Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells
Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites (p < 0.05) in RBC metabolism using only measurements of these five biomarkers. The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.
- Research Organization:
- Univ. of California, San Diego, CA (United States); Univ. of California at San Diego, La Jolla, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- SC0008701
- OSTI ID:
- 1347468
- Alternate ID(s):
- OSTI ID: 1345712; OSTI ID: 1355929; OSTI ID: 1366707
- Journal Information:
- PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Vol. 13 Journal Issue: 3; ISSN 1553-7358
- Publisher:
- Public Library of Science (PLoS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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