Microbial strain-level population structure and genetic diversity from metagenomes

Abstract

Among the human health conditions linked to microbial communities, phenotypes are often associated with only a subset of strains within causal microbial groups. Although it has been critical for decades in microbial physiology to characterize individual strains, this has been challenging when using culture-independent high-throughput metagenomics. We introduce StrainPhlAn, a novel metagenomic strain identification approach, and apply it to characterize the genetic structure of thousands of strains from more than 125 species in more than 1500 gut metagenomes drawn from populations spanning North and South American, European, Asian, and African countries. The method relies on per-sample dominant sequence variant reconstruction within species-specific marker genes. It identified primarily subject-specific strain variants (<5% inter-subject strain sharing), and we determined that a single strain typically dominated each species and was retained over time (for >70% of species). Microbial population structure was correlated in several distinct ways with the geographic structure of the host population. In some cases, discrete subspecies (e.g., for Eubacterium rectale and Prevotella copri) or continuous microbial genetic variations (e.g., for Faecalibacterium prausnitzii) were associated with geographically distinct human populations, whereas few strains occurred in multiple unrelated cohorts. We further estimated the genetic variability of gut microbes, with Bacteroides species appearing remarkably consistent (0.45% median number of nucleotide variants between strains), whereas P. copri was among the most plastic gut colonizers. We thus characterize here the population genetics of previously inaccessible intestinal microbes, providing a comprehensive strain-level genetic overview of the gut microbial diversity.

Subspecies in the global human gut microbiome

“…While many subspecies appeared to be distributed without any recognizable geographic pattern, some did show striking regional enrichments (Figs 1A and EV1), consistent with a recently published, independent study (Truong et al , 2017). Overall, the most restricted geographic ranges across subspecies were observed in the Chinese samples, followed by the ones from Kazakhstan, while European and American samples appeared more similar in their gut subspecies compositions (Fig 1A).…”

“…Using these positions, we could assign subspecies in a substantially expanded set of samples with low sequence coverage (on average, a 60% increase in the number of samples profiled per species; see Materials and Methods and Table EV2). We found that 47 of the 112 identified subspecies lack a representative reference genome, sometimes even the most prevalent ones (Fig 1A, Table EV2), as similarly noted by independent studies (Lloyd‐Price et al , 2017; Truong et al , 2017). Hence, the metagenomic SNP profiles not only increased our power to investigate the relation between subspecies and host properties, but also allowed for subspecies identification and quantification in new samples.…”

“…Overall, the most restricted geographic ranges across subspecies were observed in the Chinese samples, followed by the ones from Kazakhstan, while European and American samples appeared more similar in their gut subspecies compositions (Fig 1A). For example, for Eubacterium rectale , one subspecies ( E. rectale MGSS3) was almost exclusive to Chinese samples (see also Truong et al , 2017), while the other two were found in all other countries, including neighboring Kazakhstan (Fig EV2). Strong geographic segregation might explain why associations between E. rectale and host physiology have often proven to be unstable when testing in different cohorts, as these subspecies could not be profiled previously (David et al , 2014).…”

“…To be able to determine population structure without relying on limited or biased sets of pan‐genomes (Gordienko et al , 2013), we explored the natural genomic variation of the human gut microbiome (Schloissnig et al , 2013; Luo et al , 2015; Nayfach et al , 2016; Lloyd‐Price et al , 2017; Truong et al , 2017). To this end, we assessed the microbial genetic landscape of 2,144 deeply sequenced human stool metagenomes from nine countries, spanning three continents, including published (Huttenhower et al , 2012; Qin et al , 2012; Karlsson et al , 2013; Le Chatelier et al , 2013; Zeller et al , 2014) as well as 298 newly generated ones (Table EV1).…”

“…Due to limited throughput and biased by growth requirements, cultivation‐based assessments are unviable for the study of complex microbial communities. Recently, the advent of culture‐independent metagenomic approaches and newly developed analysis tools have made it possible to study the population structure and genomic variation of microbes in their natural environment (Luo et al , 2015; Nayfach et al , 2016; Truong et al , 2017). Here, we devised a novel approach for broadly delineating subspecies in the majority of abundant gut microbes and determined their associated single‐nucleotide variants in order to quantify and characterize them in their natural habitat.…”