Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies.

BACKGROUND: Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2. RESULTS: We detected over 19,000 independent lead cis-eQTLs and over 6000 independent lead trans-eQTLs, targeting over 10,000 gene targets (eGenes), with a false discovery rate (FDR) < 5%. Of previously published significant GWAS SNPs, 48% are identified to be significant eQTLs in our study. Some trans-eQTLs point toward novel mechanistic explanations for the association of the SNP with the GWAS-related phenotype. We also identify 59 distinct blocks or clusters of trans-eQTLs, each targeting the expression of sets of six to 229 distinct trans-eGenes. Ten of these sets of target genes are significantly enriched for microRNA targets (FDR < 5%). Many of these clusters are associated in GWAS with multiple phenotypes. CONCLUSIONS: These findings provide insights into the molecular regulatory patterns involved in human physiology and pathophysiology. We illustrate the value of our eQTL database in the context of a recent GWAS meta-analysis of coronary artery disease and provide a list of targeted eGenes for 21 of 58 GWAS loci.

A systems biology approach toward understanding seed composition in soybean.

BACKGROUND: The molecular, biochemical, and genetic mechanisms that regulate the complex metabolic network of soybean seed development determine the ultimate balance of protein, lipid, and carbohydrate stored in the mature seed. Many of the genes and metabolites that participate in seed metabolism are unknown or poorly defined; even more remains to be understood about the regulation of their metabolic networks. A global omics analysis can provide insights into the regulation of seed metabolism, even without a priori assumptions about the structure of these networks. RESULTS: With the future goal of predictive biology in mind, we have combined metabolomics, transcriptomics, and metabolic flux technologies to reveal the global developmental and metabolic networks that determine the structure and composition of the mature soybean seed. We have coupled this global approach with interactive bioinformatics and statistical analyses to gain insights into the biochemical programs that determine soybean seed composition. For this purpose, we used Plant/Eukaryotic and Microbial Metabolomics Systems Resource (PMR, http://www.metnetdb.org/pmr, a platform that incorporates metabolomics data to develop hypotheses concerning the organization and regulation of metabolic networks, and MetNet systems biology tools http://www.metnetdb.org for plant omics data, a framework to enable interactive visualization of metabolic and regulatory networks. CONCLUSIONS: This combination of high-throughput experimental data and bioinformatics analyses has revealed sets of specific genes, genetic perturbations and mechanisms, and metabolic changes that are associated with the developmental variation in soybean seed composition. Researchers can explore these metabolomics and transcriptomics data interactively at PMR.

Gene expression in intestinal mucosal biopsy specimens obtained from dogs with chronic enteropathy.

OBJECTIVE: To characterize mucosal gene expression in dogs with chronic enteropathy (CE). ANIMALS: 18 dogs with CE and 6 healthy control dogs. PROCEDURES: Small intestinal mucosal biopsy specimens were endoscopically obtained from dogs. Disease severity in dogs with CE was determined via inflammatory bowel index scores and histologic grading of biopsy specimens. Total RNA was extracted from biopsy specimens and microchip array analysis (approx 43,000 probe sets) and quantitative reverse transcriptase PCR assays were performed. RESULTS: 1,875 genes were differentially expressed between dogs with CE and healthy control dogs; 1,582 (85%) genes were downregulated in dogs with CE, including neurotensin, fatty acid-binding protein 6, fatty acid synthase, aldehyde dehydrogenase 1 family member B1, metallothionein, and claudin 8, whereas few genes were upregulated in dogs with CE, including genes encoding products involved in extracellular matrix degradation (matrix metallopeptidases 1, 3, and 13), inflammation (tumor necrosis factor, interleukin-8, peroxisome proliferator-activated receptor γ, and S100 calcium-binding protein G), iron transport (solute carrier family 40 member 1), and immunity (CD96 and carcinoembryonic antigen-related cell adhesion molecule [CEACAM] 18). Dogs with CE and protein-losing enteropathy had the greatest number of differentially expressed genes. Results of quantitative reverse transcriptase PCR assay for select genes were similar to those for microchip array analysis. CONCLUSIONS AND CLINICAL RELEVANCE: Expression of genes encoding products regulating mucosal inflammation was altered in dogs with CE and varied with disease severity. Impact for Human Medicine-Molecular pathogenesis of CE in dogs may be similar to that in humans with inflammatory bowel disease.

