Background & aims

Interactions between mucosal cell types, environmental stressors, and intestinal microbiota contribute to pathogenesis in inflammatory bowel disease (IBD). Here, we applied metaproteomics of the mucosal-luminal interface to study the disease-related biology of the human colonic mucosa.

Methods

We recruited a discovery cohort of 51 IBD and non-IBD subjects endoscopically sampled by mucosal lavage at 6 colonic regions, and a validation cohort of 38 no-IBD subjects. Metaproteome data sets were produced for each sample and analyzed for association with colonic site and disease state using a suite of bioinformatic approaches. Localization of select proteins was determined by immunoblot analysis and immunohistochemistry of human endoscopic biopsy samples.

Results

Co-occurrence analysis of the discovery cohort metaproteome showed that proteins at the mucosal surface clustered into modules with evidence of differential functional specialization (eg, iron regulation, microbial defense) and cellular origin (eg, epithelial or hemopoietic). These modules, validated in an independent cohort, were differentially associated spatially along the gastrointestinal tract, and 7 modules were associated selectively with non-IBD, ulcerative colitis, and/or Crohn's disease states. In addition, the detailed composition of certain modules was altered in disease vs healthy states. We confirmed the predicted spatial and disease-associated localization of 28 proteins representing 4 different disease-related modules by immunoblot and immunohistochemistry visualization, with evidence for their distribution as millimeter-scale microgeographic mosaic.

Conclusions

These findings suggest that the mucosal surface is a microgeographic mosaic of functional networks reflecting the local mucosal ecology, whose compositional differences in disease and healthy samples may provide a unique readout of physiologic and pathologic mucosal states.

Abnormalities of the intestinal microbiota are implicated in the pathogenesis of Crohn's disease (CD) and ulcerative colitis (UC), two spectra of inflammatory bowel disease (IBD). However, the high complexity and low inter-individual overlap of intestinal microbial composition are formidable barriers to identifying microbial taxa representing this dysbiosis. These difficulties might be overcome by an ecologic analytic strategy to identify modules of interacting bacteria (rather than individual bacteria) as quantitative reproducible features of microbial composition in normal and IBD mucosa. We sequenced 16S ribosomal RNA genes from 179 endoscopic lavage samples from different intestinal regions in 64 subjects (32 controls, 16 CD and 16 UC patients in clinical remission). CD and UC patients showed a reduction in phylogenetic diversity and shifts in microbial composition, comparable to previous studies using conventional mucosal biopsies. Analysis of weighted co-occurrence network revealed 5 microbial modules. These modules were unprecedented, as they were detectable in all individuals, and their composition and abundance was recapitulated in an independent, biopsy-based mucosal dataset 2 modules were associated with healthy, CD, or UC disease states. Imputed metagenome analysis indicated that these modules displayed distinct metabolic functionality, specifically the enrichment of oxidative response and glycan metabolism pathways relevant to host-pathogen interaction in the disease-associated modules. The highly preserved microbial modules accurately classified IBD status of individual patients during disease quiescence, suggesting that microbial dysbiosis in IBD may be an underlying disorder independent of disease activity. Microbial modules thus provide an integrative view of microbial ecology relevant to IBD.

Objective

To compare colonic microbial composition in systemic sclerosis (SSc) patients and healthy controls and to determine whether certain microbial genera are associated with gastrointestinal (GI) tract symptoms in patients with SSc.

Methods

Healthy controls were age- and sex-matched (1:1) with adult SSc patients. Cecum and sigmoid mucosal lavage samples were obtained during colonoscopy. The microbiota in these samples were determined by Illumina HiSeq 2000 16S sequencing, and operational taxonomic units were selected. Linear discriminant analysis effect size was used to identify the genera that showed differential expression in SSc patients versus controls. Differential expression analysis for sequence count data was used to identify specific genera associated with GI tract symptoms.

Results

Among 17 patients with SSc (88% female; median age 52.1 years), the mean ± SD total GI Tract 2.0 score was 0.7 ± 0.6. Principal coordinate analysis illustrated significant differences in microbial communities in the cecum and sigmoid regions in SSc patients versus healthy controls (both P = 0.001). Similar to the findings in inflammatory disease states, SSc patients had decreased levels of commensal bacteria, such as Faecalibacterium and Clostridium, and increased levels of pathobiont bacteria, such as Fusobacterium and γ-Proteobacteria, compared with healthy controls. Bifidobacterium and Lactobacillus, which are typically reduced under conditions of inflammation, were also increased in abundance in patients with SSc. In SSc patients with moderate/severe GI tract symptoms, the abundance of Bacteroides fragilis was decreased, and that of Fusobacterium was increased, compared with patients who had no or mild symptoms.

Conclusion

This study demonstrates a distinct colonic microbial signature in SSc patients compared with healthy controls. This unique ecologic change may perpetuate immunologic aberrations and contribute to clinical manifestations of SSc.

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI.