Background: Donated blood is typically stored before transfusions. During storage, the metabolism of red blood cells changes, possibly causing storage lesions. The changes are storage time dependent and exhibit donor-specific variations. It is necessary to uncover and characterize the responsible molecular mechanisms accounting for such biochemical changes, qualitatively and quantitatively; Study Design and Methods: Based on the integration of metabolic time series data, kinetic models, and a stoichiometric model of the glycolytic pathway, a customized inference method was developed and used to quantify the dynamic changes in glycolytic fluxes during the storage of donated blood units. The method provides a proof of principle for the feasibility of inferences regarding flux characteristics from metabolomics data; Results: Several glycolytic reaction steps change substantially during storage time and vary among different fluxes and donors. The quantification of these storage time effects, which are possibly irreversible, allows for predictions of the transfusion outcome of individual blood units; Conclusion: The improved mechanistic understanding of blood storage, obtained from this computational study, may aid the identification of blood units that age quickly or more slowly during storage, and may ultimately improve transfusion management in clinics.

Document Type


Publication Date


Notes/Citation Information

Published in Metabolites, v. 7, issue 2, 12, p. 1-16.

© 2017 by the authors. Licensee MDPI, Basel, Switzerland.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Digital Object Identifier (DOI)


Funding Information

This work was supported in part by grants from the National Institutes of Health (P01-ES016731 and 1P30ES019776-01A1, Gary W. Miller, PI; R01HL095479 and P01HL08677, JDR, PI), an endowment from the Georgia Research Alliance (EOV, PI), and a NIH P & F grant to Zhen Qi from the NIH Regional Comprehensive Metabolomics Resource Core grant 1U24DK097215-01A1 (Richard M. Higashi, PI). This project was furthermore supported in part by a contract from the US National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services under contract # HHSN272201200031C (Mary Galinsky, PI).

Related Content

Supplementary Materials: The following are available online at www.mdpi.com/2218-1989/7/2/12/s1, Figure S1: Dynamics of metabolite levels for donors in group2 and group2_match, Figure S2: Dynamics of metabolite levels for donors in group1, Figure S3: Storage effect on the PK flux among all donors. Storage time effects on the PK flux are quantified and compared among all donors, Figure S4: Storage effect on fluxes PFK, ALD, and TPI. Storage time effects on fluxes PFK, ALD, and TPI are quantified and compared among all donors, Figure S5: Comparison of storage time effect on the HK flux during different weeks of storage for each donor. Storage time effects on the HK flux are compared week by week for each donor, Figure S6: Influence of donation batches on storage time effect on the flux HK, Figure S7: Influence of different kinetic models. Two available kinetic models for the flux PFK were used to quantify storage time effect. Solid lines represent results from one model, while dashed lines are from the other model, Table S1: Demographics of the donors, Table S2: Influence of secondary factors on storage time effects.

metabolites-07-00012-s001.pdf (802 kB)
Supplementary Materials