172 Matching Annotations
  1. Aug 2025
    1. (Fig 4B). However, rlm1Δ/Δ and sko1Δ/Δ had a reduced capacity to remodel during co-culture, indicating that they are positive regulators of bacterial-mediated cell wall remodeling (Fig 4B). The reduced capacity of sko1Δ/Δ to remodel was also apparent based on its mannan histograms, which had more overlap between conditions than that of the SN152 wildtype (Fig 4C). After identifying transcription factors required for the full remodeling phenotype, we wanted to determine which signaling pathways were responsible for transcription factor activation. Many of the relevant pathways are controlled by mitogen-activated protein kinase (MAPK) cascades; therefore, we chose representative MAPKs from three signaling pathways with well-established roles in the regulation of cell wall remodeling. We found that mkc1Δ/Δ, from the PKC pathway, and cek1Δ/Δ, from the Cek1-mediated pathway, had a normal remodeling response (Fig 4D). Representative kinases from the high osmolarity glycerol (HOG) pathway, hog1Δ/Δ and pbs2Δ/Δ, had a reduced capacity to remodel in response to E. coli co-culture, similar to sko1Δ/Δ and rlm1Δ/Δ (Fig 4D).

      Do you show for comparison the change in cell wall mannan content over this time period for these different mutants in monoculture, other than for the mutant in C?

    2. e standard growth conditions for C. albicans, YPD media at 30°C, also allows for growth of E. coli

      Similarly, if you are interested in realistically modeling interactions, it seems non-ideal to choose a medium optimized for yeast growth. It seems like choosing a medium that more closely represents the conditions found in the host would be a lot more compelling.

    3. ), in monoculture or co-culture with Escherichia coli strain MC1061

      I am curious about your choice of strain here. From what I can tell MC1061 is an engineered cloning strain. If you are interested in modeling commensal interactions, it seems like it would be preferable to use a commensal strain or isolate that better reflects a native E. coli?

  2. Jun 2025
    1. . Users can identify fungal clusters of interest based on its phenotypic characteristics and rapidly relate its underlying genomics, creating a systematic and targeted exploration strategy for downstream functional or genomic studies.

      How would users access any of the data here, and is it possible to obtain these strains?

    2. we performed multiple sequence alignment (MSA) on the 518 fungal 18S rRNA sequences

      Have you deposited these sequences publicly anywhere? It would be especially helpful if they were directly paired/associated with the culture collection they came from. I couldn't easily find a way to explore the NPL collection, but if such a site exists, it would be good to link it here

    3. Fungal DNA was extracted from biomass and sequenced on the Illumina NovaSeq 6000 platform.

      I think it is important to provide more detail here. Extraction of fungal DNA from environmental isolates is not trivial, especially for a large collection. What method did you use? Also, I assume you only sequenced 18S amplicons, but you don't provide any details here on the methods you used (primers used, PCR method, sequencing read length, where was the sequencing done, etc.). Was the sequencing done by a commercial service? Without these details, it is impossible for anyone to replicate what you have done.

    4. the phylogenetic tree offers a comprehensive overview of Singapore’s fungal diversity.

      Are there any existing amplicon/ shotgun metagenomic surveys of Singapore's fungal biodiversity across different environments? It would be interesting to compare the genera you recovered to know how representative this culture collection is of the full biodiversity present in these environments.

    5. omprises 518 diverse fungal strains,

      Could you clarify why you focused on these 518 strains, out the 857 that you were able to revive and get DNA from? Were you trying to select unique morphologies?

  3. May 2025
    1. data-sharing.atkinson-lab.com/vad/.

      I really appreciate that you have made the structures available and also appreciate the detailed metadata in the supp table (associated host, 'brightness' etc.). It would be great if you could set up a web portal to host all of this to make it easier for others to view and reuse your dataset (download structures per host or per cluster, etc.)

    2. Recent viral structure databases, such as the BFVD [18] and ViralZone [10] projects, have addressed this gap to a large extent. However, these resources rely on less accurate methods than the reference AlphaFold2 implementation and are limited to monomeric structural predictions.

      https://www.biorxiv.org/content/10.1101/2024.12.19.629443v1.full

      It might be nice to also compare your study to the Viro3D work, which combines AlphaFold2-ColabFold and ESMFold to predict structures from animal-infecting viruses

    1. hese raw data were assembled into a single transcriptome assembly comprising 93,510 co

      Have you deposited the raw data or the assembled transcriptome in any public databases? It would be great if you could reference here so that your work can benefit others.

      Do you have an estimate of the size of the T. autumnalis genome? It would be nice to have some context here around how many genes you expect this organism to have (even if the estimate is just based on closely related species)

    1. protective roles in the context of other viral and microbial infections

      could you expand here on what we know about the protective role of IL-27 in those contexts? What do we know about the mechanism of protection and would you predict that it is similar or different to what you observe in the placenta?

