The goal of this project is to conduct time-series proteomic analyses of a tractable and naturally- occurring model ecosystem, the newborn intestinal tract, in order to characterize links between dysbiosis and intestinal inflammation. At present, we lack a mechanistic understanding of the relationships between the microbiota and inflammatory disorders such as necrotizing enterocolitis (NEC) and inflammatory bowel disease. We also lack tools to clarify which microbial biochemical processes are active in the human gut. Over the past decade, our research group has developed methods to pair community metagenomics with high-throughput mass spectrometry-based proteomic analyses to accurately identify proteins with strain-level resolution. An important component of this approach has been the development of bioinformatics tools that enable the integration and analysis of "omic" data. Recently, we adapted these methods to simultaneously measure human and microbial proteins in time series infant fecal samples. Here, we propose to test the hypothesis that inflammation in the premature infant gut is triggered by aberrations in microbial community metabolism. This project will leverage samples and a large amount of metagenomic data obtained in a companion NIH study of the microbiota in babies with and without NEC.
The specific Aims of this project are:
Aim 1. Characterize gut microbial community function during the first month of life in healthy infants, by determining which microbial genes and metabolic pathways are most important during early colonization, with specific attention to the transition from aerobic to anaerobic community metabolism.
Aim 2. Characterize time-dependent signatures of human proteins linked to intestinal inflammation in fecal samples from newborn infants, by evaluating the abundances of human proteins linked to intestinal homeostasis, inflammation, and redox biology in the context of changes in the microbial proteome.
Aim 3. Test the hypothesis that babies with NEC developed inflammation as a consequence of a delayed transition to anaerobic microbial metabolism in the gut, by comparing temporal patterns of human and microbial protein expression in babies with and without NEC to determine if dysbiosis precedes inflammation This work will rely on a bioinformatics strategy for analysis of large time series datasets that will deployed in the context of GGKbase, a novel knowledgebase framework that will facilitate collaborative data analysis and sharing of "omic" information with the scientific community. This research uses the developing infant gut as a model system to uncover general features of gut microbial community function, and to clarify the relationships between aberrant function and inflammation. Our results and the informatics tools that we develop will contribute to an improved understanding of the dynamics of the relationship between the human body and the human microbiome. 1

Public Health Relevance

The developing infant gut is an ideal model system that can be used to uncover general features of gut microbial community function, and to clarify the relationships between aberrant function and inflammation. Intestinal inflammatory disorders, such as necrotizing enterocolitis or Crohn's Disease, can result from exaggerated immune responses;however, it is unclear whether microbiome shifts precede inflammation, or are a consequence of it. The goal of this project is to conduct time-series whole community proteomic analyses of the model ecosystem, the newborn intestinal tract, in order to characterize links between dysbiosis and intestinal inflammation.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM103600-01A1
Application #
8605019
Study Section
Special Emphasis Panel (ZGM1-GDB-2 (MC))
Program Officer
Sledjeski, Darren D
Project Start
2014-02-06
Project End
2017-11-30
Budget Start
2014-02-06
Budget End
2014-11-30
Support Year
1
Fiscal Year
2014
Total Cost
$370,486
Indirect Cost
$83,442
Name
UT-Battelle, LLC-Oak Ridge National Lab
Department
Type
DUNS #
099114287
City
Oak Ridge
State
TN
Country
United States
Zip Code
37831