Oh no! I thought the flag meant post & accidentally reported your thoughtful feedback to moderators instead of replying. Hopefully they figure it out (no obvious way to contact them or "unflag"

Comment 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

Response Easy to add

Comment 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.

Response The proteins are untagged. For the UppS and BcrC the cell shortening occurs with addition of inducer, , so strong indication expression is occurring. A western would provide information about degree of overexpression, but we don’t think is necessary to support conclusion drawn. Do you think there is an alternative possibility that needs to be excluded? We note that in another preprint (https://www.biorxiv.org/content/10.1101/2022.02.03.479048v1) the authors delete the native uppS in their inducible Phy-uppS strain (Fig S4) and at 100 uM IPTG (10X less than what we used in experiment) the cells have wt growth on LB plates, so we at least know the Phy-uppS is functional and made (or they would die!). We are introducing the uppS deletion into our strain to see if we can identify a concentration of IPTG that doesn’t affect cell growth but still induces shortening.

For MraY, the result is negative, so you are spot on – it is impossible to tell if due to lack of overexpression from data shown. We only know the strain is correctly made from sequencing. We will investigate if there is an antibody or functional fusion available. The reason we were not sure was worth doing is because the MraY reaction is reversible (15131133). This means that without a phenotype, there is no simple way to know the reaction can even be pushed forward even if the overexpression is confirmed (more negative data). We actually overexpressed some other proteins that act downstream (MraY, MurJ, AmJ) and they were also negative for shortening. Probably we should remove the negative data or reword to make the caveats of the negative result clear.

Question 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?

Response The shortening is even faster than 10 min (not only statistically significant, but also obvious qualitatively if we mount immediately after adding Mg2+ ). We did not include the first timepoint because original purpose was to check everything was ready with microscope – did not expect shortening so fast! We can definitely add that data in. When we saw, we tried to capture the transition on pads, but going from culture to pad seems to stress the cells too much in the small window where the cool stuff happens. Since growth rate doesn’t appear to be a big factor in those initial divisions, we might be able to grow at lower temp and shift to pads for adjustment period before adding Mg2+. Did not play with it much due to lack of resources atm, but a flowcell setup would probably be best. In short, we think rapid divisions right after transition do not require transcription or translation. It really “smells” more like a biophysical thing.

Question Do you have any hypotheses what is most likely to be affected by magnesium? Do you think if the membrane may be affected?

Response We have a lot of hypotheses, but all speculative. There could be an extracytoplasmic enzyme involved in envelope synthesis is sensitive to Mg2+ availability, and that at lower concentrations, its activity is affected. There is some old literature with membrane preps that suggests PG synthesis requires higher Mg2+ than teichoic acid synthesis. If Und-P is limiting, higher Mg2+ may shift make the pool more available to make the septum. Tingfeng initially hypothesized there might be a receptor/signal mechanism but has not been able to identify one. Und-P seems to be important, but “availability” is not just pool, but how fast (and where!) the flipping across the membrane occurs. If Und-PP needs to be dephosphorylated to Und-P before being flipped back to cytoplasmic side, anything that effects the PP/Pi equilibrium would be predicted to affect the reaction rate, with lower Pi (in periplasm or pseudoperiplasm in case of G+) favoring the dephosphorylation. Cell wall associated Mg2+ could shift equilibrium to be more favorable for a Und-PP phosphatase more closely associated with the divisome. I could go all day… In short, we don’t know enough!

Question 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?

Response In the window we looked at, the elongation rate is constant (not less or more) and only the division frequency changes. Some bacteria (like Caulobacter and to lesser extent E. coli) clearly elongate and divide simultaneously, so model there is some competition for substrate (like Lipid II) makes sense. Septators like Bacillus seem to delineate the two processes more, though we have found conditions where even Bacillus invaginates during division, so it’s not absolute. Like eukaryotic cells, bacterial undoubtedly have mechanisms not only commit to a round of DNA replication when there is some signal that resources are sufficient. Clearly with some bacteria, this is not the case with cell division. The alternative would be that every cell cycle there is an opportunity to divide if some threshold of something(s) is reached. There is a hypothesis from Mtb literature that it may be GTP, but it’s not at all clear that is sufficient. In yeast, size at cell division is affected by perturbing 1-C pool.

Question 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!

Response It’s a great question – lots to do! We didn’t even look at another Gram-positive, but we plan to. It’s trickier to limit Mg2+ in Gram-negatives (see 27471053 – we tried Bsub homolog for those wondering – it’s not responsible for phenotype we see).

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

Response We have hypotheses, but if my (Jen’s @rosh_ba) twitter engagement is any indication, way too speculative for public consumption at present. Need acquire preliminary data/write grant.

Question 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?

Response Several proteins involved in envelope synthesis (like UppS) are Mg2+ dependent enzymes. But at least for intracellular players, levels of Mg2+ should be more than high enough to support enzyme activity (0.8 – 3.0 mM is Bsub range I recall off top of head). Could have impact extracytoplasmically by lowering pool sponged into the cell wall, but intuition (for what that is worth) is that it is not the coordination of an enzyme with a metal that is impacted rather the equilibrium with other ions like Pi and H+ and their impact on net ATP synthesis. Lots to think about and do, and no simple answers. When Tingfeng started project idea was to find mechanism – didn’t realize we were asking “How does the cell work?” Turned out to be a bit much for a dissertation project :)

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?

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Reply to the reviewers

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

This paper provides a detailed step by step protocol of the CUT&RUN technique, which enables high-resolution chromatin mapping and probing, adapted to the malaria parasite Plasmodium falciparum. In particular, Kafsack and colleagues apply the CUT&RUN protocol to infected red blood cells from in-vitro culture and obtain very good quality genome-wide profiles of two histone modifications, H3K4me3 and H3K9me3. The results are congruent with previous ChIP-seq data with a substantial improvement in terms of coverage and chip-to-input noise. The protocol is very detailed and the figures are great.

Major comments: 1. Authors successfully adapted the CUT&RUN protocol in P. falciparum. First, the binding profiles obtained by CUT&RUN for H3K4me3 and H3K9me3 are very similar to those reported by previous ChIP-seq studies. Secondly, by down-sampling 4X and 16X the test samples, authors demonstrate that 1M PE reads of sequencing depth would be enough to obtain accurate profiling of these histone modifications.

Despite this data is convincing, only one region in chr. 8 is shown as an example in figures 2, 3 and 4.

Different regions should be included, at least as supplementary figures, to reinforce their conclusions.

__Response: __We chose that locus on chromosome 8 to provide a gene-level resolution view at a locus encompassing genes in both eu- and heterochromatin states. We have now included Supplementary Figure 1, which shows these tracks for full length chromosomes 4 and 7. Additionally, genome-wide enrichment tracks for all data sets in this study are available for download at NCBI Gene Expression Omnibus under accession number GSE210062.

Related to this, there is evidence of the impact of chromatin structure on ChIP-seq analysis. Specifically, heterochromatin is typically depleted in ChIP input controls because of technical and experimental issues and this can result in a false enrichment of heterochromatic regions in the tested sample. How represented is heterochromatin (i.e. sub-telomeric and telomeric regions) in the test and control samples using the cut&run protocol?

__Response: __The reviewer is correct that chromatin structure may alter accessibility which may bias absolute measurements but since the accessibility biases based to chromatin-structure are identical for both the histone PTM-specific antibody and the isotype control and cancel out in the enrichment score.

How biased is the cut&run sample compare to the ChIP-seq sample?

__Response: __We have included this in Supplementary Figure 1. The H3K4me3 and H3K9me3 enrichment scores are strongly correlated both between CUT&RUN replicates and between CUT&RUN and previously published ChIP-seq results.

In this sense, it would be desirable if authors provide more information about the quality analysis results, for example the chip to input signal ratio and the coverage for heterochromatic (telomeric, centromeric and subtelomeric) regions.

__Response: __We agree that this would be of interest to the reader. For this reason, the full genome-wide enrichment tracks were made available for all datasets in this study. We have added language to draw further attention to this availability.

Additionally, loci typically biased in ChIP-seq samples, i.e. clonally variant gene families in sub-telomeric regions, should be shown as examples.

__Response: __We chose that locus on chromosome 8 to provide a gene-level resolution view at a locus encompassing genes in both eu- and heterochromatin states, including 2 var genes (PF3D7_0808600 and PF3D7_0808700) and two rifin genes (PF3D7_0808800 and PF3D7_0808900). We have now included Supplementary Figure 1, which shows these tracks for full length chromosomes 4 and 7, which also include subtelomeric and non-subtelomeric heterochromatin loci containing these genes. Additionally, genome-wide enrichment tracks for all data sets in this study are available for download at NCBI Gene Expression Omnibus under accession number GSE210062.

1. For P. falciparum WGS a PCR-free library preparation is strongly recommended. We wonder if it would be possible to try to integrate this step in their CUT&RUN protocol.

__Response: __Since such biases are sequence dependent, they would impact raw coverage but cancel out in the enrichment plots since the sequence-based biases are identical in both samples. While PCR-free amplification may be desirable for some applications, we feel this is outside the scope of this study to implement these changes.

It would have been desirable to have tried the CUT&RUN protocol on other type of proteins, different to hPTMs which are highly abundant, for example one of the Pf Api-AP2 transcription factors. Assaying the CUT&RUN protocol on a different type of protein shouldn't be cost/time consuming and would provide evidence of the versatility of the approach.

Response: As indicated by the title, this protocol was optimized specifically for profiling of histone modifications. CUT&RUN has been used in other systems to profile genome-wide binding of other proteins but this was not our aim and outside the scope of this study.

1. The step by step protocol is very detailed, however there are some parts that need to be better explained:

In the section "Binding cells to Concanavalin A-coated beads": it's not mentioned the harvest time and the stage of the parasites used. In addition, several methods are proposed for iRBCs enrichment, but is not mentioned which method was used and the life stage of the parasites. In this part of the protocol authors state "resuspend cells containing 1-5x107 nuclei to a cell density to 1x107 cells/mL".

According to our calculations, to guarantee this nuclei number it would be necessary to enrich in iRBCs and late stages. Otherwise the red blood cells density should be much larger. Could you please clarify this point?

Response: For this study we enriched for trophozoites using a percoll/sorbitol density gradient, which we have now clarified in step 8. However, whether and which enrichment strategy is employed will vary based on the desired parasite stages and experimental design.

In the section "P. falciparum culturing and synchronization of erythrocytic stages" the authors indicate that the method used for synchronization was double-synchronization with sorbitol treatment to achieve a {plus minus} 6 h synchrony. The details provided appear insufficient to replicate the procedure. E.g. it's not explained how the double step synchronization was performed and for how long the culture was incubated after the synchronization.

The number of parasite cells and the life-stage used is mentioned at the end (in the section of expected outcomes). It would be more useful if this information is specified at the beginning together with the most appropriate procedure to get an iRBC culture well synchronised and enriched in late stages.

Response: The stage, synchrony and growth conditions are determined by the scientific question the experimenter is asking, not by the assay. For this reason, we provide the number of infected erythrocytes and nuclei used in our studies so that other experimenters can aim for similar numbers regardless of the stage and synchrony. For this study we used asexual blood-stages at 36±4 hp.i. We have clarified this in step 11.

• With regards to reproducibility, all experiments were done in replicate (3 Rs) and the statistics appear adequate.

Minor comments: - Abstract. A closing bracket is missing.

Response: Corrected - Step 11: Split each sample into 1mL aliquots at ?

Response: Corrected

• The affinity of proteins A/G to IgG antibodies varies based on host species and IgG subtype (see link). This link does not seem to work

Response: Corrected

• Low bind tubes are mentioned several times. Please clarify whether it refers to low bind protein or low bind DNA. Step 77.

Response: The vendor and catalog number for the low-bind tubes are specified in the Reagents, Materials & Equipment list.

Which was the desired sequencing depth per library? It could be mentioned here. It is mentioned later in "Quantification and statistical analysis" that the initial desired depth was of 40M read pairs, but what was the real depth obtained? from the Figure 4 seems to be less than 17M read pairs per sample.

Response: Thank you for catching that error. The target was 10M read pairs per library but since CUT&RUN is so specific the isotype controls release less DNA and the resulting libraries produces fewer clusters than aimed for leading to slight over sequencing of the remaining samples.

• Step 79. Please clarify/justify why 50 bp paired-end reads were chosen as sequence length. Response: After excluding the telomere repeats 100 bp (50+50) are sufficient for uniquely mapping 98.3% of the nuclear. Paired-end sequencing was chosen over single-end because it provides the actual size of each fragment.

In the section "Quantification and statistical analysis", references to Figure 3 and 4 are inverted or do not correspond with the actual figures 3 and 4.

Response: Corrected

• Figure 2. Among the replicates, sample 2 seems to have higher background, could you comment why? Response: It is inherent in biological replicates that one would have the greatest amount of noise but we unfortunately have no further insight into why Sample 2 had a higher elevated background that Samples 1 and 3. Furthermore, even with this slightly higher background the relative enrichment of signal to noise ratio enrichment peaks are readily identifiable.

• Below some suggestions that may help the authors improve the presentation of their data and conclusions:

The limitations and potential shortcomings of the protocol are mentioned along the text (e.g. the use of different antibodies, different targets, weak interactions..), but could be good if they are included in a different section, preferably at the end.

Response: A “Limitations” section was added.

Also in this section it would be good if they develop further (or at least speculate) on the differences in the protocol or things to consider if other type of proteins are assayed (i.e. TFs).

Response: As mentioned above, we have not applied to CUT&RUN to profile chromatin other than Histone PTMs, as this was not the aim of our study. Since chromatin-bound histones always occur within a nucleosomal context, we are hesitant to make claims to the utility of this specific protocol for profiling DNA-binding proteins with smaller DNA-binding footprints. That said, CUT&RUN has been used to great success in other systems to profile a wide range of chromatin-bound proteins. We have included mention of this at the end of the introduction.

Authors should better comment on the potential impact of chromatin structure and DNA sequence (i.e. AT richness) on the biased representation of heterochromatic regions in the data, the level of background and the peak calling analysis.

Response: For the enrichment scores, sequence and accessibility biases cancel out since they are the same for both the PTM-specific antibody and the isotype controls.

The coverage of critical loci, like those belonging to clonally variant gene families, should be calculated and examples of tracks included as supplemental figures.

Response: Gene Expression Omnibus under accession number GSE210062 as indicated in the Quantification & Statistical Analysis and data availability sections.

Authors claim that the CUT&RUN protocol has exceptionally low background and has been successfully used to profile chromatin interactions from very small numbers of cells. But it is not specified how many. That is, which is the standard in other fields and how it compares with the number of cells used here.

Response: As stated in the note following step 10, we did not optimize the minimum number of parasites required in this study since at the 1e7 iRBC required for each sample correspond as little as 1mL of bloodstage culture 2% parasitemia and 5% hematocrit. The down-sampling analysis in figure 3 suggests that the number of input cells can likely be reduced at least 10-fold.

Information about synchronisation, estimation of iRBCs density and nuclear content appears insufficiently described and has been fragmented in different sections so it is difficult to replicate. For example, within the section "Binding cells to Concanavalin A-coated beads" different alternative protocols for iRBCs synchronisation and enrichment are mentioned but it is not clear whether authors actually perform that step. It could be convenient to describe it and include it in the step-by-step protocol.

Response: The stage, synchrony and growth conditions are determined by the scientific question the experimenter is asking, not by the assay. For this reason, we provide the number of infected erythrocytes and nuclei used in our studies so that other experimenters can aim for similar numbers regardless of the stage and synchrony. For this study we used asexual blood-stages at 36±4 hp.i. We have clarified this in step 11.

Significance (Required) The CUT&RUN is a novel technique to profile chromatin modifications genome-wide that has been successfully adapted to P. falciparum by the authors. This technique overcomes important limitations of the traditional ChIP-seq and provides better quality data. First, fewer cells and lower sequencing depths are required which is fundamental for the analysis of certain parasite life stages. Second, the binding step is carried out in-situ using unfixed and intact cells. This allows to avoid crosslinking, which can interfere with target recognition that results in unspecific background, and also avoids the random fragmentation of the chromatin, that can bias in the analysis.

This work is significant since it represents the first CUT&RUN step by step protocol adapted to P. falciparum. The results are important for researchers from the malaria field and parasitologists in general who could eventually leverage this protocol to other Apicomplexa.

Our expertise is on transcriptional regulation, molecular parasitology, genomics and epigenomics, of malaria parasites. We hope the comments above will help the authors to improve the ms. Congratulations on the work.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): ____ In general the paper is very clear and convincing and I have only minor comments for the authors to address.

Introduction The authors state that 'crosslinking presents another challenge as it can interfere with antibody recognition.' Would it be possible to provide a reference to strengthen this statement? Response: Additional references (Baranello et al, O’Neill et al) were added.

In the third paragraph the authors mention that CUT&RUN can be used to profile chromatin interactions from very small numbers of cells. This argument would be strengthened by adding references or examples from mammalian systems, and the authors might mention that the slight modification CUT&TAG has been employed for single cell sequencing. https://doi.org/10.1038/s41587-021-00865-z.

Response: The reference was added.

Figure 2 A label of Relative fold enrichment should be added to the y axis. This applies also to Figure 4. In the legend, it isn't entirely clear from what control the fold enrichment is being generated. Based on the other figures I assume it's the isotype control, and it would be helpful to state that in the legend.

Response: Thank you for the suggestion. We have made these changes.

Figure 3 An HP1 ChIP is included but there is no track for HP1 using CUT&RUN. It isn't entirely clear to me why HP1 is included; is it to make the point that it overlaps with H3K9me3? There is a sentence at the end of the Quantifcation and statistical analysis section that indicates that an HP1 CUT&RUN experiment was performed ('Representative tracks of H3K9me3, H3K4me3, and HP1 produced using CUT&RUN or ChIPseq at chromosome 8 of P. faliciparum are shown in Figure 4), but I don't see an HP1 track for Figure 4 and I don't see CUT&RUN HP1 tracks on Figure 3.

Response: Correct, no HP1 CUT&RUN was performed, we are just trying to show that H3K9me3 CUT&RUN recapitulates ChIP-seq of both H3K9me3 and HP1, which binds to H3K9me3.

Figure 4 The downsampling of reads is a nice demonstration that low numbers of reads are required for the CUT&RUN technique. It might be helpful to include downsampling of ChIP-seq reads within this figure to compare the two techniques more directly.

Response: The reason we included the down-sampling of CUT&RUN sequence reads was to explore whether were over-sequencing our CUT&RUN libraries not to provide a comparison to ChIP-seq. For simplicity we have therefore kept the figure as is.

DNA purification by Phenol/Chloroform extraction In step 38, I noticed that RNAse was not added at this step, as described in the original paper by Skene et al. Can the authors make a brief note about why they omit this reagent?

Response: RNAse A is already present since it was included in the STOP buffer at step 35.

The authors mark the TE buffer in bold, but I don't see a description of its makeup in the buffer section, though possibly I missed it. While this is a pretty standard buffer, it might still be nice to include it for completeness.

Response: TE buffer recipe was added.

Clean-up of PCR amplified library Between step 70 and 71 the authors include a warning to not discard the beads. However, this warning is not included in the Post-ligation Clean-up, which involves much the same procedure. Response: Corrected

Typos and writing It might be helpful to define CUT&RUN in the abstract by spelling out the acronym there. Response: It is defined in the 3rd paragraph of the introduction

I've mostly seen ChIP-seq with a dash between the IP and the seq.

Response: Corrected

Powerful is used twice in consecutive sentences in the first paragraph of the introduction. Consider substituting the words 'important tool' for 'powerful tool.' Response: Corrected

Figure 2 legend. “(purple) in of three biological replicates” should be “(purple) in three biological replicates” Response: Corrected

Figure 3 legend Last sentence should include 'to' between the words 'shown' and 'the'. Response: Corrected

Post-ligation Clean-up “Wash twice with 200µl of 80% Ethanol freshly prepared” should be “Wash twice with 200µl of freshly prepared 80% Ethanol” Response: Corrected

Library PCR amplification “Fragments are PCR amplified using Kapa polymerase, which it is more efficient” should be “Fragments are PCR amplified using Kapa polymerase, which is more efficient” Response: Corrected

Figure 7 legend “Indicated in the tope left panel” Should be “Indicated in the top left panel” Response: Corrected

Expected outcomes “to dismiss any sort of contamination” should be “To dismiss contamination” Response: Corrected

Potential Solution After the sentence 'Incubation buffer is added.' The next letter 'i' should be capitalized in the word Isolate. Response: Corrected

CROSS-CONSULTATION COMMENTS Plasmodium is not my model organism, so I'd defer to Reviewer 2 on the comments regarding additional detail for synchronization and Plasmodium culture conditions. I have nothing further to add, and I'm excited to see what experiments come from the addition of this technique to the parasite field.

Reviewer #3 (Significance (Required)):

This excellent methods paper describes a detailed protocol for the adaptation of the Cleavage Under Targets & Release Using Nuclease (CUT&RUN) technique to Plasmodium falciparum, the causative agent of malaria. CUT&RUN is an alternative to ChIP-seq, and has the advantage that it does not require crosslinking of targets, which can introduce artifacts and cause issues with antibody recognition. CUT&RUN can also be performed with low numbers of cells and has an excellent signal to noise ratio, which the authors demonstrate by downsampling the number of reads used in their analysis. The authors also clearly demonstrate that profiling of histone modifications using CUT&RUN yields comparable results to ChIP-seq. Because it can be difficult to obtain large numbers of cells from Plasmodium cultures, CUT&RUN is especially useful in this important model system. Publication of a detailed protocol will help other Plasmodium researchers answer important questions regarding genomic localization for their targets of interest.

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Referee #3

Evidence, reproducibility and clarity

In general the paper is very clear and convincing and I have only minor comments for the authors to address.

Introduction

The authors state that 'crosslinking presents another challenge as it can interfere with antibody recognition.' Would it be possible to provide a reference to strengthen this statement?

In the third paragraph the authors mention that CUT&RUN can be used to profile chromatin interactions from very small numbers of cells. This argument would be strengthened by adding references or examples from mammalian systems, and the authors might mention that the slight modification CUT&TAG has been employed for single cell sequencing. https://doi.org/10.1038/s41587-021-00865-z.

Figure 2

A label of Relative fold enrichment should be added to the y axis. This applies also to Figure 4. In the legend, it isn't entirely clear from what control the fold enrichment is being generated. Based on the other figures I assume it's the isotype control, and it would be helpful to state that in the legend.

