Modeling red blood cell metabolism in health and disease

PROJECT SUMMARY Understanding the metabolism of human red blood cells (RBCs) is important for improving medicine surrounding blood transfusion and inborn errors of metabolism. This proposal aims to use computational genome-scale metabolic models (GEMs) to advance translational applications for blood transfusions and inborn errors of metabolism. By population, RBCs are the most common cell in the human body, numbering approximately 25 trillion RBCs per person. The main role of the RBC is to carry oxygen using hemoglobin and, with approximately 260 million units of hemoglobin per cell, RBCs are highly evolved to carry oxygen. Mature RBCs lack mitochondria and organelles so they cannot synthesize new proteins. Hence, they have evolved elaborate metabolic mechanisms to regulate their physiology and adjust to environmental stressors. One such stressor is RBC storage in the blood bank, a crucial part of meeting patients' transfusion needs. Another RBC stressor is inborn errors of metabolism, such as G6PDH deficiency. Studying RBC metabolism sheds light on these medical treatments and conditions. The first specific aim is to expand the current state-of-the-art human RBC GEM with data from our lab's most recent proteomics study of ultra-pure human RBCs. This will enable computational models of the human red blood cell to reflect RBC metabolism more accurately. The second specific aim is to use the update or current RBC genome-scale metabolic model to identify metabolic profiles linked to RBC failure (hemolysis) in storage. To this end, we will use the Recipient Epidemiology and Donor Evaluation Study (REDS) operated by the NIH NHLBI to enhance knowledge of RBC storage and transfusion medicine.