Distinct peripheral blood RNA responses to Salmonella in pigs differing in Salmonella shedding levels: intersection of IFNG, TLR and miRNA pathways.

Transcriptomic analysis of the response to bacterial pathogens has been reported for several species, yet few studies have investigated the transcriptional differences in whole blood in subjects that differ in their disease response phenotypes. Salmonella species infect many vertebrate species, and pigs colonized with Salmonella enterica serovar Typhimurium (ST) are usually asymptomatic, making detection of these Salmonella-carrier pigs difficult. The variable fecal shedding of Salmonella is an important cause of foodborne illness and zoonotic disease. To investigate gene pathways and biomarkers associated with the variance in Salmonella shedding following experimental inoculation, we initiated the first analysis of the whole blood transcriptional response induced by Salmonella. A population of pigs (n = 40) was inoculated with ST and peripheral blood and fecal Salmonella counts were collected between 2 and 20 days post-inoculation (dpi). Two groups of pigs with either low shedding (LS) or persistent shedding (PS) phenotypes were identified. Global transcriptional changes in response to ST inoculation were identified by Affymetrix Genechip® analysis of peripheral blood RNA at day 0 and 2 dpi. ST inoculation triggered substantial gene expression changes in the pigs and there was differential expression of many genes between LS and PS pigs. Analysis of the differential profiles of gene expression within and between PS and LS phenotypic classes identified distinct regulatory pathways mediated by IFN-γ, TNF, NF-κB, or one of several miRNAs. We confirmed the activation of two regulatory factors, SPI1 and CEBPB, and demonstrated that expression of miR-155 was decreased specifically in the PS animals. These data provide insight into specific pathways associated with extremes in Salmonella fecal shedding that can be targeted for further exploration on why some animals develop a carrier state. This knowledge can also be used to develop rational manipulations of genetics, pharmaceuticals, nutrition or husbandry methods to decrease Salmonella colonization, shedding and spread.

Helicobacter bilis colonization enhances susceptibility to Typhlocolitis following an inflammatory trigger.

BACKGROUND: Aberrant mucosal immune responses to antigens of the resident microbiota are a significant cause of inflammatory bowel diseases (IBD), as are genetic and environmental factors. Previous work from our laboratory demonstrated that Helicobacter bilis colonization of immunocompetent, defined microbiota mice induced antigen-specific immune responses to the resident microbiota, yet these mice failed to develop colitis, suggesting that the immunological provocation induced by H. bilis alone was insufficient to induce disease. AIM: The purpose of this study was to test the hypothesis that the introduction of a bacterial provocateur such as H. bilis enhances the host's susceptibility to IBD following an inflammatory event. METHODS: Defined microbiota (DM) mice colonized with H. bilis were administered low dose (1.5%) dextran sodium sulfate (DSS) in drinking water for 5 days followed by a 4-day restitution period. Severity of lesions was assessed grossly and microscopically. Differential expression of select mucosal genes and histopathologic lesions was characterized. RESULTS: Helicobacter bilis colonization increased the severity of intestinal inflammation induced by an inflammatory trigger in the form of low-dose DSS. An analysis of the molecular and cellular mechanisms associated with H. bilis colonization revealed significant increases in expression of mucosal genes associated with lymphocyte activation and inflammatory cell chemotaxis as well as increased infiltration of mucosal macrophages and T cells in mice colonized with H. bilis prior to DSS treatment versus DSS treatment alone. CONCLUSIONS: These results indicate that prior colonization with H. bilis heightens the host's sensitivity to enteric inflammation by altering mucosal homeostasis and initiating immune cell activation and migration.

Linear mixed model selection for false discovery rate control in microarray data analysis.

In a microarray experiment, one experimental design is used to obtain expression measures for all genes. One popular analysis method involves fitting the same linear mixed model for each gene, obtaining gene-specific p-values for tests of interest involving fixed effects, and then choosing a threshold for significance that is intended to control false discovery rate (FDR) at a desired level. When one or more random factors have zero variance components for some genes, the standard practice of fitting the same full linear mixed model for all genes can result in failure to control FDR. We propose a new method that combines results from the fit of full and selected linear mixed models to identify differentially expressed genes and provide FDR control at target levels when the true underlying random effects structure varies across genes.