tag line includes who but now where this is, when it was taken, context is missing from tagline, but included in paragraph beneth.

who, what, where, when in tag line

I use an app called RED, which is a widely used sharing app in China, where people can share their daily life or make recommendations. There are very detailed tag categories within the app, so it will make recommendations based on what you read regularly. At the same time, its tags are also interlinked, so it will also recommend content that you might be interested in to observe user feedback.

Which can be triggered by perhaps the most reliable Safari Extension keyboard shortcut (on iOS/iPadOS) to date: ⌘⇧E.

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Reply to the reviewers

Manuscript number: RC-2022-01723

Corresponding author(s): Daphne Avgousti, Srinivas Ramachandran

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

Summary This study by Lewis et al. examines the role of heterochromatin in the nuclear egress of herpesvirus capsids. They show that heterochromatin markers macroH2A1 and H3K27me3 are enriched at specific genome regions during the infection. They also show that when macroH2A1 is removed or H3K27me3 is depleted (both of which reduce the amount of heterochromatin at the nuclear periphery), the capsids are not able to egress as effectively. This is interesting since it could be argued that heterochromatin acts as a hindrance to the transport of viral capsids to the nuclear envelope and that the loss of it would allow capsids to reach the nuclear envelope more easily. However, this paper seems to show that heterochromatin formation, on the contrary, is necessary for efficient egress. Overall, the study seems comprehensive. The methodology is solid, and the experiments are very well controlled. However, some issues need to be addressed before publication.

Major comments

1) In line 49, the authors state, "Like most DNA viruses, herpes simplex virus (HSV-1) takes advantage of host chromatin factors both by incorporating histones onto its genome to promote gene expression and by reorganizing host chromatin during infection". In addition, HSV1 expression can be hindered by the host's interferon response via histone modifications. Ref. Johnson KE, Bottero V, Flaherty S, Dutta S, Singh VV, Chandran B. IFI16 restricts HSV-1 replication by accumulating on the HSV-1 genome, repressing HSV-1 gene expression, and directly or indirectly modulating histone modifications. PLoS Pathog. 2014 Nov 6;10(11):e1004503. doi: 10.1371/journal.ppat.1004503. Erratum in: PLoS Pathog. 2018 Jun 6;14(6):e1007113. PMID: 25375629; PMCID: PMC4223080.

We agree with the reviewer and have amended our text and added the reference. See line 57.

2) Reference 5 is misquoted in the sentence, "This redistribution of host chromatin results in a global increase in heterochromatin". In that reference, the amount of heterochromatin is not analyzed in any way. However, that particular paper shows that the transport of capsid through chromatin is the rate-limiting step in nuclear egress, which is important considering this study. Further, the article by Aho et al. shows that when the infection proceeds capsids can more easily traverse from the replication compartment into the chromatin, which means that infection can modify chromatin for easier capsid transport. For that reason, the article is an important reference, but it needs to be cited correctly.

We agree with the reviewer that this citation was misquoted and have corrected the citation. See lines 55 and 62-64.

3) The term heterochromatin channel at lines 54, 102, and 303 is misleading since the channels seen in the original referred paper are less dense chromatin areas. Also, this term is not used in the original paper where the phenomenon was first described. These less dense interchromatin channels were found by soft-X-ray tomography imaging and analyses, not by staining.

We thank the reviewer for pointing out this discrepancy and have amended the text to accurately describe the methods used in the appropriate citations. See lines 65, 115, and 383.

4) It is difficult to visualize chromatin using TEM microscopy. The values of peripheral chromatin thickness given in Figure 1e (5-15 nm) do not seem realistic given that the thickness of just one strand of histone-wrapped DNA is 11 nm. Why are the two values for WT different? If you can get so different values for WT, it is a bit worrisome (switching the WT results between the top and bottom parts of Fig. 1e would for example result in very different conclusions on the effect of macroH2A1 KO for the thickness of the chromatin layer).

*We agree with the reviewer that it is difficult to visualize chromatin by TEM. It is also important to note that comparisons can only be made between samples treated on the same day in the same way. Taking this into account, we chose to compare macroH2A1 KO cell stains to controls done at the same time, and the same for H3K27me3 depleted conditions compared to DMSO treated and prepare for EM at the same time. Visually, it is apparent that the staining in the macroH2A1 KO control cells is somewhat different than those of the H3K27me3 depleted control cells, which represents the inherent variability of this method. It is also true that one nucleosome is around 11nm, however, since the cells contain highly compacted chromatin with many other proteins present, this measurement is not appropriate to apply. Adding up the millions of nucleosomes that make up the chromosomes at 11nm each would result in a space much larger than the nucleus, therefore we focus on comparing between control and experimental conditions restricted to this assay as a relative qualitative comparison. Nevertheless, we agree with the reviewer that the notion of changing chromatin is difficult to quantify by EM and so we have taken an additional approach to test our hypothesis and confirm EM interpretations (discussed lines 391-393). We have utilized live capsid trafficking to visualize capsid movement in nuclei in the presence or absence of macroH2A1. The results from these new experiments are presented in new Figure 5 and EV5 and support our model. *

5) In lines 134-137 it says that "The enrichment of macroH2A1 and H3K27me3 was observed as large domains that were gained upon viral infection (Fig 2a), suggesting that the host landscape is altered upon infection. These gains were reflected in an increase in total protein levels measured by western blot (Fig 2b)." However, the protein levels of H3K27me3 do not seem to increase during infection. In other presented data as well (Figs. 2a, 2b, 2c, S2a) it is difficult to justify the statement that H3K27me3 is enriched in infection. When this is the case, the conclusion that the amount of heterochromatin increases in the infection (the quotation above and the one in line 315) is not supported. The statement in line 315 is also not specific since it is unclear what "newly formed heterochromatin increases" means.

We agree with the reviewer that our original description was misleading. We now have edited the text to clarify that there is redistribution of macroH2A1 and H3K27me3. In the revised manuscript, we have also included mass spectrometry data mined from Kulej et al. that show peptide counts that reflect increases in the heterochromatin markers described (see new Figure EV1a). Despite this quantitative measure, upon rigorous replicates of our western blots as requested by Reviewer 2, we concluded that the increases originally described are somewhat inconsistent by western blot. This discrepancy between mass spectrometry data and western blot is likely due to the non-linear nature of antibody binding and developing of western blots by the ECL enzymatic reaction. Therefore, our revised manuscript focuses on this redistribution as a reaction to infection and stress responses instead of a global increase as the original manuscript stated. See lines 174, 182, 196, 397 and Fig EV4d in main text and discussion sections.

• *

6) Quantitation of viral capsid location in H3K27me3-depleted cells seems somewhat arbitrary. It would have been more robust to calculate the number of capsids per unit length of the nuclear envelope with and without depletion.

We agree with the reviewer that the quantification of capsids in the H3K27me3-depleted conditions was arbitrary. In our revised manuscript, we have now repeated this quantification to accurately measure the phenotype observed, that is the chains of capsids lined up at the inner nuclear membrane. To do this, we used two measures: 1) the distance from the INM as less than 200nm and 2) the distance from other capsids as less than 300nm. Taking into account these two measures, we quantified the frequency with which multiple capsids lined up at the INM in WT and H3K27me3-depleted conditions. This is represented in the new Figure 5d. In the WT setting, we observe most often 1 single capsid at the INM, with a small fraction of 2 capsids. However, in the H3K27me3-depleted condition, we observe much greater numbers of capsids at the INM more frequently, as many as 16 at a time, leading to an average of 2-3 capsids at any single location. The source data for this figure are also provided. See lines 589 and Fig5d.

7) In lines 300-302 it says "Elegant electron microscopy work showed that HSV-1 infection induces host chromatin redistribution to the nuclear periphery2,8." However, the redistribution data in reference 8 is based on soft x-ray tomography and not on electron microscopy."

We have amended the text to accurately describe the methods used in the citations. See line 384.

8) The authors bundle together the effects of macroH2A1 removal and H3K27me3 depletion by saying that they both decrease the amount of heterochromatin at the nuclear periphery and therefore hinder capsid egress. This seems overly simplistic and macroH2A1 and H3K27me3 seem to act very differently, which is manifested in the drastic difference in nuclear capsid localization between the two cases. This difference needs to be discussed more.

We agree with the reviewer that there is a nuanced difference in the effect on nuclear egress in the absence of the two heterochromatin marks. Specifically, that macroH2A1 loss results in greater numbers of capsids dispersed throughout the nucleus, whereas depletion of H3K27me3 results in capsids reaching the INM and not escaping. To examine these differences further, we have carried out live imaging of capsid trafficking in macroH2A1 KO cells compared to control and found that capsids move much more slowly, consistent with our model, see new Figure 5h-I and EV5h-i. Conversely, H3K27me3 depletion does not prevent the capsids from reaching the INM, raising the question of whether they are successfully able to dock at the nuclear egress complex (NEC). To investigate this further, we obtained an antibody against the NEC component UL34 and probed during infection in our heterochromatin disrupted conditions. We found that UL34 levels are unchanged upon loss of macroH2A1 or depletion of H3K27me3, suggesting the levels of UL34 do not account for the decrease in titers. These data are now presented in new Figure EV3g-h. Furthermore, we have amended our model to include the two different scenarios upon loss of different types of heterochromatin (see new Figure 6) and discussion of these differences. See line 428.

Minor comments Line 45: Nuclear replicating viruses -> Nuclear-replicating viruses Line 56: is -> are Line 64: 25kDa -> 25 kDa Line 159: macroH2A1 cells -> macroH2A1 KO cells Line 289: The term gDNA is rarely used for viral DNA. Replace gDNA with viral DNA. Line 405: 8hpi -> 8 hpi Line 449: mm2 -> μm2 "Scale bar as indicated" words can be removed in the figure legends or at least should not be repeated many times within one figure legend.

We have amended the text to address these comments. See lines 52, 68, 76, 179, 334, 513, and 585.

Reviewer #1 (Significance (Required)):

These findings would appeal to a broad audience in the field of virology. Specifically, the researcher in the fields of virus-cell and virus-nucleus interactions. This manuscript analyses herpesvirus-induced structural changes in the chromatin structure and organization in the nucleus that are also likely to affect the intranuclear transport of viral capsids.

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

The manuscript "HSV-1 exploits heterochromatin for egress" describes the effects of heterochromatin at the nuclear periphery, macroH2A1 or H3K27me3 on HSV-1 replication and egress. Knocking out macroH2A1 or depleting H3K27me3 with high concentrations of tazemetostat depleted heterochromatin at the nuclear periphery, may not have affected HSV-1 protein expression and modestly inhibited the production of cell-free infectivity and HSV-1 genomes. macroH2A1 deposition was affected by infection, creating new heterochromatin domains which did not correlate directly with the levels of expression of the genes in them. The authors conclude that heterochromatin at the nuclear periphery dependent on macroH2A1 and H3K27me3 are critical for nuclear egress of HSV-1 capsids.

The experiments leading to the conclusion that HSV-1 capsids egress the nucleus through channels in the peripheral chromatin confirm previously published results (https://doi.org/10.1038/srep28844). The previously published EM micrographs show a much larger number of nuclear capsids, more consistent with the images in the classical literature, even in conditions when nuclear egress was not inhibited. Figures 1 and 4 show scarce nuclear capsids, even under the conditions when nuclear egress should be inhibited according to the model and analyses. The large enrichment in nuclear capsids in KO cells predicted by the model is not reflected in figure 4a, which shows only a modest increase in nuclear capsid density (the total number of nuclear capsids would be more informative). The number or density of nuclear capsids is not shown in H3K27 "depleted" cells. The robustness of the analyses of the number of capsids at the membrane in H3K27 "depleted" cells is unclear. For example, the analyses could be repeated with different cut offs, such as 2 or 4. If they are robust, then the conclusions will not change when the cutoff value is changed.

We appreciate the reviewer’s observation that to number of capsids we show differs from those published in the publication by Myllys et al. (Sci Rep 2016 PMID 27349677). It is important to note there are several differences between our study and that of Myllys et al. that explain the difference. First, as reviewer 1 pointed out, the Myllys et al. study used three-dimensional soft X-ray tomography combined with cryogenic fluorescence and electron microscopy to observe capsids in 3D rendered nuclei. Since our method uses only single ultrathin 50nm slices of cells, we cannot visualize the total number of capsids per nucleus, rather only per slice, which is why we have averaged slices of many nuclei to generate a statistical comparison between macroH2A1 KO or H3K27me3-depleted and control cells treated at the same time (see response to reviewer 1). Furthermore, these other methods are specialized techniques for 3D imaging that are beyond the scope of our study. Second, the Myllys et al. paper used B cells which are much smaller than HFFs, lending themselves to better tomography studies but not commonly used to study HSV-1 biology. Third, the Myllys et al. paper also used a different MOI and time point than we have. Taken together, these differences account for the disparity in visualizing capsids which is why we quantified capsid number across many images.

We agree with the reviewer that our quantification in the H3K27me3-depleted cells compared to control was somewhat arbitrary. As stated in the response to Reviewer 1 above, in our revised manuscript we have now repeated this quantification to accurately reflect the phenotype observed, that is the chains of capsids lined up at the inner nuclear membrane. To do this, we used two measures: 1) the distance from the INM as less than 200nm and 2) the distance from other capsids as less than 300nm. Taking into account these two measures, we quantified the frequency with which multiple capsids lined up at the INM in WT and H3K27me3-depleted conditions. This is represented in the new Figure 5d. In the WT setting, we observe most often 1 single capsid at the INM, with a small fraction of 2 capsids. However, in the H3K27me3-depleted condition, we observe much greater numbers of capsids at the INM more frequently, as many as 16 at a time, leading to an average of 2-3 capsids at any single location. The source data for this figure are also provided. See lines 589 and Fig 5d.

Furthermore, we have now also carried out live-imaging analysis of single capsids during infection which show the appropriate number of capsids expected when the full nucleus is visible. These results are presented in the new Figure 5 and EV5.

The quantitation of the western blots present no evidence of reproducibility and/or variability. The number of biologically independent experiments analyzed must be stated in each figure and the standard deviation must be presented. As presented, the results do not support the conclusions reached. The quality of western blots should also be improved. it is unclear why figure 2b shows viral gene expression in wild-type cells only, and not in KO or H3K27me3 depleted cells, which are only shown in the supplementary information. These blots presented in Figure S5a and S5b are difficult to evaluate as the signal is rather weak and the controls appear to indicate different loading levels. These blots do not appear to be consistent with the conclusions reached. Some blots (VP16, ICP0 in HFF) appear to indicate a delay in protein expression whereas others (VP16, ICP0 in RPE) appear to indicate earlier expression of higher levels. The claimed "depletion of H3K27me3 is not clear in in figure S5d, in which the levels appear to be highly variable in all cases, without a consistent pattern, with no evidence of reproducibility and/or variability, and using a mostly cytoplasmic protein as loading control. All western blots should be repeated to a publication level quality, the number of independent experiments must be clearly stated in each figure, and the reproducibility and/or variability must be indicated by the standard deviation.

*As reviewer 1 also pointed out, we appreciate that there is some variability with respect to the stated ‘increase’ in these heterochromatin marks during infection. As stated in response to reviewer 1, in our revised manuscript we have included a deeper analysis of these marks from global mass spectrometry that indicates an increase in total levels. Please see response to reviewer 1. *

• *

In the revised manuscript, we have now included mass spectrometry data mined from Kulej et al. that show peptide counts that reflect increases in the heterochromatin markers described (see new Figure EV1a). Despite this quantitative measure, upon rigorous replicates of our western blots as requested by Reviewer 2, we concluded that the increases originally described are somewhat inconsistent by western blot. This discrepancy between mass spectrometry data and western blot is likely due to the non-linear nature of antibody binding and developing of western blots by the ECL enzymatic reaction. Nevertheless, our genome-wide chromatin profiling showed consistent, reproducible, and statistically significant redistribution of macroH2A1 and H3K27me3 upon HSV-1 infection. Therefore, our revised manuscript now focuses on this redistribution as a reaction to infection and stress responses instead of a global increase as the original manuscript stated. See lines 174, 182, 196, 397 and Fig EV4b-c.

• *

With respect to viral protein levels, although there is slight variation in the levels of VP16 or ICP0 in RPEs compared to HFFs, we do not feel that this difference is biologically significant as several other measures of viral infection progression are unchanged (viral RNA, viral genome accumulation within infected cells). Furthermore, the significant difference in titers we observe is not explained by slight differences in ICP0 or VP16. Nevertheless, to document this variability in western blot and assuage any concern of impact infection progression, we have repeated each western blot presented in the paper three separate times and used these blots to quantify each relevant protein. Graphs of western blot quantitation can be found in each figure accompanying a western blot as follows:

Western blots:

Figures 3b-c, 4ab, EV1b, EV5a

Quantitation of western blots:

Figures 3d, 4c, EV1c, EV5b-f

• *

An enhanced analyses of the RNA-seq data, analyzing all individual genes rather than pooling them together, would provide better support to these conclusions. Then, the western blots are useful to show that the changes in mRNA result in changes in the levels of selected proteins.

• *

*We appreciate the reviewer’s interest in the RNA-seq data, however, we feel that reviewer has not understood the analysis we presented in the initial submission. To clarify, we calculated fold changes for individual genes and did not pool RNA-seq data anywhere in the manuscript. We show boxplots of log2 fold changes of individual genes. Boxplots enable summarization of the salient features of a distribution while still representing individual gene analysis. Here, the distribution being plotted is the log2 fold change of individual genes that intersect with macroH2A1 domains that change due to infection. As such, clusters 1-3 of macroH2A1 domains feature a loss in macroH2A1 due to infection and the boxplots show that the majority of genes are upregulated. To highlight this point further, in our revised manuscript we have included volcano plots of genes intersecting with each cluster also showing the split between the number of genes significantly upregulated and downregulated in each cluster at each time point (see new Figure EV3c). As expected from the boxplots, clusters 1-3 feature a much higher fraction of genes are significantly upregulated, whereas cluster 5 features a higher fraction of genes downregulated with concomitant increase in macroH2A1 due to infection. Taken together with the gene ontology analysis (new Figure Sd), these results support our model in which macroH2A1 is deposited in active regions to block transcription and promote heterochromatin formation. To further support these conclusions, we have also carried out analysis of 4sU-RNA data generated upon salt stress or heat shock and found that the regions defined by gain of macroH2A1 (i.e. clusters 5 and 6) also exhibit significant decreases in new transcription at just 1-2 hours after treatment. These data, which are presented in new Figure EV3b-c, strongly support our model in which macroH2A1 is deposited in genes downregulated upon stress response to generate new heterochromatin. *

Figure S1 raises some questions about the specificity of the macroH2A1 antibody used for CUT&Tag. As expected CUT&Tagging the cellular genome in the KO cells with the specific antibody results in lower signal than with the IgG control antibody. In contrast, viral DNA is CUT&Tagged as efficiently in the KO as in the WT cells, and in both cases significantly above the IgG controls. The simplest interpretation of these results is that the antibody cross-reacts with a protein that binds to HSV-1 genomes. The manuscript must experimentally address this possibility.

We agree with the reviewer that there is a possibility that antibodies cross react. However, we are confident that this is not the case in this scenario for the following reasons:

• *

*1 – We have carried out immunofluorescence analysis of macroH2A1 or H3K27me3 during HSV-1 infection and observe no overlap with ICP8 staining. We have included these images together with a histogram documenting the lack of overlap in the new Figure EV2f-g. *

• *

2 – CUT&Tag relies on the Tn5 transposase to insert barcodes into accessible regions of the genome. An inherent limitation of this method during viral infection is that the replicating viral genome is very dynamic and accessible, leading to easier and less specific insertion by the transposase. This is evidenced by the pattern of signal across the viral genome that is completely overlapping in the macroH2A1, H3K27me3 and IgG conditions. Snapshots of the full viral genome are now included in the new Figure EV2c-d.

• *

*Furthermore, using CUT&Tag with macroH2A1 antibody, we expect the transposition rate to be identical between WT and macroH2A1 KO conditions for the Ecoli and viral genomes. This is because we assume that the transposition in these two genomes is non-specific since there is no macroH2A1 present. Then, we expect the spike-in normalized CUT&Tag enrichment on the viral genome to be the same between WT and macroH2A1 KO conditions. Since IgG should not be affected by macroH2A1 KO, we expect the IgG enrichment to be same between WT and macroH2A1 KO conditions. Thus, non-specific background would result in higher enrichment in an apparent signal on viral genome in the macroH2A1 KO condition. *

• *

Combined with this expectation for background transposition and the following: 1) the distribution of the CUT&Tag signal across the viral genome is virtually identical between IgG, macroH2A1, and H3K27me3 CUT&Tag signal in WT and macroH2A1 KO cells (see new Figure EV2c-d), 2) that there is no colocalization between macroH2A1 or H3K27me3 with viral genomes by immunofluorescence (see new Figure EV2f-g), and 3) the whole genome correlation of the signals across CUT&Tag samples on the viral genome, but not the host, are virtually identical as presented in a heat map (see new Figure EV1g vs EV2e), we conclude that the viral CUT&Tag signal is noise. Therefore, any analysis of the signal on the viral genomes would not be biologically meaningful.

• *

Also, Figure S1 shows that the viral genome is CUT&Tag'ed with H3K27me3 antibody as efficiently in macro H2A1 WT and KO cells, and in both cases above the background signal from IgG control antibody. The authors conclude that the signal with the specific antibody "mirrors" that of the control antibody, but "mirroring" is not defined and the actual data show that there is a large increase in signal with the specific antibody. Not surprisingly, the background signal also increases, as the number of genomes increase while infection progresses. The authors conclude that "these results indicated that there was a significant background signal from the viral genome that could not be accounted for", but no evidence supporting this conclusion is presented. The data show clear signal above the background from the viral genome and that this signal is not affected by the presence or absence of macroH2A1. This section of the manuscript has to be thoroughly re-analyzed as there is clear H3K27 signal.

*We agree with the reviewer that as presented in the current manuscript it seems as though there is a real H3K27me3 signal. However, as stated in the above comment, the pattern of this signal matches that of all other conditions, including IgG, suggesting it is not a real signal, cross-reacted or otherwise, but rather an artifact of the methodology. See new Figure EV2. *

The concentration of tazemetostat used is high. Normally, concentrations of around 1µM are used in cells, and 10µM is often cytotoxic (for examplehttps://doi.org/10.1038/s41419-020-03266-3; https://doi.org/10.1158/1535-7163.MCT-16-0840). The effects on H3K27me3 presented in figure S1b appear to be normalized to mock infected treated cells. If so, they do not allow to evaluate the effectivity of the treatment. Cell viability after the four days treatment must be evaluated, the claimed "depletion" of H3K27me3 must be clearly demonstrated (the blots in figure S5 are not sufficient as presented), and levels of different histone methylations must be tested to support the claimed specificity of tazemetostat for H3K27me3 at the high concentrations used.

*While we agree with the reviewer that the cytotoxicity of any inhibitor is an important aspect to take into account, in this instance the reviewer is incorrect. The reviewer has cited papers that highlight the potential use of tazemetostat as a cancer-cell specific treatment for colorectal and B-cell cancers. In both of these cases, the primary conclusion is that tazemetostat’s cytotoxic property is largely corelated to mutation in EZH2. In fact, WT EZH2 treated cells had a more “cytostatic” response, which shows that tazemetostat is not toxic with WT EZH2 (Brach et al. Mol Cancer Ther. 2017, PMID 28835384) as is the case in our system. Furthermore, the Tan et al. study shows a non-transformed human fibroblast (CCD-18co) and embryonic colon epithelial (FHC) as “healthy controls” for their work in colorectal cancer cell lines in Figure 1D. These 2 cell lines, which are comparable to the WT HFF cells we used, show no reduction in viability at a log fold greater concentration than the 10 µM used in our paper. *

• *

*Nevertheless, we agree with the reviewer that cytotoxicity should be formally ruled out. In our original experiment, we recorded cell counts at the harvested mock, 4-, 8-, and 12 hpi and found no difference in the number of cells over the course of infection (see new Figure EV3e). We also used trypan blue staining as a measure of cell viability upon tazemetostat treatment and found no toxicity. These results are presented in new Figure EV3f. *

Furthermore, we agree with the reviewer that total H3 levels by western blot should be included in any comparison of H3 modification. While these were included in some figures, they were unintentionally omitted in others. In our revised manuscript we have now included these blots together with quantification of triplicate biological samples of H3K27me3 levels normalized to total H3. See new Figures 3, 4, EV1, and EV5.

• *

Minor comments. Reference No.27 is misquoted in lines 250-251, which state that it shows that "HSV-1 titers, but not viral replication, where reduced upon EZH2 inhibition." The reference actually shows inhibition of HSV-1 infectivity, DNA levels and mRNA for ICP4, ICP22 and ICP27. This reference uses much shorter treatments (12 h and only after infection). It also shows that inhibition of EZH2/1 up regulates expression of antiviral genes.

*We appreciate that the reviewer has pointed out a discrepancy between our results using an EZH2 inhibitor (tazemetostat) and those from reference 27 (Arbuckle et al., mBio, 2017 PMID 28811345) that requires clarification. The reviewer states that the treatments were 12 hours after infection, however, this is incorrect. In the Arbuckle et al. study, the authors used multiple different inhibitors at high doses for short treatments before infection and noted that this caused an upregulation in antiviral genes that blocked infection progression of multiple viruses including HCMV, Ad5 and ZIKA. Importantly, these genes include multiple immune signaling and interferon stimulated genes. In our study, we specifically use a much lower dose of EZH2 inhibitor, with respect to the IC50 value, and waited 3 days to ensure a steady state. In our system, any initial burst of immune response from the inhibitor would likely have subsided by the time we do our infection. Furthermore, supplemental figure EV1 from the Arbuckle et al. study states that EZH1/2 inhibitors do not affect nuclear accumulation of viral genomes and suppress HSV-1 IE expression in an MOI-independent manner (Arbuckle et al. Supplemental Figure 1). These results in fact support our conclusions that it is not any antiviral effect of inhibition of EZH2 that causes the decrease in titers that we observe. *

• *

To clarify, the IC50 value of the inhibitors used in the Arbuckle et al. study are 10 nmol/L (GSK126) and 4 nmol/L (GSK343). The IC50 is a measurement used to denote the amount of drug needed to inhibit a biological process by 50% and is commonly used in pharmacology to compare drug potency. In the Arbuckle et al. study, GSK126 was used at a concentration range of 15-30 µM, that is 1500-3000x more than the IC50 level as converted from nmol/L to µM, and GSK343 was used at a concentration range of 20-35 µM, that is 5000-8750x more than the IC50 level, to see changes in viral mRNA levels. The IC50 value for tazemetostat is 11 nmol/L which means that one would need to use a much higher molarity of tazemetostat, at least 28 µM which would be 2500x the IC50 value, to achieve the comparable biological changes as the inhibitors used in the Arbuckle et al. study. Thus, we are confident that the 10 µM concentration used in our study is an appropriate and non-toxic amount that would not impact antiviral responses at the dose and times that we used. As shown above and reported in multiple studies (for example: Knutson et al. Molecular Cancer Therapy 2014 PMID 24563539, Tan et al. Cell Death and Disease 2020 PMID 33311453 cited above, and Zhang et al. Neoplasia 2021 PMID 34246076, among others) the concentration of tazemetostat that we used is not toxic to the cells. Importantly, it was also reported that a global decrease in H3K27me3 by EZH2 inhibition using a 10 µM concentration of tazemetostat (here referred to by the identifier EPZ6438) did not impact HSV-1 RNA transcript accumulation measured by bulk sequencing (Gao et al. Antiviral Res 2020 PMID 32014498), consistent with our findings.

• *

In our revised manuscript, we have now included a discussion of these important points. See lines 409-428.

HFF are primary human cells but they are fibroblasts whereas the primary target of HSV-1 replication is epithelial cells. The wording used "they represent a common site of infection in humans" must be edited

We agree with the reviewer and have updated the text. See lines 109.

Disruption of macroH2A (1 and 2) results in general defects in nuclear architecture, not just peripheral chromatin (https://doi.org/10.1242/jcs.199216;, see also figure 1c and 5a, presenting invaginated and lobulated nuclei). The manuscript would benefit from including a broader discussion of the effects of macroH2A defects on the general nuclear architecture.

• *

We agree with the reviewer and our revised manuscript now includes a more in-depth discussion of the impact of macroH2A and other heterochromatin marks on nuclear structure. See lines 373-374 and 394.

The title should be edited, as "egress" in virology is commonly used to refer to the egress of virions from the cell, not to the nuclear egress of capsids. Adding the words nuclear and capsid should be sufficient to address this issue.

*We agree with the reviewer and will update the title to read “HSV-1 exploits host heterochromatin for nuclear egress”. Given that we are measuring multiple aspects of infection, we feel that adding the word ‘capsid’ is not necessary. *

It is unclear why preferential changes in expression of housekeeping genes would indicate "stress responses to infection". The rationale for this conclusion must be fully articulated and supported.

We agree with the reviewer that it may not be immediately clear as to why changes in house-keeping gene expression represent a stress response. In a recent study that we cite in our manuscript, Hennig et al. (PLOS Path 2018 PMID 29579120) demonstrate that changes in chromatin accessibility and gene transcription during HSV-1 infection resemble those that occur upon heat shock or salt stress. These results strongly support the model that global transcription changes caused upon stress (heat, salt, infection etc.) result in dramatic alterations to chromatin structure. In support of this notion, in our revised manuscript we now include analysis of these datasets based on our macroH2A1-defined clusters. Importantly, we found that the regions defined by gain of macroH2A1 (i.e. clusters 5 and 6) also exhibit significant decreases in new transcription at just 1-2 hours of exposure to salt and heat stress. These data, which are presented in new Figure EV3b-c, strongly support our model in which macroH2A1 is deposited on active genes to generate heterochromatin as a response to the stress of infection. We also discuss these results further in the revised manuscript, see lines 210-220, 233-236, and 424-426.

Statistical methods must be fully described in materials and methods and the number of biologically independent experiments must be stated in each figure.

*We agree with the reviewer and have included these details in each figure legend. *

Reviewer #2 (Significance (Required)):

The major strengths of the manuscript lie on the comprehensive analyses of the effects of knocking histone macroH2A in the nuclear architecture and chromatin organization. These analyses indicate that peripheral heterochromatin is defective in the KO. Another strength lies on the analyses of the news heterochromatin domains in HSV-1 infected cells. The relationship between the lack of correlation between the changes in gene expression and global heterochromatin domains defined by macroH2A1 with the main conclusion is less clear.

The major weakness is that the data presented do not strongly support the conclusions. Additional experiments are required to support the main conclusion that the effects in peripheral heterochromatin result in a biologically significant effect on capsid egress. The authors should also consider that the additional experimentation may not support the conclusion that macroH2A or H3K27me3 play critical roles in the nuclear egress of capsids.

• *

*To support our conclusions, we have carried out an entirely different set of experiments to track capsid movement. Bosse et al. PNAS 2015 PMID 26438852 and Aho et al. PLOS Path 2021 PMID 34910768 use live-imaging and single-particle tracking to characterize capsid motion relative to host chromatin. These approaches allowed the authors to discover that infection-induced chromatin modifications promote capsid translocation to the INM. They showed that 1) HSV-1 infection alters host heterochromatin such that open space is induced at heterochromatin boundaries, termed "corrals", in which viral capsids diffuse and 2) the movement of viral capsids through the host heterochromatin is the rate limiting step in HSV-1 nuclear egress. *

• *

To test our hypothesis that macroH2A1-dependent heterochromatin specifically is required, we collaborated with Dr. Jens Bosse to carry out these same experiments in our macroH2A1 KO and paired control cells. We tracked RFP-VP26 using spinning-disk confocal live imaging to track individual capsid movement within the nucleus. We found that capsids in cells lacking macroH2A1 traveled much shorter distances on average. This is represented graphically by the mean-square displacement (MSD) of capsid movement in macroH2A1 KO cells plateauing at ~0.4 µm2 vs 0.6 µm2 in WT cells, which represents the size of the “corral”, or space through which capsids diffuse. The average corral size in macroH2A1 KO cells is ~300 nm less than the average corral size in WT cells (two-thirds the size). These results are consistent with the finding that macroH2A1 limits chromatin plasticity both in vitro (Muthurajan et al. J Biol Chem 2011 PMID 21532035) and in cells (Kozlowski et al. EMBO Rep 2018 PMID 30177554). These data strongly support our hypothesis that macroH2A1-dependent heterochromatin is critical for the translocation of HSV-1 capsids through the host chromatin to reach the INM. Furthermore, these data support the model in which macroH2A1 allows for the increase of open space induced during infection. Loss of this open space restricts the movement of capsids in the nucleus, as quantified by our live-imaging experiments. These data are now included in the new Figure 5 and EV5 and described in lines 348-372 and 1011-1037.

• *

NOTE: These experiments were done in a separate lab using the same cells and MOI we used for our TEM studies. It is important to note that because this was done by live imaging where the full nucleus and cell are visible, the appropriate number of capsids is apparent.

Another major weakness is that the results of CUT&Tag of the viral genome are dismissed without proper justification. The authors conclude that the results invalidate the assays, but the results are consistent with cross-reactivity of the macroH2A1 antibody with another protein that interacts with the viral genomes and with H3K27me3 being associated with the viral genomes irrespectively of macroH2A1.

*We agree with the reviewer that as presented the viral genome reads were dismissed without thorough justification. As stated above, we are confident that the patterns we detected do not represent a biologically relevant signal but rather an artifact of the experimental set up. Furthermore, it is well known in the field that normalizing replicating viral genomes during lytic infection in any kind of chromatin profiling technique is fraught with inconsistencies as each cell may have a different copy number of viral genomes at any given time point. Therefore, we feel strongly that any analysis of the viral genome chromatin profile during a lytic replication at this point in time would require single cell sequencing which is beyond the scope of this study. We appreciate that this was not clearly presented in the original manuscript and in our revised submission we have included a full supplemental figure documenting the negative data that support our conclusions (see new Figure EV2). *

If the authors had additional data supporting the claim that these results do not reflect cross-reactivity or association with the viral genomes, these data must be presented. Without that additional data, the conclusions are not supported and these discussions must be removed from the manuscript. The authors may still opt to not analyze any association with the viral genomes, but they should not dismiss them as artifactual without actual evidence to support this claim. Previously published literature is also misquoted.

This study makes an incremental contribution to the previously published evidence showing that HSV-1 capsids egress the nucleus through channels in between the peripheral chromatin. It shows that disruption of the heterochromatin at the nuclear periphery, and the nuclear architecture in general, may have a modest effect on capsid egress. This information may be of interest mostly to a specialized audience focused on the egress of nuclear capsids.

While we agree with the reviewer on many points as stated above, we respectfully disagree that our study is merely an incremental contribution of interest only to a specialized audience focused on nuclear egress. As reviewer 2 states earlier, the strength of our study lies in the “comprehensive analyses of the effects of knocking histone macroH2A in the nuclear architecture and chromatin organization”, which would be of interest to a broad chromatin audience as well as virologists. Together with the new data presented here and a revised manuscript, we feel that our study would be of interest to a broad audience in the chromatin and virology fields as reviewers 1 and 3 also pointed out. Chromatin is generally analyzed in the context of how it might affect gene expression and the impact of chromatin on biological processes such as viral infections, and its structural role in the nucleus is not commonly considered. Here, we demonstrate an important example of the glaring effects of chromatin structure on the biological nuclear process of infection.

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

Lewis et al. reveal an unexpected role for heterochromatin formation in remodeling the nucleus to facilitate egress of the nuclear-replicating virus HSV1. By performing TEM in HSV1-infected primary human fibroblasts, the authors show that capsids accumulate at the inner nuclear membrane in regions of less densely stained heterochromatin, in agreement with studies in established cell lines. The authors go on to reveal that heterochromatin in the nuclear periphery of HSV1-infected primary fibroblasts was dependent on the histone variant macroH2A1 and is enriched with H3K27me3.CUT & Tag was used to profile macroH2A1 over time during lytic HSV1 infection and showed that both macroH2A1 and H3K27me3 were enriched over newly formed heterochromatic regions 10s-100s of Kb in length in active compartments. Remarkably, loss of macroH2A1 or H3K27me3 reduced released, cell free infection virus progeny and increased intranuclear capsid accumulation without detectably impacting the proportion of mature genome containing capsids, virus genome or protein accumulation. Their finding that newly remodeled heterochromatin forms in HSV infected cells and is a critical determinant for the association of capsids with the inner nuclear membrane is consistent with a critical role in egress.

I have only relatively minor editorial suggestions listed below to improve the manuscript:

Line 92: This subtitle should be revised to more precisely state the findings shown in the Fig 1 data. While the first part of the statement "HSV1 capsids associate with regions of less dense chromatin" is consistent with what is shown, the final phrase "...to escape the nucleus" is an interpretation of the data inferred from the static image.

We agree with the reviewer and have amended our text to more accurately describe the figure. See lines 138-139.

Line 96: I am not sure the statement that fibroblasts represent a "common" site of infection is supported by ref 15. FIbroblasts do, as indicated in ref 15, express the appropriate receptor(s) for virus entry and in culture support robust virus productive growth. However, in human tissue, infection of dermal fibroblasts appears rare, suggesting it may not be a "common" site of infection (PMCID: PMC8865408). Maybe simply revise wording to indicate fibroblasts represent "a site of infection or can be infected in tissue?".

We agree with the reviewer, as was also pointed out by reviewer 2, and have amended the text. See lines 109.

Line 126-127: As written it states that "....regions of the host genome that increase during infection", implying these genome regions are amplified (increase). I think the authors mean that infection increases binding of mH2A1 and H3K27me3 to broad regions of the host genome. Please clarify.

We agree with the reviewer that this was written ambiguously. As was pointed out by reviewers 1 and 2, the increase in these marks depends on the type of measurement. Therefore, we have modified the text in a revised manuscript to focus instead on the redistribution of these marks during infection. See line 138-139.

FIgS1, a,b,c,d: please indicate that 4,8,12 indicate hpi, correct? And indicate that in the legend M indicates Mock.

This is correct and we have updated this in the figure legend. See lines 625-627.

Line 197: "active compartments". Do the authors mean transcriptionally active compartments? Please clarify

This is correct and have clarified this in the text. See line 248.

Line 232: please replace "productive" with "infectious"

We agree with the reviewer and have amended our text. See line 295.

Line 233 - The authors conclude mH2A1 is important for egress, ruling out assembly before even bringing it up. As I read on, it is clear the authors addressed this important issue later on in the manuscript. That said, it was a bit jarring to conclude egress is important without addressing the assembly possibility at this juncture in the manuscript. One way to remedy this would be to move the Fig S6 assembly/capsid type data (lines 286-297, Fig S6) and surrounding text earlier to support the conclusion that mH2A1 did not detectably influence assembly, but is important for egress.

*We agree with the reviewer that the order of presentation makes it difficult to follow. Our revised manuscript now includes these important data within the same figure. See new Figure 5. *

Line 244: "progeny production" - it would be helpful to specify "cell free or released infectious virus progeny"

Line 248: change "produced" to released"

Line 273 replace "productive" with "infectious virus progeny released from infected cells"

Fig S5c: Was the plaque assay performed on cell free supernatants? This should be indicated.

We agree with the reviewer and have made all these changes in the text. See lines 285-287.

Reviewer #3 (Significance (Required)):

The experiments are well executed, the data are solid with appropriate statistical analysis and their analysis sufficiently rigorous, and the manuscript is clearly written. Moreover, the finding that HSV manipulates host heterochromatin marks to facilitate nuclear egress is significant and exciting. The work reveals an unexpected role for newly assembled heterochromatin in egress of nuclear replicating viruses like HSV1.

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

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Lewis et al. reveal an unexpected role for heterochromatin formation in remodeling the nucleus to facilitate egress of the nuclear-replicating virus HSV1. By performing TEM in HSV1-infected primary human fibroblasts, the authors show that capsids accumulate at the inner nuclear membrane in regions of less densely stained heterochromatin, in agreement with studies in established cell lines. The authors go on to reveal that heterochromatin in the nuclear periphery of HSV1-infected primary fibroblasts was dependent on the histone variant macroH2A1 and is enriched with H3K27me3.CUT & Tag was used to profile macroH2A1 over time during lytic HSV1 infection and showed that both macroH2A1 and H3K27me3 were enriched over newly formed heterochromatic regions 10s-100s of Kb in length in active compartments. Remarkably, loss of macroH2A1 or H3K27me3 reduced released, cell free infection virus progeny and increased intranuclear capsid accumulation without detectably impacting the proportion of mature genome containing capsids, virus genome or protein accumulation. Their finding that newly remodeled heterochromatin forms in HSV infected cells and is a critical determinant for the association of capsids with the inner nuclear membrane is consistent with a critical role in egress.

I have only relatively minor editorial suggestions listed below to improve the manuscript:

Line 92: This subtitle should be revised to more precisely state the findings shown in the Fig 1 data. While the first part of the statement "HSV1 capsids associate with regions of less dense chromatin" is consistent with what is shown, the final phrase "...to escape the nucleus" is an interpretation of the data inferred from the static image.

Line 96: I am not sure the statement that fibroblasts represent a "common" site of infection is supported by ref 15. FIbroblasts do, as indicated in ref 15, express the appropriate receptor(s) for virus entry and in culture support robust virus productive growth. However, in human tissue, infection of dermal fibroblasts appears rare, suggesting it may not be a "common" site of infection (PMCID: PMC8865408). Maybe simply revise wording to indicate fibroblasts represent "a site of infection or can be infected in tissue?".

Line 126-127: As written it states that "....regions of the host genome that increase during infection", implying these genome regions are amplified (increase). I think the authors mean that infection increases binding of mH2A1 and H3K27me3 to broad regions of the host genome. Please clarify.

FIgS1, a,b,c,d: please indicate that 4,8,12 indicate hpi, correct? And indicate that in the legend M indicates Mock.

Line 197: "active compartments". Do the authors mean transcriptionally active compartments? Please clarify

Line 232: please replace "productive" with "infectious"

Line 233 - The authors conclude mH2A1 is important for egress, ruling out assembly before even bringing it up. As I read on, it is clear the authors addressed this important issue later on in the manuscript. That said, it was a bit jarring to conclude egress is important without addressing the assembly possibility at this juncture in the manuscript. One way to remedy this would be to move the Fig S6 assembly/capsid type data (lines 286-297, Fig S6) and surrounding text earlier to support the conclusion that mH2A1 did not detectably influence assembly, but is important for egress.

Line 244: "progeny production" - it would be helpful to specify "cell free or released infectious virus progeny"

Line 248: change "produced" to released"

Line 273 replace "productive" with "infectious virus progeny released from infected cells"

Fig S5c: Was the plaque assay performed on cell free supernatants? This should be indicated.

Significance

The experiments are well executed, the data are solid with appropriate statistical analysis and their analysis sufficiently rigorous, and the manuscript is clearly written. Moreover, the finding that HSV manipulates host heterochromatin marks to facilitate nuclear egress is significant and exciting. The work reveals an unexpected role for newly assembled heterochromatin in egress of nuclear replicating viruses like HSV1.

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

The manuscript "HSV-1 exploits heterochromatin for egress" describes the effects of heterochromatin at the nuclear periphery, macroH2A1 or H3K27me3 on HSV-1 replication and egress. Knocking out macroH2A1 or depleting H3K27me3 with high concentrations of tazemetostat depleted heterochromatin at the nuclear periphery, may not have affected HSV-1 protein expression and modestly inhibited the production of cell-free infectivity and HSV-1 genomes. macroH2A1 deposition was affected by infection, creating new heterochromatin domains which did not correlate directly with the levels of expression of the genes in them. The authors conclude that heterochromatin at the nuclear periphery dependent on macroH2A1 and H3K27me3 are critical for nuclear egress of HSV-1 capsids.

The experiments leading to the conclusion that HSV-1 capsids egress the nucleus through channels in the peripheral chromatin confirm previously published results (https://doi.org/10.1038/srep28844). The previously published EM micrographs show a much larger number of nuclear capsids, more consistent with the images in the classical literature, even in conditions when nuclear egress was not inhibited. Figures 1 and 4 show scarce nuclear capsids, even under the conditions when nuclear egress should be inhibited according to the model and analyses. The large enrichment in nuclear capsids in KO cells predicted by the model is not reflected in figure 4a, which shows only a modest increase in nuclear capsid density (the total number of nuclear capsids would be more informative). The number or density of nuclear capsids is not shown in H3K27 "depleted" cells. The robustness of the analyses of the number of capsids at the membrane in H3K27 "depleted" cells is unclear. For example, the analyses could be repeated with different cut offs, such as 2 or 4. If they are robust, then the conclusions will not change when the cutoff value is changed.

The quantitation of the western blots present no evidence of reproducibility and/or variability. The number of biologically independent experiments analyzed must be stated in each figure and the standard deviation must be presented. As presented, the results do not support the conclusions reached. The quality of western blots should also be improved. it is unclear why figure 2b shows viral gene expression in wild-type cells only, and not in KO or H3K27me3 depleted cells, which are only shown in the supplementary information. These blots presented in Figure S5a and S5b are difficult to evaluate as the signal is rather weak and the controls appear to indicate different loading levels. These blots do not appear to be consistent with the conclusions reached. Some blots (VP16, ICP0 in HFF) appear to indicate a delay in protein expression whereas others (VP16, ICP0 in RPE) appear to indicate earlier expression of higher levels. The claimed "depletion of H3K27me3 is not clear in in figure S5d, in which the levels appear to be highly variable in all cases, without a consistent pattern, with no evidence of reproducibility and/or variability, and using a mostly cytoplasmic protein as loading control. All western blots should be repeated to a publication level quality, the number of independent experiments must be clearly stated in each figure, and the reproducibility and/or variability must be indicated by the standard deviation. An enhanced analyses of the RNA-seq data, analyzing all individual genes rather than pooling them together, would provide better support to these conclusions. Then, the western blots are useful to show that the changes in mRNA result in changes in the levels of selected proteins.

Figure S1 raises some questions about the specificity of the macroH2A1 antibody used for CUT&Tag. As expected CUT&Tagging the cellular genome in the KO cells with the specific antibody results in lower signal than with the IgG control antibody. In contrast, viral DNA is CUT&Tagged as efficiently in the KO as in the WT cells, and in both cases significantly above the IgG controls. The simplest interpretation of these results is that the antibody cross-reacts with a protein that binds to HSV-1 genomes. The manuscript must experimentally address this possibility.

Also, Figure S1 shows that the viral genome is CUT&Tag'ed with H3K27me3 antibody as efficiently in macro H2A1 WT and KO cells, and in both cases above the background signal from IgG control antibody. The authors conclude that the signal with the specific antibody "mirrors" that of the control antibody, but "mirroring" is not defined and the actual data show that there is a large increase in signal with the specific antibody. Not surprisingly, the background signal also increases, as the number of genomes increase while infection progresses. The authors conclude that "these results indicated that there was a significant background signal from the viral genome that could not be accounted for", but no evidence supporting this conclusion is presented. The data show clear signal above the background from the viral genome and that this signal is not affected by the presence or absence of macroH2A1. This section of the manuscript has to be thoroughly re-analyzed as there is clear H3K27 signal.

The concentration of tazemetostat used is high. Normally, concentrations of around 1µM are used in cells, and 10µM is often cytotoxic (for example https://doi.org/10.1038/s41419-020-03266-3; https://doi.org/10.1158/1535-7163.MCT-16-0840). The effects on H3K27me3 presented in figure S1b appear to be normalized to mock infected treated cells. If so, they do not allow to evaluate the effectivity of the treatment. Cell viability after the four days treatment must be evaluated, the claimed "depletion" of H3K27me3 must be clearly demonstrated (the blots in figure S5 are not sufficient as presented), and levels of different histone methylations must be tested to support the claimed specificity of tazemetostat for H3K27me3 at the high concentrations used.

Minor comments.

Reference No.27 is misquoted in lines 250-251, which state that it shows that "HSV-1 titers, but not viral replication, where reduced upon EZH2 inhibition." The reference actually shows inhibition of HSV-1 infectivity, DNA levels and mRNA for ICP4, ICP22 and ICP27. This reference uses much shorter treatments (12 h and only after infection). It also shows that inhibition of EZH2/1 up regulates expression of antiviral genes.

HFF are primary human cells but they are fibroblasts whereas the primary target of HSV-1 replication is epithelial cells. The wording used "they represent a common site of infection in humans" must be edited

Disruption of macroH2A (1 and 2) results in general defects in nuclear architecture, not just peripheral chromatin (https://doi.org/10.1242/jcs.199216;, see also figure 1c and 5a, presenting invaginated and lobulated nuclei). The manuscript would benefit from including a broader discussion of the effects of macroH2A defects on the general nuclear architecture.

The title should be edited, as "egress" in virology is commonly used to refer to the egress of virions from the cell, not to the nuclear egress of capsids. Adding the words nuclear and capsid should be sufficient to address this issue.

It is unclear why preferential changes in expression of housekeeping genes would indicate "stress responses to infection". The rationale for this conclusion must be fully articulated and supported.

Statistical methods must be fully described in materials and methods and the number of biologically independent experiments must be stated in each figure.

Significance

The major strengths of the manuscript lie on the comprehensive analyses of the effects of knocking histone macroH2A in the nuclear architecture and chromatin organization. These analyses indicate that peripheral heterochromatin is defective in the KO. Another strength lies on the analyses of the news heterochromatin domains in HSV-1 infected cells. The relationship between the lack of correlation between the changes in gene expression and global heterochromatin domains defined by macroH2A1 with the main conclusion is less clear.

The major weakness is that the data presented do not strongly support the conclusions. Additional experiments are required to support the main conclusion that the effects in peripheral heterochromatin result in a biologically significant effect on capsid egress. The authors should also consider that the additional experimentation may not support the conclusion that macroH2A or H3K27me3 play critical roles in the nuclear egress of capsids. Another major weakness is that the results of CUT&Tag of the viral genome are dismissed without proper justification. The authors conclude that the results invalidate the assays, but the results are consistent with cross-reactivity of the macroH2A1 antibody with another protein that interacts with the viral genomes and with H3K27me3 being associated with the viral genomes irrespectively of macroH2A1. If the authors had additional data supporting the claim that these results do not reflect cross-reactivity or association with the viral genomes, these data must be presented. Without that additional data, the conclusions are not supported and these discussions must be removed from the manuscript. The authors may still opt to not analyze any association with the viral genomes, but they should not dismiss them as artifactual without actual evidence to support this claim. Previously published literature is also misquoted.

This study makes an incremental contribution to the previously published evidence showing that HSV-1 capsids egress the nucleus through channels in between the peripheral chromatin. It shows that disruption of the heterochromatin at the nuclear periphery, and the nuclear architecture in general, may have a modest effect on capsid egress. This information may be of interest mostly to a specialized audience focused on the egress of nuclear capsids.

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Reviewer #1 (Public Review):

In this work, authors seek to understand how the polycomb complex may coordinate gene expression changes that occur during sequential stages of neuronal maturation. The main strengths are 1) choice of cerebellar granule neurons which mature over a protracted period during normal cerebellar development and constitute a relatively homogeneous population of neurons, 2) use of a genetic in vivo mouse model where a histone demethylase is knocked out, combined with an in vitro culture model of maturing cerebellar granule neurons in which a histone methyltransferase is inhibited, 3) use of CUT & TAG in neuronal cultures to investigate how changes in the H3K27me3 repressor chromatin modification at promoters correlate with gene expression and chromatin accessibility changes. The authors propose a bidirectional effect of the same chromatin repressor modification that is responsible, at least in part, for the timely loss of expression of early genes and the appearance of genes expressed later in maturation. This is the major impact of the work for those interested in cerebellar development. A weakness in the work lies in its narrow focus, which is on promoter regions almost exclusively.

