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In The Journal of infectious diseases ; h5-index 82.0

BACKGROUND : Susceptibility to Vibrio cholerae infection is impacted by blood group, age, and pre-existing immunity, but these factors only partially explain who becomes infected. A recent study used 16S rRNA amplicon sequencing to quantify the composition of the gut microbiome and identify predictive biomarkers of infection with limited taxonomic resolution.

METHODS : To achieve increased resolution of gut microbial factors associated with V. cholerae susceptibility and identify predictors of symptomatic disease, we applied deep shotgun metagenomic sequencing to a cohort of household contacts of patients with cholera.

RESULTS : Using machine learning, we resolved species, strains, gene families, and cellular pathways in the microbiome at the time of exposure to V. cholerae to identify markers that predict infection and symptoms. Use of metagenomic features improved the precision and accuracy of prediction relative to 16S sequencing. We also predicted disease severity, although with greater uncertainty than our infection prediction. Species within the genera Prevotella and Bifidobacterium predicted protection from infection, and genes involved in iron metabolism also correlated with protection.

CONCLUSION : Our results highlight the power of metagenomics to predict disease outcomes and suggest specific species and genes for experimental testing to investigate mechanisms of microbiome-related protection from cholera.

Levade Inès, Saber Morteza M, Midani Firas, Chowdhury Fahima, Khan Ashraful I, Begum Yasmin A, Ryan Edward T, David Lawrence A, Calderwood Stephen B, Harris Jason B, LaRocque Regina C, Qadri Firdausi, Shapiro B Jesse, Weil Ana A


\n Vibrio cholerae\n , cholera, machine learning, metagenomics, microbiome