Figure 3

An HP1 ChIP is included but there is no track for HP1 using CUT&RUN. It isn't entirely clear to me why HP1 is included; is it to make the point that it overlaps with H3K9me3? There is a sentence at the end of the Quantifcation and statistical analysis section that indicates that an HP1 CUT&RUN experiment was performed ('Representative tracks of H3K9me3, H3K4me3, and HP1 produced using CUT&RUN or ChIPseq at chromosome 8 of P. faliciparum are shown in Figure 4), but I don't see an HP1 track for Figure 4 and I don't see CUT&RUN HP1 tracks on Figure 3.

Figure 4

The downsampling of reads is a nice demonstration that low numbers of reads are required for the CUT&RUN technique. It might be helpful to include downsampling of ChIP-seq reads within this figure to compare the two techniques more directly.

DNA purification by Phenol/Chloroform extraction In step 38, I noticed that RNAse was not added at this step, as described in the original paper by Skene et al. Can the authors make a brief note about why they omit this reagent?

The authors mark the TE buffer in bold, but I don't see a description of its makeup in the buffer section, though possibly I missed it. While this is a pretty standard buffer, it might still be nice to include it for completeness.

Clean-up of PCR amplified library

Between step 70 and 71 the authors include a warning to not discard the beads. However, this warning is not included in the Post-ligation Clean-up, which involves much the same procedure.

Typos and writing

It might be helpful to define CUT&RUN in the abstract by spelling out the acronym there.

I've mostly seen ChIP-seq with a dash between the IP and the seq.

Powerful is used twice in consecutive sentences in the first paragraph of the introduction. Consider substituting the words 'important tool' for 'powerful tool.'

Figure 2 legend.

(purple) in of three biological replicates

Should be

(purple) in three biological replicates

Figure 3 legend Last sentence should include 'to' between the words 'shown' and 'the'.

Post-ligation Clean-up Wash twice with 200µl of 80% Ethanol freshly prepared

Should be

Wash twice with 200µl of freshly prepared 80% Ethanol

Library PCR amplification Fragments are PCR amplified using Kapa polymerase, which it is more efficient

Should be

Fragments are PCR amplified using Kapa polymerase, which is more efficient

Figure 7 legend Indicated in the tope left panel

Should be

Indicated in the top left panel

Expected outcomes to dismiss any sort of contamination

Should be

To dismiss contamination

Potential Solution After the sentence 'Incubation buffer is added.' The next letter 'i' should be capitalized in the word Isolate.

CROSS-CONSULTATION COMMENTS

Plasmodium is not my model organism, so I'd defer to Reviewer 2 on the comments regarding additional detail for synchronization and Plasmodium culture conditions. I have nothing further to add, and I'm excited to see what experiments come from the addition of this technique to the parasite field.

Significance

This excellent methods paper describes a detailed protocol for the adaptation of the Cleavage Under Targets & Release Using Nuclease (CUT&RUN) technique to Plasmodium falciparum, the causative agent of malaria. CUT&RUN is an alternative to ChIP-seq, and has the advantage that it does not require crosslinking of targets, which can introduce artifacts and cause issues with antibody recognition. CUT&RUN can also be performed with low numbers of cells and has an excellent signal to noise ratio, which the authors demonstrate by downsampling the number of reads used in their analysis. The authors also clearly demonstrate that profiling of histone modifications using CUT&RUN yields comparable results to ChIP-seq. Because it can be difficult to obtain large numbers of cells from Plasmodium cultures, CUT&RUN is especially useful in this important model system. Publication of a detailed protocol will help other Plasmodium researchers answer important questions regarding genomic localization for their targets of interest.

Reviewer #2 (Public Review):

Horton et al combined computational and functional approaches to identify a role for a mouse transposable element (TE) family in the transcriptional response to interferon gamma (IFNG, also known as type II interferon). This paper builds on previous work, some of which was done by the corresponding author, in which TE families have been shown to contribute transcription factor binding sites to genes in a species-specific manner. In the current work, the authors analyzed datasets from mouse primary macrophages that had been stimulated by IFNG to identify TEs that might contribute to the transcriptional response to IFNG treatment. In addition to previously identified endogenous retrovirus subfamilies, the authors identified sites from another TE family, B2_Mm2, that they found contained STAT1 transcription factor binding sites and whose binding by STAT1 was induced following IFNG stimulation. To test the hypothesis that a B2_Mm2 element was providing IFNG-inducibility to an associated gene, the authors chose one of the 699 mouse genes that had nearby (<50 kb) B2_Mm2 elements and was upregulated upon IFNG treatment in previous datasets. The gene they chose was Dicer1, which also is upregulated by IFNG in mouse macrophages but not in human primary macrophages, furthering the hypothesis that the presence of B2_Mm2 in mouse cells may provide IFNG-inducibility to Dicer1. Following KO of a ~500 bp region in two separate clones of immortalized mouse macrophages, the authors show a decrease in basal as well as IFNG-induced expression of Dicer1, providing support for their conclusion that a B2_Mm2 is important for IFNG-inducibility. The authors further show that two nearby genes that are also upregulated by IFNG, Serpina3f and Serpina3g, are also reduced at basal conditions as well as when stimulated with IFNG. The authors use these data to suggest that additional elements in the B2_Mm2 element in the Dicer1 gene, possibly CTCF elements, are have long distance effects on transcription of nearby genes.

Overall, this is an interesting and well written manuscript. The computational conclusions are supported by their data and add to the growing field of TEs and their role in transcription regulatory network evolution. While the authors do a good job of experimentally validating one example, inclusion of additional data, all of which they already have, as detailed below would substantially increase the applicability of their work and strengthen their conclusions about the broad role of TEs in the IFNG response in mice versus other species.

1) Following their genome-wide comparisons, the authors hone in on Dicer1 as an interesting example in which they hypothesize that a B2_Mm2 element near the Dicer1 gene could be contributing to the fact that this gene is upregulated by IFNG in mouse cells but not human cells. What would be very useful to the readers of this paper is knowing how many other examples there might be like this one. Adding DEseq values from human RNAseq data the authors already use (current references 10 and/or 37) for identifiable human orthologs to Table S7 would thus strengthen their conclusions. If Dicer1 is unique in this aspect of having (a) a nearby B2_Mm2 element and (b) a binary difference between inducibility in mouse versus human cells, that is interesting. If Dicer1 is not unique, that strengthens the authors' assertion that B2_Mm2 insertions have altered the transcriptional network in a host-specific manner. Either way, the answer is interesting, but without including this analysis, the authors leave out an important aspect of their work and it remains unclear how generalizable their conclusions are.

2) The results with Serpina3g and Serpina3F gene expression in the authors' knockout cells are very interesting. However, the authors focus almost exclusively on Serpina3g and Serpina3F, which makes it difficult to understand what is happening genome wide. Are other IFNG-induced genes (including those not on chromosome 12) similarly affected at the level of basal or induced transcription? How many genes are different in WT versus KO cells, both at basal and induced states? Does this correlate with their CUT&TAG data shown in Fig. 5? By focusing only on nearby genes (Serpina3g and Serpina3F), the authors hint that this may be a long range regulatory effect, "potentially mediated by the CTCF binding activity of the element" that they removed. But by only focusing on two nearby IFNG-induced genes, their data do not rule out the (also potentially quite interesting) possibility that there may be a more indirect role for this TE site or Dicer1 in basal transcription of IFNG-induced genes or IFNG-mediated gene expression. Providing more data on other genes throughout the genome in WT and KO cells, which the authors have generated but do not include in the manuscript, would help distinguish between these models. While a broader effect of these KOs on IFNG expression, or gene expression in general, would not fit as neatly with their model for local gene regulation, these analyses are needed to understand the effects of TE insertion on gene regulation.

Reviewer #3 (Public Review):

First of all, I enjoyed the manuscript by Horton et al. In the manuscript, they first re-analyzed published ChIP-seq data for STAT1 binding in INF-activated macrophages and found that a fourth of the >20,000 STAT1 binding sites were in transposable elements. Especially, about 10% of the total STAT1 binding sites were in B2_Mm2, a murine-specific SINE. They showed that these B2 elements are associated with H3K27ac signal upon INF treatment, thus likely serve as an INF-inducible enhancer through STAT1 binding. The authors then focus on the STAT1-bound B2_Mm2 in the Dicer1 gene (designated as B2_Mm2.Dicer1), and demonstrated that deletion of this B2 in a macrophage-like murine cell line resulted in loss of STAT1 binding, H3K27ac, and Dicer1 upregulation upon INF treatment. Their findings suggest that B2 transposition events has altered the transcriptional regulatory network in the innate immune response in the mouse.

The manuscript is well organized, and the findings are potentially interesting in terms of the evolution of species-specific regulatory networks of the innate immune response. But, I am not convinced with the enhancer role of the B2_Mm2.Dicer1 copy for the Dicer1 expression (see below).

Major Comments:

(1) In Fig. 4, the degree of Dicer1 induction by INF was small (1.2-fold or so), and accordingly the effect of the B2 deletion on the Dicer1 induction was also small. In addition, this B2 binds to CTCF, and its deletion should also eliminate CTCF binding. Therefore, it is difficult to conclude from the presented data that this B2 serve as an enhancer for Dicer1. The B2 may increase the frequency of transcription (as suggested by the authors), may serve as an obstacle for transcriptional elongation (via binding to CTCF), or may regulate the splicing efficiency. In Fig.5C, promoter acetylation level does not seem to be affected in KO1. Pol II either does not seem to be affected if the Pol II peak is compared to the background level. Taken together, the enhancer role is not supported by strong evidence.

(2) On the other hand, the authors discovered that the B2 deletion resulted in the decrease of Serpina3h, Serpina3g, Serpina3i and Serpina3f by >100-fold, which are 500 kb apart from the B2 locus. This is also interesting, and could be evidence for the B2 enhancer. Given that this B2 binds to both STAT1 and CTCF, the locus could interact with the Serpina3 locus to act as an enhancer. Were there STAT1 CUT&TAG peaks around the Serpina3 genes? Did H3K27ac and Pol II ChIP peaks in the Serpina3 promoters disappear in the KO cells? It would be interesting to see the IGV snapshots for H3K27ac, POLR2A and STAT1 ChIP-seq data around Serpina3 genes. In addition, HiC data for activated macrophages, if available, could be supportive evidence for the interaction between B2_Mm2.Dicer1 and the Serpina3 locus.

Minor Comments:

(3) Regarding Fig.1C, the authors calculated the B2 expression levels by mRNA-seq and DESeq2 analysis. But it does not accurately give the B2 transcription level, because the method does not discriminate B2 RNAs and B2-containing mRNA (and lncRNA as well). I wonder that the apparent upregulation of STAT1-binding B2 loci is due to the increase of Pol II transcription around the loci, rather than Pol III-mediated B2 transcription. This possibility should be discussed in page 6 after "Taken together, these data indicate that thousands of B2_Mm2 elements show epigenetic and transcriptional evidence of IFNG-inducible regulatory activity in primary murine bone marrow derived macrophages."

(4) Fig. 2B shows that about 70-80% of B2_Mm2 loci carry the STAT1 motif, whereas only a limited number (2-3%) of B2_Mm2 bind to STAT1. Is this because of differences in their motif sequences, in genomic locations, or in epigenomic environments? For example, do these STAT1-binding loci have a C-to-A mutation at the second last position in the GAS motif (TTCNNGGAA), like B2_Mm2.Dicer1 (shown in Fig. S4)? Can the authors discuss about it? In addition, although the consensus sequence of B2_mm2 has a GAS motif with only a single mismatch, the presence of the STAT1 motif in >70% of B2_Mm2 is surprising, given that their average divergence to the consensus sequence is about 10% (ref. 26 of the manuscript). Is the binding site significantly conserved in compare to the other regions of the B2 sequence?

I would echo Bob's sentiment here and would recommend you keep that material like this in a separate section or box all together.

If it helps to have an example, in 2006, Hawk Sugano showed off a version of a method you may be considering which broadly went under the title of Pile of Index Cards (or PoIC) which combined zettelkasten and productivity systems (in his case getting things done or GTD). I don't think he got much (any?!) useful affordances out of mixing the two. In fact, from what I can see looking at later iterations of his work and how he used it, it almost seems like he spent more time and energy later attempting to separate and rearrange them to get use out of the knowledge portions as distinct from the productivity portions.

I've generally seen people mixing these ideas in the digital space usually to their detriment as well—a practice I call zettelkasten overreach.

Another method with questionable accuracy. A big question that arises is how can you tell different fish apart from one another without a tag and release system?

Reviewer #1 (Public Review):

In this paper, the authors use purified Xenopus γ-TuRCs and experiments in cell extract combined with cutting edge imaging techniques to investigate whether binding of the γ-TuNA fragment can activate γ-TuRCs. The authors show that γ-TuNA fragments from both humans and Xenopus are obligate dimers and that dimerization is necessary for γ-TuRC binding. They further show, using direct visualisation of microtubule nucleation from individual purified γ-TuRCs, that γ-TuNA binding increases the nucleation efficiency of γ-TuRCs by ~20 fold, helping to overcome negative regulation by Strathmin.

γ-TuNA, otherwise known as the CM1 domain, CM1 motif or CM1 helix, is well conserved and found within the N-terminal region of proteins across evolution. These proteins bind and recruit γ-TuRCs to MTOCs, such as the centrosome, meaning that γ-TuRC recruitment and activation could be closely linked. Earlier studies had provided strong evidence that binding of γ-TuNA activated γ-TuRCs, hence the name "γ-TuRC mediated nucleation activator" (Choi et al., 2010), and this claim was supported by similar work a few years later (Muroyama et al. 2016). Moreover, several other studies showed that expressing in cells γ-TuNA, or equivalent protein fragments, led to ectopic microtubule nucleation in the cytoplasm, with some of the studies showing that mutations preventing the binding of these fragments to γ-TuRCs ablated this effect (Choi et al., 2010; Lynch et al., 2014; Hanafusa et al., 2015; Cota et al., 2016; Tovey et al., 2021). Collectively, therefore, it was accepted that binding of these fragments somehow activated γ-TuRCs. More recent data, however, including from the authors themselves, had provided evidence that γ-TuNA binding did not activate γ-TuRCs (Liu et al., 2019; Thawani et al., 2020). A major objective of this paper was therefore to help resolve this controversy. The author's data suggest that the ability of these fragments to activate γ-TuRCs depends upon the type and position of tag attached to the N-terminus of the γ-TuNA fragment, with large tags seemingly turning γ-TuNA into a γ-TuRC inhibitor (although they also note that one of the previous studies, which concluded γ-TuNA was an activator, had also used fragments with large N-terminal tags). The authors also insist that the new results benefit from a much-improved γ-TuRC purification protocol that results in higher yield and purity. This purification approach uses the affinity of the γ-TuNA fragment and so could be adopted by others in the field.

The major strength of this paper is directly showing, using very powerful single molecule imaging and their improved protocols, that γ-TuNA is a γ-TuRC activator, thus resolving the controversy that has existed for the last few years. The weakness is that we still don't learn how γ-TuNA binding activates γ-TuRCs (this has been proposed to be via structural changes but other mechanisms can be considered), and thus there is little conceptual advance from the original Choi et al. 2010 paper, which had already concluded that γ-TuNA binding increased the nucleation efficiency of γ-TuRCs. Moreover, the authors do not include experiments with the other proposed γ-TuRC activator, XMAP215, which they have investigated previously (Thawani et al., 2020), and so we are left wondering whether γ-TuNA and XMAP215 work together or as part of separate activation pathways.

Overall, this paper is timely as it finally resolves the controversy over γ-TuNA and it is admirable that the authors are willing to directly address and correct their previous conclusion. The data is solid and well-presented and the text is clear and has appropriate citations. In my opinion, papers that clarify the literature are just as important as those that make conceptual advances.

Reviewer #2 (Public Review):

This is the first report that establishes gamma-TuNA as an activator of gamma-TuRC-dependent microtubule-nucleation, using purified components. This is an in-depth study that establishes experimental conditions under which gamma-TuNA can function as an activator (dimerization of gamma-TuNA, appropriately sized N-terminal tag) and clarifies why similar attempts to study gamma-TuNA have failed in the past. I think that the information in this manuscript will be of immense value to the scientific community, as it resolves a long-standing mystery concerning the function of gamma-TuNA. A key question that still remains unanswered is whether the gamma-TuNA-dependent activation mechanism involves a conformational change of the gamma-TuRC, from an asymmetric to a ring-shaped template structure, but this may be beyond the scope of the present submission.

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I think you could have used a <footer>tag instead of creating a footer class.

Similar comments - I would use unordered lists and paragraph elements where necessary!

Also, I found there is an < address > tag you could use. Learn more: https://www.w3schools.com/tags/tag_address.asp

Introduction ends

Reviewer 4. Christina Lynggaard

This manuscript assesses the variation in arthropod communities in three ecoregions in Canada. The study is well done, and the sampling was very thorough with a big sampling effort. I only have minor comments. Specially I consider that the aim can be focused on the ecoregions instead of the feasibility of the method, as this has already been shown. In addition, it would be nice to have more details in certain sections in the data analyses and in the results. I have addressed these comments below. -I am not sure why the title "Message in a bottle". -Line 65- Could you specify which indicator species have been targeted? Or cite studies that target those species? - Line 96- Based on the limitations of the ecoregions, it is not clear why ecoregions are an obvious candidate. -In line 104 seems that your aim is to demonstrate how feasible is to use metabarcoding for large-scale monitoring and that you use the ecoregions to prove that. However, showing the feasibility of this method for large-scale studies has already been done (e.g. Svenningsen et al 2021, Detecting flying insects using car nets and DNA metabarcoding; Bush et al 2020, DNA metabarcoding reveals metacommunity dynamics in a threatened boreal wetland wilderness). I suggest keeping it focused on the need to apply this method in different ecoregions. -In the Data description section, you mention that you examined phylogenetic diversity, but in the Analyses section you vaguely mention it. The phylogenetic diversity findings are discussed later on, but it is difficult to follow the discussion when the results were not presented previously. In addition, the authors use the findings in phylogenetic diversity to support the idea of a structure in the ecoregions, so I suggest making more emphasis in this in the results section. -Line 189. I agree that the higher number of BINs could be due to eDNA, but couldn't another reason be that the BINs were oversplit during data analysis? -Line 215-217. Has this been found previously in other studies using Malaise trap? If so, please reference to those findings. -Line222- This is a brief discussion about temporal turnover. However, these results are not presented previously, or at least not clearly enough. -Line 266-267- Yes, you showed compositional shifts using metabarcoding in bulk arthropod samples, but the way this sentence is structured it sounds like you are the first to show this. Compositional shifts in arthropods have been shown previously in other studies using metabarcoding. -Line 321- Did you have negative PCR controls? In line 326 you mention negative controls, but I assume you refer to the extraction negative controls. -Line 340- It is not clear why you queried the data against a bacterial library. -Line 348- What was the reason for choosing "at least three reads"? and the same for line 350 where you cluster sequences with a minimum of 5 reads per cluster. -Line 357- If you see tag switching in your negative controls that means that most likely you have it in the rest of the data. How did you ensure that the rest of the data did not have that? You may have tags switching in sequences not found in the negative controls but found in your samples. -Line 369- As you used the Bray-Curtis index in this metabarcoding data, did you convert your data to presence/absence? It is known that for metabarcoding data the use of read numbers for community analysis is not adequate (see Nichols et al 2018 "Minimizing polymerase biases in metabarcoding") .

• SEE

already installed

Done !

les tags ne prend pas en compte le majuscule et le minuscule

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

Thank you for giving us the opportunity to submit a revised draft of the manuscript “Tup1 is Required for Transcriptional Repression Necessary in Quiescence in S. cerevisiae” to Review Commons. We appreciate the time and effort that you and the other reviewers dedicated to providing feedback on our manuscript and are grateful for the insightful comments on and valuable improvements to our paper. We believe that the experiments suggested in these comments would bring clarity to the manuscript, and wish that we had the ability to perform them all. Unfortunately our lab is closing, and the only remaining lab member is the PI, so we are only able to perform limited experiments to address some of the concerns raised during review. We have incorporated several changes in response to comments from the reviewers. Those changes are highlighted within the manuscript. Please see below, in blue, for a point-by-point response to the reviewers’ comments and concerns. All page numbers refer to the revised manuscript Word file with tracked changes.

Of particular note is the discussion of cellular morphology of the tup1∆ and sds3∆ strains. We realize that our findings are purely descriptive, and are not surprised that all three reviewers had comments on this data. This was the source of much discussion among the authors and consultation with other labs; we debated even including these observations in the manuscript, since we were unable to figure out the underlying mechanism. Ultimately we decided that it was worth reporting in case other labs may benefit from the knowledge, and we have altered the language in the manuscript (page 6) to better reflect this. However, if the reviewers feel that this observation would be better left out of the manuscript, we would be willing to remove Figure 6 and any discussion of these images.

Reviewer #1 Comments:1. The authors chose to examine 3-day SP cells to interrogate quiescence because tup1∆ cells are highly flocculant, interfering with the isolation of purified quiescent cells. These cells are a mixture of both nonquiescent and quiescent cells, so it is not correct to state that they represent a quiescent cell population. The addition of EDTA to the gradients used to isolate quiescent cells could eliminate flocculation and permit the isolation of quiescent cells. EDTA is also often added to media in low amounts to reduce flocculation. The authors need to indicate the proportion of quiescent cells in their SP cultures by applying these tools.

We appreciate the suggestion, but the phenotype of this strain is not typical flocculation (see photo below, also added to the paper as Supplementary Figure 1). We did add EDTA (pH 8.0) to a final concentration of 10 mM to two separate tup1∆ and it did not visibly affect the clumping of cells. Furthermore, changes to the cell wall are a distinct feature of quiescent S. cerevisiae and contribute to the ability to separate different cell types by density-gradient centrifugation, so it is difficult to anticipate how EDTA would affect our ability to isolate Q cells. We have provided more explanation in the manuscript to better explain this (page 3).

1. The authors reported that while Xpb1 and Tup1 share many overlapping binding sites, but that Xbp1 does not regulate Tup1's binding. What other factors might be responsible for their shared binding? Could histone deacetylation play a role? This could be addressed by a Tup1 ChIP in an sds3∆ mutant.

