Hi, I found this study to be interesting and potentially very impactful for diagnostic needs, as you mention. I have a few questions: 1. Do you have any insights into which timepoints or periods were the most useful (or most noisy) for your training purposes? 2. Do you know whether introducing imaging steps every 30 minutes may have had any effects on colony growth patterns? 3. For the species/strains that were most difficult to predict, do you have any idea what factors may have contributed to this, i.e. technical difficulties in holography, heterogeneity within or across colonies, etc Thank you!

This is fascinating. I have 2 questions!

1. Natural novelty -- do you see any evidence for natural variants for F240A? Would be super interesting if so, in case that could help identify other co-varying residues that could help with kinetics. Or even other substitutions at that site that give you more options for novelty engineering. Would also be very curious to know what organisms can tolerate a trait like this.

2. Do you have any insights into how that mutation, especially for a hydrophobic residue, might affect binding or folding/dynamics? I haven't taken a close look at where it sits on the structure, but that might provide some helpful insights into how to compensate for any hit you take on kinetics.

Hi, I'm curious which proteins have unusually low or high hit rates, and what can be learned from that in terms of evolutionary history or plasticity?

interesting indeed

Why are the maximums different for X-ray vs EM?

this is great

Curious if you have a sense of how much the bound/unbound status is related to conformational state of protein copy within the crystal. Or whether this is also largely stochastic.

I found this study interesting, but as someone outside this field, I'm curious why you were surprised by this result? It's generally true in evolution that constituent parts of organisms can evolve at different rates and in different ways than the whole organism, depending on selective pressures that affect some aspects of biology more than others. This is certainly true for genes, so I would have expected this to be true for circuit elements as well. However, it seems that you may have been surprised by this result? Can you help me understand what field-level assumptions or observations cause this result to be unexpected?

I found this introduction remarkably helpful and easy to understand. Many thanks

Did you note a higher rate of "non-recovery" on the vaccinated animals? I'm wondering whether you'd hypothesize that vaccination against the EVs could be the cause of increased itching or irritation that alerts the animals to the ticks.

Would you mind sharing your rationale for isolating EVs at this time point? Do you know how EV cargo or composition might vary depending on the feeding time point when you harvest?

Curious if you know anything about the composition of these vesicles relative to each other? Are they carrying very distinct cargo?

I'm curious if you have any hypotheses about which seasonal differences are driving these changes, i.e. light/dark cycles, temperature, etc?

Did you ever try cultivating these organisms? I wonder if some of your insights could inform strategies around this. If you were able to grow any, it would be fun to see if we could reinforce any of your taxonomical results using raman spectroscopy (see here: https://research.arcadiascience.com/pub/result-raman-taxonomy/release/2). Would be happy to discuss if you're at all interested!

any idea about the source(s) of this?

Such an important problem. Thanks for tackling

i love this

This is an interesting point. What is the current explanation for this phenomenon?

Thanks for the analysis in this paper. Super interesting, and I agree that improvements on this front could have huge and fascinating implications for understanding more mtDNA biology.

I'm curious how you decided to set your final range at 3+? Did you not see much difference between 3+ vs 5+, for example in terms of change in edge bias? Or was this cut-off informed by the trade-off of losing potentially interesting mutations once you increased stringency too much.

Thanks for summarizing this so plainly and directly.

I've wondered whether there might be interesting signatures that could differentiate between 1) inappropriate transfer of functional annotations to seemingly similar proteins vs 2) incomplete annotations, i.e. where the other protein(s) may indeed have the originally hypothesized function AND a second or additional functions on top of this that confuses interpretation. Do you know of any work or models that is attempting to address this?

Curious if you might have a ballpark number in terms of % of entries for which there is direct experimental data? I've been trying to get a sense of this and agree that it's low, but haven't been able to track down a number.

Will do! Appreciate this clear call-to-action.

While this may be true, were you ever able to discuss this with any of the vendors to provide potential insights into the lots in question?

Are you aware of any other lab organisms that are sensitive to this?

Wow, this was so much work!

I really, really appreciated this report on many levels. First and foremost, I found it to be super useful and practical. Second, I enjoyed the straight forward, honest reporting on the true scientific process (and all the frustrations it brings!). And finally, love the title. Thank you!

This is super interesting. Did you ever look at the conservation of the "extra" positive residues across bacteria and whether this could predict natural transformability for other species that are less well studied? It could be fun to see how effective the absence or presence of various positive residues might correlate with this more broadly.

I'm curious what could be driving this variation. Do you know if transformation assays using different DNA sequences or lengths could lead to differences here?

