InStrain enables population genomic analysis from metagenomic data and rigorous detection of identical microbial strains

Abstract

Coexisting microbial cells of the same species often exhibit genetic differences that can affect phenotypes ranging from nutrient preference to pathogenicity. Here we present inStrain, a program that utilizes metagenomic paired reads to profile intra-population genetic diversity (microdiversity) across whole genomes and compare populations in a microdiversity-aware manner, dramatically increasing genomic comparison accuracy when benchmarked against existing methods. We use inStrain to profile >1,000 fecal metagenomes from newborn premature infants and find that siblings share significantly more strains than unrelated infants, although identical twins share no more strains than fraternal siblings. Infants born via cesarean section harbored Klebsiella with significantly higher nucleotide diversity than infants delivered vaginally, potentially reflecting acquisition from hospital versus maternal microbiomes. Genomic loci showing diversity within an infant included variants found in other infants, possibly reflecting inoculation from diverse hospital-associated sources. InStrain can be applied to any metagenomic dataset for microdiversity analysis and rigorous strain comparison. MainCells in microbial populations are not all identical to one another. Genetic polymorphisms rapidly arise through de novo mutation, and these variants can spread because they confer a fitness advantage or by lateral gene transfer (if the variant confers an advantage or is linked to a fitness-conferring variant). It is estimated that billions to trillions of bacterial genetic mutations are generated de novo every day in the microbiome of an individual adult human 1 , and these differences can be clinically relevant. For example, just three point mutations can confer antibiotic resistance in Enterobacteriaceae 2 . Studying genetic variation in microbial populations has historically involved isolating a multitude of cells from the same population and performing phenotypic analysis and/or genome sequencing. Genome-resolved metagenomic analysis, which involves extracting and sequencing DNA directly from the environment and using computational tools to assemble and bin the resulting DNA sequences into genomes in silico , presents an attractive high-throughput alternative to this process. This technique allows simultaneous analysis of microbial communities, the species populations that comprise them, and heterogeneity within these populations, and has been used to reveal fine-scale evolutionary mechanisms 3-5 , dynamics 6-12 , and strain level metabolic variation that could contribute to strain selection 1,13 .Many fundamental questions in human microbiome research relate to the transmission of microbial populations between individuals, including how we are seeded by microbes early in life [14][15][16] . However, strain diversity presents challenges for such analyses. Sequence comparisons are usually performed by aligning consensus genomes assembled from different samples 1,17 or by modifying a reference genome using mapped reads and comparing it...

Quantification of discrete gut bacterial strains following fecal transplantation for recurrentClostridioides difficileinfection demonstrates long-term stable engraftment in non-relapsing recipients

“…Recent FMT analyses have combined deep metagenomic sequencing with the computational detection of single nucleotide polymorphic (SNP) variants in common species marker genes to quantify some properties of strain composition. These approaches [30][31][32][33] have found enrichments of donor SNPs in the recipient microbiota post-FMT suggesting transmission and engraftment 31,32 of some proportion of the donor's strains in the recipient but linkage of these donor microbe SNPs to the donor's discrete bacterial strains remains elusive. While informative, these approaches require very deep metagenomic sequencing to track strains present at even shallow relative abundance 34 .…”

GUNC: Detection of Chimerism and Contamination in Prokaryotic Genomes

“…MAGs have led to the discovery of novel deepbranching lineages previously eluding cultivation-based approaches, such as the Asgardarchaeota [16] or the bacterial Candidate Phyla Radiation [11,17], thereby substantially expanding the microbial tree of life [18,19]. Moreover, MAGs can be taxonomically resolved to strain level [20,21] which is particularly beneficial in undersampled environments where reference genomic coverage is scarce [22,23].…”

Strain population structure varies widely across bacterial species and predicts strain colonization in unrelated individuals

“…These species boundaries can be detected in the SNPs of conserved genes (i.e., the average nucleotide identity; ANI) 3–6 and in the large differences in genome overlap (e.g., by pairwise genome alignment) or gene flow discontinuities driven by the strong bias of horizontal gene transfer within a species rather than across species boundaries 3,7–9 . As in microbial pathogenesis 10,11 , the functional impact of the microbiome is dependent on strain-level variation within a species 12–18 , which has driven computational advances to track strains 19–22 , cluster strains 23 , measure strain stability 7,21,24 , and analyze strain variation 25,26 . Strain-focused algorithms for both the commensal microbiome and infectious disease research have also begun to inform genomic boundaries for bacterial strains 7,21,22 .…”

“…As in microbial pathogenesis 10,11 , the functional impact of the microbiome is dependent on strain-level variation within a species 12–18 , which has driven computational advances to track strains 19–22 , cluster strains 23 , measure strain stability 7,21,24 , and analyze strain variation 25,26 . Strain-focused algorithms for both the commensal microbiome and infectious disease research have also begun to inform genomic boundaries for bacterial strains 7,21,22 . Despite the importance of strain-variation, we still lack a broad understanding of the general principles of strain population structure, such as the number of strains in each bacterial species, the stability of these strains 27 , the prevalence of each strain within a species in human and non-human reservoirs, and the fitness differences and environmental changes that drive alterations in strain prevalence 27,28 .…”

“…Understanding bacterial population structure and strain prevalence, beyond the narrow lens of the hospital environment, could provide novel tools to quantify the association of microbial strains with both infectious and complex human disease, as well as new routes to limit human disease. Early broad explorations of the gut microbiota have demonstrated contexts of enriched strain sharing for commensal microbes including the shared hospital environment for infants 22 , fecal microbiota transplantation (FMT) 19,21 , and early life co-habitation between family members 7,22 . Although selective pressures likely differ in non-MDRO organisms, the prevalence of a bacterial strain in the broader human microbiota population, beyond enriched scenarios of direct transfer like co-habitation and FMT, is a reflection of a strain’s fitness that includes both the transmissibility of the organism and its stability in the host 13 .…”