    2. s (IFIT1, DTX3L, PARP9, PARP14, PSME1;

      Are there certain immune cell types that are enriched in or that are particularly important in trophoblasts? What do we know about the regulation/role of these genes in those cell types? Does the set of genes upregulated in IL27 stimulated TOs give you any mechanistic insight into what's happening? It would be really helpful to provide more context here around the interplay of these different factors

    3. d that neutralization of IL-27 or IFNλ led to similar increases in

      Could you comment here on how/if IL-27 regulates ifn gamma and where IL-27 sits in relation to ifn gamma in immune pathways? Specifically, how would neutralizing IL-27 be expected to impact ifn gamma reponses?

  4. Apr 2025
    1. n addition, S. scabiei 87-22 produces numerous other compounds known for their antimicrobial activity against a broader range of fungi and bacteria.

      It would be great to add either here or in the introduction more context around S. scabiei. What do we know about its metabolic potential? If it is a model, is its genome sequenced, and if so, are there other things you could do to support your hypotheses around the VCs having antifungal impacts? Do you know the genes responsible for production of any of these compounds and are these genes present? Could you compare the expression of these genes in isolation/coculture ?

    2. previously reported for their antimicrobial properties

      If you have a couple candidate VCs that you think are driving what you observe in your growth assays, it would be great to measure the production of these in isolates vs. fungal coculture

    3. he 12 VCs that have previously been reported for their antimicrobial properties (Table 1).

      Based on your lit review, are any of these VCs associated with Strep species? It would be nice to put into your table the biological system that this was observed in . Are they all examples where the VC is produced by bacteria?

    4. volatilome of S. scabiei 87-22 across the five culture conditions tested (MHB, LB, ISP6, TSB, and ISP1)

      Have you done any work looking at the volatiles produced during coculture? If this is an antifungal , it seems like production may be tied to the presence of fungi. I understand this may be challenging to do in liquid with molds, but I suspect there are creative ways to solve this that you could find in the literature around VOCs in microbial interactions

    5. A), and MHB (liquid MHA) (not shown)

      It would be good to include the data here if you think this was a crucial step, as you mention in the next sentence. At the very least, it would be nice to state how you confirmed it.

    6. The VCs produced in ISP1, ISP6, LB, TSA, and MHA media resulted in complete inhibition of

      How long after inoculation were these images taken? Can you comment on how this relates to the growth rates of these different organisms? Are the control plates imaged at the same timepoint>?

    7. Pe. restrictum NS1.

      Why did you choose to do a dilution series with Pe. restricum? It may have been nice to capture the interaction earlier on, as right now the outcome seems binary (growth/ no growth)

    8. score of 100 means full growth inhibition of the tested microorganism

      I assume this is in the methods, but it would be nice to add here how these scores are determined (I assume you measured something?). The pictures are helpful but It is hard to interpret the scores in the figure without understanding what they are coming from

    9. growth of various fungal and oomycete microorganisms,

      Could you comment briefly on why you chose this panel of fungal strains? It seems from your methods that they come from various culture collections, but it would be nice to have a little information here on where they were originally isolated from and whether you expect to find Streptomyces scabiei in those same environments. Are these fungi particularly agriculturally relevant as candidates for a VOC pesticide (in terms of total crop losses, or for a specific crop, or because they lack other effective control measures?)

  5. Jan 2025
    1. show that a single GI nematode secreted miRNA can regulate host genes to promote stem cell maintenance, regeneration of host GI epithelial tissue and potentially reduce inflammation and immune cell migration.

      Cool biology and cool paper- I enjoyed reading it!

    2. (F) Alignment of seed sequence (nucleotides 2-8) of miR-5352 sequences from GI nematode species (red) with mammalian (human, murine, bovine) miR-92a (purple).

      since there is space, it would be nice to put a legend for the purple/red directly in the figure

    3. Merging bovine and ovine bioinformatic target prediction datasets for Hco-miR-5352 identified 54 potential target mRNAs,

      Why do this? did the target predictions differ significantly between ovine and bovine?

    4. L-13 and miR-5352 mimic have opposing effects on gene expression in ovine abomasal organoids.

      It might help get your point across/ be easier to interpret/learn from this figure if you 1) layered the functional enrichment analysis that you do on your differentially expressed genes in the next section onto this figure or 2) added some kind of functional grouping/color coding onto the gene names in all of these heatmaps. Otherwise, these heatmaps are pretty uninformative unless the reader knows offhand what all of these genes are, and would be just as good as a supp table. I think figures 4 and 5 would be more effective combined.

    5. Similarly, murine SI Dclk1 tdTomato reporter organoids stimulated with IL-13 and transfected with miR-5352 showed a reduced number of tdTomato+ cells relative to IL-13 plus control mimic, indicating suppression of murine tuft cell differentiation

      what is the bioinformatically-predicted target mRNA of miR-5352 in mice? is it also KLF4?