Is the inverse true? i.e. every genome containing a homolog of ΦKZ gp54/Chimallin/PhuN also encodes lmp1?

Curious how the structure-based searches were conducted? Couldn't find anything in your methods section, but maybe I missed it!

Did you detect any other impacts, such as differences in membrane structure or size?

Apologies if this has already been addressed in other work, but have there been comprehensive structure-based searches for these proteins across organisms?

Do you have any speculative thoughts on whether this strategy could also have a secondary impact on other aspects of PAO fitness? i.e. PAO–eukaryotic host interactions, surface composition in general, other T4 functions

Wow

Super interesting! Do you think this is rooted in variable binding affinities? Would be cool if that matters for biological outcomes here. And if you could predict that somehow.

Very cool how specific this is

In light of this, curious if you've done or are planning to do any predicted-structure-based searches as well, in addition to BLAST. Would be interesting to see if you pull out anything interesting using that search?

If it's helpful, our protein cartography tool could possibly help with some of this workflow. And I'm kind of curious whether there are any hits outside of phage. Such as other endosymbionts of bacteria or even hosts that might also use this strategy to regulate phage/bacteria interactions.

If you seeded sequence- or structure-based searches with only these regions, do you ever see them fused to other known functional domains besides this family?

This is so interesting!

I learned a new word today!

Could it be that they had essential functions in now-extinct mammals? No idea how one would answer this, but I always wonder in these instances.

Curious how these were chosen? Was it based primarily on availability or was there something specific about the particular ones you chose? Would have been cool to see more marsupials or monotremes represented, but I assume it was challenging to find high quality genomes that were amenable to your analyses?

I've always found this fascinating. Did ERV gene domestication happen at a particularly interesting moment in natural history for these animals with regard to reproduction? Something major that was changing in the environment that benefited from domestication events to accelerate change?

Any idea whether it could be packaging anything else besides the mRNA and RTase or integrase that could be involved in synapse regulation?

This is fascinating. What do you know about the evolutionary history of Copia vs Arc? I was wondering this w the epistasis observation as well.

So cool that you did this

for cells that aren't a part of sheets, do you see similar differentiation patterns into cysts? Any difference in outcomes/ability to do so? Curious if the multi-cellular state is somehow helpful to actively mediate this transition.

Are there any other notable differences between splash pools, like pH or metals/mineral content?

I don't know much about choano colony dynamics, so I have a bunch of questions!. How do you generally assess integration? Also, do you see much cell movement within a community structure, i.e. outside cells or newly integrated ones that eventually move to the center? Or would you expect that peripheral cells tend to stay peripheral and have some unique features that enable aggregation? And finally, is this there any known difference in these spatial features relative to species known to only clonally divide?

This was super helpful, thank you. And it's interesting to see that most of the suspected contaminants have also been seen as contaminants elsewhere! Thank you for running these analyses.

Any thoughts on what approaches your group will take in the future to avoid this? One thing we ended up finding post-study is that looking for transcripts, i.e. actual expression of genes, ended up being a decent proxy for whether a bacterium was present. Because it is suggestive of a microbe actually being alive and growing. It's not a perfect correlation, but it was surprisingly tight.

Any ideas as to why Burkholderiaceae and M. radiatus were so sensitive to these experimental procedure differences?

Thank you so much for doing this careful study. It's so important to get this right, and I know it can be challenging to wade through conflicting observations. Your overview of this in the intro is also very helpful. We also struggled with similar issues for the tick microbiome, and it took quite a bit of work to experimentally consider the "negative result" (see here: https://www.biorxiv.org/content/10.1101/198267v1). Major kudos to you for your persistence on this question!

Have you ever taken a look to see whether any of these variants, particularly the ones that affect localization, are naturally occurring in other species. I'm curious whether there could be clues in biology as to how such variants are compensated for elsewhere. Would be fun to check! Let us know if we can help at Arcadia.

This is really exciting. Curious whether you ever got to test this in any in vivo model to see whether it affects disease traits? (or maybe this is next up in your plans!)

Did you happen to check whether the other 3/8 had this effect too? Wondering if they might still impact primary cilia, despite not exhibiting the same detectable phenotype in your previous assay.

This is super interesting. For the 3 pathogenic variants that did not make it to this phenotype list, curious if you saw any other localization defects besides quantitative levels at the PM, i.e. specific spatial patterns at the PM?

Would be interested to see whether our protein structural comparison tool Protein Cartography could help you visualize a structural/evolutionary landscape of this family, and possibly use your insights to identify more GH19 chitinases that are potential antifungal enzymes!

