Abstract
The microbiome influences health and disease through complex networks of host genetics, genomics, microbes, and environment. Identifying the mechanisms of these interactions has remained challenging. Systems genetics in laboratory mice (Mus musculus) enables data-driven discovery of biological network components and mechanisms of host-microbial interactions underlying disease phenotypes. To examine the interplay among the whole host genome, transcriptome, and microbiome, we mapped QTL and correlated the abundance of cecal messenger RNA, luminal microflora, physiology, and behavior in a highly diverse Collaborative Cross breeding population. One such relationship, regulated by a variant on chromosome 7, was the association of Odoribacter (Bacteroidales) abundance and sleep phenotypes. In a test of this association in the BKS.Cg-Dock7m +/+ Leprdb/J mouse model of obesity and diabetes, known to have abnormal sleep and colonization by Odoribacter, treatment with antibiotics altered sleep in a genotype-dependent fashion. The many other relationships extracted from this study can be used to interrogate other diseases, microbes, and mechanisms.
Highlights
The microbiome influences health and disease through complex networks of host genetics, genomics, microbes, and environment
The median broad-sense heritability of microbial abundance estimated by intraclass correlation in the Collaborative Cross (CC) founder strains data from Campbell et al (2012) for each OTU (Table S1)
We evaluated whether the presence of Odoribacter in Leprdb mice could explain the altered sleep behavior reported in these mice
Summary
The microbiome influences health and disease through complex networks of host genetics, genomics, microbes, and environment. Because mice and humans harbor similar microbiota at high taxonomic levels (Ley et al 2008; Krych et al 2013), systems genetic analysis in laboratory mice can be an effective tool for discovering the mechanisms of host–microbe interactions in a large-scale, data-driven manner This quantitative genetic approach provides a means of holistic assessment of the relationships between hosts, microbes, and diseases through the use of population genetic variation, one of the greatest determinants of microbial community composition in mice (Deloris Alexander et al 2006; Campbell et al 2012). Interrogation of these networks at the level of transcripts, microbes, and phenotypes enables the study of mechanisms of microbiota influence on health and disease by identifying causal mechanisms responsible for phenotypic correlations
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