On 2018-12-01 23:22:16, user Devang Mehta wrote:

Hi, can you please upload a formatted version with in-text figures, or alternatively add the captions to the figure pages?

On 2018-11-30 20:09:57, user RobS wrote:

What are the 9 biomarkers? I can't find that information anywhere!

On 2018-11-30 17:51:43, user Josh Lï Spinoza wrote:

Here is the peer-reviewed paper:<br /> https://mbio.asm.org/conten...

On 2018-11-30 16:59:29, user Charlotte Francoeur wrote:

Is there a way to access your supplementary material?

On 2018-11-30 15:49:40, user Davide Mangani, PhD wrote:

A question regarding Supp fig.4 (hope I am not overlooking something obvious):<br /> in the gating strategy γδ1 are marked as (CD44+ CD45RB+) and γδ17 (CD44hi CD45RB-ve).<br /> However, the Vγ4 gating (green square) takes CD44+ CD45RB+ rather than negative. Are the axis or the gates swapped?

On 2018-11-30 14:22:10, user Claudiu Bandea wrote:

Evolution of giant viruses from larger ancestors

In their manuscript, Bäckström et al. describe “a metagenomics analysis of the genomes of large and giant viruses from deep sea sediments ”, and conclude that: “The results support the concept of independent evolution of giant viruses from smaller ancestors in different virus branches ” (1).

As suggested in the title of this Note, I disagree with their conclusion: the results do not support an independent evolution of giant viruses from smaller ancestors, and I challenge the authors to point to any of the results presented in the paper supporting this conclusion. So, what happened?

Likely, the authors arrived to their conclusion in context of the highly circulated hypothesis that, in general, viruses have been evolving from simple to complex, by increasing the size of their genome and proteome. However this paradigm about the evolution of viruses is in stark contrast to the fact that thousands if not millions of intracellular parasitic or symbiotic microorganisms have independently evolved toward a smaller genome/proteome.

Although, similar to their free-living ancestors or relatives, these intracellular parasitic and symbiotic cellular organisms do occasionally acquire new genetic material, there is overwhelming evidence that, overall, these species have experienced reductive evolution. This is a well-established fact. So, why would viral lineages evolve in opposite direction?

Without addressing this fundamental question, the hypothesis that viral lineages evolved from simple to complex, and the conclusion of the Bäckström et al. that giant viruses evolved from smaller ancestors are highly questionable.

Fortunately, there are only 2 broad ways of thinking about the evolution of viral lineages, and the alternative pathway is that, similar to all endo-parasitic and endo-symbiotic cellular lineages, the viral lineages have evolved by reductive evolution.

To their credit, Bäckström et al. mention this hypothesis: “It has been proposed that the giant viruses were descendants of a hypothetical, probably, extinct fourth domain of cellular life that evolved via drastic genome reduction ”. Nevertheless, they dismissed it based on the following two rationales:

(i) “phylogenetic analysis of expanded sets of translation-related proteins encoded by giant viruses has resulted in tree topologies that were poorly compatible with the fourth domain hypothesis” ;

(ii) and because of “conceptual difficulties inherent in the postulated cell to virus transition ”

The multiple studies the authors cite in support of the first assertion are more or less explanatory or supportive; however, the two citations they used in support of the second claim do not address the conceptual difficulties with the transition from cell to virus, so the authors might want to verify them.

Nevertheless, these conceptual difficulties are highly relevant, and historically they have been the central objection against the origin of viruses from cellular lineages, as eloquently described by Salvador Luria and James Darnell (2) half of century ago: " The strongest argument against the regressive origin of viruses from cellular parasites is the non-cellular organization of viruses. The viral capsids are morphogenetically analogous to cellular organelles made up of protein subunits, such as bacterial flagella, actin filaments, and the like, and not to cellular membranes…. This theory today has little to recommend it, at least in its original form. "

In the early 1980’s, I proposed a new theory on the origin and nature of viruses that overcame these conceptual difficulties and set the evolutionary origin and nature of viruses in a new light (3, 4).

According to this theory, the viral lineages originated from parasitic cellular lineages that started their intracellular life cycle by fusing with their host cells. By discarding their cellular membrane, these novel parasites had increased access to resources present in their particular environment, the host cell, including a translation machinery. After synthesizing their specific molecules and replicating their genome using the resources found in their intracellular environment, the parasites produced spore-like transmissible forms, which started a new life cycle by fusing with other host cells. These incipient viral lineages have diversified by reductive evolution into a myriad of viruses with smaller genome and diverse life cycles.

Some of the major implications of this evolutionary model are that the ‘molecular biology’ of the pre-viral parasitic lineages and that of the host cells had to be compatible (i.e . belong to the same cellular ‘domain’, which means that there was not a ‘fourth domain’; see Fig. 4 in Ref. 4), and that numerous cellular lineages have evolved into viral lineages throughout the history of life, a process that is still be active, as the current evidence indicate.

References:

1. Bäckström D, Yutin N, Jorgensen SL. et al. 2018. Virus genomes from deep sea sediments expand the ocean megavirome and support independent origins of viral gigantism . bioRxiv: doi: https://doi.org/10.1101/469... https://www.biorxiv.org/con...

2. Luria SE and Darnell JE. 1967. General Virology . Wiley. New-York.

3. Bandea CI. 1983. A new theory on the origin and the nature of viruses . Journal of Theoretical Biology 105(4), 591-602.

4. Bandea CI. 2009. The origin and evolution of viruses as molecular organisms . Nature Precedings: http://precedings.nature.co...

On 2018-11-30 13:12:24, user theempiricalmage wrote:

And of course, lack of Iranian samples from the paleolithic. "Caucasus", nowadays, has become the choice euphemism for Iranian. Note the lack of 25kybp R1a diversification in the Caucasus compared to Iran (likely origin). Difficult to reconcile that with this study. The Iranian anthropological record is impeccable, and it has long been established that the plateau was well populated by that time.

On 2018-10-02 16:04:14, user xyyman wrote:

Points to clarify <br /> 1. What is meant by “lack of extra deep ancestry”…Villabruna cluster?<br /> 2. “inference of Dzudzuana-related ancestry as the core component of ancient and present-day West Eurasia does **NOT** constitute proof for migrations specifically from the Caucasus” <br /> 3. Why use such a title when included in your text is “, the geographical and temporal extent of this population and its relatives remains unknown”<br /> 4. No Y-DNA or subclades of the mtDNA obtained?<br /> 5. Will the data files(BAM/FASTQ) be freely available?

Quotes<br /> Thus, our results prove that the European affinity of Neolithic Anatolians does not necessarily reflect any admixture **into** the Near East** from** Europe, as an Anatolian Neolithic-like population already existed in parts of the Near East by ~26kya. Furthermore, Dzudzuana shares more alleles with Villabruna-cluster groups than with other ESHG (Extended Data Fig. 5b), suggesting that this European affinity was specifically related to the Villabruna cluster, and indicating that the Villabruna affinity of PGNE populations from Anatolia and the Levant is not the result of a migration into the Near East from Europe.

Europeans are differentiated by an excess of up to ~20% Villabruna-related ancestry relative to non-European populations AND ALSO BY A RELATIVE **LACK OF **EXTRA ‘DEEP’ ANCESTRY **COMPARED**TO THE NEAR EAST AND NORTH AFRICA , a type of ancestry that may only partially be explained by the Basal Eurasian ancestry of ancient West Eurasian populations and MUST ALSO TRACE TO AFRICA (Extended Data Fig. 7). ‘Deep’ ancestry, including Basal Eurasian ancestry, is associated with **reduced** Neandertal ancestry (Supplementary Information section 5, Extended Data Fig. 8), confirming that Neandertal ancestry in West Eurasia has been diluted by admixture.

We caution that the inference of Dzudzuana-related ancestry as the core component of ancient and present-day West Eurasia does **NOT** constitute proof for migrations specifically from the Caucasus: given that this is the only ancient DNA data from this time period and broad region, the geographical and temporal extent of this population and its relatives remains unknown. Both in its past (formed by admixture with Basal Eurasians), and in its future (admixing with populations from Africa, Europe, and Siberia in post-glacial, Neolithic, and later periods), Dzudzuana stands in the middle of an ongoing process of admixture of diverse elements from which West Eurasians, in all their diversity, eventually formed.

On 2018-11-30 11:52:13, user Mubassherul wrote:

You did a nice work which makes NER faster. I really appreciate it. All the recognized name are disambiguated by identifiers which sources are from different databases. I am pretty curious that sometime "Acronyms are often homonyms".So, how the tagger manage this problem? It would be great if the user can get return of the results instead of returning the result to central BeCalm server. Great job. Thumbs up.

On 2018-11-30 10:26:39, user Janine Brunner wrote:

BioRxiv still have problems to display the Supplementary Material, but they are working on a solution. Meanwhile please request the Suppl. Material by Email. Thank you for understanding!

On 2018-11-30 08:11:18, user Markus Seeger wrote:

K+-conductance without P-loop motif: this is how TMEM175 does it.<br /> The Nb-MBP fusion on top of the channel looks stunning.<br /> Congrats to Janine and Stephan!

On 2018-11-29 10:47:10, user Chicken Tikka Masala wrote:

Nice work but it misses the Material and Methods section.

On 2018-11-29 10:18:39, user ValeriaKalenkova wrote:

Great stuff! Can you share the MM section too?

On 2018-11-29 08:14:51, user Janine Brunner wrote:

Unfortunately, the Supplementary Material is not yet displayed online, but this issue will be fixed as soon as possible. We can provide the Supplementary Material upon request by Email.

On 2018-11-30 09:56:43, user Matthias Meurer wrote:

It will take another couple of weeks until the pMaCTag plasmids will appear on Addgene. Until then please feel free to contact the corresponding author Michael Knop for any plasmid request. The web page for primer design is already up and running: http://www.pcr-tagging.com/

On 2018-11-30 07:51:51, user Thomas Felzmann wrote:

Dear colleagues,

I congratulate you on this timely and potentially important study. However, I seem to detect a contradiction. You state, as is generally accepted, that cortisol has anti-inflammatory properties. Not by accident it is used therapeutically to treat autoimmune diseases, allergies, or transplant rejection. How would you explain your observation, that the levels of pro-inflammatory cytokines match the levels of cortisol rather than mirroring it, such that high cortisol levels correlate with low pro-inflammatory cytokines and vice versa.

Kind regards, Thomas<br /> - -<br /> Thomas Felzmann, MD, MBA<br /> Associate Professor of Immunology

AUSTRIAN RED CROSS, CENTRAL BLOOD BANK<br /> Research & Development

Wiedner Hauptstrasse 32, 1040 Vienna<br /> T: +43 1 58900 <br /> M: +43 664 1317106<br /> E: thomas.felzmann@roteskreuz.at<br /> W: www.roteskreuz.at

On 2018-11-29 19:08:34, user Jen Herman wrote:

If anybody needs/wants supplemental file, email me jkherman@tamu.edu. Didn't realize it wasn't part of post.

On 2018-11-29 16:55:16, user Laia Bassaganyas Bars wrote:

CNApp is hosted at http://bioinfo.ciberehd.org... and the source code is freely available at GitHub (https://github.com/ait5/CNApp)

On 2018-11-29 14:55:17, user Marcelo Kauffman wrote:

Very interesting approach. Are the supplementary methods going to be posted?

On 2018-11-29 14:49:43, user Mujawid wrote:

As a doctor and a patient with calcium oxalate stones could you suggest for me, the dosage that would be effective. For reference, Swanson super citrimax capsules have 900mg of Hydroxycitrate per capsule. Looking forward to hearing from you. My email address is mujawids@gmail.com.

On 2018-11-29 09:29:33, user PitchFeedback wrote:

Thank you for publishing this study. I also have a strong believe that our brain is capable to acquire AP in adulthood. While in your study you work with complex sinus waves, piano tones and violin tones, I think that not only the listening part of AP is important, but that the pitch producing part is equally important. Because in language learning, every child starts with uttering sounds that it tries to align with the spoken words of the parents. I think the same is true for pitches. In general you can observe that people that sing make faster progress in musical learning. Therefore, I propose to use the voice for acquiring AP.