    6. Our data indicate that H. contortus total ES suppresses IL-13 induced secretory cell development in both murine and ovine GI organoids.

      Is this surprising? if the parasite has evolved a miRNA to specifically modulate host defense systems, would you have predicted this to be effective in a non-host system and different cell type (if the immune pathway is conserved)? It might be nice to comment here on whether this result suggests a target with high sequence conservation between mice/sheep

    7. oids. For this, we employed murine SI organoids in which tdTomato was expressed under the control of the promoter of the tuft cell-associated gene Dclk1

      could you provide a little more context in this section to clarify the differences/advantages of the two organoid systems? I think it would help your paper be more clear to a broader audience.

      for example, are gastric tuft cells and SI tuft cells equally relevant to H. contortus biology? Is the purpose of these two organoid systems to look at multiple points along the GI tract, or to show that the effects of the ES are conserved across species or just to take advantage of the ability to use a reporter system in the murine organoid? Does H. contortus ever infect mice?

    1. structure prediction workflow of Nomburg et al.

      Do you have a hypothesis as to specifically what difference in their prediction workflow leads to lower model quality? From a brief glance, it seems like they are also using ColabFold. Is your model improvement coming from also using ESMFold and then selecting the highest confidence model?

    2. Functionally annotated structure similarity network of viral proteins.

      Is it possible to explore this network in the Viro3D database (seems like no)? Or could you provide the network file with node annotations that was used to create this figure as supp data? It may be interesting for users to be able to explore these clusters based on functions of interest.

  6. Dec 2024
    1. There was full concordance for strains appearing in the statistically significant subset for both cytokine assays.

      It looks like your group has previously published on TnSeq screens with GBS. Have you done any screens with the TnSeq library that might be relevant to the findings here, and did the five genes identified here pop up in those assays as having significant fitness effects?

    2. The knockdown variants with statistical significance in both assay sets are listed in Table 1.

      It's great that you were able to experimentally follow up with the sip finding. It might be nice to at least briefly mention the other four genes identified here. What is known about them? Is it possible to hypothesize why they may impact cytokine response (and if not, might be nice to state that)?

    3. or this testing, we excluded 20 GBS variants that had not met the knockdown criterion of <0.5 control expression of their target gene.

      maybe nice to note here that this resulted in the inability to test 6(?) of your 61 target genes

    4. he wide standard deviation was driven by nine genes with less than 50% homology

      Can you comment on what these nine genes are/any insight into why they have such relatively low sequence conservation?

    1. We performed structural alignments between proteomes with the tool Foldseek

      Thanks for putting your code on github! I noticed on your github page that you masked low confidence ends of protein structures prior to alignment. This is an interesting consideration and I think is worth mentioning in the methods here.

    2. is important to note that bacterial queries were not limited to alignments with a single host target structure and single query structures contributed multiple targets to the protein IDs used in the GO analysis

      Did you do any analysis of queries that had strong hits (confident alignments) to multiple targets? I am curious about the distribution of these matches (were they all equally good matches? were they matches to proteins in the same family? etc)

    3. 5,227 unique microbe proteins

      can you clarify what this value represents? Looking at supp table 3, it looks like there are 1669 unique microbe uniprot ids? This would also make sense if Legionella only has around 3000 proteins

    4. conservation of critical residues and domains within the structural alignment.

      Could you elaborate here on how you determined critical domains? Was this something you did manually/by-eye for only a very small set of proteins? Or did you do this systematically?

    5. We selected an e-value cutoff of 0.01 for these alignments

      I've noticed that the Foldseek e-value can be strongly affected by short query proteins that have low target coverage. As you note below, these could still be biologically meaningful, as pathogens may only need to do a good job mimicking a certain functional domain, for example, rather than the full protein. Did you notice this in your data? Is it possible that with this approach you are missing out on finding more partial mimics even though you are using a lenient target coverage cutoff?

    6. Free-living proteomes that contained at least 900 structures were selected for use in the control dataset.

      I'm curious about your decision to use a cutoff of 900 here. I would expect free-living bacteria to have more on the order of 3-4000 protein-coding genes. It might be useful to note the distribution you saw and why you chose this threshold.

  7. Nov 2024
    1. In conclusion, high-throughput biological data

      I appreciate that you have made a publicly available tool that is meant to aid in the interpretation of large datasets. GO and KEGG mappings are widely used by the biological community and I agree that trying to interpret them on a large scale is challenging. I was able to download and run your app, although I was not able to get the heatmap visualization to work. Overall, my main comment on this paper is that there seem to be a lot of extraneous/unnecessary broad descriptions, but there is a strong lack of clarity around how to effectively use the app and what the actual contributions of the app are. I think this paper would benefit from removing these unnecessary details and focusing more on what was built. The github README would strongly benefit from a more step by step user tutorial if you want others to use your tool.