This is super interesting. It's making me wonder whether this could either be because it recognizes the substrate in a particular structural conformation unique to the in vivo cell wall. Or perhaps this long-range interaction allows a different processive activity along a cell wall that is required for lytic/anti-fungal activity? Did you ever compare binding of this mutant vs control to cell wall fragments, as opposed to intact cells? Would be interesting to see whether this phenotype is dependent on the state of the substrate.

Curious if you ever examined the structure of this complex in-solution any other way? Or have any guesses about how many different folded states it can occupy in solution?

What about have you looked for structural cousins of any of the Lap proteins? Would be fun to see what other organisms encode these and whether it ever includes other secretion systems (or even eukaryotes)?

Were you surprised by this?

This is super cool. It's amazing how much sequence diversity allows for this intimate/elongated of an interaction

Will be interested to follow along with future studies on this! Are you planning to pursue these yourself?

Is there any insight into whether the ticks may have fed on the same species of host >1? Or is this purely referring to average # of different host species?

Thanks for doing this study! Super interesting. Curious if you have any idea "how old" the adults were compared to each other, and whether there is any correlation between this and # hosts detected.

I have two questions around this! 1) I'm assuming that the % identity for this gene is fairly reflective of phylogenetic distances between these species as a whole (given that IF1 is related to an essential process). Is this true? 2) I'm curious if you know how you would design the peptide differently if your target species was another one of these, such as Burkholderia. (Big picture, wondering how much room there is for rationally designing specificity to avoid off-target effects with other bacterial species).

This interesting. I was curious whether you think that there could be some indirect reasons for this difference that are distinct from IF1 structure? For example, perhaps some of this difference could be due to differences in cell envelope between Gram-positive and -negative, which could impact 1) delivery of peptide across the envelope, or 2) toxic AMP-like effects via physical interactions with the envelope (as you mention later). Would love to hear more of your thoughts on this. Thanks.

I found this study really exciting, making me hopeful for the future - both as a patient and a scientist. I think your work could lead to really interesting and fundamental advances in understanding some of the key early steps of this developmental process. My curiosity is particularly piqued with regard to the peak performance time point! Thanks for your work.

I'm curious if you think that training set size would need to be increased if you pool together datasets from different clinics, introducing more noise? Perhaps this has already been evaluated elsewhere, but since I'm from outside your field, it made me wonder how this compares to those multiple-clinic ML studies. Thanks!

I found this observation fascinating. It would be so interesting to follow-up on these peak prediction windows to understand what else is happening in the cell that may be deterministic for cell fate. Do you have any plans to follow up on these time points?

Hi there, this is super interesting and promising. Quick note - it would be helpful to have the axes in Figure 2B labeled so that I can better understand the trend.

Curious how specific these cognate receptors are with peptides?

Would you be interested in extending this analysis beyond human and mice? Would be really fascinating to understand the evolutionary history of this across more species and lineages. Maybe this could even help shed new insights on to overarching biological functions these enable?

This is wild and so fun to think about. Especially since it's the tip of an iceberg of dark matter functions!

is this a typo or do you mean pre-proprecursor?

Curious if this is higher or lower than you expected? And what the implications of these levels may be with regard to their function or capping?

Love this angle. Are there classes of peptides with only one of these? And how do they compare numbers-wise? And what might be unique about having the coincident N/C mods?

Since you didn't include mice here, am I correct to assume that this is something that is NOT conserved in mice. And therefore an advantage to using pig as a model for human biology?

This was super helpful. Thank you!

Maybe this is leftover from a previous version?

As an outsider to this field, it would be helpful for me to better understand the rationale for including uterine fluids. It seems important, and I want to make sure I understand why!

Would be curious to know your interpretation of this result. Do you think these proteins are being secreted by the uterine lining or the embryo itself? i.e. in which direction is communication happening?

Curious if these transcriptional profiles provided any insights for you on other proxies you could measure in the future that may help you differentiate between cell types and stages? i.e. key metabolites/hormones that could be assayed for instead of sequencing?

Curious if these transcriptional profiles provided any insights for you on other proxies you could measure in the future that may help you differentiate between cell types and stages? i.e. key metabolites/hormones that could be assayed for instead of sequencing?

Would be curious to know your interpretation of this result. Do you think these proteins are being secreted by the uterine lining or the embryo itself? i.e. in which direction is communication happening?

Maybe this is leftover from a previous version?

This was super helpful. Thank you!

Since you didn't include mice here, am I correct to assume that this is something that is NOT conserved in mice. And therefore an advantage to using pig as a model for human biology?