Since with this study you show that acquiring AP is possible in adulthood it would be interesting to have a similar study that uses pitch singing. I have outlined such an approach in the YouTube video “Pitch Ability Method – A Scientific Approach to Absolute Pitch” Maybe someone could repeat your study with singers and compare the results to your study, which used participants with instrument training. If such a study would show that indeed singers perform better than instrumentalists this would underpin the thesis that the brain uses voice to learn not only words but also pitches.

On 2018-11-29 09:06:32, user Conrad Mullineaux wrote:

Speculative hypothesis papers can be fun and good for stimulating debate. But, to be useful, I think they need to present a plausible and coherent scenario (something that at least has a chance of being true) and they need to pay reasonable attention to the facts. I’m not sure that’s the case here. My main concerns are:<br /> 1. Fig. 3. The feedback loop looks neat, but it ignores the fact that the local [O2] around the nitrogenase need not correlate to any significant extent with the global atmospheric [O2]. Huge discrepancies could occur, due to local environmental conditions, and also due to the metabolic activity of the cell itself. Considering only the latter factor, the intracellular [O2] could be much higher than ambient (due to PSII activity) or much lower than ambient (due to respiration). If the nitrogenase doesn’t actually see the global atmospheric [O2], such a feedback loop could not clamp global [O2] at any particular level as proposed.<br /> 2. P.5 “If diazotrophic cyanobacteria are grown under conditions where they have sufficient CO2 and light, and with N2 as the sole N source, then they grow and accumulate no more than 2% oxygen in their culture atmosphere (16). The 2% O2 remains constant during prolonged culture growth because this is the O2 partial pressure beyond which nitrogenase activity becomes inhibited. With greater O2, nitrogenase is inactivated and there is no fixed N to support further biomass accumulation. With less O2, nitrogenase outpaces CO2 fixation until the latter catches up, returning O2 to 2% in the culture.” The outcome of this experiment will come as a surprise to anyone who has observed diazotrophic cyanobacteria happily growing without a combined nitrogen source at 21% ambient O2 (it depends on the cyanobacterium, of course). The result is a key plank of the authors’ argument, but it’s not clear if, when or how the experiment has been carried out. It’s not as straightforward as it seems, and nothing like that is described in the cited reference (16: Berman-Frank et al 2003). The nearest thing in that paper is a statement that a specific cyanobacterium, Plectonema boryanum, is unable to fix nitrogen above certain ambient [O2] levels. The limits are actually rather lower that the 2% quoted: 0.5% in the light and 1.5% in the dark (16). Plectonema is a specialist for microaerobic environments, and most other diazotrophic cyanobacteria are not so susceptible to O2 inhibition. <br /> 3. P.5 “Cyanobacteria have evolved mechanisms to avoid nitrogenase inhibition by oxygen, including N2 fixation in the dark, heterocysts or filament bundles as in Trichodesmium. Critics might counter that any one of those mechanisms could have bypassed O2 feedback inhibition.” Indeed they might. The authors go on to brush aside their imaginary critic on 3 grounds, none of which seem valid. “First, evolution operates without foresight”. Foresight isn’t needed: there would have been an immediate selective advantage to acquiring an O2 protection mechanism. “Second, the mechanisms that cyanobacteria use to deal with modern O2 levels appear to have arisen independently in diverse phylogenetic lineages, not at the base of cyanobacterial evolution when water oxidation had just been discovered”. Very likely so, but what about the next 2 billion years? “Third, the oldest uncontroversial fossil heterocysts trace to land ecosystems of the Rhynie chert”. It may or may not be the case that heterocysts evolved late, but, in any case, heterocysts are not significant contributors to marine nitrogen fixation: in extant cyanobacteria it’s the other protection mechanisms that allow cyanobacteria to make a huge contribution to oceanic nitrogen fixation even in the presence of 21% atmospheric O2. What about those other mechanisms? The fact that different lineages of cyanobacteria have independently come up with at least 3 different ways to protect their nitrogenase from O2 indicates that evolving such mechanisms is not really such a big deal. The authors’ scenario suggests that for a period approaching 2 billion years there was a nitrogen-limited biosphere with cyanobacterial nitrogenase operating right up against an inhibitory concentration of O2. There would have been intensive selective pressure for adaptations to protect the nitrogenase from oxygen. The scenario depends on the assumption that no cyanobacterium was able to develop a protection mechanism that would allow nitrogen fixation at >2% O2, despite selective pressure operating over a period of about 2 billion years and the availability of multiple solutions to the problem, as seen in extant cyanobacteria. I’m afraid that’s implausible, and I suggest that we need to look elsewhere for an explanation of the low O2 level through the Proterozoic.

On 2018-11-28 10:17:24, user Tanai Cardona Londoño wrote:

Hi, interesting proposal. I like it. A couple of comments.

The fossil heterocystous cyanobacteria reported by Pang et al., (2018) are not just akinetes. They are entire filaments with cells that do resemble heterocysts. I spent all of my PhD studying heterocystous cyanoabcteria, purifying them, extracting their thylakoids membrane, staining them, seeing them in a variety of microscopes... I have to say that those filaments are excellently preserved and are virtually indistinguishable from extant heterocystous cyanobacteria. I would dare to say that the fossils presented by Pang et al., (2018) are unequivocal fossils of heterocystous cyanobacteria. But I'm not a paleontologist.

You cited the review by Butterfield (2015) to validate your statement that the best fossil heterocystous cyanobacteria are from the Devonian, but in that paper that is Butterfield's own assessment, which predated Pang et al.'s paper. The Devonian fossil's cited by Butterfield are reported in a 1959 paper that I was not able to access. Were you able? Are those truly better preserved that Pang et al.'s? Butterfield does not show the Devonian fossils in his review... So, your argumentation there can be strengthened. Don't be so quick to dismiss Pang et al.'s fossils!

Your proposal also makes me wonder about the light-independent protochlorophyllide reductase. It is a nitrogenase-like enzyme and it is also oxygen sensitive (http://www.plantphysiol.org...:izB1smxE8rbmcpyKSQcHK9qJz2Y "http://www.plantphysiol.org/content/142/3/911.short)").

Could it be that the oxygen sensitivity of protochlorophyllide-reductase limited the rise of oxygen prior to the Great Oxidation Event, before the diversification and expansion of today's taxa of cyanobacteria, and before the origin of the light-dependent protochlorophyllide reductase?

Thanks.

All the best,<br /> Tanai

On 2018-11-28 18:11:32, user mialsmith wrote:

In this paper, Ueyama et al. proposed Rac-dependent paracrine signal from keratinocytes to intradermal pre-adipocytes that promotes adipogenesis. Using K5-Cre;Rac1flox/flox;Rac3-/- (Rac1/Rac3-DKO) mice, the authors showed that Rac3-/- exacerbated hairless phenotype observed in K5-Cre;Rac1flox/flox (Rac1-KO) mice (Fig. 1D), reduced skin thickness and fat content in the dermis (Fig. 3A and B). Ueyama et al. then showed that BMP2 and FGF21, potentially produced by keratinocytes in a Rac-dependent manner (Fig. 4A and B), can induce differentiation of adipocyte precursors in culture (Fig. 6B). Although the authors provided good evidence that BMP2 and FGF21 promotes differentiation of adipocyte precursors, there is not enough data supporting the claim that these signaling ligands were produced in keratinocytes in a Rac-dependent manner in vivo. My major concerns with this paper involve the expression of Rac3 in keratinocytes and potentially adipocyte precursor, the quantification method for gene expression, and the in vivo aspect of this proposed mechanism.

My first concern involves the expression of Rac3 in skin. Fig. 1 A and B showed the expression of Rac3 in keratinocytes at mRNA level, albeit not high. I think it is essential to show that Rac3 is expressed at protein level as well especially when their mRNA level is low. Although Ueyama et al. wanted to study the Rac-dependent signaling from keratinocytes to adipocytes, the Rac3-/- mouse model is not an epidermis specific knock out. Therefore, if Rac3 is expressed in adipocytes and plays a role in its differentiation, then there might be a confounding factor in this study. Expression of Rac3 has been shown in 3T3-L1 cells and adipocytes from ependymal tissue (Lira et al., 2018), hence there is a possibility that Rac3 is expressed in skin adipocytes and plays a direct role there. To eliminate this possibility, Rac3 expression in adipocytes in wild type and Rac3-/- mice should be compared at mRNA level (using qRT-PCR) and protein level (using western blot).

Secondly, Ueyama et al. only used RT-PCR to quantify expression of Rac genes and to validate ligand expression from DNA microarray data. However, this method was difficult to draw quantitative conclusion about differential gene expression. In Fig. 4B, the authors included Beta-Actin as loading control, but interestingly, Beta-Actin amplification did not increase with more cycles suggesting saturation already occurred in fewer than 25 cycles. Therefore, beta-actin RT-PCR was not a great control in this case as saturation might be reached at different cycles in DKO and Rac3-KO. I would suggest using qRT-PCR to quantify gene expression at mRNA level because it is a more standard method and provides quantitative data. In addition to quantifying gene expression at mRNA level, it would further support the paper’s argument if gene expression at protein level was also established as mRNA expression does not always correlate to protein production. For experiment in Fig. 4B, I would be more confident to conclude that those 5 factors were synthesized if a western blot of total protein lysate of keratinocytes were extracted and probed for at least BMP2 and FGF21 (available antibodies), or maybe even consider performing immunofluorescence (IF) of whole skin to look at these potential ligands in situ.

Expression of potential signaling ligands brings me to my last major concern involving the in vivo aspect of this proposed signaling mechanism. The paper showed combination of at least 2 factors could induce 3T3-L1 fibroblasts to produce lipids in vitro (Fig. 4C and D), yet this approach was not sufficient to show that these ligands would indeed induce adipogenesis in vivo. Although Ueyama et al. showed 72-hour mouse primary keratinocytes culture media can induced differentiation of 3T3-L1, the effect is extremely mild compared to addition of BMP2 and FGF21/FGF20 (Fig. 5A). In addition, for experiments in 5B and C, it was interesting that human derived NHEK culture media were used to stimulate mouse derived 3T3-L1 differentiation which is likely not what happen in a normal mouse or human tissue. I think the authors can bolster their argument if they show that compared to DKO mice, Rac3-/-, Rac1flox/flox, and K5-Cre;Rac1flox/flox mice have higher amount of candidate signaling ligands and more differentiation of intradermal white adipocytes (using in situ IF or at least western blot of keratinocyte proteins).

On 2018-11-28 13:27:21, user Francel Lamprecht wrote:

I found that some of the links you provide is not working:<br /> 1. http://www.escardio.org/sta...<br /> disease-statistics-2012.pdf (not that important for me, but others might need this information).<br /> 2. https://www.mathsworks.com. It would have been interesting to see the algorithm you used.

The link to the Biomodels database is working, but for the given model number, it is shown as inactive and is not useful at at all.

On 2018-11-16 16:10:02, user Francel Lamprecht wrote:

As a Bioinformatics student, I am currently doing research about the SBGN. Up to now I have only seen bits and pieces of the SBGN, without having the actual research results. This article made it possible for me to link the biological results with the SBGN. The article made me grasp the practical value of having such a graphical notation, as one can systematically work your way through the network and visualize the processes occurring within the body. Having the different processes and interactions that takes place in each organ/tissue, in different compartments clearly enhances understanding. The inclusion of screenshots of the various SBGN editors, as well as the XML file, gave me valuable insight into how visual map generation works.

On 2018-11-28 11:44:17, user Northern Finland Birth Cohorts wrote:

Seems like LEPR is important in infancy BMI - <br /> See ours posted a year ago <br /> https://www.biorxiv.org/con...

On 2018-11-27 18:46:32, user sandeep chakraborty wrote:

Really convincing data, but I am not surprised -

https://sanchakblog.wordpre...

On 2018-11-27 15:58:08, user Gilles Marodon wrote:

Very nice work. I'm just wondering about figure 3C. Is it correct to perform statistical testing of Treg vs total CD4+ T cells knowing that Treg are included in the latter and probably at various proportions? Anyway, the difference is so marked with Tconv (no need for statistics here!) that adding total CD4+ T cells does not add much to me.

