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What is this?

citation

word is divided

pressbook citation

Add preview questions and references

glossary

main purpose of the study

not only parents as educators, but also as transmitters of cultural knowledge and standards

parents as sexual socialization agent for their children

Again, I think a baseline here would make this claim more convincing. In other words, what aspects of the differentiation dynamics described here could only be captured by ESPRESSO?

It would be helpful to compare this result to some baseline obtained from an established method like cell painting. In other words, can existing techniques also readily distinguish these cell types?

it would be helpful to also provide some absolute measures of throughput here, such as how many FOVs of a given size and resolution can be imaged per unit time.

It sounds like the clustering was done after the embedding step; that is, using the low-dimensional embeddings from PacMAP, rather than the original feature matrix. If so, I'm worried that this will result in inaccurate clusters, as PacMAP (like all such methods) does not perfectly preserve the relationships between the original high-dimensional feature vectors.

This statement right here emphasizes the importance and power of the Pteach program. Teacher candidates are blessed with an interactive, personal experience in the field years before becoming a teacher. I believe strongly this program will prepare future teachers in a way they will lead a successful, positive, impacting career.

I would love the opportunity to observe more of my fellow teachers. This practice is done during student teaching and then rarely ever again throughout a teacher's career. Doing this could cause a lot of stress and a feeling of unsaid competition so it would need to be set up well. I think in the right learning communities this could be very powerful.

I have rarely had coaches model but I think this practice would be very helpful.

In DPS the coaching I have received has also been around the leap framework. This is helpful for LEAP scoring but I wonder if it is the best or only coaching system that should be implemented. I am curious what other districts do and if there are researches backed coaching models.

1 PET ONLY.

Built in 2012.

Built as condos. Has a/c, w/d, pool. Don't know pet policy.

Built as apts. No a/c, w/d. Has covered pool. Don't know pet policy.

Built as apts. No a/c, some units might have stackable w/d, has pool. Do not know pet policy.

Built in 1979. A/c, w/d, 1 pool. Allows 2 pets.

Waiting to find out about foundation/structure issue and if lenders will approve a purchase in this complex. Has a/c, w/d, pool. Allows 2 pets.

Older building, no a/c, w/d, or pool. Can put in mini-split. Pet policy?

Newer bldg. A/c, w/d, but no pool.

not burnt?

you've been kissed?

guy with a piza box meme

others or whitewalkers

AWW

atleast theyre nicer to him

yeah :(

pity indeed

DONT DOUBT HIM

ned you were never a schemer :(

NOO HE'D NEVER FIND OUT

ned :(

6 months?

nooo the walkers

just like he made bran look

bro

oh you poor girl JOFFRY HATES YOU

yes but no since its coming from you

sansaaaa

SANSA

urgh

yess cat is strong

thought it was more lannister pride but yeah thats beatuful

wait i thought the king already died before nvm ig

wait i love this

her first kill..

noo don't kick him

thats bran sister!!

:((

NOOO

lmao

yup..

she lovesss green

:(

Olá, Esta ferramenta parece bastante útil :) .

Ao ler o capítulo fico com a questão se derivar E-atividades em E-exercícios ou E-tarefas será útil ou acabam por ser conceitos redundantes e que apenas adicionam complexidade à taxonomia usada no contexto dos ambientes digitais.

Obrigado! Luis Dias

Considero que o cerne da questão colocada acima pela colega Idalina Santos(relativamente à reflexão proposta sobre se os estilos de aprendizagem são verdadeiros ou mitos) está nesta afirmação. Mais do que rotular alunos e processos de ensino aprendizagem é fundamental diversificar e ampliar o potencial da aprendizagem fazendo uso de uma diversidade de recursos, sem nunca esquecer a intecionalidade pedagógica que temos vindo a discutir ao longo das últimas semanas!

Concordo, que neste tipo de ensino, mais do que no ensino presencial e com estas e-atividades, o aluno tem que ser mais autónomo na busca de informação, na aprendizagem de novas ferramentas tecnológicas.

Given that you have a very low fraction of bacterial reads, which is a common problem in the field, I think a useful contribution from your data would be to create a panel of primers to amplify community members that you see are present. This would give you more resolution than 16s but allow you to avoid more of the host sequencing data. However, the usefulness of such a panel would be bounded by how it would be adopted by others in the field. It would probably be most useful if you applied it to this fish farm repeatedly, but I'm not sure if doing so is biologically interesting.

Since you have the resolution to go below phylum, I think it would be interesting to focus on that more in the discussion.

I agree, but I think this would be worth mentioning in the abstract, and perhaps in the last paragraph of the introduction, to better prepare your readers about the types of results you are going to present

The percentage or fraction? If percentage, less than 1% is incredibly small and I would question any results in this report. How many reads total was this? If you used a very high depth, you might capture a substantial portion of the community.

I imagine you might have a huge amount of drop out here by applying kraken first and then assembling with megahit. I would either: 1. Map reads to carp first, and then assemble anything that doesn't map 2. Assemble everything and then filter out carp contigs.

Why not the species level?

It might be worth mapping back to the host genome if you have one prior to performing taxonomic classification.

I would also be interested to see nonpareil curves of your sequencing data before and after host mapping. I would be curious if you reached saturation of the community -- this can usually be better assessed with raw sequencing data than with taxonomically classified reads.

Can you add what functions you used to do this in the Vegan package?

What filters did you use here? I'm curious how many reads were lost to filtering.

Do you have any idea if the bacterial load of the water, and therefore the skin, of the carp was much higher than for fish observed in the wild, or typically sequenced with 16s? I'm wondering if there was more bacteria than usual, and that was why you were able to get enough bacterial reads to perform an analysis

Would you be able to provide some specific examples of the economic importance (even half a sentence)? I'm not a carp expert so I have no idea what these might be!

Can the microbiome also be pathogenic for carp?

I think this section doesn't highlight the massive challenge in trying to get shotgun metagenomic sequencing data from fish. In the experiments where we have tried (killifish, different tissues), we end up with 98 or 99% killifish (host) reads. 16s allows us to amplify and get just the microbial signal.

We have talked about trying to do a more balanced marker gene panel, but that has methodological problems like not having as many tools and determining the best marker genes to use.

It would be nice if these challenges were better represented. I think the reason this gap exists is methodological (hard to get shotgun sequencing from fish), not for lack of interest

I'm curious the extent to which these studies investigated farm vs. wild fish, and if you think that would make a difference on microbiome. It might be helpful to include that distinction in when covering literature in the introduction, given that you see some results that you don't expect relative to other observations in the field.

I have the strong suspicion the above quote was cherrypicked. Boas is not criticizing cultural evolution directly in the quote. Instead, he is pointing out the similarities between the violent in-group tendencies of the "civilized" west and hunter-gatherer societies.

Reduces my opinion of this author and this book

Fascinating. No wonder we're all relativists

What if we just... flash froze the poppy seed (so it's not dead) and pinned it for wall decor? Is this catastrophe? The seed is still... "alive," or still has the potential to become a poppy. It's just staying a seed.

now this is a suspicious normative claim. Is it "good" for the poppy seed to become a poppy rather than a seed on my bagel? I don't think so. Just because something is likely to happen does not give it moral strength. That's letting entropy decide your morals.

empirically id bet very smart people people tend to be depressed more often than people of average intelligence

Look for a brief video on this to include in this section. decide where to put it depending on the video.

this may be a Pressbook - check license

TIL

I didn't know that most people (outside of Asia) consider "kindly" to be patronizing. The many quirks of language!

I found this confusing, because I feel as though justice systems are mutual agreements set up by men in order to keep people safe, create law, and set up punishments etc... Epicurus says that natural justice is "mutual agreements not to inflict nor allow harm," and I may be confused on his definition of natural and absolute justice and how those intertwine. He says later that how the details of justice vary, and I think I would just need more clarification on that aspect.

I found this passage disturbing and I do not necessarily agree with it. I think that because we have two different people with two different moral compasses, their views on the world are polar and there is a struggle in comparing them. This "unjust" person has an opposite view of anxiety, punishment, power, fear, etc... because they are "morally wrong," and may not experience the same emotional spectrum as a person who always does the right thing.

This sentence is interesting to me because I think humans desperately want to understand and fully comprehend everything happening around them to feel some sense of control, but they will never grasp the power of the universe/nature/death because I don't think it is something that is meant to be understood. Humans want to preoccupy themselves with science and nature and the study of those subjects, rather than looking deeper inside of their own vices of pain and desire.

mean change squared: spoke about in meeting. Just a reminder.

Progress is finally bering shown and recognizing the legal status for freedom. Although progress was shown it was no were near it should of been for full human rights the same for everyone. The goal of having everyone treated equal was still far away almost 100 years!

Douglass talks of the spirit of Abraham Lincoln’s message of "charity toward all, and malice toward none" and Ulysses S. He calls for peace in the country. Going towards what is right and not wrong for everyone to be treated equal.

This section of the poem was interesting because not only does the daughter receive endless instructions on how to become a proper girl, but she also has to learn how to be like a man. This could be interpreted in different ways, but I think it could be the mother's way of making sure her daughter survives in the world of men and teaching her to be strong.

This section displays an aggressive switch in the use of language. She goes from teaching her daughter how to sew to once again mentioning her turning into a slt. I noticed that the line "the slt you are so bent on becoming" is repeated throughout the poem, and each time it just seems so sudden. However, I think it shows how people perceive girls as being provocative no matter what they are doing or wearing or how innocent their actions are.

This line shows the expectation for girls to be conscious of every minor action they take. Eating, a basic necessity for survival, is even presented as something that girls must do in a way that does not inconvenience others. She is basically reminding her daughter that she needs to act like a "proper" lady.

This quote reminds me of something my mother often mentions. Women are expected to cook, clean, and care for the ill. The last line reminds me of home remedies women did to not have a child due to the lack of access to medicine to end a pregnancy safely.

This part advises the daughter that no matter what her feelings are towards someone, negative or positive, the daughter must smile and not show any signs that others may frown upon. This reminds me of the idea that women must be perfect and/or not complain.

This line reinforces the idea that the daughter must act like a woman and not do anything that showcases anything but that. Playing with marbles could ruin the daughter's reputation and/or show that she is not a "proper" woman.

This typewriter repairman was told computers were king. Twenty years later, he’s still in business by [[Matthew Ormseth]] on 2019-02-06 for Los Angeles Times

Version 4 of this preprint has been peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology.<br /> See the peer reviews and the recommendation.

This is SO key. Students often see themselves just as "viewers" and "consumers."

This text appears to be the same as that describing panel a. Is this intended? I assume C is an image of a blebbistatin experiment?

Out of curiosity (and ignorance) why is the line containing the repolymerized bleached monomer so thin? The volume of bleached monomer appears to be large. Is the width of the repolymerized line impacted by the relative position of the bleaching?

X - X_bar

should be m

There are 3 types of Reading Projects: 1. Doing a research project. Having a well-defined research question and answering it through means of reading. 2. Reading a set of books or a genre in itself or even author; finding out the types or history of it... Mostly applicable to fiction. 3. Becoming more engaged with a specific author or thinker; Reading as much as possible about a specific author (primary, secondary)

There is a value in reading a lot.. But it's not in the number, it is more in the concept of Exploration vs. Specialization. Some form of exploration is highly useful.

Number of Books Read has nothing to do with substance learned; enlightenment gained. It is a vanity metric.

Activity in a box?

In a box along with H5P

No me genera nada de valor

www

03/06

mercredi 3 juin

Great rule to live by and remember. This also makes me reflect on how we ask students questions on tests.

I really appreciate these points. I think this is a great foundation to ensuring learning in the classroom. I believe these are the true tests of education, not necessarily standardized tests.

I like how Brene Brown talked about similar responses like this to shame. We often demonstrate a variety of responses.

Creation over consumption!

Digital tools can be used in ways that transform the experience where without the tool, one would not be able to accomplish. I see this especially with connecting with others in the world.

That's very interesting. The observation unfolds the goals of a system. But does the observation also counts as interaction with the system?

This is similar to idea that gives rise to collective consciousness for all humans, across space and time

This is an interesting conjecture. It would imply that the mere thought of something can count as an "interaction" and thus, whenever we first discovered the uniformity of space, the rest of the "non-observable" universe came into existence for us

eLife assessment

This work is an important contribution to the development of a biologically plausible theory of statistical modeling of spiking activity. The authors convincingly implemented the statistical inference of input likelihood in a simple neural circuit, demonstrating the relationship between synaptic homeostasis, neural representations, and computational accuracy. This work will be of interest to neuroscientists, both theoretical and experimental, who are exploring how statistical computation is implemented in neural networks. There are questions about the performance of the methods in the case where other biologically significant parameters, such as firing rate and thresholds, are optimized together with the synaptic weights.

eLife assessment

This study provides valuable new insights into how multisensory information is processed in the lateral cortex of the inferior colliculus, a poorly understood part of the auditory midbrain. By developing new imaging techniques that provide the first optical access to the lateral cortex in a living animal, the authors provide convincing in vivo evidence that this region contains separate subregions that can be distinguished by their sensory inputs and neurochemical profiles, as suggested by previous anatomical and in vitro studies. This work provides a foundation for future research exploring how this part of the auditory midbrain contributes to multisensory-based behavior.

https://www.nytimes.com/2024/06/28/climate/biden-ambler-road-mining-alaska.html

https://www.youtube.com/watch?v=_RWkPlbK4EU&t=2s

r/Wikihowvideos

https://taz.de/Novelle-des-Klimaschutzgesetzes/!6016453/

https://taz.de/Frust-auf-der-Bonner-Klimakonferenz/!6020117/

https://www.derstandard.de/story/3000000226373/regierung-schafft-klimaplan-nicht-und-bittet-eu-kommission-um-mehr-zeit

https://www.derstandard.de/story/3000000226325/kein-russisches-gas-mehr-nach-oesterreich-das-geschieht-im-extremszenario

https://www.derstandard.de/story/3000000226318/artensterben-auch-kleinster-elefant-der-welt-steht-auf-der-roten-liste

Author response:

The following is the authors’ response to the current reviews.

Joint Public Review:

Xie et al. propose that the asymmetric segregation of the NuRD complex is regulated in a V-ATPase-dependent manner, and plays a crucial role in determining the differential expression of the apoptosis activator egl-1 and thus critical for the life/death fate decision.

Remaining concerns are the following:

The authors should provide the point-by-point response to the following issues. In particular, authors should provide clear reasoning as to why they did not address some of the following comments in the previous revisions. The next response should be directly answering to the following concerns.

(1) Discussion should be added regarding the criticism that NuRD asymmetric segregation is simply a result of daughter cell size asymmetry. It is perfectly fine that the NuRD asymmetry is due to the daughter cell size difference (still the nucleus within the bigger daughter would have more NuRD, which can determine the fate of daughter cells). Once the authors add this clarification, some criticisms about 'control' may become irrelevant.

We thank the reviewer for this suggestion. We will add the following text in the revised discussion on page 14, line 26:

“…We cannot rule out the possibility that NuRD asymmetric segregation results from daughter cell size asymmetry. According to this perspective, the nucleus in the larger daughter cell could possess more NuRD, potentially influencing the fate of the daughter cells. However, it is important to note that the nuclear protein histone or the MYST family histone acetyltransferase is equally segregated in daughter cells of different sizes.….”

(2) ZEN-4 is a kinesin that predominantly associates with the midzone microtubules and a midbody during mitosis. Given that midbodies can be asymmetrically inherited during cell division, ZEN-4 is not a good control for monitoring the inheritance of cytoplasmic proteins during asymmetric cell division. Other control proteins, such as a transcriptional factor that predominantly localizes in the cytoplasm during mitosis and enters into nucleus during interphase, are needed to clarify the concern.

We clarified the issue of ZEN-4 below:

The critique assumes that "midbodies can be asymmetrically inherited during cell division." However, this assumption does not apply to our study of Q cell asymmetric divisions. In our earlier research, we demonstrated that midbodies in Q cells are released post-division and subsequently engulfed by surrounding epithelial cells (Chai et al., Journal of Cell Biology, 2012). Moreover, we have shown that midbodies from the first cell division in C. elegans embryos are also released and engulfed by the P1 cell (Ou et al., Cell Research, 2013). Therefore, the notion of midbody asymmetric inheritance is irrelevant to this manuscript. Additionally, our manuscript already presents the example of the MYST family histone acetyltransferase, illustrating a nuclear protein that predominantly localizes in the cytoplasm during mitosis and symmetrically enters the nucleus during interphase.

As for pHluorin experiments, symmetric inheritance of GFP and mCherry is not an appropriate evidence to estimate the level of pHluorin during asymmmetric Q cell division. This issue remains unsolved.

We acknowledge the limitation of pHluorin in measuring the pH level in a living cell. Future studies could be performed to measure the dynamics of pH levels when advanced tools are available.

(3) Q-Q plot (quantile-quantile plot) in Figure S10 can be used for visually checking normality of the data, but it does not guarantee that the distribution of each sample is normal and has the standard deviation compared with the other samples. I recommend the authors to show the actual statistical comparison P-values for each case. The authors also need to show the number of replicate experiments for each figure panel.

We thank the reviewer for pointing this out. We will provide P-values for each case and the number of replicate experiments in the revised Figure 5-figure supplement 1 ( corresponding to Figure S10) and the figure legend.

The authors left inappropriate graphs in the revised manuscript. In Figure 3E, some error bars are disconnected and the other are stuck in the bars. In Figure S4C, LIN-53 in QR.a/p graph shows lines disconnected from error bars.

We thank the reviewer for pointing this out. We will correct these error bars.

I am bit confused with the error bars in Figure 2B. Each dot represents a fluorescent intensity ratio of either HDA-1 or LIN-53 between the two daughter cells in a single animal. Plots are shown with mean and SEM, but several samples (for example, the left end) exhibit the SEM error bar very close to a range of min and max. I might misunderstand this graph but am concerned that Figure 2B may contain some errors in representing these data sets. I would like to ask the authors to provide all values in a table format so that the reviewers could verify the statistical tests and graph representation.

