different from those of A and C. Can some clarification be provided, were the same microscope, camera, and settings used?

Figure 3 - It would be good to mention the role of brefeldin A as an ATPase inhibitor to provide context for why it is being used.

‘Surprisingly, Arl15-GFP localized to the cytosol as similar to Arf1-GFP in GM130 dispersed cells that are indicative of brefeldin A treatment in HeLa cells (Fig. 3A).’ - It may be worth clarifying the reference to a surprising result, considering the nocadozol results would this result not be expected? It may also be worth providing some comments about the possible PM localisation difference when Golgi is disrupted with nocadozol vs BrefeldinA/golgicide A. It seems that the PM localisation is also affected in the BrefeldinA/golgicideA treatments.

Figure 3A ‘Cells were treated with DMSO (as control), brefeldin A or golgicide A for 24 h followed fixation’ - Please comment on the 24-hour period, BFA would be expected to work in minutes timescale: https://rupress.org/jcb/article-pdf/118/4/813/1063231/813.pdf

Supplementary Fig 2A - The blots for Arl15 endogenous are very different between S2A and S2B. Also a 40% knockdown of Arf1 decreases the level of Arl15 by 17%. Can some comments be provided on the significance of this decrease.

Figure 4 - Is the SEM over 3 independent experiments or total number of cells from the three experiments? What was the criteria used to define a structure as filopodia?

‘However, we continued with Arl15V80A,A86L,E122K cytosolic mutant to study the functionality of Arl15 in HeLa cells’ - It may be worth specifying the reason to use the V80A,A86L,E122K form instead of the more simple V80A alone?

‘To test whether the mislocalized Cav-2 and STX6 are targeted to lysosomes in siArl15 cells’ - Please comment on why colocalisation with lysotracker or lamp1 positive structures was not examined instead of treating the cells with bafilomycin A1? Note that bafilomycin A1 also inhibits retrograde membrane traffic at the ER–Golgi boundary: https://www.molbiolcell.org/doi/10.1091/mbc.9.12.3561?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed

Figure 5 - Please clarify whether the quantification of images was done on images taken from the same microscope? Also, suggest arranging the figures in a way that the quantification and images are not so far apart from each other.

Figure 5D - It is unclear how the western blot of EGFR showing total EGFR is indicative of what happened to its trafficking, this appears to be in contrast to the increase in transferrin uptake data. Recommend normalizing the transferrin uptake to surface transferrin levels as one can have higher uptake simply because there is more transferrin receptor instead of actual changes in trafficking rates.

‘Nevertheless, the reason for the partial loss of STX6 and caveolin-2 localization from Golgi in the ASAP1/2 knockdown cells requires investigation’ - Text earlier mentioned "However, we have not observed any significant change in Arl15 and its dependent cargo (caveolin-2 and STX6) localization to Golgi in siASAP1/2 cells " and there does not appear to be any difference in the siASAP1 or siASAP2 on Fig 6. However, in Figure S3 there is a slight reduction in the intensity. Can this be clarified?

Methods ‘Post chase, cells were washed with 1X PBS, fixed with 3% formaldehyde…’ - Please report for how long and at which temperature the fixation step was completed.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Joe Biggane, Luciana Gallo, Rachel Lau, Sam Lord, Dipika Mishra, Claudia Molina. The comments were synthesized by Iratxe Puebla.

The study reports two two Bcl-2 family proteins, BNIP5 and Bcl-G, which inhibit Bak-dependent apoptosis through engagement of MODE 2 inhibition. The BH3 domains of these proteins act as selective Bak activators, while not inhibiting anti-apoptotic proteins, leading to increased binding of activated Bak to Mcl-1, which prevents apoptosis.

The reviewers raised a couple of questions about the methodology and several other suggestions for the paper, outlined below:

Methodology

Throughout the study various BH3 mimetics are used, but the combinations in which they are used and/or the doses employed could be more clearly reported. For example, in Figure 1E and 1F ABT-737 and S63845 are used at 1 μM. Then, in Figure 1H, A-331852 is substituted for ABT-737 in combination with S63845 and the concentration is not reported. In Figure 1H, ABT-737 and S63845 are used again, but this time at a concentration of 2 μM each. Other concentrations are used in Figures 2, 3, and 5. There seems to be a dose-response assay in Figure 3B, but it is used for a specific use case. It would be beneficial to report all combinations and doses employed, and the rationale for them in the main text, to allow readers to fully interpret the data presented.

In various figures, there is a concern about the statistical approach to calculate p-values based on multiple measurements or cells within each sample. The t-test and ANOVA assume that each measurement is independent, and multiple nuclei within the same sample are not independent. Recommend either not reporting p-values or averaging together the values from each biological replicate to calculate the p-value using those sample-level means. For more information, see https://doi.org/10.1371/journal.pbio.2005282 and https://doi.org/10.1083/jcb.202001064

Specific comments

• Introduction ‘The two MOMP effectors Bcl-2 associated x (Bax) and Bcl-2 antagonist killer (Bak) are inactive in resting cells as these cells exhibit low levels of proapoptotic BH3-only proteins (e.g. BIM)....and some are additionally able to activate Bax and Bak (sensitizers and direct activators, e.g. BIM)’ - Recommend revising the fragment for clarity, adding references to support the statements and possibly an introductory figure to help visualize the proteins involved.
• Introduction, last paragraph ‘We found that two Bcl-2 proteins, Bcl-2 interacting protein 5 (BNIP5) and Bcl-G, act as selective inhibitors of Bak-dependent but not Bax-dependent apoptosis…’- The fragment is unclear, BNIP5 and Bcl-G are first reported as Bak-inhibitors, then activators and back to inhibitors. Does this mean to describe protein-protein interaction and changes in conformation?

Figure 1

-Recommend using a different color scheme for Figure 1E to assist visual interpretation of the results, in particular consider using a color-blind friendly color palette.

-‘colony formation (F)’ - The text later on refers to ‘clonogenic survival’, would it be possible to clarify in the legend or text what is being assessed, i.e. recovery assay, clonogenic survival or colony formation?

-Figure 1G - Please clarify whether 2 uM of each are used in this experiment.

-‘We transduced PC9 lung cancer or A375 melanoma cells…’ - It is nice to see that different cell lines were assessed to address any cell line-specific effects. Would be interesting to see if this effect occurs in normal cell lines and not just cancer cell lines.

• Figure 2 - The inline color-coded legends are useful when bars are displayed but in the figure several bars are close to zero, consider an alternative method to label the bars.
• Results ‘...suggesting posttranscriptional regulation of Bak levels by BNIP5’ - Maybe large proteome databases of multiple cell lines (e.g CCLE) can be datamined to determine the correlation between BNIP and Bak expression?
• Figure 3B, 3D and 3E - Please clarify the concentrations used for each treatment in the figure legend.
• Methods, Cell viability and cell death measurements - The study assessed cell death or cell viability with either live cell imaging, or in fixed cells, can the methodology for this be elaborated upon? Also, propidium iodide staining is used in several sections of the results, recommend adding information about this under the Methods section.
• Methods - There are several missing references in the Methods section.
• Suggest adding Supplemental Figure 6 as a graphical abstract.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ashley Albright, Luciana Gallo, Sam Lord, Dipika Mishra, Claudia Molina, Arthur Molines, Sónia Gomes Pereira, Parijat Sil, Rinalda Proko. The comments were synthesized by Richa Arya.

The reviewers like the motivation behind the study as a lot is still unknown about the impact of fluorescent tags on various mechanisms in biology. The work is impactful. However we outline below some major questions and several minor points:

1. Related to the data analysis:

The findings are valuable however the analyses may not be sufficiently sensitive to pick up morphological changes. Maybe other more sensitive approaches for measuring interference in the biology of these neurons could also be tested, like bulk growth rates, a stimulus added to the culture medium or other?

Some of the phenotypes (see Figure 1D and Figure 3D) are relatively subtle and the manuscript relies heavily on statistics to support the claims. Independent of the statistics, the differences are not striking by eye examination. Perhaps more data is necessary to bolster some of the reported claims.

As continuation to the expression analysis, it is important to estimate the expression levels of the actin binding probes used, in order to rule out the fact that some of the observed differences between LifeACT-GFP and AC-GFP may be due to discrepancies in the extent of overexpression of these probes. It would greatly add to the study to include, at least for some of the phenotypes, whether the measured parameters respond to the low versus high expression levels of the same probe.

1. Related to the transient expression:

Figure 1: The transient expression method used in the manuscript shows a lot of variability in expression levels, between cells, and between replicates.

Expression levels could confound the interpretation. One of the constructs could be expressed more or less than the other, resulting in stronger or weaker phenotypes, not because it is more or less toxic than the other per se but because its expression level is different. It would be relevant to "normalize" for the expression level of each construct. Another way to circumvent this, at least partially, would be to substantially increase the number of cells analyzed, which would allow for a range of expression values to be represented in the data.

1. Related to FRAP analysis Last paragraph result 1: 'and depends only on their affinity to F-actin, that is similar in AC-GFP and LifeAct-GFP (Figure 1A, Figure 1B, Supplementary Video 1). In addition, our results suggest that even without photomanipulation, minor changes in actin reorganization cannot be revealed when using actin-binding probes..’- Based on the images reported, it is not possible to establish how much of the signal is due to the population of probes being bound to actin versus the population that is free floating in the cytoplasm. The recovery could be due to the diffusion of free-floating probes and therefore give no information about affinity for actin. EGFP alone was used as a baseline for cytoplasmic diffusion, the slower recovery from the EGFP-actin implies that some portion of the EGFP-actin is incorporated in filaments. Recommend replacing "Affinity" with "relative ability to incorporate into filaments." A possibility to address the issue of size-based diffusion in cytoplasm is to complete FRAP measurements in latrunculin-treated cells that depolymerize most of the actin filaments. This will enable to set a baseline for each of the probes here (which will now probably be either free or G-actin bound) and provide a complement to the Jasplakinolide treatment.

‘In addition, our results suggest that even without photomanipulation, minor changes in actin reorganization cannot be revealed when using actin-binding probes.’- There has been only two actin binding probes tested, both with similar turnover as measured in FRAP in their own assay. It might be worth making a comparison in this experiment with a very strong actin binding probe as control, such as Utrophin.

1. Figure 2: Theat measurement shown is not a very good proxy for filopodia motility.

The study used an intensity-weighted center of mass. This means that the center of mass moves, not only because the shape of the filopodia changes but also because the signal intensity changes. In other words, the shape of a filopodia could be constant (no motility) and yet have a center of mass that moves because the mCherry signal fluctuates inside it. This could be avoided if the center of mass of the shape is used, not weighed by intensity. This is especially a concern because the signal from the cytoplasmic mCherry is used for the analysis. If a folipodia locally thickens in the Z-direction, the cytoplasmic signal will increase locally, displacing the intensity weighted center of mass even if the 2D contour has not changed. Using a membrane signal would provide a better alternative. It would also be possible to make use of the resource Filotracker, that tracks the length of the filopodia as a measure of filopodia dynamics. Find the paper and the resource here: https://www.molbiolcell.org/doi/10.1091/mbc.e16-06-0461,https://github.com/olemarch/FiloTracker

1. Result 2 last para, ‘We found no significant difference in center of mass displacement between actin probe expressing cells and EGFP expressing control filopodia (Figure 2B)…’. This section needs more clarity and evidence to conclude that the probes do not alter filopodia dynamics. Maybe filopodia growth rate or some additional measurements? Failing to find significance does not equate to finding evidence of absence. It may be that this one parameter is not sufficiently sensitive. Maybe this possible uncertainty should be discussed in the last sentence of the paragraph, to note that the data highlights the possibility that the tested actin labeling proteins do not interfere.

Minor Comments

• Introduction: ‘Actin is a key cytoskeletal element in mammalian cells involved in many cellular mechanisms’. mammalian cells can be replaced with eukaryotic cells. It would also be nice to mention some of the cellular mechanisms involved such as cell division, and migration, among others.
• Introduction: it would be good to describe the various phenotypes observed in previous studies when actin was labeled or when actin-binding proteins were used. It would give readers context about the level of toxicity and what phenotypes to expect.
• Introduction last paragraph: ‘…and to exclude certain actin structures from labeling (Munsie et al., 2009)’: one more reference could be added for this statement: Sanders et al., 2013 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4197975/
• Figure1: It would be nice to have grayscale images of the actin channel in addition to the overlay.
• Figure 1 B, C: For all of the FRAP recovery curves, recommend providing insets, zooming in on the first 30 to 60 sec of the recovery, as that's when most of the recovery happens. The last 120 sec of the plots show a "flat" plateau.
• Figure 1D, in the fluorescence recovery plateau %."In addition, our results suggest that even without photo manipulation, minor changes in actin reorganization cannot be revealed when using actin-binding probes." This claim relies on the p-values. In looking at Figure 1D, left panel, EGFP-actin (orange) dots there appears to be an outlier. Independent of the outlier, the collection of dots does not appear that different by eye, recommend providing additional data to support this claim.
• Result 1: ‘In Jasplakinolide-treated neurons, as expected, we observed an almost immediate recovery of fluorescence in the EGFP expressing group, whereas the EGFP-actin signal did not recover…’. The fact that the EGFP-actin signal did not recover is surprising. Normally not all of the actin present in the protrusion is incorporated into filaments, some of it is floating around freely. Hence, some of the signal should be recovering, even after stabilization of the actin filament, simply due to diffusion. For example, the EGFP signal recovers in presence of Jasp. due to diffusion of the free-floating probe. Recommend some discussion about the absence of recovery for the EGFP-actin.
• Figure 2A: ‘Red lines show the movement track of intensity weighted center of mass..’. The red dots for the center of mass cluster and overlap, recommend color coding the dots so that it is clear visually what the displacement of the center of mass was and showing an overlay of the contours used for the analysis. Additionally, in the Supplementary Video 2 it looks like EGFP and EGFP-actin centers of mass are more displaced than AC-GFP or LifeAct-GFP. It would be good to clarify if this is exactly the same example as shown in the figure.
• Figure 2B: ‘Average intensity-weighted center of mass displacement over 60s time periods…’ Why was only a 60 sec interval considered when there are images up to 120 sec and the video goes until 180 sec? Additionally, please specify if these are the first 60 sec of imaging.
• Result 3: It is known that expression levels of actin binding probes can alter actin structures and their dynamics. It would have been great to do the following: (a) estimate the levels of expressed lifeact-GFP/AC-GFP and see how they compare with each other, (b) note or look for phenotypic differences as a function of the expression levels of these probes. It might be worth plotting the spine morphometric data in categories of low, medium and high expression levels of the two actin binding probes as well as EGFP-Actin (since this can affect nucleation/treadmilling etc at very high expression levels). Just as the identity of the actin binding probe being used is an important consideration in studies of actin dynamics, so is the expression levels of these probes.
• Result 3: use p-values to compare different cell lines, the n used in the statistics should be the number of samples, not the number of spines.
• Result 3: ‘This is like due to the known high background fluorescence level of LifeAct, originating from its affinity to G-actin (Melak, Plessner and Grosse, 2017)…’. Actin chromobody is also known to bind G actin. Is there a significant difference in G Actin binding affinity for LifeACT versus AC that can account for this explanation?
• Figure 3C ‘Expression of EGFP-actin or LifeAct-GFP for 24 hours did not influence total protrusion density’ - Please indicate whether these morphological analyses were done blinded as to what the cells were expressing, or any steps taken to reduce bias.
• Figure 3D: There is a similar concern here as for Figure 1D. Here the number of cells is higher, but the density of the points is not shown. By eye the box plots do not look very different, violin plots may be better for these data so that the distribution of data points is more apparent.
• Figure 3F: it would be useful to have a representative image of each (stubby, thin, and mushroom) class, to help non-experts better visualize what's being analyzed .
• Result 4, paragraph 1, ‘..whether dendritic arborization is altered within 24 h after the transfection of the tested probes…’ All the experiments were performed 24h after transfection, would it be worth testing different time intervals (e.g. 12-16h and/or 48h)?
• Result 4C,D E: suggest adding quantification to enhance the data.
• Materials and Methods section, ‘Live cell imaging and FRAP experiments, post-bleach in every case (Supplementary Video 2)…’. Should this read video 1.
• Materials and Methods section, ‘Live cell imaging and FRAP experiments,Then, cumulative displacement curves were calculated, and the 60 sec points were compared and statistically analysed (Supplementary Video 1)…’. Should this read video 2.
• Materials and Methods section: There are several custom-made plugins used in this work. It is good practice to make these available to the community by depositing them in a repository (e.g. GitHub, zenodo).