The work is primarily bioinformatics driven and lacks physiological significance of the gene expression changes, or how the culture timing correlates with temporal regulation and chromatin changes in vivo. However, the results do support the proposal that polycomb-associated enzymatic activities play sequential roles during successive stages of cerebellar maturation.

reply to u/noobinPython at https://www.reddit.com/r/Zettelkasten/comments/12u8gbv/is_zotero_a_reliable_software_to_transcribe/

Zotero is incredibly powerful and you could use it as a full end-to-end solution if you wanted to. It's particularly good if you're also using .pdf or other digital documents as it has the ability to pull in notes you've made digitally in a variety of .pdf annotation tools including Adobe's Acrobat (free version) which includes highlighting and notes you've made. It does have its own .pdf viewer now which also allows one to read, highlight, annotate, and tag individual pieces of text and then aggregate them into a single file. In addition to pulling in all the annotations into a single note file, one could break them into smaller individual notes per document if desired and these have addressable locations within the system.

Because Zotero is so powerful and can be dovetailed with a variety of other plugins specific to it as well as with other note taking tools like Obsidian, Logseq, etc. I'd highly recommend you try using it with a single document and take some notes to see if it'll work for you. There are surely some tutorials for using it as well as other useful plugins like Zotfile, MDnotes, etc. for your note taking workflows. It's open source and been in heavy use by many academics for over a decade and is actively developed, so it's one of the more robust systems out there. There are ways to do almost anything you'd want to with it from a note taking, reading, and citation management perspective, so searching and learning a bit about its features and functionality will get you a long way. Out of the box, it's reasonably intuitive, but there are lots of advanced features internally and even more features using a variety of plugins. Just the ability to have a browser extension and a keyboard shortcut to save all the bibliographic metadata of a source in a second or less and the ability to spit out full references for sharing with others has made it a godsend for me even if it did nothing else. Searching around will provide you with a huge amount of video tutorials and ways of using it either by itself, in conjunction with Zotfile, or dovetailing it with dozens of other tools.

Personally I use it in combination with a variety of other tools including Hypothes.is and Obsidian for a comprehensive workflow, but it could do incredibly well as a note taking tool just by itself.

.

reply to u/After-Cell at https://www.reddit.com/r/antinet/comments/12noly2/rebinding_a_book_for_more_margin_space/

The historical practice of "interleaved books" was more popular in a bygone era. If you search you can find publishers that still make bibles this way, but it's relatively rare now.

Given the popularity and ease of e-books and print on demand, you could relatively easily and cheaply get an e-book and reformat it at your local print shop to either print with larger margins or to add blank sheets every other page to have more room for writing your notes. For some classic texts (usually out of copyright) you can "margin shop" for publishers that leave more marginal space or find larger folio editions (The Folio Society, as an example) for your scribbles if you like.

Writing your notes on index cards with page references is quick and simple. These also make good temporary bookmarks. Other related ideas here: https://hypothes.is/users/chrisaldrich?q=tag:%22interleaved%20books%22

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Have I just coined "margin shopping"?

Narzędzia do Hypothes.is https://jonudell.info/h/tools.html

• facet tools - wyszukiwarka
• copy annotations - kopiowanie zaznaczeń
• tag rename - zmiana tagów
• annotation powered survey - rozszerzona wyszukiwarka
• pagefit - skrypt pozwalający dostosować szerokość strony po wysunięciu panelu hypothes.is.

Przydałoby się jeszcze narzędzie pozwalające zablokować panel tak, aby nie zwijał się w momencie interakcji z elementami strony.

General comments:

This study carefully delineates the role of magnesium in cell division versus cell elongation. The results are really important specifically for rod-shaped bacteria and also an important contribution to the broader field of understanding cell shape. Specifically, I love that they are distinguishing between labile and non-labile intracellular magnesium pools, as well as extracellular magnesium! These three pools are really challenging to separate but I commend them on engaging with this topic and using it to provide alternative explanations for their observations!

A major contribution to prior findings on the effects of magnesium is the author’s ability to visualize the number of septa in the elongating cells in the absence of magnesium. This is novel information and I think the field will benefit from the microscopy data shown here.

I completely agree with the authors that we need to be more careful when using rich media such as LB. It is particularly sad that we may be missing really interesting biology because of that! It’s worth moving away from such media or at least being more careful about batch to batch variability. Batch to batch variability is not as well appreciated in microbiology as it is for growing other cell types (for example, mammalian cells and insect cells).

For me, the most exciting finding was that a large part of the cell length changes within the first 10min after adding magnesium. The authors do speculate in the discussion that this is likely happening because of biophysical or enzymatic effects, and I hope they explore this further in the future!

I love how the paper reads like a novel! Congratulations on a very well-written paper!

Kudos to the authors for providing many alternative explanations for their results. It demonstrates critical thinking and an open-mind to finding the truth.

Specific comments:

Figure 2C → please include indication of statistical significance

Figure 3C → please include indication of statistical significance

Figure 6A → please include indication of statistical significance

Figure 8B → please include indication of statistical significance

Figure S1B → please include indication of statistical significance

Figure S3B → please include indication of statistical significance

For your overexpression experiments, do the overexpressed proteins have a tag? It would be helpful to have Western blot data showing that the particular proteins are actually being overexpressed. I think the phenotypes that you observe are very compelling so I don’t doubt the conclusions. Western blot data would just provide some additional confirmation that you are actually achieving overexpression of UppS, MraY, and BcrC.

Questions:

Based on your data, there are definitely differences in gene expression when you compare cells grown in media with and without magnesium. Because the majority in cell length increase occurs in such a short time though (the first 10min), I was wondering if you think that some or most of it is not due to gene expression? Do you have any hypotheses what is most likely to be affected by magnesium? Do you think if the membrane may be affected?

Why do you think less magnesium activates this program of less division and more elongation? Additionally why is abundant magnesium activating a program of increased cell division and less elongation? Do you think there is some evolutionary advantage, especially considering how important magnesium is for ATP production?

Related to this previous question, I also wonder if this magnesium-dependent phenotype would extend to other unicellular organisms, may be protists or algae? That would be a really exciting direction to explore!

Regarding the zinc and manganese experiments, why do you think they lead to additional phenotypes compared to magnesium? Do you have any hypotheses?

Regarding your results that Lipid I availability may be a major a problem for the cell division in the absence of magnesium, do you think that is due to effects magnesium has on the enzymes directly, or do you think magnesium affects the substrate availability/conformation by coordinating the phosphate groups? Or something else, may be membrane conformation?

Reviewer #3 (Public Review):

In this manuscript, Villalobos-Cantor et al. have implemented the method for monitoring cellular proteome that their lab has established in cell culture models of Drosophila brains. The method uses a puromycin analog (O-propargyl-puromycin, OPP) that is locked by the addition of phenylacetyl group (PhAc-OPP) that can be unlocked by expression of Penicillin G acetylase (PGA) to tag the proteins translated in a specific cell type. When unlocked, OPP can get incorporated into the newly translating nascent peptide, and abort translation while allowing click chemistry addition of various tags, such as fluorophore-azide to visualize or biotin-azide to immunopurify polypeptides. The authors demonstrate the use of the method in adult drosophila brains expressing PGA in neurons or glia, showing that the addition of OPP is indeed PGA dependent and the proteins are only tagged in the cells that express PGA. The authors also show that when fluorophore azide is used to visualize the proteome and the samples are run on a gel, bands of various sizes can be observed to have incorporated OPP, arguing the method labels the proteome indiscriminately. The authors also optimized the protocol by titrating the amount of PhAc-OPP to use to minimize cellular stress. Also, they show that driving the expression of PGA with elav-Gal4 or repo-Gal4 is not toxic and does not cause phenotypes although Actin-Gal4 driven expression causes phenotypes. Finally the authors demonstrate the use of the technique to show that there is an age-induced decrease in total protein synthesis in the fly brain. This is a nice technique to implement in fly but the characterization of the technique is not complete in its current state. It is not clear what percentage of the nascent peptides are tagged, and whether the cells in the tissue are equally represented in the lysates for immunopurification.

This is a bit fey, I think. Perhaps the name is too worn to make out. The tag itself, the handover is enough.

Thank you for including this information!!! Being able to see the actual collection site / environment provides a lot of information that is often never publicly reported and gets lost with time!

Reviewer #1 (Public Review):

The study examines how hemocytes control whole-body responses to oxidative stress. Using single cell sequencing they identify several transcriptionally distinct populations of hemocytes, including one subset that show altered immune and stress gene expression. They also find that knockdown of DNA Damage Response (DDR) genes in hemocytes increases expression of the immune cytokine, upd3, and that both upd3 overexpression in hemocytes and hemocyte knockdown of DDR genes leads to increased lethality upon oxidative stress.

Strengths

1, The single cell analyses provide a clear description of how oxidative stress can cause distinct transcriptional changes in different populations of hemocytes. These results add to the emerging them in the field that there functionally different subpopulations of hemocytes that can control organismal responses to stress.<br /> 2, The discovery that DDR genes are required upon oxidative stress to limit cytokine production and lethality provides interesting new insight into the DDR may play non-canonical roles in controlling organismal responses to stress.

Weaknesses

1, In some ways the authors interpretation of the data - as indicated, for example, in the title, summary and model figure - don't quite match their data. From the title and model figure, it seems that the authors suggest that the DDR pathway induces JNK and Upd3 and that the upd3 leads to tissue wasting. However, the data suggest that the DDR actually limits upd3 production and susceptibility to death as suggested by several results:<br /> a) PQ normally doesn't induce upd3 but does lead to glycogen and TAG loss, suggesting that upd3 isn't connected to the PQ-induced wasting.<br /> b) knockdown of DDR upregulates upd3 and leads to increased PQ-induced death. This would suggest that activation of DDR is normally required to limit, rather than serve as the trigger for upd3 production and death.<br /> c) hemocyte knockdown of either JNK activity or upd3 doesn't affect PQ-induced death, suggesting that they don't contribute to oxidative stress-induced death. Its only when DDR is impaired (with DDR gene knockdown) that an increase in upd3 is seen (although no experiments addressed whether JNK was activated or involved in this induction of upd3), suggesting that DDR activation prevents upd3 induction upon oxidative stress.

2, The connections between DDR, JNK and upd3 aren't fully developed. The experiments show that susceptibility to oxidative stress-induced death can be caused by a) knockdown of DDR genes, b) genetic overexpression of upd3, c) genetic activation of JNK. But whether these effects are all related and reflect a linear pathway requires a little more work. For example, one prediction of the proposed model is that the increased susceptibility to oxidative stress-induced death in the hemocyte DDR gene knockdowns would be suppressed (perhaps partially) by simultaneous knockdown of upd3 and/or JNK. These types of epistasis experiments would strengthen the model and the paper.

3, The (potential) connections between DDR/JNK/UPD3 and the oxidative stress effects on depletion of nutrient (lipids and glycogen) stores was also not fully developed. However, it may be the case that, in this paper, the authors just want to speculate that the effects of hemocyte DDR/upd3 manipulation on viability upon oxidative stress involve changes in nutrient stores.

Reviewer #3 (Public Review):

Male infertility is an important health problem. Among pathologies with multiple morphological abnormalities of the flagellum (MMAF), only 50% of the patients have no identified genetic causes. It is thus primordial to find novel genes that cause the MMAF syndrome. In the current work, the authors follow up the identification of two patients with MMAF carrying a mutation in the CCDC146 gene. To understand how mutations in CCDC146 lead to male infertility, the authors generated two mouse models: a CCDC146-knockout mouse, and a knockin mouse in which the CCDC146 locus is tagged with an HA tag. Male CCDC146-knockout mice are infertile, which proves the causative role of this gene in the observed MMAF cases. Strikingly, animals develop no other obvious pathologies, thus underpinning the specific role of CCDC146 in male fertility.

The authors have carefully characterised the subcellular roles of CCDC146 by using a combination of expansion and electron microscopy. They demonstrate that all microtubule-based organelles, such as the sperm manchette, the centrioles, as well as the sperm axonemes are defective when CCDC146 is absent. Their data show that CCDC146 is a microtubule-associated protein, and indicate, but do not prove beyond any doubt, that it could be a microtubule-inner protein (MIP).<br /> This is a solid work that defines CCDC146 as a novel cause of male infertility. The authors have performed comprehensive phenotypic analysis to define the defects in CCDC146 knockout mice. Surprisingly, the authors provide virtually no information on the penetrance of those defects - in most cases they simply show descriptive micrographs. The message of this manuscript would have been more convincing if the key phenotypes of the CCDC146 knockout mice were quantified, in particular those shown in Fig. 2E, 7A, 11B, 13.

The manuscript text is well written and easy to follow also for non-specialists. The introduction and discussion chapters contain important background information that allow putting the current work into the greater context of fertility research. The figures could have been designed more carefully, with additional information on the genotype and other details such as the antibodies used etc. directly added to the figure panels, which would improve their readability. The author might also consider pooling small figures with complementary content into one bigger figure in order to group related information together, and again facilitate the reading of the manuscript.

Overall, this manuscript provides convincing evidence for CCDC146 being essential for male fertility, and illustrates this with a large panel of phenotypic observations, which however mostly lack quantification in order to judge their penetrance. Together, the work provides important first insights into the role of a so-far unexplored proteins, CCDC146, in spermatogenesis, thereby broadening the spectrum of genes involved in male infertility.

reply to u/bestlunchtoday at https://www.reddit.com/r/Zettelkasten/comments/12gadut/benefits_of_sharing_permanent_notes/

I love the diversity of ideas here! So many different ways to do it all and perspectives on the pros/cons. It's all incredibly idiosyncratic, just like our notes.

I probably default to a far extreme of sharing the vast majority of my notes openly to the public (at least the ones taken digitally which account for probably 95%). You can find them here: https://hypothes.is/users/chrisaldrich.

Not many people notice or care, but I do know that a small handful follow and occasionally reply to them or email me questions. One or two people actually subscribe to them via RSS, and at least one has said that they know more about me, what I'm reading, what I'm interested in, and who I am by reading these over time. (I also personally follow a handful of people and tags there myself.) Some have remarked at how they appreciate watching my notes over time and then seeing the longer writing pieces they were integrated into. Some novice note takers have mentioned how much they appreciate being able to watch such a process of note taking turned into composition as examples which they might follow. Some just like a particular niche topic and follow it as a tag (so if you were interested in zettelkasten perhaps?) Why should I hide my conversation with the authors I read, or with my own zettelkasten unless it really needed to be private? Couldn't/shouldn't it all be part of "The Great Conversation"? The tougher part may be having means of appropriately focusing on and sharing this conversation without some of the ills and attention economy practices which plague the social space presently.

There are a few notes here on this post that talk about social media and how this plays a role in making them public or not. I suppose that if I were putting it all on a popular platform like Twitter or Instagram then the use of the notes would be or could be considered more performative. Since mine are on what I would call a very quiet pseudo-social network, but one specifically intended for note taking, they tend to be far less performative in nature and the majority of the focus is solely on what I want to make and use them for. I have the opportunity and ability to make some private and occasionally do so. Perhaps if the traffic and notice of them became more prominent I would change my habits, but generally it has been a net positive to have put my sensemaking out into the public, though I will admit that I have a lot of privilege to be able to do so.

Of course for those who just want my longer form stuff, there's a website/blog for that, though personally I think all the fun ideas at the bleeding edge are in my notes.

Since some (u/deafpolygon, u/Magnifico99, and u/thiefspy; cc: u/FastSascha, u/A_Dull_Significance) have mentioned social media, Instagram, and journalists, I'll share a relevant old note with an example, which is also simultaneously an example of the benefit of having public notes to be able to point at, which u/PantsMcFail2 also does here with one of Andy Matuschak's public notes:

[Prominent] Journalist John Dickerson indicates that he uses Instagram as a commonplace: https://www.instagram.com/jfdlibrary/ here he keeps a collection of photo "cards" with quotes from famous people rather than photos. He also keeps collections there of photos of notes from scraps of paper as well as photos of annotations he makes in books.

It's reasonably well known that Ronald Reagan shared some of his personal notes and collected quotations with his speechwriting staff while he was President. I would say that this and other similar examples of collaborative zettelkasten or collaborative note taking and their uses would blunt u/deafpolygon's argument that shared notes (online or otherwise) are either just (or only) a wiki. The forms are somewhat similar, but not all exactly the same. I suspect others could add to these examples.

And of course if you've been following along with all of my links, you'll have found yourself reading not only these words here, but also reading some of a directed conversation with entry points into my own personal zettelkasten, which you can also query as you like. I hope it has helped to increase the depth and level of the conversation, should you choose to enter into it. It's an open enough one that folks can pick and choose their own path through it as their interests dictate.

要发一篇笔记之前，搜索下类似的内容，看看小红书官方推荐什么 tag，另外可以看看相同领域的博主使用什么 tag。

Resources

Links and resources from the second half of the chat and Q&A:

• TAG Feedback Protocol via https://learninginhand.com/blog/feedbackchat
• Robin DeRosa's OER pedagogical endeavor
• Annotation Rubrics
• Faculty Focus Live - A Hyflex Course: Bridging the Gap Between Online and In-person Students
• Bringing Theories to Practice: Universal Design Principles and the Use of Social Annotation to Support Neurodiverse Students by Christian Aguiar, Fatma Elshobokshy, and Amanda Huron
• Compass Points rubric

Video

<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/KkgXkJZXa0E" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

Watch on YouTube

Author Response

Reviewer #1 (Public Review):

The authors start the study with an interesting clinical observation, found in a small subset of prostate cancers: FOXP2-CPED1 fusion. They describe how this fusion results in enhanced FOXP2 protein levels, and further describe how FOXP2 increases anchorageindependent growth in vitro, and results in pre-malignant lesions in vivo. Intrinsically, this is an interesting observation. However, the mechanistic insights are relatively limited as it stands, and the main issues are described below.

Main issues:

1) While the study starts off with the FOXP2 fusion, the vast majority of the paper is actually about enhanced FOXP2 expression in tumorigenesis. Wouldn't it be more logical to remove the FOXP2 fusion data? These data seem quite interesting and novel but they are underdeveloped within the current manuscript design, which is a shame for such an exciting novel finding. Along the same lines, for a study that centres on the prostate lineage, it's not clear why the oncogenic potential of FOXP2 in mouse 3T3 fibroblasts was tested.

We thank the reviewer very much for the comment. We followed the suggestion and added a set of data regarding the newly identified FOXP2 fusion in Figure 1 to make our manuscript more informative. We tested the oncogenic potential of FOXP2 in NIH3T3 fibroblasts because NIH3T3 cells are a widely used model to demonstrate the presence of transformed oncogenes2,3. In our study, we observed that when NIH3T3 cells acquired the exogenous FOXP2 gene, the cells lost the characteristic contact inhibition response, continued to proliferate and eventually formed clonal colonies. Please refer to "Answer to Essential Revisions #1 from the Editors” for details.

2) While the FOXP2 data are compelling and convincing, it is not clear yet whether this effect is specific, or if FOXP2 is e.g. universally relevant for cell viability. Targeting FOXP2 by siRNA/shRNA in a non-transformed cell line would address this issue.

We appreciate these helpful comments. Please refer to the "Answer to Essential Revisions #1 from the Editors” for details.

3) Unfortunately, not a single chemical inhibitor is truly 100% specific. Therefore, the Foretinib and MK2206 experiments should be confirmed using shRNAs/KOs targeting MEK and AKT. With the inclusion of such data, the authors would make a very compelling argument that indeed MEK/AKT signalling is driving the phenotype.

We thank the reviewer for highlighting this point and we agree with the reviewer’s point that no chemical inhibitor is 100% specific. In this study, we used chemical inhibitors to provide further supportive data indicating that FOXP2 confers oncogenic effects by activating MET signaling. We characterized a FOXP2-binding fragment located in MET and HGF in LNCaP prostate cancer cells by utilizing the CUT&Tag method. We also found that MET restoration partially reversed oncogenic phenotypes in FOXP2-KD prostate cancer cells. All these data consistently supported that FOXP2 activates MET signaling in prostate cancer. Please refer to the "Answer to Essential Revisions #2 from the Editors” and to the "Answer to Essential Revisions #7 from the Editors” for details.

4) With the FOXP2-CPED1 fusion being more stable as compared to wild-type transcripts, wouldn't one expect the fusion to have a more severe phenotype? This is a very exciting aspect of the start of the study, but it is not explored further in the manuscript. The authors would ideally elaborate on why the effects of the FOXP2-CPED1 fusion seem comparable to the FOXP2 wildtype, in their studies.

We thank the reviewer very much for the comment. We had quantified the number of colonies of FOXP2- and FOXP2-CPED1-overexpressing cells, and we found that both wildtype FOXP2 and FOXP2-CPED1 had a comparable putative functional influence on the transformation of human prostate epithelial cells RWPE-1 and mouse primary fibroblasts NIH3T3 (P = 0.69, by Fisher’s exact test for RWPE-1; P = 0.23, by Fisher’s exact test for NIH3T3). We added the corresponding description to the Results section in Line 487 on Page 22 in the tracked changes version of the revised manuscript. Please refer to the "Answer to Essential Revisions #5 from the Editors” for details.

5) The authors claim that FOXP2 functions as an oncogene, but the most-severe phenotype that is observed in vivo, is PIN lesions, not tumors. While this is an exciting observation, it is not the full story of an oncogene. Can the authors justifiably claim that FOXP2 is an oncogene, based on these results?

We appreciate the comment, and we made the corresponding revision in the revised manuscript. Please refer to the "Answer to Essential Revisions #3 from the Editors” for details.

6) The clinical and phenotypic observations are exciting and relevant. The mechanistic insights of the study are quite limited in the current stage. How does FOXP2 give its phenotype, and result in increased MET phosphorylation? The association is there, but it is unclear how this happens.

We appreciate this valuable suggestion. In the current study, we used the CUT&Tag method to explore how FOXP2 activated MET signaling in LNCaP prostate cancer cells, and we identified potential FOXP2-binding fragments in MET and HGF. Therefore, we proposed that FOXP2 activates MET signaling in prostate cancer through its binding to MET and METassociated gene. Please refer to the "Answer to Essential Revisions #2 from the Editors” for details.

Reviewer #2 (Public Review):

1) The manuscript entitled "FOXP2 confers oncogenic effects in prostate cancer through activating MET signalling" by Zhu et al describes the identification of a novel FOXP2CPED1 gene fusion in 2 out of 100 primary prostate cancers. A byproduct of this gene fusion is the increased expression of FOXP2, which has been shown to be increased in prostate cancer relative to benign tissue. These data nominated FOXP2 as a potential oncogene. Accordingly, overexpression of FOXP2 in nontransformed mouse fibroblast NIH-3T3 and human prostate RWPE-1 cells induced transforming capabilities in both cell models. Mechanistically, convincing data were provided that indicate that FOXP2 promotes the expression and/or activity of the receptor tyrosine kinase MET, which has previously been shown to have oncogenic functions in prostate cancer. Notably, the authors create a new genetically engineered mouse model in which FOXP2 is overexpressed in the prostatic luminal epithelial cells. Overexpression of FOXP2 was sufficient to promote the development of prostatic intraepithelial neoplasia (PIN) a suspected precursor to prostate adenocarcinoma and activate MET signaling.

Strengths:

This study makes a convincing case for FOXP2 as 1) a promoter of prostate cancer initiation and 2) an upstream regulator of pro-cancer MET signaling. This was done using both overexpression and knockdown models in cell lines and corroborated in new genetically engineered mouse models (GEMMs) of FOXP2 or FOXP2-CPED1 overexpression in prostate luminal epithelial cells as well as publicly available clinical cohort data.

Major strengths of the study are the demonstration that FOXP2 or FOXP2-CPED1 overexpression transforms RWPE-1 cells to now grow in soft agar (hallmark of malignant transformation) and the creation of new genetically engineered mouse models (GEMMs) of FOXP2 or FOXP2-CPED1 overexpression in prostate luminal epithelial cells. In both mouse models, FOXP2 overexpression increased the incidence of PIN lesions, which are thought to be a precursor to prostate cancer. While FOXP2 alone was not sufficient to cause prostate cancer in mice, it is acknowledged that single gene alterations causing prostate cancer in mice are rare. Future studies will undoubtedly want to cross these GEMMs with established, relatively benign models of prostate cancer such as Hi-Myc or Pb-Pten mice to see if FOXP2 accelerates cancer progression (beyond the scope of this study).

We appreciate these positive comments from the reviewer. We agree with the suggestion from the reviewer that it is worth exploring whether FOXP2 is able to cooperate with a known disease driver to accelerate the progression of prostate cancer. Therefore, we are going to cross Pb-FOXP2 transgenic mice with Pb-Pten KO mice to assess if FOXP2 is able to accelerate malignant progression.

2) Weaknesses: It is unclear why the authors decided to use mouse fibroblast NIH3T3 cells for their transformation studies. In this regard, it appears likely that FOXP2 could function as an oncogene across diverse cell types. Given the focus on prostate cancer, it would have been preferable to corroborate the RWPE-1 data with another prostate cell model and test FOXP2's transforming ability in RWPE-1 xenograft models. To that end, there is no direct evidence that FOXP2 can cause cancer in vivo. The GEMM data, while compelling, only shows that FOXP2 can promote PIN in mice and the lone xenograft model chosen was for fibroblast NIH-3T3 cells.

To determine the oncogenic activity of FOXP2 and the FOXP2-CPDE1 fusion, we initially used mouse primary fibroblast NIH3T3 for transformation experiments, because NIH3T3 cells are a widely used cell model to discover novel oncogenes2,3,10,11. Subsequently, we observed that overexpression of FOXP2 and its fusion variant drove RWPE-1 cells to lose the characteristic contact inhibition response, led to their anchorage-independent growth in vitro, and promoted PIN in the transgenic mice. During preparation of the revised manuscript, we tested the transformation ability of FOXP2 and FOXP2-CPED1 in RWPE1 xenograft models. We subcutaneously injected 2 × 106 RWPE-1 cells into the flanks of NOD-SCID mice. The NODSCID mice were divided into five groups (n = 5 mice in each group): control, FOXP2overexpressing (two stable cell lines) and FOXP2-CPED1- overexpressing (two cell lines) groups. The experiment lasted for 4 months. We observed that no RWPE-1 cell-injected mice developed tumor masses. We propose that FOXP2 and its fusion alone are not sufficient to generate the microenvironment suitable for RWPE-1-xenograft growth. Collectively, our data suggest that FOXP2 has oncogenic potential in prostate cancer, but is not sufficient to act alone as an oncogene.

3) There is a limited mechanism of action. While the authors provide correlative data suggesting that FOXP2 could increase the expression of MET signaling components, it is not clear how FOXP2 controls MET levels. It would be of interest to search for and validate the importance of potential FOXP2 binding sites in or around MET and the genes of METassociated proteins. At a minimum, it should be confirmed whether MET is a primary or secondary target of FOXP2. The authors should also report on what happened to the 4-gene MET signature in the FOXP2 knockdown cell models. It would be equally significant to test if overexpression of MET can rescue the anti-growth effects of FOXP2 knockdown in prostate cancer cells (positive or negative results would be informative).

We appreciate all the valuable comments. As suggested, we performed corresponding experiments, please refer to the " Answers to Essential Revisions #2 from the Editors”, to the "Answer to Essential Revisions #6 from the Editors”, and to the "Answer to Essential Revisions #7 from the Editors” for details.

Reviewer #3 (Public Review):

1) In this manuscript, the authors present data supporting FOXP2 as an oncogene in PCa. They show that FOXP2 is overexpressed in PCa patient tissue and is necessary and sufficient for PCa transformation/tumorigenesis depending on the model system. Overexpression and knock-down of FOXP2 lead to an increase/decrease in MET/PI3K/AKT transcripts and signaling and sensitizes cells to PI3K/AKT inhibition.

Key strengths of the paper include multiple endpoints and model systems, an over-expression and knock-down approach to address sufficiency and necessity, a new mouse knock-in model, analysis of primary PCa patient tumors, and benchmarking finding against publicly available data. The central discovery that FOXP2 is an oncogene in PCa will be of interest to the field. However, there are several critically unanswered questions.

1) No data are presented for how FOXP2 regulates MET signaling. ChIP would easily address if it is direct regulation of MET and analysis of FOXP2 ChIP-seq could provide insights.

2) Beyond the 2 fusions in the 100 PCa patient cohort it is unclear how FOXP2 is overexpressed in PCa. In the discussion and in FS5 some data are presented indicating amplification and CNAs, however, these are not directly linked to FOXP2 expression.

3) There are some hints that full-length FOXP2 and the FOXP2-CPED1 function differently. In SF2E the size/number of colonies between full-length FOXP2 and fusion are different. If the assay was run for the same length of time, then it indicates different biologies of the overexpressed FOXP2 and FOXP2-CPED1 fusion. Additionally, in F3E the sensitization is different depending on the transgene.

We appreciate these valuable comments and constructive remarks. As suggested, we performed the CUT&Tag experiments to detect the binding of FOXP2 to MET, and to examine the association of CNAs of FOXP2 with its expression. Please refer to the " Answer to Essential Revisions #2 from the Editors" and the " Answer to Essential Revisions #4 from the Editors" for details. We also added detailed information to show the resemblance observed between FOXP2 fusion- and wild-type FOXP2-overexpressing cells. We added the corresponding description to the Results section in Line 487 on Page 22 in the tracked changes version of the revised manuscript. Please refer to the “Answer to Essential Revisions #5 from the Editors” for details.

2) The relationship between FOXP2 and AR is not explored, which is important given 1) the critical role of the AR in PCa; and 2) the existing relationship between the AR and FOXP2 and other FOX gene members.

We thank the reviewer very much for highlighting this point. We agree that it is important to examine the relationship between FOXP2 and AR. We therefore analyzed the expression dataset of 255 primary prostate tumors from TCGA and observed that the expression of FOXP2 was significantly correlated with the expression of AR (Spearman's ρ = 0.48, P < 0.001) (Figure 1. a). Next, we observed that both FOXP2- and FOXP2-CPED1overexpressing 293T cells had a higher AR protein abundance than control cells (Figure 1. b). In addition, shRNA-mediated FOXP2 knockdown in LNCaP cells resulted in a decreased AR protein level compared to that in control cells (Figure 1. c). However, we analyzed our CUT&Tag data and observed no binding of FOXP2 to AR (Figure 1. d). Our data suggest that FOXP2 might be associated with AR expression.

Figure 1. a. AR expression in a human prostate cancer dataset (TCGA, Prostate Adenocarcinoma, Provisional; n = 493) classified by FOXP2 expression level (bottom 25%, low expression, n = 120; top 25%, high expression, n = 120; negative expression, n = 15). P values were calculated by the MannWhitney U test. The correlation between FOXP2 and AR expression was evaluated by determining the Spearman's rank correlation coefficient. b. Immunoblot analysis of the expression levels of AR in 293T cells with overexpression of FOXP2 or FOXP2-CPED1. c. Immunoblot analysis of the expression levels of AR in LNCaP cells with stable expression of the scrambled vector or FOXP2 shRNA. d. CUT&Tag analysis of FOXP2 association with the promoter of AR. Representative track of FOXP2 at the AR gene locus is shown.

Reference

1. Mayr C, Bartel DP. Widespread shortening of 3'UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell. 2009 Aug 21;138(4):673-84.
2. Gara SK, Jia L, Merino MJ, Agarwal SK, Zhang L, Cam M et al., Germline HABP2 Mutation Causing Familial Nonmedullary Thyroid Cancer. N Engl J Med. 2015 Jul 30;373(5):448-55.
3. Kohno T, Ichikawa H, Totoki Y, Yasuda K, Hiramoto M, Nammo T et al., KIF5B-RET fusions in lung adenocarcinoma. Nat Med. 2012 Feb 12;18(3):375-7.
4. Chen F, Byrd AL, Liu J, Flight RM, DuCote TJ, Naughton KJ et al., Polycomb deficiency drives a FOXP2-high aggressive state targetable by epigenetic inhibitors. Nat Commun. 2023 Jan 20;14(1):336.
5. Kaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD, Henikoff JG et al., CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun. 2019 Apr 29;10(1):1930.
6. Spiteri E, Konopka G, Coppola G, Bomar J, Oldham M, Ou J et al., Identification of the transcriptional targets of FOXP2, a gene linked to speech and language, in developing human brain. Am J Hum Genet. 2007 Dec;81(6):1144-57.
7. Lai CS, Fisher SE, Hurst JA, Vargha-Khadem F, Monaco AP. A forkhead-domain gene is mutated in a severe speech and language disorder. Nature. 2001 Oct 4;413(6855):519-23.
8. Hannenhalli S, Kaestner KH. The evolution of Fox genes and their role in development and disease. Nat Rev Genet. 2009 Apr;10(4):233-40.
9. Shu W, Yang H, Zhang L, Lu MM, Morrisey EE. Characterization of a new subfamily of winged-helix/forkhead (Fox) genes that are expressed in the lung and act as transcriptional repressors. J Biol Chem. 2001 Jul 20;276(29):27488-97.
10. Wang C, Liu H, Qiu Q, Zhang Z, Gu Y, He Z. TCRP1 promotes NIH/3T3 cell transformation by over-activating PDK1 and AKT1. Oncogenesis. 2017 Apr 24;6(4):e323.
11. Suh YA, Arnold RS, Lassegue B, Shi J, Xu X, Sorescu D et al., Cell transformation by the superoxide-generating oxidase Mox1. Nature. 1999 Sep 2;401(6748):79-82.

An annotated list of collaborative scholarly projects in the Humanities may look like existing curated catalogues of digitale editions.

reply to u/iamharunjonuzi at https://www.reddit.com/r/Zettelkasten/comments/12bvcmn/how_do_i_store_when_coming_across_an_actual_fact/

How central is the fact to what you're working at potentially developing? Often for what may seem like basic facts that are broadly useful, but not specific to things I'm actively developing, I'll leave basic facts like that as short notes on the source/reference cards (some may say literature notes) where I found them rather than writing them out in full as their own cards.

If I were a future biologist, as a student I might consider that I would soon know really well what a cell was and not bother to have a primary zettel on something so commonplace unless I was collecting various definitions to compare and contrast for something specific. Alternately as a non-biologist or someone that doesn't use the idea frequently, then perhaps it may merit more space for connecting to others?

Of course you can always have it written along with the original source and "promote" it to its own card later if you feel it's necessary, so you're covered either way. I tend to put the most interesting and surprising ideas into my main box to try to maximize what comes back out of it. If there were 2 more interesting ideas than the definition of cell in that documentary, then I would probably leave the definition with the source and focus on the more important ideas as their own zettels.

As a rule of thumb, for those familiar with Bloom's taxonomy in education, I tend to leave the lower level learning-based notes relating to remembering and understanding as shorter (literature) notes on the source's reference card and use the main cards for the higher levels (apply, analyze, evaluate, create).

Ultimately, time, practice, and experience will help you determine for yourself what is most useful and where. Until you've developed a feel for what works best for you, just write it down somewhere and you can't really go too far wrong.

Reviewer #3 (Public Review):

Bacteria sense and respond to multiple signals and cues to regulate gene expression. To define the complex network of signaling that ultimately controls transcription of many genes in cells requires an understanding of how multiple signaling systems can converge to effect gene expression and ensuing bacterial behaviors. The global transcription factor CRP has been studied for decades as a regulator of genes in response to glucose availability. It's direct and indirect effects on gene expression have been documented in E. coli and other bacteria including pathogens including Vibrio cholerae. Likewise, the master regulator of quorum sensing (QS), HapR), is a well-studied transcription factor that directly controls many genes in Vibrio cholerae and other Vibrios in response to autoinducer molecules that accumulate at high cell density. By contrast, low cell density gene expression is governed by another regulator AphA. It has not yet been described how HapR and CRP may together work to directly control transcription and what genes are under such direct dual control.

Using both in vivo methods with gene fusions to lacZ and in vitro transcription assays, the authors proceed to identify the smaller subset of genes whose transcription is directly repressed (7) and activated (2) by HapR. Prior work from this group identified the direct CRP binding sites in the V. cholerae genome as well as promoters with overlapping binding sites for AphA and CRP, thus it appears a logical extension of these prior studies is to explore here promoters for potential integration of HapR and CRP. Inclusion of this rationale was not included in the introduction of CRP protein to the in vitro experiments.

Seven genes are found to be repressed by HapR in vivo, the promoter regions of only six are repressed in vitro with purified HapR protein alone. The authors propose and then present evidence that the seventh promoter, which controls murPQ, requires CRP to be repressed by HapR both using in vivo and vitro methods. This is a critical insight that drives the rest of the manuscripts focus.

The DNase protection assay conducted supports the emerging model that both CRP and HapR bind at the same region of the murPQ promoter, but interpret is difficult due to the poor quality of the blot. There are areas of apparent protection at positions +1 to +15 that are not discussed, and the areas highlighted are difficult to observe with the blot provided.

The model proposed at the end of the manuscript proposes physiological changes in cells that occur at transitions from the low to high cell density. Experiments in the paper that could strengthen this argument are incomplete. For example, in Fig. 4e it is unclear at what cell density the experiment is conducted. The results with the wild type strain are intermediate relative to the other strains tested. Cell density should affect the result here since HapR is produced at high density but not low density. This experiment would provide important additional insights supporting their model, by measuring activity at both cell densities and also in a luxO mutant locked at the high cell density. Conducting this experiment in conditions lacking and containing glucose would also reveal whether high glucose conditions mimicking the crp results.

Throughout the paper it was challenging to account for the number of genes selected, the rationale for their selection, and how they were prioritized. For example, the authors acknowledged toward the end of the Results section that in their prior work, CRP and HapR binding sites were identified (line 321-22). It is unclear whether the loci indicated in Table 1 all from this prior study. It would be useful to denote in this table the seven genes characterized in Figure 2 and to provide the locus tag for murPQ. Of the 32 loci shown in Table 1, five were selected for further study using EMSA (line 322), but no rationale is given for studying these five and not others in the table.

Since prior work identified a consensus CRP binding motif, the authors identify the DNA sequence to which HapR binds overlaps with a sequence also predicted to bind CRP. Genome analysis identified a total of seven sites where the CRP and HapR binding sites were offset by one nucleotide as see with murPQ. Lines 327-8 describe EMSA results with several of these DNA sequences but provides no data to support this statement. Are these loci in Table 1?

Using structural models, the authors predict that HapR repression requires protein-protein interactions with CRP. Electromobility shift assays (EMSA) with purified promoter DNA, CRP and HapR (Fig 5d) and in vitro transcription using purified RNAP with these factors (Figure 5e) support this hypothesis. However, the model proports that HapR "bound tightly" and that it also had a "lower affinity" when CRP protein was used that had mutations in a putative interaction interface. These claims can be bolstered if the authors calculate the dissociation constant (Kd) value of each protein to the DNA. This provides a quantitative assessment of the binding properties of the proteins. The concentrations of each protein are not indicated in panels of the in vitro analysis, but only the geometric shapes denoting increasing protein levels. Panel 5e appears to indicate that an intermediate level of CRP was used in the presence of HapR, which presumably coincides with levels used in lane 4, but rationale is not provided. How well the levels of protein used in vitro compare to levels observed in vivo is not mentioned.

The authors are commended for seeking to connect the in vitro and vivo results obtained under lab conditions with conditions experienced by V. cholerae in niches it may occupy, such as aquatic systems. The authors briefly review the role of MurPQ in recycling of the cell wall of V. cholerae by degrading MurNAc into GlcNAc, although no references are provided (lines 146-50). Based on this physiology and results reported, the authors propose that murPQ gene expression by these two signal transduction pathways has relevance in the environment, where Vibrios, including V. cholerae, forms biofilms on exoskeleton composed of GlcNAc.

The conclusions of that work are supported by the Results presented but additional details in the text regarding the characteristics of the proteins used (Kd, concentrations) would strengthen the conclusions drawn. This work provides a roadmap for the methods and analysis required to develop the regulatory networks that converge to control gene expression in microbes. The study has the potential to inform beyond the sub-filed of Vibrios, QS and CRP regulation.

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This is interesting to think about such a low amount of media consumption. I always imagined that on top of outdoor activities and activities without media, there would also be a decent amount of time spent consuming media, even if that was radio or magazines.

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Reply to the reviewers

We would truly like to thank all 3 reviewers for insightful, helpful and thus constructive comments.

Reviewer #1

Summary

In this manuscript, Lockyer et al. provide novel insights into the mechanism by which Toxoplasma gondii avoids parasite restriction in IFNγ-activated human cells. To identify potentially secreted proteins supporting parasite survival in IFNγ-activated human foreskin fibroblasts (HFF), the authors designed a CRISPR screen of Toxoplasma secretome candidates based on hyperLOPIT protein localization data. By this approach, they identified novel secreted proteins supporting parasite growth in IFNγ-activated cells. Among the gene identified, they found MYR3 a known component of the putative translocon in charge of protein export through the parasitophorous vacuole membrane. Therefore, the authors focused their investigations on GRA57, a dense granule protein of unknown function, which affects parasite survival to a lesser extent than the MYR component. The resistance phenotype conferred by GRA57 was confirmed by fluorescence microscopy. Importantly, the authors provide evidence that the protective function of GRA57 is not as well conserved in murine cells of the same type (MEF) as in HFF. To further explore the mechanism by which GRA57 protect the parasites in IFNγ-activated cells, the authors searched for protein partners by biochemistry. By immunoprecipitation and tandem mass spectrometry, they identified two other putative dense granule proteins, GRA70 and GRA71, which co-purified with GRA57-HA tagged protein. Noteworthy, both proteins were also found in the CRISPR screens with significant score conferring resistance. High-content imaging analysis confirmed the protective effect conferred by GRA57, GRA70, and GRA71 individually at similar levels. After ruling out an effect of tryptophan deprivation in parasite clearance, or a role of GRA57 in protein export normally mediated by the MYR translocon, and a role on host cell gene expression by RNA-Seq, the authors investigated the ubiquitination of the parasitophorous vacuole membrane, a marker previously thought to initiate parasite clearance. A reduction in ubiquitin labeling around the vacuole of mutant parasites is observed, which is quite surprising given the correlated increase in parasite clearance. The authors concluded that ubiquitin recruitment may not be directly linked to the parasite clearance mechanism.

Major comments

• Figure 2C. In this figure, the restriction effect of IFNγ is about 60% (or 40% survival) for RHdeltaUPRT parasites grown in HFFs, which is quite different from the 85% mentioned earlier in the results section. How was actually done the first assay? Settings with 60% restriction sounds reasonable and indicates that a substantial fraction of the parasite population evades the restrictive effect of IFNγ, which provides a clear rationale for the main objective of this study, namely the identification of effectors supporting parasite development in human cells in the presence of IFNγ.

This discrepancy in restriction likely arises from the differences in the parasites used in these assays and the measurements of restriction. The 85%/90% restriction initially mentioned is from the pooled CRISPR screens using the effector knockout pool. This restriction level was assessed by counting of parasites retrieved following infection of IFNg-stimulated HFFs. The 60% restriction of wildtype parasites seen in Figure 2 is a separate assay. This percentage was calculated by measuring total mCherry fluorescence area within infected HFFs. We expect the restriction of the pooled CRISPR population to be higher than in restriction assays performed with either wild type parasites or single genetic knockouts. We included the 85%/90% numbers to highlight that the HFFs were highly restrictive in the screen, but we have now removed references to these numbers in the results section to avoid confusion with later results that use more accurate measures of survival. We refer to this restriction level instead in the discussion section.

Optional comment: GRA70 and GRA71 were both copurified with GRA57, but what about GRA71 expression and localization? Is there a reason why this protein partner has not been studied further just like GRA70?

Tagging of GRA71 was attempted but was not successful in a first attempt. We have not re-attempted this tagging as Krishnamurthy et al 2023 (PMID: 36916910) recently tagged and localised GRA71, demonstrating it is also an intravacuolar dense granule protein with similar localisation to GRA57 and GRA70- we feel there is minimal value in us repeating this.

*Is there any change in GRA57, GRA70, and GRA71 localization and/or amount when cells were pretreated with IFNγ? *

Thank you for this suggestion, we have now conducted further investigation to address this. We checked the localisation of GRA57-HA and GRA70-V5 in IFNg-stimulated HFFs and found no change to their localisation. This data has been added in Supplementary Figure S4 in our revised manuscript. Alignment of our RNA-Seq data to the Toxoplasma genome, now included as Supplementary Data 4, also shows there is no significant up or downregulation in expression of any of the three proteins when HFFs are pretreated with IFNg.

Do they still form a complex in the absence of IFNγ?

We did not investigate this in this manuscript, however in Krishnamurthy et al 2023 (PMID: 36916910) CoIPs using GRA57 and GRA70 in the absence of IFNγ also identified these three proteins as interaction partners, so formation of the complex is likely IFNg-independent.

• In the absence of GRA70 or GRA71 is GRA57 expression and/or localization affected?*

We did not investigate this possibility in this manuscript, however doing so would require the generation of epitope tagged lines in knockout backgrounds. We believe this represents a significant body of work and would therefore be suitable for a future study focused on the further characterisation of this complex. The RNA-Seq data shows that GRA70 and GRA71 expression levels are not significantly different in the RH∆GRA57 strain (Supplementary Data 4) which we have now included as a statement in the results section.

• *Page 13, result section. To determine whether GRA57 has any direct or indirect effect on host cell gene expression, the authors performed RNA-Seq analysis of HFF cells pretreated or not with IFNγ. First, as for proteomic data, were the data deposited on GEO or another repository database? *

Second, were any effect detected on parasite gene expression? Reads alignment could be done using the T. gondii reference genome to determine whether IFNg or gra57 KO has any effect on parasite genes. Possibly, other secreted proteins not necessarily expressed at the tachyzoite stage and therefore not captured in the hyperLOPIT protein analysis are specifically expressed in these conditions.

We will deposit the RNA-Seq data on GEO prior to final publication. We did perform read alignment using the Toxoplasma gondii reference genome, and we agree it would be useful to include this analysis. We have now provided this data in Supplementary Data 4. Comparison of parasite gene expression between RH∆Ku80 and RH∆GRA57 revealed very few major changes (L2FC 2) that were also rescued in the RH∆GRA57::GRA57 line, irrespective of IFNg stimulation. Of the few genes that were up or downregulated in the RH∆GRA57 parasites, these were all uncharacterised. Collectively this data did not provide any mechanistic insight into the function of GRA57, and we think it unlikely the GRA57 phenotype is related to major changes in host or parasite gene expression. We have amended the manuscript to highlight this.

Optional comment: RNA-Seq analysis points to a clear induction of GBPs upon IFNγ treatment in HFF. Given the clear function of GBP in parasite clearance, have the authors ever hypothesized that GRA57 could be involved in preventing GBP binding to the PVM?

We have not tested if GBP recruitment is influenced by GRA57, however GBPs have previously been shown to be dispensable for restriction of Toxoplasma growth in HFFs (Niedelman et al 2013, PMID: 24042117) despite being robustly induced by IFNg stimulation (Kim et al 2007, PMID: 17404298). We have modified the manuscript to highlight this.

Minor comments

• Page 4, introduction, 8th paragraph. Regarding the role of IST, it might be less prone to controversy to state: 'a condition that may only be met in the early stages of infection.'

We agree and have changed this.

• Page 4, end of introduction. Changing '... indicating that the three proteins function in a complex'. Changing to '... indicating that the three proteins function in the same pathway.' might be more appropriate for the conclusion.

We agree and have changed this.

• Page 4, result section, first paragraph. 'strain specific and independent effectors'. Are the authors talking about strain-specific and non-strain-specific factors?

Yes- we have changed the text to reflect this.

- Page 6, result section. 'GRA25, an essential virulence factor in mice'. It is not clear to the reviewer how a virulence factor is essential since both parasite and mouse survival is achieved in the GRA25 mutant. I suggest to replace 'essential' by 'major'.

We agree and have changed this.

- Page 7. 'showing that GRA57 resides in the intravacuolar network (IVN) (Figure 2A)'. From the image shown, GRA57 clearly localizes into the PV, but it is hard to tell whether GRA57 is associated with the intravacuolar network. Colocalization assay or electron microscopy would be necessary to draw such conclusions.

We agree and have changed all references to this localisation as ‘intravacuolar’ instead of specifically the IVN.

- 'uprt locus'. Lower case letters and italic are generally preferred to designate mutants, whereas upper case letters are generally used for wild type alleles. (Sibley et al., Parasitology Today, 1991. Proposal for a uniform genetic nomenclature in Toxoplasma gondii).

We agree and have changed this.

- The authors mentioned in the introduction that ROP1 contributes to T. gondii resistance to IFNγ in murine and human macrophages. However, they did not comment on whether ROP1 was found important in the screen performed here in human HFF cells. It may be useful to reference ROP1 in Figure 1 as GRA15, GRA25, etc.

ROP1 was not found to be important in the HFF screens (+IFNg L2FCs in RH: -0.1, PRU: -0.46). As ROP1 was characterised as an IFNg resistance effector in macrophages, this discrepancy may therefore represent a cell type-specific difference, so we feel it is not relevant to highlight for the purposes of the screens presented here.

- Figure 2D. The authors compared the restriction effect of IFNγ on parasites grown in HFF and MEF host cells. However, as represented - % + IFNγ/- IFNγ - it cannot be estimated whether the parasites grew similarly in the two host cell types in the absence of IFN. Please indicate whether or not the growth was similar in both cell types.

As these restriction assays were not carried out concurrently and were designed to measure IFNg survival, we feel it would be inaccurate to compare parasite growth between the two cell types using this data. The focus of these experiments was to investigate the restrictive effect of IFNg across parasite strains, using the -IFNg condition to control for differences in growth rate or MOI. Therefore we feel it is appropriate for the focus of our manuscript to represent the data in this way.

- pUPRT plasmid. Any reference or vector map would be appreciated.

We have added the reference for this plasmid.

- Page 9, figure 3A, mass spectrometry analysis. I did not find the MS data in supplementals. Were the data deposited in on PRIDE database or another data repository?

The table was included as Supplementary Data 2, however this was not referred to in the main text. We have now amended the text to include this. The data will be deposited on PRIDE prior to final publication.

- Figures 3E and 3F. It might be worth mentioning, at least in the figure legend, that GRA3 localizes at PV membrane and is exposed to the host cell cytoplasm (to mediate interactions with host Golgi). The signal for GRA3 following saponin treatment is here an excellent control that should be highlighted, indicating that saponin effectively permeabilized the host cell membrane.

We agree and have updated the figure legend and the main text. We have also added a reference to Cygan et al 2021__ (__PMID: 34749525) in support of this data, which found GRA57, but not GRA70 or GRA71, enriched at the PVM.

• Page 11, section title. I think that the authors meant 'GRA57, GRA70 and GRA71 confer resistance to vacuole clearance in IFNγ-activated HFFs.'

We agree and have changed this.

• Page 11, in the result section comparing the effect of GRA57 mutant with MYR component KO, the authors are referring to host pathways that are counteracted by MYR-dependent effectors released into the host cell. It is not clear which pathways the authors are referring to.

It is not known exactly which host pathways mediate vacuole clearance or parasite growth restriction, or which MYR-dependent parasite effectors specifically resist these defences, therefore we have removed this statement from the text for clarity.

• Page 16, discussion, end of 4th paragraph. '... to promote parasite survival in IFNγ activated cells' sounds better.

We agree and have changed this.

• Page 22-23, Methods section, c-Myc nuclear translocation assays and elsewhere. Please indicate how many events were actually analyzed. For example, in this assay, to determine the median nuclear c-Myc signal, how many infected cells were analyzed for each biological replicate?

We have updated the methods section for the c-Myc nuclear translocation and ubiquitin-recruitment assays to include details on how many events were analysed.

**Referees cross-commenting**

Overall, I agree with most of the co-reviewers' remarks. I agree with reviewer #2 that this manuscript reports interesting data for the field of parasitology, but that the broad interest for immunologists is somewhat limited by the lack of a description of the mechanism by which these effectors oppose IFNgamma-inducible cell-autonomous defenses. I also agree with the other reviewers' comments regarding the GRA57, 70, and 71 heterotrimeric complex, which would require further description. In its present form, the manuscript undoubtedly represents an interesting starting point for further investigations and any additional data regarding the mode of interaction of the identified effectors and their function related or not to ubiquitylation would bring a significant added value.