    2. Moreover, the AI study shows that official conversational artificial intelligence created by different developers can output useful conclusions of what is happening in the case studied, all by submitting the prompt created input that gives AI the information needed to give a non-expert user the keys to deeply understand what happens in his data.

      I am still not sure how this is involved at all. I saw the AI prompt file but I don't see any discussion of outputs that you got back from it that were informative? Also I do not understand the relevance to the tool you have built

    3. The software is completely able to retrieve information from online databases, like Kyoto Encyclopedia of Genes and Genomes (KEGG) and QuickGo by using the methods explained previously, being also able to construct usable and optimal dataframes for further processes from the data obtained.

      I think a major component missing here is a clear description of the workflow you are proposing. Do users first perform GO and KEGG mapping of their genes of interest and then use your software to pull additional layers of information? I looked at your sample files and tried running the app, and it seems that for GO, it just retrieves the first ancestor term. If this is the case, it would be much simpler to just say "our software can be used to pull the first ancestor terms for a list of go terms of interest. The user can then view this first ancestor information overlaid on the network visualization" or something like this

    4. Figure 5.

      I'm not sure that figures 1 through 5 are needed or add much to the paper. It might be more useful to show more of the outputs of the app, or walk users through a specific use case that demonstrates how the app can be used to explore a biological question.

    5. Also, a new category was added in the filter feature of the software, and it was the possibility of adding an extra column in the table named “Disease” (defined by our dataset sample case), where the diseases name would be extracted from the raw data and stored for further analysis, only useful when your file stores experimental data of more than one disease, basically it request the input column to inspect and the names of the different diseases.

      I'm not sure I understand this section/the utility of what you are describing here for the disease column. It might be helpful to revisit this section and try to reword it for clarity.

    6. 3.2 Data Pre-Processing

      It is really unclear what the point of this section is or how it relates to the application you have produced. Are you describing pre-filtering steps that you have done on your own data? Or providing instructions on steps that users should take? In either case, I do not think you need sections to explain differential expression analysis or gene set enrichment analysis, as these are well-established techniques. It would be a lot more helpful to provide a clear description of what the use cases are for this application, what the starting point is for using your application, and how the data should be formatted. As a possible example: "Users will upload a csv file to the application that contains results of differential expression analysis. Users will need to provide GO or KEGG mappings for each gene in their analysis and information on experimental groups that they would like to compare." I downloaded and ran your application, but it is still unclear to me how this section relates to your app.

    7. https://github.com/alexrodriguezmena/BIOFunctional

      Thank you for making your code available on github! You might consider adding a license so that others understand how they can use your code. For reproducibility, it might also be helpful to make a release of the current version of the scripts.

    8. The aim of this project is to develop an user-friendly application to

      I love this idea! I've also spent a lot of time figuring out the best way to visualize this information for my data. Tools that make this easier are definitely needed.

  8. May 2024
    1. detectable up to 72 hours in our consortium

      Do you mention anywhere how the time points in your lab fermentations relate to fermentation timescales in winemaking? It is hard to know what 72 hours means in this context

    2. insights gained from studying population dynamics might provide strategies to mitigate fermentations by managing the yeast population.

      It could be nice to give an example of how you imagine winemakers might use this information. E.g. Do you imagine that they may reinoculate certain species during the fermentation if an important species has died off?

    3. S. cerevisiae. Hu: H. uvarum, Lt: L. thermotolerans, Sb: S. bacillaris, Td: T. delbrueckii, Sc: S. cerevisi

      It looks like you may have space here to write out these names within the figure so that the reader doesn't need to reference the legend

    4. .

      Do you know how the dynamics discussed in this paragraph relate to what was seen in real wine fermentations? Do any of the 11 papers you referenced previously look at dynamics over time and how do these results compare?

    5. Then, the initial abundance for each species was determined from their natural initial abundance in must and early stages of fermentation

      It might be worth moving this sentence earlier in the paragraph- as I was reading it I was trying to figure out what timepoint you were using from the articles to determine abundance. The sentence before this one is also redundant with the beginning of this paragraph and the beginning of the next paragraph

  9. Mar 2024
    1. . By exploring these questions further, future studies would promote the development and enjoyment of miso and other fermented plant-based foods, facilitating product quality, sustainability, and food diversity, while deepening our knowledge of the microbiology of food fermentation, both traditional and novel.

      This is a really cool study. It's awesome you are pursuing a deeper understanding of misos and other fermented foods. I agree they have so much potential for helping us move towards a sustainable food system.

    2. Hypothesising horizontal gene transfer (HGT), we investigated where the MS strain had acquired these genes from

      From the MAG of the MS S. epidermidis strain, is there anything about the surrounding genomic region of these carotenoid genes that suggests HGT/that they may be on a mobile element? Are these genes co-localized in a biosynthetic cluster? Since carotenoid biosynthesis is pretty well-studied, can you comment on where these genes are in the pathway and what carotenoid they may produce?