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Amrita Anand, Richa Arya, Aurora Cianciarullo, Luciana Gallo, Dipika Mishra, Sanjeev Sharma, Ryman Shoko and Rajan Thakur. The comments were synthesized by Ehssan Moglad.

The study conducted by Doyle et al. aimed to test the lipid phosphatidylinositol 4-phosphate (PI4P) transfer activity of the human ORP5 protein via orthogonal targeting of different sites of membrane contact, namely, between the plasma membrane (PM) and the mitochondrial outer membrane.

Major comments

Figure 1: The idea behind the experiment is great, however, there are some questions about the data presented:

• Recommend using better representative images, the morphology of the cells in panels B and C appears distorted, are these cells undergoing some death? This would make interpretation of the data difficult.
• In Figure 1B, the control experiment should be done with a known mitochondrial marker to test if the construct works as expected.
• P14P measurement using the P4M probe is unclear from the images and quantification provided. Please provide a multi-plane image of the probe showing its distribution on the PM and at the ER-PM or ER-mito contact site.
• Please provide an additional graph to show the relative change of PI4P at the PM compared to the rest of the cell or respective contact site.
• For a better comparison, recommend showing the normal (control) distribution of PI4P in the images.

Results: ‘As expected, we did not see the accumulation of PI4P at these contact sites (see graph in Fig. 1C), presumably due to SAC1 activity in the ER. Instead, the fluorescence of PM PI4P seemed to decline’: Please indicate whether this result is statistically significant.

Figure 2: The experiment with the FKBP-PI4KC1001 construct is not discussed in the text. Also, further clarification would be helpful for the results presented in panel B. In +SAC1mito, it is showing accumulation after Rapa treatment, please discuss why PI4P is not showing accumulation.

'The rationale was that without inhibition of PI4P synthesis, observing reductions in PM PI4P catalyzed by transport of PI4P out of the PM would require a rate that exceeded synthesis, which may not be possible through reduced flux at the much smaller surface area of induced PM-mitochondria contact sites, compared to ER-PM contact sites (compare Figs. 1C and D). We also imaged by Total Internal Reflection Fluorescence Microscopy (TIRFM) to more sensitively detect changes in PM PI4P with the high-affinity PI4P biosensor, P4Mx2.': Recommend revising the fragment for clarity.

Figure 3: The shape of the cells across figure 3 varies substantially, can some text be added to discuss why this is the case?

Figure 3: The authors have already shown in a previous paper that SAC1 predominantly acts only in the 'cis' configuration. However, induced coupling of overexpressed ORP between ER-PM and mito-PM using rapamycin might bring these membranes closer than usual or cause the formation of more membrane contact sites. Thus, there may be some possibility for SAC1 to act in 'trans'. Alternatively, there could be indirect changes in PM PI4P due to increased activity of endogenous ORP5 at these induced contact sites. To address this:

• Would it be possible to confirm if there was an increase in the number/size of contact sites by checking for mapper expression and localization when ORP5 constructs are expressed and coupled with Rapamycin? Lipid binding mutants of ORP5 could also be used to show that those lipid binding mutants do not cause a depletion upon coupling with Rapamycin.
• For experiments where SAC1 (Fig 3) was overexpressed along with FRB::FKBP-ORP5-ΔTMD, please show control conditions where the SAC1 alone was expressed without the ORP5. Also, in a control condition where lipid binding mutants of ORP5 are expressed along with SAC1, there should be minimal effects on PM PI4P depletion compared to WT ORP5. Adding these controls will further confirm prior observations and substantiate the effects of FRB:: FKBP-ORP5-ΔTMD expression, and rule out any potential artifacts from overexpression of just SAC1.

A major confound across all the experiments is the activity of endogenous ORP5 that is not measured. Is it possible to perform experiments (such as in Figure 3) where the endogenous ORP5 is downregulated using siRNA or shRNA and a siRNA/shRNA-resistant version of ORP5 is overexpressed in this background? There could be potential compensatory effects from other ORP5, and this would require simultaneous knockdown of multiple ORPs.

Minor comments

Figure 1: It could be helpful to start Figure 1 using a scheme of the two hypotheses on how ORP5 regulates PI4P levels at the plasma membrane. This will help easily assess the data presented in the Figures for and against the hypotheses.

Figure 1A: It is difficult to see the co-localization in the current color scheme. It would be helpful to use a different color combination, provide zoomed-in images, or use pointers to highlight.

Figures 1C and D: Please specify in the legend the timepoint when rapamycin was added and the subcellular membrane the measurements were made from.

In discussion: 'In principle, this observation does not demonstrate lipid transfer (though it is compatible with it). Although tethering at a site of membrane contact seems to facilitate access of PI4P to SAC1, ORP5 could simply be presenting the lipid to the phosphatase, as opposed to depositing PI4P into the membrane for subsequent hydrolysis by SAC1. If ORP5 works in such a presentation mode, it is not clear to which membrane the resulting PI lipid is released: either back into the PM, or into the tethered membrane. In other words, lipid transfer is not necessarily part of the reaction': Are there ways in which this can be tested? Suggest proposing some future experiments in the text.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj and Gary McDowell. Review synthesized by Bianca Melo Trovò.

This study demonstrates the utility of an L-Methionine analog - ProSeMet - to tag and enrich proteins which have residues that are methylated in vivo, ex vivo and in vitro. Furthermore, the study demonstrates that this can be used in combination with mass spectrometry to identify these sites. Overall this is a useful, well-verified and well-described approach that will be helpful for future identification and investigation of methylation sites.

Major comments

It would be helpful if the manuscript could additionally discuss the reversibility of methylation generally, and the reversibility of the modification of protein residues by the alkyne group specifically, in the discussion, and whether that has any implications for their results. It may be that the dynamics of methylation and demethylation vary between the two; or it may be that they are the same - either way, that may affect how they suggest others use this method and interpret its results.

Perhaps related to the question of reversibility, it would be helpful if the manuscript would comment on whether these are “true” methylation sites or not; i.e. whether they consider all these methylation sites to be functional. Trying to determine this would be an interesting direction for future work, but for this study a reflection on whether these novel functional methylation sites are simply capable of being methylated, or are likely to be methylation sites that are meaningful biologically, would be helpful.

Results, ProSeMet competes with L-Met to pseudo methylate protein in the cytoplasm and nucleus: the manuscript claims that ProSeMet is not incorporated into newly synthesized proteins but rather converted to ProSeAM and used by native methyltransferases. There does appear to be some reduction in the labeling with ProSeMet on cycloheximide treatment in Figure 2D - could this suggest that it is incorporated into newly synthesized proteins as well as being converted to ProSeAM? If not, could the manuscript explain why not? This experiment clearly shows that in contrast to AHA labeling, there is still use of ProSeMet as a substrate when translation is inhibited; however, it is not clear how this demonstrates that it is not incorporated at all into newly synthesized proteins. If methyl has been incorporated in previously present proteins, perhaps this can be clarified in the text.

Results, ProSeMet competes with L-Met to pseudomethylate protein in the cytoplasm and nucleus: the conclusion that “Cell fractionation of the cytosolic and nuclear compartments followed by SDS-PAGE fluorescent analysis revealed no fluorescent labeling of the L-Met control” is correct but may be overstated as there appears to be some background in the cytosolic fraction.

Minor comments

• Introduction: Recommend including a mention to ProSeMet's permeability.
• Introduction, Figure 1: the last step with CuAAC and N3 labeling in the description of the Chemoenzymatic approach for metabolic MTase labeling is not clear. Please, add the description in the legend.
• Results, Figure 2D: the image suggests an overloaded gel, consider using an alternative gel image.
• Supplementary Material, Fig. S1: the data with L-met is only shown with T47D stacks.
• Supplementary Material, Fig. S3: please add the control for the no treatment condition.
• Results, Fig. 2A ‘ incubating for 30 m in L-Met free media’: Please confirm that the length of incubation was 30 minutes.
• Results, Enrichment of pseudo methylated proteins used to determine breadth of methyl proteome: Please provide some description for the SMARB1-deficient G401 cell line. Why smarb1 deficient?
• Results, Figure 3: Please define BP, MF, HP, NES, and label the x and y axes in panel D.
• Results, ProSeMet-directed pseudo methylation is detectable in vivo: Please, clarify if the administration was oral.

Comments on reporting

• Results, ProSeMet competes with L-Met to pseudo methylate protein in the cytoplasm and nucleus: Please verify the quantity reported: 5µg on SDS-PAGE gel seems low.
• Results, ProSeMet-directed pseudo methylation is detectable in vivo: the manuscript reports that “mice starved prior to ProSeMet injection had increased ProSeMet labeling in the heart, whereas mice fed prior to ProSeMet administration had increased labeling in the brain and lungs”. The error bars are large, it would be helpful to show the individual real data points for the graphs in Figure 4.
• Results, Figure 4C: please report the mathematical expression used to calculate the relative fluorescence.
• Supplementary Material, Fig. S7: please provide more details on the antibody employed.

Suggestions for future studies

Future studies could investigate the biological functionality of the novel methylation sites - but this is a great proof of principle.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Claudia Molina Pelayo, Demetris Arvanitis, Pablo Raneo-Robles, Sónia Gomes Pereira. The comments were synthesized by Vasanthanarayan Murugesan.

In this preprint, Hughes et al. describe the interaction between the ER protein PERK and the mitochondrial protein ATAD3A. During ER stress, PERK phosphorylates elF2a leading to reduced global protein synthesis. The authors show that increased interaction between PERK and ATAD3A during such stress attenuates elF2a phosphorylation locally around mitochondria, resulting in continued translation of mitochondrial protein despite a reduction in global protein translation. The authors present multiple lines of evidence to support this claim and the experiments were well performed. The findings may have important implications for the understanding of mitochondrial protein synthesis and the interactions between mitochondria and the ER.

The following suggestions were raised:

Experiments

1. The manuscript would benefit greatly by measuring protein translation explicitly showing that mitochondrial protein translation is retained despite a reduction in global protein synthesis under certain conditions. That would help determine whether mitochondrial protein translation is protected under certain conditions driven by ATAD3 expression.

2. The specificity of ATAD3A towards PERK activation requires further experimental validation. Some specific suggestions are:

3. Changes in activation of other pEIF2a kinases, such as GCN2 or PKR, could be measured to discard their involvement.

4. In Figure S2, protein levels of ATF6 should accompany changes in spliced XBP1.
5. ATF4 levels, a downstream marker of the signaling pathway, could be measured.

Manuscript

• Recommend providing more details about the experimental protocol when treating cells with ER stressors. Different treatment durations are found throughout the manuscript (30min, 1h, 8h…). More information would be helpful in understanding the election of those time points for different experiments.
• In Figure 2, recommend including the blots for the downstream targets ATF4, GADD34 and CHOP at the 30 minutes time point, where the upstream activation starts.
• In Figure 2, the differences shown in the representative images for p-eIF2a and ATF4 appear milder than what is shown in the graph. In particular when compared with the interpretation of blots in Fig. S2. It is suggested to include all the blots used for quantification in Figure 2 in a supplemental figure so it can be clear how overexpressing/downregulating ATAD3A has a meaningful effect on this signaling pathway.
• Figure 2B shows 5 different (phospho)proteins using the same loading control blot. This approach would require stripping of the membrane after each blotting, can this be specified in figure legends and in the Materials & Methods. Was the membrane stripped after each blot or were different membranes used? If different membranes were used, please indicate so and present the individual beta-actin blots corresponding to each protein as a supplemental figure.
• In Figure 3A, arrows indicating the contact sites between ER and the mitochondria would be helpful in highlighting the colocalization of the two proteins. Please also provide scale bars for the images.
• In Figure 3D, the #contacts per mitochondria, it is important to specify the area of images analyzed. It is unclear that n=45 images from 3 separate experiments refers to 45 images per experiment or a total of 45 images pooled from 3 experiments. Please clarify.
• Recommend discussing the limitation of experiments using a single siRNA for loss-of-functions studies and experiments using cell culture.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Pablo Ranea-Robles and Michael Robichaux. Review synthesized by Michael Robichaux.

The manuscript reports findings from new knockout human cell lines for the mitochondrial release factors mtRF1 and mtRF1a. The work contributes new insight into mitochondrial protein translation and mechanisms related to mitochondrial disease. A specific role is demonstrated for the release factor mtRF1 in the translation of COX1, a mitochondrial respiratory protein. The manuscript also identified a compensatory role for the mitochondrial ribosome-associated quality control (mtRQC) pathway when mitochondrial translation termination is impaired.