This is a good thought; histone acetylation levels may have a role in regulating Tup1 localization and we would have liked to address this if we had more time. Unfortunately, we had some difficulty performing ChIP of Tup1, because initially we used a FLAG tag which caused a phenotype similar to deletion of Tup1, and had to switch to making myc-tagged strains. This delay meant we did not have time to pursue creating myc-tagged Tup1 in an sds3∆ strain, and now we do not have the ability to follow up on this for revisions.

1. Has PolII occupancy been examined in Log vs SP cells of tup1∆ to determine if Tup1 inhibits PolII association with its genes that are repressed ?

We did not look at PolII occupancy in our Tup1 deletion, and could not find any existing datasets with this information. It is our hope that another lab is able to carry out this experiment, because it could be very enlightening, but it is beyond the scope of this work.

1. The observation that tup1∆ cells have several nuclear puncta is intriguing, although the cytological images need to be improved.

The nuclear puncta we see in the tup1 deletion are definitely a puzzle. We had limited time to investigate this phenomena, and in discussing the matter with some other labs it seemed doubtful that more advanced imaging would yield anything of use to us. We realized that we accidentally omitted important details for this figure and have updated the manuscript to add them. We imaged 2 biological replicates for each strain and imaged many yeast samples for each strain (which has been added to the caption for Figure 6) and found that our findings were statistically significant (p

Reviewer #2 Comments1. The authors acknowledge that it would be better to work with purified quiescent cells but couldn't isolate pure populations. As a result, a mixture of quiescent and nonquiescent cells are analyzed in stationary phase. They say this is because Tup1 deletion strains are flocculent. But they performed ChIP-Seq on Myc-tagged Tup1 strain. Don't these cells express Tup1? If not, could this be performed in wild-type yeast with Myc-tagged Tup1? It seems important to separate quiescent from nonquiescent yeast for the authors' conclusions.

It is true that we could have done ChIP-seq for Tup1 in purified Q cells. We considered it, but decided to look at the mixed population so that we could directly compare our RNA-seq results from the tup1∆ strain. It’s a balance between having some results that are specific to quiescence, versus being able to directly compare the effects of deletion of Tup1 at the sites where it binds. We are now unable to perform this experiment, but we have updated the language in the manuscript (page 3) to better reflect this choice.

1. The Chipseq data in Fig 1B do not have a y axis and it is consequently not clear whether these data are normalized and shown with the same axis.

Thank you for pointing this out - these data are normalized to RPKM during processing, and we have updated the caption for figure 1 and the methods on page 10 to reflect this information. Normalizing the data in IGB itself, however, causes an adjustment in the y-axis that makes the tracks appear to be inconsistent. In any case, we are not making claims about the relative amount of signal, and as it is common in the field to not include y-axes on IGB tracks, we have opted to keep the y-axis for Figure 1B as-is.

1. In Fig 2, it seems important to determine how many genes are different between WT and Tup1 deletion strains in log phase. Are just as many genes different? Or is Tup1 more important in diauxic shift and stationary phase than log phase?

We did intend to focus only on diauxic shift and stationary phase data for this paper, since there has already been so much work on the role of Tup1 in log phase. As mentioned above, comparisons of RNA between log and DS/Q is difficult. We attempted to find a publicly available dataset to perform some analysis for revisions, but unfortunately most previous work on the effect of Tup1 on transcription was performed via tiling arrays, which is not comparable.

1. Are the genes that are regulated by Tup1 normally regulated during diauxic shift or stationary phase compared with log growth?

Because there is a massive global decrease in the level of total RNA in diauxic shift and quiescence (McKnight, Boerma, et al., 2015) it is impossible to directly compare transcript levels between these states in our experiments. If there was time, we could have attempted to repeat these experiments with an external spike-in control; this is potentially something another lab could do to follow up on our findings.

1. What fraction of the genes that are differentially expressed in Tup1 knockout yeast have Tup1 binding at the promoter? Enhancer? What fraction can be explained by Tup1, Hap1, Nrg1, Mig1 individually and together?

We have added the number of genes that are differentially expressed in Tup1 knockout yeast during DS to the manuscript (page 3). Regarding enhancers, the genome of S. cerevisiae is very compact, and there is not evidence of long-distance activation of genes as seen in metazoans (Dujon, 1996; Dobi & Winston, 2007; Spiegel and Arnone, 2021). Upstream activating sequences (UASs) are generally considered the closest equivalent to enhancers in cerevisiae, and they tend to function within a few hundred base pairs of the promoter. Our analysis only identifies the nearest gene; it would be difficult to parse out locations in the promoter versus a UAS without a more advanced analysis that is beyond our capabilities now.

As for the effect of Hap1, Nrg1, and Mig1, we were able to look for their motifs in the genes that are differentially expressed in the Tup1 knockout but we do not have binding data for these factors in quiescence or stationary phase so it is impossible to conclusively state what role those TFs play. This would be a very interesting followup to our work, but is outside the scope of this manuscript.

References:

Dujon, B. 1996. The Yeast Genome Project: What did we learn? Trends Genet. 12, 263-270.

Dobi, K.C.; Winston, F. 2007. Analysis of Transcriptional Activation at a Distance in Saccharomyces cerevisiae. Mol Cell Biol. 27(15), 5575-5586. https://doi.org/10.1128/MCB.00459-007

Spiegel, JA; Arnone, J.T. 2021.Transcription at a Distance in the Budding Yeast Saccharomyces cerevisiae. Appl. Microbiol. 1(1), 142-149. https://doi.org/10.3390/applmicrobiol1010011

1. The methodology used to generate the gene ontology enrichments should be described in the methods.

Thank you for noticing this omission; we have added the relevant information to the manuscript (page 10) and have also added the related citation (page 11).

1. The authors should provide genomewide data to support the statement that Tup1 and Rpd3 ChIP datasets have substantial overlap. They should also provide genomewide data to support the statement that there is substantial overlap between Rpd3 and Tup1. How much overlap is observed and how much is expected by chance?

We have compared the existing ChIP data for Rpd3 binding in quiescent cells to our ChIP data for Tup1 in 3-day cultures and included this in the manuscript (page 4, Supplementary Figure 2B), along with a p-value.

1. For Sds3, similar to Tup1 inactivation, it would be helpful to know how many genes change in with Sds3 inactivation in log phase in addition to diauxic shift and stationary phase.

As with our response to comment #3, we focused only on diauxic shift and stationary phase data for this paper, and analysis of this data would be difficult without a spike-in control. While there are some existing datasets for RNA-seq of Rpd3 knockouts, this would include both Rpd3L and Rpd3S activity, rather than just Rpd3L, which is our focus with the Sds3 deletion strains. As such, we did not perform RNA-seq of sds3∆ in log phase.

1. If the argument is that Sds3 and Xbp1 cooperate with Tup1 to affect gene expression, testing the gene expression changes that are associated with Tup1 in Sds3 or Xbp1 knockout strains would help the authors make this point.

We do not have tup1∆/sds3∆ or tup1∆/xbp1∆ double knockout strains. We attempted to make these strains but could not, which may indicate that these double deletions are synthetic lethal. Deletion of sds3 alone causes a significant reduction in growth rate, so it is perhaps not surprising that we could not create the double knockouts.

1. The final phenotype of extra DAPI positive blobs in the nucleus is not very specific or clear.

We agree, please see our comments at the top of this letter.

Reviewer #3 (Major comments):Did tup1∆/sds3∆ double mutant show the same phenotype with tup1∆ (or sds3∆) single mutant in G0? If Tup1 actually plays role in tandem with Sds3 in the gene regulation during G0, the epistatic relationship might be estimated.

We do not have tup1∆/sds3∆ or tup1∆/xbp1∆ double knockout strains. We attempted to make these strains but could not, which may indicate that these double deletions are synthetic lethal. Deletion of sds3 alone causes a significant reduction in growth rate, so it is perhaps not surprising that we could not create the double knockouts.

The histone acetylation was not synergistically augmented in the above double mutant?

Please see the response above.

The authors showed that tup1∆ but not sds3∆ cells contain multiple DAPI signals but sds3∆ cells show abnormal cell shape in G0 phase. These phenotypic abnormalities in these mutants suggest a potential mitotic defect. Both mutants showed very similar abnormalities in H3K23 acetylation and gene expressions in quiescent state. Why these showed distinctly different abnormality in cell morphology during G0?

Unfortunately we were unable to investigate this further.

Did iswi2∆ cells also show abnormality in G0 phase?

No, they did not; thank you for asking, this is a good question. We have added this information to the manuscript (page 6).

(Minor comments)Supplementary figure1. This data seems to be very important. I recommend to use this data in the main figure with statistical analysis (p-values) to show the significant overlap of Tup1 and Rdp3 distribution.

We have compared the existing ChIP data for Rpd3 binding in quiescent cells to our ChIP data for Tup1 in 3-day cultures and included this in the manuscript (page 4, Supplementary Figure 2B) . We do feel that this data belong in the supplement, however, because the data is not exactly equivalent to our studies: quiescent cells and 3-day cultures are not the same, and knockout of Rpd3 eliminates function of both Rpd3L and Rpd3S complexes, while knocking out Sds3 targets only the Rpd3L complex.

Figure 4. Histone acetylation level data in Figure 4A and the data for gene repressions by Tup1 and Sds3 in Figure 4C seem to be very important. However, statistical analysis data (p-values) was not presented. Please show the statistical analysis data (p-values) as in figure 3 to show that the Tup1 and Sds3 contribute similarly in histone deacetylation and repression. The author did not find the significant changes of histone deacetylation in xbp1∆ cells but said that when filtered in Xbp1 binding motif Xbp1 depletion has similar effect on the acetylation level. Please show this data.

We have added language in the manuscript comparing genes with altered acetylation levels to those that are differentially expressed in our RNA-seq datasets, along with a p-value, to page 5.

https://www.reddit.com/r/antinet/comments/xyhpq4/2015_exhibition_of_roland_barthes_zettelkasten/

Johanna Daniel has some interesting reflections (in French) about Barthes' reading, note taking, and writing processes: http://johannadaniel.fr/isidoreganesh/2015/06/archives-roland-barthes/ Perhaps most importantly she's got some photos from an exhibition of his work which includes portions of his note cards and writing. Roland Barthes' note cards on the Sorrows of Young Werther by Goethe. And you thought your makeshift cardboard boxes weren't "enough"? Want more about Barthes' practice? Try my digital notes: https://hypothes.is/users/chrisaldrich?q=tag%3A%22Roland+Barthes%22

I'm pretty sure you can add this to your style.css page as it's considered a css tag?. I think this is mixing html & css into one sheet which can become confusing.

The BR tag is already self closing, therefore you don't need to have a closing tag. A BR tag by it's self is perfectly fine.

p tag!

p tag, It'll still load properly without but it's needed.

The fillable pdf IDs are found in the url for each form on the fillable pdf page

This linked article was super interesting to me. I had never heard of the term "clicktivism" before but it made a lot of sense to hear that, because many online seem to feel like helping things to trend is a notable part of activism, and this in turn can lead to behavior paltforms find suspicious. It's true that it does have its share of impact- causes and people in need can reach more people than was ever possible thanks to the internet- but it has also led to some odd cultural quirks online when it comes to giving such issues a platform. Occasionally the goal to "boost awareness" or publicly declare yourself aligned with the "right" or "correct" cause can overpower the actual execution of concrete, meaningful action. The term "performative activism" also encompasses this phenomenon, in which people publicly post a lot about a tag or trend to sort of assure their followers they too are on the right side of affairs. Accounts that are clearly mining activist circles for engagement (even if they are helping to spread awareness of issues) can definitely act similarly to bots or spammers.

🅐 제목: 약관의 제목을 입력합니다. (한국어와 영문 모두 필수)
  한국어 약관 제목은 한글, 영문, 숫자, 공백, 키보드에서 직접 입력 가능한 특수문자만 지원(최대 45자)
  영어 약관 제목은 영문, 숫자, 공백, 키보드에서 직접 입력 가능한 특수문자만 지원(최대 100자)
  영문 약관이 없더라도 영어 약관 제목은 영어로 입력, 영어 약관 제목은 사용자 기기의 언어 설정이 영어일 때 사용됨
🅑 URL: 약관 내용을 확인할 수 있는 웹 페이지 URL을 입력합니다. ('약관 조건'이 [필수 동의 항목]인 경우 필수)
🅒 조건: 서비스 약관의 조건을 선택합니다.
  [필수 동의 항목]: 가입 시 반드시 동의해야 하는 서비스 약관
  [선택 동의 항목]: 서비스 제공 범위에 따라 사용자가 추가 동의할 수 있는 서비스 약관
🅓 TAG: 서비스 약관을 가리키는 고유값입니다. (필수)
  아래 카카오 API 요청 시 태그 사용
    동의한 약관 확인하기
    약관 선택해 동의 받기
  실제 서비스 개발 시 사용 가능한 값으로 입력
  영문, 숫자, 하이픈(-), 언더스코어(_)만 입력 가능
  ${KEYWORD}_${DATE}, ${KEYWORD}_${ORDER} 등 형식 권장(예: terms_20220701, terms_01)

Review coordinated via ASAPbio’s crowd preprint review

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

---

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

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

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

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

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

Figure 3

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

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

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

I get what you're trying to do here with the "br" tag, but it falls apart when the window is resized. I think wrapping the content in an element and styling it with a max-width property would be better, as this would prevent it from become really long on wide displays while letting it resize dynamically on small displays

use root tag for style the full html file

closing tag should be use on same line.

That closing tag should be closed on the same line.

main tag is not used as it is written in instructions to use definitely .

main tag should be used as per instructions.

dl tag is good but in instructions they ask to use embedded style for remove the bullets .so read carefully all the instructions

Breaks make sense but this text is not surrounded by an element tag. Something like: <br>

Meet the Team

<br>

makes it possible to format that line.

Again, Maybe add a (p) tag as it's text.

I'm not to sure what happened here maybe it didn't go through when you committed? There's no (p) tag for the text here? there is for a couple of text's but some don't have anything? Key part!

Author Response

Reviewer #1 (Public Review):

In one of the most creative eDNA studies I have had the pleasure to review, the authors have taken advantage of an existing program several decades old to address whether insect declines are indeed occurring - an active area of discussion and debate within ecology. Here, they extracted arthropod environmental DNA (eDNA) from pulverized leaf samples collected from different tree species across different habitats. Their aim was to assess the arthropod community composition within the canopies of these trees during the time of collection to assess whether arthropod richness, diversity, and biomass were declining. By utilizing these leaf samples, the greatest shortcoming of assessing arthropod declines - the lack of historical data to compare to - was overcome, and strong timeseries evidence can now be used to inform the discussion. Through their use of eDNA metabarcoding, they were able to determine that richness was not declining, but there was evidence of beta diversity loss due to biotic homogenization occurring across different habitats. Furthermore, their application of qPCR to assess changes in eDNA copy number temporally and associate those changes with changes to arthropod biomass provided support to the argument that arthropod biomass is indeed declining. Taken together, these data add substantial weight to the current discussion regarding how arthropods are being affected in the Anthropocene.

Thank you very much for the positive assessment of our work.

I find the conclusions of the paper to be sound and mostly defensible, though there are some issues to take note of that may undermine these findings.

Firstly, I saw no explanation of the requisite controls for such an experiment. An experiment of this scale should have detailed explanations of the field/equipment controls, extraction controls, and PCR controls to ensure there are no contamination issues that would otherwise undermine the entirety of the study. At one point in the manuscript the presence of controls is mentioned just once, so I surmise they must exist. Trusting such results needs to be taken with caution until such evidence is clearly outlined. Furthermore, the plate layout which includes these controls would help assess the extent of tag-jumping, should the plate plan proposed in Taberlet et al., 2018 be adopted.

Second, without the presence of adequate controls, filtering schemes would be unable to determine whether there were contaminants and also be unable to remove them. This would also prevent samples from being filtered out should there be excessive levels of contamination present. Without such information, it makes it difficult to fully trust the data as presented.

Finally, there is insufficient detail regarding the decontamination procedures of equipment used to prepare the samples (e.g., the cryomil). Without clear explanations of the steps the authors took to ensure samples were handled and prepared correctly, there is yet more concern that there may be unseen problems with the dataset.

We are well aware of the potential issues and consequences of contamination in our work. However, we are also confident that our field and laboratory procedures adequately rule out these issues. We agree with the reviewer that we should expand more on our reasoning. Hence, we have now significantly expanded the Methods section outlining controls and sample purity, particularly under “Tree samples of the German Environmental Specimen Bank – Standardized time series samples stored at ultra-low temperatures” (lines 303-304), “Test for DNA carryover in the cryomill” (lines 448-464) and “Statistical analysis” (lines 570-575).

We ran negative control extractions as well as negative control PCRs with all samples. These controls were sequenced along with all samples and used to explore the effect of experimental contamination. With the exception of a few reads of abundant taxa, these controls were mostly clean. We report this in more detail now in the Methods under “Sequence analysis” (lines 570-575). This suggests that our data are free of experimental contamination or tag jumping issues.

We have also expanded on the avoidance of contamination in our field sampling protocols. The ESB has been set up for monitoring even the tiniest trace amounts of chemicals. Carryover between samples would render the samples useless. Hence, highly clean and standardized protocols are implemented. All samples are only collected with sterilized equipment under sterile conditions. Each piece of equipment is thoroughly decontaminated before sampling.

The cryomill is another potential source of cross-contamination. The mill is disassembled after each sample and thoroughly cleaned. Milled samples have already been tested for chemical carryover, and none was found. We have now added an additional analysis to rule out DNA carryover. We received the milling schedule of samples for the past years. Assuming samples get contaminated by carryover between milling runs, two consecutive samples should show signatures of this carryover. We tested this for singletaxon carryover as well as community-wide beta diversity, but did not find any signal of contamination. This gives us confidence that our samples are very pure. The results of this test are now reported in the manuscript (Suppl. Fig 12 & Suppl. Table 3).

Reviewer #2 (Public Review):

Krehenwinkel et al. investigated the long-term temporal dynamics of arthropod communities using environmental DNA (eDNA) remained in archived leave samples. The authors first developed a method to recover arthropod eDNA from archived leave samples and carefully tested whether the developed method could reasonably reveal the dynamics of arthropod communities where the leave samples originated. Then, using the eDNA method, the authors analyzed 30-year-long well-archived tree leaf samples in Germany and reconstructed the long-term temporal dynamics of arthropod communities associated with the tree species. The reconstructed time series includes several thousand arthropod species belonging to 23 orders, and the authors found interesting patterns in the time series. Contrary to some previous studies, the authors did not find widespread temporal α-diversity (OTU richness and haplotype diversity) declines. Instead, β-diversity among study sites gradually decreased, suggesting that the arthropod communities are more spatially homogenized in recent years. Overall, the authors suggested that the temporal dynamics of arthropod communities may be complex and involve changes in α- and β-diversity and demonstrated the usefulness of their unique eDNA-based approach.

Strengths:

The authors' idea that using eDNA remained in archived leave samples is unique and potentially applicable to other systems. For example, different types of specimens archived in museums may be utilized for reconstructing long-term community dynamics of other organisms, which would be beneficial for understanding and predicting ecosystem dynamics.

A great strength of this work is that the authors very carefully tested their method. For example, the authors tested the effects of powdered leaves input weights, sampling methods, storing methods, PCR primers, and days from last precipitation to sampling on the eDNA metabarcoding results. The results showed that the tested variables did not significantly impact the eDNA metabarcoding results, which convinced me that the proposed method reasonably recovers arthropod eDNA from the archived leaf samples. Furthermore, the authors developed a method that can separately quantify 18S DNA copy numbers of arthropods and plants, which enables the estimations of relative arthropod eDNA copy numbers. While most eDNA studies provide relative abundance only, the DNA copy numbers measured in this study provide valuable information on arthropod community dynamics.

Overall, the authors' idea is excellent, and I believe that the developed eDNA methodology reasonably reconstructed the long-term temporal dynamics of the target organisms, which are major strengths of this study.

Thank you very much for the positive assessment of our work.

Weaknesses:

Although this work has major strengths in the eDNA experimental part, there are concerns in DNA sequence processing and statistical analyses.

Statistical methods to analyze the temporal trend are too simplistic. The methods used in the study did not consider possible autocorrelation and other structures that the eDNA time series might have. It is well known that the applications of simple linear models to time series with autocorrelation structure incorrectly detect a "significant" temporal trend. For example, a linear model can often detect a significant trend even in a random walk time series.

We have now reanalyzed our data controlling for autocorrelation and for non-linear changes of abundance and recover no change to our results. We have added this information to the manuscript under “Statistical analysis” (lines 629-644).

Also, there are some issues regarding the DNA sequence analysis and the subsequent use of the results. For example, read abundance was used in the statistical model, but the read abundance cannot be a proxy for species abundance/biomass. Because the total 18S DNA copy numbers of arthropods were quantified in the study, multiplying the sequence-based relative abundance by the total 18S DNA copy numbers may produce a better proxy of the abundance of arthropods, and the use of such a better proxy would be more appropriate here. In addition, a coverage-based rarefaction enables a more rigorous comparison of diversity (OTU diversity or haplotype diversity) than the readbased rarefaction does.

We did not use read abundance as a proxy for abundance, but used our qPCR approach to measure relative copy number of arthropods. While there are biases to this (see our explanations above), the assay proved very reliable and robust. We thus believe it should indeed provide a rough estimate of biomass. As biomass is very commonly discussed in insect decline (in fact the first study on insect decline entirely relies on biomass; Hallmann et al. 2017), we feel it is important go include a proxy for this as well. However, we also discuss the alternative option that a turnover of diversity is affecting the measured biomass. A pattern of abundance loss for common species has been described in other works on insect decline.

We liked the reviewer’s suggestion to use copy number information to perform abundance-informed rarefaction. We have done this now and added an additional analysis rarefying by copy number/biomass. A parallel analysis using this newly rarefied table was done for the total diversity as well as single species abundance change. Details can be found in the Methods and Results section of the manuscript. However, the result essentially remains the same. Even abundance-informed rarefaction does not lead to a pattern of loss of species richness over time (see “Statistical analysis”).

The overall results are supporting a scenario of no overall loss of species richness over time, but a loss of abundance for common species. And we indeed see the pattern of declining abundance for once-common species in our data, for example the loss of the Green Silver-Line moth, once a very common species in beech canopy (Suppl. Fig. 10). We have added details on this to the Discussion (lines 254-260).

These points may significantly impact the conclusions of this work.

