Lipoteichoic acid (LTA) is a major virulence factor of Gram-positive bacteria including Staphylococcus aureus. Despite its pivotal role in causing sepsis, the systemic immune responses to LTA in human cells are poorly understood. Here, we produced highly-pure and structurally-intact LTA from S. aureus and examined the gene expression profile of LTA-stimulated human peripheral blood mononuclear cells (PBMCs). The LTA preparation did not contain any detectable biologically-active impurities and stimulated Toll-like receptor 2. Protein expression profiling using a cytokine array kit and ELISA revealed expression of MCP-1/CCL2, IL-6, and IL-1β. We performed transcriptional profiling of PBMCs in response to S. aureus LTA using an Affymetrix genechip microarray. A total of 208 genes were significantly (fold change>1.5 and P
Many anthropometric measures, including body mass index (BMI), waist-to-hip ratio (WHR), and subcutaneous fat thickness, are used as indicators of nutritional status, fertility and predictors of future health outcomes. While BMI is currently the best available estimate of body adiposity, WHR and skinfold thickness at various sites (biceps, triceps, suprailiac, and subscapular) are used as indices of body fat distribution. Copy number variation (CNV) is an attractive emerging approach to the study of associations with various diseases. In this study, we investigated the dosage effect of genes in the CNV genome widely associated with fat distribution phenotypes in large cohorts. We used the Affymetrix genome-wide human SNP Array 5.0 data of 8, 842 healthy unrelated adults in KARE cohorts and identified CNVs associated with BMI and fat distribution-related traits including WHR and subcutaneous skinfold thickness at suprailiac (SUP) and subscapular (SUB) sites. CNV segmentation of each chromosome was performed using Golden Helix SVS 7.0, and single regression analysis was used to identify CNVs associated with each phenotype. We found one CNV for BMI, 287 for WHR, 2, 157 for SUP, and 2, 102 for SUB at the 5?% significance level after Holm?Bonferroni correction. Genes included in the CNV were used for the analysis of functional annotations using the Database for Annotation, Visualization and Integrated Discovery (DAVID v6.7b) tool. Functional gene classification analysis identified five significant gene clusters (metallothionein, ATP-binding proteins, ribosomal proteins, kinesin family members, and zinc finger proteins) for SUP, three (keratin-associated proteins, zinc finger proteins, keratins) for SUB, and one (protamines) for WHR. BMI was excluded from this analysis because the entire structure of no gene was identified in the CNV. Based on the analysis of genes enriched in the clusters, the fat distribution traits of KARE cohorts were related to the fat redistribution associated with the aging process. In addition to structural variation, dosage effect analysis of genes based on CNV is useful to gain an understanding of the comprehensive biological phenomena underlying particular phenotypes and/or diseases.
BACKGROUND: Traditional candidate gene approach has been widely used for the study of complex diseases including obesity. However, this approach is largely limited by its dependence on existing knowledge of presumed biology of the phenotype under investigation. Our combined strategy of comparative genomics and chromosomal heritability estimate analysis of obesity traits, subscapular skinfold thickness and back-fat thickness in Korean cohorts and pig (Sus scrofa), may overcome the limitations of candidate gene analysis and allow us to better understand genetic predisposition to human obesity.
RESULTS: We found common genes including FTO, the fat mass and obesity associated gene, identified from significant SNPs by association studies of each trait. These common genes were related to blood pressure and arterial stiffness (P = 1.65E-05) and type 2 diabetes (P = 0.00578). Through the estimation of variance of genetic component (heritability) for each chromosome by SNPs, we observed a significant positive correlation (r = 0.479) between genetic contributions of human and pig to obesity traits. Furthermore, we noted that human chromosome 2 (syntenic to pig chromosomes 3 and 15) was most important in explaining the phenotypic variance for obesity.
CONCLUSIONS: Obesity genetics still awaits further discovery. Navigating syntenic regions suggests obesity candidate genes on chromosome 2 that are previously known to be associated with obesity-related diseases: MRPL33, PARD3B, ERBB4, STK39, and ZNF385B.