    3. To overcome this limitation by further characterising the microbiota independently of the set references, we applied an analysis using two marker genes—ychF and leuS—from the assembled metagenomes.

      Aside from using metaphlan or marker genes, can you tell from the assemblies what other fungi were recovered? How many different fungal MAGs did you get (even if not high quality)? Do you think sequencing depth was an issue in recovering fungal MAGs?

    4. —habanero-barley (HB), maitake-soy (MS), and toasted sesame (TS)—were co-dominated by other species of the Saccharomycetales family, mainly Millerozyma farinosa, Starmella-Candida spp., Debaryomyces hansenii and Candida glabarata.

      Can you think of any reason why these misos might favor Saccharomycetes? Any commonalities in pH or other factors that are different from the others?

  10. Feb 2024
    1. We provide analysis of their phylogeny, genomic organization, BGC content and provide an example of how this large dataset can be used to identify novel BGCs

      I left some minor suggestions, but I wanted to say that I really enjoyed this paper! Such a cool resource.

    2. sequencing

      Could you also include information on which chemistry version/flow cell version was used? Newer nanopore chemistries have a lot of improvements in error rate, so it would be good to know what was used here.

    3. We isolated almost 2000 strains of filamentous Actinomycetia from soil, and sequenced more than 1,000 of them, with ∼90% of them assembling into complete chromosomes.

      This is such an amazing contribution!

    4. he bar chart on the outer ring denote the number of BGCs.

      it might be helpful to label this on the actual figure panel somehow, I wouldnt have expect this number to be related to BGCs from just looking at the figure

    5. The coverage plot inspection revealed large scale assembly artifacts of some of the chromosomes, resulting in 11 genomes which needed to be reassembled after Filtlong removal of half the raw nanopore data. These errors related to the TIRs of Streptomyces genomes.

      It is awesome that you made the effort to make sure these genome assemblies were as high quality as possible! I am curious if there is anything specific about the TIRs of these 11 genomes- are they particularly complex ?

    6. Nanopore MinION platform, with either the SQK-RBK004 or SQK-RBK110-96 kit, both

      It would be nice here to include information on the sequencing statistics. I see the kit enables 96 barcodes, but how many samples were actually pooled per flow cell? What were the yields of the sequencing runs and what was the distribution of read sizes?

    7. ISP4, AIM-Avicel, and AIM-xylose

      maybe it would be good to explain here what these media are, or provide a reference, for readers who are not familiar with Actino-specific media? And to help explain why antifungals/gram negative inhibitors aren't needed?

    8. Soil samples were collected

      I see that this information is in the results/discussion, but it might be nice to also include here at least a list of countries that these samples were collected from, or reference where in the paper this information is

  11. Jan 2024
    1. that many researchers continue to resort to “reinventing the wheel” instead of developing tools to bridge the gaps between different resources or, alternatively, work with existing resources to improve their interoperability

      I think this is a really good point. I suspect this may in part be due to the fact that bridging resources is not as flashy as a 'new' tool. It would be great if there was a way to incentivize work to improve interoperability. I wonder if some kind of 'hackathon' with a focused goal of bridging a specific gap between resources could be a way to encourage this.

    2. Figure 4.

      It might be worth just combining d and e (like putting e as an inset in d). The way it is set up now is a little weird and it took me a bit to notice that e was physically associated with d. For c/f, you could consider adding a small legend in the figure like "# of hits" or something so it is interpretable without the figure legend. It is also not immediately clear from looking at the figure, what is going into the venn diagram in f

    3. https://vdclab-wiki.herokuapp.com/

      This wiki is awesome! As someone who did have to discover these resources individually over time, it is so nice to have all of this in one place that is easy to navigate! I especially like the 'tools by objective' organization framework.

      I also appreciate the 'ease of use' section that some tools have. Nice to know what you are getting into.

  12. Dec 2023
    1. stability of the bacterial community composition over the three sampling campaigns

      It seems like Marinomonas is consistently fairly abundant in these years compared to what you saw in the dynamic study. Any thoughts on why Marinomonas didn't show up in the dynamic study? Were the dynamic cheese samples taken in 2023?

    2. metavirome composed of 331 vOTUs >2 Kb

      It would be nice to have some more information here on the viral contigs that were assembled (e.g. the size distribution. did you see any particularly large genomes?). From the methods it looks like you did some assessments of genome quality/completeness, it would be nice to describe here also what proportion were low/high quality etc.

  13. Nov 2023
    1. tive abundance of ∼60% by day 64, which it maintained throughout the >1.5 years of follow-up sampling

      this is cool. Have you done any investigation of this specific strain's genome to see what makes it such a strong colonizer? Did this subject report any changes to their health following this shift?