As an outsider to this field, it would be helpful for me to better understand the rationale for including uterine fluids. It seems important, and I want to make sure I understand why!

these could be very interesting though!

I spent a lot of time re-reading this section because it seems very important. As a biologist without much experience in this area, I'm not entirely sure I understand it correctly. My overarching interpretation is that you have two distinct challenges that intersect to compound the complexity of image processing. Namely, the ability to track meshes across different planes or geometries. Second, the ability to follow these over time. Both are challenging, but together, really make it hard to do the types of surface analyses that could be useful here. Am I understanding correctly? Sorry if this is totally inaccurate, but I got a bit list in the problem statement about convexity, immediately followed by statements about timepoints. I would appreciate some clarification if possible!

Curious how this approach coupled with some simple cellular staining techniques (dyes that selectively stain certain cellular features such as cellular ends, blebs, etc) could maximize the power of this? I understand that takes away from some of the impact to be able to examine without such markers, but maybe it can be incorporated to guide some customized decision-making for subcellular surface compartments that may have distinct underlying intrinsic properties worthy of different optimizations.

I know this is not the punchline, but I found this exciting because I think we could be much more creative in biology about finding useful proxies

This is very cool

Curious if you saw any interesting bleb "fingerprint" differences in terms of the range distribution for short- to long-lived blebs at any given point in time as opposed to on- vs off-? or did the distribution of those mostly stay constant, but the average bleb time moved?

I found this easy to understand! Now I went back and can better absorb the intro materials. Thank you!

Agree. Would love to brainstorm ways to this could be leveraged for cell bio work at Arcadia in the future. Thank you.

Agree. Would love to brainstorm ways to this could be leveraged for cell bio work at Arcadia in the future. Thank you.

I found this easy to understand! Now I went back and can better absorb the intro materials. Thank you!

Curious if you saw any interesting bleb "fingerprint" differences in terms of the range distribution for short- to long-lived blebs at any given point in time as opposed to on- vs off-? or did the distribution of those mostly stay constant, but the average bleb time moved?

I know this is not the punchline, but I found this exciting because I think we could be much more creative in biology about finding useful proxies

Curious how this approach coupled with some simple cellular staining techniques (dyes that selectively stain certain cellular features such as cellular ends, blebs, etc) could maximize the power of this? I understand that takes away from some of the impact to be able to examine without such markers, but maybe it can be incorporated to guide some customized decision-making for subcellular surface compartments that may have distinct underlying intrinsic properties worthy of different optimizations.

these could be very interesting though!

This is very cool

I spent a lot of time re-reading this section because it seems very important. As a biologist without much experience in this area, I'm not entirely sure I understand it correctly. My overarching interpretation is that you have two distinct challenges that intersect to compound the complexity of image processing. Namely, the ability to track meshes across different planes or geometries. Second, the ability to follow these over time. Both are challenging, but together, really make it hard to do the types of surface analyses that could be useful here. Am I understanding correctly? Sorry if this is totally inaccurate, but I got a bit list in the problem statement about convexity, immediately followed by statements about timepoints. I would appreciate some clarification if possible!

This is an exciting feature!

Is this a typo? The figure says mM, which I actually thought would be super cool because it suggests your prediction of 10mM being the functional equivalent of 1mM IPTG at beginning of study was pretty clever. That you see most of the dynamic range leading up to this point where cells start growing more slowly

Curious if you have thoughts on how you might approach this if the genome for your organism wasn't super well annotated and you only had either a partially assembled genome or transcriptome?

Smart way to determine range

This is so cool! So simple yet elegant, and very well done. And it worked. Amazing

Just a small thought - may have been helpful to include this further up in results because i wondered this a few times, which distracted me a bit. Turns out, you have some super relevant analyses for it!

Curious what is the significance of this?

This was a cool control comparison! Wondering if there's the opportunity for comparison of other insect clades that specialize but not in plants?

Not knowing much about this area.... does increase in gene copy number always mean increase in functional range (i.e. diversifying specificity against intended targets) or can it sometimes mean more types of regulation (i.e. differential expression across tissue types within the insect)?

Curious if this is also true for hematophagous arthropods that moved from marine to land hosts?

Curious if you also see this effect if you add additional FBS to cells that were grown in media already containing FBS? Or is it unique to cells that haven't yet "seen" FBS?

As someone who doesn't think about LDLs very much, it could be super useful to give a little more of an explanation of the difference here and what one might expect your whole particles to contain. Maybe a schematic? I'm worried that I'm missing something.