We thank the reviewer for pointing this out. We apologize for the typo in Figure 2B figure legend. We will correct SEM to SD.

(4) The authors still do not provide evidence that the increase in sAnxV::GFP and Pegl-1gfp or the increase in H3K27ac at the egl-1 gene in hda-1(RNAi) and lin-53(RNAi) animals is not a consequence of global effects on development. Indeed, the images provided in Figure S7B demonstrate that there are global effects in these animals. no causal interactions have been demonstrated.

We cannot exclude the global effects and have discussed this issue in our previous manuscript on page 9, line 26:

“...Considering the pleiotropic phenotypes caused by loss of HDA-1, we cannot exclude the possibility that ectopic cell death might result from global changes in development, even though HDA-1 may directly contribute to the life-versus-death fate determination.”

(5) Figure 4: Due to the lack of appropriate controls for the co-IP experiment (Fig. 4), I remain unconvinced of the claim that the NuRD complex and V-ATPase specifically interact. Concerning the co-IP, the authors now mention that the co-IP was performed three times: "Assay was performed using three biological replicates. Three independent biological replicates of the experiment were conducted with similar results." However, the authors did not use ACT-4::GFP or GFP alone as controls for their co-IP as previously suggested. This is critical considering that the evidence for a specific HDA-1::GFP - V-ATPase interaction is rather weak (compare interactions between HDA-1::GFP and V-ATPase subunits in Fig 4B with those of HDA-1::GFP and subunits of NuRD in Fig S8B).

We conducted GFP pull-down experiments and MS spectrometric analysis for HDA-::GFP and ACT-4::GFP using identical protocols, yielding consistent results. We agree with the reviewer that in our Western blot, inclusion of ACT-4::GFP is a more effective negative control compared to empty beads.

(6) Based on Fig 5E, it appears that Bafilomycin treatment causes pleiotropic effects on animals (see differences in HDA-1::GFP signal in the three rows). The authors now state: "Although BafA1-mediated disruption of lysosomal pH homeostasis is recognized to elicit a wide array of intracellular abnormalities, we found no evidence of such pleiotropic effects at the organismal level with the dosage and duration of treatment employed in this study". However, the 'evidence' mentioned is not shown. It is critical that the authors provide this evidence.

We thank the Reviewer for pointing out this issue. We only checked the viability of the L1 larvae and morphology of animals at the organismal level with the BafA1 dosage and duration of treatment and did not notice any death of the animals and apparent abnormality in morphology (N > 20 for each treatment). However, as the reviewer pointed out, there can be some abnormalities at the cellular level. We thus revised this above description as the following, on page 11, line 27:

“…Although BafA1-mediated disruption of lysosomal pH homeostasis is recognized to elicit a wide array of intracellular abnormalities, we did not observe any larval deaths and apparent abnormality in morphology at the organismal level (N > 20 for each treatment) at the dose and duration of treatment employed in this study...”

---

The following is the authors’ response to the previous reviews.

eLife assessment

The authors propose that the asymmetric segregation of the NuRD complex in C. elegans is regulated in a V-ATPase-dependent manner, that this plays a crucial role in determining the differential expression of the apoptosis activator egl-1, and that it is therefore critical for the life/death fate decision in this species. If proven, the proposed model of the V-ATPase-NuRD-EGL-1-Apoptosis cascade would shed light onto the mechanisms underlying the regulation of apoptosis fate during asymmetric cell division, and stimulate further investigation into the intricate interplay between V-ATPase, NuRD, and epigenetic modifications. However, the strength of evidence for this is currently incomplete.

Public Review:

Xie et al. propose that the asymmetric segregation of the NuRD complex is regulated in a V-ATPase-dependent manner, and plays a crucial role in determining the differential expression of the apoptosis activator egl-1 and thus critical for the life/death fate decision.

While the model is very intriguing, the reviewers raised concerns regarding the rigor of the method. One issue is with statistics (either insufficient information or inadequate use of statistics), and second is the concern that the asymmetry observed may be caused by one cell dying (resulting in protein degradation, RNA degradation etc). We recommend that the authors address these issues.

We extend our sincere thanks to the Editors and Reviewers for their insightful comments on this study.

Major #1:

There are still many misleading statements/conclusions that are not rigorously tested or that are logically flawed. These issues must be thoroughly addressed for this manuscript to be solid.

(1) Asymmetry detected by scRNA seq vs. imaging may not represent the same phenomenon, thus should not be discussed as two supporting pieces of evidence for the authors' model, and importantly each method has its own flaw. First, for scRNA seq, when cells become already egl-1 positive, those cells may be already dying, and thus NuRD complex's transcripts' asymmetry may not have any significance. The data presented in FigS1D, E show that there are lots of genes (6487 out of 8624) that are decreased in dying cells. Thus, it is not convincing to claim that NuRD asymmetry is regulated by differential RNA amount.

We agree with the reviewer's comment. Indeed, scRNA-seq reveals phenomena different from those observed in protein imaging, and NuRD asymmetry may not be regulated by differential RNA levels. Seven years ago, when we started this project, NuRD asymmetry during asymmetric neuroblast division was unknown. We first found NuRD mRNA asymmetry using scRNA-seq and then NuRD protein asymmetry using fluorescence imaging. We have documented the whole process of discovering NuRD asymmetry, although the asymmetry of NuRD complex transcripts does not necessarily imply protein asymmetry. We have revised statements related to "NuRD asymmetry being regulated by differential RNA amounts" and discussed this issue in the revised manuscript on page 14, line 2:

" The transcript asymmetry detected by scRNA-seq may not correspond to the protein asymmetry detected by microscopic imaging. Our scRNA-seq data shows that 6487 out of 8624 genes were not detected in egl-1-positive cells, the putative apoptotic cells. Cells that are egl-1 positive may be undergoing apoptosis, rendering the asymmetry of NuRD complex transcripts insignificant in inferring protein asymmetry. Thus, the observed transcript asymmetry of the NuRD subunits between live and dead cells may be coincidental with NuRD protein asymmetry during asymmetric neuroblast division, rather than serving as a regulatory mechanism."

(2) Regarding NuRD protein's asymmetry, there are still multiple issues. Most likely explanation of their asymmetry is purely daughter size asymmetry. Because one cell is much bigger than the other (3 times larger), NuRD components, which are not chromatin associated, would be inherited to the bigger cell 3 times more than the smaller daughter. Then, upon nuclear envelope reformation, NuRD components will enter the nucleus, and there will be 3 times more NuRD components in the bigger daughter cell. It is possible that this is actually the underling mechanism to regulate gene expression differentially, but this possibility is not properly acknowledged. Currently, the authors use chromatin associated protein (Mys-1) as 'symmetric control', but this is not necessarily a fair comparison. For NuRD asymmetry to be meaningful, an example of protein is needed that is non-chromatin associated in mitosis, distributed to daughter cells proportional to daughter cell size, and re-enter nucleus after nuclear envelope formation to show symmetric distribution. And if daughter size asymmetry is the cause of NuRD asymmetry, other lineages that do not undergo apoptosis but exhibit daughter size asymmetry would also show NuRD asymmetry. The authors should comment on this (if such examples exist, it is fine in that in those cell types, NuRD asymmetry may be used for differential gene expression, not necessarily to induce cell death, but such comparison provides the explanation for NuRD asymmetry, and puts the authors finding in a better context).

For more than one decade, we have meticulously explored the relationship between protein asymmetry and cell size asymmetry during ACDs of Q cells. A notable example of even protein distribution is the cytokinetic kinesin ZEN-4, as documented in our 2012 publication in the Journal of Cell Biology (Chai et al., JCB, 2012). This study, primarily focusing on the fate of the midbody post-cell division, also showcased the dynamics of GFP-tagged ZEN-4 during ACDs of QR.a cells in movie S1. Intriguingly, beyond its role in the cytokinetic ring, we observed a uniform dispersal of ZEN-4 throughout the cytoplasm. Remarkably, following cell division, ZEN-4 transitions evenly into the nuclei of the daughter cells, a phenomenon with implications yet to be fully understood. One hypothesis is that ZEN-4's nuclear localization may prevent the formation of ectopic microtubule bundles in the cytosol during interphase. Below, we present a snapshot from our original movie, clearly showing the symmetrical distribution of ZEN-4 into the nuclei of the two daughter cells.

(3) For the analysis of protein asymmetry between two daughters in Fig S4C, the method of calibration is unclear, making it difficult to interpret the results.

In Figure S4C, we quantified the relative total fluorescence of the Q cell, with the quantification method illustrated in Figure S4A. To further clarify our quantification approach, we have updated Figure S4A and the "Live-Cell Imaging and Quantification" section in the Materials and Methods:

“…To determine the ratios of fluorescence intensities in the posterior to anterior half (P/A) of Q.a lineages or A/P of Q.p lineages, the cell in the mean intensity projection was divided into posterior and anterior halves. ImageJ software was used to measure the mean fluorescence intensities of two halves with background subtraction. The slide background's mean fluorescence intensity was measured in a region devoid of worm bodies. The background-subtracted mean fluorescence intensities of the two halves were divided to calculate the ratio. The same procedure was used to determine the fluorescence intensity ratios between two daughter cells. Total fluorescence intensity was the sum of the posterior and anterior fluorescence intensities or the sum of fluorescence intensities from two daughter cells (Figure S4A). …”

(4) As for pHluorin experiments, the authors were asked to test the changes in fluorescence observed are due to changes in pH or changes in the amount of pHluorin protein. They need to add a ratio-metric method in this manuscript. A brief mention to Page 12 line 12 is insufficient to clarify this issue.

We appreciate the concerns about potential changes in pH or pHluorin protein levels. While we cannot completely dismiss the impact of changes in the amount of pHluorin protein, it appears improbable that the asymmetry of pHluorin fluorescence is attributed to an asymmetric amount of pHluorin protein. This inference is supported by the observation that other fluorescent proteins, such as GFP or mCherry, did not exhibit any asymmetry during ACDs of Q cells. An example of GFP alone during the ACD of QL.p is illustrated in figure 5A from Ou and Vale, JCB, 2009. The fluorescence intensities in the large QL.pa cell and the small QL.aa are indistinguishable.

Major #2:

Some issues surrounding statistics must be resolved.

(1) Fig. 1FG, 2D, 3BDEG, 5BD and 6B used either one-sample t-test or unpaired two-tailed parametric t-test for statistical comparison. These t-tests require a verification of each sample fitting to a normal distribution. The authors need to describe a statistical test used to verify a normal distribution of each sample.

(2) Fig. 2D, 3D, and 3G have very small sample size (N=3-4, N=6, N=3, respectively), it is possible that a normal distribution cannot be verified. How can the authors justify the use of one-sample t-test and unpaired parametric t-test ?

(3) Statistical comparison in Fig. 2D and Fig. 6B should be re-assessed. For Fig. 2D, the authors need to compare the intensity ratio of HDA-1/LIN53 between sister cells dying within 35 min and those over 400 min. For Fig. 6B, they need to compare the intensity ratio of VHA-17 between DMSO- and BafA1- treated cells at the same time point after anaphase.

We appreciate the reviewer's advice on the statistical analysis of our data. In response, we performed normality tests on the datasets presented in Figures 1F, 1G, 3B, 5B, 5D, and 6B, all of which passed the tests (as demonstrated in Figure S10). We also acknowledge the reviewer's comment on the inadequate sample sizes in Figures 2D, 3D, 3E, and 3G for fitting a normal distribution. Therefore, we have revised our statistical analysis methods for these figures and updated both the figures and their legends. The revised statistical results support the primary conclusions of this study.

In response to the reviewer's observation regarding the small sample size in Figure 2D , which precluded normality verification, and the suggestion to compare sister cells that die within 35 minutes to those surviving over 400 minutes, we adapted our approach. We implemented the Kruskal-Wallis test to evaluate the differences among the groups. To assess the specific differences between each group and the 400 min MSpppaap group, we conducted the Dunn’s multiple comparisons test. The revised Figure 2D illustrates the updated statistical significance.

For Figure 3D, due to the small sample size precluding normality verification, we applied the Wilcoxon test with 1 as the theoretical median. The revised Figure 3D illustrates the updated statistical significance.

For Figure 3E, where the sample size also hindered normality verification, we conducted the Kruskal-Wallis test to evaluate the overall effect. Additionally, Dunn’s multiple comparisons test was utilized to examine the differences between groups. The revised Figure 3E illustrates the updated statistical significance.

For Figure 3G, the reviewer pointed out the small sample size and the limited statistical power due to having only three data points per group. To address this, we revised the figure to visually present each data point, aiming to more clearly illustrate the variation trends.

For Figure 6B, following the reviewer's suggestion, we compared the DMSO group directly with the Baf A1 group, updating Figure 6B to reflect this comparison as advised.

These adjustments have been made to ensure the statistical analyses are robust and appropriate given the sample sizes and to align with the reviewer's recommendations, enhancing the clarity and accuracy of our findings.

Recommendations for the authors:

We recommend using grey scale (instead of 'heatmap' representation) to show the protein distribution of interest. Heatmap does not help at all, because 'total protein amount per cell' (instead of signal intensity on each pixel) is what matters in the context of this paper. Heatmap presentation does not allow readers to integrate signal intensity with their eyes.

We thank the editor for pointing this out. We have changed heatmaps to inverted fluorescence images in grey scale.

eLife assessment

The authors make the intriguing proposal that the NuRD complex in C. elegans, which has been linked to regulation of the cell death protein EGL-1 before, becomes asymmetrically distributed after cell division and that this asymmetry relies on V-ATPase activity. Whereas some disagreement remained between the reviewers' and the authors' interpretation, the final version incorporated alternative possibilities in the text, and with careful interpretation, the current manuscript's model is supported by solid data, and represents a valuable contribution to the field.

deploy it with Tilt

(now, I'm unsure whether Tilt is used here only because it automatically rebuilds the code, or because it is actually useful to debug it. If the code is already deployed, can I ignore Tilt?)

important: the image built with the Dockerfile must have the Delve debugger

(it's not just "Notice that", it should be highlighted more)

the idea: launch the application in a way we can connect remotely

Tilt rebuilds and pushes the deployments at each code change

Muito interessante ao pensarmos na vertente do ensino universitario, para que o ensino universitário permaneça relevante e competitivo, é essencial que as instituições adotem uma abordagem mais aberta às inovações disruptivas. Isso requer não apenas a implementação de novas tecnologias e métodos, mas também uma reavaliação das estratégias educacionais e da estrutura organizacional para fomentar a criatividade e a inovação. Assim, as universidades podem equilibrar a busca por resultados imediatos com a exploração de novas oportunidades, garantindo sua relevância e impacto no futuro da educação.

eLife assessment

The study presents a valuable tool for searching molecular dynamics simulation data, making such datasets accessible for open science. The authors provide convincing evidence that it is possible to identify noteworthy molecular dynamics simulation datasets and that their analysis can produce information of value to the community.

Reviewer #1 (Public Review):

Summary:

Tiemann et al. have undertaken an original study on the availability of molecular dynamics (MD) simulation datasets across the Internet. There is a widespread belief that extensive, well-curated MD datasets would enable the development of novel classes of AI models for structural biology. However, currently, there is no standard for sharing MD datasets. As generating MD datasets is energy-intensive, it is also important to facilitate the reuse of MD datasets to minimize energy consumption. Developing a universally accepted standard for depositing and curating MD datasets is a huge undertaking. The study by Tiemann et al. will be very valuable in informing policy developments toward this goal.

Strengths:

The study presents an original approach to addressing a growing concern in the field. It is clear that adopting a more collaborative approach could significantly enhance the impact of MD simulations in modern molecular sciences.

The timing of the work is appropriate, given the current interest in developing AI models for describing biomolecular dynamics.

Weaknesses:

The study primarily focuses on one major MD engine (GROMACS), although this limitation is not significant considering the proof-of-concept nature of the study.

Reviewer #2 (Public Review):

Summary:

Molecular dynamics (MD) data is deposited in public, non-specialist repositories. This work starts from the premise that these data are a valuable resource as they could be used by other researchers to extract additional insights from these simulations; it could also potentially be used as training data for ML/AI approaches. The problem is that mining these data is difficult because they are not easy to find and work with. The primary goal of the authors was to discover and index these difficult-to-find MD datasets, which they call the "dark matter of the MD universe" (in contrast to data sets held in specialist databases).

The authors developed a search strategy that avoided the use of ill-defined metadata but instead relied on the knowledge of the restricted set of file formats used in MD simulations as a true marker for the data they were looking for. Detection of MD data marked a data set as relevant with a follow-up indexing strategy of all associated content. This "explore-and-expand" strategy allowed the authors for the first time to provide a realistic census of the MD data in non-specialist repositories.

As a proof of principle, they analyzed a subset of the data (primarily related to simulations with the popular Gromacs MD package) to summarize the types of simulated systems (primarily biomolecular systems) and commonly used simulation settings.

Based on their experience they propose best practices for metadata provision to make MD data FAIR (findable, accessible, interoperable, reusable).

A prototype search engine that works on the indexed datasets is made publicly available. All data and code are made freely available as open source/open data.

Strengths:

- The novel search strategy is based on relevant data to identify full datasets instead of relying on metadata and thus is likely to have many true positives and few false positives.

- The paper provides a first glimpse at the potential hidden treasures of MD simulations and force field parametrizations of molecules.

- Analysis of parameter settings of MD simulations from how researchers *actually* run simulations can provide valuable feedback to MD code developers for how to document/educate users. This approach is much better than analyzing what authors write in the Methods sections.

- The authors make a prototype search engine available.