Reviewer #1 (Significance (Required)):

Despite the fact that humans are accidental intermediate hosts for Toxoplasma gondii, the parasite may develop a persistent infection, demonstrating that it has effectively avoided host defenses. While Toxoplasma gondii has been extensively studied in mice, much less is known about the mechanisms by which the parasite establishes a chronic infection in humans. In this context, this article described very interesting data about the way this parasite counteracts human cell-autonomous innate immune system. This is a fascinating and important topic lying at the interface between parasitology and immunology. Indeed, the highly specialized secretory organelles characteristics of apicomplexan parasites are key to govern host-cell and parasite interactions ranging from host cell transcriptome modification to counteracting immune defense mechanisms. Overall, this article presents a significant contribution to the field of parasitology by identifying novel players involved in Toxoplasma gondii's evasion of human cell-autonomous immunity. Most conclusions are generally well supported by cutting-edge approaches and state of the art methods. Despite being a highly competitive field, this article stands out as the first screen designed specifically to identify virulence factors for human cells and extends our understanding of the secreted dense granule proteins resident of the parasitophorous vacuole. Importantly, the authors provide evidence that these players are active in different strain backgrounds and act in a way that is independent of the export machinery in charge of delivering effector proteins directly into the host cell. However, substantial further research is needed to fully understand the mechanism by which these novel players confer resistance to the parasite in IFNγ activated human cells and how their mode of action differs from that mediated by the translocation machinery (MYR complex). As a microbiologist and biochemist, I find this work of a particular interest to a broad audience, especially to parasitologists and immunologists, as it may unveil unexpected aspects of human innate immunity involved in parasite clearance with proteins unique to Apicomplexa phylum.

Reviewer #2

This paper reports high-quality genetic screening data identifying three novel Toxoplasma virulence factors (Gra57,70, and 71) that promote survival of two distinct Toxoplasma strains (type I RH and type II Pru) inside IFN-gamma primed human fibroblasts. Follow-up studies, exclusively focused on type I RH Toxoplasma, confirm the screening data. Gra57 IP Mass-Spec data suggest that Gra57, 70, and 71 may form a protein complex, a model supported by comparable IF staining patterns

Major:

- It is unclear what statistical metric was used to define screen hits as strain-dependent vs strain-independent. A standard approach would be to use a specific z-score value (often a z-score of 2) above or below best fit linear relationship between L2FCU for RH vs Pru as depicted in Fig.1D. Gra25 and Gra35 appear to be specific for Pru but it would be helpful to approach this type of categorization statistically. Also, such an analysis may reveal that only Pru-specific but not RH-specific hits were identified. Could the authors speculate why that would be?

We did not use a specific statistical metric to define screen hits as strain-dependent vs strain-independent, but GRA57 was selected as a strain-independent hit based on having a L2FC of RH specific: TGME49_309600 (GRA71) & CST9

PRU specific: GRA35, GRA25, ROP17, GRA23 & GRA45

Strain-independent: MYR3, GRA57, TGME49_249990 (GRA70) & MYR1

This agrees with our selection of strain-independent hits. However, we feel that using either L2FC or Z-score cut-offs is equally arbitrary, and we would therefore prefer to leave the data displayed without these cut-offs. It is indeed interesting that there appear to be more strain-specific hits in the PRU screen, but we cannot speculate as to why this may be as we did not explore this further here.

*- The paper proposes that Gra57, 70, and 71 form a heterotrimeric complex. This is based on the Mass-spec data from the original Gra57 pulldown, similar IF staining patterns, and comparable phenotypic presentation of the individual KO strains. However, only the MS data provide somewhat direct evidence for the formation a trimeric complex, and these data are by no means definitive. As this is a key finding of the MS, it should be further supported by additional biochemical data. Ideally, the authors should reconstitute the trimeric complex in vitro using recombinant proteins. Admittedly, this could be quite an undertaking with various potential caveats. Alternatively, reciprocal pulldowns of the 3 components could be performed. Super-resolution microscopy of the 3 Gra proteins might present another avenue to obtain more compelling evidence in support of the central claim of this work, *

We attempted a reciprocal pulldown using our GRA70-V5 line which unfortunately failed to verify the MS data, but we believe this is primarily due to differences in the affinity matrix that we used for this pulldown (anti-V5 vs anti-HA) and would require further optimisation or generation of a GRA70-HA line. However, while these revisions were being performed, another group published data demonstrating through pulldown of GRA57 and GRA70 that these proteins interact with each other, GRA71, and GRA32__ (__Krishnamurthy et al 2023, PMID: 36916910). We also identified GRA32 as enriched in our MS data, but to a less significant degree than GRA70 and GRA71. Together we believe that this independent data set is a robust validation of our findings, and strongly justifies the conclusion that these proteins form a complex.

We agree with the reviewer that further biochemical characterisation of the complex will be an interesting avenue for future research, but we feel it would require a substantial amount of further work. As suggested, super-resolution microscopy of the 3 proteins would require the generation of either double or triple tagged Toxoplasma lines, or antibodies against one or more of the complex members. Again, we feel this would represent a substantial body of further work. Reconstitution of the complex in vitro would require recombinant expression and purification of multiple large proteins that are all multidomain and possibly membrane associated/integrated. Assuming a 1:1:1 stoichiometric assembly this complex would be 446kDa. Purification of such proteins and reconstitution of the complex in vitro is therefore likely to represent many challenges and we do not feel this would be trivial to accomplish.

- The ubiquitin observations made in this paper are a bit preliminary and the authors' interpretation of their data is vague. The authors may want to re-consider that ubiquitylated delta Gra57 PVs are being destroyed with much faster kinetics than ubiquitylated WT PVs. The reduced number of ubiquitylated delta Gra57 PVs compared to ubiquitylated WT PVs across three timepoints (as shown by the authors in Fi. S8) does not disprove the 'fast kinetics model.' To test the fast kinetics ubiquitin-dependent null hypothesis, video microscopy could be used to measure the time from PV ubiquitylation onset to PV destruction

We agree with the reviewer that the possibility remains that GRA57 knockouts are cleared within the first hour of infection, and we have amended our text to reflect this. However, we think this is unlikely given that GRA57 knockouts are also less ubiquitinated in unstimulated cells, yet do not show any growth differences in unstimulated HFFs. Also considering the new data we have provided showing reduced recognition of GRA57 knockouts by the E3 ligase RNF213 (Figure 5D), we expect that the observed reduction in ubiquitination is highly likely to be unlinked to the increased susceptibility of GRA57 knockouts to IFNg. We have amended the discussion to state this conclusion more strongly.

The recently published manuscript that also identified GRA57/GRA70/GRA71 as effectors in HFFs showed that deletion of these effectors leads to premature egress from IFNg-activated HFFs__ (__Krishnamurthy et al 2023, PMID: 36916910). In light of this new data, we hypothesised that early egress could be causing the apparent reduction in ubiquitination. We have now provided data that disproves this hypothesis (Figure S10), as inhibition of egress did not rescue the ubiquitination phenotype. We also did not observe enhanced restriction of GRA57 knockout parasites at 3 hours post-infection (Figure S10B), suggesting clearance, or egress, happens after this time point.

We agree with the reviewer that determining the kinetics of IFNg restriction of these knockouts in HFFs would be interesting, however we feel this is more suited to future work. Imaging ubiquitin recruitment in live cells would also require the generation of new reporter host cell lines which would require a substantial amount of further work.

- Related to the point above. We know that different ubiquitin species are found at the PVM in IFNgamma-primed cells but to what degree each Ub species exerts an anti-parasitic effect is not well established. The paper only monitors total Ub at the PVM. Could it be that delta Gra57 PVs are enriched for a specific Ub species but depleted for another? The authors touch on this in the Discussion but these are easy experiments to perform and well within the scope of the study. At least the previously implicated ubiquitin species M1, K48, and K63 should be monitored and their colocalization with Toxo PVMs quantified

We agree that these experiments are within the scope of this study. We have now investigated the ubiquitin phenotype further by assessing the recruitment of M1, K48 and K63 ubiquitin linkages to the vacuoles of GRA57 knockouts. We observed depletion of both M1 and K63 linked ubiquitin. This data is now included in Figure 5 and Figure S8.

The E3 ligase RNF213 has recently been shown to facilitate recruitment of M1 and K63-linked ubiquitin to Toxoplasma vacuoles in HFFs (Hernandez et al 2022, PMID: 36154443 & Matta et al 2022, DOI: https://doi.org/10.1101/2022.10.21.513197 ). We therefore additionally assessed the recruitment of RNF213 to GRA57 knockouts, and found RNF213 recruitment was also reduced. Given that a reduction in RNF213 recruitment should correlate with a decrease in restriction, this data further supports our conclusion that the ubiquitin and restriction phenotypes are not causally linked. The observation that GRA57 knockouts are less susceptible to recognition by RNF213 also opens an exciting avenue for further research into the host recognition of Toxoplasma vacuoles by RNF213, for which currently the target is unknown.

Minor:

- For readers not familiar with Toxo genetics, the authors should include a sentence or two in the results section explaining the selection of HXGPRT deletion strains for the generation of Toxo libraries

We agree and have added this in.

- the highest scoring hits from the Pru screen (Gra35 &25) weren't investigated further. These hits appear to be specific for Pru. Some discussion as to why there are Pru-specific factors (but maybe not RH-specific factors) seems warranted

As mentioned above, we agree that it is indeed interesting that there appear to be more strain-specific hits in the PRU screen, but we cannot speculate as to why this may be as we did not explore the reasons for this further in this manuscript. Without substantial further investigation it cannot be determined whether these represent true strain-specific differences or reflect technical variability between the independent screens. We therefore feel it is sufficient to highlight effectors with the strongest phenotypes in each screen, without drawing strong conclusions regarding strain-specificity.

**Referees cross-commenting**

My reading of the comments is that there's consensus that this is a high quality study revealing novel Toxo effectors that undermine human cell-autonomous immunity and an important study in the field of parasitology. I might be the outlier that doesn't see much of an advance for the field of immunology since we don't really know what these effectors are doing, and the preliminary studies addressing this point are not well developed, with some confusing results.

My major comment #2 and rev#1's major comment #2 are, I think, essentially asking for the same thing, namely some more robust data on substantiating the formation of a trimeric complex.

My co-reviewers made great comments all across and I don't see any real discrepancies between the reviewers' comments - just some variation in what we, the reviewers, focused on

Reviewer #2 (Significance (Required)):

The discovery of a novel set of secreted Gra proteins critical for enhanced Toxoplasma survival specifically in IFNgamma primed human fibroblasts (but not mouse fibroblasts) is an important discovery for the Toxoplasma field. However, the study is somewhat limited in its scope as it fails to determine which, if any, specific IFNgamma-inducible cell-autonomous immune pathway is antagonized by Gra57 &Co. Instead, the paper reports that parasitophorous vacuoles (PVs) formed by Gra57 deletion mutants acquire less host ubiquitin than PVs formed by the parental WT strain. Because host-driven PV ubiquitylation is generally considered anti-parasitic, this observation is counterintuitive, and no compelling model is presented to explain these unexpected findings. Overall, this is a well conducted Toxoplasma research study with a few technical shortcomings that need to be addressed. However, in its current form, the study provides only limited insights into possible mechanisms by which Toxoplasma undermines human immunity. This study certainly provides an exciting starting point for further explorations.

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

Summary:

Toxoplasma gondii virulence and immune responsed upon infection in mice are well described. In contrast, little is known about human responses, particularly upon IFNγ-activation. However, host ubiquitination of the parasitophorous vacuole has been shown to be associated with parasite clearence in human cells.

Targeted CRISPR screens were used in the type I RH and type II Pru strain of Toxoplasma gondii to identify dense granule and rhoptry proteins. Human foreskin fibroblasts (HFFs) stimulated with IFNγ were used for infection of the knock-out parasites to identify guide RNAs and thus their corresponding genes to identify genes conferring growth benefits. Beside components of the MYR translocon, gra57 was identified. This gene was then knock-out or epitope-tagged in RH. The tagged line confirmed GRA57 localisation in the intravacuolar network confirming previously published work from another lab. Knock-out of gra57 lead to a moderate decrease in survival in HFFs, but not in mouse cells. Co-immunoprecipitation experiments with GRA57 identified 2 dense granule proteins that also display IFNγ-specific phenotypes with similar localisation as GRA57, and all are resistance factors in IFNγ-activated HFFs. Knock-out of GRA57 does not impact tryptophan metabolism, effector export of gene expression of the host cells. However, deletion of GRA57 or its interaction partners reduces ubiquitination of the parasitophorous vacuole.

Major comments:

This is a well executed study with informative, novel data. Here a few comments and questions:

- LFC cut-off of the CRISPR screen should be clearly stated.

We have amended this in the text.

- What is the rationale for using Prugniaud as the type II strain of choice and not ME49?

Both ME49 and PRU strains are widely used in the field, but as the PRU strain was used previously by our group for in vivo screens of Toxoplasma effectors (Young et al 2019 PMID: 31481656, Butterworth et al 2022 PMID: 36476844) ,using PRU here allows for direct comparison of our screening datasets.

- Figure 4A does not list all the significant genes that are then mentioned in the text below. This should be amended.

It is unclear what the reviewer is referring to here (Figure 4A displays restriction assay data).

*- RNA-Seq data is inadequately presented. Although, the actual genes regulated may be of secondary importance in this study, it would still be good to have a few key genes mentioned as a quality control statement. *

This was also raised by reviewer 1. We have now modified the manuscript to highlight that we observed robust induction of interferon-stimulated genes in our IFNg-treated conditions, but minimal differential gene expression between HFFs infected with the different parasite strains.

*- It is stated that "...GRA57 is not as important for survival in MEFs as in HFFS". With no significant change observed, it should be re-phrased to something like ""...indicatin that GRA57 is s important for survival in MEFs as in HFFS." *

We have re-phrased this statement.

*- Optional: GRA57 was described by the Bradley lab to be in the PV in tachyzoites and in the cyst wall in bradyzoites. Although it tissue cysts are not the focus of this paper and the knock-out is created also in a cyst-forming strain, it would have been useful to look for a phenotype of the knockout in cysts, in vitro at least, better both in in vitro and in vivo. In future, this could also be useful for the authors bringing in more citations. *

We agree with the reviewer that the impact of GRA57 on cyst formation would be an interesting topic for further exploration, however the focus of our study is on the role of secreted Toxoplasma effectors during the acute stages of infection.

Minor comments:

- Line numbers would be useful for an efficient review process.

We have added these to the revised manuscript.

- Strictly speaking, we have to talk about the sexual development taking place in felid and not feline hosts (Introduction; Felidae versus Felinae).

We have amended this in the text.

- Please insert spaces between numbers and units.

We have corrected this.

- Domain structures are presented, but maybe the AlphaFold 3D predictions could be added in a supplemental figure?

For GRA70 and GRA71 the AlphaFold 3D predictions are readily available on ToxoDB, whereas for GRA57 the prediction is not available due its size. We therefore independently analysed GRA57 using the full implementation of AlphaFold 2 (not ColabFold). We attempted submissions of putative discrete domains as well as the full-length protein, however both approaches yielded predictions with low confidence and low structural content, except for a ~100aa region of helical residues. We chose not to include the AlphaFold 3D predictions for all three proteins as the confidence for these predictions is low with pLDDT scores of commonly *- To improve the confidence of the co-immunoprecipitation, it would be necessary to use another tagged protein GRA70 or 71) and see if the same complex can be pulled down. Like this, one could also address what happens in a GRA57KO line? Do GRA70 and 71 stay together in the absence of GR57 forming a dimer? *

Reviewer 2 raised a similar point regarding the reciprocal pulldown, please see above for our detailed response to this. As suggested, we attempted a reciprocal pulldown using our GRA70-V5 line which unfortunately did not reconstitute the complex, but we believe this was due to technical differences in the epitope tag (V5 vs HA) and affinity matrix used. Overall, we believe that more detailed study of the assembly and biochemistry of this complex will require substantially more work and the generation of further cell lines, which would be beyond the scope of this study.

Reviewer #3 (Significance (Required)):

Significance:

This study endeavours to start closing an important knowledge gab of host defence in non-rodent hosts, especially humans. The data is solid using two different strains and yields novel insights into players of host cell resistance in humans against T. gondii. Using a targeted screening approach of rhoptry and dense granule proteins, they focused their interest on a subcategory of secreted proteins. The authors have not limited themselves to the screening and localisation study, but also investigated effect on host cells and host cell response. The identification of GRA57 being an important resistance factor and forming a heterodimer with GRA70 and GRA71 is novel. This study is of interest to cell biologists in the field of cyst-forming Coccidia, especially T. gondii and researchers interested in host resistance, parasite clearance by the host and parasite virulence.

I am a cell biologist working in Toxoplasma gondii and other Coccidians.

Author Response

Reviewer #2 (Public Review):

The authors unexpectedly found that the protein Grb2, an adaptor protein that mediates the recruitment of the Ras guanine-nucleotide exchange factor, SOS, to the EGF receptor, can be recruited to membranes by the immune cell tyrosine kinase Btk. The authors show, using total internal reflection fluorescence (TIRF) microscopy that the interaction with Grb2 is reversible, dependent on the proline-rich region of Btk, and independent of PIP3. These experiments are well performed and unambiguous.

The authors next asked whether Grb2 binding to Btk influences its kinase activity, by evaluating (i) Btk autophosphorylation and (ii) the phosphorylation of a peptide from the endogenous substrate PLCy1. The readout relies on non-specific antibody-mediated detection of phosphotyrosine but nevertheless reveals a concentration-dependent increase in both Btk autophosphorylation and PLCy1 phosphorylation. The experiments, however, have only been performed in duplicate and, particularly in the case of PLCy1 phosphorylation, exhibit enormous variability which is not reflected in the example blot the authors have chosen to display in Figure 3C. Comparison of the same, duplicate experiment presented in Figure 3 Supplement 2 paints a very different picture.

We added an experiment wherein we measure phosphorylation of the PLC𝛾2-peptide fusion by Btk in the presence of different concentrations of Grb2, and we have carried out LC-MS/MS to probe which Tyr are phosphorylated in these experiments. We have also modified our presentation of the Western blot data to allow readers to view each replicate separately. We believe this makes it easier to evaluate the trends observed in each replicate, and because the intensity measured here is only semi-quantitative, due to limitations of the technique, we believe this is a more accurate way to present our results. Both Tyr of the PLC𝛾2-peptide are phosphorylated, as well as one Tyr at the very C-terminus of GFP (Figure 3 – Supplements 3-5).

The authors next sought to determine which domains of Grb2 are required for activation of Btk. Again, these experiments were only performed in duplicates, and the authors’ claims that Grb2 can moderately stimulate the SH3-SH2-kinase module of Grb2 are not well supported by their data (Figure 4C-D).

We have opted to remove the data for the activation of the SH3-SH2-kinase construct (Src module) from the revised manuscript. Upon further inspection, we agree that these experiments only showed a weak trend and believe that much more experimentation is needed to draw firm conclusions regarding this construct. We do still speculate that SH2 linker displacement may contribute to our observations of enhanced catalytic activity of Btk in the presence of Grb2, however this speculation is based solely on previous work with Btk and other kinases (Aryal et al., 2022; Moarefi et al., 1997).

The authors next asked whether Grb2 stimulates Btk by promoting its dimerization and trans- autophosphorylation. The authors measured the diffusion coefficient of Btk on PIP3- containing supported lipid bilayers in the presence and absence of Grb2. They noted that the diffusion coefficient of individual Btk particles decreases with increasing unlabeled Btk, which they interpret as Btk dimerization. Grb2 does not appear to influence the diffusion of Btk on the membrane (Figure 5A). Presumably, the diffusion coefficient reported here is the average of a number of single-molecule tracks, which should result in error bars. It is unclear why these have not been reported. Next, the authors assessed the ability of Grb2 to stimulate a mutant of Btk that is impaired in its ability to dimerize on PIP3-containing membranes. In contrast to wild-type Btk, autophosphorylation of dimerization-deficient Btk is not enhanced by Grb2. Whilst the data are consistent with this conclusion, again, the experiment has only been repeated once and the western blot presented in Figure 5 Supplement 2 is unreadable. It is also puzzling why Grb2 gets phosphorylated in this experiment, but not in the same experiment reported in Figure 3 Supplement 2.

The diffusion coefficient reported here is determined from a large number of single molecule tracks. We have expanded our explanation of how this is done in the Materials and Methods, as well as providing an example of the data and fits from one of the conditions in Figure 4 – Supplement 3. We are now including standard deviation for each diffusion coefficient determined from the fit of the step size distribution.

We have opted to remove the data involving the dimerization-deficient Btk construct. We agree that these results are difficult to interpret.

We have investigated the Grb2 phosphorylation signal and concluded that this is an off-target effect of the antibody. MS/MS cannot detect and phosphorylation on Grb2. We now comment on this in the figure legend of Figure 3 – Supplement 2.

Finally, the authors argue that Grb2 facilitates the recruitment of Btk to molecular condensates of adaptor and scaffold proteins immobilized on a supported lipid bilayer (SLB) (Figure 6). This is a highly complex series of experiments in which various components are added to supported lipid bilayers and the diffusion of labelled Btk is measured. When Btk is added to SLBs containing the LAT adaptor protein (phosphorylated in situ by Hck immobilized on the membrane via its His tag), it exhibits similar mobility to LAT alone, and its mobility is decreased by the addition of Grb2. The addition of the proline-rich region (PRR) of SOS further decreases this mobility. In this final condition, the authors incubate the reactions for 1 h until LAT undergoes a phase transition, forming gel-like, protein-rich domains on the membrane, shown in Figure 6B. The authors’ conclusion that Btk is recruited into these phase-separated domains based on a slow-down in its diffusion is not well supported by the data, which rather indicates that Btk is excluded from these domains (Figure 6B – Btk punctae (green) are almost exclusively found in between the LAT condensates (red)). As such, the restricted mobility of Btk that the authors report may simply reflect the influence of barriers to diffusion on the membrane that result from LAT condensation into phase- separated domains. The authors also present data in Figure 6 Supplement 1 indicating that Grb2 recruitment to Btk is out-competed by SOS-PRR and that Btk does not support the co- recruitment of Grb2 and SOS-PRR to the membrane. These data would appear to suggest that the authors’ interpretation of the decreased mobility of Btk on the membrane may not be correct.

We have now included an example of one of the single molecule videos, overlayed with the surrounding LAT phase, to more directly display the data that was recorded for this experiment. In this video, it is possible to see that the LAT dense phase occupies only some of the observed window, and although it is possible that these dense “islands” function as barriers to Btk diffusion, Btk would be expected to diffuse freely outside of the LAT dense areas of the bilayer. This property can be clearly seen in the video we have now included. This is reminiscent of what was observed previously during the LAT phase transition for tracking of LAT itself (Sun et al., 2022). Given the extensive previous analysis of LAT diffusion on supported lipid bilayers (Lin et al., 2022; Sun et al., 2022), we believe the necessary controls have been included to support our conclusions. However, we agree there is much to be learned about this interaction and we hope that future studies will further investigate the relationship between cytoplasmic kinases and plasma membrane associated signaling clusters.

Reviewer #3 (Public Review):

The study of Nocka and colleagues examines the role of membrane scaffolding in Btk kinase activation by the Grb2 adaptor protein. The studies appear to make a case for a reinterpretation of the "Saraste dimer" of Btk as a signaling entity and assigns roles to the component domains in the Src module in Btk activation. The point of distinction from earlier studies is that this work ascribes a function to an adaptor protein as promoting the kinase activation, rather than vice versa, and also illustrates why Btk can be activated via modes distinct from its close relative, such as Itk. Importantly, these studies address these key questions through membrane tethering of Btk, which is a successful, reductionist way to mimic cellular scenarios. The writing could be improved and can absolutely be more economical in word choice and use; currently, there is a good deal of background to each section that is not always comprehensive or crucial to contextualise the findings, while key information is often omitted. The results are currently not described in a detailed manner so there is an imbalance between the findings, which should be the focus, relative to background and interpretations or models.

We have assessed the manuscript and made many improvements to shift the focus to the findings, while providing only the necessary background for readers unfamiliar with the specifics of Btk and Grb2 signaling and structure.

Reviewer #2 (Public Review):

The authors unexpectedly found that the protein Grb2, an adaptor protein that mediates the recruitment of the Ras guanine-nucleotide exchange factor, SOS, to the EGF receptor, can be recruited to membranes by the immune cell tyrosine kinase Btk. The authors show, using total internal reflection fluorescence (TIRF) microscopy that the interaction with Grb2 is reversible, dependent on the proline-rich region of Btk, and independent of PIP3. These experiments are well performed and unambiguous.

The authors next asked whether Grb2 binding to Btk influences its kinase activity, by evaluating (i) Btk autophosphorylation and (ii) the phosphorylation of a peptide from the endogenous substrate PLC1. The readout relies on non-specific antibody-mediated detection of phosphotyrosine but nevertheless reveals a concentration-dependent increase in both Btk autophosphorylation and PLCy1 phosphorylation. The experiments, however, have only been performed in duplicate and, particularly in the case of PLCy1 phosphorylation, exhibit enormous variability which is not reflected in the example blot the authors have chosen to display in Figure 3C. Comparison of the same, duplicate experiment presented in Figure 3 Supplement 2 paints a very different picture.

The authors next sought to determine which domains of Grb2 are required for activation of Btk. Again, these experiments were only performed in duplicates, and the authors' claims that Grb2 can moderately stimulate the SH3-SH2-kinase module of Grb2 are not well supported by their data (Figure 4C-D).

The authors next asked whether Grb2 stimulates Btk by promoting its dimerization and trans-autophosphorylation. The authors measured the diffusion coefficient of Btk on PIP3-containing supported lipid bilayers in the presence and absence of Grb2. They noted that the diffusion coefficient of individual Btk particles decreases with increasing unlabeled Btk, which they interpret as Btk dimerization. Grb2 does not appear to influence the diffusion of Btk on the membrane (Figure 5A). Presumably, the diffusion coefficient reported here is the average of a number of single-molecule tracks, which should result in error bars. It is unclear why these have not been reported. Next, the authors assessed the ability of Grb2 to stimulate a mutant of Btk that is impaired in its ability to dimerize on PIP3-containing membranes. In contrast to wild-type Btk, autophosphorylation of dimerization-deficient Btk is not enhanced by Grb2. Whilst the data are consistent with this conclusion, again, the experiment has only been repeated once and the western blot presented in Figure 5 Supplement 2 is unreadable. It is also puzzling why Grb2 gets phosphorylated in this experiment, but not in the same experiment reported in Figure 3 Supplement 2.

Finally, the authors argue that Grb2 facilitates the recruitment of Btk to molecular condensates of adaptor and scaffold proteins immobilized on a supported lipid bilayer (SLB) (Figure 6). This is a highly complex series of experiments in which various components are added to supported lipid bilayers and the diffusion of labelled Btk is measured. When Btk is added to SLBs containing the LAT adaptor protein (phosphorylated in situ by Hck immobilized on the membrane via its His tag), it exhibits similar mobility to LAT alone, and its mobility is decreased by the addition of Grb2. The addition of the proline-rich region (PRR) of SOS further decreases this mobility. In this final condition, the authors incubate the reactions for 1 h until LAT undergoes a phase transition, forming gel-like, protein-rich domains on the membrane, shown in Figure 6B. The authors' conclusion that Btk is recruited into these phase-separated domains based on a slow-down in its diffusion is not well supported by the data, which rather indicates that Btk is excluded from these domains (Figure 6B - Btk punctae (green) are almost exclusively found in between the LAT condensates (red)). As such, the restricted mobility of Btk that the authors report may simply reflect the influence of barriers to diffusion on the membrane that result from LAT condensation into phase-separated domains. The authors also present data in Figure 6 Supplement 1 indicating that Grb2 recruitment to Btk is out-competed by SOS-PRR and that Btk does not support the co-recruitment of Grb2 and SOS-PRR to the membrane. These data would appear to suggest that the authors' interpretation of the decreased mobility of Btk on the membrane may not be correct.

This reminds me of farm business behavior, when farmers buy and sell cattle, pigs, etc. based on whether or not they will serve them well in their business.

https://docs.google.com/presentation/d/1gR4gyZxr9CAc8KoN0Cdlpjpp0305CZ8ZpNWRHfMq83w/edit#slide=id.g228c93eec09_0_0

TAG Feedback Protocol Prompts for responding to classmates - Tell some things you like - Ask thoughtful questions - Give suggestions

(Source: https://learninginhand.com/blog/feedbackchat)

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Via: https://hypothes.is/a/8iTBtM8dEe29byc2oLQk-w

话语分析的工具有什么

reply to Christoph Thiede at https://norden.social/@LinqLover/110011970287271976

@LinqLover@norden.social @academia@a.gup.pe @zettelkasten@a.gup.pe @zettelkasten@mobilize.berlin @academicchatter@a.gup.pe @academicsunite@a.gup.pe If I understand your question properly, you're presumably using a paper zettelkasten and not a digital one? The issue is that of "multiple storage". Niklas Luhmann solved this by numbering his cards (using a Dewey-like system) and then creating an index for the subjects to be able to find them. John Locke did roughly the same thing with his indexing method for commonplace books.

cf. https://hypothes.is/users/chrisaldrich?q=tag%3A%22multiple+storage%22 and https://publicdomainreview.org/collection/john-lockes-method-for-common-place-books-1685

In the digital domain I rely on relational databases or heavy tagging and digital search. For an example, see again the Hypothesis link above.

"Classical" ZK prior to Luhmann simply made multiple copies and distributed them, though updating them was nearly impossible.

Jon Udell's Tag Rename tool for Hypothes.is.