On 2018-11-27 13:09:02, user wint wrote:

cool

On 2018-11-27 10:17:30, user Camila wrote:

I find your paper very interesting and I appreciate that you implement Gene Ontologies to build your two dimensionality reduction models. It is a sensible idea to use the biological knowledge that has been collected so far. I would maybe like to see how robust your method is on a bigger and more diverse dataset. And maybe you don’t mention it on your paper, but it would be nice that you had a GitHub repository with the documented implementation of your code. And one last thing is that instead of using ZIFA you could try to compare the performance with SIMLR which is a multi kernel learning method built by some of the authors that developed ZIFA, that has less stringent conditions on the data and has already proved to outperform ZIFA for dimensionality reduction of Single-Cell RNA Seq Data.

On 2018-11-27 10:00:11, user Alexey Kozlov wrote:

All alignments and analysis results are available here: https://figshare.com/s/6123...

On 2018-11-27 08:10:26, user Klaus Fiedler wrote:

The similarity of Wnt - p24-GOLD domain interaction shown here to XWnt8 -<br /> Fz8-CRD interaction seen in the cited work [32] can be observed when <br /> comparing the figure 2A of Janda et al.

On 2018-11-27 08:09:56, user Klaus Fiedler wrote:

The manuscript version no. 1 page 13 should read in the last paragraph: <br /> "Further inspection shows, that also N-glycans of TMED proteins shown to <br /> be modified by carbohydrates<br /> within the GOLD-domain, had already been<br /> further analyzed (65,75). For TMED7 and TMED9, N-glycans processed to <br /> complex and high mannose/complex N-glycans, respectively, had been <br /> found. Comparison of N-glycosylation sequons of each TMED of Mus <br /> musculus suggests that TMED4 and TMED11 could be N-glycosylated within, <br /> and likely TMED6 and 10 exterior to the GOLD-domain putative glycan <br /> binding pocket (data not shown). Asn103 in TMED7 is not located within <br /> the binding site of complex/hybrid N-glycans as gleaned from the <br /> structural comparative sequence analysis (Fig. 3A, Fig. 4B and Suppl. <br /> S5). The three TMED proteins TMED4, TMED9 and TMED11 may thus be impeded<br /> in putative glycan binding to the concave lectin surface if themselves <br /> covalently glycosylated within the GOLD-domain. It is possible that all <br /> other TMED proteins are free to interact with ligands via their concave <br /> patch GOLD-domain without steric hindrance."<br /> The paragraph has been corrected here (v2).

On 2018-11-27 08:06:35, user Klaus Fiedler wrote:

The link to an update on the structural analysis and modelling:

https://figshare.com/articl...

Regards

On 2018-11-24 15:37:21, user Klaus Fiedler wrote:

The manuscript version no. 1 page 13 should read in the last paragraph: Further inspection shows, that also N-glycans of TMED proteins shown to be modified by carbohydrates<br /> within the GOLD-domain, had already been further analyzed (65,75). For TMED7 and TMED9, N-glycans processed to complex and high mannose/complex N-glycans, respectively, had been found. Comparison of N-glycosylation sequons of each TMED of Mus musculus suggests that TMED4 and TMED11 could be N-glycosylated within, and likely TMED6 and 10 exterior to the GOLD-domain putative glycan binding pocket (data not shown). Asn103 in TMED7 is not located within the binding site of complex/hybrid N-glycans as gleaned from the structural comparative sequence analysis (Fig. 3A, Fig. 4B and Suppl. S5). The three TMED proteins TMED4, TMED9 and TMED11 may thus be impeded in putative glycan binding to the concave lectin surface if themselves covalently glycosylated within the GOLD-domain. It is possible that all other TMED proteins are free to interact with ligands via their concave patch GOLD-domain without steric hindrance.<br /> The file should be updated-

On 2018-11-26 23:42:41, user Fraser Lab wrote:

We reviewed this at a journal, posting the text of the review here (note the version we reviewed may be a bit different than the one posted here)...

This paper describes a novel adaptation of the principles of self-similar molecular structure self-assembly. Here they use biological building blocks, some of which they’ve played with before: https://onlinelibrary.wiley..., to expand their potential assembly properties and, hopefully, their applications. They use Rosetta to help guide components and linkers to produce a fractal-like structure self-similar over three orders of magnitude in scale, with assembly/disassembly and cargo capture tunable by phosphorylation and quantifiable by dynamic light scattering. It is very impressive how their assembly design involves molecular modeling and simulations, and a selected assembly is carried through all stages of experimental validation. Presumably some of the motivation of this work, other than just being cool, is to build off of some limitation encountered during their previous work: https://onlinelibrary.wiley... - it's a little unclear how the authors' thinking evolved between these two papers (the citation itself is a bit “hidden” in the manuscript).

The claims made regarding the properties of the fractal-like assemblies are experimentally validated by multiple methods and controls (in particular the dependence on components and linker lengths). Self-similarity and assembly morphology are independently assessed by cryo-electron tomography, helium ion microscopy and atomic force microscopy with good agreement. The mechanism of assembly, factors controlling morphology, reversible assembly upon phosphorylation/dephosphorylation, and improved cargo capture of fractal-like over aggregate structures are all experimentally validated.

The computational approaches involved in design of these structures are also well-justified, including any necessary simplifications and assumptions such as the choice of coarse-grained model for large-scale simulations. Furthermore, the measured fractional dimensionalities of the synthesized structures are in close agreement with the predictions based on simulations.

The major success of this paper is the thorough proof of concept of the design and synthesis of novel molecular structures with very exciting applications. The methods detailed here are sufficient for other groups to apply this technique with different building blocks, linkers, constraints on flexibility, regulatory mechanisms, and target morphologies. The major limitation is the apparent restriction of cargo capture to the time of structure assembly — if reversible cargo capture and release could be demonstrated independently of assembly/disassembly, the applications of this method would be even more wide-reaching.

One aspect that could use a bit more clarification is whether the assembly and disassembly kinetics make sense from a first principles point of view. Is there unusual cooperativity in either process? In particular the disassembly kinetics would be interesting to model - given a nM affinity and the fact that the SH2 domain should be competitive for the phosphatase substrate, the off rate should be limiting and the constraints by multivalent interactions should make rebinding faster than diffusion.

In summary, this paper demonstrates a powerful technique that is novel in its use of proteins as the basic subunits and in the use of phosphorylation to control assembly/disassembly, advances which are important to the biological relevance of these structures and which, supported by the various forms of experimental validation the authors also describe, make it highly relevant to the field of bioengineering and, above all, very cool!

Minor points:<br /> DNA nanotechnology has been employed to some similar ends: https://www.nature.com/arti..., necessitating a bit more nuance to the claims on page 3 lines 18-20.<br /> The term "design elements" on page 4 line 15 is ambiguous, as the first two items listed are concrete structures and the third is described as a quality. Whichever perspective is taken should be consistent across all three items and between the listings on lines 15-19 and on page 4 line 19 through page 5 line 1.<br /> Authors should comment on the significance of 2.1 ± 0.1 fractional dimensionality in Fig. 4 and on the precision of this measurement.<br /> In Fig. 4F, the authors may push the limits of interpretability of a charge density map.<br /> In Fig. 5C, the y axis would better read "% protein capture or release" and the components captured or released would be better identified individually in the legend.<br /> The phrasing "we believe that three factors..." in the supplementary results and discussion does not belong in a scientific manuscript. The same information may instead be phrased as a hypothesis, claim, observation, findings, analysis, approach, or similar. <br /> On page 46 the term "avidity" is used twice, once seemingly associated with monovalent binding and once associated with divalent binding. In case the reader has missed its definition earlier, this section could be slightly rearranged to remove this ambiguity.<br /> Readability suffers slightly from one placement typo (desired the, p. 13 l. 12), excess capitalization of method names (e.g. Helium Ion and Atomic Force microscopy), several autocorrected spellings (Volt(a phase plate), p. 10 l. 8; mcm/px in SI 3.4; Thermal (Thermo) Fisher in SI 3.5; sale precipitation in the Fig. S19 legend) and a missing Ångstrom symbol (120A, SI 3.6).

We review non-anonymously, James Fraser and Iris Young (UCSF). We will be posting this review as a public comment on the preprint version of the manuscript on BioRxiv.

On 2018-11-25 15:23:58, user Jakub Gemperle wrote:

https://www.ncbi.nlm.nih.go...

On 2018-11-22 11:39:25, user Martin R. Smith wrote:

If you used a random tree instead of a molecular tree to generate hypothetical ancestors, would your approach improve the congruence of morphological results with the random tree?

On 2018-11-21 16:54:24, user Helgo Schmidt wrote:

Now published in eLife https://elifesciences.org/a... (doi: 10.7554/eLife.39163)

On 2018-11-21 09:57:49, user dalloliogm wrote:

Some comments about the paper:

• Since there are many colors used in Figure 2 and 3, it is difficult to read which method is which (especially if you printed the paper in grey scale, like I did :-) ).

• in Fig 4, it would be useful to have the maximum number of total rejection for each column. It took me a while to understand that the columns have different scale. For example, I would expect BH and q-value to have the same color intensity within each dataset (e.g. ChIPseq, etc..) as these methods do not make use of covariates. However the colors are different, even when the total number of tests is the same (e.g. in many scRNA datasets).

On 2018-11-06 09:33:43, user dalloliogm wrote:

The methods presented in the paper are very interesting. However there is still something I don't fully understand yet. The authors suggest to use independent covariates when adjusting for FDR. For example, in a eQTL study you may want to give more importance to the SNPs closer to the target gene, as these are more likely to be real associations. However, what is the advantage of including these weights at the FDR level, instead of covariates during the previous analysis? Shouldn't a FDR correction be agnostic to any hypothesis?

On 2018-11-21 08:01:55, user Nikolic wrote:

Very nice and useful work !

On 2018-11-21 01:08:56, user Jordan Gauthier wrote:

I like the biology underlying your study, yet I am skeptical much can be achieved with such limited numbers (n = 14). Also including autologous transplants is a bad idea. Would increase your numbers and perform external validations.

On 2018-11-20 22:59:27, user Claire McWhite wrote:

Hi, I'd like to take a look at the molecular model of the GAPDH-CP12-PRK, 6GVE, but I couldn't find it in PDB?

On 2018-11-20 21:44:11, user Arie Horowitz wrote:

Dear readers,<br /> The last text version uploaded today includes minor but numerous changes and corrections. My apologies for not having made them earlier.

Arie

On 2018-11-20 17:15:04, user Tom Gybels wrote:

An additional phase IV study, coordinated by 52 independent dermatologists with in total 199 patients, confirmed the spectacular results of this proof of concept! We at YUN are so proud!

On 2018-11-20 13:13:47, user Ingmar Claes wrote:

Very proud of being part of the bacterial revolution! At YUN we are convinced that the live biotherapeutic products (LBP) field can jointly reduce the (over/mis)use of antibiotics!

Many thanks to the University of Antwerp and the University Hospital of Antwerp! This wouldn't have been possible without this collaboration. <br /> @SarahLebeer @Eline_Oerlemans @Filip_Kiekens @Tim_Henkens @Julien_Lambert

On 2018-11-20 16:30:28, user Zhenguang Zhang wrote:

Interesting paper. Is the P1 gating for lung epithelial cells right in SF8b?

On 2018-11-20 16:08:19, user google-1c72a6296b34b022a62ed224dfa04c87 wrote:

I've uploaded the supplementary note to the S-LD4M github repository: https://github.com/lukejoco...

On 2018-11-20 05:29:43, user X. Chen wrote:

the github url of VariantKey in the paper is unreachable

On 2018-11-19 21:06:35, user Orob350 wrote:

Peer-reviewed version has been published:

https://www.sciencedirect.c...

On 2018-11-19 11:46:46, user Mark Leake wrote:

Accepted paper at https://academic.oup.com/na... Biorxiv version mistakenly does not contain main figs, and has error in spelling of 1st author name. Any issue with obtaining corrected pdf contact Mark Leake (mark.leake@york.ac.uk)

On 2018-11-18 23:32:03, user Marcus wrote:

Following on from my previous comment, immunos on endogenous PIN1 by some of the same authors already show a very different polarity pattern to the one proposed in this paper. A pattern much more, if not identical to the patterns of PIN1-GFP in other studies. E.g. Fig. 2A (de Reuille et al 2006) shows early reversals of PIN1 polarity and clear polarity away from the meristem towards convergences way before P6 - contradicting this study. I find this data quite compelling.

On 2018-11-14 06:34:51, user Marcus wrote:

Interesting paper and great effort at quantification.

Controversial though, as I'm sure the authors realise.

So here are some points that I think are important.

