Cluster analysis of genome-wide expression differences in disease-unaffected ileal mucosa in inflammatory bowel diseases

Abstract

Whole human genome (Agilent) expression profiling was conducted on disease-unaffected ileal RNA collected from the proximal margin of resected ileum from 47 ileal Crohn's disease (CD), 27 ulcerative colitis (UC) and 25 control patients without inflammatory bowel diseases (IBD). Cluster analysis combined with significance analysis of microarrays (SAM) and principal component analysis (PCA)and was used to reduce the data dimension to identify geneprobe clusters associated with early pathogenic changes in ileal CD and UC. Ingenuity Pathway Analysis (IPA) was used to identify the biological pathways associated with each cluster. We reduced the dimensions of the 26,765 gene probe set to 43 gene-probe clusters. Most of these clusters could be labeled as related to different biological pathways, such as Paneth cell antimicrobial peptides, the formation of organized lymphoid structures, or nuclear receptor signaling and xenobiotic metabolism. Molecular phylogenetic 16S rRNA sequence analysis was completed on 83 DNA samples from the same samples used to generate the gene expression profiles. We conducted an exploratory study to determine if the first principle component (PC1) of these clusters could be linked to specific phyla/subphyla taxa.

Investigating the biological and clinical significance of human dysbioses

“…As Sauer et al have noted in regards to genetic analysis "The (traditional) reductionist approach has successfully identified most of the components and many of the interactions but … the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models" [79]. A potentially promising approach could be the development of statistical methods for analyzing microbiome data from a biological causal pathway perspective [80, 81]. This analytical methodology is based on structural equations modeling (SEM), a set of interrelated regression equations with random independent as well as dependent variables that allow formulation and testing of directional causal pathway hypotheses [82].…”