again loading control? also there is still some p53 detected in the column-bound without CHCH present? also;;;; binding to his6 tag? Need another experiment to confirm interaction, this and imaging is not really enough?

```js var name = 'Alfred'; var age = 47;

function greet(){ console.log(arguments[0]); console.log(arguments[1]); console.log(arguments[2]); } greetI'm ${name}. I'm ${age} years old.; ```

Author Response

Reviewer #1 (Public Review):

How morphogens spread within tissues remains an important question in developmental biology. Here the authors revisit the role of glypicans in the formation of the Dpp gradient in wing imaginal discs of Drosophila. They first use sophisticated genome engineering to demonstrate that the two glypicans of Drosophila are not equivalent despite being redundant for viability. They show that Dally is the relevant glypican for Dpp gradient formation. They then provide genetic evidence that, surprisingly, the core domain of Dally suffices to trap Dpp at the cell surface (suggesting a minor role for GAGs). They conclude with a model that Dally modulates the range of Dpp signaling by interfering with Dpp's degradation by Tkv. These are important conclusions, but more independent (biochemical/cell biological) evidence is needed.

As indicated above, the genetic evidence for the predominant role of Dally in Dpp protein/signalling gradient formation is strong. In passing, the authors could discuss why overexpressed Dlp has a negative effect on signaling, especially in the anterior compartment. The authors then move on to determine the role of GAG (=HS) chains of Dally. They find that in an overexpression assay, Dally lacking GAGs traps Dpp at the cell surface and, counterintuitively, suppresses signaling (fig 4 C, F). Both findings are unexpected and therefore require further validation and clarification, as outlined in a and b below.

a) In loss of function experiments (dallyDeltaHS replacing endogenous dally), Dpp protein is markedly reduced (fig 4R), as much as in the KO (panel Q), suggesting that GAG chains do contribute to trapping Dpp at the cell surface. This is all the more significant that, according to the overexpression essays, DallyDeltaHS seems more stable than WT Dally (by the way, this difference should also be assessed in the knock-ins, which is possible since they are YFP-tagged). The authors acknowledge that HS chains of Dally are critical for Dpp distribution (and signaling) under physiological conditions. If this is true, one can wonder why overexpressed dally core 'binds' Dpp and whether this is a physiologically relevant activity.

According to the overexpression assay, DallyDeltaHS seems more stable than WT Dally (Fig. 4B’, E’, 5H, I). As the reviewer suggested, we addressed the difference using the two knock-in alleles and found that DallyDeltaHS is more stable than WT Dally (Fig.4 L, M inset), further emphasizing the insufficient role of core protein of Dally for extracellular Dpp distribution.

(During the revising our figure, we found labeling mistake in Fig. 4M, N and Fig. 4Q, R and corrected the genotypes.)

In summary, we showed that, although Dally interacts with Dpp mainly through its core protein from the overexpression assay (Fig. 4E, I), HS chains are essential for extracellular Dpp distribution (Fig. 4R). Thus, the core protein of Dally alone is not sufficient for extracellular Dpp distribution under physiological conditions. These results raise a question about whether the interaction of core protein of Dally with Dpp is physiologically relevant. Since the increase of HS upon dally expression but not upon dlp expression resulted in the accumulation of extracellular Dpp (Fig. 2) and this accumulation was mainly through the core protein of Dally (Fig. 4E, I), we speculate that the interaction of the core protein of Dally with Dpp gives ligand specificity to Dally under physiological conditions.

To understand the importance of the interaction of core protein of Dally with Dpp under physiological conditions, it is important to identify a region responsible for the interaction. Our preliminary results overexpressing a dally mutant lacking the majority of core protein (but keeping the HS modified region intact) showed that HS chains modification was also lost. Although this is consistent with our results that enzymes adding HS chains also interact with the core protein of Dally (Fig. 4D), the dally mutant allele lacking the core protein would hamper us from distinguishing the role of core protein of Dally from HS chains.

Nevertheless, we can infer the importance of the interaction of core protein of Dally with Dpp using dally[3xHA-dlp, attP] allele, where dlp is expressed in dally expressing cells. Since Dally-like is modified by HS chains but does not interact with Dpp (Fig. 2, 4), dally[3xHA-dlp, attP] allele mimics a dally allele where HS chains are properly added but interaction of core protein with Dpp is lost. As we showed in Fig.3O, S, the allele could not rescue dallyKO phenotypes, consistent with the idea that interaction of core protein of Dally with Dpp is essential for Dpp distribution and signaling and HS chain alone is not sufficient for Dpp distribution.

b) Although the authors' inference that dallycore (at least if overexpressed) can bind Dpp. This assertion needs independent validation by a biochemical assay, ideally with surface plasmon resonance or similar so that an affinity can be estimated. I understand that this will require a method that is outside the authors' core expertise but there is no reason why they could not approach a collaborator for such a common technique. In vitro binding data is, in my view, essential.

We agree with the reviewer that a biochemical assay such as SPR helps us characterize the interaction of core protein of Dally and Dpp (if the interaction is direct), although the biochemical assay also would not demonstrate the interaction under the physiological conditions.

However, SPR has never been applied in the case of Dpp, probably because purifying functional refolded Dpp dimer from bacteria has previously been found to be stable only in low pH and be precipitated in normal pH buffer (Groppe J, et al., 1998)(Matsuda et al., 2021). As the reviewer suggests, collaborating with experts is an important step in the future.

Nevertheless, SPR was applied for the interaction between BMP4 and Dally (Kirkpatrick et al., 2006), probably because BMP4 is more stable in the normal buffer. Although the binding affinity was not calculated, SPR showed that BMP4 directly binds to Dally and this interaction was only partially inhibited by molar excess of exogenous HS, suggesting that BMP4 can interact with core protein of Dally as well as its HS chains. In addition, the same study applied Co-IP experiments using lysis of S2 cells and showed that Dpp and core protein of Dally are co-immunoprecipitated, although it does not demonstrate if the interaction is direct.

In a subsequent set of experiments, the authors assess the activity of a form of Dpp that is expected not to bind GAGs (DppDeltaN). Overexpression assays show that this protein is trapped by DallyWT but not dallyDeltaHS. This is a good first step validation of the deltaN mutation, although, as before, an invitro binding assay would be preferable.

Our overexpression assays actually showed that DppDeltaN is trapped by DallyWT and by dallyDeltaHS at similar levels (Fig. 5H-J), indicating that interaction of DppDeltaN and HS chains of Dally is largely lost but DppDeltaN can still interact with core protein of Dally.

(Related to this, we found typo in the sentence “In contrast, the relative DppΔN accumulation upon DallyΔHS expression in JAX;dppΔN was comparable to that upon DallyΔHS expression in JAX;dppΔN (Fig. 5H-J).” and corrected as follows, “In contrast, the relative DppΔN accumulation upon Dally expression in JAX;dppΔN was comparable to that upon DallyΔHS expression in JAX;dppΔN (Fig. 5H-J).”

We thank the reviewer for the suggesting the in vitro experiment. Although we decided not to develop biophysical experiments such as SPR for Dpp in this study due to the reasons discussed above, we would like to point out that our result is consistent with a previous Co-IP experiment using S2 cells showing that DppDeltaN loses interaction with heparin (Akiyama2008).

However, in contrast to our results, the same study also proposed by Co-IP experiments using S2 cells that DppDeltaN loses interaction with Dally (Akiyama2008). Although it is hard to conclude since western blotting was too saturated without loading controls and normalization (Fig. 1C in Akiyama 2008), and negative in vitro experiments do not necessarily demonstrate the lack of interaction in vivo. One explanation why the interaction was missed in the previous study is that some factors required for the interaction of DppDeltaN with core protein of Dally are missing in S2 cells. In this case, in vivo interaction assay we used in this study has an advantage to robustly detect the interaction.

Nevertheless, the authors show that DppDeltaN is surprisingly active in a knock-in strain. At face value (assuming that DeltaN fully abrogates binding to GAGs), this suggests that interaction of Dpp with the GAG chains of Dally is not required for signaling activity. This leads to authors to suggest (as shown in their final model) that GAG chains could be involved in mediating the interactions of Dally with Tkv (and not with Dpp. This is an interesting idea, which would need to be reconciled with the observation that the distribution of Dpp is affected in dallyDeltaHS knock-ins (item a above). It would also be strengthened by biochemical data (although more technically challenging than the experiments suggested above). In an attempt to determine the role of Dally (GAGs in particular) in the signaling gradient, the paper next addresses its relation to Tkv. They first show that reducing Tkv leads to Dpp accumulation at the cell surface, a clear indication that Tkv normally contributes to the degradation of Dpp. From this they suggest that Tkv could be required for Dpp internalisation although this is not shown directly. The authors then show that a Dpp gradient still forms upon double knockdown (Dally and Tkv). This intriguing observation shows that Dally is not strictly required for the spread of Dpp, an important conclusion that is compatible with early work by Lander suggesting that Dpp spreads by free diffusion. These result show that Dally is required for gradient formation only when Tkv is present. They suggest therefore that Dally prevents Tkv-mediated internalisation of Dpp. Although this is a reasonable inference, internalisation assays (e.g. with anti-Ollas or anti-HA Ab) would strengthen the authors' conclusions especially because they contradict a recent paper from the Gonzalez-Gaitan lab.

Thanks for suggesting the internalization assay. As we discussed in the discussion, our results suggest that extracellular Dpp distribution is severely reduced in dally mutants due to Tkv mediated internalization of Dpp (Fig. 6). Thus, extracellular Dpp available for labelling with nanobody is severely reduced in dally mutants, which can explain the reduced internalization of Dpp in dally mutants in the internalization assay. Therefore, we think that the nanobody internalization assay would not distinguish the two contradicting possibilities.

The paper ends with a model suggesting that HS chains have a dual function of suppressing Tkv internalisation and stimulating signaling. This constitutes a novel view of a glypican's mode of action and possibly an important contribution of this paper. As indicated above, further experiments could considerably strengthen the conclusion. Speculation on how the authors imagine that GAG chains have these activities would also be warranted.

Thank you very much!

Reviewer #2 (Public Review):

The authors are trying to distinguish between four models of the role of glypicans (HSPGs) on the Dpp/BMP gradient in the Drosophila wing, schematized in Fig. 1: (1) "Restricted diffusion" (HSPGs transport Dpp via repetitive interaction of HS chains with Dpp); (2) "Hindered diffusion" (HSPGs hinder Dpp spreading via reversible interaction of HS chains with Dpp); (3) "Stabilization" (HSPGs stabilize Dpp on the cell surface via reversible interaction of HS chains with Dpp that antagonizes Tkv-mediated Dpp internalization); and (4) "Recycling" (HSPGs internalize and recycle Dpp).

To distinguish between these models, the authors generate new alleles for the glypicans Dally and Dally-like protein (Dlp) and for Dpp: a Dally knock-out allele, a Dally YFP-tagged allele, a Dally knock-out allele with 3HA-Dlp, a Dlp knock-out allele, a Dlp allele containing 3-HA tags, and a Dpp lacking the HS-interacting domain. Additionally, they use an OLLAS-tag Dpp (OLLAS being an epitope tag against which extremely high affinity antibodies exist). They examine OLLAS-Dpp or HA-Dpp distribution, phospho-Mad staining, adult wing size.

They find that over-expressed Dally - but not Dlp - expands Dpp distribution in the larval wing disc. They find that the Dally[KO] allele behaves like a Dally strong hypomorph Dally[MH32]. The Dally[KO] - but not the Dlp[KO] - caused reduced pMad in both anterior and posterior domains and reduced adult wing size (particularly in the Anterior-Posterior axis). These defects can be substantially corrected by supplying an endogenously tagged YFP-tagged Dally. By contrast, they were not rescued when a 3xHA Dlp was inserted in the Dally locus. These results support their conclusion that Dpp interacts with Dally but not Dlp.

They next wanted to determine the relative contributions of the Dally core or the HS chains to the Dpp distribution. To test this, they over-expressed UAS-Dally or UAS-Dally[deltaHS] (lacking the HS chains) in the dorsal wing. Dally[deltaHS] over-expression increased the distribution of OLLAS-Dpp but caused a reduction in pMad. Then they write that after they normalize for expression levels, they find that Dally[deltaHS] only mildly reduces pMad and this result indicates a major contribution of the Dally core protein to Dpp stability.

Thanks for the comments. We actually showed that compared with Dally overexpression, Dally[deltaHS] overexpression only mildly reduces extracellular Dpp accumulation (Fig. 4I). This indicates a major contribution of the Dally core protein to interaction with Dpp, although the interaction is not sufficient to sustain extracellular Dpp distribution and signaling gradient.

The "normalization" is a key part of this model and is not mentioned how the normalization was done. When they do the critical experiment, making the Dally[deltaHS] allele, they find that loss of the HS chains is nearly as severe as total loss of Dally (i.e., Dally[KO]). Additionally, experimental approaches are needed here to prove the role of the Dally core.

Since the expression level of Dally[deltaHS] is higher than Dally when overexpressed, we normalized extracellular Dpp distribution (a-Ollas staining) against GFP fluorescent signal (Dally or Dally[deltaHS]). To do this, we first extracted both signal along the A-P axis from the same ROI. The ratio was calculated by dividing the intensity of a-Ollas staining with the intensity of GFP fluorescent signal at a given position x. The average profile from each normalized profile was generated and plotted using the script described in the method (wingdisc_comparison.py) as other pMad or extracellular staining profiles.

Although this analysis provides normalized extracellular Dpp accumulation at different positions along the A-P axis, we are more interested in the total amount of Dpp or DppDeltaN accumulation upon Dally or dallyDeltaHS expression. Therefore, we plan to analyze the normalized total amount of Dpp against GFP fluorescent signal (Dally or Dally[deltaHS]) in the revised ms. In this case, normalization will be performed by dividing total signal intensity of extracellular Dpp staining (ExOllas staining) divided by GFP fluorescent signal (Dally or Dally[deltaHS]) in ROI in each wing disc.

We agree with the reviewer that additional experimental approaches are needed to address the role of the core protein of Dally. As we discussed in the response to the reviewer1, to understand the importance of the interaction of core protein of Dally with Dpp, it is important to identify a region responsible for the interaction. Our preliminary results overexpressing a dally mutant lacking the majority of core protein (but keeping the HS modified region intact) showed that HS chains modification was also lost. Although this is consistent with our results that enzymes adding HS chains also interact with the core protein of Dally (Fig. 4D), the dally mutant allele lacking the core protein would hamper us from distinguishing the role of the core protein of Dally from HS chains.

Nevertheless, we can infer the importance of the interaction of core protein of Dally with Dpp using dally[3xHA-dlp, attP] allele, where dlp is expressed in dally expressing cells. Since Dally-like is modified by HS chains but does not interact with Dpp (Fig. 2, 4), dally[3xHA-dlp, attP] allele mimics a dally allele where HS chains are properly added but interaction of core protein with Dpp is lost. As we showed in Fig.3O, S, the allele could not rescue dallyKO phenotypes, consistent with the idea that interaction of core protein of Dally with Dpp is essential for Dpp distribution and signaling.

Prior work has shown that a stretch of 7 amino acids in the Dpp N-terminal domain is required to interact with heparin but not with Dpp receptors (Akiyama, 2008). The authors generated an HA-tagged Dpp allele lacking these residues (HA-dpp[deltaN]). It is an embryonic lethal allele, but they can get some animals to survive to larval stages if they also supply a transgene called “JAX” containing dpp regulatory sequences. In the JAX; HA-dpp[deltaN] mutant background, they find that the distribution and signaling of this Dpp molecule is largely normal. While over-expressed Dally can increase the distribution of HA-dpp[deltaN], over-expression of Dally[deltaHS] cannot. These latter results support the model that the HS chains in Dally are required for Dpp function but not because of a direct interaction with Dpp.

Our overexpression assays actually showed that both Dally and Dally[deltaHS] can accumulate Dpp upon overexpression and the accumulation of Dpp is comparable after normalization (Fig. 5H-J), consistent with the idea that interaction of DppdeltaN and HS chains are largely lost. As the reviewer pointed out, these results support the model that the HS chains in Dally are required for Dpp function but not because of a direct interaction with Dpp.

In the last part of the results, they attempt to determine if the Dpp receptor Thickveins (Tkv) is required for Dally-HS chains interaction. The 2008 (Akiyama) model posits that Tkv activates pMad downstream of Dpp and also internalizes and degrades Dpp. A 2022 (Romanova-Michaelides) model proposes that Dally (not Tkv) internalizes Dpp.

To distinguish between these models, the authors deplete Tkv from the dorsal compartment of the wing disc and found that extracellular Dpp increased and expanded in that domain. These results support the model that Tkv is required to internalize Dpp.

They then tested the model that Dally antagonizes Tkv-mediated Dpp internalization by determining whether the defective extracellular Dpp distribution in Dally[KO] mutants could be rescued by depleting Tkv. Extracellular Dpp did increase in the D vs V compartment, potentially providing some support for their model. However, there are no statistics performed, which is needed for full confidence in the results. The lack of statistics is particularly problematic (1) when they state that extracellular Dpp does not rise in ap>tkv RNAi vs ap>tkv RNAi, dally[KO] wing discs (Fig. 6E) or (2) when they state that extracellular Dpp gradient expanded in the dorsal compartment when tkv was dorsally depleted in dally[deltaHS] mutants (Fig. 6I). These last two experiments are important for their model but the differences are assessed only visually. In fact, extracellular Dpp in ap>tkv RNAi, dally[KO] (Fig. 6B) appears to be lower than extracellular Dpp in ap>tkv RNAi (Fig. 6A) and the histogram of Dpp in ap>tkv RNAi, dally[KO] is actually a bit lower than Dpp in ap>tkv RNAi, But the author claim that there is no difference between the two. Their conclusion would be strengthened by statistical analyses of the two lines.

We will provide the statistical analyses in the revised ms.

Strengths:

1) New genomically-engineered alleles

A considerable strength of the study is the generation and characterization of new Dally, Dlp and Dpp alleles. These reagents will be of great use to the field.

Thanks. We hope that these resources are indeed useful to the field.

2) Surveying multiple phenotypes

The authors survey numerous parameters (Dpp distribution, Dpp signaling (pMad) and adult wing phenotypes) which provides many points of analysis.

Thanks!

Weaknesses:

1) Confusing discussion regarding the Dally core vs HS in Dpp stability. They don't provide any measurements or information on how they "normalize" for the level of Dally vs Dally[deltaHS]? This is important part of their model that currently is not supported by any measurements.

We explained how we normalized in the above section. We will update the analysis in the revised ms.

2) Lacking quantifications and statistical analyses:

a) Why are statistical significance for histograms (pMad and Dpp distribution) not supplied? These histograms provide the key results supporting the authors' conclusions but no statistical tests/results are presented. This is a pervasive shortcoming in the current study.

Thanks. We will provide statistics in the revised ms.

b) dpp[deltaN] with JAX transgene - it would strengthen the study to supply quantitative data on the percent survival/lethal stage of dpp[deltaN] mutants with or without the JAK transgene

In this study, we are interested in the role of dpp[deltaN] during the wing disc development. Therefore, we decided not to perform the detailed analysis on the percent survival/lethal stage of dpp[deltaN] mutants with or without the JAX transgene in the current study. Nevertheless, the fact that dpp[deltaN] allele is maintained with a balanced stock and JAX;dpp[deltaN] allele can be maintained as homozygous stock indicates that the lethality of dpp[deltaN] allele comes from the early stages. Indeed, our preliminary results showed that pMad signal is severely lost in the dpp[deltaN] embryo without JAX (data not shown), indicating that the allele is lethal at early embryonic stages.

c) The graphs on wing size etc should start at zero.

Thanks. We corrected this in the current ms.

d) The sizes of histograms and graphs in each figure should be increased so that the reader can properly assess them. Currently, they are very small.

Thanks. We changed the sizes in the current ms.

The authors' model is that Dally (not Dlp) is required for Dpp distribution and signaling but that this is not due to a direct interaction with Dpp. Rather, they posit that Dally-HS antagonize Tkv-mediated Dpp internalization. Currently the results of the experiments could be considered consistent with their model, but as noted above, the lack of statistical analyses of some parameters is a weakness.

Thanks. We will perform the statistical analyses in the revised ms.

One problematic part of their result for me is the role of the Dally core protein (Fig. 7B). There is a mis-match between the over-expression results and Dally allele lacking HS (but containing the core). Finally, their results support the idea that one or more as-yet unidentified proteins interact with Dally-HS chains to control Dpp distribution and signaling in the wing disc.

Our results simply suggest that Dpp can interact with Dally mainly through core protein but this interaction is not sufficient to sustain extracellular Dpp gradient formation under physiological conditions (dallyDeltaHS) (Fig. 4Q). We find that the mis-match is not problematic if the role of Dally is not simply mediated through interaction with Dpp. We speculate that interaction of Dpp and core protein of Dally is transient and not sufficient to sustain the Dpp gradient without HS chains of Dally stabilizing extracellular Dpp distribution by blocking Tkv-mediated Dpp internalization.

There is much debate and controversy in the Dpp morphogen field. The generation of new, high quality alleles in this study will be useful to Drosophila community, and the results of this study support the concept that Tkv but not Dally regulate Dpp internalization. Thus the work could be impactful and fuel new debates among morphogen researchers.

Thanks.

The manuscript is currently written in a manner that really is only accessible to researchers who work on the Dpp gradient. It would be very helpful for the authors to re-write the manuscript and carefully explain in each section of the results (1) the exact question that will be asked, (2) the prior work on the topic, (3) the precise experiment that will be done, and (4) the predicted results. This would make the study more accessible to developmental biologists outside of the morphogen gradient and Drosophila communities.

Thanks. We will modify our texts to help non-experts understand our story in the revised ms.

Reviewer #2 (Public Review): 

The authors are trying to distinguish between four models of the role of glypicans (HSPGs) on the Dpp/BMP gradient in the Drosophila wing, schematized in Fig. 1: (1) "Restricted diffusion" (HSPGs transport Dpp via repetitive interaction of HS chains with Dpp); (2) "Hindered diffusion" (HSPGs hinder Dpp spreading via reversible interaction of HS chains with Dpp); (3) "Stabilization" (HSPGs stabilize Dpp on the cell surface via reversible interaction of HS chains with Dpp that antagonizes Tkv-mediated Dpp internalization); and (4) "Recycling" (HSPGs internalize and recycle Dpp). 

To distinguish between these models, the authors generate new alleles for the glypicans Dally and Dally-like protein (Dlp) and for Dpp: a Dally knock-out allele, a Dally YFP-tagged allele, a Dally knock-out allele with 3HA-Dlp, a Dlp knock-out allele, a Dlp allele containing 3-HA tags, and a Dpp lacking the HS-interacting domain. Additionally, they use an OLLAS-tag Dpp (OLLAS being an epitope tag against which extremely high affinity antibodies exist). They examine OLLAS-Dpp or HA-Dpp distribution, phospho-Mad staining, adult wing size. 

They find that over-expressed Dally - but not Dlp - expands Dpp distribution in the larval wing disc. They find that the Dally[KO] allele behaves like a Dally strong hypomorph Dally[MH32]. The Dally[KO] - but not the Dlp[KO] - caused reduced pMad in both anterior and posterior domains and reduced adult wing size (particularly in the Anterior-Posterior axis). These defects can be substantially corrected by supplying an endogenously tagged YFP-tagged Dally. By contrast, they were not rescued when a 3xHA Dlp was inserted in the Dally locus. These results support their conclusion that Dpp interacts with Dally but not Dlp. 

They next wanted to determine the relative contributions of the Dally core or the HS chains to the Dpp distribution. To test this, they over-expressed UAS-Dally or UAS-Dally[deltaHS] (lacking the HS chains) in the dorsal wing. Dally[deltaHS] over-expression increased the distribution of OLLAS-Dpp but caused a reduction in pMad. Then they write that after they normalize for expression levels, they find that Dally[deltaHS] only mildly reduces pMad and this result indicates a major contribution of the Dally core protein to Dpp stability. The "normalization" is a key part of this model and is not mentioned how the normalization was done. When they do the critical experiment, making the Dally[deltaHS] allele, they find that loss of the HS chains is nearly as severe as total loss of Dally (i.e., Dally[KO]). Additionally, experimental approaches are needed here to prove the role of the Dally core.

Prior work has shown that a stretch of 7 amino acids in the Dpp N-terminal domain is required to interact with heparin but not with Dpp receptors (Akiyama, 2008). The authors generated an HA-tagged Dpp allele lacking these residues (HA-dpp[deltaN]). It is an embryonic lethal allele, but they can get some animals to survive to larval stages if they also supply a transgene called “JAX” containing dpp regulatory sequences. In the JAX; HA-dpp[deltaN] mutant background, they find that the distribution and signaling of this Dpp molecule is largely normal. While over-expressed Dally can increase the distribution of HA-dpp[deltaN], over-expression of Dally[deltaHS] cannot. These latter results support the model that the HS chains in Dally are required for Dpp function but not because of a direct interaction with Dpp. 

In the last part of the results, they attempt to determine if the Dpp receptor Thickveins (Tkv) is required for Dally-HS chains interaction. The 2008 (Akiyama) model posits that Tkv activates pMad downstream of Dpp and also internalizes and degrades Dpp. A 2022 (Romanova-Michaelides) model proposes that Dally (not Tkv) internalizes Dpp.  

To distinguish between these models, the authors deplete Tkv from the dorsal compartment of the wing disc and found that extracellular Dpp increased and expanded in that domain. These results support the model that Tkv is required to internalize Dpp. They then tested the model that Dally antagonizes Tkv-mediated Dpp internalization by determining whether the defective extracellular Dpp distribution in Dally[KO] mutants could be rescued by depleting Tkv. Extracellular Dpp did increase in the D vs V compartment, potentially providing some support for their model. However, there are no statistics performed, which is needed for full confidence in the results. The lack of statistics is particularly problematic (1) when they state that extracellular Dpp does not rise in ap>tkv RNAi vs ap>tkv RNAi, dally[KO] wing discs (Fig. 6E) or (2) when they state that extracellular Dpp gradient expanded in the dorsal compartment when tkv was dorsally depleted in dally[deltaHS] mutants (Fig. 6I). These last two experiments are important for their model but the differences are assessed only visually. In fact, extracellular Dpp in ap>tkv RNAi, dally[KO] (Fig. 6B) appears to be lower than extracellular Dpp in ap>tkv RNAi (Fig. 6A) and the histogram of Dpp in ap>tkv RNAi, dally[KO] is actually a bit lower than Dpp in ap>tkv RNAi, But the author claim that there is no difference between the two. Their conclusion would be strengthened by statistical analyses of the two lines. 

Strengths: 

1. New genomically-engineered alleles

A considerable strength of the study is the generation and characterization of new Dally, Dlp and Dpp alleles. These reagents will be of great use to the field.

2. Surveying multiple phenotypes

The authors survey numerous parameters (Dpp distribution, Dpp signaling (pMad) and adult wing phenotypes) which provides many points of analysis.

Weaknesses: 

1. Confusing discussion regarding the Dally core vs HS in Dpp stability. They don't provide any measurements or information on how they "normalize" for the level of Dally vs Dally[deltaHS]? This is important part of their model that currently is not supported by any measurements.

2. Lacking quantifications and statistical analyses: 

a. Why are statistical significance for histograms (pMad and Dpp distribution) not supplied? These histograms provide the key results supporting the authors' conclusions but no statistical tests/results are presented. This is a pervasive shortcoming in the current study. 

b. dpp[deltaN] with JAX transgene - it would strengthen the study to supply quantitative data on the percent survival/lethal stage of dpp[deltaN] mutants with or without the JAK transgene <br /> c. The graphs on wing size etc should start at zero. <br /> d. The sizes of histograms and graphs in each figure should be increased so that the reader can properly assess them. Currently, they are very small. 

The authors' model is that Dally (not Dlp) is required for Dpp distribution and signaling but that this is not due to a direct interaction with Dpp. Rather, they posit that Dally-HS antagonize Tkv-mediated Dpp internalization. Currently the results of the experiments could be considered consistent with their model, but as noted above, the lack of statistical analyses of some parameters is a weakness. One problematic part of their result for me is the role of the Dally core protein (Fig. 7B). There is a mis-match between the over-expression results and Dally allele lacking HS (but containing the core). Finally, their results support the idea that one or more as-yet unidentified proteins interact with Dally-HS chains to control Dpp distribution and signaling in the wing disc. 

There is much debate and controversy in the Dpp morphogen field. The generation of new, high quality alleles in this study will be useful to Drosophila community, and the results of this study support the concept that Tkv but not Dally regulate Dpp internalization. Thus the work could be impactful and fuel new debates among morphogen researchers. <br />

The manuscript is currently written in a manner that really is only accessible to researchers who work on the Dpp gradient. It would be very helpful for the authors to re-write the manuscript and carefully explain in each section of the results (1) the exact question that will be asked, (2) the prior work on the topic, (3) the precise experiment that will be done, and (4) the predicted results. This would make the study more accessible to developmental biologists outside of the morphogen gradient and Drosophila communities.

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Reply to the reviewers

Reviewer 1:

We would like to thank you for taking the time to review our manuscript. Your thoughtful and insightful comments have greatly improved the quality of our work. We appreciate your thoroughness in evaluating our study and providing valuable feedback.

Your constructive criticism and suggestions have helped us identify areas that needed further clarification and improvement, and we are grateful for your efforts in guiding us towards a stronger manuscript.

Thank you again for your time and expertise in reviewing our work. We hope that you find our revisions satisfactory and look forward to hearing your thoughts on the revised manuscript.

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

In this manuscript by Sharma and colleagues, the authors investigate the transcriptional regulation of the TAL1 isoforms - that derive from differential promoter usage and/or alternative splicing - and the contribution of TAL1 long and TAL1 short protein isoforms in normal haematopoietic development and disease.

The study suggests that TAL1 transcript isoforms are fine-tuned regulated. By using CRISPR/Cas9 techniques, the authors show that the enhancer -8 (MuTE) and enhancer -60 differentially regulate the TAL1 isoforms. Whether the remaining enhancers at the TAL1 locus (see Zhou Y et al, Blood 2013) also differentially regulate TAL1 transcription remains to be elucidated.

The authors found that TAL1 short isoform interacts strongly with T-cell specific transcription factors such as TCF3 and TCF12, as compared to TAL1 long isoform. TAL1 short shows an apoptotic transcription signature and it fails in rescuing cell growth as compared to TAL1 long in T-ALL. In addition, TAL1 short promotes erythropoiesis.

Lastly, the authors suggest that altering TAL1 long and TAL1 short protein isoforms ratio could have a potential therapeutic application in disease, but further studies are needed. *

We would like to thank you for your time and effort in reviewing our manuscript. Your constructive feedback and insightful comments have been immensely valuable in improving the quality of our work. Your expertise in the field has undoubtedly contributed to the credibility and accuracy of this research. In addition, your dedication and attention to detail have been instrumental in shaping the final version of the manuscript.

* I have a number of comments: Figure 1 It was not mentioned that MOLT4 cells also have MuTE. Do Jurkat and MOLT4 share a similar profile in terms of TAL1 transcript isoforms? It would have been very interesting to see whether the TAL1 transcript isoforms are similar in SIL-TAL1+ cells (e.g RPMI-8402). In these cells, TAL1 activation results from a deletion that fuses the 5' non-coding region of SIL with TAL1. *

Thank you for your comment. We apologize for the confusion regarding the MOLT4 cells in our analysis. We have now updated the manuscript to explicitly mention the presence of MuTE in MOLT4 cells (Line 127). Additionally, we agree that it would be interesting to investigate whether the TAL1 transcript isoforms are similar in SIL-TAL1+ cells, such as RPMI-8402. To address this point, we have included the CCRF-CEM cell line that harbors the SIL-TAL1 recombination in our analysis. We have updated the manuscript with these new findings (Fig. 1C&D and S1A&B). Thank you for bringing this to our attention.

Figure 2 * It is not very clear how the expression of the short isoform delta exon 3 is quantified. Detailed information and a schematic of the primer location could be helpful. *

Thank you for your comment. We apologize for any confusion regarding the quantification of the expression of the short isoform (delta exon 3). The detailed information and schematic of the primer location can be found in Supplementary Figure 2B. We have included the location of each primer used in real-time PCR analysis for the quantification of all TAL1 isoforms. We hope this additional information will address your concerns.

* The results on Figure 2 derive from complex Cas9/CRISPR experiments. A schematic representation showing the location of the following elements is missing: CTCF sites, CTCF gRNA target region, dCas9-p300 gRNA target region and -60 enhancer. *

We agree that providing a schematic representation of the Cas9/CRISPR experiments would be helpful for better understanding the data in Figure 2. We have now included a detailed schematic of the location of the CTCF sites, CTCF gRNA target region, dCas9-p300 gRNA target region and -60 enhancer in Supplementary Figure 2E. We believe this new figure will provide a clearer overview of the experiments performed and will aid in the interpretation of the results.

* Are the levels of dCas9-p300 WT and dCas9-p300 MUT comparable in transfected HEK 293 cells? Were those possibly measured by qPCR or Western Blot? Why the authors chose to use 293T cells for the CTCF del as the enhancer usage around the locus must be so different from haematopoietic cells. *

Thank you for your question. We have added Western Blot analysis to compare the levels of dCas9-p300 WT and dCas9-p300 MUT in transfected HEK293T cells, as suggested. The results are presented in Supp. Fig. S2H.

Regarding the choice of HEK293T cells for the CTCF deletion experiment, we selected this cell line for its low expression of TAL1, which contributes to a high dynamic range when tethering p300 core to a closed chromatin region. We have added a clarification of our rationale for using HEK293T cells in the revised manuscript (Lines 177-8). Thank you for your valuable feedback.

* Is CPT - camptothecin? A control gene that is sensitive to CPT treatment would ensure the inhibitor is working. *

Thank you for your comment. Indeed, CPT stands for camptothecin, and this information is already included in the methods section. We have also added this information to the results section (Line 221) to make it clearer.

Regarding the suggestion to use a control gene sensitive to CPT treatment, we agree that this could be a useful addition to our experimental design. To address this, we have quantified the amount of TAL1 transcript to an endogenous control which is not transcribed by RNA Polymerase II (RNAPII) (18s rRNA). As a positive control, we compared Cyclo A, our endogenous control, to 18s rRNA and observed a reduction (Supp. Fig. S2K). This allows us to confidently conclude that the inhibitor is working as intended.

Thank you for bringing up this point, and we hope that our response addresses your concern.

*

In supplementary Figure 2D, the reduction in expression in Jurkat Del-12 is restricted to TSS2. There is no reduction in TAL1 TSS1 and TAL1 TSS4 (this is not clear from the result description section). As seen, these isoforms are upregulated and that could suggest a compensatory mechanism mediated by alternative promoter activation. The fact that Jurkat Del-12 express TAL1 from MSCV-TAL1 could also suggest that TSS1 and TSS4 are upregulated by TAL1 or indirectly, by other members of the TAL/LMO complex (see Sanda T et al, Cancer Cell 2012) *

Certainly, we appreciate your feedback. Supplementary Figure 2D indeed shows that the MuTE enhancer has a differential effect on the promoters, and we have now included this in the text of the manuscript. Regarding the TAL1-long isoform, while MSCV-TAL1 in the Jurkat Del-12 cell line does give rise to this isoform, our results from Figure 3A did not find TAL1-long to have a differential effect on TAL1 promoters. It is important to note that the experiment conducted was an exogenous construct in HEK293T cells, which has its limitations. Thus, the speculation that TAL1-long drives the result in supplementary Figure 2D is possible, and we have added this to the text. Thank you for bringing up this important point (Lines 167-9).

Figure 3 * A. Are the levels of TAL1 short cDNA and TAL1 long cDNA comparable in the co-transfection luciferase experiments? The overexpression of the isoforms does not reflect the endogenous expression levels in cell lines where one of the isoforms is more predominantly expressed (e.g Jurkat cells express low levels of TAL1 short). *

Thank you for your comment. To address your concern, we have added real time (Supp. Fig. S3A) as well as Western blot in a new figure (Supp. Fig. S3B) to show that the levels of TAL1-short and TAL1-long cDNA are comparable in the co-transfection luciferase experiments. Additionally, we observed a very low amount of endogenous TAL1 isoforms in the cell line (Supp. Fig. S3A&B), which was below detection using these methods. This suggests that the effect of the endogenous TAL1 in this cell line is low. We appreciate your feedback, and we hope this additional information addresses your concern.

* Figure 4 Are the levels of flag-TAL1 long and flag-TAL1 short comparable? The levels of expression could explain the low intensity signal for TAL1 long. *

Thank you for your insightful comment. Indeed, the issue of isoform quantification is critical in understanding the functional differences between TAL1-short and TAL1-long. To address this concern, we performed careful quantification of the isoforms and made sure that the amount was equal or slightly in favor of TAL1-long before conducting the experiments in this manuscript. We have also added a Western blot in Supp. Fig. S3A and real time in Supp. Fig. S3B showing the similar amount of the two isoforms. Furthermore, in Figure 4A, we provided the amount of each isoform in the input section, showing a higher amount of TAL1-long. This strengthens our result, which shows that TAL1-short binds stronger to TCF-3 and 12. Protein levels for ChIP-seq experiment (Fig. 4B-H) is now in Supp. Fig. S4B. We thank you for bringing up this important point, and we hope that our additional data and clarifications have addressed your concern

*Is there any reason for not performing a depletion of endogenous TAL1 prior to the ChIP seq flag experiment? *

Thank you for your comment. In our experience, infecting Jurkat cells with shRNA or an expressing vector systems can induce some cellular stress, and we did not want to add additional stress to the cells by depleting endogenous TAL1. Since we immunoprecipitated using a Flag-tagged protein, we did not see a need to deplete the endogenous TAL1 protein. However, in our RNA-seq experiment, depletion of endogenous TAL1 was critical, and we have added this additional step in this experiment.

* Could the authors speculate about MAF motif enrichment in both isoforms and not in TAL1-total? *

Thank you for bringing up this interesting point. It is worth noting that while all ChIP-seq experiments were performed in Jurkat cells, not all of them were conducted by us. In particular, ChIP-seq of TAL1 total was performed by Sanda et al., 2012, using an endogenous antibody against both isoforms, whereas we conducted ChIP-seq for TAL1-short and TAL1-long using a FLAG tag antibody in cells expressing each of the isoforms. Therefore, the different conditions of these experiments may have contributed to the observed MAF motif enrichment in both isoforms and not in TAL1-total. While we cannot provide a definitive explanation, we speculate that the overexpression of the isoforms or the presence of the FLAG tag may have facilitated the detection of the MAF motif. We have added this discussion to the manuscript to acknowledge and address this interesting observation (Lines: 307-8).

1. Sanda et al., Core transcriptional regulatory circuit controlled by the TAL1 complex in human T cell acute lymphoblastic leukemia. Cancer Cell 22, 209-221 (2012).

   * Do TAL1 long and TAL1 short recognise the same DNA motif? *

This is indeed a very interesting question but a difficult one to answer since TAL1 does not bind to the DNA alone but in a complex. In this situation, the ChIP-seq de-novo binding results suggest motifs that could be recognized by TAL1 or any of its complex partners. Using previous data, TAL1’s binding motif is CAGNTG (Hsu et al., 1994), while this motif was not identified in our analysis of the TAL1-total or FLAG-TAL1-long ChIP-seq results, we did, however, identify this sequence in FLAG-TAL1-short ChIP-seq results (p value=1e-93). We predict that this discrepancy is due to the complex nature of transcription factors binding and the fact that the ChIP-seq results were not all done in the same way. We have now added this to the discussion (Lines: 419-25).

1. L. Hsu et al., Preferred sequences for DNA recognition by the TAL1 helix-loop-helix proteins. Mol Cell Biol 14, 1256-1265 (1994).

* Figure 6 In A and B, are the levels of flag-TAL1 long and flag-TAL1 short in transduced K562 comparable? In C and D, are the TAL1 levels reduced at the protein level?*

Thank you for your question. To answer your question, we added Western Blot analysis to show the comparable levels of flag-TAL1-long and flag-TAL1-short in transduced K562 cells (Supp. Fig. S6C). In Figure 6C and D, we also added Western Blot analysis to show the reduction in TAL1 protein levels upon shRNA-mediated knockdown(Supp. Fig. S6B).

* Minor points: Figure 1 A. Include a scale bar *

To address this, we included coordinates of the components of the gene marked in the figure.

* C. Loading control such as GAPDH is missing in the Western Blot. Are CUTLL cells the same as CUTTL-1? *

We added loading controls as requested now supplementary Fig. 1C, S2C, S3A, S4B, S6B&C. Yes, CUTLL is the same as CUTLL-1 we have now fixed this in the text (Line 120).

D. Adjust scale of the CHIP seq tracks in K562 cells in order to see the peak summit. *Include genome build *

Thank you for your comment. We have adjusted the scale of the ChIP-seq tracks in K562 cells as suggested to improve the visualization of the peak summit. However, one of the peaks still had a much higher signal and the summit is still missing from this particular peak. To address this, we have added a new figure in the supp. Fig. S1C materials where we adjusted the peak to show the summit. Please note that in this track, the chromatin structure at the enhancers is missing, and therefore, we did not include it in the main figure. Thank you for bringing this to our attention.

We have added a genome build hg19 to the figure legend.

* In supplementary Figure 1B, the symbol scheme is not clear *

Thank you for this note, we have replaced the figure and added text to make it clearer.

* Figure 2 A & C. Remove 'amount' from the Y axis. Is the total mRNA amount calculated as % of the reference genes? It could be specified on the y axis or figure legend. *

We have removed the word "amount" from the Y axis as requested. Total mRNA amount is normalized relative to the reference genes (∆∆Cq) by Bio-Rad's CFX Maestro software (version 2.3) according to the formula:

where:

• RQ = Relative Quantity of a sample
• Ref = Reference target in a run that includes one or more reference targets in each sample
• GOI = Gene of interest (one target)

* In supplementary Figure 2C, a loading control is missing.*

We have added alpha-tubulin to this figure.

* Figures 4, 5 and 6 Size of the figures should be increased. *

We have increased the figure size as suggested. *

Reviewer #1 (Significance (Required)): The study from Sharma and colleagues is novel and it extends the knowledge on TAL1 regulation and the role of TAL1 in development and disease. Although the study suggests that there is a correlation between enhancers, chromatin mark deposition at exons and regulation of alternative splicing, the mechanistic link is not fully elucidated.*

To further elucidate the mechanistic link between the MuTE enhancer, broad H3K4me3 modification spanning 7.5 Kbp from TAL1 promoter 1 to promoter 5 (as shown in Fig. 1D), and alternative splicing, we conducted experiments where we manipulated KMT2B, a component of the SET1/COMPASS complexes responsible for methylating H3K4. Our findings indicate that silencing KMT2B in Jurkat cells led to a significant 30% increase in TAL1-∆Ex3 (Fig. 2H and Supp. Fig. S2I&J). These results contribute to a more comprehensive understanding of the molecular mechanisms underlying TAL1 alternative splicing regulation.

The findings on TAL1 short protein are interesting but the data on TAL1 long lacks some refinement so then robust conclusions can be drawn. * The experimental data lacks a few controls. The text is clear and prior studies could be better referenced. *

We have made an effort to better reference out manuscript.

* As TAL1 is a very crucial transcription factor oncogene in T-ALL, the study is important as it addresses a very relevant question in the field that is the regulation of the transcription of TAL1 and the functional relevance of both TAL1 short and TAL1 long isoforms. *

Reviewer 2: *

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

Summary: Sharma et al. thoroughly characterized the regulation of TAL1 by mapping the use of its five promoters and enhancers, which together transcribe five transcripts, coding for two protein isoforms. For that purpose the authors used few cell lines: Jurkat as a T-ALL cell line, chronic myeloid leukemia (CML) cell line K562 and HEK293T with low TAL1 expression, as well as CutLL and MOLT4. They profiled the chromatin marks H3K27ac and H3K4me3 at the TAL1 locus, and show that when a the -8 enhancer is compromised tha chromatin marks change, and not only the expression level of TAL1 is reduced, the level of exon 3 skipping is increased. When the -60 enhancr was activated, TAL1 expression increased, and exon 3 skipping was reduced. Those findings indicate that in tal1, transcription and alternative splicing are co-regulated, independent of RNAPII. The authors also show that as an autoregulator, TAL1-short has a preference to TSS1-3 of TAL1, which is not shared by TAL1-long, and that each of the 5' UTR affect Tal1 expression differently. TAL1-short binds E-proteins more strongly than TAL1-long, binds many more sites than TAL1-long and stronger, and each isoform has unique set of targets. Finally, the authors set to identify the different functions of the TAL1 isoforms, and showed that Tal1-short slows cell growth and leads to TAL1-short but not TAL1-long leads to exhaustion of hematopoietic stem cells and promotes differentiation into erythroids. This paper used for the first time TAL1 isoform specific ChIP-seq, which enable accurate definition of isoform-specific targets in Jurkat cells. They demonstrated an interaction between choice of TSS and alternative splicing, and isoform specific functions. Given the clinical importance of TAL1 and the meticulous work performed to characterize its isoform specific regulation and function, I find this manuscript of interest, and only have minor suggestions to improve readability. *

Thank you for taking the time to carefully review our manuscript on the regulation and function of TAL1 isoforms. We appreciate your positive feedback on our comprehensive characterization of TAL1 regulation using chromatin profiling and isoform-specific ChIP-seq. We are glad that you found our findings on the co-regulation of transcription and alternative splicing, as well as the isoform-specific functions of TAL1, to be of interest.

We also appreciate your suggestions to improve the readability of the manuscript and have made the necessary revisions accordingly. Your feedback has been invaluable in strengthening the quality of our work, and we are grateful for your contribution to the scientific community.

* Minor comments: Add explicitly the motivation for choosing the cell line in each part. *

We have added motivation (Lines: 157-8, 177-8, 192-194, 235-6 text that was on the previous version: 192-194, 379-80).

* Figure 1 - Consider marking the promoter numbers and the enhancers names in the same names as in text (-8,-60 etc.), to make it easier for the readers to understand which enhancers is being discussed. *

This in a very important point. We have added the numbering to Figure 1D and Supp. Fig. S2A, B & E.

*P5, P18 - ProtParam is only a prediction tool, and does not supply an experimental measurement, as may be assumed from text. Please rephrase accordingly. *

The words “prediction tool” were added in the indicated paragraphs (Lines 115 and 427).

* Figure 2B/D - y axis label unclear, not explained in text. In accordance, unclear if the change is in the amount of RNA, or the ratio between the long and short variants. *

Thank you for this comment. We greatly appreciate your feedback and suggestions. To make our calculations, which are the norm in the splicing field, clearer, we have now added text to Figure 4 and provided more detailed explanations in lines 670-73. We hope that these modifications will improve the clarity and comprehensiveness of our manuscript.

*Consider removing the bars and increasing the dots, to make the graphs cleaner. *

We removed the bars throughout the manuscript for a cleaner look.

* P8 - The term '5C' may require more explanation, depending on target audience. *

We have added text to explain the technique (Lines 179-81).

* Figure 3 - the trend is that TAL1-short promotes transcription from all five TSSs. However, only in TSS1-3 is the difference significant, but the difference between the long and short forms is not significant. It is unclear if "The mean of three independent experiments done with three replicates" means overall there are three replicates per condition or nine. Please rephrase to clarify. *

Thank you for your comment. To clarify, we want to state that each biological experiment was done in three technical replicates, resulting in a total of nine replicates for each condition. We apologize for any confusion and have now rephrased to: The mean was calculated from three independent biological experiments, each performed with three technical replicates (Lines: 696 and 699).

*Fig 4 A - it seems that many of the sites bound by Tal1 total are not bind by either Tal1-short or Tal1-long. Indeed very little overlap between Tal1-short and Tal-1-total is seen in Fig 4I as well. It seems Tal1-long has very few peaks. Consider adding a discussion of possible reasons. *

We agree that these findings are noteworthy and warrant further discussion. We added text to the discussion section to explore potential reasons for these observations (Lines 416-25).

* Fig 4c - it is hard to distinguish the different lines. Consider a more clear visualization. Also, some text is in a font size too small to read. *

We have changed the format of the figure and took out the input data from the main figure to help the visualization. The input data appear in the Supp. Fig. S4C.

* Fig 4 D-H - will be useful to see the numbers, not just the % divided by %. *

A table with the specific numbers can be found in Supp Figure 4F-J.

* Fig 4 legend - 'I&L' possibly means 'I-L'. P14 - refer to where the results of the 'validation using real-time PCR' are shown. P16 - symbol replaced by an empty rectangle 20 􀀀M *

Thank you for these valuable comments, we have fixed/added these in the manuscript.

* Figure 6D - Y axis value seem strange (fold change relative to day 0 should be 1 at day 0). Consider different Y axis label for C and D to clarify. *

Thank you for this comment, we have changed the y-axis to: Fold-change relative to day 1.

* P18 - It is unclear which "two isoforms with posttranslational modifications which affected the migration rate of the protein (Fig. 1C)" were shown. Only two isoforms are mentioned throughout the paper. *

We have added text to clarify we are referring to TAL1-short and long (Lines 409-10).

*

P18 - "Our ChIP-seq results suggest that the isoforms bind at the same location (Fig. 4B)." - in 4B it seems most of TAL1-short bound positions are not bound by TAL1 long. Please clarify. *

* Worth mentioning that the Total TAL1 is taken from Jurkat cells but from a different experiment. * We have changed the statement and added the text referring to the experiments done independently (Lines 422-3).

*

Reviewer #2 (Significance (Required)): This paper used for the first time TAL1 isoform specific ChIP-seq, which enable accurate definition of isoform-specific targets in Jurkat cells. They demonstrated an interaction between choice of TSS and alternative splicing, and isoform specific functions. Given the clinical importance of TAL1 and the meticulous work performed to characterize its isoform specific regulation and function, I find this manuscript of interest, and only have minor suggestions to improve readability. *

Author Response

Reviewer #1 (Public Review):

The manuscript by Lujan and colleagues describes a series of cellular phenotypes associated with the depletion of TANGO2, a poorly characterized gene product but relevant to neurological and muscular disorders. The authors report that TANGO2 associates with membrane-bound organelles, mainly mitochondria, impacting in lipid metabolism and the accumulation of reactive-oxygen species. Based on these observations the authors speculate that TANGO2 function in Acyl-CoA metabolism.

The observations are generally convincing and most of the conclusions appear logical. While the function of TANGO2 remains unclear, the finding that it interferes with lipid metabolism is novel and important. This observation was not developed to a great extent and based on the data presented, the link between TANGO2 and acyl-CoA, as proposed by the authors, appears rather speculative.

We thank you for your advice and now include additional data that lends support to the role of TANGO2 in lipid metabolism. We have changed the title accordingly.

1) The data with overexpressed TANGO2 looks convincing but I wonder if the authors analyzed the localization of endogenous TANGO2 by immunofluorescence using the antibody described in Figure S2. The idea that TANGO2 localizes to membrane contact sites between mitochondria and the ER and LDs would also be strengthened by experiments including multiple organelle markers.

We agree that most of the data on TANGO2 localization are based on the overexpression of the protein. As suggested by the reviewer and despite the lack of commercial antibodies for immunofluorescence-based evaluation, see the following chart, we tested the commercial antibody described in Figure 2 on HepG2 and U2OS cells. Moreover, we used Förster resonance energy transfer (FRET) technology to analyze the proximity of TANGO2 and Tom20, a specific outer mitochondrial membrane protein. In addition, we visualized cells expressing tagged TANGO2 and tagged VAP-B, an integral ER protein in the mitochondria-associated membranes (doi:10.1093/hmg/ddr559) or tagged TANGO2 and tagged GPAT4-Hairpin, an integral LD protein (doi:10.1016/j.devcel.2013.01.013). These data strengthen our proposal and are presented in the revised manuscript.

As suggested by the reviewer, we have also visualized two additional cell lines (HepG2 and U2OS) with the anti-TANGO2( from Novus Biologicals) that have been used for western blot (see chart above). As shown in the following figure, the commercial antibody shows a lot of staining in addition to mitochondria, especially in U2OS cells, where it also appears to label the nucleus.

2) The changes in LD size in TANGO2-depleted cells are very interesting and consistent with the role of TANGO2 in lipid metabolism. From the lipidomics analysis, it seems that the relative levels of the main neutral lipids in TANGO2-depleted cells remain unaltered (TAG) or even decrease (CE). Therefore, it would be interesting to explore further the increase in LD size for example analyze/display the absolute levels of neutral lipids in the various conditions.

We agree with the reviewer and now present the absolute levels of lipids of interest in the various conditions of the lipidomics analyses (Figure S 3).

3) Most of the lipidomics changes in TANGO2-depleted cells are observed in lipid species present in very low amounts while the relative abundance of major phospholipids (PC, PE PI) remains mostly unchanged. It would be good to also display the absolute levels of the various lipids analyzed. This is an important point to clarify as it would be unlikely that these major phospholipids are unaffected by an overall defect in Acyl-CoA metabolism, as proposed by the authors.

As stated above, we have now included the absolute levels of lipids of interest in the various conditions of the lipidomics analyses (Figure S 3).

Reviewer #1 (Public Review):

The manuscript by Lujan and colleagues describes a series of cellular phenotypes associated with the depletion of TANGO2, a poorly characterized gene product but relevant to neurological and muscular disorders. The authors report that TANGO2 associates with membrane-bound organelles, mainly mitochondria, impacting in lipid metabolism and the accumulation of reactive-oxygen species. Based on these observations the authors speculate that TANGO2 function in Acyl-CoA metabolism.

The observations are generally convincing and most of the conclusions appear logical. While the function of TANGO2 remains unclear, the finding that it interferes with lipid metabolism is novel and important. This observation was not developed to a great extent and based on the data presented, the link between TANGO2 and acyl-CoA, as proposed by the authors, appears rather speculative.

1. The data with overexpressed TANGO2 looks convincing but I wonder if the authors analyzed the localization of endogenous TANGO2 by immunofluorescence using the antibody described in Figure S2. The idea that TANGO2 localizes to membrane contact sites between mitochondria and the ER and LDs would also be strengthened by experiments including multiple organelle markers.

2. The changes in LD size in TANGO2-depleted cells are very interesting and consistent with the role of TANGO2 in lipid metabolism. From the lipidomics analysis, it seems that the relative levels of the main neutral lipids in TANGO2-depleted cells remain unaltered (TAG) or even decrease (CE). Therefore, it would be interesting to explore further the increase in LD size for example analyze/display the absolute levels of neutral lipids in the various conditions.

3. Most of the lipidomics changes in TANGO2-depleted cells are observed in lipid species present in very low amounts while the relative abundance of major phospholipids (PC, PE PI) remains mostly unchanged. It would be good to also display the absolute levels of the various lipids analyzed. This is an important point to clarify as it would be unlikely that these major phospholipids are unaffected by an overall defect in Acyl-CoA metabolism, as proposed by the authors.

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Reply to the reviewers

We would like to thank the reviewers for their extensive review of our manuscript and constructive criticism. We have attempted to address the points raised in the reviewer's comments and have performed additional experiments and have edited the text of the manuscript to explain these points. Please see below, our point-by-point response to the reviewer’s comments. In the submitted revised manuscript, some figure numbers have changed from the prior reviewed version.

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

In this MS, Mrj - a member of the JDP family of Hsp70 co-chaperones was identified as a regulator of the conversion of Orb2A (the Dm ortholog of CPEB) to its prion-like form.

In drosophila, Mrj deletion does not cause any gross neurodevelopmental defect nor leads to detectable alterations in protein homeostasis. Loss of Mrj, however, does lead to altered Orb2 oligomerization. Consistent with a role of prion-like characteristics of Orb2 in memory consolidation, loss of Mrj results in a deficit in long-term memory.

Aside from the fact that there are some unclarities related to the physicochemical properties of Orb2 and how Mrj affects this precisely, the finding that a chaperone could be important for memory is an interesting observation, albeit not entirely novel.

In addition, there are several minor technical concerns and questions I have that I feel the authors should address, including a major one related to the actual approach used to demonstrate memory deficits upon loss of Mrj.

Reviewer #1 (Significance (Required)):

Figure 1 (plus related Supplemental figures): • There seem to be two isoforms of Mrj (like what has been found for human DNAJB6). I find it striking to see that only (preferentially?) the shorter isoform interacts with Orb2. For DNAJB6, the long isoform is mainly related to an NLS and the presumed substrate binding is identical for both isoforms. If this is true for Dm-Mrj too, the authors could actually use this to demonstrate the specificity of their IPs where Orb2 is exclusively non-nuclear?

According to Flybase, Mrj has 8 predicted isoforms of which four are of 259 amino acids (PA, PB, PC, and PD), 3 are of 346 amino acids (PE, PG, and PH) and one is of 208 amino acids (PF) length (Supplementary data 1). We isolated RNA from flyheads and used this in RT-PCR experiments to check which Mrj isoforms express in the brain. Since both the 346 amino acid (1038 nucleotide long) and 259 amino acids (777 nucleotides long) form, which we refer to as the long and middle isoform, has the same N and C terminal sequences we used the same primer pair for this, but on RT-PCR the only amplicon we got corresponds to the 259 amino acid form. For the 208 amino acids (624 nucleotides long) form we designed a separate forward primer and attempted to amplify this using RT-PCR but were unable to detect this isoform also. This data is now presented in Supplemental Figure 4B. Since the only isoform detected from fly head cDNA corresponded to the 259 amino acid form, we think this is the predominant isoform of Mrj expressing in Drosophila and this is what is in our DnaJ library and what we have used in all our experiments here. This is also the same isoform described in previous papers on Drosophila Mrj (Fayazi et al, 2006; Li et al, 2016b). For this 259 amino acid Mrj isoform, we see its expression in both the nucleus and cytoplasm (Supplemental Figure 4C). As the long 346 AA fragment was undetectable in the brain, it was not feasible to address the reviewer’s point of using the long and short forms of Mrj for IP with Orb2. However, we have performed IP of human CPEB2 (hCPEB2) with the long and short isoforms of human DnaJB6 and have detected interaction of hCPEB2 with both the long and short isoforms of DnaJB6 (Supplemental Figure 6E).

