On 2022-07-27 18:58:54, user Moritz Oberlander wrote:

There is an opportunity to discuss the infectious study in the paper J Galmès, 2013 and her French thesis: https://tel.archives-ouvert... with TTMV-Ly1,-Ly2,-Ly3 virus particles in lung epithelia or embryonic kidney cells. It would be interesting to compare them but it seems to me, there is not significant difference in the infection of the TTMV-Ly1, Ly2, Ly3 viral particles (Figure 45).

I looked at the TTMV-Ly2 because it has two characteristic repeats in the 5’-noncoding region, the second repeat was probably created by the insertion of about 66nts (insert: gccggaaaaccacataatttgcatggctaaccacaaactgatatgctaattaacttccacaaaaca). I searched (Blast) for a homologues direct lineage of TTMV-Ly2 (to exclude a recombination) based on this insert and found several homologues of Ly-2. I wanted to see if they hold a similar spike structure as well. Some lineage-homologs seem to be similar but safia-668-2 and 314-17 may show more changes in spike region of Ly2:

YTGANLPGDTTQIPVADLLPLTNPRINRPGQSLNEAKITDHITFTEYKNKFTNYWGNP TTMV-ly2 2979nts<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKTNYKNYWGNP safia-367-10 2991nts<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKTNYKNYWGNP safia-692-0 2992<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKTNYKNYWGNP safia-418-10 2941<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKINYKNYWGNP safia-569-10 2849<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIAAKTDFNKYKTNYKNYWGNP safia-388-14 2991<br /> YAPGPPIPTAENLKVGDLIPLTNPRDNVSGESFFEQQTTTHETWKQYFTNYKKHWGNI safia-668-2 2977<br /> YAPGPPIPTAENLKVGDLIPLTNPRDNVSGESFFEQQTTTHETWKQYFTNYKKHWGNI safia-314-17 2977

FNKHIQEHLDMILYSLKSPEAIKNEWTTENMKWNQLNNAGTMALTPFNEPIFTQIQYN ly2<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia 367-10<br /> FNVEIQEHIQDILYSLKSPEAIKSAWSTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-692-0<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-418-10<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-569-10<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-388-14<br /> FNVHTSEHLEDLLYSLKSPEAIAKKALENENKTDLKWSELDNAANMALTPFDQPIFIP safia-668-2<br /> FNVHTSEHLEDLLYSLKSPEAIAKKALENENKTDLKWSELDNAANMALTPFDQPIFIP safia-314-17

In the phylogenetic tree, the TTMV5-TGP96 (no the second insert) is the closest homolog to TTMV-Ly2:<br /> YTGTNPPSDTSQIKVADIIAVTDKKNNKPGESYHDQQTTSNKNWQQYFENYQQFWGNP TTMV5-TGP96

It seems to be difficult to find a direct lineage for TTMV-Ly1 or its homologs with similar structure and discuss that......

On 2022-07-27 14:46:46, user Karen Lange wrote:

Overall this is a very thorough study looking at the roles of Septin9 and ARHGEF18 in ciliogenesis. I especially like Figure 3 where the authors compare the severity of partial knock down of SEPTIN9 with siRNA vs in the knock-out CRISPR cell line. The complete lack of cilia in Figure 3J/K is very striking. The experiments using centrin-fused RhoA and ARHGEF18 are very creative. It is impressive how long the cilia are in Figure 5D! I find it interesting that expressing the Centrin-GFP-DHPH construct doesn’t cause the same long cilia phenotype in the Septin9 KO cells. This observation is based on the images shown in the panels because I do not see cilia length measured in Septin9KO + Centrin-GFP-DHPH.

I appreciate the use of scatter plots to highlight the variability/reproducibility of the data. I assume the different colors relate to different replicates, but this is not clearly stated.

I have a few specific comments with regards to Figure 7:

I found it difficult to see the 3 RPGRIP1L puncta. I think if the panels were more zoomed in or larger it would help to better highlight the shift from 3 to 2 puncta in the Septin9 KO cells.

In the Septin9 KO cells RPGRIP1L and TCTN2 staining was not observed at the transition zone. This observation is not surprising since the Septin9 KO cells do not have cilia (Figure 3J/K). As such, I do not believe that the conclusion that Septin9 “mediates the localization of transition zone components to the ciliary base” is founded. Septin9 could have a function early in ciliogenesis that is not necessarily specific to mediating the localization of transition zone components. Perhaps this could be better resolved in the Septin9 siRNA knock down where short cilia are present.

The data showing that expression of constitutively active RhoA restores RPGRIP1L and TCTN2 at the transition zone (7I,7J,7M,7N) is very nice and consistent with this construct restoring cilia in the Septin9 KO. However, I think the data in 7M and 7N would be clearer if you included the Sept9 KO data on this graph.

One last point – Figures 7A, 7B, and the inset on 7C state RPGRIP1 while the text only refers to RPGRIP1L. The Materials and Methods do not mention which antibody was used so I am not clear which protein was the focus of this study. Similarily, I think it is a typo in 7F and the Figure 7 legend where it says TCTN1.

On 2022-07-26 20:14:37, user Justin Hackett wrote:

Please add update that this paper has been published. https://www.nature.com/arti...

On 2022-07-26 16:54:39, user DrDiagnosis wrote:

The authors should take a look at https://doi.org/10.1002/mp....

On 2022-07-26 16:45:31, user Andy Villunger wrote:

Hi Sebastian, this is highly interesting stuff, but I think the title is a bit of a stretch and makes too strong a claim right now. Using the BH3 swap mutants is nice, but does not tell you much about the activity of the full-length (endogenous) protein. You may be aware that the "BH3-only" version of BCL-G is barely expressed in most cells and the version with BH2 & BH3 domain is much more abundant (PMID_23059823). So, studying the BH3 domain in isolation appears somewhat artificial to me. Also, in the CT26 xenotransplant experiment you show would benefit from overexpressing a BH3_mut version of BNIP5 and some data that documents it is really impaired BAK-dependent cell death that gives the growth advantage.....there could be many alternative explanations. So, comparing CT26 lacking Bax or Bak ± BNIP5 wt vs. mut would be a strong experiment to test your hypotheses. Happy to chat more.....

On 2022-07-26 09:48:35, user Iratxe Puebla wrote:

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/jou... and https://doi.org/10.1083/jcb...<br /> 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/nme.... 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.... 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.

On 2022-07-26 03:42:41, user MarkD wrote:

For the most part. this paper treats gene annotations as an opaque collection that is used regardless of the specific type of gene.

It is especially fascinating that RefSeq has genes that don't have exons.

On 2022-07-22 22:26:51, user Guillermo Gómez wrote:

Amazing findings! exNef was my PhD thesis and in those days (2005) I insisted that Nef by itself was able to cause severe immune depression independent of viral replication. Here in bioRXiv we published in Dec 2021 that ORF8 is the "Nef" of COVID-19, ie, a superantigen with enough abilities (at least in silico) to cause disease. Happy to see that research about viral toxins are still alive!

On 2022-07-22 15:49:08, user Still Too Slow wrote:

Lines 25/26: 75.6 ± 0.6 mmol C/L day; 0.06 g/L "day" - the "day" should be hour

Also the mmol C is not intuitive. I spent way too long working out the rates to figure out why the rates and concentrations weren't meshing.

On 2022-07-21 15:49:11, user Marie-Cecile Caillaud wrote:

eLife Sciences Publications, Ltd

https://doi.org/10.7554/eLi...

Manuscript has been accepted

Acceptance Date 2022 Jul 9

Publication: eLife

This paper has been accepted for publication so its publisher has pre-registered a Crossref DOI.

This persistent identifier and link (10.7554/eLife.73837) can already be

shared by authors and readers, as it will redirect to the published

article when available

On 2022-07-21 14:39:58, user Benjamin Buchmuller wrote:

Very exciting work! Reminds me of Giehr et al. (NAR, 2018) "Hairpin oxidative bisulfite sequencing" https://dx.doi.org/10.1093/... I feel that their work should be cited and discussed in the manuscript.

On 2022-07-20 18:37:37, user Ariel Mundo wrote:

This paper has been published in Statistics in Medicine

On 2022-07-20 16:07:55, user Laboratório Biofísico-química wrote:

Final version available at https://www.tandfonline.com...

On 2022-07-20 09:54:59, user Herranz Lab wrote:

Amazing work!<br /> I was trying to check some of the Supplementary Table data, but I can't seem to be able to download it. Are you planning on making them available/downloadable too?<br /> Thanks

On 2022-07-19 18:14:29, user T Sawaya wrote:

I'm wondering if you could publish the disaggregated data for immunity against BA.4 and BA.5 separately. In the abstract, you mention this vaccine providing neutralizing antibody titers against BA.4 but in the figures, BA.4 and 5 figure together. I wonder if that means the Novavax vaccine isn't protective against BA.5 or if it was just a typo in the abstract on your end. Either way, it would be helpful to see antibodies against BA.4 and BA.5 separately and if that is not possible, have an explanation as to why they were aggregated.

On 2022-07-18 12:50:30, user Marc RobinsonRechavi wrote:

Dear authors,

I noticed that sequencing data was generated in this study, but there is no declaration of availability. Can you please deposit the data and provide availability in the manuscript?

Best regards<br /> Marc

On 2022-07-18 10:31:38, user Prof. T. K. Wood wrote:

There is little doubt that S. pneumoniae, as probably all bacteria, make persister cells, but Fig. 5 shows there is primarily cell death still occurring at 25 hr, not persistence being studied. Arguably, the rate of dying has decreased but persistence is not reached. Also, there is little credible evidence that TAs are involved in persistence (line 343).

On 2022-07-18 09:58:46, user Irilenia Nobeli wrote:

NOTE FROM THE AUTHORS:<br /> We are currently investigating the implications of counting reads across overlapping features in prokaryotic genomes that may affect the results and conclusions of this manuscript. If this turns out to be the case, we will be deleting this manuscript from bioRvix. If not, we will simply upload a newer version.<br /> Please watch this space for updates.<br /> I.N.

On 2022-07-17 12:24:43, user Tiago Lubiana wrote:

Hey, the link to e DUB Portal in the abstract seems to be broken, I cannot click it at least

On 2022-07-16 17:09:16, user carrental wrote:

Very cool and fresh story. How comes that the C-terminal fragment of the receptor enters the mitochondria?

On 2022-07-16 07:41:26, user Iratxe Puebla wrote:

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 <br /> - Could the effects seen be due to cells spending different amounts of time in the channels? Do all cells migrate at a similar speed? <br /> - 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/jou... and https://doi.org/10.1083/jcb...

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.

On 2022-07-16 04:46:40, user Hui Zheng wrote:

Little is known about whether and how Vitamin C specifically targets SARS-CoV-2 infection and its underlying molecular mechanisms, although some reports suggest that vitamin C may be beneficial in the treatment of SARS-CoV-2 infection. This study was finished through repeated verification with the help of many scientists. We sincerely hope that this easy and flexible strategy is useful to restrict the large-scale spread of SARS-CoV-2 in the population and to reduce the disease severity of COVID-19 in the early stage of SARS-CoV-2 infection.

On 2022-07-15 13:55:40, user Anirudh .Shanbhag wrote:

The article has been published in ACS Synthetic biology entitled 'Validated In Silico Population Model of Escherichia coli'. Link to the article is as follows:<br /> https://pubs.acs.org/doi/10...

On 2022-07-14 15:54:12, user Qian Zhu wrote:

I am author of the smfishHMRF package (part of Giotto) that is used in one of your comparisons in Figure 6. I am highly doubtful about the results your presented of Giotto in Figure 6 and same of SpaGCN. I believe much of the results you are seeing is due to the selection of genes to find spatial domains than having to do with the underlying method. We also do not rule out improper usage of our package in this comparison. We will share our findings with you in a separate thread.

On 2022-07-14 06:25:43, user André Rendeiro wrote:

Very interesting study. There is no methods section on the acquisition of microscopy images and quantification of nuclear features (Fig1c). Could the authors elaborate on how nuclear dispersion is measured?

On 2022-07-13 22:14:10, user JKlumel wrote:

1. Does the half-life of the fusion protein is expected to be 4-8 hours similar to YTI enhanced IgG bound to FcRn receptor?
2. In classical situation FcRn orchestrates processing of IgG-opsonized immune complexes (IC) in concert with classical Fcγ receptors to present the antigenic peptides to APCs and ensure clearance of the IC. How the fusion protein opsonized immune complexes are going to be recognized by to APCs and cleared when the Fc is silenced? There will be no risk of neutrophil extracellular trap induced collateral damage resulting from excessive attempts to remove the Fc silenced IC?
3. In studies with recombinant ACE2 it was shown that rACE2 in concentrations close to physiological range in plasma could enhance SC2 infection, while high concentrations achieved inhibitory effects on the infection. Is there a risk that nasally administered fusion protein shall get in plasma in physiological range?
4. Is there a risk of IgG suppression in the mucosa of the upper respiratory tract due to FcRn binding to the fusion protein?
5. Could the invention be financially competitive product in comparison to other nasal prophylactic agents: hypertonic saline, polysaccharides, povidone iodine, nitric oxide?

On 2022-07-13 13:46:46, user Iratxe Puebla wrote:

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 occassions. 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.

On 2022-07-13 12:08:26, user Pietro Parisse wrote:

A revised version of the manuscript has been published in Nanomedicine NMB https://www.sciencedirect.c...

On 2022-07-12 20:32:14, user Xing Jian wrote:

It is not clear to me which “case” patient sample from the Coriell Institute was used, and where the whole-exome sequencing data was obtained. These information should be presented in the published version.

On 2022-07-12 15:56:36, user Sara Paver wrote:

This is a neat study - I look forward to reading it in more detail. I think your introduction might benefit from incorporating the work of Salazar and colleagues (https://sfamjournals.online...

On 2022-07-12 11:01:20, user Gregor Gorjanc wrote:

Nice work @Yanyu! See also https://www.biorxiv.org/con...

On 2022-07-12 07:16:11, user sho kubota wrote:

Excellent marker! Figure 2 data are very beutiful.

On 2022-07-10 17:36:37, user Ashraya Ravikumar wrote:

Summary:

In this paper, the authors address the important question of how Aminoacyl-tRNA synthetases (AARSs) have evolved. A key attribute of AARSs is that they have specialized to transfer specific amino acids to their cognate tRNAs, with minimal cross-reactivity. Although there are two major classes of AARSs (Class I and II), they focus specifically on Class I AARSs (since they could not perform a stable phylogenetic analysis on Class II). To this end, they have employed structure based sequence alignment of HUP domains of different Class I AARSs, based on which they built phylogenetic trees and performed ancestral sequence reconstruction. They make interesting, but counterintuitive, observations on the evolutionary trajectory of AARSs in comparison to the timeline of emergence of amino acids themselves. Specifically, they note that AARSs which charge amino acids that emerged later in time appear as early branches in the phylogenetic tree and vice versa. They also observe that one of the AARS ancestor (Anc-all-minus) had a wide substrate binding pocket that did not confer amino acid sidechain selectivity, but rather selected for L-configuration ɑ-amino acids. Based on these results, the authors propose a new model of evolution of Class I AARSs called generalist-maintaining (GM), where the early ancestor with non-specific/generalist activity is maintained and as amino acids emerged later in time, became starting point for the evolution of specialized AARSs. Overall, the paper is concise and self-sufficient. The conclusions drawn by the authors are significant and well supported by data. There are a few minor points that we want to bring to the attention of authors, which could improve the manuscript further.

Minor points:

The authors discuss one set of ancestral reconstruction throughout the paper. Were there any alternatives generated by the software used? If yes, on what basis was this particular reconstruction chosen? Perhaps, if there are alternatives, the authors could build the ancestry based on them and see if it yields similar results. If it does, it could make the conclusions from this paper more robust.<br /> The authors mention in a single sentence in Methods - “Ancestral states were inferred using codeml from the PAML package”. Since this is one of the most important steps in this work, some explanation about how this was done and any parameter choices or tuning can be useful.<br /> On Page 7, they say “The anticodon binding domains are thought to have emerged later, in agreement with our analysis, which indicated that the anticodon domains of Class I AARSs relate to at least three separate evolutionary emergences (Table S1)”. The three emergences of anticodon domains is not clear from Table S1. If we are to go by the different H groups according to ECOD, there are only two in the anticodon binding domain column in Table S1. Some clarity on this will be helpful<br /> Since the authors frequently compare the specialization of AARSs with the emergence of amino acids, a schematic showing the order of appearance of amino acids will be more illustrative than making the readers refer to multiple papers. Potentially using real amino acids in Figure 4 would be more clarifying than A,B,C,D,E?<br /> The phylogenetic trees shown in Figure S4 are key to this work. The authors could make the Raxml tree (since this was used for ancestral reconstruction) a main figure to guide the reader about this important part of the paper.<br /> The expansions of many abbreviations used throughout the paper haven’t been given (for example - HUP, HIGH, ECOD)<br /> The importance of outgroups in ancestral reconstruction is remarked upon twice “due to the lack of a suitable outgroup, making ancestor reconstruction intractable.”... “Using Clade 2 as an outgroup, we succeed in the construction” - but would benefit from a sentence or two (and reference) explaining this necessity to readers from non-phylogenetic backgrounds.<br /> The discussion of the structural/functional characteristics of the inferred ancestors could be expanded. For example: “Further, the inferred Anc-All-minus pocket bears no hallmarks of a Val activating enzyme,” it is unclear which hallmarks you are referring to and what is different in the ancestor. Predicted AlphaFold2 structures of the ancestors might help here, especially aligned with substrate bound/docked structures of extant AARSs.

Ashraya Ravikumar and James Fraser (UCSF)

James Fraser had a long scientific relationship and personal friendship with the late Dan Tawfik and objectivity (which is always something in the eye of the beholder) may be particularly impacted here.

On 2022-07-10 01:25:17, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint.

I think this provides a number of useful references and comparisons.

I also have a few comments:

1) While rMATS and MISO are popular programs, I don't think they are necessarily my preferred starting point for splicing analysis.

For example, I might consider the following as starting points for splicing analysis:

a) QoRTS + JunctionSeq (an extension of DEXSeq with a separate dispersion estimate for junctions versus exons)<br /> b) Custom analysis of the tab-delimited splice junction counts (SJ.out.tab) from STAR.

Perhaps b) is too open-ended. However, if you labeled the junctions like genes or transcripts, you could calculate Count-Per-Million (CPM) values and perhaps use traditional differential expression methods (DESeq2, edgeR, limma-voom, etc.).

Unlike a), there would not really be specialized splicing visualization for b) without additional coding.

However, both of these strategies do not fit the description of "alternative splicing events (ASEs) are conceptualized as binary choices". Instead, each junction is treated like a feature.

So, I can see how that can complicate comparisons. Nevertheless, limma-voom and DESeq2 are used as part of this package. If I look at the ASE-methods.R code and the supplemental methods, I think the main difference is the "Treatment:Isoform" interaction variable.

In other words, if you had 2 groups with 3 replicates and 2 junctions as part of splicing event, am I understanding the that the difference between SpliceWiz and what I would have otherwise expected is that you now have 12 count measurements (2 junctions) instead of 6 count measurements (1 junction)?

So, if I am understanding everything, I am not sure if you have comments with respect to why a strategy more like JunctionSeq (or what I might write with custom code for individual junctions) is not preferable and not provided as an option within SpliceWiz?

2) I thought Figure S4 was interesting and useful.

However, the retained intron counts are quite different, as also mentioned in the text: "Of the 15,188 extra retained introns annotated by SpliceWiz, 15,144 (99.7%) of these would have been excluded due to this extra criterion."

Identifying additional differential intron retention events might be helpful, but I also wonder if perhaps this also corresponds to additional false positives. For example, I am also not sure how often there might be pre-mRNA and/or pseudogenes where no introns are spliced (not a single retained intron event). For example, if there are confounding factors not sufficiently modeled in simulated data, then I would expect estimates of accuracy could be considerably different than in actual data.

3) As a minor point, there is a typo in the description for Figure 5: "Heirarachical" instead of "Hierarchical".

Thanks Again,<br /> Charles

On 2022-07-09 18:08:54, user Brian Stevenson wrote:

Please note that observation of punctate patterns of DNA in Borrelia burgdorferi has been published before. Similar patterning was also observed in relapsing fever Borrelia. The pattern observed here should not have been a "surprise" (line 100).

References that should be cited are:

Jutras et al., 2012, EbfC (YbaB) is a new type of bacterial nucleoid-associated protein and a global regulator of gene expression in the Lyme disease spirochete, J. Bacteriol 194:3395-3406,<br /> pubmed.ncbi.nlm.nih.gov/225...

Kitten and Barbour, 1992, <br /> The relapsing fever agent Borrelia hermsii has multiple copies of its chromosome and linear plasmids, Genetics 132:311-324, <br /> pubmed.ncbi.nlm.nih.gov/142...<br /> (this paper is currently cited for a different reason)

On 2022-07-09 16:30:13, user K.R. Caspar wrote:

You might want to consider this paper, which previously discussed and dismissed the idea of self-domestication in social bathyergids:

https://www.frontiersin.org...

On 2022-07-08 10:20:41, user Kees Jalink wrote:

Please note that this work has in the mean time been published in largely unaltered form in Scientific Reports, 2021 Oct 20;11(1):20711; DOI: 10.1038/s41598-021-00098-9.

For clarity, we also share the reviewer comments and our responses to that with you:

Reviewer Comments:

Reviewer 1<br /> This is a very elaborate and interesting study proposing a dynamic genetic screen based on FRET-FLIM and which allows for a more refined understanding of the impact of gene knockouts on cellular signaling and metabolic processes compared to the simple cell viability and colony formation readouts widely used in the past (and also currently). The authors used an innovative FRET-FLIM sensor (created by them) expressed in stable cell lines to monitor changes in the levels of cyclic AMP by modulating phosphodiesterase-induced breakdown via treatment with silencing RNA (siRNA) oligonucleotides.

I only have a few comments regarding the text of the article, as follows.

1. It is very difficult to understand from the abstract the purpose of the paper, at least from the standpoint of the general FRET enthusiast with no specific knowledge regarding the biological application described. (i) Specifically, in the second paragraph, the agonist of what receptor are the authors referring to? What is the connection between the” 22 different phosphodiesterases (PDEs)” and the baseline levels of cAMP. Is that what the sentence refers to? It is hard to guess from the current sentence structure.<br /> (ii) The authors used “HeLa cells stably expressing our FRET-FLIM sensor.” Precisely what sensor does “our” stand for? (iii) The rest of the paragraph does not seem much easier either, especially given the numerous undefined acronyms. All these questions are fully addressed in the body of the paper, though not in the abstract.<br /> The last 75% of the abstract has been completely rephrased to address hopefully all of the reviewers concerns. Changed part is indicated with track-changes.

2. On page 3, the authors state: “FLIM is a robust and inherently quantitative method for FRET detection which requires no additional calibrations or correction parameters.” That is not entirely correct, for a couple of reasons: (i) FLIM generally requires separate knowledge of the donor lifetime in the absence of FRET. <br /> The reviewer is right. We condensed the text so much that we cut some corners. We have now rephrased that claim, and mentioned that the donor lifetime is a necessary calibration. See page 3.

(ii) One has to fully separate the donor emission from acceptor emission, which is usually done achieved band-pass filters (it is also done, though not very often, using spectral resolution). This is not unlike what is done in intensity-based measurements, in which, at least one is attempting to unmix the donor and acceptor signals or at least apply some post-measurement corrections for bleed through (caused by spectral overlap between donor and acceptor emission). The fact that FLIM researchers often choose to ignore this kind of corrections may not be interpreted, in my view, as an advantage of FLIM. Please consider adjusting the text.<br /> We could not agree more! In particular in view of our own contributions to obtain truly quantitative sensitized emission FRET (van Rheenen et al, BJ 2004), we are keenly aware of the dangers of spectral overlap. That is the reason that in this study we used our Epac-SH189 FRET sensor which has dark (i.e. non-emitting; Y145W mutation; Klarenbeek et al, PLoSOne 2015 ) acceptors. Given the high QY of the donor, the lifetime of this sensor has ignorable contribution of acceptor emission even if a large spectral window is selected. We did not attempt unmixing approaches, because our pilots had indicated near-identical lifetimes when the emission was taken just from the part of the spectrum that is exclusively occupied by mTurquoise. This information is presented towards the end of the introduction on page 5, in the paragraph where we present a more specific outlook to the contents. It has been rephrased in part for better emphasis.

1. The results shown in Figures 6 and 7 are fascinating. To let the reader more easily follow the story, could the authors insert “agonist” or “antagonist” as necessary within the following sentences? “We first stimulated HeLa cells with 40 nM isoproterenol which caused a rapid rise in cAMP levels and subsequently added propranolol at 60 nM concentration which caused a sharp decline following the stimulation. Finally, 25 µM forskolin…”<br /> We have adapted the text (page 14) and legend of figure legends (page 15) according to these suggestions.

Once again, in my evaluation, this is an excellent, detailed, and rich in information study worthy of publication in a high caliber journal.

Reviewer 2<br /> The manuscript submitted by Harkes et al. on the topic of FRET-FLIM HCS with siRNA screen to monitor dynamically the change of cAMP concentration using a FRET biosensor is well written with interesting results and shows the high potential of this method. In order to increase its impact and for clarification I have few queries:<br /> [1] I found the introduction of interest but previous work in the scope of HCS FLIM is missing. I suggest to add references of several groups working in this direction (French, Tramier, Esposito...).<br /> In our introduction we sought to emphasize work that is geared towards screening of fast dynamic changes in lifetime in living cells, which implies imaging with very high photon fluxes and with methods that do not waste photons unnecessarily, so as to avoid unnecessary cell damage. We agree with the reviewer that this does not acknowledge much of the work of those who contributed to high-content and high-speed FLIM imaging, in particular from pioneers like Drs French, Tramier, Esposito, but also e.g. Gerritsen, Ameer-Beg and others. We have now rewritten that part (page 4) and added 4 references to just a few of the very relevant contributions.

[2] For lifetime analysis, authors have used biexponential fit with two fixed lifetimes 3.4 and 0.6 ns with a final determination of mean lifetime using the different preexpo factors. I'm not sure that this approach is the more appropriate. First, what means these two fixed lifetimes? is it pertinent with the biosensor under study? this is not really discussed in the manuscript.<br /> Second, if finally you use a mean parameter for the concentration curve fitting, why not using a mean analysis such as the mean arrival time? This parameter is now directly calculated in the FALCON version and seems pertinent because HyD have very low noise. From my point of view, this will increase the sensitivity and the speed of the measurement. In any case, this has to be discussed.<br /> In fact, we had given this quite a bit of thought but for brevity, it did not make it to the final draft. In brief, we performed 2-component fits because these fitted much better than single-component fits. The lifetimes of 3.4 and 0.6 ns were selected because these were the dominant components in large numbers of global (i.e. whole-image) fits. We expected a dominant component of 3.4 ns, which is that of the Epac sensor in its low-FRET configuration. The low lifetime, 0.6 ns, is in fact significantly below the resting-state lifetime of the cells (minimally 1.9 ns in some cells) but it is the second dominant Tau in the two component fits and the phasor analysis also indicates a intercept below 1 ns. Our data were thus collected and exported (data reduction) for those two components, enabling us to analyze both effects on long as well as on short lifetime contributions upon PDE knockdown.<br /> In preparing figures for the manuscript, we noted no clear advantage of separately presenting data of both lifetimes and their amplitudes, so we decided to extract the weighted mean lifetime. This has now been described more completely in the Methods section (page 6). For the reviewer, we also note that we are not fond of using the FALCONs mean photon arrival time because 1st, unlike the fitting, it proved to be quite sensitive to environmental (background) light, and 2nd, there is a small bug in our version of the software which sometimes causes erroneously high lifetimes when recalculating old data with the ‘mean photon arrival time’ option. 3rd, this method has the same number of free fit parameters as a single component fit. We experienced that the result with this method had less pixel to pixel variation compared to a single component fit.

[3] When showing screen results, only fitting parameters are presented in figures. Is it too difficult to present few curves in which differences can be shown? This will increase the understanding of the reader before to present the statistics.<br /> We thank the reviewer for this excellent suggestion. Two panels have now been added to the boxplot in Fig. 4

[4] From my point of view, details regarding how to add drugs in multi-well plate has to be presented. Is it pipeting ? and in this case it does not really make High Content automated approach. Moreover, how is managed the focus since probably you loose it during pipeting... or is it more automated device that you use in the context of multiwell plate. In addition, how is selected the FOV? how to manage the human choice? This has to be detailled and discussed.<br /> We have extended the text in Materials and Methods to cover all of these aspects, see page 6, 7. In brief, in this study we present only data from studies where stimulus addition and mixing where done manually, although we have also implemented automated addition of stimuli (3 channels). For the protocol involving rapid sequential agonist-antagonist stimulation we found that, at least with our equipment, automated addition of stimuli depended too much on diffusion, and therefore results were more variable than with manual mixing. For keeping in focus, we routinely used the Leica hardware focusing option, AFC.

One last important issue: in revising our manuscript, we noted that the reported sequences for siRNAs used for PDE8A accidentally had become mixed up. We now corrected those entries in Supplementary Table S1. This correction does not affect any of the experiments, microscopy data or interpretations whatsoever, it solely affects one row in that table. <br /> We expect that with those changes, we have adequately addressed all issues raised by the reviewers. We feel that the manuscript has significantly improved in the review process and we are indebted to both reviewers for their time and thoughtful comments.

Sincerely, on behalf of all authors, <br /> Kees Jalink

On 2022-07-07 16:03:17, user Kathrin Liszt wrote:

Hi,<br /> in your methods at "Affinity based Cas9-Mediated Enrichment" you describe to collect the supernatant which must have around 740 µL volume that includes your DNA sample that need to be washed then with the Ampure XP beads. How much beads did you add for that wash. How did you do the wash with the Ampure XP beads?<br /> Regards,<br /> Kathrin

On 2022-07-07 10:16:54, user Prof. T. K. Wood wrote:

Congratulations. Never was any credible evidence that anti-phage systems like toxin/antitoxin and CBASS systems, etc. kill cells; just wild claims without evidence. Note the the first TA system found to inhibit phage by transcription shutoff should be cited (Hok/Sok, https://journals.asm.org/do... ) since it was discovered 25 years before ToxIN.

On 2022-07-06 17:55:48, user Iratxe Puebla wrote:

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?

On 2022-07-06 11:54:24, user Luca Jovine wrote:

Now published: https://doi.org/10.1038/s41...

On 2022-07-05 22:26:50, user Prof. T. K. Wood wrote:

Would be great to see the supplemental information. Also, would be interesting to quantify the 100S fraction over time.

On 2022-07-05 13:23:20, user Jianfeng Lee wrote:

This work was officially published in Brilifling in bioinformatics:

Jianfeng Li, Benben Miao, Shixiang Wang, Wei Dong, Houshi Xu, Chenchen Si, Wei Wang, Songqi Duan, Jiacheng Lou, Zhiwei Bao, Hailuan Zeng, Zengzeng Yang, Wenyan Cheng, Fei Zhao, Jianming Zeng, Xue-Song Liu, Renxie Wu, Yang Shen, Zhu Chen, Saijuan Chen, Mingjie Wang, Hiplot Consortium, Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization, Briefings in Bioinformatics, 2022;, bbac261, https://doi.org/10.1093/bib...

On 2022-07-04 10:14:10, user 吴豪达 wrote:

Thanks for the development of bioRxiv!

The paper was published in Frontiers in Immunology (https://urldefense.proofpoi... ).

On 2022-07-04 02:13:23, user Zhiyun Yu wrote:

Online figures are available in supplementary material. More data and methods can be shared upon request.

On 2022-07-02 08:02:09, user Aram P. wrote:

Hi all. Thanks for Your work. I want to mention two possible errors in Your paper.<br /> 1. The paper say that Late Armenia cluster do have a partial continuity with Early Armenia cluster. The estimated proportion is 50%. That's looks good. But the other 50% can't be from Steppe as You state. Because Late Armenia cluster is shifted to Near East. So it's more likely that the extra 50% is from South not from Steppe <br /> 2. The other issue I see is the place of modern Kurdish samples on the PCA. Near Lybians.

Thanks in advance for Your attention.

On 2022-06-07 03:30:39, user Sean Dugaw wrote:

I share Davidski’s criticisms regarding the geographic labels. In addition, I would add that Egypt is not ever considered part of the Levant. I understand why you have grouped the populations of Egypt and the Levant together, however a label which includes both terms seems called for.

On 2022-05-19 13:21:33, user R. Rocca wrote:

Authors, the labels in the ENA Browser completely mismatch the BAM files. For example, the row with the label ID R11563 has a link to 1554.bam. Most, if not all the labels are wrong. If they are used for future research, there will no doubt create a lot of erroneous results.

On 2022-05-18 22:14:12, user Davidski wrote:

Hello authors,

Unfortunately, there are some serious problems with the geographic concepts in your preprint:

• your Steppe region includes a large swath of Eastern Europe that is mostly forest and forest steppe. Only about a third or less of this region is actually a steppe (the Pontic-Caspian steppe). Calling this region Eastern Europe would be more useful and in tune with geographic conventions.

• what you call Eastern Europe is not generally, by itself, known as Eastern Europe, especially since the fall of the Iron Curtain. That is, Czechia, Hungary and Slovakia (often along with Poland) are nowadays more commonly described as East Central Europe.

• what you call SE Central Europe is actually much of the Balkans, and thus straight up Southeastern Europe.

Honestly, calling Bulgaria Central Europe, while, at the same time, calling Czechia (inc. Bohemia) Eastern Europe just doesn't look right.

On 2022-07-01 14:28:11, user Andrea wrote:

Interesting hypothesis.<br /> But since I noticed that the changes occur close to the binding site (see https://aquaria.app/SARS-Co... ): Have you considered that the increased PPAR mimicry could be of functional importance? -- A quick Google search brought e.g.: https://www.frontiersin.org...

On 2022-07-01 10:23:35, user Iratxe Puebla wrote:

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).

On 2022-07-01 02:26:15, user Sciency wrote:

This is spectacular work. I have a feeling I'll be referencing it a bunch. I have two small questions.<br /> - You used the Reef Life Survey Database, and for each sampling location, one of the parameters was whether that location is within 10km of a reef? I'm not yet familiar with the database, but it sounds like it covers more than reefs and reef-adjacent areas? (assuming anything further than 10km wouldn't count as reef-adjacent)

• I'm not super-sure the phrasing of "hypothesize that MPAs counterbalance (mediate) the effect of coral reefs, human density, and thermal stress on species richness" is fully clear. In the diagram, I see that a coral reef increases species richness by 54% if the reef is within an MPA, and by only 15% if the reef is not in an MPA. So, to me, this would not be MPAs "mediating" the effects of the reefs. Maybe enhancing or supporting? Mediating usually has the connotation of making a negative effect less negative or a positive effect less positive, no?

Thanks for the paper. Beautiful work.

On 2022-07-01 00:21:53, user Dylan Glubb wrote:

This looks like a really interesting paper but the supplementary tables don't appear to be available.

On 2022-06-30 18:01:00, user QuiPrimusAbOris wrote:

Interesting piece of work substantiating the role of CAFs in tumorigenesis with some specific mechanism. The authors emphasize here the obvious TRANS effect (Fibroblast --> Epithelium). But the key question alas remains not answered: What makes the BRCA1 mutated epithelial cell convert the normal fibroblasts into (pre)CAFs? Can wildtype fibroblasts also become, with same ease, tumor promoting CAFs in this model?<br /> it shold be reminded that with germline BRCA1 mutation we have the rater unusual but interesting setting of having an oncogenic mutation in both the epithelium and stroma. <br /> The more usual setting is to have the mutated epithelium be surrounded by genetically wildtype fibroblasts - which still are converted into CAFs.<br /> This aspect is not addressed, not even discussed. It is also not clear what the authors mean by "control" when they say it since they do not specify it (there are many questions in this regard): BRCA1 wildtype cells from cancer-free healthy individuals or from precancerous, non-BRCA1 tissues.. Would be nice to have both types f controls. etc. Also the genotype of the fibroblasts with respect to BRCA shold be specified in every experiment.

On 2022-06-30 16:17:06, user Arianna Basile wrote:

Hello :) Very interesting, thank you for this tool and best luck for its publication. May I ask if you are planning to release the code on a public GitHub page?

Best,<br /> Arianna

On 2022-06-30 05:39:56, user ＮＡＴＴＡＳＩＴ ＰＲＡＰＨＡＷＩ wrote:

Hi, you have done a great work! <br /> 1) I noticed that you might need a correction on Fig.7 <br /> "Fig.7 Chromatin condensation promotes ~~osteogenesis~~ adipogenesis of hMSCs cultured on soft hydrogel"<br /> 2) It would be great if hMSCs cultured on stiff hydrogel were included as a control in every experiment.<br /> Best,

On 2022-06-29 20:42:56, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint. I think this is an interesting manuscript.

At the moment, I have one question:

Is there a reason why there is not a plot for Asian ancestry in Figure 3?

I see mention of "East Asian" populations in the ADMIXTURE analysis section of the methods, and I also see a plot similar to what I might expect in Supplementary Figure 11. It looks like the East Asian contribution is usually small, but the African contribution in Figure 3 is also often small.

I mention this because there was a paper reporting mixed Native American ancestry for the MDA-MB-468 cell line (from Hooker et al. 2019), but this was not reproduced in the Cellosaurus ancestry estimates (from Dutil et al. 2019). It was prepared for a slightly different reason, but I have some additional notes here. Basically, I think difference may be due to Hooker et al. 2019 using 2 Chinese populations (CHB and CDX) as a proxy for Native American ancestry.

So, if others might make a similar assumption, then I thought being able to visualize separate East Asian and Native American estimations might be helpful. I don't know how common that might happen, but I was aware of at least one example.

Thank you very much!

Sincerely,<br /> Charles

On 2022-06-29 15:36:43, user Iratxe Puebla wrote:

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/jou... and https://doi.org/10.1083/jcb....

On 2022-06-28 20:25:39, user Irina Novikova wrote:

InaZ fibrils have been recorded in this preprint paper. It should be cited.<br /> https://www.biorxiv.org/con...

On 2022-06-28 18:41:39, user Shashank Srivastava wrote:

Very interesting and well conducted study! I found a few reference errors though. Foltz et al. found UBR7 association with histone H3.1 (not with CENP-A) in Nature Cell Biology, 2006 not 2009 Cell paper. It should be corrected in Page 7 and 12 of the manuscript. <br /> Also, Hogan et al., EMBO, 2021 is relevant with regards to histone chaperone's role in histone deposition, and therefore should be considered to be referenced and discussed.

On 2022-06-28 12:02:36, user CT wrote:

Hello,

This is a very interesting paper, but I would like to point that the mobility data used here have been determined with a fluorescent parS/ParB system that is now known to introduce artefacts in the Ter region, because it is sticky and therefore make the foci less mobile. Could you maybe discuss your results in light of this?<br /> See: Stouf et al 2013, Crozat et al 2020<br /> Many thanks

On 2022-06-28 11:43:11, user Giulio Spinozzi wrote:

Article published in Biomolecules: https://www.mdpi.com/2218-2...

On 2022-06-27 17:36:28, user Gibs wrote:

Seems interesting (great name also)! Currently the GitHub link does not work. I imagine the repo is private - would it be possible to make it public now that the pre-print is out?

On 2022-06-26 19:21:28, user Donald R. Forsdyke wrote:

POSSIBLE MACROEVOLUTIONARY EXPLANATION

Graham Coop appears content with this bioRxiv preprint publication, which would seem to present a true “version of record” of his thinking in 2016. (He did not have his text modified in response to the whims of journal reviewers or need to suffer the stylistic corrections of journal copyeditors.)

Among those who provided feedback on earlier drafts, Coop lists Vince Buffalo who later (2021), more formally addressed the same topic in consultation with Coop (1). However, rather than naming bioRxiv directly, Buffalo cited Coop (2016) as having published in “Cold Spring Harbor Labs Journals” (2). This would seem not to have been picked up by automatic search engines, so no link to Buffalo (2021) is so far evident at this site.

This is regrettable since Buffalo goes beyond Coop (and other commentators; 3) in suggesting that we should be considering macroevolutionary explanations for the failure of consensus population size (Nc) to relate to population diversity (“Lewontin’s paradox”):

Finally, beyond just accounting for phylogenetic non-independence, macroevolution and phylogenetic comparative methods are a promising way to approach across-species population genomic questions. For example, one could imagine that diversication processes could contribute to Lewontin’s Paradox. If large-Nc species were to have a rate of speciation that is greater than the rate at which mutation and drift reach equilibrium (which is indeed slower for large Nc species), this could act to decouple diversity from census population size. That is to say, even if the rate of random demographic bottlenecks were constant across taxa, lineage-specic diversication processes could lead certain clades to be systematically further from demographic equilibrium, and thus have lower diversity than expected for their census population size.

I have further addressed this topic elsewhere (4).

1. Buffalo, V. (2021) Quantifying the relationship between genetic diversity and population size suggests natural selection cannot explain Lewontin’s paradox. Elife 10: e67509.

2. Coop G. (2016) Does linked selection explain the narrow range of genetic diversity across species? Cold Spring Harbor Labs Journals; 2016.

3. Charlesworth, B. & Jensen, JD. (2022) How can we resolve Lewontin's Paradox? Genome Biology & Evolution (doi/10.1093/gbe/evac096/6615374).

4. Forsdyke, DR. (2022) Social Sciences Research Network. Speciation, Natural Selection, and Networks: Three Historians Versus Theoretical Population Geneticists

On 2022-06-25 19:18:11, user Tom Kerppola wrote:

This paper ia also available on the Immunology web site with the doi 10.1111/imm13527. The URL is https://onlinelibrary.wiley.com/doi/10.1111/imm.13527

On 2022-06-25 19:02:04, user Tom Kerppola wrote:

A paper that examines the specificity and mechanisms for Keap1 moderation of the transcription of virus induced genes is now available on the Immunology web site with the doi 10.1111/imm13527. The URL is https://onlinelibrary.wiley.com/doi/10.1111/imm.13527

On 2022-06-24 19:05:28, user Larissa Dougherty wrote:

We want to thank the participating reviewers in ASAPbio’s Crowd Review for taking the time to provide thoughtful feedback for our preprint. We have responded to some comments below and in the next version, will revise the manuscript accordingly.

“The majority of the conclusions about MAPK 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.”

We have cited Molina et al. 2009 who showed specificity for BCI hydrochloride in zebrafish. BCI targets primarily DUSP6, but also exhibited some activity towards DUSP1. In this study, the authors had also used zebrafish embryos to check expression of 2 other FGF inhibitors, spry 4 and XFD, in the presence of BCI but found that their effects were not reversed. In addition, they checked the ability for BCI to suppress activity of other phosphatases including Cdc25B, PTP1B, or DUSP3/VHR and found that BCI could not suppress these phosphatases. Though this is not to say that BCI is not inhibiting these proteins mentioned, but BCI inhibition has previously been found to be more specific to MAPK phosphatases.

In addition, we have previously confirmed that U0126 has a slight lengthening effect on Chlamydomonas which further implicates this pathway in cilium length tuning (Avasthi et al. 2012).

“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.”

While ERK activity spikes early after BCI treatment, what we are assaying here are downstream effects following ERK activation. Our experiments primarily address these eventual outcomes rather than the immediate molecules participating in signaling. Here we show that ciliary shortening is a downstream effect, though we also show that ciliogenesis is immediately inhibited as well (30 minute and 60 minute timepoints included) to show that these processes are stopped in their tracks, but it takes 2 hours to see the measurable large-scale changes to the cell. We agree that MAPK is unlikely to still be active at the 2 hour time point given that ERK activation is decreased within 60 minutes.

“Specific comments<br /> Introduction:”

Thanks for the suggestions on wording. We will make minor edits to the wording per the helpful suggestions for clarity.<br /> “Results: <br /> Figure 1 <br /> 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.”

We will revise the figures to indicate individual P-values from multiple comparisons. In Figure 1C, both 15 µM and 30 µM are significantly different from 0 µM. In Figure 1H and J, the differences between the control and 1.56 µM as well as the control and 3.13 µM are significant for ciliary length. For percent ciliation, they are not significantly different.

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

We have also measured Arl13B as a marker with and without acetylated tubulin staining and found consistent results regarding ciliary shortening in hTERT-RPE1 cells. In addition, we have measured acetylated tubulin in Chlamydomonas cells and have found consistent results with ciliary length changes compared to other markers such as non-acetylated B-tubulin and FAP138-GFP.

“Figure 2<br /> 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.”

While there may be additional effects on intra-ciliary KAP-GFP distribution that impact ciliary phenotype, we expect the decreased ciliary KAP-GFP to largely explain the profound effect on ciliary growth.

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

Figure SF 2C acts as a control for Figure 3H. After a single regeneration event, cilia cannot initially regrow in BCI, but ultimately, at this lower concentration of BCI used, cilia can slowly begin to regrow possibly after overcoming the acute ERK activation with BCI. Additionally, after a single regeneration, there is enough ciliary protein present to normally regenerate cilia to half length (Rosenbaum et al., 1969). In Figure 3H, we show that upon completely depleting the protein pool through 2 regenerations (the first in the protein synthesis inhibitor cyclohexamide), cilia cannot begin to regrow after several hours until it is washed out. What we see here is that with existing ciliary protein present, though this protein cannot participate in immediate ciliogenesis until the cell overcomes BCI, the cilia can ultimately regrow. Following complete ciliary protein depletion and washout of BCI, cilia cannot regrow for several hours, which indicates a defect in ciliary protein synthesis during the BCI treatment period.

“Figure 3 <br /> 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.”

In Figure 3B, we are quantifying the amount of KAP-GFP in the cell body versus in cilia. Consistent with our data that there is reduced entry of KAP-GFP into the cilia, we see this occur when we quantify this protein. These data are a fractionation of the cell body and cilia protein rather than a readout of protein synthesis. BCI prevents entry of KAP-GFP into cilia. These data suggest that although the quantities of protein are similar in BCI vs. control cells, the distribution of KAP-GFP is increased in the cell body and decreased in the cilia. It is not that there is a ciliary protein synthesis defect that we are seeing in Figure 3B, but the localization of ciliary proteins are altered in BCI.

“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.”

By “partially” disrupts the transition zone, we are referring to BCI altering some protein composition without altering the complete transition zone structure. This suggests that BCI is not directly impacting or disrupting the entire transition zone, just parts of it. We see a change in NPHP4, but the lack of structural changes by EM suggests that the proteins giving rise to the EM-visible structures are relatively unperturbed.

We agree that it might be easier to see visible changes with higher concentrations of BCI. Interestingly though, we do not see a dose dependent change in NPHP4 fluorescence at the transition zone. The addition of BCI decreases the signal uniformly at all concentrations. It remains, however, a possibility that other transition zone proteins may be affected more drastically with BCI than NPHP4.

“Figure 5<br /> 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.”

In reference to impacting cell viability, for these experiments, we could not wash out 30 µM BCI paired with 36 µM BFA. Either this was too toxic or had very potent effects on the cell that prevented them from reassembling cilia. However, with the slightly lower concentration of 20 µM BCI paired with 36 µM BFA, we were able to wash out the drugs successfully and rescue ciliary regrowth. At this lower concentration, we noted that cilia shorten faster and more drastically than in either drug alone which is represented in the graph. We did not graph the higher concentration of 30 µM BCI paired with 36 µM BFA due to inability for cilia to regrow post washout. Given that the lower concentrations allowed us to draw conclusions about the membrane source, we plan to remove the sentence about toxicity at 30 µM BCI.

In reference to morphology, we cite Dentler 2013 who went into detail about how 36 µM BFA collapses the Golgi using EM. Dentler also shows that the Golgi is an important source of membrane for cilia which is ultimately why cilia shorten in BFA. In our study, we wanted to see if BCI impacted Golgi-derived membrane traffic. We looked at the Golgi with EM and did not see collapse despite the faster ciliary resorption seen with coupling 20 µM BCI and 36 µM BFA, though we did not look at EM with the paired drugs.

“Figure 6<br /> Figure 6C – The three categories mentioned in the text are not mentioned in the figure.”

We have included measurements for full microtubule cages only for clarity in the main data; however, in the supplement we have included distinctions in the measured data between full vs. partial cages to provide a more complete story where the full-cage-only measurement may not tell the whole story.

On 2022-06-22 15:49:19, user Iratxe Puebla wrote:

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.

On 2022-06-24 15:33:29, user Alejandro Heuck wrote:

Congrats for a great work. Nice to see the translocon modeled as an hexadecameric oligomer, as shown in vitro for the homologue system in P. aeruginosa in <br /> J Biol Chem. 2016;291(12):6304-15<br /> https://pubmed.ncbi.nlm.nih...

On 2022-06-24 13:38:10, user Patricia Champion wrote:

This work validates the first step of our recently published model for ESX-1 transport in M. marinum (https://doi.org/10.1073/pnas.2123100119). Happy to see this work!

On 2022-06-24 01:15:41, user Jichen Bao wrote:

The manuscript has been accepted by ACS synthetic biology. https://pubs.acs.org/doi/10...

On 2022-06-23 14:34:06, user Andres Romanowski wrote:

Hi people! This is great. Thanks for doing this! I hope you get this published soon. I was wondering if you plan to update AtRTDv3 to use the Col-CEN T2T genome. Thank you!

On 2022-06-23 10:55:01, user Chiara Damiani wrote:

Hi. There is some imprecision in referencing to our scFBA tool. The paper states "Existing analysis tend to portray the average change of intermixed and heterogeneous cell subpopulations within a given tissue [22-24]". However, in ref 22 we do predict single-cell fluxes! Simply we do not use neural networks, but we use Linear Programming to do that. Best regards, Chiara

On 2022-06-23 03:42:57, user John King wrote:

Here is the DOI link of the published version of the paper, https://doi.org/10.1371/jou...

On 2022-06-22 20:32:02, user Iratxe Puebla wrote:

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. <br /> 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.

On 2022-06-22 13:39:06, user Prof. T. K. Wood wrote:

Simply no evidence ANY of these phage systems cause cell death. When did we stop seeking evidence for something before invoking it?

On 2022-06-22 12:18:53, user shashi shekhar singh wrote:

Highly significant research done by authors and this manuscript may encourage the researchers to develop precise strategies for treating the pan-resistant bacteria.

On 2022-06-22 06:51:59, user Agustina Rimondi wrote:

This preprint has been published and a link will be forthcoming

On 2022-06-22 06:31:57, user Jakub Gemperle wrote:

Now published in eLife<br /> https://doi.org/10.7554/eLi...

On 2022-05-31 15:16:38, user Jakub Gemperle wrote:

Plasmids generated in this study are now available from Addgene: https://www.addgene.org/bro...

On 2022-06-21 13:17:37, user Davidski wrote:

Hello authors,

Thanks for making the genotype data available so quickly. A few points after running the data, copy pasting from my blog...

• in terms of fine scale ancestry, the Erfurt Jews show enough variation to be divided into three or four clusters, as opposed to just two as per Waldman et al.

• some of the Erfurt Jews show excess "Mediterranean" ancestry, while others excess "North African" ancestry, and this cannot be explained with ancestral populations similar to Lebanese and/or South Italians, but rather with significant gene flow from the western Mediterranean and possibly North Africa

• several of the Erfurt Jews show relatively high levels of "East Asian" ancestry that cannot be explained with admixture from Russians, or even any Russian-like populations, because such populations almost lack this type of ancestry, and instead show significant "Siberian" admixture

• as far as I can see, there are no correlations between any of the observations above and the quality of the samples. That is, low coverage doesn't appear to be causing the aforementioned excess "Mediterranean", "North African" and/or "East Asian" ancestry proportions.

More at this link:

https://eurogenes.blogspot....

Cheers, David Wesolowski

On 2022-06-21 06:12:51, user Effie Bastounis wrote:

In Figure 3h the figure legend is correct but in the actual figure you need to correct the colors. I.e. blue should be the local isotropic stress and red the change in the area of the hole!

On 2022-06-21 04:30:22, user Kunio Ihara wrote:

There is no Figure legend in Fig1 and p245 (which is appeared in Fig.1) in not found in Table 5 (plasmid list).

On 2022-06-20 01:43:16, user Artem B wrote:

Hello! Has this work eventually been published?:)

On 2022-06-18 21:26:56, user Gagandeep singh wrote:

Nice article. However as a suggestion, you should expand the ADMET table as it is not covering all the ADMET properties.

Also you can do metadynamics analysis of the trajectory (Free energy landscape, PCA, mode vector etc), MMGB/PBSA calculations, Network analysis of the residues, density distribution of RMSD/RMSF/RG, Binding pocket dynamics in terms of appearance/disappearance of pocket/change in pocket volume. Such kind of additional trajectory analysis will support your manuscript.

Best Regards,<br /> Gagandeep Singh<br /> Assistant Research Officer <br /> CCRAS, Ministry of AYUSH, Gov. of India,

Ph.D. Scholar<br /> KSBS, IIT Delhi

gagan.sk.1994@gmail.com

On 2022-06-18 19:15:13, user Alex Cope wrote:

Published in Plos Genetics: https://doi.org/10.1371/jou...

On 2022-06-18 13:53:05, user Marc RobinsonRechavi wrote:

Dear Yamaguchi et al,

You write under Data accessibility:

All code necessary to repeat the analysis described in this study have been made available. SLiM source codes of our model for speciation dynamics will be hosted on Dryad Digital Repository upon acceptance. There are no data to be archived.

This is a publication, i.e. it is made public as part of the scientific record and is citable, thus I strongly invite you to make the corresponding source code available without delay.

On 2022-06-17 19:18:38, user Yuangao Wang wrote:

After months' negotiation with the manufacturer, the Solution A, one of the key reagent of this protocol for the success of obtaining pure eccDNA, has become globally available now.

On 2022-06-17 09:46:09, user N van Hilten wrote:

The peer reviewed version of this paper is now published in the Journal of Chemical Theory and Computation (JCTC). https://pubs.acs.org/doi/fu...

On 2022-06-17 01:31:40, user Angie Renton wrote:

A version of this article has been published with Scientific Reports, and is available here: https://doi.org/10.1038/s41...

On 2022-06-16 16:47:00, user Elena L. Paley wrote:

Leading corresponding authors are not available for contact/comments<br /> No citation of our earlier studies on the tryptamine-related subject:<br /> https://pubmed.ncbi.nlm.nih...<br /> Tryptamine induces tryptophanyl-tRNA synthetase-mediated neurodegeneration with neurofibrillary tangles in human cell and mouse models<br /> https://pubmed.ncbi.nlm.nih...<br /> Diet-Related Metabolic Perturbations of Gut Microbial Shikimate Pathway-Tryptamine-tRNA Aminoacylation-Protein Synthesis in Human Health and Disease<br /> Abstract<br /> Human gut bacterial Na(+)-transporting NADH:ubiquinone reductase (NQR) sequence is associated with Alzheimer disease (AD). Here, Alzheimer disease-associated sequence (ADAS) is further characterized in cultured spore-forming Clostridium sp. Tryptophan and NQR substrate ubiquinone have common precursor chorismate in microbial shikimate pathway. Tryptophan-derived tryptamine presents in human diet and gut microbiome. Tryptamine inhibits tryptophanyl-tRNA synthetase (TrpRS) with consequent neurodegeneration in cell and animal models. Tryptophanyl-tRNA synthetase inhibition causes protein biosynthesis impairment similar to that revealed in AD. Tryptamine-induced TrpRS gene-dose reduction is associated with TrpRS protein deficiency and cell death. In animals, tryptamine treatment results in toxicity, weight gain, and prediabetes-related hypoglycemia. Sequence analysis of gut microbiome database reveals 89% to 100% ADAS nucleotide identity in American Indian (Cheyenne and Arapaho [C&A]) Oklahomans, of which ~93% being overweight or obese and 50% self-reporting type 2 diabetes (T2D). Alzheimer disease-associated sequence occurs in 10.8% of C&A vs 1.3% of healthy American population. This observation is of considerable interest because T2D links to AD and obesity. Alzheimer disease-associated sequence prevails in gut microbiome of colorectal cancer, which linked to AD. Metabolomics revealed that tryptamine, chorismate precursor quinate, and chorismate product 4-hydroxybenzoate (ubiquinone precursor) are significantly higher, while tryptophan-containing dipeptides are lower due to tRNA aminoacylation deficiency in C&A compared with non-native Oklahoman who showed no ADAS. Thus, gut microbial tryptamine overproduction correlates with ADAS occurrence. Antibiotic and diet additives induce ADAS and tryptamine. Mitogenic/cytotoxic tryptamine cause microbial and human cell death, gut dysbiosis, and consequent disruption of host-microbe homeostasis. Present analysis of 1246 participants from 17 human gut metagenomics studies revealed ADAS in cell death diseases.<br /> More links to our relevant published articles available and delivered to corresponding authors. Our earlier studies demonstrated that tryptamine induces glucose metabolism alterations in blood and in brain. Furthermore, human gut microbial tryptamine increases revealed in the human population with a high prevalence of diabetes. More references are at the web site: www.stopalzheimerstest.com<br /> I have more comments on this preprint.

On 2022-06-16 02:27:16, user Sciency wrote:

This is a fascinating article to read, and I look forward to learning more. I'm going to take it step by step, commenting on clarity as I read through the paper.

• I stumbled a few times in the abstract. " A deeper sampling of individual ants from two colonies that included all available castes (pupae, larvae, workers, female and male alates), from both before and after adaptation to controlled laboratory conditions, revealed that ant microbiomes from each colony, caste, and rearing condition were typically conserved within but not between each sampling category." <br /> What does "deeper" mean, is it that you sampled ants from each caste? (the way it's phrased now, it sounds somewhat detached, like stating that the colony had castes without stating that you sampled them) <br /> So colony number, caste, and wild vs lab are sampling categories? What would it mean "within, but not between each sampling category"?

• What kind of sequencing did you do? I'd like to see at a glance which -omics you are doing, right at the start of the paper, because I sometimes look for papers that use a particular method.

• You use "Tenericute" in the abstract, and "Mollicute" in the Importance section. For the readers unfamiliar with the two, it might be good to disambiguate.

• The Importance section is somewhat long and repeats a lot of the abstract. What made you want to do this study? That no one has studied this ant's microbiomes? That the findings might extrapolate to other ants? That you could say something about individuality and colonial organization or evolution from the members' microbiomes?

• "Honey bee queens, workers, and drones also each have unique gut microbiomes, where worker microbiomes are more diverse than those of queens and drones, possibly due to worker foraging (9)." "Unique" has the connotation of being individual, rather than a group characteristic. Would "discrete" be a better term? And I'm a bit confused by "more diverse". Diverse how? Is the meaning that the range of species within the microbial community is somehow wider on the taxonomic tree? Or something else?

• But now reading that honey bees have a core microbiome that is found in all colonies and castes. But were we not talking about "more diverse"?

• "However, strains [...]" just need to be a bit clearer on what are these strains of.

• What makes these microbiomes "low-diversity"?

• " the samples collected from each colony were not differentiated from each other" is unclear. Do you mean that the team collected ants of caste X from all 25 colonies into a single blended sample? Try to rephrase " Whether the 19 common bacteria found in Texas T. septentrionalis and form a conserved microbiome that is found in other geographic regions or castes is also unknown." is unclear to me. Maybe try to break it up into shorter sentences.

• "major driver" might suggest causality. I think you mean that the differences in the presence or absence of those symbionts are producing the statistical effect of seeing differences between microbiomes, is that correct?

• "Colony JKH000270 lab-maintained ants were sampled after a year and 4 months (some male alates were sampled earlier) and Colony JKH000307 lab-maintained ants were sampled after 4 months."<br /> I'm wondering if the time factor would be important in microbiome adaptation. If it is, can the two colonies really be compared to each other? Would you mind adding a couple of sentences to explain the procedure?

• I'm not sure I understand how pupae and ant guts and whole ants will act as confirmatory datasets. Would you mind elaborating?

• "Reads that were not classified as belonging to the kingdom Bacteria (i.e., those identified as Archaea or Eukaryote) using the SILVA database v128 (43, 44) were removed." I understand that including viruses, other fungi, diatoms, etc. would change the scope of the project. I'd be interested in learning about that part of the microbiome, and hope you write the next paper on it.

On 2022-06-16 00:58:14, user Sciency wrote:

Overall, I love this paper. It's an original approach, and an important topic. The language is clear and engaging. I do have some questions and comments that I hope might be helpful.

• "integral parts of an imperialist enterprise [20]. Imperialism granted Western scientists unprecedented access to the world, which they translated into scientific authority, power, and wealth," When I first read that, I thought you meant "power and wealth" as in technology that spurred the Industrial Revolution. Later on in the paper, you elaborate that it is the power of Global North academics over academics in the Global South. I'm also thinking that maybe applied science, such as concentration of Big Pharma exploration and production would generate power and wealth today? Later on, you write " Implicit in this perspective is that first authorship, and authorship of publications in general, are ways to establish authority and accumulate power in knowledge production, which in itself is worth questioning. For example, how do established authorship norms promote inequity and dominance in Western science (e.g. [74])?" <br /> Pretty much the question I had. So, would it be possible to phrase the statement that is at the start of the paper in some way that lets me know to wait for that discussion at the end of the paper?<br /> You go on to explain: "The imperialistic dynamics that created the current structures of access and power within Western science between the Global North and South have also enabled Western science to assert dominance in global knowledge production [76], while erasing, appropriating, and subjugating Indigenous knowledge and authority. "<br /> Which leads me to believe that you only meant academia power. So, basically, I'm a bit confused by the flow. But the ideas themselves are powerful and I'm glad to read them.

• "through foundational practices" I understand that you mean foundational to science, not the imperialism here?

• "as expertise about the natural world continues to be disproportionately claimed by the Global North through publication practices. Our findings serve as a case study that reflects the inequitable structures at the core of Western biodiversity science and their resulting disparities, e.g. in access, labor, collaboration, power, and designations of expertise and authority.". To me, this is the most important statement. And yet it's pretty far down, at the very end of the Introduction section. On the one hand, I understand that it is the last step in a logical chain. On the other, I would still like it to be one of the first things I see, to let me know what I'm reading towards.

• "This study shows how historical inequity continues to shape present day research practices" I'm not sure it does? To me, it shows the historical inequity, and it shows that inequity is still here today. But I don't know if the paper describes the mechanism by which one generates the other. And don't think it needs to, what the paper focuses on is far more important. So I'd just rephrase that a bit to remove the suggestion of causality. "Things are bad. Things are still bad. We should think about that and fix it."

• "1960". I'm cool with missing the years between 1950 and 1960, I just want to see a phrase acknowledging that, and saying you accounted for it in the statistics.

• "we did not include descriptions in which the species was extinct at the time of description" Could you add a quick phrase explaining why? As a fresh reader, I thought "I'd feel it an honor to have a dinosaur named after me". And I'm genuinely curious what is the quality of that honor that makes it different from having a current species.

• "The prevalence of first authors from the Global South increases significantly toward the present (R2 = 0.143, p = 0.014), but the prevalence of first authors from the Global North remains consistent (R2 = 6.749e-5, p = 0.947) "<br /> Is it not a zero-sum calculation? Earlier, you make a similar statement, but without the word "first", so I concluded that Global South authors were being included as author lists generally grew in length. But there can be only one first author per paper, so I'm confused by the calculation here.

• In this paper, you speak of inclusion of Global South stakeholders in Western science. You also speak of inclusion of Indigenous knowledge and worldviews in science. So the "The patterns of authorship we observed show that researchers from the Global South have increasing opportunities to participate in Western science" statement feels like it's missing a piece.

• "prioritizes the Global North’s power to theorize and conceptualize [...] frames the value of people and their perspectives in terms of how they can benefit those currently in power"<br /> and<br /> "Paradoxically, as taxonomic work has been devalued, [...] the roles of different individuals, and how different roles are supported and valued (intellectually and materially)" Loved reading this.

• "Adhering to the fallacy of neutrality (which is in fact a non-neutral stance and one embedded in white supremacy;" I understand that thinking one is neutral is a fallacy. But how is it a non-neutral stance embedded in white supremacy? Is it because privileged people have the ability to not see problems and therefore think they're being neutral? I would want to see a bit more clarity in this phrasing.

Thank you for writing the paper, look forward to seeing it in print.

On 2022-06-14 23:54:40, user Raj_Operon wrote:

MPST provides sulfur to the downstream enzymes such as MOCS3 which then transfers to Urm1. Urm1 then transfers the sulfur to CTU1/CTU2 complex that thiolates the tRNA at U34 position.<br /> How just the shMPST could be concluded as the main enzyme playing the role in oxidative stress in glioblastama.<br /> There is clearly a possibility that shMPST knockdown decreases the tRNA thiolation levels which then leads to the effect seen here.

And the Dimedone switch labelling method for detecting the persulfided proteins is not selective as claimed in the method. A coomassie stained gel for the Figure 5 A, B would be helpful since the P-SSH lane seems to have degraded/low molecular proteins alone persulfided (from the intensity).

On 2022-06-14 18:49:19, user CJ San Felipe wrote:

In this paper, the authors analyze an intrinsically disordered region (IDR) of the yeast general recognition factor Abf1 with the aim of identifying functional determinants of Abf1’s IDR. The advantage of the authors’ plasmid shuffle experiments is that it allows the study of many mutations and variations of Abf1. The authors reveal that Abf1 possesses an essential motif (EM) as well as several contextual residues that work together to mediate Abf1’s function. Upon further investigation of compositionally and functionally similar IDR’s, the authors hypothesize that sequence specificity and chemical context in IDRs functionally overlap with each other rather than act independently, and propose a 2D model to describe the contributions of each in IDRs. <br /> The major success of this paper is in developing a model that reconciles two contributors to IDR function: sequence specificity and chemical context. The major weakness of the paper is that the model is not comprehensively backed with control experiments. The 2D landscape model presented argues that modulation of essential motifs and contextual amino acids can produce several binding modes; however, no data is presented to show that these chimeras are viable because they interact with the same factors or function in the same way that IDR2 does. Therefore, we can’t be certain if these are off-target effects or the same interactions that occur with IDR2 as put forward in the model. In addition, we found some aspects of the organization of the paper may require more clarity. Overall, the paper reveals some of the functional determinants for Abf1’s IDR and proposes an intriguing model for the functional determinants of other IDRs, but it could be difficult for these findings to be generalized.

Major points<br /> p.4: <br /> It is unclear to us why the minimal viable construct IDR2 449-662 is the background reference construct. Is it possible that IDR1 (absent in this construct) could provide unknown benefits in particular situations? For example, given the unknowns of Abf1’s interactome, is it possible that IDR1 helps to activate transcription of other genes that could rescue IDR2 mutants? Perhaps the presence of IDR1 could confer viability for IDR2 mutants that were deemed not viable in later experiments. Plasmid shuffle assays with IDR2 mutants that also have IDR1 present could be control experiments that answer this question.

p.4 <br /> The constructs generated in this paper are tested for viability via plasmid shuffle assay, but there is no control experiment to ensure that these constructs are still interacting with the same partners or functioning in the same way that wildtype IDR2 does. One possible control experiment to test this could be to choose an Abf1-interacting partner based on proteomic literature on Abf1, and perform a co-immunoprecipitation/Western blot to see if the partner is still present across different IDR2 mutants. This control experiment should be done with full length Abf1, the background reference construct (with no IDR1), as well as a construct without the EM and a shuffled construct to represent the two extremes of the 2D landscape.

p.5: <br /> The decision to choose the G4 motif does not have a strong justification or explanation. In figure 3F it is shown from the alignment between Abf1 and Gal4 that the region considered to have sub-homology does not overlap with the essential motif of Abf1 nor does it show similarity in its sequence. Therefore, in our view, it does not appear that Gal4 has an EM that is homologous to the EM of Abf1.

Figure S1 PDF:<br /> By eye, it appears that there is large variation between the strains considered inviable – for example, FUS_1_163_WT clone 3 on page 6 and Shuffle 3 clones 2 and 3 on page 3 are both marked as inviable yet differ in growth. It could be helpful to readers if an explanation about why a binary classification of viable vs inviable was used in this study, as opposed to a sliding scale quantification.

Minor points<br /> For a future direction, after identifying the essential motif in IDR2 (EM), we think it would be compelling to go back to the orthologs initially tested to see how conserved the essential motif is evolutionarily and to see how divergent the orthologs that we’re inviable were. We also feel that this could be incorporated into the paper’s discussion.

Figure 3: <br /> Panels G-K were difficult for us to understand due to the sheer number of constructs presented. To us, the contrast between sequence-specific motif and chemical context would be clearer if panels E and K were combined, perhaps with labels “sequence specificity” and “chemical context” below the respective constructs, to underscore the two ends of the spectrum that these panels represent and to emphasize the unexpected viability of the constructs in K.

p.2-3: <br /> The hypothesis that poorly conserved IDRs may still retain functional conservation is compelling, but the proteome-wide analysis of disorder leading up to this hypothesis could be clarified in the methods section. In particular, it would be helpful to include an explanation of why and how disorder score from metapredict and predicted pLDDT were used in conjunction with each other, as opposed to using the predicted consensus disorder score from metapredict alone.

We review non-anonymously: Daphne Chen, CJ San Felipe, James Fraser (UCSF).

On 2022-06-14 16:05:45, user Peter Lipke wrote:

A revised version of this work has been accepted in Frontiers in Molec. Biosciences.

On 2022-06-14 16:04:56, user Peter Lipke wrote:

A revised version of this work has been accepted in Frontiers in Molec. Biosciences- Peter Lipke

On 2022-06-14 09:10:13, user Dr Josh Berryman wrote:

Peer-reviewed full text is now open-access at https://www.nature.com/arti...