- The guidelines for FAIR MD data are based on experience gained from trying to make sense of the data.

Weaknesses:

- So far the work is a proof-of-concept that focuses on MD data produced by Gromacs (which was prevalent under all indexed and identified packages).

As discussed in the manuscript, some types of biomolecules are likely underrepresented because different communities have different preferences for force fields/MD codes (for example: carbohydrates with AMBER/GLYCAM using AMBER MD instead of Gromacs).

- Materials sciences seem to be severely under-represented - commonly used codes in this area such as LAMMPS are not even detected, and only very few examples could be identified. As it is, the paper primarily provides an insight into the *biomolecular* MD simulation world.

The authors succeed in providing a first realistic view on what MD data is available in public repositories. In particular, their explore-expand approach has the potential to be customized for all kinds of specialist simulation data, whereby specific artifacts are<br /> used as fiducial markers instead of metadata. The more detailed analysis is limited to Gromacs simulations and primarily biomolecular simulations (even though MD is also widely used in other fields such as the materials sciences). This restricted view may simply be correlated with the user community of Gromacs and hopefully, follow-up studies from this work will shed more light on this shortcoming.

The study quantified the number of trajectories currently held in structured databases as ~10k vs ~30k in generalist repositories. To go beyond the proof-of-principle analysis it would be interesting to analyze the data in specialist repositories in the same way as the one in the generalist ones, especially as there are now efforts underway to create a database for MD simulations (Grant 'Molecular dynamics simulation for biology and chemistry research' to establish MDDB' DOI 10.3030/101094651). One should note that structured databases do not invalidate the approach pioneered in this work; if anything they are orthogonal to each other and both will likely play an important role in growing the usefulness of MD simulations in the future.

Reviewer #3 (Public Review):

Molecular dynamics (MD) simulations nowadays are an essential element of structural biology investigations, complementing experiments and aiding their interpretation by revealing transient processes or details (such as the effects of glycosylation on the SARS-CoV-2 spike protein, for example (Casalino et al. ACS Cent. Sci. 2020; 6, 10, 1722-1734 https://doi.org/10.1021/acscentsci.0c01056) that cannot be observed directly. MD simulations can allow for the calculation of thermodynamic, kinetic, and other properties and the prediction of biological or chemical activity. MD simulations can now serve as "computational assays" (Huggins et al. WIREs Comput Mol Sci. 2019; 9:e1393. https://doi.org/10.1002/wcms.1393). Conceptually, MD simulations have played a crucial role in developing the understanding that the dynamics and conformational behaviour of biological macromolecules are essential to their function, and are shaped by evolution. Atomistic simulations range up to the billion atom scale with exascale resources (e.g. simulations of SARS-CoV-2 in a respiratory aerosol. Dommer et al. The International Journal of High Performance Computing Applications. 2023; 37:28-44. doi:10.1177/10943420221128233), while coarse-grained models allow simulations on even larger length- and timescales. Simulations with combined quantum mechanics/molecular mechanics (QM/MM) methods can investigate biochemical reactivity, and overcome limitations of empirical forcefields (Cui et al. J. Phys. Chem. B 2021; 125, 689 https://doi.org/10.1021/acs.jpcb.0c09898).

MD simulations generate large amounts of data (e.g. structures along the MD trajectory) and increasingly, e.g. because of funder mandates for open science, these data are deposited in publicly accessible repositories. There is real potential to learn from these data en masse, not only to understand biomolecular dynamics but also to explore methodological issues. Deposition of data is haphazard and lags far behind experimental structural biology, however, and it is also hard to answer the apparently simple question of "what is out there?". This is the question that Tiemann et al explore in this nice and important work, focusing on simulations run with the widely used GROMACS package. They develop a search strategy and identify almost 2,000 datasets from Zenodo, Figshare and Open Science Framework. This provides a very useful resource. For these datasets, they analyse features of the simulations (e.g. atomistic or coarse-grained), which provides a useful snapshot of current simulation approaches. The analysis is presented clearly and discussed insightfully. They also present a search engine to explore MD data, the MDverse data explorer, which promises to be a very useful tool.

As the authors state: "Eventually, front-end solutions such as the MDverse data explorer tool can evolve being more user-friendly by interfacing the structures and dynamics with interactive 3D molecular viewers". This will make MD simulations accessible to non-specialists and researchers in other areas. I would envisage that this will also include approaches using interactive virtual reality for an immersive exploration of structure and dynamics, and virtual collaboration (e.g. O'Connor et al., Sci. Adv.4, eaat2731 (2018). DOI:10.1126/sciadv.aat2731)

The need to share data effectively, and to compare simulations and test models, was illustrated clearly in the COVID-19 pandemic, which also demonstrated a willingness and commitment to data sharing across the international community (e.g. Amaro and Mulholland, J. Chem. Inf. Model. 2020, 60, 6, 2653-2656 https://doi.org/10.1021/acs.jcim.0c00319; Computing in Science & Engineering 2020, 22, 30-36 doi: 10.1109/MCSE.2020.3024155). There are important lessons to learn here, for simulations to be reproducible and reliable, for rapid testing, for exploiting data with machine learning, and for linking to data from other approaches. Tiemann et al. discuss how to develop these links, providing good perspectives and suggestions.

I agree completely with the statement of the authors that "Even if MD data represents only 1 % of the total volume of data stored in Zenodo, we believe it is our responsibility, as a community, to develop a better sharing and reuse of MD simulation files - and it will neither have to be particularly cumbersome nor expensive. To this end, we are proposing two solutions. First, improve practices for sharing and depositing MD data in data repositories. Second, improve the FAIRness of already available MD data notably by improving the quality of the current metadata."

This nicely states the challenge to the biomolecular simulation community. There is a clear need for standards for MD data and associated metadata. This will also help with the development of standards of best practice in simulations. The authors provide useful and detailed recommendations for MD metadata. These recommendations should contribute to discussions on the development of standards by researchers, funders, and publishers. Community organizations (such as CCP-BioSim and HECBioSim in the UK, BioExcel, CECAM, MolSSI, learned societies etc) have an important part to play in these developments, which are vital for the future of biomolecular simulation.

Author response:

The following is the authors’ response to the original reviews.

eLife assessment

The study presents a valuable tool for searching molecular dynamics simulation data, making such data sets accessible for open science. The authors provide convincing evidence that it is possible to identify useful molecular dynamics simulation data sets and their analysis can produce valuable information.

Public Reviews

Reviewer #1 (Public Review):

Summary:

Tiemann et al. have undertaken an original study on the availability of molecular dynamics (MD) simulation datasets across the Internet. There is a widespread belief that extensive, well-curated MD datasets would enable the development of novel classes of AI models for structural biology. However, currently, there is no standard for sharing MD datasets. As generating MD datasets is energy-intensive, it is also important to facilitate the reuse of MD datasets to minimize energy consumption. Developing a universally accepted standard for depositing and curating MD datasets is a huge undertaking. The study by Tiemann et al. will be very valuable in informing policy developments toward this goal.

Strengths:

The study presents an original approach to addressing a growing concern in the field. It is clear that adopting a more collaborative approach could significantly enhance the impact of MD simulations in modern molecular sciences.

The timing of the work is appropriate, given the current interest in developing AI models for describing biomolecular dynamics.

Weaknesses:

The study primarily focuses on one major MD engine (GROMACS), although this limitation is not significant considering the proof-of-concept nature of the study.

We thank the reviewer for his/her comments. Moving forward, our plan includes expanding this research to encompass other MD engines used in biomolecular simulations and materials sciences, such as NAMD, Charmm, Amber, LAMMPS, etc. However, this requires parsing associated files to supplement the sparse metadata generally available for the related datasets

Reviewer #2 (Public Review):

Summary:

Molecular dynamics (MD) data is deposited in public, non-specialist repositories. This work starts from the premise that these data are a valuable resource as they could be used by other researchers to extract additional insights from these simulations; it could also potentially be used as training data for ML/AI approaches. The problem is that mining these data is difficult because they are not easy to find and work with. The primary goal of the authors was to discover and index these difficult-to-find MD datasets, which they call the "dark matter of the MD universe" (in contrast to data sets held in specialist databases).

The authors developed a search strategy that avoided the use of ill-defined metadata but instead relied on the knowledge of the restricted set of file formats used in MD simulations as a true marker for the data they were looking for. Detection of MD data marked a data set as relevant with a follow-up indexing strategy of all associated content. This "explore-and-expand" strategy allowed the authors for the first time to provide a realistic census of the MD data in non-specialist repositories.

As a proof of principle, they analyzed a subset of the data (primarily related to simulations with the popular Gromacs MD package) to summarize the types of simulated systems (primarily biomolecular systems) and commonly used simulation settings.

Based on their experience they propose best practices for metadata provision to make MD data FAIR (findable, accessible, interoperable, reusable).

A prototype search engine that works on the indexed datasets is made publicly available. All data and code are made freely available as open source/open data.

Strengths:

The novel search strategy is based on relevant data to identify full datasets instead of relying on metadata and thus is likely to have many true positives and few false positives.

The paper provides a first glimpse at the potential hidden treasures of MD simulations and force field parametrizations of molecules.

Analysis of parameter settings of MD simulations from how researchers *actually* run simulations can provide valuable feedback to MD code developers for how to document/educate users. This approach is much better than analyzing what authors write in the Methods sections.

The authors make a prototype search engine available.

The guidelines for FAIR MD data are based on experience gained from trying to make sense of the data.

Weaknesses:

So far the work is a proof-of-concept that focuses on MD data produced by Gromacs (which was prevalent under all indexed and identified packages).

As discussed in the manuscript, some types of biomolecules are likely underrepresented because different communities have different preferences for force fields/MD codes (for example: carbohydrates with AMBER/GLYCAM using AMBER MD instead of Gromacs).

Materials sciences seem to be severely under-represented --- commonly used codes in this area such as LAMMPS are not even detected, and only very few examples could be identified. As it is, the paper primarily provides an insight into the *biomolecular* MD simulation world.

The authors succeed in providing a first realistic view on what MD data is available in public repositories. In particular, their explore-expand approach has the potential to be customized for all kinds of specialist simulation data, whereby specific artifacts are used as fiducial markers instead of metadata. The more detailed analysis is limited to Gromacs simulations and primarily biomolecular simulations (even though MD is also widely used in other fields such as the materials sciences). This restricted view may simply be correlated with the user community of Gromacs and hopefully, follow-up studies from this work will shed more light on this shortcoming.

The study quantified the number of trajectories currently held in structured databases as ~10k vs ~30k in generalist repositories. To go beyond the proof-of-principle analysis it would be interesting to analyze the data in specialist repositories in the same way as the one in the generalist ones, especially as there are now efforts underway to create a database for MD simulations (Grant 'Molecular dynamics simulation for biology and chemistry research' to establish MDDB' DOI 10.3030/101094651). One should note that structured databases do not invalidate the approach pioneered in this work; if anything they are orthogonal to each other and both will likely play an important role in growing the usefulness of MD simulations in the future.

We thank the reviewer for his/her comments. As mentioned to Reviewer 1, we intend to extend this work to other MD engines in the near future to go beyond Gromacs and even biomolecular simulations. Furthermore, as the value of accessing and indexing specialized MD databases such as MDDB, MemprotMD, GPCRmd, NMRLipids, ATLAS, and others has been mentioned by the reviewer, it is indeed one of our next steps to continue to expand the MDverse catalog of MD data. This indexing may also extend the visibility and widespreaded adoptability of these specific databases.

Reviewer #3 (Public Review):

Molecular dynamics (MD) simulations nowadays are an essential element of structural biology investigations, complementing experiments and aiding their interpretation by revealing transient processes or details (such as the effects of glycosylation on the SARS-CoV-2 spike protein, for example (Casalino et al. ACS Cent. Sci. 2020; 6, 10, 1722-1734 https://doi.org/10.1021/acscentsci.0c01056) that cannot be observed directly. MD simulations can allow for the calculation of thermodynamic, kinetic, and other properties and the prediction of biological or chemical activity. MD simulations can now serve as "computational assays" (Huggins et al. WIREs Comput Mol Sci. 2019; 9:e1393.

https://doi.org/10.1002/wcms.1393). Conceptually, MD simulations have played a crucial role in developing the understanding that the dynamics and conformational behaviour of biological macromolecules are essential to their function, and are shaped by evolution. Atomistic simulations range up to the billion atom scale with exascale resources (e.g. simulations of SARS-CoV-2 in a respiratory aerosol. Dommer et al. The International Journal of High Performance Computing Applications. 2023; 37:28-44. doi:10.1177/10943420221128233), while coarse-grained models allow simulations on even larger length- and timescales. Simulations with combined quantum mechanics/molecular mechanics (QM/MM) methods can investigate biochemical reactivity, and overcome limitations of empirical forcefields (Cui et al. J. Phys. Chem. B 2021; 125, 689 https://doi.org/10.1021/acs.jpcb.0c09898).

MD simulations generate large amounts of data (e.g. structures along the MD trajectory) and increasingly, e.g. because of funder mandates for open science, these data are deposited in publicly accessible repositories. There is real potential to learn from these data en masse, not only to understand biomolecular dynamics but also to explore methodological issues. Deposition of data is haphazard and lags far behind experimental structural biology, however, and it is also hard to answer the apparently simple question of "what is out there?". This is the question that Tiemann et al explore in this nice and important work, focusing on simulations run with the widely used GROMACS package. They develop a search strategy and identify almost 2,000 datasets from Zenodo, Figshare and Open Science Framework. This provides a very useful resource. For these datasets, they analyse features of the simulations (e.g. atomistic or coarse-grained), which provides a useful snapshot of current simulation approaches. The analysis is presented clearly and discussed insightfully. They also present a search engine to explore MD data, the MDverse data explorer, which promises to be a very useful tool.

As the authors state: "Eventually, front-end solutions such as the MDverse data explorer tool can evolve being more user-friendly by interfacing the structures and dynamics with interactive 3D molecular viewers". This will make MD simulations accessible to non-specialists and researchers in other areas. I would envisage that this will also include approaches using interactive virtual reality for an immersive exploration of structure and dynamics, and virtual collaboration (e.g. O'Connor et al., Sci. Adv.4, eaat2731 (2018). DOI:10.1126/sciadv.aat2731)

The need to share data effectively, and to compare simulations and test models, was illustrated clearly in the COVID-19 pandemic, which also demonstrated a willingness and commitment to data sharing across the international community (e.g. Amaro and Mulholland, J. Chem. Inf. Model. 2020, 60, 6, 2653-2656 https://doi.org/10.1021/acs.jcim.0c00319; Computing in Science & Engineering 2020, 22, 30-36 doi: 10.1109/MCSE.2020.3024155). There are important lessons to learn here, for simulations to be reproducible and reliable, for rapid testing, for exploiting data with machine learning, and for linking to data from other approaches. Tiemann et al. discuss how to develop these links, providing good perspectives and suggestions.

I agree completely with the statement of the authors that "Even if MD data represents only 1 % of the total volume of data stored in Zenodo, we believe it is our responsibility, as a community, to develop a better sharing and reuse of MD simulation files - and it will neither have to be particularly cumbersome nor expensive. To this end, we are proposing two solutions. First, improve practices for sharing and depositing MD data in data repositories. Second, improve the FAIRness of already available MD data notably by improving the quality of the current metadata."

This nicely states the challenge to the biomolecular simulation community. There is a clear need for standards for MD data and associated metadata. This will also help with the development of standards of best practice in simulations. The authors provide useful and detailed recommendations for MD metadata. These recommendations should contribute to discussions on the development of standards by researchers, funders, and publishers. Community organizations (such as CCP-BioSim and HECBioSim in the UK, BioExcel, CECAM, MolSSI, learned societies etc) have an important part to play in these developments, which are vital for the future of biomolecular simulation.

We thank the reviewer for his/her comments. Beyond the points mentioned to Reviewers 1 and 2, as the reviewer suggested, it would be of great interest to combine innovative and immersive approaches to visualize and possibly interact with the data collected. This is indeed more and more amenable thanks to technologies such as WebGL and programs such as Mol*, or even - as also pointed out by the reviewer - through virtual reality, for example with the mentioned Narupa framework or with the UnityMol software. For a comprehensive review on MD trajectory visualization and associated challenges, we refer to our recent review article https://doi.org/10.3389/fbinf.2024.1356659.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Some minor text editing would improve the readability of the manuscript.

It would be very useful if the authors could share their perspectives on the best and most efficient approach to sharing datasets and code associated with a publication. My concern lies in the fact that Github, which is currently the dominant platform for sharing code, is not well-suited for hosting large MD datasets. As a result, researchers often need to adopt a workflow where code is shared on Github and datasets are stored elsewhere (e.g., Zenodo). While this is feasible, it adds extra work. Ideally, a transparent process could be developed to seamlessly share code and datasets linked to a study through a unified interface.

We thank the reviewer for this excellent suggestion. To our knowledge, there is yet no easy framework to jointly store and share code and data, linked to their scientific publication. Of course, code can be submitted to “generic” databases along with the data, but at the current state, those do not provide such useful features like collaborative work & track recording as done to the extent of GitHub.

Although GitHub is indeed a suitable platform to deposit code, we strongly advise researchers to archive their code in Software Heritage. In addition to preserving source code, Software Heritage provides a unique identifier called SWHID that unambiguously makes reference to a specific version of the source code.

So far, it is the responsibility of the scientific publication authors to link datasets and source codes (whether in GitHub or Software Heritage) in their paper, but also to make the reverse link from the data and code sharing platforms to the paper after publication.

As mentioned by the reviewer, a unified interface that could ease this process would significantly contribute to FAIR-ness in MD.

Reviewer #2 (Recommendations For The Authors):

L180: I am not aware that TRR files contain energy terms as stated here, my understanding was that EDR files primarily served that purpose.