Reviewer #3 (Public Review):

The aim of Weber and colleagues' study was to generate arthropod environmental DNA extracted from a unique 30-year time series of deep-frozen leaf material sampled at 24 German sites, that represent four different land use types. Using this dataset, they explore how the arthropod community has changed through time in these sites, using both conventional metabarcoding to reconstruct the OTUs present, and a new qPCR assay developed to estimate the overall arthropod diversity on the collected material. Overall their results show that while no clear changes in alpha diversity are found, the βdiversity dropped significantly over time in many sites, most notable in the beech forests. Overall I believe their data supports these findings, and thus their conclusion that diversity is becoming homogenized through time is valid.

Thank you for the positive assessment.

While overall I do not doubt the general findings, I have a number of comments. Firstly while I agree this is a very nice study on a unique dataset - other temporal datasets of insects that were used for eDNA studies do exist, and perhaps it would be relevant to put the findings into context (or even the study design) of other work that has been done on such datasets. One example that jumps to my mind is Thomsen et al. 2015 https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2656.12452 but I am sure there are others.

We have expanded the introduction and discussion on this citing this among other studies now (lines 71-72, 276-278).

From a technical point of view, the conclusions of course rely on several assumptions, including (1) that the biomass assay is effective and (2) that the reconstructed levels of OTU diversity are accurate,

With regards to biomass although it is stated in the manuscript that "Relative eDNA copy number should be a predictor for relative biomass ", this is in fact only true if one assumes a number of things, e.g. there is a similar copy number of 18s rDNA per species, similar numbers of mtDNA per cell, a similar number of cells per individual species etc. In this regard, on the positive side, it is gratifying to see that the authors perform a validation assay on 7 mock controls, and these seem to indicate the assay works well. Given how critical this is, I recommend discussing the details of this a bit more, and why the authors are convinced the assay is effective in the main text so that the reader is able to fully decide if they are in agreement. However perhaps on the negative side, I am concerned about the strategy taken to perform the qPCR may have not been ideal. Specifically, the assay is based on nested PCR, where the authors first perform a 15cycle amplification, this product is purified, then put into a subsequent qPCR. Given how both PCR is notorious for introducing amplification biases in general (especially when performed on low levels of DNA), and the fact that nested PCRs are notoriously contamination prone - this approach seems to be asking for trouble. This raises the question - why not just do the qPCR directly on the extracts (one can still dilute the plant DNA 100x prior to qPCR if needed). Further, given the qPCRs were run in triplicate I think the full data (Ct values) for this should be released (as opposed to just stating in the paper that the average values were used). In this way, the readers will be able to judge how replicable the assay was - something I think is critical given how noisy the patterns in Fig S10 seem to be.

We agree with this point, and this is why we do not want to overstate the decline in copy number. This is an additional source of data next to genetic and species diversity. We have added to our discussion of turnover as another potential driver of copy number change (lines 257-260). We have also added text addressing the robustness of the mock community assay (lines 138-141).

However, we are confident of the reliability and robustness of our qPCR assay for the detection of relative arthropod copy number. We performed several validations and optimizations before using the assay. We have added additional details to the manuscript on this (see “Detection of relative arthropod DNA copy number using quantitative PCR”, lines 548-556). We got the idea for the nested qPCR from a study (Tran et al.) showing its high accuracy and reproducibility. We show that our assay has a very high replicability using triplicates of each qPCR, which we will now include in the supplementary data on Dryad. The SD of Ct values is very low (~ 0.1 on average). NTC were run with all qPCRs to rule out contamination as an issue in the experiments. We also find a very high efficiency of the assay. At dilutions far outside the observed copy number in our actual leaf data, we still find the assay to be accurate. We found very comparable abundance changes across our highly taxonomically diverse mock communities. This also suggests that abundance changes are a more likely explanation than simple turnover for the observed drop in copy number. A biomass loss for common species is well in line with recent reports on insect decline. We can also rely on several other mock community studies (Krehenwinkel et al. 2017 & 2019) where we used read abundance of 18S and found it to be a relatively good predictor of relative biomass.

The pattern in Fig. S10 is not really noisy. It just reflects typical population fluctuations for arthropods. Most arthropod taxa undergo very pronounced temporal abundance fluctuations between years.

Next, with regards to the observation that the results reveal an overall decrease in arthropod biomass over time: The authors suggest one alternate to their theory, that the dropping DNA copy number may reflect taxonomic turnover of species with different eDNA shedding rates. Could there be another potential explanation - simply be that leaves are getting denser/larger? Can this be ruled out in some way, e.g. via data on leaf mass through time for these trees? (From this dataset or indeed any other place).

This is a very good point. However, we can rule out this hypothesis, as the ESB performs intensive biometric data analysis. The average leaf weight and water content have not significantly changed in our sites. We have addressed this in the Methods section (see ”Tree samples of the German Environmental Specimen Bank – Standardized time series samples stored at ultra-low temperatures”, lines 308-311).

With regards to estimates of OTU/zOTU diversity. The authors state in the manuscript that zOTUs represent individual haplotypes, thus genetic variation within species. This is only true if they do not represent PCR and/or sequencing errors. Perhaps therefore they would be able to elaborate (for the non-computational/eDNA specialist reader) on why their sequence processing methods rule out this possibility? One very good bit of evidence would be that identical haplotypes for the individual species are found in the replicate PCRs. Or even between different extractions at single locations/timepoints.

We have repeated the analysis of genetic variation with much more stringent filtering criteria (see “Statistical analysis”, lines 611-615). Among other filtering steps, this also includes the use of only those zOTUs that occur in both technical replicates, as suggested by the reviewer. Another reason to make us believe we are dealing with true haplotypic variation here is that haplotypes show geographic variation. E.g., some haplotypes are more abundant in some sites than in others. NUMTS would consistently show a simple correlation in their abundance with the most abundant true haplotype.

With regards to the bigger picture, one thing I found very interesting from a technical point of view is that the authors explored how modifying the mass of plant material used in the extraction affects the overall results, and basically find that using more than 200mg provides no real advantage. In this regard, I draw the authors and readers attention to an excellent paper by Mata et al. (https://onlinelibrary.wiley.com/doi/full/10.1111/mec.14779) - where these authors compare the effect of increasing the amount of bat faeces used in a bat diet metabarcoding study, on the OTUs generated. Essentially Mata and colleagues report that as the amount of faeces increases, the rare taxa (e.g. those found at a low level in a single faeces) get lost - they are simply diluted out by the common taxa (e.g those in all faeces). In contrast, increasing biological replicates (in their case more individual faecal samples) increased diversity. I think these results are relevant in the context of the experiment described in this new manuscript, as they seem to show similar results - there is no benefit of considerably increasing the amount of leaf tissue used. And if so, this seems to point to a general principal of relevance to the design of metabarcoding studies, thus of likely wide interest.

Thank you for this interesting study, which we were not aware of before. The cryomilling is an extremely efficient approach to equally disperse even traces of chemicals in a sample. This has been established for trace chemicals early during the operation of the ESB, but also seems to hold true for eDNA in the samples. We have recently done more replication experiments from different ESB samples (different terrestrial and marine samples for different taxonomic groups) and find that replication of extraction does not provide much more benefit than replication of PCR. Even after 2 replicates, diversity approaches saturation. This can be seen in the plot below, which shows recovered eDNA diversity for different ESB samples and different taxonomic groups from 1-4 replicates. A single extract of a small volume contains DNA from nearly all taxa in the community. Rare taxa can be enriched with more PCR replicates.

Reviewer #1 (Public Review):

In one of the most creative eDNA studies I have had the pleasure to review, the authors have taken advantage of an existing program several decades old to address whether insect declines are indeed occurring - an active area of discussion and debate within ecology. Here, they extracted arthropod environmental DNA (eDNA) from pulverized leaf samples collected from different tree species across different habitats. Their aim was to assess the arthropod community composition within the canopies of these trees during the time of collection to assess whether arthropod richness, diversity, and biomass were declining. By utilizing these leaf samples, the greatest shortcoming of assessing arthropod declines - the lack of historical data to compare to - was overcome, and strong timeseries evidence can now be used to inform the discussion. Through their use of eDNA metabarcoding, they were able to determine that richness was not declining, but there was evidence of beta diversity loss due to biotic homogenization occurring across different habitats. Furthermore, their application of qPCR to assess changes in eDNA copy number temporally and associate those changes with changes to arthropod biomass provided support to the argument that arthropod biomass is indeed declining. Taken together, these data add substantial weight to the current discussion regarding how arthropods are being affected in the Anthropocene.

I find the conclusions of the paper to be sound and mostly defensible, though there are some issues to take note of that may undermine these findings.

Firstly, I saw no explanation of the requisite controls for such an experiment. An experiment of this scale should have detailed explanations of the field/equipment controls, extraction controls, and PCR controls to ensure there are no contamination issues that would otherwise undermine the entirety of the study. At one point in the manuscript the presence of controls is mentioned just once, so I surmise they must exist. Trusting such results needs to be taken with caution until such evidence is clearly outlined. Furthermore, the plate layout which includes these controls would help assess the extent of tag-jumping, should the plate plan proposed in Taberlet et al., 2018 be adopted.

Second, without the presence of adequate controls, filtering schemes would be unable to determine whether there were contaminants and also be unable to remove them. This would also prevent samples from being filtered out should there be excessive levels of contamination present. Without such information, it makes it difficult to fully trust the data as presented.

Finally, there is insufficient detail regarding the decontamination procedures of equipment used to prepare the samples (e.g., the cryomil). Without clear explanations of the steps the authors took to ensure samples were handled and prepared correctly, there is yet more concern that there may be unseen problems with the dataset.

interesting... I looked this up and (info coming from not the most reliable website) it seems as though this spelling may just have been one of Whitman's idiosyncracies. No real reason behind it, just a quirk of Whitman's

Clark, Brooke. “Walt Whitman – Wow – Canada!” Wow, https://wowcanada.wordpress.com/tag/walt-whitman/.

Review coordinated via ASAPbio’s crowd preprint review

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

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

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

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

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

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

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

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

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

Methods

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

.

Header tag can be used here. ex: <Header><h1>Assignment b</h1></Header>

• from : https://hypothes.is/a/YXVbMtv0EeyGCyNoHE3dvg

The DSL for discourse graph introduced here, describes an intertwingled associative complex of domain specific terms (in a meta graph as it were) that can be used to mark and name some discourse relevant aspect of what is being talked about.

A network of such terms (a tag bundle) are used here to name pages such that they combine discourse graoh DSL terms with 'concept handles'

TrailMark symmanthetic plain text mark in notation provides the means of articulating such DSLs as you write need to read it but at first glance it seems that you are using these specific discours graph types as qualifier of content and in fact part of the identity of a page, This is what we do with TrailMarks except that leave it for the user to extend the vocabulary. You may do that as well for all I know. And indeed these 'trailmarks' as we call them are like terms in an AST for a DSL

Hypothes.is & 📒ShrewdNotes Web Page Annotation

I'm blogging about how I'm using Hypothes.is as a starting point for documentation about 📒ShrewdNotes Web Page Annotation. Because I'm learning how to organize efficient workflows with 📒ShrewdNotes. Then I can be certain that my documentation is accurate for when I get more people involved.

So these are practical notes that guide me. In order to become reference material for anonymous learners, followers, subscribers, and members. Also, these notes can become a framework for Hypothes.is-style 📒ShrewdNotes that I can adapt for Sciwheel and other annotation services.

Hypothes.is Groups and Tags

I use Hypothes.is Groups as follows: * Public Group for Anonymous Learners. * 🗃ShrewdSubjects Groups. * My Private Group.

Public Group for Anonymous Learners

I make most of my Hypothes.is 📒ShrewdNotes in public. Mainly on my own websites. But increasingly whenever I find anything useful or interesting. Especially where I find good content relating to one of my 🗃ShrewdSubjects. So anyone can follow my Public 📒ShrewdNotes.

To recognize 🗃ShrewdSubjects, I use Hypothes.is Tags. Currently, these are Food, Gout, and Learning. Where Learning includes any topic not covered by the other subjects. In this way, as I recognize new subjects, I will separate them from Shrewd Learning.

Note that Public annotations for my subject tags include notes from all Hypothes.is users. So, tags are a great way to collect notes by everyone who is interested in a topic. Also, you can mix and match User, Group, and Tag searches. As well as using free-format search.

Additionally, I've started using Hypothes.is Tags to help me organize my workflows. Currently, I use 4, but I may extend this as I incorporate annotation into my daily life. My current 4 tags are: * New - denotes new content that I'm preparing. * Change - denotes changes I plan to make. * editing - means that I've saved the note that I'm currently working on. Or that I've forgotten to delete my editing tag! I use this to avoid losing text as I'm writing it, as Hypothes.is does not have autosave. Though there are ways round that. * BuildInPublic - read more at #BuildInPublic.

🗃ShrewdSubjects Groups

Though I like to BuildInPublic, I also like to collaborate. Now, collaboration is possible using public annotation. But I'm trying to create services built around teams. So I've created private groups for each 🗃ShrewdSubject.

Currently, joining these private Hypothes.is groups starts by subscribing to one or more newsletters: * Foodary Nexus Newsletter * GoutPal Links Newsletter * Shrewd Learning Newsletter

My Private Web Annotation Group

I try to do most of my content creation in public. Or in teams. So I only currently have one group for private notes. But Hypothes.is allows you to create as many private groups as you need. Which is useful where you want to keep notes separate. However, I recommend using tags unless you are sure you want separate groups of notes.

Research reports show that anger motivates people to react more than happiness and sadness. When people are angry, they will quickly spread information and discuss, and maintain a high mood. When a tag, such as # hasjustlanedyet, is searched many times, it will become a hot spot, which will further lead more curious people to search the tag.

This would be a great starter for my lesson on connotation/denotation. They will need to post a lot of words for that. A graffiti board would be great.

Chalk talk is in my plans this week, so I will give this strategy a try!

Embedded in the meta head tag.

you are missing the body tag that goes after this and before the H1 entry.

Fruktansvärt. Få tag på dessa galningar omedelbart.

If this was broken up into separate lines it might be a little easier to read and edit

Perhaps try putting the < small > element on a separate line.

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

Learn more at Review Commons

---

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): ____ *A significant criticism of the paper is an assumption that readers will be familiar with all of the findings in the author's previous 2016 paper and the PGL-1 papers by Aoki et al. Minimal context is given for each approach. *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

*Some conclusions are not well supported and require further analysis, proper controls, and more extensive descriptions of the experiments performed. *

We have addressed the reviewer’s concerns as detailed below.

Most importantly, the central conclusion and title of the paper is that composition can buffer the dynamics of individual proteins within liquid-like condensates. In other words, in vitro condensation assays often do not recapitulate LLPS behavior in vivo. That said, the findings in this study would be significantly strengthened and complemented by observing endogenously tagged PGL-3 and PGL-3 mutants in living worms, considering the efficiency of using CRISPR in C. elegans to insert tags and make precise mutations.

The original manuscript already contained data where we microinjected wild-type PGL-3 and mutant PGL-3 proteins (recombinantly purified) into adult C. elegans gonads to assay how the P granule phase supports diffusion of these proteins.

In the revised version, we now include additional data which shows “dynamics buffering” in transgenic worms generated using CRISPR/Cas9 technology. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

*To improve readability, the introduction to P granules should be expanded, and include the reasons for looking at the nematode-specific PGL-3 protein among all the other known P granule proteins. A recap of previous findings on PGL-3 phase separation, in vivo and in vitro, is warranted, starting with the significant results of Saha et al 2016. Setting up the investigative questions in the context of recent work on PGL-1 (Aoki, et al) is also necessary. *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

The physiological concentration of PGL-3 should be more transparent, including why some experiments in this study are done at physiological concentrations while others are not. Describing why salt concentrations, crowding agents, and protein abundance are similar or different for each experiment is necessary and relevant. For example, after showing in Figure 1 that PGL-3 protein phase separates, the paragraph starting on line 161 says that it was previously shown that PGL-3 doesn't phase separate at physiological concentrations without RNA. One has to go back to Figure 1 to realize it was done differently than Figure 2 and Saha 2016.

The concentrations of PGL-3 protein and use of crowding agents (if any) have already been specified within figures or figure legends. Salt concentrations used are specified within figure legends or materials and methods section.

We have added the following paragraph to the materials and methods section of the revised manuscript.

“Saha et al. 2016 showed that at physiological concentrations (approx. 1 mM), the PGL-3 protein is unable to phase separate into condensates. At these concentrations, mRNA promotes phase separation of PGL-3. To assay for mRNA-dependence of condensate assembly, it is therefore essential to use physiological concentrations of the PGL-3 protein or mutants (e.g. Figure 2). However, these condensates are generally too small to assay rate of internal rearrangement of PGL-3 molecules within condensates using fluorescence recovery after photobleaching experiments. Therefore, to generate large condensates for measuring internal rearrangement of PGL-3 or mutant molecules, we primarily used higher concentrations of these proteins where binding to RNA is not essential for phase separation. However, to mimic the in vivo P granule phase as closely as possible, we generally added constituent proteins in proportion to their in vivo abundance estimated in Saha et al. 2016.”

The added paragraph in the Introduction section of the revised manuscript may be helpful to the readers. * *

*Statements in the same paragraph like "in contrast to full-length PGL-3, mRNA does not support phase separation..." should be qualified by stating the concentration observed, with or without salts or other crowding agents. Similarly, line 230 "suggests that interactions involving the disordered C-terminal region of PGL-3 are not essential for the fast dynamics" and should be qualified with "at non-physiological concentrations and with XX crowding agents or salt concentration." It would be more consistent if physiological concentrations were consistent from figure to figure, as extra variables weaken some of the stated conclusions. *

We thank the reviewer for this suggestion. However, we feel the statements (without full experimental details within main text) help convey the conceptual essence of the findings better. Of course, all these statements contain reference to figures or prior publications which provide relevant details about experimental conditions.

*The 2010 review reference stating that there are 40 P granule enriched proteins is outdated. More recent reviews put the number much higher. This is relevant because the approach to put PGL-3 in a more physiological environment by including just PGL-1, GLH-1 and mRNA with the condensate assays, out of ~100 P granule enriched proteins, may not be sufficient to conclude "that the influence of complex composition on dynamics is modest" (line 223), or imply that the multicomponent nature of the P granule is reconstituted by adding these components (line 355). *

We revised the text to indicate that P granules contain approx. 70 proteins and added appropriate references.

• *

Based on current information of constitutive P granule components (PGL-1, PGL-3, GLH-1, GLH-2, GLH-3, GLH-4, DEPS-1, MIP-1 and mRNA), (Kawasaki et al, 1998, 2004; Spike et al, 2008a, 2008b; Price et al, 2021; Cipriani et al, 2021; Phillips & Updike, 2022) we reconstituted P granule-like phase in vitro with mRNA, PGL- and GLH- proteins that likely constitute the most abundant components within P granules in vivo (based on concentration estimates in Saha et al. 2016).

We do appreciate the reviewer’s comment that more components can be added to our in vitro reconstitution in addition to the limited set of components used in our study. However, we feel it is interesting to observe that a limited set of components can support dynamics buffering (the main message of the paper). Further, the complementary in vivo experiments show that the P granule phase can also support dynamics buffering.

*Figure 1C needs to include PGL-3(370-693) in the analysis. Figure 1E is also incomplete without a comparison of FRAP recovery between PGL-3(1-452) and full PGL-3 as the control.

*

Fig. 1c already includes data with PGL-3 (370-693) [top row, central panel]. FRAP recovery data with full-length PGL-3 is already available in Supplementary Fig. 2c, g.

*Figure 4C is missing an essential control where PGL-3 and S1 FRAP is performed without PGL-1, GLH-1, and mRNA. *

In the revised version, we have added Supplementary Fig. 5f, where FRAP recovery of the following condensates are plotted together: 1) PGL-3 alone, 2) S1 alone, 3) PGL-3 + PGL-1, GLH-1 and mRNA, 4) S1 + PGL-1, GLH-1 and mRNA.

*It would also help show sup Fig4A in the main figure to show concentration dependence. *

We revised Fig. 4 to address the reviewer’s suggestion.

Consider adding subtitles to supplementary figures.

We considered the suggestion but felt it may not be essential.

*M&M should include an explanation for statistical analysis *

We added a paragraph describing statistical analysis within the Materials and Methods section.

*CROSS-CONSULTATION COMMENTS I am also in agreement with the comments and critiques of reviewers 2 and 3.

* Reviewer #1 (Significance (Required)): The paper by Saha and colleagues investigate the in vitro liquid-liquid phase separation propensity of a P granule protein PGL-3 and its structural domains. The findings largely replicate and support the phase-separation properties of a paralogous protein called PGL-1, as recently described by Aoki et al. 2021. Furthermore, they show that the dynamics demonstrated by recombinant PGL-3 may be maintained or buffered by the complex composition of P granules.

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

*Jelenic et al. describe the effect of partner proteins on the FRAP dynamics of recombinant PGL-3 protein and variants in in vitro condensates and C elegans p-granules. The study shows that the N terminal a-helical dimerization domains is required for condensate formation and modulate of it alters aggregation and the FRAP dynamics of its condensates. Interestingly, a construct including the entire IDR region (370-693) by itself does not phase separate on its own at these conditions. The K126E K129E mutant (known previously to disrupt dimerization) and the deletion mutant abrogate llps. A mutant construct that shuffles the sequence in the region 423-453 called S1 here reduces the helicity and the condensate FRAP dynamics but recovered in the presence of a few P granule components. Also, the reduced dynamics of partially unfolded PGL-3 condensates are also rescued by the p-granule components to a certain degree of the unfolded PGL3 concentrations. This threshold concentration for recovering the condensate dynamics is further reduced in the helix reducing S1 mutant, which is also dependent on the number and the nature of P granule components.

Overall, the study aims to probe how "composition can buffer protein dynamics within liquid-like condensates" - yet several underlying aspects of the study do not fully support that conclusion. The introduction does not sufficiently introduce the known structural information of the two dimerization domains in C elegans PGL proteins for which structures are known. The region is discussed as "alpha helical" but really there are two evolutionarily conserved independently folding dimerization domains (referring to the mutants as "reduced alpha helicity" is not helpful - these are mutations that destabilize a folded domain).*

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

*Additionally, the abstract and introduction ignore the aspects of aggregation (touched on in discussion) - this is likely what the disruption to the helical region in residue 450 region is doing (the helix is not on the dimer interface based on homology / sequence identity to the crystal structure of PGL-1 central dimerization domain. *

We think elucidating the molecular mechanism of apparent aggregation of PGL-3 (D425-452) could be an interesting direction for future investigation. Here, we focused our analysis predominantly on the mutant S1 since it generates liquid-like condensates with ~20- fold slower dynamics (compared to wild-type) in contrast to non-dynamic condensates/aggregates. Therefore, influence of other P granule components on the dynamics of PGL-3 in liquid-like condensates is easier to address using the mutant S1 rather than PGL-3 (D425-452). We didn’t find evidence that S1 aggregates as we did not detect aggregates of S1 molecules using fluorescence confocal microscopy and the slow dynamics in condensates of S1 does not change significantly over 24 h (Supplementary Fig. 3f).