• I would be interested to know a bit more about the other 5 JDPs that are interactors with Orb2: are the human orthologs of those known? It is striking that these other 5 JDPs interact with Orb2 in Dm (in IPs) but have no impact on Sup35 prion behavior. Importantly, this does not imply they may not have impact on the prion-like behavior of other Dm substrates, including Dm-Orb2.

We have performed BlastP analysis of CG4164, CG9828, CG7130, DroJ2, and Tpr2 protein sequences against Human proteins. Based on this we have listed the highest-ranking candidate identified here for each of these genes.

Drosophila Gene

Human gene

Query cover

Percent identity

E value

CG4164

dnaJ homolog subfamily B member 11 isoform 1

98 %

62.96%

2e-150

CG9828

dnaJ homolog subfamily A member 2

92%

39.41%

3e-84

CG7130

dnaJ homolog subfamily B member 4 isoform d

56%

69.44%

2e-30

Tpr2

dnaJ homolog subfamily C member 7 isoform 1

93%

46.22%

6e-139

DroJ2

dnaJ homolog subfamily A member 4 isoform 2

98%

60.60%

2e-169

In the context of the chimeric Sup35-based assay where Orb2A’s Prion-like domain (PrD) is coupled with the C-terminal domain of Sup35, the only protein which could convert Orb2A PrD-Sup35 C from its non-prion state to prion state was Mrj. Within the limitations of this heterologous-system based assay, the other 5 DnaJ domain proteins as well as the Hsp70’s were unable to convert the Orb2A PrD from its non-prion to prion-like state. What these other 5 interacting JDP proteins are doing through their interaction with Orb2A and if they are even expressing in the Orb2 relevant neurons will need to be tested separately and will be the subject of our future studies.

• The data in panels H, I indeed suggest that Mrj1 alters the (size of) the oligomers. It would be important to know what is the actual physicochemical change that is occurring here. The observed species are insoluble in 0.1 % TX100 but soluble in 0.1% SDS, which suggest they could be gels, but not real amyloids such as formed by the polyQ proteins that require much higher SDS concentrations (~2%) to be solubilized. This is relevant as Mrj1 reduces polyQ amyloidogenesis whereas is here is shown to enhance Orb2A oligomerization/gelidification. In the same context, it is striking to see that without Mrj the amount of Orb2A seems drastically reduced and I wonder whether this might be due to the fact that in the absence of Mrj a part of Orb2A is not recovered/solubilized due to its conversion for a gel to a solid/amyloid state? In other words: Mrj1 may not promote the prion state, but prevents that state to become an irreversible, non-functional amyloid?

On the reviewer’s point to address what is the actual physicochemical change occurring here, we will need to develop methods to purify the Orb2 oligomers in significant quantities to examine and distinguish if they are of gel or real amyloid-like nature. Currently, within the limitations of our ongoing work, this has not been possible for us to do and we can attempt to address this in our future work. Cryo-EM derived structure of endogenous Orb2 oligomers purified from a fly head extract from 3 million fly heads, made in the TritonX-100 and NP-40 containing buffer, the same buffer as what we have used here for the first soluble fraction, showed these oligomers as amyloids (Hervas et al, 2020). If the oligomers extracted using 0.1% and 2% SDS are structurally and physicochemically different, within the limitations of our current work, had not been possible to address.

The other point raised by the reviewer is, if in the absence of Mrj (in the context of Figure 4 of our previously submitted manuscript), a part of Orb2 is not solubilized due to us using a lower 0.1% SDS for extraction. To address this, we attempted to see how much of leftover Orb2 is remaining in the pellet after extraction with 0.1 % SDS. Towards this, according to the reviewers’ suggestion, we used a higher 2% SDS containing buffer to resuspend the leftover pellet after 0.1% SDS extraction, and post solubilisation ran all the fractions in SDD-AGE. We did this experiment with both wild-type and Mrj knockout fly heads. Under these different extractions, we first observed while there is more Orb2 in the soluble fraction (Triton X-100 extracted) of Mrj knockout, this amount is reduced in both the 0.1% SDS solubilized and 2% SDS solubilized fractions. So, even though there is leftover Orb2 after 0.1% SDS extraction, which can be extracted using 2% SDS, this amount is reduced in Mrj knockout. The other observation here is the Orb2 extracted using 2% SDS shows a longer smear in comparison to the 0.1% SDS extracted form suggesting a possibility of more and higher-sized oligomers present in this fraction. Since we do not have the exact physicochemical characterization of these oligomers detected with 0.1% and 2% SDS-containing buffer, we are not differentiating them by using the terms gels and real amyloids, but refer to them as 0.1% SDS soluble Orb2 oligomers and 2% SDS soluble Orb2 oligomers. Overall, our observations here suggest in absence of Mrj, both of these kinds of Orb2 oligomers are decreased and so Mrj is most likely promoting the formation of Orb2 oligomers. It is possible that the 0.1% SDS soluble Orb2 oligomers gradually accumulate and undergo a further transition to the 2% SDS soluble Orb2 oligomers, so if in absence of Mrj, the formation of the 0.1% SDS soluble Orb2 oligomers is decreased, the next step of formation of 2% SDS soluble Orb2 oligomers also be decreased. This data is now presented in Figure 5H, I and J).

On the other possibility raised by the reviewer that Mrj can prevent the oligomeric state of Orb2 to become an irreversible non-functional amyloid, we think it is still possible for Mrj to do this but this could not be tested under the present conditions.

• It may be good for clarity to refer to the human Mrj as DNAJB6 according to the HUGO nomenclature. Also, the first evidence for its oligomerization was by Hageman et al 2010.

We have now changed mentions of human Mrj to DNAJB6. We apologize for missing the Hageman et al 2010 reference and have now cited this reference in the context of Mrj oligomerization.

• It is striking to see that Mrj co-Ips with Hsp70AA, Hsp70-4 but not Hsp70Cb. The fact that interactions were detected without using crosslinking is also striking given the reported transient nature of J-domain-Hsp70 interactions Together, this may even suggest that Mrj-1 is recognized as a Hsp70 substrate (for Hsp70AA, Hsp70-4 but not Hsp70Cb) rather than as a co-chaperone. In fact, a variant of Mrj-1 with a mutation in the HPD motif should be used to exclude this option.

In IP experiments we notice Mrj interacts with Hsp70Aa and Hsc70-4 but not with Hsc70-1 and Hsc70Cb. In our previously submitted manuscript, we realized we made a typo on the figure, where we referred to Hsp70Aa as Hsc70Aa. We have corrected this in the current revised manuscript. On the crosslinking point raised by the reviewer, we reviewed the published literature for studies of immunoprecipitation experiments which showed an interaction between DnaJB6 and Hsp70. We noted while one of the papers (Kakkar et al, 2016) report the use of a crosslinker in the experiment which showed an interaction between GFP-Hsp70 and V5-DnaJB6, in another two papers the interaction between endogenous Mrj and endogenous Hsp/c70 (Izawa et al, 2000) and Flag-Hsp70 and GFP-DnaJB6 (Bengoechea et al, 2020) could be detected without using any crosslinker. Our observations of detecting the interaction of Mrj with Hsp70Aa and Hsc70-4 in the absence of a crosslinker are thus similar to the observations reported by (Izawa et al, 2000; Bengoechea et al, 2020).

On the point of if Mrj is a substrate for Hsp70aa and Hsc70-4 and not a co-chaperone, we feel in the context of this manuscript, since we are focussing on the role of Mrj in the regulation of oligomerization of Orb2 and in memory, the experiment with HPD motif mutant is probably not necessary here. However, if the reviewers suggest this experiment to be essential, we can attempt this experiment by making this HPD motif mutant.

• The rest of these data reconfirm nicely that Mrj/DNAJB6 can suppress polyQ-Htt aggregation. Yet note that in this case the oligomers that enter the agarose gel are smaller, not bigger. This argues that Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid like state.

Figure 2 and Supplemental Figure 4 discuss the effect of Mrj on Htt aggregation. We have used 2 different Htt constructs here. For Figure 2I, we used only Exon1 of Htt with the poly Q repeats. Here in SDD-AGE, for the control lane, we see the Htt oligomers as a smear for the control. For Mrj, we see only a small band at the bottom which can be interpreted most likely as either a monomer or as small oligomers since we do not observe any smear here. However, for the 588 amino acid fragment of HttQ138 in the SDD-AGE we do not see a difference in the length of the smear but in the intensity of the smear of the Htt oligomers (Supplemental Figure 4E). Based on this we are suggesting in presence of Mrj, there are lesser Htt oligomers. On the point of Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid-like state, our experiments with the Mrj knockout show reduced Orb2 oligomers (both for 0.1% and 2% SDS soluble forms), suggesting Mrj plays a role in the conversion of Orb2 to the oligomeric state. If Mrj inhibits the conversion of oligomers to a more amyloid-like state, this is possible but we couldn’t test this hypothesis here. However, for Htt amyloid aggregates, previous works done by other labs with DnaJB6 as well as our experiments with Mrj suggest this as a likely possibility.

Figure 3: • The finding that knockout of DNAJB6 in mice is embryonic lethal is related to a problem with placental development and not embryonic development (Hunter et al, 1999; Watson et al, 2007, 2009, 2011) as well recognized by the authors. Therefore, the finding that deletion of Dm-Mrj has no developmental phenotype in Drosophila may not be that surprising.

We agree with the reviewer’s point that DNAJB6 mutant mice have a problem with placental development. However, one of the papers cited here (Watson et al, 2009) suggests DNAJB6 also plays a crucial role in the development of the embryo independent of the placenta development defect. The mammalian DNAJB6 mutant embryos generated using the tetraploid complementation method show severe neural defects including exencephaly, defect in neural tube closure, reduced neural tube size, and thinner neuroepithelium. Due to these features seen in the mice knockout, and the lack of such developmental defects in the Drosophila knockout, we interpreted our findings in Drosophila as significantly different from the mammals.

• It is a bit more surprising that Mrj knockout flies showed no aggregation phenotype or muscle phenotype, especially knowing that DNAJB6 mutations are linked to human diseases associated with aggregation (again well recognized by the authors). However, most of these diseases are late-onset and the phenotype may require stress to be revealed. So, while important to this MS in terms of not being a confounder for the memory test, I would like to ask the authors to add a note of caution that their data do not exclude that loss of Mrj activity still may cause a protein aggregation-related disease phenotype. Yet, I also do think that for the main message of this MS, it is not required to further test this experimentally.

We agree with the reviewer and have added this suggestion in the discussion that loss of Mrj may still result in a protein aggregation-related disease phenotype, probably under a sensitized condition of certain stresses which is not tested in this manuscript.

Figure 4:

• IPs were done with Orb2A as bite and clearly pulled down substantial amounts of GFP-tagged Mrj. For interactions with Orb2B, a V5-tagged Mrj was use and only a minor fraction was pulled down. Why were two different Mrj constructs used for Arb2A and Orb2b?

In the previously submitted manuscript, we have used HA-tagged Mrj (not V5) for checking the interaction with full-length Orb2B tagged with GFP. This was done with the goal of using the same Mrj-HA construct as that used in the initial Orb2A immunoprecipitation experiment. Since this has raised some concern as in the IPs to check for interaction between truncated Orb2A constructs (Orb2A325-GFP and Orb2AD162-GFP) and Mrj (Mrj-RFP), we used a different GFP and RFP tag combination. To address this, we have now added the same tag combinations for the IPs (Mrj-RFP with Orb2A-GFP and Orb2B-GFP). In these immunoprecipitation experiments where Mrj-RFP was pulled down using RFP Trap beads, we were able to detect positive interaction with GFP-tagged Orb2A and Orb2B. This data is now added in Figure 4F and 4I. We also have added the IP data for interaction between Mrj-HA and untagged Orb2B in Figure 4K, similar to the combination of initial experiment between Mrj-HA and untagged Orb2A.

• In addition, I think it would be important what one would see when pulling on Mrj1, especially under non-denaturing conditions and what is the status of the Orb2 that is than found to be associated with Mrj (monomeric, oligomeric and what size).

We have now performed IP from wild-type fly heads using anti Mrj antibody and ran the immunoprecipitate in SDS-PAGE and SDD-AGE followed by immunoblotting them with anti-Orb2 antibody. Our experiments suggest that immunoprecipitating endogenous Mrj brings down both the monomeric and oligomeric forms of Orb2. This data is now added in Figure 4L, M and N.

• This also relates to my remark at figure 1 and the subsequent fractionation experiments they show here in which there is a slight (not very convincing) increase in the ratio of TX100-soluble and insoluble (0.1% SDS soluble) material. My question would be if there is a remaining fraction of 0.1% insoluble (2% soluble) Orb2 and how Mrj affects that? As stated before, this is (only) mechanistically relevant to understanding why there is less oligomers of Orb2 in terms of Mrj either promoting it or by preventing it to transfer from a gel to a solid state. The link to the memory data remains intriguing, irrespective of what is going on (but also on those data I do have several comments: see below).

We have addressed this in response to the reviewer’s comments on Figure 1. We find in both wild type and Mrj knockout fly heads, there are Orb2 oligomers that can be detected using 0.1% SDS extraction and with further extraction with 2% SDS. The 2% SDS soluble Orb2 oligomers were previously insoluble during 0.1% SDS-based extraction. However, the amounts of both of these oligomers are reduced in Mrj knockout fly heads. Since we do not have the physicochemical characterization of both of these kinds of oligomers, we are not using the terms gel or solid state here but referring to these oligomers as 0.1% SDS soluble Orb2 oligomers and 2% SDS soluble Orb2 oligomers. We speculate that the 0.1% SDS soluble Orb2 oligomers over time transition to the 2% SDS soluble Orb2 oligomers. As in the absence of Mrj in the knockout flies, both the 0.1% SDS soluble and 2% SDS soluble Orb2 oligomers are decreased, this suggests Mrj is promoting the formation of Orb2 oligomers. On the reviewer’s point, if Mrj can prevent the transition from 0.1% SDS soluble to 2% SDS soluble Orb2 oligomers, we think it is possible for Mrj to both promote oligomerization of Orb2 as well as prevent it from forming bigger non-functional oligomers, but the second point is not tested here. The relevant data is now presented in Figure 5H, I and J.

• I also find the sentence that "Mrj is probably regulating the oligomerization of endogenous Orb2 in the brain" somewhat an overstatement. I would rather prefer to say that the data show that Mrj1 affects the oligomeric behavior/status of Orb2.

Based on the reviewer’s suggestion we have now changed the sentence to Mrj is probably regulating the oligomeric status of Orb2

Figure 5:

• To my knowledge, the Elav driver regulates expression in all neurons, but not in glial cells that comprise a significant part of the fly heads/brain. The complete absence of Mrj in the fly-heads when using this driver is therefore somewhat surprising. Or do we need to conclude from this that glial cells normally already lack Mrj expression?

On driving Mrj RNAi with Elav Gal4, we did not detect any Mrj in the western. We attempted to address the glial contribution towards Mrj’s expression we used a Glia-specific driver Repo Gal4 line to drive the control and Mrj RNAi line and performed a western blot using fly head lysate with anti-Mrj antibody. In this experiment, we did not observe any difference in Mrj levels between the two sets. As the Mrj antibody raised by us works in western blots but not in immunostainings, we could not do a colocalization analysis with a glial marker. However, we used the Mrj knockout Gal4 line to drive NLS-GFP and performed immunostainings of these flies with a glial marker anti-Repo antibody. Here we see two kinds of cells in the brain, one which have GFP but no Repo and the other where both are present together. This suggest that while Glial cells have Mrj but probably majority of Mrj in the brain comes from the neurons. We also found a reference where it was shown that Elav protein as well as Elav Gal4 at earlier stages of development expresses in neuroblasts and in all Glia (Berger et al, 2007). So, another possibility is when we are driving Mrj RNAi using Elav Gal4, this knocks down Mrj in both the neurons as well as in the glia. This coupled with the catalytic nature of RNAi probably creates an effective knockdown of Mrj as seen in the western blot. This data is now added in Supplementary Figure 5G and H.

• Why not use these lines also for the memory test for confirmation? I understand the concerns of putative confounding effects of a full knockdown (which were however not reported), but now data rely only on the mushroom body-specific knockdown for the 201Y Gal4 line, for which the knockdown efficiency is not provided. But even more so, here a temperature shift (22oC-30oC) was required to activate the expression of the siRNA. For the effects of this shift alone no controls were provided. The functional memory data are nice and consistent with the hypothesis, but can it be excluded that the temperature shift (rather than the Mrj) knockdown has caused the memory defects? I think it is crucial to include the proper controls or use a different knockdown approach that does not require temperature shifts or even use the knockout flies.

We have now performed the memory experiments with Mrj knockout flies. Our experiments show at 16 and 24-hour time points Mrj knockout flies have significantly reduced memory in comparison to the control wildtype. This data is now added in Figure 6B.

Figure 6:

The finding of a co-IP between Rpl18 and Mrj (one-directional only) by no means suffices to conclude that Mrj may interact with nascent Orb2 chains here (which would be the relevant finding here). The fact that Mrj is a self-oligomerising protein (also in vitro, so irrespective of ribosomal associations!), and hence is found in all fractions in a sucrose gradient, also is not a very strong case for its specific interaction with polysomes. The finding that there is just more self-oligomerizing Orb2A co-sedimenting with polysomes in sucrose gradients neither is evidence for a direct effect of Mrj enhancing association of Orb2A with the translating ribosomes even though it would fit the hypothesis. So all in all, I think the data in this figure and non-conclusive and the related conclusions should be deleted.

We have now performed the reverse co-IP between Rpl18-Flag and Mrj-HA using anti-HA antibody and could detect an interaction between the two. This data is now added in Supplementary Figure 6A.

To address if Mrj is a self-oligomerizing protein that can migrate to heavier polysome fractions due to its size, we have loaded recombinant Mrj on an identical sucrose gradient as we use for polysome analysis. Post ultra-centrifugation we fractionated the gradients and checked if Mrj can be detected in the fraction numbers where polysomes are present. In this experiment, we could not detect recombinant Mrj in the heavier polysome fractions (data presented in Supplementary Figure 6B). Overall, our observations of Mrj-Rpl18 IPs, the presence of cellularly expressed Mrj in polysome fractions, and the absence of recombinant Mrj from these fractions, suggest a likelihood of Mrj’s association with the translating ribosomes.

On the reviewer’s point of us concluding Mrj may interact with nascent Orb2 chains, we have not mentioned this possibility in the manuscript as we don’t have any evidence to suggest this. We have also added a sentence: This indicates that in presence of Mrj, the association of Orb2A with the translating ribosomes is enhanced, however, if this is a consequence of increased Orb2A oligomers due to Mrj or caused by interaction between polysome-associated Orb2A and Mrj will need to be tested in future.

Based on these above-mentioned points, we hope we can keep the data and conclusions of this section.

Overall, provided that proper controls/additional data can be provided for the key experiments of memory consolidation, I find this an intriguing study that would point towards a role of a molecular chaperone in controlling memory functions via regulating the oligomeric status of a prion-like protein and that is worthwhile publishing in a good journal.

However, in terms of mechanistical interpretations, several points have to be reconsidered (see remarks on figure 1,4); this pertains especially to what is discussed on page 13. In addition, I'd like the authors to put their data into the perspective of the findings that in differentiated neurons DNAJB6 levels actually decline, not incline (Thiruvalluvan et al, 2020), which would be counterintuitive if these proteins are playing a role as suggested here in memory consolidation.

We have addressed the comments on Figures 1 and 4 earlier. We have also added new memory experiment’s data with the Mrj knockout in Figure 6.

We have attempted to put the observations with Drosophila Mrj in perspective to observations in Thiruvalluvan et al, on human DnaJB6 in the discussions as follows:

Can our observation in Drosophila also be relevant for higher mammals? We tested this with human DnaJB6 and CPEB2. In mice CPEB2 knockout exhibited impaired hippocampus-dependent memory (Lu et al, 2017), so like Drosophila Orb2, its mammalian homolog CPEB2 is also a regulator of long-term memory. In immunoprecipitation assay we could detect an interaction between human CPEB2 and human DnaJB6, suggesting the feasibility for DnaJB6 to play a similar role to Drosophila Mrj in mammals. However, as the human DnaJB6 level was observed to undergo a reduction in transitioning from ES cells to neurons, (Thiruvalluvan et al, 2020) how this can be reconciled with its possible role in the regulation of memory. We speculate, such a reduction if is happening in the brain will occur in a highly regulatable manner to allow precise control over CPEB2 oligomerization only in specific neurons where it is needed and the reduced levels of DnaJB6 is probably sufficient to aid CPEB oligomerization Alternatively, there may be additional chaperones that may function in a stage-specific manner and be able to compensate for the decline in levels of DNAJB6.

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

Summary: The manuscript describes the role of the Hsp40 family protein Mrj in the prion-like oligomerization of Orb2. The authors demonstrate that Mrj promotes the oligomerization of Orb2, while a loss in Mrj diminishes the extent of Orb2 oligomerization. They observe that while Mrj is not an essential gene, a loss in Mrj causes deficiencies in the consolidation of long-term memory. Further, they demonstrate that Mrj associates with polysomes and increases the association of Orb2 with polysomes.

Major comments: None

Minor comments:

1. In the section describing the chaperone properties of Mrj in clearing Htt aggregates (Fig 2), the legend describes that "Mrj-HA constructs are more efficient in decreasing Htt aggregation compared to Mrj-RFP". It would be helpful to add Mrj-RFP to the quantification in Fig 2G to know exactly the difference in efficiency. Is there an explanation for why the 2 constructs behave differently?

We have added the quantitation of Htt aggregates in presence of Mrj-RFP in the revised version (Data presented in Figure 2G). While the efficiency of Mrj-RFP to decrease Htt aggregates is significantly less in comparison to Mrj-HA, it is still significantly better in comparison to the control CG7133-HA construct. It is possible, due to the tagging of Mrj with a larger tag (RFP), this reduces its ability to decrease the Htt aggregates in comparison to the construct where Mrj is tagged with a much smaller HA tag.

Figs A, B, C, G need to have quantification of the percentage of colocalization with details about the number of cells quantified for each experiment.

We have now added the intensity profile images and colocalization quantitation (pearson’s coefficient) in the Supplemental Figure 5A and B. This quantitation is done from multiple ROI’s taken from at 4-6 cells.

In Fig 6 B, C, F, G it would be helpful to label the 40S, 60S and 80S peaks in the A 254 trace.

We have now labeled the 80S, and polysome peaks in the Figure 7B, C, F and G. We could not separate the 40S and 60S peaks in the A254 trace.

It's interesting that Mrj has opposing functions with regard to aggregation when comparing huntingtin with Orb2. From the literature presented in the discussion, it appears as though chaperones including Mrj have an anti-aggregation role for prions. It would be helpful to have more discussion around why, in the case of Orb2, this is different. The discussion states that "The only Hsp40 chaperone which was found similar to Mrj in increasing Orb2's oligomerization is the yeast Jjj2 protein" - this point needs elaboration, as well as a reference.

In the discussions section we have now added the following speculations on this:

One question here is why Mrj behaves differently with Orb2 in comparison to other amyloids. Orb2 differs from other pathogenic amyloids in its extremely transient existence in the toxic intermediate form (Hervás et al, 2016). For the pathogenic amyloids, since they exist in the toxic intermediate form for longer, Mrj probably gets more time to act and prevent or delay them from forming larger aggregates. For Orb2, Mrj may help to quickly transition it from the toxic intermediate state, thereby helping this state to be transient instead of longer. An alternate possibility is post-transition from the toxic intermediate state, Mrj stabilizes these Orb2 oligomers and prevents them from forming larger aggregates. This can be through Mrj interacting with Orb2 oligomers and blocking its surface thereby preventing more Orb2 from assembling over it. Another difference between the Orb2 oligomeric amyloids and the pathogenic amyloids is in the nature of their amyloid core. For the pathogenic amyloids, this core is hydrophobic devoid of any water molecules, however for Orb2, the core is hydrophilic. This raises another possibility that if the Orb2 oligomers go beyond a certain critical size, Mrj can destabilize these larger Orb2 aggregates by targeting its hydrophilic core.

On the Jjj2 point raised by the reviewer, we have added the (Li et al, 2016a) reference now and elaborated as:

The only Hsp40 chaperone which was found similar to Mrj in increasing Orb2’s oligomerization is the yeast Jjj2 protein. In Jjj2 knockout yeast strain, Orb2A mainly exists in the non-prion state, whereas on Jjj2 overexpression the non-prion state could be converted to a prion-like state. In S2 cells coexpression of Jjj2 with Orb2A lead to an increase in Orb2 oligomerization (Li et al, 2016a). However, Jjj2 differs from Mrj, as when it is expressed in S2 cells, we do not detect it to be present in the polysome fractions.

The Jjj2 polysome data is now presented in Supplementary Figure 6C.

Reviewer #2 (Significance (Required)):

General assessment:

Overall, the work is clearly described and the manuscript is very well-written. The motivation behind the study and its importance are well-explained. I only have minor comments and suggestions to improve the clarity of the work. The study newly describes the interaction between the chaperone Mrj and the translation regulator Orb2. The experiments that the screen for proteins that interact with Orb2 and promote its oligomerization are very thorough. The experiments that delve into the role of Mrj in protein synthesis are a good start, and need to be explored further, but that is beyond the scope of this study.

Advance: The study describes a new interaction between the chaperone Mrj and the translation regulator Orb2. The study is helpful in expanding our knowledge of prion regulators as well factors that affect memory acquisition and consolidation.

Audience: This paper will be of most interest to basic researchers.

My expertise is in Drosophila genetics and neuronal injury.

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

The manuscript submitted by Desai et al. identifies a chaperone of the Hsp40 family (Mrj) that binds Orb2 and modulates its oligomerization, which is critical for Orb2 function in learning and memory in Drosophila. Orb2 are proteins with prion-like properties whose oligomerization is critical for their function in the storage of memories. The main contribution of the article is the screen of Hsp40 and Hsp70-family proteins that bind Orb2. The authors show IP results for all the candidates tested, including those that bind Fig. 1) and those that don't (Supp Fig 3). There is also a figure devoted to examining the interaction of Mrj with polyglutamine models (Htt). They also generate a KO mutant that is viable and shows no gross defects or protein aggregation. Lastly, they show that the silencing of Mrj in the mushroom body gamma neurons results in weaker memories in a courtship paradigm. Although the data is consistent and generally supportive of the hypothesis, key details are missing in several areas, including controls. Additionally, the interpretation of some results leaves room for debate. Overall, this is an ambitious article that needs additional work before publication.

Specific comments:

1. General concern over the interpretation of IP experiments and colocalization. These experiments don't necessarily reflect direct interactions. They are consistent with direct interaction but not the only explanation for a positive IP or colocalization.

This paper is centred on the interaction between Orb2A and Mrj, which we have detected using immunoprecipitation. The reviewer’s concern here is, this experiment is not able to distinguish if this can be a direct protein-protein interaction or if the two proteins are part of a complex.

To address this concern we have used purified recombinant protein-based pulldowns. Our experiments with purified protein pulldowns (GST tagged Mrj from E.coli with Orb2A from E.coli or Orb2A-GFP from Sf9 cells) suggest Orb2A and Mrj can directly interact amongst themselves. This data is now presented in Figure 1J and K.

The Huntingtin section has a few concerns. The IF doesn't show all controls and the quantification is not well done in terms of what is relevant. A major problem is the interpretation of Fig 2F. The idea is that Mrj prevents the aggregation of Htt, which is the opposite of what is observed with Orb2. The panel actually shows a large Htt aggregate instead of multiple small aggregates. This has been reported before in Drosophila and other systems with different polyQ models. Mrj and other Hsp40 and Hsp70 proteins modify Htt aggregation, but in an unexpected way. This affects the model shown in Fig. 6H. Lastly, Fig 2H and 2I show very different level of total Htt.

In Figure 2F of the previously submitted manuscript, we have shown representative images of HttQ103-GFP cells coexpressing with a control DnaJ protein CG7133-HA and Mrj-HA. In Figure 2G we quantitated the number of cells showing aggregates within the population of doubly transfected cells. On the reviewer’s point of figure 2F showing large Htt aggregates instead of multiple small aggregates, we do not see a large Htt aggregate in presence of Mrj in this figure, the pattern looks diffused here and very different from the control CG7133 where the aggregates are seen. We have performed the same experiment with a different Htt construct (588 amino acids long fragment) tagged with RFP, and here also we notice in presence of Mrj, the aggregates are decreased and the expression pattern looks diffused (Supplementary Figure 4E, 4F).

If the comment on large Htt aggregates in presence of Mrj is concerning figure 2E, here we show Mrj-RFP to colocalize with the Htt aggregates. Here, even though Mrj-RFP colocalizes with Htt aggregates, it rescues the Htt aggregation phenotype as in comparison to the control CG7133, the number of cells with Htt aggregates is still significantly less here. We have added this quantitation of rescue by Mrj-RFP in the revised manuscript now. The observation of colocalization of Mrj-RFP with Htt aggregates is similar to previous reports of chaperones rescuing Htt aggregation and yet showing colocalization with the aggregates. Both Hdj-2 and Hsc70 suppress Htt aggregation and yet were observed to colocalize with Htt aggregates in the cell line model as well as in nuclear inclusions in the brain (Jana et al, 2000). In a nematode model of Htt aggregation, DNJ-13 (DnaJB-1), HSP-1 (Hsc70), and HSP-11 (Apg-2) were shown to colocalize with Htt aggregates and yet decrease the Htt aggregation (Scior et al, 2018). Hsp70 was also found to colocalize with Htt aggregates in Hela cells (Kim et al, 2002).

Regarding Figures 2H and 2I, while figure 2H is of an SDS-PAGE to show no difference in the levels of monomeric HttQ103 (marked with *) in presence of Mrj and the control CG7133, figure 2I is for the same samples ran in an SDD-AGE where reduced amount of Htt oligomers as seen with the absence of a smear in presence of Mrj. The apparent difference in Htt levels between 2H and 2I is due to the detection of Htt aggregates/oligomers in the SDD-AGE which are unable to enter the SDS-PAGE and hence undetected. In Supplementary Figure 4E, similar experiments were done with the longer Htt588 fragment and here we notice in the SDD-AGE reduced intensity of the smear made up of Htt oligomers, again suggesting a reduction in Htt aggregates. Thus our results are not in contradiction to previous studies where Mrj was found to rescue Htt aggregate-associated toxicity.

Endogenous expression of Mrj using Gal4 line: where else is it expressed in the brain / head and in muscle. Fig 3G shows no muscle abnormalities but no evidence is shown for muscle expression. It is nice that Fig 3E and F show no abnormal aggregates in the Mrj mutant, but this would be maybe more interesting if flies were subjected to some form of stress.

We have now added images of the brain and muscles to show the expression pattern of Mrj. Using Mrj Gal4 line and UAS- CD8GFP, we noticed enriched expression in the optic lobes, mushroom body, and olfactory lobes. We also noticed GFP expression in the larval muscles and neuromuscular junction synaptic boutons. This data is now presented in Supplementary Figure 5C, D, E and F.

On the reviewer’s point of subjecting the Mrj KO flies to some form of stress, we have not performed this. We have added in the discussions a note of caution, that loss of Mrj may still result in a protein aggregation-related disease phenotype, probably under a sensitized condition of certain stresses which is not tested in this manuscript.

Fig. 5B shows no Mrj detectable from head homogenates in flies silencing Mrj in neurons with Elav-Gal4. It would be nice if they could show that ONLY neurons express Mrj in the head. Also noted, Elav-Gal4 is a weak driver, so it is surprising that it can generate such robust loss of Mrj protein

We have used an X chromosome Elav Gal4 driver to drive the UAS-Mrj RNAi line and here we could not detect Mrj in the western. To address the reviewer’s point on the glial contribution towards expression of Mrj, we used a Glial driver Repo Gal4 to drive Mrj RNAi. In this experiment, we did not detect any difference in Mrj levels between the control and the Mrj RNAi line (presented now in Supplementary Figure 5G). We also used the Mrj knockout Gal4 line to drive NLS-GFP and immunostained these using a glial marker anti-Repo antibody. Here, we were able to detect cells colabelled by GFP as well as Repo, suggesting Mrj is likely to be present in the glial cells (presented now in Supplementary Figure 5H). We also looked in the literature and found a reference where it was shown that Elav protein as well as Elav Gal4 at earlier stages of development expresses in neuroblasts and in all Glia (Berger et al, 2007). So, another possibility is when we are driving Mrj RNAi using Elav Gal4, this knocks down Mrj in both the neurons as well as in the glia.

Fig 4-Colocalization of Orb2 with Mrj lacks controls. The quantification could describe other phenomena because the colocalization is robust but the numbers shown describe something else.

We have now added the intensity profile and colocalization quantitation (pearson’s coefficient) in Supplemental Figure 5A and B. This quantitation is done from multiple ROI’s taken from 4-6 cells. Also, to suggest the interaction of Orb2 isoforms with Mrj, we are not depending on colocalization alone and have used immunoprecipitation experiments to support our observations.

Fly behavior. The results shown for Mrj RNAi alleles is fine but it would be more robust if this was validated with the KO line AND rescued with Mrj overexpression.

We have now performed memory assays with the Mrj knockout. Our experiments showed Mrj knockouts to show significantly decreased memory in comparison to wild-type flies at 16 and 24-hour time points (presented in Figure 6B). We have not been able to make an Mrj Knockout-UAS Mrj recombinant fly, most likely due to the closeness of the two with respect to their genomic location in second chromosome.

Minor comments:

Please, revise minor errors, there are several examples of words together without a space.

We have identified the words without space and have corrected them now.

Intro: describe the use of functional prions. Starting the paragraph with this sentence and then explaining what prion diseases are is a little confusing. Also "prion proteins" can be confusing because the term refers to PrP, the protein found in prions.

We have now altered the introduction and have described functional prions.

Results, second subtitle in page 5. This sentence is quite confusing using prion-like twice

We have now changed the heading to “Drosophila Mrj converts Orb2A from non-prion to a prion-like state.”

Page 6: "conversion from non-prion to prion-like form...". This can be presented differently. Prion-like properties are intrinsic, proteins don't change from non-prion to prion-like. They may be oligomeric or monomeric or highly aggregated but the prion-like property doesn't change

We agree with the reviewer's point of Prion-like properties are intrinsic, but the protein might or might not exist in the prion-like state or confirmation. When we are using the term conversion from non-prion to prion-like form we mean to suggest a conformational conversion leading to the eventual formation of the oligomeric species. We also noted the terminology of non-prion to prion-like state change is used in several papers (Satpute-Krishnan & Serio, 2005; Sw & Yo, 2012; Uptain et al, 2001).

Scale bars and text are too small in several figures

We have now mentioned in the figure legends the size of the scale bars. For several images we have made the scale bars also larger.

Not sure why Fig 4C is supplemental, seems like an important piece of data.

We have kept this data in the supplemental data as we performed this experiment with recombinant protein which is tagged with 6X His and we are not sure if this high degree of oligomerization/aggregation of recombinant Mrj and further precipitation over time, happens inside the cells/ brain.

Intro to Mrj KO in page 7 is too long. Most of it belongs in the discussion

We have now moved the portions on mammalian DNAJB6 which were earlier in the results section to the discussions section.

Change red panels in IF to other color to make it easier for colorblind readers.

We have now changed the red panels to magenta. We apologize for our figures not being colorblind friendly earlier.

The discussion is a little diffuse by trying to compare Orb2 with mammalian prions and amyloids and yeast prions.

We looked into the functional prion data and couldn’t find much on chaperone mediated regulation of these. Also, we felt comparing with the amyloids and yeast prions brings out the contrast with respect to the Mrj mediated regulatory differences between the two.

Reviewer #3 (Significance (Required)):

This is a paper with a broad scope and approaches. The paper describes the role of Mrj in the oligomerization of Orb2 by protein biochemistry techniques and determine the role of loss of Mrj in the mushroom bodies in fly behavior.

The audience for this content is basic research and specialized. The role of Mrj in Orb2 aggregation and function sheds new light on the mechanisms regulating the function of this protein involved in a novel mechanism of learning and memory.

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So, it'll be scrapable, and presumably online

Imagine if they answered "Google Docs, and then pick 'HTML' when you save it".

It's the sort of thing that seems obviously wrong on its face, but in practice is more expedient and not egregiously worse than the accepted alternative.

Reviewer #1 (Public Review):

According to current knowledge, zebrafish neurons maintain the capacity of regenerating with the exception of adult cerebellar Purkinje cells (PC), which are thought to have lost this property. Regeneration instead occurs at larval stages but whether newly generated PC form fully functional circuits is still unclear. This elegant and well-performed study takes advantage of a transgenic zebrafish line that enables inducing apoptosis under a tamoxifen-inducible system and at the same time visualizes PCs morphology through a membrane tagged RFP. Using this line (and other lines that tag radial glial and ventricular progenitors) in combination with morphological and functional analysis, the authors show that ventricular progenitors retain the lifelong ability to regenerate PCs. At larval stages, the newly regenerated PCs form fully functional circuits that lead to normal behavior. In adults, PC regeneration is less efficient (and PCs are also less prone to undergo apoptosis) but sufficient to support exploratory behavior. This study resolves the controversial issue of whether adult PC regeneration is possible and demonstrates that newly formed PCs at larval and adult stages can form functional circuits that support normal behavior.

This is a well-performed and carefully executed and quantified study. There is however a point that needs clarification:

The authors state that acute regeneration occurs between 5-10dpt. However, the graphs in Fig 1D, F, and 2F indicate that most PC generation occurs from 20-30 days. What happens in this period? Does proliferation increase? Can the authors perform BrdU incorporation between 6 days and 1 month? Related to this, as the authors indicate in lines 129-131, the regeneration of new PCs overlaps with normal development. Are other neuronal cell types generated in appropriate numbers?

Author Response

Reviewer #1 (Public Review):

This study addresses the role of the general transcription factor TBP (TATA-binding protein), a subunit of the TFIID complex, in RNA polymerase II transcription. While TBP has been described as a key component of protein complexes involved in transcription by all three RNA polymerases, several previous studies on TBP loss of function and on the function of its TRF2 and TRF3 paralogues have questioned its essential role in RNA polymerase II transcription. This new study uses auxin induced TBP degradation in mouse ES cells to provide strong evidence that its loss does not affect ongoing polymerase II transcription or heat-shock and retinoic acid-induced transcription activation, but severely inhibits polymerase III transcription. The authors coupled TBP degradation with TRF2 knock out to show that it does not account for the residual TBP-independent transcription. Rather the study provides evidence that TFIID can assemble and is recruited to promoters in the absence of TBP.

All together the study provides compelling evidence for TBP-independent polymerase II transcription, but a better characterization of the residual TFIID complex and recruitment of other general transcription factors to promoters would strengthen the conclusions.

We thank the reviewer for their accurate summary of our findings and the public assessment of our manuscript.

Reviewer #2 (Public Review):

The paper is intriguing, but to me, a main weakness is that the imaging experiments are done with overexpressed protein. Another is that the different results for the different subunits of TFIID would indicate that there are multiple forms of TFIID in the nucleus, which no one has observed/proposed before. Otherwise, the experimental data would have to be interpreted in a more nuance way. Additionally, there is no real model of how a TBP-depleted TFIID would recruit Pol II. Do the authors suggest that when TBP is present, it is not playing a role in Pol II transcription, despite being at all promoters? Or that in its absence an alternative mechanism takes over? In the latter case, are they proposing that it is just based on the rest of TFIID? How? The authors do not provide a mechanistic explanation of what is actually happening and how Pol II is being recruited to promoters.

We thank the reviewer for their public review of our manuscript. Although the reviewer poses many interesting questions raised from our findings, they would be a great focus for future directions.

We agree that our imaging experiments using over-expressed constructs have limitations. Though they provide insight that is unique and orthogonal to the genomics analyses, we agree that they are still preliminary, and therefore we have removed them from the manuscript, with the hope of further developing these experiments into a follow-up manuscript.

While we cannot exclude different forms of TFIID in the cell, previous studies have identified different TAF-containing complexes. Indeed, we referenced several of these studies in our manuscript, including TFTC/SAGA. Furthermore, in our Discussion section, we speculated how a large multi-subunit complex like TFIID may not behave as a monolith but rather have distinct dynamics/behavior among the subunits. Some studies are now revealing that biochemically defined complexes behave more as a hub, with subunits having distinct dynamics coming in and out of the complex, but in a way such that a snapshot at any given time would show a stably formed complex.

What TBP does for Pol II is an intriguing question, and one that we had thought we could answer with our rapid depletion system. One possibility is that Pol II initiation has evolved to have so many redundant mechanisms such that removal of one factor (TBP) would not disrupt the whole system. And yet, TBP remains a highly essential gene (perhaps mostly for its essential role in Pol III transcription), and therefore, its binding to Pol II gene promoters has been maintained, almost in a vestigial way. Of course, this is speculative, and our rapid depletion system only shows us that TBP is not required for Pol II transcription, not what it does when it binds to promoters.

Lastly, we believe that our study tested 3 potential mechanisms that could explain TBP-independence for Pol II transcription. 1) We tested the possibility that TBP is only needed for induction and not for subsequent re-initiation. We provide evidence using two orthogonal induction systems that this is not the case. 2) We tested whether the TRF2 paralog could functionally replace TBP, and show that this is also not the case. 3) We show that TFIID can form in the absence of TBP. While we agree that there are more mechanisms to test, addressing all of them would require a re-examination of over 50 years of research that would not be feasible to report in a single manuscript, especially for a system as complex as Pol II initiation.

Reviewer #3 (Public Review):

In this study, the authors set out to study the requirement of the TATA binding protein (TBP) in transcription initiation in mESCs. To this end they used an auxin inducible degradation (AID) system. They report that by using the AID-TBP system after auxin degradation, 10-20% of TBP protein is remaining in mESCs. The authors claim that as, the observed 80-90% decrease of TBP levels are not accompanied by global changes in RNA polymerase II (Pol II) chromatin occupancy or nascent mRNA levels, TBP is not required for Pol II transcription. In contrast, they find that under similar TBP-depletion conditions tRNA transcription and Pol III chromatin occupancy were impaired. The authors also asked whether the mouse TBP paralogue, TBPL1 (also called TRF2) could functionally replace TBP, but they find that it does not. From these and additional experiments the authors conclude that redundant mechanisms may exist in which TBP-independent TFIID like complexes may function in Pol II transcription.

The major strengths of this manuscript are the numerous genome-wide investigations, such as many different CUT&Tag experiments, and NET-seq experiments under control and +auxin conditions and their analyses. Weaknesses lie in some experimental setups (i.e. overexpression of Halo-tagged TAFs), mainly in the overinterpretation (or misinterpretation) of the data and in the lack of a fair discussion of the obtained data in comparison to observations described in the literature. As a result, very often the interpretation of data does not fully support the conclusions. Nevertheless, the findings that 80-90% decrease in cellular TBP levels do not have a major effect on Pol II transcription are interesting, but the manuscript needs some tuning down of many of the authors' very strong conclusions, correcting several weaker points and with a more careful and eventually more interesting Discussion.

We thank the reviewer for their public review of our manuscript. We would like to add that, in addition to testing the TBP paralog for redundancy, we also tested a mechanism in which TBP would be required for the initial round of transcription but not for subsequent ones. We show that data from orthogonal experiments that this mechanism is not the case. As in our response to Reviewer 2, we agree that our over-expression imaging experiments are still somewhat preliminary, and therefore we have removed these experiments and potential over/misinterpretation of these results from the manuscript.

Reviewer #3 (Public Review):

In this study, the authors set out to study the requirement of the TATA binding protein (TBP) in transcription initiation in mESCs. To this end they used an auxin inducible degradation (AID) system. They report that by using the AID-TBP system after auxin degradation, 10-20% of TBP protein is remaining in mESCs. The authors claim that as, the observed 80-90% decrease of TBP levels are not accompanied by global changes in RNA polymerase II (Pol II) chromatin occupancy or nascent mRNA levels, TBP is not required for Pol II transcription. In contrast, they find that under similar TBP-depletion conditions tRNA transcription and Pol III chromatin occupancy were impaired. The authors also asked whether the mouse TBP paralogue, TBPL1 (also called TRF2) could functionally replace TBP, but they find that it does not. From these and additional experiments the authors conclude that redundant mechanisms may exist in which TBP-independent TFIID like complexes may function in Pol II transcription.

The major strengths of this manuscript are the numerous genome-wide investigations, such as many different CUT&Tag experiments, and NET-seq experiments under control and +auxin conditions and their analyses. Weaknesses lie in some experimental setups (i.e. overexpression of Halo-tagged TAFs), mainly in the overinterpretation (or misinterpretation) of the data and in the lack of a fair discussion of the obtained data in comparison to observations described in the literature. As a result, very often the interpretation of data does not fully support the conclusions.<br /> Nevertheless, the findings that 80-90% decrease in cellular TBP levels do not have a major effect on Pol II transcription are interesting, but the manuscript needs some tuning down of many of the authors' very strong conclusions, correcting several weaker points and with a more careful and eventually more interesting Discussion.

Author Response

Reviewer #1 (Public Review):

The authors present data identifying the role of the bacterial enhancer binding protein (bEBP) SypG in the regulation of the Qrr1 small RNA, which is known to be a key regulator of Vibrio fischeri bioluminescence production and squid colonization. Previously, only the bEBP LuxO was known to activate Qrr1 expression. LuxO and Qrr1 are conserved in the Vibrionaceae, and the authors show that SypG is conserved in ~half of the Vibrio family, suggesting that this Qrr1 regulatory OR gate controlled by LuxO or SypG may play important roles in physiology processes in other species.

Successful squid colonization by Vibrio fischeri is a complex process, known to be influenced by several factors, including the formation of and dispersal from cellular aggregates prior to entering squid pores, and inoculation of the light organ crypts, and biofilm formation within the crypts. Previously, it was shown that strains lacking qrr1 were at a deficit for crypt colonization in the presence of wild-type V. fischeri. Conversely, cells lacking binK, which encodes a hybrid histidine kinase, were at an advantage for crypt colonization in the presence of wild-type cells. However, the authors identified BinK as a negative regulator of Qrr1 expression in a transposon screen. The authors used genetic epistasis experiments and found that Qrr1 transcription can be activated by either phosphorylated LuxO at low cell densities (in the absence of quorum sensing signals) or by SypG, presumably by binding to the two upstream activation sequences in the promoter of qrr1 to activate transcription by the required alternative sigma factor sigma-54. The competition between these bEBPs has not been tested. The model proposed is an OR gate through which quorum sensing and aggregation signals control Qrr1. However, there are several untested aspects of this model. First, the role of phosphorylation in SypG activity, and the connection to BinK, are not addressed in this manuscript, which may confound the observed effects observed on qrr1 transcription. Further, the authors did not test whether SypG directly binds to the qrr1 promoter, nor did they assess the individual role of LuxO binding to the two LuxO binding sites in the absence of SypG. The study is lacking an in vivo assessment of SypG and LuxO binding/competition at the Qrr1 promoter based on the authors' model of the OR gate.

Major comments:

• What is known about the connection between BinK and SypG? BinK is a hybrid HK (intro states this). Does BinK phosphorylate/dephosphorylate SypG - directly or indirectly? I saw a published paper (Ludvik et al 2021) with a diagram suggesting BinK does inhibit SypG, but the connection is unclear. This diagram also suggested that SypG needs to be phosphorylated. Can the authors comment - does SypG need to be phosphorylated to be active? Because SypG has the same sequence as the LuxO linker (Fig. S2), then I presume that SypG would also need to be phosphorylated to be active (like LuxO)? The authors utilize a phosphomimic of LuxO to test function under constitutive activity (Fig. S3), but they do not use a phosphomimic of SypG (Fig 4). If the authors used a constitutive allele, would those assays reveal more about the competition between SypG and LuxO, in the presence of phosphorylated LuxO at low cell density? The authors should include a putative cartoon model for how BinK HK activity connects to SypG, based on what is already in the literature, to aid the reader.

We have added information & corresponding cartoon model in the results section about the signaling pathway involving BinK and SypG, including that SypG must be phosphorylated to be active and that BinK acts as a phosphatase towards SypG. We have also generated a SypGD53E mutant and found increased Pqrr1 activity, which suggests that phosphorylation of SypG has a major impact on SypG-dependent activation of Pqrr1.

• Line 246: Figure S3: nucleotide substitutions in both UAS regions showed loss of Pqrr1-gfp, but this could be due to binding/activation by SypG or LuxO. This should be tested in a sypG- strain to determine the sole effect of LuxO binding to these two UASs. In Figures 4G and 7, the luxO- sypG- Ptrc-sypG strain backgrounds allow the independent analysis of the two bEBPs. It is important to test which of these two sites is critical for LuxO-dependent activation of Pqrr1, given the conservation of the LuxO-Qrr1 region in other Vibrios (line 327, Fig. S5). Thus, the authors could also discuss whether these two proteins would compete at both sites. Further, the authors should comment that they have not shown biochemical evidence that SypG binds to the two UASs in the Qrr1 promoter. The regulation of this locus by SypG is only shown by genetic assays in this manuscript.

We have added a paragraph in the discussion highlighting how useful protein-DNA assays would be to address competition along with the barriers encountered with approaches to purify SypG. Regarding the contribution of each UAS to LuxO-dependent activation, we refer to the phosphomimic data of LuxO (Fig. S4) in the supplement that highlight G-131 and G-97 do not affect LuxO-dependent activation (as pointed out by reviewer #2), which has contributed to our test of a G-131T mutant in the co-colonization experiment.

• Examination of the binding of LuxO and SypG (e.g., ChIP-seq) in combination with their transcriptional reporter under varying conditions (low cell density vs high cell density, with or without rscS* overexpression) would be extremely beneficial in testing the model proposed.

We agree but have not had success in our attempts to perform ChIP due to protein instability. For example, we have tried SypG with a C-terminal TAP tag, which my colleague Dr. Lu Bai at Penn State has used extensively for ChIP, ChIP-seq, and ChIP-exo, but we could not observe a signal even when RscS* allele was included in the strain.

Reviewer #2 (Public Review):

The study by Surrett et al. uncovers a novel regulatory axis in Vibrio fischeri that controls the expression of the qrr1 small RNA, which post-transcriptionally controls various quorum-dependent outputs. This study is timely and addresses a major question about the physiology of this important model symbiosis and potentially other Vibrio species. The results should be of broad interest within the field of microbiology.

While it was previously believed that qrr1 expression is under the strict control of the LuxO-dependent quorum sensing cascade, the authors demonstrate that qrr1 expression can be induced by another bEBP, SypG, in a manner that is quorum-independent. It was previously shown that qrr1 is important for colonization, and the authors recapitulate and extend this finding here. However, bacteria are likely at high cell density prior to entry into the crypts, which would repress qrr1 expression. Thus, despite the importance of qrr1 expression for crypt colonization, it would counterintuitively be repressed. The discovery of the SypG quorum-independent induction of qrr1 in this study may help resolve this conundrum. The authors take a largely genetic approach to characterize this novel regulatory pathway in combination with a squid colonization model. The experiments performed are generally well controlled and the data are clearly presented. The authors, however, fail to provide experimental evidence to support the physiological relevance of SypG-dependent control of qrr1 expression during host colonization.

Fig. 2 - It is unclear why there is a disconnect between qrr1 expression and qrr1-dependent effects. Data in 2B, indicate that qrr1 is induced in the ∆binK mutant according to the Pqrr1-gfp reporter but this expressed qrr1 does not have any effect on phenotypes like bioluminescence according to the data presented in 2C. While the authors reveal an effect of the binK deletion when rscS is overexpressed, it is unclear why this is necessary since simple deletion of bink without rscS is sufficient to induce qrr1 in 2B. Could this discrepancy be due to the fact that experiments in 2B are done on solid media while the experiments in 2C are performed in liquid media? Do cells in liquid not express qrr1? Or conversely, perhaps testing the bioluminescence of cells scraped off of plates could reveal a phenotype for the binK mutant similar to those seen in the rscS background in liquid. Or alternatively, if cells in a liquid culture still express qrr1, perhaps there is a posttranscriptional mechanism that prevents qrr1 from exerting an effect on bioluminescence? The latter possibility would alter the proposed model.

To help explain why we chose to overexpress RscS, we have added the cartoon in Fig. 2C, which highlights how BinK dephosphorylates SypG. We believe that the conditions used in the bioluminescence assay do not phosphorylate SypG, which prevents an effect by BinK. However, overexpression of RscS permits phosphorylation of SypG, which enables a phenotype to emerge in a binK mutant. We have tested the bioluminescence of cells within spots but did not detect a difference.

The authors propose a model in which sypG dependent activation of qrr1 is required for appropriate temporal regulation of this small RNA and contributes to optimal fitness of V. fischeri during colonization, however, this was not directly tested, and experimental evidence to support a physiological role for spyG-dependent regulation of qrr1 remains lacking. Data in Fig. S3 and Fig. 4G-H suggest that the Gs at -131 and -97 in Pqrr1 are largely dispensable for LuxO-dependent activation, but are important for SypG-dependent activation of Pqrr1. Also, the Pqrr1 mutations at C -130 and -96 completely prevent sypG-dependent activation while only partially reducing LuxO-dependent activation. If SypG-dependent activation of qrr1 is critical for the fitness of V. fischeri, a strain harboring these Pqrr1 promoter mutations should be attenuated in a manner that resembles the qrr1 deletion mutant as shown in Fig. 3C.

We thank the reviewer for this suggestion, which led us to generate and test a G-131T mutant in vivo.

Fig. S4 - these data suggest that LuxO cannot enhance transcription of PsypA and PsypP at native expression levels. But sypG-dependent induction of qrr1 was largely tested with Ptrc-dependent overexpression of SypG. Would overexpression of LuxO induce PsypA and PsypP? The authors should at least acknowledge this possibility in the text.

As requested, we have added text that acknowledges this possibility.

The authors adopt three distinct strategies to induce sypG-dependent activation of qrr1 in distinct figures throughout the manuscript: deletion of binK, overexpression of rscS (rscS*), and direct overexpression of sypG. It is not entirely clear why distinct approaches are used in different figures. This is particularly true for Fig. 5 since the authors already demonstrated that the direct overexpression of sypG can be used, which is a more direct way of addressing this question. Similarly, sypG overexpression should inhibit bioluminescence in Fig. 2 based on the proposed model, which would have tested the claims made more directly. Additional text to clarify this would be helpful.

As requested, we have added Fig. 2C and text to describe how SypG is regulated, which provides ways to test SypG-dependent activation of qrr1.

The Fig. 5D legend indicates that the strains harbor a Ptrc-GFP reporter. However, the text would suggest that these strains should harbor a Pqrr1-GFP reporter to test the question posed.

This has been corrected.

The conclusion that SypG and LuxO share UASs in the qrr1 promoter is based on fairly limited genetic evidence where point mutations were introduced into 3 bp of the predicted LuxO UASs within the qrr1 promoter. This conclusion needs to be qualified in the text or additional experimental evidence is needed to support this claim. For example, in vivo ChIP-exo could be used to map the SypG and LuxO binding sites. Or SypG and LuxO could be purified to assess binding to the qrr promoter in vitro (to map binding sites or test competitive interactions of these proteins to the qrr promoter).

As described above and in the text, we have not been able to construct a functional tagged SypG that would enable these types of studies.

On a related note, SypG binding to the qrr1 promoter is speculated based on indirect genetic evidence. But the authors do not directly demonstrate this. This should be acknowledged in the text or additional experimental evidence should be provided to support this claim.

As requested, we have added text in the discussion that highlights this problem.

Reviewer #3 (Public Review):

In this manuscript, Surrett and coworkers aimed to identify the mechanism that regulates the transcription of Qrr1 sRNA in the squid symbiont Vibrio fischeri. In many Vibrio species, Qrr1 transcription is regulated by quorum sensing (QS) and activated only at low cell density. Qrr1 is important for V. fischeri to colonize the squid host. In the QS systems that have been studied so far, LuxO is the only known response regulator that activates Qrr sRNA transcription. However, the authors argued that since V. fischeri forms aggregates before entering into the light organ of the squid, Qrr1 would not be made as high cell density QS state is likely induced within the aggregates. Therefore, they hypothesized that additional regulatory systems must exist to allow Qrr1 expression in V. fischeri to initiate colonization of the light organ. In turn, the authors identified that disruption of the function of the sensor kinase BinK allowed Qrr1 expression even at high cell density. Through a series of cell-based reporter assays and an in vivo squid colonization assay, they concluded that BinK is also involved in Qrr1 regulation within the squid light organ. They went on to show that another sigma54-dependent response regulator SypG is also involved in controlling Qrr1 expression. The authors propose dual regulation of LuxO and SypG on Qrr could be a common regulatory mechanism on Qrr expression in a subset of Vibiro species.

