Objective To compare colonic microbial composition of systemic sclerosis (SSc) patients and healthy controls and to determine whether certain microbial genera are associated with SSc-gastrointestinal (GIT) symptoms. Methods Healthy controls were age- and gender-matched to adult SSc patients (1:1). Cecum and sigmoid mucosal lavage samples were obtained during colonoscopy. The microbiota from these samples were determined by Illumina HiSeq 2000 16S sequencing, and operational taxonomic units were selected using the Greengenes database at 97% identity. Linear discriminant analysis effect size was used to identify the genera that showed differential expression in SSc versus controls. Differential expression analysis for sequence count data was used to identify specific genera associated with GIT symptoms. Results Among 17 patients with SSc (88% Female; Median age 52.1 years), the mean (SD) total GIT 2.0 score was 0.7 (0.6). Principal coordinate analysis illustrated significant microbial community differences in SSc versus healthy controls in the cecum (p=0.001) and sigmoid (p=0.001) regions. Similar to inflammatory disease states, SSc patients had decreased commensal bacteria, such as Faecalibacterium and Clostridium, and increased pathobiont bacteria, such as Fusobacterium and γ-Proteobacteria, compared with healthy controls. However, SSc patients had increased Bifidobacterium and Lactobacillus, which are typically reduced in inflammation. SSc patients with moderate/severe GIT symptoms had decreased Bacteroides fragilis and increased Fusobacterium compared with SSc patients with none/mild symptoms. Conclusions This study demonstrates a distinct colonic microbial signature in SSc patients compared with healthy controls. This unique ecological change may perpetuate immunological aberrations and contribute to clinical manifestations of SSc.

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.

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.

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 ANOVA, 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.

This analysis shows 2010-2012 utilization and medication patterns of IBD health care in the United States and suggests that improvement can be obtained through enhanced guidelines adherence.