1. Firstly the up-the gradient model referenced (Jonsson et al.. 2006) does not propose that auxin maxima only move according to growth and are stuck to underlying cells. In fact it was shown that additional mechanisms have to be in place for maxima not to move, e.g. localized influx carrier expression (Heisler and Jonsson 2006, J. of Plant Growth Regulation).

2. More importantly, the authors find a mismatch between PIN1 polarity patterns and auxin distribution patterns. Although automatic segmentation is used, critically the authors rely on one particular PIN1-GFP (from the Friml lab) for their analysis. From our in-lab comparisons this PIN1-GFP is not nearly as polar or dynamic in expression level as other PIN1-GFPs with a different GFP insertion site, even though both versions can complement. For instance compare Fig. 3A in this paper to Fig. 1 from Heisler et al., 2005 in CB. Ideally immunos on endogenous PIN1 would be used for quantification. At the very least a complemented line.

This is critical because there is a clear conflict with the previous literature in terms of when polarity reversals occur and whether PIN1 ever points away from the meristem towards new convergence sites. From both Fig.1 and especially Movie S2 in Heisler et al., in Curr Biol (2005), it is clear that PIN1 reversals in these cases occur quite early, certainly before P5 as indicated in this paper. Furthermore, the reversal is correlated with a drop in PIN1-GFP expression level which is not seen in the current paper and I strongly suspect this is due to the particular PIN1-GFP used.

Lastly, auxin depletion also correlates with reductions in MONOPTEROS (MP) expression. Since MP activity orients PIN1, it also makes sense that PIN1 polarities change when depletion occurs.

1. A hysteresis mechanism is inferred because 6 hrs of auxin treatment leads to more widespread DR5 compared to 30 min treatment. Wouldn't a simpler explanation be that auxin infiltration into the SAM takes time? A good control experiment would be to look at auxin levels using the ratiometric auxin marker to see whether concentrations also increase over time of treatment. If concentrations increase then this would be a much more likely explanation in my view.

2. The proposal that the the central zone is a central organiser of polarity would seem to be disproven by the experiment in Reinhardt et al. (2003) Dev, in which the central zone was ablated while organ formation continued.

3. Lastly, given the topic of this paper is the relationship between PIN1 polarity patterns and auxin response, I have to admit I am a little disappointed that Caggiano et al., (2017) in eLife wasn't cited since this paper was on the same overall topic and seems highly relevant.

Apart from that I think the paper is a fantastic effort.

All the best,<br /> Marcus.

On 2018-11-18 17:13:19, user Juan Sánchez-Arcila wrote:

Anna, I looked in /Users/myAcct/Library/Preferences/ in FlowJoX (10.5.2 ver ) and I could find a FlowJo10.prefs file one. Do I need to create one?

Thanks.

On 2018-11-18 16:43:52, user Juan Sánchez-Arcila wrote:

Hi Anna, will you offer this type of analysis for R?<br /> Cheers!

On 2018-11-18 15:39:43, user theempiricalmage wrote:

Still using CHG from up to 11000bc years ago, and comparing them to Iranian Neolithics from only 7500bc. Anyways, as trivial as it seems, it is clear the 'green component' is more Iranic than CHG, in contrast to what the author states.

On 2018-11-18 13:13:41, user mgm14392 wrote:

This is a very interesting paper. I am sure this approach considering the amino acids neighbor preferences and relative positions will be very useful. I wonder if the authors have a summary for the 14,647 PDB structures obtained using NCBI VAST. I understand this is a non-redundant dataset but I think it would be interesting to see if there are some protein families more represented than others when they obtained the statistics.

On 2018-11-18 13:13:33, user Erlembaldo wrote:

Very interesting paper. I was wondering if they have been released the raw data about the samples used.

On 2018-11-17 21:25:31, user ???? Dr. Jennifer Glass ???? wrote:

This is a fascinating paper! Has it been peer reviewed yet? Wondering on publication status.

On 2018-11-17 21:16:43, user theempiricalmage wrote:

Observe the limited point of sampling (extreme western fringe) for the "Anatolian" population. The same dubious type of conclusion came from an earlier study which I recall mentioning. <br /> However, from the sparse sampling just a couple hundred miles East of that, and indeed, the Iranian Neolithic component shows. Feldman et al, 2018...that's just poor science.

On 2018-11-16 23:39:09, user Well Left wrote:

Open source is the right way to do HASCIA. Thanks for this contribution, all.

On 2018-11-16 21:46:09, user Alex Naka wrote:

There's one unfortunate omission in this preprint - we forgot to acknowledge Dr. Connie Cepko, who very graciously shared CreDOG AAVs to help us pilot those experiments.

On 2018-11-16 21:09:26, user Patrick ZQ. LI wrote:

Hi Yvon,<br /> Many thanks for sharing the ROP6/PS work with us and I enjoyed reading it,

I have some questions regarding the data in Fig S5,

When PM PS is depleted (pssi) or ROP6/PS interaction is abolished. ROP6 and ROP6(7Q) have a two-fold increase regarding its Nano-domains formation (Fig S5 E-G). Does this mean that ROP6 use its C-ter to cluster with PM PS and another domain to cluster at PM-nanodomains with reduced PS (Fig 5A)?

Yes, most ROP6(7Q) are not localized to PM and less likely to have PM-related ROP6 function. However, for WT ROP6, under Auxin treatment, there could be auxin-activated ROP6 promotes the formation of nanodomains that have both ROP6 and PS, and another ROP6 population clusters at PS depletion sites, which is not directly regulated by auxin but benefits from auxin promoted PS re-distribution (Fig 5 A and C). Since you also have observed that ROP6 and PS only partially reside in the same PM-Nano domains in response to Auxin, I am wondering if you have given a thought about the function of the ROP6 nanodomains with depleted PS, since the paper highlights the importance of PS variations?

Best, Patrick

On 2018-11-16 16:25:46, user Eryn McFarlane wrote:

Dear Authors,

We are a group of Phd students and Postdocs at the University of Edinburgh that meet weekly to discuss life history papers. We noticed that the tone of our discussions could be a little negative, so, to counteract this, we decided that the most positive thing would be to review pre-print papers, and then share our reviews with the authors. Hopefully, this acts to both give us experience as reviewers, and provide feedback to researchers who have posted their manuscripts on bioRkiv.

We hope that this review is useful to you, and will help to improve your paper. Please feel free to contact us if you have any questions or clarifications.

Best wishes,<br /> Eryn McFarlane<br /> eryn.mcfarlane@ed.ac.uk<br /> on behalf of UoE Life History Journal Club

Major comments on ‘Loci, genes and gene networks associated with life history variation in a model ecological organism, Daphnia pulex (complex)’.

This paper asks some interesting questions about the genomic and transcriptomic underpinning of life history traits in wild caught Daphnia pulex. Malcom et al’s linking of the genotype, transcriptome, phenotype in ecologically relevant traits in a model system is a thorough exploration of this on-going problem in ecological genomics. Below, in no particular order, are our main suggestions to improve this manuscript.

Description of methods in main text: In general, we found the paper a bit difficult to follow with the methods after the discussion. We would suggest that either all results are reported with a brief description of the method used, or the methods be incorporated early in the ms (i.e. after the introduction). Ideally, if there were to be increased narration of the methods in the results section, this would include a description of sample sizes. In general, we were not clear on the experimental design until we had read the methods, which made much of the paper difficult to follow.

Quantitative genetics: We think that the authors have used a mix of appropriate and inappropriate quantitative genetics techniques. For example, we agree with how they have estimates H2. However, the genetic covariances described don’t account for error around the breeding value estimates. This is problematic, and can lead to anticonservative estimates (Hadfield et al. 2010 Am Nat 175(1):116-125). We suggest that, instead, the authors use multivariate statistics that estimate variance covariance matrices, with error. Further, we don’t agree that the authors have accounted for maternal (genetic) effects using their experimental design (line 443 – 445). A standardised environment over several generations does not preclude (heritable) differences in maternal investment. If there are maternal genetic effects that co-vary with environment or clone, then we expect that these will lead to an inflated H2 estimate.

Hybridization: the presence of unidentified hybrids among the clones is concerning to us. If this is a hybrid complex, then many of the downstream genomic and transcriptomic analyses are inappropriate, as they assume populations, rather than hybrid zones. For example, LD is imagined to be very high in recent hybrids, which could lead to GWAS hits that are representative of large portions of the genome. We suggest that STRUCTURE (Pritchard et al. 2000 Genetics 155(2): 945-959 or similar) is used to determine the admixture score of each clone between the 2 species in the complex. Then, admixture mapping could be applied which would take advantage of this hybridization. Similarly, if there are hybrids among the samples, then they could be utilized to examine allele specific expression, which should not be disregarded. These are some interesting additional questions that could be posed using a dataset that includes hybrid individuals.

GWAS: 96 individuals for a GWAS analyses is a quite low sample size (although might be more reasonable for admixture mapping, see below). Additionally, this is a tall order with only 4000 markers. As all clones are wild caught, we wonder what the LD is between the 4000 makers. Kardos et al. (Molecular Ecology Resources 2016 16:727-741) have a description of the problem of quickly dropping LD with few markers and small individual sample sizes which illustrates the problem of GWAS on data sets such as this one.

GO term analyses: These analyses should demonstrate enrichment of GO terms, not just presence of significant GO terms. Would these GO terms come up at this frequency just by chance because they are representative of the GO annotation of this species?

On 2018-11-16 14:50:17, user Christopher O'Connell wrote:

Does it work with Nikon PFS which relies on refractive index mismatch to lock focus? With aqueous buffer you can lock and image without z drift. Seems problematic if you can't use PFS.

On 2018-11-16 04:54:47, user razibkhan wrote:

fig 4 the label is GHI instead of GIH

On 2018-11-16 01:40:56, user 成瀬清 wrote:

@LischikC Do you test gRNA for slc2a15b?　KO of this gene disrupt the leucophore development.

On 2018-11-15 22:28:30, user Ravishankar Palanivelu wrote:

The Supplemental information is not loading up. I get an error message.

On 2018-11-15 07:07:58, user Rod Page wrote:

See work by @nickynicolson on linking duplicate plant specimens https://doi.org/10.6084/m9.... and https://arxiv.org/abs/1809.... which is relevant to linking sequences to specimens (which may be geocoded)

On 2018-11-14 18:12:40, user Jian Wang wrote:

Amazing work! Congratulations!

On 2018-11-14 16:34:41, user Jennifer Harder wrote:

For those who visited our poster at our ASN Kidney Week 2018, we just uploaded our manuscript so you can have a closer look at the data.

On 2018-11-14 16:34:20, user Yuji Kondo wrote:

Dear author. Thanks for exciting paper. I have a question about figure 1F especially nuclear lamina and nuclear pore. What do you think about overall nuclear staining even in limited expression of APEX2 in nuclear lamina or pore? I am a bit confused with mis-match of co-staining of avidin and APEX2. I want to hear your thought. Thank you so much in advance.

On 2018-10-31 22:43:48, user Tom Smith wrote:

Dear authors. Could you let me know what the GEO accession is for the raw data. In the methods it simply states: "All sequencing data available through GEO" but I am unable to find it in GEO. A search for "apex-seq" only returns the data from the recent Padron et al study into RNA granules. Also, I'm sure you're aware by now but the section "Data analysis using DESeq2" and "FPKM data sources" are duplicated in your methods section

On 2018-10-31 04:49:31, user F_M_Fazal wrote:

We thank you all for your interest in the APEX-seq atlas preprint! We have submitted the methods as supplementary material on bioRxiv. Should post soon.

On 2018-10-30 21:29:04, user Devang Mehta wrote:

A preprint is expected to reflect a complete scientific manuscript. The lack of a Methods section here is very troubling given that the authors include every other section including the Acknowledgements!

On 2018-10-30 20:44:03, user Michael Hoffman wrote:

This preprint seems to be missing a Methods section referred to within. Methods are not described with the minimum level of detail one might expect for a scientific publication.

On 2018-11-14 15:56:42, user dalloliogm wrote:

This is an interesting method, however I don't understand the application to GWASes. Isn't the p-value in a GWAS dependent on the allele frequency?

On 2018-11-14 01:28:31, user Manuel Kleiner wrote:

This is a very nice comprehensive dataset on transmission mode. The interpretation of the results is, however, problematic as there are some serious biases in the data. For example, for vertically transmitted insect symbionts every single symbiont strain and host species has been included in table S1 as a separate entry, while for horizontally transmitted rhizobial symbionts, where hundreds of symbiont strains and host species are known, everything was lumped together at genus level and on the host side just "legumes". So there is a strong bias leading to underestimation of horizontal transmission in terrestrial settings. <br /> Also, the definition of symbiont is unclear. The manuscript seems to only include obligate mutualists. What about the broader definition of symbiosis which includes parasites, or what about mixed mode and horizontally transmitted symbionts of vertebrate animals. Particularly for the intestinal microbiota of many mammals, we do know quite a bit about transmission mode, but not much about the ecological impact of the symbionts.<br /> As mentioned above, I think this is a very valuable dataset, but the mansucript would need to be recast in terms of conclusions, more precise definitions what was looked at, inclusion of additional data and better data grouping to avoid biases caused by use of different taxonomic levels.<br /> Thank you for making this available!

On 2018-10-19 20:36:29, user Harald Gruber-Vodicka wrote:

Strict vertical transmission spanning hundreds of millions of years of cospeciation has been shown in marine invertebrates. Check out our data for 15 species of Paracatenula flatworms in our 2011 PNAS paper, figures 4 and S6. https://doi.org/10.1073/pna...

On 2018-10-04 14:35:17, user Nicole Dubilier wrote:

Host dependency also influences symbiont transmission, check out this excellent paper by Roberta Fisher, Lee Henry, Charlie Cornwallis, Toby Kiers, Stu West:<br /> https://www.nature.com/arti...

On 2018-11-13 20:43:58, user Wilfried Guiblet wrote:

Please find the last version of this manuscript at:<br /> https://genome.cshlp.org/co...

On 2018-11-13 19:00:07, user chinghaowang wrote:

Check out our new work on the "information-speed" trade-off in biochemical signaling. We also develop a theoretical/computational framework for designing signaling pathways that maximize information capacity!

On 2018-11-13 16:29:16, user luca magnani wrote:

Published Manuscript can be found here<br /> https://www.nature.com/arti...

On 2018-11-12 22:58:58, user hari easwaran wrote:

Cool work on elucidating mechanisms during Senescence associated methylation.

On 2018-11-12 14:23:07, user drvivienrolfe wrote:

This looks like a fantastic initiative - thank you for developing it. I'm wondering, are you searching AYUSH or other Indian/Chinese databases, as I would think there is a wealth of extra literature there other than what is available on Medline? Also - will it be possible to link to the publications through clicking on the bar chart of + and - results - it would be good to see the evidence behind the data for each spice? (I work for a herbal tea company, and this is going to be very useful for us).

On 2018-11-12 12:32:50, user Larry Lain wrote:

Just curious. Why are the other 50 million plus statin users not candidates?

On 2018-11-12 11:03:45, user Cho Fred Ntsang wrote:

As lead author, I will welcome all contributions to the growth of knowledge.<br /> Thanks

On 2018-11-12 11:01:37, user Cho Fred Ntsang wrote:

It's my greatest pleasure to share.

On 2018-11-11 23:57:21, user Vlad wrote:

Well done, people!<br /> I thought about you publishing something like that

On 2018-11-11 17:36:13, user Betty______Lin wrote:

Extracting information from huge scientific publications is extremely importance, due to this importance lots of information extraction systems have been reported. However this automated hypothesis generation is extraordinary, which combined with semi-supervised machine learning can be successfully utilized and iterated in the future work.

On 2018-11-10 15:11:38, user Michael Hoffman wrote:

This manuscript is incomplete as it is missing a Methods section referred to within. Methods are not described in the usual level of detail for a scientific publication.

On 2018-11-10 14:54:26, user Elana Fertig wrote:

Amazing concept, but a methods section is really needed.

On 2018-11-10 06:51:22, user cabbageleek wrote:

Nice work, but you need to improve the write up to get any traction!<br /> I hope my comments will help you improve the manuscript, please take them that way.<br /> The title will be meaningless to most people. species2vec is not the novel method, but it is the code that implements the model.<br /> What is "species representation"? This is not a commonly used term for anything ecological.<br /> The abstract should explain the research in much clearer language. Currently, it is only comprehensible to a tiny number of people.<br /> Say what you are trying to achieve in very simple language. For example, you should not start the abstract with the words "Word embeddings", because that is meaningless to most people who might be interested to read it. The explanation of word embedding it in the methods, but it should be moved to the introduction.<br /> You mention the skip-gram model in the abstract with no explanation at all. <br /> There is no point having the species names on figure 3, as they are unreadable. <br /> Figures should be understandable without reference to the text...<br /> Figure 4 needs a better legend. What do the colour codings mean?<br /> Figure 5 needs a better legend.<br /> The DOI of the GBIF download should be cited.<br /> I assume the words "substitude" and "continious" are spelling mistakes.<br /> I would avoid using the term subword without explanation, it is a rather obscure term and only known to small cliche of researchers and not to species distribution modellers.<br /> In figure 3 the cluster Africa is further from Europe than South America and Australia is closer to Europe than South America. This doesn't make sence from a biogeographic point of view. Can you explain this? Did you remove the introduced species before doing the analysis?

On 2018-11-09 14:54:15, user H. Etchevers wrote:

This article is a very interesting followup to your 2017 work. I suggest that you define what you mean by "deep phenotyping" in the introduction.

Perhaps in the discussion, you could also suggest an example of how this approach could be applied to clinical manifestations in human mosaic patient populations, eg. with a known hotspot mutation but unique distributions of the mosaic cells.

On 2018-11-09 06:45:27, user Wendy Adams wrote:

"Long-term use of antibiotics is associated with serious complications, including post-treatment Lyme disease syndrome (PTLDS)."<br /> Unclear what this means - if it means that LT abx cause PTLDS, then that's not a true statement. LT antibiotics may be prescribed for PTLDS (or chronic Lyme) but PTLDS by definition is dx'ed after a standard course of doxy or amox, not a long course, of antibiotics. You may have meant "PTLDS may be treated with LT abx, which may be associated with serious complications". Also important to remember that PTLDS is only a small subset of those patients who are still symptomatic after initial treatment for Lyme disease.

On 2018-11-09 05:45:10, user Ben Busby wrote:

Minor comment: This is awesome; a link to the image in dockerhub would be helpful!

On 2018-11-08 21:35:25, user snärkē Ph. D. wrote:

Hello,

Thanks for depositing your paper in the archives! I'm reading through it at the moment.

I have some concerns about supplementary figure 13 https://uploads.disquscdn.c... . There a lot of portions of the plot that exhibit apparently perfectly linear behavior. There aren't good biological explanations for this.

There are several possible explanations, but the one I'm currently thinking of is: the parallel lines are the result of the digital (non-continuous) nature of the data at low expression levels plus the influence of data normalization on the results.

Even if that's not the source of this result, it's a good idea in general to include sample normalization results as a covariate in your exploration of genomic data.

Nathan

On 2018-11-08 13:42:48, user Hao Luo wrote:

Thanks for your code!!!

On 2018-11-08 07:41:49, user Plinio Casarotto wrote:

Hi, there is a typo in the abstract at bioRxiv page. Where it is written 'independent podcast series (median = 24, average = 96)...', should be (median = 16, average = 48) according to the pdf.

On 2018-11-07 21:57:58, user Alec Tarashansky wrote:

Github repo for this method found here:

https://github.com/atarasha...

On 2018-11-07 19:55:50, user Emil Ruff wrote:

Great article! A broad discussion on the topic is long overdue. In our latest study on deep-sea methane seeps we show that it may take years to decades for a methane filter to grow back after a disturbance. A possible impact of deep-sea mining and bottom trawling on methane emission is indicated in the conclusion of the article. If you are interested check it out here: https://www.nature.com/arti...

On 2018-11-07 18:05:29, user Ryan Morin wrote:

Final version of this is at Nature Communications: https://www.nature.com/arti...

On 2018-11-07 15:23:37, user Tanai Cardona Londoño wrote:

I just had a look at this tool and put it to the test. It is amazing. Thank you.

I have a quick question... when you say the following: "In contrast, gene functions with extremely low homoplasy include sporulation, photosynthesis, and core processes such as transcription, replication, and protein synthesis".

Do you mean that these are more likely to have been inherited vertically?

The reason I ask is because one of the biggest controversies in the evolution of photosynthesis is whether the distribution of phototrophy has been driven by horizontal gene transfer or losses. The distribution of photosynthesis in bacteria is well known to be quite patchy, with only few phyla known to be phototrophic.

I have argue that even though the distribution of photosynthesis in bacteria is patchy, the phylogeny of many of the core proteins of photosynthesis indicate vertical inheritance with losses as the dominant evolutionary force, although at least one unambiguous cases of horizontal gene transfer is known of full phototrophy is known.

What is your opinion on this? Unfortunately, it is hard for me to understand how the homoplasy metrics were calculated.

Another thing:

I did a search using pfam, PF00124, a core photosystem protein (Type II reaction centre protein). This protein is known to be found in Cyanobacteria, Proteobacteria, Chloroflexi, Gemmatimonadetes, and in some of the newly assembled WPS-2 metagenomes.

My search retrieved 881 genomes with hits in 11 phyla of bacteria. No hits for WPS-2, which is not unexpected, leaving 7 new phyla not previously known to have phototrophy.

The sequences in this 7 phyla represented 1% of the total sequences, most, if not all of them likely to be “contaminated” genomes. I BLASTed all of these sequences: a few of these hit to photosynthetic eukaryotic algae, one genome classified as Fusobacteria had a sequence with 96% sequence identity to a gymnosperm! It is unlikely that these represent true horizontal gene transfer, and it is more likely to represent genomes with contaminating sequences. Something that is not uncommon at all.

I had experienced similar things before, see this for example: https://tanaiscience.blogsp...

Of course, 1% is relatively low, but how something like that could affect your analysis of patchiness and homoplasy, would 1% be considered negligible?

I know the evolution and distribution of these proteins pretty well, so it is easy for me to notice when something is off. I wonder if these phenomenon extrapolates across all protein families and genomes. In such case, 1% “contaminating” sequences, let’s call them false positives, of nearly 40 million annotations would be about 400 thousand sequences… what do you make of that? I know that you cannot control the quality of the available genome data, but something like that could result on overestimation of horizontal gene transfer occurrences in bacteria, for example.

I was just thinking that a word of caution or a bit of discussion regarding possible artifacts could be useful for non-expert readers who would want to use your tool, given that is so accessible and easy to use.

All the best,<br /> Tanai

On 2018-11-06 20:39:12, user 9632628567 wrote:

Abstract "We find that differences in drought & herbivory drive survival differences between habitats"

Correlation does not equal causation. The positive relationship they found between survival with herbivory strongly suggests that herbivory is not driving any survival difference, unless herbivory increased survival somehow. Instead, ones which survived longer were probably more likely to get herbivory. Correlating survival and soil moisture in Mediterranean annual plants - which die during the summer, nomatter the moisture, which declines at the same time, has its own problems, not mentioned here.

On 2018-11-06 15:40:25, user Laura Sanchez wrote:

Dear Stubbendieck et al, this preprint was discussed in a lab meeting and we would like to offer the following for review. Thank you for posting this very interesting manuscript. Best, The Sanchez Lab:

The research article by Stubbendieck et al, describes the discovery of iron-chelating siderophores produced by Corynebacterium spp., most notably C. propinquum. These siderophores were shown to compete for iron in the environment and inhibited the growth of Staphylococcus epidermidis isolates, presumably through nutrient sequestration. The biosynthetic gene cluster responsible for the production of dehydroxynocardamine, the siderophore identified through comparative metabolomics, was shown to be expressed in the nasal cavity in vivo. In the well-organized introduction, the authors argue that the study provides a better understanding of microbial competition in the nasal cavity, which is an important addition to host-microbe interaction research. However, there is a lack of explanation in the experimental design and more thorough explanations of the results could greatly enhance the clarity of the manuscript. The following points are provided as suggestions to improve the manuscript.

Major critiques

The authors provide a thorough explanation in their introduction of known examples of nutrient sequestration by microbes of their hosts, but they do not fully explain how their studies may be applied to any health issue related to the nasal cavity. We could not fully agree what the main point of the manuscript was, if it were arguing that it sees differences in biosynthetic capability between Corynebacterium subspecies, or whether it was important that the growth of a Staphylococcus species could be inhibited by this mechanism. Perhaps with the result in hand, the introduction could be edited to better reflect the take home message of the manuscript.

