This application addresses broad Challenge Area (08): Genomics and specific Challenge Topic, 04-DK-101: Role of the Human Gut Microbiome in NIDDK Diseases. The human gastrointestinal tract is colonized by a climax population of microbes (the gut microbiome) that interacts intimately with its host. Linkages between the gut microbiome and host metabolic and immune functions are complex and vital;aberrations in the acquisition or ultimate composition of the gut microbiome are believed to be an important predisposition factor in complex NIDDK diseases such as obesity, inflammatory bowel disease, and gastric cancers later in life. Like these multifactorial diseases, composition of the gut microbiome is in itself a complex trait, affected by both environmental factors (chance exposure, diet) and a number of host genetic factors such as those influencing mucosal immunity. Similarly, those host genetic loci that affect composition of the gut microbiome, especially those portions of it that are known to be factors in disease, are likely to contribute to one's overall predisposition to disease. With an integrated set of synergistic specific aims, we will explore the host genetic control over gut microbiome composition in mice. Our rationale for this research is that it provides a new means for identifying disease predisposition loci through the effects of these loci on composition of the gut microbiome. Our working hypothesis is that composition of the gut microbiome is a polygenic trait and that host genes controlling its composition can be identified by high- resolution quantitative trait loci (QTL) mapping. Using deep pyrosequencing to quantify the relative abundance of individual taxa of the gut microbiome as """"""""host traits"""""""", our preliminary data show clearly that strong QTL can be successfully identified by co-segregation patterns of gut microbiome composition traits with SNP markers in 200 mice from an F4 intercross mapping population between two common mouse strains, C57BL/6J and HR (derived from ICR). Our current objective is to establish mouse quantitative genetic models systematically as an approach to comprehensively map QTL affecting composition of the microbiome. This will be accomplished through a series of analyses and association studies in mouse populations with increasing complexity and diversity. First, we will expand our C57 x HR F4 analysis to include all 800 mice in this population. Gut microbiome QTL identified in this cohort will then be fine-mapped using an F10 intercross population from the same origin. Second, we will expand our genetic base by phenotyping the ~40 primary Mouse Phenome Database inbred lines and applying haplotype-based QTL association mapping. And third, we will take our studies into the Collaborative Cross (CC), a large panel of recombinant inbred mouse lines derived from intercrossing of eight highly divergent inbred lines including wild germplasm. The CC is the only mammalian resource that has high and uniform genome-wide variation effectively randomized across a large, heterogeneous population which also supports integration across environmental and biological conditions and over time. The CC captures the complexity of the mammalian genome and permits modeling of complex systems and interactions that influence disease. We will use the CC to explore the genomic search space in a manner impossible in humans to develop specific, high confidence models to be tested in future mouse validation and new human studies. Our innovative experiments capitalize on powerful genetic resource populations and an extensive array of expertise in creating and evaluating large populations of experimental mice, analysis of high-density SNP data, QTL mapping and statistical genomics, pyrosequencing analysis of complex microbiomes, phylogenetic analysis, and analysis of large complex data sets. The team represented in this proposal has forged impressive collaborations to create a new experimental approach and favorable research environments.
This research will help us better identify genes contributing to NIDDK diseases through their effects on composition of the gut microbiome, and specifically, aberrant patterns of colonization. A more complete understanding of the genetics of NIDDK diseases will lead to new and better treatments and improve our ability to screen for and prevent such burdensome conditions as obesity, IBD and some forms of cancer. This research will help us better identify genes contributing to NIDDK diseases through their effects on composition of the gut microbiome, and specifically, on aberrant patterns of colonization. A more complete understanding of the genetics of NIDDK diseases will lead to new and better treatments and improve our ability to screen for and prevent such burdensome conditions as obesity, IBD and some forms of cancer.
