A genome-wide methylation study of body fat traits in the Norfolk Island isolate.

BACKGROUND AND AIMS: Natural variation in body fat is explained by both genetic and environmental effects. Epigenetic mechanisms such as DNA methylation can mediate these effects causing changes in gene expression leading to onset of obesity. Studies of genetic isolates have the potential to provide new epigenetic insights with advantages such as reduced genetic diversity and environmental exposures. METHODS AND RESULTS: This was an exploratory study of genome-wide DNA methylation in relation to body fat traits in 47 healthy adults from the genetic isolate of Norfolk Island. Quantitative body fat traits (body fat percentage, body mass index, hip circumference, waist circumference, waist-hip-ratio and weight) were carefully measured. DNA methylation data was obtained from peripheral blood using Illumina 450K arrays. Multi-trait analysis was performed using Principal Component Analysis (PCA). CpG by trait association testing was performed using stepwise linear regressions. Two components were identified that explained approximately 89% of the phenotypic variance. In total, 5 differential methylated positions (DMPs) were identified at genome-wide significance (P≤ 2.4 × 10-7), which mapped to GOT2-CDH8, LYSMD3, HIBADH, ADGRD1 and EBF4 genes. Gene set enrichment analysis of 848 genes containing suggestive DMPs (P≤ 1.0 × 10-4) implicated the Cadherin (28 genes, Padj = 6.76 × 10-7) and Wnt signaling pathways (38 genes, Padj = 7.78 × 10-6). CONCLUSION: This study provides new insights into the epigenetically influenced genes and pathways underlying body fat variation in a healthy cohort and provides targets for consideration in future studies of obesity risk.

Diagnosing sepsis in the ICU: Comparison of a gene expression signature to pre-existing biomarkers.

PURPOSE: We aimed to identify a gene signature that discriminates between sepsis and aseptic inflammation in patients administered antibiotics in the intensive care unit and compare it to commonly utilised sepsis biomarkers. METHODS: 91 patients commenced on antibiotics were retrospectively diagnosed as having: (i) blood culture positive sepsis; (ii) blood culture negative sepsis; or (iii) aseptic inflammation. Bloods were collected after <24 h of antibiotic commencement for both gene expression sequencing analysis and measurement of previously identified biomarkers. RESULTS: 53 differentially expressed genes were identified that accurately discriminated between blood culture positive sepsis and aseptic inflammation in a cohort of patients given antibiotics [aROC 0.97 (95% CI, 0.95-0.99)]. This gene signature was validated in a publicly available database. The gene signature outperformed previously identified sepsis biomarkers including C-reactive protein [aROC 0.72 (95% CI, 0.57-0.87)], NT-Pro B-type Natriuretic Peptide [aROC 0.84 (95% CI, 0.73-0.96)], and Septicyte™ LAB [aROC 0.8 (95% CI, 0.68-0.93)], but was comparable to Procalcitonin [aROC 0.96 (95% CI, 0.9-1)]. CONCLUSIONS: A gene expression signature was identified that accurately discriminates between sepsis and aseptic inflammation in patients given antibiotics in the intensive care unit.