Line 143 states that nasal lavage samples were acquired from children, however, metatranscriptome sequencing for the detection of the dehydroxynocardamine BGC was done on samples acquired from adults in a different study with a different collection method. Are these translatable? Why were the samples taken from two different populations?

In Lines 285-288, the authors report that they identified dehydroxynocardamine using matching MS/MS fragmentation with the following GNPS search parameters: 2.0 Da parent mass tolerance and 0.5 Da fragment ion tolerance, ≥0.6 cosine score, and 3 matched peaks. For high resolution data, parameters could be narrowed, these are very wide windows to reflect similarity and are surprising given that the data was collected on an instrument with high resolving power. We could suggest generating the molecular network at 0.5 Da parent mass tolerance and 0.2 Da fragment ion tolerance, ≥0.7 cosine score, and 5 matched peaks. Was the parent mass filtered? Often times in qTOF data the parent mass does not completely fragment and can confound the analysis.

Figure 1A has three images of Staphylococcus colonies, but they look as though they have different morphologies. Why are there three colonies? Would the authors consider including a cartoon to aid readers in understanding the experiment? It is difficult to tell which colony in the coculture is which species.

In Figure 1B there is an Actinobacterium outside of the Corynebacterium clade that produces a siderophore but does not inhibit S. epidermidis growth. Is there a reason for this or can you comment on a potential reason? A chi-square test is cited for statistical significance, but the input is not explained.

Are you comparing the scores from strong, weak, and none (2, 1, and 0) in this test? Also in Figure 1B there are other seven Actinobacterium nasal isolates showing strong inhibition scores against S. epidermidis. Could you comment on a potential reason of strong inhibition of S. epidermidis by those isolates as well?

Lines 411-415 state that the authors grew colonies of Corynebacterium species to assess iron limitation on colony formation for Figure 2C. Can the authors replicate this experiment using Staphylococcus species? If the Staphylococcus species cannot grow under iron limitation, this would add significantly to their hypothesis as a positive control.

The previous point ties into Figure 3B. The iron supplementation seems to boost the growth of both microbes. Additionally, C. propinquum and C. genitalium appear to have the same effect on S. epidermidis. The growth of S. epidermidis alone is required to support the authors’ claims.<br /> Is it possible for the authors to generate a dehydroxnocardamine knockout to analyze against S. epidermidis? This could definitively verify that the siderophore is responsible for iron acquisition. Simply knowing that the species is capable of producing this molecule does not directly infer that is responsible for the inhibition unless the pure compound can be tested.

In supplement to the point above, is it possible to purchase a standard of dehydroxynocardamine, isolate the compound, or synthesize it to spot next to a S. epidermidis colony? This would link the molecule to the inhibition if the same effect is generated.

In lines 447-448, the authors claim, “this result indicates that inhibition of S. epidermidis in vitro by C. propinquum is due to iron sequestration.” Have the authors detected any metal-bound species of dehydroxynocardamine? Is it possible that it is sequestering other metals, like zinc or copper? ICP analysis or mass spectrometry could answer this and would definitely verify that the compound is actually a siderophore, otherwise this should clearly be stated as putative.

In lines 511-512, the authors state that the dehydroxynocardamine BGC is expressed in vivo. How do they know which organism is responsible for the transcriptomic response?

Minor critiques

On lines 365-367, for Figure 1 the authors claim, “Given the limited variation in staphylococcal inhibition by other genera of Actinobateria, we focused on Corynebacterium spp.” Many other Actinobacteria appear to generate the same inhibition pattern, some presenting more inhibition. Is there any further explanation for selection of this clade for further evaluation?

For Figure 2A, labels would greatly enhance readers’ understanding. It was difficult to interpret this chart.

A cartoon, similar to Figure 1A would be helpful in elucidating which colony is which. Colonies of C. propinquum in 3A are circular and resemble the colonies of S. epidermidis in 3B.<br /> Is figure 3C scored using the same system as Figure 1A? Is there any statistical analysis done?

Figure 4B could be moved into supplemental information so that focus of this figure is on the cluster of interest.

In Figure 4C, the node corresponding to dehydroxynocardamine could be labeled for clarity. Otherwise, the analogues in 4D could be labeled with their corresponding matches.

In Table 1, donF should be dnoF.

On 2018-11-06 11:27:18, user Nibi wrote:

Regarding the wash solution in the "DNA Preparation for NGS in RGEN-D Protocol" section of the methods, what composition did you use?

And also, regarding the SRA data "SRP151278", I couldn't access using the accession number. Have you deposited the data?

On 2018-11-06 11:24:13, user Nibi wrote:

Regarding the wash solution in the "DNA Preparation for NGS in RGEN-D Protocol" section at page 24-25, what composition did you use?<br /> And also, regarding the SRA data "SRP151278" at page 36, I couldn't access using the accession number. Have you deposited the data?

On 2018-11-06 04:10:33, user Philippe Henry wrote:

This is a fantastic preprint, thank you Chris and team!

On 2018-11-06 02:55:07, user Joseph Kirschvink wrote:

We are open to any comments or criticisms.<br /> Joe K.

On 2018-11-05 21:25:19, user Fraser Lab wrote:

I reviewed this paper for a journal:<br /> The major goal of this manuscript is to explore whether large crystals can be made amenable for microED by using FIB milling to reduce them in size. This manuscript is somewhat reactive to a recent report in PNAS (Duyvesteyn et al), which used essentially an identical crystal preparation scheme, but a different data collection scheme.

The major issue with the manuscript is that the introduction is missing a paragraph on WHY milling will be helpful. Even though the audience of this journal is fairly well informed about microED, a few sentences on the technical issues of why diffraction is not observed from larger crystals and the contrast between the interactions of electrons vs. X-rays over a large number of unit cells should be pointed out. This will help to motivate the present study of FIB milling crystals and help to set up a parallel paragraph in the discussion that outlines some of the next steps and the theoretical limits (how many unit cells are really needed? relationships btw number of cells and multiple scattering events, best milling procedures/currents, etc). This could be a breakthrough workflow for microED and it would be great if this manuscript stood on its own to explain exactly why it might be!

Minor points:<br /> Ironically, one of the best ways to answer definitively whether gallium is incorporated into the lattice would be to use X-ray crystallography for its significant anomalous signal!

the change in resolution relative to their best samples by microED is a bit puzzling. have they tried milling multiple lamella from the same macro crystal and testing whether the resolution is consistent across one “original” lattice? This could also use more speculation!<br /> In contrast to the reduced resolution speculation due to damage from milling - the Duyvesteyn paper also notes that they have high angle spots (1.8A - obviously for a different sample), but are limited by severe radiation damage. Presumably the differences in data collection allow a less damaged set to be collected here - can the authors clarify and contrast with the other study a bit more?

I review non-anonymously, James Fraser (UCSF). I’ll also be posting this as a comment on the authors preprint, which they have posted on BioRxiv.

On 2018-11-05 18:42:08, user Diego Folco wrote:

New great tool for the Pombe community!<br /> I was wondering if these spore-autonomous fluorophores could work similarly in azygotic asci.

On 2018-11-05 17:25:57, user Dr. James D. Johnson wrote:

Wondering why you guys chose not to use INS as the marker for beta-cells?

On 2018-11-04 23:16:52, user Ya Sa Min wrote:

Being able to easily resolve commonly used compact URIs in biological science, will certainly be very useful.

On 2018-11-04 12:28:17, user Mazer22 wrote:

How do I get access to the full article? I am trying some batches at home. Thanks!

On 2018-11-04 12:10:41, user Jon wrote:

How do we see the full article?

On 2018-11-04 08:49:40, user Levi Yant wrote:

Note that Preite, Sailer, and Syllwasschy contributed equally. Correspondence may be directed to either me or Ute Krämer.

On 2018-11-04 01:08:08, user jvkohl wrote:

The difference in the energy of two photons links light-activated microRNA biogenesis from mitochondrial calcium to autophagy and RNA-mediated cell type differentiation in all living genera. See also: The Influence of MicroRNAs on Mitochondrial Calcium<br /> https://www.frontiersin.org...

On 2018-11-02 23:26:27, user Wouter De Coster wrote:

Dear authors,

Thank you for this interesting work. I'll read it later more in depth, but I have some quick feedback. It seems the URL to your tool is only available at the very end of the manuscript, although it would be convenient to have it earlier, preferably at the end of the abstract. Since your tool is the main deliverable of this work you should put it more in the spotlight and easier to find. My second comment is about the use of python 2.7. It seems your code is not compatible with python 3, which is a problem. Python2.7 is not going to be supported for that long anymore, and by publishing tools with an outdated python interpreter now you are not writing future-proof software.

Cheers,<br /> Wouter

On 2018-11-02 21:06:55, user Molly Liu wrote:

Thank you for posting this preprint. Anjali Pandey presented this paper for the Vosshall lab journal club on 10/31, and we loved it! We were convinced that your optogenetic perturbations induced flies to perform idiothetic local search and intrigued by the effects of starvation and changing search landscapes. Your data are complex, yet analyzed and presented extremely clearly, with multiple sensible metrics supporting your claims. In a spirit of excited inquiry, here are some questions we had:

Figure 1: Your results have interesting gradations in that activation with some drivers definitely induce local search (Gr43a, Gr5a), some definitely do not (orco, Or59b, ppk28, R58E02), and some induce a “modest local search” (Ir76b, Or42b, NPF).<br /> - Do these categories reflect different biological contributions for each set of neurons, or technical differences in the strengths of the drivers? We suggested performing dose-response curves looking at magnitude of search over a range of frequency of IR pulses for a few of the drivers, seeing if there are differences in search magnitude even when the drivers plateau in strength.<br /> - Is there a way to statistically compare the differences between genotypes, as well as looking at differences within genotypes (red vs. blue in 1H-J)? We suggested taking the difference between between activation and baseline (red - blue) and comparing them across genotypes to see if the structure of the data contain the three groups you suggest in the text.

We were also briefly confused about how starved the animals were in this first experiment, so we suggested describing the starvation length earlier in the text, instead of waiting until the text re: Figure 2.

A complementary strategy: Are there any neurons that, when activated or inactivated upon simultaneous encounter with food, can block search?

Figure 2: We were curious about the magnitude of the responses seen in 2E-J here relative to the responses in the less starved flies of Figure 1. Did you perform controls in parallel with fed animals, similar to 2A-D? Similar to our suggestion for 1, would it be possible to statistically quantify the changes in response magnitude by comparing between treatments instead of merely within treatments?

Figure 3: We found this to be the most intriguingly unexplored figure in this paper. Currently, 3A-C only makes a claim about the search persistence for Gr43a driven flies. We wondered what a similar analysis would find with the other drivers: Do the “modest local search” drivers induce less persistent searches? This question would be particularly interesting with NPF: does peripheral activation induce a more lasting central state than directly activating central brain neurons?

3D-F sparked so many questions, and we hope you continue to investigate the intricacies of a two-centered search. Are there any patterns in how flies switch their search centers? How long does it take for a fly to “forget” one of the centers, if they ever do? Do they search around both centers without receiving continual reinforcement from the LED stimulus?

Figure 4: We adored flyadelphia! Some questions:<br /> - Is this constrained search “harder” or “easier” for flies than the free search in the arena? (Can the flies learn to count “blocks,” and is that easier than counting their steps?) We were curious to learn more of your thoughts on this. One way to get at this question might be to put some of the “modest local search” driven flies in this arena and see if their performance improves relative to the Gr43a driven flies. Does the time<br /> - Orderly blocks are relatively easy to navigate. Are flies still able to search if you put them in a “fly West Village” or “fly Boston” with chaotic and seemingly randomly angled blocks? Do the number of revisits or bout duration decline with increasing difficulty of the task? <br /> - We were intrigued by the possibility of using flyadelphia to play fly “Pac-man”, perhaps by using another driver expressed in neurons signalling negative valence to drive a blue-light responsive rhodopsin, and competing blue and red light.

Overall, we were very impressed by this paper and excited to see the new avenues of research that it will produce. Congratulations!