    2. ese disrupted species exhibited much lower rates of recovery in subjects with lasting responses:

      Do you know if there are any strain level differences among the disrupted species in transient vs lasting that might explain this?

    3. pre- perturbation community diversity

      Even if there were not global differences in the amount of diversity, do you see any differences in common in the composition of the starting communities that might make them more sensitive to antibiotic perturbation? Fewer predicted antibiotic resistance genes?

  14. Oct 2023
    1. Specifically, for Bt there was an enrichment of ISOSDB412 insertions in susC-D/tonB and EPS biogenesis genes, whereas Bf acquired an abundance of insertions in susC-D/tonB genes

      this is a super cool validation!

    2. tonB

      TonB is also associated with siderophore uptake/ iron acquisition. Iron availability is known to impact the growth of potentially pathogenic bacteria in the gut. Wondering if this could be related to fitness effects of IS in these genes

      other question- is the tonB genomically co-localized with these sus genes?

    3. d much higher abundance of IS elements associated with pathobionts including Escherichia and Prevotella species (

      is there any way to normalize this statement relative to taxonomic abundance of these organisms in the MDG samples vs the others? Or state that there was no difference in the abundance of Escherichia/Prevotella in MDG samples vs others if that is the case?

    4. pseudoR pipeline that utilizes ISOSDB to identify IS element insertions

      you previously referred to OASIS as being a rigorously tested tool for high-throughput identification of multiple genomes. Since this was mentioned by name earlier, can you specify what advantages pseudoR has over OASIS specifically vs. just in general over previous tools?

    5. Gene classes targeted by IS elements are primarily metabolic, cell surface, and mobile genetic element accessory genes.

      are there any gene classes that seem very underrepresented for being targeted by IS elements and if so is there a reasonable hypothesis for why?

    6. The ISOSDB and pseudoR pipeline is freely available at https://github.com/joshuakirsch/pseudoR.

      I really appreciate you putting your code on Github! And for specifying the dependency versions. The documentation looks really nice. So awesome. One suggestion- it might be nice to organize yours scripts/ databases into folders to make the repo a little cleaner

  15. Aug 2023
    1. Conclusion

      This is a cool concept! You are addressing an important issue of non-viral reads in 'virome' data. Really interesting to think about the roles that non-viral extracellular DNA might be playing in the ecosystem. I wonder how your findings transfer to non-marine environments. Your proposal of the involvement of EVs is a valuable perspective. Although unclear if the everything classified here as EV is actually an extracellular vesicle, it is useful to think about what unknown agents might be involved here.

    2. Transposons have been shown to mobilize not only themselves but also adjacent ’passenger genes’, genes that are located in proximity to transposons and are therefore co-mobilized by transposons

      to this point, if you have the MAG regions that these reads mapped to, did you do any classification of the functions associated with these regions? it seems like this may be more interesting and possibly better than classifying partial orfs from the reads?

    3. we compared the SSU rRNA alignment rates

      Is there a reason that alignments are done to SSU rRNA only? Could you use a read based classifier like sourmash or kraken to fully identify bacterial reads present?

    4. he remaining DNA makes up the sequence space of protected extracellular DNA, peDNA (bottom panel).

      Why is the DNA on the outside of EVs protected from DNase treatment (bottom left example in the peDNA box)?

    5. Figure 1:

      I think that the little gray face under 'DNase treatment' is a DNase enzyme? Although is it cute, I'm not sure that this is the best representation. You might consider only keeping the text for this step, or showing dna being cleaved.

  16. Jul 2023
    1. MinION promises to become able to function as a rapid and accurate pathogen species identification technique for clinical specimens.

      It could be nice to comment here on how you think this might compare to the use of Illumina sequencing for clinical detection. Short read sequencing usually requires lower DNA input and sequencing can be highly multiplexed. The ability to do MinION in-house and get immediate results is definitely appealing- but how does this balance out with DNA requirements, accuracy of detection, etc.? Could be interesting to discuss briefly here.

    2. Figure 1)

      Can you explain in the legend for this figure what is meant my the anticipated number of reads? Is this a % of the total read output based on input DNA %? I think that '4 hr MinION run' may not be the most informative title for this figure. You could consider removing the blue background and the text/title at the top to make this figure look cleaner. I also do not think it is necessary to include the ng information here. Species names could be italicized.

    3. The MINION sequencing was performed with R9.4 flow cells

      Was an entire minion flow cell used for each sample that was sequenced? I see in table 1 that the run time was very short, suggesting that a full cell was not used. Either way it would be nice to explicitly state this here so that the reader can better evaluate your sequencing results. If any flow cells were reused for any experiments, it would be good to add this information to the methods, including the relevant reagent information if washes were done.