“…available in one dataset. Interestingly, we found 1,406 .trr files, Which contain trajectory but also additional information such as velocities, energy of the system, etc’ While the file is especially useful in terms of reusability, the large size (can go up to several 100GB) limits its deposition in most…”

Indeed, our formulation was ambiguous. The EDR files contain the detailed information on energies, whereas TRR files contain numerous values from the trajectory such as coordinates, velocities, forces and to some extent also energies

(https://manual.gromacs.org/current/reference-manual/file-formats.html#trr)

L207: The text states that the total time was not available from XTC files, only the number of frames. However, XTC files record time stamps in addition to frame numbers. As long as these times are in the Gromacs standard of picoseconds, the simulation time ought to be available from XTCs.

“…systems and the number of frames available in the files (Fig. 3-B). Of note, the frames do not directly translate to the simulation runtime - more information deposited in other files (e.g. .mdp files) is needed to determine the complete runtime of the simulation. The system was up…”.

Thank you for the useful comment, we removed this sentence. We now mention that studying the simulation time would be of interest in the future, especially when we will perform an exhaustive analysis of XTC files.

“Of note, as .xtc files also contain time stamps, it would be interesting to study the relationship between the time and the number of frames to get useful information about the sampling. Nevertheless, this analysis would be possible only for unbiased MD simulations. So, we would need to decipher if the .xtc file is coming from biased or unbiased simulations, which may not be trivial.”

Analysis of MDP files: Were these standard equilibrium MD or can you distinguish biased MD or free energy calculations?

Currently we do not distinguish between biased and unbiased MD, but in the future we may attempt to do so, e.g. by correlating it with standard equilibration force-fields/parameters, timesteps or similar. Nevertheless, a true distinction will remain challenging.

L336: typo: pikes -> spikes (or peaks?)

“…simulations of Lennard-Jones models (Jeon et al., 2016). Interestingly, we noticed the appearance of several pikes at 400K, 600K and 800K, which were not present before the end of the year 2022. These peaks correspond to the same study related to the stability of hydrated crystals (Dybeck et al., 2023)’ Overall, thhis analysis revealed that a wide range of temperatures have been explored,…”

Thank you. We have corrected this typo.

Make clear how multiple versions of data sets are handled, e.g., if v1, v2, and v3 of a dataset are provided in Zenodo then which one is counted or are all counted?

We collected the latest version only of datasets, as exposed by default by the Zenodo API. To reflect this, we added the following sentence to the Methods and Materials section, Initial data collection sub-section:

“By default, the last version of the datasets was collected.”

L248 Analysis of GRO files seems fairly narrow because PDB files are very often used for exactly the same purpose, even in the context of Gromacs simulations, not the least because it is familiar to structural biologists that may be interested in representative MD snapshots. Despite all the shortcomings of abusing the PDB format for MD, it is an attempt at increased interoperability. Perhaps the authors can make sure that readers understand that choosing GRO for analysis may give a somewhat skewed picture, even within Gromacs simulations.

Thanks for this comment. We collected about 12,000 PDB files that could indeed be output from Gromacs simulations and easily be shared due to the universality of this format, but that could as well come from different sources (like other MD packages or the PDB database itself). We purposely decided to limit our study to files strictly associated with the Gromacs package, like MDP and XTC file types. However, we will extend our survey to all other structure-like formats and especially the PDB file type. We reflected this purpose in the following sentence (after line 281)

“Beyond .gro files, we would like to analyze the ensemble of the ~12,000 .pdb files extracted in this study (see Figure 2-B) to better characterize the types of molecular structures deposited.”

A simple template metadata file would be welcome (e.g., served from a GitHub/GitLab repository so that it can be improved with community input).

Thank you for this suggestion that we fundamentally agree with. However, the generation of such a file is a major task, and we believe that the creation of a metadata file template requires far-reaching considerations, therefore is beyond the scope of this paper and should not be decided by a small group of researchers. Indeed, this topic requires a large consensus of different stakeholders, from users, to MD program developers, and journal editors. It would be especially useful to organize dedicated workshops with representatives of all these communities to tackle this specific issue, as mentioned by Reviewer3 in his/her public review. As a basis for this discussion, we humbly proposed at the end of this manuscript a few non-constraining guidelines based on our experience retrieving the data.

To emphasize this statement, we added the following sentence at the end of the “Guidelines for better sharing of MD simulation data” section (line 420):

“Converging on a set of metadata and format requires a large consensus of different stakeholders from users, to MD program developers, and journal editors. It would be especially useful to organize specific workshops with representatives of all these communities to collectively tackle this specific issue.”

In "Data and code availability" it would be good to specify licenses in addition to stating "open source". Thank you for pointing out that GitLab/GitHub are not archives and that everyone should be strongly encouraged to submit data to stable archival repositories.

We added the corresponding licenses for code and data in the “Data and code availability” section.

Reviewer #3 (Recommendations For The Authors)

The paper is well written, with very few typographical or other minor errors.

Minor points:

Line 468-9 "can evolve being more user-friendly" should be "can evolve to being more user-friendly", I think.

Thank you, we have changed the wording accordingly.

hello22222

hello

eLife assessment

The authors propose that the asymmetric segregation of the NuRD complex in C. elegans is regulated in a V-ATPase-dependent manner, that this plays a crucial role in determining the differential expression of the apoptosis activator egl-1, and that it is therefore critical for the life/death fate decision in this species. If proven, the proposed model of the V-ATPase-NuRD-EGL-1-Apoptosis cascade would shed light onto the mechanisms underlying the regulation of apoptosis fate during asymmetric cell division, and stimulate further investigation into the intricate interplay between V-ATPase, NuRD, and epigenetic modifications. However, the strength of evidence for this is currently incomplete.

Mabye rather simpler.

I would prefer just Content.

Here, it's not about GitHub anymore!

eLife assessment

The manuscript describes a careful, quantitative analysis of Myosin 10 molecules in U2OS cells, a widely used model for studying filopodia, and how many are present in the cytosol versus filopodia. This important study provides key parameters that are required for building a biophysical model of filopodia which is required to gain a complete understanding of these major actin-based structures. The evidence for the conclusions is compelling, but there are also certain areas of the manuscript that require clarification.

Reviewer #1 (Public Review):

Summary:

The manuscript proposes an alternative method by SDS-PAGE calibration of Halo-Myo10 signals to quantify myosin molecules at specific subcellular locations, in this specific case filopodia, in epifluorescence datasets compared to the more laborious and troublesome single molecule approaches. Based on these preliminary estimates, the authors developed further their analysis and discussed different scenarios regarding myosin 10 working models to explain intracellular diffusion and targeting to filopodia.

Strengths:

I confirm my previous assessment. Overall, the paper is elegantly written and the data analysis is appropriately presented. Moreover, the novel experimental approach offers advantages to labs with limited access to high-end microscopy setups (super-resolution and/or EM in particular), and the authors proved its applicability to both fixed and live samples.

Weaknesses:

Myself and the other two reviewers pointed to the same weakness, the use of protein overexpression in U2OS. The authors claim that Myosin10 is not expressed by U2OS, based on Western blot analysis. Does this completely rule out the possibility that what they observed (the polarity of filopodia and the bulge accumulation of Myo10) could be an artefact of overexpression? I am afraid this still remains the main weakness of the paper, despite being properly acknowledged in the Limitations.

I consider all the remaining issues I expressed during the first revision solved.

Reviewer #2 (Public Review):

Summary:

The paper sought to determine the number of myosin 10 molecules per cell and localized to filopodia, where they are known to be involved in formation, transport within, and dynamics of these important actin-based protrusions. The authors used a novel method to determine the number of molecules per cell. First, they expressed HALO tagged Myo10 in U20S cells and generated cell lysates of a certain number of cells and detected Myo10 after SDS-PAGE, with fluorescence and a stained free method. They used a purified HALO tagged standard protein to generate a standard curve which allowed for determining Myo10 concentration in cell lysates and thus an estimate of the number of Myo10 molecules per cell. They also examined the fluorescence intensity in fixed cell images to determine the average fluorescence intensity per Myo10 molecule, which allowed the number of Myo10 molecules per region of the cell to be determined. They found a relatively small fraction of Myo10 (6%) localizes to filopodia. There are hundreds of Myo10 in each filopodia, which suggests some filopodia have more Myo10 than actin binding sites. Thus, there may be crowding of Myo10 at the tips, which could impact transport, the morphology at the tips, and dynamics of the protrusions themselves. Overall, the study forms the basis for a novel technique to estimate the number of molecules per cell and their localization to actin-based structures. The implications are broad also for being able to understand the role of myosins in actin protrusions, which is important for cancer metastasis and wound healing.

Strengths:

The paper addresses an important fundamental biological question about how many molecular motors are localized to a specific cellular compartment and how that may relate to other aspects of the compartment such as the actin cytoskeleton and the membrane. The paper demonstrates a method of estimating the number of myosin molecules per cell using the fluorescently labeled HALO tag and SDS-PAGE analysis. There are several important conclusions from this work in that it estimates the number of Myo10 molecules localized to different regions of the filopodia and the minimum number required for filopodia formation. The authors also establish a correlation between number of Myo10 molecules filopodia localized and the number of filopodia in the cell. There is only a small % of Myo10 that tip localized relative to the total amount in the cell, suggesting Myo10 have to be activated to enter the filopodia compartment. The localization of Myo10 is log-normal, which suggests a clustering of Myo10 is a feature of this motor.

One of the main critiques of the manuscript was that the results were derived from experiments with overexpressed Myo10 and therefore are hard to extrapolate to physiological conditions. The authors counter this critique with the argument that their results provide insight into a system in which Myo10 is a limiting factor for controlling filopodia formation. They demonstrate that U20S cells do not express detectable levels of Myo10 (supplementary Figure 1E) and thus introducing Myo10 expression demonstrates how triggering Myo10 expression impacts filopodia. An example is given how melanoma cells often heavily upregulation Myo10.

In addition, the revised manuscript addresses the concerns about the method to quantitate the number of Myo10 molecules per cell and therefore puncta in the cell. The authors have now made a good faith effort to correct for incomplete labeling of the HALO tag (Figure 2A-C, supplementary Figure 2D-E). The authors also address the concerns about variability in transfection efficiency (Figure 1D-E).

A very interesting addition to the revised manuscript was the quantitation of the number of Myo10 molecules present during an initiation event when a newly formed filopodia just starts to elongate from the plasma membrane. They conclude that 100s of Myo10 molecules are present during an initiation event. They also examined other live cell imaging events in which growth occurs from a stable filopodia tip and correlated with elongation rates.

Weaknesses:

The authors acknowledge that a limitation of the study is that all of the experiments were performed with overexpressed Myo10. They address this limitation in the discussion but also provide important comparisons for how their work relates to physiological conditions, such as melanoma cells that only express large amounts of Myo10 when they are metastatic. Also, the speculation about how fascin can outcompete Myo10 should include a mechanism for how the physiological levels of fascin can complete with the overabundance of Myo10 (page 10, lines 401-408).

Reviewer #3 (Public Review):

Summary

The work represents progress in quantifying the number of Myo10 molecules present in the filopodia tip. It reveals that cells overexpressing fluorescently labeled Myo10 that the tip can accommodate a wide range of Myo10 motors, up to hundreds of molecules per tip.

The revised, expanded manuscript addresses all of this reviewer's original comments. The new data, analysis and writing strengthen the paper. Given the importance of filopodia in many cellular/developmental processes and the pivotal, as yet not fully understood role of Myo10 in their formation and extension, this work provides a new look at the nature of the filopodial tip and its ability to accommodate a large number of Myo10 motor proteins through interactions with the actin core and surrounding membrane.

Specific comments -

(1) One of the comments on the original work was that the analysis here is done using cells ectopically expressing HaloTag-Myo10. The author's response is that cells express a range of Myo10 levels and some metastatic cancer cells, such as breast cancer, have significantly increased levels of Myo10 compared to non-transformed cell lines. It is not really clear how much excess Myo10 is present in those cells compared to what is seen here for ectopic expression in U2OS cells, making a direct correspondence difficult.

In response to comments about the bulbous nature of many filopodia tips the authors point out that similar-looking tips are seen when cells are immunostained for Myo10, citing Berg & Cheney (2002). In looking at those images as well as images from papers examining Myo10 immunostaining in metastatic cancer cells (Arjonen et al, 2014, JCI; Summerbell et al, 2020, Sci Adv) the majority of the filopodia tips appear almost uniformly dot-like or circular. There is not too much evidence of the elongated, bulbous filopodial tips seen here.

However, in reconsidering the approach and results, it is the case that the finding here do establish the plasticity of filopodia tips that can accommodate a surprisingly (shockingly) large number of motors. The authors discuss that their results show that targeting molecules to the filopodia tip is a relatively permissive process (lines 262 - 274). That could be an important property that cells might be able to use to their advantage in certain contexts.

(2) The method for arriving at the intensity of an individual filopodium puncta (starting on line 532 and provided in the Response), and how this is corrected for transfection efficiency and the cell-to-cell variation in expression level is still not clear to this reviewer. The first part of the description makes sense - the authors obtain total molecules/cell based on the estimation on SDS-PAGE using the signal from bound Halo ligand. It then seems that the total fluorescence intensity of each expressing cell analyzed is measured, then summed to get the average intensity/cell. The 'total pool' is then arrived at by multiplying the number of molecules/cell (from SDS-PAGE) by the total number of cells analyzed. After that, then: 'to get the number of molecules within a Myo10 filopodium, the filopodium intensity was divided by the bioreplicate signal intensity and multiplied by 'total pool.' ' The meaning of this may seem simple or straightforward to the authors, but it's a bit confusing to understand what the 'bioreplicate signal intensity' is and then why it would be multiplied by the 'total pool'. This part is rather puzzling at first read.

Since the approach described here leads the authors to their numerical estimates every effort should be made to have it be readily understood by all readers. A flow chart or diagram might be helpful.

(3) The distribution of Myo10 punctae around the cell are analyzed (Fig 2E, F) and the authors state that they detect 'periodic stretches of higher Myo10 density along the plasma membrane' (line 123) and also that there is correlation and anti-correlation of molecules and punctae at opposite ends of the cells.

In the first case, it is hard to know what the authors really mean by the phrase 'periodic stretches'. It's not easy to see a periodicity in the distribution of the punctae in the many cells shown in Supp Fig 3. Also, the correlation/anti-correlation is not so easily seen in the quantification shown in Fig 2F. Can the authors provide some support or clarification for what they are stating?

(4) The authors are no doubt aware that a paper from the Tyska lab that employs a completely different method of counting molecules arrives at a much lower number of Myo10 molecules at the filopodial tip than is reported here was just posted (Fitz & Tyska, 2024, bioRxiv, DOI: 10.1101/2024.05.14.593924).

While it is not absolutely necessary for the authors to provide a detailed discussion of this new work given the timing, they may wish to consider adding a note briefly addressing it.

Author response:

The following is the authors’ response to the original reviews.

eLife assessment

This valuable study reports on the packing of molecules in cellular compartments, such as actin-based protrusions. The study provides solid evidence for parameters that enable the building of a biophysical model of filopodia, which is required to gain a complete understanding of these important actin-based structures. Some areas of the manuscript require further clarification.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript proposes an alternative method by SDS-PAGE calibration of Halo-Myo10 signals to quantify myosin molecules at specific subcellular locations, in this specific case filopodia, in epifluorescence datasets compared to the more laborious and troublesome single molecule approaches. Based on these preliminary estimates, the authors developed further their analysis and discussed different scenarios regarding myosin 10 working models to explain intracellular diffusion and targeting to filopodia.

Strengths:

Overall, the paper is elegantly written and the data analysis is appropriately presented.

Weaknesses:

While the methodology is intriguing in its descriptive potential and could be the beginning of an interesting story, a good portion of the paper is dedicated to the discussion of hypothetical working mechanisms to explain myosin diffusion, localization, and decoration of filopodial actin that is not accompanied by the mandatory gain/loss of function studies required to sustain these claims.

To be fair, the detailed mechanisms that we raise related to diffusion, localization, and decoration are based on extensive work by others. Many prior papers use domain deletions of Myo10 and fall in the category of gain/loss-of-function studies. It is true that we have not repeated those extensive studies, but it seems appropriate to connect with and cite their work where appropriate.

Reviewer #2 (Public Review):

Summary:

The paper sought to determine the number of myosin 10 molecules per cell and localized to filopodia, where they are known to be involved in formation, transport within, and dynamics of these important actin-based protrusions. The authors used a novel method to determine the number of molecules per cell. First, they expressed HALO tagged Myo10 in U20S cells and generated cell lysates of a certain number of cells and detected Myo10 after SDS-PAGE, with fluorescence and a stained free method. They used a purified HALO tagged standard protein to generate a standard curve which allowed for determining Myo10 concentration in cell lysates and thus an estimate of the number of Myo10 molecules per cell. They also examined the fluorescence intensity in fixed cell images to determine the average fluorescence intensity per Myo10 molecule, which allowed the number of Myo10 molecules per region of the cell to be determined. They found a relatively small fraction of Myo10 (6%) localizes to filopodia. There are hundreds of Myo10 in each filopodia, which suggests some filopodia have more Myo10 than actin binding sites. Thus, there may be crowding of Myo10 at the tips, which could impact transport, the morphology at the tips, and dynamics of the protrusions themselves. Overall, the study forms the basis for a novel technique to estimate the number of molecules per cell and their localization to actin-based structures. The implications are broad also for being able to understand the role of myosins in actin protrusions, which is important for cancer metastasis and wound healing.