Overall, the experiments were carefully performed and the findings that BinK and SypG are involved in Qrr1 regulation are interesting. This paper is of potential interest to an audience in the field of QS and Vibrio-host interaction. However, experimental deficiencies and alternative explanations of the results have been identified in the manuscript that prevents a thorough mechanistic understanding of the interplay between QS and these new regulators.

1) The premise that Qrr1 expression in the light organ has to be regulated by systems other than QS is unclear. In lines 108-109, it was stated that "...prior to entering the light organ, bacterial cells are collected from the environment and form aggregates that are densely packed", however, in lines 184-185, it was stated that "The majority of crypt spaces each contained only one strain type (Fig. 3B), which is consistent with most populations arising from only 1-2 cells that enter the corresponding crypt spaces". So, if the latter case is true (i.e., 1-2 cells/crypt), why Qrr1 could not be made at that time point as predicted by a QS regulation model?

We have not changed this section because if Qrr1 is expressed only after the cells have already entered the crypt space, then the Δqrr1 mutant would colonize a number of crypt spaces comparable to that of wild type cells.

2) The involvement of the rscS allele for the ∆binK mutant to show an altered bioluminescence phenotype is confusing. It is unclear why a WT genetic background was sufficient to show the high Qrr1 phenotype in the original genetic screen that identified BinK (Fig. 2A-B), while the rcsS allele is now required for the rest of the experiments to show the involvement of BinK in bioluminescence regulation (Fig 2C). Is the decreased bioluminescence phenotype observed in rcsS* ∆binK mutant (fig. 2C) dependent on LuxU/LuxO/Qrr1/LitR? Could it be through another indirect mechanism (e.g., SypK as discussed in line 403)? A better explanation of the connection between RcsS/Syp and BinK and perhaps additional mutant characterization are necessary to interpret the observed phenotypes.

As described above, we have added a cartoon that illustrates the pathway involving BinK (Fig. 2C) and additional justification in the results section, which better explains why RscS overexpression was used.

3) In squid colonization competition assays (Fig. 3), it was concluded that the ∆qrr1 allele is epistatic to the ∆binK allele (line 204), and the enhanced colonization of the ∆binK mutant is dependent on Qrr1 (section title, line 162). This conclusion is hard to interpret. The results can be interpreted as ∆qrr1 mutation lowers the colonization efficiency of the ∆binK mutant which could imply BinK regulates Qrr1 in vivo. Alternatively, it could be interpreted that the ∆binK mutation increases the colonization efficiency of the ∆qrr1 mutant. Direct competition between single and double mutants in the same animals may resolve the complexity. And direct comparison of Qrr1 expression of WT and ∆binK mutants inside the animals, if possible, will also help interpret these results.

We thank the reviewer for the suggestion and were able to test the ΔbinK and ΔbinK Δqrr1 mutants directly (Fig. S2). We were unable to interpret the data using the Pqrr1 reporter due to unexpected heterogeneity in Pqrr1 activity throughout the crypt spaces.

4) Similar concern to above (#2), in Fig. 4, the link between BinK and Qrr1 regulation is not fully explored. What connects BinK and Qrr1 expression? Does BinK function via LuxU (or other HPT) to control SypG like the other QS kinases? And what is the role of other known kinases (e.g., SypF) in the signaling pathway? And did the authors test other bEBPs found in V. fischeri for their role in Qrr1 regulation?

We have added to the discussion content that highlights examining LuxU as a direction worthwhile to pursue to understand how BinK affects signaling that activates Qrr1.

5) In addition to the genetic analysis, additional characterization of SypG is required to demonstrate the proposed regulatory mechanism: What is the expression level (and phosphorylation state) of SypG and LuxO at different cell densities? Does purified SypG directly bind to the qrr1 promoter region? c. How do these two bEBPs compete with each other if they are both made and active?

We agree that these are interesting questions, but as described above, we were unable to purify SypG to address the biochemistry.

6) The molecular OR logic gate is used to describe the relationship between LuxO and SypG, but this logic relationship is not always true in all conditions (if at all). In WT, deletion of luxO completely abolished Qrr1 expression (Fig. 4C). Even in the binK mutant, LuxO still seems to be the more prominent regulator (Fig. 4D) as deletion of luxO already caused a smaller but significant drop in Qrr1 expression. The authors may need to use this term more precisely.

We note that in wild-type cells, SypG is not active under the conditions tested, so SypG would not contribute to activating Qrr1 expression. The level of Pqrr1 activity by the SypG(D53E) variant surpasses the basal level of LuxO, which suggests that LuxO does not always serve as the prominent regulator. We have added content to the discussion to highlight how LuxO may contribute more to the regulation.

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

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Reply to the reviewers

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

I summarise the major findings of the work below. In my opinion the range and application of approaches has provided a broad evidence base that, in general, supports the authors conclusions. However, there are, in my opinion, particular failures to utilise and communicate this evidence. The manuscript may be much improved with attention in the following areas. In each case I will give general criticism with a few examples, but the principals of my comments could be applied throughout the work.

1) Insufficient quantification. The investigation combines various sources of qualitative data (EM, fluorescence microscopy, western blotting) to generate a reasonably strong evidence base. However, the work is over-reliant on representative images and should include more quantification from repeat experiments. When there are multiple fluorescence micrographs with intensity changes (not necessarily just representative images) (e.g. Figure 1 or 2) the authors should consider making measurements of these. Also the VLP production assays, which are assessed by western blotting would particularly benefit from a quantitative assessment (either by densitometry or, if samples remain, ELISA/similar approach).

We have performed quantification of immunofluoresence, western blotting and VLP experiments from existing data. These quantification are presented in our revised manuscript. An overview of new quantification is shown below:

Data shown

Quantification now shown in

Method

Analysis

Figure 1A

Supp F1C

IF

HAE (-/+ SARS-CoV-2)

• Tetherin total fluorescence intensity

Figure 1D

Supp F1E

IF

HeLa+ACE2 (-/+ SARS-CoV-2 )

• Tetherin total fluorescence intensity

Figure 2C

Supp F2B

IF

A549+ACE2 (-/+ SARS-CoV-2)

• Tetherin total fluorescence intensity

Figure 2G

Supp F2D

IF

T84 (-/+ SARS-CoV-2)

• Tetherin total fluorescence intensity

Supp F4A

Supp F4B

IF

HeLa + ss-HA-Spike transients (-/+ HA stained cells) - Tetherin total fluorescence intensity

Figure 4D

Supp F4E

IF

HeLa + TetOne ss-HA-Spike stables (-/+ Dox)

• Tetherin total fluorescence intensity

Figure 4F

Supp F4G

W blot

HeLa + TetOne ss-HA-Spike stables (-/+ Dox)

– Tetherin abundance

Figure 4G

Supp F4I

W blot – lysates

Spike VLP experiments

– tetherin abundance

Figure 4G

Supp F4J

W blot - VLPs

Spike VLP experiments

• N-FLAG abundance

Figure 6A

Supp F7A

W blot – lysates

ORF3a VLP experiments

– tetherin abundance

Figure 6A

Supp F7B

W blot - VLPs

ORF3a VLP experiments

• N-FLAG

For immunofluoresence anaysis, the mean, standard deviation, number of cells analysed and number of independent experiments are shown in the updated figure legends. Statistical analysis is also detailed in figure legends. Methods for the quantificaiton of fluoresence intensity is included in the Methods section.

Densitometry was performed on western blots and VLP experiments as suggested. The mean, standard devisation and number of independent expreiments analysed are expressed in figure legends. Methods for densityometry quantification is now included.

2) Insufficient explanation. I found some of the images and legends contained insufficient annotation and/or description for a non-expert reader to appreciate the result(s). Particularly if the authors want to draw attention to features in micrographs they should consider using more enlarged/inset images and annotations (e.g. arrows) to point out structures (e.g DMVs etc.). This short coming exacerbates the lack of quantification.

Additional detail has been provided to the figure legends, and we have updated several figures to draw attention to features in micrographs. Black arrowheads have been added to Figures 1E, 2D, 2H to highlight plasma membrane-associated virions, and asterisks to highlight DMVs in Figures 1E, 2D and Supplemental Figures 2C, 2E. Similarly, typical Golgi cisternae are highlighted by white arrowheads micrographs in Figure 2E. These figure legends have also been modified to highlight these additions.

3) Insufficient exploration of the data. I had a sense that some aspects of the data seem unconsidered or ignored, and the discussion lacks depth and reflection. For example the tetherin down-regulation apparent in Figures 1 and 2 is not really explained by the spike/ORF3a antagonism described later on, but this is not explicitly addressed.

We have made changes throughout the manuscript, but the discussion especially has been modified. We now discuss the ORF3a data in more depth, discuss possible mechanisms by which ORF3a alone enhances VLP release, and discuss our ORF7a data in context to previous reports.

The discussion has been updated to now include a better description of our data, and additional writing putting our work in to context with previously published work. See discussion section of revised manuscript.

Also, Figure 6 suggests that ORF3a results in high levels of incorporation of tetherin in to VLPs, but I don't think this is even described(?). The discussion should also include more comparison with previous studies on the relationship between SARS-2 and tetherin.

We have added a section to discuss how ORF3a may enhance VLP release,

‘We found that the expression of ORF3a enhanced VLP independently of its ability to relocalise tetherin (Figure 6A). This may be due to either the ability of ORF3a to induce Golgi fragmentation [38] which facilitates viral trafficking [39], or due to enhanced lysosomal exocytosis [37]. Tetherin was also found in VLPs upon co-expression with ORF3a (Figure 6A) which may also indicate to enhanced release via lysosomal exocytosis [37].

The secretion of lysosomal hydrolases has been reported upon expression of ORF3a [31] and whilst this may in-part be due to enhanced lysosome-plasma membrane fusion, our data highlights that ORF3a impairs the retrograde trafficking of CIMPR (Supplemental Figures 6B, 6F, 6G), which may similarly increase hydrolase secretion.’ – (Line 625-654).

The discussion has been developed to compare the relationship between SARS-CoV-2 and tetherin in previous studies,

‘SARS-CoV-1 ORF7a is reported to inhibit tetherin glycosylation and localise to the plasma membrane in the presence of tetherin [18]. We did not observe any difference in total tetherin levels, tetherin glycosylation, ability to form dimers, or surface tetherin upon expression of either SARS-CoV-1 or SARS-CoV-2 ORF7a (Figures 4A, 4B, 4C).

Others groups have demonstrated a role for ORF7a in sarbecovirus infection and both SARS-CoV-1 and SARS-CoV-2 virus lacking ORF7a show impaired virus replication in the presence of tetherin [18,41]. A direct interaction between SARS-CoV-1 ORF7a and SARS-CoV-2 ORF7a and tetherin have been described [18,41], although the precise mechanism(s) by which ORF7a antagonises tetherin remains enigmatic. We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a can potently antagonise IFN signalling [42] which would impair tetherin induction in many cell types.’ – (Line 667-704).

I have no minor comments on this draft of the manuscript.

Reviewer #1 (Significance (Required)):

Tetherin, encoded by the BST2 gene, is an antiviral restriction factor that inhibits the release of enveloped viruses by creating tethers between viral and host membranes. It also has a capacity for sensing and signalling viral infection. It is most widely understood in the context of HIV-1, however, there is evidence of restriction in a wide variety of enveloped viruses, many of which have evolved strategies for antagonising tetherin. This knowledge informs on viral interactions with the innate immune system, with implications for basic virology and translational research.

This study investigates tetherin in the context of SARS-CoV-2. The authors use a powerful collection of tools (live virus, gene knock out cells, recombinant viral and host expression systems) and a variety of approaches (microscopy, western blotting, infection assays), which is, itself, a strength. The study provides evidence to support a series of conclusions: I) BST2/tetherin restricts SARS-CoV-2 II) SARS-CoV-2 ablates tetherin expression III) spike protein can modestly down-regulate tetherin IV) ORF3A dysregulates tetherin localisation by altering retrograde trafficking. These conclusions are broadly supported by the data and this study make significant contributions to our understanding of SARS-CoV-2/tetherin interactions.

My enthusiasm is reduced by, in my opinion, a failure of the authors to fully quantify, explain and explore their data. I expect the manuscript could be significantly improved without further experimentation by strengthening these aspects.

This manuscript will be of interest to investigators in virology and/or cellular intrinsic immunity. Given the focus on SARS-CoV-2 it is possible/likely that it will find a slightly broader readership.

I have highly appropriate skills for evaluating this work being experienced in virology, SARS-CoV-2, cell biology and microscopy.

We wish to thank Reviewer #1 for their comments which have helped us to improve the quality of our revised manuscript.

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

BST2/tetherin can restrict the release and transmission of many enveloped viruses, including coronaviruses. In many cases, restricted viruses have developed mechanisms to abrogate tetherin-restriction by expressing proteins that antagonize tetherin; HIV-1 Vpu-mediated antagonism of tetherin restriction is a particularly well studied example. In this paper, Stewart et al. report their studies of the mechanism(s) underlying SARS-CoV-2 antagonism of tetherin restriction. They conclude that Orf3a is the primary virally encoded protein involved and that Orf3a manipulates endo-lysosomal trafficking to decrease tetherin cycling and divert the protein away from putative assembly sites.

Major comments:- In my view some of the claims made by the authors are not fully supported by the data. For example, the bystander effect discussed in line 162 may suggest that infected cells can produce IFN but does not 'indicate' that they do

This text has now been edited,

‘The levels of tetherin in uninfected HAE cells is lower than observed in uninfected neighbours in infected wells demonstrating that infected HAE cells are able to generate IFN to act upon uninfected neighbouring cells, enhancing tetherin expression.’ - (Lines 163-172).

Most of the EM images show part of a cell profile, so statements such as (line 192) 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' should be backed up with appropriate images, or indicated as 'not shown', or removed (the observation is not so important for this story). Line 196, DMVs can't be seen in these micrographs.

The statement 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' has been removed. The micrographs in Figure 1E have been re-cropped, and image iii replaced with an image showing DMVs and budding virions. Plasma membrane-associated virions are highlighted by black arrowheads, DMVs by black asterisks, and intracellular virion by a white arrow.

Line 391, I can't see much change in CD63 distribution.

CD63 reproducibly appears clustered towards the nuclei in ORF3a expressing cells, whilst CD63 positive puncta are abundant in the periphery of mock cells. CD63 puncta are also larger, and the staining of CIMPR and VPS35 also appears to be associated with larger organelles. We have amended the text to now read,

‘Expression of ORF3a also disrupted the distribution of numerous endosome-related markers including CIMPR, VPS35, CD63, which all localised to larger and less peripheral puncta (Supplemental Figure 6B), and the mixing of early and late endosomal markers’ - (Line 469).

Quantification of the diameter of CD63 puncta indicate that they are larger in ORF3a expressing cells than in mock cells. Mock cells - 0.71μm (SD; 0.19), ORF3a - 1.15μm (SD;0.35). At least 75 organelles per sample, from 10 different cells. We have not included this data as we do not wish to labor this point but are happy to include this quantification if required to do so.

Line 321, the authors show that ORF7a does not affect tetherin localization, abundance, glycosylation or dimer formation, but they don't show that it doesn't restrict SARS-CoV-2. Can they be sure that epitope tagging this molecule does not abrogate function (or the functions of any of the other tagged proteins for that matter), or that ORF7a works in conjunction with one of the other viral proteins?

We are careful in the manuscript not to claim that ORF7a has no effect on tetherin. Our data indicate that ‘ORF7a does not directly influence tetherin localisation, abundance, glycosylation or dimer formation’ - (Line 361-362).

We were unable to reproduce an effect of ORF7a on tetherin glycosylation. Our data conflicts with that presented by Taylor et al, 2015, where ORF7a impaired tetherin glycosylation and ORF7a localised to the plasma membrane in tetherin expressing cells. The experiments performed by Taylor et al used HEK293 cells and ectopically expressed tagged tetherin. The differences in results may be attributed to the differences between cell lines or due to differences between endogenous or ectopic / tagged tetherin.

The study by Taylor et al uses SARS-CoV-1 ORF7a-HA from Kopecky-Bromberg et al., 2007 (DOI: 1128/JVI.01782-06), where the -HA tag is positioned at the C-terminus. Our ORF7a-FLAG constructs have a C-terminal epitope tag. While we cannot exclude the possibility that tagged proteins may act differently from untagged ones, the differences between our findings and previous work appear unlikely to be due to epitope tags.

Our manuscript states that although we cannot find any effect of ORF7a on tetherin localisation, abundance, glycosylation, or dimer formation, we cannot exclude that ORF7a impacts tetherin by another mechanism. For example, ORF7a has been found to antagonise interferon responses. Tetherin is abundantly expressed in HeLa cells and expression does not require induction through interferon. None of our experiments above would be impacted by interferon antagonism yet this could impact other cell types besides infection in vivo. These possibilities may explain the reported differential impact of ORF7a by different labs. An addition comment has been added to the discussion to reflect this,

’We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a potently antagonises IFN signalling [38], which would impair tetherin induction in many cell types. - (Line 701-704).

Note - Reference 38 has been added to the manuscript – Xia et al., Cell Reports DOI: 10.1016/j.celrep.2020.108234

In the ORF screen, a number of the constructs are expressed at low level, is it possible they [the authors] are missing something?

Some of the ORFs expressed in the miniscreen appear poorly expressed. We accept that in the use of epitope tagged constructs expression levels of individual viral proteins may impact upon a successful screen. However, this screen was performed to identify any potential changes in tetherin abundance or localisation, and the screen did successfully identify ORF3a, which we were able to follow-up and verify.

Line 376, the authors refer to ORF3a being a viroporin. A recent eLife paper (doi: 10.7554/eLife.84477; initially published in BioRxiv) refutes this claim and builds on other evidence that ORF3a interacts with the HOPS complex. The authors should at least mention this work, especially in the discussion, as it would seem to provide a molecular mechanism to support their conclusions.

This paper had not been peer reviewed at the time of our initial submission. We have now included the following text,

‘SARS-CoV-2 ORF3a is an accessory protein that localises to and perturbs endosomes and lysosomes [29]. It may do so by acting either as a viroporin [30] or by interacting with, and possibly interfering with the function of VPS 39, a component of the HOPS complex which facilitates tethering of late endosomes or autophagosomes with lysosomes [29,31]. Given ORF3a likely impairs lysosome function, the observed increased….’ - (Lines 444-449).

Fig 3, the growth curves illustrated in Fig3 C and D do not have errors bars; how many times were these experiments repeated?

These experiments require more repeats to include error bars. Infection and plaque assay (Figure 3C, 3D) are currently ongoing and we plan to complete them in the next 6-8 weeks and include them in the finalised manuscript.

In the new experiments, infections will additionally be performed at MOI 0.01, in addition to the previous MOIs (1 and 5).

Line 396, the authors show increased co-localization with LAMP1. As LAMP1 is found in late endosomes as well as lysosomes, they cannot claim the redistributed tetherin is specifically in lysosomes.

We have altered the text to now say:

‘The ORF3a-mediated increase in tetherin abundance within endolysosomes could be due to defective lysosomal degradation.’ - (Line 475).

There seems to be a marked difference in the anti-rb555 signal in the 'mock' cells in panels 5H and Suppl 6E. Is there a good reason for this, or does this indicate variability between experiments?

Antibody uptake experiments in Figure 5H and Supp Figure 6E were performed and acquired on different days. Relatively low levels of signal are available in these antibody uptake experiments, and the disperse labelling seen in the mocks does not aid this.

Fig 6a, why is there negligible VLP release from cells lacking BST2 and ORF3a-strep? How many times were these experiments performed? Is this a representative image? I think it confusing to refer to the same protein by two different names in the same figure (i.e. BST2 and tetherin). Do the authors know how the levels of ORF3a expressed in cells in these experiments compares to those seen in infected cells?

We have changed the blot in Figure 6A for one with clearer FLAG bands. Three independent experiments were performed for Figure 6A. Quantification of VLPs is now included in Supplemental Figure 7B.

We have changed ‘Bst2’ to ‘tetherin’ in all previous figures relating to protein; Figure 4G, Figure 6A, B, C.

We have no current information to compare ORF3a levels in these experiments versus in infected cells. We can investigate quantifying this if necessary.

My final point is, perhaps, the trickiest to answer, but nevertheless needs to be considered. As far as we know, SARS-CoV-2 and at least some other coronaviruses, bud into organelles of the early secretory pathway, often considered to be ERGIC. In the experiments shown here the authors provide evidence that ORF3a can influence tetherin recycling, but the main way of showing this is through its increased association with endocytic organelles. Do the authors have any evidence that Orf3a reduces tetherin levels in the ERGIC or whether the tetherin cycling pathway(s) involve the ERGIC?

This is an interesting point, and as the reviewer concedes, this is tricky to answer. Expression of ORF3a causes the redistribution or remodeling of various organelles (Figures 1E, 2D, 2F, Supp Figures 2C, 2E, 3E, 6B, 6C, 6D). We have been unable to test the direct involvement of ERGIC, despite attempts with a number of commercial antibodies. Given the huge rearrangements of organelles during SARS-CoV-2 infection, it is unclear exactly what will happen to the distribution of ERGIC.

Minor comments: Line 53, delete 'shell' its redundant and confusing when the authors have said coronaviruses have a membrane.

Deleted.

Line 61, delete 'the'

Deleted.

Line 72, delete 'enveloped'; coronaviruses already described as enveloped viruses (line 53)

Deleted.

Lines 93 - 100, lop-sided discussion of the viral life cycle; this paragraph is mostly about entry, which is not relevant to this paper, and does not really deal with the synthesis and assembly side of the cycle.

We have now added the following text,

‘….liberating the viral nucleocapsid to the cytosol of the cell. Upon uncoating, the RNA genome is released into the host cytosol and replication-transcription complexes assemble to drive the replication of the viral genome and the expression of viral proteins. Coronaviruses modify host organelles to generate viral replication factories - so-called DMVs (double-membrane vesicles) that act as hubs for viral RNA synthesis [10]. SARS-CoV-2 viral budding occurs at ER-to-Golgi intermediate compartments (ERGIC) and newly formed viral particles traffic through secretory vesicles to the plasma membrane where they are released to the extracellular space.’ - (Lines 95-104).

Line 103, why are the neighbouring cells 'naive'?

‘naïve’ removed.

Line 112 - 113, delete last phrase; tetherin is described as an IFN stimulated gene in line 111; to be accurate, the beginning of the sentence should be 'Tetherin is expressed from a type 1 Interferon stimulated gene ...'

Amended.

Line 118 - 119, should say 'For tetherin-restricted enveloped viruses' as not all enveloped viruses are restricted by tetherin.

Amended.

Line 131, coronaviruses are not the only family of tetherin-restricted viruses that assemble on intracellular membranes, e.g. bunyaviruses.

This has been modified and now reads,

‘In order for tetherin to tether coronaviruses, tetherin must be incorporated in the virus envelope during budding which occurs in intracellular organelles.’ - (Lines 133-135).

Line 192, there is no EM data in Supplemental Fig 1C.

This has now been removed.

Line 251, 'a synchronous infection event' should be 'synchronous infection' as there will be multiple infection events.

This has been changed.

Page 13 (and elsewhere), unlike Southern, 'Western' should not have a capital letter, except at the start of a sentence.

These have been updated throughout the manuscript (Lines 183, 341, 3549, 356, 392, 509, 763, 1330, 1399).

Lines 330 and 352, can the authors quantitate S protein-induced reduction in cell surface tetherin rather than using the somewhat subjective 'mild'?

These are now changed to,

‘Transient transfection of cells with ss-HA-Spike caused a 32% decrease in tetherin as observed by immunofluorescence (Supplemental Figure 4A, 4B), with…’ – (Line 370).

‘To explore whether the Spike-induced tetherin downregulation altered virus release, we performed experiments with virus like particles (VLPs) in HEK293T …’ – (Line 399).

Line 379, OFR, should be ORF.

Yes, changed.

Line 448, 'Tetherin retains the ability' - did it ever loose it?

This has been rephrased to,

‘Tetherin has the ability to restrict a number of different enveloped viruses that bud at distinct organelles.’ - (Line 547).

Line 451, 'luminal' is confusing in this context.

This has been modified to,

‘Tetherin forms homodimers between opposing membranes (e.g., plasma membrane and viral envelope) that are linked via disulphide bonds.’ - (Line 549).

Line 453, the process of virus envelopment is likely to be more than a 'single step'

This now reads,

‘…virus during viral budding, which occurs in modified ERGIC organelles.’ - (Line 552).

Line 457, in my view the notion that Vpu abrogation of tetherin restriction is just due to redistribution of tetherin to the TGN is somewhat simplistic and disregards a lot of other work.

We have removed mention of mechanisms of tetherin antagonism by other viruses. The key point we wish to make here is that tetherin is lost from the budding compartment. This now reads,

‘Many enveloped viruses antagonise tetherin by altering its localisation and removing it from the respective site of virus budding.’ – (Line 552-553).

Line 472, what is meant by 'resting states'?

This should have been ‘in the absence of stimulation’ and have now been re-written,

‘Tetherin is an IFN-stimulated gene (ISG) [13], and many cell types express low levels of tetherin in the absence of stimulation.’ - (Line 577).

Line 1204, how were 'mock infected cells .......... infected'?

This has now been re-written,

‘Differentiated nasal primary human airway epithelial (HAE) cells were embedded to OCT….’ - (Line 1385).

Reviewer #2 (Significance (Required)):

This study builds on published work supporting the notion that SARS-Cov-2 ORF3a is an antagonist for the restriction factor tetherin. Importantly, it provides insights to the the mechanism of ORF3a mediated tetherin antagonism, specifically to ORF3a inhibits tetherin cycling, diverting the protein to lysosomes and away from compartment(s) where virions assemble. Overall, the authors provide good supporting evidence for these conclusions, however there are issues that the authors need to address.

We wish to thank Reviewer #2 for their insightful comments and suggestions for improving this work.

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

Restriction factors are major barriers against viral infections. A prime example is Tetherin (aka BST2), which is able to physically tether budding virions to the plasma membrane preventing release of the infectious particles. Of note, tetherin has broad anti-viral activity and has been established as a crucial innate immune defense factor against HIV, IAV, SARS-CoV-2 and other important human pathogens. However, successful viruses like SARS-CoV-2 evolved strategies to counteract restriction factors and promote their replication. Important restriction factors, such as tetherin, may often be targeted by multiple viral strategies to ensure complete suppression of their anti-viral activities by the pathogen. Of note, it was previously published that the accessory protein ORF7a of SARS-CoV-2 binds to (Petrosino et al, Chemistry Europe, 2021) and antagonizes it (Martin-Sancho et al, Molecular Cell, 2021). Previous data on SARS-CoV also revealed that ORF7a promotes cleavage of tetherin (Taylor et al, 2015, J Virol). In this manuscript, the authors show that tetherin restricts SARS-CoV-2 by tethering virions to the plasma membrane and propose that tetherin is targeted by two proteins of SARS-CoV-2. Whereas the Spike protein promotes degradation of tetherin, the accessory protein ORF3a redirects tetherin away from newly forming SARS-CoV-2 virions. While the overall findings that both S and ORF3a are additionally targeting tetherin is both novel and intriguing, additional evidence is needed to support this. In addition, the authors show that in their experimental setups ORF7a does not induce cleavage of tetherin. This is in direct contrast to previously published data both on SARS-CoV(-1) and -2 (Taylor et al, 2015, J Virol; Petrosino et al, Chemistry Europe, 2021; Martin-Sancho et al, Molecular Cell, 2021). From my point of view that needs further experimental confirmation. While the authors state that the impact of Spike on tethrin is mild, the experiments should still allow the conclusion whether there is a (mild) effect or not. The mechanism of ORF3a is fortunately more robustly assessed and provides some novel insights. Unfortunately, the whole manuscript suffers from a striking lack of quantifications. In addition, it is not clear whether and how many times experiments were repeated to the same results. Overall, the data in this manuscript seem very speculative and preliminary and thus do not support the authors conclusions.

Major:

Much of the data seems like it was only done once. As I am sure that this is a writing issue, please clearly state how many times the individual assays were repeated, provide the quantification graphs and appropriate statistics. Some experiments may need additional quantification and confirmation by other methods to be convincing.

Quantification is provided throughout the revised manuscript. Figure legends have also been updated to provide information on quantification and statistical analysis.

For example, Figure 1A, C and D: Please quantify the levels of tetherin and use an alternative readout, e.g. Western blotting of infected cells.

Quantification has been performed and included in our revised manuscript in Supplemental Figures 1C, 1E. Tetherin is not shown in Figure 1C.

A table is provided (above) to highlight the additional quantification.

Figure 2A: Please quantify.

We are not sure we understand this point. The western blot shown in Figure 2A demonstrates the ectopic expression of ACE2 in our A549 cell line. A549 cells have been used by many labs to study SARS-CoV-2 infection, but express negligible ACE2.

Fig 3A: Please show and confirm successful tetherin KO in the cell lines that are used not only in microscopy.

A new blot is now shown in Figure 3A, including a blot demonstrating tetherin loss in both KO lines.

Figure 4C: Please quantify

Currently flow cytometry experiments have been performed twice each and this is now detailed in the figure legends. The data shown in each panel is representative and the data has been explored using analogous approaches. For example, Figure 4C is complemented by Figures 4A and 4B, Figures 4E is complemented by 4D and 4F. We do not feel that repeating these flow cytometry analysis will significantly improve the manuscript.

Figure 4D: Please quantify the effects are not obvious from the images provided.

Quantification is now provided in Supplemental Figure 4E.

Figure 4E, F Please provide a quantification of multiple independent repeats, the claimed differences are neither striking nor obvious.

Quantification of 4F is now provided in Supplemental Figure 4G. Tetherin levels were quantified to be reduced by 25% (SD: 8%) by addition of Doxycycline and induction of ss-HA-Spike. Information for quantification is provided in figure legends.

Figure 5A: Please quantify

These experiments have currently been performed twice and this is now described in the figure legends. Data shown is representative. We can perform one more repeat of these experiments to quantify if neccessary, but do not feel it will significantly alter the manuscript.

Figure 3C and D: At timepoint 0 the infection input levels are different. The initial infection levels have to be the same to draw the conclusion that tetherin KO affects virion release and not the initial infection efficiency. Can the authors either normalize or ensure that the initial infection is the same in all conditions and that variations in the initial infection efficiency do not correlated with the impact of tetherin on replication/release ? How often were those experiments repeated? Are the marginal differences in infectious titre significant? Overall the impact of tetherin on SARS-CoV-2 is very underwhelming but that may be due to efficient viral tetherin-counteraction strategies. Why is the phenotype inverted at 72 h?

Equal amounts of virus, as measured by plaque-forming units (PFU), were used for both HeLa cell lines and thus at 0 hpi the variation seen is within the parameters of the assay used. It remains possible that tetherin affects virus entry but this is unlikely and this assay was not designed to investigate that effect.

Growth curve assays are currently being repeated using an MOI of 0.01, 1 and 5. We are removing the 72 hpi sample from future experiments. At this time point, we find that the extensive cell death caused by viral replication (especially at higher MOIs) makes it difficult to accurately separate the released from intracellular fractions and conclusions cannot be accurately drawn from the data.

Additional repeats of these experiments are in progress and will be included in the finalised manuscript.

Figure 4B and C: Can the authors provide an explanation why SARS-CoV ORF7a is not inducing cleavage/removes glycosylation of tetherin. To show that the assays work, an independent positive control needs to be included. The FACS data in C is unfortunately not quantified.

See above comments (Reviewer #2) regarding discussion on ORF7a. Additional text has been included to discuss ORF7a data,

‘SARS-CoV-1 ORF7a is reported to inhibit tetherin glycosylation and localise to the plasma membrane in the presence of tetherin [18]. We did not observe any difference in total tetherin levels, tetherin glycosylation, ability to form dimers, or surface tetherin upon expression of either SARS-CoV-1 or SARS-CoV-2 ORF7a (Figures 4A, 4B, 4C).

Others groups have demonstrated a role for ORF7a in sarbecovirus infection and both SARS-CoV-1 and SARS-CoV-2 virus lacking ORF7a show impaired virus replication in the presence of tetherin [18,41]. A direct interaction between SARS-CoV-1 ORF7a and SARS-CoV-2 ORF7a and tetherin have been described [18,41], although the precise mechanism(s) by which ORF7a antagonises tetherin remains enigmatic. We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a can potently antagonise IFN signalling [42] which would impair tetherin induction in many cell types.’ – (Line 667-704).

Fig 4G: The rationale and result of this experiment are not clear.

The rationale for Spike VLP experiments is explained at Line 403. Given that Spike caused a reproducible decrease in cellular tetherin, we examined whether this downregulation was sufficient to antagonise tetherin and increase VLP yield.

Fig 6: What is the benefit of doing the VLP assays as opposed to genuine virus experiments? To me it rather seems to be making the data unnecessarily complex. Again, no quantifications or repeats are provided.

VLPs are used to separate the budding and release process from the replication process of RNA viruses. VLPs have been used in a number of SARS-CoV (DOI: 1002/jmv.25518) and HIV-1 (DOI: https://doi.org/10.1186/1742-4690-7-51) studies to analyse the impact of tetherin (and tetherin mutants) on release.

VLP experiment quantification are now included throughout.

Minor: Fig 1D: How do the authors explain the mainly intracellular Spike staining?

We do not understand this point. Spike staining is intracellular, whether expressed alone or in the context of infected cells.

Please add statistical analyses on the data e.g. Fig. 3 C and D

Additional repeats of these experiments are in progress and will be included in the finalised manuscript.

Fig. 4B and F: Why do the annotated sizes of tetherin differ between the blots?

Figures 4B and 4F are run in non-reduced and reduced conditions respectively. In order to best show the dimer deficient C3A-Tetherin, blots are typically run in non-reduced conditions to exemplify dimer formation and to highlight any defects in dimer formation. The rest of the blots in the manuscript are run in denaturing conditions to aid blotting of other proteins. (Lines 957-958) and now (Lines 1356-1357).

Fig. 5A: What is ORF6a? Do the authors mean ORF6?

Yes, this has been changed.

An MOI of 1 is NOT considered a low or relevant MOI. Can the authors either rephrase or repeat experiments with an actual low or relevant MOI i.e. 0.01 ?

We are currently repeating these experiments and are including MOIs of 0.01, 1 and 5.

Why were the cell models switched between Figure 1 and 2 and essentially the same experiments repeated?

HeLa cells express high levels of tetherin at steady state, whilst A549 cells require IFN stimulation. HeLa cells demonstrate that tetherin downregulation occurs via an IFN-independent manner. A549 and T84 cells are more physiologically relevant cell types for SARS-CoV-2 infection. These points are stated in Lines 230 and 261.

The manuscript may benefit a lot from streamlining and removing unessential deviations from the main message (e.g. discussions why multistep/single step growth curves are used/not relevant; why are they shown if the authors conclude that a single step is not relevant?). The discussion is extremely lengthy and does not provide sufficient discussion of the presented data.

The multistep/single step growth curve text will be adapted, but it will be re-written after additional infection experiments.

We have removed from the Discussion a small section discussing ORF7a mutants, given that the emphasis of our manuscript is not on ORF7a.

We have also removed a small section describing the rearrangements of intracellular organelles by SARS-CoV-2 as it does not directly relate to the central message of our manuscript.

According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin. My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

Reviewer #3 (Significance (Required)):

According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin. My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

We thank Reviewer#3 for their comments and suggestions for improving this work.

self-issued names

SVG

Author Response

Reviewer #2 (Public Review):

The idea of using fluorescently labeled tandem SH2 domains to target tagged RTKs is brilliant and could potentially provide a powerful new way to assess the activation of RTKs in situ and in multiple physiological contexts. Thus, it was disappointing that there was insufficient characterization of the system to be able to interpret the data it generates. Although the paper shows that tagging the EGFR appears to have minimal impact on its biological activity, the readout for receptor kinase activity is % clearance of the fluorescent reporter tag from the cytosol. Such clearance is likely to depend on a variety of different factors, including the ratio of tagged receptors to probe, the number of functional pools in which the probe exists, the exchange rate between these pools, and the affinity of the probes for the tagged receptor. Without determining how each of these factors impacts % clearance, it is difficult to interpret either the dose-response curves or response kinetics.

We appreciate the reviewer’s point that the paper would be improved by a thorough analysis of how membrane translocation depends on our biosensor’s expression levels. We have attempted to address this thoroughly in our response to the Editor’s summary comments above. Briefly, we have now added 3 new supplementary figures (Figures S2-S4) in which we quantify ZtSH2 translocation as a function of expression levels. We find that the ratio of EGFR/ZtSH2 expression predicts the extent of ZtSH2 translocation in both NIH3T3 and HEK293T cells, matching results from our computational model. We have also added a new section to the main text to clearly explain these results (Lines 190-235). We hope that these data clarify the design constraints for two-component biosensors of this type.

For example, the difference in activation kinetics between EGFR and ErbB2 is very interesting, but the almost instantaneous rise (Fig S4B) is very surprising. The kinetics of activation of the EGFR have been extensively studied by mass-spectrometry and are generally limited by ligand binding, which has a characteristic time of several minutes, not seconds (pmid: 26929352; pmid: 1975591). Thus, such a response is suggestive of a freely exchanging ZtSH2 reporter pool that is mostly depleted in seconds with the slow secondary kinetics reflecting a slowly exchanging ZtSH2 reporter pool. Alternately, the cells could be accumulating an intracellular pool of activated receptors over time. That the authors are using concentrations of EGF >100-fold physiological levels (pmid: 29268862) further complicates the interpretation of these experiments.

We thank the reviewer for bringing these papers to our attention. However, we strongly disagree with their interpretation of the results. In a paper cited by the reviewer (PMID:26929352), phosphotyrosine responses are extremely fast, with phosphorylation occurring within tens of seconds even in response to 20 nM EGF (see Figure 2 from Reddy et al PNAS 2016). Reddy et al further claim in their abstract “Significant changes were observed on proteins far downstream in the network as early as 10 s after stimulation.” While the timescale of EGFR phosphorylation may be of some debate, the response timescale we observe is consistent with previously published observations.

It is also important to point out that the secondary gradual rise of ZtSH2 recruitment is only observed upon treatment with EGF, not EREG or EPGN (Figure 3A). The gradual rise can also be observed upon treatment with EREG in the presence of a GBM-associated EGFR mutation that alters receptor dimerization (Figure 3E). These data indicate that the secondary rise is not an intrinsic feature of the ZtSH2 reporter, and instead represents a feature of ligand-receptor activation itself.

The reviewer suggests that perhaps there is some internal pool of ZtSH2 or EGF, but we find no evidence for such a pool in our microscopy imaging. To clarify this point to the reader, we have now added a new supplementary figure (Figure S6) showing representative cells for all stimulation conditions used in Figure 3A, showing consistent, high levels of EGFR and ZtSH2 enrichment at the plasma membrane and uniform cytosolic intensity for at least 30 min after stimulation across all ligands.

Finally, while the reviewer mentions the use of high EGF doses in our paper, we would like to point out that we performed extensive experiments at other doses in the manuscript, testing 14 total doses of three EGFR ligands in Figure 3, and present additional data at 20 ng/mL EGF throughout Figures 2, S2, and S7. It is also very important to test high input doses for our negative controls to ensure that the ZtSH2 biosensor retains specificity for ITAM sequences and fails to show recruitment to untagged EGFR even under saturating conditions. It is also quite customary in the field: for example, the Erk KTR paper that the reviewer mentions in a later comment (Regot et al, Cell 2014) exclusively tests their biosensors using saturating doses of 50 ng/mL anisomycin, 100 ng/mL FGF, and 10 μM forskolin to characterize p38, Erk and PKA biosensor responses.

There is also insufficient attention paid to either controlling or measuring important parameters, such as expression levels of tagged receptors or levels of endogenous receptors. 3T3 cells, contrary to the statement of the authors, do not have "negligible" numbers of EGFR: they have ~40K, which is typical for mouse fibroblasts. This is much higher than MCF7 cells, which are frequently used as a model system to study EGFR responses. Yet they do not see transactivation of their ErbB2 construct in 3T3 cells without expressing additional EGFR (Fig. 4C), suggesting low sensitivity of the assay. Conversely, they show a significant response mediated by endogenously tagged EGFR in HEK 293 cells, which are frequently used as an EGFR-negative cell line (PMID: 26368334). This indicates that their assay is extremely sensitive. Which is it? As mentioned above, it likely depends on the expression level and affinity of the different components of their system.

After extensive searching we have not found any publications with an estimate as high as 40K EGFR receptors/cell in NIH3T3 cells. Livneh et al 1986 report that NIH3T3 cells express as little as 500 EGFR receptors per cell and do not respond mitogenically to EGF, and subsequent Schlessinger lab papers use NIH3T3 cells as an EGFR-null background for introduction of receptor variants. Eierhoff et al PLOS Pathogens 2010 use NIH3T3s as an EGFR-null control, showing immunoblot data of undetectable pEGFR responses. The paper we found with the highest stated EGFR expression per cell in NIH3T3 cells is Verbeek et al, FEBS Lett 1998, which reports a value of 3,000 receptors per cell, but does so without any literature citation or measurement. These references are consistent with our experience: over nearly a decade of MAPK signaling experiments in the lab, we have only seen weak or undetectable EGF-stimulated responses in unmodified NIH3T3s, depending on the assay. We are quite confident that more potent responses are elicited in HEK293T cells, where we observe EGFR expression by fluorescence imaging of CRISPR-tagged cells, immunofluorescence staining, and immunoblotting, and where we observe robust signaling responses using biosensors. We also now cite some of these references to support our claim (Line 144).

The reviewer makes an excellent point in the last sentence of their comment: indeed, it is essential to match the expression level of our SH2-based biosensor to the expression level of EGFR in any system in order to observe potent membrane translocation! This was imperative for visualizing any translocation in our CRISPR-tagged HEK293Ts: we had to switch to an exceptionally bright fluorophore and select cells with very low ZtSH2 expression to observe translocation. The ZtSH2/EGFR ratio is a crucial design parameter, which we now present extensive data and modeling to support (Figure S2-S4; Lines 190-235). Our data suggests that quite sensitive biosensor responses are possible with appropriate balance between ZtSH2 and EGFR expression levels (Figure 6) and, in general, biosensor responses can be matched to a dynamic range of interest by scaling ZtSH2 expression with EGFR levels.

A great advantage of using the EGFR system as a test case for the new system is that thousands of investigations have been performed over the last four decades. This provides a strong foundation for determining whether the new technology is working correctly. For example, the dynamics of EGFR activation and trafficking at the single cell level have been documented in many studies, which show a remarkable consistency (e.g. see pmid: 24259669; pmid: 11408594; pmid: 25650738). Unfortunately, instead of using differences between the new results and previously reported data as a basis for refining their technique, the authors attempt to apply their raw data to address complex questions of EGFR dynamics, with less than satisfactory results.

For example, they attempt to use their technique to understand the basis of different signaling dynamics between EGFR ligands. Rather than being a relatively recent observation, differences in EGFR ligand signaling have been explored for over 30 years (pmcid: PMC361851), and are generally ascribed to differences in trafficking (pmid: 7876195). Based on these observations and resulting mathematical models, novel EGFR ligands have been designed with enhanced potency (pmid: 8195228 , pmid: 9634854 ). All this work was done over 20 years ago. Since then, new natural ligands for the EGFR have been discovered from sequence analysis and differences in their potency have similarly been ascribed to differences in their intracellular trafficking patterns (pmid: 19531065 - cited by the authors). An alternate hypothesis was proposed more recently by Freed et al (2017) as described by the authors, but that is what it is: an alternative hypothesis.

We thank the reviewer for pointing out many excellent, classic studies on EGFR endocytosis and trafficking. We agree that this is a well-established field and that EGFR is certainly internalized, recycled, and degraded in a manner that depends on ligand affinity on the cell surface and in endosomes. These seminal studies lead the reviewer to propose an alternative hypothesis to explain our kinetic data in Figure 3: that differences in trafficking and maintenance of EGFR levels at the plasma membrane are the source of the altered kinetics between high- and low-affinity ligands. To address this question, we have now included new supplementary data examining endocytosis and trafficking in multiple contexts.

First, we examine membrane EGFR levels in 3T3 cells overexpressing our EGFR-pYtag system (or ITAM-less EGFR as a control) after EGF stimulation (Figure S5A-C). We find that EGFR membrane intensity is virtually unchanged after 60 min of saturating EGF stimulation, a response that does not depend on whether ITAMs are appended to the receptor. We also now include still images of cells at every concentration examined in our dose-response experiments for all 3 ligands (Figure S6), which do not show clear differences in the subcellular distribution of EGFR before and after stimulation as a function of ligand identity. We also remind the reviewer that our interpretation is not simply an untested hypothesis – we experimentally tested a GBM-associated EGFR variant whose effect on receptor dimerization has been quantified, and observe EGF-like response kinetics even after EREG stimulation, a result predicted by our model (Figure 3D-E).

We believe that the sustained membrane-localized signaling we observe might be ascribed to two factors: our choice of cell line and its expression level of EGFR. This conjecture is supported by some data: in contrast to our EGFR-overexpressing NIH3T3 cells, HEK293Ts harboring endogenous or low EGFR levels exhibit a dramatic redistribution of EGFR after EGF stimulation (Figure S3, Figure 6). This is clearly a context where transient versus sustained signaling might depend on the choice of ligand and its consequences on internalization.

We also note that our data identify ligand-specific signaling differences that are distinct from prior studies, which focused on transient vs sustained signaling downstream of different EGFR ligands. In contrast, we identify a biphasic increase in EGFR activity after stimulation with EGF versus a rapid approach to steady state after stimulation with EREG or EPGN, despite the continued presence of high levels of membrane-localized EGFR in each case.

Unfortunately, the model that the authors use to test this hypothesis does not even include endocytosis or receptor trafficking but instead uses variable "scaling" factors to see if the data can fit the dimerization hypothesis. In the supplement, they state that "Since our simulations were run on relatively short time scales (~30 min post-stimulation), we did not consider trafficking and degradation of receptors." However, the half-life of EGFR internalization is generally ~3-4min (pmid: 1975591) and degradation ~1hr, so most of the signal shown in Figure 3 is likely to come from internalized rather than surface-associated ligand-EGFR complexes. A further complication is that internalization rates are strongly influenced by receptor expression levels (pmid: 3262110), which are not controlled for here. Thus, the omission of trafficking in their model is not appropriate. This does not mean that the authors are wrong, it simply means that without validation or calibration, their new technology is not ready to resolve current problems in the field.

We thank the reviewer for pointing out ways to improve our modeling (endocytosis) and discussion of its parameterization (scaling factors). We address both points below:

Scaling factors: We thank the reviewer for their comments & agree that our discussion of model parameterization was lacking. To clarify: our base-case model for EGF includes 9 parameters, 6 of which are obtained from literature and 3 which reflect lumped kinetic processes of EGFR dimerization and activation and which we set to match our data. We then used experimentally-determined values to change the base-case model to simulate low-affinity ligand stimulation: a fold-change in ligand affinity and a fold-change in receptor dimerization. This is why we simulate EREG with β=50 and γ=100, reflecting the 10-to-100-fold differences in binding affinity and receptor dimerization that have been experimentally measured for this low-affinity ligand. Similar experimentally defined values constrain β and γ in the case of GBM-associated mutations. A more thorough explanation of our model and these scaling parameters is now included in Lines 334-362.

Endocytosis: We wholeheartedly agree that our model is quite simplified, and a thorough treatment of endocytosis and trafficking would be essential for capturing nuances associated with these steps of the cascade. However, while we appreciate the 3-4 min rule of thumb for EGFR internalization that the reviewer mentions, it is simply not reflective of the membrane-associated EGFR levels we observe in our cells. Examples can be observed in Figure 1C, Figure 2A, Figure 5F, Figure S1B, Figure S2A-B, Figure S5A, and Figure S6, as well as in the quantification of membrane associated EGFR at 0 and 60 min in Figure S5B. It is quite likely that endocytosis and trafficking are operating throughout this time course, but are balanced to maintain similarly high level of EGFR at the cell surface. We wholeheartedly agree with the reviewer’s helpful note that EGFR expression levels heavily influence internalization, which our data also support, and may explain our results. For example, we also see rapid EGFR membrane clearance in HEK293T CRISPR cells (Figure 6) and in HEK293Ts that express low levels of EGFR but not high levels of EGFR (Figure S3A).

In sum, we argue that our inclusion of additional data showing sustained EGFR protein levels and ZtSH2 recruitment at the plasma membrane should help justify our assumption of membrane-associated signaling in our model. However, we happily concede that this is a highly simplified model, and that endocytosis is a very important process that should be accounted for in future studies (e.g., Line 344-346: “However, we expect that internalization and trafficking can play a crucial role in EGFR dynamics in many contexts, which would need to be included in future models to adequately assess those scenarios”).

Reviewer #2 (Public Review):

The idea of using fluorescently labeled tandem SH2 domains to target tagged RTKs is brilliant and could potentially provide a powerful new way to assess the activation of RTKs in situ and in multiple physiological contexts. Thus, it was disappointing that there was insufficient characterization of the system to be able to interpret the data it generates. Although the paper shows that tagging the EGFR appears to have minimal impact on its biological activity, the readout for receptor kinase activity is % clearance of the fluorescent reporter tag from the cytosol. Such clearance is likely to depend on a variety of different factors, including the ratio of tagged receptors to probe, the number of functional pools in which the probe exists, the exchange rate between these pools, and the affinity of the probes for the tagged receptor. Without determining how each of these factors impacts % clearance, it is difficult to interpret either the dose-response curves or response kinetics.

For example, the difference in activation kinetics between EGFR and ErbB2 is very interesting, but the almost instantaneous rise (Fig S4B) is very surprising. The kinetics of activation of the EGFR have been extensively studied by mass-spectrometry and are generally limited by ligand binding, which has a characteristic time of several minutes, not seconds (pmid: 26929352; pmid: 1975591). Thus, such a response is suggestive of a freely exchanging ZtSH2 reporter pool that is mostly depleted in seconds with the slow secondary kinetics reflecting a slowly exchanging ZtSH2 reporter pool. Alternately, the cells could be accumulating an intracellular pool of activated receptors over time. That the authors are using concentrations of EGF >100-fold physiological levels (pmid: 29268862) further complicates the interpretation of these experiments.

There is also insufficient attention paid to either controlling or measuring important parameters, such as expression levels of tagged receptors or levels of endogenous receptors. 3T3 cells, contrary to the statement of the authors, do not have "negligible" numbers of EGFR: they have ~40K, which is typical for mouse fibroblasts. This is much higher than MCF7 cells, which are frequently used as a model system to study EGFR responses. Yet they do not see transactivation of their ErbB2 construct in 3T3 cells without expressing additional EGFR (Fig. 4C), suggesting low sensitivity of the assay. Conversely, they show a significant response mediated by endogenously tagged EGFR in HEK 293 cells, which are frequently used as an EGFR-negative cell line (PMID: 26368334). This indicates that their assay is extremely sensitive. Which is it? As mentioned above, it likely depends on the expression level and affinity of the different components of their system.

A great advantage of using the EGFR system as a test case for the new system is that thousands of investigations have been performed over the last four decades. This provides a strong foundation for determining whether the new technology is working correctly. For example, the dynamics of EGFR activation and trafficking at the single cell level have been documented in many studies, which show a remarkable consistency (e.g. see pmid: 24259669; pmid: 11408594; pmid: 25650738). Unfortunately, instead of using differences between the new results and previously reported data as a basis for refining their technique, the authors attempt to apply their raw data to address complex questions of EGFR dynamics, with less than satisfactory results.

For example, they attempt to use their technique to understand the basis of different signaling dynamics between EGFR ligands. Rather than being a relatively recent observation, differences in EGFR ligand signaling have been explored for over 30 years (pmcid: PMC361851), and are generally ascribed to differences in trafficking (pmid: 7876195). Based on these observations and resulting mathematical models, novel EGFR ligands have been designed with enhanced potency (pmid: 8195228 , pmid: 9634854 ). All this work was done over 20 years ago. Since then, new natural ligands for the EGFR have been discovered from sequence analysis and differences in their potency have similarly been ascribed to differences in their intracellular trafficking patterns (pmid: 19531065 - cited by the authors). An alternate hypothesis was proposed more recently by Freed et al (2017) as described by the authors, but that is what it is: an alternative hypothesis.

Unfortunately, the model that the authors use to test this hypothesis does not even include endocytosis or receptor trafficking but instead uses variable "scaling" factors to see if the data can fit the dimerization hypothesis. In the supplement, they state that "Since our simulations were run on relatively short time scales (~30 min post-stimulation), we did not consider trafficking and degradation of receptors." However, the half-life of EGFR internalization is generally ~3-4min (pmid: 1975591) and degradation ~1hr, so most of the signal shown in Figure 3 is likely to come from internalized rather than surface-associated ligand-EGFR complexes. A further complication is that internalization rates are strongly influenced by receptor expression levels (pmid: 3262110), which are not controlled for here. Thus, the omission of trafficking in their model is not appropriate. This does not mean that the authors are wrong, it simply means that without validation or calibration, their new technology is not ready to resolve current problems in the field.

Interesting method of finding example sentences in words.

what to call these words? illiterate words?

1930s Wilson Memindex Co Index Card Organizer Pre Rolodex Ad Price List Brochure

archived page: https://web.archive.org/web/20230310010450/https://www.ebay.com/itm/165910049390

Includes price lists

List of cards includes: - Dated tab cards for a year from any desired. - Blank tab cards for jottings arranged by subject. - These were sold in 1/2 or 1/3 cut formats - Pocket Alphabets for jottings arranged by letter. - Cash Account Cards [without tabs]. - Extra Record Cards for permanent memoranda. - Monthly Guides for quick reference to future dates. - Blank Guides for filing records by subject.. - Alphabet Guides for filing alphabetically.

Memindex sales brochures recommended the 3 x 5" cards (which had apparently been standardized by 1930 compared to the 5 1/2" width from earlier versions around 1906) because they could be used with other 3 x 5" index card systems.

In the 1930s Wilson Memindex Company sold more of their vest pocket sized 2 1/4 x 4 1/2" systems than 3 x 5" systems.

Some of the difference between the vest sized and regular sized systems choice was based on the size of the particular user's handwriting. It was recommended that those with larger handwriting use the larger cards.

By the 1930's at least the Memindex tag line "An Automatic Memory" was being used, which also gave an indication of the ubiquity of automatization of industrialized life.

The Memindex has proved its success in more than one hundred kinds of business. Highly recommended by men in executive positions, merchants, manufacturers, managers, .... etc.

Notice the gendering of users specifically as men here.

Features: - Sunday cards were sold separately and by my reading were full length tabs rather than 1/6 tabs like the other six days of the week - Lids were custom fit to the bases and needed to be ordered together - The Memindex Jr. held 400 cards versus the larger 9 inch standard trays which had space for 800 cards and block (presumably a block to hold them up or at an angle when partially empty).

The Memindex Jr., according to a price sheet in the 1930s, was used "extensively as an advertising gift".

The Memindex system had cards available in bundles of 100 that were labeled with the heading "Things to Keep in Sight".

better range for bad actors to try to steal the data from my tag?

Can this unique reference number be used to identify me (assuming they've already identified me another way and associated this number with me)? Yes!!

So this answer is a bit incomplete/misleading...

ABAB is a rhyme scheme, that the first and third line end with rhyming words (A) and the second and fourth lines end with different rhyming words (B). The rhyme scheme is determined by the last word of each line. Lines that end with a rhyme are labeled with the same letter.

Example:

I have a cat. (A)

I have a mouse. (B)

I have a hat. (A)

I have a house. (B)

https://poetscollective.org/poetryforms/tag/ababa/

Reviewer #2 (Public Review):