However, in the revised version, we now include additional in vivo data with C. elegans expressing the aggregation-prone PGL-3 (D425-452)-mEGFP. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

Finally, the "dynamics buffering" is not really clearly established and could also be explained as small concentrations of aggregated proteins act like clients while increasing the concentration results in aggregation and "cross linking" in the entire droplet - and this concentration is never achieved in the in worm experiments so it is not clear. In other words, the change in FRAP dynamics not observed in worms is perhaps not surprising if small amount of recombinant proteins are incorporated into the granules. *

*

Data with the S1 mutant establishes that dynamics buffering can be observed in condensates with different sets of additives both in vitro (Fig. 5a, b) and in vivo (Fig. 4a, b). Further, data with condensates of S1 containing the additives PGL-3 (K126E K129E) or S1 (K126E K129E) demonstrate that dynamics (half-time of FRAP recovery) within S1 condensates, and in turn “dynamics buffering” depend on inter-molecular interactions. With respect to the hypothesis proposed by the reviewer, we did not detect aggregates within S1 condensates using confocal fluorescence microscopy.

In contrast to S1 condensates, condensates containing partially unfolded PGL-3-mEGFP together with PGL-1, GLH-1 and mRNA showed spatial inhomogeneities in fluorescence signal throughout the condensate (Fig. 4g). We have not tested if areas with higher fluorescence signal represent aggregates. It is a possibility that the partially unfolded PGL-3-mEGFP fluorescence signal becomes more homogeneous if higher concentrations of additives (PGL-1, GLH-1 and mRNA) are used. However, the presented data demonstrate the significant effect of the P granule components (PGL-1, GLH-1 and mRNA) on the FRAP recovery rate of partially unfolded PGL-3-mEGFP in condensates (compare figures Fig. 3e and Fig. 4g).

However, consistent with dynamics buffering, the P granule phase in vivo supports wild-type dynamics of different PGL-3 constructs over a range of concentrations - PGL-3(D425-452)-mEGFP at physiological concentration (CRISPR transgenic strain, Fig. 4e) or at higher concentrations (microinjected S1 and partially unfolded PGL-3-mEGFP, Fig. 4b).

• *

*It is also not clear what the mechanism of the changes is - is the protein driven to fold more properly (despite S1 disruption of its conserved sequence) inside the condensate? Does it still self interact and act as a dimerization domain? Does this change disrupt interactions? *

We agree with the reviewer that identifying the precise structural changes of the S1 protein within the condensate vs. dilute phase could be an interesting direction for future investigation. However, we have already discussed the issues raised by the reviewer in the original manuscript.

“Our data is consistent with the model that other regions of S1 molecules cooperate with residues 425-452 (shuffled) to generate stronger inter-molecular interactions. For instance, addition of the mutant S1 (K126E K129E) enhances dynamics of S1 within condensates in contrast to maintaining the slower dynamics observed within condensates of S1 alone. This suggests that the interactions disrupted by the mutations K126E and K129E also contribute to slow S1 dynamics. One possibility is that interactions involving the residues K126 and K129 favor S1 conformations that enhance 425-452 (shuffled)-dependent interactions. Indeed, the mutations K126E K129E have been reported to interfere with interactions among N-termini of PGL-3 molecules (Aoki et al, 2021). While two self-association domains within the α-helical N-terminus of PGL-3 have been mapped (Aoki et al, 2021, 2016), structural insights into those associations are limited. However, PGL-3 shares significant sequence similarity with another protein PGL-1. Crystal structures are available for fragments of the PGL-1 protein that show the two self-association domains at the N-terminus are predominantly α-helical and globular in nature (Aoki et al, 2016, 2021). Therefore, one possibility is that shuffling the sequence 425-452 of PGL-3 or heat-induced unfolding of PGL-3 exposes hydrophobic residues that become available to participate in inter-molecular interactions.”

What is the real mechanism by which PGL-3 phase separates if not via the disordered domains? *

*

We agree with the reviewer that elucidating the detailed mechanism of phase separation of PGL-3 is an interesting direction for future investigation. However, we feel this is not required to support the main message of this manuscript.

Throughout the manuscript, the term "dynamics" is used to indicate FRAP, but it would be better to define what is meant (diffusion of PGL-3 in condensates) instead of using dynamics a term that could mean many things. Secondly, FRAP cannot directly measure liquidity etc (see recent critiques by McSwiggen elife 2019, etc) so it is better to be cautious in the claims. Finally, discussing "dyanmics buffering" adds more terminology where it is not needed - perhaps say "changes to diffusion of PGL-3 in condensates".

We feel it is useful to introduce a term that describes our observation. To our knowledge, our observation is novel and therefore requires a new term to describe it.

However, we do appreciate the concern raised by the reviewer. We used a more generic term “dynamics buffering” in contrast to the more specific “diffusion buffering” since we did not directly estimate diffusion behavior at the ‘single-molecule’ level. However, we already described what we mean by “dynamics buffering” in the text as follows.

“We used condensates of similar size for our analysis (average ± 1 SD of diameter of condensates are 6.4 ± 1.7 mm (Fig. 5a) and 5.9 ± 0.4 mm (Fig. 5b)). Therefore, dynamics buffering here is likely to represent similar diffusion rates of S1 within condensates.”

• *

*The "N-terminus" is not 65% of the protein. One could define this as the N-terminal domain, but again there are two clear folded domains in the first 65% of the protein and this needs to be described better. *

We revised the text to replace the terms “N-terminus” and “N-terminal domain” to “N-terminal fragment”.

*The description of "stickers" and the references to tau and hnRNPA1 are confusing as this is a predominantly ordered domain while those are IDRs. *

• *

We feel this is important as it aids discussing our work in the context of current literature describing the mechanisms of macromolecular phase separation.

The suggestion in the discussion that "P granule components support dynamics by participating in intermolecular interactions wth PGL-3-mEGFP molecules" is not well supported because no interaction assays are performed and no mutaitons are made that disrupt these interactions to test this.

Indeed, we have not conducted interaction assays or mutational analysis to directly test this. However, our detailed analysis with the S1 mutant supports this suggestion.

While partially unfolded PGL-3-mEGFP molecules lose 30% of a-helicity, the a-helicity of the S1 mutant is reduced by 15% compared to wild-type PGL-3. Data with S1 and partially unfolded PGL-3-mEGFP molecules show that loss of a-helicity correlates with slower diffusion of protein molecules within condensates. Using the mutants PGL-3 (K126E K129E) and S1 (K126E K129E), we show that diffusion rate of S1 molecules within condensates depend on inter-molecular interactions, and presence of other P granule components support faster diffusion rate of S1 molecules within condensates. Therefore, we feel it is safe to speculate that intermolecular interactions with P granule components can support dynamics of a “more unfolded” (compared to S1) version of PGL-3 molecule. * *

*More detailed analysis of some of the claims: Claim 1: An a-helical region mediates the phase separation of PGL-3, and the C-terminal disordered region by itself does not phase separate. The N-terminal dimerization is essential for LLPS. The C-terminal IDR interactions with mRNA facilitate the LLPS. Comments: The authors show sufficient experimental data using microscopy and FRAP on truncated constructs with the N-terminal and C-terminal regions - but see above regarding how these are described - a proper domain structure with the folded domains shown and the RGG motifs highlighted should be added and integrated throughout the discussion. *

In the revised version of the manuscript, we described the predicted PGL-3 domains within a paragraph in the introduction: “The interactions that support phase separation of the PGL-3 protein remains unclear. Structural studies on the orthologous PGL-1 protein revealed two dimerization domains. This raises the possibility that PGL-3 also contains similar dimerization domains, and phase separation depends on interactions involving these domains.”

Our Fig. 1a already includes the schematic representation of PGL-3 with predicted N-terminal and Central Dimerization domains and RGG repeats.

*They show that the N-terminus is necessary and adequate for LLPS, and the C-terminus by itself does not phase separate. But, how does the N-terminal domains phase separate? This is not explained - what are the interactions? *

• *

Also, a di-mutant (K126E K129E) that is known, and also authors use SEC-MALS to show their N-terminal construct is consistent with the published results. Disrupting the n-terminal dimerization prevents phase separation, suggesting the importance of these residues in the N-terminus for self-assembly and LLPS. The Microscopy data backs the claim that the mRNA-mediated LLPS is facilitated by binding with C-terminus. However, the m-RNA binding to IDR is not sufficient for LLPS. Yet, the authors do not explain how higher salt prevents phase separation - again the mechanism of phase separation is unclear. Is it multivalent interaction of the two dimerization domains? A basic model (that is tested) would be important.

We agree with the reviewer that elucidating the detailed mechanism of phase separation of PGL-3 is an interesting direction for future investigation. However, we feel this is not required to support the main message of this manuscript.

However, our manuscript already provides some relevant insights as follows.

“To investigate the underlying mechanism further, we began by testing if the N-terminal α-helical region of PGL-3 can self-associate. Our analysis using size exclusion chromatography followed by multi-angle light scattering (SEC-MALS) showed that this PGL-3 fragment 1-452 forms a dimer (Supplementary Fig. 2f). Mutation of two residues (K126E K129E) have been shown to interfere with interactions among the N-termini of PGL-3 molecules (Aoki et al, 2021). We mutated these two residues within the full-length PGL-3 protein (K126E K129E) (Fig. 1a) and found that this mutant PGL-3 (K126E K129E) protein cannot phase separate even at high protein concentrations up to ~130 µM (Fig. 1b, c). Addition of mRNA does not trigger phase separation of this protein at physiological concentrations either (Fig. 2a, b). Taken together, our data is consistent with a model where association among folded N-termini of PGL-3 molecules is essential for phase separation.”

A likely possibility is that phase separation of PGL-3 depends on electrostatic inter-molecular interactions among the folded N-terminal fragment of PGL-3 molecules. Therefore, high salt prevents phase separation.

Are the tags removed to ensure that phase separation is not caused by tags or remaining linker regions? Is the protein purified to be without nucleic acid contamination or other purity metrics?

Most of the experiments were done with only 5% of total protein tagged with 6x-His-mEGFP. No additional tags were present on the constructs. For recombinant expression and purification, proteins were cloned such that it is possible to remove the 6xHis-mEGFP tag following treatment with TEV protease. Following removal of the 6xHis-mEGFP tag, the residual linker is just two amino acid residues long. We used 100% tagged-protein for our experiments only in very few cases (indicated in the figure legends).

To demonstrate purity of recombinant proteins, SDS-PAGE gels with all protein constructs used in this study are shown in Supplementary Fig. 1.

To minimize contamination of nucleic acids, we treated samples with Benzonase during the course of purification.

To assess the extent of nucleic acid contamination, the ratio of absorbance at 260 nm and 280 nm (A260/A280) was monitored. In exceptional cases with high A260/A280 values, we analyzed samples further by purifying RNA from the sample using RNA purification kit (Qiagen) and found that RNA represented 1% or less of the sample mass.* *

Claim2: The N-terminal a-helical region modulates the dynamics within condensates. The IDR region has minimal effect on the fast dynamics of PGL-3. Comments: The authors show that the full-length PGL-3 condensates have modest influence of components by comparing the FRAP half times with or without the P granule components, including mRNA. However, have the authors tried this in the presence of mRNAs for the constructs lacking the IDRs as they have several RGG domains and bind with mRNA and are likely to change the dynamics.

We thank the reviewer for this suggestion. However, this experiment is not essential to support the claim made in the context of homotypic condensates of PGL-3 : “The N-terminal a-helical region modulates the dynamics within condensates. The IDR region has minimal effect on the fast dynamics of PGL-3.”

*The authors report the importance of the N-terminal a-helical region by making a construct that lacks/disrupts a part of the helices lowers the thermal stability and significantly lowers the dynamics of the condensates. Also unfolding of helices is shown to reduce the dynamics. One primary concern is whether these "rescued" protein dynamics imply protein functionality. *

An assay of “functionality” e.g. an enzymatic activity of the PGL-3 protein is not available.

However, we compared the fecundity of C. elegans worms expressing from the native pgl-3 locus, PGL-3-mEGFP or the mutant protein PGL-3(D425-452)-mEGFP, to assay the functionality of P granules in these strains. We found that worms of both genotypes produced similar number of offspring (Fig. 4d). This suggests that deletion of residues 425-452 of PGL-3 does not result in significant loss of function of P granules.

Are these semi denatured proteins refolded in the presence of P-granule components?

We feel that identifying the precise structural changes of the semi-denatured PGL-3 proteins within the condensate vs. dilute phase could be an interesting direction for future investigation.

Finally, it is not clear why the authors chose to disrupt folding of the central dimerization domain?

The manuscript included a paragraph to describe the rationale.

“This suggests that interactions involving the disordered C-terminal region of PGL-3 are not essential for the fast dynamics within condensates. Therefore, we addressed the role of the N-terminal α-helical region (1-452) in driving dynamics. In order to avoid engineering mutations that result in significant misfolding of PGL-3 and concomitant loss of its ability to phase separate, we focused our mutational analysis close to the junction of the folded N-terminus and the disordered C-terminus of PGL-3. Surprisingly, we found that a full-length PGL-3 construct (D425-452) that lacks only 27 residues phase separates into condensates that are non-dynamic (Fig. 3a, c). Sequence analysis of the PGL-3 protein predicts that this region 425-452 spans two α-helices (one complete helix and fraction of a second helix) (Supplementary Fig. 3d). We generated a PGL-3 construct (hereafter called ‘S1’) (Fig. 3a) in which the sequence in the region, 425-452, is shuffled while keeping the overall amino acid composition unchanged. We found that S1 phase separates into condensates that are 20- fold less dynamic than with wild-type PGL-3 (Fig. 3d, Supplementary Fig. 3c).”

Saying that "reduced alpha-helicity of PGL-3 correlates with slower dynamics in condensates" may be factual in these assays but "correlation" should be expanded upon to include mechanism and to me it seems that the statement should read "aggregation of PGL-3 causes slower dynamics in condensates" (both the partially destabilized mutant and the fully unfolded WT show similar effects perhaps to different degrees).

We feel that identifying the precise structural changes of the semi-denatured PGL-3 proteins within the condensate vs. dilute phase could be an interesting direction for future investigation.

We did not use the term "aggregation" since we did not detect aggregates of S1 molecules using fluorescence confocal microscopy.

*CROSS-CONSULTATION COMMENTS I agree with the other reviewer's comments and critiques, I have concerns about the biological relevance and also the biophysical mechanisms. Reflecting on the other reviewers' comments, the papers could provide more depth in one or both of these areas to come to firm conclusions that are either revealing about PGL biology or elucidate a (possible) general biophysical mechanism. *

In the revised version, we now include additional data which shows “dynamics buffering” in transgenic worms generated using CRISPR/Cas9 technology. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

Reviewer #2 (Significance (Required)): *Hence, although the authors shows how inclusion of other components can alter the one protein component phase separation, this is done with entirely artificial means of destabilizing the fold of one of the domains which likely leads to aggregation. So the true impact of the work is hard to understand because the mutations impact on the basic biophysical properties of the domain (stability, interaction) are not completely characterized and the reason for disrupting this folding is not clear. *

A major impact of our work is elucidation of a novel “dynamics buffering” property within biomolecular condensates in vitro. Our in vivo data is consistent with this finding.

• *

We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificially-generated” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

• *

My field of expertise is protein phase separation and protein structure. * *

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

Summary: P granules are liquid condensates found in the developing germlines and embryos of C. elegans. Prior work by the authors and others have established P granules as a tractable model to investigate the basic biophysical properties of liquid condensates. Much of the prior published work focused on specific P granule scaffold proteins, PGL-1 and PGL-3. How attributes of these PGL proteins and the effect of other P granule components affect condensate properties is not fully understood. Here, Jelenic, et al. probe the biophysical properties of PGL-3. Using recombinant protein, they show that an N-terminal, alpha-helical region of PGL-3 is sufficient for liquid condensate formation and that N-terminal assembly is required for this formation. Creation of a scrambled alpha-helical region in PGL-3 and heat treatment affects PGL-3 fluidity. This fluidity can be "rescued" in vivo and in vitro with the inclusion of other P granule factors, including wildtype PGL-3, PGL-1, GLH-1 and mRNA. The authors note an inverse correlation between fluidity and mutant PGL-3 fluorescent intensity. They propose a model that heterotypic compositions of condensates can buffer their fluidity against components with stronger multivalent interactions. *

MAJOR: 1. PGL-3 is a fantastic model to study the biophysical properties of a liquid condensate. But as the authors address in their discussion, the S1 mutant will likely affect the central domain folding, at its minimum causing exposure of a hydrophobic surface not typically exposed in biology. These helices are found at the terminal portion of the domain determined in the crystal structure and as depicted in the authors' Figure 1A. While the cause of S1's enhanced molecular interactions does not affect the in vitro work presented in this manuscript, it does affect how the conclusions connect to the biological nature of P granules and liquid condensates more generally. *

We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificial” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

• Recombinant PGL-3 experiments added PGL-1, GLH-1 and mRNA simultaneously and measured fluidity. It will be interesting to know which components contribute to fluidity and whether fluidity enhancement of each component is dependent on one another. Addition experiments with each component should be included and/or at least discussed in the main text. *

Our data with S1-mEGFP or PGL-3-mEGFP (pre-heated at 50°C) proteins microinjected into C. elegans gonads, and the transgenic strain expressing PGL-3(D425-452)-mEGFP from the pgl-3 locus showed that the P granule phase can support fast dynamics of these mutant PGL-3 constructs. Since P granules have a complex composition, one possibility is that fast dynamics of these constructs is supported by interactions involving many P granule components. We found that using only a limited set of P granule components (PGL-1, GLH-1 and mRNA) can buffer dynamics of S1 in condensates in vitro.

In absence of a systematic analysis investigating the individual role of approx. 70 P granule proteins in buffering S1 dynamics in condensates in vitro, we have claimed in the text that dynamics-buffering of S1 in condensates is supported by interactions among two or more components. However, we do appreciate the reviewer’s comment and feel it would be interesting to investigate the contribution of individual P granule components towards fluidity in future studies. We have discussed this in the ‘Discussion’ section of the manuscript.

• The biological relevance of PGL-1, GLH-1, and mRNA were not discussed in the main text. How these factors contribute to P granule assembly and function should be mentioned in the Introduction or Results. *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

*MINOR: 1. Line 20, "most non-membrane-bound compartments...have complex composition": Are there examples of condensates that do not have complex composition? *

Not all non-membrane-bound compartments may have been characterized. To accommodate this possibility, we refrained from making a more general statement, but stated “most non-membrane-bound compartments…”.

• Lines 40-43, RNA interactions driving LLPS: Please include citations from the Parker Lab (e.g. Van Treeck and Parker, Cell. 2018 doi: 10.1016/j.cell.2018.07.023) *

We added the reference suggested by the reviewer.

• *

• Line 60, condensates contain hundreds of different proteins and RNA: Please cite at least a few examples of condensates with their components identified. *

We added some references following suggestion by the reviewer.

• Lines 82-84, PGL-3 drives assembly: Please cite Kawasaki, et al. Genetics 2004 for the discovery of PGL-3. *

We added the reference suggested by the reviewer.

• Lines 88-89, PGL-3 N-terminal fragment predominantly alpha-helical: The PGL domain structures should be cited here as supporting evidence that these regions are composed primarily of alpha helices (Aoki, et al 2016, 2021) *

• *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

• Lines 158-159, driving forces for phase separation: This statement should be removed or expanded. The authors point regarding the protein concentrations is not clear here but clarified in the Discussion (Lines 691-693). Recommend removing due to its speculative nature. *

We retained the speculative comment in the results section. We feel that this prepares the readers for the discussion later in the manuscript.

• Lines 210: Add commas before and after "PGL-1 and GLH-1"*

We addressed the reviewer’s suggestion.

• Lines 218-219: add "and" instead of comma between PGL-1 and GLH-1 *

We addressed the reviewer’s suggestion.

• Lines 238-239, alpha-helices: The PGL CDD structure should also be referenced here (Aoki, et al 2016). *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

• Lines 680-682, MEG proteins: Please cite accordingly. *

We added the reference suggested by the reviewer.

• Lines 694-695, heterotypic interactions: Please cite Saha, et al. 2016. *

We added the reference suggested by the reviewer.

• Figure 1: Add space between 1 and mM DTT *

We addressed the reviewer’s suggestion.

• Figure 2b: Please provide statistics between condensate numbers. *

We provide statistics between condensate numbers in Fig. 2b.

• Figure 4A: The region of the germline imaged and analyzed should be mentioned in the caption or the main text. *

We revised the Figure legend of Fig. 4a to address this issue.

• Figure 4B,C: Please include statistics between the FRAP curves. *

We have included statistics comparing FRAP curves in Supplementary Fig. 4a-c.

• Figure 4D: It will be helpful to compare this curve to Figure S4A in the same graph. Please also include graph statistics. *

We have revised Fig. 4 to address the reviewer’s suggestion.

• Figure 5: The data points are difficult to resolve. Recommend use of color.*

We considered the suggestion, but felt it works better in the original form.

• Figure 6: This is a very general model that does not highlight the extensive experimental work performed by the authors. Recommend incorporating PGL-3, mutants and P granule factors into this model. *

We thank the reviewer for appreciating our extensive work. However, we retained the original Fig. 6 for the sake of simplicity.

• Methods, Line 939, C. elegans section: What worms were used? TH623? Please describe the genotype. *

We have included a table listing the strains used in the study and their genotype. * CROSS-CONSULTATION COMMENTS While my review was arguably the more favorable of the three, I agree with the other reviewers' comments and evaluation, particularly with Reviewer #1. As written in my review, my primary concern was the biological relevance of the work.*

Reviewer #3 (Significance (Required)):

Overall, the in vitro work presented investigating the biophysical properties of this minimal P granule system was thorough and well-analyzed, and the manuscript was clearly written. Additional citations and statistics will improve the manuscript and the strength of the conclusions, respectively. The biological relevance of this study to P granule form and function in vivo, and to condensates in vivo, is debatable. This work will interest those who study condensate biology, the biophysics of protein-protein and protein-RNA interactions, and RNA biochemists more generally.

