We need to show comments in issues (after creation). It will be similar to Adding a comment but users should know this exists. e.g. 3. After creating an issue, you can open the issue again and tag team members you wish to collaborate with on this issue. <add screenshot>

Author Response

Reviewer #1 (Public Review):

1) The authors show that there are several classes of Snf1 targets (Fig. 3e), most notably some that are phosphorylated immediately after Snf1 activation by glucose (<5 min) and others that are only phosphorylated after 15 min. In a simple view, all direct Snf1 targets should be phosphorylated immediately after Snf1 activation. Is that the case? What is the overlap between the direct targets found using the OBIKA assay and the slow and fast responding in vivo targets? What about the phosphorylation motif, does it differ between the groups? These points are not discussed in the text except to point out that the direct Snf1 target Msn4 is among the slowly phosphorylated group.

This is a very good point and we have performed the suggested analysis, which resulted in an interesting finding that we describe now in the text as follows:

“Notably, of the 145 confirmed target sites, 81 (i.e. 72%) were significantly regulated after both 5 min and 15 min. Of the remaining 64 sites, 32 responded only after 5 min, while the other 32 responded only after 15 min. Some of the former residues are located within Snf1 itself, the -subunit of the Snf1 complex (i.e. Sip1), the Snf1-targeting kinase Sak1, or Mig1, while some of the latter are located within the known Snf1-interacting proteins such as Gln3, Msn4, and Reg1. These observations indicate that Snf1-dependent phosphorylation initiates, as expected, within the Snf1 complex and then progresses to other effectors. Interestingly, based on the residues that responded exclusively after 5 min, we retrieved a perfect Snf1 consensus motif (i.e. an arginine residue in the -3 position and a leucine residue in the +4 position; Supplementary figure 2A). The one retrieved for the residues that respond exclusively at 15 min, in contrast, significantly deviated from this consensus motif (Supplementary figure 2B). The slight temporal deferral of Snf1 target phosphorylation may therefore perhaps in part be explained by reduced substrate affinity due to consensus motif divergence.”

2) The data showing that Snf1-dependent phosphorylation of Pib2 plays a key role in triggering inhibition of TORC1 is convincing but is entirely dependent on a rescue of the TORC1 inhibition defect seen in cells where Snf1 is inhibited. That is, TORC1 is normally inactivated during glucose starvation; this does not occur when Snf1 is inhibited by 2nm-pp1 but does occur when Snf1 is inhibited in a strain carrying a phosphomimetic version of Pib2 (Pib2SESE). This indicates that Pib2 phosphorylation is sufficient to replace Snf1 signaling and inhibit TORC1 during glucose starvation. However, in a simple model, a phosphodead version of Pib2 (SASA) should have the opposite effect. That is TORC1 should remain active during glucose starvation in the Pib2SASA strain-but that is not the case (Fig. 4g). This point is not discussed in the paper; why do the authors think that TORC1 is inhibited normally in the SASA mutant inhibits TORC1 normally?

We fully agree with this statement and have highlighted and discussed this issue now in the last paragraph of the results section (where we think this fits best) as follows:

“In contrast, the separated and combined expression of Sch9S288A and Pib2S268A,S309A showed, as predicted, no significant effect in the same experiment. Unexpectedly, however, the latter combination did not result in transient reactivation of TORC1, like we observed in glucose-starved, Snf1-compromised cells. This may be explained if TORC1 reactivation would rely on specific biophysical properties of the non-phosphorylated serines within Sch9 and Pib2 that may not be mimicked by respective serine-to-alanine substitutions. Alternatively, Snf1 may employ additional parallel mechanisms (perhaps through phosphorylation of Tco89, Kog1, and/or other factors; see above) to prevent TORC1 reactivation even when Pib2 and Sch9 cannot be appropriately phosphorylated. While such models warrant future studies, our current data still suggest that Snf1-mediated phosphorylation of Pib2 and Sch9 may be both additive and together sufficient to appropriately maintain TORC1 inactive in glucose-starved cells”

Reviewer #2 (Public Review):

1) Because PIB2 is a major focus of the manuscript, I was surprised that it was not discussed in the introduction. I think it would be appropriate to discuss prior evidence linking this protein to TORC1.

We thank the reviewer for this suggestion. Pib2 and its role in TORC1 control is now described in the introduction.

2) The authors introduce mutations into PIB2 at two sites determined to be phosphorylated by SNF1, at S268 and S309. Somewhat confusing results are obtained, in that the PIB2 null and phosphomimic mutants (S268E and S309E) confer a similar TORC1 phenotype, compared to the S268A S308A mutant. These results require further explanation than simply that "TORC1 inactivation defect in SNF1-compromised cells is due to a defect in PIB1 phosphorylation". This is particularly intriguing given that the opposite results are observed with the SCH9 mutants, where the null and alanine mutants confer a similar phenotype compared to the S to E mutants.

The finding that both loss of Pib2 and expression of the phosphomimetic allele yield the same phenotype is indeed counterintuitive. Hence, we fully agree with the criticism put forward here. We believe that the underlying reason for our observation is based on the unique property of Pib2 in having both a C-terminal TORC1-activating domain (CAD) and an-N-terminal TORC1-inhibitory domain (NID). We have addressed this point briefly in the discussion ("Our current data favor a model according to which Snf1-mediated phosphorylation of the Kog1-binding domain in Pib2 weakens its affinity to Kog1 and thereby reduces the TORC1-activating influence of Pib2 that is mediated by the C-terminal TORC1-activating (CAD) domain via a mechanism that is still largely elusive"), but now also address this issue in the results section as suggested.

3) The authors conclude, based on the co-IP data in Figure 4H, that interactions between KOG1 and PIB2 are direct. However, it remains possible that interactions between these proteins are mediated by other components of TORC1 or within cells. This should be addressed.

Please note that the Kog1-Pib2 interaction has previously been demonstrated by different methods. Accordingly, Pib2 has not only been shown to interact with Kog1 (or TORC1) in co-IP studies in vivo (PMID: 30485160, PMID: 29698392), but also by co-IP studies in vitro (PMID: 29698392, PMID: 28483912, PMID: 34535752). In addition, the interaction between Kog1-Pib2 has also been dissected (down to defined domains) by classical two hybrid analyses (PMID: 28481201). All of these studies are cited now in the introduction where Pib2 is discussed.

4) The authors demonstrate convincingly that the PIB2 and SCH9 SNF1-specific phospho-site mutants have a detectable effect on TORC1, primarily by examining TORC1-dependent phosphorylation of SCH9. What is unclear is whether phosphorylation at these sites has a significant physiological impact on cells. It appears that the rapamycin hyper-sensitivity displayed in Figure 6E is the only data presented to address this question. It would be appropriate for the authors to comment further on the significance of SNF1-dependent phosphorylation of these two substrates.

To further address the physiological role of the Snf1-dependent phosphorylation of Sch9 and Pib2 combined, we newly assessed the growth rate of the strain that expresses the Sch9SE and Pib2SESE alleles combined. Accordingly, we found the snf1as pib2SESE sch9SE strain to exhibit a significantly higher doubling time than the snf1as strain on both low-nitrogen-containing media and standard synthetic complete media. This is now included in the text (results section).

Reviewer #3 (Public Review):

1) Conceptually, the manuscript shows that Snf1 activity is important for the acute inhibition of TORC1 during glucose starvation. However, this is mainly restricted to 10 and 15 minutes of glucose starvation. After 20 minutes, TORC1 is inhibited by some unknown mechanisms independent of Snf1 (Hughes Hallet et al). This raises concern regarding the physiological relevance of Snf1-mediated TORC1 inhibition during acute glucose stress. The authors show that this regulation is important for the survival of cells under TORC1 inhibition. How do the authors envision that the acute role of Snf1 plays an important long-term physiological relevance during rapamycin treatment? Providing more support for the physiological relevance of this regulation will make this study of interest to a broad readership.

Please see our response to point 4 of reviewer #2.

2) Another major concern of the manuscript is the inconsistencies between the various representative immunoblots and their quantifications. The effect of AMPK activity on TORC1 signaling under glucose starvation seems very subtle. A few specific concerns are mentioned below:

a) In figure 1A, the increase in TORC1 activity upon inhibition of analogue sensitive Snf1as by 2NM-PP1 is very marginal. Although quantification shows a significant increase, a representative western blot figure should be shown.

We have replaced the original immunoblots with more representative ones in Figure 1A.

b) Does deleting Snf1 itself have any effect on TORC1 activity? Lane 4 of figure 1A shows reduced activity compared to lane 1.

TORC1 activity is generally assessed as the ratio between phosphorylated Sch9 and total Sch9 (see also below under (e)). Accordingly, based on the quantification of 6 blots (we added two more experiments to address this point; Figure 1B), loss of Snf1 has no significant impact on TORC1 activity in exponentially growing cells, as we expected.

c) To show the effect of Snf1 on the repression of TORC1, the time-course experiments are run on two separate gels in figure 1C. Hence, it is difficult to compare the effect of Snf1 on unscheduled reactivation of TORC1 under glucose starvation.

Please note that the data of the two blots were cross-normalized to the sample from exponentially growing cells (labeled “Exp”; i.e. the same sample was loaded on the two blots) in order to compare and quantify the effects of Snf1.

d) In figure 1E, the effect of Reg1 deletion on TORC1 activity seems minor as both phospho- and total levels of Sch9 are reduced.

As correctly pointed out by this reviewer, we consistently found the total Sch9 levels to be lower in reg1Δ cells when compared to wild-type cells. To assess TORC1 activity, we therefore always determine the ratio between phosphorylated Sch9 and total Sch9, and the respective ratio is significantly different in reg1∆ cells when compared to wild-type cells. We speculate that the reduced Sch9 levels in this mutant are caused by the reduced growth rate (PMID: 22140226) and hence lower protein synthesis rate (to which translation of SCH9 mRNA may be specifically sensitive).

Since further mechanistic insights are based on these initial findings of figure 1, solidifying these observations is very important.

3) In figure S1, the analogue sensitive Snf1as shows significant reduction in its activity (reduced S79 phosphorylation of ACC1-GFP). This raises the concern of whether this genetic background is an ideal system to resolve the mechanism of TORC1 suppression.

The Snf1as allele is indeed hypomorphic, which we acknowledge appropriately in the text. We would like to point out however, that we took great care in each experiment to include the DMSO control that allowed us to unequivocally assign any observed effects to the specific drug-mediated inhibition of Snf1as. Importantly, we think that the hypomorphic nature of the Snf1as allele (which allows normal growth on non-fermentable carbon sources) represents a minor trade-off when compared to the advantages that this allele provides over the use of a snf1∆ strain, which exhibits a fundamentally reprogrammed transcriptome/proteome (PMID: 17981722). Accordingly, this allele allows the assessment of Snf1 inhibition on very short time scales while minimizing confounding large-scale proteome rearrangements that may indirectly affect the studies. Moreover, use of the Snf1as allele also allowed us to compare our results more directly with other phosphoproteome studies that used the same allele (PMID: 25005228, PMID: 28265048). Finally, please also note that our main conclusions (on Snf1-mediated control of TORC1) are corroborated by additional genetic data such as the ones in Figure 1A/E where we use snf1∆ and reg1∆ cells.

4) In figure 2, during glucose restimulation, there is increased retention of Snf1as-pThr210 in the presence of 2NM-PP1. This suggests that the upstream glucose sensing pathway as well as Snf1 might be more active than in DMSO-treated cells. This also raises concerns regarding the suitability of the genetic background for the study. Can authors comment on why this phosphorylation persists? Does the phosphoproteomic analysis give any hint for this phenotype?

This is a very good point. In fact, we forgot to mention in the text that the observed effect of the 2NM-PP1 treatment on Snf1-Thr210 phosphorylation has already been studied and mechanistically explained earlier (PMID: 23184934). Accordingly, the entry of the drug into the broader catalytic cleft of the Snf1as mutant causes the catalytic domain to be stabilized in a conformation, which prevents dephosphorylation of pThr210 by the dedicated Glc7-Reg1 phosphatase heterodimer. This can be observed each time when we compared 2NM-PP1- and DMSO-treated cells and probed for Snf1-Thr210 phosphorylation. This is, in fact, an independent control for proper 2NM-PP1 functioning. We have now added a sentence (including reference) that pinpoints this issue in the text.

5) In figure 4H, where authors claim reduced binding of Kog1 to Pib2SESE, levels of Kog1 in input are also reduced. Can authors provide further support using colocalization studies? Also, does Pib2SESE has any defect in forming Kog1 bodies?

We took great care to load equal amounts of IPed Pib2-myc variants and then normalized the co-IPed Kog1-HA on the IPed Pib2-myc variant levels. The Kog1-HA input levels vary a bit between the 4 experiments, but they are on average not significantly lower in Pib2SESE-myc-expressing cells when compared to WT cells. In addition, in our Co-IP experiments, the beads are saturated with Pib2-myc variants and Kog1-HA levels are generally not limiting. We therefore deem it fair to say that the Pib2SESE has a reduced affinity for Kog1. Based on our experience with other co-localization studies of membrane-bound proteins and protein complexes (e.g. TORC1 versus EGOC), we find it extremely difficult to quantify local interactions by fluorescence microscopy (unless they are close to all or nothing). In this case, where we have a partial defect in the interaction between Kog1 and Pib2SESE, we anticipate that such analyses will not allow us to draw additional conclusions.

Regarding the issue of Kog1/TORC1-body formation: all of our mutations in PIB2 and SCH9 were introduced (by CRISPR-Cas9) in the genome of our snf1as strain, which was used throughout this study. To analyze Kog1/TORC1-bodies, we have therefore first tried to C-terminally tag KOG1 with GFP in the genome of our strain background (similarly as was done in the original description of Kog1 bodies; PMID: 26439012). However, because all our attempts failed to create KOG1-GFP in our strain, we assumed that this construct may be lethal in our strain background. This is not completely unexpected, as it is known that the Kog1-GFP allele is hypomorphic and temperature sensitive (PMID: 19144819). In an alternative approach, we have therefore set out to study TORC1 body formation in our strains by using a GFP-TOR1 allele that can be integrated into the genome and that expresses functional TORC1 (PMID: 25046117). As we have described earlier, the respective GFP-Tor1 construct localized on vacuolar membranes and on foci that we previously have shown to correspond to signaling endosomes (PMID: PMID: 30732525, 30527664). Unexpectedly, however, when we starved the respective cells for glucose, the number of GFP-Tor1 foci did only marginally increase (20%) in our strain background over a period of up to 1 hour. Given these various unexpected issues, we prefer to not include any of these preliminary data in the current version of our manuscript, but to rather follow up on these observations in a separate study. We deem this particularly justified as the current literature on TORC1-body and TOROID formation also appears controversial and may need further clarification. For instance, while TORC1-body formation has been suggested to represent a Snf1-dependent process that is dispensable for TORC1 inhibition (PMID: 30485160), TOROID formation has been suggested to represent a Snf1-independent process that is mechanistically linked to TORC1 inhibition (PMID: 28976958).

6) In figure 5F, where the authors claim the Sch9SE mutant has lower TORC1 activity, the difference is very minor. Furthermore, corresponding lanes also show reduced levels of Snf1as expression. Hence, improved blots are required here. Also, an in vitro kinase assay with full-length Sch9 KD with and without the Ser288 mutation could solidify the observation that phosphorylation of Ser288 indeed affects TORC1-mediated phosphorylation.

We have replaced the blots in Figure 5F with an alternative set that more clearly highlights the (statistically significant) differences, while also exhibiting more equal levels of Snf1as levels. Regarding the in vitro kinase assays: we have repeatedly tried to perform TORC1 kinase assays on full length Sch9KD without success. We currently believe that proper TORC1-mediated phosphorylation of Sch9 may have to occur on membranes to which both TORC1 and Sch9 are tethered through phospholipid interactions (PMID: 29237820). We are trying to set up such a system on liposomes, but we assume that this will be a major effort that cannot be resolved in due time.

7) In figure 6E, the Sch9SE mutant shows no effect in the presence of rapamycin. Thus, in vivo, phosphorylation at Ser288 may not be perturbing the phosphorylation of Sch9 by TORC1.

When cells are grown on glucose where TORC1 is highly active (as in Fig. 6E or 6A/B in Exp), expression of Sch9SE has no significant effect indeed. However, in glucose-starved cells, where TORC1 activity is low, expression of the Sch9S288E allele clearly and significantly contributes to inhibition of Sch9-Thr737 phosphorylation by TORC1 (Figure 6A/B and Figure 5F/G).

8) According to the author's proposed mechanism, TORC1 activity in Pib2SASA or Pib2SASA/Sch9SA backgrounds should be higher during glucose starvation compared to the control strains. However, glucose starvation shows a similar level of reduction in TORC1 activity in these backgrounds. This raises concern regarding the proposed mechanism. The authors mainly base their conclusions on Ser to Glutamate mutants. The authors should be cautious that Ser to Glutamate changes may also affect the protein structure which can confer similar phenotypes. How do the authors justify this discrepancy?

Please see our response to point 2 of reviewer #1.

Its really intersting seeing how they have their ideas on topics that you'd think is shut and close, challenged and how to rethink this topic. I remember in class discussing how we found objects to be ineffective but with this, It makes me think of it differently. There is more meaning and makes you quesition some aspects. Although I still think the polar bear tag might need more explanation

It's not "recent". The Android support was introduced in 2015, 6 months after the initial iOS only version. https://github.com/facebook/react-native/releases/tag/v0.1.0 https://github.com/facebook/react-native/releases/tag/v0.11.0

Rainbow money

This is more common because the end-product is similar to human systems.

A single or a double plasmid is possible. There are two options for the heavy chain:

1. The "Kozak" is also a restriction binding site. It allows translation.
2. The His tag is optional - it can be changed to something else.

For IgGs - there is a hinge region in the middle.

The same genes in a mammalian plasmid are also present.

one of my enduring beliefs is that we should put down some public stake in what we believe, something that declares what we think. and that we can re-assess that latter and just or not. are we willing to, years latter, affirm our previous claims? do we believe otherwise? is there visible nuance & complexity within us, or are we acting superficial, responsively? this, to me, is where relevation, self lies: whether we are dynamic, or merely transient creatures.

i don't know how to tag this.

Viewing others annotations can be very helpful. It allows for new unthought of view points that gives a student a differing perspective than what they had thought of before.

By using tags composed of several words, I am being too specific, and making my tag system less flexible.

reply to u/stjeromeslibido at https://www.reddit.com/r/antinet/comments/10lqfsn/comment/j63y2k9/?utm_source=reddit&utm_medium=web2x&context=3

Every card has holes pre-punched into it in exactly the same place (see the photo in the original post at the top) so that one might put a knitting needle (or other thin instrument) through the whole deck in each of the positions. Then one should decide on what each hole's meaning will be by position.

As an example, imagine you're using your cards in a rolodex fashion and you want to distinguish the six categories: family, friends, service providers, neighbors, co-workers, and organizations/businesses. For family members you cut/remove the additional paper between the first hole (representing "family") and the edge of the paper. You do the same thing for all the other cards based on their respective categories. So, for example, your brother Joe who lives across the street from you and works with you at the office in the family business would have cuts removed for positions 1, 4, and 5. For an entity that fits all six categories, cuts would be made such that the sheet would no longer stay in u/I-love-teal (the original poster's) six ring binder notebook.

At the end of the year you want to send Christmas cards to your friends, family and neighbors, so you put the knitting needles into position 1 and pull up separating your family out, then you repeat for positions 4 and 5 until you have your full list. (Pro tip: you probably wouldn't want to pull them out of the deck completely, but might rather pull them up and set them at a 90 degree angle thus preventing you from needing to do the work of refiling them all in a particular order.)

Obviously if you have multi-row edge punches or dozens of edge notches you can discern a lot more categories or data types using basic logic. Just abstract this to your particular note card system. Herman Hollerith used this in early versions of the U.S. Census in the late 1800s and it and variations were used heavily in early computer programming applications.

A variation of this sort of trick can also be done by coloring in (or not) the edges of parts of your cards as well. See for example the general suggestions in these photos which help to layout the idea of the "Pile of Index Card" system used back in 2006 with respect to Getting Things Done (GTD) philosophy:

• https://www.flickr.com/photos/hawkexpress/189967397/
• https://www.flickr.com/photos/aki_oda/2684257358
• https://www.flickr.com/photos/hawkexpress/189972896

On my mathematics specific notes which I generally put on graph paper cards, I use colored edge "notches" like these to represent broad categories like theorems, proofs, definitions, corollaries, etc. or method of proof (induction, direct, contradiction, contraposition, construction, exhaustion, probabilistic, combinatorial, etc.) This makes finding specific cards a bit easier as I tip through various sections.

A historian might use colored edges to visually label dates by decades or centuries depending on the timespan of their studies. The uses can be endless and can be specific to your field of study or needs.

Some might also attach the idea of tags/categories to the colors of their cards, so you might use white cards for ideas which are your own, yellow cards which are quotes of others' material, blue cards which represent synopses of other's ideas, etc. One might also profitably use a multi-pen with different colored inks to represent these sorts of meta-data as well.

The variations are endless...

Author Response

Reviewer #1 (Public Review):

In this study, the protein composition of exocytotic sites in dopaminergic neurons is investigated. While extensive data are available for both glutamatergic and GABA-ergic synapses, it is far less clear which of the known proteins (particularly proteins localized to the active zone) are also required for dopamine release, and whether proteins are involved that are not found in "classical" synapses. The approach used here uses proximity ligation to tag proteins close to synaptic release sites by using three presynaptic proteins (ELKS, RIM, and the beta4-subunit of the voltage-gated calcium channel) as "baits". Fusion proteins containing BirA were selectively expressed in striatal dopaminergic neurons, followed by in-vivo biotin labelling, isolation of biotinylated proteins and proteomics, using proteins labelled after expression of a soluble BirAconstruct in dopaminergic neurons as reference. As controls, the same experiments were performed in KO-mouse lines in which the presynaptic scaffolding protein RIM or the calcium sensor synaptotagmin 1 were selectively deleted in dopaminergic neurons. To control for specificity, the proteomes were compared with those obtained by expressing a soluble BirA construct. The authors found selective enrichments of synaptic and other proteins that were disrupted in RIM but not Syt1 KO animals, with some overlap between the different baits, thus providing a novel and useful dataset to better understand the composition of dopaminergic release sites.

Technically, the work is clearly state-of-the-art, cutting-edge, and of high quality, and I have no suggestions for experimental improvements.

We thank the reviewer for this summary and for pointing out the high quality of the work.

On the other hand, the data also show the limitations of the approach, and I suggest that the authors discuss these limitations in more detail. The problem is that there is very likely to be a lot of non-specific noise (for multiple reasons) and thus the enriched proteins certainly represent candidates for the interactome in the presynaptic network, but without further corroboration it cannot be claimed that as a whole they all belong to the proteome of the release site.

We fully agree with the reviewer. Most importantly, we have changed the final section from “Conclusions” to “Summary of conclusions and limitations” (lines 501-518) to summarize the limitations with equal weight to the conclusions. In the revised manuscript, we also included many specific additional points in this respect throughout the discussion and the results: many hits could be noise (lines 458, 478-479), thresholding affects the inclusion of proteins in the release site dataset (lines 208-215), the seven-day time window could deliver interactors from the soma to the synapse (lines 493-495), specific oddities (for example histones, lines 482-485), iBioID does not deliver an interactome per se but is simply based on proximity (lines 505-508), and several more. We also clearly state that each specific hit needs follow-up studies (lines 501-503: ” Each protein will require validation through morphological and functional characterization before an unequivocal assignment to dopamine release sites is possible.”), and a similar statement was added on lines 374-375.

Reviewer #2 (Public Review):

The Kaiser lab has been on the forefront in understanding the mechanism of dopamine release in central mammalian neurons. assessing dopamine neuron function has been quite difficult due to the limited experimental access to these neurons. Dopamine neurons possess a number of unique functional roles and participate in several pathophysiological conditions, making them an important target of basic research. This study here has been designed to describe the proteome of the dopamine release apparatus using proximity biotin labeling via active zone protein domains fused to BirA, to test in which ways its proteome composition is similar or different to other central nerve terminals. The control experiments demonstrating proper localization as well as specificity of biotinylation are very solid, yielding in a highly enriched and well characterized proteome data base. Several new proteins were identified and the data base will very likely be a very useful resource for future analysis of the protein composition of synapse and their function at dopamine and other synapses.

We thank the reviewer for this positive assessment of our work.

Major comment:

The authors find that loss of RIM leads to major reduction in the number of synaptically enriched proteins, while they did not see this loss of number of enriched proteins in the Syt1-KO's, arguing for undisrupted synaptome. Maybe I missed this, but which fraction of proteins and synaptic proteins are than co-detected both in the Syt1 and control conditions when comparing the Venn diagrams of Fig2 and Fig 3 Suppl. 2? This analysis may provide an estimate of the reliability of the method across experimental conditions.

We thank the reviewer for proposing to be clear in the comparison of the control and Syt-1 cKODA data. A direct comparison of hit numbers is included on lines 323-324, with 37% overlap between control and Syt-1 cKODA (vs. 15% between control and RIM cKODA). A direct mapping of this overlap is included in Fig. 4E. We think that this direct comparison is complicated by a number of factors, as outlined below, and did our best to include these complications in the discussion, including the last section (lines 501-518).

First, to assess overall similarity, the initial comparison should be to assess axonal proteins identified in the BirA-tdTomato samples. These datasets are quite similar, with 671 (control) and 793 (Syt-1 cKODA) proteins detected, and a high overlap of 601 proteins. We think that this indicates that the experiment per se is quite reproducible. The comparison of the release site proteome between control and Syt-1 cKODA is more complicated. We think that the main point of this comparison is that the overall number of hits is quite similar, with 450 hits in the Syt-1 cKODA proteome and 527 hits in the control proteome, and we now show that this similarity holds across multiple thresholds (lines 298-301; ≥ 1.5: Syt-1 cKODA 602 hits, control 991, ≥ 2.0: 450/527, ≥ 2.5: 252/348). Detailed analyses of overlap reveals that known active zone proteins such as Bassoon, CaV2 channels, RIMs, and ELKS proteins are present in both proteomes, but the overlap is partial and incomplete with 191 proteins found in both proteomes. As discussed throughout and summarized on lines 501-518, the reasons for this partial overlap may be manifold. Trivially, it could be explained by noise or non-saturation (“incompleteness”) of the proteome. We also think that the Syt-1 proteome is not expected to be identical because there is a strong release deficit in these mice. If Syt-1 has a dopamine vesicle docking function (which it does at conventional synapses [4]), this could influence the proteome. We note that protein functions in the dopamine axon are not well established, but inferred from studies of classical synapses.

We have scrutinized the manuscript to not express that the control and Syt-1 cKODA proteomes are identical; we know they are not and discuss the example of α-synuclein specifically (Fig. 6, lines 347-362). Rather, the striking part is that the extent of the proteomes with high hit number, much higher than RIM cKODA, are similar. Specific hits have to be assessed in a detailed way, one hit at a time, in future studies, as expressed unequivocally on lines 501-503).

Reviewer #3 (Public Review):

In this study Kershberg et al use three novel in vivo biotin-identification (iBioID) approaches in mice to isolate and identify proteins of axonal dopamine release sites. By dissecting the striatum, where dopamine axons are, from the substantia nigra and VTA, where dopamine somata are, the authors selectively analyzed axonal compartments. Perturbation studies were designed by crossing the iBioID lines with null mutant mice. Combining the data from these three independent iBioID approaches and the fact that axonal compartments are separated from somata provides a precise and valuable description of the protein composition of these release sites, with many new proteins not previously associated with synaptic release sites. These data are a valuable resource for future experiments on dopamine release mechanisms in the CNS and the organization of the release sites. The BirA (BioID) tags are carefully positioned in three target proteins not to affect their localization/function. Data analysis and visualization are excellent. Combining the new iBioID approaches with existing null mutant mice produces powerful perturbation experiments that lead and strong conclusions on the central role of RIM1 as central organizers of dopamine release sites and unexpected (and unexplained) new findings on how RIM1 and synaptotagmin1 are both required for the accumulation of alpha-synuclein at dopamine release sites.

We thank the reviewer for assessing our paper, for summarizing our main findings, and for expressing genuine enthusiasm for the approach and the outcomes.

It is not entirely clear how certain decisions made by the authors on data thresholds may affect the overall picture emerging from their analyses. This is a purely hypothesis-generating study. The authors made little efforts to define expectations and compare their results to these. Consequently, there is little guidance on how to interpret the data and how decisions made by the authors affect the overall conclusions. For instance, the collection of proteins tagged by all three tagging strategies (Fig 2) is expected to contain all known components of dopamine release sites (not at all the case), and maybe also synaptic vesicles (2 TM components detected, but not the most well-known components like vSNAREs and H+/DA-transporters), and endocytic machinery (only 2 endophilin orthologs detected). Whether or not a more complete collection the components of release sites, synaptic vesicles or endocytic machinery are observed might depend on two hard thresholds applied in this study: (a) "Hits" (depicted in Fig 2) were defined as proteins enriched {greater than or equal to} 2-fold (line 178) and peptides not detected in the negative control (soluble BirA) were defined as 0.5 (line 175). How crucial are these two decisions? It would be great to know if the overall conclusions change if these decisions were made differently.

We agree with the reviewer that the thresholding decisions are important and have now better incorporated the rationale for these decisions in the manuscript.

Two-fold enrichment threshold. As outlined in the response to point 1 in the editorial decision letter, we now include figure supplements to illustrate the composition of the control proteome if we apply 1.5- or 2.5-fold enrichment thresholds (Fig. 2 – figure supplements 1 and 2) instead of the 2.0-fold threshold used in Fig. 2. This leads to more or less hits (991 and 348, respectively) compared to the 2.0-fold threshold (527 hits). It is noteworthy that the SynGO-overlap is the highest with the 2.0 threshold (37% vs. 31% at 1.5 and 33% at 2.5, Fig. 2 – figure supplement 3), justifying this threshold experimentally in addition to what was done in previous work [1,2]. These data are now described on lines 208-215 of the manuscript. When we apply these different thresholds to RIM and Syt-1 cKODA datasets, the finding that RIM ablation disrupts release site assembly persists. The following hit numbers were observed in the mutants at the 1.5, 2.0 and 2.5 enrichment thresholds, respectively: RIM cKODA 268, 198 and 82 hits; Syt cKODA 602, 450 and 252 hits. Hence, the extent of the release site proteome remains much smaller after RIM ablation independent of the enrichment threshold, bolstering the conclusion that RIM is an important scaffold for these release sites. This is included in the revised manuscript on lines 298301.

Undetected peptides in BirA-tdTomato. We did not express this well enough in the manuscript. The undetected proteins were set to 0.5 such that a protein that was detected with a specific bait but not with BirA-tdTomato could be illustrated with a specific circle size, not to determine inclusion in the analyses. If the average peptide count across repeats with a specific bait was 1, this resulted in inclusion in Fig. 2 and consecutive analyses with the smallest circle size. Hence, this decision was made to define circle size. It did not affect inclusion in Fig. 2 beyond the following two points. If one were to further decrease it, this might result in including peptides that only appeared once as a single peptide for some of the experiments, which we wanted to avoid. If one would set it higher (to 1), this artificial threshold would be equal to proteins that were actually detected experimentally multiple times, which we wanted to avoid as well. We have now clarified this on lines 165-167 and lines 1119-1121.

Expected proteins. In general, interpreting our dataset with a strong prior of expected proteins is difficult. The literature on release site proteins specifically characterized for dopamine is limited. We have found Bassoon, RIM, ELKS and Munc13 to be present using 3D-SIM superresolution microscopy [5,6], and we indeed found these proteins in the data as discussed on lines 227-232 and lines 423-445 in the revised manuscript. The prediction for vesicular and endocytic proteins is complicated. Release sites are sparse [5,7], and vesicle clusters are widespread in the dopamine axon, in some cases filling most of the axon (for example, see extended vesicle clusters filling much of the dopamine axon in Fig. 7E of [5]). Furthermore, docking in dopamine axons has not been characterized, and it is unclear how frequently vesicles are docked. Hence, it is not clear whether vesicular proteins should be concentrated at release sites compared to the rest of the axon (the BirA-tdTomato proteome we use for normalization). Similar points can be made for proteins for endocytosis and recycling of dopamine vesicles. Within the dopamine system, it is unclear whether the recycling pathway is close to the exocytic sites. One consistent finding across functional studies is that depletion after activity is unusually long-lasting in the dopamine system, for tens of seconds, even after only mild stimulation [5,8–13]. Hence, endocytosis and RRP replenishment might be very slow in these axons. It is not certain that endocytic factors are predeployed to the plasma membrane, and if they are, it is unclear how close to release sites they would be. As such, we agree with the reviewer that the proteome we describe is a hypothesisgenerator. With the limited knowledge on dopamine release, predictions beyond the previously characterized proteins in dopamine axons are difficult to make.

We thank the reviewer for suggesting to include a better analysis of different thresholds and for giving us the opportunity to clarify the other points that were raised.

Given the good separation of the axonal compartment from the somata (one of the real experimental strengths of this study), it is completely unexpected to find two histones being enriched with all three tagging strategies (Hist1h1d and 1h4a). This should be mentioned and discussed.

We agree with the reviewer and have addressed this point in the manuscript. This could either reflect noise, or there could be more specific reasons behind it. The manuscript now states on lines 482-485: “It is surprising that Hist1h1d and Hist1h4a, genes encoding for the histone proteins H1.3 and H4, were robustly enriched (Fig. 2A). These hits might be entirely unspecific, or their co-purification could be due to biotinylation of H1 and H4 proteins (Stanley et al., 2001). It is also possible that there are unidentified synaptic functions of some of the unexpected proteins.” Ultimately, we do not know why these proteins are enriched, and we state clearly in the section “Summary of conclusions and limitations” that each new hit has to be validated in future studies (lines 501-503).

It would also help to compare the data more systematically to a previous study that attempted to define release sites (albeit not dopamine release sites) using a different methodology (biochemical purification): Boyken et al (only mentioned in relation to Nptn, but other proteins are observed in both studies too, e.g. Cend1).

We agree with the reviewer that Boyken et al, 2013 [14] is an important resource for our paper and for the assessment of the proteomic composition of release sites. We have now introduced links and citations to this paper multiple times (for example, on lines 231, 241, 430, 443, 481) and have expanded the discussion of overlap between these proteomes, including on Cend1 (lines 479482).

We think that a systematic comparison with Boyken et al, 2013 [14] is complicated because (1) so little is known about dopamine release mechanics and (2) because the approach is very different between the two papers. In respect to (1), most prominently, it is not certain how frequently vesicles are docked in the dopamine axon. Only ~25% of the varicosities contain these release sites, and vesicle docking has not been characterized in striatal dopamine axons to the best of our knowledge. Hence, how a docking site at a classical synapse compares to a dopamine release site remains unclear at the outset. For point (2), the key difference is that “within dataset normalizations” are very different in these two studies. In our iBioID dataset, we normalize to soluble proteins defined as proximity to BirA-tdTomato. In ref. [14], the authors express enrichment over “light”, regular synaptic vesicles purified with the same approach. This has a major impact on the proteome that strongly influences a direct comparison of hits, because there are large differences in the normalization. While each normalization makes sense for the respective paper, it complicates direct comparison.

