An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography

By Stephen Nayfach, Beltran Rodriguez-Mueller, Nandita R. Garud, Katherine S. Pollard. Genome Research, 2016

416 citations recorded by the source corpus.

We present the Metagenomic Intra-species Diversity Analysis System (MIDAS), which is an integrated computational pipeline for quantifying bacterial species abundance and strain-level genomic variation, including gene content and single-nucleotide polymorphisms (SNPs), from shotgun metagenomes. Our method leverages a database of more than 30,000 bacterial reference genomes that we clustered into species groups. These cover the majority of abundant species in the human microbiome but only a small proportion of microbes in other environments, including soil and seawater. We applied MIDAS to stool metagenomes from 98 Swedish mothers and their infants over one year and used rare SNPs to track strains between hosts. Using this approach, we found that although species compositions of mothers and infants converged over time, strain-level similarity diverged. Specifically, early colonizing bacteria were often transmitted from an infant’s mother, while late colonizing bacteria were often transmitted from other sources in the environment and were enriched for spore-formation genes. We also applied MIDAS to 198 globally distributed marine metagenomes and used gene content to show that many prevalent bacterial species have population structure that correlates with geographic location. Strain-level genetic variants present in metagenomes clearly reveal extensive structure and dynamics that are obscured when data are analyzed at a coarser taxonomic resolution.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c5d9dc21457c4e6ae512b7a60a1f66d34740ba13

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Citations in the corpus

The Human Gut Microbiome: From Association to Modulation. (2018, neutral): “, 2013), several tools to call microbial SNVs and to profile subspecies to strain-level variation have been developed (Costea et al., 2017c; Nayfach et al., 2016; Quince et al., 2017; Scholz et al., 2016; Truong et al., 2017) and applied to the human gut microbiome.”
Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 (2020, background): “…as taxonomic profiling (Lu et al., 2017; Milanese et al., 2019; Truong et al., 2015; Wood et al., 2019) , strain identification (Luo et al., 2015; Nayfach et al., 2016; Scholz et al., 2016; Truong et al., 2017) , or functional profiling (Franzosa et al., 2018; Kaminski et al., 2015;…”
The First Microbial Colonizers of the Human Gut: Composition, Activities, and Health Implications of the Infant Gut Microbiota (2017, background): “In order to trace vertical transmission events, several in silico pipelines have recently been developed (45, 523).”
Mother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome (2018, background): “, 2015) and early intimate relations, particularly with the mother (Asnicar et al., 2017; Backhed et al., 2015; Korpela et al., 2018; Nayfach et al., 2016), also play a pivotal role in the early microbial acquisition and community succession in the infant.”
Linking pangenomes and metagenomes: the Prochlorococcus metapangenome (2018, methodology): “Recently, pangenomic approaches have been used to characterize the gene content of microbial populations in environmental samples through metagenomic read recruitment ( Delmont & Eren, 2016 ; Scholz et al., 2016 ; Nayfach et al., 2016 ).”
Microbial strain-level population structure and genetic diversity from metagenomes (2017, positive): “When compared to other recent strain-level metagenomic profilers, StrainPhlAn achieved substantially better results than MIDAS (Nayfach et al. 2016) and ConStrains (Luo et al. 2015), based on per-nucleotide and overall strain-tracking accuracies, respectively (Supplemental Tables S1 – S5;…”