Red blood cells (RBC) stored in approved additive solutions undergo a set of metabolic and physicochemical changes referred to as 'storage lesions'reducing the efficacy and safety of older transfused RBC units. Though the consequences of the storage lesion are slowly becoming well documented, a major reason for delayed progress in developing new technologies for quality and safety of RBC transfusion is the lack of global understanding of metabolic decline during storage. There has been interest to utilize high-throughput metabolite profiling for global understanding of RBC metabolic decline but data analysis of complex datasets has been a daunting challenge. The proposed program will develop the first, robust computational platform involving statistical analysis, systems biology of metabolic networks, and data-driven kinetic models to fully interpret and analyze RBC metabolite-profiles in a complete network context. The program will utilize time- course global, quantitative metabolite profiling to track intracellular and extracellular RBC metabolites under standard storage conditions. Deep metabolic understanding will be obtained through computational analysis to quantitatively predict optimal additive solution strategies based on expected biological efficacy, physico- chemical considerations, compound cost, and potential regulatory hurdles. Predicted additives will be chosen for experimental testing in Phase II.
Recent studies raise serious concerns on the safety of transfusing red blood cell units older than 20 days. The field is open for innovation as most technologies are derived from work from the 1980s. As part of this proposal, novel computational methods will be developed and applied to comprehensively understand the degradation of red blood cells under storage conditions to predict new additive solutions for increasing transfusion quality and extending shelf life, an area that accounts for 1% of all hospital costs
| Yurkovich, James T; Bordbar, Aarash; Sigurjónsson, Ólafur E et al. (2018) Systems biology as an emerging paradigm in transfusion medicine. BMC Syst Biol 12:31 |
| Yurkovich, James T; Zielinski, Daniel C; Yang, Laurence et al. (2017) Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks. J Biol Chem 292:19556-19564 |
| Bordbar, Aarash; Yurkovich, James T; Paglia, Giuseppe et al. (2017) Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci Rep 7:46249 |
| Bordbar, Aarash; Johansson, Pär I; Paglia, Giuseppe et al. (2016) Identified metabolic signature for assessing red blood cell unit quality is associated with endothelial damage markers and clinical outcomes. Transfusion 56:852-62 |