With these points in mind, we have compared hits across both datasets class-by-class. For some classes, the datasets have reasonable overlap for ≥ 2-fold enriched proteins: for example for active zone proteins (3 of 7 hits in [14] appear in our control proteome) and adhesion and cell surface proteins (8 of 18). For other classes, the overlap is limited: for example for nucleotide metabolism/protein synthesis (0 of 16 hits in [14] appear in our dataset) and cytoskeletal proteins (5 of 29). We hope the reviewer agrees, that given these factors, the analyses and discussion needed for a systematic comparison goes beyond the scope of our paper. We have instead added a number of references to Boyken et al., 2013 [14], as outlined above, when direct comparison is meaningful.

Reviewer #1 (Public Review):

This study investigated the roles of sams-1 and sams-4, two enzymes that generate the major methyl donor SAM, in heat stress response and the associated molecular changes. The authors provided evidence that loss of sams-1 resulted in enhanced resistance to heat stress, whereas loss of sams-4 resulted in heightened sensitivity to heat stress. The authors further showed that whereas the basal level of the histone modification H3K4me3 in intestinal nuclei was substantially reduced in sams-1 loss-of-function mutants, H3K4me3 level greatly increased upon heat stress, and this increase depended on sams-4. Additional RNA-seq results revealed largely distinct heat stress-induced RNA expression changes in the sams-1 mutant and sams-4 knockdown worms. The authors further profiled genomic locations of H3K4me3 in sams-1 mutant and sams-4 knockdown worms. Unfortunately, the lack of sufficient technical detail made it difficult to evaluate the H3K4me3 profiling data.

The paper provided several conceptual advances:<br /> - Uncovering interesting and opposing heat stress phenotype associated with the loss of two related SAM synthases. Thus, even though both SAMS-1 and SAMS-4 produce SAM, the source of SAM production appears to have distinct consequences on the organismal heat stress response.<br /> - Demonstration that SAMS-4 appeared able to compensate for the loss of SAMS-1 upon heat shock, resulting in restoration of the histone mark H3K4me3 in intestinal cells.<br /> - Revealing largely different gene expression changes upon heat shock in animals lacking sams-1 or sams-4. Thus, the gene expression profiles corroborated the differential heat stress response.

This paper describes one of the first adaptations of CUT&TAG in C. elegans, which can be of high impact on the field. Unfortunately, the lack of experimental detail made it difficult to evaluate the quality of the CUT&TAG data and the consequent interpretations.

Overall, the paper reported a number of interesting findings that will be of substantial interest to the field. However, the paper in its current form has substantial shortcomings, particularly related to the difficulty in evaluating the validity of H3K4me3 profiling data. The paper would also benefit from further discussion that attempts to reconcile some of the inconsistent results.

Reviewer #2 (Public Review):

In this manuscript titled "S-adenosylmethionine synthases specify distinct H3K4me3 populations and gene expression patterns during heat stress", the authors Godbole et al investigated how C. elegans SAM synthases, SAMS-1 and SAMS-4, affected gene expression, H3K4 trimethylation (H3K4me3), and the survival under heat stress. They found in this study that SAMS-4 was required for survival during heat shock. They reasoned that SAM supplied by SAMS-4 but not SAMS-1 might be responsible for generating H3K4me3 under heat shock and claimed that the two SAM synthases differentially affected histone methylation and thus gene expression in the heat shock response. This study suggested a stress-responsive mechanism by which the specific isozyme of SAM synthetase provided a specific pool of cellular SAM for H3K4me3. Overall, this study is interesting but descriptive. Lacking necessary controls and mechanistic details weakened the significance of this work.

Strengths: Very interesting survival phenotypes in the loss of different SAM synthetases; technical success in CUT&tag in C. elegans.

Weaknesses: No clear conclusion can be drawn about whether and how SAM synthetases affect H3K4me3.

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

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Reply to the reviewers

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

Summary:

The submitted manuscript is comparing the effect of individual chaperones and heat-resistant obscure (Hero) proteins on the overall folding of the TDP-43 LCD-domain and its relation to aggregation propensity. Therefore, the authors apply smFRET in order to deduce eventual morphological changes of the LCD-domain from FRET efficiencies. The authors observe that the LCD domain extends its structure upon binding of chaperone/Hero proteins whereas it is collapsed in the absence of those. Furthermore, immunoblotting of filter trap assays indicate that overexpression of chaperones and Hero proteins reduce aggregation of TDP-43 in vivo. Both, the morphological effects on the LCD-domain and the aggregation propensity are significantly enhanced for the TDP-43 A315T mutant. Moreover, the authors tested a charge depleted Hero protein version with reduced "chaperone-like" behaviour. Therefore, the authors conclude that the binding or chaperone activity of the Hero protein is based on its residue specific charges. Finally, the authors conclude that Hero proteins can act similar to chaperones in order to keep protein homeostasis under stress conditions.

We thank the Reviewer for their insightful evaluation of our study.

Major comments:

The similar effect of chaperones and Hero proteins on the morphology of TDP-43 found by the authors is intriguing and the applied experimental procedures seem well described and conducted.

However, the assumption of the authors that a change in morphology of the LCD-domain by the chaperones and Hero proteins is directly connected to the reduction of TDP-43 aggregation is not entirely clear. Whether an overexpression of individual chaperones and Hero proteins has a direct effect on TDP-43 aggregation cannot be tested in vivo, only. It cannot be excluded that inside the cell the here tested chaperones and Hero proteins control intermediate processes or work as co-factors for other proteins involved in protein homeostasis rather directly influencing the aggregation of TDP-43. Therefore, I recommend in vitro aggregation experiments, using ThT signal as a readout. By adding chaperones, Hero proteins and a negative (BSA or others) control individually, a direct effect on TDP-43 aggregation could be concluded. Those experiments have been extensively used in the field and are quick and straightforward to handle.

As the Reviewer explains, indirect effects on TDP-43 aggregation in cells may be accounted for by conducting aggregation experiments in vitro, with recombinant proteins. We are currently designing such experiments based on a previously described full-length recombinant TDP-43 with a TEV-cleavable MBP tag (Wang 2018 EMBO J). This can be incubated with Hero/DNAJA2/Control, and aggregation induced by cleavage of the tag, after which aggregation can be measured via filter trap similar to the method described in our work. We will include these results in our revised manuscript.

We thank the Reviewer for their advice. While we note that it is controversial whether ThT binds to aggregates formed from full-length TDP-43 (used in all our assays in the current manuscript), it is reasonable to apply this assay to the LCD fragment as in the paper referenced by the Reviewer below (Lu 2022 Nat Cell Biol). Such an assay is also a reasonable method for confirming effects of Hero protein and DNAJA2 in vitro, and we can conduct this assay as a back-up if the above does not work.

In addition, focusing on the LCD-domain as a main driver for TDP-43 aggregation is limiting this study. In particular, recent studies [1] indicate that the RRM1 and RRM2 sites of TDP-43 have a major impact on TDP-43 gelation and maturation to solid aggregates. Unfortunately, those sites have not been studied in this manuscript.

We thank the Reviewer for their insight. While we are keen to investigate the impact of other regions on the aggregation of TDP-43 in the future, we chose to focus on the LCD in our current study because our smFRET assay is particularly suitable to monitor the dynamic conformational nature of this flexible, unfolded region.

However, we agree with the Reviewer that it is possible the RRMs have an effect on the activities of Hero11 and DNAJA2. We will create constructs for the RRM-depleted variant, TDP43ΔRRM1&2, and RNA-binding deficient variant, TDP435FL for use in our cell-based assay. This will allow us to investigate how this domain influences the effects of Hero and DNAJA2, and we will include this in our revised manuscript.

As an optional alternative for using Hero11KR->G could be the alteration of buffer conditions and using higher number of salts to promote charge screening. It would be of interest whether the results with the Hero11KR->G could be reproduced with wild type Hero11.

We will perform our assays with Hero11 in high salt conditions for charge screening. While we agree that it may be a great alternative experiment, we note that changing the salt concentration may directly affect the LCD conformation, possibly complicating interpretation of results.

[1] Lu et al. Nat Cell Biol;24(9):1378-1393 (2022)

Minor comments:

Overall, the text is clearly written, and the figures are appropriate.

Whether the activity of individual chaperones or Hero proteins on TDP-43 aggregation "may result in the overall fitness of the cell" or "reinforcing the conformational health of the proteome" is disputable without knowing how the overexpression of certain chaperones or Hero proteins alter the formation of toxic TDP-43 oligomers.

We thank the Reviewer for their balanced critique. We will remove or weaken this point regarding how Hero proteins "may result in the overall fitness of the cell" or may be "reinforcing the conformational health of the proteome" from the discussion.

Reviewer #1 (Significance (Required)):

Studying the mechanistic effects of chaperones on aggregating proteins is of major interest for the field in order to understand aging related disbalance of protein homeostasis and the progression of neurological decline, such as seen for amyotrophic lateral sclerosis (ALS). Furthermore, finding homolog proteins, also being able to inhibit protein aggregation, can help to understand overall mechanisms of protein aggregation and processes preventing such fatal behaviour. However, the technique used in this manuscript are not very novel and have been used numerously times before. smFRET is a common technique to look at protein folding/unfolding and is used frequently as a molecular ruler. The manuscript is of interest for the field of protein aggregation and folding, smFRET and neurodegeneration.

My expertise lies in the field of protein aggregation and inhibition due to chaperones, measuring molecular interactions and neurodegenerative diseases.

We greatly appreciate the Reviewer’s expert opinion on our work. As the Reviewer explains, we believe our work will contribute to the fields of protein aggregation and folding, smFRET and neurodegeneration. While the smFRET method may not be novel on its own, to our knowledge this is the first observation of the TDP-43 LCD, with the effect of a pathogenic mutation, at the single-molecule level. In fact, the production, dye-labeling and isolation of individual molecules is extremely challenging for TDP-43. This was made possible by our technical advances using genetic code expansion to site-specifically introduce an unnatural amino acid in TDP-43, purifying and labeling the TDP-43 from HEK cells, and isolation on glass slides.

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

In this manuscript, the authors build on their findings (Tsuboyama 2020) that electrostatically charged IDPs (Heros) can protect proteins from denaturation and aggregation. In their previous work, they demonstrate that these Hero proteins could decrease the fraction of insoluble GFP-TDP43∆NLS in mammalian cell lines and that this mode of action was related to the electrostatic charge of the proteins and not sequence dependent. Although this protective mode of action appears to be similar to that of canonical chaperones, it is unclear how the Hero proteins compare. In this study, the authors compare Hero11 to a panel of canonical chaperones in their cell-based assays and show that it prevents aggregation in a comparable way to DNJA2. It appears that Hero11 decreases the GFP-TDP43∆NLS aggregates better than some other chaperones. They then utilise their expertise in smFRET analysis (Tsuboyama, 2018) to compare what effect DNJA2 and Hero11 (along with Hero11KR-->G (non-charged control)) have on the dynamic structures of the GFP-TDP43∆NLS (labelled with complementary fluorophores in the LCD domain). Based on analysis of the WT GFP-TDP43∆NLS and the A315T GFP-TDP43∆NLS, the authors suggest that the presence of Hero11 and DNJA2 maintain the LCD-domain of TDP43 in an extended conformation and that by doing so, aggregation can be prevented (as assessed in the cell-based assay).

Despite finding the results very interesting, I feel that the study is preliminary and the conclusions drawn are not fully substantiated by the presented experimental work. Many questions need addressing to validate these findings and conclusions (please see more in the "Significance" section). I have tried to list the main concerns below.

We thank the Reviewer for their detailed and critical assessment of our current study.

Questions/concerns:

Authors used double transient transfections but have not shown quantification of protein levels of the chaperones versus TDP43 - western blots to confirm proper expression (and levels) of the chaperones/Hero protein is crucial without it, we cannot assume that the differences in TDP-43 aggregation are a result of effective chaperoning or due to a lack of expression of any of the chaperone proteins, or high expression of others.

We agree with the Reviewer that this is an important and straightforward validation experiment. We will perform the Western Blotting to confirm the proper and comparable expression of the chaperones/Hero proteins.

Authors used quite a high BSA concentration in the smFRET work; it would be useful to see what the TDP43 smFRET trace looks like without BSA incubation (to ensure it is not causing some effect). Also, is there a concentration dependence? The Authors mention they are unable to identify a Hero/TDP43 complex; but if the amount of Hero protein is high (given that it is single molecules tethered), the change in compaction may not relate to the levels/ratios found in the cells (where changes to aggregation are occurring). have the authors considered whether positively charged polymers (poly-Lys) have any impact on the TDP-43 smFRET distribution? Given that the smFRET trace is so heterogeneous, to understand what is happening here would require the comparison of more than 2 variants.

As the Reviewer suggests, we will include additional smFRET experiments in our revision.

First, we will perform the smFRET experiment of the TDP-43 alone in the PBS buffer. However, we would like to clarify the reason we used BSA incubation for comparison in the current experiment is to account for the possibility of non-specific macromolecular crowding effects on the conformation of the LCD (an effect reported for IDPs in general, for example in Banks 2018 Biophys. J.); we expected that it would be fair to compare Hero11 against another protein, rather than buffer alone. As the Reviewer suggested, we can also perform the same experiments at lower concentrations of Hero11 and DNAJA2, including equimolar concentrations (as suggested below). Moreover, we can also test poly-K peptides for comparison.

Although the A315T variant has a very distinct smFRET profile, it is clear that the effects of Hero11KR-->G (that is proposed to have no effect on aggregation or on the smFRET of WT TDP43) has a clear impact on A315T. Why is this?

We thank the Reviewer for raising this interesting point. We envision that the observed effect is due to weak interactions between the LCD domain of TDP-43 and Hero11KR->G; even without K and R, there many other functional amino acids that are fully accessible due to the extremely disordered nature of the protein. The effect is easier to be observed with the A315T mutant, compared to the WT TDP-43, presumably because the mutant tends to take more compact conformations on its own. Nonetheless, unlike WT Hero11, Hero11KR->G fails to accumulate the very extended form of the LCD (FRET signal of ~0; please see below for the explanation of this value), which appears to be associated with suppression of aggregation. We will include these in our discussion.

The LCD region is prone to PMTs - as the tethered protein is taken from expression in mammalian cells, how can the authors be sure that it has no PMTs? Although a clear difference is observed between WT and A315T in terms of "compactness" of the LCD domain, we cannot assume that the effect of DNAJ2 and Hero11 are the same - in fact, the Hero11 KR-->G control for the A315T is clearly different from the negative control (BSA) and the effect that was seen in WT. As the LCD domain is well-known to be the site of post-translational modifications (ie. Phosphorylation - this would have an effect on an electrostatic Hero11), could the effects be related to changes in PMTs as well?

We thank the Reviewer for their insight. We would like to clarify that we make no assumption that our dye-labeled TDP-43 is free of post-translational modifications. Indeed, the fact that it is derived from HEK293 cells suggests it should have post-translational modifications relevant to humans and may be even considered an advantage of our method. (Most structural methods require purification of a large amount of protein, often only possible through recombinant expression in E. coli, thus lacking human-relevant PTMs.) As the Reviewer points out, the LCD is known to have many phosphorylation sites, which may help explain how the positively charged Hero11 interacts with it. Thus, we will perform mass spectrometry of TDP-43 and the A315T variant expressed in HEK cells to identify what post-translational modifications are present.

The authors mention other studies on DNJA proteins on TDP-43; is the mechanism by which they suppress aggregation known? If the authors want to compare the unknown effects of Hero11, it would be useful to know what DNJ2A is doing, otherwise, the results are still not conclusive, only that "change is similar" in two experiments. What is known about DNJ2A interactions with TDP-43? Did the authors do any pulldown assays to detect a complex in cellulo?

While previous studies have identified various DNAJ (specifically J-domain protein B-subfamily) proteins that suppress aggregation of overexpressed TDP-43, not much is known of this specific interaction (Udan-Johns 2014 Hum Mol Genet, Chen 2016 Brain, Park 2017 PLOS Genet). To address the Reviewer’s questions, we will include experiments characterizing the effects of DNAJA2 on TDP-43. We will perform colocalization experiments, explaining effects of DNAJA2 and Hero11 on TDP-43 in the cell. As explained below, we will also perform Pulse Shape Analysis (PulSA), a flow cytometry-based method that can be used to study protein localization patterns in cell, which will also provide insight into the effects on the distribution of TDP-43 in cells. We can also perform co-IP of TDP-43 to detect if there is a detectable, stable complex with DNAJA2 and/or Hero11. Together, these will clarify the similarities and differences between DNAJA2 and Hero11.

It is unclear how the findings of the smFRET relate to structural understanding of the LCD-domain of TDP43 (i.e. NMR studies?); is it known whether PTMs are more prominent with the A315T variant as this may explain it's more compact nature? As well, putative helical structure in the LCD domain may lend to the changes in compaction.

The Reviewer brings up an interesting and careful discussion. Currently, it is unknown if PTMs actually cause more compaction, or if they are more prominent in the A315T variant, but we will perform mass spectrometry to detect PTMs.

As the Reviewer mentions, it would be very interesting to compare our smFRET results to other studies of specific LCD structures. However, it is not trivial to deduce lengths (and structure) from smFRET data as various other factors, for example, dye orientation and local chemical environment, may affect FRET efficiency. Nonetheless, we can still cautiously provide a discussion of how our FRET results compare with previous studies.

For the dye pair used in our study, Cy3 and ATTO647N, the low/no FRET signals promoted by DNAJA2 and Hero11 correspond to a range of end-to-end distances of 6.9 nm to 10.2 nm (FRET signals of 0.1 to 0.01, respectively). Assuming that the LCD behaves like a ~140 amino acid worm-like chain (WLC) with persistence length (Lp) = 0.8 nm, we expect a mean end-to-end distance of 7.35 nm. Thus, the low FRET peak can be well explained by promotion of an extended WLC behavior of the LCD by DNAJA2 and Hero11. On the other hand, the FRET peaks of WT LCD and the A315T mutant (in the absence of Hero11 or DNAJA2) correspond to ~4 and ~3.3 nm, respectively. We will include a careful discussion of how our results relate to known structural understanding of the LCD in the revised discussion.

It is unclear how there can be such a prominent FRET ~0 peak and in fact negative values.

We regret that we did not clearly explain this in the manuscript. Negative values arise when applying correction factors from the alternating laser scheme (ALEX) to FRET signals. FRET efficiency, E, is the ratio of acceptor signal intensity, IA, over the total signal intensity, ID+IA, (with the application of a correction factor, γ, but this doesn’t affect the negative values and won’t be discussed further here) and is given by the equation: E=IA/(γ×ID+IA). However, due to leakage of the donor signal into the acceptor channel and direct excitation of the acceptor dye by the donor laser, raw IA values, IA,raw, are erroneously higher than in reality. For example, the ~0 FRET peaks in question appear to be around 0.1–0.2 before correction. These are accounted for by applying the respective correction factors, Dleakage and Adirect, through the equation: IA=IA,raw–Dleakage×ID–Adirect×IAA. (IAA is the acceptor signal during excitation of the acceptor dye.) These two correction factors are determined by observing the traces and choosing the mean values using iSMS software (2015 Preus Nat Methods) and applied uniformly to all traces in an experiment. When IA is especially low, such as when FRET is almost 0, the magnitude of the correction factor terms may be larger than IA,raw, resulting in negative values. This does not mean that values less than 0 are invalid, but merely that they have been overcompensated in the error application. For the dye pair in our study, FRET efficiencies less than 0.1 correspond to distances greater than 6.9 nm, meaning peaks around zero represent LCD behaviors with end-to-end distances greater than around 7 nm. Please also note that kernel density estimation often gives distributions with values beyond the (0,1) range just because of how these plots are constructed. This will be added to the methods in the revised manuscript.

Conclusion is that Hero11 and DNJA2 maintain the TDP43 LCD-domain (soluble protein) in an extended form and that this is linked with the decrease in aggregates found in the cell; however, with the cell-based assay, no analysis to quantify the expression levels of the TDP43 and the chaperones/Hero are presented, and more importantly, no analysis on the complementary soluble fraction (to the filter assay) has been done to show that indeed, these biomolecules maintain the proteins in a soluble form. It is possible that the TDP-43 is being degraded?

As described above, we plan to perform Western Blotting to examine the expression levels of these proteins. To address the concerns about solubility, we will perform Pulse Shape Analysis (PulSA) to quantitatively measure the expression and soluble/aggregated distribution GFP-tagged TDP43 in HEK293T cells. Measuring the soluble diffuse signals and the punctate aggregate signals will also tell us if there are differences in how GFP-TDP43 is aggregated between Hero11, DNAJA2 and controls. In addition, to support results from the FTA, we will provide sedimentation assays, where the soluble and aggregate fraction from cells is separated by centrifugation and analyzed (Krobitsch 2000 PNAS). These will provide information on TDP-43 in the soluble fraction.

Reviewer #2 (Significance (Required)):

Contextually, this study has novelty and potential value for basic research. Firstly, understanding the underlying mechanisms by which Hero protein prevent aggregation would be valuable towards understanding the players in protein homeostasis which can be imbalanced with respect to disease. Secondly, the use of smFRET as a tool in understanding the dynamics of TDP-43 and mutational variants can be powerful in defining structural attributes with pathological consequences in ALS. Although this work shows comparisons between the effect of a canonical chaperone (DNJA2) and Hero11 on the dynamics of monomeric protein and the effect on cellular aggregation, proposing a general mechanism on the data from two TDP-43 variants and a cell-based aggregation assay is premature and more experimental evidence is needed to define the critical link that prevents aggregation of TDP-43 within the cell. Mechanistically, the study does not give a lot of additional insight into the mode of action of Hero11 in the process of preventing aggregation (nor does it explain what DNJA2 is doing and therefore how Hero11 compares and contrasts). The proposed "extended versus collapsed" switch is simplistic and doesn't account for the complexity of TDP-43 structural dynamics. To support their proposed mechanism of action, the authors needs to examine TDP-43 mutational variants (specifically disease-related ones) using their smFRET to understand exactly what the "collapsed" and "extended" data is defining before making the leap that this effect is what is preventing aggregation. There are some structural studies about residual structure in this region (via NMR) that should be considered (https://doi.org/10.1016/j.str.2016.07.007). Although the A315T variant has a very distinct smFRET profile, it is clear that the effects of Hero11KR-->G (that is proposed to have no effect on aggregation or on the smFRET of WT TDP43) has a clear impact on A315T. Why is this? Have the authors considered that the LCD domain of TDP43 is prone to post-translational modifications? Is this variant more phosphorylated - a PMT like phosphorylation is surely to have an impact on interactions with Hero proteins as they are positively charged. Given that the protein is expressed in mammalian cells, it is likely that PMTs have occurred (but the authors should analyse for this).

With regards to the cell-based aggregation assays, the authors again present a simplified relationship - however, a number of control experiments and additional questions arise. It appears that there is less aggregation with co-expression of some chaperones and the Hero11, but what about the soluble fraction? What is the impact of these biomolecules? Is this that it is maintaining soluble protein, enhancing degradation, propagating soluble oligomers? Equally, how do we know that the levels of the chaperones/Heros and the TDP-43 is the same in each cell - these are transient transfections, and no western blots are shown to confirm the levels of the proteins. In fact, the authors state that "co-transfection of HSP70 (HSPA8), HSP90 (HSP90AB1) or HOP all failed to suppress TDP-43 aggregation compared to GST" and mention that this is in contrast to other studies, but could this be a failure to express these in the cell models? Some western blot/lysate analysis is needed. Chaperones often form complexes with their client proteins, is there any evidence of complexes in these cell models (i.e. using immunoprecipitation)?

We thank the Reviewer for their detailed evaluation and interest in our work. As the Reviewer describes, smFRET is a powerful tool for studying the conformational dynamics of TDP-43, and we hope that this study will contribute to our understanding of how Hero proteins and chaperones prevent aggregation.

We are also grateful to the Reviewer for their constructive criticism of our current model, and we will revise it accordingly. We completely agree with the Reviewer that there are complex structural dynamics within the LCD that determine aggregation and phase separation behaviors. Our simple model was intended to explain how external factors that suppress aggregation, DNAJA2 and Hero11, could affect the conformation of LCD at the single-molecule level. As discussed above, we were cautious to over-interpret how our FRET observations correlate to specific conformations, leading to this simplistic model. We do not intend for our explanation of “extended versus collapsed” in the model to explain all structural dynamics of the LCD; rather, we wanted to highlight the characteristic low FRET state promoted by DNAJA2 and Hero11. We believe that the experiment plan explained above will address the Reviewer’s concerns in full, and we thank the Reviewer again for helping us to significantly improve our manuscript.

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

- In a recent study (PLosBiol, 2020) the same authors described an interesting class of proteins they call 'Hero'. Based on their analyses in cultured cells and transgenic Drosophila models the authors concluded that 'Hero' proteins protect against protein instability and aggregation. So far, this class of proteins has not been analyzed by independent groups.

In the current manuscript, they mainly confirm their own previous finding that Hero 11 prevents There are several concerns about the presented data:

We thank the Reviewer for their critical comments on our current manuscript.

- Based on the filter trap assays shown in figure 1 and 3 the authors conclude that DNAJB8 and Hero11 specifically interfere with the aggregation of TDP-43. However, they do not show that the expression levels of TDP-43 are not altered by the co-expression of the different proteins and are comparable in the different samples. In order to make a relevant statement about the anti-aggregation activity of the analyzed proteins, the ratio between soluble and aggregated TDP-43 has to be analyzed.

We would like to clarify that the Reviewer means DNAJA2, not DNAJB8. Following the Reviewer’s advice, we will perform Western Blotting combined with sedimentation assays, where the soluble and aggregate fraction from cells is separated by centrifugation and analyzed to examine the expression levels. We will also perform colocalization experiments and Pulse Shape Analysis (PulSA), a flow cytometry-based method that can be used to study protein localization patterns in cell, which will provide insight into the anti-aggregation activities.

- The FRET assays shown in figures 2 and 4 indicate a slightly higher FRET efficiency in the presence of Hero11 and DNAJA2 and Hero11. The authors postulate that is phenomenon is causally linked to the activity of Hero11 to prevent aggregation of TDP-43. First, it remains unclear whether the slight increase is really significant. Second, I could not find any experimental evidence to support the assumption that a more collapse conformation of the TDP-43 LCD measured in single molecule FRET assays, correlates with an increased aggregation tendency of TDP-43.

We apologize that we are not sure what the Reviewer refers to by “a slightly higher FRET efficiency in the presence of Hero11 and DNAJA2 (and Hero11).” We would like to clarify that, in the presence of Hero11 and DNAJA2, what we observed was a very low (not slightly higher) FRET efficiency of ~0 (Figure 2g and h), suggesting an extended conformation. In contrast, the aggregation-prone A315T variant of TDP-43 shows a very high FRET efficiency of ~0.9 (Figure 4a), which indicates a collapsed conformation.

A minor comment, if the authors would like to compare the specific activity of different proteins, they should use equal molarities of the different proteins and not equal amounts.

As the Reviewer suggests, we will include experiments at equal molarities in the revision.

- For a one-way ANOVA, the response variable residuals have to be normally distributed. With an n = 3 this cannot be tested. Thus, the quantifications of the results shown in figure 1 and 3 are not reliable.

We thank the Reviewer for their critical comment on the statistical analysis. We would like to clarify that statistically significant differences in aggregation between conditions compared to a control are based on Dunnett’s test. While ANOVA is typically first performed to test for any significant difference in means before performing a post-hoc test, Dunnett’s test is independent and can be performed without ANOVA.

Following the Reviewer’s advice, we carefully re-examined our assumption of normality for this data. It is reasonable to perform Dunnett’s test on a sample size of n = 3, and it is generally safe to assume that data from three independent experiments will be reasonably normally distributed. In support of this, performing Kolmogorov-Smirnov test on our data in Figure 1 showed none of the groups differ significantly from normal distributions with the respective mean and standard deviation (p-values greater than 0.05). Thus, we believe it is reasonable to assume the data are normally distributed, the residuals normally distributed, and our statistical analyses reliable. This analysis will be included in the revision to support the normality assumption.

However, even if we did not assume a normal distribution of our data in Figure 1, we still would have obtained statistically significant differences; If we had relied on a Kruskal-Wallis test as a non-parametric equivalent of ANOVA, thus making no assumption of normality, we would have seen p = 0.005176, a value much lower than our significance level of α = 0.05, indicating sufficient evidence that there is a difference in aggregation among these groups.

- The title is imprecise and overstate the presented data:

'canonical chaperone' suggest that their results are valid for chaperones in general. However, they only tested DNAJA2 in the single -molecule FRET assay. Moreover, HAPA8, another canonical chaperone, obviously had an opposite effect on TDP-43 aggregation (Fig.1). Similarly, they only tested Hero11. Thus, 'canonical chaperone' has to be replaced by 'DNAJA2' and 'a heat-resistant obscure (Hero) protein' by 'Hero11'. Similarly, the term 'conformational modulation' is not as concise one would one expect for the title of a research paper.

We would like to clarify that the Reviewer means HSPA8 (not HAPA8). According to the Reviewer’s suggestion, we will change the title to “DNAJA2 and Hero11 mediate similar conformational extension and aggregation suppression of TDP-43”.

Reviewer #3 (Significance (Required)):

In a recent study (PLosBiol, 2020) the same authors described an interesting class of proteins they call 'Hero'. Based on their analyses in cultured cells and transgenic Drosophila models the authors concluded that 'Hero' proteins protect against protein instability and aggregation. So far, this class of proteins has not been analyzed by independent groups.

In the current manuscript, they mainly confirm their own previous finding that Hero 11 prevents aggregation of TDP-43 and present very few new data that would provide new insights. Specifically, only the FRET assays shown in figure 2 and 4 are really new, which, by the way, could easily be shown in one figure.

We thank the Reviewer for their critical evaluation of our current study. As the Reviewer suggests, we believe our smFRET results provide new insights into how Hero11 and DNAJA2 function. We would like to emphasize that, rather than confirming our previous findings, our current manuscript mainly addresses a critical point that remained unknown in our previous study by investigating the mechanism of how Hero proteins prevent aggregation. Moreover, to our knowledge, this is the first observation of the TDP-43 LCD, with the effect of a pathogenic mutation, at the single-molecule level.

the train is a medium too. think about mediums other than paper. literally everything that exists in this world could be a medium

!- similar to : Nagarjuna’s tetra lemma - https://jonudell.info/h/facet/?user=stopresetgo&max=50&tag=Nagarjuna

!- quotable : John Zerzan - this is similar to: David Loy, Byung-Chui Han, Jay Garfield, John Varvaeke - https://jonudell.info/h/facet/?user=stopresetgo&tag=david+loy&max=100&exactTagSearch=true&expanded=true&addQuoteContext=true - https://hyp.is/go?https://hyp.is/go?url=https%3A%2F%2Fwww.meaningcrisis.co%2F&group=world

!- comment : progress as an "uncontested good" - progress trap is the contestation - see annotations on progress trap: https://jonudell.info/h/facet/?user=stopresetgo&tag=progress+trap&max=100&exactTagSearch=true&expanded=true&addQuoteContext=true

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Reply to the reviewers

1. General Statements [optional]

We would like to thank the reviewers for taking time in reviewing and commenting on our paper. The comments were very constructive and conscientious, thanks to their expertise in the field. These comments and the revisions would surely make this paper a better and more robust finding in the field.

The comments were about clearer explanations, increasing the quality of the data and additional experiments for a stronger conclusion, all of which we are eager to accomplish. Now we have sorted out the problems and planned the experiments required in the revision, as detailed below.

2. Description of the planned revisions

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

Summary In this manuscript, Komori et al. examined the role of the LRRK2 substrate and regulator Rab29 in the lysosomal stress response. Briefly, in chloroquine (CQ)-treated HEK293 cells the authors observed an apparent LRRK2-independent increased in Rab29 phosphorylation which was accompanied by translocation of Rab29 to lysosomes. Intriguingly, the authors detected a similar increase in Rab29 phosphorylation when Rab29 was tethered to lysosomes in the absence of CQ treatment. Using mass spectrometry, mutagenesis and a phospho-specific anti-body, the authors mapped the CQ-induced phosphorylation site to S185 and demonstrated its independence from LRRK2. Next, the authors found that PKCa was the kinase responsible for S185 phosphorylation and lysosomal translocation of Rab29. Lastly, the authors showed that in addition to PKCa the lysosomal translocation of Rab29 was also regulated by LRRK2. Overall, Komori and colleagues provide interesting new insights into the phosphorylation-dependent regulation of Rab29. However, there are. Number of technical and conception concerns which should be addressed.

Major points 1) Figure 1F: the localization of Rab29 to lysosomes is not convincing at all. The authors should either provide more representative image examples or image the cells at a higher resolution. The authors should also confirm the CQ-induced lysosomal localization of Rab29 in a different cell type (e.g., HEK293).

We will replace Fig 1F pictures with slightly more magnified images with higher resolution. We will also include additional cell types (HEK293, and other cells that are predicted to express endogenous Rab29); Reviewer #2 also raised this point (see Reviewer #2 comment on Significance).

Moreover, the authors should show that prenylation of Rab29 is required for its CQ-induced phosphorylation.

We will test the effect of lovastatin, a HMG-CoA reductase inhibitor that causes the depletion of the prenylation precursor geranylgeranyl diphosphate from cells (Binnington et al., Glycobiology 2016, Gomez et al, J Cell Biol 2019), or 3-PEHPC, a GGTase II specific inhibitor that also causes the inhibition of Rab prenylation (Coxon FP et al, Bone 2005).

2) The rapalog-induced increase in Rab29 phosphorylation in Figure 2D is not convincing since there is at least 2-3-fold more Rab29 in FRB-LAMP1 expressing cells compared to their FRB-FIS1 counterparts. An independent loading control is also missing. This is a key experiment and should be properly controlled and quantified. In addition, can CQ treatment drive 2xFKBP GFP-Rab29 from mitochondria to lysosomes (in the presence of rapalog and FRB-Fis1)?

We will carefully examine another round of rapalog-induced phosphorylation of Rab29, with an independent loading control such as alpha-tubulin. The immunoblot analysis will be made against the intensity of non-p-Rab29. The response to the latter question was described in the section 4 below.

3) Figure 4A-C: Are these stable Rab29 expressing cells? If not, the quantification of "the size of largest lysosome in EACH cell" becomes very problematic. This analysis should be repeated with stable Rab29 variant cells in a background lacking endogenous Rab29. Furthermore, the LAMP1 signal is too dim to see any convincing colocalization (e.g., with WT) or the lack thereof (e.g., in the case of S185D).

The cells shown in Figure 4 are HEK293 cells transiently expressing Rab29, and the issue of quantification was described in the section 3 below. We agree that the signal of LAMP1 was dim, and it turned out that the confocal microscope we used had problems with the sensitivity of the red channels. We will be taking another round of these images with a new confocal microscope.

Lastly, the authors should corroborate their findings with an ultrastructural analysis since the electron microscopy would definitively be more suitable for this type of measurements.

We are planning to obtain electron microscopic images, according to this reviewer’s request. We plan to invite an expert in electron microscopy analysis as a co-author.

4) The lysosomal colocalization of Rab29 in Figure 5C is again not convincing. This analysis needs to be repeated with high resolution imaging.

Again, we will repeat this experiment with a new confocal microscope, with the hope that it would yield better images.

5) The authors need to show the level of LRRK2 depletion (Figure 6). Given the role of LRRK2 in driving lysosomal Rab29 translocation, the importance of the LRRK2 independent pS185 for this process remains unclear.

We will add the level of LRRK2 on its knockdown; we have experienced that LRRK2 knockdown usually occurs with more than 50% efficiency every time. The response to the latter comment was described in the section 3 below.

6) In general, the authors employ an alternative, biochemical assay (e.g., LysoIP) for the lysosomal translocation of Rab29. This would in particular help to clarify the effect of the Rab29 variants and LRRK2 inhibition.

