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

Abstract

Background: The development of high-throughput technologies capable of whole cell measurements of genes, proteins, and metabolites has led to the emergence of systems biology. Integrated analysis of the resulting omic data sets has proved to be hard to achieve. Metabolic network reconstructions enable complex relationships amongst molecular components to be represented formally in a biologically relevant manner while respecting physical constraints. In silico models derived from such reconstructions can then be queried or interrogated through mathematical simulations. Proteomic profiling studies of the mature human erythrocyte have shown more proteins present related to metabolic function than previously thought; however the significance and the causal consequences of these findings have not been explored.

Results: 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.

Conclusion: 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.

Figure 1

(A) Workflow for building a comprehensive in silico erythrocyte metabolic network. The three major data types required are: the human genome sequence, high-throughput data (specifically, proteomics for an enucleated cell), and primary literature. The global human metabolic network, Recon 1, was constructed from the human genome sequence and annotation. To build the erythrocyte network, iAB-RBC-283, proteomics was used to remove non-erythrocyte related open reading frames (ORFs) or genes. Detailed curation utilizing protein, metabolite, and transport experimental literature was needed to build a high-quality metabolic reconstruction. (B) Without network reconstruction and rigorous curation, the experimentally generated proteomic data is raw and difficult to interpret. The process detailed in panel A provides a means to build a meaningful knowledge-base of available high-throughput data that can then be probed and tested.

Figure 2

Topological map of the human erythrocyte metabolic network (iAB-RBC-283). Utilizing proteomic data, a much expanded metabolic network was reconstructed accounting for additional carbohydrate and nucleotide metabolism. In addition, the erythrocyte plays roles in amino acid, cofactor, and lipid metabolism. Abbreviations: PPP - pentose phosphate pathway, Arg - arginine, Met - methionine.

Figure 3

The metabolite connectivity of iAB-RBC-283, Recon 1, and the similarly sized organelles in Recon 1. Recon 1 and the mitochondria have high network connectivity, with many data points above the reference lines. The erythrocyte network and other organelles have less metabolic connectivity denoted by data point being on or below the reference lines. This characteristic can be attributed to either 1) an inherently 'fragmented' erythrocyte network, or 2) incomplete proteomic coverage.

Figure 4

To test the potential use of erythrocytes as biomarkers, we identified the known morbid SNPs and drug targets of erythrocyte enzymes account for in the reconstructed network. 142 morbid SNPs were identified with the majority dealing with non-erythrocyte related pathologies (see Table 1). In addition, over 230 FDA-approved, FDA-withdrawn, and experimental drugs have known protein targets in the human erythrocyte.

Figure 5

Flux variability of the exchange reactions can be used to detect metabolic signatures of simulated morbid SNPs and drug treated erythrocytes. (A) Exchange reactions are artificial reactions that allow the mathematical model to uptake and secrete metabolites into the extracellular space. Uptake of a metabolite into the erythrocyte is expressed as a negative value and secretion is expressed as a positive value. There are four major differences that can occur for an exchange reaction in two different states: i) the reaction is either active (non-zero minimum or maximum flux) or inactive (zero minimum and maximum flux), ii) the exchange becomes fixed in one direction (uptake or secretion only), iii) there is a magnitude change in exchange, iv) the reaction is unaffected and is the same for both states. (B) We detected 71% and 62% of the morbid SNPs and drugs associated with the erythrocyte. The distribution shows that most perturbed conditions have between one to ten differentially active exchange reactions.