    4. Antimicrobial resistance application (AMRA)

      It would be nice to provide a reference here and maybe a sentence or two on why you selected this application, since there are a number of applications that find AMR genes in sequence data. Is the detection performed on raw reads and do you think that detection may be more accurate in assembled data?

    5. Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF MS microflex LT from Bruker Daltonics, Bremen, Germany) was used, with scores >2 verifying the strain identity before analysis.

      I think this section may benefit from a more detailed description of the method. If this is a very commonly used protocol, it would be nice to provide a reference to the protocol.

    6. ‥… it will make a tremendous difference both to diagnostics and to science it will quickly make its way into many labs and the clinic’

      I think your punctuation might be off here. It looks like this is meant to be a quotation from another article? The quote itself also seems like two sentences have been erroneously joined.

  17. Jun 2023
    1. We used 16S rRNA gene and 16S rRNA amplicon sequencing of co-extracted DNA and reverse-transcribed RNA to assess community structure across our samples

      I see from this reference (https://journals.asm.org/doi/full/10.1128/mSystems.00003-19) that this method has been benchmarked against cell activity staining. Have you considered benchmarking the 16S rRNA ratio approach against a cell activity analysis used for metagenomic sequencing (like iRep, (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5538567/)) to see how it performs?

    2. by combining 16S rRNA with 16S rRNA gene amplicon sequencing

      I think that this sentence could be reworded to maybe make it more clear what you mean by combining 16S rRNA with amplicon sequencing. I had to read further into the introduction to understand. Maybe something like this : "The active subset of the whole community was determined based on a threshold for the ratio of RNA sequencing reads to DNA reads for 16S rRNA genes."

    3. Figure 6:

      It is extremely hard to get any information of the phyla color distribution outside of the first few bars. If you want to display this information, maybe removing the black lines between the phyla would make the colors more visible.

    4. Figure 1:

      There seem to be some white text boxes covering up things that they shouldn't in this figure. For example, under FireClassification, 'recovered' looks half-covered up & 'SiteID' looks maybe a little cut off on the bottom. Maybe just a format conversion issue but you might want to check.

  18. Apr 2023
    1. indicating a blind spot in bulk-sequencing

      Looking at the metagenomes you searched, it seems like they were solely wastewater metagenomes- is there a reason to think that lncP PDPs are not found in other environments? Think the claims in this paragraph may just be a little too broad stated

    2. Figure 4

      Should there be text referencing Figure 4 d and e? I am having trouble finding it. Having corresponding text referencing d and e would be really helpful in following the story and in creating a convincing argument that these viruses are missing from metagenomes

    3. DNA genomes with covalently bound proteins2

      would looking at metatranscriptomes be informative here? would you expect that phages that drop out of metagenomes for some of these reasons to still show up in metatranscriptomes?

    1. Figure 3:

      I like the use of a heatmap to represent the data in A. I wonder if the choice of color scale is confusing here, as these three colors are used to represent the three enzyme classes in the rest of the figures. It may be more intuitive to have no change shown in white/gray and positive/negative changes shown in contrasting colors.

      It could also be good to move Figure S2 into a second panel of A here, as it may be nice to directly visually compare the growth impact of interactors to their impact on enzyme activity.

    2. Figure 4:

      Do you also have information about the gene expression changes of the cross-feeder species in these interactive contexts vs. alone? It's not necessary here but it could be interesting/aid in interpretation of what is happening mechanistically.

    3. Figure 5:

      It may make sense to combine figures 5 and 6 here, as they seem to be showing the same information but for the two degrader species. This would make comparing the degrader response easier .

    4. The underlying question we aim to answer is whether bacterial species without the genetic repertoire to perform a given function can influence that function when measured at the level of consortia

      This is a super interesting question!

    1. To assess the distribution of pink berry-associated bacteria and phages, genome-wide read coverages were analyzed for individual pink berry metagenomes

      It would be nice to provide a summary of what % of reads from the metagenome map to the genomes in Figure 3, and what % are unmapped. And also if are large portion are unmapped, if you have an idea what they are (eg run some taxonomic analysis on the unmapped reads)

    2. NARBL

      is this a commonly used tool/ are there better tools that might exist? Looking at the GitHub page associated with NARBL, the documentation seems extremely scarce and it makes me concerned about the reproducibility of using this

    3. Co-assembly of the three pink berry metagenomes yielded 184 contigs totaling 4.35 Mb in length

      Could you add a sentence here explaining how diverse pink berry microbial communities are? And a statement of whether this assembly size seems reasonable? Is it surprising that the total assembly is only 4.35 Mb (about 1 bacterial genome?) It might be nice to know what % of reads are represented in this assembly

    1. These results highlight the stochasticity of transmission kinetics through the intestinal gut’s distinct “island” niches34,

      I can see this as being one possibility, but I'm not sure based on this data that this is the only possible explanation. It may be worth rephrasing to make this more of a possible explanation rather than a claim. It may also be worth briefly defining island niches- I tried looking at reference 34 but don't see this term in there.

