iAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states

Erythrocyte proteomic data was used to reconstruct the most expansive description of erythrocyte metabolism to date, following extensive manual curation, assessment of the literature, and functional testing. The reconstruction contains 281 enzymes representing functions from glycolysis to cofactor and amino acid metabolism. Such a comprehensive view of erythrocyte metabolism implicates the erythrocyte as a potential biomarker for different diseases as well as a 'cell-based' drug-screening tool. The analysis shows that 94 erythrocyte enzymes are implicated in morbid single nucleotide polymorphisms, representing 142 pathologies. In addition, over 230 FDA-approved and experimental pharmaceuticals have enzymatic targets in the erythrocyte.The advancement of proteomic technologies and increased generation of high-throughput proteomic data have created the need for a means to analyze these data in a coherent manner. Network reconstructions provide a systematic means to integrate and analyze proteomic data in a biologically meaning manner. Analysis of the red cell proteome has revealed an unexpected level of complexity in the functional capabilities of human erythrocyte metabolism.The advancement of high-throughput data generation has ushered a new era of "omic" sciences. Whole-cell measurements can elucidate the genome sequence (genomics) as well as detect mRNA (transcriptomics), proteins (proteomics), and small metabolites (metabolomics) under a specific condition. Though these methods provide a broad coverage in determining cellular activities, little integrated functional analysis has been performed to date.Genome-scale network reconstructions are a common denominator for computational analysis in systems biology as well as an integrative platform for experimental data analysis [1,2]. There are several applications of reconstructions including: 1) contextualization of high-throughput data, 2) directing hypothesis-driven discovery, and 3) network property discovery [1].