In vertebrates, including humans, individuals harbor gut microbial communities whose species composition and relative proportions of dominant microbial groups are tremendously varied. Although external and stochastic factors clearly contribute to the individuality of the microbiota, the fundamental principles dictating how environmental factors and host genetic factors combine to shape this complex ecosystem are largely unknown and require systematic study. Here we examined factors that affect microbiota composition in a large ( n = 645) mouse advanced intercross line originating from a cross between C57BL/6J and an ICR-derived outbred line (HR). Quantitative pyrosequencing of the microbiota defined a core measurable microbiota (CMM) of 64 conserved taxonomic groups that varied quantitatively across most animals in the population. Although some of this variation can be explained by litter and cohort effects, individual host genotype had a measurable contribution. Testing of the CMM abundances for cosegregation with 530 fully informative SNP markers identified 18 host quantitative trait loci (QTL) that show significant or suggestive genome-wide linkage with relative abundances of specific microbial taxa. These QTL affect microbiota composition in three ways; some loci control individual microbial species, some control groups of related taxa, and some have putative pleiotropic effects on groups of distantly related organisms. These data provide clear evidence for the importance of host genetic control in shaping individual microbiome diversity in mammals, a key step toward understanding the factors that govern the assemblages of gut microbiota associated with complex diseases.

Background: Individuality in the species composition of the vertebrate gut microbiota is driven by a combination of host and environmental factors that have largely been studied independently. We studied the convergence of these factors in a G₁₀ mouse population generated from a cross between two strains to search for quantitative trait loci (QTLs) that affect gut microbiota composition or ileal Immunoglobulin A (IgA) expression in mice fed normal or high-fat diets. Results: We found 42 microbiota-specific QTLs in 27 different genomic regions that affect the relative abundances of 39 taxa, including four QTL that were shared between this G₁₀ population and the population previously studied at G₄. Several of the G₁₀ QTLs show apparent pleiotropy. Eight of these QTLs, including four at the same site on chromosome 9, show significant interaction with diet, implying that diet can modify the effects of some host loci on gut microbiome composition. Utilization patterns of IghV variable regions among IgA-specific mRNAs from ileal tissue are affected by 54 significant QTLs, most of which map to a segment of chromosome 12 spanning the Igh locus. Despite the effect of genetic variation on IghV utilization, we are unable to detect overlapping microbiota and IgA QTLs and there is no significant correlation between IgA variable pattern utilization and the abundance of any of the taxa from the fecal microbiota. Conclusions: We conclude that host genetics and diet can converge to shape the gut microbiota, but host genetic effects are not manifested through differences in IgA production.

Multilocus-genotyping methods have shown that Escherichia coli O157:H7 is a geographically disseminated clone. However, high-resolution methods such as pulse-field gel electrophoresis demonstrate significant genomic diversity among different isolates. To assess the genetic relationship of human and bovine isolates of E. coli O157:H7 in detail, we have developed an octamer-based genome-scanning methodology, which compares the distance between over-represented, strand-biased octamers that occur in the genome. Comparison of octamer-based genome-scanning products derived from >1 megabase of the genome demonstrated the existence of two distinct lineages of E. coli O157:H7 that are disseminated within the United States. Human and bovine isolates are nonrandomly distributed among the lineages, suggesting that one of these lineages may be less virulent for humans or may not be efficiently transmitted to humans from bovine sources. Restriction fragment length polymorphism analysis with lambdoid phage genomes indicates that phage-mediated events are associated with divergence of the lineages, thereby providing one explanation for the degree of diversity that is observed among E. coli O157:H7 by other molecular-fingerprinting methods.

Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM ), providing a useful tool for analyzing microbiome data.

The activity of ς ^B in Listeria monocytogenes is stimulated by high osmolarity and is necessary for efficient uptake of osmoprotectants. Here we demonstrate that, during cold shock, ς ^B contributes to adaptation in a growth phase-dependent manner and is necessary for efficient accumulation of betaine and carnitine as cryoprotectants.

Non-sorbitol-fermenting, β-glucuronidase-negative Escherichia coli O157:H7 strains are regarded as a clone complex, and populations from different geographical locations are believed to share a recent common ancestor. Despite their relatedness, high-resolution genotyping methods can detect significant genome variation among different populations. Phylogenetic analysis of high-resolution genotyping data from these strains has shown that subpopulations from geographically unlinked continents can be divided into two primary phylogenetic lineages, termed lineage I and lineage II, and limited studies of the distribution of these lineages suggest there could be differences in their propensity to cause disease in humans or to be transmitted to humans. Because the genotyping methods necessary to discriminate the two lineages are tedious and subjective, these methods are not particularly suited for studying the large sets of strains that are required to systematically evaluate the ecology and transmission characteristics of these lineages. To overcome this limitation, we have developed a lineage-specific polymorphism assay (LSPA) that can readily distinguish between the lineage I and lineage II subpopulations. In the studies reported here, we describe the development of a six-marker test (LSPA-6) and its validation in a side-by-side comparison with octamer-based genome scanning. Analysis of over 1,400 O157:H7 strains with the LSPA-6 demonstrated that five genotypes comprise over 91% of the strains, suggesting that these subpopulations may be widespread.