Accurate estimation of intraspecific microbial gene content variation in metagenomic data with MIDAS v3 and StrainPGC

Abstract

Metagenomics has greatly expanded our understanding of the gut microbiome by revealing vast diversity within and across human hosts. Even within a single species, different strains can have highly divergent gene content, affecting traits such as antibiotic resistance, metabolism, and virulence. Methods that harness metagenomic data to resolve strain-level differences in functional potential are crucial for understanding the causes and consequences of this intraspecific diversity. The enormous size of pangenome references, strain mixing within samples, and inconsistent sequencing depth present challenges for existing tools that analyze samples one at a time. To address this gap, we updated the MIDAS pangenome profiler and developed StrainPGC, an approach to strain-specific gene content estimation that combines strain tracking and correlations across multiple samples. We validate our tool using a synthetic community and find that it outperforms existing approaches. Analyzing a large, publicly available metagenome collection from inflammatory bowel disease patients and healthy controls, we catalog the functional repertoires of thousands of strains across hundreds of species, capturing extensive diversity missing from reference databases. Finally, we apply StrainPGC to metagenomes from a clinical trial of fecal microbiota transplantation for the treatment of ulcerative colitis. We identify two Escherichia coli strains from two different donors that are both frequently transmitted to patients, but have notable differences in functional potential. StrainPGC and MIDAS v3 together enable precise, intraspecific pangenomic investigations using large collections of metagenomic data without microbial isolation or de novo assembly.

Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM)

“…To perform pangenome profiling, we utilized the Genes module from MIDAS v3 [11], which features careful curation of the pangenome database and comprehensive functional annotation. Specifically, a single Bowtie2 index was built for all 71 species, and QC-ed paired-end reads for each sample were aligned to this index.…”

“…MicroSLAM implements a generalized linear mixed-effects model that enables two complementary statistical tests of association between a host trait and within-species genetic variation (Fig 1A;Methods). Both tests use the presence/absence of genes from a given species' pangenome across samples, which can be quantified from metagenomic sequencing data using tools such as MIDAS v3, panX, and Roary [11][12][13] . The inputs are a gene presence/absence matrix, any covariates one wishes to include, and the trait data, which can be quantitative or binary (e.g., case/control).…”

“…The examples in this study focus on the human gut microbiome and IBD case/control cohorts, but microSLAM can be applied to any trait or environment. MicroSLAM implements a generalized linear mixed-effects model that enables two species' pangenome across samples, which can be quantified from metagenomic sequencing data using tools such as MIDAS v3, panX, and Roary [11][12][13] . The inputs are a gene presence/absence matrix, any covariates one wishes to include, and the trait data, which can be quantitative or binary (e.g., case/control).…”

“…As microbiome species evolve, individual lineages lose and gain genes through horizontal gene transfer [6,7] and other processes that create structural variation [8][9][10] . The resulting pangenome can be quantified from shotgun metagenomics data [11][12][13] , which has revealed immense genetic diversity between and within human hosts [14] . Even when two people harbor the same microbial species, the cells within those populations are likely to perform different functions [10,15] .…”

“…The gut microbiome has long-standing links to IBD, including species abundance and gene associations [15,20,29,[32][33][34][35][36][37][38][39][40][41] . Here, we combined MIDAS v3 pangenome profiling [11] with microSLAM to quantify associations between IBD and relative abundance, population structure, and gene presence/absence across 71 common members of the human gut microbiome. These analyses identified 49 species with IBD-associated population structure and 83 significant gene families, which we interpreted at the pathway level within and across microbiome species.…”