A novel network-based approach for discovering dynamic metabolic biomarkers in cardiovascular disease
| Autor: | Alexander Lassnig, Verena Niederkofler, Verena Spath-Blass, Robert E. Gerszten, Christian Baumgartner, Sonja Langthaler, Daniela Baumgartner, Katharina Maria Bergmoser, Aarti Asnani, Theresa Margarethe Rienmüller |
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| Rok vydání: | 2018 |
| Předmět: |
Proteomics
0301 basic medicine Computer and Information Sciences Cell Physiology Pharmacological therapy Science Myocardial Infarction Disease Computational biology Biochemistry Mass Spectrometry Metabolic Networks 03 medical and health sciences Drug Metabolism Metabolites Medicine and Health Sciences Humans Pharmacokinetics Pharmacology Multidisciplinary Metabolic biomarkers Novelty Biology and Life Sciences Computational Biology Cell Biology Cell Metabolism 3. Good health Kinetics Metabolic pathway Metabolism 030104 developmental biology Cardiovascular Diseases Metabolic Disorders Purine Metabolism Medicine Biomarker (medicine) Identification (biology) Metabolic Pathways Network Analysis Biomarkers Metabolic Networks and Pathways Research Article |
| Zdroj: | TU Graz PLoS ONE PLoS ONE, Vol 13, Iss 12, p e0208953 (2018) |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0208953 |
| Popis: | Metabolic biomarkers may play an important role in the diagnosis, prognostication and assessment of response to pharmacological therapy in complex diseases. The process of discovering new metabolic biomarkers is a non-trivial task which involves a number of bioanalytical processing steps coupled with a computational approach for the search, prioritization and verification of new biomarker candidates. Kinetic analysis provides an additional dimension of complexity in time-series data, allowing for a more precise interpretation of biomarker dynamics in terms of molecular interaction and pathway modulation. A novel network-based computational strategy for the discovery of putative dynamic biomarker candidates is presented, enabling the identification and verification of unexpected metabolic signatures in complex diseases such as myocardial infarction. The novelty of the proposed method lies in combining metabolic time-series data into a superimposed graph representation, highlighting the strength of the underlying kinetic interaction of preselected analytes. Using this approach, we were able to confirm known metabolic signatures and also identify new candidates such as carnosine and glycocholic acid, and pathways that have been previously associated with cardiovascular or related diseases. This computational strategy may serve as a complementary tool for the discovery of dynamic metabolic or proteomic biomarkers in the field of clinical medicine. |
| Databáze: | OpenAIRE |
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