Overall the experiments and results presented in the manuscript are supportive of the conclusions described in the text. These findings are impactful toward understanding mitochondrial translation termination.

Major comments

• In the results section related to Figure 1d, an increase in reactive oxygen species (ROS) is measured using the mitoSOX probe. Considering that mitoSOX measures superoxide accumulation in the mitochondria, please consider specifying in the text that the ROS measured is of mitochondrial origin. In addition, since mitoSOX labeling may be affected by changes in mitochondrial membrane potential or mitochondrial shape and size, please consider adding an experimental condition using a membrane-potential-responsive, redox-insensitive probe. Finally, please clarify the results presented in Figure 1d with more technical detail. What do the n-values signify? Technically, how is ROS production measured?
• For Figure 2b-d, in gel activity for complex I and IV are measured; please provide further technical details for these experiments. Please describe what kind of activity is being measured and how it is measured. Also consider adding a density graph of these gel data for clarification of the results.
• Referencing Figure 2b-c, specifically, it is stated in the Results section that “mtRF1 loss does not affect complex I..”; however, the figure shows an increase in activity of ~20% for the mtRF1-/- condition. Please consider rephrasing or clarifying this point.
• For Figure 3, there is a possible discrepancy in these results that may need to be addressed. For example, the difference of the relative intensity of ND6 between the WT vs. mtRF1a-/- conditions shown in Fig 3a is significantly less than what is quantified for the same comparison in the bar graph in Fig 3e. It is possible these analyses were performed differently; if so, please report this.

Minor comments

• It is stated in the first Results section: “In the absence of mtRF1a, cells tend to produce more reactive oxygen species (ROS)...”, which is vague, please rewrite more technically since it is describing the quantitative data in Fig 1D. From this same section, the final statement: “Thus, both release factors are critical for mitochondrial function and cellular growth” is perhaps too conclusive based only on the results from Figure 1.
• Related to Fig 1c: consider converting the graph to a log scale, which may help illustrate the difference in growth rates between conditions. In addition to measuring cellular growth, please also consider measuring/counting mitochondria and examining cell morphology changes, which may be easy, additive experiments to include here.
• In the Results section related to Fig. 2a-d, the respiratory chain complexes are presented with no context. Consider mentioning these complexes in the Introduction or contextualizing them better in this section.
• In this same Results section please add appropriate citations for “Figure 2g” when referencing results related to that figure panel.
• A portion of the results text related to Figure 4a-b states: “With the exception of MTND6 (mRNA encoding for ND6), all of the mt-mRNAs arise from the polycistronic transcript synthesized from the heavy strand. If the loss of mitochondrial RFs would affect mitochondrial transcription, one would expect an overall decrease in all mitochondrial transcripts. However, as we observe a selective decrease in specific transcripts in the individual knockouts, we conclude that it is more likely an issue of RNA stability rather than synthesis.” This may be more appropriate to include in the Discussion section.
• In the Results section related to Figure 5, please again consider properly citing the figures when describing the results presented in those figures and panels.
• While Figure 6 is an informative model figure, please consider explaining the model with respect to results in the manuscript.

Comments on reporting

• Please consider adding more detail in the Methods section about the statistical analyses performed in this study. In addition, other statistical tests may be needed for some group comparisons (e.g., two-way ANOVA for the data in Fig. 5d).
• For all the western blot data presented in the manuscript, please consider adding the full blot scans to the supplemental material.
• Referencing Supplementary Table S1, please consider adding validation references for the antibodies used in this study. This is of great benefit to other researchers.

Suggestions for future studies

Future studies may test the effect of the combined ablation of the mtRF1 and mtRF1a release factors.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ashley Albright, Samuel Lord, Arthur Molines and Sónia Gomes Pereira. Review synthesized by Iratxe Puebla.

The paper studies the involvement of aneuploidy in promoting chromosomal instability and suggests the aneuploid state of cancer cells as a point-mutation independent source of genome instability. The paper reports a considerable amount of data. We outline below some suggestions regarding presentation and the analyses reported:

‘mis-segregation in otherwise pseudo-diploid human cells’ - Please provide some explanation for the term ‘pseudo-diploid’.

‘suggesting that dormant replication origins’ - Please provide a sentence clarifying the meaning of ‘dormant replication’.

‘Cells activate dormant origins in response to reduced fork rate and stalled forks to ensure that the genome gets fully replicated in time’ - Please provide a reference to support this statement.

Figure 3

• Recommend re-arranging the order and position of the panels for greater clarity.
• ‘Interestingly, we found a positive correlation between S phase length and frequency of abnormal mitoses (mean S phase length in control: 603,3 ± 55,4; aneuploid: 728,7 ± 46,2) (Fig. 3c).’ - Figure 3C shows that the cells that have an abnormal mitosis had a slightly longer S phase on average, however there is no correlation analysis done or an analysis around "frequency of abnormal mitosis", recommend revising the sentence.
• Figure 3C - Cells with a longer S phase (or cell cycle in general) will receive more light before reaching mitosis. Is it possible that the correlation mentioned is due to photo-toxicity? Longer S phase -> more photo-toxicity -> abnormal mitosis. Recommend adding a control to account for the potential phototoxicity of the imaging.

Figure 4 - Panels C and D show that, among the cells that have foci, the number of foci is increased, either by aneuploidy or by the drugs. However, it is unclear from the data if the number of cells with foci also increases. Would it be possible to plot the % of cells with more than 1 foci for each condition? (as in Figure 4G). Also, C and D are aggregates of multiple experiments, it would be good to show the data per replicates.

‘there was a sub-population of senescent cells in the aneuploid sample (Fig. 5a)’ - Was senescence tested in the normal (euploid) population too (at the same passage)? Is that the sample named as "control" in the figure legend?

‘in aneuploid cycling cells was comparable to that of the controls for at least 3 generations by live-cell imaging (Fig. 6a-c)’ - Suggest clarifying here what the control is, in addition to naming it in the figure legend.

Comments on analyses/reporting

• In various figures (including Figures 1H,J,L,N,O; 2C,G,E; 3I; 4C,D; 5H,I; 6H,I,J), there is a concern about the statistical approach to calculate p-values based on multiple measurements or cells within each sample. The t-test assumes that each measurement is independent, and multiple cells within the same sample are not independent. Recommend either not reporting p-values or averaging together the values from each sample and calculating the p-value using those sample-level means. For more information, see https://doi.org/10.1371/journal.pbio.2005282 and https://doi.org/10.1083/jcb.202001064
• For each bar graph throughout the paper, recommend reporting the value of n, in the figure itself, the figure legend, or in the text. Using Figure 1C as an example, this reports a doubling in the number of cells with greater than 10 errors, but the significance of that would vary depending on the number of cells analyzed. Some plots in panels c and f have no error bars, and it would be useful to report the number of experiments.
• Almost every figure features representative images. The manuscript includes a massive amount of data already, but it may be relevant to show additional images in the supplement in cases where representative images are used in figures.
• Data analysis for RNAseq ‘results were filtered only based on p-value’ - Please clarify why the False Discovery Rate was not taken into the filtering step.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Luciana Gallo, Lauren Gonzalez, Claudia Molina, Arthur Molines, Srimeenakshi Sankaranarayanan and Sanjeev Sharma. Review synthesized by Iratxe Puebla.

The manuscript studies the role of the long-coding RNA lncRNA H19 in cellular senescence. The results show that H19 levels decline as cells undergo senescence and repression of H19 is triggered by the loss of CTCF and prolonged activation of p53. The loss of H19 leads to increased let7b-mediated targeting of EZH2. The mTOR inhibitor rapamycin maintains lncRNA H19 levels throughout the cellular lifespan preventing reduction of EZH2 and cellular senescence.

The reviewers found the methodology appropriate but raised some comments and suggestions about the paper as outlined below:

Introduction ‘H19 is a highly conserved, maternally expressed imprinted gene and encodes a 2.3 kb long non-coding RNA (lncRNA). It is located immediately downstream of the neighboring gene IGF2.’ - An additional reference to the expression pattern/levels of lncRNA H19 across 'normal' tissues/developmental stages would be useful to provide immediate insight into the contexts where H19 is important and note the conditions where its levels are altered.

‘To characterize the role of H19 in the cellular senescence of somatic cells, we examined H19 expression during replicative senescence of human cardiac fibroblasts’ - The data on changes in expression of H19 with age/culture time is very interesting. Suggest providing some comments on the choice of experimental systems for each experiment and why HCF cells were used to study replicative senescence while other experiments were completed in skin samples.

Figure 1

• Figure 1a - Please indicate in the legend how far apart or what are the passage numbers for 'early' and 'late' passages for the cell culture experiments. Is the reduction in H19 gradual or does it sharply decrease after a certain number of passages? What biological meaning would either of these observations have and how does it relate to mouse data in vivo?
• Supplementary Figure 1 shows a sharp drop between PD 20 and PD 50. Would it be possible to provide a finer analysis of H19 levels across many cell passages?
• Figure 1b - Recommend using the same normalization in a) and b). In a) levels are normalized to the first condition "early" while in b) levels are normalized to the second condition "old".
• Figures 1d and g - Please provide further information on how Cumulative population doublings were measured and clarification for the numbers on the Y axis.
• ‘decreased the lifespan of cells (Figure 1d; Figure 1-figure supplement 1c)’ - Figure 1d measures cells' doubling time, not lifespan. If lifespan is being inferred from doubling time, please provide some clarification on how this is being done. There are fewer cells after 15 days but it does not mean that cells are dying, it could be that they are growing slower. Please also provide details for the methodology followed to obtain the data in this panel.

Figure 2

• ‘CTCF mRNA and protein levels decreased in the late passage cells (Figure 2a and b), and CTCF knockdown in early passage cells induced premature senescence characterized by increased SA-β-gal staining and reduction in proliferation (Figure 2-figure supplement 2a). In contrast, treatment with rapamycin mitigated CTCF depletion, which is consistent with the effect of rapamycin maintaining H19 levels (Figure 2a and b). Furthermore, the regulatory link between CTCF and H19 is supported by decreased H19 expression in CTCF-targeted cells (Figure 2c).’ - CTCF knockdown and rapamycin treatment can affect many pathways, recommend toning down this conclusion. In Supplemental Figure 2a, the % of positive cells in the siNeg condition is significantly higher than in Figure 1e (close to 50% in Sup Fig 2a vs 30 % in Fig 1e). Recommend providing some comments on the variability of the control value as that level of variability can confound the conclusions. For example, the siCTCF condition is lower than the siNeg control condition when compared with the value from Sup Fig 2a but not when compared with the value from Fig 1e.
• Figure 2d - Remove "presentation last saved just now" from the panel.

‘a stress-dependent downregulation of CTCF through proteasomal degradation of CTCF protein in endothelial cells (51)’ - The paper cited here discusses epithelial cells, should the reference to endothelial cells be updated?

Figure 3 - Please provide further clarification regarding acute stress or prolonged activation of p53. What are the timescales? How do these relate to replicative senescence seen with aging or as cells at late passages?

‘Together these results confirm that activation of p53 is responsible for the downregulation of H19 as part of DNA damage response’ - Please provide further clarification regarding the reference to DNA damage. Is this an inference from the statement about "activation of p53 is crucial for establishing senescence as part of DDR"? p53, like CTCF and mTOR, can play different roles.

‘Given the mounting evidence suggesting the role of lncRNA H19 as a competing endogenous RNA (ceRNA) or miRNA sponge (60–62), we speculated that H19 might mediate the senescence program by regulating miRNA availability. To determine which miRNAs are directly regulated by lncRNA H19 during senescence, we evaluated miRNA expression profiles in control and H19 targeted cells (Figure 4a).’ - Can some further clarification be provided for this claim, if H19 is acting as a miRNA sponge, it wouldn't affect its overall levels, but rather its ability to bind its target genest? Based on the data presented, the link between let7b and H19 appears to be more related to let7b expression than sequestration. Consider removing the fragment or revising it to clarify the mechanistic link drawn between H19 and let7b. To show that H19 is acting as a sponge in this system, it may be necessary to mutate the complementary sequence and check whether let7b's activity increases (i.e. its target genes are down-regulated).

‘Among the top miRNAs upregulated in H19 depleted cells were members of the let7 family; specifically, let7b expression was significantly upregulated (Figure 4b’ - Suggest adding some more information about the other miRNAs that are affected.

Figure 4f ‘Senescence-associated secretory’ - Please clarify why SERPINE mRNA level is considered instead of IL-6 as in Figure 1f.

‘suggests the loss of EH2 results in a general decrease in PRC2 activity’ - should EH2 read EZH2?

Figure 5 - What happens to CDKN2A levels when H19 is depleted or overexpressed? Can the H3Kme3 antibody binding data be supported with expression data for CDKN2A? It may be relevant to see whether it follows the expectation that loss of H19 reduces EZH2 expression and increases p16 expression.

Figure 6 - Please provide some brief clarification for what the solid and dashed lines represent in the model.

‘More importantly, prolonged treatment with mTOR inhibitor rapamycin maintains lncRNA H19 levels by preventing the loss of CTCF expression and activation of p53, thus preventing the induction of senescence.’ - There is a question as to whether the experiments presented support this statement, suggest reframing the fragment. The strongest mechanistic experiments in the study are those regarding let7b, because they use the mimic to "rescue" its function.

Supplementary Figure 1d - It is nice to see authors tested 2 different siRNAs for H19 and these showed the same effect in Panel d. Can some discussion be provided for why overexpression of H19 leads to an increase in senescence markers and reduced proliferation.The outcomes of siRNA experiments may not sufficiently support the correlation between H19 levels and senescence induction. This is an example where both excess H19 and reduced levels of H19 have the same effect and it is a very important result. Would it be possible to titrate the expression of H19 to achieve different levels of overexpression and then analyze senescence markers under these conditions? It may also be possible to generate a siRNA-resistant overexpression construct to rescue the effects seen with siRNA-mediated depletion of H19.

Supplementary Figure 5 - Recommend updating the presentation to more clearly highlight the decrease in binding as mentioned in the main text.

Methods

• ‘10g of plasmid DNA was transfected’ - should this read 10 micrograms?
• ‘ΔΔCT method’ - Please clarify the control for calculating relative mRNA levels.
• ‘Cells were incubated with EdU stain (100mM Tris (pH8.5), 1mM CuSO4, 1.25 μM Azide Fluor 488, and 50mM ascorbic acid) at room temperature for 30 mins. Cells were washed with PBS twice and imaged using EVOS FL Auto microscope (Thermo Fisher)’ - Please report the duration that the cells were incubated with EdU in culture before the cells were fixed and EdU incorporated in the DNA was stained.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Richa Arya, Samuel Lord, Arthur Molines and Sónia Gomes Pereira. Review synthesized by Richa Arya.

