Noteworthy things — Week 27 (01/07/2024)

A weekly summary of what caught our eye in the field of microbiome research, microbial genomics and ecology, and others. Comments in blue are personal and hopefully useful!

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From our lab

  • Harnessing human microbiomes for disease prediction
    Yang Liu et al. Trends in Microbiology — 20 January 2024 (July Issue)
    Comment: Our review on leveraging the gut microbiome for predicting disease risk and outcome that have a (known or unknown) microbial component has finally been assigned to the July issue of TiM. Have a look if you haven’t already! 🙂

Noteworthy studies and publications

(a) Microbiome

  • Time of sample collection is critical for the replicability of microbiome analyses
    Celeste Allaband et al. Nature Metabolism — 1 July 2024.
    Comment: This 16S rRNA-based study on fecal samples from 16 male mice found that the time of sample collection within a 4-hour window significantly affected the gut microbiome’s diversity and composition, more so than dietary changes, suggesting the necessity for standardized sampling times (accounting for diurnal variations in microbiota) to maximise data accuracy in microbiome studies.

  • Spatial mapping of mobile genetic elements and their bacterial hosts in complex microbiomes
    Benjamin Grodner et al. Nature Microbiology — 25 June 2024.
    Comment: Great study and figures! Here, authors use a novel high-resolution imaging method combining DNA FISH with rRNA-FISH to visualize mobile genetic elements and their bacterial hosts in oral biofilms, no less! This revealed spatial clusters of AMR genes and bacteriophages, indicating heterogeneous distribution and potential barriers to their spread within biofilms. Very impressive! Also check the thread on Twitter from the lead author.

  • Dietary fibre directs microbial tryptophan metabolism via metabolic interactions in the gut microbiota
    Anurag K. Sinha, Martin F. Laursen et al. Nature Microbiology — 25 June 2024.
    Comment: Fibre-rich diets are thought to have the potential to beneficially modulate the gut microbial metabolic output. In this in vitro/in vivo study, authors show that dietary fibre alters gut microbial tryptophan metabolism by suppressing indole production in favour of beneficial indolelactic acid (ILA) and indolepropionic acid (IPA). This shift occurs as fibre-degrading bacteria inhibit indole-producing E. coli via catabolite repression, thereby increasing tryptophan availability for other taxa to generate metabolites.

  • Birthmode and environment-dependent microbiota transmission dynamics are complemented by breastfeeding during the first year
    Marta Selma-Royo, Léonard Dubois, Serena Manara et al. Cell Host & Microbe — 12 June 2024.
    Comment: In this small mother-infant cohort, authors compare hospital versus at-home delivery on infant microbiota transmission and show an impact. They show that human milk is a key modulator of infant microbiota during the first year, with a variation across delivery modes and locations, except for Bifidobacteria, with B. longum persisting and diversifying more according to breastfeeding duration.

(b) Microbial genetics, ecology, evolution and AMR

  • Time-calibrated phylogenetic and chromosomal mobilome analyses of Staphylococcus aureus CC398 reveal geographical and host-related evolution
    Javier Eduardo Fernandez et al. Nature Communications — 1 July 2024.
    Comment: The CC398 lineage of Staphylococcus aureus is an important zoonotic one, shown to colonize livestock, pets and humans and is a very interesting model for studying host adaptation in S. aureus. Here, authors analyzed >3000 global S. aureus CC398 genomes, constructing a time-calibrated phylogeny that reveals distinct evolutionary lineages, with a notable focus on the equine-associated EP5-Leq lineage. They also identify interesting key MGEs driving adaptation and AMR in CC398.

  • Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium
    Tuan-Anh Tran et al. Nature Communications — 13 June 2024.
    Comment: Pretty innovative study in which authors used imaging and ML to predict ciprofloxacin susceptibility in Salmonella. They analyzed 16 clinical isolates and 4 lab strains and captured detailed morphological data from bacterial cells exposed to various ciprofloxacin concentrations over 24 hours. ML classifiers were trained on key imaging features to distinguish susceptible from resistant isolates, even without direct antimicrobial exposure. Not sure how this could be scaled-up and translated in practice but this is quite original and interesting!

(c) Other general interest