Host–microbe relationships in inflammatory bowel disease detected by bacterial and metaproteomic analysis of the mucosal–luminal interface

Authors


  • Supported in part by NIH grants 5R01AI078885 and P01DK46763, and Crohn's and Colitis Foundation of America grant 1567 and Microbiome in IBD award. And Grant Support from the Edythe and Eli Broad Medical Research Program (J.B.).[correction made here after initial online publication].

Abstract

Background:

Host–microbe interactions at the intestinal mucosal–luminal interface (MLI) are critical factors in the biology of inflammatory bowel disease (IBD).

Methods:

To address this issue, we performed a series of investigations integrating analysis of the bacteria and metaproteome at the MLI of Crohn's disease, ulcerative colitis, and healthy human subjects. After quantifying these variables in mucosal specimens from a first sample set, we searched for bacteria exhibiting strong correlations with host proteins. This assessment identified a small subset of bacterial phylotypes possessing this host interaction property. Using a second and independent sample set, we tested the association of disease state with levels of these 14 “host interaction” bacterial phylotypes.

Results:

A high frequency of these bacteria (35%) significantly differentiated human subjects by disease type. Analysis of the MLI metaproteomes also yielded disease classification with exceptional confidence levels. Examination of the relationships between the bacteria and proteins, using regularized canonical correlation analysis (RCCA), sorted most subjects by disease type, supporting the concept that host–microbe interactions are involved in the biology underlying IBD. Moreover, this correlation analysis identified bacteria and proteins that were undetected by standard means-based methods such as analysis of variance, and identified associations of specific bacterial phylotypes with particular protein features of the innate immune response, some of which have been documented in model systems.

Conclusions:

These findings suggest that computational mining of mucosa-associated bacteria for host interaction provides an unsupervised strategy to uncover networks of bacterial taxa and host processes relevant to normal and disease states. (Inflamm Bowel Dis 2012;)

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