Integration of oxidative stress into systems medicine view for obesity and obesity related complications
The project aims is to include knowledge on different oxidized versions of lipids and proteins into the integrated view on obesity and obesity related complications by systems medicine. The project will combine clinical parameters and omics data targeting oxidized lipids and lipid-modified proteins, as well as freely accessible multi-omics datasets in order to mathematically model the impact of oxidative stress on adipose tissue to distinguish metabolically healthy obese individuals from those with high metabolic risks. The proposed project goes far beyond the current strategies by integrating quantitative data obtained by oxlipidomics and proteomics of LPP-modified proteins (lipox-proteomics) from AT tissue and plasma samples in combination with publicly available omics datasets into mathematical models of adipose tissue and obesity.
The project will rely on combination of a wide array of approaches including omics data acquisition, clinical characterization, statistical evaluation, and mathematical modeling. Using previously published omics data and mathematical models of adipose tissue as a scaffold, newly generated data on oxidized lipids and proteins will be integrated via genome-scale metabolic modeling and regulatory protein networks analysis to obtain systems medicine view on metabolically healthy and diseased obesity phenotypes and identify potential biomarkers and therapeutic targets, which will be further validated in blood plasma samples of obese and lean individuals.
The results will have high diagnostic, prognostic, and therapeutic potentials by providing significant impacts on our understanding of obesity outcomes and additionally on biomarker discovery and validation, design of new intervention therapies, and diagnostic/prognostic assays. These reliable biomarkers will push clinical developments by
- (i) accelerating disease progression monitoring and therapy evaluations,
- (ii) providing information on drug-target interactions, general changes in the pathophysiology of diseases and improvement of a clinical status, and
- (iii) reflecting possible side effects of therapeutic treatments.
- Moreover, the systematic view on obesity and obesity related complications assessable via enriched genome-scale metabolic models of AT (iAdipocyte1850) will guide the development of personalized, predictive, preventive, and participatory medicine (P4 Medicine).
Ni, Z., Angelidou, G., Hoffmann, R., Fedorova, M., 2017a. LPPtiger software for lipidome-specific prediction and identification of oxidized phospholipids from LC-MS datasets. Sci Rep 7, 15138. doi.org/10.1038/s41598-017-15363-z
Ni, Z., Angelidou, G., Lange, M., Hoffmann, R., Fedorova, M., 2017b. LipidHunter Identifies Phospholipids by High-Throughput Processing of LC-MS and Shotgun Lipidomics Datasets. Anal. Chem. 89, 8800–8807. doi.org/10.1021/acs.analchem.7b01126