A major impact of our work is elucidation of a novel “dynamics buffering” property within biomolecular condensates in vitro. Our in vivo data is consistent with this finding.

We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificially-generated” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

• *

Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

*I have expertise in P granules, protein/RNA biochemistry, condensate assembly, and C. elegans. *

References

Aoki ST, Kershner AM, Bingman CA, Wickens M & Kimble J (2016) PGL germ granule assembly protein is a base-specific, single-stranded RNase. Proceedings of the National Academy of Sciences of the United States of America

Aoki ST, Lynch TR, Crittenden SL, Bingman CA, Wickens M & Kimble J (2021) C. elegans germ granules require both assembly and localized regulators for mRNA repression. Nat Commun 12: 996

Cipriani PG, Bay O, Zinno J, Gutwein M, Gan HH, Mayya VK, Chung G, Chen J-X, Fahs H, Guan Y, et al (2021) Novel LOTUS-domain proteins are organizational hubs that recruit C. elegans Vasa to germ granules. Elife 10: e60833

Frottin F, Schueder F, Tiwary S, Gupta R, Körner R, Schlichthaerle T, Cox J, Jungmann R, Hartl FU & Hipp MS (2019) The nucleolus functions as a phase-separated protein quality control compartment. Science 365: 342–347

Kawasaki I, Amiri A, Fan Y, Meyer N, Dunkelbarger S, Motohashi T, Karashima T, Bossinger O & Strome S (2004) The PGL family proteins associate with germ granules and function redundantly in Caenorhabditis elegans germline development. Genetics 167: 645–661

Kawasaki I, Shim YH, Kirchner J, Kaminker J, Wood WB & Strome S (1998) PGL-1, a predicted RNA-binding component of germ granules, is essential for fertility in C. elegans. Cell 94: 635–645

Phillips CM & Updike DL (2022) Germ granules and gene regulation in the Caenorhabditis elegans germline. Genetics 220: iyab195

Price IF, Hertz HL, Pastore B, Wagner J & Tang W (2021) Proximity labeling identifies LOTUS domain proteins that promote the formation of perinuclear germ granules in C. elegans. Elife 10: e72276

Saha S, Weber CA, Nousch M, Adame-Arana O, Hoege C, Hein MY, Osborne Nishimura E, Mahamid J, Jahnel M, Jawerth L, et al (2016) Polar Positioning of Phase-Separated Liquid Compartments in Cells Regulated by an mRNA Competition Mechanism. Cell 166: 1572-1584.e16

Spike C, Meyer N, Racen E, Orsborn A, Kirchner J, Kuznicki K, Yee C, Bennett K & Strome S (2008a) Genetic analysis of the Caenorhabditis elegans GLH family of P-granule proteins. Genetics 178: 1973–1987

Spike CA, Bader J, Reinke V & Strome S (2008b) DEPS-1 promotes P-granule assembly and RNA interference in C. elegans germ cells. Development (Cambridge, England) 135: 983–993

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Consolidated peer review report (9 September 2022)

GENERAL ASSESSMENT

This interesting preprint by Suárez-Delgado et al. explores the mechanism by which activation of the Hv1 voltage-activated proton channel is dependent upon both the voltage and pH difference across the membrane. The authors are the first to incorporate the fluorescent unnatural amino acid, Anap, into the extracellular regions of the S4 helix of human Hv1 to monitor transitions of S4 upon changes in voltage or pH. The authors first checked that Anap is pH insensitive for practical use in Hv1, where changes in local pH are known to occur when the voltage sensor activates and the proton pore opens. Anap was incorporated at positions throughout the S3-S4 linker and the extracellular end of S4 (up to the 202nd residue) of hHv1 and some positions showed clear voltage-dependent changes in fluorescence intensity. The authors also obtained fluorescence spectra at different voltages and observed no spectral shifts, raising the possibility that voltage dependent changes in fluorescence intensity could primarily be due to fluorescence quenching. Upon mutation of F150, the Anap signal at the resting membrane voltage increased, suggesting dequenching upon removal of F150. The authors also discovered that the kinetics of Anap fluorescence upon membrane repolarization have two phases (rapid and slow) under certain pH conditions and that there is a pH- dependent negative shift of the conductance-voltage (G-V) relation compared with the fluorescence-voltage (F-V) relation in some mutants. The biphasic kinetics of the fluorescence decay upon repolarization were explained by modelling a slower transition of return from intermediate resting state to a resting state. The pH-dependent shift of the G-V relation from the F-V relation provides insight into mechanisms of ΔpH-dependent gating of Hv1, a longstanding enigma. Overall, the approaches are rigorous, the figures show important results, and this work paves the way for the use of Anap fluorescence to study Hv1 gating and modulation.

RECOMMENDATIONS

Revisions essential for endorsement:

1) In its current form, the narrative of the preprint has two threads. One on the mechanisms of Anap fluorescence changes (mainly quenching) and another on a previously unappreciated transition of the voltage sensor, as revealed by Anap. Our impression is that the preprint suffers somewhat from this split focus, which could be resolved by explaining why Anap was used to explore voltage sensor activation in Hv1 in the introduction. Perhaps the authors could also explain the advantage of smaller sized fluorophores compared to other maleimide-based fluorophores earlier in the introduction, or the utility of being able to insert Anap into transmembrane segments. The authors should more clearly point out how they exploited the advantages of Anap as a tool in this study. It would furthermore be helpful to discuss previous studies using nongenetic tools for VCF and spell out how they have delineated key aspects of Hv1, which would help to emphasize how several positions studied here (for example, 201 and 202) could not be labelled with cysteine-based fluorophores.

2) We think the authors should be cautious about understanding the physicochemical nature of Anap using prodan as a model. It would be helpful to discuss the possibility that undetected spectral shifts due to a nonquenching mechanism could be overlooked, even though major signal changes can be explained by fluorescence quenching in their data. Regarding the mechanisms of remaining voltage-dependent fluorescence changes of F150A-A197Anap, it would be helpful for the authors to suggest possible ideas about which residues might account for remaining signals.

The beautiful spectral data for Anap is impressive. However, the physicochemical basis of the fluorescence change of Anap cannot be understood by simple extension of findings for prodan, which shows structural similarity to Anap. Our understanding is that changes in Anap fluorescence can only reveal a change in the structural relationship between Anap and one of its neighbors because the physicochemical basis of Anap fluorescence is complicated. For example, fluorescence could also be affected by the electrostatic environment, stretch of peptide bond, etc. Previous studies, including those of TRP channels, showed that the kind of environmental changes that Anap faces in ion channels do not necessarily induce large spectral shifts, unlike in cell-free spectral analyses using distinct solvents. Further, only minor shifts in spectra occur upon local structural change, as seen in previous work including Xu et al. Nat. Commun. 2020 11:3790. Such minor shifts could be perhaps overlooked even when Anap is incorporated into S4 and exposed to environmental change. Therefore, it is not easy to decode the physicochemical basis of Anap fluorescence changes. F150A-A197Anap has increased fluorescence and no change in spectral pattern, leading the authors to conclude that F150 quenches Anap fluorescence of A197 position. However, a significant amount of fluorescence change still occurs upon changes in membrane potential after F150 is changed to alanine (Figure 4). It is very likely that quenching is not the only mechanism underlying the observed voltage induced change of Anap fluorescence of Hv1. The authors suggest that remaining voltage-dependent fluorescence change of F150A-A197Anap could be due to interaction with other aromatic residues, but this has not been tested.

3) The current version of the preprint is missing important control experiments, ideally performed using western blots to measure protein expression or, if that is not possible, proton current and fluorescence measurements, to demonstrate that protein expression or functional channels are not seen for all mutants in the absence of ANAP (but in the presence of the tRNA and Rs construct).  A similar control for imaging would be to use ANAP alone without encoding. 

4) Aromatics in the S4 segment were ruled out as potential quenchers on the assumption that they would move together with Anap during gating. It should be noted, however, that Hv1 is a dimer and therefore a fluorophore attached to S4 in one subunit could be quenched by S4 aromatics in the neighboring subunit if were close to the dimer interface. In Fujiwara et al. J. Gen. Physiol. 2014 143:377-386, for example, W207 does not appear very far from labeled positions in the adjacent S4. This possibility should be mentioned in the discussion.

5) It is not clear whether the Anap spectra purely represent Hv1 incorporated into the plasma membrane or perhaps include signals from the cytoplasm or channels in internal membranes (whether assembled or incompletely assembled). It would be helpful to provide a more complete presentation of the data obtained and to provide more information in the Methods Section. In the Methods section, it is stated “The spectra of both fluorophores (Anap and mCherry) were recorded by measuring line scans of the spectral image of the cell membrane, and the background fluorescence from a region of the image without cells was subtracted”. How are signals from cell membranes specified in this method being discriminated from those associated with the cytoplasm and intracellular membranes? If spectral data include signals from free Anap in the cytoplasm or Hv1 in intracellular membranes, spectral shifts upon membrane potential changes will be difficult to detect, even when Anap is incorporated into Hv1 and senses environmental change by voltage-induced conformational change. In Figure 3E, wavelength spectra were shown as standardized signals for different voltages. Amplitude change would be demonstrated (spectrum at different voltages without standardization would be shown). In Figure 4, spectra were compared between A197Anap and F150A-A197Anap, showing increases of fluorescence in F150A-A197Anap. Was this signal measured at resting membrane potential? How does the spectrum change when the membrane potential is changed?

Rationales for the confirmation of signals originating from the cell surface for Hv1 Anap might include the observations that: a) some mutants showed slightly different spectral patterns (in particular, Q191Anap showed a small hump at longer wavelengths, which is proposed to represent FRET between mCherry and Anap) and b) signal intensity was voltage dependent (if signals originate  from endomembranes, they should not be voltage dependent). Mentioning these two points earlier in the text might help to alleviate concerns about the location of the protein that contributes to the measured signals.

6) In Fig 5, the fluorescence kinetics do not really match the current activation kinetics for panels A, B, and C.  Is there an explanation for this mismatch?  It would be helpful to have the fitted data in the figure. A more thorough comparison of the kinetics of currents and fluorescence would be helpful throughout the study. 

7) Which construct of hHv1 was used to obtain the data in Figure 6? Unless we missed it, this information is not provided in the text or figure legend. Is it for L201Anap? This figure also shows an intriguing finding that the G-V relationship is negatively shifted from the F-V relationship at pHo7-pHi7 but not at pHo5.5-pHi5.5. A shifted G-V relation with the same ΔpH contrasts with what has been reported in other papers. However, the authors did not really discuss this surprising finding in the light of previous references. Could the shift of the G-V relation between two pH conditions with the same ΔpH be due to any position-specific effect of Anap? If Figure 6 represents L201Anap mutant, the presence of Anap at L201 probably makes such shift of G-V curve in Figure 6C? The authors should openly discuss this finding in relation to what has been reported in the literature.

8) The authors suggest that the small hump near 600 nm in Figure 1E represents FRET between Anap and mCherry. It is surprising that FRET can take place across the membrane. Can the authors point to another case of FRET taking place across a cell membrane? One possibility might be that misfolded proteins place mCherry and Anap close to each other. It is also curious that only A191Anap did not show such a FRET-like signal. Also, if there is FRET, why wouldn’t this also contribute to the voltage-dependent changes in fluorescence?

9) F150A-A197Anap shows a leftward shift of the F-V relation compared with the G-V relation only when ΔpH=1. Another unusual finding with F150A-A197Anap is the very small shift of the G-V relation between ΔpH=0 and ΔpH=1, when other reports in the literature suggest it should be 40 mV or more. Are these peculiar properties simply due to the absence of Phe at position 150, which might play a critical role in gating as one of the hydrophobic plugs of Hv1?  To address this possibility, it would be ideal to compare different ΔpH values with and without F150 when Anap is incorporated at a different position (such as L201Anap). Regardless, it would be helpful to discuss this point.

10) In Figure 1E, I202Anap exhibits a blue shift in its spectrum suggesting the environment of Anap on I202 is more hydrophobic than the other sites. We presume these spectra were obtained at a negative membrane voltage, but the text or legend should clearly state how these were obtained. The authors should also explain whether the whole cell or edge was imaged. If these are at negative membrane voltages, might the Anap spectrum shift to higher wavelengths (i.e. more hydrophilic) when the membrane is depolarized? Did the authors find any spectral shift for I202Anap when doing a similar test as depicted in Figure 3E?

11) In Figure 3E, spectra are shown as normalized signals for different voltages, but an amplitude change should also be demonstrated by providing raw spectra at different voltages.

12) In Figure 4, spectra are compared between A197Anap and F150A-A197Anap, showing increase of fluorescence in F150A-A197Anap. Were these obtained at a negative membrane voltage? How do these spectra change when membrane potential is changed?

Additional suggestions for the authors to consider:

1) The authors propose that Anap fluorescence tracks an S4 movement involved in the opening of the channel. They also argue that the existence of more than one open state could explain why the increase in florescence upon depolarization lags the proton current in most cases. While they convincingly show that Anap is not pH sensitive per se, when incorporated into the protein, the fluorescence efficiency of the fluorophore could still be affected by protonation of channel residues in the immediate environment when the channel opens, even after S4 has completed its movement. To address this alternative explanation, the authors could use Hv1 mutants with strongly reduced proton conductance. Channels bearing mutations corresponding to N214R or D112N were used successfully to isolate Hv1 gating currents from the much larger proton currents (De La Rosa & Ramsey, Biophys. J. 2018 114:2844-2854; Carmona et al. PNAS 2018 115:9240-9245; Carmona et al. PNAS 2021 118: e2025556118). Perhaps, they could be used with patch clamp fluorometry as well?

2) The data showing that Hv1-197Anap is quenched by Phe at position 150 are very nice. Yet, it would be useful to show that the quenching is specific to F150 using a negative control. F149, for instance, is just next to F150 but points in a different direction, so its mutation to alanine should not affect Hv1-197Anap fluorescence.

3) A major finding of this work is the identification of a slow kinetic component that is highly sensitive to ΔpH. Earlier studies found that the ability of Hv1 to sense ΔpH is altered by some channel modifications, e.g., in the loop between TMH2 and TMH3 (Cherny et al. J. Gen. Physiol. 2018 150:851-862). Did the authors check whether any of these modifications alter the transition responsible for the slow kinetic component? For instance, a suppression of the transition resulting from a H168X mutation would help tighten the link to ΔpH sensing.

4) We understand that it is difficult to tightly control intracellular and extracellular pH when Hv1 is heterologously expressed in mammalian cells. The G-V relation is not always reliable because accumulation of protons or depletion of protons upon Hv channel activity will alter gating, as the authors have previously published (De La Rosa et al., J. Gen. Physiol. 2016 147:127-136). Could the kinetic analysis of Anap fluorescence be affected by similar alterations to proton concentration in the vicinity of Hv1? It would be helpful for the authors to comment on this specifically.

5) Quenching of Anap by Phe could be verified in cell free conditions using a spectrophotometer with different concentrations of Phe, or citing the literature if it has already been reported.

6) The authors did not cite any example of Anap incorporation into S4 helices, but there are several recent papers where Anap was utilized to probe motion of S4 in other channels. Examples include Dai et al., Nat. Commun. 2021 12:2802 and Mizutani et al. PNAS 2022 119:e2200364119.

7) In the Anap-free negative control (with only A197TAG plasmid transfection), the mCherry signal seems positive (Supplementary Figure 1, left row, second from the top). Is this due to unexpected skipping of the TAG codon to make mCherry-containing partial polypeptides? It would seem like an explanation is needed.

8) The data of Figure 3E are shown as data with different membrane voltages. But there is no information about membrane voltage for Fig. 1E and Fig. 2A and Fig. 4B. Are these from unpatched cells? Please clarify.

9) G-V relations are shown for F150A-A197Anap, but current traces of F150A-A197Anap are missing.

10) On Page 11, Line 303 says “experimental F-V relationship is positively shifted by 10 mV with respect to the G-V curve”. But looking at the data Fig5D, the shift at ΔpH=2 seems the opposite. Perhaps “positively” should be “negatively” in this sentence?

REVIEWING TEAM

Reviewed by:

Yasushi Okamura, Professor, Osaka University, Japan: voltage-sensing proteins, electrophysiology and fluorescence spectroscopy

Francesco Tombola, Associate Professor, University of California, Irvine, USA: ion channel mechanisms, electrophysiology and fluorescence spectroscopy

Christopher A. Ahern, Professor, University of Iowa, USA: ion channel mechanisms, non-canonical amino acidic mutagenesis

Curated by:

Kenton J. Swartz, Senior Investigator, NINDS, NIH USA

(This consolidated report is a result of peer review conducted by Biophysics Colab on version 1 of this preprint. Minor corrections and presentational issues have been omitted for brevity.)

Author Response

Reviewer #1 (Public Review):

High resolution mechanistic studies would be instrumental in driving the development of Cas7-11 based biotechnology applications. This work is unfortunately overshadowed by a recent Cell publication (PMID: 35643083) describing the same Cas7-11 RNA-protein complex. However, given the tremendous interest in these systems, it is my opinion that this independent study will still be well cited, if presented well. The authors obviously have been trying to establish a unique angle for their story, by probing deeper into the mechanism of crRNA processing and target RNA cleavage. The study is carried out rigorously. The current version of the manuscript appears to have been rushed out. It would benefit from clarification and text polishing.

We thank the reviewer for the positive and helpful comments that have made the manuscript more impactful.

To summarize the revisions, we have resolved the metal-dependence issue, updated the maps in both main and supplementary figures that support the model, re-organized the labels for clarity, and added the comparison between our and Kato et al.’ structures.

In addition, we describe a new result with an isolated C7L.1 fragment that retains the processing and crRNA binding activities.

Reviewer #2 (Public Review):

In this manuscript, Gowswami et al. solved a cryo-EM structure of Desulfonema ishimotonii Cas7-11 (DiCas7-11) bound to a guiding CRISPR RNA (crRNA) and target RNA. Cas7-11 is of interest due to its unusual architecture as a single polypeptide, in contrast to other type III CRISPR-Cas effectors that are composed of several different protein subunits. The authors have obtained a high-quality cryo-EM map at 2.82 angstrom resolution, allowing them to build a structural model for the protein, crRNA and target RNA. The authors used the structure to clearly identify a catalytic histidine residue in the Cas7-11 Cas7.1 domain that is important for crRNA processing activity. The authors also investigated the effects of metal ions and crRNAtarget base pairing on target RNA cleavage. Finally, the authors used their structure to guide engineering of a compact version of Cas7-11 in which an insertion domain that is disordered in the cryo-EM map was removed. This compact Cas7-11 appears to have comparable cleavage activity to the full-length protein.

The cryo-EM map presented in this manuscript is generally of high quality and the manuscript is very well illustrated. However, some of the map interpretation requires clarification (outlined below). This structure will be valuable as there is significant interest in DiCas7-11 for biotechnology. Indeed, the authors have begun to engineer the protein based on observations from the structure. Although characterization of this engineered Cas7-11 is limited in this study and similar engineering was also performed in a recently published paper (PMID 35643083), this proof-of-principle experiment demonstrates the importance of having such structural information.

The biochemistry experiments presented in the study identify an important residue for crRNA processing, and suggest that target RNA cleavage is not fully metal-ion dependent. Most of these conclusions are based on straightforward structure-function experiments. However, some results related to target RNA cleavage are difficult to interpret as presented. Overall, while the cryo-EM data presented in this work is of high quality, both the structural model and the biochemical results require further clarification as outlined below.

We thank the reviewer for the positive and helpful comments that have made the manuscript more impactful.

To summarize the revisions, we have resolved the metal-dependence issue, updated the maps in both main and supplementary figures that support the model, re-organized the labels for clarity, and added the comparison between our and Kato et al.’ structures.

In addition, we describe a new result with an isolated C7L.1 fragment that retains the processing and crRNA binding activities.

1. The DiCas7-11 structure bound to target RNA was also recently reported by Kato et al. (PMID 35643083). The authors have not cited this work or compared the two structures. While the structures are likely quite similar, it is notable that the structure reported in the current paper is for the wild-type protein and the sample was prepared under reactive conditions, resulting in a partially cleaved target. Kato et al. used a catalytically dead version of Cas7-11 in which the target RNA should remain fully intact. Are there differences in the Cas7-11 structure observed in the presence of a partially cleaved target RNA in comparison to the Kato et al. structure? Such a comparison is appropriate given the similarities between the two reports. A figure comparing the two structures could be included in the manuscript.

We have added a paragraph on page 12 that describe the differences in preparation of the two complexes and their structures. We observed minor differences in the overall protein structure (r.m.s.d. 0.918 Å for 8114 atoms) but did observe quite different interactions between the protein and the first 5’-tag nucleotide (U(-15) vs. G(-15)) due to the different constructs in pre-crRNA, which suggests an importance of U(-15) in forming the processing-competent active site. We added Figure 2-figure supplementary 3 that illustrates the similarities and the differences.

2.The cryo-EM density map is of high quality, but some of the structural model is not fully supported by the experimental data (e.g. protein loops from the alphafold model were not removed despite lack of cryo-EM density). Most importantly, there is little density for the target RNA beyond the site 1 cleavage site, suggesting that the RNA was cleaved and the product was released. However, this region of the RNA was included in the structural model. It is unclear what density this region of the target RNA model was based on. Further discussion of the interpretation of the partially cleaved target RNA is necessary. Were 3D classes observed in various states of RNA cleavage and with varied density for the product RNAs?

We should have made it clear in the Method that multiple maps were used in building the structure but only submitted the post-processed map to reviewers. When using the Relion 4.0’s local resolution estimation-generated map, we observed sufficient density for some of the regions the reviewer is referring to. For instance, the site 1 cleavage density does support the model for the two nucleotides beyond site 1 cleavage site (see the revised Figure 1 & Figure 1- figure supplement 3).

However, there are protein loops that remain lack of convincing density. These include 134141 and 1316-1329 that are now removed from the final coordinate.

The “partially cleaved target RNA” phrase is a result of weak density for nucleotides downstream of site 1 (+2 and +3) but clear density flanking site 2. This feature indicates that cleavage likely had taken place at site 1 but not site 2 in most of the particles went into the reconstruction. To further clarify this phrase, we added “The PFS region plus the first base paired nucleotide (+1*) are not observed.” on page 4 and better indicate which nucleotides are or are not built in our model in Figure 1.