We have previously shown that the overexpressed Rab29 (and LRRK2) is enriched in the lysosomal fraction from CQ-treated cells, which was performed using dextran-coated magnetite (Eguchi et al, PNAS 2018). Using the same biochemical method, we will show the enrichment of endogenous Rab29 in the lysosomal fraction.

Minor points

9) Figure 2C is lacking the control IF staining for mitochondria (to which 2xFKBP-GFP-Rab29 is assumed be recruited upon co-expression with FRB-FIS1).

We will stain the cells with MitoTracker to ensure that anchoring away of 2xFKBP-GFP-Rab29 by FRB-Fis1 results in mitochondrial localization.

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

The data in the manuscript convincingly demonstrates that lysosomal overload by Chloroquine treatment induces Rab29 localisation to the lysosomes and that this membrane association is dependent on PKCalpha-dependent phosphorylation at Ser185.

We have a number of rather minor comments listed below:

Figure 2

The increasing levels of non-phosphorylated Rab29 over the indicated time course of AP21967 treatment in Figure 2B are concerning. First, could you provide an explanation for this clear increase in both non-p-Rab29 and p-Rab29 in the phostag but not the normal gel? Second, could all quantifications of p-Rab29 be made relative to the non-p-Rab29?

We will try another round of rapalog-induced phosphorylation of Rab29, with an independent loading control. The immunoblot analysis will be made against the intensity of non-p-Rab29. Reviewer #1 raised a similar concern on Figure 2D.

Figure 5

To further demonstrate that PKCalpha phosphorylates endogenous Rab29 at Ser185, we recommend reperforming the Go3983/PMA treatment in figure B with the anti-p-Ser185 antibody. It may be sufficient to perform the treatment only at 4 or 8 hours, simply to provide stronger evidence regarding the phosphorylation of endogenous Rab29.

We will give a try, although the anti-phosphorylated protein antibodies that we tried never worked for phos-tag SDS-PAGE. With the conventional western blot, we will be able to try this experiment.

It is not clear whether the activity of PMA in the assay is due to inhibition of PKCalpha. Are the effects ablated by PKCalpha KD

We will test the knockdown of PKCalpha, beta, gamma and delta by siRNAs to further narrow down the effects of PKC-dependent phosphorylation of Rab29.

Reviewer #2 (Significance (Required)):

These cell biology findings are important in the field as both Rab29 and LRRK2 are implicated in the pathogenesis of Parkinson disease. The phosphorilation of Ser185 of Rab29 by PKCalpha is novel and contributes to our understanding of Rab29 and LKRR2 regulation. One limitation of the study is that is conducted in only two cell types quite unrelated to the disease, so how general and disease relevant are the findings it is not clear. Most of the data are solid. There are two experiments whose results are difficult to interpret and a few controls missing. Also a few issues with quantifications, all of which is described in details above and will need to be fixed prior to publication. My expertise for this paper is in the cell biology of lysosomal function.

The issue that only two cell types were analyzed was also raised by reviewer #1, so we will examine additional cell types, especially those that are predicted to express endogenous Rab29. Our responses to other issues raised are described elsewhere. Thank you for these insightful comments.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Figure 4A-C: Are these stable Rab29 expressing cells? If not, the quantification of "the size of largest lysosome in EACH cell" becomes very problematic. This analysis should be repeated with stable Rab29 variant cells in a background lacking endogenous Rab29. (Reviewer #1)

As described in the section 2 above, the cells shown in Figure 4 are HEK293 cells transiently expressing Rab29. We are sorry that the description “the size of largest lysosome in each cell” was misleading. As we analyzed only cells overexpressing GFP-Rab29 that were marked with GFP fluorescence, we believe that transient expression should not be a problem. To avoid any misunderstandings, we have described in Figure 4 legends that only lysosomes in Rab29-positive cells (and all cells expressing Rab29) were included in the analysis of the largest lysosome of each cell.

Regarding the effect of endogenous Rab29 in Figure 4 experiments, Reviewer #2 similarly raised the issue on whether Rab29 phosphomimetics are acting as dominant active, preventing lysosomal enlargement. On this point, we have previously reported that knockdown of endogenous Rab29 causes the enhancement of lysosomal enlargement upon CQ treatment (Figure 5I,J of Eguchi et al, PNAS 2018), suggesting that the lysosome-deflating effect by phosphomimetics is a dominant active effect rather than dominant negative suppressing endogenous Rab29. This point is considered significant, and thus has been explained in the results section (page 7, lines 168-171).

Along similar lines: why not all cells in Figure 5E and Figure 5G show Rab29- and LRRK2-positive structures? How do the authors know which of these phenotypes is the prevalent one? (Reviewer #1)

As for the ratio of cells with Rab29- and LRRK2-positive structures, it seems reasonable given that different cells have different levels of exposure to lysosomal stress and that the response is transient and does not occur simultaneously. The ratio of these positive cells may also vary depending on the cell culture conditions. Since Rab29- and LRRK2-positive structures are rarely seen in control cells, we think this would be a meaningful phenotype even if only 20-30% of cells show such structures. The result that the ratio of localization changes is not 100% is now noted in the results section explaining Figure 1G (page 4-5, lines 108-110) where the immunocytochemical data first appears.

Given the role of LRRK2 in driving lysosomal Rab29 translocation, the importance of the LRRK2 independent pS185 for this process remains unclear. (Reviewer #1)

Our data suggested that Rab29 is stabilized on lysosomes only when LRRK2-mediated phosphorylation and S185 phosphorylation both occur on Rab29 molecule (as shown in Figure 7 scheme), so we believe there is no contradiction. We have now described more clearly about this notion at the end of the results section (page 9, lines 235-236).

It is not clear what the authors mean by "lysosomal overload stress". Since mature lysosomal incoming pathways such as autophagy or endocytosis are disrupted by CQ, it is difficult to picture an overload. Maybe rephrasing would help to clarify this. (Reviewer #1)

Chloroquine (CQ) is known as a lysosomotropic agent that accumulates within acidic organelles due to its cationic and amphiphilic nature, causing lysosome overload and osmotic pressure elevation, and this is what we call “lysosomal overload stress”. The well-known effects of CQ to disrupt lysosomal incoming pathways are ultimately caused by the above consequences. Also, we have previously reported that lysosomal recruitment of LRRK2 is caused by CQ but not by bafilomycin A1, the latter being an inducer of lysosomal pH elevation, or by vacuolin-1 that enlarges lysosomes without inducing lysosomal overload/pH elevation (Eguchi et al, PNAS 2018), and further found that not only CQ but also other lysosomotropic agents commonly induced LRRK2 recruitment (Kuwahara et al, Neurobiol Dis 2020). We thus have described the effect of CQ as “overload”. However, it is true that we have not provided a clear explanation for readers, so we have added some notes for lysosomal overload stress in the introduction section (page 3, lines 69-71).

Which cell type is used for the IF analysis in Figure 2C? This information is in general quite sparse. The authors should clearly state the cell type for each experiment/Figure. (Reviewer #1)

We have added cell type information that was missing in several places in the manuscript. We are very sorry for the inconveniences. For clarification, HEK293 cells were used in Figure 2C.

Are the images in figure 1F representative? i.e. does Rab29 always colocalise to such enlarged lysosomes upon CQ treatment and does CQ treatment always drastically alter the cellular distribution of Rab29? (Reviewer #2)

The images in Figure 1F are representative of when Rab29 is recruited, but it is not seen in all cells, and the ratio of recruitment (~80%) is shown in Figure 1G. Reviewer #1 also asked why Rab29 recruitment is not seen in all cells, and we gave the same answer above. It may be reasonable to speculate that different cells have different levels of exposure to lysosomal stress and that the response is transient and does not occur simultaneously. The ratio of these positive cells may also vary depending on the cell culture conditions. For the readers’ clarity, we have added that the ratio of localization change of Rab29 is not 100% and is comparable to that of LRRK2 previously reported (page 4-5, lines 108-110).

Considering that the "forced localisation technique" induces a non-physiological colocalization of non-endogenous Rab29 to lysosomes, it may be an overestimation to conclude just from these data that phosphorylation of Rab29 occurs on the lysosomal surface. This is also quite in contrast with the later finding that phosphorylation by PKCalpha promotes lysosome localization of Rab29. It seems more reasonable to conclude that Rab29 can be phosphorylated when localised at the lysosomes (as opposed to other organelles such as mitochondria). If the authors feel strongly about this point they might need to find a less non-physiological assay. (Reviewer #2)

Yes, it could be an overestimation, and as we do not have better means to conduct a less non-physiological assay, we have modified the description from “occurred on the lysosomal surface” to “could occur on the lysosomal surface” (page 5, line 112 (subtitle) and line 128).

Regarding the comparison with the later finding that phosphorylation by PKCalpha promotes lysosome localization of Rab29, these data (Figure 2 and 5) could be explained with a single speculation: phosphorylation of Rab29 on lysosomal membranes could retain Rab29 on the membranes for a longer time. It is not easy to decipher which comes first, association with membranes or phosphorylation of Rab29, in a physiological assay, but considering reports that show PKCalpha activation happens on membranes (Prevostel et al., J Cell Sci 2000), at least the data favor our conclusion over the idea of PKCalpha phosphorylating Rab29 in the cytoplasm and then promoting lysosomal localization. This point is now clearly described in the discussion (page 10, lines 248-251).

It is not clear how the Rab29 phosphomimetics are acting as dominant active preventing lysosomal enlargement. Authors should speculate or repeat the experiments in absence of endogenous Rab29 to clarify the matter. (Reviewer #2)

A similar concern about the effect of endogenous Rab29 was also raised by Reviewer #1 (see above). We have previously reported that knockdown of endogenous Rab29 causes the enhancement of lysosomal enlargement upon CQ treatment (Figure 5I,J of Eguchi et al, PNAS 2018), suggesting that the lysosome-deflating effect by phosphomimetics is a dominant active effect rather than dominant negative suppressing endogenous Rab29. This point is considered important and thus has been explained in the results section (page 7, lines 168-171).

Overall, there is some missing information regarding repeats for Western blots, such as those in figure 3C, 3D and 3E. Please add indications about repeats in the figure legend or methods. (Reviewer #2)

We have added the repeat information to each figure legend where it was missing. We are very sorry for the inconveniences.

The model in figure 7 however seems to suggest that Rab29 associates to lysosomal membranes independently, and is then stabilised at the membranes by LRRK2 and PKCalpha - a point which is not directly supported by the data. (Reviewer #2)

As noted earlier, we consider that phosphorylation of Rab29 on lysosomal membranes could retain Rab29 on the membranes for a longer time, given the present data and previous reports that phosphorylation of Rab29 is more likely to happen on the lysosomal membrane than in the cytosol. Also, as inhibition of either of the two phosphorylations ends up in disperse Rab29 localization, we have made this figure as a model of what is plausible right now. This explanation is now added in the discussion (page 10, lines 248-251).

English proofreading should be improved: "CQ was treated to HEK293" (page 4), "As we assumed that this phosphorylation is independent of LRRK2" as an opening line (page 5) (Reviewer #2)

Thank you for pointing out these incorrect wordings. They were corrected.

4. Description of analyses that authors prefer not to carry out

In addition, can CQ treatment drive 2xFKBP GFP-Rab29 from mitochondria to lysosomes (in the presence of rapalog and FRB-Fis1)? (Reviewer #1)

We do not think that a comparison between the affinities of FKBP-rapalog-FRB and Rab29-[unknown factor that directs Rab29 to lysosomes] is necessary, as the former has a Kd in the single digit nM range (Banaszynski et al, JACS 2005), whereas the latter (based on estimations from related PPIs) is estimated to be in the μM range, which shows a much weaker affinity than the former (McGrath et al, Small GTPases 2019). Furthermore, even if Rab29 appears to have migrated from mitochondria to lysosomes as a result of this experiment, one cannot rule out the possibility that a small portion of the mitochondrial membrane was incorporated into the lysosomal membrane that was enlarged by CQ treatment.

Molecular weight markes should be provided for all immunoblot experiments. (Reviewer #1)

The immunoblot pictures without molecular weight markers in our paper are all Phos-tag SDS-PAGE blot analyses. Phos-tag SDS-PAGE results in band shifts of phosphorylated proteins, and writing in markers would be misleading. Moreover, previous representative studies heavily using Phos-tag (e.g., Kinoshita et al, Proteomics 2011, Ito et al, Biochemical Journal 2016) also did not show the molecular weight markers. Here we performed phos-tag SDS-PAGE analysis only to find differences in the phosphorylation state of Rab proteins.

The use of the quantification ratio of cells with Rab29-positive lysosomes in figure 1G might be slightly misleading as it does not allow the reader to understand to what extent Rab29 localisation at lysosomes upon CQ treatment. We recommend using a simpler quantification, such as by measuring the average colocalisation of Rab29 and LAMP1 per cell. (Reviewer #2)

For figure 5D and 5F, As with figure 1G, we recommend using a more straightforward and impartial method of quantification such as simply measuring the colocalisation of Rab29 with LAMP1. (Reviewer #2)

Popular colocalization analyses using Pearson’s or Mander’s coefficients would be a good choice if the amounts of Rab29 varied greatly between lysosomes. However, this may not apply in this case; the amount of Rab29 or LRRK2 on each lysosome is considered to saturate quickly and a relatively low amount of them may not be detected on immunofluorescence observations, whereas the probability of finding these structures has been shown to exhibit a moderate sigmoid curve (as seen in Figure 1E or 2H of Eguchi et al., PNAS 2018). Therefore, the amount of Rab29 or LRRK2 could be approximated to a Bernoulli distribution in terms of colocalization with lysosomes, and this is the reason why we chose to quantify “the ratio of cells with Rab29-positive lysosomes”.

We recommend using a more transparent and simple quantification method, such as average size of lysosomes per cell. (Reviewer #2)

As one can see in the inset of Figure 4B, unenlarged lysosomes are unfortunately too small for the quantification of their size, much less tell two small lysosomes apart in our experimental settings and laboratory resources, so we decided to analyze the largest lysosome in each cell as a representative of the cells to minimize measurement errors. This measurement only includes GFP-Rab29 positive cells, and by comparing against CQ-untreated cells we intended to increase the validity of this analysis. This quantification method was also used in our previous report (Eguchi et al, PNAS 2018).

Author Response

Reviewer #1 (Public Review):

The authors devised a new mRNA imaging approach, MASS, and showed that it can be applied to investigate the activation of gene expression and the dynamics of endogenous mRNAs in the epidermis of live C. elegans. The approach is potentially useful, but this manuscript will benefit by addressing the following questions:

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

Major comments:

1) In Figure 1-figure supplement 1, the authors claimed that MASS could verify the lamellipodia-localization of beta-actin mRNAs. However, the image showed the opposite of the authors' claim as the concentration of beta-actin mRNA was lower in lamellipodia than the rest of the cytosol. This result disagreed with ref. 17 (Katz, Z.B. et al., Genes and Development, 2012). Hence, the authors cannot make the statement that "MASS can be readily used to image RNA molecules in live cells without affecting RNA subcellular localization". To thoroughly test this notion, the authors should image beta-actin mRNA using MASS and the conventional MS2 system side by side and calculate the polarization index in the same way as shown in Katz, Z.B. et al., Genes and Development, 2012.

We noticed that b-ACTIN mRNAs were less polarized in our image compared to that shown in Katz, Z.B. et al. (Genes and Development, 2012). It is likely due to different cell lines being used. In the previous study, mouse embryonic fibroblasts (MEFs) were used. In our initial experiment, HeLa cells were used. Our data showed b-that ACTIN mRNAs labeled with MASS could be localized to the lamellipodia.

As suggested by the reviewer, we performed new experiments to image b-ACTIN mRNAs using MASS and the conventional MS2 system side by side in NIH3T3 cells, a mouse fibroblast cell line (MEF cells are not available in our lab). We did not find cells with extensively polarized b-ACTIN mRNAs localization, potentially due to different cell lines. We, therefore, did not calculate the polarization index. However, we found that b-ACTIN mRNAs detected by both methods showed a similar localization pattern. These new data suggest that MASS does not affect RNA subcellular localization. We added the new results and updated Figure 1-figure supplement 3.

2) The experiments that validate this new RNA imaging method are not sufficient. The authors need to systematically compare MASS and the MS2 system, including their RNA signal intensity, signal-to-background ratio.

We have systematically compared MASS and the conventional MS2 system, including signal intensity and signal-to-noise ratio, and measured the velocities of mRNA movement. We found that MASS showed a similar signal-to-noise ratio and higher signal intensity to the conventional MS2 system. We have now revised the information in the text on pages 4 and 5, and in Figure 1-figure supplement 4, 5, and 6.

3) In line with this, does beta-actin mRNA display the same behavior as in (Figure 1C-F) when the mRNA was imaged with the MS2 system? The movies do not indicate the type of motility expected of mRNA. For instance, it seems that almost all of the GFP dots, which are presumably single beta-actin mRNAs, stayed stationary over a time course of tens of seconds (Movie 1). This seems to be very different from what has been observed before. It's not clear that the dots are real mRNAs molecules. This further stresses the importance for them to compare their new imaging system with the conventional MS2 application.

We noticed that the mobility of b-ACTIN mRNAs vary in different cells. It is possible that the mobility of mRNAs was regulated in a cell context-dependent manner.

To confirm that the GFP foci detected are real mRNA molecules, we performed MASS combined with single-molecule RNA FISH. We found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH. In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN-8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP (Wu et al., Biophysical journal 2012). As only 8xMS2 was used in MASS, it is likely that some mRNAs were not entirely bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. Thus these results suggest that GFP dots are real mRNA molecules. We have added the new data in Figure 1, Figure 1-figure supplement 1, and the text on page 3.

We measured the velocity of (b-ACTIN mRNA movement tracked by MASS and the conventional MS2 system. We added this information in Figure 1-figure supplement 5 and to the text on pages 4 and 5. With the conventional MS2 system, we observed similar behavior to those observed by MASS.

4) The authors claimed that a major advantage of MASS is that it has only 8xMS2 stemloops (350 nt) and overcomes "the previous obstacle of the requirement of inserting a long 1,300 nt 24xMS2". This statement lacks experimental support in this manuscript. The authors need to quantitatively compare the genomic tagging efficiency of 8xMS2 and 24xMS2.

It has been reported by several decent studies that the knock-in efficiency decreases dramatically with increasing insert size. For example:

~10-fold decrease of knockin frequency with a 1085 bp compared to a 767 bp insertion of DNA fragment (Extended Data Fig.8. Wang, J. et al. Nature methods, 2022).

~30-fold decrease of knockin frequency with an 1122 bp compared to a 714 bp insertion of DNA fragment (Figure 3 and Table S1. Paix, A. et al. PNAS, 2017).

In this study, we did not directly examine the knock-in efficiency of 8xMS2 and 24xMS2. Based on published data from other laboratories, we assumed that the efficiency of the knock-in of 8xMS2 (350 nt) would be higher than that of 24xMS2 (~1300 nt).

5) MASS has the same strategy as SunRISER (Guo, Y. & Lee, R.E.C., Cell Reports Methods, 2022). Both methods use Suntag to amplify signals of MS2- or PP7-tagged RNA. The authors need to elaborate the discussions and describe the similarities and differences of the two studies. In particular, the Guo paper needs to be properly referenced.

We have cited the paper and discussed the similarities and differences between our method and the SunRISER (page 7). Taking both studies together, Guo and we demonstrated that it is an efficient strategy to combine the MS2 system and the Suntag system as a signal amplifier for long-term and endogenous mRNA imaging in live cells.

6) In Guo, Y. & Lee, R.E.C., Cell Reports Methods, 2022, they showed that 8XPP7 with 24XSunTag configuration led to fewer mRNA per cell (Figure 5B of the Cell Reports Methods paper). Does MASS, which has 8xMS2 with 24xSunTag, similarly lead to few mRNAs? The authors should compare the number of mRNAs detected by MASS and the conventional MS2, or by FISH.

We compared the number of mRNAs detected by MASS and by smFISH. We performed MASS combined with single-molecule RNA FISH and found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH.

In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP. As only 8xMS2 was used in MASS, it is likely that some mRNAs were not entirely bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. These data indicated that MASS could label the majority of mRNAs from a specific gene in live cells.

We have added the new data in Figure 1, Figure 1-figure supplement 1, and the text on page 3.

Reviewer #2 (Public Review):

Hu et al. developed a new reagent to enhance single mRNA imaging in live cells and animal tissues. They combined an MS2-based RNA imaging technique and a Suntag system to further amplify the signal of single mRNA molecules. They used 8xMS2 stem-loops instead of the widely-used 24xMS2 stem-loops and then amplified the signal by fusing a 24xSuntag array to an MS2 coat protein (MCP). While a typical 24xMS2 approach can label a single mRNA with 48 GFPs, this technique can label a single mRNA with 384 GFPs, providing an 8-fold higher signal. Such high amplification allowed the authors to image endogenous mRNA in the epidermis of live C. elegans. While a similar approach combining PP7 and Suntag or Moontag has been published, this paper demonstrated imaging endogenous mRNA in live animals. Data mostly support the main conclusions of this paper, but some aspects of data analysis and interpretation need to be clarified and extended.

Strengths:

Because the authors further amplified the signal of single mRNA, this technique can be beneficial for mRNA imaging in live animal tissues where light scattering and absorption significantly reduce the signal. In addition, the size of an MS2 repeat cassette can be reduced to 8, which will make it easier to insert into an endogenous gene. Also, the MCP24xSuntag and scFv-sfGFP constructs can be expressed in previously developed 24xMS2 knock-in animal models to image single mRNAs in live tissues more easily.

The authors performed control experiments by omitting each one of the four elements of the system: MS2, MCP, 24xSuntag, and scFV. These control data confirm that the observed GFP foci are the labeled mRNAs rather than any artifacts or GFP aggregates. And the constructs were tested in two model systems: HeLa cells and the epidermis of C. elegans. These data demonstrate that the technique may be used across different species.

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

Weaknesses:

Although the paper has strength in providing potentially useful reagents, there are some weaknesses in their approach.

Each MCP-24xSunTag is labeled with 24 GFPs, providing enough signal to be visualized as a single spot. Although the authors showed an image of a control experiment without MS2 in Figure 1B, the authors should at least mention this potential problem and discuss how to distinguish mRNA from MCP tagged with many GFPs. MCP-24xSunTag labeled with 24 GFPs may diffuse more rapidly than the labeled mRNA. Depending on the exposure time, they may appear as single particles or smeared background, but it will certainly increase the background noise. Such trade-offs should be discussed along with the advantage of this method.

With MCP-24xSuntag, in theory, there will be up to 24 GFP molecules tethered to one MCP molecule, which may lead to the formation of GFP puncta. However, under our imaging conditions (100 ms to 500 ms) with a spinning disk confocal microscopy, puncta of MCP24xSuntag were not detected. As the reviewer suggested, it might be because MCP24xSuntag is diffusing too fast to be detected as a spot.

For the signal-to-noise ratio, we did more experiments and analyses. We imaged overexpressed b-ACTIN mRNAs using the conventional 24xMS2 system or MASS with different repeats of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). For the conventional 24xMS2 system, we followed the previous protocol that added a nuclear localization signal (NLS) to MCP, and b-ACTIN mRNAs were nicely detected with a signal-to-noise ratio of 1.21.

We found that MASS showed a comparable or better signal-to-noise ratio than the conventional 24xMS2 system. (MASS with MCP-24xSuntag: 1.79, MASS with MCP12xSuntag: 1.48, MASS with MCP-6xSuntag: 1.42). These data indicate that using Suntag as a signal amplifier did not increase background noise.

Also, more quantitative image analysis would be helpful to improve the manuscript. For instance, the authors can measure the intensity of each GFP foci, show an intensity histogram, and provide some criteria to determine whether it is an MCP-24xSuntag, a single mRNA, or a transcription site. For example, it is unclear if the GFP spots in Figure 2D are transcription sites or mRNA granules.

Under our imaging conditions, MCP-24xSuntag was not detected as GFP foci.

We performed MASS combined with single-molecule RNA FISH and found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH.

In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP. As only 8xMS2 was used in MASS, it is likely that some mRNAs were not entirely bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. These data indicated that MASS could label the majority of mRNAs from a specific gene in live cells.

We have added the new data in Figure 1, Figure 1-figure supplement 1, and the text on page 3.

The GFP spots in Figure 2D are not transcription sites, as they were localized in the cytoplasm, not in the nucleus. We imaged exogenous BFP-8xMS2 mRNAs in the epidermis of C. elegans and found that the size of the GFP foci of endogenous C42D4.38xMS2 mRNAs is larger than that of BFP-8xMS2 mRNAs. Those data suggest that the GFP spots in Figure 2D (C42D4.3-8xMS2 mRNA) are mRNA granules. We added those new data in Figure 2-figure supplement 5 and the text on page 7.

Another concern is that the heavier labeling with 24xSuntag may alter the dynamics of single mRNA. Therefore, it would be desirable to perform a control experiment to compare the diffusion coefficient of mRNAs when they are labeled with MCP-GFP vs MCP- 24xSuntag+scFv-sfGFP.

We thank the reviewer for raising this critical issue. We have performed live imaging of bACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). We then measured the velocity of mRNA movement in each imaging condition. We found that compared to the conventional 24xMS2 system, mRNA labeled with MCP-24xSuntag or by MCP-12xSuntag showed a smaller velocity, indicating that heavier labeling affected mRNA movement speed.<br /> In contrast, we found that mRNAs labeled with MCP-6xSuntag showed a similar velocity to that tagged with the conventional 24xMS2 system. Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP6xSuntag) should be used. We added those new data in Figure 1-figure supplement 5 and to the text on pages 4, 5.

The authors could briefly explain about the genes c42d4.3 and mai-1. Why were these specific genes chosen to study gene expression upon wound healing? Did the authors find any difference in the dynamics of gene expression between these two genes?

The function of C42D4.3 and mai-1 is currently not known. Through mRNA deep sequencing, It has been shown that the expression level of C42D4.3 and mai-1 was quickly increased after wounding of the epidermis of C. elegans. We, therefore, choose those two mRNAs for imaging. We added more information about C42D4.3 and mai-1 to the text on page 6.

We observed similar dynamics of gene expression between C42D4.3 and mai-1 (Video 7 ,8, 9).

Reviewer #3 (Public Review):

It is a brilliant idea to combine the MS2-MCP system with Suntag. As the authors stated, it reduces the copies of the MS2 stem loops, which can create challenges during cloning process. The Suntag system can easily amplify the signal by several to tens of folds to boost the signal for live RNA tagging. One of the best ways to claim that MASS works better than the MS2 system by itself is to compare their signal-to-noise ratios (SNRs) within the same model system, such as HeLa cells or the C. elegans epidermis. Because the authors' main argument is that they made an improvement in live RNA tagging method, it is necessary to compare it with other methods side-by-side. The authors claim that MASS can significantly improves the efficiency of CRISPR by reducing the size of the insert, it still requires knocking in several transgenes, which can be even more challenging in some model systems where there are not many selection markers are available. Another possible issue is that the bulky, heavy tagging (384 scFv-sfGFP along with 24xSuntag) can affect the mobility or stability of the target mRNAs. If it also tags preprocessed RNA in the nucleus, it may affect the RNA processing and nuclear export. A few experiments to address these possibilities will strengthen the authors' arguments. I am proposing some experiments below in detailed comments.

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

1) For the experiments with HeLa cells, it is not clear whether the authors used one focal plane or the whole z-stack for their assessment of mRNA kinetics, such as fusion, fission, and anchoring. If it was from one z-plane, it was possible that many mRNAs move along the z-axis of the images to assume kinetics. If the kinetics is true, is it expected by the authors? Are beta-actin mRNAs bound to some RNA-binding proteins or clustered in RNP complexes?

One focal plane was used in the experiments showing mRNAs' fusion, fission, and anchoring behavior. We have now added this information in the figure legend of figure 1. Yes, b-ACTIN mRNA are bound to specific RNA-binding proteins, for example, ZBP1, and it has been reported that ZBP1 forms granules with b-ACTIN mRNAs (Farina, K.L., et al., Journal of cell biology, 2003).

2) Some quantifications on beta-actin mRNA kinetics, such as a plot of their movement speed or fusion rate, etc., would help readers better understand the behaviors of the mRNAs and assess whether the MASS tagging did not affect them.

We thank the reviewer for raising this critical issue. We have performed live imaging of bACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). We then measured the velocity of mRNA movement in each imaging condition. We found that compared to the conventional 24xMS2 system, mRNA labeled with MCP-24xSuntag or by MCP-12xSuntag showed a smaller velocity, indicating that heavier labeling affected mRNA movement speed.<br /> In contrast, we found that mRNAs labeled with MCP-6xSuntag showed a similar velocity to that tagged with the conventional 24xMS2 system. Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP6xSuntag) should be used. We added those new data in Figure 1-figure supplement 5 and the text on pages 4 and 5.

3) Using another target gene for MASS tagging would further confirm the efficacy of the system. Assuming the authors generated a parental strain of HeLa cell, where MCP24xSuntag and scFv-sfGFP are already stably expressed (shown in Fig. 1B), CRISPR-ing in another gene should be relatively easy and fast.

For exogenous genes, in addition to b-ACTIN, we imaged mRNAs from three more genes, C-MYC, HSPA1A, and KIF18B, with MASS in HeLa cells. For endogenous genes, we imaged C42D4.3 and mai-1 in the epidermis of C. elegans. These data indicated that MASS is able to image both exogenous and endogenous mRNAs in live cells. We have now added those new data in Figure 1-figure supplement 2, Figure 2-figure supplement 2, and to the text on pages 3, 4, and 6.

4) Adding a complementary approach to the data presented in Fig. 1, such as qRT-PCR for beta-actin, with or without the MASS system would ensure the intense tagging did not affect the mRNA expression or stability.

To address this question, we performed more experiments to test whether MASS affected the mRNA expression and stability. Because b-ACTIN mRNA is very stable; thus it is not suitable for measuring mRNA stability. We, therefore, tested three genes, including C-MYC, HSPA1A, and KIF18B, which were reported as medium-stable mRNAs. We found that MASS did not affect the stability of those three mRNAs in HeLa cells. We also tested the expression level and the stability of endogenous C42D4.3 mRNA in the epidermis of C. elegans and found that both the expression and the stability were not affected by MASS. We have now added those new data in Figure 1-figure supplement 2, Figure 2-figure supplement 2, and to the text on pages 3, 4, and 6.

5) For experiments with the C. elegans epidermis, including at least one more MASS movie clip for C42D4.3 and a movie for mai-1 would be helpful for readers to appreciate the RNA labeling and its dynamics.

We showed two movies (video 7 and video 8) and the snapshots for C42D4.3 mRNA (Figure 2D and Figure 2-figure supplement 3). We also added a movie (Video 9) for mai-1.

6) The difference between Fig. 2D and Fig. 2-fig supp. 3 is unclear. The authors should address the different patterns of RNA signal propagation. Is it due to the laser power used too much, resulting in photobleach in Fig. 2D?

We have noticed the difference between Figure 2D and Figure 2-figure supplement 3. In Figure 2D, GFP foci did not appear within the injury area after wounding. In Figure 2-figure supplement 3, GFP foci quickly appeared within the injury area. Although we kept the laser power setting constant when performing the laser wounding experiment, there are indeed variations in the actual laser power used. As the reviewer suggested, the difference may be due to photobleaching in Figure 2D. Alternatively, it is possible that the location of the injury site or the degree of injury could affect the dynamics of gene expression.

However, we would like to point out that the dynamics of gene expression pattern in Figure 2D (Video 7) and Figure 2-figure supplement 3 (Video 8) is similar. GFP foci of C42D4.3 mRNAs were first detected around the injury sites. Then GFP foci gradually appeared from the area around the injury site to distal regions.

7) Movie 7 is the key data the authors are presenting, but there are a few discrepancies between their arguments and what is seen from the movie. The authors say the RNAs are "gradually spread" (the line 120 in the manuscript). However, it seems that the green foci just appear here and there in the epidermis and the majority of them stay where they were throughout the timelapse. This pattern seems to be different from the montage in Fig. 2-fig supp. 3, which indeed looks like the mRNA spots are formed around the lesion and spread overtime. Additional explanation on this will strengthen the arguments. Given the dramatic increase of c42d4.3 mRNA abundance 1 min. after the laser wounding, there must be a tremendous boost of transcription at the active transcription sites, which should be captured as much bigger and fewer green foci that are located inside the nucleus. Is this simply because those nuclear sites are out of focus or in a similar size as mRNA foci? Regardless, this should be addressed in the discussion.

We apologize for the confusing description of our original data. We wrote "gradually spread", but we did not mean that mRNAs were transcribed at the wounding site and moved to the distal regions. We actually mean that GFP foci first appeared close to the wounding site and more GFP foci gradually appeared at the distal regions. We have changed our writing to "the appearance of GFP foci gradually spreads from the area around the injury site to distal regions".

For the difference between Figure 2D and Figure 2-figure supplement 3, please see our discussion for comment 6.

For transcription, we also expected a boost of transcription after wounding. However, we failed to detect the appearance of bigger GFP foci in the nucleus. We agree with the reviewer that this is because the active nuclear sites are out of focus. The epidermis of C. elegans is a syncytium with 139 nuclei located in different regions and focal planes. With our microscopy, we were able to image only one focal plane, in which there are usually only four to ten nuclei. Therefore, it is likely that the nuclei with active transcription were out of focus. We have now discussed this point in the revised manuscript (page 6).

8) One clear way to confirm that MASS labels mRNAs and does not affect their stability/localization is to compare the imaging data with single-molecule RNA fluorescence in situ hybridization (smFISH) that the Singer lab developed decades ago. The authors can target the endogenous c42d4.3 or mai-1 RNAs using smFISH and compare their abundance and subcellular localization patterns with their data.

To confirm that the GFP foci detected are real mRNA molecules, we performed MASS combined with single-molecule RNA FISH and found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH. In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN-8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP. As only 8xMS2 was used in MASS, it is likely that some mRNAs were not fully bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. These data indicated that MASS could detect single mRNA molecules and label the majority of mRNAs from a specific gene in live cells. We have now added the new data in Figure 1, Figure 1-figure supplement 1, and to the text on page 3.

We performed more experiments to test whether MASS affected the mRNA expression and stability. Because b-ACTIN mRNA is very stable; thus it is not suitable for measuring mRNA stability. We, therefore, tested three genes, including C-MYC, HSPA1A, and KIF18B, which were reported as medium-stable mRNAs. We found that MASS did not affect the stability of those three mRNAs in HeLa cells. We also tested the expression level and the stability of endogenous C42D4.3 mRNA in the epidermis of C. elegans and found that both the expression and the stability were not affected by MASS. We have now added those new data in Figure 1-figure supplement 2, Figure 2-figure supplement 2, and to the text on pages 3, 4, and 6.

To test whether MASS affected the mRNA localization, we performed new experiments to image b-ACTIN mRNAs using MASS and the conventional MS2 system side by side in NIH3T3 cells, which is a mouse fibroblast cell line. We found that b-ACTIN mRNAs showed similar localization in both methods. These new data suggest that MASS does not affect RNA subcellular localization. We have now added the new results in Figure 1-figure supplement 2.

9) One of the main purposes to live image RNAs is to assess their dynamics. Adding some more analyses, such as the movement speed of the foci, would be helpful to show how effective this system is to assess those dynamics features.

We thank the reviewer for raising this critical issue. We have performed live imaging of bACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). We then measured the velocity of mRNA movement in each imaging condition. We found that compared to the conventional 24xMS2 system, mRNA labeled with MCP-24xSuntag or by MCP-12xSuntag showed a smaller velocity, indicating that heavier labeling affected mRNA movement speed.