This is a follow-up study by the senior author, who previously showed in a 2021 JBC paper that levels of Paternally Expressed Gene 10 (PEG10) protein, among many other protein changes, are increased in the spinal cord of Ubqln2 knockout (KO) animals (JBC 2021). In this report, they provide more direct evidence that PEG10 levels are regulated by ubqln2 and that PEG10 can be proteolytically cleaved generating fragments, which when overexpressed, induce alterations in gene expression. Through proteomic analysis of spinal cord tissue from control and ALS patients, they found that PEG10 levels and the signature of genes regulated by its products are increased in ALS, proposing that elevation in PEG10 is a novel marker and driver of ALS.

PEG10 resembles a retrotransposon, encoding virus-like gag-pol products. It is only found in eutherian mammals. Although it has lost its ability to transpose, it still retains the retroviral-like translation frameshifting property generating two main products, gag (reading frame 1, RF1) and gag-pol (RF1/2). PEG10 is essential for survival. It plays an important role in RNA-binding and trophoblast stem cell specification, being required for placental development. It is also expressed in several adult tissues, but its function in them is obscure. A recent study showed PEG10 RF1 and RF1/2 bind the deubiquiting enzyme USP9X, and that loss of USP9X destabilizes RF1 but not RF1/2, suggesting USP9X regulates ubiquitination and proteasomal degradation of PEG10 (Abed et al. PLOS One 2021). Additionally, Abed et al. showed PEG10 products support virus-like particle (VLP) assembly and that both RF1 and RF1/2 localize to the cytoplasm, whereas a portion of RF1/2 is found in the nucleus of some cells. They further showed PEG10 binds and regulates RNA expression, most probably through interaction with the 3'-ends of the RNAs but found no common binding motif suggesting interaction could be with the secondary structure.

As mentioned, the senior author previously reported in a JBC article in 2021 that PEG10 levels are elevated in ubqln2 knock out (KO) mice, but that its levels were slightly decreased in the P497S mouse model of ALS. They validated PEG10 as an interactor of ubqln2 by proximity-dependent biotin labeling. A review of the current manuscript follows.

1. Evidence that ubqln2 regulates PEG10 accumulation (Fig 1). The authors use human embryonic stem cells to investigate how knockout (KO) of different ubqln isoforms (1, 2, and 4) affects PEG10 accumulation, showing that only KO of ubqln2 increases the RF1/2 product.

a) There is considerable variation in PEG10 expression in the duplicate sample sets provided, but this is not reflected by the error bars (fig 1 A and B). For example, RF1/2 is quite different in the two ubqln4 KO lysates, yet the error bars do not capture the variation. Better loading and quantification is needed. Also, in the KO cells, gag levels are slightly increased, which is consistent with alterations in proteasomal degradation. Alternatively, the changes in RF1/2 could also result from changes in read-through translation, but this is not investigated. Also, it would be helpful to include blots showing the lower Mol weight PEG10 products, to see how they change relative to Fig 3.

Fig 1G. The authors examined if removal of the poly proline rich region (PPR) from PEG10 affects RF1/2 regulation by ubqln, confirming its requirement.

b) The mechanism why deletion of the PPR abolished RF1/2 regulation by ubqlns was not examined. Is it from accelerated degradation? Also, it is not clear why the authors use the triple ubqln KO cells and did not perform that tests in the different ubqln KO cells. The latter comment applies for several of their investigations, leading to uncertainty regarding the specificity of ubqln2 in PEG10 regulation. It is possible that removal of most ubqlns stalls protein degradation affecting PEG10 turnover?

2. The authors investigated the phylogenetic relationship between PEG10 and ubqln2 demonstrating that PEG10 levels from marsupials that lack a PPR can be increased by appending a PPR from human PEG10. They used triple ubqln KO cells for these investigations.

a) The change they describe is not obvious in Fig2C and E as they appear quite small. They also conclude that ubqln2 regulates PEG10 by these studies, but really the experiments show it is from loss of all ubqlns, not ubqln2 specifically.

3. The authors show PEG10 is capable of self-cleavage of the RF1 product, generating 2 detectable N-terminal products, and several other fragments, including a C-terminal nuclear capsid (NC) fragment (Fig3). They show expression of HA-tagged NC fragment localizes to mainly the nucleus, whereas several other PEG10 products and fragments localize to the cytoplasm. They provide strong support that PEG10 is capable of self-cleavage by mutation of an aspartate residue (D) in a DSG motif in the protein to alanine (A to → ASG), which abolished cleavage. They also conducted a nice experiment showing the ASG mutant can be cleaved in trans by introduction of WT PEG10.<br /> a) The authors never show evidence for liberation and accumulation of the NC fragment, only for an artificially tagged protein by immunofluorescence. Use of a tag to study its localization and affects is problematic as the could influence its properties. They need to show that the fragment is detectable because of their central claim that it is responsible for inducing changes in genes. Biochemical fractionation studies could also reveal the extent of the partitioning of the fragment in the nucleus and cytoplasm. The mechanism by which the NC fragment induces changes in gene expression is not clear.

4. The authors show differences in gene expression upon transfection of HEK293 cells with PEG10 RF1, RF1/2, and NC expression constructs. They show that two PEG10-regulated genes, DCLK1 and TXNIP, are both increased in the spinal cord in sporadic ALS cases compared to controls.<br /> a) It is not clear from the studies whether the changes found in ALS are related to changes in PEG10 specifically, or for other reasons. Additionally, more rigorous comparison in many more ALS and controls is needed. PEG10 levels increase upon cell differentiation (Abed et al.) so the changes in ALS may reflect a compensatory and protective response.

5. To investigate if PEG10 RF1/1 levels are altered by ALS mutations in ubqln2 they transfected ubqln TKO cells with either wt ubqln2, or with mutants carrying either the P497H or P506T ALS mutations. They show PEG10 RF1/2 levels are reduced by overexpression of both the wt and P497H mutant, but not by the P506T mutant. They claim that P497H expression did not affect RF1/2 levels. The authors conducted a proteomic comparison of extracts from the spinal cord of two controls, one P497H ubqln2 case, and six sporadic ALS cases. They found increased levels of RF1/2 in the ALS cases. They also found neurofilament medium and neurogranin were both reduced in the ALS cases. Based on these changes they speculate that PEG10 is a novel marker for ALS.<br /> a) The conclusion that the P497S mutant did not affect RF1/2 is incorrect. It reduced RF1/2 slightly more than wt ubqln2. In fact, it appears that expression of all three (wt and the 2 ALS mutants) ubqln2 proteins reduce RF1/2 significantly, compared to the TKO cells.<br /> b) The changes in PEG10 found in the ALS cases are difficult to evaluate because too few controls and ALS cases were used for the comparison. Huge variations in the levels of PEG10 and of the other proteins graphed In Fug 6B-F were seen in the two controls. The comparison needs to be done with many more samples for sound statistical comparison. Were the samples compared from the same region of the spinal cord?

General comments

1. In the Discussion the authors write that because ubqln2 is the only ubqln capable of regulating PEG10 RF1/2 levels, the PXX domain that is only present in ubqln2 is likely responsible for the regulation. There is no proof in support of this hypothesis. Only one ALS-causing mutation (P506T) in the PXX domain, but not the P497H mutation in the same PXX domain, affected RF1/2 accumulation, inconsistent with general involvement of the PXX domain in PEG10 regulation.

2. The authors claim that ubqln2 may have specifically evolved to restrain PEG10 expression, but don't mention USP9X as being another regulator. The common theme that emerges from these studies is that PEG10 levels are regulated by any mechanism that interferes with ubiquitination/proteasomal degradation. Indeed, immunoblots of the gag-pol (RF1/2) in the different ubqln KO cells show a smear at high molecular weight consistent with the accumulation of ubiquitinated PEG10. The authors imply that the transcriptional changes caused by the alteration in PEG10 levels by ubqln2 are responsible for ALS (title of the paper), but this is merely speculation as the effects of the changes are not known. The changes found could be protective. They also claim PEG10 may serve as a novel biomarker for ALS, but such a claim is not justified from the limited analysis conducted so far, which will require more extensive proof.

Emphasis: last sentence

reply to u/OldSkoolVFX at https://www.reddit.com/r/ObsidianMD/comments/11jiein/comment/jb6np3f/?utm_source=reddit&utm_medium=web2x&context=3

I've studied (and used) Luhmann and other related systems more closely than most, so I'm aware of zettelkasten.de and the variety of numbering systems available including how Luhmann's likely grew out of governmental conscription numbers in 1770s Vienna. As a result your answer comes close to a generic answer, but not to the level of specificity I was hoping for. (Others who use a timestamp should feel free to chime in here as well.)

How specifically does the anonymity of the notes identified this way create surprise for you? Can you give me an example and how it worked for you? As an example in my own practice using unique titles in Obsidian, when I type [[ and begin typing a word, I'll often get a list of other notes which are often closely related. This provides a variety of potential links and additional context to which I can write the current note in light of. I also get this same sort of serendipity in the autocomplete functionality of my tagging system which has been incredibly useful and generative to me in the past. This helps me to resurface past notes I hadn't thought of recently and can provide new avenues of growth and expansion.

I've tried the datetime stamp in the past, but without aliasing them all with other titles, things tend to get lost in a massive list of generally useless numbers in an Obsidian folder—i.e. looking at the list gives me absolutely no information without other actions. Further the aliasing to remedy this just becomes extra administrative work. I've also never experienced the sort of surprise you mention when using datetime stamps, or at least not as the result of the timestamps themselves. As a separate concrete example in this video https://share.tube/w/4ad929jjNYMLc6eRppVQmc?start=49s using Denote, there is a clever naming method which simultaneously uses timestamps, Luhmann IDs, titles, and tags. However in this scheme the timestamps is one of the least useful (other than for simply searching by creation date/time, as in "I remember doing this on my birthday last year", or "it was sometime in Winter 2015"...) compared with the Luhmann identifiers, the title, or the tag for search and discovery within the search functionality. Consequently, I'm looking for concrete reasons why people would use datetime stamps and affordances they provide other than to simply have an identifier.

Funny (and ironic) that you should ask...

I myself have been asking lately, what problem does the now-standard "Run npm install after you clone the repo" approach solve? Can you state the NPM hypothesis?

See also: builds and burdens

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giac126), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1: Shiping Liu

How to model the statistical distribution of the gene expression, is a basic question for the field of single cell sequencing data mining. Dharmaratne and colleagues looked details at the distribution of very gene. By using the generalized linear models (GLM), the authors present a new program scShapes, which matched a specific gene with a distribution from one of the four shapes, Poisson, Negative Binomial (NB), Zero-inflated Poisson (ZIP), and Zero-inflated Negative Binomial (ZINB). As the authors present in this manuscript, not all genes adapted to a single distribution, neither NB or Poisson, and some of the genes actually adapted to the zero-inflated models because of the property of high drop-out rate in the modern single cell sequencing, says 3' tag sequenced. It is has been popular to employ GLM in single cell data mining recently, but it also got both praise and blame. So it is a good forward step to model a specific model for an individual gene. But the bad side is the computing cost, especially for the number of cells been sequenced reach to millions in currently research, and it believed that the dataset will be reached even bigger in the future. So it make a great obstacle arise to the application of the method presented by the author here. How to speed up the calculation using the mixed model or scShapes? The authors also performed the scShapes on some datasets, including the metformin, human T cells, and PBMCs. They found some potential genes that changed the distribution shape, but didn't easy to be identified by other methods. It demonstrated that scShapes could identified the subtle change in gene expression.

Major points: (1) We didn't see any details about the metformin dataset, the segueing depth and quality, number of genes/UMIs per cell, and so on. It makes hard to evaluate the quality and reliability of the results generated by scShapes. If this dataset is another manuscript could not possible to be presented at the same time, I suggest the author could perform on alternative dataset, as there are so many single cell datasets has been published could be used in this study.

(2) Even the authors taken the cell type account in the GLM, I wonder for a specific gene, whether the distribution shape will change in different cell type. If so, it will becoming more complex, that is need to model the distribution shape for individual gene for every cell type alone.

(3) To identify the different gene expression in scShapes, the author didn't consider the influence of different cell number, or the proportion of cell number, in the different cell type. A possible way to evaluate or eliminate this bias is to down sampling from a big dataset, instead of just simulated total number 2k ~ 5k from the PBMC. To evaluate the influence both the total number cell and the proportion in cell type.

(4) The author should present the comparative results of the computational cost for different methods. Says the accuracy, time and memory consuming under different number of cells. I suggest the authors use much a larger dataset, because currently single cell research may include millions of cells, and the ability to process big data is very important to the application and becoming a widely used one.

Minor points: (1) No figure legends for Fig.2 c and d.

(2) It is unclear whether the total 30% genes undergo shape change, or just the proportion of the remaining after the pipeline. So please clarify the details.

Reviewer 2: Yuchen Yang

In this manuscript, authors presented a novel statistical framework scShapes using GLM approach for identifying differential distributions in genes across scRNA-seq data of different conditions. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution. scShapes was shown to be able to identify biologically-relevant switch in gene distribution shapes between different conditions. However, there are still several concerns required to be addressed.

1. In this study, authors compared scShapes to scDD and edgeR. However, besides these two, there are many other methods for calling DEGs from scRNA-seq. Wang et al. (2019) systematically evaluated the performance of eight methods specifically designed for scRNA-seq data (SCDE, MAST, scDD, D3E, Monocle2, SINCERA, DEsingle, and SigEMD) and two methods for bulk RNA-seq (edgeR and DESeq2). Thus, it is also worthy to compare scShapes to other methods, such as SigEMD, DEsingle and DESeq2, which were supposed to perform better than scDD or edgeR.

2. When scShapes was compared to scDD, authors mainly focused on the distribution shifting. However, to users, it would be better to present a venn diagram showing the numbers of the genes detected by both scShapes and scDD, and the genes specifically identified by scShapes and scDD, respectively. In addition, authors showed the functional enrichment results for DEGs identified by scShapes. It is also worthy to perform enrichment analysis for the genes detected by both scShapes and scDD or specifically identified by scShapes or scDD.

3. Since scShapes detects differential gene distribution between different conditions, it would be better to show users how to interpret the significant results biologically. For example, authors mentioned that RXRA is differentially distributed between Old and Young and Old and Treated, so what does this results mean? Can this differential distribution be associated with differential expression?

4. In Discussion, authors mentioned that scRATE is another tool that can model droplet-based scRNA-seq data. It would be clearer to discuss that why authors develop their own algorithm rather than using scRATE to model the distribution.

5. In Introduction, authors talked about the zero counts in scRNA-seq data, and presented evidence in Results part. Since 2020, there are several publications also focusing on this issue, such as Svensson, 2020 and Cao 2021. These discussions should be included in this manuscript.

Do you think combining Ac4ManNAz and rPAL labeling could be a good way to both specifically identify Neu5Ac-ligated RNA and amplify that signal using orthogonal labels (perhaps Biotin and a FLAG tag) with different fluorophores?

Author Response

Reviewer #1 (Public Review):

The paper addresses an interesting question - how genetic changes in Y. pestis have led to phenotypic divergence from Y. pseudotuberculosis - and provides strong evidence that the frameshift mutation in rcsD is involved. Overall, I found the data to be clearly presented, and most of the conclusions well supported by the data. The authors convincingly show that (i) the frameshift mutation in rcsD alters the regulation of biofilm formation, (ii) this effect depends upon expression of a small protein that corresponds to the C-terminal portion of RcsD, and (iii) the frameshift mutation in rcsD prevents loss of the pgm locus. I felt that the discussion/conclusions about what phosphorylates/dephosphorylates RcsB and how this impacts biofilm formation are overstated, as there are no experiments that directly address this question. I also felt that the authors' model for what phosphorylates/dephosphorylates RcsB in Y. pestis should be more clearly articulated, even if it is only presented as speculation. Lastly, the authors propose that full-length RcsD is made in Y. pestis and contributes to phosphorylation of RcsB, but the evidence for this is weak (faint band in Figure 2d). It may be that the N-terminal domain of RcsD is functional. I recommend either softening this conclusion or testing this hypothesis further, e.g., by introducing an in-frame stop codon early in rcsD after the frame-shift.

Thanks for your comments. We have provided a model and revised the discussion about phosphorylation/dephosphorylation of RcsB and how this impacts biofilm formation (Figure 8 and Supplementary Figure 4). In addition, we have introduced an in-frame stop codon in rcsD before the frameshift and showed that full-length RcsD is only made in wildtype Y. pestis but not in the rcsDpe-stop mutant (Supplementary Figure 1g).

Reviewer #2 (Public Review):

Guo et al. have investigated the consequences of a frameshift mutation in the rcsD gene in the Yersinia pseudotuberculosis progenitor that is conserved in modern Y. pestis strains. Interestingly, they identify a start codon with a ribosome binding site that enables production of an Hpt-domain protein from the C-terminus in Y. pestis. Targeted deletion of this Hpt-domain increased biofilm production in Y. pestis. They find that the ancestral RcsDpstb (full length) is a positive regulator of biofilm in Y. pestis while the Hpt-domain version (RcsDYP) represses biofilm in vitro. When fleas were infected with Y. pestis expressing the ancestral RcsDPSTB protein, there was no difference in bacterial survival or rate of proventricular blockage. This strain also killed mice the same rate (in a different Y. pestis strain background). However, replacing RcsDYP with RcsYPTB dramatically increases the frequency of pgm locus deletion (containing Hms ECM and yersiniabactin genes) during flea infection. The authors predict that this would reduce the invasiveness of the bacteria in mammals and/or flea blockage in subsequent flea-rodent-flea transmission cycles. They also measured global gene expression differences between RcsDPSTB compared to the wild-type strain. They argue that the frameshift of RcsD maintaining the Hpt-domain (RcsDYP) was needed to regulate biofilm while limiting loss of the pgm locus.

Loss of the pgm locus was not tested in the Y. pestis rcsD mutant strain (lacking the entire gene or just the C-terminal Hpt domain). Therefore, the claim that maintaining the Hpt-domain protein was important lacks convincing evidence. Additionally, it is possible that the population of rcsDpe::rcsDpstb after in vitro growth for 6 days would still be proficient at infecting and blocking fleas, even though many of the bacteria would have lost the pgm locus. Production of Hms polysaccharide by pgm+ could trans-complement those that are pgm-. The nature of the pgm locus loss is assumed to be due to recombination between IS elements. This is certainly the likeliest explanation but not the only one. The authors checked for pgm loss by phenotype (CR binding) and by two sets of primers, one targeting the hmsS gene and another set that is unspecified. Loss of the entire pgm (especially yersiniabactin genes) should be clarified.

Thanks for your comments. We have now provided the data to show that deletion of RcsD-Hpt resulted in increased loss of the pgm locus (Figure 5d) to strengthen the claim that maintenance of the Hpt-domain is significant for retention of the pgm locus. We also agree that 6-day old cultures of a mixture of pgm+ and pgm- rcsDpe::rcsDpstb will still be capable of infecting and blocking fleas. However, these strains will be less efficient at causing disease in the vertebrate host in the absence of the pgm locus. We agree that recombination between IS elements might not be the only cause of loss of the pgm locus. To verify the loss of the pgm locus, we have used two sets of primers. One set targets the hmsS gene and another set targets the upstream and downstream sequences of the pgm locus (Supplementary Table 3). We have clarified this in the revised manuscript (Line 610-613).

Reviewer #3 (Public Review):

The Rcs phosphorelay plays an important role in regulating gene expression in bacteria; most of the current knowledge about the Rcs proteins is from E. coli. Yersinia pestis, carrying mutations in two central components of the Rcs machinery, provides an interesting example of how evolution has shaped this system to fit the life cycle of this bacteria. In bacteria other than Y. pestis, most Rcs activating signals are sensed via the outer membrane lipoprotein RcsF; from there, signalling depends on inner membrane protein IgaA, a negative regulator of RcsD. Histidine kinase RcsC is the source of the phosphorylation cascade that goes from the histidine kinase domain of RcsC to the response regulator domain of RcsC, from there to the histidine phosphotransfer (Hpt) domain of RcsD, and finally to the response regulator RcsB. RcsB, alone or with other proteins, regulates transcription of many genes, both positively and negatively. These authors have previously shown that RcsA, a co-regulator that acts with RcsB at some promoters, is functional in Y. pseudotuberculosis but mutant in Y. pestis, and that this leads to increased biofilm in the flea. The authors also noted that rcsD in Y. pestis contains a frameshift after codon 642 in this 897 aa protein; in theory that should eliminate the Hpt domain from the expressed protein. However, they found evidence that the frame-shifted gene had a role in regulation. This paper investigates this in more depth, providing clear evidence for expression of the Hpt domain (without the N-terminal domain), and demonstrating a critical role for this domain in repressing biofilm formation. The Y. pseudotuberculosis RcsD does not express a detectable amount of the Hpt domain nor does it repress biofilm formation. The ability of the Hpt domain protein to keep biofilm formation low explains most of what is observed for the full-length frame-shifted protein.

1) The authors provide a substantial amount of data supporting the expression of the C-terminus of RcsD is sufficient and necessary for low biofilm levels, and that this is dependent upon the active site His in the RcsD Hpt domain (H844A) as well as other components of the basic phosphorelay (RcsC and RcsB). However, it is only possible to see this protein by Western blot in 100-fold "Enriched" lysates (Figure 2). No small protein was detected in the RcsDpstb strain, although the enriched lysate was not shown for this. Without that experiment, it is not possible to evaluate whether the small protein is also made from the rcsDpstb gene. Either answer would be interesting, and would allow other conclusions to be drawn. Is the RBS and start codon the same for the HPT region of this rcsD gene (it could be added to Supplementary Table 6). If the small protein is made, is its ability to function blocked by the excess full length protein in terms of interactions with RcsC? Or is the expression of the small protein dependent upon loss of overlapping translation from the upstream start?

The small Hpt protein may be produced from expression of the epitope tagged rcsDpstb gene as it can be detected in an enriched isolation of this sample (Supplementary Figure 1f). Because only a small amount of the RcsD-Hpt is produced from the rcsDpstb substitution, it might only function at low levels in the presence of large amounts of RcsDpstb. The RBS and start codon are the same for the RcsD-Hpt in Y. pestis and Y. pseudotuberculosis, we have added them in the Supplementary Table 6. In addition, we have provided a model to show the function and regulation of RcsD and Hpt (Supplementary Figure 4).

2) In many phosphorelays, the protein kinase also acts as a phosphatase, and which direction P flows is critical for regulation. It is often difficult to follow what the model for this is in this paper, and that is important to understand for evaluating the results. Most of this paper uses two assays, biofilm formation and crystal violet staining (also related to biofilm formation) to assess the functioning of the Rcs phosphorelay. Based on the behavior of the rcsB mutant, it would seem that functional Yersinia pestis Rcs (RcsDpe) represses this behavior, and this correlates with RcsB phosphorylation (Figure4). What is the basis (Line 443-44) for saying that RcsD phosphorylates RcsB while RcsDHpt dephosphorylates? Yersinia pseudotuberculosis RcsD(pstb) shows no difference with the rcsB mutant. Doesn't that suggest that RcsDpstb is no longer repressing (phosphorylating)? In the presence of the RcsDpstb as well as multicopy RcsF, an activating signal in other organisms, RcsDpstb seems able to phosphorylate. This all suggests that the full-length protein, like the Hpt domain, is capable of phosphorylating, but that it may be doing nothing in the absence of signal (or dephosphorylating). Given these results, saying that RcsDpstb is positively regulating biofilm formation (Fig.1 title, and elsewhere) is somewhat misleading. What it presumably does is prevent the Hpt domain, expressed from the chromosomal locus in Figure1b, from signalling to RcsB. By itself, it is not clear it is doing anything. Understanding this clearly is important for interpreting this system and the tested mutants. A clear model and how phosphate is flowing in the various situations would help a lot. Currently Supplementary Figure3 seems to reflect the appropriate directional arrows, but the text does not. Moving the rcsB data earlier in the paper (after Figure1, 2, or maybe earlier, before Figure3) would certainly help.

RcsD dephosphorylates RcsB while RcsD-Hpt phosphorylates RcsB. Expression of RcsDpstb in the wild type strain and the N-term deletion mutant resulted in increased biofilm, indicating RcsB is less phosphorylated (Figure 1b and 1c). While over-expression of RcsD-Hpt resulted in decreased biofilm formation, indicating RcsB is more phosphorylated. In addition, the Phos-tag experiments showed that the RcsDpstb strain has a lower level of phosphorylated RcsB (Figure 4b). Expression of RcsDpstb in the wild type strain showed similar results as a rcsB mutant indicating a lower level of phosphorylated RcsB in the presence of RcsDpstb.

It is possible that the RcsDpstb interferes with the ability for RcsD-Hpt to phosphorylate RcsB. However, plasmid expression of the rcsDpstb-H844A mutant in the Y. pestis rcsDN-term deletion mutant formed significantly less biofilm than wild type rcsDpstb indicating H844 might be important for RcsD to dephosphorylate RcsB (Supplementary Figure 2b and Line 180-183). In addition, it is known that RcsD plays a dual role in phosphorylation and dephosphorylation of RcsB in other organisms (Majdalani N, et al., 2005, J. Bacteriol. https://doi.org/10.1128/JB.187.19.6770-6778.2005; Wall EA, et al., 2020, Plos Genetics, https://doi.org/10.1371/journal.pgen.1008610; Takeda S., et al., 2001, Mol. Microbiol., https://doi: 10.1046/j.1365-2958.2001.02393.x). We therefore think it is safe to say that the full length RcsD might function to dephosphorylate RcsB. We have modified the model in the revised manuscript (Supplementary Figure 4 and Figure 8). Regulation of RcsB has been investigated previously. The main finding of our manuscript is regulation of RcsB by the mutated RcsD (RcsD-Hpt). Thus, we have moved the known rcsB deletion mutant data to Figure 1 in the revised manuscript as suggested. We kept the rest of data in Figure 4 the same. We think it might be better to first show the mutation of rcsD alters Rcs signaling and then show how this occurs (by affecting RcsB phosphorylation).

3) The authors show (in their pull-down) that there is a bit of full-length RcsD even in the frame-shifted protein. Is there any clear evidence this does anything here? Does the N-terminus (truncated after the frame-shift) have a function?

We have introduced a stop codon in rcsDpe and showed that full-length RcsD is made by rcsDpe but not by rcsDpe with the stop codon (Supplementary Figure 1g). RcsDN-term seems do not have a function in our tested condition (Figure 1e).

4) While the RNA seq data is useful addition here, it is difficult to interpret without a bit more data on the strain used for the RNA seq, including the biofilm phenotypes of the WT and mutant derivatives, as well as the relevant rcsD sequences, and maybe expression of a few genes or proteins (Hms or hmsT). Are these similar in the parallel strains used earlier in the paper and the one for RNA seq, in WT, rcsB- and the RcsDpstb derivative? It would appear that rcsB- and rcsDpstb have opposite effects, at least at 25{degree sign}C, while in Figure4, these two derivatives have similar effects on biofilm. Is this due to temperature, strains, or biofilm genes that are not shown here? It is certainly possible that the ability of the full-length RcsD changes its kinase/phosphatase balance as a function of temperature, or dependent on other differences in these Y. pestis strains.

The strain used for RNA seq is a derivative of the biovar Microtus strain 201 which has a similar in vitro phenotype as the strain KIM6+ (Line 297-298). We used this strain for RNA seq because it has the virulence plasmid pCD1 and we wanted to analyze the gene expression of this plasmid, which is required for virulence, as well. RNAseq data showed that rcsB- and rcsDpstb have opposite effects on mRNA level of some genes. However, no significant change in expression of biofilm genes was noted in the RNAseq data set. In fact, our previous data has shown that the biofilm related (hmsT and hmsD) genes are only moderately (Less than 2-fold change between wild type and rcsB mutant) regulated by RcsB based on RT-PCR and β-gal analysis (Sun YC, et al., 2012, J. Bacteriol. https:// doi: 10.1128/JB.06243-11and Guo XP, et al., 2015, Sci. Rep. https://doi: 10.1038/srep08412 and Figure 4c).

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Referee #2

Evidence, reproducibility and clarity

RC-2022-01803 "UBXN1 maintains ER proteostasis and represses UPR activation by modulating translation independently of the p97 ATPase" By Ahlstedt et al.

Comments to the Author

UBXN1 is a VCP adaptor UBX domain protein which is known to be involved in elimination of ubiquitylated cytosolic proteins bound to the BAG6 complex. In this study, authors demonstrated that cells depleted of UBXN1 have elevated UPR activation, even without external ER stresses. Cells devoid of UBXN1 have significant and global up-regulation of UPR-specific target genes, and these cells are more sensitive to ER stress than their wildtype counterparts. Using quantitative tandem mass tag proteomics of UBXN1 deleted cells, authors found that significant enrichment of the abundance of ER proteins involved in protein translocation, protein folding, quality control, and the ER stress response in an ERAD-independent manner. Notably, they observed no change in the abundance of proteins in the cytosol or nucleus, and significant decrease in the expression of several mitochondrial proteins when UBXN1 was depleted. Authors further demonstrate that UBXN1 is a translation repressor, and its UBA domain is critical for suppressing protein synthesis. Thus, increased influx of proteins into the ER in UBXN1 KO cells causes UPR activation. Authors concluded that they have identified a new regulator of protein translation and ER proteostasis.

My specific comments were provided as follows.

Comments

1. Authors found that significant enrichment of the ER proteins in UBXN1 KO cells, while there is no change in the abundance of proteins in the cytosol or nucleus. Mitochondrial proteins are even down-regulated in UBXN1 KO cells. I found these observations very interesting. However, I was frustrated that authors did not investigated the reason why such differences are associated in UBXN1-suppressed cells. Authors demonstrate that depletion of UBXN1 resulted in suppression of protein synthesis, but did not address whether ER proteins are specifically repressed by UBXN1 or it represses translation globally, as noted in their Discussion section. Do the mRNAs encoding signal sequence at the N-terminus of their products are specifically translated in UBXN1-suppressed cells? Do the translations of mRNAs encoding mitochondria translocation signals are suppressed in UBXN1 KO cells? It should be possible to investigate these issues by using appropriate model ER- or mitochondrial proteins with or without specific signal sequences. Such kind of analysis should be necessary to support the claim of this manuscript.
2. Related to my previous comments, ER-targeted mRNAs are known to be degraded by a process termed RIDD in the case of ER stressed condition. Since the rapid degradation of mRNAs through RIDD functions to alleviate ER stress by preventing the continued influx of new polypeptides into the ER, I wondered why UBXN1 depletion greatly stimulates ER protein synthesis, escaping IRE1-dependent mRNA degradations. Does UBXN1 depletion suppress RIDD?
3. Authors mentioned that the elevated levels of ER proteins are not due to increased transcription of target genes. However, they only provided the quantification of prp transcript levels, which was unchanged between wildtype and UBXN1 KO cells. To support this important conclusion, it is necessary to provide whole transcriptome data to compare the expression levels of corresponding ER proteins (quantified by their proteomics data) and transcripts (quantified by, for an example, RNA-seq analysis).
4. Authors claimed that UBXN1 loss is detrimental to cell viability and have elevated levels of the apoptosis in the face of ER stress. However, authors did not examine apoptotic cell death in UBXN1 KO cells. They only provided evidence for defective proliferation of cells and transient induction of CHOP expression, but these are not enough to support the ER-stress induced apoptosis.
5. Authors showed that UBA domain of UBXN1 is critical for suppressing protein synthesis. Could you provide a bit more detailed discussion how UBA domain modulates protein translational events and promote expressions of ER-related proteins. Have you ever checked whether UBA domain of UBXN1 is necessary for suppressing UPR-specific target gene expressions?

Significance

Although the discovery in this manuscript might be potentially interesting for broad audience, the presented study did not provide enough mechanistic insights and their data lacks vital evidences to support their conclusion. I found that the data are preliminary to discuss the validity of this finding. The inadequacy of these points makes this manuscript unsuitable for publication at this stage.

My expertise is cell biology and biochemistry for protein quality control.

OpenAI’s use of human taggers

To search for Tag list user:LeaAnn_Bethany tag: in the search bar.

And only private. It seems you can not highlight publicly, unless you put at least one tag. A highlight with a comment is an annotation. An annotation without a highlight is Page Note (you need add it in separate pane).

reply to u/laystitcher at https://www.reddit.com/r/Zettelkasten/comments/11ay28d/how_did_you_teach_yourself_zettelkasten/

Roughly in order: - Sixth grade social studies class assignment that used a "traditional" index card-based note taking system. - Years of annotating books - Years of blogging - Havens, Earle. Commonplace Books: A History of Manuscripts and Printed Books from Antiquity to the Twentieth Century. New Haven, CT: Beinecke Rare Book and Manuscript Library, 2001. - Locke, John, 1632-1704. A New Method of Making Common-Place-Books. 1685. Reprint, London, 1706. https://archive.org/details/gu_newmethodmaki00lock/mode/2up. - Erasmus, Desiderius. Literary and Educational Writings, 1 and 2. Edited by Craig R. Thompson. Vol. 23 & 24. Collected Works of Erasmus. Toronto, Buffalo, London: University of Toronto Press, 1978. https://utorontopress.com/9781487520731/collected-works-of-erasmus. - Kuehn, Manfred. Taking Note, A blog on the nature of note-taking. December 2007 - December 2018. https://web.archive.org/web/20181224085859/http://takingnotenow.blogspot.com/ - Ahrens, Sönke. How to Take Smart Notes: One Simple Technique to Boost Writing, Learning and Thinking – for Students, Academics and Nonfiction Book Writers. Create Space, 2017. - Sertillanges, Antonin Gilbert, and Mary Ryan. The Intellectual Life: Its Spirit, Conditions, Methods. First English Edition, Fifth printing. 1921. Reprint, Westminster, MD: The Newman Press, 1960. http://archive.org/details/a.d.sertillangestheintellectuallife. - Webb, Beatrice Potter. Appendix C of My Apprenticeship. First Edition. New York: Longmans, Green & Co., 1926. - Schmidt, Johannes F. K. “Niklas Luhmann’s Card Index: The Fabrication of Serendipity.” Sociologica 12, no. 1 (July 26, 2018): 53–60. https://doi.org/10.6092/issn.1971-8853/8350. - Hollier, Denis. “Notes (On the Index Card).” October 112, no. Spring (2005): 35–44. - Wilken, Rowan. “The Card Index as Creativity Machine.” Culture Machine 11 (2010): 7–30. - Blair, Ann M. Too Much to Know: Managing Scholarly Information before the Modern Age. Yale University Press, 2010. https://yalebooks.yale.edu/book/9780300165395/too-much-know. - Krajewski, Markus. Paper Machines: About Cards & Catalogs, 1548-1929. Translated by Peter Krapp. History and Foundations of Information Science. MIT Press, 2011. https://mitpress.mit.edu/books/paper-machines. - Goutor, Jacques. The Card-File System of Note-Taking. Approaching Ontario’s Past 3. Toronto: Ontario Historical Society, 1980. http://archive.org/details/cardfilesystemof0000gout.

And many, many others as I'm a student of intellectual history.... If you want to go spelunking on some of my public notes, perhaps this is an interesting place to start: https://hypothes.is/users/chrisaldrich?q=tag%3A%22note+taking%22 I also keep a reasonable public bibliography on this and related areas: https://www.zotero.org/groups/4676190/tools_for_thought

this is a small peptide tag

orbitdb

Hyperlinks like this drive me nuts! On a news site, linking to 'recent posts' when referring to another specific article, term, or concept is ridiculous, particularly when the tag referenced seems to have nothing to do with the desired end result. I see this often on large news sites and articles. It makes web information impossible to reproduce and retrace when digging through archives, etc., and the cost to keep links updated if the name of the article changes surely can't be that substantial - right?

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Reply to the reviewers

We thank all the reviewers for having raised constructive criticism to fortify the main message and improve the clarity of the manuscript. We appreciate that all reviewers found that our work addresses an important topic and is of interest to a broad audience. We believe that we have thoroughly addressed the concerns of the reviewers, especially with regard to 1) performing another SMC3 chromatin immunoprecipitation and sequencing (ChIP-seq) replicate and control, 2) including a later time point for the transcriptional data, and 3) performing additional characterization of the growth phenotype of the SMC3 conditional knockdown.

Reviewer #1

(Evidence, reproducibility and clarity (Required)):*

Summary The present work by Rosa et al., provides convincing data about the presence and functional relevance of the cohesin complex in Plasmodium falciparum blood stages. In accordance with other organisms, the composition of the cohesin complex containing SMC1, SMC3 RAD21 and putatively STAG could be confirmed via pulldown and mass spectrometry. Basic characterization of endogenous tagged SMC3 demonstrated the expression and nuclear localization during IDC, as well as the relatively stable accumulation at centromeric regions, consistent with the known cohesin function in chromatid separation. Furthermore, dynamic and stage-dependent binding to intergenic regions observed in ChIPseq and major transcriptome aberrations upon knockdown of SMC3 (__Response: __As we regularly perform ChIP-seq experiments in the lab, we have generated multiple negative control datasets. In our opinion, the most stringent negative control for an HA-tagged protein is performing ChIP with an HA antibody in a WT strain. We have recently published an in-depth analysis of this (and other) negative ChIP-seq controls (Baumgarten & Bryant, 2022, https://doi.org/10.12688/openreseurope.14836.2). We show in this publication that non-specific ChIP-seq experiments (such as negative controls) result in an over-representation of HP1-heterochromatinized regions due to differences in sonication efficiency of heterochromatin and technical challenges with mapping regions with high levels of homology. In the anti-HA in WT ChIP negative control (performed at 12hpi), we do not see any enrichment at centromeric regions, but rather at heterochromatinized regions where clonally variant gene families are located. We performed peak calling analysis and found no significant overlap between the negative control ChIP-seq and the SMC3-3HA ChIP-seq data at 12hpi.

In addition, we have now performed a second biological replicate of the SMC3-3HA ChIP-seq with a different clone at all time points. We compared this data to that from the original clone and found significant overlap of the peaks called (see what is now Table 4 and Supp. Fig. 3A). We generated a stringent list of peaks that were shared between both clones at each time point and repeated all downstream analyses (see what are now Tables 5-8). We found that our conclusions were largely unchanged. Text describing these experiments and analyses have been added throughout the results section.

• Proposed mechanism of repressive effect of SMC3 early in IDC on genes, that get de-repressed in late stages: To claim this mode of function, it would be necessary to include a KD on late stage parasites. If there is an early repressive role of SMC3, upregulated genes should not be affected by late SMC3-KD. __Response: __To be clear, we are most interested in the transcriptional role of SMC3 during interphase, where results are not confounded by its potential role in mitosis. However, we did collect a 36hpi time point in the SMC3-3HA-glmS and WT strain, with and without glucosamine. We have added this last time point and the WT data from the other two time points to the manuscript (see Tables 11-13). Unfortunately, and for reasons unknown, the WT replicates treated with glucosamine showed a significantly advanced “transcriptional age” compared to the other replicates at 36hpi (see what is now Supp. Fig. 5B). Thus, we did not feel comfortable performing the RNA-seq analysis as we did with the other two time points (i.e. subtracting up- and down-regulated genes from the WT control from the SMC3-3HA-glmS data sets). We have added this information to the results section (Lines 256 and 261). As the WT parasites treated with glucosamine were approximately 8 hours in advance of the untreated WT parasites for the 36hpi time point, any up- and down-regulated genes might have been due to differences in the cell cycle rather than due to glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

To address the question of whether genes that are upregulated upon depletion of SMC3 at early stages are affected at the 36hpi time point, we performed differential expression analysis of the SMC3-3HA-glmS parasites with and without glucosamine at 36hpi (we have added this data in Table 11). Again, significantly up- and down-regulated genes were not filtered using the WT dataset. With this analysis, we see only three genes from the list of invasion-related genes (Hu et al., 2010) that are up-regulated, but none of them have a significant q-value (Tab 5 of Table 18). Thus, depletion of SMC3 in late stage parasites does not lead to up-regulation of the same genes that are upregulated at 12 and 24hpi. We have added this information to the text (Line 273).

Furthermore, the hypothesized repressive effect of SMC3 does not explain the numerous genes downregulated in KD.

__Response: __As we state on line 350, we do not observe enrichment of SMC3 at downregulated genes, suggesting an indirect or secondary effect of SMC3 KD on these genes.

• Due to the fact, that the KD was induced at the exact same timepoint and analysed 12h and 24h after induction it is possible that identified, differentially expressed genes at 24h are not directly regulated by SMC3, but rather due to a general deregulation of gene expression. Did the authors attempt to analyse gene expression upon induction at ring, trophozoite and schizont stage? Response: __As we state on line 230, in order to achieve SMC3 KD at the protein level, we had to treat the parasite with glucosamine for two cell cycles (approximately 96 hours). After two cell cycles of glucosamine treatment, the parasites were tightly synchronized and sampled 12 and 24 hours later. Thus, SMC3 KD takes place over the course of multiple days, but parasites are collected after stringent synchronization. Giemsa staining and bioinformatic analysis (line 250) of the RNA-seq data from parasites (with or without glucosamine) harvested at 12 and 24 hpi show that these parasites were synchronous and that there were no gross differences in genome-wide transcript levels. It is certainly possible that differentially expressed genes at 12 or 24hpi are not directly regulated by SMC3, and this is precisely why we perform ChIP-seq of SMC3: to provide evidence of direct involvement via binding, as stated on line 281. __

• *Based on rapid parasite growth, the authors hypothesize a higher invasion rate due to upregulation of invasion genes. This hypothesis is not supported by quantitative invasion assays or quantification of invasion factors on the protein level. An alternative explanation could be a shorter cell cycle (__Response: __We have repeated the growth curve analysis with additional clones and no longer observe a growth phenotype in the SMC3 knockdown condition. We have added images of Giemsa-stained parasites from the knockdown time course we performed to what is now Supp. Fig. 5A. We see no obvious differences in cell morphology caused by glucosamine treatment in the WT or SMC3-3HA-glmS parasites.

• Correlation of SMC3-occupancy/ATAC/expression profile of the exemplary genes rap2 and gap45 (Figure 4C,D,E): is this representative for all upregulated genes? __Response: __SMC3 occupancy shown at rap2 and gap45 is representative for all upregulated genes (see Fig. 4A and B). It is difficult to provide a general representation of the average expression profiles of all up-regulated genes over the course of the IDC, but Fig. 3E shows that the vast majority of up-regulated genes normally reach their peak expression in late stage parasites. With regard to ATAC-seq profiles, we have performed a metagene analysis of chromatin accessibility (data taken from (Toenhake et al., 2018)) at all up-regulated genes at time points that closely correspond to the time points used in our study: 15, 25, and 35, and 40 hpi (new Fig. 4C). This metagene analysis confirms what we observe at individual genes: increasing chromatin accessibility over the course of the IDC at these genes’ promoters. While metagene analyses offer important information, we always try to show the raw data (as in new Figs. 4D-F) from individual examples as proof of principle.

• Given that SMC3 appears to be not essential for parasite growth, the authors could generate a null mutant for SMC3, which might allow for easier analysis of differences in gene regulation, cell cycle progression and/or invasion efficiency. __Response: __As we explain on line 327, very little cohesin is required for normal growth and/or mitosis in our study and two studies in S. cerevisiae and D. melanogaster. However, SMC3 is essential in S. cerevisiae. We were unable to knock out SMC3, and a recent mutagenesis study suggests that SMC3 and SMC1 are essential to the parasite during the intraerythrocytic developmental cycle (Zhang et al. Science, 2018). This is why we chose an inducible knockdown system.

*Reviewer #1 (Significance (Required)):

Own opinion The authors provide a basic characterization of the cohesin component SMC3 using NGS methods to investigate chromatin binding sites and its potential influence on gene expression. *

__Response: __We respectfully disagree that our study offers only a basic characterization of SMC3. We combine IFA, mass spectrometry, and both ChIP-seq and RNA-seq of SMC3 across the entire intraerythrocytic developmental cycle to provide the most detailed and comprehensive functional analysis of SMC3 in P. falciparum to date.

The localisation of SMC3 at centromers as described previously (Batugedara 2020) was confirmed. However, the dynamic binding to other regions in the genome, potentially mediated by other proteins, could not be resolved unequivocal with only one replicate of ChIPseq per time point.

__Response: __With regard to the replicates for ChIP-seq, please see our response to this same point above.

Similarly, the RNAseq data demonstrate the relevance of SMC3 for gene expression, but no clear picture of a regulatory mechanism can be drawn at his point. Lacking information about the mode of binding as well as the setup of transcriptome analysis (only two time-shifted sampling points after simultaneous glmS treatment for 96h resulting in incomplete knockdown) cannot definitely elucidate, if SMC3/cohesin is a chromatin factor that affects transcription of genes in general or a specific repressor of stage-specific genes. __Response: __We agree that we have not established a regulatory mechanism for how SMC3 achieves binding specificity. However, the combination of inducible knockdown (as SMC3 is essential to the cell cycle) and differential expression analysis with ChIP-seq from the same time points across the intraerythrocytic developmental cycle is the most stringent and standard approach in the field of epigenetics for determining the direct role of a chromatin-associated protein in gene expression. We provide a detailed explanation of how the transcriptome analysis was set up in the Results (lines 229-234) and Materials and Methods (lines 715-719) section. With regard to our sampling points being “time-shifted,” we provide bioinformatic analysis (line 246-251, what is now Supp. Fig. 5B) of the RNA-seq data from untreated and glucosamine-treated parasites showing highly similar “ages” with regard to progression through the intraerythrocytic developmental cycle. While we of course also monitor progression through the cell cycle with Giemsa staining (Supp. Fig. 5A), this bioinformatic analysis is the most stringent method of determining specific times in the cell cycle.

*The work will be interesting to a general audience, interested in gene regulation and chromatin remodelling

The reviewers are experts in Plasmodium cell biology and epigenetic regulation.*

Reviewer #2

(Evidence, reproducibility and clarity (Required)):

Rosa et al, Review Commons The manuscript by Rosa et al. addresses the function of the cohesion subunit Smc3 in gene regulation during the asexual life cycle of P. falciparum. Cohesin is a conserved protein complex involved in sister chromatin cohesion during mitosis and meiosis in eukaryotic cells. Cohesin also modulates transcription and DNA repair by mediating long range DNA interactions and regulating higher order chromatin structure in mammals and yeast. In P. falciparum, the Cohesin complex remains largely uncharacterized. In this manuscript, the authors present mass spectrometry data from co-IPs showing that Smc3 interacts with Smc1 and a putative Rad21 orthologue (Pf3D7_1440100, consistent with published data from Batugedara et al and Hilliers et al), as well as a putative STAG domain protein orthologue (PF3D7_1456500). Smc3 protein appears to be most abundant in schizonts, but ChIPseq indicates predominant enrichment of Smc3 in centromers in ring and trophozoite stages. In addition, Smc3 dynamically binds with low abundance to other loci across the genome; however, the enrichment is rather marginal and only a single replicate was conducted for each time point making the data interpretation difficult. Conditional knock-down using a GlmS ribozyme approach indicates that parasites with reduced levels of Smc3 have a mild growth advantage, which is only evident after five asexual replication cycles and which the authors attribute to the transcriptional upregulation of invasion-linked genes following Smc3 KD. Indeed, Smc3 seems to be more enriched upstream of genes that are upregulated after Smc3 KD in rings than in downregulated genes, indicating that Smc3/cohesin may have a function in supressing transcription of these schizont specific genes until they are needed. The manuscript is concise and very well written, however it suffers from the lack of experimental replicates for ChIP experiments and a better characterization of the phenotype of conditional KD parasites. * Major comments • In the mass spectrometry analysis, many seemingly irrelevant proteins are identified at similar abundance to the putative rad21 and ssc3 orthologues, and therefore the association with the cohesion complex seems to be based mostly on analogy to other species rather than statistical significance. Hence, it would be really nice to see a validation of the novel STAG domain and Rad21 proteins, for example by Co-IP using double transgenic parasites.*

__Response: __While our IP-MS data did not yield high numbers of peptides, the top most enriched proteins were SMC3 and SMC1. As we state on line 157, two previous studies have already shown a robust interaction between SMC1, SMC3, and RAD21 in Plasmodium, supporting the existence of a conserved cohesin complex. While the identification of the STAG domain-containing protein is interesting, the purpose of our IP-MS was less about redefining the cohesin complex in P. falciparum and more about confirming that the epitope-tagged SMC3 we generated was incorporated correctly into the cohesin complex and was specifically immunoprecipitated by the antibody we later use for western blot, immunofluorescence, and ChIP-seq analyses. However, to validate the results of ours and others’ mass spectrometry results, we generated two new parasite strains – SMC1-3HA-dd and STAG-3HA-dd – and an antibody against SMC3 (see what is now Supp. Fig. 1). We performed co-IP and western blot analysis with these strains and show an interaction between SMC1 and SMC3 and STAG and SMC3 (see what is now Supp. Fig. 2). This information has been added to the manuscript on lines 162-167.

• *The ChIPseq analysis presented here is based on single replicates for each of the three time points. The significance cutoffs for the peaks are rather high (q __Response: __In our experience, a significance cutoff of FDR As we regularly perform ChIP-seq experiments in the lab, we have generated multiple negative control datasets. In our opinion, the most stringent negative control for an HA-tagged protein is performing ChIP with an HA antibody in a WT strain. We have recently published an in-depth analysis of this (and other) negative ChIP-seq controls (Baumgarten & Bryant, 2022, https://doi.org/10.12688/openreseurope.14836.2). We show in this publication that non-specific ChIP-seq experiments (such as negative controls) result in an over-representation of HP1-heterochromatinized regions due to differences in sonication efficiency of heterochromatin and technical challenges with mapping regions with high levels of homology. In the anti-HA in WT ChIP negative control (performed at 12hpi), we do not see any enrichment at centromeric regions, but rather at heterochromatinized regions where clonally variant gene families are located. We performed peak calling analysis and found no significant overlap between the negative control ChIP-seq and the SMC3-3HA ChIP-seq data at 12hpi.

In addition, we have now performed a second biological replicate of the SMC3-3HA ChIP-seq with a different clone at all time points. We compared this data to that from the original clone and found significant overlap of the peaks called (see what is now Table 4 and Supp. Fig. 3A). We generated a stringent list of peaks that were shared between both clones at each time point and repeated all downstream analyses (see what are now Tables 5-8). We found that our conclusions were largely unchanged. Text describing these experiments and analyses have been added throughout the results section.

The SMC3 ChIP from Batugedara et al., 2020 was performed with an in-house generated antibody (not a commercially available, widely validated antibody as we use) at a single time point in the IDC: trophozoites. Batugedara et al. performed one replicate and did not have an input sample for normalization. Rather, it seems that they incubated beads, which were not bound by antibody or IgG, with their chromatin and used any sequenced reads from this beads sample to subtract from their SMC3 ChIP signal as means of normalization. According to ENCODE ChIP-seq standards, this is not a standard nor stringent way of performing ChIP-seq and the subsequent analysis. Because they did not generate a dataset for their ChIP input, it is not possible to call peaks as we do in our study and compare those peaks with ours.

• The authors argue that during schizogony, cohesin may no longer be required at centromers, explaining the low ChIPsignal at this stage (Line 301). However, during schizogony parasites undergo repeated rounds of DNA replication (S-phase) and mitosis (M-phase) to generate multinucleated parasites; and concentrated spots of Smc3 are observed in each nucleus in schizonts by IFA. In turn, the strong presence of Smc3 at centromers in ring stage parasites is surprising, particularly since the Western Blot in Figure 1D shows most expression of Smc3 in schizonts and least in rings; and Smc3 is undetectable in rings by IFA. Yet, the ChIP signal shows very strong enrichment at centromers, long before S phase produces sister chromatids. What could be the reason for this discrepancy? Again, ChIP replicates and controls would be helpful in distinguishing technical problems with the ChIP from biologically relevant differences. __Response: __We discuss in lines 337-342 not that cohesin is no longer required at centromeres during schizogony, but that its removal from centromeres may be required specifically for separation of sister chromatids, as is seen in other eukaryotes. We also discuss that the unique asynchronous mitosis in Plasmodium may lead to a mixed population of parasites at the time point sampled where there may be some centromeres with SMC3 present and some where it is absent to promote sister chromatid separation. Even though SMC3 may be evicted from centromeres to promote sister chromatid separation, it is likely re-loaded onto centromeres once this process is complete. This is most likely why we see foci of SMC3 in each nucleus of mature schizonts by IFA. With regard to the discrepancy between SMC3 levels in rings seen in total nuclear extracts (by western blot) and at centromeres (by ChIP-seq): the total level of a protein in the nucleus does not necessarily dictate the genome-wide binding pattern or the level of enrichment of that protein at specific loci in the genome. Moreover, if one molecule of SMC3 binds to each centromere, 14 molecules would be needed in a ring stage parasite while over 500 would be needed in a schizont (assuming that there are ~36 merozoites present). SMC3 binds to centromeres in interphase cells in other eukaryotes as well, and we speculate that this binding may play a role in the nuclear organization of centromeres, as we discuss starting on line 333.

• It is surprising that a conserved protein like Smc3 shows such a subtle phenotype, given that it is predicted to be essential and its orthologues have a function in mitosis. Generally, only limited data are presented to characterize the Smc3 KD parasites, and more detail should be included. For example validation of the parasite line using a PCR screen for integration and absence of wt, parasite morphology after KD, and/or analysis of the KD parasites for cell cycle status. __Response: __First, we have repeated our growth curve analysis several times and with more clones and have concluded that there is not a significant growth phenotype in SMC3 KD parasites (see what is now Supp. Fig. 4B). As we discuss on line 342, very little intact cohesin complex seems to be required for normal growth and mitosis in S. cerevisiae and D. melanogaster, which is probably why we do not see an obvious growth or morphological phenotype. Because we could not generate SMC3 knockout parasites, there may be just enough SMC3 left to perform its vital function in our KD strain. We have added PCR data to demonstrate integration of the 3HA tag- and glmS ribozyme-encoding sequence in the clonal strains we are using for all experiments (see what is now Supp. Fig. 1A). Sanger sequencing was performed on these PCR products to confirm correct sequences. We also added images of Giemsa-stained parasites in untreated and glucosamine-treated parasites at all time points to demonstrate a lack of an obvious morphological phenotype in SMC3 KD parasites (see what is now Supp. Fig. 5A).

• Synchronization was performed at the beginning of the growth time course, which would be expected to result in a stepwise increase in parasitemia every 48 hours; however, the parasitemia according to Fig. 4F rises steadily, which would indicate that the parasites are actually not very synchronous. __Response: __We did indeed tightly synchronize these parasites and hope that the stepwise increase in parasitemia is seen better in our new growth curve analysis (see what is now Supp. Fig. 4B).

• The question of whether Smc3 causes a shorter parasite life cycle (quicker progression) or more invasion is important and could be experimentally addressed by purifying synchronous schizont stage parasites and determining their invasion rates as well as morphological examination of the Giemsa smears over the time course. __Response: __We have repeated our growth curve analysis several times and with more clones and have concluded that there is not a significant growth phenotype in SMC3 KD parasites (see what is now Supp. Fig. 4B).

• Please also compare Smc3 transcriptional levels in transgenic parasites to those in wt parasites to rule out that the genetic modification has lead to artificial upregulation of Smc3 transcription. __Response: __We have added this data to what is now Supp. Fig. 4C, showing that there is no significant difference in SMC3 transcript levels between WT and SMC3-3HA-glmS strains. We have added this information to the text of the manuscript (Line 243). As we also generated an SMC3 antibody, we could demonstrate that there is no appreciable difference in SMC3 protein levels between WT and SMC3-3HA-glmS strains (see what is now Supp. Fig. 1D).

• According to Figure S2, even more genes were deregulated at the 12 hpi time point in the WT parasites than in Smc3 parasites, and even to a much higher extent. What "transcriptional age" did the WT control parasites have at each time point? __Response: __We have now included the transcriptional age of all strains, replicates, and treatments in what is now Supp. Fig. 5B. At the 12 hpi time point in particular, regardless of glucosamine treatment, the SMC3-3HA-glmS and WT parasites were highly synchronous. The only large discrepancy we see in transcriptional age is between untreated and glucosamine-treated WT parasites at 36 hpi, which is why we did not include this time point in our transcriptional analysis. We were also surprised by the number of genes that were de-regulated with simple glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