1. The authors argue that site 1 cleavage of target RNA is independent of metal ions. This is a potentially interesting result, but it is difficult to determine whether it is supported by the evidence provided in the manuscript. The Methods section only describes a buffer containing 10 mM MgCl2, but does not describe conditions containing EDTA. How much EDTA was added and was MgCl2 omitted from these samples? In addition, it is unclear whether the site 1 product is visible in Figures 2d and 3d. To my eye, the products that are present in the EDTA conditions on these gels migrated slightly slower than the typical site 1 product. This may suggest an alternate cleavage site or chemistry (e.g. cyclic phosphate is maintained following cleavage). Further experimental details and potentially additional experiments are required to fully support the conclusion that site 1 cleavage may be metal independent.

As we pointed out in response to Reviewer 1’s #8 comment, this conclusion may have been a result of using an older batch of DiCas7-11 that contains degraded fragments.

As shown in the attached figure below, “batch Y” was an older prep from our in-house clone and “batch X” is a newer prep from the Addgene purchased clone (gel on right), and they consistently produce metal-independent (batch Y) or metal-dependent (batch X) cleavage (gel on left). It is possible that the degraded fragments in batch Y carry a metal-independent cleavage activity that is absent in the more pure batch X.

We further performed mass spectrometry analysis of two of the degraded fragments from batch Y (indicated by arrows below) and discovered that these are indeed part of DiCas7-11. We, however, cannot rationalize, without more experimental evidence, why these fragments might have generated metal-independent cleavage at site 1. Therefore, we simply updated all our cleavage results from the new and cleaner prep (batch X) (For instance, Figure 3c). As a result, all references to “metal-independence” were removed.

With regard to the nature of cleaved products, we found both sites could be inhibited by specific 2’-deoxy modifications, consistent with the previous observation that Type III systems generate a 2’, 3’-cyclic product in spite of the metal dependence (for instance, see Hale, C. R., Zhao, P., Olson, S., Duff, M. O., Graveley, B. R., Wells, L., ... & Terns, M. P. (2009). RNA-guided RNA cleavage by a CRISPR RNA-Cas protein complex. Cell, 139(5), 945-956.)

We added this rationale based on the new results and believe that these characterizations are now thorough and conclusive

1. The authors performed an experiment investigating the importance of crRNA-target base pairing on cleavage activity (Figure 3e). However, negative controls for the RNA targets in the absence of crRNA and Cas7-11 were not included in this experiment, making it impossible to determine which bands on the gel correspond to substrates and which correspond to products. This result is therefore not interpretable by the reader and does not support the conclusions drawn by the authors.

Our original gel image (below) does contain these controls but we did not include them for the figure due to space considerations (we should have included it as a supplementary figure). We have now completely updated Figure 3e with much better quality and controls. Both the older and the updated experiments show the same results.

Original gel for Figure 3e containing controls.

Typo in the tag here; "transculsion" should be "transclusion".

i think you forgot to use the header tag.

I think you have added extra link tag for css file. gone through all code and i felt it you have added extra tags .

This is interesting that you used a 'mark' tag, because I have not heard about it before.

would recomend dropping this end tag to the line below, just to make it easier to see the end tag as

is a block level element.

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Reply to the reviewers

1. General Statements [optional]

In our work, we quantified the abundance and positions of major kinetochore proteins within the metaphase kinetochore in budding yeast using single-molecule localization microscopy. Based on these measures, we revised the current model of the kinetochore and provided a nanoscale view of the complex.

We now revised our manuscript according to reviewers’ points. We performed new analyses to quantify the measurement errors and to justify our data analysis workflows. We further exploited the correlation-based analysis and found a correlation between the spreads of kinetochore proteins perpendicular to the spindle axis and their positions along the axis. We also discussed the potential non-centromeric pools and revised our model of the kinetochore. Further information on our analyses was now provided to improve the clarity. Changes to the text were implemented to better reflect our data. Information from relevant works was incorporated to better connect this work to the field.

We thank the reviewers for their points, which help us show the rigorousness of our analyses, further demonstrate the potential of our work, and improve clarity.

2. Point-by-point description of the revisions

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

The authors have developed a rigorous methodology for using single-molecule imaging of exogenously labeled kinetochore proteins to count and estimate their copy numbers and the average distance from the kinetochore protein Spc105. Although the method is technically sound, its application to the kinetochore raises some crucial questions below. My biggest concern is the effect of non-centromeric pools of the centromeric proteins Cse4, Cep3, and Ctf19 on the estimated copy number per kinetochore. The authors should be able to address most, if not all, questions by presenting a more in-depth data analysis.

Major points

1. Accounting for tilt of the yeast spindle relative to the image plane: It is not clear to me how the authors ascertain whether the spindle being imaged is nearly parallel to the image plane. In the companion fission yeast study, spindle poles are used for this purpose, but this study seems to rely only on the labeled kinetochore proteins. The criteria used to select the in-plane spindles should be clearly defined.

We thank the reviewer for pointing this out. We selected the in-plane spindles based on their average PSF size, which informs the z positions of the center of the kinetochore cluster (for simplicity, now all ’half-spindle’ was changed to ‘kinetochore cluster’). To calibrate the z position of kinetochore clusters, we first measured the width of the kinetochore cluster by fitting a cylindrical distribution. Overall, the kinetochores are likely symmetrically distributed around the spindle axes. Therefore, the height and the width of a kinetochore cluster should be the same. We then calibrated the z positions of the PSF size based on fluorescent bead data. Next, we plugged in the cylindrical distribution to the calibration curve to correlate the mean PSF size and position of the kinetochore cluster. We only took the kinetochore clusters with a mean PSF size

1. The effects of PSF depth on counting kinetochore proteins: The authors use a well-characterized nuclear pore protein as the reference to estimate kinetochore protein counts per half-spindle. Although this method appears rigorous in principle, I am unsure about the effect of the spatial distribution of kinetochores on the accuracy of the estimated number. Nuclear pore proteins are all localized within an 100 nm away from the focal plane even when the spindle is perfectly parallel to the focal plane. A discussion of this possibility, its effect on the protein count/distance estimates, and any mitigating factors is essential to highlight the caveats associated with the conclusions.

Based on the cylindrical distribution (see please the reply to point 1) of kinetochore clusters and their positions in z, we calculated the upper and lower boundaries of the distribution of kinetochore proteins in z, given a specific mean PSF size cutoff of a kinetochore cluster. Regardless of how stringent the cutoff is (130 and 135 nm), we made sure the boundaries do not exceed the imaging depth defined by our choice of the PSF size filtering (

1. Presentation of the cross-correlation analysis: The authors use cross-correlation for an unbiased calculation of the axial separation between a protein of interest and Cse4, but I am curious about the structure of the underlying data, and the intensity image in Figure 1 is not easy to examine. It will be helpful to include more analysis of the underlying data for at least a subset of the proteins (e.g., proteins at short, intermediate, and long distances from Cse4) as supplementary data.

2. The authors should include X and Y projections of the cross-correlation function.

3. Do the widths of cross-correlation functions (i.e., their spread perpendicular to the spindle axis) match across all proteins and experiments? This should be an almost invariant characteristic of the measurements, assuming that proteins within each kinetochore tightly cluster around the 25 nm microtubule. This line of thinking makes the large width of the cross-correlation shown in Figure 1 somewhat surprising.

4. It will also be interesting to test if the correlation between the positions of Spc105 molecules, especially perpendicular to the spindle axis, is comparable to the known separations between adjacent microtubules in the yeast spindle (the authors could use Winey et al. 1995 for serial-section EM of yeast spindles for comparison).

The reviewer is interested in the spread, or the size of the distribution, of a protein in a kinetochore along and perpendicular to the spindle axis. This is an interesting idea and can be done practically. However, the information can be more easily obtained based on auto-correlation instead of cross-correlation, due to its better signal-to-noise ratio along the dimension perpendicular to the spindle axis. Cross-correlations in that dimension are convoluted with background localizations and different localization precisions of the two channels. These factors are hard to interpret and disentangled. In auto-correlations, although the background is still present, it can be modeled and then removed easily, as now mentioned on page 15 lines 500-516.

Accordingly, we performed auto-correlation analysis on all the proteins and compared them to simulations representing different sizes. We find that the size of the distribution correlates to the position of the protein along the spindle axis. The results are now included as the new Fig. S5 and discussed on page 6 lines 169-176.

The cross-correlation analysis was based on only the position of the maximum value, not the projections. To keep the figure concise, we decided not to include the projections. However, the auto-correlation analysis was indeed based on projections, which we now included in Fig. S5.

Regarding the correlation between the positions of Spc105 molecules, we believe the reviewer actually refers to the correlation between the positions of kinetochores. Auto-/cross-correlations contain the information of the cluster sizes, based on the first peak (as shown in Fig. S5), and the relative distance (if the pattern is periodic). Unfortunately, the positions of kinetochores perpendicular to the spindle axis are not periodically distributed. Therefore, we cannot comment on the separations between adjacent microtubules.

1. Cse4 count (4 per kinetochore) and the model presented: One of the surprising conclusions of the study is that there are two nucleosomes associated with each microtubule attachment, with Mif2/CENP-C potentially interacting with both nucleosomes. There are two critical issues that the authors must consider.

(1) Fluorescent protein chimeras of Cse4 and CBF3 and COMA complex members do not exclusively localize to kinetochores. Biochemical studies show that both Cse4 and CBF3 proteins interact with non-centromeric DNA, e.g., see work from the Biggins lab regarding Cse4 over-expression and also from the Henikoff group that used ChIP-seq. I can't think of a similar reference for the CBF3 complex, but the DNA-binding proteins are also likely to interact with other parts of the genome. The non-centromeric protein is visible as a significant background fluorescence in wide-field microscopy, e.g., see Cep3 localization here: https://images.yeastrc.org/imagerepo/viewExperiment.do?id=202308&experimentGroupOffset=3&experimentOffset=0&experimentGroupSize=3

Similar background fluorescence can be detected for Cse4 and Ctf19. This extra-centromeric localization of Cse4, Cep3, and Ctf19 makes it possible that the protein counts included by the authors are "contaminated" to some extent by the extra-centromeric protein. The authors should discuss this possibility and how it might affect their counts.

After consideration, we agree with the reviewer that, specifically, a fraction of counted Cse4 molecules should be considered non-centromeric. We agree that the previous data is certainly sufficient to conclude it. The reviewer made a similar suggestion about COMA and CBF3 subcomplexes. In recent years a substantial portion of inner kinetochore components has been reconstituted. In Harrison et al. 2019, the Ctf19 complex structure has been solved. Two copies of the complex were observed. Therefore, the non-centromeric pool of COMA is certainly possible and we now made the adjustments to the text (page 8, lines 219-225) and Fig. 4. Accordingly, we now also modified the abstract (page 1, lines 26-27) and restructured the sections (page 10) to accommodate the different possibility of Cse4 copy numbers. While, fluorescence imaging of CBF3 presents a signal throughout the nuclear region we observed only four copies of Cep3 (part of CBF3). A CBF3 structure also has been resolved by Yan et al. 2018, in which the complex was proposed to exist as a dimer. This translates into four copies of Cep3. Therefore, we find it more suitable to leave all observed Cep3 (CBF3) molecules within a kinetochore model.

(2) The model drawn in Figure 4 makes explicit assumptions about the positioning of the four Cse4 molecules (or two nucleosomes) in each kinetochore relative to the rest of the kinetochore components. Yet, the data shown do not justify this specific arrangement. Lawrimore et al. 2011 claim that the non-centromeric Cse4 nucleosomes must be randomly distributed in the pericentromeric chromatin to evade detection in biochemical tests. Therefore, the nearest-neighbor analysis suggested above will be valuable for gaining new insights into the relative positioning of the centromeric- and non-centromeric Cse4 nucleosomes. A similar analysis for Cep3 and Ctf19 will also be helpful. If stereotypical positioning of these molecules cannot be detected, then the model should be revised accordingly (alternative models that are also consistent with the data can be included).

The reviewer has pointed out that Lawrimore et al. 2011 proposed and justified the existence of a non-centromeric Cse4 pool. This arrangement, also potentially along other inner kinetochore components, makes sense and our data did not indicate it otherwise. Therefore, we now revised our model accordingly by applying changes in the main text on page 10 lines 302-305 __as well as in __Fig. 4.

(3) I suggest one experiment that can help the authors better understand protein organization in one kinetochore. Joglekar et al. 2006 used a dicentric chromosome to isolate single kinetochores on the spindle axis to test the assumption that each kinetochore consists of approximately the same number of molecules of kinetochore proteins. The strains are easy to construct (transform existing strains with a linearized plasmid). Single kinetochores can be seen with a low but reasonable frequency. I leave the decision to perform the experiment to the authors' discretion depending on whether the experiment will be worth the effort in strengthening or enhancing their conclusions.

We performed the suggested experiment using the strain published in Joglekar et al. 2006 (kindly provided by Prof. Kerry Bloom) with Cse4 additionally tagged with mMaple. However, we always observed several super-resolved Cse4 clusters (likely of several kinetochores) overlapping with Nuf2-GFP diffraction-limited signal, therefore unable to assign a single isolated kinetochore to the lagging centromere.

1. Information regarding the degree of correction applied to calculate protein count per half-spindle: It will be helpful to include data regarding the degree of correction applied to the expected and measured numbers of NPC protein as supplementary data so that the readers can see the magnitude of this correction relative to the measured counts.

We would like to clarify that we did not correct the data. Instead, we calibrate the copy number, given that the copy number of Nup188 per NPC is known. We assume the same ratio between localization and copy number applies to both Nup188 and the kinetochore proteins. We now include a new Table S4 listing calibration factors of all experiments shown in Fig. 3.

Minor points:

1. McIntosh et al. JCB 2013 used microtubule plus-ends in serial section electron micrographs of yeast spindles to align the centromeric region and found a disk-shaped structure that roughly corresponds to the size of a single nucleosome ~ 80 nm away from the tip of the microtubule and centered the microtubule axis. The authors should refer to this finding in their discussion of the model that they present with two nucleosomes. In my opinion, this is compelling evidence for a nucleosome-like structure serving as the kinetochore foundation.

We agree with this reviewer's comment. The study, among others, present compelling evidence for a point-centromere. We now included the finding in the discussion on page 10, lines 293-294.

1. As discussed by the authors, the number of Cse4 molecules per kinetochore has been the subject of some controversy. Biochemical data from the Biggins group and ChIPseq data from the Westermann group (Altunkaya et al. 2016 Current Biology) strongly suggest that Cse4 molecules can only be found centered on the centromeric sequence. The latter reference should be included in the discussion.

Thank you for pointing this out. Indeed, this is important. We have now added the relevant reference in the discussion on__ page 10 lines 291-292__.

1. Although microscopy-based methods have estimated anywhere from 1, 2, to 6 Cse4 molecules per kinetochore, these studies generally agree on the stoichiometry between Cse4 and the rest of the kinetochore proteins, e.g., Ndc80 complex proteins are ~ 4-fold more abundant that Cse4, etc. The present study seems to disagree with protein stoichiometry. The authors may find it worthwhile to note this feature of their data.

We now discuss the stoichiometry difference between our results and others on page 11 lines 322-324.

1. Omission of the Dam1 complex from this study is disappointing to me personally, but I am sure that the authors have good reasons for this. They should briefly comment on the absence of the Dam1 complex in this study.

To provide information on the Dam1 complex, we imaged Ask1, a component of the complex. The measured positioning and copy number of the protein are now included in Fig. 2 and Fig. 3 respectively, and described and discussed in respective parts of the manuscript.

Reviewer #1 (Significance (Required)):

Cieslinski and colleagues present a single-molecule localization-based study to define the copy numbers and relative organization of kinetochore proteins in budding yeast. These numbers confirm and significantly refine prior measurements of the same aspects of the kinetochore. They also raise new questions and point to new research directions. The measurements also reveal a model of the protein organization of the budding yeast kinetochore in metaphase. For these reasons, the manuscript is of significant interest to the cell division field.

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

In this study, Cielinski and colleagues have applied single molecule localization microscopy to map the positions of proteins in the yeast kinetochore. This has not been reported previously and this study is both well-conducted and the data appear solid. They also use a modification of this technique to assess the stoichiometry of kinetochore proteins. The results that they obtain are broadly in line with several previous studies that use other methodology. There may be an improvement in accuracy using this new approach that has not been obtained previously and there are some important novel conclusions from this work. I would like the authors to address the following concerns prior to publication:

Major points

1. One interesting finding is that there is a discrepancy in the length of both the MIND and NDC80 complexes (from crystallographic data) with their relative positions. The authors suggest that the outer complexes could be twisted or rotated in respect of the spindle axis. It would be great if the authors could illustrate this in their model (or discuss it in the text), to demonstrate the required angle of twist/rotation of both complexes to account for the discrepancy. A twisted filament structure to the outer kinetochore does have some implications for its response to tension - a key determinant of kinetochore-microtubule attachment. It also may provide some flexibility to the structure under tension.

The discussion about this discrepancy has now been incorporated in the main text, page 9 lines 263-267. For clarity, we only partially reflect this in our schematic model (Fig. 4A; the MIND complex) but we already reflected this in the illustrative structural model in Fig. 4B.

1. For the experiment with cycloheximide, the authors state "Although we observed minor changes in copy numbers, the overall effect of CHX was small." For some proteins, Cse4i for example, there appears to be a significant decrease in intensity (30-40%) after cycloheximide treatment, see Figure S3. While the conclusion that tag maturation does not affect copy number measurements is sound, I suggest modifying this section to reflect the data.

We now modified the section accordingly by pointing out that Cse4i under CHX measurements led to reduction of the signal. The modification can be found on page 8 lines 207-211.

1. Page 5. The statement "These data agree reasonably well with previous diffraction-limited dual-color microscopy studies ..." provides readers with little ability to compare the data. I would like to see a supplementary figure comparing these new data with previous studies, especially those of Joglekar et al 2009, see Figure 3 in this paper.

We thank the reviewer for suggesting such a table. This will allow readers a direct comparison of the data between our study and Joglekar at al. 2009. The comparison can be found in new Table S1 __and __Fig. S4, which are now mentioned on page 5.

1. In terms of the distances quoted, are they in one dimension (as per Jogelkar et al 2009) or in three? The results section is entitled "...positions of kinetochore proteins along the metaphase spindle axis", which suggests a single dimension. Please make this very clear in the results section. In the discussion, is the statement "we mapped the relative positions of 15 kinetochore proteins along the kinetochore axis", which is not entirely clear. It seems from the methods that this is one dimension "...we determined the average distance between the two proteins along the spindle axis. “I suggest clarifying the results section briefly and clearly to indicate that this is a single dimension being measured and also using consistent wording of the axis measured throughout the text.

We agree the previous description may not be clear to the viewers. We now changed the text accordingly in the results section, page 5 lines 129-130.

Minor points:

Abstract: I would drop "all" from "For all major kinetochore proteins...", since full characterisation was performed on 14 proteins (9 in terms of copy number).

We now deleted “all” in the abstract as the reviewer suggested__.__

Page 2: "trough" to through.

Corrected.

Page 2 "S. cerevisiae" to italics

Corrected.

Methods p11. How do the MKY strains relate to common yeast genetic backgrounds? (e.g. are they S288C?).

MKY strains are derivative of S288C. The information was now updated in the Methods section and in Table S2.

Reviewer #2 (Significance (Required)):

This manuscript, together with an accompanying one from Virat et al., are nice complementary studies that provide the first single molecule localization studies of the yeast kinetochore. Although other labs have used super-resolution methods to study individual kinetochore proteins; both of these new studies map distances between many proteins at the kinetochore and thus are able to produce maps of the overall kinetochore structure. Like the previous study using standard resolution methods (Joglekar et al, 2009. Current Biology 19, 694-699); these studies will likely provide a benchmark for future studies on eukaryotic kinetochore architecture, including those in mammalian systems. Additionally, this work will appeal to super-resolution microscopists.

My expertise is as a yeast kinetochore cell biologist.

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Referee #2

Evidence, reproducibility and clarity

In this study, Cielinski and colleagues have applied single molecule localization microscopy to map the positions of proteins in the yeast kinetochore. This has not been reported previously and this study is both well-conducted and the data appear solid. They also use a modification of this technique to assess the stoichiometry of kinetochore proteins. The results that they obtain are broadly in line with several previous studies that use other methodology. There may be an improvement in accuracy using this new approach that has not been obtained previously and there are some important novel conclusions from this work. I would like the authors to address the following concerns prior to publication:

Major points

1. One interesting finding is that there is a discrepancy in the length of both the MIND and NDC80 complexes (from crystallographic data) with their relative positions. The authors suggest that the outer complexes could be twisted or rotated in respect of the spindle axis. It would be great if the authors could illustrate this in their model (or discuss it in the text), to demonstrate the required angle of twist/rotation of both complexes to account for the discrepancy. A twisted filament structure to the outer kinetochore does have some implications for its response to tension - a key determinant of kinetochore-microtubule attachment. It also may provide some flexibility to the structure under tension.
2. For the experiment with cycloheximide, the authors state "Although we observed minor changes in copy numbers, the overall effect of CHX was small." For some proteins, Cse4i for example, there appears to be a significant decrease in intensity (30-40%) after cycloheximide treatment, see Figure S3. While the conclusion that tag maturation does not affect copy number measurements is sound, I suggest modifying this section to reflect the data.
3. Page 5. The statement "These data agree reasonably well with previous diffraction-limited dual-color microscopy studies ..." provides readers with little ability to compare the data. I would like to see a supplementary figure comparing these new data with previous studies, especially those of Joglekar et al 2009, see Figure 3 in this paper.
4. In terms of the distances quoted, are they in one dimension (as per Jogelkar et al 2009) or in three? The results section is entitled "...positions of kinetochore proteins along the metaphase spindle axis", which suggests a single dimension. Please make this very clear in the results section. In the discussion, is the statement "we mapped the relative positions of 15 kinetochore proteins along the kinetochore axis", which is not entirely clear. It seems from the methods that this is one dimension "...we determined the average distance between the two proteins along the spindle axis."I suggest clarifying the results section briefly and clearly to indicate that this is a single dimension being measured and also using consistent wording of the axis measured throughout the text.

Minor points:

Abstract: I would drop "all" from "For all major kinetochore proteins...", since full characterisation was performed on 14 proteins (9 in terms of copy number).

Page 2: "trough" to through.

Page 2 "S. cerevisiae" to italics

Methods p11. How do the MKY strains relate to common yeast genetic backgrounds? (e.g. are they S288C?).

