Abstract
BackgroundHuman tissues perform diverse metabolic functions. Mapping out these tissue-specific functions in genome-scale models will advance our understanding of the metabolic basis of various physiological and pathological processes. The global knowledgebase of metabolic functions categorized for the human genome (Human Recon 1) coupled with abundant high-throughput data now makes possible the reconstruction of tissue-specific metabolic models. However, the number of available tissue-specific models remains incomplete compared with the large diversity of human tissues.ResultsWe developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). mCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. mCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. Using our method, we reconstructed draft genome-scale metabolic models for 126 human tissue and cell types. Among these, there are models for 26 tumor tissues along with their normal counterparts, and 30 different brain tissues. We performed pathway-level analyses of this large collection of tissue-specific models and identified the eicosanoid metabolic pathway, especially reactions catalyzing the production of leukotrienes from arachidnoic acid, as potential drug targets that selectively affect tumor tissues.ConclusionsThis large collection of 126 genome-scale draft metabolic models provides a useful resource for studying the metabolic basis for a variety of human diseases across many tissues. The functionality of the resulting models and the fast computational speed of the mCADRE algorithm make it a useful tool to build and update tissue-specific metabolic models.
Highlights
As these transcriptional regulatory network (TRN) cannot yet be comprehensively and accurately reconstructed and modeled in human cells, recent efforts have turned to employing context-specific expression data to create models that are representative of active metabolism in specific human tissues and cell types either across a wide range of experimental conditions or under a particular condition [10,11,12,13,14,15,16,17,18,19]
We have developed a method called metabolic Contextspecificity Assessed by Deterministic Reaction Evaluation that leverages gene expression evidence, network structure, and metabolic function to construct context-specific models in an automated, deterministic, and high-throughput fashion
We identified many amino acid metabolic pathways as enriched in 30 brain tissue models in Tissue-Specific Encyclopedia of Metabolism (TSEM), which agrees with the known role of amino acids in neurotransmitter metabolism
Summary
We developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). mCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. mCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. We developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). MCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. MCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. We reconstructed draft genome-scale metabolic models for 126 human tissue and cell types. There are models for 26 tumor tissues along with their normal counterparts, and 30 different brain tissues. We performed pathway-level analyses of this large collection of tissue-specific models and identified the eicosanoid metabolic pathway, especially reactions catalyzing the production of leukotrienes from arachidnoic acid, as potential drug targets that selectively affect tumor tissues
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