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 gene- probe 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. patients undergoing either right hemicolectomy or total colectomy. Of these 99 subjects, we have completed molecular phylogenetic analysis of the same biopsy samples based on 16S rRNA sequence analysis in 83 subjects. To identify biological pathways associated with early pathogeneic changes in the disease unaffected ileum, we aim to construct a system model including clinical information, genetic data and microbiota composition. In order to integrate these large data sets, we developed a dimension reduction scheme combining several computational tools, including cluster analysis, significance analysis of microarray (SAM) (4) and principal component analysis (PCA), to summarize information from our whole expression profiling experiments. Cluster analysis of microarray data based on similarity of gene expression values has been used for dimension reduction purpose (5-7), but has been criticized for lacking of statistical significance (4). IPA as well as direct inspection of the gene lists within each cluster was used to identify biological pathways. To illustrate the use of this approach towards demonstration - reduction, we present an exploratory analysis integrating the results of our cluster analysis with genotype, phenotype and human microbiome data.
[Show abstract][Hide abstract] ABSTRACT: In this work, we propose a novel genetic pathway discovery and comparison analysis framework integrating newly generated gene expression microarray data and existing biological pathway information. Starting with the significance analysis of microarray (SAM), a list of differentially expressed genes among groups is obtained. This gene list is then imported to the Ingenuity Pathway Analysis (IPA) to yield potentially relevant biological pathways. Finally, a newly-developed covariate structural equation modeling method is applied to evaluate gene-gene interactions and group difference. We illustrate this novel comparative pathway analysis pipeline using the whole human genome expression profiling data collected from a study of inflammatory bowel diseases (IBD) with 99 subjects from three phenotypic groups: ileal Crohn's disease (CD), ulcerative colitis (UC) and control non-IBD.
IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2011, Orlando, FL, USA, February 3-5, 2011; 01/2011
[Show abstract][Hide abstract] ABSTRACT: Culture-independent microbiological technologies that interrogate complex microbial populations without prior axenic culture, coupled with high-throughput DNA sequencing, have revolutionized the scale, speed and economics of microbial ecological studies. Their application to the medical realm has led to a highly productive merger of clinical, experimental and environmental microbiology. The functional roles played by members of the human microbiota are being actively explored through experimental manipulation of animal model systems and studies of human populations. In concert, these studies have appreciably expanded our understanding of the composition and dynamics of human-associated microbial communities (microbiota). Of note, several human diseases have been linked to alterations in the composition of resident microbial communities, so-called dysbiosis. However, how changes in microbial communities contribute to disease etiology remains poorly defined. Correlation of microbial composition represents integration of only two datasets (phenotype and microbial composition). This article explores strategies for merging the human microbiome data with multiple additional datasets (e.g. host single nucleotide polymorphisms and host gene expression) and for integrating patient-based data with results from experimental animal models to gain deeper understanding of how host-microbe interactions impact disease.
Trends in Microbiology 07/2011; 19(9):427-34. DOI:10.1016/j.tim.2011.06.005 · 9.19 Impact Factor
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