Predicting Vibrio cholerae Infection and Disease Severity Using Metagenomics in a Prospective Cohort Study.

Journal Article (Journal Article)

BACKGROUND: Susceptibility to Vibrio cholerae infection is affected by blood group, age, and preexisting immunity, but these factors only partially explain who becomes infected. A recent study used 16S ribosomal RNA 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 were 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.

Full Text

Duke Authors

Cited Authors

  • Levade, I; Saber, MM; Midani, FS; Chowdhury, F; Khan, AI; Begum, YA; Ryan, ET; David, LA; Calderwood, SB; Harris, JB; LaRocque, RC; Qadri, F; Shapiro, BJ; Weil, AA

Published Date

  • February 3, 2021

Published In

Volume / Issue

  • 223 / 2

Start / End Page

  • 342 - 351

PubMed ID

  • 32610345

Pubmed Central ID

  • PMC7857355

Electronic International Standard Serial Number (EISSN)

  • 1537-6613

Digital Object Identifier (DOI)

  • 10.1093/infdis/jiaa358

Language

  • eng

Conference Location

  • United States