{"doi":"10.1101/2020.08.17.20176891","title":"Blood donor exposome and impact of common drugs on red blood cell metabolism","abstract":"Abstract Computational models based on recent maps of the red blood cell proteome suggest that mature erythrocytes may harbor targets for common drugs. This prediction is relevant to red blood cell 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 post-transfusion efficacy. To test this prediction, here we provide a comprehensive characterization of the blood donor exposome, including the detection of common prescription and off-the-counter drugs in 250 units donated by healthy volunteers from the REDS-III RBC Omics study. Based on high-throughput drug screenings of 1,366 FDA-approved drugs, we report a significant impact of ∼65% of the tested drugs 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 significant impact on erythrocyte metabolism. We then focused on ranitidine – a common antiacid – as a representative drug with the potential to improve human erythrocyte storage quality and post-transfusion performances in mice. By combining tracing experiments with 1,2,3- 13 C 3 -glucose, proteome integral solubility alteration assays, genetic ablation of S1P synthesis capacity, in silico docking and 1D NMR, we show that ranitidine triggers metabolic mechanisms involving sphingosine 1-phosphate (S1P)-dependent modulation of erythrocyte glycolysis and/or direct binding to hemoglobin. Graphical Abstract RBC exposome from the REDS III study revealed that blood from a subset of donors contains traces of the most common drugs in the United States. RBCs can uptake these drugs, in some cases can metabolize them to their bioactive metabolites and in others the drug can directly impact RBC metabolism during storage. Key points Blood donor exposomes include metabolites of environmental exposure, traces of common prescription or off-the-counter drugs; 65% of 1366 FDA- approved drug significantly affect RBC metabolism. Ranitidine significantly impacts glycolysis and S1P metabolism.","journal":"medRxiv","year":2020,"id":120975,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9509,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":110267,"name":"Davide Stefanoni","orcid":"0000-0002-7756-6788","position":1,"is_corresponding":false},{"id":297835,"name":"Aarash Bordbar","orcid":"0000-0001-6460-6771","position":2,"is_corresponding":false},{"id":297836,"name":"Aaron Issaian","orcid":"0000-0001-9762-5098","position":3,"is_corresponding":false},{"id":5441,"name":"Bernhard Ø. Palsson","orcid":"0000-0003-2357-6785","position":4,"is_corresponding":false},{"id":297837,"name":"Larry J. Dumont","orcid":"0000-0002-1715-0748","position":5,"is_corresponding":false},{"id":297838,"name":"Ariel Hay","orcid":"0000-0002-9003-6415","position":6,"is_corresponding":false},{"id":300020,"name":"Anren Song","orcid":null,"position":7,"is_corresponding":false},{"id":297839,"name":"Yang Xia","orcid":"0000-0002-8059-6179","position":8,"is_corresponding":false},{"id":300021,"name":"Jasmina S. Redzic","orcid":null,"position":9,"is_corresponding":false},{"id":297840,"name":"Elan Eisenmesser","orcid":"0000-0002-6967-0892","position":10,"is_corresponding":false},{"id":110273,"name":"James C. Zimring","orcid":"0000-0002-1063-4010","position":11,"is_corresponding":false},{"id":300022,"name":"Steve Kleinman","orcid":null,"position":12,"is_corresponding":false},{"id":11001,"name":"Kirk C. Hansen","orcid":"0000-0001-5054-838X","position":13,"is_corresponding":false},{"id":36841,"name":"Michael P. Busch","orcid":"0000-0002-1446-125X","position":14,"is_corresponding":false},{"id":28360,"name":"Angelo D’Alessandro","orcid":"0000-0002-2258-6490","position":15,"is_corresponding":false},{"id":560018,"name":"for the Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) RBC-Omics Study","orcid":null,"position":16,"is_corresponding":false},{"id":110269,"name":"Travis Nemkov","orcid":"0000-0001-8566-7119","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-18T23:14:38.147936Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}