In the manuscript titled “Nanog organizes transcription bodies” Kuznetsova et al. discuss how the transcriptional bodies are assembled. They show that Nanog and Sox19b cluster before the transcription actually starts and initiate the formation of transcription bodies.

The following comments and suggestions were raised to help strengthen the manuscript:

1. Result: RNA Pol II transcription localizes to two transcription bodies when zebrafish genome activates…

The heading of the section is a bit confusing. Does it refer to the transcription of RNA pol II or transcripts of RNA pol II?

Within the result section: ‘…as 64-cell stage…’ Would be good for the reader to clarify that this is division number 6 to make it clearer that it is way earlier than what is reported in the previous sentence.

‘...productive transcription starts in two transcription bodies in the nucleus. These transcription bodies are isolated, large, long-lived, and appear at a predictable time during development…’. At this stage the study has reported localization data but not activity data (this is included later). The formation of clusters (which is what is detected) might suggest but cannot conclude about the activity of the enzyme or whether RNA is actually being produced.

1. Figure 1

‘…the percentage of nuclei with at least one Pol II (Ser5P or Ser2P) cluster is indicated…’. The injection is happening at the 1-cell stage. Then observations are made at the 64/128/256 cell stage. Are all the nuclei labelled at these stages? or only a subset? Recommend providing some clarification about the percentages reported? Are they a consequence of the embryo being mosaic, some cells containing the label injected at the 1-cell stage and some not? Or is it biological noise? Or a combination of both? Is this the ratio of (# of cells with puncta)/(total # of cells) or is it (# of cells with puncta)/(# of cells containing some labelled Pol II)?

'C. Tracks of transcription bodies at 64-, 128-, and 256-cell stage. The presence of Pol II Ser5P, Ser2P, or both, is indicated by red, blue, and white circles, respectively. Time on the x-axis in minutes after mitosis.’. The sample size seems too small, can some clarification be provided to help with the interpretation of this data:

In the first plot, one embryo is 64 cells. Even taking in consideration the fact that the embryo might be a mosaic with 50% of the cells labelled and that one can not image all of the cells due to the thickness of the sample, it should leave a few cells imaged per embryo (5-10 cells). It would be good if one experimental replication was made of multiple embryos injected in parallel. So, with all these considerations, the 20-ish tracks displayed on the first plots seem like a small number. If one experimental replicate is 3-4 embryos and 5 cells can be imaged per embryo then around 20 tracks would be the result of 1 replicate (vs 3 as indicated in the methods). If 10% of the cells can be imaged at 256-cell stage, with 3 replicates each made of multiple embryos, it would give more than 60-ish tracks.

‘For wt, N=3, n=111; for mir430 mutant N=3, n=72…’. Please clarify what the two n refer to in the figure legend?

The methods state "A minimum of 3 biological and 3 technical replicates was generated for each experiment. The number of experimental replicates (N) as well as the number of measured nuclei (n) are reported for each conducted experiment individually in the respective figure legend." - Recommend including a shorter but similar clarification in the figure legend.

1. Result: Transcription factors cluster prior to, and independent of transcription

'and visualized each transcription factor in combination with the initiating form of RNA Pol II (Figure 2A, and Movies S4-6…’ Suggest adding a clarification about when zygotic translation starts in zebrafish and whether translation starts before transcription in zygotes.

‘We conclude that transcription factors cluster prior to, and independent of transcription elongation….’ From the data it should be possible to estimate a mean delta T from TF clustering to Pol II clustering, it may be relevant to report such a number.

1. Figure 2: Pairwise non-parametric Wilcoxon tests: There is a concern about the use of a pairwise test as the two conditions CTRL and amanitin are two different conditions.

2. Result: RNA accumulation results in dissociation of transcription factor clusters

‘... the appearance of RNA Pol II Ser5P (initiation) clusters was also delayed in the absence of transcription elongation (Figure 2E)…’. Suggest calculating the delta in time between TF cluster appearance and Pol II cluster (as suggested above). It appears the "delay" in the apparition of the Pol II puncta is the delay observed for the TF, which would indicate that with or without transcription Pol II joins the TF cluster at the same time.

‘…while accumulation of RNA causes them to dissolve…’. Is this based on the observation that inhibition of transcription results in a longer cluster lifetime? RNA accumulation might promote clusters to dissolve, but whether it is the "cause" of their dissolution has not been tested. Recommend reframing the fragment to avoid conclusions about RNA accumulation.

1. Result: Nanog organizes transcription bodies

'…cycle (Figure 3A)…'. There is a concern about comparing Nanog and Sox apparition time if they are not observed within the same embryo / nuclei. The present data are convolved by variations between embryos and between nuclei, recommend providing some clarification and looking at the time difference between each TF and the corresponding Pol II cluster.

‘...Nanog RNA Pol II Ser5P could still be detected…’. Suggest re-phrasing this part as "to determine if RNA Pol II Ser5P could still be detected in the absence of Nanog".

1. Figure 3.

‘In C-D, the percentage of nuclei with the indicated pattern is indicated…’. Suggest some further clarification about the percentages reported. In C does this indicate that 9 % of the cells form Sox clusters in absence of Nanog? And in D that 27 % of cells form Pol II clusters in absence of Nanog? If that is the case, recommend discussing it as it might impact the conclusion that Nanog is "required" for Pol II clustering.

'Pairwise non-parametric Wilcoxon tests were performed, ns indicates P > 0.05…'. Reconsider the use of pairwise tests, as noted above.

1. Figure 4 - ‘percentages indicate how often the shown phenotype is observed. For D and E, N ≥ 3 and n ≥ 18.’ - Please clarify how these percentages are calculated. Is this the percentage of nuclei with the described phenotype per embryo? Or the percentage of embryos with at least one nucleus with the depicted phenotype? In Figure 1 the percentage for Pol II in WT at 128-cell stage is 80%. Figure 4 reports 100%, is it evaluating the same thing? If it is preferable not to write exactly N and n values for all the conditions, maybe these could be shown in the figure itself.

2. Result: Nanog DBD as well as IDR are required to organize transcription bodies

‘In this study we analyzed the assembly of two transcription bodies…’. Recommend placing this under a separate Discussion/conclusions section.

‘…and RNA accumulates, transcription factor clusters disassemble.’ It is not clear that the statement is supported by the data, consider reframing the fragment.

1. STAR METHODS

Please provide additional details about the different aspects of methodology. Also consider depositing the custom scripts to a public platform such as github or zenodo where these materials can be publicly accessed and referenced, supporting reproducibility.

‘Preparation of embryos for use in live-cell microscopy….At 16- to 32-cell stage…’. In the movies (or at least their legends), the embryos shown are at the latter cell stages. Would it be possible to clarify whether later staged embryos were prepared and how? If the approach involved waiting until the desired development stage was achieved, please indicate so.

‘Image pre-processing with Noise2Void… The network was trained on and applied to the raw spinning disk confocal data in full 3D with both color channels being present…’. Are there specific parameters that should be specified? How many stacks / movies were used for training? How was it evaluated that the training was sufficient?

‘Signal normalization…..The denoised and max-projected 2D image data was normalized…’. Please report the details of the process e.g what was the normalization?

‘Determining developmental stage and mid-point between interphases……This method is very reliable as the inter-nuclear distances in these early stages are highly stereotypic…’. Was this method previously described and/or used? If so, please provide references.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj and Michael Robichaux. Review synthesized by Michael Robichaux.

The manuscript presents a cryo-electron microscopy focused study of a recombinant type V-K CRISPR-associated Cas12k transposon recruitment complex from Scytonema hofmanni that is DNA-bound and includes a complete R-loop formation. In addition to mapping the assembly and interactions within this transposon complex, the study also details the discovery of ribosomal protein S15 as an essential component for the transposition activity of the complex. The work presented in this manuscript may contribute to the development of new programmable CRISPR-associated genome-engineering tools in eukaryotic cells.

Major comments

• The figures in the manuscript are generally well-organized and clear. In particular, the 2D diagram of the Cas12k-TnsC complex in Figure 1A is a useful figure panel; however, please consider refining the diagram for readability by replacing the current nucleotide sequence rearrangement with simpler shapes or graphics.
• For the structural complex models in Figure 2, please consider adding annotations that highlight both the completed R-loop as well as the 122॰ angled confirmation of the PAM distal to proximal DNA, which are both features that are highlighted in the Results section text.
• The title for the “TniQ nucleates TnsC filament formation” Results section and the title for Figure 4 are both possibly overstated since these mechanistic conclusions are based solely on transposition assay results.
• In the discussion, please consider revising the language used to describe the mechanism of transposon complex assembly (the model in Figure 7) to better justify a rationale for proposing a “cooperative” assembly mechanism that is based on the data in this manuscript, which is a structural assessment of the whole complex and its sub-complex interactions.

Minor comments

• In the first section of Results section, consider adding a description of the recombinant system used to purify the protein complex used for cryo-EM as done for the Figure 1 legend (“V-K CRISPR-associated transposon system from Scytonema hofmanni (Strecker et al., 2019)”).
• For Figure S1B, the orientation map is not clear, an adjustment to the color contrast may improve the clarity of this panel.
• For the cryo-EM data in Figures S2, please better define the TnsC oligomer organization (i.e., hexameric, variable). Also for Figure S2, please consider improving the image contrast for the angular distribution images in panel B.
• For Figure S3, both the incomplete R-loop and the missing Cas12k-sgRNA + TsnC contacts described in the text for this non-productive complex structure are not evident or identifiable in the models presented in the figure. Please consider annotations or descriptions in the figure legend.
• For Figure S4, please consider defining all rotations and dispositions that make up the conformational rearrangements in the RuvC domain, as described in the Results section text.
• For Figure 2, please consider adding a 2D diagram of the current complex structure in comparison to previously-reported structural models.
• The organization of Figure 3 is too busy, please consider re-formatting for clarity.
• For Figure S8, please consider including a “zoomed-out” image of the Cas12k+S15 structure.
• In the concluding paragraph of the Discussion section, please elaborate more on how the findings from this work may impact the “genome engineering application of CRISPR-associated transposons”.

Comments on reporting

• As outlined in Figure S1, 75K particles were used for the final cryo-EM reconstruction of the Cas12k-TsnC recruitment complex. Please consider discussing the structural elements or discrepancies of the other classified particles.
• Table S2 and S3 appear to be missing.
• In the “TniQ recognizes tracrRNA and R-loop” Results section, please specify which TniQ and tracrRNA mutations reduced transposition activity.

Suggestions for future studies

Please consider future studies that address the relevance of this transposon complex structure to physiological processes via cell-based assays.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Richa Arya, Joseph Biggane, Luciana Gallo, Arthur Molines, Sónia Gomes Pereira. Review synthesized by Vasanthanarayan Murugesan.

In this preprint, the authors describe a novel pathway that maintains protein homeostasis in cells recovering from heat stress termed stress-induced protein disaggregase activation pathway (siDAP). siDAP induces the DNAJA1+DNAJB1-Hsp70 protein disaggregase and targets aggregates of tightly misfolded proteins. This pathway is distinct from more-known ubiquitin-dependent quality control and works in sequence with it. Further, the authors show that this pathway is compromised in aging cells. The authors have provided a wealth of convincing data to support the claims made.

The following items were raised:

Major comments

Manuscript:

• It is recommended to revise the manuscript to better integrate the data and the text. The paper provides extensive data to support the study claims, but further background material for the experiments in the introductory or results section would support interpretation e.g., concepts required to understand the final two figures are not discussed in the introduction.
• Reducing the number of supplementary figures may make the manuscript easier to follow and help in tightening the narrative.

Experiments:

• Results ‘Immediately after HS, DNAJA1 and DNAJB1 rapidly relocalized to nucleoli’ - It is unclear from the DAPI stain what happens to the nucleolus at 0h after HS. It seems to be present in some cells but not all. Could a marker of the nucleolus be used and/or some clarification included?
• Results ‘This suggests that predominantly newly synthesized DNAJA1 and DNAJB1 molecules drive the assemblage of the DNAJA1+DNAJB1-Hsp70 disaggregase in cells after HS’ - Fig S5D shows that B1 forms puncta after HS even in CHX treated cells, which suggests that protein synthesis is not needed. Can some clarification be added for this fragment.
• Results ‘diffuse GFP fluorescent signal (cyan) indicating that protein aggregates were largely absent’ - The presence of aggregates or puncta before HS cannot be ruled out, the puncta or aggregate could be too small to be resolved. Recommend commenting on this.
• Results ‘Blocking Hsp70 activity by VER-155008 also caused DNAJA1+DNAJB1 scaffolds to persist up to 24h after HS, presumably due to their continuous association with the aggregates (Figure 2D).’ - The HSP70 aggregates look different after treatment with VER, they look more like the A1/B1 puncta than in the DMSO condition, it may be worth commenting on this.
• In Figure 6, the distinction between biological aging and replicative aging could be stated more clearly. Cell lines derived from donors of different biological ages form siDAP puncta and recover from heat shock. However, the cells lose this ability when cultured in dishes at passage 12 or 18 irrespective of biological age. Hence it is not clear if passaging cells mimics biological aging with regard to protein homeostasis.

