Bioinformatics platform for biomarker identification for End Stage Renal Disease through GC/MS metabolomics

• Applicant: Professor Dr. Karsten Hiller
• Subject Area: Bioinformatics and Theoretical Biology
• Term: from 2008 to 2011
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 72434747

Patients initiating chronic hemodialysis due to End Stage Renal Disease (ESRD) carry a several hundred fold increased risk to die of cardio vascular disease and infections compared to the general population. Only 50% of all death among chronic dialysis patients are explanable by traditional risk factors, like hypertension, dyslipidemia, age or gender. To uncover the metabolic principles underlying the yet unknown non-traditional risk factors and to establish useful diagnostic biomarkers metabolome analysis of patients body fluids were performed in a cohort study of chronic hemodialysis patients. This project aims to develop bioinformatics tools to allow the automated interpretation of high resolution, high throughput GC-MS data. For this purpose a novel deconvolution and metabolite detection method will be implemented. For the comparison of obtained complex metabolomes the performance of emergent self organizing maps (ESOM), principal component analysis (PCA) and hierarchical cluster analysis (HCA) will be analyzed and the best performing method will be applied. Finally, all developed methods will be applied for the detection of metabolic biomarkers characteristic for ESRD related mortality.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Dr. Gregory Stephanopoulos