Good morning everyone!
Today’s digest presents several antibiotic resistance papers, as well as hospital ICU microbiome study, a microbial nomenculture paper, bunch of human microbiome papers (both gut and vaginal), as well as two computational platforms for analyzing microbial data.
In a more personal note– recently, my PhD research (at ARO and The Hebrew U, Israel) resulted in two manuscripts dealing with the exciting world of bacterial secondary metabolites. The first one is a review paper where we present state-of-the-art tools and methodologies to study and identify novel secondary metabolites gene clusters within the widely diverse soil and root bacterial communities. We present a conceptual pipeline for an efficient use of these culture-based and culture-independent platforms to identify novel antimicrobials-producing gene clusters in-vivo.
The second one (a bioRxiv pre-print) utilized some of these techniques to explore differences in secondary metabolites gene clusters between soil and roots bacterial communities, in terms of diversity, composition and taxonomy. We then took advantage of this data and deeply focused on root-enriched and abundant sequences (those that were associated with NRPS and PKS, mega-enzymes that produces two very important families of secondary metabolites- nonribosomal peptides and polyketides). Then, we used a unique culture-independent platform (eSNaPD, from Sean Brady lab @ Rockefeller U, NY) and were able to actually recover 5 clones (~40Kb in length) that harbored five novel gene clusters. At least one of these probably encode for an antifungal metabolites (derived from Actinobacteria). This piepline/platform is applicable for other uses of course, depending on your research question and the functions you’re intrested in. I’ll be happy for any feedback regarding the bioRxiv MS (either directly there or via my tweeter).
General microbiology
Roadmap for naming uncultivated Archaea and Bacteria, Alison E. Murray, Nature Microbiology
Human microbiome
Re-evaluating the relationship between missing heritability and the microbiome, Gavin Douglas , Microbiome
Microbes and mental health: Can the microbiome help explain clinical heterogeneity in psychiatry?, Christina L. Hayes, Front. Neuroendocrinology
Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations, Nicolai Karcher, Genome Biology
Exploring potential of vaginal Lactobacillus isolates from South African women for enhancing treatment for bacterial vaginosis, Anna Ursula Happel , PLOS Pathogens
Hospital microbiome
Temporal variations in bacterial community diversity and composition throughout intensive care unit renovations, Jessica Chopik , Microbiome
Antibiotic Resistance
The household resistome – frequency of beta-lactamases, class 1 integron and antibiotic resistant bacteria in the domestic environment, Laura S. Schages , bioRxiv
Environmental conditions dictate differential evolution of vancomycin resistance in Staphylococcus aureus, Henrique Machado, bioRxiv
Coexistence of Antibiotic Resistance Genes and Virulence Factors Deciphered by Large-Scale Complete Genome Analysis, Yu Pan, mSystems
Bioinformatics
A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems, Begum D. Topcuoglu, mBio
PVAmpliconFinder: a workflow for the identification of human papillomaviruses from high-throughput amplicon sequencing, Alexis Robitaille, BMC Bioinformatics
Microbiome Digest - Bik's Picks 
Have you looked into study global metabolite variations using deep proteomic methods at all. My lab is working on this technology in particular and I am curious about your experience using techniques, if applicable to the work you’ve been doing. Great insights in this article by the way
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that is a brilliant suggestion, and a very interesting one. I must say my study, and our lab in general, mostly uses genomic data (DNA/RNA) and not any kind of proteomic. As these metabolites are quite hard to extract (not to mention the challenge associated with focusing on a specific one), we rather clone the target gene cluster in a host and then try to isolate it (rather than do it directly from our soil samples for instance). I would love to read more about your research though! thanks for taking the time to read and give your feedback.
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Awesome technique you might try to put to use. If you have access to any proteomic technology (mass spectrometer, etc.) this’ll work. We use an isobaric mass label called TMT which binds to all lysine side chains for every protein in a given sample. With the isobaric tag, running it through MS allows identification of <100,000 unique peptides. You can isolate metabolites by, prior to labelling, pulling down specific species (pulling down phosphopeptides in a phospho-enrichment, ubituinated, etc.)
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