Minor comments

• Figure 1H: Recommend including some comments on why the size of HSF is more at 0 hr, and commenting on whether HSF-1 depletion changes HSP70 levels.
• Figure 2 (B-D) - The size of cells in U vs 0 hour appear different, the 0 hr cells look bigger. Suggest adding a scale bar and clarification on whether the magnification is the same.
• In Figure S4/S5, it is hard to infer the state of the nucleolus during stress with DAPI staining and subsequently the localization of DNAJA1 and DNAJB1 to the nucleolus is not clear.
• In Figure S4D, it is shown that CHX doesn’t affect the formation of puncta but the text states that newly synthesized DNAJA1 and DNAJB1 are required for the assembly of DNAJA1-DNAJB1-HSP70. Please provide some clarification for this contradiction.
• In Fig S8, statistical analysis of different siDAP induction is suggested.
• In Fig 3, please provide clarification for the choice of experiments in CHX-treated cells for testing the effect of VER-155008.
• In Fig 5, the caption mentions cells with/without VER-155008 treatment which cannot be seen in the figure.
• ‘In fact, we found that human cells can tune the activation of siDAP according to the level of protein damage sustained after HS’ - It may be informative to check if the cytotoxicity levels differ from HS at 39ºC and at 42ºC.
• In Fig 6, quantification of PLA^Dt, similar to Fig 1F is suggested. Please also report the conditions used for heat shock in these experiments, 42oC for 2 hrs?
• ‘Moreover, siDAP was fully active in all fibroblast lines tested (Figure 6A; Figure S22A and B). Similar to immortalized HeLa cells, primary dermal fibroblasts only induced the DNAJA1+DNAJB1 JDP scaffold after HS (Figure S22C)’ - May be worth mentioning that the apparently higher intensity of the fluorescence signal in the cells derived from aged subjects. The fluorescence signal per cell looks much greater in 70 yo (Fig. S22 only) and 75 yo (Figs. 6 and S22). The next few lines discuss the relevance of decreased fluorescence (representative of loss of siDAP induction) with serial passaging/replicative age. However, upon HS, siDAP signal seems to go up with chronological age, but then in the replicative aging experiments, siDAP is lost quickly.
• Discussion ‘There is some evidence to suggest that cellular surveillance systems that usually keep protein aggregation in check deteriorate during aging….’ - There may be some conflation of biological aging and "replicative aging". There seemed to be conflicting results when looking at differently biologically aged samples, which may affect interpretation of whether replicative aging in a dish recapitulates aging processes.
• Methods Cell culture - Please provide further information about the age and other details for the 6 primary fibroblast cell lines.
• Recommend increasing the size of the microscopic image panels in several figures to better highlight the features mentioned.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Rasmus Norrild and Akihito Inoue. Review synthesized by Bianca Trovò.

Antibody-based technologies for the detection and quantification of analytes in complex biological samples present challenges regarding the infrastructure and chemical modifications involved. There is therefore interest in developing alternative biosensor platforms that leverage split luciferase enzymes. Single-component luminescent biosensors can be more easily produced and work in both homogenous and immobilized assay formats. The manuscript reports the design of BAT, a single-component, NanoLuc-based, Binding Activated Tandem split enzyme biosensor for the detection of the SARS-CoV-2 spike protein in multiple assay formats.

The reviewers praised the efficiency and practical value of the reporter system described, as the reporter protein works even in crude bacterial cell lysate, as well as the novelty of the mechanism of action for the reporter system. A few comments and suggestions raised are outlined below.

Major comments

1. In the introduction, the manuscript mentions the single-component, NanoLuc-based, Binding Activated Tandem split enzyme (BAT) biosensor, which is said to “not rely on a large conformational change in the binding module or competition with a tethered decoy as with other single component platforms”. The manuscript argues for the uniqueness and generality of the BAT approach based on a mechanism that does not rely on conformational change. Further references and explanation for the mode of action would be helpful to support the argument. Given the lack of conformational change in the binding module, can an explanation be included for what causes the split components to come closer and reconstitute again?
2. Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: the explanation of Figure 1a and Figure S1f provided in the context of the model for the mechanism of BATs could be strengthened with crystal diffraction data to validate the hypothesis, especially for clarifying how steric hindrance occurs when binding with the antigen (although this will not elucidate any conformational change happening in LCB1 upon binding).
3. Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: the manuscript reports a full mutational analysis, or deep mutational scanning (DMS) leading to the generation of “a point mutant in the S-BAT binding module at Asp30 (“S-BAT”), designed to ablate salt bridges formed with Lys417 and Arg403 in the Spike receptor binding domain (RBD)*”. Would it be possible to have this mutation motivated in the manuscript, and why was it chosen over other possible mutations in that context?
4. Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: the statement that “the absence of a hook effect at super-stoichiometric concentrations of Spike binding sites to sensor copies supports a predominantly cis activation mechanism” is a strong point but further clarification on this point is recommended, for example, further context on the Hook effect, and what would have been expected if trans activation was the major mode of action.
5. Discussion: the manuscript has shown different versions of the same assay, so a discussion on advantages of one version over the other would be important.

Minor comments

1. Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: “In addition to having high thermal stability, rigidity, and no disulfide bonds to complicate purification” - Please clarify which protein these qualities refer to.
2. Results, Design and optimization of SARS-CoV-2 biosensor “S-BAT”: “This suggests that cis activation is likely the predominant source of signal in the assay, but we cannot rule out the contribution of a trans activating mechanism. In the trans mechanism, simultaneous binding to multiple protomers in a single Spike might increase the effective concentration, driving activation.” - Please provide further comments on the cis and trans mechanisms.
3. Results, S-BAT is functional in multiple assay formats: “Adsorption-based immobilization is advantageous in that it requires no chemical modification to the protein reagent”, recommend reporting the efficiency of this step and how much signal is left after the washing steps.
4. Methods, Cloning: the manuscript reports that all BAT constructs were subcloned using NcoI and BamHI restriction sites via Gibson assembly. Restriction site cloning and Gibson assembly seem to be two orthogonal methods, suggest providing further information on the cloning procedure.
5. Methods, Recombinant Protein Production: "The un-cleaved BAT sensors were concentrated to ∼1.0 mL, and the concentration was calculated from the A280 value”. It is unclear if this was done using highly pure imidazole or if the signal was subtracted from Imidazole? A280 quantification is known to be difficult with Imidazole present.
6. In Figure 1c: “Performance (Signal to Noise (S/N) multiplied by the magnitude of signal change (S-N))” please provide the mathematical expression for this analysis.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Anchal Chandra, Luciana Gallo, Joachim Goedhart, Sónia Gomes Pereira, Samuel Lord, Dipika Mishra, Ehssan Moglad, Arthur Molines, Sanjeev Sharma. Review synthesized by Iratxe Puebla.

The study reports single-cell intracellular pH (pHi) measurements in different cell lines to measure spatiotemporal pHi dynamics during cell cycle progression. The manuscript reports an increase in pHi at the G2/M transition, decreased pHi at the G1/S boundary, S/G2 boundary, and prior to division, and increases during mid-S phase and G2, and suggests that pHi dynamics are necessary for cell cycle progression.

The reviewers praised the topic of the study, measuring intracellular pH during the cell cycle and looking at the heterogeneity between cells are both important questions. However, there were some questions raised about the methodology as well as the interpretation of the data, as outlined below.

Comments about methodology

The pH sensor used in the study has been used previously but the single-cell level use requires new types of control and validations. It would be relevant to report:

• What is the measurement error?
• How efficient is the permeabilization protocol?
• How homogeneous is the expression of the sensor? How does the expression level impact the pHi readout?

These technical parameters could explain the heterogeneity in pHi reported in Figures 1,2,3, and they are relevant to understand if the fluctuations reported are relevant biologically or at the level of technical variability.

Recommend providing additional details on the methodology for single cell pHi measurements, to ensure the experiments can be fully reproduced. Please report sample sizes.

There is apparent intra-cellular heterogeneity present within each cell. The text should highlight whether the cytoplasm is heterogenous in pH. The study uses a single ROI per cell to measure intracellular pHi, however, if the cytoplasm is heterogeneous as some images show, the location of the ROI can influence the readout. It is recommended to use image analysis tools to segment cells and use the whole signal rather than a selected portion.

There are concerns about the statistical analysis for several figures (including Figs 2I, 3I, and 5), in particular regarding the calculation of p-values based on multiple measurements or cells within each sample. The t-test and ANOVA assume that each measurement is independent, while multiple nuclei within the same sample are not independent. Recommend not reporting p-values or averaging together the values from each sample and then calculate the p-value using those sample-level means. For more information, see https://doi.org/10.1371/journal.pbio.2005282 and https://doi.org/10.1083/jcb.202001064

The median is used as the reporter of the populations, the context for this choice is unclear. There are concerns about reporting standard deviation to estimate the spread around the median.

Specific comments

Introduction ‘In normal cells, intracellular pH (pHi) is near neutral (∼7.2)...’ - Could the text specify the type of cells the statement relates to, does it apply to all eukaryotes, mammalian cells, or even more specific and only demonstrated for human cells?

Results ‘single-cell standardization is performed using buffers of known pH containing the protonophore nigericin (Fig. 1A, see methods for details).’ - The experiments use two pH extremes (~6.7 and 7.7 per the Materials & Methods)) and assume a linear relationship of the emission ratio between these extremes. Is this linear relationship verified? The supplementary Fig S2. shows an increase in signal across just two points. Suggest presenting an analysis of the biosensor across 4-5 different pH points to demonstrate linearity and dynamic range within the first set of figures. Plotting the ratio as well as the fluorescence intensities of individual channels across these pH ranges would also be relevant.

Figure 1

Is there an explanation for the signal from the nucleus? It seems initially more acidic than the cytoplasm and then it does not change as much as the cytoplasm during the nigericin treatment. Is this due to bad permeabilization?

‘B) NL20, C) A549, and D) H1299’ - Please indicate which cells are normal and which cancerous.

‘E-G) Histograms of single-cell pHi in E) NL20 (n=173, 3 biological replicates), F) A549 (n=424, 4 biological replicates), and G) H1299 (n=315, 3 biological replicates).’ - The distributions are aggregates of 3 or 4 biological replicates. What do the distributions look like in each replicate? Are the differences between conditions visible? If the E, F and G histograms are generated using data pooled from different replicates, recommend separating the replicates and presenting the distributions separately for each replicate experiment.

‘We next measured single-cell pHi in individual NL20-mCh-pHl (Fig. 1B), A549-mCh-pHl (Fig. 1C), and H1299-mCh-pHl (Fig. 1D) cells’ - From the methods: "Individual Regions of Interest (ROI) are drawn for each cell in each condition (initial, high pH nigericin, and low pH nigericin), and mCherry aggregates are removed using thresholding holes." From the cells in the image, it appears that the cytoplasmic signal is not homogenous and suggests that the choice of ROI will affect the reading for each cell. In this condition, to do single cell measurements, it is recommended to use the signal from the entire cell (cytoplasm) rather than using an ROI.

‘Representative pHluorin and mCherry channels and single-cell standardization lines can be found in Fig. S2’ - The pH probe appears to be comprised of a straight fusion between the pH sensitive GFP (pHluorin) and pH insensitive mCherry. One would expect that the ratio of GFP to mCherry is only determined by pH (and not by expression level or excitation intensity). A question arises around the dynamic range (shown in fig. S2) being different between the different cell lines. For instance, the ratios observed for pH=7 and pH=7.8 are 3 and 8 for NL20, 3 and 5 for A549, and 0.5 and 2 for H1299. Can an explanation be provided for the differences between cell lines? Were the single cell measurements verified with a dye (BCECF/SNARF/SNAFL)? Was the permeabilization protocol validated?

‘(NL20-mCh-pHl) (Fig. 1E; 7.42±0.07).’ - The first sentence of the results section indicates "In normal epithelial cells, pHi is near neutral (∼7.2), while cancer cells have a constitutively increased pHi (pHi>7.4)." According to this statement, the NL20 cell line has a pHi corresponding to cancer cells, can this be clarified?

‘These data show the advantages of measuring single-cell pHi under physiological culture conditions that match population averages, but also provide pHi distributions lost at the population level.’ - The single cell data reveal the heterogeneity, can further explanation be provided for the advantage gained by these data over bulk measurements?

‘These data also show that pHi is heterogeneous even in clonal, genetically identical, cell lines, suggesting pHi may be a biomarker for non-genetic cell phenotype’ - The data show heterogeneity, but do not address how much and what the source of heterogeneity is. It would be helpful to: report the error on the measurement, compare the spread of pHi to something else to get a sense of the normal level of noise in the measurement. Could this be compared to the spread of mCherry intensity, to check if there is more spread in pHi than in expression level of the construct.

Independent measurement of the heterogeneity of the pH (e.g. with another probe/dye) would shed some light. The heterogeneity (or spread) of basal biosensor distributions could be compared against the distributions achieved after nigericin treatment - to bring out the differences in biological heterogeneity versus measurement error. The results could then further elaborate on whether the biological heterogeneity has relevance in the regulation of cellular processes.

‘pHi in physiological environments’ - Can some clarification be provided for how prior studies did not follow physiological conditions, while the current set up would provide such physiological conditions?

‘We synchronized H1299-mCh-pHl cells using Palbociclib’ - The study uses H1299 line in most figures hereafter, A549 line in some while not the NL-20 lung cells, can some justification be provided for the selection of cell lines for specific experiments.

‘In this representative replicate, we observed single-cell pHi significantly decreased between 0 and 4 h, significantly increased between 4 and 8 h, decreased between 8 and 12 h, and increased again between 12 and 24 h (Fig. 2D).’ - It is not clear whether these data are consistent with the other replicates (Figure S3). For example, another replicate shows a consistent decrease of pHi between 0-4h and 4-8h, which is not the case for the example shown in the main figure. Can some clarification be added about discrepancies between replicates. In Figure S3 the different time points were statistically compared to their previous time point, can the same statistical analysis be applied to the replicate in Figure 2?

Figure 2

‘Box and whisker plots of F) cyclin E1, G) cyclin A2, and H) cyclin B1 immunoblot data across 3 biological replicates’ - There is a concern about the use of boxplots for n=3 as they summarize the data into 5 statistics (2x whiskers, Q1, Q3 and the median): www.nature.com/articles/nmeth.2813. It is recommended to show the individual data with a dotplot.

‘Figure 2I. Violin plots of raw pHi across 3 biological replicates’ - A superplot is recommended for identifying the biological vs. technical replicates: https://doi.org/10.1083/jcb.202001064. The significance should be determined based on n=3 (not on the pooled technical replicates).

‘Cyclin immunoblots and pHi agreed across 3 biological replicates, and additional blots are shown in Fig. S3.’ -The replicates from Fig. S3 and Fig. 2 do not appear to show a clear behavior. For example at 4h, two replicates show a decrease while the third shows an increase in pHi. Could some clarification be added for this?

‘When pHi measurements on Palbociclib-treated cells were compared over three biological replicates, we found that pHi significantly decreased at the G1/S transition (4 h, 7.75±0.15) and in late S phase (12 h, 7.69±0.09), significantly increased at G2/M (24 h, 7.82±0.11) (Fig. 2I), and then significantly decreased once more at the end of the experiment in asynchronous cells (36 h, 7.67±0.10) (Fig. 2I).’ - The population in Fig 1 shows a large spread from around 7.4 to 8. This emcopasses all the distribution shown in Fig 2 and if the individual time points are undersampled, small fluctuations are expected in the mean and the median. Can some comment be provided about the potential influence of undersampling on the fluctuation? If the fluctuations were due to undersampling they would be random and could explain why the replicates are not in very good agreement. Also, can some clarification be added about how many cells were measured in each time point.