Strengths:

The paper addresses an important fundamental biological question about how many molecular motors are localized to a specific cellular compartment and how that may relate to other aspects of the compartment such as the actin cytoskeleton and the membrane. The paper demonstrates a method of estimating the number of myosin molecules per cell using the fluorescently labeled HALO tag and SDS-PAGE analysis. There are several important conclusions from this work in that it estimates the number of Myo10 molecules localized to different regions of the filopodia and the minimum number required for filopodia formation. The authors also establish a correlation between number of Myo10 molecules filopodia localized and the number of filopodia in the cell. There is only a small % of Myo10 that tip localized relative to the total amount in the cell, suggesting Myo10 have to be activated to enter the filopodia compartment. The localization of Myo10 is log-normal, which suggest a clustering of Myo10 is a feature of this motor.

Weaknesses:

One main critique of this work is that the Myo10 was overexpressed. Thus, the amount in the cell body compared to the filopodia is difficult to compare to physiological conditions. The amount in the filopodia was relatively small - 100s of molecules per filopodia so this result is still interesting regardless of the overexpression. However, the overexpression should be addressed in the limitations.

This is a reasonable perspective and we now note this caveat in the Limitations section so that readers will take note. Our goal here was to understand a system in which Myo10 is the limiting reagent for filopodia, rather than a native system that expresses high Myo10 on its own. Because U2OS cells do not express detectable levels of Myo10 (see below), the natural perturbation here is overexpressing Myo10 to stimulate filopodial growth.

The authors have not addressed the potential for variability in transfection efficiency. The authors could examine the average fluorescence intensity per cell and if similar this may address this concern.

Indeed, cells are heterogenous and will naturally express different levels of Myo10 not only due to transfection efficiency, but also due to their state (cell cycle stage, motile behavior, and more). In fact, we measure the transfection efficiency of each bioreplicate and account for it in our calibration procedure. We also measure the fluorescence intensity per cell, which lets us calculate the total Myo10s per cell and the cell-to-cell variability. These Myo10 distributions across cells are shown in Fig. 1D-E.

We note here an error that we made in applying this transfection efficiency correction in the first submission. When we obtain the total Myo10 molecules by SDS-PAGE, we should divide by the total number of transfected cells. However, due to an operator precedence error, the transfection efficiency appeared in the numerator rather than the denominator. We have now corrected this error, which has the effect of increasing the number of molecules in all of our measurements. The effect of this correction has strengthened one of the paper’s main conclusions, that Myo10 is frequently overloaded at filopodial tips.

The SDS PAGE method of estimating the number of molecules is quite interesting. I really like this idea. However, I feel there are a few more things to consider. The fraction of HALO tag standard and Myo10 labeled with the HALO tagged ligand is not determined directly. It is suggested that since excess HALO tagged ligand was added we can assume nearly 100% labeling. If the HALO tag standard protein is purified it should be feasible to determine the fraction of HALO tagged standard that is labeled by examining the absorbance of the protein at 280 and fluorophore at its appropriate wavelength.

This is a fair point raised by the reviewer, and we have now measured a labeling efficiency of 90% in Supplementary Figure 2A-C. We have adjusted all values according to this labeling efficiency.

The fraction of HALO tagged Myo10 labeled may be more challenging to determine, since it is in a cell lysate, but there may be some potential approaches (e.g. mass spec, HPLC).

As noted, this value is considerably more challenging. Instead, we determined conditions under which labeling in cells is saturated. We have now stained with a concentration range for both fixed and live cell samples. Saturation occurs with ~0.5 μM HaloTag ligand-TMR in fixed/permeabilized cells and in live cells (Supplementary Figure 2D-E). This comparison of live cells vs. permeabilized cells allows us to say that the intact plasma membrane is not limiting labeling under these conditions.

In Figure 1B, the stain free gel bands look relatively clean. The Myo10 is from cell lysates so it is surprising that there are not more bands. I am not surprised that the bands in the TMR fluorescence gel are clean, and I agree the fluorescence is the best way to quantitate.

Figure 1B shows the focused view at high MW, and there is not much above Myo10. The full gel lanes shown in Supp. Fig. 1C show the expected number of bands from a cell lysate.

In Figure 3C, the number of Myo10 molecules needed to initiate a filopodium was estimated. I wonder if the authors could have looked at live cell movies to determine that these events started with a puncta of Myo10 at the edge of the cell, and then went on to form a filopodia that elongated from the cell. How was the number of Myo10 molecules that were involved in the initiation determined? Please clarify the assumptions in making this conclusion.

We thank the reviewer (and the other reviewers) for this excellent suggestion. We have now carried out these live cell experiments. These experiments were quite challenging, because we needed to collect snapshots of ~50 cells to measure the mean fluorescence intensity of transfected cells and then acquire movies of several cells for analysis. The U2OS cells were also highly temperature-sensitive and would retract their filopodia without objective heating.

We have now analyzed filopodial initiation events and measured considerably more Myo10 at the first signs of accumulation– in the 100s of molecules. The dimmer spots that we measured in the first draft were likely unrelated to filopodial initiation, and we have corrected the discussion on this point.

We now also track further growth from a stable filopodial tip (the phased-elongation mechanism from Ikebe and coworkers) and find approximately 500 molecules bud off in those events. We also track filopodial elongation rates as a function of Myo10 numbers. We have added additional live cell imaging sections that include these results.

It is stated in the discussion that the amount of Myo10 in the filopodia exceeds the number of actin binding sites. However, since Myo10 contains membrane binding motifs and has been shown to interact with the membrane it should be pointed that the excess Myo10 at the tips may be interacting with the membrane and not actin, which may prevent traffic jams.

This is also an excellent point to consider, and we have expanded the relevant discussion along these lines. We agree that the Myo10 at the filopodial tip is likely membrane-bound. We now estimate the 2D membrane area occupied by Myo10, and find that it reaches nearly full packing in many cases (under a number of assumptions that we spell out more fully in the manuscript).

Reviewer #3 (Public Review):

Summary:

The unconventional myosin Myo10 (aka myosin X) is essential for filopodia formation in a number of mammalian cells. There is a good deal of interest in its role in filopodia formation and function. The manuscript describes a careful, quantitative analysis of Myo10 molecules in U2OS cells, a widely used model for studying filopodia, how many are present in the cytosol versus filopodia and the distribution of filopodia and molecules along the cell edge. Rigorous quantification of Myo10 protein amounts in a cell and cellular compartment are critical for ultimately deciphering the cellular mechanism of Myo10 action as well as understand the molecular composition of a Myo10-generated filopodium.

Consistent with what is seen in images of Myo10 localization in many papers, the vast majority of Myo10 is in the cell body with only a small percentage (appr 5%) present in filopodia puncta. Interestingly, Myo10 is not uniformly distributed along the cell edge, but rather it is unevenly localized along the cell edge with one region preferentially extending filopodia, presumably via localized activation of Myo10 motors. Calculation of total molecules present in puncta based on measurement of puncta size and measured Halo-Myo10 signal intensity shows that the concentration of motor present can vary from 3 - 225 uM. Based on an estimation of available actin binding sites, it is possible that Myo10 can be present in excess over these binding sites.

Strengths:

The work represents an important first step towards defining the molecular stoichiometry of filopodial tip proteins. The observed range of Myo10 molecules at the tip suggests that it can accommodate a fairly wide range of Myo10 motors. There is great value in studies such as this and the approach taken by the authors gives one good confidence that the numbers obtained are in the right range.

Weaknesses:

One caveat (see below) is that these numbers are obtained for overexpressing cells and the relevance to native levels of Myo10 in a cell is unclear.

A similar concern was raised by Reviewer 2; please see above.

An interesting aspect of the work is quantification of the fraction of Myo10 molecules in the cytosol versus in filopodia tips showing that the vast majority of motors are inactive in the cytosol, as is seen in images of cells. This has implications for thinking about how cells maintain this large population in the off-state and what is the mechanism of motor activation. One question raised by this work is the distinction between cytosolic Myo10 and the population found at the ‘cell edge’ and the filopodia tip. The cortical population of Myo10 is partially activated, so to speak, as it is targeted to the cortex/membrane and presumably ready to go. Providing quantification of this population of motors, that one might think of as being in a waiting room, could provide additional insight into a potential step-by-step pathway where recruitment or binding to the cortical region/plasma membrane is not by itself sufficient for activation.

As mentioned in our response to Reviewer 2, we have now carried out quantitation in live cells to capture Myo10 transitions from cell body into filopodial movement. We attempted to identify this membrane-bound population of motors in our new live cell experiments but were unable to make convincing measurements. Notably, we see no noticeable enrichment of Myo10 at the cortex relative to the cytosol. Although we believe there is a membrane-bound waiting room (akin to the 3D-2D-1D mechanism of Molloy and Peckham), we suspect that the 2D population is diffusing too rapidly to be detected under our imaging conditions.

Specific comments:

(1) It is not obvious whether the analysis of numbers of Myo10 molecules in a cell that is ectopically overexpressing Myo10 is relevant for wild type cells. It would appear to be a significant excess based on the total protein stained blot shown in Fig S1E where a prominent band the size of tagged Myo10 seen in the transfected sample is almost absent in the WT control lane.

Even “wildtype” cells vary considerably in their Myo10 expression levels. For example, melanoma cells often heavily upregulate Myo10, while these U2OS cells produce nearly none (Supplementary Figure 1E). Thus, there is no single, widely acceptable target for Myo10 expression in wildtype cells.

Please note that the new Supplementary Figure 1E is a Myo10 Western blot, not total protein staining as before.

Ideally, and ultimately an important approach, would be to work with a cell line expressing endogenously tagged Myo10 via genome engineering. This can be complicated in transformed cells that often have chromosomal duplications.

Indeed, we chose U2OS cells for this work because they do not express detectable levels of Myo10, and thus we can avoid all of these complications. Here we can examine how Myo10 levels control filopodial production through ectopic expression.

However, even though there is an excess of Myo10 it would appear that activation is still under some type of control as the cytosolic pool is quite large and its localization to the cell edge is not uniform. But it is difficult to gauge whether the number of molecules in the filopodium is the same as would be seen in untransfected cells. Myo10 can readily walk up a filopodium and if excess numbers of this motor are activated they would accumulate in the tip in large numbers, possibly creating a bulge as and indeed it does appear that some tips are unusually large. Then how would that relate to the normal condition?

As noted above, the normal condition depends on the cellular system. However, endogenous Myo10 also accumulates in bulges at filopodial tips, so this is not a phenotype unique to Myo10 overexpression. For example, the images from Figure 1 of the Berg and Cheney (2002) citation show bulges from endogenous Myo10 in endothelial cells.

(2) Measurements of the localization of Myo10 focuses in large part on ‘Myo10 punctae’. While it seems reasonable to presume that these are filopodia tips, the authors should provide readers with a clear definition of a puncta. Is it only filopodia tips, which seems to be the case? Does it include initiation sites at the cell membrane that often appear as punctae?

We define puncta as any clusters/spots of Myo10 signal detected by segmentation, not limited to any location within the surface-attached filopodia. We exclude puncta that appear in the cell interior (~5 of which appear in Fig. 1A). These are likely dorsal filopodia, but there are few of these compared to the surface attached filopodia of U2OS cells. In Figure 2, “puncta” includes all Myo10 clusters along the filopodia shaft, though a majority happen to be tip-localized (please see Supplementary Figure 4B). We have edited the main text for clarification.

Along those lines, the position of dim punctae along the length of a filopodium is measured (Fig 3D). The findings suggest that a given filopodium can have more than one puncta which seems at odds if a puncta is a filopodia tip. How frequently is a filopodium with two puncta seen? It would be helpful if the authors provided an example image showing the dim puncta that are not present at the tip.

We have now provided an example image of dim puncta along filopodia in Supplementary Figure 4C.

(3) The concentration of actin available to Myo10 is calculated based on the deduction from Nagy et al (2010) that only 4/13 of the actin monomers in a helical turn are accessible to the Myo10 motor (discussion on pg 9; Fig S4). Subsequent work (Ropars et al, 2016) has shown that the heads of the antiparallel Myo10 dimer are flattened, but the neck is rather flexible, meaning that the motor can a variable reach (36 - 52 nm). Wouldn’t this mean that more actin could be accessible to the Myo10 motor than is calculated here?

Although we see why the reviewer might believe otherwise, the 4/13 fraction of accessible actin holds. This fraction is obtained from consideration of the fascin-actin bundle structure alone, independent of the reach of any particular myosin motor. Every repeating layer of 13 actin subunits (or 36 nm) has 4 accessible myosin binding-sites. The remaining 9 sites are rejected because a single myosin motor domain will have a steric clash with a neighboring actin filament in the bundle. A myosin with an exceptionally long reach might reach the next 13 subunit layer, but that layer also has only 4 binding sites. Thus, we can calculate the number of binding sites per unit length along the filopodium. This number would hold for a dimeric myosin with any reach, including myosin-5 or myosin-2.

(4) Quantification of numbers of Myo10 molecules in filopodial puncta (Fig 3C) leads the authors to conclude that ‘only ten or fewer Myo10 molecules are necessary for filopodia initiation’ (pg 7, top). While this is a reasonable based on the assumption that the formation of a puncta ultimately results from an initiation event, little is known about initiation events and without direct observation of coalescence of Myo10 at the cell edge that leads to formation of a filopodium, this seems rather speculative.

As noted above, we have now performed the necessary live cell imaging of filopodial nucleation events and have updated our conclusions accordingly.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I have made a series of comments that might help the authors improve their manuscript:

- A full calibration of the methodology would require testing a wider range of protein amounts, to exhaustively detect the dynamic range of the technique. The authors acknowledge in the discussion that “Furthermore, our estimates of molecules are predicated on the calibration curve of the Halo Standard Protein on the SDS-PAGE gels, which is likely the highest source of error on our molecule counts”. A good way of convincing a nasty reviewer is to provide a calibration with more than 3 reference points. At least this will help exclude from the analysis cells where Myo10 estimates are not in the linear regime of detection.

We completely agree with the reviewer’s suggestion to build a robust calibration curve. The SDS gel shown in Figure 1C originally contained 4 reference points, but the highest HaloTag standard protein point oversaturated the detector at the set exposure in the TMR channel and was omitted. We have now re-run the SDS gel to include a HaloTag standard protein curve comprising 5 points, alongside all three bioreplicates from the fixed cell experiments and all three bioreplicates from the live cell experiments (updated in Figure 1B-C). We had saved frozen lysates from the original fixed cell work, so we were able to reanalyze our data with the new set of standards. The Myo10 quantities are consistent, but with much tighter CIs from the standard curve.

- As already said this methodology is intriguing, however, a correlative validation with a conventional SMLM approach to address the bona-fide of the method would be ideal.

Unfortunately, single molecule approaches for validation are impractical for us. Due to the relatively high magnification of our TIRF microscope and the large spread area of the U2OS cells, single cells typically extend beyond the field of view. We acknowledge the benefits of SMLM quantitative techniques and other approaches cited in the introduction section. To avoid use of special tools/instruments, we offer our methodology, based off Pollard group’s quantitative Western blotting of GFP, as a simpler alternative accessible to anyone.

- TMR is a small ligand likely interacting also with Halo in its denatured state. However, to clear any doubts a parallel Native-PAGE investigation should be included, or if existing a specific reference should be provided.

Perhaps there is a misunderstanding here. One of the key advantages of the HaloTag labeling system is that the engineered dehalogenase is covalently modified by the ligand (the TMR-ligand is a suicide substrate). This means that the TMR remains bound even under denaturing conditions, which allows its detection in SDS-PAGE. Native gels are unnecessary here.

- Moreover, SDS-PAGE is run at alkaline pH, have the authors considered these points when designing the methodology? Fluorescence images were taken in PBS, which has a different pH. Could the authors, or the literature, exclude these aspects as potential pitfalls in the methodology? Also temperature is affecting fluorescence emission, but it is easier to control with certain tolerance in the room-temperature regime.

Our method does not compare fluorescence values that cross the experimental systems (SDS-PAGE vs. microscopy). Cellular proteins and HaloTag protein standards are compared in a single setting of SDS-PAGE to obtain the average number of Myo10s per transfected cell. Likewise, all measurements on intact (live or fixed) cells are conducted in that single setting to obtain average fluorescence per cell. Thus, there is no issue with the different buffers or temperatures affecting fluorescence emission.

- The authors should test their approach also with truncation variants of Myosin10 (for instance lacking the PH or motor domain). This is a classical approach that might prove the potential of the technique when altering the capacity of the protein to interact with a main binding partner. Also, treatments that induced filopodia formation might prove useful (i.e., hypotonic media induce filopodia formation in some fibroblast cell lines in our hands).

The reviewer raises interesting suggestions that we aim to address in future experiments, but truncation variants and environmental perturbations are beyond the focus of the current manuscript. Here, we report on the otherwise unperturbed state when we add exogenous full-length Myo10 to the U2OS cells. But indeed, experiments with Myo10 domain truncations, PI3K and PTEN inhibition, and cargo protein / activating cofactor knock-downs (among others) are on our drawing board.

- Most of the mechanisms hypothesized in the discussion are sound and plausible. However, the authors have chosen an experimental model where transient transfection of exogenous Myo10 in U2OS is performed. This approach poses two main and fundamental questions that are not resolved by the data provided:

A) how do different expression levels affect the Myo10 counting?

Our counting procedure does not assume uniform expression across a population of cells– quite the opposite, in fact. We directly measure Myo10 expression levels on a cell-by-cell basis with microscopy, once we know the number of molecules in our total pool (see the Methods for details). As an example of the final output, Figs. 1D and 1E show the total number of Myo10 molecules per cell for fixed and live cells, respectively.

B) how does endogenous and unlabeled Myo10 hamper the bonafide of counts? The authors claimed “U2OS cells express low levels of Myo10, so there is a small population of unlabeled endogenous Myo10 unaddressed by this paper”. As presented, the low levels of endogenous Myo10 sound an arbitrary parameter, and there are no data presented that can limit if not exclude this bias in the analysis. To produce data in a genetically modified cell line with Halo-tag on the endogenous protein will represent a much cleaner system. Alternatively, the authors should look for Myo10 KO cell lines where they can back-transfect their Halo-Tagged Myo10 construct in a more consistent framework, focusing on cells with low-to-mid levels of expression.