On 2022-06-14 09:07:35, user Prof. T. K. Wood wrote:

1. Abstract: (i) No evidence of “programmed cell suicide” by anti-phage systems based on TAs and Pycsar, CBASS, etc.; merely metabolism is reduced.<br /> (ii) No undiscounted evidence that toxin MazF is a suicide protein.

2. Not sure why the seminal discovery of phage inhibition by the toxin/antitoxin Hok/Sok system is not mentioned here. See doi:10.1128/jb.178.7.2044-2050.1996.

3. Discussion: no credible link between (p)ppGpp and TAs.

On 2022-06-14 08:57:51, user Joachim Goedhart wrote:

The authors observe fluorescence in cells and biological tissue. The fluorescence is attributed to proteins and these are named human fluorescent protein I and II (HFP1, HFP2).

However, there is no evidence that the fluorescence originates from a protein. The source of the emitted signal can be any (auto)fluorescent molecule (e.g. riboflavin).

The labels in figure 3&4 are too small to read and prevent evaluation of those results.

On 2022-06-14 08:39:13, user Jade Bruxaux wrote:

Hi!<br /> Would it be possible to get access to the Supplemental methods / code mentioned in the text?<br /> Thanks in advance!

On 2022-06-14 05:08:23, user Kazuo Takayama wrote:

This study was published in "Communications Biology"

Cell response analysis in SARS-CoV-2 infected bronchial organoids.<br /> Sano E, Suzuki T, Hashimoto R, Itoh Y, Sakamoto A, Sakai Y, Saito A, Okuzaki D, Motooka D, Muramoto Y, Noda T, Takasaki T, Sakuragi JI, Minami S, Kobayashi T, Yamamoto T, Matsumura Y, Nagao M, Okamoto T, Takayama K. <br /> Commun Biol. 2022 May 30;5(1):516. doi: 10.1038/s42003-022-03499-2.<br /> https://www.nature.com/arti...

On 2022-06-13 21:43:37, user Robert Ihry wrote:

The manuscript has now cleared peer review!

https://pubs.acs.org/doi/fu...

On 2022-06-13 14:21:01, user 伊藤司 wrote:

here is the published version. https://doi.org/10.1264/jsm...<br /> The bioRxiv system links preprints to the published versions soon.

Ito

On 2022-06-13 08:30:22, user Sravasti Mukherjee wrote:

This is an interesting study that shows very nicely the importance of biosensor expression levels in order to make accurate quantitative measurements and to adapt it for high-throughput screenings. However, the authors mention that genetically encoded biosensors (for e.g. cAMP sensors) are rarely used in high-throughput screenings. Probably they have overlooked a recent paper - [(Harkes and Kukk et al., 2021 - Dynamic FRET-FLIM based screening of signal transduction pathways) https://www.nature.com/arti...] in which they use an Epac based cAMP FRET sensor with FLIM as the readout and perform high-throughput arrayed siRNA based screen to study the breakdown kinetics of cAMP by the PDEs. Using FLIM as the readout of FRET sensors also circumvents some of the sensor expression level issues that are highlighted in this pre-print. Probably these are some points that the authors would like to take into account when revising their pre-print.

On 2022-06-13 08:08:35, user Olivier Gandrillon wrote:

This is a quite provocative view of the absence of cell types that could be identified through specific gene expression patterns in single-cell RNAseq data. My first comment is that Waddington was not the first one to propose the existence of distinct cell types, harbouring different functions. My second comment is more of a question: when you are using differentially expressed genes why do you still find no cluster structure? This seems weird to me. By definition of DE genes, they should define clusters. I fully agree that there should be some continuity in between cell types but at the same time tehre should be differences in between cell types.

On 2022-06-11 23:52:23, user DD wrote:

The main peer reviewed published article can be found here:<br /> https://doi.org/10.1038/s41...

On 2022-06-11 01:40:19, user KeninSydney wrote:

I may have misread the paper but shouldn’t one step have been to have two groups of mice without tumours and attempt to train the ants to select one of the groups?

Maybe ants can distinguish individual mice by urine?

On 2022-06-10 11:49:16, user Khaled wrote:

Hello,<br /> I think there is a mistake in the affiliation order as all authors with 2 are in EMBL but in the affiliations 2 it's mentioned FHT, Italy.

On 2022-06-10 06:33:11, user Jingyi Jessica Li wrote:

Here is our formal response: https://www.biorxiv.org/con...

In this response to the correspondence by Hejblum et al. [1], we clarify the reasons why we ran the Wilcoxon rank-sum test on the semi-synthetic RNA-seq samples without normalization, and why we could only run dearseq with its built-in normalization, in our published study [2]. We also argue that no normalization should be performed on the semi-synthetic samples. Hence, for fairer method comparison and using the updated dearseq package by Hejblum et al., we re-run the six differential expression methods (DESeq2, edgeR, limma-voom, dearseq, NOISeq, and the Wilcoxon rank-sum test) without normalizing the semi-synthetic samples, i.e., under the "No normalization" scheme in [1]. Our updated results show that the Wilcoxon rank-sum test is still the best method in terms of false discovery rate (FDR) control and power performance under all settings investigated.

References<br /> 1. Hejblum BP, Ba K, Thibaut R, Agniel D: Neglecting normalization impact in semisynthetic RNA-seq data simulation generates artificial false positives. bioRxiv 2022.<br /> 2. Li Y, Ge X, Peng F, Li W, Li JJ: Exaggerated false positives by popular differential<br /> expression methods when analyzing human population samples. Genome biology<br /> 2022, 23:1--13.

On 2022-05-16 21:15:23, user Jingyi Jessica Li wrote:

We thank Dr. Hejblum et al for sending us a draft of this article on May 3 before posting it. Below I'm pasting our reply sent to Dr. Hejblum et al on the same day. We believe that our discussion will be beneficial for the community.

Dear Dr. Hejblum and all,

Thank you for sending us your correspondence draft. We appreciate your professionalism.

The main message of our article is that using popular methods without a sanity check may lead to inflated FDR, and permutation offers an easy sanity check.

We agree that normalization is a tricky issue, and when samples do not need normalization (as is the case for permuted samples, which all come from the same "condition"), normalization may introduce unwanted bias, violate the null hypothesis, and thus deteriorate the FDR control. Meanwhile, we stand with our fundamental assumption that permuted samples should contain no true DE genes. Since many DE methods include normalization as an internal step and only accept count data as input, the only way to fairly compare them is to apply each method as a whole pipeline, not just its DE statistical test step, to the permuted samples. (That is, the "normalization first" approach in your manuscript is inapplicable to the DE methods that only accept count data, unless we dissect these methods and modify their code, which is beyond the scope of our benchmark study.) As a result, any bias introduced by normalizing the permuted samples (which do not need normalization) would be reflected in the actual FDR inflation. The Wilcoxon test is an exception because it is not a DE analysis pipeline, so we applied it to permuted samples without doing normalization in Figure 2A. This explains why our Figure 2A differs from your Figure 1A.

We would like to clarify that our study is not a comprehensive benchmark because (1) there are numerous DE methods and (2) we did not want to dilute the cautionary message against using the popular DESeq2 and edgeR without a sanity check. Hence, we did not do a dissection of each method to find out how to fix the inflated FDR issue. Our dearseq results are based on dearseq (asymptotic), not dearseq (permuted), because we deemed dearseq (asymptotic) more appropriate when the sample size is large.

We appreciate your clarification about the effect of normalization on the dearseq performance, and your results motivated us to think about the problem more clearly. However, we respectfully disagree with your conclusion that dearseq outperforms Wilcoxon in your results. Our reasoning is that only dearseq (asymptotic), not dearseq (permuted) has a slight power advantage over Wilcoxon, but dearseq (asymptotic) does not guarantee to control the FDR when the sample size is under 40; on the other hand, Wilcoxon only sacrifices power but not FDR control when the sample size is small. Nevertheless, we agree that dearseq is advantageous in that it can account for more complex experimental designs.

We would be happy to publicly respond to your correspondence when needed. We believe that our discussion will be beneficial for the community.

Best,<br /> Jessica

Jingyi Jessica Li, Ph.D.

Associate Professor<br /> Department of Statistics<br /> University of California, Los Angeles

http://jsb.ucla.edu

On 2022-06-10 00:42:21, user Shunsuke Tagami wrote:

This has been published in ACS Synthetic Biology.<br /> https://pubs.acs.org/doi/10...

On 2022-06-10 00:40:53, user Shunsuke Tagami wrote:

This has been published in Nature Communications.<br /> https://www.nature.com/arti...

On 2022-06-10 00:10:43, user commenter wrote:

Now published in The EMBO Journal<br /> https://doi.org/10.15252/em...<br /> Arabidopsis HEAT SHOCK FACTOR BINDING PROTEIN is required to limit meiotic crossovers and HEI10 transcription

On 2022-06-09 13:45:55, user C.S. Raman wrote:

This preprint is now in press at ACS Catalysis. A link will be posted soon.

On 2022-06-08 05:41:00, user simon LECLERC wrote:

This manuscript is finished at more than 70%.<br /> There are left a couple of experiment to conclude this research article.<br /> Feel free to comment here to improve this manuscript as much as possible!<br /> Thanks,<br /> Simon

On 2022-06-07 04:07:46, user Gita Madhu wrote:

Is there any reason why the word Introner is used with a capitalised first letter?

On 2022-06-06 16:13:35, user Raphael Kopan wrote:

this article is now in Print in Development

On 2022-06-04 21:05:09, user Dong-Ha Oh wrote:

Now published in Nat Plants https://doi.org/10.1038/s41...

On 2022-06-03 00:00:03, user Alex Cope wrote:

Published at BMC Genomics: https://doi.org/10.1186/s12...

On 2022-06-02 15:44:26, user disqus_w4VlVyfN45 wrote:

I'd like to hint self-plagiarism of one image of Ubx WT expression in figure 2 A, A’ (p.15). The <br /> same image is shown in another paper from the Patel Lab with changed color and labels: “Comprehensive analysis of Hox gene <br /> expression in the amphipod crustacean Parhyale hawaiensis” by Serano et al. (2016) figure 6 H <br /> (https://doi.org/10.1016/j.y....