• A negative correlation with transcription is well established in S. cerevisiae, particularly at inducible genes. How does Smc3 enrichment generally look like for genes that show maximal expression at each of the time point? __Response: __We have performed a metagene analysis of SMC3 enrichment at all genes at each respective time point, which we divided into quartiles of expression based on their FPKM values in the RNA-seq data from the corresponding time point in untreated SMC3-3HA-glmS parasites. This quartile analysis considers all genes, including genes that are not transcribed at all and regardless of whether a gene has a significant SMC3 peak or is differentially expressed upon SMC3 knockdown. At the 12 hpi time point, we do see an inverse correlation between SMC3 enrichment and gene transcription level, but this enrichment is most pronounced across genes bodies. We see the highest SMC3 enrichment at genes in the 4th (lowest) quartile category. For the other two time points, we do not see any obvious pattern of SMC3 enrichment with regard to transcriptional status.

• Line 590: according to the methods, a 36 hpi KD time point was also harvested. Why are the data not shown/analysed? __Response: __To be clear, we are most interested in the transcriptional role of SMC3 during interphase, where results are not confounded by its potential role in mitosis. However, we did collect a 36hpi time point in the SMC3-3HA-glmS and WT strain, with and without glucosamine. We have added this last time point and the WT data from the other two time points to the manuscript (see Tables 11-13). Unfortunately, and for reasons unknown, the WT replicates treated with glucosamine showed a significantly advanced “transcriptional age” compared to the other replicates at 36hpi (see what is now Supp. Fig. 5B). Thus, we did not feel comfortable performing the RNA-seq analysis as we did with the other two time points (i.e. subtracting up- and down-regulated genes from the WT control from the SMC3-3HA-glmS data sets). We have added this information to the results section (Lines 256 and 261). As the WT parasites treated with glucosamine were approximately 8 hours in advance of the untreated WT parasites for the 36hpi time point, any up- and down-regulated genes might have been due to differences in the cell cycle rather than due to glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

Minor Comments • Line 103/104: the hinge domain and ATPase head domain are mentioned, please annotate these in Figure 1A.

__Response: __We have annotated the hinge and ATPase domains.

• Figure 1D: the kDa scale is missing from the H3 WB. __Response: __We have added a kDa scale.

• What is the scale indicated by different colors in Fig. 2A? __Response: __The different colors (blue, coral, and green) only represent the 12, 24, and 36hpi time points, respectively. This color scheme is used throughout the manuscript. If the reviewer is referring to the color gradation within each circos plot, this does not indicate a specific scale. The maximum y-axis value for all circos plots is 24, as indicated in the figure legend.

• Line 189: it would also be interesting how many peaks are "conserved" between the different time points studied, so not only to compare the gene lists of closest genes but also the intersecting peaks and then the closest genes to the intersecting peaks. __Response: __We have added this information in Table 7 and in the manuscript starting on Line 203. Using the new dataset of consensus peaks between two replicates, there were 88 genes associated with an SMC3 peak across all three time points, most of which were close to a centromeric region.

• What is the distribution of the peaks over diverse genetic elements, such as gene bodies, introns, convergent/ divergent/ tandem intergenic regions? In yeast, cohesion is particularly enriched in convergent intergenic regions, so it would be interesting to see how this behaves in P. falciparum. __Response: __We would have liked to define how many peaks were in intergenic versus genic regions of the genome, but the dataset of “genes” from PlasmoDB includes UTRs. Thus, we would need a better annotation of the genome to perform this analysis. Regardless, we calculated the average SMC3 peak enrichment (shared between both replicates) in intergenic regions between convergent and divergent genes (see what is now Supp. Fig. 3B and Table 6). As we now state in the manuscript on line 198, we see a slight enrichment in regions between convergent genes at all time points, but the differences were not significant.

• Line 130 intra-chromosomal interactions (word missing) __Response: __Thank you for pointing this out. We have corrected this.

• Contrary to Figure 1D, the WB in Figure 3A indicates strong expression of Smc3 in rings. Please comment on this discrepancy. __Response: __While extracts from all time points were run on the same western blot in Fig. 1D and thus developed for the same amount of time, this was not the case for Fig. 3A. In Fig. 3A, the samples were run on different blots and exposed for different times, so while we can compare SMC3-HA levels between – and + glucosamine for each time point, the levels at 12 hpi cannot be quantitatively compared to those at 24 or 36hpi.

• What time point after glucosamine addition represents the WB in Fig. 3A? __Response: __The “12hpi” parasites were sampled approximately 108 hours post glucosamine addition and the “24hpi” parasites sampled approximately 120 hours post glucosamine addition. Basically, the parasites were treated with glucosamine for 96 hours, synchronized, and then harvested 12 and 24 hours later.

• Line 233 / Suppl Figure 3: Isn't it a bit concerning that the untreated control parasites at 24 hpi statistically corresponded to 18-19 hpi? And to what timepoint did the wt parasites correspond? __Response: __We are not concerned by this, and we have included the WT parasites in what is now Supp. Fig. 5B for better comparison. In the analysis presented in Supp. Fig. 5B, regardless of glucosamine presence or absence, the differences among replicates and strains at 12 and 24hpi are, in our opinion, minimal, amounting to one or two hours of the 48-hour IDC. In our extensive experience with RNA-seq across the P. falciparum lDC, this synchronization is extremely tight. As we describe on line 430 of the Materials and Methods, there is a ±3 hour window in our synchronization method, meaning that parasites harvested at 24hpi could be anywhere from 21-27hpi. In addition, the dataset that was used for comparison (from Bozdech et al., 2003) was generated in 2003 in a different laboratory using different strains with microarray. While comparing more recent RNA-seq data to this classic study has become well-established practice and is useful for comparing transcriptional age between replicates and strains, it is inevitable that the calculated “hpi” from (Bozdech et al., 2003) will differ somewhat from our experimental “hpi”. We have indeed seen this small discrepancy in predicted transcriptional age in several of our RNA-seq datasets (unrelated to this study) from trophozoites harvested at 24hpi.

• Line 264: "whether naturally or via knockdown" - the meaning of this sentence is not entirely clear __Response: __We are referring to depletion of SMC3 at promoters, either naturally (i.e. lack of binding at the promoter at 36hpi that is not the result of SMC3 knockdown, as we show in Fig. 4B) or via SMC3 knockdown, which is not natural but artificial.

• Figure 4 Legend: A, B, C etc. are mixed up. Response: Thank you for pointing this out. We have corrected this.

• Figure 4D: the differences seem to be marginally significant, even not significant at all (q=0.8) for gap45 at 12hpi. __Response: __If one defines a significance cutoff of q = 0.05 (as is common practice in differential expression analyses), then the differences are significant. For a small minority of invasion genes (such as gap45), we do observe significance at either 12 hpi or 24 hpi, but not both. Thus, we have removed the word “significant” from the descriptions of each dataset in Tab 1 of what is now Table 18. however, we do not believe that this rules out a role for SMC3 at such a gene during interphase. What is now Table 18 offers a longer list of invasion-related genes, most of which are more “significantly” affected than rap2 and gap45.

• Figure 4F shows FACS data using SYBR green as a DNA stain. The authors could exploit this data to look at the relative DNA content per cell as a measure of parasite stage, since more mature parasites will have more DNA (mean fluorescence intensity). How did the corresponding parasite cultures look in Giemsa smears? Response: We have repeated our growth curve analysis several times and with more clones and have concluded that there is not a significant growth phenotype in SMC3 KD parasites (see what is now Supp. Fig. 4B). We have added images of Giemsa-stained parasites in untreated and glucosamine-treated parasites at all time points to demonstrate a lack of an obvious morphological phenotype in SMC3 KD parasites (see what is now Supp. Fig. 5A).

• Are RNAseq replicates biological replicates from independent experiments or technical replicates? __Response: __RNA-seq replicates are technical replicates from the same parasite clone.

• Why does the number of genes analysed for differential gene expression differ between the comparisons? __Response: __If the reviewer is referring to the discrepancy between the total number of genes for different time points [for example, between what is now Table 9 (12hpi) and Table 10 (24hpi)], this is because in the RNA-seq/differential expression analysis, there have to be reads mapping back to a gene in order for that gene to be included in the analysis. Thus, if a gene is not transcribed at a given time point in the treated or untreated samples, it will not be included in the analysis. Gene transcription fluctuates significantly over the course of the IDC, so different time points will have different total numbers of transcribed genes.

• Line 372: Do you mean the proteins or the genes? AP2-I has a peak at 24 hpi and 36 hpi, and its interacting AP2 factor Pf3D7_0613800 at all time points. __Response: __We are referring to the genes. With the new ChIP-seq analysis including the second replicate, there are no consensus SMC3 peaks associated with ap2-I, bdp1, or Pf3D7_0613800 (see what is now Table 7).

• Line 480: no aldolase was shown. __Response: __We have removed this sentence.

• Line 838: include GO analysis in methods __Response: __We have added this.

Reviewer #2 (Significance (Required)): The paper addresses the function of the cohesin complex in gene regulation of malaria parasites for the first time. Due to the conserved nature of the complex, the data may be interesting for a broad audience of scientists interested in nuclear biology and cell division/ gene regulation.

Reviewer #3

(Evidence, reproducibility and clarity (Required)):

*Summary:

In the presented manuscript by Rosa et al. the authors investigate the longstanding question of how P. falciparum achieves the tight transcriptional regulation of its genome despite the apparent absence of many canonical sequence specific transcription factor families found in other eukaryotes. To do this the authors investigate the role of the spatial organization of the genome in this context, by performing a functional characterization of the conserved cohesion subunit SMC3 and its putative role in transcriptional regulation in P. falciparum. Using Cas9 mediated genome editing the authors generated a SMC3-3xHA-glmS parasite line, which they subsequently used to show expression of the protein over the asexual replication cycle by western blot and IFA analysis. In addition, using co-IP experiments coupled with mass spectrometry they identified the additional components of the cohesion complex also found in other eukaryotes as interaction partners of SMC3 in the parasite, thereby confirming the presence of the conserved cohesin complex in P. falciparum. By using a combination of ChIP-seq and RNA-seq experiments in SMC3 knockdown parasites the authors furthermore show that a reduction of SMC3 resulted in the up-regulation of a specific set of genes involved in invasion and egress in the early stages of the asexual replication cycle and that this up-regulation in transcription is correlated with a loss of SMC3 enrichment at these genes. From these observations the authors conclude, that SMC3 binds dynamically to a subset of genes and works as a transcriptional repressor, ensuring the timely expression of the bound genes. Overall, the presented data is intriguing, of high quality and very well presented. However, there are some points, which should be addressed to bolster the conclusions drawn by the authors.

Major points: I was not able to find the deposited datasets in the BioProject database under the given accession number. This should obviously be addressed and would have been nice to be able to have a look at these datasets also for the review process. *__Response: __We apologize for not giving the reviewers access. As the manuscript has been made available as a pre-print (which includes data accession numbers), but has not yet been published, we have not activated access to the data on the database.

*SMC3-ChIP-seq experiments:

"168 were bound by SMC3 across all three time points (Fig. 2D). However, most SMC3-bound genes showed a dynamic binding pattern, with a peak present at only one or two time points (Fig. 2B,D)."

Here it would be interesting to actually have more than one replicate of each of these ChIP-seq time points. This could provide a better idea of how "dynamic" these binding patterns actually are. Furthermore, I was missing a list of these 168 genes, which are constantly bound by SMC3. Anything special about those? What actually happens to this subset of genes in the SMC3 knockdown parasites? Do they show similar transcriptional changes?*

__Response: __We have now performed a second biological replicate of the SMC3-3HA ChIP-seq with a different clone at all time points. We compared this data to that from the original clone and found significant overlap of the peaks called (see what is now Table 4 and Supp. Fig. 3A). We generated a stringent list of peaks that were shared between both clones at each time point and repeated all downstream analyses (see what are now Tables 5-8). We found that our conclusions were largely unchanged. Text describing these experiments and analyses have been added throughout the results section. Using the new dataset of consensus peaks between two replicates, there were 88 genes associated with an SMC3 peak across all three time points (see what is now Table 7). The genes that are associated with an SMC3 peak at all time points are, in general, those closest to centromeric/pericentromeric regions and show no obvious functional relationship to each other. Out of these 88 genes, four are significantly up- or downregulated at 12 hpi and 26 are significantly up- or downregulated at 24 hpi. The most significantly downregulated of these genes in both datasets is smc3 itself.

*SMC3-knockdown experiments:

In Sup. Fig. 1 there is a double band in the HA-western blot in the 2nd cycle -GlcN. sample. This second band is absent in all other HA-western shown. Have the authors any idea where that second band comes from?*

__Response: __As the reviewer says, we do not see this second band in most of our western blots. It is possible that it is just a small amount of degradation in the lysate.

In Figure 3A, the WB data shown is slightly contrasting the RNA-seq quantification (3B). The knock-down on protein level seems to be stronger in the 12 hpi samples here than in the 24 hpi samples. Although the band for HA-SMC3 is stronger at the 12 hpi TP there's no band visible in the + GlcN. sample. There's however in the 24 hpi samples. Could the authors comment on this?

Response: __With regard to the discrepancy of the knockdown and protein versus RNA level, it is quite common for transcript levels to not agree with protein levels. This is why we always confirm a transcriptional knockdown with western blot analysis using appropriate loading controls. We are not sure why there is a more dramatic knockdown of SMC3 at 12hpi than at 24hpi, as these samples came from the same culture, but were simply harvested 12 hours apart. __

*"Comparison of our RNA-seq data to the time course transcriptomics data from (Painter et al., 2018) revealed that SMC3 depletion at 12 hpi caused downregulation of genes that normally reach their peak expression in the trophozoite stage (18-30 hpi), with the majority of upregulated genes normally reaching their peak expression in the schizont and very early ring stages (40-2 hpi) (Fig. 3E). At 24 hpi, a similar trend is observed, with most downregulated genes normally peaking in expression in trophozoite stage (24-32 hpi) and the majority of upregulated genes peaking in expression at very early ring stage (2 hpi) (Fig. 3F)."

I'm not fully convinced by these presented results/conclusions. This dataset would greatly benefit from the inclusion of additional later time points.*

__Response: __To be clear, we are most interested in the transcriptional role of SMC3 during interphase, where results are not confounded by its potential role in mitosis. However, we did collect a 36hpi time point in the SMC3-3HA-glmS and WT strain, with and without glucosamine. We have added this last time point and the WT data from the other two time points to the manuscript (see Tables 11-13). Unfortunately, and for reasons unknown, the WT replicates treated with glucosamine showed a significantly advanced “transcriptional age” compared to the other replicates at 36hpi (see what is now Supp. Fig. 5B). Thus, we did not feel comfortable performing the RNA-seq analysis as we did with the other two time points (i.e. subtracting up- and down-regulated genes from the WT control from the SMC3-3HA-glmS data sets). We have added this information to the results section (Lines 256 and 261). As the WT parasites treated with glucosamine were approximately 8 hours in advance of the untreated WT parasites for the 36hpi time point, any up- and down-regulated genes might have been due to differences in the cell cycle rather than due to glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

We performed differential expression analysis of the SMC3-3HA-glmS parasites with and without glucosamine at 36hpi (we have added this data in Table 11). Again, significantly up- and down-regulated genes were not filtered using the WT dataset. With this analysis, we see only three genes from the list of invasion-related genes (Hu et al., 2010) that are up-regulated, but none of them have a significant q-value (Tab 5 of Table 18). Thus, depletion of SMC3 in late stage parasites does not lead to up-regulation of the same genes that are upregulated at 12 and 24hpi. We have added this information to the text (Line 277).

*The presented upregulation of the egress and invasion related genes is hard to pinpoint to be a direct effect of transcriptional changes due to the SMC3 knockdown. While there's a slight upregulation of these genes they still seem to be regulated in their normal overall transcriptional program as shown in Figure 4D/E. *

__Response: __We provide evidence of a direct effect of SMC3 binding by combining differential expression analysis performed upon SMC3 knockdown with SMC3 ChIP-seq at corresponding time points. As we show in what is now Fig. 4C and D, promoter accessibility of these egress/invasion genes correlates with their transcriptional activity. However, SMC3 binding to the promoters of these same genes shows inverse correlation with their transcriptional activity (what is now Fig. 4B and D). While we believe that SMC3 does contribute to the repression of these genes at specific time points during the cell cycle, it is highly likely that SMC3 is just one protein of many that regulates these genes. Moreover, and especially since we do not see a growth phenotype in the SMC3 KD, it is possible that another protein or even SMC1 could compensate for loss of SMC3 at these promoter regions. We now state these possibilities on lines 346 383 of the Discussion.

*So the changes could in theory also be explained by the differences in cell cycle progression which are present between +/- GlcN. cultures (Sup. Fig. 3). The presented normalization to the microarray data is a well-established practice to correct for this but, as presented seems to have its limitation with these parasite lines (line 233, glucosamine treated parasites harvested at 24 hpi correspond statistically to approximately 18-19 hpi (Supp. Fig. 3).) *

__Response: __In the analysis presented in what is now Supp. Fig. 5B, regardless of glucosamine presence or absence, the differences among replicates and strains at 12 and 24hpi are, in our opinion, minimal, amounting to one or two hours of the 48-hour IDC. In our extensive experience with RNA-seq across the P. falciparum lDC, this synchronization is extremely tight. As we describe on lines 416-421 of the Materials and Methods, there is a ±3 hour window in our synchronization method, meaning that parasites harvested at 24hpi could be anywhere from 21-27hpi. In addition, the dataset that was used for comparison (from Bozdech et al., 2003) was generated in 2003 in a different laboratory using different strains with microarray. While comparing more recent RNA-seq data to this classic study has become well-established practice and is useful for comparing transcriptional age between replicates and strains, it is inevitable that the calculated “hpi” from (Bozdech et al., 2003) will differ somewhat from our experimental “hpi”. We have indeed seen this small discrepancy in predicted transcriptional age in several of our RNA-seq datasets from trophozoites harvested at 24hpi.

By including additional later time points, one could actually follow the expression profiles over the whole cycle and elucidate if there's an actual transcriptional up-regulation of the genes, or if the + GlcN. parasites show a faster cell cycle progression, with a shifted peak expression timing compared to the - GlcN. parasites. __Response: __We did collect a 36hpi time point in the SMC3-3HA-glmS and WT strain, with and without glucosamine. We have added this last time point and the WT data from the other two time points to what is now Supp. Fig. 5. Unfortunately, and for reasons unknown, the WT replicates treated with glucosamine showed a significantly advanced “transcriptional age” compared to the other replicates at 36hpi. Thus, we did not feel comfortable performing the RNA-seq analysis as we did with the other two time points (i.e. subtracting up- and down-regulated genes from the WT control from the SMC3-3HA-glmS data sets). We have added this information to the results section (Lines 256 and 261). As the WT parasites treated with glucosamine were approximately 8 hours in advance of the untreated WT parasites for the 36hpi time point, any up- and down-regulated genes might have been due to differences in the cell cycle rather than due to glucosamine treatment. The glmS system of inducible knockdown is widely used in P. falciparum; however, to our knowledge, no lab has investigated whether glucosamine treatment affects transcription in wildtype cells over the course of the IDC. Thus, for accurate phenotypic characterization of any protein with this system with regard to transcriptomics, we thought it was important to provide an RNA-seq dataset to define the cohort of genes affected by glucosamine treatment in WT parasites. We hope that our study will demonstrate the importance of using stringent controls when using inducible knockdown systems.

*"These genes show SMC3 enrichment at their promoter regions at 12 and 24 hpi, but not at 36 hpi (Fig. 4C), and depletion of SMC3 resulted in upregulation at both 12 and 24 hpi (Fig. 4D). Comparison of the SMC3 ChIP-seq data with published Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data (Toenhake et al., 2018) and mRNA dynamics data (Painter et al., 2018) from similar time points in the IDC revealed that SMC3 binding at the promoter regions of these genes inversely correlates with chromatin accessibility (Fig. 4C) and their mRNA levels (Fig. 4E), which both peak in schizont stages. These data are consistent with a role of SMC3 in repressing this gene subset until their appropriate time of expression in the IDC."

The presented correlations certainly make an intriguing point towards the authors conclusion that SMC3/cohesin depletion from the promoter regions of the genes results in a de-repression of these genes and their transcriptional activation. However, the SMC3 knockdown is not complete and only up to 69% as presented on RNA level in these parasites. Therefore a control experiment which needs to be done is to actually show the loss of SMC3 from the presented activated example genes in the knockdown parasites. This could easily be done by ChIP-qPCR or even ChIP-seq, to get a global picture of the actual changes in SMC3 occupation in the knockdown parasites in correlation with changes in transcript levels. *__Response: __While SMC3-3HA-glmS knockdown is not complete at the RNA level, it is fairly robust at the protein level, especially at 12hpi (Fig. 3A).

*"These data suggest that SMC3 knockdown results in a faster progression through the cell cycle or a higher rate of egress/invasion."

The authors could greatly strengthen their conclusions by investigating this thoroughly. Pinpointing the observed phenotype to an actual increase in invasion or egress would add to the authors main conclusion that the loss of SMC3 de-regulates the timing of gene expression for these invasion related genes thereby increasing their transcript levels and thus leading to a higher rate of egress/invasion. To determine cell cycle progression simple comparisons between DNA content using a flow cytometer at timepoints together with visual inspection of Giemsa stained blood smears would give a ggod indication towards changes in cell cycle progression. In addition invasion/egress assays by counting newly invaded rings per schizont could reveal, if there are changes in the rate of egress/invasion upon SMC3 knockdown.*

Response: __We have repeated our growth curve analysis several times and with more clones and have concluded that there is not a significant growth phenotype in SMC3 KD parasites (see what is now Supp. Fig. 4B). We have added images of Giemsa-stained parasites from the knockdown time course we performed to what is now Supp. Fig. 5A. We see no obvious differences in cell morphology caused by glucosamine treatment in the WT or SMC3-3HA-glmS parasites. As we discuss on line 327, very little intact cohesin complex seems to be required for normal growth and mitosis in S. cerevisiae and D. melanogaster, which is probably why we do not see an obvious growth or morphological phenotype. We believe that SMC3 is probably only a part of a complex controlling transcription of these invasion or egress genes. Thus, the up-regulation of these genes upon SMC3 KD might not be enough to lead to a significant growth or invasion phenotype. __

*Minor points:

In the MM section on the Cas9 experiments it says dCas9 where it should be Cas9 (line 425)*

__Response: __Thank you for pointing this out. We have corrected this.

It would be great to add which HP1 antibody was used in which dilution in the IFAs to the MM section. __Response: __We have added this information to the Materials and Methods section.

In Figure 4C for the gap45 gene there's is some green peak floating around which should not be there. __Response: __Thank you for pointing this out, we have corrected it.

*Reviewer #3 (Significance (Required)):

Significance: The manuscript investigates a very timely topic by trying to uncover new molecular mechanisms of transcriptional regulation in P. falciparum. Investigating the role of the cohesin complex/SMC3 in this context provides valuable new insights to the field. While the first part with the description of the SMC3 cell line and the co-IP experiments largely confirms published data on the existence and composition of the cohesin complex in Plasmodium and its enrichment at the centromeres, the second part is especially intriguing since it investigates the molecular function of SMC3 in more detail. The results pointing to a role of SMC3/cohesin as a transcriptional repressor are of great interest to the field and will open up new concepts for future investigation.*

*Audience: The work is particularly interesting for people interested in gene regulatory processes in Plasmodium and Apicomplexan parasites in general. At the same time it also nicely points towards shared principles of gene regulation to other eukaryotes in relation to the spatial organization of the genome making the work also very interesting for a broader audience with interest in the general principles of gene regulatory processes in eukaryotic organisms.

Expertise: P. falciparum epignetics and chromatin biology / gene regulation / Cas9 gene editing*

CROSS-CONSULTATION COMMENTS

All reviewers agree that the paper addresses an important topic and provides convincing evidence for enrichment of the cohesin component Smc3 at P. falciparum centromers. In contrast, evidence for a function of Smc3 as a transcriptional repressor of genes in the first part of the parasite life cycle is less well supported. All reviewers agree that the statistical significance of the ChIP experiments needs to be impoved by including biological replicates. In addition, the phenotype of the conditional knock-down should be analysed in more detail by clarifying whether faster cell cycle progression or higher invasion rate are responsible for the observed growth adavantage. Inclusion of transcriptional data from a later time point in addition to the presented data for 12 hpi and 24 hpi was also requested by all reviewers. Finally, several inconsistencies require clarification.

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

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Reply to the reviewers

Response to reviewers' comments

We thank the reviewers for their constructive evaluation of our manuscript. In the following point-by-point response, we explain how we will implement the suggested modifications.

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

Summary:

The formation of meiotic double-stranded DNA breaks is the starting point of meiotic recombination. DNA breaks are made by the topoisomerase-like SPO11, which interacts with a number of regulatory factors including REC114, MEI4 and IHO1. Despite the key role this process has in the continuation, and genetic variation, or eukaryotic life, there is very little known about how this process is regulated. Laroussi et al make use of biochemical, biophysical and structural biological approaches to extensively characterise the REC114-MEI4-IHO1 complex.

This is an outstanding biochemical paper. The experiments are well planned and beautifully executed. The protein purifications used are very clean, and the figures well presented. Importantly Laroussi et. al describe, and carefully characterise through point mutational analysis, the direct physical interaction between IHO1 and REC114-MEI4. This is an interaction that has, at least in yeast, previously been suggested to be driven by liquid-liquid separation. The careful and convincing work presented here represents an important paradigm-shift for the field.

I am fully supportive of publication of this excellent and important study.

We thank the reviewer for his/her positive comments, appreciation of the importance of our study and suggested modifications.

Major comments:

Point 1:

My only major concern is regarding Figure 4, and specifically the AF2 model of the coiled-coil tetramer of IHO1. Given the ease with which MSAs of coiled-coils can become "contaminated" with non-orthologous sequences, I would urge some caution with this model. This is especially since the yeast ortholog of IHO1, Mer2, has been previously reported to be an anti-parallel tetramer (albeit, not very well supported by the data). The authors have several choices here. 1) They could simply reduce the visibility of the IHO1 tetramer model, and indicate caution in the parallel tetramer model. 2) They could consider using a structure prediction algorithm that doesn't use an MSA (e.g. ESMFold). 3) They could try to obtain experimental evidence for a parallel coiled-coil tetramer, e.g. through EM, SAXS or FRET approaches. I would like to make it crystal clear, however, that I would be *very* supportive of approach 1) or 2). An experimental approach is *not* necessary.

Assuming the authors don't take a wet-lab approach, this shouldn't take more than a couple of weeks.

This is a very good suggestion. We are aware of the previously reported anti-parallel architecture of the yeast IHO1 ortholog Mer2 (Claeys Bouuaert et al., Nature 2021). It should be noted, that in the recent preprint, posted by the Claeys Bouuaert lab (BioRxiv, https://doi.org/10.1101/2022.12.16.520760), a high confidence model of yeast Mer2 (and for human) parallel tetrameric coliled-coil is presented, apparently consistent with their previous XL-MS results (Claeys Bouuaert et al., Nature 2021).

To clarify this issue we will follow the suggestions of Reviewer 1 and 2.

1. As suggested also by Reviewer 2, we will produce a tethered dimer of IHO1125-260, connected by a short linker and determine its MW by SEC-MALLS (and SAXS).
2. In the meantime we followed the suggestion of Reviewer 1 and modelled the IHO1130-281 by the ESMfold, which is another recent powerful AI-based program that does not use multiple sequence alignments. Remarkably, the predicted structure is very similar to the one predicted by AlphaFold, also predicting the parallel arrangement of IHO1. This model will be included as a supplementary figure.
3. We will also point out in the text that these models, despite being very convincing, remain models.

   Minor comments:

Point 2:

The observation that REC114 and MEI4 can also form a 4:2 complex is very interesting and potentially important. Did the authors also try to model this higher order complex in AF2?

Yes, we did this with the hope that we could identify residues whose mutation could limit the fast exchange between the 2:1 and 4:2 states. Unfortunately, no convincing additional contacts are modelled by AlphaFold. This PAE plot will be included as a supplementary figure.

Point 3:

Similarly to above, what does the prediction of the full-length REC114:MEI4 2:1 complex look like? Presumably the predicted interaction regions align well with experimental data, but it would be interesting to see (and easy to run).

The AlphaFold modelling of the FL REC114:MEI4 (2:1) complex will be included as supplementary figure. It is consistent with the model comprising only the interacting regions. No additional convincing contacts are predicted.

Point 4:

Did the authors carry out SEC-MALS experiments on any IHO1 fragment lacking the coiled-coil domain? It was previously reported for Mer2 that the C-terminal region can form dimers, for example (OPTIONAL).

We can easily do that. We have the N- and C- terminal regions lacking the coiled-coil expressed as MBP fusions and they will be analysed by SEC-MALLS.

Point 5:

Given that full-length REC114 is used for the IHO1 interaction studies, do the authors have any data as to the stoichiometry of the REC114FL-MEI41-127 complex? (OPTIONAL)

We have repeatedly analysed the REC114-MEI4-IHO1 complex sample by SEC-MALLS and native mass spectrometry, but in both cases the sample is too complex to be interpreted. This is like due to the fast exchange between REC114-MEI4 2:1 and 4:2 complexes and low binding affinity of IHO1 for REC114.

Point 6:

Did the authors try AF2 modelling of the REC114-IHO1 interaction using orthologs from other species?

Yes, but not extensively. We will repeat this modelling again.

**Referees cross commenting**

I will add cross-comments to the comments of Reviewer #2

Firstly, the comments made by Reviewer #2 are technically correct. Firstly, reviewer #2 points out that the oligomerization states that the authors report could, in part, be artifactual the based on the his-tag purification method. This is indeed correct. However, given that none of the oligomerization states reported are per se unusual, given what is already known (including pre-prints from the Keeney and Claeys Bouuaert laboratories), I think the authors could forego this step.

Secondly, the use of an experimental structural method, such as SAXS, would certainly add value to the paper. Also Reviewer #2 is correct in pointing out the availability of the ESRF beamlines to the authors. However, while SAXS is a useful method, I personally consider the use of mutants to validate the interactions, an even stronger piece of evidence that the AlphaFold2 interactions are correct. I must disagree somewhat with Reviewer #2 with their argument that SAXS would validate the fold. Certainly if one of the AF2 predicted structures is radically wrong, then SAXS would produce scattering data, and a subsequent distance distribution plot that is incompatible with the AF2 model. However, a partly correct AF2 model, of roughly the right shape, might still fit into a SAXS envelope.

Reviewer #2 shares my concern on the parallel coiled-coil of IHO1, and their suggested solution is very elegant.

In my view, due to the time constraints imposed by the partially competing work from the Keeney and Claeys Bouuaert laboratories (recently on biorxiv). I would support the authors if they chose the quickest route to publication.

Reviewer #1 (Significance (Required)):

General assessment: The strengths of the paper are as follows:

1) Quality of experiments - The proteins used have been properly purified (SEC) and properly handled. The experiments are carefully carried out and controlled.

2) Detail - The authors carry out the considerable effort of characterising protein interactions. through separation-of-function mutants. This adds to the quality of the paper, and renders the AF2 models as convincing as experimentally determined structures

3) Conceptual advances - The most important conceptual advance is the direct binding of the N-term of IHO1 to REC114. That this is the same region as used by both TOPOVIBL and ANKRD31 points to a complex regulation.

4) Integrity - the authors have taken great care not to "oversell" the results. The data are presented, and analysed, without hyperbole.

Limitations - The only limitation of the paper is the lack of in vivo experiments to test their findings. However given the time and effort required, and the pressing need to publish this exciting study, this is not a problem.

Advance: The paper provides advances from a number of directions, both conceptual and mechanistic. Mechanistically the description of the REC114-MEI14 complex is important, and in particular the observation that it can also form a higher order 4:2 structure. Likewise, while IHO1 was inferred to be a tetramer (based on work on Mer2) this was never proven formally. Most importantly, is the physical linkage between IHO1 and REC114, and that this is an interaction that is incompatible with TOPOVIBL and ANKRD31.

Audience:

Given the central role of meiotic recombination in eukaryotic life, any studies that shed additional light on the regulation of meiosis are suitable for a broad audience. However, this subject paper will be more specifically of interest to the meiosis community. The elegant methodology will also be of interest to structural biologists and protein biochemists.

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

This manuscript addresses the structure of the REC114-MEI4-IHO1 complex, which controls the essential process of programmed DSB induction by SPO11/TOPOVIBL in meiosis.

The manuscript carefully combines biochemistry, biophysics and modelling in an integrative manner to report the architecture of the full REC114-MEI4-IHO1 complex that is not itself amenable to direct structure elucidation such as by X-ray crystallography. These are important results that will be of interest to the recombination and meiosis fields. The data are generally convincing and interpretations appear correct, so the manuscript is certainly suitable for publication. I have included some suggestions below that I believe would strengthen the manuscript and enhance our confidence in the findings. Whilst the manuscript is publishable in its current format, I believe the suggestions given below would make it into a much stronger paper.

We thank the reviewer for his/her positive comments on our study and the suggestions below.

I have two general suggestions:

Point 1:

Analyses have been performed on fusion proteins (His, His-MBP etc). we have previously observed that bulky tags such as MBP can interfere with oligomeric state through steric hindrance, and that His-tags can mediated formation of larger oligomers, seemingly through coordination of metals leached from IMAC purification. This latter point has also been observed by others

https://www.sciencedirect.com/science/article/pii/S1047847722000946.

Where possible, I would repeat SEC-MALS experiments using untagged proteins, or at least following incubation with EDTA to mitigate the potential for His-mediated oligomerization.

We agree with this reviewer’s comment that expression tags can have unexpected impact of the protein behaviour.

1. For REC114-MEI4 complex the stoichiometry is assessed by several techniques. Figure 1f,g shows analytical ultracentrifugation, which was performed on the minimal REC114226-254-MEI41-43 complex that contains no fusion tag showing that this stoichiometry is independent of fusion tags. We will nevertheless repeat the SEC-MALLS on REC114-MEI41-127 after removing the His-tag of MEI4 as suggested.
2. For the REC114 dimer, we cannot remove the His-MBP tag since this short fragment of REC114226-254 is no stable without MBP. The dimerization of Rec114 was already reported in (Claeys Bouuaert et al., Nature 2021). The dimerization is sensitive to specific point mutations within REC114. We will however, repeat the SEC-MALLS experiment following incubation with EDTA to mitigate the potential for His-mediated oligomerization.
3. The presented SEC-MALLS on IHO1 fragments (Figure 4b) was done on proteins without fusion tags. Reviewer 1 and 2 also agreed that additional repeats of the experiments without fusion tags are not necessary.

The authors have relied upon mutagenesis to validate Alphafold2 models. Their results are convincing but only confirm that contacts involved in structures rather than the specific fold per se. Their finding would be greatly strengthen by collecting SEC-SAXS data and fitting models directly to the scattering data. This is extremely sensitive, so a close fit provides the best possible evidence of accuracy of the model. SAXS is affected by unstructured regions and tags, so would have to be performed using structural cores of untagged proteins rather than full-length constructs. Given the local availability of world-class SAXS beamlines at the ESRF, which is next door to the leading author's institute, it seems that the collection of SAXS data would be practical for them.

The usage of SAXS is discussed in the specific points below. We will attempt to do SEC-SAXS on the REC114-MEI4 complex. Due to instability of REC114226-254 without MBP, SAXS cannot be done. We will also do SAXS on the IHO1 tetramer.

My specific comments are below:

Point 2:

Figure 1d

The SEC-MALS shows multiple species, with 2:1 and 4:2 representing a minority of total species present. What are the larger oligomers? Could these be an artefactual consequence of the His-tags (as described above)?

This SEC-MALLS will be repeated without the His-tag on MEI4.

Point 3:

Figure 1f,g

The AUC changes over concentration and pH are intriguing - have they tried MALS in these conditions? This would be much more informative as it would reveal the range of species present. Low concentrations could be analysed by peak position even if scattering is insufficient to provide interpretable MW fits. I would advise doing this without his tag or adding EDTA (as described above).

We will perform this experiment as suggested.

Point 4:

Figure 2

I would like to see the models validated by SAXS using minimum core untagged constructs. This could be sued to test the validity of the 2:1 model directly, and to model the 4:2 complex by multiphase analysis and/or docking together of 2:1 complexes.

The hydrophobic LALALAII region of MEI4 is interesting and the mutagenesis data do agree with the model. However, it is important to point out that any decent model would place this hydrophobic helix in the core of the complex, and so would be disrupted by mutagenesis. Hence, the mutagenesis results confirm that the hydrophobic helix is critical for the interaction, but does not confirm that the specific alphafold model is more valid than any other model in which the helix is similarly in a core position.

We will attempt to perform the SEC-SAXS measurements. The challenge here will be obtaining a sample that is monodisperse in solution being required for SAXS. We showed the fast exchange between the 2:1 and 4:2 oligomeric state. The AUC data indicates that the sample has a predominantly 2:1 stoichiometry at 0.2 mg/ml, pH 4.5 and 500mM NaCl. Given the small size of the complex, the signal at 0.2 mg/ml is likely to be noisy.

Point 5:

Figure 3

This would also benefit from SAXS validation of the structural core. The mutagenesis here provides convincing evidence in favour of the structure as specific hydrophobics ether disrupt or have no effect, exactly as predicted. Hence, their data strongly support the dimer model. As this provides the framework for the 2:1 complex, these data make me far more confident of the previous 2:1 model in figure 2. I am wondering whether it would be better to present these data first such that the reader can see the 2:1 model being built upon these strong foundations?

We agree with this suggestion and will present the REC114 dimerization data before the REC114-MEI4 complex. However, REC114226-254 is not stable without the MBP tag so is not suitable for SAXS analysis.

Point 6:

Figure 4

The MALS data convincingly show formation of a tetramer. How do we know that it is parallel? The truncation supports this but coiled-coils do sometimes form alternative structures when truncated (e.g. anti-parallel can become parallel when sequence is removed), and alphafold seems to have a tendency of predicting parallel coiled-coils even when the true structure of anti-parallel (informal observation by us and others). A simple test would be to make a tethered dimer of 110-240, with a short flexible linker between two copies of the same sequence - if parallel it should form a tetramer of double the length, if anti-parallel it should form a dimer of the same length - determinable by MALS (and SAXS).

To address this point we will perform this experiment as suggested by Reviewer 2. We will produce a tethered dimer of IHO1 110-240, connected by a short linker and determine its MW by MALS (and possibly SAXS). We also performed ESMfold modelling (Reviewer 1, Point 1), resulting in the same model. As the IHO1 tetramer is likely suitable for SAXS analysis, we will also perform SAXS on it.

Point 7:

Figures 5/6

The interaction is clear albeit low affinity (but within the biologically interesting range). It would be nice to obtain MALS (using superose 6) data showing the stoichiometry of the complex - are the data too noisy to be interpretable owing to dissociation? The alpahfold model and mutagenesis data strongly support the conclusion that the IHO1 N-term binds to the PH domain, as presented.

We have repeatedly analysed the REC114-MEI4-IHO1 complex sample by SEC-MALLS (on Superose 6) and native mass spectrometry, but in both cases the sample is too complex to be interpreted. This is likely due to the fast exchange between REC114-MEI4 2:1 and 4:2 complexes and low binding affinity of IHO1 for REC114.

**Referees cross commenting**

Just to clarify a couple of points regarding consultation comments from reviewer 1:

The suggestion regarding tags was mostly directed to the cases in which MALS data are noisy, with multiple oligomeric species (such as figure 1d). In these cases, i wondered whether the large MW species may be artefactual and could be resolved by removal of the tags. In cases where oligomers agree with those reported by other labs, I agree that there's no need to explore these further.

In terms of SAXS, I agree that fitting models into envelopes will not distinguish between similar folds. However, fitting models directly to raw scattering data is extremely sensitive and I have never seen good fits with low chi2 values for incorrect models (even when very similar in overall shape to the correct structure).

Reviewer #2 (Significance (Required)):

The manuscript carefully combines biochemistry, biophysics and modelling in an integrative manner to report the architecture of the full REC114-MEI4-IHO1 complex that is not itself amenable to direct structure elucidation such as by X-ray crystallography. These are important results that will be of interest to the recombination and meiosis fields. The data are generally convincing and interpretations appear correct, so the manuscript is certainly suitable for publication. I have included some suggestions below that I believe would strengthen the manuscript and enhance our confidence in the findings. Whilst the manuscript is publishable in its current format, I believe the suggestions given below would make it into a much stronger paper.

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

Laroussi et al used Alphafold models to predict the assembly of REC114-MEI4-IHO1 complex, and verified the structure using different biochemical experiments. Both Alphafold predictions and experiment data are convincing for the overall protein complex assembly. Importantly, they identified a motif on IHO1 that share the same binding site on REC114 with TOPOVIBL and ANKRD31, suggesting that REC114 acts as a regulatory base coordinating different binding partners during meiosis progression. Overall, I believe this is a nice biochemistry paper, but lacks insights into the biology (I believe those in vivo data is beyond the scope of this paper), at least more discussions are needed in terms of these findings.

We thank the reviewer for the supportive comments on our manuscript and its evaluation. We agree with the reviewer, that including in vivo data, that might provide further biological insights, would be useful. However, there is currently no good cellular model for meiotic recombination in mouse and thus our structure-based mutations will need to be tested in transgenic mice. Such data will take a long time to obtain and would delay the publication these in-vitro results that already provide novel insight into the REC114-MEI4-IHO1 complex architecture. We will, nevertheless, as suggested, strengthen the discussion of the biological implications of our findings.

Some minor points:

Point 1:

Any data showing MEI4 forms a dimer on its own?

As mentioned in the manuscript, full-length MEI4 is difficult to produce in bacteria or insect cells. Thus, we worked with the N-terminal fragment which in absence of REC114 is nor very stable. We will perform SEC-MALLS to assess its oligomeric state. Alphafold suggests dimeric arrangement of MEI4, but only with low confidence.

Point 2:

In Fig2 and Sup Fig4, HisMBP-MEI4, you see more MBP than the fusion protein, especially more obvious in the mutants. What's your explanation?

The N-terminus of MEI4 is well produced when co-expressed with REC114. For the pull-down experiments in Figure 2 we expressed it as His-MBP fusion in absence of REC114. In this situation, there is a degradation between MBP and MEI4. We find this very often for proteins that not very stable, which is the case of MEI4 without REC114. This is the best way we could produce at least some MEI4 in absence of REC114. The MBP protein could probably be removed by other chromatography techniques, but we think that for the purpose of the pull-down its presence is not interfering with the REC114-MEI4 binding.

Point 3:

TOPOVIBL and ANKRD31, I am curious if you have looked at the conserved residues on these motifs.

We show a strong conservation of the IHO1 among vertebrates (Fig. 6c). We will further analyse the sequence conservation in more distant species.

Point 4:

Reference needed when stating that IHO1 was not interacting with REC114 in previous biochemical assay in the discussion part.

This will be corrected

Point 5:

Also, have you run AlphaFold that gives multiple models? Sometimes, with short motifs, 1 or 2 models of several models give good confidence for the interaction.

Using in-house Alphafold installation producing 25 models did not reveal better models.

Reviewer #3 (Significance (Required)):

While most of the interactions between REC114 and MEI4 or IHO1 were established with Y2H or other biochemical assays before. This paper used the AlphaFold, and finally verified the findings with biochemical experiments, which helps to establish the exact motifs/residues involved in the interaction. For example, the MEI4-REC114 interfaces are novel, more interestingly, the IHO1 shares the same interface with ANKRD31 and TOPOVIBL. Thus, this finding of REC114-MEI4-IHO1 complex assembly would be interesting to people with different working areas. I would like to see more studies on the coordination IHO1 with ANKRD31 or TOPOVIBL in the future.

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Referee #2

Evidence, reproducibility and clarity

This manuscript addresses the structure of the REC114-MEI4-IHO1 complex, which controls the essential process of programmed DSB induction by SPO11/TOPOVIBL in meiosis.

The manuscript carefully combines biochemistry, biophysics and modelling in an integrative manner to report the architecture of the full REC114-MEI4-IHO1 complex that is not itself amenable to direct structure elucidation such as by X-ray crystallography. These are important results that will be of interest to the recombination and meiosis fields. The data are generally convincing and interpretations appear correct, so the manuscript is certainly suitable for publication. I have included some suggestions below that I believe would strengthen the manuscript and enhance our confidence in the findings. Whilst the manuscript is publishable in its current format, I believe the suggestions given below would make it into a much stronger paper.

I have two general suggestions:

1. Analyses have been performed on fusion proteins (His, His-MBP etc). we have previously observed that bulky tags such as MBP can interfere with oligomeric state through steric hindrance, and that His-tags can mediated formation of larger oligomers, seemingly through coordination of metals leached from IMAC purification. This latter point has also been observed by others https://www.sciencedirect.com/science/article/pii/S1047847722000946. Where possible, I would repeat SEC-MALS experiments using untagged proteins, or at least following incubation with EDTA to mitigate the potential for His-mediated oligomerisation.
2. The authors have relied upon mutagenesis to validate Alphafold2 models. Their results are convincing but only confirm that contacts involved in structures rather than the specific fold per se. Their finding would be greatly strengthen by collecting SEC-SAXS data and fitting models directly to the scattering data. This is extremely sensitive, so a close fit provides the best possible evidence of accuracy of the model. SAXS is affected by unstructured regions and tags, so would have to be performed using structural cores of untagged proteins rather than full-length constructs. Given the local availability of world-class SAXS beamlines at the ESRF, which is next door to the leading author's institute, it seems that the collection of SAXS data would be practical for them.

My specific comments are below:

Figure 1d The SEC-MALS shows multiple species, with 2:1 and 4:2 representing a minority of total species present. What are the larger oligomers? Could these be an artefactual consequence of the His-tags (as described above)?

Figure 1f,g The AUC changes over concentration and pH are intriguing - have they tried MALS in these conditions? This would be much more informative as it would reveal the range of species present. Low concentrations could be analysed by peak position even if scattering is insufficient to provide interpretable MW fits. I would advise doing this without his tag or adding EDTA (as described above).

Figure 2 I would like to see the models validated by SAXS using minimum core untagged constructs. This could be sued to test the validity of the 2:1 model directly, and to model the 4:2 complex by multiphase analysis and/or docking together of 2:1 complexes. The hydrophobic LALALAII region of MEI4 is interesting and the mutagenesis data do agree with the model. However, it is important to point out that any decent model would place this hydrophobic helix in the core of the complex, and so would be disrupted by mutagenesis. Hence, the mutagenesis results confirm that the hydrophobic helix is critical for the interaction, but does not confirm that the specific alphafold model is more valid than any other model in which the helix is similarly in a core position.

Figure 3 This would also benefit from SAXS validation of the structural core. The mutagenesis here provides convincing evidence in favour of the structure as specific hydrophobics ether disrupt or have no effect, exactly as predicted. Hence, their data strongly support the dimer model. As this provides the framework for the 2:1 complex, these data make me far more confident of the previous 2:1 model in figure 2. I am wondering whether it would be better to present these data first such that the reader can see the 2:1 model being built upon these strong foundations?

Figure 4 The MALS data convincingly show formation of a tetramer. How do we know that it is parallel? The truncation supports this but coiled-coils do sometimes form alternative structures when truncated (e.g. anti-parallel can become parallel when sequence is removed), and alphafold seems to have a tendency of predicting parallel coiled-coils even when the true structure of anti-parallel (informal observation by us and others). A simple test would be to make a tethered dimer of 110-240, with a short flexible linker between two copies of the same sequence - if parallel it should form a tetramer of double the length, if anti-parallel it should form a dimer of the same length - determinable by MALS (and SAXS).

Figures 5/6 The interaction is clear albeit low affinity (but within the biologically interesting range). It would be nice to obtain MALS (using superose 6) data showing the stoichiometry of the complex - are the data too noisy to be interpretable owing to dissociation? The alpahfold model and mutagenesis data strongly support the conclusion that the IHO1 N-term binds to the PH domain, as presented.

Referees cross commenting

Just to clarify a couple of points regarding consultation comments from reviewer 1:

The suggestion regarding tags was mostly directed to the cases in which MALS data are noisy, with multiple oligomeric species (such as figure 1d). In these cases, i wondered whether the large MW species may be artefactual and could be resolved by removal of the tags. In cases where oligomers agree with those reported by other labs, I agree that there's no need to explore these further.

In terms of SAXS, I agree that fitting models into envelopes will not distinguish between similar folds. However, fitting models directly to raw scattering data is extremely sensitive and I have never seen good fits with low chi2 values for incorrect models (even when very similar in overall shape to the correct structure).

Significance

The manuscript carefully combines biochemistry, biophysics and modelling in an integrative manner to report the architecture of the full REC114-MEI4-IHO1 complex that is not itself amenable to direct structure elucidation such as by X-ray crystallography. These are important results that will be of interest to the recombination and meiosis fields. The data are generally convincing and interpretations appear correct, so the manuscript is certainly suitable for publication. I have included some suggestions below that I believe would strengthen the manuscript and enhance our confidence in the findings. Whilst the manuscript is publishable in its current format, I believe the suggestions given below would make it into a much stronger paper.

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

Learn more at Review Commons

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Referee #1

Evidence, reproducibility and clarity

Summary:

The formation of meiotic double-stranded DNA breaks is the starting point of meiotic recombination. DNA breaks are made by the topoisomerase-like SPO11, which interacts with a number of regulatory factors including REC114, MEI4 and IHO1. Despite the key role this process has in the continuation, and genetic variation, or eukaryotic life, there is very little known about how this process is regulated. Laroussi et al make use of biochemical, biophysical and structural biological approaches to extensively characterise the REC114-MEI4-IHO1 complex.

This is an outstanding biochemical paper. The experiments are well planned and beautifully executed. The protein purifications used are very clean, and the figures well presented. Importantly Laroussi et. al describe, and carefully characterise through point mutational analysis, the direct physical interaction between IHO1 and REC114-MEI4. This is an interaction that has, at least in yeast, previously been suggested to be driven by liquid-liquid separation. The careful and convincing work presented here represents an important paradigm-shift for the field.

I am fully supportive of publication of this excellent and important study.

Major comments:

My only major concern is regarding Figure 4, and specifically the AF2 model of the coiled-coil tetramer of IHO1. Given the ease with which MSAs of coiled-coils can become "contaminated" with non-orthologous sequences, I would urge some caution with this model. This is especially since the yeast ortholog of IHO1, Mer2, has been previously reported to be an anti-parallel tetramer (albeit, not very well supported by the data). The authors have several choices here. 1) They could simply reduce the visibility of the IHO1 tetramer model, and indicate caution in the parallel tetramer model. 2) They could consider using a structure prediction algorithm that doesn't use an MSA (e.g. ESMFold). 3) They could try to obtain experimental evidence for a parallel coiled-coil tetramer, e.g. through EM, SAXS or FRET approaches. I would like to make it crystal clear, however, that I would be very supportive of approach 1) or 2). An experimental approach is not necessary.

Assuming the authors don't take a wet-lab approach, this shouldn't take more than a couple of weeks.

Minor comments:

1. The observation that REC114 and MEI4 can also form a 4:2 complex is very interesting and potentially important. Did the authors also try to model this higher order complex in AF2?
2. Similarly to above, what does the prediction of the full-length REC114:MEI4 2:1 complex look like? Presumably the predicted interaction regions align well with experimental data, but it would be interesting to see (and easy to run).
3. Did the authors carry out SEC-MALS experiments on any IHO1 fragment lacking the coiled-coil domain? It was previously reported for Mer2 that the C-terminal region can form dimers, for example (OPTIONAL).
4. Given that full-length REC114 is used for the IHO1 interaction studies, do the authors have any data as to the stoichiometry of the REC114FL-MEI41-127 complex? (OPTIONAL)
5. Did the authors try AF2 modelling of the REC114-IHO1 interaction using orthologs from other species?

Referees cross commenting

I will add cross-comments to the comments of Reviewer #2

Firstly, the comments made by Reviewer #2 are technically correct. Firstly, reviewer #2 points out that the oligomerization states that the authors report could, in part, be artifactual the based on the his-tag purification method. This is indeed correct. However, given that none of the oligomerization states reported are per se unusual, given what is already known (including pre-prints from the Keeney and Claeys Bouuaert laboratories), I think the authors could forego this step.

Secondly, the use of an experimental structural method, such as SAXS, would certainly add value to the paper. Also Reviewer #2 is correct in pointing out the availability of the ESRF beamlines to the authors. However, while SAXS is a useful method, I personally consider the use of mutants to validate the interactions, an even stronger piece of evidence that the AlphaFold2 interactions are correct. I must disagree somewhat with Reviewer #2 with their argument that SAXS would validate the fold. Certainly if one of the AF2 predicted structures is radically wrong, then SAXS would produce scattering data, and a subsequent distance distribution plot that is incompatible with the AF2 model. However, a partly correct AF2 model, of roughly the right shape, might still fit into a SAXS envelope.

Reviewer #2 shares my concern on the parallel coiled-coil of IHO1, and their suggested solution is very elegant.

In my view, due to the time constraints imposed by the partially competing work from the the Keeney and Claeys Bouuaert laboratories (recently on biorxiv). I would support the authors if they chose the quickest route to publication.

Significance

General assessment: The strengths of the paper are as follows:

1. Quality of experiments - The proteins used have been properly purified (SEC) and properly handled. The experiments are carefully carried out and controlled.
2. Detail - The authors carry out the considerable effort of characterising protein interactions. through separation-of-function mutants. This adds to the quality of the paper, and renders the AF2 models as convincing as experimentally determined structures
3. Conceptual advances - The most important conceptual advance is the direct binding of the N-term of IHO1 to REC114. That this is the same region as used by both TOPOVIBL and ANKRD31 points to a complex regulation.
4. Integrity - the authors have taken great care not to "oversell" the results. The data are presented, and analysed, without hyperbole.

Limitations - The only limitation of the paper is the lack of in vivo experiments to test their findings. However given the time and effort required, and the pressing need to publish this exciting study, this is not a problem.

Advance: The paper provides advances from a number of directions, both conceptual and mechanistic. Mechanistically the description of the REC114-MEI14 complex is important, and in particular the observation that it can also form a higher order 4:2 structure. Likewise, while IHO1 was inferred to be a tetramer (based on work on Mer2) this was never proven formally. Most importantly, is the physical linkage between IHO1 and REC114, and that this is an interaction that is incompatible with TOPOVIBL and ANKRD31.

Audience: Given the central role of meiotic recombination in eukaryotic life, any studies that shed additional light on the regulation of meiosis are suitable for a broad audience. However, this subject paper will be more specifically of interest to the meiosis community. The elegant methodology will also be of interest to structural biologists and protein biochemists.