This is super interesting. I think I am missing something though - why do you think the proteolytic degradation of your fraction earlier in the study led to the destroying of aggregation activity? Is this because the protein(s) were required for assembly LDLs in the first place?

This is cool. I also found this way of explaining it really clear/helpful. Presumably there are advantages to this strategy too - allowing Capsaspora to respond to a diversity of cues through response mechanisms so they aren't strictly dependent on the molecular cues themself for the downstream consequences. Would be really interesting to see how promiscuous this response is to different kinds of LDLs.

that's an interesting thought

why do you think Capsaspora has evolved the requirement of two molecules, and not just one? What does the simultaneous presence of these two chemical factors represent in its environment, and what advantages does it provide to have two different knobs that can be tuned?

Very cool way to look at the results.

Would be super interested in why the blue replicate is an outlier in terms of curve shape. It makes me wonder whether there is anything fundamentally different about the associated roots (growth rate, morphology).

This was such an important figure for helping you understand all downstream analyses. It would have been great to have a normal light photo or cartoon schematic to the side to help orient me on what I was looking at.

It was a cool paper that presented a newly developed system that can be used to longitudinally track microbes along growing roots of a plant. There are going to be a lot of cool applications from this, and I would expect that this platform sets up all sorts of cool projects from this group and others in the future. I'm excited to see what develops!

It took me a few reads though to fully appreciate how cool it was because the title led me to expect a slightly different set of questions/conclusions. Namely, I think two things on this point: 1) it wasn't clear at the outset that they were engineering a new tool. 2) the word "actively" made me think that they were going to provide very direct evidence of active transport of some kind. However, it was largely focused on microscopy-based tracking and interesting correlations that point to some kind of co-distribution of protist/bacteria in a protist-dependent way. But it didn't necessarily shed light on whether this was truly direct and "active" in the classic sense of the word (ATP). It also didn't necessarily conclude on directionality (i.e. protists driving bacteria rather than the other way around), although the dependency on protists and other lines of evidence in literature would certainly strongly suggest this!

And to be totally clear, I LOVE tool/platform papers, including this one! It just took a little effort to work align my expectations with the title.

why do you think Capsaspora has evolved the requirement of two molecules, and not just one? What does the simultaneous presence of these two chemical factors represent in its environment, and what advantages does it provide to have two different knobs that can be tuned?

that's an interesting thought

As someone who doesn't think about LDLs very much, it could be super useful to give a little more of an explanation of the difference here and what one might expect your whole particles to contain. Maybe a schematic? I'm worried that I'm missing something.

This is cool. I also found this way of explaining it really clear/helpful. Presumably there are advantages to this strategy too - allowing Capsaspora to respond to a diversity of cues through response mechanisms so they aren't strictly dependent on the molecular cues themself for the downstream consequences. Would be really interesting to see how promiscuous this response is to different kinds of LDLs.

This is super interesting. I think I am missing something though - why do you think the proteolytic degradation of your fraction earlier in the study led to the destroying of aggregation activity? Is this because the protein(s) were required for assembly LDLs in the first place?

Curious if you also see this effect if you add additional FBS to cells that were grown in media already containing FBS? Or is it unique to cells that haven't yet "seen" FBS?

Very cool way to look at the results.

Would be super interested in why the blue replicate is an outlier in terms of curve shape. It makes me wonder whether there is anything fundamentally different about the associated roots (growth rate, morphology).

It was a cool paper that presented a newly developed system that can be used to longitudinally track microbes along growing roots of a plant. There are going to be a lot of cool applications from this, and I would expect that this platform sets up all sorts of cool projects from this group and others in the future. I'm excited to see what develops!

It took me a few reads though to fully appreciate how cool it was because the title led me to expect a slightly different set of questions/conclusions. Namely, I think two things on this point: 1) it wasn't clear at the outset that they were engineering a new tool. 2) the word "actively" made me think that they were going to provide very direct evidence of active transport of some kind. However, it was largely focused on microscopy-based tracking and interesting correlations that point to some kind of co-distribution of protist/bacteria in a protist-dependent way. But it didn't necessarily shed light on whether this was truly direct and "active" in the classic sense of the word (ATP). It also didn't necessarily conclude on directionality (i.e. protists driving bacteria rather than the other way around), although the dependency on protists and other lines of evidence in literature would certainly strongly suggest this!

And to be totally clear, I LOVE tool/platform papers, including this one! It just took a little effort to work align my expectations with the title.

This was such an important figure for helping you understand all downstream analyses. It would have been great to have a normal light photo or cartoon schematic to the side to help orient me on what I was looking at.