Modeling Red Blood Cell Metabolism in the Omics Era

Abstract

Red blood cells (RBCs) are abundant (more than 80% of the total cells in the human body), yet relatively simple, as they lack nuclei and organelles, including mitochondria. Since the earliest days of biochemistry, the accessibility of blood and RBCs made them an ideal matrix for the characterization of metabolism. Because of this, investigations into RBC metabolism are of extreme relevance for research and diagnostic purposes in scientific and clinical endeavors. The relative simplicity of RBCs has made them an eligible model for the development of reconstruction maps of eukaryotic cell metabolism since the early days of systems biology. Computational models hold the potential to deepen knowledge of RBC metabolism, but also and foremost to predict in silico RBC metabolic behaviors in response to environmental stimuli. Here, we review now classic concepts on RBC metabolism, prior work in systems biology of unicellular organisms, and how this work paved the way for the development of reconstruction models of RBC metabolism. Translationally, we discuss how the fields of metabolomics and systems biology have generated evidence to advance our understanding of the RBC storage lesion, a process of decline in storage quality that impacts over a hundred million blood units transfused every year.

Metabolite and protein shifts in mature erythrocyte under hypoxia

“…Multiomics analysis integrates the erythrocyte metabolome, proteome, and transcriptome for disease modeling and a more comprehensive dynamic deciphering of the biological complexity of the disease. 183 …”

RBC-GEM: a Knowledge Base for Systems Biology of Human Red Blood Cell Metabolism

“…Therefore, both untargeted and tracing metabolomic experiments of human RBCs [4,23,26,44,[46][47][48][49]93] were utilized to further substantiate the presence of enzymatic activity and validate the existence of related metabolites (substrates/products). Through the use of high-throughput multi-omic data, we were able to gain a comprehensive understanding of the RBC proteome [29,30,94].…”

“…The innovation and advances in affordable, high-throughput omics technologies continue to drive change in blood science and personalized transfusion medicine. These advancements promote the paradigm shift away from the long held views that RBCs are relatively inert cells [29][30][31][32], and "not a hapless sack of hemoglobin", as Greenwalt put it [33]. Thus, there is a pressing need for an updated RBC knowledge base that reflects the last decade of advances made in RBC omics research.…”

“…Connectivity analysis demonstrated the RBC metabolic network is not inherently fragmented, and that the achievements made in understanding RBCs through various omics approaches have revealed a richer, more connected metabolic network than previously known. Through the use of high-throughput multi-omic data, a comprehensive understanding of the catalytically active RBC proteome is also achievable[29,30,94]. Furthermore, coalescence of information about pathological metabolic 30 states in RBCs illuminated the physiological significance of 'moonlighting' functions of enzymes and previously overlooked pathways.RBC-GEM is available in a version-controlled GitHub repository (https://github.com/zhaiman/RBC-GEM) where the versions of the reconstruction are hosted as fully annotated SBML files (S3 File) and as as flat table files.…”