Project 3 will develop new computational tools for understanding how gut microbial communities change their membership, gene content, gene expression, and metabolic activities in obesity and during diet interventions, in both humans and 'humanized'gnotobiotic mouse models. Its overarching goals are to: (i) understand the levels at which multi-omics data should be collected and analyzed in order to maximize our understanding of complex multifactorial pathophysiological conditions such as obesity and its associated metabolic abnormalities;(ii) develop improved genome assembly techniques and predictions about culture conditions and syntrophic interactions to improve the utility of personalized bacterial culture collections and data derived from them;and (iii) understand how best to characterize the diversity of gut microbial communities, and the functional profiles of these communities, observed in the human population and use them for patient stratification. Project 3 has three Aims: (1) develop new tools for relating """"""""multi-omics"""""""" data across analysis levels and relating information from mouse models, specifically gnotobiotic humanized mice characterized in Project 1 and Core A, to information about the discordant twins (phenotyped in Project 2 and Core A) from which those animal models were derived and to the human population at large;(2) to develop improved methods for assembly of complete bacterial genomes as a reference for shotgun metagenomic and meta- transcriptomic data, including meta-transcriptome data collected from gnotobiotic mice colonized with bacterial culture collections generated from the fecal microbiota of co-twins in discordant twin pairs where the complete bacterial genomes are known, thus eliminating a major computational bottleneck and providing more relevant types of assemblies for downstream annotation and interpretation tasks;and (3) to provide a broader understanding of the major patterns of variation in human gut microbial communities and their genes, transcripts and metabolites in individuals with and without obesity and obesity associated abnormalities, and to test whether these major patterns can be used for stratification of human subjects in terms of their response to specific dietary or other interventions. Project 3 and Core B will work closely together to make new analysis tools and datasets available to the scientific community.
Efforts to characterize the human gut microbiome in health and disease are producing vast amounts of data about its variations. Understanding which of this variation is clinically important, and which features of this variation are reproducible across studies, populations, and host species (mice and humans), is critical for using the results to define clinical interventions. We will develop new computational tools that assist in gaining this understanding about the role of gut microbes in the pathogenesis of obesity and associated metabolic abnormalities, and in guiding preclinical tests for microbiota-directed therapeutics.
|Faith, Jeremiah J; Colombel, Jean-FrÃ©dÃ©ric; Gordon, Jeffrey I (2015) Identifying strains that contribute to complex diseases through the study of microbial inheritance. Proc Natl Acad Sci U S A 112:633-40|
|Rosenbaum, Michael; Knight, Rob; Leibel, Rudolph L (2015) The gut microbiota in human energy homeostasis and obesity. Trends Endocrinol Metab 26:493-501|
|Heath, Andrew C (2015) Metagenomics: a new frontier for translational research and personalized therapeutics in psychiatry? Biol Psychiatry 77:600-1|
|Peterson, Daniel A; Planer, Joseph D; Guruge, Janaki L et al. (2015) Characterizing the interactions between a naturally primed immunoglobulin A and its conserved Bacteroides thetaiotaomicron species-specific epitope in gnotobiotic mice. J Biol Chem 290:12630-49|
|Subramanian, Sathish; Blanton, Laura V; Frese, Steven A et al. (2015) Cultivating healthy growth and nutrition through the gut microbiota. Cell 161:36-48|
|Dey, Neelendu; Wagner, Vitas E; Blanton, Laura V et al. (2015) Regulators of gut motility revealed by a gnotobiotic model of diet-microbiome interactions related to travel. Cell 163:95-107|
|Luo, Chengwei; Knight, Rob; Siljander, Heli et al. (2015) ConStrains identifies microbial strains in metagenomic datasets. Nat Biotechnol 33:1045-52|
|Ussar, Siegfried; Griffin, Nicholas W; Bezy, Olivier et al. (2015) Interactions between Gut Microbiota, Host Genetics and Diet Modulate the Predisposition to Obesity and Metabolic Syndrome. Cell Metab 22:516-30|
|Duncan, Alexis E; Sartor, Carolyn E; Jonson-Reid, Melissa et al. (2015) Associations between body mass index, post-traumatic stress disorder, and child maltreatment in young women. Child Abuse Negl 45:154-62|
|Walters, William A; Xu, Zech; Knight, Rob (2014) Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett 588:4223-33|
Showing the most recent 10 out of 74 publications