Exp Clin Endocrinol Diabetes 2014; 122 - P098
DOI: 10.1055/s-0034-1372115

Identification of gene-networks associated with specific lipid metabolites by Weighted Gene Co-Expression Network Analysis (WGCNA)

MA Osterhoff 1, 2, T Frahnow 1, AC Seltmann 1, AS Mosig 3, 4, K Neunübel 3, S Sales 5, J Sampaio 5, S Hornemann 1, M Kruse 1, AFH Pfeiffer 1, 2
  • 1German Institute of Human Nutrition, Potsdam-Rehbruecke, Department of Clinical Nutrition, Nuthetal, Germany
  • 2Charité – University Medicine, Campus Benjamin Franklin, Endocrinology, Diabetes and Nutrition, Berlin, Germany
  • 3Jena University Hospital, Molecular Haemostaseology, Jena, Germany
  • 4Jena University Hospital, Center for Sepsis Control and Care, Jena, Germany
  • 5Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

Aim: Little is known about complex gene-networks regulating the metabolism of specific classes of lipids in humans. Lipid metabolism might be either genetically determined or by individual factors. The aim of the study was to correlate lipidome and genome data of human subjects to identify specific gene-modules responsible for the regulation of specific lipid metabolites.

Methods: In the NUGAT-Study all subjects first received a carbohydrate-rich low-fat diet for 6 weeks (CID1 thereafter), immediately followed by a high-fat diet with CID2 after 1 and CID3 after additional 5 weeks. At each CID periumbilical fat biopsies were taken for RNA isolation and Agilent 8 × 40K gene micro arrays, as well as plasma for measurement of lipid metabolites. WGCNA was used for identification of co-expressed gene-networks and their correlation with lipidome data.

Results: By analysis of the 5000 strongest regulated genes 10 gene-modules were identified whereof 3 different specific modules revealed a high correlation (p = 0.003 to 0.04), among others, with glycerolipids with an odd or even number of C-atoms, respectively. These reflected common gene-ontology processes like lipid metabolic, glycerophospholipid biosynthetic and a set of phospholipid remodeling processes but also diverged in certain processes like proliferative or cytokine related processes specific to the modules. All modules had pathways leading to phospholipase 2A dependent processes in common generating several potent signaling molecules.

Conclusion: We detected highly significant covariations between changes of lipid species and gene expression indicating potent and specific gene regulatory mechanisms. WGCNA appears to be useful to model novel gene-networks being related to the function of specific lipid metabolites or lipid classes, either for metabolism or as signaling molecules, and may allow to distinguish between groups of subjects with different patterns concerning their individual diet to gene-expression relation.