Significance

This manuscript, together with an accompanying one from Virat et al., are nice complementary studies that provide the first single molecule localization studies of the yeast kinetochore. Although other labs have used super-resolution methods to study individual kinetochore proteins; both of these new studies map distances between many proteins at the kinetochore and thus are able to produce maps of the overall kinetochore structure. Like the previous study using standard resolution methods (Joglekar et al, 2009. Current Biology 19, 694-699); these studies will likely provide a benchmark for future studies on eukaryotic kinetochore architecture, including those in mammalian systems. Additionally, this work will appeal to super-resolution microscopists.

My expertise is as a yeast kinetochore cell biologist.

(Could not highlight what attended to) Another good addition to making their web much more accesible to all the people that might go on to there website. And that is a descriptive tag for images on the website, for screen readers to be able to identify and describe the photo that is placed there. This allows people with visual impairments to identify everything that is going on, all with just listening to the Text To Speech.

This is optional we can add optional tag in bottom at all place

the formatting here makes it difficult to identify where the paragraph tag closes.

It would be better if you place the content next to the opening tag and the close it there, not in a new line.

p tag is not used. Few inline tags are used.

More minor nit-picking, the 'li' element is missing its closing tag

no errors

keep the tag along with the content it is related to

No error. good

This looks good! Only improvement could be to format the elements in such a away that would communicate the hierarchy. For example each 'li' element could be on its own line and further indented from the 'ul' tag.

again just needs opening and closing 'p' tags

I have tried to find mistakes but i am not able to do so. This is just example of perfect coding.

no errors

related, but not drawn from as I've been thinking about the continuum of taxonomies and subject headings for a while...

On the Spectrum of Topic Headings in note making

Any reasonable note one may take will likely have a hierarchical chain of tags/subject headings/keywords going from the broad to the very specific. One might start out with something broad like "humanities" (as opposed to science), and proceed into "history", "anthropology", "biological anthropology", "evolution", and even more specific. At the bottom of the chain is the specific atomic idea on the card itself. Each of the subject headings helps to situate the idea and provide the context in which it sits, but how useful within a note taking system is having one or more of these tags on it? What about overlaps with other broader subjects (one will note that "evolution" might also sit under "science" / "biology" as well), but that note may have a different tone and perspective than the prior one.

This becomes an interesting problem or issue as one explores ideas in a pre-designed note taking system. As a student just beginning to explore anthropology, one may tag hundreds of notes with anthropology to the point that the meaning of the tag is so diluted that a search of the index becomes useless as there's too much to sort through underneath it. But as one continues their studies in the topic further branches and sub headings will appear to better differentiate the ideas. This process will continue as the space further differentiates. Of course one may continue their research into areas that don't have a specific subject heading until they accumulate enough ideas within that space. (Take for example Daniel Kahneman and Amos Tversky's work which is now known under the heading of Behavioral Economics, a subject which broadly didn't exist before their work.) The note taker might also leverage this idea as they tag their own work as specifically as they might so as not to pollute their system as it grows without bound (or at least to the end of their lifetime).

The design of one's note taking system should take these eventualities into account and more easily allow the user to start out broad, but slowly hone in on direct specificity.

Some of this principle of atomicity of ideas and the growth from broad to specific can be seen in Luhmann's zettelkasten (especially ZK II) which starts out fairly broad and branches into the more specific. The index reflects this as well and each index heading ideally points to the most specific sub-card which begins the discussion of that particular topic.

Perhaps it was this narrowing of specificity which encouraged Luhmann to start ZKII after years of building ZKII which had a broader variety of topics?

Author Response

Reviewer #3 (Public Review):

1) Validation of reagents: The authors generated a pY1230 Afadin antibody claiming that (page 6) "this new antibody is specific to tyrosine phosphorylated Afadin, and that pY1230 is targeted for dephosphorylation by PTPRK, in a D2-domain dependent manner". The WB in Fig 1B shows a lot of background, two main bands are visible which both diminish in intensity in ICT WT pervanadate-treated MCF10A cell lysates. The claim that the developed peptide antibody is selective for pY1230 in Afadin would need to be substantiated, for instance by pull down studies analysed by pY-MS to substantiate a claim of antibody specificity for this site. However, for the current study it would be sufficient to demonstrate that pY1230 is indeed the dephosphorylated site. I suggest therefore including a site directed mutant (Y1230F) that would confirm dephosphorylation at this site and the ability of the antibody recognizing the phosphorylation state at this position.

We would like this antibody to be a useful and freely accessible tool in the field and have taken on board the request for additional validation. To this end we have significantly expanded Supplementary Figure 2 (now Figure 1 - figure supplement 2) and included a dedicated section of the results as follows: 1. We have now included information about all of the Afadin antibodies used in this study, since Afadin(BD) appears to be sensitive to phosphorylation (Figure 1 - figure supplement 2A). 2. We have demonstrated that the Afadin pY1230 antibody detects an upregulated band in PTPRK KO MCF10A cells, consistent with our previous tyrosine phosphoproteomics (Figure 1 - figure supplement 2B). This indicates that the antibody can be used to detect endogenous Afadin phosphorylation. 3. We have included two new knock down experiments demonstrating the recognition of Afadin by our antibody (Figure 1 - figure supplement 2C). There appear to be two Afadin isoforms recognised in HEK293T cells by both the BD and pY1230 antibody, consistent with previous reports (Umeda et al. MBoC, 2015). We have highlighted these in the figure. 4. We have performed mutagenesis to demonstrate the specificity of the antibody. We tagged Afadin with a fluorescent protein tag, reasoning that it would cause a shift in molecular weight that could be resolved by SDS PAGE, as is the case. We noted that the phosphopeptide used spans an additional tyrosine, Y1226, which has been detected as phosphorylated (although to a much lower extent than Y1230) on Phosphosite plus. The data clearly show that Afadin cannot be phosphorylated when Y1230 is mutated to a phenylalanine (compared to CIP control), indicating that this is the predominant site recognised by the antibody. In addition, the endogenous pervanadate-stimulated signal is completely abolished by CIP treatment (Figure 1 - figure supplement 2D). 5. We have included densitometric quantification of the dephosphorylation assay shown in Figure 1B, which was part of a time course and shows preferential dephosphorylation by the PTPRK ICD compared to the PTPRK D1. The signal stops declining with time, which could indicate antibody background, or an inaccessible pool of Afadin-pY1230 (Figure 1 - figure supplement 2E). 6. To further demonstrate that this site is modulated by PTPRK in post-confluent cells, we have used doxycycline (dox)-inducible cell lines generated in Fearnley et al, 2019. Upon treatment with 500 ng/ml Dox for 48 hours PTPRK is induced to lower levels than wildtype, however, normalized quantification of the Afadin pY1230 against the Afadin (CST) signal clearly indicates downregulation by PTPRK WT, but not the catalytically inactive mutant (Figure 1 - figure supplement 2F and 2G). Together these data strengthen our assertion that this antibody recognises endogenously phosphorylated Afadin at site Y1230, which is modulated in vitro and in cells by PTPRK phosphatase activity. For clarity, we have highlighted and annotated the relevant bands in figures. We have also included identifiers for each Afadin total antibody was used in particular experiments.

2) The authors claim that a short, 63-residue predicted coiled coil (CC) region, is both necessary and sufficient for binding to the PTPRK-ICD. The region is predicted to have alpha-helical structure and as a consequence, a helical structure has been used in the docking model. Considering that the authors recombinantly expressed this region in bacteria, it would be experimentally simple confirming the alpha-helical structure of the segment by CD or NMR spectroscopy.

To clarify, the helical structure in the docking model was independently predicted by several sequence and structural analysis programmes including AlphaFold2, RobettaFold, NetSurfP and as annotated in Uniprot (as a coiled coil). We did not stipulate prior to the AF2 prediction that it was helical. Isolated short peptides frequently adopt helical structure, therefore prediction of a helix within the context of the full Afadin sequence is, in our opinion, stronger evidence than CD of an isolated fragment.

3) Only two mutants have been introduced into PTPRK-ICD to map the Afadin interaction site. One of the mutations changes a possibly structurally important residues (glycine) into a histidine. Even though this residue is present in PTPRM, it does not exclude that the D2 domain no longer functionally folds. Also the second mutation represents a large change in chemical properties and the other 2 predicted residues have not been investigated.

The residues that were selected for mutation are all localised to the protein surface and therefore are unlikely to be involved in stable folding of PTPRK. In support of the correct folding of the mutated PTPRK, we include in Figure 1 below SEC elution traces for wild-type and mutant D2 showing that they elute as single symmetric peaks at the same elution volume as the WT protein. This is consistent with them having a similar shape and size, and not being aggregated or unfolded.

Figure 1. PTPRK-D2 wild-type and mutant preparative SEC elution profiles. A280nm has been normalised to help illustrate that the different proteins elute at the same volume. The main peak from these samples was used for binding assays in the main paper.

Furthermore, the yield for the double mutant was very high (4 mg of pure protein from a 2 L culture, see A280 value in graph below), whereas poorly folded proteins tend to have significantly reduced yields. This protein was also very stable over time whereas unfolded proteins tend to degrade during or following purification.

Figure 2. Analytical SEC elution profile for the PTPRK-D2 DM construct showing the very high yield consistent with a well-folded, stable protein.

Finally, we have carried out thermal melt curves of the WT and mutant PTPRK D2 domains showing that they all possess melting temperatures between 39.3°C and 41.7°C, supporting that they are all equivalently folded. We include these data as an additional Supplementary Figure (Figure 4 - figure supplement 3) in the paper.

4) The interface on the Afadin substrate has not been investigated apart from deleting the entire CC or a central charge cluster. Based on the docking model the authors must have identified key positions of this interaction that could be mutated to confirm the proposed interaction site.

We have now made and tested several additional mutations within both the Afadin-CC and PTPRK-D2 domains to further validate the AF2 predicted model of the complex.

For Afadin-CC we introduced several single and double mutations along the helix including residues predicted to be in the interface and residues distal from the interface. These mutations and the pulldown with PTPRK are described in the text and are included as additional panels to a modified Figure 3. All mutations have the expected effect on the interaction based on the predicted complex structure. To help illustrate the positions of these mutations we have also included a figure of the interface with the residues highlighted.

For the PTPRK-D2 we have also introduced two new mutations, one buried in the interface (F1225A) and one on the edge of the interface encompassing a loop that is different in PTPRM (labelled the M-loop). GST-Afadin WT protein was bound to GSH beads and tested for their ability to pulldown WT and mutated PTPRK. These new mutations (illustrated in the new Figure 4 – figure supplement 2) further support the model prediction. F1225A almost completely abolishes binding as predicted, while the M-loop retains binding. These mutations and their effects are now described in the main text and the pull-down data, including controls and retesting of the original DM mutant, are included as panel H in a newly modified Figure 4 focussed solely on the PTPRK interface.

5) A minor point is that ITC experiments have not been run long enough to determine the baseline of interaction heats. In addition, as large and polar proteins were used in this experiment, a blank titration would be required to rule out that dilution heats effect the determined affinities.

All control experiments including buffer into buffer, Afadin into buffer and buffer into PTPRK were carried out at the same time as the main binding experiment and are shown below overlaid with the binding curve. These demonstrate the very small dilution heats consistent with excellent buffer matching of the samples.

We were able to obtain excellent fits to the titration curves by fitting 1:1 binding with a calculated linear baseline (see Figure 2B,D). Very similar results were obtained by fitting to the sum (‘composite’) of fitted linear baselines obtained for the three control experiments for each titration.

"The <meta> tag defines metadata about an HTML document. Metadata is data (information) about data. <meta> tags always go inside the <head> element, and are typically used to specify character set, page description, keywords, author of the document, and viewport settings."

Our justice system in a nutshell. The one who fits the tag of a media defined criminal instantly made to look guilty without validation or solid evidences.

.code code css code, kbd, pre, tt, .ace_editor.ace-github-light, .blame-sha, .blob-code-inner, .blob-num, .branch-name, .commit .sha, .commit .sha-block, .commit-desc pre, .commit-ref, .commit-tease-sha, .default-label .sha, .export-phrase pre, .file-editor-textarea, .file-info, .gollum-editor .expanded textarea, .gollum-editor .gollum-editor-body, .hook-delivery-guid, .hook-delivery-response-status, .input-monospace, .news .alert .branch-link, .oauth-app-info-container dl.keys dd, .quick-pull-choice .new-branch-name-input input, .tag-references > li.commit, .two-factor-secret, .upload-files .filename, .url-field, .wiki-wrapper .wiki-history .commit-meta code { font-family: 'Fira Code' !important; }

joined :)

Historically, the related topics section of each GoutPal page identified relationships using the WordPress Tag feature. However, after I invented WordPress Transmigration, I need a better approach.

So I must replace this with a navigation map. And create new workflows that enable me to update these maps efficiently.

#BuildInPublic

My latest research summarries about edamame and gout are available to GoutPal Links Subscribers. So far, my research notes cover topics for new edamame pages. Including reviews of studies about edamame and uric acid. Also, I'm researching general health benefits of edamame in my Food Research Channel. So I'll re-purpose some of that for gout sufferers where there is an impact on inflammation.

Such a good tool that I wish I knew about earlier.

this also seems unfortunately like not the right solution, though. should nice clothes always cost a lot of money? how would a utopian society deal with this sort of weighty clothes? are we all making clothes for each other? but then we lose the idea of fashion/designers/luxury - in some sense i wonder how capital and design are intertwined

Text-Based Adventure Game

Taken from Maciej Cegłowski https://idlewords.com/talks/website_obesity.htm.

I'm not sure if there is supposed to be a forward slash before the closing bracket in order to properly close the tag

I'm not sure if there is supposed to be a forward slash before the closing bracket in order to properly close the tag

annotation meta: may need new tag: fear would be used for ... valid uses for it beyond ...

annotation meta: may need new tag: you don't have a _; you have a _

annotation meta: may need new tag: applies to siblings only or applies to same level only

Make sure every element has one opening and closing tag. (with the exception of some elements don't require closing tags).

So in this case, the middle "p" tag isn't doing anything.

• It's good practice to put new elements on a new line
• You have two opening paragraph tags (

  ) before a closing tag (

  ), paragraph content should be between

annotation meta: may need new tag:

dynamic behavior vs. static analysis [not quite parallel]

or can we reuse something else like?: lexical semantics vs. run-time semantics

annotation meta: may need new tag: - don't want to change or remove existing feature [because...] - solving problem B will reduce misunderstandings around feature A

annotation meta: may need new tag:

distinction between nothing, false, and empty array

verbiage that allows for ...

Context: Lindsey Graham had just proposed a bill for a nationwide abortion ban after 15 weeks of pregnancy.

McConnell's position seems to be one that choice about abolition is an option, but one which is reserved for white men of power over others. This is painful because that choice is being left to people without any of the information and nuance about specific circumstances versus the pregnant women themselves potentially in consultation with their doctors who have broad specific training and experience in the topics and issues at hand. Why are these leaders attempting to make decisions based on possibilities rather than realities, particularly when they've not properly studied or are generally aware of any of the realities?

If this is McConnell's true position, then why not punt the decision and choices down to the people directly impacted? And isn't this a long running tenet of the Republican Party to allow greater individual freedoms? Isn't their broad philosophy: individual > state government > national government? (At least with respect to internal, domestic matters; in international matters the opposite relationships seem to dominate.)

tl;dr:<br /> Mitch McConnell believes in choice, just not in your choice.

Here's the actual audio from a similar NPR story:<br /> https://ondemand.npr.org/anon.npr-mp3/npr/me/2022/09/20220914_me_gop_sen_lindsey_graham_introduces_15-week_abortion_ban_in_the_senate.mp3#t=206

---

McConnell is also practicing the Republican party game of "do as I say and not as I do" on Graham directly. He's practicing this sort of hypocrisy because as leadership, he's desperately worried that this move will decimate the Republican Party in the midterm elections.

There's also another reading of McConnell's statement. Viewed as a statement from leadership, there's a form of omerta or silent threat being communicated here to the general Republican Party membership: you better fall in line on the party line here because otherwise we run the risk of losing power. He's saying he's leaving it up to them individually, but in reality, as the owner of the purse strings, he's not.

---

Thesis:<br /> The broadest distinction between American political parties right now seems to be that the Republican Party wants to practice fascistic forms of "power over" while the Democratic Party wants to practice more democratic forms of "power with".

Pillar of Oenomaus should be a tag added. Wish there was a better location description

Another possible tag? How many alters should we consider important to tag?

Could this be a potential tag on the map that we dont have?

I can see how this statement could relate to the previous reading (Ch. Introduction The Digital Humanities Moment) about there being too many people practicing the digital humanities. When hashtags get used and everyone starts contributing information, misinformation can spread just as easily as facts. Maybe this is an example of the kind of situation Gold implied when he said people can ask "disruptive questions"?

Nachdem Gott den Menschen am sechsten Tag schuf, sagte er dass es sehr gut war. Nachdem er [der Mensch] dann im Garten Eden das einzige Gebot brach, dass seinen geistlichen Tod herbeiführte und ihn von Gott trennte, wurde es nötig, dass Gott den Menschen von dieser Trennung erlöst. Die Erlösung kommt nur durch den Glauben an den Erlöser (Jesus Christus) selber, er befreit uns vom geistlichen Tod und hat unsere Schuld auf sich selbst genommen. Er hat uns Erlöst von unserer Schuld, sie kommt nur durch ihn.

• SEE

THIS WILL HELP WITH ORGANIZATION! USE IT (talking to myself here)

black box

opaque score

Introduction: The introduction to a scholarly article describes the topic or problem the authors researched. Sometimes it's labeled, sometimes it's not. The authors will present the thesis of their argument or the goal of their research. The introduction may also discuss the relevance or importance of the research question. An overview of related research and findings, called a literature review, may appear in the introduction, though the literature review may be in its own section.

The idea of social cost of carbon (SCC) is fascinating: seemingly it aims to make the social costs of climate crisis objective by giving them a price tag. But then it becomes clear that the price tag depends on political / value judgements concerning the future, on which the idea of "discounting" depends.

I totally agree! It was cool to have a walk through on what a civilization entails to the author rather than them operating on assumption.

Always start component names with a capital letter.

Author Response

Reviewer #3: (Public Review):

In this ms Li et al. examine the molecular interaction of Rabphilin 3A with the SNARE complex protein SNAP25 and its potential impact in SNARE complex assembly and dense core vesicle fusion.

Overall the literature of rabphilin as a major rab3/27effector on synaptic function has been quite enigmatic. After its cloning and initial biochemical analysis, rather little new has been found about rabphilin, in particular since loss of function analysis has shown rather little synaptic phenotypes (Schluter 1999, Deak 2006), arguing against that rabphilin plays a crucial role in synaptic function.

While the interaction of rabphilin to SNAP25 via its bottom part of the C2 domain has been already described biochemically and structurally in the Deak et al. 2006, and others, the authors make significant efforts to further map the interactions between SNAP25 and rabphilin and indeed identified additional binding motifs in the first 10 amino acids of SNAP25 that appear critical for the rabphilin interaction.

Using KD-rescue experiments for SNAP25, in TIRF based imaging analysis of labeled dense core vesicles showed that the N-terminus of SN25 is absolutely essential for SV membrane proximity and release. Similar, somewhat weaker phenotypes were observed when binding deficient rabphilin mutants were overexpressed in PC12 cells coexpressing WT rabphilin. The loss of function phenotypes in the SN25 and rabphilin interaction mutants made the authors to claim that rabphilin-SN25 interactions are critical for docking and exocytosis. The role of these interaction sites were subsequently tested in SNARE assembly assays, which were largely supportive of rabphilin accelerating SNARE assembly in a SN25 -terminal dependent way.

Regarding the impact of this work, the transition of synaptic vesicles to form fusion competent trans-SNARE complex is very critical in our understanding of regulated vesicle exocytosis, and the authors put forward an attractive model forward in which rabphilin aids in catalyzing the SNARE complex assembly by controlling SNAP25 a-helicalicity of the SNARE motif. This would provide here a similar regulatory mechanism as put forward for the other two SNARE proteins via their interactions with Munc18 and intersection, respectively.

We thank the reviewer #3 for the summary of the paper and for the praise of our work. The point-to-point replies are as follow:

While discovery of the novel interaction site of rabphilin with the N-Terminus of SNAP25 is interesting, I have issues with the functional experiments. The key reliance of the paper is whether it provides convincing data on the functional role of the interactions, given the history of loss of function phenotypes for Rabphilin. First, the authors use PC12 cells and dense core vesicle docking and fusion assays. Primary neurons, where rabphilin function has been tested before, has unfortunately not been utilized, reducing the impact of docking and fusion phenotype.

We have discussed these questions as mentioned in our response to Essential Revisions 3 and added this corresponding passage to the Discussion section (pp.18-19, lines 407-427).

In particular the loss of function phenotype in figure 3 of the n-terminally deleted SNAP25 in docking and fusion is profound, and at a similar level than the complete loss of the SNARE protein itself. This is of concern as this is in stark contrast to the phenotype of rabphilin loss in mammalian neurons where the phenotype of SNAP25 loss is very severe while rabphilin loss has almost no effect on secretion. This would argue that the N-terminal of SNAPP25 has other critical functions besides interacting with rabphilin. In addition, it could argue that the n-Terminal SNAP25 deletion mutant may be made in the cell (as indicated from the western blot) but may not be properly trafficked to the site of release

To test whether the N-peptide deletion mutant of SN25 can properly target to the plasma membrane, we overexpressed the SN25 FL or SN25 (11–206) with C-terminal EGFP-tag in PC12 cells and monitored the localization of SN25 FL-EGFP and SN25 (11–206)-EGFP near the plasma membrane by TIRF microscopy. We observed that the average fluorescence intensity of SN25 (11–206)-EGFP showed no significant difference with SN25 FL-EGFP as below, suggesting that the N-peptide deletion mutant may not influence the trafficking of SN25 to plasma membrane.

(A) TIRF imaging assay to monitor the localization of SN25-EGFP near the plasma membrane. Overexpression of SN25 FL-EGFP (left) and SN25 (11–206)-EGFP (right) using pEGFP-N3 vector in PC12 cells. Scale bars, 10 μm. (B) Quantification of the average fluorescence intensity of SN25-EGFP near the plasma membrane in (A). Data are presented as mean ± SEM (n ≥ 10 cells in each). Statistical significance and P values were determined by Student’s t-test. ns, not significant.