In contrast, we found that mRNAs labeled with MCP-6xSuntag showed a similar velocity to that tagged with the conventional 24xMS2 system. Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP6xSuntag) should be used. We added those new data in Figure 1-figure supplement 5 and to the text on pages 4 and 5.

Reviewer #4 (Public Review):

Hu et al introduced the MS2-Suntag system into C. elegans to tag and image the dynamics of individual mRNAs in a live animal. The system involves CRISPR-based integration of 8x MS2 motifs into the target gene, and two transgene constructs (MCP-Suntag; scFv-sfGFP) that can potentially recruit up to 384 GFP molecule to an mRNA to amplify the fluorescent signal. The images show very high signal to background ratio, indicating a large range of optimization to control phototoxicity for live imaging and/or artifacts caused by excessive labeling. The use of epidermal wound repair as a case study provides a simplified temporal context to interpret the results, such as the initiation of transcription upon wounding. The preliminary results also reveal potentially novel biology such as localization of mRNAs and dynamic RNP complexes in wound response and repair. On the other hand, the system recruits a large protein complex to an mRNA molecule, an immediate question is to what extent it may interfere with in vivo regulation. Phenotypic assays, e.g., in development and wound repair, would have been a powerful argument but are not explored. In all, C. elegans is powerful system for live imaging, and the genome is rich in RNA binding proteins as well as miRNAs and other small RNAs for rich posttranscriptional regulation. The manuscript provides an important technical progress and valuable resource for the field to study posttranscriptional regulation in vivo.

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

Reviewer #4 (Public Review):

Hu et al introduced the MS2-Suntag system into C. elegans to tag and image the dynamics of individual mRNAs in a live animal. The system involves CRISPR-based integration of 8x MS2 motifs into the target gene, and two transgene constructs (MCP-Suntag; scFv-sfGFP) that can potentially recruit up to 384 GFP molecule to an mRNA to amplify the fluorescent signal. The images show very high signal to background ratio, indicating a large range of optimization to control phototoxicity for live imaging and/or artifacts caused by excessive labeling. The use of epidermal wound repair as a case study provides a simplified temporal context to interpret the results, such as the initiation of transcription upon wounding. The preliminary results also reveal potentially novel biology such as localization of mRNAs and dynamic RNP complexes in wound response and repair. On the other hand, the system recruits a large protein complex to an mRNA molecule, an immediate question is to what extent it may interfere with in vivo regulation. Phenotypic assays, e.g., in development and wound repair, would have been a powerful argument but are not explored. In all, C. elegans is powerful system for live imaging, and the genome is rich in RNA binding proteins as well as miRNAs and other small RNAs for rich posttranscriptional regulation. The manuscript provides an important technical progress and valuable resource for the field to study posttranscriptional regulation in vivo.

Henry David Thoreau repeated himself frequently in his journals. Damion Searls who edited an edition of his journals suggested that some of this repetition was for the beauty and pleasure of the act, but that in many examples his repetition was an act of drafting, revising, and constructing.

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Scott Scheper has recommended finding the place in one's zettelkasten where one wants to install a card before writing it out. I believe (check this) that he does this in part to prevent one from repeating themselves, but one could use the opportunity and the new context that brings them to an idea again to rewrite or rework and expand on their ideas while they're so inspired.

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Thoreau's repetition may have also served the idea of spaced repetition: reminding him of his thoughts as he also revised them. We'll need examples of this through his writing to support such a claim. As the editor of this volume indicates that he removed some of the repetition, it may be better to go back to original sources than to look for these examples here.

(This last paragraph on repetition was inspired by attempting to type a tag for repetition and seeing "spaced repetition" pop up. This is an example in my own writing practice where the serendipity of a previously tagged word auto-populating/auto-completing in my interface helps to trigger new thoughts and ideas from a combinatorial creativity perspective.)

https://omnivore.app/<br /> Open source version of readwise

Originally bookmarked from phone on Sun 2023-01-15 11:25 PM

updated: 2023-01-17 with tag: "accounts"

I'm copying @kael seeing if I can follow mrcolbyrussell since he has some intriguing comments, but then again I don't know how tag actions work at all...

Das Interview der taz mit Olaf Scholz zeigt, dass für die Bundesregierung nach wie vor das weitere Wachstum der Wirtschaft Priorität vor dem Klimaschutz hat, und dass es dabei vor allem darum geht den Wirtschaftsstandort Deutschland so zu sichern, wie er jetzt gerade funktioniert. Einsparen von Energie hat dabei keine Priorität. Scholz spricht sich für eine Steigerung der Stromproduktion durch Erneuerbare aus und fordert 3-4 neue Windräder pro Tag.

in reply to subject of https://hyp.is/VeTJlpN0Ee2mNKOVyQ-B5g/github.com/mikel/mail/issues/902

References to "the World Wide Wruntime" is a play on words. It means "someone's Web browser". Viz this extremely salient annotation: https://hypothes.is/a/i0jxaMvMEey_Elv_PlyzGg

Reviewer #3 (Public Review):

Cahoon set out to demonstrate that sexual dimorphic outcomes of meiosis are caused by different regulations of the synaptonemal complex (SC). In the employed model organism C. elegans it has been shown that the SC consists of at least 6 different proteins (SYP-1-6) and that their assembly into this intricate structure is mutually dependent and that crossover formation is drastically, if not completely abolished, in the absence of individual SC mutants (SYP-5 and SYP-6 are functionally redundant).

The authors employ FRAP analysis and examine the rate of reincorporation of the synapsis components SYP-2 and SYP3 in three different regions of the gonad and compare the incorporation after photobleaching in hermaphrodite and male gonads. They find that SYP-2 dynamics is increased in spermatocytes, whereas in oocytes SYP-3 dynamics is increased. They also found differing profiles of incorporation during the progression of prophase I for those two synapsis components in the two sexes.

Furthermore, the authors show that syp-2/+ and syp-3/+ show signs of haploinsufficiency, as demonstrated by increased embryonic lethality and the missegregation of the X chromosome. In these mutants, the authors examined the kinetics of the appearance of recombination foci, where they used RAD-51 as a measure for progress of homologous recombination and repair pathway choice (repair via the sister versus the homolog and/or non-homologous end joining), MSH-5 for stabilisation of the strand invasion product and COSA-1 as a marker for crossover designation.<br /> The authors show that in the hypomorphs the behaviour of some recombination markers change. The counts of the numbers of COSA-1 are not explaining the missegregation of the X chromosome. The localisation of the crossovers shifts towards the pairing centre chromosome ends in the hypomorphs.

The manuscript is descriptive and the link that dimorphic incorporation rates of SYP-2 and SYP-3 are causative for recombination dimorphisms is not substantiated by the shown experiments. The observed phenomena in the heterozygous syp mutants could be due to general SC defects and not the lack of a critical amount at a specific point during recombination. Overall, the FRAP experiments do not address the possible different synthesis rates of the employed markers (it would be more meaningful to examine the incorporation under protein synthesis inhibitory conditions) or use a photoconvertible tag, that allows the assessment of new synthesis. It has been well documented that in the more distal regions of the gonad gene expression is upregulated. It is not clear what the contribution of differing gene expression of the examined synapsis proteins to the different dynamic behaviour actually is.

I created weekly tweets for another class and it was a fun way to interact with others/break up the usual assignments for the class. The tweets had their own respective tag so it was easy to keep track of who tweeted what/look at their opinions

Is this from the tag?

https://forum.obsidian.md/t/group-tasks-by-page-tags-using-dataview/47354/2

A good description of tags in Obsidian and how Dataview views them at the YAML, page level, and task level.

BIOmatodonte

https://mastodon.art/@fediblock

I boost everything from the #fediblock hashtag that isn't noise, reruns, or user-level. Do your own homework beyond that.

<small><cite class='h-cite via'>ᔥ <span class='p-author h-card'>@welshpixie@mastodon.art</span> in "If you're an instance admin/mod struggling to keep up with the fediblock tag, @fediblock is a 'curated' version that filters through the trolling/misuse of the tag and repeat entries, and only boosts the actual proper fediblock content. :)" - Mastodon.ART (<time class='dt-published'>01/05/2023 11:17:52</time>)</cite></small>

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

Learn more at Review Commons

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Referee #3

Evidence, reproducibility and clarity

This study by Ghosh et al. proposes a role for phosphatidylinositol 5-phosphate 4-kinase (PIP4K) in regulating PI3P levels in vivo. They use loss-of-function Drosophila model of the only PIP4K gene (dPIP4K29) to investigate the PI3P and PI(3,5)P2 metabolizing enzymes. First, they showed that loss of function of PIP4K leads to reduced cell size in larval salivary glands and this was attributed to the elevated level of PI3P. Then, they modulated enzymes involved in PI3P metabolism (kinases and phosphatases) and propose the implication of the PI3P phosphatase myotubularin (Mtm) and the Pi3k Class III (PI3K59F) in PIP4K-dependent cell seize control. Finally, as PI3P has an established role in autophagy, they modulate the autophagy related gene (atg1) and connect the observed increase of PI3P level to the upregulation of autophagy in dPIP4K29 model. The authors used genetic manipulations of dPIP4K29 models as well as specialized lipidomic expertise (phosphoinositide measurement using mass spectrometry and PI-kinase/phosphatase assays) to address their conclusions. The experimental strategies were well designed and major conclusions were in line with the obtained results.

Major comments:

• Are the key conclusions convincing?

Almost yes, however there is two major concerns for me: Concern 1 is about the level of PIP2/PI4,5P2, the product of PIP4K, in the dPIP4K29 model. This was not measured in the study. The authors claim page 5 that: "This observation suggests that the ability of dPIP4K to regulate cell size does not depend on the pool of PI(4,5)P2 that it generates... based on the fact that re-expression a mutation that hPIP4Kβ[A381E] in the salivary glands of dPIP4K29 (AB1> hPIP4Kβ[A381E]; dPIP4K29) (Figure S1A) did not rescue the reduced cell size. This mutation hPIP4Kβ[A381E] was generated in a study by Kunz et al. (2002) where they demonstrated by in vitro kinase assay that it cannot utilize PI5P as a substrate but can produce PI(4,5)P2 using PI4P as a substrate. In the same study, using MG-63 cells, Kunz et al. propose that the A381E mutation did not metabolize PI5P as it lost its plasma membrane localization. In my opinion the author should strength their claim about the role of dPIP4K independently of PI(4,5)P2 by addressing the level of PI(4,5)P2 in their model biochemically by mass spectrometry as they have this powerful tool and support this by using PH-PLCd probe to detect PI(4,5)P2. Also, as they use completely different model as Kunz et al. they should verify, if possible, the localization of hPIP4Kβ[A381E] vs WT PIP4Kβ in salivary glands.

Concern 2: Page 7: The author used Mtm tagged constructs (mCherry and GFP) and measure its phosphatase activity toward PI(3,5)P2 and they did not show any obvious activity. I would like to suggest the use of untagged (or small tag construct, Flag or HA) for the expression experiment in S2R+ cell as it is known that active myotubularins in other cell model as well as in vitro have a strong 3-phosphatase activity toward PI(3,5)P2. By looking at the graph FigS2 Bii, we could clearly see a big disparity within mCherry-Mtm data points. This experiment should be more strengthen by additional experimental points but also by using a positive CTRL where PI(3,5)P2 level drops (inhibition of PIKfyve by Apilimod).

Concern 3: Page 10: "we tagged dPIP4K with the tandem FYVE domain at the C-terminus end of the protein (dPIP4K2XFYVE) to target it to the PI3P enriched endosomal compartment and reconstituted this in the background of dPIP4K29. We did not observe a significant change in the cell size of dPIP4K29" I really don't understand the relevance of this experiment. FYVE tandem will bind to PI3P whenever it was in the cell (Lysosomes, autophagosome). Why the authors claim that the expression of restricted dPIP4K2XFYVE will be restricted to the endosomes. I think that this experiment is confusing and should be removed. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

See concern 1 to 3. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Yes, the proposed experiments in concern 1-3 are not difficult to address as the authors have all the appropriate tools to manage this. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

Yes. It is not time consuming and not costly according to their expertise, available tools and materials that they used through the study. - Are the data and the methods presented in such a way that they can be reproduced?

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

Yes

Minor comments:

• Specific experimental issues that are easily addressable.

  1. Address the level of PI(4,5)P2 in dPIP4K29 model by mass spectrometry.
  2. Address the localization of hPIP4Kβ[A381E] vs WT PIP4Kβ in salivary glands.
  3. Test the Mtm phosphatase activity toward PI(3,5)P2 using untagged or small tagged (HA or Flag) Mtm and repeat/homogenize the PI(3,5)P2-phosphatase assay (FigS2ii).
  4. Are prior studies referenced appropriately?

Yes - Are the text and figures clear and accurate?

The figures needsmore organization. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

NO

Referees cross-commenting

Overall, Reviewer #1 and #2 found the study by Ghosh et al interesting well designed and written providing insights into the role of PIP4K in regulating cell seize. However, they comment few points that would be very helpful to improve the study. I am agreeing with both reviewers for the raised comments.

Significance

The author addressed how elevated PI3P in dPIP4K29 model impacted cell seize. Indeed, they connected this cell phenotype to the autophagy where PI3P plays a crucial role. However, I am still questioning how deletion of PIP4K enhances PI3P level.

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

The role of PIP4K in cellular homeostasis and organismal physiology is still unclear. This study brings additional insights into how PIP4K could be involved in important cellular process such as autophagy by regulating additional phsophoinositides.<br /> - State what audience might be interested in and influenced by the reported findings.

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

Phosphoinositides, Myotubularin, endolysosomal trafficking, skeletal muscle.

Sorry, I'm only just seeing this now Jan. I tag a lot in Hypothes.is to help make things a bit more searchable/findable in the future. Everything in Hypothes.is gets pulled into my Obsidian vault where it's turned into [[WikiLinks]] rather than tags. (I rarely use tags in Obsidian.) Really I find tagging is better for broad generic labels (perhaps the way many people might use folders) though I tend to tag things as specifically as I can as broad generic tags for things you work with frequently become unusable over time. I recommend trying it out for yourself and seeing what works best for you and the way you think. If you find that tagging doesn't give you anything in return for the work, then don't do it. Everyone can be different in these respects.

Author Response

eLife assessment:

This study addresses whether the composition of the microbiota influences the intestinal colonization of encapsulated vs unencapsulated Bacteroides thetaiotaomicron, a resident micro-organism of the colon. This is an important question because factors determining the colonization of gut bacteria remain a critical barrier in translating microbiome research into new bacterial cell-based therapies. To answer the question, the authors develop an innovative method to quantify B. theta population bottlenecks during intestinal colonization in the setting of different microbiota. Their main finding that the colonization defect of an acapsular mutant is dependent on the composition of the microbiota is valuable and this observation suggests that interactions between gut bacteria explains why the mutant has a colonization defect. The evidence supporting this claim is currently insufficient. Additionally, some of the analyses and claims are compromised because the authors do not fully explain their data and the number of animals is sometimes very small.

Thank you for this frank evaluation. Based on the Reviewers’ comments, the points raised have been addressed by improving the writing (apologies for insufficient clarity), and by the addition of data that to a large extent already existed or could be rapidly generated. In particularly the following data has been added:

1. Increase to n>=7 for all fecal time-course experiments

2. Microbiota composition analysis for all mouse lines used

3. Data elucidating mechanisms of SPF microbiome/ host immune mechanisms restriction of acapsular B. theta

4. Short- versus long-term recolonization of germ-free mice with a complete SPF microbiota and assessment of the effect on B. theta colonization probability.

5. Challenge of B. theta monocolonized mice with avirulent Salmonella to disentangle effects of the host inflammatory response from other potential explanations of the observations.

6. Details of all inocula used

7. Resequencing of all barcoded strains

Additionally, we have improved the clarity of the text, particularly the methods section describing mathematical modeling in the main text. Major changes in the text and particularly those replying to reviewers comment have been highlighted here and in the manuscript.

Reviewer #1 (Public Review):

The study addresses an important question - how the composition of the microbiota influences the intestinal colonization of encapsulated vs unencapsulated B. theta, an important commensal organism. To answer the question, the authors develop a refurbished WITS with extended mathematical modeling to quantify B. theta population bottlenecks during intestinal colonization in the setting of different microbiota. Interestingly, they show that the colonization defect of an acapsular mutant is dependent on the composition of the microbiota, suggesting (but not proving) that interactions between gut bacteria, rather than with host immune mechanisms, explains why the mutant has a colonization defect. However, it is fairly difficult to evaluate some of the claims because experimental details are not easy to find and the number of animals is very small. Furthermore, some of the analyses and claims are compromised because the authors do not fully explain their data; for example, leaving out the zero values in Fig. 3 and not integrating the effect of bottlenecks into the resulting model, undermines the claim that the acapsular mutant has a longer in vivo lag phase.

We thank the reviewer for taking time to give this details critique of our work, and apologies that the experimental details were insufficiently explained. This criticism is well taken. Exact inoculum details for experiment are now present in each figure (or as a supplement when multiple inocula are included). Exact microbiome composition analysis for OligoMM12, LCM and SPF microbiota is now included in Figure 2 – Figure supplement 1.

Of course, the models could be expanded to include more factors, but I think this comment is rather based on the data being insufficiently clearly explained by us. There are no “zero values missing” from Fig. 3 – this is visible in the submitted raw data table (excel file Source Data 1), but the points are fully overlapped in the graph shown and therefore not easily discernable from one another. Time-points where no CFU were recovered were plotted at a detection limit of CFU (50 CFU/g) and are included in the curve-fitting. However, on re-examination we noticed that the curve fit was carried out on the raw-data and not the log-normalized data which resulted in over-weighting of the higher values. Re-fitting this data does not change the conclusions but provides a better fit. These experiments have now been repeated such that we now have >=7 animals in each group. This new data is presented in Fig. 3C and D and Fig. 3 Supplement 2.

Limitations:

1) The experiments do not allow clear separation of effects derived from the microbiota composition and those that occur secondary to host development without a microbiota or with a different microbiota. Furthermore, the measured bottlenecks are very similar in LCM and Oligo mice, even though these microbiotas differ in complexity. Oligo-MM12 was originally developed and described to confer resistance to Salmonella colonization, suggesting that it should tighten the bottleneck. Overall, an add-back experiment demonstrating that conventionalizing germ-free mice imparts a similar bottleneck to SPF would strengthen the conclusions.

These are excellent suggestions and have been followed. Additional data is now presented in Figure 2 – figure supplement 8 showing short, versus long-term recolonization of germ-free mice with an SPF microbiota and recovering very similar values of beta, to our standard SPF mouse colony. These data demonstrate a larger total niche size for B. theta at 2 days post-colonization which normalizes by 2 weeks post-colonization. Independent of this, the colonization probability, is already equivalent to that observed in our SPF colony at day 2 post-colonization. Therefore, the mechanisms causing early clonal loss are very rapidly established on colonization of a germ-free mouse with an SPF microbiota. We have additionally demonstrated that SPF mice do not have detectable intestinal antibody titers specific for acapsular B. theta. (Figure 2 – figure supplement 7), such that this is unlikely to be part of the reason why acapsular B. theta struggles to colonize at all in the context of an SPF microbiota. Experiments were also carried to detect bacteriophage capable of inducing lysis of B. theta and acapsular B. theta from SPF mouse cecal content (Figure 2 – figure supplement 7). No lytic phage plaques were observed. However, plaque assays are not sensitive for detection of weakly lytic phage, or phage that may require expression of surface structures that are not induced in vitro. We can therefore conclude that the restrictive activity of the SPF microbiota is a) reconstituted very fast in germ-free mice, b) is very likely not related to the activity of intestinal IgA and c) cannot be attributed to a high abundance of strongly lytic bacteriophage. The simplest explanation is that a large fraction of the restriction is due to metabolic competition with a complex microbiota, but we cannot formally exclude other factors such as antimicrobial peptides or changes in intestinal physiology.

2) It is often difficult to evaluate results because important parameters are not always given. Dose is a critical variable in bottleneck experiments, but it is not clear if total dose changes in Figure 2 or just the WITS dose? Total dose as well as n0 should be depicted in all figures.

We apologized for the lack of clarity in the figures. Have added panels depicting the exact inoculum for each figure legend (or a supplementary figure where many inocula were used). Additionally, the methods section describing how barcoded CFU were calculated has been rewritten and is hopefully now clearer.

3) This is in part a methods paper but the method is not described clearly in the results, with important bits only found in a very difficult supplement. Is there a difference between colonization probability (beta) and inoculum size at which tags start to disappear? Can there be some culture-based validation of "colonization probability" as explained in the mathematics? Can the authors contrast the advantages/disadvantages of this system with other methods (e.g. sequencing-based approaches)? It seems like the numerator in the colonization probability equation has a very limited range (from 0.18-1.8), potentially limiting the sensitivity of this approach.

We apologized for the lack of clarity in the methods. This criticism is well taken, and we have re-written large sections of the methods in the main text to include all relevant detail currently buried in the extensive supplement.

On the question of the colonization probability and the inoculum size, we kept the inoculum size at 107 CFU/ mouse in all experiments (except those in Fig.4, where this is explicitly stated); only changing the fraction of spiked barcoded strains. We verified the accuracy of our barcode recovery rate by serial dilution over 5 logs (new figure added: Figure 1 – figure supplement 1). “The CFU of barcoded strains in the inoculum at which tags start to disappear” is by definition closely related to the colonization probability, as this value (n0) appears in the calculation. Note that this is not the total inoculum size – this is (unless otherwise stated in Fig. 4) kept constant at 107 CFU by diluting the barcoded B. theta with untagged B. theta. Again, this is now better explained in all figure legends and the main text.

We have added an experiment using peak-to-trough ratios in metagenomic sequencing to estimate the B. theta growth rate. This could be usefully employed for wildtype B. theta at a relatively early timepoint post-colonization where growth was rapid. However, this is a metagenomics-based technique that requires the examined strain to be present at an abundance of over 0.1-1% for accurate quantification such that we could not analyze the acapsular B. theta strain in cecum content at the same timepoint. These data have been added (Figure 3 – figure supplement 3). Note that the information gleaned from these techniques is different. PTR reveals relative growth rates at a specific time (if your strain is abundant enough), whereas neutral tagging reveals average population values over quite large time-windows. We believe that both approaches are valuable. A few sentences comparing the approaches have been added to the discussion.

The actual numerator is the fraction of lost tags, which is obtained from the total number of tags used across the experiment (number of mice times the number of tags lost) over the total number of tags (number of mice times the number of tags used). Very low tag recovery (less than one per mouse) starts to stray into very noisy data, while close to zero loss is also associated with a low-information-to-noise ratio. Therefore, the size of this numerator is necessarily constrained by us setting up the experiments to have close to optimal information recovery from the WITS abundance. Robustness of these analyses is provided by the high “n” of between 10 and 17 mice per group.

4) Figure 3 and the associated model is confusing and does not support the idea that a longer lag-phase contributes to the fitness defect of acapsular B.theta in competitive colonization. Figure 3B clearly indicates that in competition acapsular B. theta experiences a restrictive bottleneck, i.e., in competition, less of the initial B. theta population is contributed by the acapsular inoculum. There is no need to appeal to lag-phase defects to explain the role of the capsule in vivo. The model in Figure 3D should depict the acapsular population with less cells after the bottleneck. In fact, the data in Figure 3E-F can be explained by the tighter bottleneck experienced by the acapsular mutant resulting in a smaller acapsular founding population. This idea can be seen in the data: the acapsular mutant shedding actually dips in the first 12-hours. This cannot be discerned in Figure 3E because mice with zero shedding were excluded from the analysis, leaving the data (and conclusion) of this experiment to be extrapolated from a single mouse.

We of course completely agree that this would be a correct conclusion if only the competitive colonization data is taken into account. However, we are also trying to understand the mechanisms at play generating this bottleneck and have investigated a range of hypotheses to explain the results, taking into account all of our data.

Hypothesis 1) Competition is due to increased killing prior to reaching the cecum and commencing growth: Note that the probability of colonization for single B. theta clones is very similar for OligoMM12 mouse single-colonization by the wildtype and acapsular strains. For this hypothesis to be the reason for outcompetition of the acapsular strain, it would be necessary that the presence of wildtype would increase the killing of acapsular B. theta in the stomach or small intestine. The bacteria are at low density at this stage and stomach acid/small intestinal secretions should be similar in all animals. Therefore, this explanation seems highly unlikely

Hypothesis 2) Competition between wildtype and acapsular B. theta occurs at the point of niche competition before commencing growth in the cecum (similar to the proposal of the reviewer). It is possible that the wildtype strain has a competitive advantage in colonizing physical niches (for example proximity to bacteria producing colicins). On the basis of the data, we cannot exclude this hypothesis completely and it is challenging to measure directly. However, from our in vivo growth-curve data we observe a similar delay in CFU arrival in the feces for acapsular B. theta on single colonization as in competition, suggesting that the presence of wildtype (i.e., initial niche competition) is not the cause of this delay. Rather it is an intrinsic property of the acapsular strain in vivo,

Hypothesis 3) Competition between wildtype and acapsular B. theta is mainly attributable to differences in growth kinetics in the gut lumen. To investigate growth kinetics, we carried our time-courses of fecal collection from OligoMM12 mice single-colonized with wildtype or acapsular B. theta, i.e., in a situation where we observe identical colonization probabilities for the two strains. These date, shown now in Figure 3 C and D and Figure 3 – figure supplement 2, show that also without competition, the CFU of acapsular B. theta appear later and with a lower net growth rate than the wildtype. As these single-colonizations do not show a measurable difference between the colonization probability for the two strains, it is not likely that the delayed appearance of acapsular B. theta in feces is due to increased killing (this would be clearly visible in the barcode loss for the single-colonizations). Rather the simplest explanation for this observation is a bona fide lag phase before growth commences in the cecum. Interestingly, using only the lower net growth rate (assumed to be a similar growth rate but increased clearance rate) produces a good fit for our data on both competitive index and colonization probability in competition (Figure 3, figure supplement 5). This is slightly improved by adding in the observed lag-phase (Figure 3). It is very difficult to experimentally manipulate the lag phase in order to directly test how much of an effect this has on our hypothesis and the contribution is therefore carefully described in the new text.

Please note that all data was plotted and used in fitting in Fig 3E, but “zero-shedding” is plotted at a detection limit and overlayed, making it look like only one point was present when in fact several were used. This was clear in the submitted raw data tables. To sure-up these observations we have repeated all time-courses and now have n>=7 mice per group.

5) The conclusions from Figure 4 rely on assumptions not well-supported by the data. In the high fat diet experiment, a lower dose of WITS is required to conclude that the diet has no effect. Furthermore, the authors conclude that Salmonella restricts the B. theta population by causing inflammation, but do not demonstrate inflammation at their timepoint or disprove that the Salmonella population could cause the same effect in the absence of inflammation (through non-inflammatory direct or indirect interactions).

We of course agree that we would expect to see some loss of B. theta in HFD. However, for these experiments the inoculum was ~109 CFUs/100μL dose of untagged strain spiked with approximately 30 CFU of each tagged strain. Decreasing the number of each WITS below 30 CFU leads to very high variation in the starting inocula from mouse-to-mouse which massively complicates the analysis. To clarify this point, we have added in a detection-limit calculation showing that the neutral tagging technique is not very sensitive to population contractions of less than 10-fold, which is likely in line with what would be expected for a high-fat diet feeding in monocolonized mice for a short time-span.

This is a very good observation regarding our Salmonella infection data. We have now added the fecal lipocalin 2 values, as well as a group infected with a ssaV/invG double mutant of S. Typhimurium that does not cause clinical grade inflammation (“avirulent”). This shows 1) that the attenuated S. Typhimurium is causing intestinal inflammation in B. theta colonized mice and 2) that a major fraction of the population bottleneck can be attributed to inflammation. Interestingly, we do observe a slight bottleneck in the group infected with avirulent Salmonella which could be attributable either to direct toxicity/competition of Salmonella with B. theta or to mildly increased intestinal inflammation caused by this strain. As we cannot distinguish these effects, this is carefully discussed in the manuscript.

6) Several of the experiments rely on very few mice/groups.

We have increased the n to over 5 per group in all experiments (most critically those shown in Fig 3, Supplement 5). See figure legends for specific number of mice per experiment.

Reviewer #2 (Public Review):

The goal of this study was to understand population bottlenecks during colonization in the context of different microbial communities. Capsular polysaccharide mutants, diet, and enteric infection were also used paired to short-term monitoring of overall colonization and the levels of specific strains. The major strength of this study is the innovative approach and the significance of the overall research area.

The first major limitation is the lack of clear and novel insight into the biology of B. theta or other gut bacterial species. The title is provocative, but the experiments as is do not definitively show that the microbiota controls the relative fitness of acapsular and wild-type strains or provide any mechanistic insights into why that would be the case. The data on diet and infection seem preliminary. Furthermore, many of the experiments conflict with prior literature (i.e., lack of fitness difference between acapsular and wild-type strain and lack of impact of diet) but satisfying explanations are not provided for the lack of reproducibility.

In line with suggestions from Reviewer 1, the paper has undergone quite extensive re-writing to better explain the data presented and its consequences. Additionally, we now explicitly comment on apparent discrepancies between our reported data and the literature – for example the colonization defect of acapsular B. theta is only published for competitive colonizations, where we also observe a fitness defect so there is no actual conflict. Additionally, we have calculated detection limits for the effect of high-fat diet and demonstrate that a 10-fold reduction in the effective population size would not be robustly detected with the neutral tagging technique such that we are probably just underpowered to detect small effects, and we believe it is important to point out the numerical limits of the technique we present here. Additionally for the Figure 4 experiments, we have added data on colonization/competition with an avirulent Salmonella challenge giving some mechanistic data on the role of inflammation in the B. theta bottleneck.

Another major limitation is the lack of data on the various background gut microbiotas used. eLife is a journal for a broad readership. As such, describing what microbes are in LCM, OligoMM, or SPF groups is important. The authors seem to assume that the gut microbiota will reflect prior studies without measuring it themselves.

All gnotobiotic lines are bred as gnotobiotic colonies in our isolator facility. This is now better explained in the methods section. Additionally, 16S sequencing of all microbiotas used in the paper has been added as Figure 2 – figure supplement 1.

I also did not follow the logic of concluding that any differences between SPF and the two other groups are due to microbial diversity, which is presumably just one of many differences. For example, the authors acknowledge that host immunity may be distinct. It is essential to profile the gut microbiota by 16S rRNA amplicon sequencing in all these experiments and to design experiments that more explicitly test the diversity hypotheses vs. alternatives like differences in the membership of each community or other host phenotypes.

This is an important point. We have carried out a number of experiments to potentially address some issues here.

1) We carried out B. theta colonization experiments in germ-free mice that had been colonized by gavage of SPF feces either 1 day prior to colonization of 2 weeks prior to colonization. While the shorter pre-colonization allowed B. theta to colonize to a higher population density in the cecum, the colonization probability was already reduced to levels observed in our SPF colony in the short pre-colonization. Therefore, the factors limiting B. theta establishment in the cecum are already established 1-2 days post-colonization with an SPF microbiota (Figure 2 - figure supplement 8). 2) We checked for the presence of secretory IgA capable of binding to the surface of live B. theta, compared to a positive control of a mouse orally vaccinated against B. theta. (Fig. 2, Supplement 7) and could find no evidence of specific IgA targeting B. theta in the intestinal lavages of our SPF mouse colony. 3) We isolated bacteriophage from the intestine of SPF mice and used this to infect lawns of B. theta wildtype and acapsular in vitro. We could not detect and plaque-forming phage coming from the intestine of SPF mice (Figure 2 – figure supplement 7).

We can therefore exclude strongly lytic phage and host IgA as dominant driving mechanisms restricting B. theta colonization. It remains possible that rapidly upregulated host factors such as antimicrobial peptide secretion could play a role, but metabolic competition from the microbiota is also a very strong candidate hypothesis. The text regarding these experiments has been slightly rewritten to point out that colonization probability inversely correlates with microbiota complexity, and the mechanisms involved may involve both direct microbe-microbe interactions as well as host factors.

Given the prior work on the importance of capsule for phage, I was surprised that no efforts are taken to monitor phage levels in these experiments. Could B. theta phage be present in SPF mice, explaining the results? Alternatively, is the mucus layer distinct? Both could be readily monitored using established molecular/imaging methods.

See above: no plaque-forming phage could be recovered from the SPF mouse cecum content. The main replicative site that we have studied here, in mice, is the cecum which does not have true mucus layers in the same way as the distal colon and is upstream of the colon so is unlikely to be affected by colon geography. Rather mucus is well mixed with the cecum content and may behave as a dispersed nutrient source. There is for sure a higher availability of mucus in the gnotobiotic mice due to less competition for mucus degradation by other strains. However, this would be challenging to directly link to the B. theta colonization phenotype as Muc2-deficient mice develop intestinal inflammation.

The conclusion that the acapsular strain loses out due to a difference of lag phase seems highly speculative. More work would be needed to ensure that there is no difference in the initial bottleneck; for example, by monitoring the level of this strain in the proximal gut immediately after oral gavage.

This is an excellent suggestion and has been carried out. At 8h post-colonization with a high inoculum (allowing easy detection) there were identical low levels of B. theta in the upper and lower small intestine, but more B. theta wildtype than B. theta acapsular in the cecum and colon, consistent with commencement of growth for B. theta wildtype but not the acapsular strain at this timepoint. We have additionally repeated the single-colonization time-courses using our standard inoculum and can clearly see the delayed detection of acapsular B. theta in feces even in the single-colonization state when no increased bottleneck is observed. This can only be reasonably explained by a bona fide lag-phase extension for acapsular B. theta in vivo. These data also reveal and decreased net growth rate of acapsular B. theta. Interestingly, our model can be quite well-fitted to the data obtained both for competitive index and for colonization probability using only the difference in net growth rate. Adding the (clearly observed) extended lag-phase generates a model that is still consistent with our observations.

Another major limitation of this paper is the reliance on short timepoints (2-3 days post colonization). Data for B. theta levels over 2 weeks or longer is essential to put these values in context. For example, I was surprised that B. theta could invade the gut microbiota of SPF mice at all and wonder if the early time points reflect transient colonization.

It should be noted that “SPF” defines microbiota only on missing pathogens and not on absolute composition. Therefore, the rather efficient B. theta colonization in our SPF colony is likely due to a permissive composition and this is likely to be not at all reproducible between different SPF colonies (a major confounder in reproducibility of mouse experiments between institutions. In contrast the gnotobiotic colonies are highly reproducible). We do consistently see colonization of our SPF colony by wildtype B. theta out to at least 10 days post-inoculation (latest time-point tested) at similar loads to the ones observed in this work, indicating that this is not just transient “flow-through” colonization. Data included below:

For this paper we were very specifically quantifying the early stages of colonization, also because the longer we run the experiments for, the more confounding features of our “neutrality” assumptions appear (e.g., host immunity selecting for evolved/phase-varied clones, within-host evolution of individual clones etc.). For this reason, we have used timepoints of a maximum of 2-3 days.

Finally, the number of mice/group is very low, especially given the novelty of these types of studies and uncertainty about reproducibility. Key experiments should be replicated at least once, ideally with more than n=3/group.

For all barcode quantification experiments we have between 10 and 17 mice per group. Experiments for the in vivo time-courses of colonization have been expanded to an “n” of at least 7 per group.

