Inflammatory bowel disease as a model for translating the microbiome

Abstract

The inflammatory bowel diseases (IBDs) are among the most closely studied chronic inflammatory disorders that involve environmental, host genetic, and commensal microbial factors. This combination of features has made IBD both an appropriate and a high-priority platform for translatable research in host-microbiome interactions. Decades of epidemiology have identified environmental risk factors, although most mechanisms of action remain unexplained. The genetic architecture of IBD has been carefully dissected in multiple large populations, identifying several responsible host epithelial and immune pathways but without yet a complete systems-level explanation. Most recently, the commensal gut microbiota have been found to be both ecologically and functionally perturbed during the disease, but with as-yet-unexplained heterogeneity among IBD subtypes and individual patients. IBD thus represents perhaps the most comprehensive current model for understanding the human microbiome's role in complex inflammatory disease. Here, we review the influences of the microbiota on IBD and its potential for translational medicine.

Copyright © 2014 Elsevier Inc. All rights reserved.

Figures

Figure 1. Disease progression can be modeled as the dynamical response of a multi-stable, multi-factorial system

A systems-level perspective on health and disease states in IBD and other microbiome-associated conditions can be illustrated using an energy landscape model. The contours of the landscape (i.e., the depth of the healthy vs. disease state) determine how likely it is for an individual to progress from one state to another. Individuals with a lower “activation energy” to this landscape, for example those carrying a NOD2 mutation, may be predisposed to a shift in the microbial community that correlates with IBD (see Section “Human Genetic Mechanisms of Microbial Interaction in IBD”). Both healthy and disease states are characterized by distinct microbial configurations and immune responses. A disturbance or perturbation, such as the introduction of an inflammation-promoting pathobiont or treatment with antibiotics, might cause the system to transition to a new “disease” state. The underlying contours of the landscape are determined by host genotype as well as environmental and physiological factors. A susceptible individual (e.g., carrying disease risk alleles; below, in purple) would be more sensitive to minor perturbations, while a tolerant individual (bearing protective regulatory variants; right, in green) would exhibit robust behavior against strong stimuli.

Figure 2. Interactions between the gut microbiota and the intestinal mucosa in IBD

This illustration depicts the major alterations to the composition of the gut microbiota in IBD, host mechanisms to correct this dysbiosis, and their functional consequences on the host mucosa. The lumen (yellow), mucus layer (brown), epithelium (purple brush-border-containing cells), and lamina propria (bottom purple section) are shown. The most consistent observations from microbial profiling studies are shown (clades with decreased abundance in IBD are in green, increased abundance in red). Specific microbial mechanisms supported by strong experimental evidence are included. These mechanisms include the expansion of the Treg cell compartment by microbially produced butyrate and the inhibition of intestinal natural killer T cell function by microbially produced sphingolipids to promote tolerance, as well as epithelially secreted antimicrobial factors and Goblet cell production of mucin to expand the mucus layer and limit microbial activity at the epithelium. These mechanisms are grouped into themes, displayed in gray boxes.

Figure 3. Modified Hill’s criteria for assessing a causal role for the gut microbiota in IBD

Experimental design considerations for a study of the role of the gut microbiota in IBD should include factors that directly address causation. Shown at the top are 5 of the Hill’s criteria that are most applicable to gut microbiota studies in IBD, including biological plausibility (Does the study provide evidence of a mechanistic link between the microbe or community of microbes and disease?), association strength (Is there a strong statistical association between the microbe or community of microbes and disease?), association specificity (Is the microbe or community of microbes also known to be involved in other unrelated diseases or is there high specificity for the disease in question?), association consistency (Do independent laboratories report the same association?), and association temporality (Does the putative disease-causing microbe or community of microbes appear at the anticipated time prior to the onset of disease?).

Figure 4. Experimental design considerations in gut microbiome studies, using IBD as an example

The development of gut microbiome study designs in IBD can serve as an example for other gut microbial dysbioses, as they demonstrate the interplay of biology, sample availability, and financial constraints. Observations typically start with “top down,” descriptive studies of the stool or (less often due to availability) mucosal microbiome in modestly sized populations. “Bottom up” molecular detail can be added efficiently by perturbation studies in cell lines, but these are limited in relatability to primary populations. Two-stage study designs offer cost-effective scalability to larger cohorts (Tickle et al., 2013), which are also better powered for genetic profiling. Patient samples can be used to derive primary cell lines (Miyoshi and Stappenbeck, 2013) or organoids (Sato et al., 2011) for controlled perturbations that more closely recapitulate in vivo conditions. Likewise stool samples can be transferred to gnotobiotic animal models (Goodman et al., 2011) to determine the causal contribution of the microbiome to phenotype, leading in more complex designs to longitudinal perturbations in multiple genetic models or environmental conditions. Finally, in-depth profiling of moderately sized patient populations over time can provide multiple views on gut microbial contributions to phenotype. This remains cost-effective when combined with staged study design (i.e., not all assays are run on all samples), and sample availability and perturbations such as treatment changes are typically contingent on clinical care.