Signed, the Vosshall lab:<br /> Molly Liu, Trevor Sorrells, Ben Matthews, Nipun Basrur, Laura Duvall

On 2018-11-02 03:56:36, user Stuart Cook wrote:

Great study. The finding that IL-11 is specifically increased in activated fibroblasts in colitis and predicts resistance to anti-TNF therapy is consistent with a previous study (PMID: 19700435). Notably, OSM and IL-13 signalling - both also predicting response to anti-TNF therapy in this study - have an absolute requirement for IL-11 to stimulate fibroblast activation (PMID: 15699166, PMID: 29160304).

On 2018-11-01 10:41:41, user Rolf Baumberger wrote:

well done!

On 2018-10-31 20:15:37, user Dong-Ha Oh wrote:

This manuscript is now published online on DNA Research: https://academic.oup.com/dn... DOI: https://doi.org/10.1093/dna...

On 2018-10-31 10:04:29, user Rana Aldisi wrote:

The authors here present a methodology in which genes can be identified in pathway figures. I found this quiet interesting, since most of the research in text mining is focused on extracting information out of the text in literature. This step could further enrich the process of literature text mining and pathway analysis. I'm looking forward to see the advances that can be made to this research.

On 2018-10-31 09:35:04, user NeilD wrote:

Dear authors,

Congratulations on putting together a fantastic piece of work investigating the role of network structure in Turing pattern formation. However, I have a minor comment that I think you might want to consider for a journal version of this work.

Your network pruning approach identifies 21 and 1934 networks with two or three nodes respectively (page 3). Our calculations also lead to this number. However, when assigning species as diffusible/non-diffusible, one should consider isomorphisms not only in the network topology but also by accounting for the diffusible species assignments. As such, there are not simply 1934 x 3 = 5802 unique networks (reported later on page 3), but slightly fewer. This is because some networks will contain symmetries with respect to the assignment of diffusible/non-diffusible species. Perhaps the clearest example of such a symmetry is shown by the equivalence of networks #1, #2 and #3 in Supplementary Document 1. Here, #2 and #3 are identical with respect to their species labels. #1 is also isomorphic to #2 and #3, with species relabelling A <-> B. Another trivial example occurs for {#5800, #5801, #5802}. There are several other networks with such symmetries in the set, which should be identified.

We suggest that you include an additional step of network pruning following the assignment of diffusible species in your analysis.

Kind regards,<br /> Neil Dalchau<br /> Microsoft Research

On 2018-10-31 07:26:38, user Alex Crits-Christoph wrote:

"providing evidence for this sequence representing the first genome of a jumbo phage (genome size > 200kbp)to be identified in the human gut microbiome" - however, earlier this year megaphages with genomes greater than 500 Kbp in size were reported in human microbiomes: https://www.biorxiv.org/con... - this error should be amended.

On 2018-10-31 07:04:22, user Pseudomonas wrote:

Very<br /> interesting software! I used it on my own data set and would like to <br /> compare the results with your results. Unfortunately, I cannot make use<br /> of your pretrained models without knowing on which data they were <br /> trained. Could you provide this kind of information?<br /> I<br /> noticed you have very high AUCs and F1 scores, did you use a balanced <br /> dataset? Did you also calculate correlations like PCC or Kendall's tau?

On 2018-10-31 00:00:08, user Ken Chen wrote:

Are these data publicly downloadable yet?

On 2018-10-30 20:15:07, user Whereisthedata? wrote:

"under the accession numbers of xxx-xxx. The unbinned scaffolds with CRISPR-CasY system were deposited under the NCBI accession numbers of xxx-xxx. All genomic data can be explored and downloaded from ggKbase following publication of this manuscript."

When authors are unwilling to share the data necessary to evaluate their claims it limits the value of preprints.

On 2018-10-30 13:36:28, user Samantha Smith wrote:

I am getting 404s on some of the github links https://github.com/smangul1...

On 2018-10-30 00:57:17, user Andrew Kern wrote:

Dan Schrider and I have responded to the criticisms contained in this preprint in the following response: http://doi.org/10.5281/zeno...

On 2018-10-29 22:56:17, user Ed C wrote:

It is missing 2003 reference showing FAD2 acetylenases as likely components of the polyacetylene biosynthetic pathway https://www.ncbi.nlm.nih.go...

On 2018-10-29 20:15:31, user SohamGhosh wrote:

Our work of nuclear mechanics and chromatin reorganization

On 2018-10-28 21:18:16, user Lillian Chong wrote:

The uploaded Movie S1 does not work. Here is the movie on YouTube: <br /> https://www.youtube.com/wat...

On 2018-10-28 08:37:11, user George wrote:

The paper is now published with some changes after peer-review: J Med Genet. 2018 Oct 20. pii: jmedgenet-2018-105477. doi:10.1136/jmedgenet-2018-105477. [Epub ahead of print]. PMID:30343275

On 2018-10-28 08:05:43, user Erez Braun wrote:

This paper was published eventually in eLife and our full comment in PubPeer.

The comment below was submitted to eLife but was rejected for no deep reason.<br /> The question whether a particular gene reacts in isolation, or alternatively its reaction is part of a more global response, can only be answered experimentally by a global observation of the system over extended time scales. This is simply because a local measurement by itself, without a global view, might be consistent with both types of reactions, local or global; in contrast, a global view will distinguish between these two possibilities.<br /> This paper analyses local data on a gene of interest alone at only two time points and reaches a conclusion that contradicts previously published global data.<br /> Irrespective of the interpretation of the global response, the fact that numerous genes show coordinated dynamics with the rewired gene (the gene of interest for the metabolic challenge) means that specific tuning of this particular gene cannot serve as an explanation for the cell response.<br /> Read the comment below and judge for yourself.

Can specific tuning of single-gene expression be deduced from a local measurement of that gene alone?

Comment on the paper by Freddolino et al.: "Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitry"

Erez Braun and Naama Brenner<br /> Technion, Haifa 32000, Israel

Abstract<br /> The living cell is a complex system in which metabolism, gene expression and regulation, protein and other molecular interactions are interconnected, leading to strong cross-talks between different parts of the genome. Modularity and specificity are sometimes found in cellular responses to external cues such as nutrient change or a common stress response. However, under more general stressful conditions with no pre-existing regulatory program, ad-hoc solutions must be invoked, which likely involve multiple components of the system. Experiments have shown that indeed cells exhibit a remarkable ability to overcome such unforeseen challenges without any pre-defined program (1, 2). While the underlying mechanisms are not well understood yet, the phenomenon can be quantitatively characterized in terms of growth patterns (3) gene expression (4-6) and more. Such measurements have demonstrated that large portions of the genome are involved in the response to the challenge, with coherence across the genome and with a stochastic non-repeatable nature. Many features of this global response remain to be studied. <br /> A recent paper by Freddolino et al. (7), proposes that under similar stressful conditions improved fitness is achieved by the stochastic tuning of an individual gene. Their experiments follow precisely our methodology developed to understand the adaptation of cells to an unforeseen challenge (1). However, in contrast to our experimental approach which consists of global measurements of the genome over extended time scales, they focused on local measurements of a single gene of interest and one other control gene, at two time points. The results are used to deduce an underlying mechanism for cellular adaptation, proposed to rely on the stochastic tuning of a single gene which in turn determines cellular fitness.<br /> The question whether the expression of a gene reflects its own tuning, or is a part of a more global response, cannot be answered by local measurements on the gene of interest alone. This is a general statement on experimental methodology that follows simply because anything that is measured locally is consistent with both local and global effects. We therefore argue that as a necessary stage, prior to interpretation of mechanisms, the phenomenon needs to be characterized on a global scale to determine its extent across the genome and its characteristic timescales. In this case, the conclusion drawn from local measurements by Freddolino et al. is found to contradict previously published global data.<br /> *Correspondence: EB erez@physics.technion.ac.il

Introduction<br /> The living cell is a complex system in which many of the underlying processes are dynamically interconnected. In particular, gene regulation is strongly interconnected with metabolism, protein interactions and other processes in the cell. This implies that, in general, the expression of any gene is in not isolated from other genes; they are coupled through metabolic and other processes which integrate the activity of different parts of the genome. Understanding the intricate relations across the genome and their integration into a metabolic function is still not well understood and constitutes a major challenge of biological research. <br /> While it is true that under some conditions, specifically under known external conditions shaped in evolution, certain genes react in a modular way and their response can be somewhat isolated from the rest of the genome, this may not represent a common behavior. Adaptation of a cell to a stressful challenge, e.g. to a novel and unforeseen challenge for which there is no pre-defined regulatory program, represents a generic situation where the complex nature of the cell will be reflected in its response (1). As a result, one cannot a-priori assume that genes respond in a local way disconnected from the response of other parts of the genome.<br /> The question whether a particular gene reacts in isolation, or alternatively its reaction is part of a more global response, can only be answered experimentally by a global observation of the system over extended time scales. This is simply because a local measurement by itself, without a global view, might be consistent with both types of reactions, local or global; in contrast, a global view will distinguish between these two possibilities. <br /> Beyond the phenomenological description of a cellular response, stands the question of biological function (e.g. cell growth and division) and how it relates to individual genes. We argue that, in general, one may not assume a-priori which component is relevant for the functioning of the cell and conduct experiments that zoom only on that single component. Yet, in many cases the complexity of the cell is ignored by the motivation to search for a simplistic explanation for complex phenomena. <br /> The paper "Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitry" by Freddolino et al. (7) takes exactly this simplistic approach. It describes the idea that “hard-wired” regulatory pathways cannot account for the adaptation of cells to environments never encountered before. This idea is not new and indeed was proposed and studied by us as the basis of the ability of cells to adapt to unforeseen challenges and evolve (1). The authors propose a specific mode of gene regulation that might be in operation under such challenges: individual genes achieve optimal expression through a ‘stochastic tuning’ that leads to improved fitness. The paper describes computer simulations demonstrating this idea, followed by a set of experiments to support it. Yeast cells were engineered to have the metabolic gene URA3 detached from its native regulation and placed under different foreign promoters. The adaptation of cells is manifested in their ability to grow on uracil-deficient medium in which URA3 is essential. Based on analyzing the expression levels of the rewired URA3 gene at two time points (alone or in comparison to another non-essential gene), and on genomic analysis around the rewired locus, the authors conclude that the observed adaptation is carried out by the suggested mechanism of stochastic tuning demonstrated in the simulations.

The purpose of this comment is to argue that the proposed mechanism of stochastic tuning of individual genes cannot explain the observed adaptation as it contradicts a host of previous results from experiments on the same phenomenon. First, from the general considerations mentioned above, measuring only the response of one metabolic gene (even if this gene is central to the specific metabolic process) while not testing the rest of the system, as done by Freddolino et al., does not provide enough support to the claim for a specific tuning. More importantly, as explained below, previous experiments on yeast cells utilizing a similar type of challenge and using precisely the same methodology, showed that adaptation of the cells to the challenge involved large parts of the genome and an intricate complex interplay of metabolism and gene regulation. These response also have a ‘stochastic’ element to them, but the data manifestly contradicts the mechanism of specific tuning of a gene as described in Freddolino et al. Unfortunately these data and their contradiction with the hypothesized mechanism were altogether ignored by the new article.