Reviewer #3 (Public Review):

The study addresses a tough question in the study of wild bats: what and where they eat, using both acoustic bio-logging and DNA metabarcoding. As a result, it was found that greater mouse-eared bats made more frequent attack attempts against passively gleaning prey with lower predation success but higher prey profitability than aerial hawking with higher predation success. This is a precious study that reveals essential new insights into the foraging strategies of wild bats, whose foraging behavior has been challenging to measure. On the other hand, the detection of capture attempts, success or failure of predation, and whether it was by passively gleaning prey or aerial hawking were determined from the audio and triaxial accelerometer analysis, and all results of this study depend entirely on the veracity of this analysis. Also, although two different weights and a tag nearly 15% of its weight were used, it is essential for the results of this data that there be no effect on foraging behavior due to tag attachment. Since this is an excellent study design using state-of-the-art methods and very valuable results, readers should carefully consider the supplemental data as well.

Would this 2010 Joyce be stunned by the 2023 school library? What has changed in the last 10 plus years?

Use the follow-up tag to delve further.

via u/The_Bee_Sneeze

Example of someone using Obsidian with a zettelkasten focus to write screenplays.

Thought the example appears in r/Zettelkasten, one must wonder at how Luhmann-esque such a practice really appears?

annotation meta: may need new tag: - can't effectively monitor

Immaturity on Jane's part, I think. Both for being so picky about eating as to make it the pressing concern of all your relations, to refuse perfectly good arrowroot, and then to scour the meadows for your own.

Arrowroot is a food of low nutritional value that was sought after by people who had various food intolerances.

There are connections to racism as it relied on slavery for mass production and exportation and the obsession with 'purity' also has ideological similarities with reference to race.

http://www.digitalussouth.org/vegetable/vegetable.php?vegName=Arrow%20Root

https://janeaustensworld.com/tag/historical-food/

Here is a link to a post about the Romany of England during the regency. It says that to even have spoken with these people during the time was a crime and that might account for some of Harriet's fear. It also adds another dimension to the conflation of 'othering' of children, the poor, and racism.

https://janeaustensworld.com/tag/gypsies-in-regency-england/

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Referee #2

Evidence, reproducibility and clarity

Summary:

Cohen et al. presented a high-throughput approach to analyze protein-(putative) substrate interactions in yeast using BirA biotin ligase and its acceptor peptide AVI tag. Using this system, the authors identified the common and unique substrates of translocation pores, Sec61 and Ssh1. Interestingly, the differential substrates between Sec61 and Ssh1 seem to be explained by the degree of hydrophobicity in signal peptide sequences, which was also nicely demonstrated by an experiment showing that swapping the first three amino acids of substrate proteins is sufficient to convert the substrate specificity. While I appreciate that the approach is high-throughput and simple (does not require mass spectrometers), there are some technical comments to be addressed.

1. Why was BirA used to study transient interactions? Biotinylation through BirA is slow (that is why it takes several hours to label proximity proteins) and thus it may not be suitable for capturing transient interactions. Instead, TurboID would be more suitable as the biotinylation reaction is faster than BirA. A reasonable explanation using BirA is required.
2. One key question is whether biotinylated proteins identified by this method are substrates or proteins just proximal to Sec61 or Ssh1 due to close cellular localization (e.g. ER membrane) or same protein complex members. An experiment or analysis would be required to confirm that the proteins they identified are indeed potential substrates.
3. Along the same line, if proteins identified by this approach are bona fide substrates of Sec61 and Ssh1, proteins having signal peptides should be enriched in the candidate list of substrates. However, it does not look like that according to Figure 2A where the secretome proteins/total proteins ratio appears to be similar among the 4 categories (e.g., Ssh1 preferring, No preference, and Not interacting or excluded). The authors should comment on this.
4. Figures 1-2: They should comment on the reproducibility of the method. How many independent experiments were performed? If performed, how was reproducibility of results?
5. Figure 3: It is important to know the overlap of proteins commonly identified in both the interaction screening and protein localization assay. A Venn diagram that compares results between the two high-throughput assays would be useful.
6. Figure 4A (GO term): The authors mentioned that " the most consistent and repeating GO term group was those related to budding and polarity process. These include: "Establishment or maintenance of cell polarity"; "Development process involved in reproduction"; "Bipolar cellular bud site selection"; "Cell budding" and "Structural constituent of cell wall". Are protein sets in these functional categories similar or different? I am asking because GO enrichment analysis often provides apparently different functional categories but similar protein sets are included. 

Referees cross-commenting

The comments from reviewer #1 are reasonable and would further strengthen the quality of the paper.

Significance

The approach is high-throughput and simple (does not require mass spectrometers).

The differential substrates between Sec61 and Ssh1 seem to be explained by the degree of hydrophobicity in signal peptide sequences, which was also nicely demonstrated by an experiment showing that swapping the first three amino acids of substrate proteins is sufficient to convert the substrate specificity.

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Reply to the reviewers

Reviewer 1

Although this is an interesting, and generally well-performed study, it is primarily observational and there are few mechanistic insights provided into how MUC13 modulates barrier function. The authors propose a presumably direct interaction between MUC13 and PKC, which apparently sequesters PKC, preventing this kinase from triggering PKC-dependent increases in TJ barrier function; however, there is no evidence that a MUC13-PKC interaction occurs, that MUC13 is phosphorylated by PKC, or that phosphorylation of MUC13 has any impact on its function or overall barrier function. Thus, the hypothesis is not directly tested and all observations in this manuscript are generally correlative in nature.

While the MUC13 cytoplasmic tail contains a putative PKC-binding motif, we indeed do not show a direct interaction between MUC13 and a member of the PKC family in this manuscript. Unfortunately, we have so far not been able to successfully perform (co-)immunoprecipitation of MUC13 with our current anti-MUC13 antibodies.

To provide more insights into the possible MUC13-PKC interaction, we plan to perform several experiments.

• First, we will determine the expression levels of the different PKC isotypes (PKC alpha, beta, gamma, delta, epsilon, and zeta) in the HRT18 cell lines by western blot.
• Next, we will determine the localization of the relevant PKC isoforms and MUC13 by immunofluorescence microscopy. We are curious to see if we can find a colocalization between MUC13 and a PKC member on the lateral or apical membrane. If we can demonstrate a colocalization, we could follow up with a proximity ligation assay, but this would require the MUC13 antibody directed against the cytoplasmic tail (which only detects the lateral population) and might therefore be challenging.
• Furthermore, since PKC delta protein levels were upregulated in the total lysate of ∆MUC13 cells, we will test a PKC delta-specific inhibitor in the TEER assay.

  Consider quantifying all blots (Fig. 5C, Fig. 6B).

As suggested, we will quantify both blots.

Consider using dot-plots for all quantified data.

The graphs will be altered to include individual measurement points.

Reviewer 2

Fig2E showed two bands with different size in the two MUC13 WT control cell lines. They hypothesized that this could be the consequences of glycosylation different patterns. A sample with untransfected HRT18 might be included in the western blot panel. Additionally, what is the 100kDa band?

Mucin blots are notoriously difficult and these MUC13 blots are the result of a lot of trial and error. We repeated the Western Blot with original HRT18 cells, HRT18 original cell line, as well as the two CRISPR control cells used in the study (WT 1 and WT 2) and one of the full-length MUC13 knockout cells. The higher band was absent from the MUC13 knockout cells, but a small shift in the MUC13 band size can be noted in the WT 1 cells compared to the original and the WT 2 cell lines, possibly indicating a change in the glycosylation pattern. The 100 kDa band remains detectable in all cell lines including the ∆MUC13 cell line, therefore we consider this to be an aspecific background band of the MUC13 antibody. We will add a more extensive Western Blot analysis to the manuscript.

Did the transfection of the inducible GFP-MUC13 plasmid induce any decrease of Claudin1/3/4 in HRT18 or Caco2 cells? Same question regarding PKCdelta.

These are indeed interesting questions. We will perform these experiments with our MUC13-overexpression HRT18 cells.

Reviewer 3

Moreover, the authors should determine if MUC13∆CT localize to TJs, as suggested by the working model in Figure 7C. The subcellular localization of MUC3∆CT could give critical clues for its function, but Figure 2G fails to provide any information and the authors do not present any additional data concerning the localization of MUC13∆CT. Detection of MUC13 in membrane fractions of WT, MUC13∆CT and cells lacking the mucin domain could be a feasible strategy forward.

We will perform additional immunofluorescence experiments to determine the subcellular localization of MUC13-∆CT more accurately. However, detection of the extracellular domain by western blot, as suggested, is not possible due to the incompatibility of the extracellular MUC13-directed hybridoma antibody with the western blot technique. We currently do not have a suitable antibody that recognizes the ED and can be used for western blot.

The authors introduce an inducible MUC13-GFP fusion protein into WT and ∆MUC13 cells and show that it reverses the enhanced TEER upon MUC13 deletion. Unfortunately, the "Materials and Methods" section lacks adequate information on how this fusion protein was designed. Critical questions are the position of the GFP tag within MUC13, whether the fusion protein is correctly processed in HRT18 cells, and if it localizes to the apical or apico-lateral membrane domains? Figure 2H is of low magnification and fails to provide information on the subcellular localization of the MUC13-GFP fusion protein.

The materials and methods section will be adjusted to describe all the design details of the fusion protein. The GFP tag was added to the MUC13 C-terminus with a GGGS linker sequence in between. Processing of the fusion protein seems correct as we observed MUC13-GFP localization to both lateral and apical membranes and no access intracellular build up. As suggested by the reviewer, we will add more detailed immunofluorescence pictures to the manuscript.

Figures 6B-C suggest that PKCdelta levels increase in ∆MUC13 cells, which correlates with higher enrichment of Claudins in membrane fractions. The authors then inhibited PKCdelta and observed reduced recruitment of Claudins to membrane fractions. Since the family of Claudins are differentially regulated by phosphorylation (PMID: 29186552), the authors should investigate the TEER phenotype of WT, ∆MUC13 and MUC13∆CT upon PKC inhibition.

We must clarify that figures 6C-D are done using the PKC inhibitor targeting all conventional PKCs (alpha, beta, gamma) as well as delta (https://www.tocris.com/products/gf-109203x_0741). We recently obtained a PKCdelta-specific inhibitor which we will test in the TEER build-up experiments.

Moreover, the authors predict phosphorylation sites in MUC13CT and suggest a link between PKC and MUC13 (Figure. 6A), however no evidence is presented to support this hypothesis. The authors should either determine if PKC phosphorylates MUC13 and if this modification has implication on MUC13 localization and TJ function, or remove statements regarding MUC13 phosphorylation. The data provided suggest that PKC regulates TJ proteins independent of MUC13.

We will adjust the manuscript to put less emphasis on the putative PKC motifs in the MUC13 cytoplasmic tail. For further details on how we will proceed regarding the possible MUC13-PKC interaction see question 1 from reviewer #1.

Figure 5C. Quantification of at least 3 independent experiments is required.

These data will be added to the manuscript.

Figure 6B. Quantification of at least 3 independent experiments is required.

These data will be added to the manuscript.

Reviewer 4

OPTIONAL: MUC13 is expressed both, in the basolateral membranes and in the apical membrane of intestinal epithelial cells (IECs). Does the authors check the relevance of MUC13 in the formation of microvilli in IECs? Are microvilli different (microvilli staining, number of positive cells to microvilli, length, width or distribution of microvilli) in ΔMUC13 and in MUC13-ΔCT? How the glycocalyx looks like in these cells genetically modified for MUC13?

HRT18 cells do not seem to develop microvilli. However, we plan to stain these cells with a microvilli-specific antibody (ACTUB). The HRT18 cells express mostly MUC13 and relatively low levels of the larger TM mucin MUC1. To study changes in the glycocalyx, we will stain using a MAL-II antibody which targets α-2,3 sialic acids, which are abundantly present in mucins. In this way, we will determine any big changes in the total glycocalyx that may occur in response to the removal of MUC13.

In the figure 1D would be nice to represent the co-localization of MUC13 together with occluding in a graph in each Z-stack so you can visualize in which part of the cell is maximum colocalization of these both components.

These data will be provided.

In the figure 1E, would be great to compare between the two different MUC13 antibodies the apical fraction stained in HRT18 and Caco-2. Specially in the HRT18 cell line since the first antibody did not label apical MUC13 expression meanwhile the second antibody detects the apical expression in these cells. How much lateral lateral stain the C terminal antibody compare with the extracellular antibody for MUC13 and how much stain apically the C terminal antibody compare with the extracellular antibody? Would be nice to see some comparative results using the intensity by Z-stack and plotting in a graph.

This is a good suggestion as it is quite intriguing that both MUC13 antibodies seem to target (partially) different MUC13 populations. We will perform co-staining with both MUC13 antibodies to provide information on which MUC13 populations are detected by each antibody (apical vs lateral membrane).

Manuscript would be improved if in the figure 2H to compare within the same cell line the number of MUC13 positive cells in the WT, number of MUC13 positive cells in WT+pMUC13 and the number of MUC13 positive cells in the ΔMUC13+pMUC13

We will quantify the percentage of MUC13-GFP positive cells in both the WT and ΔMUC13 backgrounds by either microscopy or flow cytometry.

In figure 5C would be helpful to plot in a graph the normalized expression of each TJ protein and compare between the different cells used (WT, ΔMUC13 and MUC13-ΔCT) as you did in figure 5A

We will provide the quantification data of three independent experiments.

Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer 1

In addition, this model does not explain why all kinase inhibitors tested reverse the increase in TER observed in deltaMUC13 cell lines. Does this reflect the lack of inhibitor specificity or the likelihood that many kinases are involved?

As stated in the manuscript, we think that MLCK, ROCK, and PKC are all essential for TER buildup in the ∆MUC13 cells. Because the roles of MLCK and ROCK are well established, we choose to follow up on the PKC results. We adjusted the text to clarify this point.

The authors do observe that there is an increase in expression of several tight junction-associated proteins, including the claudins, in deltaMUC13 cells. Affected CLDNs include 1, 2, 3, 4, 7, 12. (1) While it appears the authors are arguing that this increased claudin expression results in increased barrier function, they do not sufficiently highlight the well-known role that CLDN2 has in cation transport, and both CLDN-4 and -7 have also been implicated in paracellular ion flux (although this is apparently cell-type specific). These observations would seem to argue against a simple correlation between claudin expression and tight junction barrier function.

The reviewer is right about the different functions of claudins. Claudin-2, -4 and -7 have (potentially) pore-forming properties, while the other claudins restrict paracellular passage. It has been previously demonstrated that the magnitude of paracellular ion and water flux is reflected by the specific repertoire of claudin family members (Shashikanth et al., 2022). In this paper, overexpression of claudin-4 was shown to mobilize and affect polymeric strands of claudin-2, thus blocking its channel activity. Our mass spectrometry data demonstrated a striking increase in claudin-1, -2, -3, -4, -7, and -12 in the MUC13 knockout membranes compared to WT. We hypothesize that the claudin repertoire in the MUC13 knockout cells leads to a more restricted paracellular route (as observed in the TEER and tracer experiments). The pore-forming claudins may be subject to “interclaudin interference” therefore leading to restriction of the total paracellular ion and water flux. We have adjusted the text of the manuscript to clarify this point.

We attempted to investigate claudin-2 expression levels in isolated membranes by Western Blot but were unsuccessful as the antibody did not detect any protein while claudins-1 and -4 could be detected with the same method.

Furthermore, the authors should note the disconnect between paracellular ion flux mediated by claudins and the flux of markers such as dextrans and lucifer yellow, which can be dissociated from claudin function.

We acknowledge that the flux of larger particles (the leak pathway) is not regulated by claudins (which regulates the pore pathway). We aimed to assess both the pore and the leak paracellular pathways, by using different techniques including TEER, small solutes (Lucifer Yellow CH), and larger molecules (4 and 70 kDa FITC-Dextrans). HRT18 wild type cells are already very restrictive to the pass of larger molecules (FITC-Dextrans) but are more permeable to smaller solutes such as Lucifer Yellow (400 Da). We observed that removal of the MUC13 cytoplasmic tail did not affect the TEER, but reduced the paracellular passage of Lucifer Yellow, demonstrating that manipulation of MUC13 can affect both the pore and leak pathways. We adjusted to text to include this point.

The increased expression of claudins in the nominally tail-minus MUC13 without a corresponding change in TER would again seem to argue against a simple correlation;

MUC13-dCT cells showed consistently increased levels of claudins-1 and -2, but not the other claudins. This claudin repertoire (with high claudins-1 and -2, but lower claudin-3, -4, -7, and -12) is apparently not enough to increase TEER. We think that this again reflects the importance of the total claudin composition for the control of the paracellular pathway.

Watch the use of decimal points instead of commas (lines 253 and 256).

Corrected.

Line 543: MilliQ is not a washing agent (or is it?). (Line 535) We use MilliQ as a final step before mounting the glass slides to remove any possible salt deposition that would affect the visualization by microscopy.

We have specified this in the text.

Line 553: TER is the product of total resistance times the area. The units are ohms times area.

Indeed, we have changed this mistake (line 545).

Line 630: Please provide the transfer conditions (voltage, amp, watts?) and transfer buffer when describing the Western blot protocol.

For immunoblotting of MUC13, protein lysates were transferred to 0.2 µm PVDF membranes using the Trans-Blot Turbo Transfer system (Biorad). The transfer was run using the protocol (High MW) which consisted in running for 10 min at 25 volts (V) and 1,3 amperes (A). These experimental data were added to the manuscript.

Reviewer 2

My main concern about this manuscript is that the authors analyzed MUC13 role in intestinal homeostasis and function using colorectal cancer cells. As helpful as cancer cells are, we should always be cautious about extrapolating roles in normal intestinal epithelium or IBD pathology. Obviously, these finding are also interesting in a cancer context. Using GEPIA (http://gepia.cancer-pku.cn/), I observed that MUC13 is overexpressed in colorectal cancer COAD-TCGA dataset (compared to normal colon from GTEX). Similar results were obtained previously by Gupta et al. (ref #10). I am aware that this would be difficult to confirm the main findings in a non-cancerous intestinal cell line but this limit (normal intestine using cancer cells) should be at least discussed in the manuscript.

We appreciate the reviewers’ comments and are aware of the downsides of using cancer-derived cell lines. We have performed the GEPIA analysis ourselves and have an ongoing project about the possible role of MUC13 in colorectal cancer progression. In a separate project, we are collaborating with the Gaultier Laboratory at the University of Virginia which has generated a MUC13 knockout mouse. This model will allow us to study the role of MUC13 in non-cancerous tissue. We recently received intestinal biopsies from these mice which will be stained with MUC13 and claudin antibodies to determine localization in healthy tissue. These experiments will reveal if MUC13 colocalizes with claudin on the lateral membrane in the healthy mouse intestinal tract. In future experiments, we will also address MUC13 localization and function in human intestinal organoids. We have adjusted the discussion to refer to the limitations of using cancer cell lines.

Massey et al (Micro 2021, PMC7014956) previously showed that MUC13 overexpression increased rigidity in PDAC cells and discussed involvement MUC13 link with EMT. MUC13-Her2 interaction was also associated with decrease of E-cadherin suggesting an EMT phenotype. This should be included in the discussion section.

The discussion has been adjusted to include the link with EMT.

The authors performed mass spectrometry analysis. Results are deposited on ProteomeXchange but are not yet publicly released. Among the 1189 membrane protein identified. Did the authors observed alteration of EMT proteins? (decrease of vimentin for example). In the discussion section (lane 347), the authors mentioned the relationship between other membrane bound mucins such as MUC1, MUC4, MUC16 or MUC17 and AJ/TJproteins. Did the authors observed any alteration of these mucin in the mass spectrometry data?

The mass spec analysis was performed on membrane fractions, therefore our dataset will not contain true cytosolic proteins. One of the key EMT proteins, Vimentin, is a cytosolic protein, and indeed it was not found in our dataset. Other EMT-related proteins are shown in the following table. TGF beta 1 was slightly decreased, while E-cadherin and Integrin beta 6 were slightly increased in the ∆MUC13 cells compared to WT cells.

Gene Name

Mean WT

Mean ∆MUC13

Mean MUC13-∆CT

TGFBI (TGB beta 1)

20,54

16,48

18,83

CDH1 (E-cadherin)

22,69

24,57

24,24

ITGB6 (Integrin beta 6)

18,86

21,74

19,19

Vimentin - Cytosolic

-

-

-

CDH2 (Cadherin-2, N-cadherin)

-

-

-

Mucins are large proteins comprised of densely O-glycosylated mucin domains, which makes them extremely challenging to study by mass spectrometry (MS) (Rangel-Angarita et al., 2021). We did not specifically employ mucin-directed technologies in this dataset, thus making the detection of mucins hard. No mucins other than MUC13 were detected. For MUC13, two peptides corresponding to the EGF-like domains in the extracellular domain, a region that is less densely glycosylated. We added a sentence to the description of the mass spec results to include the EMT proteins and other mucins.

Minor points:

Lane 126: HRT18 and Caco2 colon cancer cells instead of intestinal epithelial cells

Corrected.

Lane 181 and lane 514: add "full length" MUC13 DNA sequence

Corrected.

Lane 234: TEER was measured every 12h. How the authors did observed the largest increase at 42h? Was it 48h? Please clarify.

We aimed at measuring every 12 h, however the exact measurements were done at 18h, 24h, and 42 h post-infection. We have corrected this in the manuscript.

Reviewer 3

Line 43 and 46. "Enterocytes" should be replaced with "intestinal epithelial cells", since enterocytes are themselves a distinct subpopulation of IECs.

We have changed it in the manuscript.

Lines 58-60. References in support of the statements should be added.

We added a reference to this sentence.

Lines 188-190. Authors comment on "roundness" of different cell lines. If the parameter is critical for the manuscript, the authors should quantify this phenotype.

The parameter is not critical for the manuscript. We removed the sentence.

Figure 3A. Staining of cell lines should include panels showing localization of MUC13.

Co-staining of MUC13 with occludin in HRT18 cell lines can be found in figure 1D, and MUC13 with E-cadherin in supplementary figure 1.

Lines 323-327 and 390-392. Sentences on these lines contradict each other. The sentences should describe/discuss quantified data presented in Figure 6D.

The reviewer is right that we should be discussing the quantified data in 6D. We adjusted the sentence in line 323-327.

Proteomic data sets should be made publicly available on data depositories.

All proteomics raw data were deposited to the ProteomeXchange Consortium with the dataset identifier PXD029606.

Reviewer 4

OPTIONAL: In the figure 2E, is the extracellular antibody still detecting the MUC13-ΔCT?

No, unfortunately the antibody directed against the MUC13 ED is not compatible with western blot.

In the figure 2G, would be nice to comment possible reasons why the deletion in the first cell line of the MUC13-CT you can still detect with the extracellular antibody some lateral expression of MUC13 meanwhile in the second cell line, the same deletion (MUC13-CT) you cannot see any lateral MUC13 staining with the extracellular antibody.

Yes, this is indeed a puzzling finding, especially because the CRISPR deletion is the same in both cell lines. We will add a sentence about possible reduced stability of the MUC13 without CT domain that leads to a different outcome in both cell lines.

It would be nice that the results from Figure 3H are better explained since it is difficult to follow.

We adjusted the text to explain the experiment in more detail.

2. Description of analyses that authors prefer not to carry out

Reviewer 1

The authors may be overly reliant on TER measurements. Epithelial cells have two parallel resistive pathways: transcellular and paracellular. TER measure the contribution of both. Thus, an increase in TER could result from a decrease in transcellular ion transport. The authors need to measure transcellular ion flow or selectively measure the junctional resistance in a select set of experiments to rule this possibility out.

The reviewer is right that TEER is a sum of the resistance of the transcellular and paracellular pathways. However, due to the high resistance of cell membranes, the current predominantly travels via the paracellular route (Elbrecht et al., 2016). For this reason, TEER measurements are widely accepted techniques for the assessment of ions passage through the paracellular pathway (Shen et al., 2011).

Reviewer 3

Figure 1C. Caco2 and HRT18 cells exhibit distinct MUC13 expression patterns when probed with an antibody against the MUC13 CT; MUC13 localizes almost exclusively to lateral cell junction in HRT18 cells, while a higher portion of MUC13 is present on the apical surface of Caco2 cells. This observation has two possible explanations: 1) the two cell lines express distinct forms of MUC13, or 2) the two cell lines carry distinct machineries for anchoring MUC13 to apical versus apico-lateral membranes. Thus, The authors should take the opportunity to determine the impact of MUC13 deletion on TEER and TJ function in Caco2 cells. Proteomic analysis and functional assays in Caco2 cells may provide more a general mechanism for how MUC13 regulates TJ proteins.

Yes, this would be a great line of investigation. However, we aimed to knockout MUC13 in Caco-2 cell lines (with the same CRISPR/Cas9 protocol as the HRT18 cells) but were unable to obtain Caco-2 knockout clones. We think this might be a consequence of the poor capability of Caco-2 cells to grow as single colonies (a required step in the protocol). Another option is Caco-2 MUC13 knockout cells have reduced viability.

The authors generate cell lines that either lack MUC13 or express MUC13 lacking the cytoplasmic domain. Loss of MUC13 cells resulted in enhanced TEER and increased recruitment of TJ proteins to membrane fractions. MUC13∆CT cells show moderate recruitment of TJ proteins to membranes and no increase in TEER but inhibit paracellular diffusion of Luciferase Yellow across monolayers. Figure 3A suggests that Occludin redistributes to tricellular junctions in ∆MUC13 cells, whereas it is found more laterally in WT and MUC13∆CT cells. These finding suggest that full-length MUC13 interferes with TJ protein complexes. However the impact of the extracellular and intracellular (CT) domains is not fully elucidated. Does the O-glycosylated mucin domain interfere with the extracellular domains Occludin and Claudins? The authors should clarify the contribution of the mucin domain to the observed phenotype, for example by performing the described experiments in a cell line expressing MUC13 lacking the mucin domain.

Mucins are type I membrane proteins with the N-terminal part of the protein on the extracellular site. Therefore, a CRISPR method to specifically remove the glycosylated domain but leave the remainder of the protein in frame is challenging. An additional difficulty is that the ED contains a lot of repeats, complicating the design of specific guide RNAs. To specifically address the contribution of the glycosylated domain, we could complement the MUC13 knockout cell with a construct lacking the ED. However, this would not be comparable to the endogenous MUC13∆CT cell line presented in this manuscript. In future studies, we will strive to address the functions of the different MUC13 domains in more detail.

Figure 5A. Turnover of TJ proteins in membrane fractions occurs faster than over a period of 1-3 days (PMID: 18474622). The authors should determine TJ protein turnover over a period of minutes and hours.

We acknowledge the findings in this interesting paper concerning the continuous remodeling of tight junctions. However, the readout of our biotinylation assay is degradation and the timeframe of degradation turns out to be days and not hours. Within this timeframe remodeling is taking place but it cannot be captured in the total lysate.

Reviewer 4

OPTIONAL: The authors show that the probiotic Lactobacillus plantarum increase epithelial barrier independently of MUC13. Have the authors considered to use other probiotics as Lactobacillus paracasei (10.3389/fcimb.2015.00026), Akkermansia muciniphila (10.1038/emm.2017.282) or some metabolic products from intestinal microbiota as short-chain fatty acids (SCFAs) (10.3389/fphys.2021.650313) to check what is the role of MUC13 and if it is related with other microbe or microbiota metabolite?

Thank you for the suggestion. We have an ongoing project in which we investigate the impact of different probiotic bacteria and plan to investigate whether they have an impact on the epithelial barrier function in a MUC13-dependent manner. This study will lead to a separate publication.

OPTIONAL: The authors successfully delete MUC13 in IECs, both, full length and the cytosolic tail. Have the authors considered targeting the deletion of the PTS domain in MUC13? Could affect that something different from paracellular trafficking as the extracellular detection of microbes and microbial products?

Removal of a domain in the extracellular domain of MUC13 with CRISPR is challenging because mucins are type I membrane proteins, the repeats and possible frameshift, as described above.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The authors describe a novel function for transmembrane mucin MUC13 in regulation of tight junctions (TJs) that create an impermeable cell monolayer that allows. paracellular diffusion of very small molecules. The authors use cultured intestinal epithelial cell monolayers. to demonstrate that MUC13 localizes to the apical aspects of IECs as well laterally to tight junctions. CRSIPR/Cas-mediated deletion of MUC13 increased transepithelial resistance (TEER) and reduced the extent of paracellular diffusion of <0.5 kDa molecules across the monolayer. Proteomic analysis revealed that specific TJ proteins are enriched in cell membrane fractions upon deletion of MUC13, while pharmacological inhibition of PKC involved in actomyosin contractility, resulted in loss of TJ proteins from cell membranes and TEER reduction. See major comments for a detailed discussion concerning the findings.

Major comments:

1. Figure 1C. Caco2 and HRT18 cells exhibit distinct MUC13 expression patterns when probed with an antibody against the MUC13 CT; MUC13 localizes almost exclusively to lateral cell junction in HRT18 cells, while a higher portion of MUC13 is present on the apical surface of Caco2 cells. This observation has two possible explanations: 1) the two cell lines express distinct forms of MUC13, or 2) the two cell lines carry distinct machineries for anchoring MUC13 to apical versus apico-lateral membranes. Thus, The authors should take the opportunity to determine the impact of MUC13 deletion on TEER and TJ function in Caco2 cells. Proteomic analysis and functional assays in Caco2 cells may provide more a general mechanism for how MUC13 regulates TJ proteins.
2. The authors generate cell lines that either lack MUC13 or express MUC13 lacking the cytoplasmic domain. Loss of MUC13 cells resulted in enhanced TEER and increased recruitment of TJ proteins to membrane fractions. MUC13∆CT cells show moderate recruitment of TJ proteins to membranes and no increase in TEER but inhibit paracellular diffusion of Luciferase Yellow across monolayers. Figure 3A suggests that Occludin redistributes to tricellular junctions in ∆MUC13 cells, whereas it is found more laterally in WT and MUC13∆CT cells. These finding suggest that full-length MUC13 interferes with TJ protein complexes. However the impact of the extracellular and intracellular (CT) domains is not fully elucidated. Does the O-glycosylated mucin domain interfere with the extracellular domains Occludin and Claudins? The authors should clarify the contribution of the mucin domain to the observed phenotype, for example by performing the described experiments in a cell line expressing MUC13 lacking the mucin domain. Moreover, the authors should determine if MUC13∆CT localize to TJs, as suggested by the working model in Figure 7C. The subcellular localization of MUC3∆CT could give critical clues for its function, but Figure 2G fails to provide any information and the authors do not present any additional data concerning the localization of MUC13∆CT. Detection of MUC13 in membrane fractions of WT, MUC13∆CT and cells lacking the mucin domain could be a feasible strategy forward.
3. The authors introduce an inducible MUC13-GFP fusion protein into WT and ∆MUC13 cells and show that it reverses the enhanced TEER upon MUC13 deletion. Unfortunately, the "Materials and Methods" section lacks adequate information on how this fusion protein was designed. Critical questions are the position of the GFP tag within MUC13, whether the fusion protein is correctly processed in HRT18 cells, and if it localizes to the apical or apico-lateral membrane domains? Figure 2H is of low magnification and fails to provide information on the subcellular localization of the MUC13-GFP fusion protein.
4. Figures 6B-C suggest that PKCdelta levels increase in ∆MUC13 cells, which correlates with higher enrichment of Claudins in membrane fractions. The authors then inhibited PKCdelta and observed reduced recruitment of Claudins to membrane fractions. Since the family of Claudins are differentially regulated by phosphorylation (PMID: 29186552), the authors should investigate the TEER phenotype of WT, ∆MUC13 and MUC13∆CT upon PKC inhibition. Moreover, the authors predict phosphorylation sites in MUC13CT and suggest a link between PKC and MUC13 (Figure. 6A), however no evidence is presented to support this hypothesis. The authors should either determine if PKC phosphorylates MUC13 and if this modification has implication on MUC13 localization and TJ function, or remove statements regarding MUC13 phosphorylation. The data provided suggest that PKC regulates TJ proteins independent of MUC13.

Minor comments:

1. Line 43 and 46. "Enterocytes" should be replaced with "intestinal epithelial cells", since enterocytes are themselves a distinct subpopulation of IECs.
2. Line 59. The authors should note that MUC13 does not have a canonical SEA domain that generates a cleaved heterodimer (PMID: 16369486).
3. Lines 58-60. References in support of the statements should be added.
4. Lines 188-190. Authors comment on "roundness" of different cell lines. If the parameter is critical for the manuscript, the authors should quantify this phenotype.
5. Figure 3A. Staining of cell lines should include panels showing localization of MUC13.
6. Figure 5A. Turnover of TJ proteins in membrane fractions occurs faster than over a period of 1-3 days (PMID: 18474622). The authors should determine TJ protein turnover over a period of minutes and hours.
7. Figure 5C. Quantification of at least 3 independent experiments is required.
8. Figure 6B. Quantification of at least 3 independent experiments is required.
9. Lines 323-327 and 390-392. Sentences on these lines contradict each other. The sentences should describe/discuss quantified data presented in Figure 6D.
10. Proteomic data sets should be made publicly available on data depositories.

Significance

Mucins participate in critical functions in the human intestine. Gel-forming mucins form the mucus layers that separate the gut microbiota from the underlying intestinal epithelial cells (IECs) (PMID: 18806221). Transmembrane mucins are instead anchored to the plasma membrane of various populations of IECs (PMID: 32169835; PMID: 28052300). Despite its discovery over 20 years ago, the functional role of MUC13 in the intestinal epithelium is still debated. MUC13 is expressed in human small intestine and colon under baseline conditions and is dysregulated during inflammation and tumorigenesis, as described by the authors. Thus, understanding how MUC13 expression and localization impact cell function is of great importance for elucidating its function in health and disease. Studies so far have identified transmembrane mucins as biophysical barriers against bacteria (PMID: 33596425) or facilitators of bacterial invasion (PMID: 33824202). The current manuscript can potentially offer novel conceptual insights into how transmembrane mucins govern the integrity of the epithelial monolayer that serves as a firewall between the multitude of microbes in the gut lumen and the immune system. Such insights have implication for both basic and clinical research on inflammatory bowel disease (IBD) and colorectal cancer (CRC). However, while the authors present convincing data that deletion of MUC13 enhances TEER and recruitment of TJ proteins, the study in its current form fail to provide mechanistic proof of how MUC13 impacts individual TJ proteins. Moreover, it is not clear if findings in a specific cultured cell line (HRT18) can be extrapolated to other frequently used intestinal cell lines (e.g. Caco2) and IECs in an in vivo setting. The latter is particularly important since the authors argue that their findings have important implication in intestinal inflammation and cancer.

restorasi gambar

Reviewer #1 (Public Review):

This paper investigates whether bistable rhodopsins can be used to manipulate GPCR signalling in zebrafish. As a first step, the authors compared the performance of bistable rhodopsins fused with a flag tag or with a fluorescent protein tag (TagCFP). Constructs were compared by expressing in HEK cells followed by calcium imaging with aequorin or cAMP monitoring with GloSensor. This showed that the protein with a smaller flag tag performed better. Then, a series of transgenic zebrafish lines were made, in which tagged rhodopsins were expressed in reticulospinal neurons or cardiomyocytes.

The data indicate that bistable rhodopsin can be used to manipulate Gq and Gi/o signalling in zebrafish. The Gq-coupled SpiRh1 was effective in manipulating reticulospinal neurons, as indicated by analysis of tail movements and calcium imaging of the neurons. Gi/o signalling could be manipulated by Opn3 from mosquitoes, TMT from pufferfish, and parapinopsin from lamprey, as shown by their effects on the heartbeat. Lamprey parapinopsin has the interesting property that it can be turned on and off by different wavelengths of light, and this was used to stop and restart the heart. Finally, the authors show that the cardiac effects are mediated by an inward-rectifier K+ channel, through the use of pharmacological inhibitors.

