Accurately Modelling Human Metabolism

Scientists in Korea have developed a highly accurate computational model of human metabolism by including reactions caused by different protein isoforms.

AsianScientist (Oct. 31, 2017) – A group of researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed a computational framework that can comprehensively reconstruct human metabolism. They published their findings in the Proceedings of the National Academy of Sciences.

Understanding of metabolic phenotypes allows researchers to design therapeutic strategies for various chronic and infectious diseases. A human computational model called the genome-scale metabolic model (GEM) contains information on thousands of metabolic genes and their corresponding reactions and metabolites. This information has played an important role in predicting metabolic phenotypes.

Although several versions of human GEMs have been released, biological information arising from alternative splicing of the human genome had not been incorporated into the model. Alternative splicing is a genetic mechanism that allows a gene to give rise to multiple products and is strongly associated with pathology.

In this study, Mr. Ryu Jae Yong, Dr. Kim Hyun Uk and Distinguished Professor Lee Sang Yup, all from the Department of Chemical and Biomolecular Engineering at KAIST, developed a computational framework that systematically generates metabolic reactions and adds them to the human GEM.

The research team first updated the biological contents of a previous version of the human GEM. The updated biological contents include metabolic genes and their corresponding metabolites and reactions. In particular, metabolic reactions catalyzed by already-known protein isoforms—variants of proteins generated from individual genes through the alternative splicing process—were additionally incorporated into the human GEM.

Each protein isoform is often responsible for the operation of one metabolic reaction. Although multiple protein isoforms generated from one gene can play different functions by having different sets of protein domains and subcellular localizations, such information was not properly considered in previous versions of human GEMs.

Upon the initial update of the human GEM, named Recon 2M.1, the research team subsequently implemented a computational framework that systematically generates information on Gene-Transcript-Protein-Reaction Associations (GeTPRA) to identify protein isoforms that were previously not identified. More than 11,000 GeTPRA were automatically predicted and thoroughly validated with this framework.

Additional metabolic reactions were then added to Recon 2M.1 based on the predicted GeTPRA for previously uncharacterized protein isoforms. This upgrade was then renamed Recon 2M.2. Finally, Recon 2M.2 was integrated with RNA sequencing data from 446 individuals to build patient-specific cancer models. These patient-specific cancer models were used to more accurately predict cancer metabolism activities and anticancer targets.

The development of a new version of human GEMs along with the computational framework for GeTPRA is expected to boost studies in fundamental human genetics and medicine. Model files of the human GEMs Recon 2M.1 and 2M.2, a full list of the GeTPRA and the source code for the computational framework to predict the GeTPRA are all available as part of the publication.

“The predicted GeTPRA from the computational framework is expected to serve as a guideline for future experiments on human genetics and biochemistry, whereas the resulting Recon 2M.2 can be used to predict drug targets for various human diseases,” said Lee.

The article can be found at: Ryu et al. (2017) Framework and Resource for More than 11,000 Gene-transcript-protein-reaction Associations in Human Metabolism.


Source: Korea Advanced Institute of Science and Technology; Photo: Shutterstock.
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