We agree, this is an important point to nail down (and is often neglected in the literature). We have now measured the endogenous Myo10 levels in U2OS cells by Western blotting and found that it is undetectable compared to our HaloTagged construct expression. Please see Supp. Fig 1E. Thus, for all intents and purposes, every Myo10 molecule in these experiments came from our expression plasmid. Accordingly, we have removed this caveat from the paper.

Minor points

- Figure 1B. To help the reader SDS-PAGE gels annotations should be clearer already from the figure.

We have updated the annotations for clarity.

- Methods should be organized in sessions. As it stands, it is hard for the reader to look for technical details.

We have expanded and added subsections to the Methods as requested.

- The good practice of indicating the gene and transcript entry numbers and the primer used to amplify and clone into the backbone vectors is getting lost in many papers. I would strongly encourage the authors to add this information to the methods.

We have included the gene entries to the methods and will include a full FASTA file of the coding sequence as supplementary information to avoid any ambiguity here.

The authors write “It is unclear how myosins navigate to the right place at the right time, but our results support an important interplay between Myo10 and the actin network.” It is a bit scholastic to say that Myo10 and actin have an important interplay, they are major binding partners. What is the new knowledge contained in this sentence?

Agreed– we have deleted the sentence in question.

Reviewer #2 (Recommendations For The Authors):

The authors should address all the weaknesses indicated in the public review.

There were a few other places that require clarification.

On page 4, the last paragraph. It is stated that the targeting of Myo10 was reported/proposed based on previous work (ref 31). The next few sentences are not referenced and thus likely refer to ref 31. The authors did not measure the parameters discussed in these sentences, so it is important to clarify that they are referring to previous work and not the current study.

Indeed, the next few sentences still refer to old reference 31, so we have now edited the paragraph for clarity.

On page 7, the reference to Figure 3A indicates that the trend of higher Myo10 correlating with more filopodia. However, the reference to Figure 3B indicates total intracellular Myo10 weakly correlates with more filopodia. However, the x-axis on Figure 3B is filopodia molecules not the intracellular Myo10. Please clarify.

We appreciate the reviewer for catching our mistake. Those plots are now in Fig. 2 and have been edited accordingly.

Reviewer #3 (Recommendations For The Authors):

The Discussion of results at the end of each section is rather brief and could be expanded on a bit more.

Before we were operating under the constraints of an eLife Short Report. We have now expanded the discussion for a full article.

The authors mention that actin filaments at the tips of filopodia could be frayed, citing Medalia et al, 2007 (ref 40). That paper describes an early cryoEM analysis of filopodia from the amoeba Dictyostelium. EM images of mammalian filopodia tips, e.g. Svitkina et al, 2003, JCB, do not show quite the same organization of actin as seen in the Dictyostelium filopodia tips. However, recent work from the Bershadsky lab, Li et al, 2023, presents a few cryoEM images of tips of left-bent filopodia that are tightly adhered to a substrate and there it looks like actin filaments become disorganized in tips, along with membrane bulging. The authors should consider expanding discussion of the filopodia tips to take into account what is known for mammalian filopodia.

We thank the reviewer for bringing these enlightening papers to our attention. We have now included these citations in the discussion.

Fig 1D - The x-axis is a bit odd, it goes from 0 then to 2.5e+06 with no indication of the bin size. Can this be re-labelled or the scale displayed a bit differently?

We have double-checked the axis breaks, which are large because the underlying values are large. We have also provided the bin size as requested for all histograms.

Fig 4A - What is the bin size for the histogram?

As above, we have now updated the figure legends (now in Fig. 3) to include the bin size.

Methods -

- Please provide an accession number for the Myo10 nucleotide sequence used for this work as there are at least two known isoforms.

Thank you for noting this. We are using the full-length, not the headless isoform. We have now updated the Methods accordingly.

- No mention is made of the SDS sample buffer used, was that also added to the sample?

We have now updated the Methods accordingly.

- How are samples boiled at 70 deg C? Do the authors actually mean ‘heated’?

Indeed. We have now corrected “boiled” to “heated.”

- Could the authors please briefly explain the connected component analysis used to identify filopodia?

We have now updated the Methods accordingly.

- The intensity of filopodia was determined by dividing tip intensity by the total bioreplicate sum of intensities then multiplying it by the total pool, if this reviewer understands correctly. It sounds like intensities are being averaged across a whole cell population instead of cell-by-cell. Is that correct? If so, can the authors please provide the underlying rationale for this? If not, then please better describe what was actually done.

We apologize for the confusion. Intensities are being averaged (summed) across a whole cell population, but importantly that step is only used to obtain a scale factor that converts the fluorescence signal at the microscope to the number of molecules. We then use that scale factor for all cells imaged in the bioreplicate, to both 1) find the total Myo10 in that cell, and 2) find the total amount of that Myo10 in any given location within that cell.

To further clarify, each bioreplicate has a known total number of Myo10 molecules associated with the number of cells loaded onto the SDS gel. From the SDS gel, we have an average number of Myo10 molecules per positively transfected cell. If 50 cell images are analyzed, then there is a Myo10 ‘total pool’ of (50 cells) * (average Myo10 molecules/cell). The fluorescence signal intensities in microscopy were summed for all cells within the bioreplicate (50 cells in this example). However, due to variation in expression, not every cell has the same signal intensity when imaged under the same conditions. It would be inaccurate to assume each cell contains the average Myo10 molecules/cell. Therefore, to get the number of molecules within a given Myo10 cell (or punctum), the summed cell (punctum) intensity was divided by the bioreplicate fluorescence signal intensity sum and multiplied by ‘total pool.’

- The authors quantify Myo10 protein amounts by western blotting using Halo tag fluorescence, a method that should provide good accuracy. The results depend on the transfection efficiency and it is rarely the case that it is 100%. The authors state that they use a ‘value correction for positively transfected cells’ (pg 11). It is likely that there was a range of expression levels in the cells, how was a cut-off for classifying a cell as non-expressing determined or set?

As described in the Methods, “microscopy was used to count the percentage of transfected cells from ~105-190 randomly surveyed cells per bioreplicate.” Cells were labeled and located with DAPI. If no TMR signal could be visually detected by microscopy, then the cell was deemed to be non-Myo10 expressing. We did not set a cutoff fluorescence value, as untransfected cells have no detectable signal. Please see Supplementary Figure 1F for examples.

- “In-house Python scripts” are used for image analysis. Will these be made publicly available?

Yes, we will package these up on GitHub.

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eLife assessment

Chang et al. have investigated the catalytic mechanism of I-PpoI nuclease, a one-metal-ion dependent nuclease, by time-resolved X-ray crystallography using soaking of crystals with metal ions under different pH conditions. This convincing study revealed that I-PpoI catalyzes the reaction process through a single divalent cation. The study uncovers important details of the roles of the metal ion and the active site histidine in catalysis.

Reviewer #1 (Public Review):

This study is convincing because they performed time-resolved X-ray crystallography under different pH conditions using active/inactive metal ions and PpoI mutants, as with the activity measurements in solution in conventional enzymatic studies. Although the reaction mechanism is simple and may be a little predictable, the strength of this study is that they were able to validate that PpoI catalyzes DNA hydrolysis through "a single divalent cation" because time-resolved X-ray study often observes transient metal ions which are important for catalysis but are not predictable in previous studies with static structures such as enzyme-substrate analog-metal ion complexes. The discussion of this study is well supported by their data. This study visualized the catalytic process and mutational effects on catalysis, providing new insight into the catalytic mechanism of I-PpoI through a single divalent cation. The authors found that His98, a candidate of proton acceptor in the previous experiments, also affects the Mg2+ binding for catalysis without the direct interaction between His98 and the Mg2+ ion, suggesting that "Without a proper proton acceptor, the metal ion may be prone for dissociation without the reaction proceeding, and thus stable Mg2+ binding was not observed in crystallo without His98". In future, this interesting feature observed in I-PpoI should be investigated by biochemical, structural, and computational analyses using other metal-ion dependent nucleases.

Reviewer #2 (Public Review):

Summary:

Most polymerases and nucleases use two or three divalent metal ions in their catalytic functions. The family of His-Me nucleases, however, use only one divalent metal ion, along with a conserved histidine, to catalyze DNA hydrolysis. The mechanism has been studied previously but, according to the authors, it remained unclear. By use of a time resolved X-ray crystallography, this work convincingly demonstrated that only one M2+ ion is involved in the catalysis of the His-Me I-PpoI 19 nuclease, and proposed concerted functions of the metal and the histidine.

Strengths:

This work performs mechanistic studies, including the number and roles of metal ion, pH dependence, and activation mechanism, all by structural analyses, coupled with some kinetics and mutagenesis. Overall, it is a highly rigorous work. This approach was first developed in Science (2016) for a DNA polymerase, in which Yang Cao was the first author. It has subsequently been applied to just 5 to 10 enzymes by different labs, mainly to clarify two versus three metal ion mechanisms. The present study is the first one to demonstrate a single metal ion mechanism by this approach.

Furthermore, on the basis of the quantitative correlation between the fraction of metal ion binding and the formation of product, as well as the pH dependence, and the data from site-specific mutants, the authors concluded that the functions of Mg2+ and His are a concerted process. A detailed mechanism is proposed in Figure 6.

Even though there are no major surprises in the results and conclusions, the time-resolved structural approach and the overall quality of the results represent a significant step forward for the Me-His family of nucleases. In addition, since the mechanism is unique among different classes of nucleases and polymerases, the work should be of interest to readers in DNA enzymology, or even mechanistic enzymology in general.

Weaknesses:

Two relatively minor issues are raised here for consideration:<br /> p. 4, last para, lines 1-2: "we next visualized the entire reaction process by soaking I-PpoI crystals in buffer....". This is a little over-stated. The structures being observed are not reaction intermediates. They are mixtures of substrates and products in the enzyme-bound state. The progress of the reaction is limited by the progress of the soaking of the metal ion. Crystallography has just been used as a tool to monitor the reaction (and provide structural information about the product). It would be more accurate to say that "we next monitored the reaction progress by soaking....".

p. 5, the beginning of the section. The authors on one hand emphasized the quantitative correlation between Mg ion density and the product density. On the other hand, they raised the uncertainty in the quantitation of Mg2+ density versus Na+ density, thus they repeated the study with Mn2+ which has distinct anomalous signals. This is a very good approach. However, there is still no metal ion density shown in the key Figure 2A. It will be clearer to show the progress of metal ion density in a figure (in addition to just plots), whether it is Mg or Mn.

Author response:

Public Reviews: 

Reviewer #1 (Public Review): 

This study is convincing because they performed time-resolved X-ray crystallography under different pH conditions using active/inactive metal ions and PpoI mutants, as with the activity measurements in solution in conventional enzymatic studies. Although the reaction mechanism is simple and may be a little predictable, the strength of this study is that they were able to validate that PpoI catalyzes DNA hydrolysis through "a single divalent cation" because time-resolved X-ray study often observes transient metal ions which are important for catalysis but are not predictable in previous studies with static structures such as enzyme-substrate analog-metal ion complexes. The discussion of this study is well supported by their data. This study visualized the catalytic process and mutational effects on catalysis, providing new insight into the catalytic mechanism of I-PpoI through a single divalent cation. The authors found that His98, a candidate of proton acceptor in the previous experiments, also affects the Mg2+ binding for catalysis without the direct interaction between His98 and the Mg2+ ion, suggesting that "Without a proper proton acceptor, the metal ion may be prone for dissociation without the reaction proceeding, and thus stable Mg2+ binding was not observed in crystallo without His98". In future, this interesting feature observed in I-PpoI should be investigated by biochemical, structural, and computational analyses using other metal-ion dependent nucleases. 

We appreciate the reviewer for the positive assessment as well as all the comments and suggestions.

Reviewer #2 (Public Review): 

Summary: 

Most polymerases and nucleases use two or three divalent metal ions in their catalytic functions. The family of His-Me nucleases, however, use only one divalent metal ion, along with a conserved histidine, to catalyze DNA hydrolysis. The mechanism has been studied previously but, according to the authors, it remained unclear. By use of a time resolved X-ray crystallography, this work convincingly demonstrated that only one M2+ ion is involved in the catalysis of the His-Me I-PpoI 19 nuclease, and proposed concerted functions of the metal and the histidine. 

Strengths: 

This work performs mechanistic studies, including the number and roles of metal ion, pH dependence, and activation mechanism, all by structural analyses, coupled with some kinetics and mutagenesis. Overall, it is a highly rigorous work. This approach was first developed in Science (2016) for a DNA polymerase, in which Yang Cao was the first author. It has subsequently been applied to just 5 to 10 enzymes by different labs, mainly to clarify two versus three metal ion mechanisms. The present study is the first one to demonstrate a single metal ion mechanism by this approach. 

Furthermore, on the basis of the quantitative correlation between the fraction of metal ion binding and the formation of product, as well as the pH dependence, and the data from site-specific mutants, the authors concluded that the functions of Mg2+ and His are a concerted process. A detailed mechanism is proposed in Figure 6. 

Even though there are no major surprises in the results and conclusions, the time-resolved structural approach and the overall quality of the results represent a significant step forward for the Me-His family of nucleases. In addition, since the mechanism is unique among different classes of nucleases and polymerases, the work should be of interest to readers in DNA enzymology, or even mechanistic enzymology in general. 

Thank you very much for your comments and suggestions.

Weaknesses: 

Two relatively minor issues are raised here for consideration: 

p. 4, last para, lines 1-2: "we next visualized the entire reaction process by soaking I-PpoI crystals in buffer....". This is a little over-stated. The structures being observed are not reaction intermediates. They are mixtures of substrates and products in the enzyme-bound state. The progress of the reaction is limited by the progress of the soaking of the metal ion. Crystallography has just been used as a tool to monitor the reaction (and provide structural information about the product). It would be more accurate to say that "we next monitored the reaction progress by soaking....". 

We appreciate the clarification regarding the description of our experimental approach. We agree that our structures do not represent reaction intermediates but rather mixtures of substrate and product states within the enzyme-bound environment. We will revise the text accordingly to more accurately reflect our methodology.

p. 5, the beginning of the section. The authors on one hand emphasized the quantitative correlation between Mg ion density and the product density. On the other hand, they raised the uncertainty in the quantitation of Mg2+ density versus Na+ density, thus they repeated the study with Mn2+ which has distinct anomalous signals. This is a very good approach. However, there is still no metal ion density shown in the key Figure 2A. It will be clearer to show the progress of metal ion density in a figure (in addition to just plots), whether it is Mg or Mn. 

Thank you for your insightful comments. We recognize the importance of visualizing metal ion density alongside product density data. As you commented, distinguishing between Mg2+ and Na+ is challenging, and in Fig 2A, no distinguishable density was observed at 20s. Mn2+, with its higher electron density, is detectable even at low occupancy. To address this, we will include figure panels in Figure 3 or supplementary figures to present Mn2+ and product densities concurrently.

Reviewer #3 (Public Review):

Summary:

Baek and colleagues present important follow-up work on the role of serum glucose in the management of neonatal sepsis. The authors previously showed high glucose administration exacerbated neonatal sepsis, while strict glucose control improved outcomes but caused hypoglycemia. In the current report they examined the effect of a more tailored glucose management approach on outcomes and examined hepatic gene expression, plasma metabolome/proteome, blood transcriptome, as well as the the therapeutic impact of hIAIP. The authors leverage multiple powerful approaches to provide robust descriptive accounts of the physiologic changes that occur with this model of sepsis in these various conditions.

Strengths:

(1) Use of preterm piglet model.

(2) Robust, multi-pronged approach to address both hepatic and systemic implications of sepsis and glucose management.

(3) Trial of therapeutic intervention - glucose management (Figure 6), hIAIP (Figure 7).

Weaknesses:

(1) The translational role of the model is in question. CONS is rarely if ever a cause of EOS in preterm neonates. The model. uses preterm pigs exposed at 2 hours of age. This model most likely replicates EOS.

(2) Throughout the manuscript it is difficult to tell from which animals the data are derived. Given the ~90% mortality in the experimental CONS group, and 25% mortality in the intervention group, how are the data from animals "at euthanasia" considered? Meaning - are data from survivors and those euthanized grouped together? This should be clarified as biologically these may be very different populations (ie, natural survivor vs death).

(3) With limited time points (at euthanasia ) for hepatic transcriptomics (Figure 2), plasma metabolite (Figure 3) blood transcriptome (Figure 4), and plasma proteome (Figure 5) it is difficult to make conclusions regarding mechanisms preceding euthanasia. Per methods, animals were euthanized with acidosis or clinical decompensation. Are the reported findings demonstrative of end-organ failure and deterioration leading to death, or reflective of events prior?

(4) Data are descriptive without corresponding "omics" from interventions (glucose management and/or hIAIP) or at least targeted assessment of key differences.

eLife assessment

This interesting and important study follows up on the authors' observations that lower glucose parental nutrition leads to lower rates of sepsis from Staphylococcus epidermis in a preterm pig model. Sepsis in early life, particularly in premature infants, has significant morbidity and mortality and the authors present convincing evidence that glycemic state affects hepatic metabolism-dependent immune function and improved clearance of coagulase-negative staphylococcal infection. The authors provide a robust multi-omic dataset for the use of the scientific community. However, there are also several concerns that will limit the impact of the work, including that the model does not reflect early onset sepsis that is observed in premature infants, and the question of whether low glucose parental nutrition (PN) is protective versus high glucose PN is harmful as the levels of glucose in the high PN were incredibly high.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors follow up on their published observation that providing a lower glucose parental nutrition (PN) reduces sepsis from a common pathogen [Staphylococcus epidermitis (SE)] in preterm piglets. Here they found that a higher dose of glucose could thread the needle and get the protective effects of low glucose without incurring significant hypoglycemia. They then investigate whether the change in low glucose PN impacts metabolism to confer this benefit. The finding that lower glucose reduces sepsis is important as sepsis is a major cause of morbidity and mortality in preterm infants, and adjusting PN composition is a feasible intervention.