    2. The bacterial community dynamics are unperturbed by the entry of E. coli into the community (Fig. 1g)

      I see that the overall diversity of the community does not seem to be impacted, but does this necessarily mean that the community dynamics don't change? In other words, does the relative composition change but remain equally diverse or does the composition not change and therefore diversity doesn't change? This may be reflected in your frequency weighting/q metrics but it may be worth stating more clearly here or just having a supp figure showing the relative abundance plots from the 16s sequencing.

    3. The dominant cluster, C1, was always grouped with Lachnospiraceae, whereas two other low-frequency clusters, C7 and C8 grouped persistently with Lactobacillaceae, the canonical member of gut microbiota (Fig. 2c). Interestingly, it was previously shown in invasion studies of pathogenic strains of E. coli and Lachnospiraceae that these bacteria utilize similar sugars and thrive in the same environment37.

      It could be interesting to also know something about the species/ strain diversity of the resident bacteria, and whether these clusters interact with specific species/strains

    1. It may be worth reframing to consider some of limitations to the approach and factor in some of the ways in which abiotic and biotic factors simply cannot be fully decoupled. For example, if Chlamy is differentiating into a gamete in the 12 day vs 3 day (a known physiological change triggered by nitrogen starvation), how would this change your interpretation of whether biotic and abiotic effects were fully decoupled? Likewise, bacteria in stationary phase (and possibly Chlamy, too?) may be upregulating production of specialized metabolites which can have inhibitory effects on community composition.

  19. Mar 2023
    1. To assess the distribution of pink berry-associated bacteria and phages, genome-wide read coverages were analyzed for individual pink berry metagenomes

      It would be nice to provide a summary of what % of reads from the metagenome map to the genomes in Figure 3, and what % are unmapped. And also if are large portion are unmapped, if you have an idea what they are (eg run some taxonomic analysis on the unmapped reads)

    2. Co-assembly of the three pink berry metagenomes yielded 184 contigs totaling 4.35 Mb in length

      Could you add a sentence here explaining how diverse pink berry microbial communities are? And a statement of whether this assembly size seems reasonable? Is it surprising that the total assembly is only 4.35 Mb (about 1 bacterial genome?) It might be nice to know what % of reads are represented in this assembly

  20. Feb 2023
    1. The dominant cluster, C1, was always grouped with Lachnospiraceae, whereas two other low-frequency clusters, C7 and C8 grouped persistently with Lactobacillaceae, the canonical member of gut microbiota (Fig. 2c). Interestingly, it was previously shown in invasion studies of pathogenic strains of E. coli and Lachnospiraceae that these bacteria utilize similar sugars and thrive in the same environment37.

      It could be interesting to also know something about the species/ strain diversity of the resident bacteria, and whether these clusters interact with specific species/strains

    2. These results highlight the stochasticity of transmission kinetics through the intestinal gut’s distinct “island” niches34,

      I can see this as being one possibility, but I'm not sure based on this data that this is the only possible explanation. It may be worth rephrasing to make this more of a possible explanation rather than a claim. It may also be worth briefly defining island niches- I tried looking at reference 34 but don't see this term in there.

    3. The bacterial community dynamics are unperturbed by the entry of E. coli into the community (Fig. 1g)

      I see that the overall diversity of the community does not seem to be impacted, but does this necessarily mean that the community dynamics don't change? In other words, does the relative composition change but remain equally diverse or does the composition not change and therefore diversity doesn't change? This may be reflected in your frequency weighting/q metrics but it may be worth stating more clearly here or just having a supp figure showing the relative abundance plots from the 16s sequencing.

  21. Oct 2022
    1. Figure 4:

      Do you also have information about the gene expression changes of the cross-feeder species in these interactive contexts vs. alone? It's not necessary here but it could be interesting/aid in interpretation of what is happening mechanistically.

    2. Figure 3:

      I like the use of a heatmap to represent the data in A. I wonder if the choice of color scale is confusing here, as these three colors are used to represent the three enzyme classes in the rest of the figures. It may be more intuitive to have no change shown in white/gray and positive/negative changes shown in contrasting colors.

      It could also be good to move Figure S2 into a second panel of A here, as it may be nice to directly visually compare the growth impact of interactors to their impact on enzyme activity.

  22. Sep 2022
    1. It may be worth reframing to consider some of limitations to the approach and factor in some of the ways in which abiotic and biotic factors simply cannot be fully decoupled. For example, if Chlamy is differentiating into a gamete in the 12 day vs 3 day (a known physiological change triggered by nitrogen starvation), how would this change your interpretation of whether biotic and abiotic effects were fully decoupled? Likewise, bacteria in stationary phase (and possibly Chlamy, too?) may be upregulating production of specialized metabolites which can have inhibitory effects on community composition.