Guild and Niche Determination Enable Targeted Alteration of the Microbiome
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Abstract
Microbiome science has greatly contributed to our understanding of microbial life and its essential roles for the environment and human health 1–5 . However, the nature of microbial interactions and how microbial communities respond to perturbations remains poorly understood, resulting in an often descriptive and correlation-based approach to microbiome research 6–8 . Achieving causal and predictive microbiome science would require direct functional measurements in complex communities to better understand the metabolic role of each member and its interactions with others. In this study we present a new approach that integrates transcription and translation measurements to predict competition and substrate preferences within microbial communities, consequently enabling the selective manipulation of the microbiome. By performing metatranscriptomic (metaRNA-Seq) and metatranslatomic (metaRibo-Seq) analysis in complex samples, we classified microbes into functional groups (i.e. guilds) and demonstrated that members of the same guild are competitors. Furthermore, we predicted preferred substrates based on importer proteins, which specifically benefited selected microbes in the community (i.e. their niche) and simultaneously impaired their competitors. We demonstrated the scalability of microbial guild and niche determination to natural samples and its ability to successfully manipulate microorganisms in complex microbiomes. Thus, the approach enhances the design of pre- and probiotic interventions to selectively alter members within microbial communities, advances our understanding of microbial interactions, and paves the way for establishing causality in microbiome science.
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in each SynCom member thus determining their substrate preference, i.e. their niche
was this done within the community context or as individual cultures?
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A total of 88 genes coding for import proteins were detected in the SynCom at the metagenomic level, of which 40 genes (45%) were transcribed and translated
i think the idea to use importers is a super cool hypothesis. how effective this approach is should be in part determined by how well-annotated these are (which you also mention in the discussion). i'm wondering what level of confidence in the substrate specificity you have in these 88 importers? was there any manual curation done, or were these all the ones that were predicted to have a specific substrate?
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Chitinophaga specifically inhibited the growth of Mucilaginibacter
for mucilaginibacter on the chitinophaga lawn, there is a nice zone of inhibition, although it might be helpful to highlight the zone with a bar or arrow or bracket so it is easier to see. For the others plates, it seems like some of the lawns aren't growing confluently, which makes the results difficult to interpret (although it is hard to tell with the plate glare). also, is mucilaginibacter stimulating the growth of marmoricola?
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e) five individual members were experimentally removed from the SynCom prior to incubation (asterisks), and relative abundances of all remaining members were compared to the non-modified SynCom.
this is a really interesting and important result! however, the figure overall is super challenging to interpret. panel e is probably the most crucial, and the easiest to interpret. i'm not sure a-d are adding much.
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we experimentally removed individual members
might be clearer to say "experimentally omitted" or excluded
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Metabolic pathway prioritization differed between bacteria grown axenically or in the SynCom, highlighting the importance of performing functional analysis directly in community settings
this is such a cool result!
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d) phylogenetic tree based on 16S rRNA sequences shows substantial differences with the TE-based guild dendrogram c), indicating that guilds are not based solely on phylogeny.
i think it would be really informative to have a genome-based phylogeny here instead of a 16S-based cladogram. It would be really interesting to see whether the branch lengths provide any insight into functional differences. one possible genome-based approach: https://tygs.dsmz.de/ If that isn't possible, at a minimum it would be good to use a phylogeny here instead of a cladogram.
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