Adaptation of yeast cells to unforeseen challenge: experimental methodology and growth characteristics

The idea that cells can adapt to an unforeseen challenge, never before encountered in their history, is not new. Starting in 2006, our lab has been developing the concepts and experimental methodology for studying it (1). Our approach involved rewiring an essential metabolic gene in yeast cells, detaching it from its native regulatory system and placing it under a foreign promoter. Growing these cells in an environment where expression of the rewired gene is essential, while its natural regulation is compromised, are the basic ingredients of this paradigm (2). Our first experiments were focused on the HIS3 gene, an essential metabolic enzyme in an environment lacking histidine, rewired to a promoter of the GAL system which is repressed in a glucose environment. Further pressure was applied in some of the experiments by supplying the medium with the HIS3p inhibitor, 3AT. Similar experiments were repeated by rewiring HIS3 to various cell-cycle promoters, with essentially similar results (4). Despite the severe regulatory challenge, yeast cells adapted and established stable growth, in a process that was consequently studied by us in the following decade (reviewed in (1)). These experiments had already established some generality to the phenomenon of adaptation to an unforeseen challenge in rewired yeast cells, with quantitative details depending on the promoter to which the essential gene is rewired. <br /> The work of Freddolino et al. (7) follows this paradigm step-by-step, with two differences: rewiring of URA3 instead of HIS3 (and using its corresponding inhibitor 6AU instead of 3AT), and rewiring to several different natural and semi-synthetic promoters. They report the same phenomenology of cellular adaptation as our previous work, suggesting its robustness also to these differences.<br /> More specifically, all typical growth patterns of yeast cells adapting to the regulatory challenge on plates, reported in Freddolino et al., reproduce our previous results: (1) A significant delay (~100 hours) of growth on plates following exposure to the challenging environment (compare ref (2), Fig. 1 and ref (4) Fig. 1 to Freddolino et al. (7), Fig. 3.). (2) A temporary arrest of growth at variable colony size (between few to hundreds of cells), and for variable arrest times. This results in a broad distribution of adaptation times leading to colony growth, and a broad distribution of colony sizes at a given time (compare ref (2), Fig. 1 and ref (8), Fig.1 to Freddolino et al. (7) Fig. 3). (3) Appearance of multiple growth centers in parallel in growth-arrested colonies (compare ref (8), Fig. 1 and Freddolino et al. (7) Fig. 3). (4) Eventually a fraction of yeast cells resume normal growth and division in the face of the unforeseen challenge, within a relatively short timescale (compared to evolutionary timescales). The exact fraction and adaptation time depends on promoter and level of inhibitor (0.5-0.8 in our experiments with the GAL promoter (2, 9), and a broader range with cell-cycle promoters, including the case of no observed adaptation after 25 days (4); 10-3-0.1 in Freddolino et al. (7)). (5) The adapted state was stably inherited across generations (2, 4, 7, 9). This is a set of unusual growth characteristics, which we view as the hallmarks of yeast cells adapting to unforeseen regulatory challenge (see Fig. 1 in this comment). <br /> In their paper, Freddolino et al. note that the adaptation phenomenon they observe is common to a range of different synthetic and natural promoters, with different quantitative features (e.g. fraction of adapting cells). Taken together with our previous results, it appears that the experimental results of Freddolino et al (7) belongs to the same class of adaptation of yeast cells to regulatory challenge that has been studied by us for over a decade, from different angles and by different growth techniques (chemostat, batch culture and plates; see (1)). Indeed our previous results had shown that such adaptation is generally characterized by a high level of variability, multiple possible routes to adaptation, and non-repeatability of details (see the review (1)).

Measuring gene expression in the adaptation dynamics: global versus local response

We next discuss the experimental characterization of gene expression in the adaptation dynamics described above. In their Fig. 4, Freddolino et al. (7) show the results of flow-cytometry measurements, comparing the expression levels of the rewired URA3-mRuby to another marker attached to a non-essential gene on a sister chromatin, DHFR-GFP. The measurements are taken at two time points, before and after the transition into the growth phase. The differential increase of URA3 in coordination with the time at which cells resume growth, is taken as support for their proposed mechanism of individual stochastic tuning as driving adaptation.<br /> However, extensive previous measurements of genome-wide expression at multiple time-points during adaptation reveal a completely different picture which is incompatible with this interpretation. DNA micro-arrays show that hundreds of genes exhibit a strong and coherent response, with dynamics coordinated with the adaptation process ((4, 5)). Moreover, real-time PCR measurements of tens of genes involved in metabolism and regulation at high temporal resolution, show the temporal complexity of the gene expression response over extended timescales during adaptation (6). Thus, adaptation involves global gene expression dynamics, including also the rewired gene HIS3, showing similar and coordinated response with numerous other genes. This response cannot be deduced from measurement of one or two genes at two time points. Since the rewired gene responds in coordination with numerous other genes, residing on different parts of the genome, the isolated measurement of its response can lead to the misleading interpretation that its expression is specifically tuned toward functional needs of the cell to overcome the challenge. <br /> By constructing a flow-cytometer online with the chemostat, we have further characterized the HIS3-GFP expression dynamics (the rewired gene which is challenged) in real time, at high temporal resolution over extended periods throughout the adaptation process (3). These measurements revealed that an increase in expression at the onset of population adaptation, is actually followed by non-monotonic dynamics, converging to steady state only after a long period of ~100 generations (see Fig. 2 in (3)). The expression response of the rewired gene was thus found to be an integral part of the global response (6). Moreover, it also shows that measuring the response at only two time points is not enough; an extended-time measurements are needed to separate transient and long term responses toward adaptation.<br /> Taken together, these analyses show that the hypothesis of stochastic tuning – which Freddolino et al. base on a limited set of gene expression measurements – stands in contradiction to more extensive data in term of number of genes and timespan. The simultaneous complex dynamics of numerous genes across the genome, which are in coordination with that of the rewired gene and in correlation with the adaptation dynamics, show that specific tuning the expression of the rewired gene by itself is not the mechanism supporting adaptation. Measuring the response of the rewired gene by itself in comparison to a non-functional gene at the same locus on the sister chromatin is simply not enough of a support to deduce a local response. Note that this conclusion is completely detached from the question of the true underlying mechanism. We still do not understand the complex behavior of the living cell to a degree that allows detailed interpretation of the global response; this is a topic of ongoing investigation (10). However, irrespective of the interpretation of the global response, the fact that numerous genes show coordinated dynamics with the rewired gene (the gene of interest for the metabolic challenge) means that specific tuning of this particular gene cannot serve as an explanation for the cell response.

The role of genetic mutations in the adaptation process

Finally, we remark on the role of genetic mutations in the adaptation of rewired yeast cells to an unforeseen regulatory challenge. Freddolino et al.(7) first claim that, if genetic mutations underlie adaptation, the high expression of URA3 should remain high even when switching to a ura+ medium. However, the level of gene expression by itself cannot be a test for the presence of mutation; it is widely accepted that any given genotype can give rise to multiple patterns of gene expression. Second, they performed whole-genome sequencing, but scanned only the region within 25-kb of the rewired gene. In this region, they found variability between replicate adapting lineages, where some are completely free of mutations while others show a variety of mutations that cannot explain a fitness increase. They conclude that mutations can arise during the adaptation process but cannot be the cause of the process. These results are consistent with our extensive genetic study of the adapting populations (8, 11), demonstrating explicitly that adaptation can proceed without the involvement of mutations at all; that when mutations do arise, they can arise in different parts of the genome, not necessarily in cis; that they are insufficient to induce adaptation so other mechanisms must be involved; and that they emerge very late during the growth of the colony, further demonstrating that at least in some cases these mutations are induced by the process rather than causing it (8). Importantly, a genome-wide sequencing analysis of the entire genome in complete linages, clearly proves that there is no hypermutation involved in the adaptation process (8).

Freddolino et al. were aware of our work when writing their paper: in their discussion, they state that our experiments are irrelevant to their case because “… genetic mutations are the primary mechanism of adaptation, possibly driven by hypermutability of the genes involved in the response of interest…”. This statement is clearly false and misrepresents our findings in different experiments as presented above. This claim certainly cannot be used to avoid true discussion of our contradictory data to their interpretation, of very similar experimental results utilizing exactly the same methodology. We hope this comment will encourage a thorough and honest discussion of the points raised in it and will benefit the community, as they touch deep issues related to our quest for understanding the living cell.

References:

1. Braun, E. 2015. The unforeseen challenge: from genotype-to-phenotype in cell populations. Rep. Prog. Phys. 78:036602.
2. Stolovicki, E., T. Dror, N. Brenner, and E. Braun. 2006. Synthetic gene recruitment reveals adaptive reprogramming of gene regulation in yeast. Genetics 173:75-85.
3. Moore, L. S., E. Stolovicki, and E. Braun. 2013. Population Dynamics of Metastable Growth-Rate Phenotypes. PLoS ONE 8:e81671.
4. Katzir, Y., E. Stolovicki, S. Stern, and E. Braun. 2012. Cellular Plasticity Enables Adaptation to Unforeseen Cell-Cycle Rewiring Challenges. Plos One 7:e45184.
5. Stern, S., T. Dror, E. Stolovicki, N. Brenner, and E. Braun. 2007. Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge. Molecular Systems Biology 3:106.
6. Stolovicki, E., and E. Braun. 2011. Collective dynamics of gene expression in cell populations. Plos One 6:e20530.
7. Freddolino, P. L., J. Yang, A. Momen-Roknabadi, and S. Tavazoie. 2018. Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitry. eLIFE 7:e31867.
8. Moore, L. S., W. Wei, E. Stolovicki, T. Benbenishty, S. Wilkening, L. M. Steinmetz, E. Braun, and L. David. 2014. Induced Mutations in Yeast Cell Populations Adapting to an Unforeseen Challenge. PLoS ONE 8:e111133.
9. David, L., E. Stolovicki, E. Haziz, and E. Braun. 2010. Inherited adaptation of genome-rewired cells in response to a challenging environment. HFSP Journal 4:131–141.
10. Schreier, H., Y. Soen, and N. Brenner. 2017. Exploratory adaptation in large random networks. Nature Communications 8:14826.
11. David, L., Y. Ben-Harosh, E. Stolovicki, L. S. Moore, M. Nguyen, R. Tamse, J. Dean, E. Mancera, L. M. Steinmetz, and E. Braun. 2013. Multiple Genomic Changes Associated with Reorganization of Gene Regulation and Adaptation in Yeast. Mol. Biol. Evol. 30:1514–1526.

On 2018-10-27 17:58:18, user Wouter De Coster wrote:

Dear authors,

Thank you for sharing your work as a preprint. To other readers: the authors opened a thread on biostars to ask for comments, which you can find here: https://www.biostars.org/p/...

Cheers,<br /> Wouter

On 2018-10-25 16:59:40, user Dave Vuono wrote:

Full disclosure to all readers of this discussion: the comment (by Eveline van den Berg) was written by a colleague that has published some work on C:NO3 control on denitrification versus ammonification. As an important distinction, our manuscript does not refute the results of others. Our manuscript does not suggest that other's research is wrong, rather we argue that literature on this topic is not yet complete. We simply demonstrate through a more extensive experimental design that previous work has had a limited view of C:NO3 control on denitrification versus ammonification. This body of literature (denitrification versus ammonification) has focused narrowly on a range of C:NO3 ratios and has limited experimental evidence for C:NO3 ratios less than 1.5 and more importantly, has not tested the effects of nutrient concentration on pathway selection. We simply point out that previous literature such as Kraft 2014 Science, Yoon 2015 ISME, van den Berg 2015 ISME, van den Berg 2016 Frontiers, van den Berg 2017 AMB Express, have only tested a limited state-space for C:NO3 ratios, nutrient concentrations, and are biased towards microorganisms using high-potential pool quinones. This is a scientific fact supported by the literature. Thus we report that C:NO3 control is a confounding variable (it is true within a narrow range of conditions), and the actual mechanisms of pathway selection are based on fundamental principles of thermodynamics and enzyme biochemistry.

Hence: although the past literature on C:NO3 control is interesting, it is only valid within the narrow state-space of the author's experimental design (i.e., a limited view of the environmental landscape) and thus advise caution in the interpretation those results.

On 2018-10-25 10:13:03, user Alberto wrote:

Thanks for a great paper! I wrote a blog post about it where you can find feedback (and discuss if needed):

https://adnaera.com/2018/10...

On 2018-10-23 16:07:29, user Guest7 wrote:

In your linguistic supplement you write

"However, it remains unclear whether this gene flow [the Siberian ancestry in Saami-like samples] should be associated with the arrival of Uralic...or with an earlier immigration of pre-Uralic, so-called “Paleo-Lakelandic” groups."

Lamnidis et al. 2018 which you've referenced confirms that during the Bronze Age, and before the Saami expansion towards Lapland, a population with significantly more Siberian ancestry than Saamis was present in northern Fennoscandia. This group might be ancestral to the "Paleo"-populations.

On 2018-10-23 15:06:38, user KM wrote:

Great work, particularly in obtaining such great samples from an understudied region. You performed good analysis, uncovered a complex story, and weaved it into a comprehensible narrative, and that's so appreciated.

... But I'm going to have to complain about a system of nomenclature in which terms as similar as "Ancient North Eurasians", "Ancient North Siberians" and "Ancient Paleosiberians" describe meaningfully different clades. Is there no way that you could replace "Ancient" with a slightly more specific temporal descriptor in the ANS + AP labels you're coining?

On 2018-10-25 09:53:41, user m wrote:

Is there a typo in equation 1, or is the second sum supposed to be all zero terms?