Figure 3 - The various replicates provided here and in Sup. Fig. 4 show variability. For example, only the replicate in the main figure shows a decrease at 4h and 12h. The third and fourth replicates are in good agreement and for those pHi stays roughly the same and then drops between 12h and 24h. Should this be reflected in the text?

From the values on the y axis for each time point and replicate, it seems that the sample size varies between replicates. There is the risk of undersampling, and also that if one replicate contains much more cells than others, it would dominate the distributions once the data are pulled together. Can the sample size for each time point and replicates be reported?

‘and decreases at 12 h and 24 h (Fig. S5B-C)’ - The text previously reported that pHi increased between 12 and 24h for H1299 cells, here it reports that there is a decrease at 24h. Please provide a clarification.

‘we established a time-lapse approach to track pHi dynamics over an entire cell cycle in a single cell.’ - This is a robust approach to detect pH changes over time. ‘we selected prophase as a “normalization point” for each individual dividing cell’ - Recommend referring to "synchronization point" instead of ‘normalization’.

Figure 4

The paper shows that synchronization alters baseline pHi. Could a similar experiment be completed without synchronization?

‘A) Representative stills of Video S1 of a dividing H1299-mCh-pHl cell at indicated time (h)’ - It would be good to compare this to the metastatic cells used to establish how much of the pHi fluctuations observed during the cell cycle are "cancer" related.

‘Furthermore, the pHluorin increases observed over time in dividing cells are not correlated with increased mCherry fluorescence, which indicates pHluorin increases are not due to increases in biosensor expression (Fig. S8B-C).’ - It is great that this measurement was completed. However, from the plots provided Sup. Fig. 8 B and C, in dividers and non-dividers, it looks like the two signals (mCh intensity and pHluorin) are well correlated (first a decrease for a few hours, then it rises until 10h then it decreases). Could this indicate that the readout is influenced by protein concentration / expression? Suggest plotting the two signals vs each other’s on a scatter plot and formally testing for correlation.

Figure 5

For the FUCCI reporter, plotting mVenus and mCherry intensities normalized between the max and min value for each cell allows clear identification of transition between phases. It may be helpful to present example single cell traces from 5-10 cells for each treatment, to more clearly appreciate the cell cycle phase transitions and their durations on panels F,G and H.

‘D) Single-cell pHi of H1299-FUCCI cells treated with EIPA and SO859 (E+S, n=233) to lower pHi, untreated (CRL, n=267), or treated with ammonium chloride (NH4Cl, n=202) to raise pHi (see methods for details)‘ - Please clarify how or when delta pHi was calculated for data in Fig. 5D.

‘previous work in lower- order organisms’ - "lower-order" has a negative connotation, please consider re-phrasing to include the species or at least family of organisms.

Discussion - Recommend further discussion about altered progression through cell cycle phases at different pHi and how it could be altered in cancer cells. Is increased intracellular pH in cancer cells related in any way to their increased proliferation? If so, which cell cycle steps are affected? High intracellular pH seems to elongate all phases except the M phase.

Methods

‘Multiple Z-planes were collected with the center focal plane maintained using a Perfect Focus System (PFS).’ - Please report whether pH was analyzed on a projection, a single z-slice, each z-slice?

Single-cell pHi measurements - Please provide additional detail for the protocol for the single cell pHi measurement. Include information on whether the work involves single image, stacks, projections, etc, and the size and location of the ROIs. Please also provide further context for the "mCherry aggregates", does this mean the construct is cleaved and the mCh aggregate? Does the GFP aggregate too?

NIS Analysis Software//GraphPad Prism - Please report the version of the software used.

‘Individual Regions of Interest (ROI) are drawn for each cell in each condition’ - Could the ROI on a few of the cells be drawn and highlighted in the main figures to show the size and location of the ROI?

‘8% laser power for GFP; 700 ms exposure time and 10% laser power for TxRed; and 100 ms exposure time and 5% laser power DAPI’ - Please report the exact wavelengths used to excite the fluorophore, (e.g. 8% power of a 488 laser (GFP excitation)).

Supplementary figures

Figure S3 panel A - Should the calibration slope be the same for every cell? Can some explanation be provided for why some cells have a steeper slope than others?

Figure S4 - Replicates appear to show different trends in pHi and Cyclins, which makes it difficult to interpret the data.

Figure S8 panel A - This plot shows correlation between the two quantities, they both rise and fall at the same time. Can some clarification be provided.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Oana Nicoleta Antonescu, Ruchika Bajaj, Sree Rama Chaitanya and Akihito Inoue. Review synthesized by Ruchika Bajaj.

This study has characterized the function of Hero proteins in improving the recombinant expression of TAR DNA-binding protein in E. coli and restoration of enzymatic activity of firefly luciferase during heat and stress conditions. This study may be useful for future applications of Hero proteins in life sciences research. Please see below a few points offered as suggestions to help improve the study.

• In introduction, 3rd paragraph, in context with “amino acid composition and length of Hero proteins”, please elaborate on the effect of these two factors on the function and stability of hero proteins.
• The manuscript refers to “cis and trans” terms on several occasions. Please explain these terms in context with the association of Hero protein with the target proteins.
• Introduction - A paragraph describing the origin of Hero proteins and the differences between the types of Hero proteins in the introduction section would be helpful for readers to understand the background on these proteins. For example, please explain the background on naming these proteins as Hero 7, 9, 11 etc. The genes SERF2, C9orf16, C19orf53, etc are mentioned in the plasmid construction section in the Material and methods. Please provide a brief explanation for the relationship between these genes and Hero proteins.
• Please add more details in the Material and methods section, especifically in western blotting and the luciferase assay, to support the reproducibility of these experiments.
• Figure 1A. Please explain the role of each component (for example factorXa) either in the text or the legend.
• Figure 1B: Please add clarification regarding the normalization of lanes by total protein concentration.
• Fig 1C. Please provide an explanation for the higher order bands in the western blot. The western blot using anti-FLAG antibodies shows non-specific bands. Alternative tags or antibodies or detection methods may be used, for example, GFP tag and in-gel fluorescence can be used to check the expression.
• Figure 1D and 1E, the error bars are high. Suggest checking the data and providing the mathematical expressions used to calculate relative yields.
• Figure 2D and E, the error bars are high, access to the raw data behind the graphs may aid interpretation. An explanation for the choice of temperatures 33 C and 37 C would be helpful. Is there any relation between the choice of temperature and the Tm of the protein? The protein is directly being treated at high temperature, similar experiments with cell-based assays would be helpful to understand the effect of the Hero proteins on the stability of Fluc. Would it be possible to report the mathematical expressions used to calculate “Remaining Fluc activity”. Recommend indicating n if these activities are calculated per mg of the protein. Please explain if the reduction in activity is due to loss of protein or loss of luminescence activity from each molecule of the protein.
• Figure S1, access to the raw data would be helpful to understand the signal to noise ratio for activity.
• Figure 2 and 3 show similar experiments with wild type and mutants, it may be possible to combine the figures (for example, to avoid the redundancy in Figure 2C and 3A).
• Figure 3D and G, access to the raw data would be helpful to interpret the signal and noise ratio especially given the low values.
• Figure 4, Can some further discussion be provided for the reason for higher residual activity for SM and DM than wild type? Tm experiments during stress conditions (heat shock and freeze thaw cycles) may be helpful to define the stability of Fluc and Fluc mutants.
• Figure 5: Suggest including an explanation for choosing Proteinase K -among other proteases- for these experiments.
• The residual activity is different in Figure 4 and 5, which could be due to different stress conditions. Please include some discussion about possible explanations.
• In section “Hero proteins protect Fluc activity better in cis than in trans”, ‘When the molarity of recombinant GST, Hero9, and Hero11 proteins was increased by 10-fold...’ does molarity refer to the concentration of protein ?
• In the first paragraph of the discussion, “physical shield that prevents collisions of molecules leading to denaturation” and “maintaining the proper folding” is mentioned. Is it the hypothesis for the mechanism behind the stability provided by Hero proteins? Can further discussion on this be provided, along with a relevant reference.
• In the discussion section, it is mentioned that “Hero may be reminiscent of polyethylene glycol (PEG)”. Please provide further explanation for why hero proteins are correlated with PEG in this fragment.
• A discussion on why specific Hero proteins may be better for specific target proteins may be helpful.
• In the second paragraph, of the Discussion “Hero protein can behave differently depending on the client protein and condition” and “important to test multiple Hero proteins to identify one that best protects the protein of interest” are mentioned. Suggest adding further discussion of these points, for example around any alternatives or computational predictions or simulations to test individual Hero proteins for specific client proteins.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Sree Rama Chaitanya Sridhara and Wei Chen. Review synthesized by Bianca Melo Trovò.

Genetic transcription happens through individual Transcription Factors (TFs) whose binding events can, in some systems, temporally correlate with the stochastic firing of transcriptional bursts. The determinant of bursting is however unclear, specially whether the DNA binding kinetics solely contributes to that. The study develops an imaging-based synthetic recruitment assay called CRISPRburst in order to measure the TFs impact on bursting kinetics. The authors find that the association of TFs with specific protein partners determines their bursting output, and train a model to predict the kinetic signatures of all human TFs.

Major comments

The manuscript reports that “the maximal intensity per transcription site (TS) is likely limited by physical constraints of the transcription machinery as a limited number of RNA polymerase molecules can be loaded per gene due to polymerase velocity and spacing”. It is recommended to describe how this limitation correlates with the value of active fraction, or could be part of further analysis of this functional data.

‘Functional characterization of TFs using an imaging-based synthetic recruitment assay’ section: “If the frequency and duration of active periods were solely defined by TF binding” [...] “TFs recruited via dCas9 would all exhibit similar active fractions”. This prediction appears to rely on the assumption that the binding rate is the same for all TFs, which is usually not the case.

‘Functional characterization of TFs using an imaging-based synthetic recruitment assay’ section: Given that the TFs that do not bind to the LTR also show high correlation, it is unclear how the correlation for the 6 factors that directly bind LTR justifies that dCas9 recruits TFs in a similar way to the physiological conditions. What is the explanation for the high correlation coefficient for the TFs that do not bind LTR? There is a question as to whether the dCas9 system represents the physiological conditions because the DNA binding kinetics for each TFs are distinct, and different from that for PYL1 binding to ABI1. It would be expected that those different DNA-binding kinetics also contribute to the frequency, duration, or intensity of bursting. Some clarification could be provided around this point.

‘Interactions with co-activators are more predictive of TF kinetic specificity than IDR features’ section “This model was unable to classify TFs into kinetic classes (Figure 3B, right), demonstrating that TF-cofactor interactions play a greater role in specifying kinetic function than IDR sequence content”: Given that TFs interact with cofactors through their transactivation domains, which are intrinsically disordered, why do the TF-cofactor interactions not lead to correlation between IDRs and the kinetic function? Could the protein-protein interactions besides IDR-cofactor (e.g. cofactor-cofactor interactions) play a role in the kinetic function? Do the cofactors cluster into the different kinetic function groups?

Minor comments

Introduction ‘differ in features typically used to classify TFs, such as DNA binding domain homology’: it may be worth making a mention in the introduction to what other binding partners TFs interact with.

First paragraph of results ‘CRISPRburst, an inducible dCas9-mediated recruitment platform to study transcription kinetics’: What is the binding strength of PYL1 to ABI1? How does that compare to the typical TF-DNA binding strength?

Figure 1C: “3) Live cells are imaged 16 h post-recruitment.” This is the end time point. Are there time-dependent data available?

Figure 1 F, G: The error bars are high. Can further information be provided in the legend on how these error bars were calculated (biological vs technical replicates)?

Figure 1, ‘An average of 220 cells were analyzed per TF’ Does this imply that 220 transcription sites were scored? Considering each imaged cell has single integration of the reporter gene?

‘In total, the LTR-MS2 cell line stably expresses 1) the LTR-MBS reporter gene’: Is there information on where in the genome the reporter gene is integrated? And does it impact the transcription bursts? (considering the role of (epi)genetics in the transcriptional outcome as rightly reinforced by the data related to Fig.4).

Functional characterization of TFs using an imaging-based synthetic recruitment assay: Please provide a description for the Krüppel associated box.

“Upon recruitment, 28 TFs generate an increase in reporter active fraction”. It would be helpful to provide further clarification on how the reporter active fraction is defined and how the criteria "ratio > 1.30" was determined. A mathematical equation may also aid the description.

‘0.64 to 3.04 for active fraction and 0.68 to 1.64 for intensity (Figure 1F-G, S1E) ‘: It may be helpful to divide the active fraction (0.64 to 3.04) into different categories, for example, 3.04 - 2.5, 2.5-2.0 etc. to check whether these categories are correlated to function.

Regarding intrinsically disordered regions (IDRs) in the Results section ‘Bursting kinetics define distinct TF classes’: Can further clarification be provided in the main text for the meaning of cumulatively longer IDRs.

“these findings suggest that while the biophysical properties of IDRs may tune the amplitude of TFs’ effects, they likely do not solely encode TF kinetic specialization”: does this include post-translational modifications? If so, are there any relevant examples or illustrations?

In the section ‘TF families exhibit broad kinetic diversity’ section, “the family-defining KRAB domain does not bind DNA but recruits cofactors, consistent with the idea that DNA binding domains provide little information on kinetic specialization (Figure S6B)”. It may be relevant to discuss potential solutions to these issues in the Discussion section.

Discussion section “Our study centered on the simple HIV promoter thus provides a robust conceptual framework to investigate more complex systems, e.g. how TFs synergize with one another, interact with core promoter motifs, or communicate to promoters from distal enhancers”: all the future directions mentioned here are very relevant and exciting. Could the discussion of these items be expanded e.g., how do developmental cues drive TF kinetics or bursts?

Methods section: Are there any anomalies observed in the subcellular localization of the TFs when tagged with PYL1 or under the ABA treatment?

Comments on reporting

The manuscript reports a partial least-squares multivariate regression model in which a predictive weight to each possible interactor was assigned. Can further description and a related equation be provided for this model?

Fig. 3: Can further context be provided for the choice of SEM instead of SD which may provide a better representation of data variability?

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Bobby Hollingsworth, Gary McDowell and Michael Robicheaux. Review synthesized by Michael Robicheaux.

The preprint manuscript by Trendel et al., “Translational Activity Controls Ribophagic Flux and Turnover of Distinct Ribosome Pools”, presents a dataset that examines the lifecycle of human ribosomes, and their constituent subunit proteins, in response to translational inhibition using proteomics and cryo-EM approaches. The study focuses on the fate of 80S monosomes, which are shown to be inactive and to form a dynamic pool separate from active polysomes and nascent ribosomal subunits.

