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Blood donor exposome and impact of common drugs on red blood cell metabolism
Travis Nemkov, Davide Stefanoni, Aarash Bordbar, Aaron Issaian, Bernhard O. Palsson, Larry J. Dumont, Ariel Hay, Anren Song, Yang Xia, Jasmina S. Redzic, Elan Z. Eisenmesser, James C. Zimring, Steve Kleinman, Kirk C. Hansen, Michael P. Busch, Angelo D’Alessandro, the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell–Omics (REDS-III RBC-Omics) Study
Travis Nemkov, Davide Stefanoni, Aarash Bordbar, Aaron Issaian, Bernhard O. Palsson, Larry J. Dumont, Ariel Hay, Anren Song, Yang Xia, Jasmina S. Redzic, Elan Z. Eisenmesser, James C. Zimring, Steve Kleinman, Kirk C. Hansen, Michael P. Busch, Angelo D’Alessandro, the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell–Omics (REDS-III RBC-Omics) Study
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Research Article Hematology Metabolism

Blood donor exposome and impact of common drugs on red blood cell metabolism

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Abstract

Computational models based on recent maps of the RBC proteome suggest that mature erythrocytes may harbor targets for common drugs. This prediction is relevant to RBC storage in the blood bank, in which the impact of small molecule drugs or other xenometabolites deriving from dietary, iatrogenic, or environmental exposures (“exposome”) may alter erythrocyte energy and redox metabolism and, in so doing, affect red cell storage quality and posttransfusion efficacy. To test this prediction, here we provide a comprehensive characterization of the blood donor exposome, including the detection of common prescription and over-the-counter drugs in blood units donated by 250 healthy volunteers in the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell–Omics (REDS-III RBC-Omics) Study. Based on high-throughput drug screenings of 1366 FDA-approved drugs, we report that approximately 65% of the tested drugs had an impact on erythrocyte metabolism. Machine learning models built using metabolites as predictors were able to accurately predict drugs for several drug classes/targets (bisphosphonates, anticholinergics, calcium channel blockers, adrenergics, proton pump inhibitors, antimetabolites, selective serotonin reuptake inhibitors, and mTOR), suggesting that these drugs have a direct, conserved, and substantial impact on erythrocyte metabolism. As a proof of principle, here we show that the antacid ranitidine — though rarely detected in the blood donor population — has a strong effect on RBC markers of storage quality in vitro. We thus show that supplementation of blood units stored in bags with ranitidine could — through mechanisms involving sphingosine 1–phosphate–dependent modulation of erythrocyte glycolysis and/or direct binding to hemoglobin — improve erythrocyte metabolism and storage quality.

Authors

Travis Nemkov, Davide Stefanoni, Aarash Bordbar, Aaron Issaian, Bernhard O. Palsson, Larry J. Dumont, Ariel Hay, Anren Song, Yang Xia, Jasmina S. Redzic, Elan Z. Eisenmesser, James C. Zimring, Steve Kleinman, Kirk C. Hansen, Michael P. Busch, Angelo D’Alessandro, the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell–Omics (REDS-III RBC-Omics) Study

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Figure 6

Ranitidine impacts S1P metabolism and S1P-regulated glycolysis in stored RBCs from the REDS-III RBC-Omics Study.

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Ranitidine impacts S1P metabolism and S1P-regulated glycolysis in stored...
(A) Only one subject was identified to be positive for ranitidine among 599 samples from 250 independent donors in the recalled REDS-III RBC-Omics donor cohort. (B and C) S1P levels in RBCs from this subject on storage days 10, 23, and 42 ranked in the top 13% among all 599 samples tested in this study. (D) Schematic representation of the mechanisms through which S1P levels in RBCs modulate glycolysis, as previously described (34). (E) Consistently, faster consumption of early glycolytic intermediates (fructose bisphosphate [FBP]) and increased levels of glyceraldehyde 3-phosphate (G3P) and the final byproduct of glycolysis, lactate, were detected in the subject positive for ranitidine, with end-of-storage lactate levels ranking second among all the 599 samples tested (top 0.5%). (F) Given the structural similarity between ranitidine and SAH, we hypothesized and confirmed an interaction of this metabolite with the levels of SAH and related metabolites (SAM and SAM/SAH ratios). (G) Heat map of the top 25 metabolites that differ significantly between the subject positive for ranitidine on any storage day (10, 23, and 42) and the median of the rest of the population. (H) The map highlighted that several metabolic markers of the RBC storage lesion are decreased in the stored RBCs from this subject, including markers of posttransfusion recovery hypoxanthine, arachidonic acid, and 12-HETE. In E, F, and H, y axes indicate integrated peak areas (in AU), except for SAM/SAH (ratios between the 2 metabolites) and arachidonic acid and 12-HETE (nM).

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