Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism

Abstract

Alternative splicing plays important roles in generating different transcripts from one gene, and consequently various protein isoforms. However, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in the context of the entire human metabolism. Here, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in the human metabolism. The framework in this study generated 11,415 GeTPRA corresponding to 1,106 metabolic genes for both principal and nonprincipal transcripts (PTs and NPTs) of metabolic genes. The framework further evaluates GeTPRA, using a human genome-scale metabolic model (GEM) that is biochemically consistent and transcript-level data compatible, and subsequently updates the human GEM. A generic human GEM, Recon 2M.1, was developed for this purpose, and subsequently updated to Recon 2M.2 through the framework. Both PTs and NPTs of metabolic genes were considered in the framework based on prior analyses of 446 personal RNA-Seq data and 1,784 personal GEMs reconstructed using Recon 2M.1. The framework and the GeTPRA will contribute to better understanding human metabolism at the systems level and enable further medical applications.

Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers

“…Use of DeepEC as a third-party software component of the GeTPRA framework. In the GeTPRA framework (27), information on the predicted EC numbers and subcellular locations for 80,678 protein isoforms from 21,169 human genes was used to identify new flux-carrying metabolic reactions that can be considered for further studies on human metabolism and an update of existing human GEMs. Subcellular locations were predicted using Wolf PSORT (30).…”

“…Use of DeepEC as a Third-Party Software Component for the Generation of Human Metabolic Reactions. Finally, DeepEC was used as a part of a computational framework; namely, the gene-transcriptprotein-reaction associations (GeTPRA) framework (27). The GeTPRA framework identifies metabolic reactions to be newly added to a human genome-scale metabolic model (GEM) if the reactions carry fluxes on their addition to the human GEM and have relevant experimental evidence.…”

“…In this study, 80,678 human protein isoforms generated by 21,169 human genes obtained from Ensembl BioMart (28) were used as input for the GeTPRA framework. EFICAz2.5 was initially used for the GeTPRA framework to assign the protein sequences with EC numbers (27), but DeepEC was used in this study. As a result, a total of 5,838 protein isoforms generated from 2,324 human genes were predicted by DeepEC to have at least one EC number ( Fig.…”

“…The newly predicted EC numbers for the protein sequences were used to retrieve metabolic reactions from the KEGG database (29), which were subsequently compartmentalized by predicting subcellular locations of the protein sequences, using Wolf PSORT (30). When these metabolic reactions were compared with those in Recon 2M.2 (27), a human GEM previously prepared using the GeTPRA framework with EFICAz2.5, 212 metabolic reactions mediated by 340 protein isoforms (encoded by 183 genes) appeared to be absent in the Recon 2M.2, although these reactions have experimental evidence available at UniProt (23), BRENDA (31), and the Human Protein Atlas (32) (Dataset S3). Thus, DeepEC can identify additional reactions through EC numbers that could not be predicted by other tools, and consequently allows more accurate reconstruction of GEMs.…”

Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues

“…Such an approach provides guidelines for the development of the next-generation of data contextualization methods. Ryu et al [20] presented a systematic framework for the generation of gene-transcript-protein-reaction associations in the human metabolism and addition of new reactions from Recon 2.2 to build Recon 2M.2 that is biochemically consistent and transcript-level data compatible. Such gene-transcriptprotein-reaction information enabled more accurate simulation of cancer metabolism and prediction of anticancer targets.…”