General comments

• The manuscript is well-written and organized, and the methodology is thorough and detailed.

• The effort to validate mass spectrometry quantitative measurements, particularly the peptide sum normalization (PSN), is commendable. The description of total sum normalization and its weaknesses in this methodology is well articulated. This work will be useful for others working on similar problems in quantitative mass spectrometry.

• The described pulse-SILAC methods are quite successful at monitoring protein stability in response to different perturbations; however, the statements in favor of ribosome subunit decay through ribophagy/selective autophagy require further support. Since ribosome component decay can be due to a variety of additional pathways (see cited reference #17, An et al., 2020), it may be necessary to soften the conclusions regarding ribophagy. Additional pulse-SILAC experiments in cell lines that lack key autophagy components (e.g., ATG12/FIP200 KO cells) could be considered to directly test the ribophagy model.

• There are questions as to whether the cryo-EM processing supports the conclusions stated in the manuscript. Specific comments regarding this are provided below. In addition, additional processing detail in the flowcharts presented within the supplemental data would be helpful to better understand processing choices (e.g., D classes that move forward for additional analysis/classification/refinement).

• It would be relevant to discuss how the proteomic half-life measurements compare to those published by Li et al. 2021 (Mol Cell), which use a different method (cyclohexamide chase).

• The manuscript reports significant differences in the half-lives of the 40S/60S ribosomal subunits vs 80S/polysome fractions (Fig 1E), and states that these make up separate ribosomal pools without free exchange. However, it should be considered as an alternative that the decay rate of assembled ribosomes could be much less than the unassembled group so that the pool of free components becomes gradually depleted. In this case, exchange could still occur with a decreasing rate as the pool of free ribosome proteins are degraded faster than assembled ones. It would also be relevant to discuss the possibility that nascent 40S and 60S subunits form 80S monosomes in an alternative “life cycle” pathway.

Specific comments and suggestions

• In paragraph 1 of the Introduction, please specify the context of “serum withdrawal” as the stimulus for idle 80S ribosome accumulation. Is this from cell culture or some other system?

• In paragraph 1 of the Introduction, the sentence, “Degradation of ribosomal complexes, especially under nutrient-poor conditions, is mediated by ribophagy, a selective form of autophagy [14–17]” could be more nuanced as it does not describe other non-autophagic ribosomal degradation pathways, such as those described in cited reference #17 (An et al., 2020).

• In the “A Highly Robust Normalization Procedure...” Results section, the manuscript states that the intensive ribosomal purification methods lead to high variability in the mass spectrometry measurements. Based on this, have alternative methodologies been considered for ribosome purification?

• In panel E of Figure 1, the color scheme makes the data difficult to differentiate, could also consider separate figures for the large and small subunit datasets.

• In the “Protein Half-Lives in Polysome Profiling Fractions...'' Results section, “On average ribosomal proteins of the small subunit had 3-fold longer half-lives within the 80S fraction compared to the 40S fraction (p=5.2E-8, Wilcoxon ranksum test), whereas large subunit proteins had 4.6-fold longer half-lives within the 60S fraction compared to the 80S fraction (p=1.0E-10).” Are the “60S” and “80S” fractions mixed up at the end of the sentence?

• In the “The Monosome Fraction Predominantly Contains Inactive 80S Ribosomes...” Results section, the manuscript reports, based on their cryo-EM data (Fig. 2), that 80S monosomal complexes are idle and distinct from polysomal 80S complexes. This conclusion of a single ribosome state would need supportive evidence. From the initial particle stack (>1 million) that yielded <60k high-resolution particles after classification: were there other low-resolution class averages or heterogeneous particles that may represent actively translating ribosomes? Conclusions about ribosome activity from less than 5% of the total pool of ribosomes could be due to the conformational plasticity of translating ribosomes. In a different paper (Brown et al., eLife. 2018), several structures/states of the ribosome come out of a smaller dataset. Furthermore, a structure of comparable resolution from the polysome fraction appears necessary to support the conclusion that the 80s monosome complex is functionally distinct. The same comparative data is recommended for conclusions drawn from the cryo-EM structural analysis of arsenite treated 80S particles (Fig .S6).

• In the “The Monosome Fraction Predominantly Contains Inactive 80S Ribosomes..” Results section, this section introduces ribosomal P-stalk proteins, their plasticity and role in active ribosomes, which are concepts that could be included in the Introduction section of the manuscript.

• In Figure 2, it is unclear from the figure legend if the 80s monosome density in panel B is from the low-salt treated preparation in panel A or from a different prep.

• In the “Inhibition of Translation Produces Inactive 80S Ribosomes...” Results section, recommend revising the text to reframe the conclusion as "supports our model".

• In the “An Increased Pool of Inactive 80S Ribosomes..” Results section, recommend toning down the claims about decay rates which may require control experiments in cells lacking key autophagy proteins, such as ATG12.

• In the “An Increased Pool of Inactive 80S Ribosomes...” Results section, consider reframing the conclusions from the previous study (Trendel et al. 2019) to indicate that ribophagy is the predominant mechanism of ribosomal protein turnover in response to arsenite treatment. The prior study did not examine ribosomes treated with arsenite when autophagy was blocked. Additional quantitative tests for flux into lysosomes (Lyso-IP, Ribo-Keima shift assay) should be considered to support that ribophagic flux, specifically, eliminates proteins from ribosomal pools. Based on this comment, the inclusion of ribophagy in Fig. 5 and the statements in the final paragraph of the Discussion may require revision.

• In the “An Increased Pool of Inactive 80S Ribosomes...” Results section, the manuscript describes proteomic data in response to increasing concentrations of arsenite. The effects of these treatments on polysome profiles could be useful future experiments.

• In the “Constrained Conformational Plasticity...” Results section, there are questions about this analysis due to the small size of the final particle stack for both proteins. An alternative analysis pipeline is to mix the particles from both datasets for the simultaneous analysis of all pooled particles, from which the number of particles in a given state can be quantified.

• In the “Distinct Pools of Ribosomal Subunits...” Discussion section, the discussion of inactive 80S complexes potentially re-entering the polysome “assembly line” is quite interesting to consider in terms of its dynamics and follow-up experiments that would test this theory (including subcellular localization).

• In the “Distinct Pools of Ribosomal Subunits...” Discussion section, the manuscript posits that the degradation of newly synthesized ribosomal subunits is not energetically favorable; however, it should be considered that intrinsically disordered proteins, such as transcription factors, can be produced and quickly degraded in oscillatory patterns (e.g. see https://pubmed.ncbi.nlm.nih.... A quality control pathway that would eliminate immature or nascent ribosomal subunits is conceivable.

• Consider depositing all EM data in EMPIAR and relevant structures in EMDB/PDB, and depositing the mass spectrometry raw data in ProteomeXchange or similar database. A data availability statement could be added with relevant accession links and IDs.

• It would be helpful to build a tool to browse protein-level half lives and re-analyze raw data (e.g., tidy script depositing in Github or similar).

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Michael Robicheaux, Akihito Inoue, Justin Ouedraogo and Kunal Shah. Review synthesized by Ruchika Bajaj.

The paper reports the use of an optimized computational model, GMMA, by preparing a randomly mutated protein library and screening the mutant library using an in-vivo genetic sensor for folding for successful protein engineering efforts.

Here are a few points of feedback on the paper.

1. In the second paragraph of section “Resilience towards mutations reflects thermodynamic stability”, the manuscript refers to the design of degenerate primers, “These were designed to cover the C-terminal half of edF106, amino acid residues 48-97”. Further explanation for designing degenerate primers for these specific positions would be helpful to the reader, for example by adding references from the literature. In the same paragraph, when mentioning the size of the library (10,000 - 20,000), it would be good to explain the reason for the specified size of this library.
2. The section “GMMA analysis discovers stability effects”, mentions that, “Reliable stability effects could be assigned to 374 out of the 838 unique substitutions in the library”. Further explanation may be provided in this regard, for example to describe why/what variables could lead to unreliable stability scores for tested substitutions.
3. The study evaluated MM3 and MM6 combinations for the additivity of stability. It would be relevant to mention if other combinations like MM4 and MM5 were evaluated.
4. Fig 2b: more points could be taken on the steep regions.
5. For Supplementary Figures 4 and 5, please provide an explanation of curves or straight lines and explain the angle of fitted lines.
6. In the section “stability measurements validate GMMA”, in the sentence, “Thus, mutations which could be stabilizing in the fusion might behave differently outside of the CPOP context.”, would it be possible to elaborate more on this statement to clarify how the behavior may differ?.
7. Please indicate specific mutated amino acids in multiple mutants: MM3, MM6 and MM9, for comparison.
8. Please label residues in Figure 6b.
9. In the section, “Crystal structures show increased similarity to the 1FB0 design template”, the statement, “Only one or two would yield crystals indicative of a conformational change taking place in order to stabilize the crystal lattice.” Conformational change is questionable here, especially with low RMSD values. Would it be possible to elaborate on the statement or reframe it.
10. “showed much better agreement with its original design template spinach thioredoxin (PDB: 1FB0).” It may be helpful to provide some further context about this in the Introduction and conclusions sections.
11. In the section “structure and sequence-based methods do not predict most stabilizing variants”, the text mentions the discrepancies in rosetta and GMMM. It may be relevant to provide some further discussion on what may be behind those discrepancies.
12. Although the mutated protein has been crystallized, a discussion on protein expression or oligomerization after the mutation and its relation to thermal stability would be helpful for the study.
13. A major point which has not been mentioned in this study is scoring of these mutations according to their function, functional aspects are important for the purpose of protein engineering and thus this could be relevant. It will also be good to correlate the stability of these mutants with its function to comprehend the protein engineering effort.
14. Minor point: In the section “Initial library transformation”, please change “scraped off” to picked off.
15. Supplementary Table 4: Wilson B factor value is missing for eMM9. A possible explanation for the difference in the number of macromolecules in MM9 and eMM9 variants would be helpful. Is there a possibility of change in crystallographic oligomerization ? Any information regarding regions of protein where Ramachandran outliers are located would be helpful.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Richa Arya, Luciana Gallo, Lauren Gonzalez, Sam Lord, Dipika Mishra, Arthur Molines, Mugdha Sathe, Ryman Shoko, Ewa Maria Sitarska. Review synthesized by Ehssan Moglad.

Study conducted by Chieh-Ren Hsia et al. which looked at nuclear deformation in confined migration and its effect in chromatin organization and function.

Major comments

Results ‘To distinguish between true changes in chromatin modifications and effects of physical compression of the nuclear content due to deformation, we normalized the heterochromatin mark intensity to the euchromatin mark intensity in each cell.’ - The results are normalized to H3K9ac, with the assumption that its levels do not change during migration/confinement. Has this assumption been confirmed? For example, by normalizing both H3K27me3 and H3K9ac to total H3 instead - and showing that K27me3 increases with confined migration while H3K9ac doesn't.

Results ‘Increased heterochromatin formation should result in an increased ratio of heterochromatin marks to euchromatin marks, whereas physical compression of chromatin would increase both marks, and thus not alter their ratio…’ - Can some comments be provided on what the meaning would be for heterochromatin to "increase" and euchromatin to not change? There are two ways in which heterochromatin could "increase" - either the portion of the genome in heterochromatin could increase (which would mean the portion in euchromatin would decrease), or the portion of the genome in heterochromatin could stay the same but K27me3 levels could be higher in those regions (which might not affect euchromatin levels). One way to distinguish between these would be to stain for K36me3 as the "euchromatin" marker instead of K9ac - because K36me3 and K27me3 are mutually exclusive.

Figure 1 - Could the effects seen be due to cells spending different amounts of time in the channels? Do all cells migrate at a similar speed? - Panels D, F, I: it is unclear if the cells shown in the plot for the change in heterochromatin marks are all that migrated or only those that show the difference. Suggest including a dot plot to also show individual data. Can some clarification be provided for how to interpret that controls "before" in 1D and 1F are statistically different?

Counts in Fig S2A-D are sometimes very low (same applies to Fig 1I, Fig 2B,C,E.), it may be nice to compare some more cells.

Results ‘Although the effect was less pronounced than in the ≤2×5 μm2 confined channels (Fig. 1C-F)’ - Can the normal size of these cells be reported ? Also the size of nuclei. is it bigger than the pore size?

There are concerns about the statistical analysis related to SEM and p-values based on multiple measurements or cells within each sample. The t-test and ANOVA assume that each measurement is independent, and multiple cells within the same sample are not independent. Suggest to either not report p-values or average together the values from each sample and calculate the p-value using those sample-level means. For more information, see https://doi.org/10.1371/journal.pbio.2005282. and https://doi.org/10.1083/jcb.202001064.

Minor comments

Results ‘custom-made polydimethylsiloxane (PDMS) microfluidic devices with precisely defined constrictions that mimic interstitial space’ - The manuscript report the size of the channels, and notes that it mimics interstitial spaces, it would be helpful to also report the size range for interstitial spaces in vivo.

Figure IH: Are these the same cells as in the reference (cells in which vertical confinement is sufficient to induce a nuclear response)? Are 5 um channels squeezing the nucleous?

“significantly larger increase in heterochromatin than cells migrating through the 10-μm tall channels (Fig. 1H, I), demonstrating that the observed effect is primarily attributed to the confinement and not the migration process per se” - There is a statistical difference between the confined migration and non-confined migration groups, but there is also a statistically significant increase in heterochromatin in the non-confined migration group compared to baseline (and with larger sample sizes than in the confined group), so it may be worth commenting on the possibility of the effect of migration alone.

“Cells maintained CMiH even after completing at least one round of mitosis, without any trend of reversion in their heterochromatin levels (Fig. 2C; Fig. S4A, B), suggesting that the epigenetic modifications were inheritable through DNA replications” - This is an intriguing concept, however, it is unclear whether the cells that migrated did so before or after dividing. To support the claim about inheriting CMiH, it would be relevant to see heterochromatin levels in a mother cell increase after it squeezes through a channel, then the daughter cell (which doesn't squeeze through a channel) having a higher heterochromatin level than the "before" cells. That's not possible with immunofluorescence, maybe the GFP-HP1a could be useful for such a live-imaging approach? Otherwise, if all these "mitotic cells" divided after squeezing through a channel, that could be stated in the text, legend, and/or methods. Alternatively, the conclusion could be nuanced/toned down.

Figure 3 - The number of samples analyzed in some cases appears small. Suggest showing the data as dot plots to allow interpretation of the sample sizes for each group and the differences between the groups.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Sónia Gomes Pereira, Rachel Lau, Sam Lord, Sanjeev Sharma, Parijat Sil. Review synthesized by Richa Arya.

