Core B will provide database infrastructure and coordinate data deposition to public resources such as INSDC (the International Nucleotide Sequence Database Collaboration), as well as coordinate the data for the project overall to facilitate comparison with other datasets and deployment of advanced algorithms. It will build on the Knight lab's extensive experience with meta-analysis, sequence databases, and data visualization to provide these methods to Project 1, Project 2, and Project 3, and will work closely with Core A to mirror metabolomics data and integrate metabolomics datasets with the rest of the multi-omic data to be collected. Core B has three Aims.
Aim 1 -organize the data and metadata collected in Project 1 from mice and Project 2 from humans, curate these datasets, and ensure that analyses are reproducible in an automated fashion using virtual machines.
Aim 2 -deposit the data and metadata in standards-compliant form to INSDC, the Gene Expression Omnibus, and other resources (e.g., metabolomics repositories) as they emerge.
Aim 3 -Through analyses of existing microbiome datasets, provide best-practices recommendations to investigators in Project 1 and Project 2 to optimize experimental design. Core B will build on an extensive multi-omics data repository funded by multiple sources that is able to accommodate the types of data to be collected in the project overall, including links between human subjects with defined family relationships (e.g. dizygotic twins), humanized gnotobiotic mice colonized with strains derived from these human subjects, timeseries study designs in both humans and mice, combinations of data at multiple levels including 16S rRNA gene sequencing, RNA-Seq, and metabolomics, and other advanced features of this complex project. A key component of our approach is to enable investigators in the laboratory collecting the datasets to perform their own first-pass analyses while at the same time making the data available more broadly within the project for additional advanced analyses, such as those being developed in Project 3, to be applied, and also making the data available to the public in a relatively user-friendly form to supplement public deposition in permanent government-backed sequence data repositories.

Public Health Relevance

Efforts to characterize the human gut microbiome in health and disease are producing vast amounts of data about its organismal and gene content and variations. Storing this complex data in a database and depositing it for public use is critical for making the information generally available. We will organize the data and deposit it in major sequence repositories in order to support efforts to gain understanding about the role of microbes in obesity, and guiding preclinical tests for microbiome-directed therapeutics.

Agency
National Institute of Health (NIH)
Type
Research Program Projects (P01)
Project #
2P01DK078669-06A1
Application #
8742499
Study Section
Special Emphasis Panel (ZDK1)
Project Start
Project End
Budget Start
Budget End
Support Year
6
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Washington University
Department
Type
DUNS #
City
Saint Louis
State
MO
Country
United States
Zip Code
Faith, Jeremiah J; Colombel, Jean-Frédéric; 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

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