A strength of this paper is the testing of a range of bistable rhodopsins, with a total of 10 proteins tested. This provides a good resource for future experiments. A weakness is the failure to show that some experiments involved repeated sampling of the same animal. Figure 3 gives the impression that there are 48 independent datapoints. However, there are 8 animals, with 6 datapoints coming from each. Similarly, Figure 4 shows the data from 6 trials of 4 animals, not 24 independent animals. Repeated sampling should be reflected in the data presentation, and in the statistical analysis. Was there an effect of trial number, which is suggested in Figure 6?

Delta F/F refers to relative change, which should be (F-F0)/F0. This should be zero when t = 0. The values in Figure 3E, and 3F are ~ 1 when t = 0, however. Are these figures showing F/F0?

The authors' conclusions that the bistable rhodopsins are useful tools in the zebrafish system appear largely justified. This is consistent with findings from other organisms, including mouse (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8097317/, https://www.sciencedirect.com/science/article/pii/S0896627321001616). The tools here are likely to find broad use by scientists who use the zebrafish as the experimental system for a variety of different areas.

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Reply to the reviewers

Reviewer 1.

Major point:

(1) The authors rely upon the redistribution of RNA to measure the inheritance of extant RNAs following cell cycle release. Blocking transcription nicely shows new synthesis is not required for this inheritance. This is also consistent with the idea any newly synthesized RNA would be 'dark,' or not EU labeled, but the transcription inhibitor experiments are critical controls and nicely done. As hinted at the end of their discussion, however, a lack of RNA localizing to G1 chromosomes could be formally attributable to differential RNA stability. Might altered RNA stability of NEAT1, MALAT1, or U2 also contribute to the observed altered localizations upon interphase reentry? The authors could use qPCR or measure RNA half-life to test this possibility. These data would nicely compliment the authors' existing FISH experiments and allow them to specifically argue for differential RNA localization.

We have addressed this point by measuring the stability of MALAT1, U2, and NEAT1 in G2 cells after transcription inhibition using RNA FISH. We find that U2 and MALAT1 exhibit very little RNA degradation after 2.5 hours of transcription inhibition, which is consistent with the reported half-lives for each of these transcripts (10 hours for MALAT1 and >24hrs for U2; PMC3337439). We conclude that differential RNA stability cannot account for differential RNA import observed for these two transcripts. In contrast, NEAT1 transcript is almost undetectable after 1.5 hours of transcription inhibition, which is also consistent with the reported half-life of this transcript (22406755, 3337439). Therefore, RNA degradation during mitosis could contribute to a lack of NEAT1 nuclear import in G1. We have included this new data in a modified Figure 2E (text p5 lines 154-166).

Minor Points:

(1) The authors examine published datasets identifying RNA associated with chromatin and state the reason why these data show little overlap is "primarily attributable to purification methodology." This statement seems speculative, and its basis seems unclear.

We have changed the wording of this section to remove unwarranted speculation (p4-5 lines 116-129).

(2) The SAF-A-AA experiments failed to reveal insight into mechanisms of RNA sorting, although they do suggest the AA construct functions as a gain-of-function due to a) increased RNA reincorporated into chromosomes b) dramatic increase of chromosome targeting of SAF-A. These effects make it difficult to interpret the SAF-A-AA data. Related to this point, the analysis of altered RNA distributions relative to SAF-A is underdeveloped. Because the authors only examined one lncRNA (MALAT1), the conclusion that “forced retention of SAF-A on mitotic chromatin does not lead to an increase in the nuclear inheritance of specific transcripts” seems like an overstatement.

We have reworded this conclusion about the role of SAF-A-AA on mitotic chromatin retention to more accurately reflect our findings (p6 line 197). (3) The authors find the U2 spliceosomal RNA is preferentially inherited. Might they speculate why this would be advantageous?

We have added a sentence to the discussion speculating about the importance of U2 inheritance (p8 line 269-271). (4) Optional: it would be exciting to test the significance of U2 RNA inheritance

We agree with the reviewer that this would be an exciting future direction to test. We envision that testing this idea rigorously would require the development of several new degron cell lines and is outside the scope of this study. (5) For Figure 1, please add statistics to figures and legend; add N=cells examined.

We have added a new supplemental Excel spreadsheet that contains the N of cells measured for each experiment and added statistics to figure legends and figures where tests were significant. (6) For Figure 2, single channel panel of U2 RNA should be added. Figure 2E seems to reproduce the same data shown in Figure 2D (right-most columns) shown with different axes.

We have added a single channel image of U2 to Figure 2 and replaced panel 2E with analysis of MALAT1, NEAT1, and U2 stability after transcription inhibition. (7) Figure 3, it is unclear why the authors selected MALAT1 for analysis, but not NEAT1 (or the single (unlabeled) antisense RNA also enriched in the SAF-A IP (figure 2C).

We examined MALAT1 in greater detail because it is the most abundant lncRNA bound by SAF-A and most robust RNA FISH probe. The unlabeled antisense transcript is hnRNPUas1 and was not detectable in DLD1 cells by RNA FISH. (8) Figure 4B, please add statistics to figure and legend.

For this experiment we prefer not to add statistics to the figure. This experiment was performed on a limited number of cells (21 and 8 respectively) and we do not believe that it is statistically appropriate to treat each cell as an independent N. The data confirms results in our previously published work (Sharp et al 2020) using live cell imaging. (9) Methods: in their description of the published lists of chromatin-bound RNAs, the authors should cite those works and provide a data availability statement with the associated GEO

We have cited these works in the text and methods sections and added GEO accession numbers associated with these studies. (p21 line 442).

Reviewer 2

Major comments:

The authors pose an interesting question -- how does nuclear RNA segregate following mitosis. In many ways, the results presented in this manuscript are rather preliminary. Key controls and validation are missing. Because of this, it is difficult to assess the validity of the main conclusions of the study. More specifically:

1. The main conclusion of the manuscript ("about half of nuclear RNA is inherited by G1 cells following division") is primarily dependent on the experiment described in Fig 1A-B. The authors labeled synchronized cells with EU and quantified nuclear signal after release from synchronization. However, key controls are missing. What is the synchronization efficiency of the RO3306 treatment? How many cells in their acquired fields of cells are in G2 vs in other cell cycle stages? Following their drug release, what percentage of the synchronized cells have undergone telophase? What is the potential error rate in identifying the cell cycle stage using their visual imaging analysis? Without these key controls, it is unclear how to interpret the data presented in Fig 1B.

One reason that nuclear inheritance has not been properly addressed in the literature is the difficulty in obtaining pure populations of cells synchronized in telophase or recently divided cells in early G1. There are no drugs available which can uniquely target these cell stages. In addition, the ability of human cells to all release perfectly synchronously from a drug-induced arrest can vary with cell type. For this reason we used a strategy employing synchronization methods designed to enrich cell populations for telophase or early G1 events, combined with single cell analysis of events with the distinct cytological features of each stage. Cells that have recently divided are extremely distinctive and easily identified using a combination of DAPI morphology to assess nuclear size and condensation state and the presence of Aurora-B/Midbody staining to indicate a recent cytokinesis. Our approach of using single cell analysis coupled with quantitative imaging therefore does not require a high efficiency of synchronization in cell populations. To gain confidence that our observations were reproducible we analyzed a large number of cells, performed multiple experimental replicates, and applied statistical tests to the data.

To clarify these important points we have added text to the descriptions of how these experiments were performed (p3 line 72) and added information about the number of biological replicates to all figure legends and number of cell analyzed in each experiment to Supplementary Table 1.

1. The use of transcriptional inhibitors in Fig 1 is really nice and is important for showing that it's not due to new transcription following mitosis. Well done!

2. One potential mechanism that could explain the observed 25% relocalized nuclear RNA is through passive diffusion. That is, a proportion of molecules that are randomly diffusing during mitosis get trapped inside the newly formed nuclear membrane in early G1. This would be considered noise, and not a specific process that actively relocalizes nuclear RNA back into the nucleus. However, the authors' assay does not have a measure of the noise in their system. One potential experiment that may help quantify this noise is to express GFP in their cells, perform the experiment described in Fig 1A, and quantify the nuclear signal after telophase. This quantification would be the lower bound of the random process. A similar experiment with GFP-NLS could be performed to assess the upper bound of the 'inherited' molecules after mitosis. Without this type of control to quantify noise/random diffusion levels, it is unclear how much of the 25% EU signal that the authors detect is specific to the process they are testing.

We appreciate the point that the reviewer has raised. To address this concern we examined the localization of the abundant mRNA b-actin. We examined the fraction of all b-actin FISH signal that is present in the nucleus in G2 and G1 cells following division. If a significant fraction of RNA is trapped in the reforming nucleus then we would have expected the fraction of b-actin in the G1 nucleus to increase. We observed that less b-actin RNA was present in the G1 nucleus, suggesting that passive entrapment of RNA is unlikely to be a mechanism of RNA inheritance. This is consistent with a lack of inheritance of MALAT1 and NEAT1 lncRNAs following mitosis. We have added these results to a new Supplemental Figure 2 and added text describing the results to the Results section of the manuscript (p4 lines 101-113). Additionally, this result is consistent with recent work showing that mitotic chromosomes condense through histone deacetylation and exclude negatively charged macromolecules (PMID: 35922507) and that chromosome clustering by Ki67 in early G1 phase excludes the cytoplasm from the new nucleus (PMID: 32879492). These references and ideas are now included in the results section of the manuscript.

Related to the comment 1 and 2, EU labeling for 3 hrs in G2 cells would label ALL transcribed RNA, which would include mature mRNAs that will be translated in the cytoplasm. That is, this method is not specific to labeling nuclear RNAs only. How much of their signal is from mRNAs that got trapped inside the newly formed nuclear membrane? One way to test this is to measure the nuclear EU signal at later time points following telophase. Presumably, the nuclear transport mechanism would lead to export of non-nuclear RNAs and only the retained nuclear RNAs would contribute to the signal.

Please see our response to point 3 with regard to entrapment. The laboratory that originally described EU RNA labeling demonstrated a 3 hour EU labeling period results in labeling nuclear RNA, and that longer labeling periods are required to visualize EU labeling of cytoplasmic RNAs after export (18840688). We have also observed in our previously published work that the 3 hour period labels nuclear RNA during interphase (33053167, 32035037). The nuclear EU signal reflects RNAs undergoing transcription, nuclear retained RNAs, and mature mRNAs prior to nuclear export.

To identify nuclear RNAs that could be relocalized following mitosis, the authors analyzed data from "two different studies using different methodologies and a total of three different cell lines". From this analysis, the authors "found very little overlap in the chromatin-bound RNAs identified in these studies (Fig 2A)". This analysis seems fraught with problems. What is the rationale for using these studies? How valid is it to compare results from different methodologies and from different cell lines from the DLD-1 cells used in this study?

We analyzed the data from these two studies because they were the only published studies that identified RNAs that were tightly linked to chromatin. We chose to compare the results from three different human cell lines because we sought to identify nuclear RNAs that were cell type-independent, so that we could analyze the transcripts behavior in DLD1 cells. In support of using these two studies all the RNAs that we analyzed were nuclear in our RNA FISH assays.

A known problem of assessing chromatin-bound RNAs is that the level of contamination from cytoplasmic RNAs is highly variable and highly dependent on the assay. Indeed some of the most common contaminants of nuclear RNA assays are sn-, and sno-RNAs, and these are the main classes of RNA that the authors identified as common among the three data sets. What validation was used to assess whether these are the common noise/contaminants in the data?

Our goal in using the two previously published studies was to identify cell type-independent nuclear RNAs that could be studied in detail using FISH. For validation in our study we performed RNA FISH on MALAT1, NEAT1, and U2. We found that each of these RNAs are highly enriched in the nucleus, consistent with previous publications. Since snRNAs function in splicing and snoRNA primarily function in the modification of tRNA and rRNA in the nucleolus it seems unlikely that these are contaminants of nuclear preparations. Each of the published studies performed their own validations of their purification and sequencing methodology. For the purpose of our work nuclear enrichment of a transcript by RNA FISH satisfied our requirements.

One experimental validation that can be performed is biochemical fractionation of EU labeled cells, which would allow for fractionating nuclear from cytoplasmic RNA. The same problems arise with the analysis shown in Fig 3C when comparing SAF-A RIP-seq with this merged list of chromatin bound RNAs.

In support of the nuclear enrichment of each of the transcripts that we examined RNA-FISH analysis demonstrated significant nuclear enrichment. Additionally, many previous studies have shown that each of these transcripts are enriched in the nucleus (U2: 11489914, 10021385, 7597053; NEAT1: 17270048; MALAT1: 12970751, 17270048). New text describing our use of these studies is present in the results section (p4-5 lines 117-129).

Throughout the manuscript, the authors pose their findings as "RNA inheritance" following mitosis. However, this terminology is misleading. In fact, unless RNAs are lost/kicked out of the cell as they divide, aren't all RNAs inherited following cell division since they are present in the new daughter cells? Instead, what the authors mean is that some nuclear RNAs retain their function following cell division by relocalizing back into the nucleus in the new G1 cells, whereas other nuclear RNAs are unable to relocalize into the nucleus, and then presumably turned over by degradation process. The authors should take better care of their terminology throughout the manuscript.

Thank you for pointing this out to us. As the reviewer stated most nuclear RNAs are removed from chromatin during mitosis. Only a subset are reimported into the nucleus. We have modified our wording to clearly state that we are discussing nuclear RNA inheritance by daughter cell nuclei rather than inheritance into daughter cells in general. These text changes can be found throughout the manuscript.

Minor comments: 1. In all of the figures showing quantification of nuclear EU/FISH signal, the colors (red v blue) are not described (not found in the legend or methods). Presumably they are biological replicates, but this should be clearly stated.

We have modified the plots and figure legends to more clearly explain what is plotted (See text in Figure Legends). 2. Is figure 2E the same data presented in Fig 2D but in different y-axis? If so, state clearly

We have removed the data in the previous version of Figure 2E and replaced it with new data examining stability of MALAT1, NEAT1, and U2 in response to Reviewer 1 (p5 lines 154-166).

Figure 3A. This experiment is using the SAF-A-AID-mCherry system. Therefore the label in Fig 3A should be SAF-A-KD (Knockdown) instead of KO (knockout)

We have corrected this in Figure 3. 4. Typo in Fig 4B y-axis. It should be "Chromatin-localized SAF-A" instead of "Chromain-localized SAF-A"

Thank you for pointing this out, we have corrected it. 5. The methods section indicate the "precise N or replicates in indicated figure legends" but none of the figure legends have the N values listed.

We have listed number of biological replicates in all figure legends and included a new Supplemental Table 1 that contains the number of cells measured for each experiment.

Reviewer 3

The authors investigate an interesting question focussed on whether nuclear RNA from the previous cell cycle is present in the subsequent G1. It turns out that this is more complex than expected with some classes of RNA being inherited whilst others are not. SAF-A or HNRNPU had been implicated in this process but the authors suggest that its role is limited.

Figure 1 In panel A the authors write on image SAF-A-mCh. What does this refer to?

We have added information to the Figure legends indicating that this refers to SAF-A-AID-mCherry knocked-in to the endogenous SAF-A locus (see Figure Legends).

Panel B and other panels can the authors present this data as a boxplot or distribution plot to get a better feel of the data distribution spread.

We have modified all the plots in the manuscript to the Superviolin form to provide a clearer depiction of experimental replicates, mean, and standard deviation.

Presumably labelled RNAs are naturally turned over. Have the authors considered that some loss of signal could be because of this?

We have addressed the stability of specific RNAs using RNA FISH. We find that U2 and MALAT1 show essentially no degradation during the time course of our experiments. This data has now been included in an updated Figure 2. We have also modified our text to address this point more clearly (Figure 2E and p5 lines 154-166).

Panel E, have the authors considered labelling RNA before RO3306 treatment? What effect would this have?

We have performed this experiment in RPE1 cells and the presence of RO3306 did not affect cytological detection of transcript labeling. We have not included this experiment in the manuscript because it is performed in a different cell line than we use for the remainder of these studies.

Shouls TI be added before RO3306 washout?

We added transcription inhibitors after RO washout and entry into mitosis because transcription is naturally suppressed during mitosis. We were concerned that transcriptional inhibition in late G2 could lead to failure to properly enter into M phase.

Also, it is unclear what the arrows are pointing at. In panel F there is a difference between the red and blue experiments. In the methods the authors say that inhibition was for either 1.5 or 2 h. Is this the source of the difference?

We have modified the figure legends to state clearly that different colors indicate biological replicate experiments (See Figure Legends). Figure 2 In panel A there are clear differences between the cell lines. Is it right to compare them? Particularly the GRID-seq vs diMARGI? B, how relevant is it focussing on the "42" overlapping RNAs? In my mind this is not very informative.

Our goal with this analysis was to identify cell type-independent chromatin bound RNAs to analyze in greater detail. Therefore, we analyzed three different cell lines because we planned to analyze transcript behavior in DLD1 cells, which were not included in either study. We have explained this rationale in greater detail in a revised version of the text (p4-5 lines 116-129).

D-E, at a glance it is not clear that E is an expanded view of D. It might be easier if the panels were at same height.

We have removed Panel E and replaced it with a new experiment examining the stability of NEAT1, MALAT1, and U2 after transcription inhibition (p5 lines 154-166). Figure 3 Is it correct to describe IAA treated degron cells as a KO? I also could not see a WB showing how complete SAF-A KD was.

We previously characterized these cell lines in great detail (Sharp et al. JCB 2020). We have now provided quantitative measurement of SAF-A-mCherry fluorescence after different times of auxin addition to provide a quantitative estimate of SAF-A depletion (Supplemental Figure 3C).

2 h treatment seems quite short, is this enough time to obtain sufficient knock down? How heterogenous is SAF-A KD in the cell population?

We examined SAF-A depletion by auxin addition at 2 hours and 24 hours and achieve comparable depletion levels. This data in now included in Supplementary Figure 3C. There is some heterogeneity in the KD as is evident in Figure S3C, but these cells are easily identifiable by the presence of SAF-A-AID-mCherry fluorescence.

Previous studies have shown that SAF-A does not like being tagged. How certain are the authors that these cells behave typically?

We have generated two different cell lines (DLD1 and RPE1) where a C-terminal tag is inserted into the both copies of the endogenous SAF-A gene. SAF-A is one of the common essential genes (https://depmap.org/portal/gene/HNRNPU?tab=overview), however each of our cell lines exhibits no growth defects. We have recently shown that C-terminally tagged SAF-A fully rescues SAF-A knockout phenotypes (Sharp et al. JCB. 2020). Additionally, we have also performed RNA-seq (not published) on RPE1, RPE1 with endogenously tagged SAF-A and RPE-1 depleted of SAF-A and rescued with WT SAF-A-GFP and observed no changes in gene expression or mRNA splicing. Based on these assays we are confident that C-terminally tagged SAF-A expressed at endogenous levels functions normally. Figure 4 I'm struggling with the heading, and wonder if this is not supported by the data. Similarly the final sentence "The highly dynamic exchange of SAF-A:RNA complex" does not really provide an explanation.

We have expanded the text in this section to explain this phenotype in greater detail (p7 lines 216-218).

https://peergos.net/#%7B%22secretLink%22:true%2c%22link%22:%22#6MDZhRRPT4ugkJuUfcR4HUBKWFaAUE9Lc5F8GiPr4gYNR2K3729dZfUirK3CUe/6MDZhRRPT4ugkJuUfccsrqELFsuKZiwACb3uN1JqNM2perh5qRRvLkqMu8Ak2S/2MHRVbXfR5Via4rQ8xM2WJGkzpxpEfGM4ypFCVzdwFQ1RZV3ipBFi3AsXS9Yiz8k9rDHmDkPGSxHvqry8i5SkrufTtWu5TG/5Pf7SvmJqyjVp3YfWj9CEoYztefHw9LUQY1FoL2tufSa8henP6b%22%2c%22open%22:true%2c%22filename%22:%22UCAN%20call%202022%2012%2020%201.html%22%7D

= - trail

• https://web.archive.org/web/20170809093734/https://www.cypherpunks.to/erights/history/actors/actor-induction.pdf

• http://erights.org/history/actors.html

• http://erights.org/history/index.html
• http://erights.org/index.html

From Chris "cdata" Joel @ Subconscious to Everyone 07:58 PM http://skyhunter.com/marcs/ewalnut.html From Alan Karp to Everyone 07:59 PM http://erights.org/

= Alan Karp

Object Oriented program and

Globally addressable objects

=- bible

From Chris "cdata" Joel @ Subconscious to Everyone 07:58 PM http://skyhunter.com/marcs/ewalnut.html From Alan Karp to Everyone 07:59 PM http://erights.org/

xxxx

asked in chat

From Me to Everyone 07:39 PM I do have one question  From Jess Martin to Everyone 07:40 PM @gyuri: if it doesn't get covered in this call, feel free ask in the fission Discord and tag me, as I have a feeling I was on the livestream of which you speak 🙂 From Blaine Cook to Everyone 07:40 PM 💯 From Me to Everyone 07:41 PM I reallu liked the idea of no signup needed to control wenbative identity. This may have implications to the way the whole webnative SDK based app are to be created/distributed and thought off. Any comments would be apprefiatd. This may not be the right forum, but it is live From Brooklyn Zelenka (@expede) to Everyone 07:42 PM Im checking in sorry folks gimme a minute From Me to Everyone 07:42 PM Yes will do.

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Reply to the reviewers

1. General Statements [optional]

We would like to thank the reviewers for taking time in reviewing and commenting on our paper. The comments were very constructive and conscientious, thanks to their expertise in the field. These comments and the revisions would surely make this paper a better and more robust finding in the field.

The comments were about clearer explanations, increasing the quality of the data and additional experiments for a stronger conclusion, all of which we are eager to accomplish. Now we have sorted out the problems and planned the experiments required in the revision, as detailed below.

2. Description of the planned revisions

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

Summary In this manuscript, Komori et al. examined the role of the LRRK2 substrate and regulator Rab29 in the lysosomal stress response. Briefly, in chloroquine (CQ)-treated HEK293 cells the authors observed an apparent LRRK2-independent increased in Rab29 phosphorylation which was accompanied by translocation of Rab29 to lysosomes. Intriguingly, the authors detected a similar increase in Rab29 phosphorylation when Rab29 was tethered to lysosomes in the absence of CQ treatment. Using mass spectrometry, mutagenesis and a phospho-specific anti-body, the authors mapped the CQ-induced phosphorylation site to S185 and demonstrated its independence from LRRK2. Next, the authors found that PKCa was the kinase responsible for S185 phosphorylation and lysosomal translocation of Rab29. Lastly, the authors showed that in addition to PKCa the lysosomal translocation of Rab29 was also regulated by LRRK2. Overall, Komori and colleagues provide interesting new insights into the phosphorylation-dependent regulation of Rab29. However, there are. Number of technical and conception concerns which should be addressed.

Major points 1) Figure 1F: the localization of Rab29 to lysosomes is not convincing at all. The authors should either provide more representative image examples or image the cells at a higher resolution. The authors should also confirm the CQ-induced lysosomal localization of Rab29 in a different cell type (e.g., HEK293).

We will replace Fig 1F pictures with slightly more magnified images with higher resolution. We will also include additional cell types (HEK293, and other cells that are predicted to express endogenous Rab29); Reviewer #2 also raised this point (see Reviewer #2 comment on Significance).

Moreover, the authors should show that prenylation of Rab29 is required for its CQ-induced phosphorylation.

We will test the effect of lovastatin, a HMG-CoA reductase inhibitor that causes the depletion of the prenylation precursor geranylgeranyl diphosphate from cells (Binnington et al., Glycobiology 2016, Gomez et al, J Cell Biol 2019), or 3-PEHPC, a GGTase II specific inhibitor that also causes the inhibition of Rab prenylation (Coxon FP et al, Bone 2005).

2) The rapalog-induced increase in Rab29 phosphorylation in Figure 2D is not convincing since there is at least 2-3-fold more Rab29 in FRB-LAMP1 expressing cells compared to their FRB-FIS1 counterparts. An independent loading control is also missing. This is a key experiment and should be properly controlled and quantified. In addition, can CQ treatment drive 2xFKBP GFP-Rab29 from mitochondria to lysosomes (in the presence of rapalog and FRB-Fis1)?

We will carefully examine another round of rapalog-induced phosphorylation of Rab29, with an independent loading control such as alpha-tubulin. The immunoblot analysis will be made against the intensity of non-p-Rab29. The response to the latter question was described in the section 4 below.

3) Figure 4A-C: Are these stable Rab29 expressing cells? If not, the quantification of "the size of largest lysosome in EACH cell" becomes very problematic. This analysis should be repeated with stable Rab29 variant cells in a background lacking endogenous Rab29. Furthermore, the LAMP1 signal is too dim to see any convincing colocalization (e.g., with WT) or the lack thereof (e.g., in the case of S185D).

The cells shown in Figure 4 are HEK293 cells transiently expressing Rab29, and the issue of quantification was described in the section 3 below. We agree that the signal of LAMP1 was dim, and it turned out that the confocal microscope we used had problems with the sensitivity of the red channels. We will be taking another round of these images with a new confocal microscope.

Lastly, the authors should corroborate their findings with an ultrastructural analysis since the electron microscopy would definitively be more suitable for this type of measurements.

We are planning to obtain electron microscopic images, according to this reviewer’s request. We plan to invite an expert in electron microscopy analysis as a co-author.

4) The lysosomal colocalization of Rab29 in Figure 5C is again not convincing. This analysis needs to be repeated with high resolution imaging.

Again, we will repeat this experiment with a new confocal microscope, with the hope that it would yield better images.

5) The authors need to show the level of LRRK2 depletion (Figure 6). Given the role of LRRK2 in driving lysosomal Rab29 translocation, the importance of the LRRK2 independent pS185 for this process remains unclear.

We will add the level of LRRK2 on its knockdown; we have experienced that LRRK2 knockdown usually occurs with more than 50% efficiency every time. The response to the latter comment was described in the section 3 below.

6) In general, the authors employ an alternative, biochemical assay (e.g., LysoIP) for the lysosomal translocation of Rab29. This would in particular help to clarify the effect of the Rab29 variants and LRRK2 inhibition.

We have previously shown that the overexpressed Rab29 (and LRRK2) is enriched in the lysosomal fraction from CQ-treated cells, which was performed using dextran-coated magnetite (Eguchi et al, PNAS 2018). Using the same biochemical method, we will show the enrichment of endogenous Rab29 in the lysosomal fraction.

Minor points

9) Figure 2C is lacking the control IF staining for mitochondria (to which 2xFKBP-GFP-Rab29 is assumed be recruited upon co-expression with FRB-FIS1).

We will stain the cells with MitoTracker to ensure that anchoring away of 2xFKBP-GFP-Rab29 by FRB-Fis1 results in mitochondrial localization.

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

The data in the manuscript convincingly demonstrates that lysosomal overload by Chloroquine treatment induces Rab29 localisation to the lysosomes and that this membrane association is dependent on PKCalpha-dependent phosphorylation at Ser185.

We have a number of rather minor comments listed below:

Figure 2

The increasing levels of non-phosphorylated Rab29 over the indicated time course of AP21967 treatment in Figure 2B are concerning. First, could you provide an explanation for this clear increase in both non-p-Rab29 and p-Rab29 in the phostag but not the normal gel? Second, could all quantifications of p-Rab29 be made relative to the non-p-Rab29?

We will try another round of rapalog-induced phosphorylation of Rab29, with an independent loading control. The immunoblot analysis will be made against the intensity of non-p-Rab29. Reviewer #1 raised a similar concern on Figure 2D.

Figure 5

To further demonstrate that PKCalpha phosphorylates endogenous Rab29 at Ser185, we recommend reperforming the Go3983/PMA treatment in figure B with the anti-p-Ser185 antibody. It may be sufficient to perform the treatment only at 4 or 8 hours, simply to provide stronger evidence regarding the phosphorylation of endogenous Rab29.

We will give a try, although the anti-phosphorylated protein antibodies that we tried never worked for phos-tag SDS-PAGE. With the conventional western blot, we will be able to try this experiment.

It is not clear whether the activity of PMA in the assay is due to inhibition of PKCalpha. Are the effects ablated by PKCalpha KD

We will test the knockdown of PKCalpha, beta, gamma and delta by siRNAs to further narrow down the effects of PKC-dependent phosphorylation of Rab29.

Reviewer #2 (Significance (Required)):

These cell biology findings are important in the field as both Rab29 and LRRK2 are implicated in the pathogenesis of Parkinson disease. The phosphorilation of Ser185 of Rab29 by PKCalpha is novel and contributes to our understanding of Rab29 and LKRR2 regulation. One limitation of the study is that is conducted in only two cell types quite unrelated to the disease, so how general and disease relevant are the findings it is not clear. Most of the data are solid. There are two experiments whose results are difficult to interpret and a few controls missing. Also a few issues with quantifications, all of which is described in details above and will need to be fixed prior to publication. My expertise for this paper is in the cell biology of lysosomal function.

The issue that only two cell types were analyzed was also raised by reviewer #1, so we will examine additional cell types, especially those that are predicted to express endogenous Rab29. Our responses to other issues raised are described elsewhere. Thank you for these insightful comments.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Figure 4A-C: Are these stable Rab29 expressing cells? If not, the quantification of "the size of largest lysosome in EACH cell" becomes very problematic. This analysis should be repeated with stable Rab29 variant cells in a background lacking endogenous Rab29. (Reviewer #1)

As described in the section 2 above, the cells shown in Figure 4 are HEK293 cells transiently expressing Rab29. We are sorry that the description “the size of largest lysosome in each cell” was misleading. As we analyzed only cells overexpressing GFP-Rab29 that were marked with GFP fluorescence, we believe that transient expression should not be a problem. To avoid any misunderstandings, we have described in Figure 4 legends that only lysosomes in Rab29-positive cells (and all cells expressing Rab29) were included in the analysis of the largest lysosome of each cell.

Regarding the effect of endogenous Rab29 in Figure 4 experiments, Reviewer #2 similarly raised the issue on whether Rab29 phosphomimetics are acting as dominant active, preventing lysosomal enlargement. On this point, we have previously reported that knockdown of endogenous Rab29 causes the enhancement of lysosomal enlargement upon CQ treatment (Figure 5I,J of Eguchi et al, PNAS 2018), suggesting that the lysosome-deflating effect by phosphomimetics is a dominant active effect rather than dominant negative suppressing endogenous Rab29. This point is considered significant, and thus has been explained in the results section (page 7, lines 168-171).

Along similar lines: why not all cells in Figure 5E and Figure 5G show Rab29- and LRRK2-positive structures? How do the authors know which of these phenotypes is the prevalent one? (Reviewer #1)

As for the ratio of cells with Rab29- and LRRK2-positive structures, it seems reasonable given that different cells have different levels of exposure to lysosomal stress and that the response is transient and does not occur simultaneously. The ratio of these positive cells may also vary depending on the cell culture conditions. Since Rab29- and LRRK2-positive structures are rarely seen in control cells, we think this would be a meaningful phenotype even if only 20-30% of cells show such structures. The result that the ratio of localization changes is not 100% is now noted in the results section explaining Figure 1G (page 4-5, lines 108-110) where the immunocytochemical data first appears.

Given the role of LRRK2 in driving lysosomal Rab29 translocation, the importance of the LRRK2 independent pS185 for this process remains unclear. (Reviewer #1)

Our data suggested that Rab29 is stabilized on lysosomes only when LRRK2-mediated phosphorylation and S185 phosphorylation both occur on Rab29 molecule (as shown in Figure 7 scheme), so we believe there is no contradiction. We have now described more clearly about this notion at the end of the results section (page 9, lines 235-236).

It is not clear what the authors mean by "lysosomal overload stress". Since mature lysosomal incoming pathways such as autophagy or endocytosis are disrupted by CQ, it is difficult to picture an overload. Maybe rephrasing would help to clarify this. (Reviewer #1)

Chloroquine (CQ) is known as a lysosomotropic agent that accumulates within acidic organelles due to its cationic and amphiphilic nature, causing lysosome overload and osmotic pressure elevation, and this is what we call “lysosomal overload stress”. The well-known effects of CQ to disrupt lysosomal incoming pathways are ultimately caused by the above consequences. Also, we have previously reported that lysosomal recruitment of LRRK2 is caused by CQ but not by bafilomycin A1, the latter being an inducer of lysosomal pH elevation, or by vacuolin-1 that enlarges lysosomes without inducing lysosomal overload/pH elevation (Eguchi et al, PNAS 2018), and further found that not only CQ but also other lysosomotropic agents commonly induced LRRK2 recruitment (Kuwahara et al, Neurobiol Dis 2020). We thus have described the effect of CQ as “overload”. However, it is true that we have not provided a clear explanation for readers, so we have added some notes for lysosomal overload stress in the introduction section (page 3, lines 69-71).

Which cell type is used for the IF analysis in Figure 2C? This information is in general quite sparse. The authors should clearly state the cell type for each experiment/Figure. (Reviewer #1)

We have added cell type information that was missing in several places in the manuscript. We are very sorry for the inconveniences. For clarification, HEK293 cells were used in Figure 2C.

Are the images in figure 1F representative? i.e. does Rab29 always colocalise to such enlarged lysosomes upon CQ treatment and does CQ treatment always drastically alter the cellular distribution of Rab29? (Reviewer #2)

The images in Figure 1F are representative of when Rab29 is recruited, but it is not seen in all cells, and the ratio of recruitment (~80%) is shown in Figure 1G. Reviewer #1 also asked why Rab29 recruitment is not seen in all cells, and we gave the same answer above. It may be reasonable to speculate that different cells have different levels of exposure to lysosomal stress and that the response is transient and does not occur simultaneously. The ratio of these positive cells may also vary depending on the cell culture conditions. For the readers’ clarity, we have added that the ratio of localization change of Rab29 is not 100% and is comparable to that of LRRK2 previously reported (page 4-5, lines 108-110).

Considering that the "forced localisation technique" induces a non-physiological colocalization of non-endogenous Rab29 to lysosomes, it may be an overestimation to conclude just from these data that phosphorylation of Rab29 occurs on the lysosomal surface. This is also quite in contrast with the later finding that phosphorylation by PKCalpha promotes lysosome localization of Rab29. It seems more reasonable to conclude that Rab29 can be phosphorylated when localised at the lysosomes (as opposed to other organelles such as mitochondria). If the authors feel strongly about this point they might need to find a less non-physiological assay. (Reviewer #2)

Yes, it could be an overestimation, and as we do not have better means to conduct a less non-physiological assay, we have modified the description from “occurred on the lysosomal surface” to “could occur on the lysosomal surface” (page 5, line 112 (subtitle) and line 128).

Regarding the comparison with the later finding that phosphorylation by PKCalpha promotes lysosome localization of Rab29, these data (Figure 2 and 5) could be explained with a single speculation: phosphorylation of Rab29 on lysosomal membranes could retain Rab29 on the membranes for a longer time. It is not easy to decipher which comes first, association with membranes or phosphorylation of Rab29, in a physiological assay, but considering reports that show PKCalpha activation happens on membranes (Prevostel et al., J Cell Sci 2000), at least the data favor our conclusion over the idea of PKCalpha phosphorylating Rab29 in the cytoplasm and then promoting lysosomal localization. This point is now clearly described in the discussion (page 10, lines 248-251).

