Autoimmune diseases are debilitating and often life-threatening conditions that afflict a substantial minority of the human population. Aside from some known genetic variations, the causes of autoimmunity remain unclear, as does the reason for the rise in disease prevalence in Western countries. Recent studies of the human microbiome implicate various microbes and microbial proteins in the development of and response to autoimmune diseases. We propose to improve understanding of the relationship between the microbiome and autoimmune disease by investigating how the microbiome interacts with its host over the time course of disease onset and progression. These studies will use a mouse model of inflammatory bowel disease (IBD) where TFGb signaling is blocked on T cells by transgenic expression of a dominant negative form of TGFbRII (DNR). This system enables us to conduct carefully controlled experiments and to probe early, pre- symptomatic time points that are not easily accessible in human clinical studies.
Our first aim i s to characterize weight change and immunological markers from birth through severe IBD in DNR mice and use this data to identify disease checkpoints (initiation, pre-activation, post-activation severe disease). To explore the relationship between gut microbial communities and autoimmune disease, our second aim will use shotgun metagenomic sequencing and cutting-edge bioinformatics tools to profile the microbiome's protein repertoire at each disease checkpoint in DNR mice versus healthy wildtype (WT) littermates. We will map metagenomic sequencing reads into protein families and pathways and then use generalized linear models to test for significant differences in these physiological profiles between lines. These tests will identify candidate biomarkers that predict IBD onset or progression.
Our third aim i s to identify temporal biomarkers for IBD, microbial proteins and pathways that have different longitudinal trajectories. We will sequence metagenomes from additional time points?from birth through severe IBD?and use mixed effects models to identify microbial biomarkers that correlate with changes in host immunology and also distinguish DNR and WT mice. Our findings will then be related to metagenomic studies of IBD in humans to develop testable hypotheses about mechanisms of disease induction and to identify genes and pathways from our study that might also serve as inexpensive, early-onset and temporal diagnostics of IBD in humans. The overall goal of this study is to clarify the relationship between IBD and the mammalian gut microbiome. This study will establish the feasibility of using mouse models to study the role of the microbiome in human autoimmune disease and ultimately to develop microbiome-based therapeutics.

Public Health Relevance

Recent evidence suggests that the human microbiome, the collection of microorganisms that live in and on the human body, has a role in the development of various autoimmune diseases. This project uses new genomic technologies and computational approaches to investigate the relationship between autoimmune disease and the biological functions of the microbiome. Our goal is to identify microbial proteins associated with autoimmune disease onset and progression that can be used for diagnosis and development of new therapies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21AI108953-01A1
Application #
8772189
Study Section
Gastrointestinal Mucosal Pathobiology Study Section (GMPB)
Program Officer
Rothermel, Annette L
Project Start
2014-07-01
Project End
2016-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
J. David Gladstone Institutes
Department
Type
DUNS #
City
San Francisco
State
CA
Country
United States
Zip Code
94158
Sharpton, Thomas; Lyalina, Svetlana; Luong, Julie et al. (2017) Development of Inflammatory Bowel Disease Is Linked to a Longitudinal Restructuring of the Gut Metagenome in Mice. mSystems 2:
Nayfach, Stephen; Pollard, Katherine S (2016) Toward Accurate and Quantitative Comparative Metagenomics. Cell 166:1103-1116