Strengths:

(1) They address a highly significant problem of neonatal sepsis in preterm infants using a preterm piglet model.

(2) They have compelling data in this paper (and in a previous publication, ref 27) that low glucose PN confers a survival advantage. A downside of the low glucose PN is hypoglycemia which they mitigate in this paper by using a slightly high amount of glucose in the PN.

(3) The experiment where they change PN from high to low glucose after infection is very important to determine if this approach might be used clinically. Unfortunately, this did not show an ability to reduce sepsis risk with this approach. Perhaps this is due to the much lower mortality in the high glucose group (~20% vs 87% in the first figure).

(4) They produce an impressive multiomics data set from this model of preterm piglet sepsis which is likely to provide additional insights into the pathogenesis of preterm neonatal sepsis.

Weaknesses:

(1) The high glucose control gives very high blood glucose levels (Figure 1C). Is this the best control for typical PN and glucose control in preterm neonates? Is the finding that low glucose is protective or high glucose is a risk factor for sepsis?

(2) In Figure 1B, preterm piglets provided the high glucose PN have 13% survival while preterm piglets on the same nutrition in Figure 6B have ~80% survival. Were the conditions indeed the same? If so, this indicates a large amount of variation in the outcome of this model from experiment to experiment.

(3) Piglets on the low glucose PN had consistently lower density of SE (~1 log) across all time points. This may be due to changes in immune response leading to better clearance or it could be due to slower growth in a lower glucose environment.

(4) Many differences in the different omics (transcriptomics, metabolomics, proteomics) were identified in the SE-LOW vs SE-HIGH comparison. Since the bacterial load is very different between these conditions, could the changes be due to bacterial load rather than metabolic reprogramming from the low glucose PN?

Reviewer #2 (Public Review):

Summary:

The authors demonstrate that a low parenteral glucose regimen can lead to improved bacterial clearance and survival from Staph epi sepsis in newborn pigs without inducing hypoglycemia, as compared to a high glucose regimen. Using RNA-seq, metabolomic, and proteomic data, the authors conclude that this is primarily mediated by altered hepatic metabolism.

Strengths:

Well-defined controls for every time point, with multiple time points and biological replicates.

The authors used different experimental strategies to arrive at the same conclusion, which lends credibility to their findings.

The authors have published the negative findings associated with their study, including the inability to reverse sepsis-related mortality after switching from SE-high to SE-low at 3h or 6h and after administration of hIAIP.

Weaknesses:

(1) The authors mention, and it is well-known, that Staph epi is primarily involved in late-onset sepsis. The model of S. epi sepsis used in this study clearly replicates early-onset sepsis, but S. epi is extremely rare in this time period. How do the authors justify the clinical relevance of this model?

(2) The authors find that the neutrophil subset of the leukocyte population is diminished significantly in the SE-low and SE-high populations. However, they conclude on page 10 that "modulations of hepatic, but not circulating immune cell metabolism, by reduced glucose supply..." and this is possible because the authors have looked at the entire leukocyte transcriptome. I am curious about why the authors did not sequence the neutrophil-specific transcriptome.

(3) The authors use high (30g/k/d) and low (7.2g/k/d) glucose regimens. These translate into a GIR of 21 and 5 mg/k/min respectively. A normal GIR for a preterm infant is usually 5-8, and sometimes up to 10. Do the authors have a "safe GIR" or a threshold they think we cannot cross? Maybe a point where the metabolism switch takes place? They do not comment on this, especially as GIR and glucose levels are continuous variables and not categorical.

(4) In Figures 2B and C the authors show that SE-high and SE-low animals have differences in the oxphos, TCA, and glycolytic pathways. The authors themselves comment in the Supplementary Table S1B, E-F that these same metabolic pathways are also different in the Con-Low and Con-high animals, it is just the inflammatory pathways that are not different in the non-infected animals. How can they then justify that it is these metabolic pathways specifically which lead to altered inflammatory pathways, and not just the presence of infection along with some other unfound mechanism?

(5) The authors mention in Figure 1F that SE-low animals had lower bacterial burdens than SE-high animals, but then go on to infer that the inflammatory cytokine differences are attributed to a rewiring of the immune response. However, they have not normalized the cytokine levels to the bacterial loads, as the differences in the cytokines might be attributed purely to a difference in bacterial proliferation/clearing.

(6) The authors mention that switching from SE-high to SE-low at 3 or 6 h time points does not reduce mortality. Have the authors considered the reverse? Does hyperglycemia after euglycemia initially, worsen mortality? That would really conclude that there is some metabolic reprogramming happening at the very onset of sepsis and it is a lost battle after that.

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Version 2 of this preprint has been peer-reviewed and recommended by Peer Community in Ecology.<br /> See the peer reviews and the recommendation.

Mytest

eLife assessment

This study provides an important advance in the molecular understanding of the lipopolysaccharide export mechanism and machinery in bacteria. By using advanced spectroscopy approaches, the experiments provide solid biophysical support for the dynamic behavior of the multisubunit Lpt transport system. This work has implications for understanding bacterial cell envelope biogenesis and may contribute to the development of drugs that target Gram-negative pathogens.

Reviewer #2 (Public Review):

Lipopolysaccharide (LPS) is a major component of the outer membrane of Gram-negative bacteria and plays a critical role in bacterial virulence. The LPS export mechanism is a potential target for new antibiotics. Inhibiting this process can render bacteria more susceptible to the host immune system or other antibacterial agents. Given the rise of antibiotic-resistant bacteria, novel targets are urgently needed. The seven LPS transport (Lpt) proteins, A-G, move LPS from the inner to the outer membrane. This study investigated the conformational changes in the LptB2FG-LptC complex using site-directed spin labeling (SDSL) electron paramagnetic resonance (EPR) spectroscopy, revealing how ATP binding and hydrolysis affect the LptF β-jellyroll domain and lateral gates. The findings highlight the role of LptC in regulating LPS entry, ensuring efficient and unidirectional transport across the periplasm.

The β-jellyrolls are not fully resolved in the vanadate-trapped structure of LptB2FG and LptB2FGC. Therefore, the current study provides valuable information on the functional dynamics of these periplasmic domains, their interactions, and their roles in the unidirectional transport of LPS. Additionally, the dynamic perspective of the lateral gates in LptFG in the presence and absence of LptC is another strength of this study. Moreover, at least in detergent samples, more comprehensive intermediates of the ATP turnover cycle are studied than in the available structures, providing crucial missing mechanistic details.

Other major strengths of the study include high-quality DEER distance measurements in both detergent and proteoliposomes, the latter providing valuable dynamics information in the lipid environment. However, lipid composition is not mentioned. The proteoliposome study is crucial since the previous structural study (Li, Orlando & Liao 2019) was done in rather small-diameter nanodiscs, which might affect the overall dynamics of the complex. It would have been beneficial if the investigators had reconstituted the complex in lipid nanodiscs with the same composition as proteoliposomes. The mixed lipid/detergent micelles provide an alternative. It seems the ATPase activity of the protein complex is much lower in detergent compared with lipid nanodiscs (Li, Orlando & Liao 2019). In the current study, ATPase activity in proteoliposomes is not provided. Also, the reviewer assumes cysteine-less (CL) constructs of the complex components were utilized. The ATPase assay on CL complex is not presented.

Additionally, from previous structural studies and the mass spectrometry data presented here, LPS co-purifies and is already bound to the complex, thus the Apo state may represent the LPS-bound state without nucleotides.

The selection of sites to probe lateral gate 2, which forms the main LPS entry site, may pose an issue. Although the authors provide justification based on the available structures, one site (position 325 in LptF) is located on a flexible loop, and position 52 in LptG is on the neighboring transmembrane helix, separated by a potentially flexible loop from the gating TM1. These labeling sites could exhibit significant local dynamics, resulting in a broader distribution of distances and potentially masking the gating-related conformational changes.

Reviewer #1 (Public Review):

Summary:

The current manuscript uses electron spin resonance spectroscopy to understand how the dynamic behavior and conformational heterogeneity of the LPS transport system change during substrate transport and in response to the membrane, bound nucleotide (or transition state analog), and accessory subunits. The study builds on prior structural studies to expand our molecular understanding of this highly significant bacterial transport system.

Strengths

This series of well-designed and well-executed experiments provides new mechanistic insights into the dynamic behavior of the LPS transport system. Notable new insights provided by this study include its indication of the spatial organization of the LptC domain, which was poorly resolved in structures, and how the LptC domain modulates the dynamic behavior of the gate through which lipids access the binding site. In addition, a mass spectrometry approach designed to examine LPS binding at different stages in the nucleotide-dependent conformational cycle provides insight into the order of operations of LPS binding and transport.

Reviewer #3 (Public Review):

Summary:

The manuscript by Dajka and co-workers reports the application of a biophysical approach to analyse the dynamics of the LptB2FG-C ABC transporter, involved in LPS transport across the cell envelope in Escherichia coli. LptB2FG-C belongs to a new class of ABC transporters (type VI) and is essential and conserved in several Gram-negative pathogens. Since LPS is the major component of the outer membrane of the Gram-negative cell and is responsible for the low permeability of this membrane to several antibiotics, a deep understanding of the mechanism and function of the LptB2FG-C transporter is crucial for the development of new drugs targeting Gram-negative pathogens.

Several structural studies have been published so far on the LptB2FG-C transporter, disclosing important aspects of the transport mechanism; nevertheless, lack of resolution of some regions of the individual proteins as well as the dynamic nature of the transport mechanism per se (e.g. the insertion and removal of the TM helix of LptC from the TMDs of the transporter during the LPS transport cycle) has greatly limited the understanding of the mechanism that couples ATP binding and hydrolysis with LPS transport. This knowledge gap could be filled by applying an approach that allows the analysis of dynamic processes. The DEER/PELDOR technique applied in this work fits well with this requirement.

Strengths:

In this study, the authors provide some new pieces of information on the LptB2FG-C function and the role of LptC in the transporter. Notably, they show that:

-there is high heterogeneity in the conformational states of the entry gate of LPS in the transporter (gate-2) that are reduced by the insertion of LptC, and the heterogeneity observed is not altered by ATP binding or hydrolysis (as expected since LPS entry is ATP-independent).

-ATP binding induces an allosteric opening of LptF β-jellyroll domain that allows for LPS passage to the β-jellyroll of LptC, which is stably associated with the β-jellyroll of LptF throughout the cycle.

- the β-jellyroll of LptG is highly flexible, indicating an involvement in the LPS transport cycle.

The manuscript is timely and overall clear.

Weaknesses:

I list my concerns below and provide suggestions that, in my opinion, should be addressed to reinforce the findings of this study.

(1) Protein complex controls: the authors assess the ATPase activity of the spin-labelled variants of their protein complexes to rule out the possibility that engineering the proteins to enable spin labelling could affect their functionality (Figure S4). It has been reported that the association of LptC to LptB2FG complex inhibits its ATPase activity. However, in the ATPase assay data shown in Figure S4, the inhibitory effect of the LptC TM is not visible (please compare LptB2FG F-A45C G-I335C and F-L325C G-A52C with and without LptC). This can lead to suspect that the regulatory function of LptC is missing in the LptC-containing complexes used in this work. I suggest the authors include wt LptB2FGC in the assay to compare the ATPase activity of this complex with wt LptB2FG. The published inhibitory effect of TM LptC has been observed in proteoliposomes. Since it is not clear from the paper if the ATPase assay in Figure 4 has been conducted in DDM or proteoliposomes, the lack of inhibitory effect could be due to the assay conditions. A comparative test could answer this question.

(2) Figure 2: NBD closure upon ATP binding to LptB2FG is convincingly demonstrated both in DDM micelles and proteoliposomes, validating the experimental system. However, since under physiological conditions, ATP binding should take place before the displacement of the TM of LptC (Wilson and Ruiz, Mol microbiol 2022), I suggest the authors carry out the experiments with LptC-containing complexes to investigate conformational changes (if any) that are triggered when ATP binding occurs before the TM displacement.

(3) Proteoliposomes: in the experiments shown in Figures 3 and 4, unlike those in Figure 2, measurements in proteoliposomes give different results from the experiments in DDM, showing higher heterogeneity. Could this be related to the presence (or absence) of LPS in liposomes? It is not mentioned in the materials and methods section whether LPS is present. Could the authors please discuss this?

(4) The authors show large conformational heterogeneity in gate-2 (using the spin-labelled pair F-L325R1-G-A52R1) and suggest that deviation from the corresponding simulations could be due to the need for enhanced dynamics to allow for gate interaction with LPS or LptC. The effect of LptC is probed in the experiments shown in Figure 6, but I suggest the authors add LPS to the complexes to evaluate the possible stabilizing effect of LPS on the conformations shown in Figure 4.

(5) Figure 6: the measurement of lateral gate 1 and 2 dynamics in the LptC-containing complexes clearly supports the hypothesis, proposed based on the available structures, that TM LptC dissociates from LptB2FG upon ATP binding. However, direct evidence of this movement is still missing. Would it be possible to monitor the dynamics of the TM LptC by directly labelling this protein domain? This would give a conclusive demonstration of the displacement during the ATPase cycle.

(6) LPS release assay: Figure 6 panels H-I-J show the MS spectra relative to LPS-bound and free proteins obtained from wt LptB2FG upon ATP binding and ATP hydrolysis conditions. From these spectra the authors conclude that LPS is completely released only upon ATP hydrolysis. However, the current model predicts that LPS release into the Lpt bridge made by LptC-A-D is triggered by ATP binding. For this reason, I suggest the authors assess LPS release also from the LptB2FGC complex where, in the absence of LptA, LPS would be expected to be mostly retained by the complex under the same conditions.

In the context of your course, how would you benefit from the active learning approach the most?

Reviewer #1 (Public Review):

Summary:

Schafer et al. tested whether the hippocampus tracks social interactions as sequences of neural states within an abstract social space defined by dimensions of affiliation and power, using a task in which participants engaged in narrative-based social interactions. The findings of this study revealed that individual social relationships are represented by unique sequences of hippocampal activity patterns. These neural trajectories corresponded to the history of trial-to-trial affiliation and power dynamics between participants and each character, suggesting an extended role of the hippocampus in encoding sequences of events beyond spatial relationships.

The current version has limited information on details in decoding and clustering analyses which can be improved in the future revision.

Strengths:

(1) Robust Analysis: The research combined representational similarity analysis with manifold analyses, enhancing the robustness of the findings and the interpretation of the hippocampus's role in social cognition.

(2) Replicability: The study included two independent samples, which strengthens the generalizability and reliability of the results.

Weaknesses:

I appreciate the authors for utilizing contemporary machine-learning techniques to analyze neuroimaging data and examine the intricacies of human cognition. However, the manuscript would benefit from a more detailed explanation of the rationale behind the selection of each method and a thorough description of the validation procedures. Such clarifications are essential to understand the true impact of the research. Moreover, refining these areas will broaden the manuscript's accessibility to a diverse audience.

Reviewer #2 (Public Review):

Summary:

Using an innovative task design and analysis approach, the authors set out to show that the activity patterns in the hippocampus related to the development of social relationships with multiple partners in a virtual game. While I found the paper highly interesting (and would be thrilled if the claims made in the paper turned out to be true), I found many of the analyses presented either unconvincing or slightly unconnected to the claims that they were supposed to support. I very much hope the authors can alleviate these concerns in a revision of the paper.

Strengths & Weaknesses:

(1) The innovative task design and analyses, and the two independent samples of participants are clear strengths of the paper.

(2) The RSA analysis is not what I expected after I read the abstract and tile of the result section "The hippocampus represents abstract dimensions of affiliation and power". To me, the title suggests that the hippocampus has voxel patterns, which could be read out by a downstream area to infer the affiliation and power value, independent of the exact identity of the character in the current trial. The presented RSA analysis however presents something entirely different - namely that the affiliation trials and power trials elicit different activity patterns in the area indicated in Figure 3. What is the meaning of this analysis? It is not clear to me what is being "decoded" here and alternative explanations have not been considered. How do affiliation and power trials differ in terms of the length of sentences, complexity of the statements, and reaction time? Can the subsequent decision be decoded from these areas? I hope in the revision the authors can test these ideas - and also explain how the current RSA analysis relates to a representation of the "dimensions of affiliation and power".

(3) Overall, I found that the paper was missing some more fundamental and simpler RSA analyses that would provide a necessary backdrop for the more complicated analyses that followed. Can you decode character identity from the regions in question? If you trained a simple decoder for power and affiliation values (using the LLE, but without consideration of the sequential position as used in the spline analysis), could you predict left-out trials? Are affiliation and power represented in a way that is consistent across participants - i.e. could you train a model that predicts affiliation and power from N-1 subjects and then predict the Nth subject? Even if the answer to these questions is "no", I believe that they are important to report for the reader to get a full understanding of the nature of the neural representations in these areas. If the claim is that the hippocampus represents an "abstract" relationship space, then I think it is important to show that these representations hold across relationships. Otherwise, the claim needs to be adjusted to say that it is a representation of a relationship-specific trajectory, but not an abstract social space.

