inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains

Area of Science:

• Microbial genomics
• Population genetics
• Bioinformatics

Background:

• Microbial populations exhibit genetic variation (microdiversity) influencing traits like nutrient use and pathogenicity.
• Accurate comparison of microbial populations requires methods that account for this intra-population diversity.

Purpose of the Study:

• Introduce inStrain, a novel program for profiling and comparing microbial microdiversity using metagenomic data.
• Assess the accuracy of inStrain against existing genomic comparison methods.
• Investigate microbial strain sharing and diversity patterns in fecal metagenomes of premature infants.

Main Methods:

• inStrain utilizes paired-end metagenomic reads to analyze whole-genome microdiversity within microbial populations.
• Benchmarking of inStrain against established methods to evaluate its accuracy in genomic comparisons.
• Application of inStrain to over 1,000 fecal metagenomes from premature infants.

Main Results:

• inStrain significantly improves the accuracy of microbial population comparisons.
• Infant siblings share more microbial strains than unrelated infants; identical twins do not share more than fraternal twins.
• Infants delivered via Cesarean section exhibit higher nucleotide diversity in Klebsiella compared to vaginally delivered infants, suggesting hospital-acquired origins.
• Variable genomic loci within individual infants contain variants found across other infants, indicating diverse sources.

Conclusions:

• inStrain provides a robust framework for analyzing microbial microdiversity and performing strain-level comparisons.
• Mode of delivery influences early-life microbial community composition and diversity.
• The inStrain tool is broadly applicable to any metagenomic dataset for detailed microdiversity analysis.