General comments

It may be helpful to elaborate on how it is established that CHIP mobility is dependent on activity. The conclusion in the paper has been primarily drawn from the catalytically inactive H260Q mutant which is less mobile. However the fact that the puncta of the mutant are brighter and larger than the wild type and that it recovers slowly also indicates the protein might be inherently more prone to aggregation upon heat shock.

Related to the above point, under conditions such as VER treatment and Act-D treatment, the nucleolar recruitment is unaltered but recovery is affected (which implies mobility may be affected). This leads to the accumulation of CHIP in the nucleus. In these scenarios, it may be relevant to report on the status of wild type CHIP activity? Conducting the ubiquitination assay as in Figure 5A with Act-D and Ver treatment would be informative. If no difference in ubiquitination is observed, it can be concluded that it is not the change in CHIP mobility that affects its activity, but rather it's activity that promotes CHIP mobility/dynamics (the conclusion from Figure 5).

o Figure 1: The question arises as to why the control and recovery show puncta in panel C, but not the HS condition. Also, to make it easier to appreciate the nucleolar localization of CHIP in the HS condition, zoomed in regions and overlay images would be useful.

Figure 1b: To support interpretation of the results, it would be helpful to highlight some examples of the nucleolar localization of CHIP. Additionally, it looks like there are specific dots (that could be like condensates) in the Control and Recovered, but not during the Heat Shock cells, not in panel B. Maybe some quantification such as number of dots per cell/ intensity/size could accompany the images. Similar parameters of the condensate structures in the nuclei in the transiently transfected cells could be quantified.

Figure 1: Quantifications such as 2B and 2C could also be done for Figure 1, for both Hsp70 and CHIP.

Figure 1E.’K30A mutant exhibited impaired CHIP migration to nucleoli after heat shock (Fig. 1E)…’ How strong is this impairment? Could it be quantified either by fluorescence intensity or via Western blot of the different cellular fractions.

o Figure 2: It would be helpful to have additional clarification on what the different parameters such as -"% of cells with EGFP-CHIP in the nucleolus' or 'CHIP intensity in the nucleolus' represent, as well as clarification on the transition from measuring CHIP nucleolar-to-nucleus intensity ratios for immunostaining (as in Fig S1E) to measuring just nucleolar CHIP intensities in the main Figure for the EGFP-CHIP overexpression experiments. Perhaps a western blot showing HSP70 expression with VER might be helpful in demonstrating that total protein expression is not affected and that it is only its activity being affected.

‘a small molecule inhibitor of HSP70…’ Some suggestions alongside the loss of function assays such as knockdown and inhibitor treatment:

What happens to Hsp70 and thereby CHIP translocation to the nucleus in cells with high, medium versus low levels of HSP70 expression? Do the high-expressing cells show more enhanced CHIP recruitment to the nucleolus? Can it be quantified as to how correlated the efficiency of recruitment of CHIP is to the expression level of Hsp70? How does the nucleolar translocation of Hsp70 itself correlate with its expression level?

Figure 2a: It is clear that the HSP70 co-localises with CHIP upon heat shock. Overlaid images might be better to highlight this but the use of green and red is not ideal for colour-blind readers. May be changed for bar graphs too (2d,e).

Figure 2b,c: There is a question about the statement that mutant CHIP was unable to localise in the nucleoli due to lack of HSP70 binding in Fig 1E. In Fig 2B and 2C CHIP was able to migrate into the nucleoli (albeit at a lesser extent) with HSP70 knockdown? Maybe images corresponding to this experiment might help as well to allow the reader to see the difference in localisation? It is mentioned that CHIP auto-ubiquitination is important in its localisation in Fig 5 so does the CHIP K30A mutant necessarily verify that the lack of HSP70 binding is causing impaired migration to the nucleus in Fig 1E? Could K30A also affect its auto-ubiquitination? Suggest referencing supplementary figure 2 alongside Fig 2B and 2C, and changing the dots in this graph to red, to make it consistent with panel F.

Figure 2d,e: Bar plots could be replaced with scatter plots showing individual data points as done in Supp. Fig 1E. Adding t1/2 values with FRAP traces would support the changes observed for recovery times across conditions. Calculating mobile fraction and reporting would also be helpful.

Figure 2f,g: Suggest updating the figure legend to clearly distinguish both curves. Some additions may complement the FRAP analysis presented:

• How does the FRAP mobility of CHIP compare between absence and presence of heat shock?
• How does the FRAP mobility of CHIP compare in the recovery phase in presence and absence of heat Ver?
• Is the CHIP mobility different in nucleolus versus nucleus?

‘and HSP70 inhibition did not si+gnificantly reduce its dynamics (Fig. 2F)…’ Would there be any change in CHIP dynamics in siHSP70 cells? It would be helpful to mention this following Fig 2B/C. Maybe use 'mobility' instead of dynamics, to be more specific.

o Figure3: It will be helpful to include an overlay/merged image of the two channels, and to explain in the legend how the measured correlation coefficient is obtained. It would be nice to see what kind of sub-structures show the maximum colocalization.

Fig 3c: HS+Rec condition should show a loss of correlation between CHIP and NPM1 and is an important control in this figure. Comparison with Fibrilarin is good, demonstrating a loss of correlation between the NPM1 and CHIP themselves under different conditions and data for Ctrl only conditions would also add value.

o ‘it altered CHIP distribution, which more prominently overlapped with Act D-induced NPM1 ring formations (Fig. 4D)…’ Can this be quantified? Maybe it will show more pronounced colocalization compared to heatshock alone.

o 'this observation suggests that proper nucleolar assembly may be necessary for CHIP dynamics'. It may be worth specifying the reference to Dynamics here:

1. Mobility measured via FRAP
2. Translocation efficiency done via intensity measurement or ration of nucleolus/nucleus intensity. FRAP measurement of CHIP may be helpful to conclude about the mobility of CHIP in the nucleolus upon heat shock, in presence or absence of Act D pre-treatment. A change in mobility may support the lack of translocation during the recovery phase in presence of Act D.

o Figure 4: (a) It may be worth commenting on why the Hoechst staining looks different between the Control and the Act-D conditions. Fig4d: It could be helpful to add images of NPM1 localization in cells treated with Act D, but not under heat shock. In other words, are these NPM1 rings specific to the heat shock response? The size of the cells and the nucleus are different for HS versus Act-D+HS panels. If the scale bar is consistent and this is a normally observed morphological change upon Act-D treatment, it might be helpful to note this size difference in the legend.

o ‘We found that the activity of CHIP is not indispensable for heat shock-induced migration to the nucleolus (Fig. 5B). However, FRAP analysis of the nucleolar CHIP H260Q mutant showed a decrease in its dynamics compared to CHIP WT…’ Maybe the fragment could be rewritten for clarity (e.g. is dispensable). What happens to the mutant CHIPH260Q localization upon recovery? Is it slower than wt? Is more mutant CHIP retained in the nucleolus upon recovery?

o Figure 5: Suggest showing a wt image as comparison, in panel B. An alternate interpretation for the observations with H260Q mutant could be that the mutation leads to instability and misfolding of CHIP (as suggested in the paper) which leads to increased aggregation (larger and brighter droplets, low mobility) upon heat shock with itself and other interacting proteins. This interpretation does not need to invoke a loss of ubiquitination activity as a cause, it could be another consequence of misfolded CHIP.

Figure 5c: How do the mobility of wild type CHIP compare with the H260Q mutant in the nucleus or in absence of heat shock? If the mobility is the same during pre-heat shock/pre-translocation to the nucleolus, the wild type and mutant protein have inherently similar dynamics. And if this gets altered only in the nucleolus of heat shocked cells, it would support the conclusion that it is the activity of CHIP that helps retain its mobility in the nucleolus and possibly prevent its aggregation in this compartment.

Figure 5f: If there were two independent experiments, can both be represented? Or was the data pooled from the two experiments?? Suggest representing the data as two points for CHIP wild type and mutant each, from two independent experiments.

Figure 5g,h,i: Dot plot overlay on the boxplot might be nice to see the spread of datapoints.

o ‘Interestingly, sizeable intra-nucleolar CHIP droplet-like structures could be observed after overnight heat shock in cells expressing the CHIP H260Q mutant, outnumbering their WT protein counterparts (Fig. 5E-I)…’ In Figure 1C some bright foci are also observed in control and recovered cells. Are these similar to the "droplet-like structures" described here?

o ‘These differences between CHIP WT and mutant assemblies may stem from the alterations in CHIP H260Q dynamics within the nucleolus (Fig. 5C and D)’. Similar measurement as in Fig 5C could be done upon overnight heatshock to support this statement.

o ‘Surprisingly, we found comparable redistribution of all CHIP variants to nucleoli during heat shock, suggesting an…'. Is this a cell line-specific difference, or could it be due to differences in approach, i.e. stable cell line vs. transient overexpression? Similar transient expressions in HeLa may help clarify this.

o Based on Fig S1E, it appears there might be both an HSP70 activity-dependent (smaller) and HSP70 activity-independent (larger) contributions to CHIP localization. VER treatment reduces CHIP relocalization to the nucleus by a small but significant amount both in control and HS-treated cells.

o Cell transfection - Suggest reporting the confluency of the cells before transfection (or at which they were seeded).

Methods - In Figs 3C and 5G-I, there is a concern about the statistical approach to calculate p-values based on multiple measurements (nuclei) within each sample. The t-test and ANOVA assume that each measurement is independent, and multiple nuclei within the same sample are not independent. Recommend to either not report p-values or to average together the values from each sample and calculate the p-value using those sample-level means. For more information, see https://doi.org/10.1371/journal.pbio.2005282 and https://doi.org/10.1083/jcb.202001064.

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Joachim Goedhart, Sónia Gomes Pereira, Ricardo Bruno Carvalho, Anchal Chandra, Akanksha Verma, Claudia Molina, Richa Arya, Rachel Lau, Xianrui Cheng, Ehssan Moglad, Rinalda Proko, Luciana Gallo, Parijat Sil, Yogaspoorthi Subramaniam. Review synthesized by Vasanthanarayan Murugesan.

The reciprocal regulatory relationship between the cell cycle and ciliogenesis is poorly understood. This study by Dougherty et al. aims to better understand how MAPK signaling pathways control ciliary assembly in Chlamydomonas and RPE1 cells. ERK1/2 is a MAPK protein that is activated predominantly by MEK1/2 and deactivated by DUSP6.

For this, the study activates ERK, a well-known MAPK pathway, by inhibiting its phosphatase DUSP6 through the compound BIC ((E)-2-benzylidine-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one). The study shows that BIC leads to impaired ciliary assembly and maintenance in Chlamydomonas and impaired ciliary growth in hTERT-RPE1 cells. It further shows, in Chlamydomonas, that BCI inhibits ciliogenesis by disrupting total protein synthesis, microtubule organization, membrane trafficking, and partial kinesin-2 motor dynamics.

The use of superplots to distinguish between the biological and technical replicates was well received by the community. The discussion is well written and ties together the various experiments conducted in this study. Certain sections could be rephrased to provide more clarity for readers.

The following items of feedback were raised, to help solidify the claim that BCI affects ciliary assembly only through MAPK signaling:

The majority of the conclusions about MAP signaling are drawn based on the treatment with the BCI compound whose selectivity is unclear. It is possible that BCI could directly inhibit other phosphatases involved in ciliogenesis such as CDC14, PPP1R35. A reference pointing to the selectivity of BCI towards MKPs or alternatively rescue experiments with the inhibitor U0126 could address this issue.

It is shown that BCI leads to transient activation of the ERK activity which peaks within 30 minutes and starts fading away after around 60 minutes. However, most of the effects are studied at 2 hours, when the changes in the cilia length are most apparent. But the ERK activity at this time point is unclear. Simultaneous measurements of ERK activity and cilia length would strengthen the correlation between the two processes.

Specific comments

Introduction:

“The cell cycle and ciliogenesis utilize the same structures at different times” can be written as “The cell cycle and ciliogenesis utilize centrioles at different times” as centrioles is the only structure mentioned in the text.

Small item - “Ciliogenesis occurs when cells the exit cell cycle” to “exit the cell cycle”

Recommend revising the segment “The ERK pathway controls the cell cycle” as it mostly talks about ERK regulation rather than how the pathway regulates cell cycle.

Small item – “In C. elegans, mutations to MAPK15 directly regulate...” can be rewritten as “In C. elegans, MAPK15 directly regulates…”

Results:

Recommend revising the section “BCI-induced ERK1/2 phosphorylation disrupts ciliary maintenance and assembly in hTERT-RPE1 cells”. The timings of ciliary shortening and its relationship to ERK activation is unclear. In the concluding statement, ciliary assembly was used instead of ciliary shortening despite the data in Figure 1H showing that ciliary assembly is unaffected by BCI.

“Decreased KAP-GFP at the basal bodies” – This appears to be in contradiction to Figure 2B.

“These data suggest that BCI inhibits the mechanisms and proteins involved in cytoplasmic microtubule reorganization.” – Recommend adding further clarification about this sentence.

Figure 1

Figure 1D – It is unclear in the figure whether the P-value is calculated between concentrations 0 µM and 45 µM, or between 0 and all three other concentrations. A similar comment applies to Figure 1H and Figure 1J.

Figure 1F – Was any axonemal maker other than acetylated tubulin used to control for tubulin acetylation defects?

Figure SF 1E – Though the MKP2 mutant does not regenerate to wild type length, it does return to its own original length, can the text be adjusted to reflect this?

Figure 1G-J – The conclusion in the text that BCI prevents cilia assembly could be clarified, as the data shows growth inhibition rather than assembly inhibition.

Figure 2

Figure 2C – The legend is slightly cut off from the image.

Figure 2C – It is unclear if there is a difference in the fluorescence intensity distribution. A line profile along the cilia would indicate if there is any change in the spatial distribution of KAP.

Figure SF 2C – Is it possible to elaborate more on what specific conclusion this data suggests.

Figure 3

Figure 3B – Is there any reason why the BCI-induced regulation of MAPK signaling affects ciliary protein synthesis in particular? There seems to be no reduction in total protein synthesis.

Figure 4

Figure 4A – A clearer description of how BCI “partially” disrupts the transition zone would be beneficial. Cross-sectional imaging of the transition zone with higher concentration of BCI might make changes in the structure more apparent.

Figure 5

Figure 5A – 36 µM BFA affects cell morphology and may affect the viability of the cells, can some further clarification be added about this and the concentration used.

Figure 6

Figure 6C – The three categories mentioned in the text are not mentioned in the figure.