(4) To determine that the location of a specific character can be decoded from the hippocampal activity patterns, the authors use a sequential analysis in a low-dimensional space (using local linear embedding). In essence, each trial is decoded by finding the pair of two temporally sequential trials that is closest to this pattern, and then interpolating the power/affiliation values linearly between these two points. The obvious problem with this analysis is that fMRI pattern will have temporal autocorrelation and the power and affiliation values have temporal autocorrelation. Successful decoding could just reflect this smoothness in both time series. The authors present a series of control analyses, but I found most of them to not be incisive or convincing and I believe that they (and their explanation of their rationale) need to be improved. For example, the circular shifting of the patterns preserves some of the autocorrelation of the time series - but not entirely. In the shifted patterns, the first and last items are considered to be neighboring and used in the evaluation, which alone could explain the poor performance. The simplest way that I can see is to also connect the first and last item in a circular fashion, even when evaluating the veridical ordering. The only really convincing control condition I found was the generation of new sequences for every character by shuffling the sequence of choices and re-creating new artificial trajectories with the same start and endpoint. This analysis performs much better than chance (circular shuffling), suggesting to me that a lot of the observed decoding accuracy is indeed simply caused by the temporal smoothness of both time series.

(5) Overall, I found the analysis of the brain-behavior correlation presented in Figure 5 unconvincing. First, the correlation is mostly driven by one individual with a large network size and a 6.5 cluster. I suspect that the exclusion of this individual would lead to the correlation losing significance. Secondly, the neural measure used for this analysis (determining the number of optimal clusters that maximize the overlap between neural clustering and behavioral clustering) is new, non-validated, and disconnected from all the analyses that had been reported previously. The authors need to forgive me for saying so, but at this point of the paper, would it not be much more obvious to use the decoding accuracy for power and affiliation from the main model used in the paper thus far? Does this correlate? Another obvious candidate would be the decoding accuracy for character identity or the size of the region that encodes affiliation and power. Given the plethora of candidate neural measures, I would appreciate if the authors reported the other neural measures that were tried (and that did not correlate). One way to address this would have been to select the method on the initial sample and then test it on the validation sample - unfortunately, the measure was not pre-registered before the validation sample was collected. It seems that the correlation was only found and reported on the validation sample?

Author response:

a) that the investigation is very interesting and inventive, and has the potential to reveal some novel insights.

We thank the reviewers and are excited to improve upon the manuscript through their suggestions.

b) that the problem of temporal autocorrelation in the fMRI and behavioral data has not been dealt with clearly and convincingly

We agree that convincingly accounting for fMRI temporal autocorrelation is important to our claims. To reduce its effects, we used field standard methods: prewhitening and autocorrelation modeling with SPM’s FAST algorithm (shown by Olszowy et al. 2019 to be superior to SPM’s default setting), as well as a high-pass filter of 128 Hz. There is still some first-order autocorrelation structure present across voxels in the left hippocampal beta series: across participants there is slightly positive autocorrelation between the betas of decision trials on successive trials, that decays to ~0 at subsequent lags. We note that our task is a narrative, and some patterns over time are expected; instead of attempting to fully eliminate all temporal structure in the data, we aim to show that the temporal distance between trials is unlikely to explain our effects.

In the within versus between social dimension representational similarity analysis, the average temporal distance between trials is the same within and between dimensions. The clustering analysis is a between subject analysis about individual differences–and the same overall temporal structure is experienced by all participants.

The trajectory analysis does not focus on consecutive trials across characters, but rather on consecutive trials within characters, where the time gap between successive trials is relatively large and highly variable. An average of over a minute of time elapses between successive decision trials for a given character (versus ~20 seconds across characters), which is on average almost 11 narrative slides and 3 decision trials. Across characters, the temporal gap between decision trials ranges between 12 seconds to more than 10 minutes, reducing the likelihood that temporal autocorrelation drives character-related estimates. We also highlight the shuffled choices control model, which shares the same temporal autocorrelation structure as the model of interest but had significantly poorer social location decoding–a strong indication that temporal autocorrelation alone can’t explain these results. For each participant, we shuffled their choices and re-computed trajectories that preserved the origin and end locations but produced different locations along the way. Our model decoded location significantly better than this null model, and this difference in performance can't be explained by differences in temporal autocorrelation in the neural or behavioral data.

In the revision, we will further address this concern. For example, we will report more details on the task structure to aid in interpretation and will more precisely characterize the temporal autocorrelation profile. Where appropriate, we will also improve on and/or add more control analyses that preserve the autocorrelation structure.

c) that a number of important interesting questions have not been addressed: Are the differences between social partners encoded in the hippocampus? Are the social dimensions encoded in a consistent manner across social partners?

We believe that we should be able to decode other interesting task- and relationship-related features from the hippocampal patterns, as suggested by the reviewers. In the revision, we will attempt several such analyses, while taking care to control for temporal autocorrelation.

d) that the cluster analysis in the brain-behavior correlation analysis is not well motivated or validated and should be clarified.

We agree with the reviewers that this clustering analysis should be better described and validated. We aimed to ask whether less diverse and distinctive cognitive representations of the relationship trajectories relate to smaller real-world social networks. This question of impoverished cognitive maps was first raised by Edward Tolman; we think it is relevant here, as well. In the revision, we will clarify its motivations and implications, and better evaluate it for its robustness. Here, we address a few comments made by the reviewers.

Reviewer 2 noted that other analyses could be used to ask whether social cognitive map complexity relates to real-world social network complexity. While the proposed alternatives are interesting (e.g., correlating decoding accuracy with social network size), we believe these analyses ask different questions. The current co-clustering analysis was intended to estimate map complexity jointly from the behavioral and neural signatures of the social map across characters. In contrast, the spline location decoding is within character; the accuracy of this decoding does not say much about representations across characters. And although we think character decoding is an interesting possible addition to this manuscript, its accuracy may reflect other aspects of the relationships, beyond just spatial representation. Thus, we will provide a clearer and better validated version of the current analysis to address this question.

We would also like to clarify that we did not collect the Social Network Index questionnaire in the Initial sample; as such these results are more tentative than the other analyses, due to the inability to confirm them in a separate sample. Reviewer 2 also suggests that a single outlier could drive this effect; but estimating the effect with robust regression also returns a right-tailed p < 0.05, showing that the relationship is robust to outliers.

References

Olszowy, W., Aston, J., Rua, C. & Williams, W.B. Accurate autocorrelation modeling substantially improves fMRI reliability. Nature Communications. (2019).

Reviewer #1 (Public Review):

Summary:

An online database called MRAD has been developed to identify the risk or protective factors for AD.

Strengths:

This study is a very intriguing study of great clinical and scientific significance that provided a thorough and comprehensive evaluation with regard to risk or protective factors for AD. It also provided physicians and scientists with a very convenient, free as well as user-friendly tool for further scientific investigation.

Weaknesses:

(1) The paper mentions that the MRAD database currently contains data only from European populations, with no mention of data from other populations or ethnicities. Given potential differences in Alzheimer's Disease (AD) across different populations, the limitations of the data should be emphasized in the discussion, along with plans to expand the database to include data from more racial and geographic regions.

(2) Sufficient information should be provided to clarify the data sources, sample selection, and quality control methods used in the MRAD database. Readers may expect more detailed information about the data to ensure data reliability, representativeness, and research applicability.

(3) While the authors mention that the MRAD database offers interactive visualization interfaces, the paper lacks detailed information on how to interpret and understand these visual results. Guidelines on effectively using these visualization tools to help researchers better comprehend the data are essential.

(4) In the conclusion section of the paper, it is advisable to explicitly emphasize the practical applications and potential clinical significance of the MRAD database. The paper should articulate how MRAD can contribute to the early identification, diagnosis, prevention, and treatment of AD and its potential societal and clinical value more clearly.

(5) Grammar and Spelling Errors: There are several spelling and grammar errors in the paper. Referring to a scientific editing service is recommended.

A corriger: 1932.

A corriger et compléter: Administrateur général de la Bibliothèque nationale de 1930 à 1964. Juif, il est révoqué de son poste par le gouvernement de Vichy en 1940. Arrêté en 1941 pour activités anti-allemandes, il est déporté en 1944 à Buchenwald. Il retrouve son poste en 1945."

Nouveau lien: https://edoc.hu-berlin.de/handle/18452/19042 Voir aussi: https://mediarep.org/server/api/core/bitstreams/735f0a0d-8dab-43d1-98e3-df5efadd16b7/content

Le projet d'un cataloque national des ressources bibliographiques allemandes a été abandonné dès 1939.

eLife assessment

The study presents a valuable finding in advancing our understanding of the cellular and molecular mechanisms that regulate the switching of the migration mode from parallel to radial in cerebellar granule cell development. The evidence supporting the claims of the authors is solid and supports the main conclusion; the highlight was the imaging system's visualization of the cell-recognition event associated with neuronal migration, which established a new standard for the field. This study would be of interest to cell biologists and neurodevelopmental biologists working on cell-cell interaction and neuronal migration.

Reviewer #1 (Public Review):

This study by Hallada et al. reported the detailed characterization of cis and trans-binding of JAM-C in mediating the developmental migration of CGNs, combining ex vivo cultures, time-lapse imaging, and mathematical analyses. Overall, the study was comprehensively carried out, and the conclusion is important in our understanding of the signaling mechanism of cerebellar development.

Weaknesses:

Several technical concerns need to be clarified.

(1) The efficiency of shRNA knockdown of endogenous JAM-C. The entire study was based on the assumption that the endogenous wild-type JAM-C was depleted to the extent that it would not influence the observed phenotypes. However, this point requires verification, particularly in the ex vivo cultures.

(2) The expression levels of mutant JAM-C proteins. It is unclear whether the exogenous expression of mutant JAM-C proteins would be comparable to that of the endogenous JAM-C. In addition, the levels of exogenously expressed JAM-C may likely alter over the time course of experiments, e.g., in some experiments over 48 hours.

(3) The resolution of imaging methods. Different imaging methods were utilized in the study, and it is essential to clearly state the resolution of each imaging dataset (e.g., 0.2 x 0.2 um per pixel). This information is crucial to assess the reliability of observed phenotypes, which in some cases were relatively unimpressive.

Reviewer #2 (Public Review):

Summary:

Lamination is a layered neuronal arrangement that provides a basic frame to establish functional connectivity in the brain. The formation of a layered structure requires a highly coordinated interaction between migrating neurons and the developing microenvironment. Earlier studies revealed that to reach specific locations, migrating neurons typically follow various morphogen gradients. Here, Hallada et al. showed that cerebellar granule neurons (CGNs) could navigate via adhesive interaction with Junctional Adhesion Molecule C (JAM-C) followed by recruitment and distribution of intercellular partners (Pard3 and debris) at the contact sites. These results show that neuronal migration could be structured by specific interactions with adhesion molecules and spatial re-arrangements of downstream effectors.

Strengths:

The authors concluded that cis/trans binding sites of JAM-C on CGNs are crucial for contact formation with cerebellar glial cells (Bergman glial cells, BGs) and recruitment of Pard3 and drebrin to contact sites. This conclusion was based on the data obtained utilizing several advanced tools and technical approaches, such as cutting-edge microscopy, detailed visualization of cell-cell recognition, and a new correlation analysis.

Weaknesses:

(1) Despite multiple advanced methodologies, the study has weaknesses related primarily to the lack of specific evidence in support of findings and data interpretation issues. For example, it is unclear how JAM-C-mediated adhesion facilitates the entry of CGNs into the cerebellar molecular layer (ML). The authors described that CGN-CGN JAM recognition recruits more Pard3 and drebrin compared to CGN-BG recognition, which could increase the dwelling time of CGNs before moving to ML. However, such a mechanism does not explain what would initiate the entry of CGNs into ML. Perhaps the authors could provide a detailed explanation of this phenomenon in the Discussion (but certainly not in the Abstract). Also, the authors could consider revising the content of the Abstract, emphasizing their findings, and leaving out the speculations.

(2) To allow for comparison, it would be very helpful to indicate specific numerical values for each data point throughout the manuscript. For example, the authors stated that a change in instantaneous migration angle due to JAM-C silencing negatively affects CGNs movement to the ML (Figure 2) and that spatial distribution of negative JAM-Drebrin correlation is altered at CGN-CGN contacts (Figure 7). However, without specific values, it remains unclear what the magnitude of the discussed changes is or whether they were actually significant. It was not certainly straightforward to make specific conclusions based on graphical presentation alone.

Reviewer #3 (Public Review):

Summary:

This study elucidated the mechanism controlling the switch from parallel migration to radial migration during the development of cerebellar granule cells by analyzing the behavior of cell-type-specific JAM-mediated adhesion and the downstream factors that promote migration. The research represents a detailed analysis, employing probes to capture cell recognition events between different cell types, a co-culture system (monolayer culture and slice imaging), and imaging techniques, building upon the authors' prior research on JAM-Pard3 interactions. As a result, the authors found that:

(1) JAM-C-mediated interactions between granule cells (GCNs) are formed earlier and are more robust than JAM-C/JAM-B interactions between GCNs and glia;

(2) Recruitment of migration-promoting factors Pard3/Drebrin by JAM interactions is more efficient in GCN-GCN (JAM-C/JAM-C) interactions; and

(3) The distribution pattern of Pard3/Drebrin differs between GCN-GCN and GCN-Glia interactions, as revealed by detailed imaging analysis.

Consequently, the authors discovered that these differences contribute to a time lag between parallel and radial migration, which serves as a temporal checkpoint sorting mature cerebellar granule cells.

Strengths:

Cell migration is a commonly observed phenomenon in neural development. It is crucial for sorting specific cell populations and positioning them appropriately to develop proper neural circuits. While the regulation of these migrations is known to be mediated by secreted guidance factors, this study demonstrates that combinations of cell adhesion molecules (JAM) mediate cell type-specific interactions that contribute to the timing control of cell migration. This finding significantly advances our understanding of the mechanisms governing cell migration in neural development.

Weaknesses:

The author's hypothesis has been validated using in vitro systems. While in vitro systems allow for a more detailed design of experimental parameters, validation in vivo would still be necessary to demonstrate whether the temporal checkpoint of migration mediated by cell-cell interactions works. For example, knockout of JAM-C in cerebellar granule cells could be considered for such validation. Furthermore, the behavioral analysis of these mutant mice would be interesting.

Additionally, the author's observation that recruitment patterns of Pard3 and Drebrin at adhesive sites vary between interacting cell pairs is intriguing and suggests exciting implications. It would be highly informative if the relationship between these differences and ML entry timing could be demonstrated.

eLife assessment

Wittkamp et al. investigated the spatiotemporal dynamics of expectation of pain using an original fMRI-EEG approach. The methods are solid and the evidence for a substantially different neural representation between the anticipatory and the actual pain period is convincing. These important findings would benefit from a general framework to encompass their research questions, hypotheses, and interpretation of results. Furthermore, a more in-depth discussion about the choice of conditions would be desirable, specifically whether the definitions of nocebo and placebo in the study are comparable with traditional paradigms, and whether the control condition can be considered as a situation with no expectation or no prediction.

Reviewer #1 (Public Review):

Summary:

In this important paper, the authors investigate the temporal dynamics of expectation of pain using a combined fMRI-EEG approach. More specifically, by modifying the expectations of higher or lower pain on a trial-to-trial basis, they report that expectations largely share the same set of activations before the administration of the painful stimulus, and that the coding of the valence of the stimulus is observed only after the nociceptive input has been presented. fMRI-informed EEG analysis suggested that the temporal sequence of information processing involved the Dorsolateral prefrontal cortex (DLPFC), the anterior insula, and the anterior cingulate cortex. The strength of evidence is convincing, and the methods are solid, but a few alternative interpretations about the findings related to the control group, as well as a more in-depth discussion on the correlations between the BOLD and EEG signals would strengthen the manuscript.

Strengths:

In line with open science principles, the article presents the data and the results in a complete and transparent fashion.

From a theoretical standpoint, the authors make a step forward in our understanding of how expectations modulate pain by introducing a combination of spatial and temporal investigation. It is becoming increasingly clear that our appraisal of the world is dynamic, guided by previous experiences, and mapped on a combination of what we expect and what we get. New research methods, questions, and analyses are needed to capture these evolving processes.

Weaknesses:

The control condition is not so straightforward. Across the manuscript it is defined as "no expectation", and in the legend of Figure 1 it is mentioned that the third state would be "no prediction". However, it is difficult to conceive that participants would not have any expectations or predictions. Indeed, in the description of the task it is mentioned that participants were instructed that they would receive stimuli during "intermediate sensitive states". The results of the pain scores and expectations might support the idea that the control condition is situated in between the placebo and nocebo conditions. However, since this control condition was not part of the initial conditioning, and = participants had no reference to previous stimuli, one might expect that some ratings might have simply "regressed to the mean" for a lack of previous experience.

General considerations and reflections:

Inducing expectations in the desired direction is not a straightforward task, and results might depend on the exact experimental conditions and the comparison group. In this sense, the authors' choice of having 3 groups of positive, negative, and "neutral" expectations is to be praised. On the other hand, also control groups form their expectations, and this can constitute a confounder in every experiment using expectation manipulation, if not appropriately investigated.

In addition, although fMRI is still (probably) the best available tool we have to understand the spatial representation of cortical processing, limitations about not only the temporal but even the spatial resolution should be acknowledged. Given the anatomical and physiological complexity of the cortical connections, as we know from the animal world, it is still well possible that subcircuits are activated also for positive and negative expectations, but cannot be observed due to the limitation of our techniques. Indeed, on an empirical/evolutionary basis it would remain unclear why we should have a system that waits for the valence of a stimulus to show differential responses.

Also, moving in a dimension of network and graph theory, one would not expect single areas to be responsible for distinct processes, but rather that they would integrate information in a shared way, potentially with different feedback and feedforward communications. As such, it becomes more difficult to assume the insula is a center for coding potential pain, perhaps more of a node in a system that signals potential dangers for the integrity of the body.

The authors analyze the EEG signal between 0.5 to 128 Hz, finding significant results in the correlation between single-trial BOLD and EEG activity in the higher gamma range (see Figure 6 panel C). It would be interesting to understand the rationale for including such high frequencies in the signal, and the interpretation of the significant correlation in the high gamma range.