It is not clear how the Rab29 phosphomimetics are acting as dominant active preventing lysosomal enlargement. Authors should speculate or repeat the experiments in absence of endogenous Rab29 to clarify the matter. (Reviewer #2)

A similar concern about the effect of endogenous Rab29 was also raised by Reviewer #1 (see above). We have previously reported that knockdown of endogenous Rab29 causes the enhancement of lysosomal enlargement upon CQ treatment (Figure 5I,J of Eguchi et al, PNAS 2018), suggesting that the lysosome-deflating effect by phosphomimetics is a dominant active effect rather than dominant negative suppressing endogenous Rab29. This point is considered important and thus has been explained in the results section (page 7, lines 168-171).

Overall, there is some missing information regarding repeats for Western blots, such as those in figure 3C, 3D and 3E. Please add indications about repeats in the figure legend or methods. (Reviewer #2)

We have added the repeat information to each figure legend where it was missing. We are very sorry for the inconveniences.

The model in figure 7 however seems to suggest that Rab29 associates to lysosomal membranes independently, and is then stabilised at the membranes by LRRK2 and PKCalpha - a point which is not directly supported by the data. (Reviewer #2)

As noted earlier, we consider that phosphorylation of Rab29 on lysosomal membranes could retain Rab29 on the membranes for a longer time, given the present data and previous reports that phosphorylation of Rab29 is more likely to happen on the lysosomal membrane than in the cytosol. Also, as inhibition of either of the two phosphorylations ends up in disperse Rab29 localization, we have made this figure as a model of what is plausible right now. This explanation is now added in the discussion (page 10, lines 248-251).

English proofreading should be improved: "CQ was treated to HEK293" (page 4), "As we assumed that this phosphorylation is independent of LRRK2" as an opening line (page 5) (Reviewer #2)

Thank you for pointing out these incorrect wordings. They were corrected.

4. Description of analyses that authors prefer not to carry out

In addition, can CQ treatment drive 2xFKBP GFP-Rab29 from mitochondria to lysosomes (in the presence of rapalog and FRB-Fis1)? (Reviewer #1)

We do not think that a comparison between the affinities of FKBP-rapalog-FRB and Rab29-[unknown factor that directs Rab29 to lysosomes] is necessary, as the former has a Kd in the single digit nM range (Banaszynski et al, JACS 2005), whereas the latter (based on estimations from related PPIs) is estimated to be in the μM range, which shows a much weaker affinity than the former (McGrath et al, Small GTPases 2019). Furthermore, even if Rab29 appears to have migrated from mitochondria to lysosomes as a result of this experiment, one cannot rule out the possibility that a small portion of the mitochondrial membrane was incorporated into the lysosomal membrane that was enlarged by CQ treatment.

Molecular weight markes should be provided for all immunoblot experiments. (Reviewer #1)

The immunoblot pictures without molecular weight markers in our paper are all Phos-tag SDS-PAGE blot analyses. Phos-tag SDS-PAGE results in band shifts of phosphorylated proteins, and writing in markers would be misleading. Moreover, previous representative studies heavily using Phos-tag (e.g., Kinoshita et al, Proteomics 2011, Ito et al, Biochemical Journal 2016) also did not show the molecular weight markers. Here we performed phos-tag SDS-PAGE analysis only to find differences in the phosphorylation state of Rab proteins.

The use of the quantification ratio of cells with Rab29-positive lysosomes in figure 1G might be slightly misleading as it does not allow the reader to understand to what extent Rab29 localisation at lysosomes upon CQ treatment. We recommend using a simpler quantification, such as by measuring the average colocalisation of Rab29 and LAMP1 per cell. (Reviewer #2)

For figure 5D and 5F, As with figure 1G, we recommend using a more straightforward and impartial method of quantification such as simply measuring the colocalisation of Rab29 with LAMP1. (Reviewer #2)

Popular colocalization analyses using Pearson’s or Mander’s coefficients would be a good choice if the amounts of Rab29 varied greatly between lysosomes. However, this may not apply in this case; the amount of Rab29 or LRRK2 on each lysosome is considered to saturate quickly and a relatively low amount of them may not be detected on immunofluorescence observations, whereas the probability of finding these structures has been shown to exhibit a moderate sigmoid curve (as seen in Figure 1E or 2H of Eguchi et al., PNAS 2018). Therefore, the amount of Rab29 or LRRK2 could be approximated to a Bernoulli distribution in terms of colocalization with lysosomes, and this is the reason why we chose to quantify “the ratio of cells with Rab29-positive lysosomes”.

We recommend using a more transparent and simple quantification method, such as average size of lysosomes per cell. (Reviewer #2)

As one can see in the inset of Figure 4B, unenlarged lysosomes are unfortunately too small for the quantification of their size, much less tell two small lysosomes apart in our experimental settings and laboratory resources, so we decided to analyze the largest lysosome in each cell as a representative of the cells to minimize measurement errors. This measurement only includes GFP-Rab29 positive cells, and by comparing against CQ-untreated cells we intended to increase the validity of this analysis. This quantification method was also used in our previous report (Eguchi et al, PNAS 2018).

Reviewer #1 (Public Review):

In this study, the protein composition of exocytotic sites in dopaminergic neurons is investigated. While extensive data are available for both glutamatergic and GABA-ergic synapses, it is far less clear which of the known proteins (particularly proteins localized to the active zone) are also required for dopamine release, and whether proteins are involved that are not found in "classical" synapses. The approach used here uses proximity ligation to tag proteins close to synaptic release sites by using three presynaptic proteins (ELKS, RIM, and the beta4-subunit of the voltage-gated calcium channel) as "baits". Fusion proteins containing BirA were selectively expressed in striatal dopaminergic neurons, followed by in-vivo biotin labelling, isolation of biotinylated proteins and proteomics, using proteins labelled after expression of a soluble BirA-construct in dopaminergic neurons as reference. As controls, the same experiments were performed in KO-mouse lines in which the presynaptic scaffolding protein RIM or the calcium sensor synaptotagmin 1 were selectively deleted in dopaminergic neurons. To control for specificity, the proteomes were compared with those obtained by expressing a soluble BirA construct. The authors found selective enrichments of synaptic and other proteins that were disrupted in RIM but not Syt1 KO animals, with some overlap between the different baits, thus providing a novel and useful dataset to better understand the composition of dopaminergic release sites.

Technically, the work is clearly state-of-the-art, cutting-edge, and of high quality, and I have no suggestions for experimental improvements. On the other hand, the data also show the limitations of the approach, and I suggest that the authors discuss these limitations in more detail. The problem is that there is very likely to be a lot of non-specific noise (for multiple reasons) and thus the enriched proteins certainly represent candidates for the interactome in the presynaptic network, but without further corroboration it cannot be claimed that as a whole they all belong to the proteome of the release site.

Donwell Abbey is fiction, perhaps based on the real-life Claremont Park in Surrey, Highbury as Kenneth Smith evidences in his essay. However, Austen did not make up the popularity of strawberry season. Berry-picking could start as early as May reach its peak in July and and end in September (Eat the Seasons UK has a chart to follow). According to Austen's chronology, it is the "middle of June" in this segment and, as Mr. Knightley assures Mrs. Elton, the strawberries are already "ripening fast". Joanne Major's essay "Strawberries and cream: A Wimbledon tradition with a hong history" provides a brief history of British strawberry fanaticism paired with images that illustrate just how long the berry has remained a British summer staple. Why does Austen contrast "strawberries" with "cabbage-beds" then? The Royal Horticultural Society answers that cabbages can be harvested year round. Together with potatoes they make up the latter part of another common British staple "bubble and squeak". Even the mundanity of cabbages, Austen tells us, might "tempt" Mrs. Elton in her current state of restlessness.

Donwell Abbey is fiction, perhaps based on the real-life Claremont Park in Surrey, Highbury as Kenneth Smith evidences in his essay. However, Austen did not make up the popularity of strawberry season. Berry-picking could start as early as May reach its peak in July and and end in September (Eat the Seasons UK has a chart to follow). According to Austen's chronology, it is the "middle of June" in this segment and, as Mr. Knightley assures Mrs. Elton, the strawberries are already "ripening fast". Joanne Major's essay "Strawberries and cream: A Wimbledon tradition with a hong history" provides a brief history of British strawberry fanaticism paired with images that illustrate just how long the berry has remained a British summer staple. Why does Austen contrast "strawberries" with "cabbage-beds" then? The Royal Horticultural Society answers that cabbages can be harvested year round. Together with potatoes they make up the latter part of another common British staple "bubble and squeak". Even the mundanity of cabbages, Austen tells us, might "tempt" Mrs. Elton in her current state of restlessness.

一般使用在template上，不支持x-transition

https://lukechandler.wordpress.com/tag/standing-stone/

annotation meta: may need new tag: - developer use case - more complicated to express than they need to be.

annotation meta: may need new tag: the runaround?

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Reply to the reviewers

Reviewer 1

This paper identifies a role for the hereditary spastic paraplegia protein spatacsin in lysosome morphology, positioning and dynamics, and undertakes detailed mechanistic studies to try to identify the mechanism for this effect. In doing so the paper elucidates further mechanistic information about the properties of two other hereditary spastic paraplegia proteins, spastizin and AP5Z1. The work is done in mammalian cells and uses a combination of over-expression, depletion and biochemical studies. The main findings are:

1. The authors present evidence that spatacsin is an ER-localised protein.
2. Murine embryonic fibroblasts lacking spatacsin have a reduced number of tubular lysosomes and the remaining lysosomes are less motile. In general, a relationship between tubular lysosome morphology and lysosome motility, often in association with the endoplasmic reticulum (ER), is demonstrated. These tubular lysosomes are catalytically active and acidic.
3. In terms of mechanism of this effect, by combining a yeast-two hybrid and siRNA phenotypic screen, the authors identify a number of spatacsin-interacting proteins that also regulate lysosomal tubulation. The most important of these for the purposes of this paper is UBR4, an E3 ubiquitin ligase.
4. The authors show that spatacsin and UBR4 promote degradation of AP5Z1, and that this property required the ability of spatacsin to interact with UBR4. Somewhat surprisingly, as AP5Z1 is a coat protein, this degradation appeared to occur within the lumen of the lysosome - the authors speculate how this could be in the discussion.
5. The authors then demonstrate that AP5Z1 and spastizin, both hereditary spastic paraplegia proteins, compete for binding with spatacsin.
6. The relationship between spatacsin, spastizin, AP5Z1 and motor proteins in then examined. There is a known interaction between spastizin and KIF13A and expression of a dominant negative KIF13A protein reduced lysosomal tubulation. The authors then demonstrate an interaction between AP5Z1 and the p150Glued dynein/dynactin complex member, then showed that expression of a dominant negative p150Glued protein reduced lysosomal tubulation.
7. Finally, that authors demonstrate the relevance of these findings to neurons, the target cells of hereditary spastic paraplegia, by showing that lysosomal tubulation and axonal transport are reduced in mouse neurons lacking spastacsin, and that depletion of UBR4 or AP5Z1 affected these as expected from the experiments above.

Major comments:

Overall I believe that the key conclusions of this paper are generally convincing and that the work is of high quality. However, I do have some reservations:

1. The localisation of spatacsin on the ER. It is always difficult to be convinced about colocalization of a diffuse punctate marker and the ER. From the STED experiments in figure 1, while it definitely seems that there is some spatacsin on the ER, there also appears to be some spatacsin puncta that are not. I'd like to know if these puncta represent lysosome-associated spatacsin. This is important for interpretation of the subsequent experiments (see point 3 below). I also think quantification of these co-localisation will increase confidence in the results. In addition, a caveat of the immunofluorescence studies is that they use over-expressed spatacsin. I appreciate that there are no good antibodies to endogenous spatacsin, but I don't think this limitation is sufficiently acknowledged. As the claim of ER-localisation is critical for the proposed mechanistic model, and in the absence of experiments with endogenously tagged spatacsin, this makes the biochemical fractionation studies of figure 1C very important. To make these more convincing I would prefer to see additional control markers to verify the separation of lysosomal and ER compartments - e.g. lamp1, lamp2, an ER tubular marker such as a REEP5 or a reticulon.

Authors response : We agree with the reviewer that the localization of spatacsin is critical, and we appreciate the knowledge of the reviewer concerning the lack of good antibodies to endogenous spatacsin. We better acknowledged this limitation in our revised manucript (p. 5 and p. 15). We performed extra experiments to convincingly show that spatacsin is indeed localized at the ER. First, we performed 3-color STED experiments to visualize in the same cell spatacsin, the ER and lysosomes. The preliminary data seem to indicate that some spatacsin is associated with lysosomes at ER-lysosomes contact site. We plan to add quantifications of colocalization between spatacsin and ER staining at STED resolution to better support the fact that spatacsin is a protein of the ER.

Moreover, as requested, we have performed a western blot with Lamp2 and REEP5 antibodies on the ER- and lysosome-enriched fractions (New Figure 1B). This western blot shows that a significant proportion of Lamp2 is present in the ER-enriched fraction, which may be explained by the strong association of ER with late endosomes and lysosomes. Yet the lysosome-enriched fractions that contained no ER markers do not present spatacsin staining, suggesting that spatacsin is either in the ER or in lysosomes associated with the ER that are not positive for cathepsin D. We reformulated the text of Figure 1 according to the new included data (p. 5-6).

The authors generally do a good job of quantifying their results. However, this is lacking for the biochemical experiments (immunoblotting and IP) in figures 4 and 5, and I would prefer to see these quantified (the quantification should include data from repeat experiments so that we can judge the reproducibility of the results).

Authors response : We agree that our presentation did not indicate that the western blots were repeated several times. We have added quantifications for the western blots present in Figures 4 and 5.

On page 10, referring to the proximity ligation results, the authors comment: "This suggests that the spatacsin-spastizin interaction occurs at contact sites between the ER and lysosomes to allow spastizin recruitment to lysosomes". I'm not sure this statement is fully supported, as mentioned at point 1 above it is possible that some steady state spatacsin is at lysosomes. To fully support this, we'd need to see the PLA signal also convincingly co-localise with an ER marker.

Authors response : We will perform extra PLA experiment to indeed show that the spots where spatacsin and spastizin colocalize with an ER marker. This data will be added in Figure 5.

In figure 6C and D the effect of spastizin on lysosomal tubulation and dynamics is investigated. Wartmannin treatment is used to do this, as it is known to remove spastizin from lysosomes. However, this is a very indirect manipulation that could have many other consequences and it would be better to demonstrate this directly by showing the effect of depletion of spastizin on lysosomal morphology/dynamics. I also think the role of AP5Z1 in tubulation/dynamics would be better supported with additional experiments to deplete the protein - at present only over-expression is examined.

Authors response: *We added new data to answer this comment. Downregulation of spastizin using siRNA led to lower number of tubular lysosomes and decreased the proportion of dynamic lysosomes, showing that spastizin is required to regulate lysosome motility (Figure 6B-6C Supplementary Figure 7B). We have also added new data regarding downregulation of AP5Z1 (Figure 6A-6C-Supplementary 7A). Both overexpression and downregulation of AP5Z1 using siRNA decreased the number of tubular lysosomes and decreased the proportion of dynamic lysosomes (Figure 6A-6C-Supplementary Figure 6C-D). *

This observation suggests that the levels of AP5Z1 must be tightly regulated to control lysosome motility. We added discussion about this point as well (p.12-13).

While the experiments showing that over-expression of dominant negative forms of KIF13A and p150Glued affect lysosomal tubulation/dynamics provide good circumstantial evidence that spatacsin influences these lysosomal properties via its interactions with spastizin and AP5Z1 (which bind to these motor proteins), the authors have not shown that the interaction of the motor proteins with spastizin and AP5Z1 is required for this ability to regulate lysosome tubulation/dynamics. This means that the model presented in figure 7 is not fully supported by the data. If the authors have been able to map the binding regions for these interactions then perhaps this could be investigated with rescue experiments, although I appreciate that this is potentially a major piece of work and perhaps outside the scope of this paper. An alternative would be that the authors acknowledged this part of the model as somewhat speculative.

Authors response : We agree with the reviewer that our data do not show that KIF13A and p150Glued interact directly with spastizin and AP5Z1 to regulate lysosome dynamics. It has previously been shown that the adaptor complex AP2 interacts with p150glued via the ear domain of AP2 b subunit (Kononenko et al, 2017). It is therefore likely that the interaction of adaptor complex 5 with p150-Glued also occurs via AP5B1 subunit, and thus interaction of AP5Z1 with p150 glued would be indirect. *We discussed this point carefully (p.16). *

*Regarding the interaction of Spastizin with KIF13A, it was identified by yeast-two hybrid screen and validated by GST-pulldown (Sagona et al, 2010). This showed that KIF13A interacts with the C-terminal domain of Spastizin, and we discussed this point. To confirm that KIF13A interaction with spastizin is required to promote its role in tubular lysosome formation and dynamics, we can perform an experiment where we downregulate endogenous mouse spastizin using siRNA and express either full length human spastizin to rescue the effect of the siRNA, or overexpress a human spastizin lacking its C-terminal domain required for the interaction with KIF13A (where we would expect no rescue). This would strengthen our conclusion on the role of KIF13A in link with spastizin to regulate the formation and dynamics of tubular lysosomes. We could add these data in Figure 6 (or Supplementary Figure 7). *

• Are the experiments adequately replicated and statistical analysis adequate?

In general I am not convinced that the statistical tests are applied rigorously in this paper. Most experiments are done three times, but the "n" used for statistical testing is typically chosen as, e.g. the number of cells, number of lysosomes, rather than number of biological repeat experiments. This means that inter-experimental variability is not rigorously taken into account. A more rigorous practice would be to use the mean measures for each of three biological repeats and apply the statistical tests to the three means, so n=3 if three repeats were done. Superplots would be a nice way to graphically display these data.

Authors response : We agree with the comments of the reviewer regarding data presentation. We have therefore changed the presentation of all graphs of the manuscript using superplots that allow us to show all the points that were analyzed as well as the mean value for each biological replicate, and performed statistical analyses by comparing the biological replicates as proposed in Lord et al, JCB 2020 (10.1083/jcb.202001064).

Minor comments:

1. In supplementary figure 3D I cannot honestly say that I see the smaller band.

Authors response : We agree that this western blot is not clear. We will provide a new western blot.

When first called out, I expected supplementary tables 1 and 2 to show the list of interactors with wild-type spatacsin and spatacsind32-34 respectively, but this is not what they show.

Author response : We have added two supplementary data tables (Now Supplementary Tables 1 and 2) to give the list of interactors of wild-type C-terminal domain of spatacsin and spatacsinD32-34, respectively.

Supplementary Tables 3 and 4 now refer to the analysis of the downregulation experiments by respectively the neural network method and the tubular lysosome detection method.

The experiments in Figure 4A are a little problematic in the way that they are called out. The first call refers to just a small subset of the data in the figure, and the figure is then called out at various points later in the paper. This is quite confusing. Is there any way this could be simplified?

Authors response :We agree with the reviewer that Figure 4A was called at various points of the manuscript. This was to avoid duplicating data into two separate figures. However, we have modified the presentation of Figure 4 and Figure 5. We have included new Figure 4C to show that downregulation of UBR4 prevents the degradation of AP5Z1 upon overexpression of Spatacsin-GFP, but also in basal conditions in wild-type fibroblasts. The co-IP that was originally presented in Figure 4A has now been moved into Supplementary Figure 6A.

The section on page 10: "Spatacsin also interacts with spastizin, and is required to recruit spastizin to lysosomes (Hirst et al., 2021). ........ We hypothesized that spatacsin interaction with spastizin was required for spastizin localization to lysosomes." Is odd, as the authors seem to be hypothesising an observation that they have just said has already been demonstrated.

Authors response : We agree that these sentences were odd. We have rephrased the paragraph (p. 11).

Can the authors explain why there is so little interaction between wild-type KIF13A and spastizin?

Authors response : The interaction domain of spastizin with KIF13A is close to the motor domain according to the two-hybrid data published by Sagona et al (2010). The dominant negative construct of KIF13A that is devoid of the motor domain (KIF13A-ST) may thus facilitate access of spastizin to binding domain. We have commented on this point in the text (p.13).

In figure 6G p150Glued signal is also present in the control IP lane, which casts doubt on the specificity of the interaction. Could the authors generate a cleaner result?

Authors response : We have repeated the experiment 3 times, always with some p150Glued signal present in the control IP. Of note, as stated in the method section, we have increased the concentration of NaCl in the washing of this co-IP to decrease non-specific binding of p150glued to control beads, but we could not get cleaner results so far. We will try to get cleaner western blot to illustrate Figure 6G.

I would be interested to see how AP5Z1 expression differs between neurons with and without spatacsin- we would expect similar results to those shown in the MEFS.

*Authors response : We have not checked the levels of AP5Z1 in neurons with and without spatacsin yet. However, the complete knockout of spatacsin strongly modifies the levels of its partners. We previously showed that spastizin levels are decreased by >90% in Spg11 knockout brain (Branchu et al, 2017). Furthermore, the levels of AP5Z1 have been shown to be decreased by ~50% in fibroblasts of SPG11 and SPG15 patients (Hirst et al, 2015). *

*Our work shows that spatacsin promotes the degradation of AP5Z1 by lysosomes. It is possible that other degradation mechanism(s) may exist and could explain the lower levels of AP5Z1 in knockout cells. We discussed this point (p.15). *

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

In this study Pierga et al. report that SPG11 (spatacsin) is an ER-resident protein involved in the regulation of ER-lysosome contact sites (in particular tubular lysosomes) and subsequent faster motility of tubular lysosomes, as well in the degradation of AP5Z1 (SPG48), which forms a heterotrimeric complex with SPG15 (spastizin) and SPG11. This complex has been localized by several groups on the cytoplasmic side of LAMP-1-positive lysosomes. In addition, mutations in SPG11, SPG15, and SPG48 patients share various clinical features and were supported by biochemical/cell biological data from Spg11 and Spg15 KO mouse models and cultured cells both from patients and mice, respectively, demonstrating e.g. accumulation of autolysosome storage material, defects in the autophagic lysosome reformation process, and the loss of cortical motoneurons and Purkinje cells.

Major concerns:

i) Fig. 1, 2, 3: major disadvantage of this study is the analysis of overexpressed proteins (SPG11-V5, GFP-Sec61, and Lamp1-mCherry) which might contribute to the observed strong expression of SPG11-V5 in the ER/ER-enriched fraction. The results should be compared with the endogenous expressed proteins.

Authors response :* As stated by reviewer 1, there are no good antibodies to endogenous spatacsin, and therefore we have to rely on expression of tagged spatacsin to study its localization by immunohistochemistry. For the colocalization with the ER, we stained the latter by GFP-Sec61 that is a widely used marker for this compartment. To confirm our results, we plan to try to perform new STED imaging with REEP5 antibody to stain the ER, and Lamp1 antibody to label lysosomes, avoiding overexpression of proteins to label the subcellular compartments. Furthermore, as it is not possible to localize endogenous spatacsin by immunostaining, we addressed its localization by biochemical fractionation and western blots comparing wild-type and Spg11 knockout samples. *

For Figure 2, the data presented were indeed obtained using transfection of Lamp1-mCherry. However, we confirmed our observation of Figure 2A using alternative staining of lysosomes (Lysotracker or loading of lysosomes with Texas-Red Dextran). We therefore think that our data presented in figure 2 are valid, and that the effect we observed on tubular lysosomes was not affected by expression of Lamp1-mCherry.

In Figure 3, the lysosome were labelled with Texas-Red Dextran, and thus all the data presented in figure 3 do not rely on overexpression.

In Fig. 1C the lack of the mature Cathepsin D form which is proteolytically generated only in lysosomes from the higher molecular mass precursor is misleading and should be related to presence of lysosomal membrane proteins.

Authors response: As requested, we have performed a western blot to show the lysosomal membrane protein Lamp2 on the ER- and lysosome-enriched fractions (Figure 1B). This western blot shows that a significant proportion of Lamp2 is actually present in the ER-enriched fraction, which may be explained by the strong association of ER with late endosomes and lysosomes previously described (Friedman et al, 2013). Yet the lysosome-enriched fractions that contained no ER markers do not present spatacsin staining, suggesting that spatacsin is either in the ER or in lysosomes associated with the ER. We reformulated the text of Figure 1 according to the new included data (p 5-6). The 3-colours STED experiment that we plan to perform to answer reviewer 1 comments will help discriminate between these possibilities.

Fig. 1D: the TEM image shows only a single lysosome and proposed ER contact zones in wt-MEFs without comparison with Spg11 KO MEFs (only in the quantification). Without double immunogold labeling of SPG11 (and their lack on SPG11 KO cell lysosomes) and known ER contact-site proteins this image and the conclusion are insufficient.

Authors response : We have added an image of a lysosome taken from a knockout fibroblast (Figure 1E). As stated above there are no good antibodies to spatacsin for immunostaining, so it will not be possible to perform double immunogold labelling. This prevents us from claiming that spatacsin is a protein enriched at contact site. We therefore modulated our result section and discussion accordingly (p.5-6 and p.16).

ii) The rationale for the selection of the deleted Spg11 region D32-34 is not clear. What are the symptoms of this Spg11 knock-in mouse? A more detailed description of the phenotype is required Is the phenotype including the accumulation of LC3-positive material similar to the phenotype of the SPG11 KO mouse which has been published by Varga et al.(2015) and Branchu et al. (2017) ? If not, is the new mechanisms reported here not so important?

Author response : We have added new data (Supplementary Figure 3E-F) showing motor and cognitive impairment in mice expressing truncating spatacsin, although the motor dysfunction is slightly less marked than in Spg11 knockout animals. We also checked for accumulation of autophagy markers. We did not use LC3, but p62 that labels substrates to be degraded by autophagy. We observed accumulation of p62 in Spg11 knockout and in Spg11D32-34/D32-34 mouse neurons (Supplementary Figure 3G). These data support the functional importance of the domain encoded by exons 32 to 34 of Spg11. We commented on this in the text (p.9).

iii) p8/Fig. 3F/Suppl.Fig.3F- the most important part of the manuscript: what are the parameters of lysosomal staining in images that were used to identify genes important for lysosome tubulation by the neural network?

Authors response : For screening in Figure 3, lysosomes were stained by loading fibroblasts with Texas-Red Dextran overnight, followed by a wash of at least 4 hours. The neural network was first trained to discriminate between control and Spg11-/- fibroblasts, using any parameters of the lysosomal staining, not necessarily lysosome tubulation. This is a completely unsupervised and unbiased method, but one of its drawbacks is that we do not know which parameters were used by the network to discriminate between control and Spg11-/- fibroblasts. Therefore, we validated the classification performed by the neural network on a data set independent from the training set before using it for the screening. We rephrased the paragraph to make it clearer (p.9).

I cannot understand how the authors predict the probability of the cell to be considered as an Spg11 KO fibroblast (why not as an Spg11 D32-34 knock-in fibroblast?) as the basis for the selection of interaction candidates.

Author response : The neural network was trained on sets of images obtained from wild-type and Spg11 KO fibroblasts, which were expected to represent extreme lysosomal phenotypes linked to spatacsin function. We could therefore predict the probability of cells to be considered as Spg11 KO, not as Spg11 *D32-34 fibroblasts. We clarified this in the text (p9). *

A simple statement that the neural network approach identified those genes is too weak and requires more convincing experimental data. It has to be shown at least for the 8 positive genes in both approaches how the siRNA treatments of these genes phenocopied the lysosomal changes and of course the effect of the downregulation on the protein level of their products both in wild-type control and Spg11 D32-34 knock-in MEF. The Suppl. Fig.3F is completely unclear. How were the Y2H interaction partner validated? Did the authors use the identified 8 interaction candidates as full length bait to demonstrate the interaction with the Spg11 exons 32-34 ?

Author response : The purpose of the siRNA screen was to quickly identify putative candidates important for the regulation of lysosome dynamics. We identified 8 candidates possibly implicated in lysosomes dynamics based on the two analysis methods. We have added in Supplementary Figure 4 C-D the effect of both siRNA on lysosomal function by the two methods of analysis compared to the effect of siSPG11. However, here we aimed to identify candidates and we do not claim that every one of these eight proteins were indeed implicated in the regulation of lysosome dynamics. We corrected the text, accordingly, stating that the products of the 8 identified genes are good candidates to regulate lysosomal function (p.10). We validated the role of one of the identified candidates, UBR4, and we showed that the UBR4 siRNA indeed downregulates the protein level (Figure 4C). We only validated the interaction of spatacsin Cter with UBR4 by co-immunoprecipitation (Figure 4B).

*For the 7 remaining candidates, full characterization would indeed be required to validate their role and elucidate their mechanisms of action, but this is out of the scope of this manuscript. *

p8/Fig.3F: the genes identified in both approaches have to be listed in the Fig. 3F-Table.

Authors response : We have added in new Figure 3F the list of the 8 candidate genes that could contribute to regulate lysosome function.

The GO process- ubiquitin-dependent protein catabolic process is neither positive for the neural network nor for the directed analysis but positive for both analyses? Please explain. Similarly, the GO process proteolysis involved in cellular protein catabolic process -is not positive for the neural network analysis but again positive for both analyses.

Authors response : We agree with the reviewer that Table 3F in its older version could be a bit confusing. GO analysis is based on “enrichment” of biological processes within a list of proteins. As we did not have the same number of proteins in the 3 analyses provided in original Table 3F, we got variability in the identified biological processes. To simplify, we have therefore chosen to present only the GO analysis for the 8 candidates that were most likely implicated in lysosomal dynamics according to our two analyses of the siRNA screen which is the most relevant for our study (new Figure 3G).

For Fig. 3G the mutant ubiquitin-K0 staining in wild-type MEF cells has to be shown as well as for the Spg11 ki/KO MEFs (+ quantification of the respective data)

Authors response : As stated by Reviewer 4, the expression of lysine-null ubiquitin may impact many different cellular pathways. We therefore removed this part of the data in order to simplify the manuscript (p.10)

iv) The interpretation of the Y2H-interactome analysis by the authors is hard to follow. They searched with the exon 32-34 cDNA for binding partner, selected 3 degradative GO processes and showed by overexpression of a mutant Ub-K0 plasmid in wild-type MEFs a decreased number of tubular lysosomes, as well as their dynamics (without showing the control data in Spg11 KO or ki-MEFs). Thus, poly-ub of proteins should be in some way responsible for a lysosomal phenotype of Spg11ki MEFs.

Now they went to AP5Z1, the second binding partner of SPG11, which is reduced in its abundance upon overexpression of Spg11-GFP. I would expect to do the respective control experiment to show that in the absence of SPG11 or in the knock-in cells the amount of AP5Z1 has to increase. However, in the studies by the Huebner group by deletion of Spg11 or the other binding partner Spg15, no increase of AP5Z1 protein levels has been observed. The authors have to comment on this discrepancy.

*Authors response : We agree that this is an important point to discuss, and we failed to do it in our first version. *

*The complete knockout of spatacsin strongly modifies the levels of its partners. We previously showed that spastizin levels are decreased by >90% in Spg11 knockout (Branchu 2017). Furthermore, the levels of AP5Z1 have been shown to be decreased by ~50% in fibroblasts of SPG11 and SPG15 patients (Hirst et al, 2015). *

Our work shows that spatacsin promotes the degradation of AP5Z1 by lysosomes. It is possible that other degradation mechanism may exist, and could explain the lower levels of AP5Z1 in knockout cells. Furthermore, it was proposed that AP5Z1 stability may depend on the presence of spatacsin and spastizin (Hirst et al., 2013)*. Therefore spatacsin may contribute to tightly regulate AP5Z1 levels by contributing both to its stability, and to its degradation. We have carefully discussed this point (p.16). Furthermore, the experiments requested by reviewer 2 in point (vi) that we are planning to perform will help clarify the mechanisms of AP5Z1 degradation both in presence and absence of spatacsin. *

Then the authors found that the selected interaction partner of the exon 32-34 sequence, UBR4, does not bind to the Spg11-GFP construct lacking the domain encoded by exons 32-34 but to the C-terminal domain of Spg11-GFP. Unfortunately, all these IP-experiments were shown as cut and paste figures, preventing the direct comparison between the input and the IP protein amounts (since the information is missing what percentage of the input and the IP has been loaded per lane, the evaluation and significance of these Co-IPs are unclear).

Authors response : We have added in the Figure legend the fact that the input represents 5% of lysate added to the immunoprecipitation assays

v) p9: AP5 (Z1) is a cytoplasmic protein and can be localized on the cytoplasmic surface of lysosomes. How should the GFP-mcherry-AP5Z1 protein enter the lumen of lysosomes justifying the quenching of the GFP signal? A positive control has to be included in the experiment shown in Fig. 4E demonstrating the effect of MG132 under identical conditions of a protein substrate for proteasomal degradation.

Authors response :* We agree this is an important control. We plan to add a control showing accumulation of ubiquitin in lysates upon MG132 treatment to show it was indeed effective. *

vi) Fig. 5A: In contrast to GFP-mcherry-AP5Z1, spastizin-GFP is localized at the cytoplasmic surface of lysosomes (co-staining with LAMP1-mcherry) in wild-type MEFs. In regard to the incomplete data commented under "minor points Fig.4/Suppl.Fig.4", I suggest to perform a simple control experiment with overexpressed GFP-spastizin and mCherry-AP5Z1 in wild-type MEFs (at the best also in Spg11 KO MEF) with and without bafA treatment, which will clearly demonstrate whether single components of the trimeric Spg11, spastizin-AP5Z1 complex are degraded independently of each other in lysosomes.

*Authors response : As stated above, we will perform this control experiment, and will add the data in Figure 5 in future revision. This will help clarify the mechanism of degradation of AP5Z1 and spastizin both in presence and absence of spatacsin. Discussion of this point will also help to clarify the point iv raised by reviewer #2. *

vii) why did the authors neither mention nor discuss the described role of SPG11 in autophago-lysosome reformation (ALR)?

*Authors response : We did not discuss ALR in our first version as we did not investigate autophagic conditions. However, due to the well-described role of spatacsin in ALR, we agree that we should discuss ALR in our manuscript, and we added a paragraph (p.15). *

Minor points

• Figure 1 A, B, D, and G: ER-lysosome contact sites. The quantification of the co-localization of spatacsin-V5 with the ER marker protein GFP-Sec61b has to be given.

Authors response :* We plan to add quantification data performed on STED images showing localization of Spatacsin-GFP together with ER and lysosomal markers. This data will be added in Figure 1. *

Moreover, the authors analyzed overexpressed tagged-proteins only. The results should be compared with the endogenous proteins.

Authors response :* As stated above, there are no good antibodies to endogenous spatacsin for immunostaining. We will add new STED images with antibodies against endogenous Reep5 and Lamp1 to label the ER and lysosomes together with overexpressed spatacsin. Regarding endogenous spatacsin, we could only investigate its localization by subcellular fractionation and western blots comparing wild-type and Spg11 knockout samples. We added biochemical data suggesting that spatacsin is enriched either in the ER or in lysosome membrane associated with the ER. These data have been added in Figure 1 and in text (p.5) and we added a paragraph in discussion regarding spatacsin subcellular localization (p.15). *

p8/Figure 3: what does the 'analysis of trained neural networks' mean?

Authors response : We did not analyzed the trained neural network, but we used this trained neural network to perform image analysis. We clarified the text (p.10).

Figure 4: what happens with the other AP5 subunits?

Authors response : This is a very interesting question. We will test whether overexpression of spatacsin-GFP induces a degradation of some other AP5 subunit, provided we get specific antibody. We will add the data in Figure 4A.

Fig.4F/Suppl.Fig4: live images of GFP-mcherry-AP5Z1 + lysotracker staining have to be shown both for wild-type MEFs with and without bafilomycin A treatment(as in Fig.4F), and in Spg11 KO and Ki MEFs +/- bafA.

Authors response : We will add these data in Figure 4 (WT Mefs +/- Baf A) and in Supplementary Figure 5 (Spg11KO and SPG11D32-34 Mefs +/- Baf).

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

This manuscript highlights an interesting localization of spatacsin in the endoplasmic reticulum (ER)-lysosomes contact sites. In addition, it implicates spatacsin in regulating tubular dynamic lysosomes. Mechanistically, the authors propose that spatacsin interacts with UBR4 to promote the autophagic degradation of its binding partner AP5Z1 at the lysosomes. In turn, this would also regulate the amount of spastizin at the lysosomes, which is known to interact with anterograde motors. The authors further show that AP5Z1 interacts with p150Glued. Thus, the balance between AP5Z1 and spastizin at the lysosomes would determine lysosomal trafficking directionality.

Major Comments

1. Several crucial results of the manuscript are based on quantifications performed on immunofluorescence stainings. Data points in graphs show individual cells or individual lysosomes and the authors apply statistical tests on replicates that cannot be considered biologically independent, since they come from the same experiment or even the same cell. It is recommended to show superplots where both the individual data and the average of each independent experiment is indicated as recommended by Lord et al. (J Cell Biol 2020 219 (6): e202001064.). Statistics should be performed only on independent biological replicates.

Authors response : We agree with the comments of the reviewer regarding data presentation. We have therefore changed the presentation of all graphs of the manuscript using superplots that allow us to show all the points that were analyzed as well as the mean value for each biological replicate, and performed statistical analyses by comparing the biological replicates as proposed in Lord et al, JCB 2020 (10.1083/jcb.202001064).

The authors have used yeast two-hybrid to search for spatacsin interactors. Although in the manuscript they refer to supplementary tables that should show these interactors, the available Tables are confusing and refer to the following downregulation experiments.

Author response : We have added two supplementary data tables (Now Supplementary Tables 1 and 2) to give the list of interactors of wild-type C-terminal domain of spatacsin and spatacsinD32-34, respectively.

Supplementary Tables 3 and 4 now refer to the analysis of the downregulation experiments by respectively the neural network method and the tubular lysosome detection method.

An experiment to demonstrate that endogenous UBR4 and spatacsin interact by co-immunoprecipitation would be crucial.

Authors response : We agree with the reviewer that it would be important to test whether endogenous spatacsin and UBR4 are interacting by co-immunoprecipitation. So far we have not managed to immunoprecipitate either endogenous spatacsin or endogenous UBR4 with the antibodies we tested, which prevents us to test the interactions of endogenous proteins by co-immunoprecipitation. We are not sure we can provide this result.

Several important experiments to unravel the mechanistic role of spatacsin (Figure 4 and 5) are performed upon overexpression. This is a major limitation of the study and the authors should address it as much as possible. Western blots and immunoprecipitations are shown that appear to have been performed only once and have no quantification. As an example, in Fig 4A the difference in levels of AP5Z1 upon spatacsin overexpression or UBR4 downregulation are very minor. I would be very careful in drawing big conclusions, without additional repetitions and additional experiments in an endogenous setting.

*Authors response : We agree that a lot of our experiments used overexpression. We have now added to the manuscript new data obtained in MEFs where we downregulated spastizin or AP5Z1 (Figure 6). They confirm the role of spastizin in the regulation of lysosome dynamics. Furthermore, our new data show that levels of AP5Z1 must be tightly regulated as both overexpression and downregulation of AP5Z1 affects lysosome dynamics (p.12). We also discussed these data carefully (p.16 ). *

Furthermore, we agree that our presentation did not indicate that the western blots were repeated several times. We have now added quantifications for the western blots presented in Figures 4 and 5. Furthermore, we have also added the data showing that downregulation of UBR4 led to higher levels of AP5Z1 in control fibroblasts (Figure 4C).

The authors suggest a model by which UBR4 recruited by spatacsin is involved in autophagic degradation of AP5Z1. The data shown do not support this conclusion. First, in Figure 4A downregulation of UBR4 does not increase levels of AP5Z1 above the control in lane 1, but only when spatacsin is overexpressed. The effect of downregulation of UBR4 in wilt-type cells on AP5Z1 should be investigated. Secondly, there is no experiment directly proving that the stability of AP5Z1 depends on UBR4.

Authors response : We have added new western blots (and quantification) in Figure 4C showing that downregulation of UBR4 increased levels of AP5Z1 in control conditions. The fact that downregulation of UBR4 increased levels of AP5Z1 in control conditions suggests that UBR4 contributes to regulating the levels of AP5Z1. However, we do not show whether UBR4 directly promotes the degradation of UBR4, which has been added in the discussion (p15). To test whether UBR4 affects the stability of AP5Z1, we will monitor whether downregulation of UBR4 by siRNA increases the half-life of AP5Z1. These data will be added on Figure 4.

The authors suggest that the interaction of spatacsin with spastizin or AP5Z1 are in competition. This is an interesting hypothesis, however to conclusively demonstrate this, pull-down experiments in KO cells and not upon extreme overexpression should be performed.

Authors response : We agree that testing the interaction of spatacsin with its partners in SPG15 KO or AP5Z1 KO fibroblasts would be a very good control of our hypothesis. However, we previously showed that the levels of AP5Z1 are lower in SPG15 KO than in control fibroblasts (Hirst et al, 2015), which introduces a bias in the analysis. We therefore plan to concentrate on AP5Z1 fibroblasts and investigate whether interaction of spatacsin with spastizin is modified in these cells. An alternative would be to monitor the effect of siRNA downregulating AP5Z1 on the interaction between spatacsin and spastizin. We will add these data in Figure 5.

Minor comments

1. In figure 1G and 1H the overlapping area between lysosomes and ER is quantified. Considering that the ER occupies a large portion of the field a 90{degree sign} flipped control for both WT and KO would be important to sort out random colocalization. In this direction, it would be also essential to show that the total amount of lysosomes is not different in WT and KO, especially because in figure 1A the lysosomes in WT and KO seem to be different not just in shape but also in number and size. A different number or size of lysosomes affects this analysis.

Authors response :* We added quantifications in Supplementary Figure 1F showing that 90° flipped controls are indeed not capturing the same proportion of contacts between the ER and lysosomes. We also added quantifications in Supplementary Figure 1D-E showing that the average size of lysosomes and the number of lysosomes per unit area are similar in control and Spg11 KO fibroblasts and mentioned it in the text (p.6). If the lysosomal staining appears different in Spg11 KO fibroblasts it is because lysosomes are clustered around the nucleus, an observation that we reported previously (Boutry et al, 2019). *

In the second chapter of the Results, the authors state: "we observed by live imaging a higher number of lysosomes with tubular shape in Spg11+/+ compared to Spg11-/- cells", however the number of elongated lysosomes is quantified per area. Why the number of elongated lysosomes is not quantified over the total amount of lysosomes?

Authors response : The point raised by the reviewer is a fair point. The purpose of our analysis was to compare the number of lysosomes with tubular shape in control and Spg11 KO cells. As the number of lysosomes per unit area is invariant between control and Spg11 KO cells as shown in new data included in Supplementary Figure 1D, normalization to total number of lysosomes or to cell surface reflects the same difference in phenotype.

The In the fourth chapter of the Results, the authors state:" In wild-type MEFs, mCherry was colocalized with lysosomes. In contrast, GFP that is sensitive to pH was poorly colocalized with lysosomes, suggesting that AP5Z1 was mainly inside the acidic subcellular compartment (Figure 4F)." If the aim of the authors is to shown that AP5Z1 is mainly into the lysosome, the amount AP5Z1-mcherry inside and outside the lysosome need to be compared, with a proper statistical analysis. There is also a lot of GFP signal in the cytosol. Why is that?

*Authors response : We agree with the reviewer, we will add quantification of the proportion of AP5Z1-mCherry inside lysosomes on Supplementary Figure 5. *

Regarding the GFP-AP5Z1 signal in the cytosol, AP5Z1 has no transmembrane domain and may thus exist as a cytosolic protein. Since GFP is quenched in the acidic environment of lysosomes, the GFP fluorescence of the mCherry-GFP-AP5Z1 protein is outside lysosomes, and it appears partly cytosolic. Of note, there is also some cytosolic mCherry signal that is less visible due to the high level of mCherry fluorescence in lysosomes. We will clarify this point with the quantification of the proportion of mCherry signal compared to GFP inside the lysosomes and add it in Figure 4.

construct used in the paper is a C-terminal tagged version of spatacsin. The authors should consider to test an N-terminal tagged construct at least for the localization experiments.

Authors response : We added an immunostaining image of Spatacsin with an N-terminal tag (Supplementary Figure 1B) and mentioned it in the text (p.6). As spatacsin with a C-terminal tag, it presents a diffuse distribution that poorly co-localizes with lysosomes.

Figure 5C: a negative control and the quantification are missing.

Authors response : A non-transfected cell is present on Figure 5C, visible thanks to the Lamp1 immunostaining, and that we considered as a negative control. In this non-transfected cell, we detected no PLA signal. We added an asterisk to point the non-transfected cell on Figure 5C. Quantification will also be added in the revised version after we have performed the PLA experiment required by Reviewer 1.

Reviewer #3 (Significance (Required)):

Since spatacsin, AP5Z1 and spastizin are all implicated in hereditary spastic paraplegia, the data are of potential interest not only for basic cell biology, but also to understand the pathogenesis of the disease. In addition, the manuscript proposes a novel model regulating trafficking of dynamic lysosomes.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Pierga et al. reveal subtle differences in lysosome morphology, ER-contact, and trafficking in the absence of Spatascin. These data are replicated with a truncated Spatascin, presumably a loss of function. Two-hybrid screening of the deleted sequence from this truncation for interactors and then asked whether these hits could phenocopy the lysosome morphology changes. This led to an assertion for a role for ubiquitination in these effects. Rather than these hits the group then investigates previously known Spatascin interactors and reports similar complex but subtle abnormalities via overexpression or knockdown of these. While data show overlapping phenotypes by modulation Spatascin, AP5z1, and Spastizin, the manuscript is confusing, leaps from experiment to experiment, and does not provide novel rigorous mechanisitic insight. It conflates all the discrete lysosomes aspects into a collective to link them. The title is over-stated and not appropriate for the experiments.

The localization of endogenous Spatascin is lacking - over-expression is prone to artifact and the punctate data on the V5 suggests much more work is needed to understand where in the cell it is. It would seem much more work is needed here.

Authors response : As stated by reviewer 1, there are no good antibody to endogenous spatacsin, and therefore we have to rely on expression of tagged spatacsin to study its localization by immunofluorescence. When performing the images, we avoided the cells with the highest ovexpression of tagged spatacsin. Yet, we agree that this is still overexpression. That’s why we included subcellular fractionation data where we can detect endogenous spatacsin (Figure 1A-1B). These data confirmed that spatacsin is enriched in the ER or in lysosome fraction tightly associated with the ER.

Furthermore, the EM data (1E) would suggest the far majority of lysosomes are in contact with ER - these seems uncharacteristic.

Authors response : The EM data in figure 1E indeed shows that the majority of lysosomes are in contact with the ER, as previously shown by other groups (Friedman et al, 2013, Höglinger et al, 2019).

The phenotypes analyzed are very subtle, and while statistically significant the biological impact is unclear - in many cases individual lysosomes (or lysosome-ER contacts) are considered as an 'n'. While these results are probed across multiple independent experiments the batch effects and how uniform per cell the events are is unclear.

Authors response : We agree with the comments of the reviewer regarding data presentation. ‘n’ represented individual cells, but did not actually take into account the variability across experiments. We have therefore changed the presentation of all graphs of the manuscript using superplots that allow us to show all the points that were analyzed as well as the mean value for each biological replicate, and performed statistical analyses by comparing the biological replicates as proposed in Lord et al, JCB 2020 (10.1083/jcb.202001064).

In fig 2H critical data are missing - the effect of Spatascin KO on the transition between these morphologies should be considered as in G. Otherwise the relevance is unclear.

Authors response : We have added this quantification on Figure 2I. It shows that transition of morphology of lysosomes from round to tubular in Spg11 KO cells is still associated with a change of speed, although the average speed attained is halved compared to conditions where spatacsin is present. This shows that loss of spatacsin does not abolish morphological transition of lysosomes but limit their speed in the tubular shape. We commented on this new data in the text (p.8).

The impact of over-expressing a lysine-null Ub ( Fig 3) is far too crude and non-specific to have meaning here. It is assumed that the only proteins affected are those of interest. This is consistent with much of the paper where "true-true-and unrelated" is more likely than the presumption of causality.

Authors response : It is true that the expression of lysine-null ubiquitin is really crude and may impact many different cellular pathways. Furthermore, the results obtained with the lysine-null ubiquitin do not contribute to the rest of the paper. We therefore removed the original Fig3G, H, I and Fig 4B and updated the text accordingly (p.10).

The blots in Fig4 are a relatively poor quality and not quantified over repetition.

*Authors response :Spatacsin and spastizin are large proteins, and there is not much choice for antibodies able to detect these proteins. Yet we have validated their specificity by western blot using knockout cells (spatacsin) (Supplementary Figure 4 A-B) or siRNA (spastizin) (Supplementary Figure 7B). We agree that our presentation did not indicate that the western blots were repeated several times. We have added quantifications for the western blots present in Figures 4 and 5. We also changed some illustrative western blots to improve quality. *

Controls are missing and Fig5 suffers from a reliance on over-expression - there is a massive over-expression of AP5Z1 which may be affected the stoichiometry of these overall interactions, but with an n=1 its hard to know and its not clear what these data add. Again, while statistically significant (5E and F) due to the nature of data analysis (every lysosome=n of 1) it is not clear how biologically significant UBR4 siRNA or AP5Z1 over-expression is - as the accumulation of AP5Z1 in these two conditions is orders of magnitude apart - again likely unrelated.

Authors response : We added quantification for this western blot (Supplementary Figure 6A).

*As stated above we have changed the representation of the graphs. Each point represents one cell, and we included the mean value for each biological replicate. *

Preventing degradation of AP5Z1 by UBR4 siRNA or overexpression of AP5Z1 do not indeed have the same effect on total AP5Z1 but do have a similar effect on the interaction of spatacsin with its partners evaluated by co-immunoprecipitation, as illustrated by the quantifications that we have added. We clarified this in the text (p.12). As requested by reviewer 3, we will also investigate the effect of AP5Z1 knockout or downregulation on the interaction between spatacsin and spastizin assessed by co-immunoprecipitation. These data will be added in Figure 5 and will strengthen our conclusions.

Fig 6 begins to conflate the fact that different lysosome morphologies appear to have different trafficking properties even in WT cells and that many of these targets affect morphology - therefore to conclude a direct effect on trafficking seems inappropriate.

Authors response : In original Figure 6, we showed that Kif13A-ST and p150CC1 changed the proportion of tubular lysosomes (previous Figure 6 and H), and the data showing that these constructs changed the trafficking of lysosomes were presented in Supplementary Figure 5 B-C. We have now moved the data showing the effect of Kif13A-ST and p150CC1 in the main Figure (Figure 6F and 6I) to facilitate the interpretation of the data. Therefore, expression of Kif13A-ST and p150CC1 do not only affect the morphology of lysosomes, but also impaired their trafficking. We thus do not extrapolate lysosome dynamics from their morphology, we actually quantify lysosome dynamics.

Fig 7 extends this into polar cells (neurons) but still it is not clear whether form (morphology) dictates function (likelihood of trafficking or directionality.

Authors response : We did not only analyzed neurons because they are polarized cells, but because neurons are the main cells affected by neurodegeneration observed in absence of spatacsin (Branchu et al, 2017). We added new data on Figure 7 showing that tubular lysosomes in axons are actually more dynamic than round lysosomes, as observed in fibroblasts. We added these data in Figure 7 and text (p.13).

Investigation of lysosome trafficking in axons also allowed us to investigate the directionality of movement, which is difficult in MEFs. We clarified this point in the text (p.13).

In sum, there is a lot of data that collectively points to a partial localization of Spatascin at Er-lysosome contacts and an influence on morphology and trafficking of lysosomes in the cell, but at the end of the day very new mechanism is brought to light.

Authors response : The mechanisms regulating trafficking of lysosomes are far from being fully resolved. Our manuscript shows that spatacsin contributes to this regulation by modulating the degradation of AP5Z1. This in turn regulate the lysosomal association of AP5Z1 and spastizin that interact with motor proteins to control lysosomal dynamics.

Reviewer #4 (Significance (Required)):

This manuscript is directed to the basic cell biology community - involving ER, lysosome, and microtubule dependent trafficking. There are some new analytical tools employed and many co-factors and binding partners of Spatascin considered but frankly too many to adequately and rigorously control for. Because of this the manuscript is very unfocused, hard to follow and makes too many assumptions about shared dynamics ? necessarily arising from shared morphology - lysosomes are highly dynamic and can be affected by virtually any change in intracellular trafficking or protein/membrane transport. This is not appropriately considered.

Authors response : We have clarified our manuscript to show that dynamics is not necessarily arising from a tubular morphology. It turns out that lysosomes with a tubular morphology indeed are more dynamic that lysosomes with a round morphology. Importantly, in all our experiments dealing with lysosomal dynamics, we have actually included a quantification of lysosome dynamics using time lapse imaging as detailed in methods (p.21).

digital bearer asset means a token (or luggage tag?)

Predatory inclusion is a form of rent-seeking in which one preys on customers using monopoly power to extract excessive value for small add-on services.

So, does this mean that hyperlinks influence the PageRank? If you were to tag a website multiple times, it's PageRank index will significantly increase?

It's from the first project "Fluffy Tag". In this essay I wrote a story about my chilhood and how this memory was recalled. I had a dream to my grandma sewing my worn pillowcase, which reminds me of her throwing away my favorite pillowcase with my special smell on its tag when years ago. But I woke up that morning holding the tag on my comforter and caught a trace of the familiar smell, and I finally found my unique smell back.

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

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

In this manuscript by Rodriguez-Real et al, the authors address the contribution of the centrosome to cellular process unrelated to organizing the microtubule cytoskeleton, namely DNA repair. As many proteins contributing to the DNA damage response physically associate with centrosomes, this appears a relevant question that has been neglected so far and led to a number of studies that appeared controversial. To do so, the authors exploit a variety of tissue culture models that are well established in the fields of centrosomes and DNA repair, including U2OS and RPE1 cells, exposed to perturbations promoting DNA damage (such as ionizing radiation or pharmacologic perturbation of DNA stability) in conjunction with siRNA mediated depletion of candidate centrosomal proteins., followed by the visualization of repair events either using fluorescent reporters, or visualizing endogenous repair foci by immunofluorescence. On this basis, the authors propose that a discrete centrosomal sub-structure, namely sub-distal appendages and the CEP170 protein therein concur to promote a particular nuclear DNA repair process, namely homologous recombination.

The manuscript suffers of two main limitation:

1. the authors provide no mechanistic understanding of how CEP170, a protein that resides at centriolar subdistal appendages and shows no nuclear translocation upon DNA damage, concurs to regulate processes in the nucleus. The fact that all reported phenomena appear to be independent of microtubules suggests that neither the LINC complex nor the precise position of the centrosome in the vicinity of nuclear pore complexes contribute to the reported phenomena.
2. some of the experimental perturbations performed in the manuscript might elicit the reported phenotypes due to spurious effects on cellular processes that have not been considered with sufficient caution.

Given that uncovering the mechanism underlying the contribution of CEP170 to homologous recombination might prove very demanding, my comments will focus primarily on the second point.

Major comments:

The centriolar depletion using centrinone is known to impinge on cell proliferation in p53 WT cells. Thus, I am not convinced that the data shown in Figure S1B and S1C will sufficiently document that the observed unbalance between HDR and NHEJ are not simply reflecting a different cell cycling speed/behavior. Moreover, it would be important to address whether centrinone or depletion of CEP170 (an essential gene, according to the authors!) will trigger DNA damage by themselves. In fact, even a small extent of chronic genotoxic stress caused by the perturbations used in the manuscript might explain the reported differential proficiency of HDR.

Minor comments:

It is a pity that CEP170 is not amenable to functional dissection using a complete knockout. The fact that in PMID: 27818179 a complete knockout of CEP128 has been achieved, suggests however that subdistal appendage mediated DNA repair is not the essential process in itself. As the authors employ other cell lines stemming from the same laboratory, they could consider acquiring CEP128 KO to complement their own experiments.

The proposal that CEP170 phosphorylation of by ATM/ATR upon DNA damage might require SDA localization of the protein is plausible, yet not circumstantiated by any experimental evidence. If the authors could monitor the phosphorylation of the endogenous CEP170 protein in WT vs CEP128 KO cells (phosphor-specific antibody, MS-based proteomics or simply "phos-tag" gels), this could provide a first spark towards a mechanistic understanding of the reported phenomenon.

The entire Figure 4 is based on quantifications of clonogenic potential.

1. it would be helpful if the data were accompanied by images displaying representative crystal violet stained dishes.
2. clonogenic potential potential is discussed as a mere readout of cell survival, yet a combination between survival and proliferation concur to the reported differential clonogenic potential

Odf2 contribution to both DAs and SDAs: while Odf2 has been initially proposed to be necessary for the assembly of both types of appendages, its contribution to distal appendages has been disputed by Tanos et al using siRNA (PMID: 23348840), also confirmed by our group using CRISPR (unpublished). Thus, the role of Odf2 in SDA assembly appears more crucial than for DA assembly.

CEP164 contribution to ATM/ATR activation: this has been disputed in this paper by the Morrison lab (PMID: 26966185). Thus, a cautionary note should be mentioned when referring to this concept.

Significance

Taken together, this manuscript addresses the contribution of the centrosome to DNA repair. This is in itself a very interesting topic with the potential to attract the interest of both cell/molecular biologists as well as cancer researchers. The major advance strength is represented by pinpointing a specific centriolar substructure, namely subdistal appendages, in the control of HDR. CEP170 had been previously shown to be target of phosphorylation by ATM/R and the present study highlights that the abovementioned phosphorylation is not a mere passenger event during DNA repair, but that potentially reflects a decisive event informing the repair pathway of choice. However, several experiments have alternative explanations/interpretations and no understanding of the underlying mechanism is provided.

The expertise of this reviewer is the study of cell cycle regulation and on the centrosome structure/function.

Mastodon has RSS feeds available for tags!

I'm enamored of this idea as well and this is a fascinating example.

It seems similar to the related (and also difficult-to-name) concept which I've called combinatorial creativity. One of the earliest versions I've seen is that of Raymond Llullus' work with respect to combinatorial mnemonics described in Frances Yates' The Art of Memory (1966). Farnam Street's post is a good start https://fs.blog/networked-knowledge-and-combinatorial-creativity/, but I've been collecting other examples: https://hypothes.is/users/chrisaldrich?q=tag%3A%22combinatorial+creativity%22 and other names for it over time.

I can't help but wonder what Ericsson's role of deliberate practice would look like with arts as the subject? What motivates long term deliberate practice?

Yates, Frances A. The Art of Memory. 1966. Reprint, Chicago, IL: University of Chicago Press, 2001. https://www.amazon.com/Art-Memory-Frances-Yates/dp/0226950018.

Ericsson, K. Anders, Ralf Th. Krampe, and Clemens Tesch-Romer. The Role of Deliberate Practice in the Acquisition of Expert Performance. Psychological Review, 1993.

add the mailto: tag and add a fullstop

"In case you want a white logo, contact us."(hyperlink contact us)

A protein that is popularly used as a tag for the purification of recombinant proteins. It can be fused to either ends of the desired protein, usually the end that does not affect the function of the target protein.

A recombinant protein is produced by cloning a gene into a system that allows the expression of that gene and the translation of its gene product.

BIOmastodonte

Authors' response (28 November 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.

We thank the reviewers for the careful reading and assessment of our manuscript and for the constructive criticism. We have tried to respond to all the essential revisions, both by rewriting sections and performing some experiments or new analysis. Below we respond one by one to all the points raised. Please also note that we have added an author to the manuscript, who has carried out new experiments included in this revised version of the preprint.

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.

We think that this is a very useful suggestion and we have expanded the introduction to more pointedly indicate the contributions of previous voltage-clamp fluorometry experiments in Hv1 channels and to clearly explain why we chose to pursue the use of a genetically-encoded small fluorophore such as Anap.

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.

Thank you for pointing out our oversimplified discussion of the mechanisms of Anap fluorescence changes in Hv1 channels. We have taken into account your comments and present a more nuanced and toned-down discussion of the possible mechanisms at play in our experimental system.

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.

We thank the reviewers for this recommendation. We show that the number of cells showing mCherry fluorescence is greatly diminished in the absence of L-Anap, but in the presence of the tRNA and synthetase. As suggested, we have included results of control experiments in which we attempted to record currents from cells expressing the constructs: F150A-A197tag, Q191tag, A197tag and L201tag co-expressed with the tRNA and synthetase-coding plasmid (pANAP) and in the absence of L-Anap. We struggled to find red fluorescing cells and recorded currents from a relatively small number of these cells, most of which was leak current. We now include these data in Figure 1-Supplement 1B. These control experiments show that there is very little leakage of expression of channels that did not incorporate Anap.

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.

We appreciate the reviewers' concern regarding the role of other aromatic residues near Anap incorporation sites, especially the ones close to the subunit interface given that Hv1 is a dimer. We now mention the possibility that other residues could be quenching groups, especially given the fact that some quenching remains in the double mutant F150A-A197Anap (line 272 in results and line 432 in discussion). We have also included a new analysis of the ratio of Anap/mCherry fluorescence (at resting membrane potential) for all insertion sites. This shows a decreased ratio as Anap gets inserted in residues closer to the c-terminus of S4, which is evidence of a quenching group located near the center of the transmembrane domains (Figure 4-Supplement 1).

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 should be shown).

We appreciate the concern related to the origin of the fluorescence signals and we have improved both the presentation and the associated figures. Since this is also a concern for the experiments that determined the pH-dependence of Anap incorporated at position Q191, we have included a figure supplement 1 to Figure 2 in which we explain how the membrane was visualized. We use mCherry fluoresce as an indication of plasma membrane-associated channels, since its red fluorescence is easier to detect in the membrane than Anap fluorescence (even though cytoplasm dialysis in whole-cell should diminish the amount of free Anap, it is difficult to distinguish Anap fluorescence in the membrane by itself). Once the membrane associated mCherry fluorescence is detected, the measurement of the spectrum from a very small membrane area is insured because the spectrograph slit delimits light collection to a very small vertical area and the horizontal line scan further limits light measurement. These procedures are now made explicit in methods section and supplementary figure mentioned before. Moreover, we explain that they were also followed in experiments where the cell was under voltage-clamp. The spectral data in Figure 3E is now presented without normalization to show the voltage-dependent change in amplitude without changes in peak emission wavelength.

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?

As in the experiments of figure 1E, the spectra were obtained in non-patched cells. Thus, the signal was measured at the HEK cell resting potential (~ -30 mV) and a ΔpH ≈ 0.2. We have now incorporated that information in the methods section and the figure description. On the other hand, we did not perform experiments measuring the double mutant spectra at different voltage steps, so we cannot respond to the second question.

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.

These are great suggestions and we have incorporated them to the text (lines 156, 190 and 216 Results section), along with a better explanation of procedures followed to measure mostly membrane-associated fluorescence (see new Figure 2-Supplement 1).

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.

We believe that the kinetics of fluorescence and current does not match because the current activation rate is overestimated due to a small amount of proton depletion present in recordings from large currents. This is an unavoidable problem in proton current recordings, even with the high concentration of proton buffer used in our experiments and the long time-intervals between each voltage pulse. For this reason, we did not undertake a systematic exploration of kinetics. Nonetheless, the current and fluorescence rates are very close and have the same voltage dependence, indicating a close correlation between voltage-sensor movement and current activation. We now explain this limitation in the manuscript text (line 223 and 327, results section).

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.

Yes, construct L201Anap was used in Figure 6. This is stated now in the figure legend and in the corresponding main text. We agree that the leftward shift of the GV with respect to the FV in pHi7-pHo7 is an intriguing finding, suggesting that coupling between S4 movement and proton permeation can be regulated by the absolute value of the pH. We discuss this in the results section. The DeCoursey group has shown evidence in W207 mutants of hHv1 that the absolute value of pH can modulate the voltage dependence of the conductance. Although we had mentioned these results, we now mention them more prominently and also discuss the possibility that this might be a unique feature of introducing Anap at L201.

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?

We thank the reviewers for bringing up this point. Based on published data, we assumed that mCherry could not be excited by 405 nm radiation, thus our conclusion that the observed emission near 604 nm is FRET between Anap and mCherry. We have now measured the excitation of the Hv1-mCherry construct and observe that the 405 nm laser is capable of exciting mCherry and produced ~2 % emission (as compared to 514 nm excitation), which is almost the same as that observed for the Hv1Anap-mCherry channels. We now conclude that the second hump in the emission spectrum near 600 nm is due to direct excitation of mCherry.

On the other hand, FRET across the membrane has been demonstrated for the membrane-bound hydrophobic anion dipicrylamine and membrane-anchored GFP (Chanda, et al. A hybrid approach to measuring electrical activity in genetically specified neurons. Nature neuroscience, 2005, vol. 8, no 11, p. 1619-1626.) and dipicrylamine and GFP in the c-terminus of CNG channels (Taraska & Zagotta, Structural dynamics in the gating ring of cyclic nucleotide–gated ion channels. Nature structural & molecular biology, 2007, vol. 14, no 9, p. 854). Finally, single-molecule FRET between dyes placed extracellularly and intracellularly in Hv1 channels has been demonstrated (Han et al. eLife 2022;11:e73093. DOI: https:// doi. org/ 10. 7554/ eLife. 73093).

A191Anap shows the hump at ~600 nm, but we think it's less evident because Anap at 191 is less quenched (see Figure 4-Supplement 1 and answer to point 4 above).

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.

We now discuss these changes in the discussion (lines 440-446).

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?

Yes, the spectrum of I202Anap was obtained at the resting potential (~ -30 mV), as were all spectra in Figure 1E. We now indicate this clearly in the methods section and in the figure legend. Fluorescence was measured from the membrane region as indicated by mCherry fluorescence and as illustrated in Figure 2-Supplement 1. We did not explore this mutant further and we cannot answer the question of whether a depolarizing potential might produce a red shift of the spectrum.

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.

We have changed figure 3E to show non-normalized data that now show the increase in fluorescence intensity and no wavelength shift in the fluorescence spectrum of Anap (see also response to point 5).

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?

See response to point 4 of "Revisions essential for endorsement" section.

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?

This is an interesting suggestion that could be explored in a follow up study.

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.

This is an interesting suggestion, but, as suggested by reviewers, we think there is a possibility that other aromatic residues could contribute to quenching. Given the absence of a reliable structure for Hv1, prediction of the relative positions of any resides is very difficult and thus we did not attempt the suggested experiment.

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.

We did not carry out any of these experiments.

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.

Thanks for this suggestion. Yes, we think that the kinetics, specially of ionic currents can be affected by even small changes in the pH gradient, for this reason we did not attempt a systematic kinetic analysis. We mention this in the text where we compare the voltage dependence of current and fluorescence activation for construct A197Anap (line 223).

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.

We attempted this experiment but were unsuccessful in observing Anap quenching by phenylalanine at the concentrations of phenylalanine that can be attained in aqueous solution. We suspect that Phe quenching of Anap could happen by electron transfer or ground-state complex formation, in which case near proximity is necessary and higher concentrations of Phe would be required to detect quenching in solution. However, we measured the absorbance of Anap in the absence and presence of phenylalanine (Phe) (and tyrosine (Tyr)) at the concentrations that can be achieved in aqueous solution (8 mM and 1mM, respectively). Absorbance measurements can detect ground-state complex formation even at relatively low concentrations (J.R. Lakowicz, 1999, Principles of Fluorescence Spectroscopy). We observed that the absorbance of Anap is modified by the presence of Tyr or Phe, indicating that these amino acids indeed interact with Anap, possibly through ground-state complex formation. We include this data for the reviewers to inspect.

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.

Thanks for this observation, we have included these important results in the discussion.

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.

Thanks for bringing this up. We do not know the exact origin of these leak expression of red fluorescence. We think that, as suggested, there is a possibility that skipping of the Amber codon can lead to a methionine at the end of S4 acting as a second translation initiation site, giving rise to truncated channels that would express mCherry but not currents. This is consistent with the fact that we cannot detect currents in the absence of Anap but we see a small number of red cells.

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.

See response to point 4 of "Revisions essential for endorsement" section.

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

We have modified the figure to include current and fluorescence traces.

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?

Thanks for pointing out this mistake. We have found that this misunderstanding was provoked because of a mistake with the image labeling of F-V and G-V curves for the ΔpH=2 data, we have now corrected the figure. The shift of F-V is indeed positive to G-V as stated before.

(This is a response to peer review conducted by Biophysics Colab on version 1 of this preprint.)

https://brainsteam.co.uk/2022/11/26/one-week-with-hypothesis/

I too read a lot of niche papers and feel the emptiness, but because I'm most often writing for myself anyway, its alright. There are times, however, when I see a growing community of people who've left their associative trails behind before I've found a particular page.

I've used the phrase "digital exhaust" before, but I like the more positive framing of "learning exhaust".

If you've not found it yet, my own experimentations with the platform can largely be found here: https://boffosocko.com/tag/hypothes.is/

Grabbing text content from one of the form elements on the left

classes[0].children[0].textContent so from here we can begin looping through the different elements on this page and once we've text matched with found === org, we'll access the children of that node and start grabbing their textcontent as well to allow us to save that to our json we'll loop and save all of these in their own Objects object for now will just be a dict with these different values, from here we could easily serialize with json could have another feature to highilight the elemtns we are working with another feature could allow us to manually pick with items we want to save so we'll create a new dict, give that one a unique id and from there, depending on the page we're on, we'll call differnt functions to add to/remove from that dict will need a way to preview json before saving it to allow us to compare and make sure we're selecting/saving the correct items, could probably do so by highligitng them in our cbs we'll pass in the selector of the item or items we want and we'lll want to return them each in a dict { 'element selector'/textcontetn: htmlode }

from there'll be able to easily refer to these and remember them

so our first one will do document.queryselector and grab the elements, from there, we'll do something to those elements finally we'll return them

into our cb we'll pass the list of nodes and iterator over each one and do something. In this case we'll add these to a javascript dict

cb(nodes,cb) takes an array of html nodes and a callbaclk iterate over nodes and calls callback on each one could use map here for each node in nodes anothercb(do stuff with nodes)

we can also store these in an array and use map to call the cb on each one of the items. cb here could save to json or do somthing else

need to convert from htmlcolletino to array first

arr.map((x) => console.log(x)) then this works console.log here would be the callbacj we'll be pasing

another feature we'll want to add here is the ability to save a chat snippet, we'll then store that in our snippets object {}

and keep adding to that with the date it as added we'll select text and then press a buttno to save that to our snippets from there we'll be able to laod those in and send them to users also ability to save bug links and other kinds of links in some sort of dict names quicklinks we'll then be able to look up the quicklinks dict and got to differnt links from there

steps click hubspot link from ic by doing .click() on the a tag wait until paod loads then call callback on this page, which will be a func that searches for these different ids and retudn them, thats one func since well be using that pattern a lot we'll actually end up switching the order of these or update the func mentioned above to talso take a callback that it will call on the items we've found, in this case we'll add it to our json dict and return

functoin findandDo(query,cb)

then well save to json and load back in with new MyObject(JSON.parse(JSON.stringify(hsontactdata))) this should be able to read in the different jsons with type prop type: contact details

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