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بخشی از مقاله انگلیسیعنوان انگلیسی:Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE~~en~~

Abstract

Background: Human 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.

Results: 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. 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.

Conclusions: This 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.

Background

Metabolic dysfunction has been implicated in a wide variety of human diseases such as obesity, diabetes, inborn errors of metabolism, neurodegenerative diseases, and cancer. The recent reconstruction of genome-scale models of human metabolism [1,2] provides an important biochemical basis for systems analysis of metabolic related aspects of human physiology and pathology [3]. Such systems approaches are critical, as metabolism itself is a molecular transformation process where numerous metabolic pathways are inextricably interlinked [4]. However, the human body consists of many distinct tissues and cell types, each only expressing a fraction of the metabolic genes encoded within the genome [5]. Additional variability arises from environmental conditions and external stimuli. None of this variation can be fully accounted for with only the generic human metabolic model. Considering the context—e.g., genomic, anatomical, environmental, or temporal—under which a subset of the genome-scale biochemical network operates is therefore essential to understanding the molecular basis for many human diseases.

The importance of tissue-specific context in disease is evident from distinct metabolic profiles of cancers arising from different tissues. For example, it has been experimentally demonstrated that MYC oncogene-induced liver tumors show increased glutamine uptake, while MYCinduced lung tumors show glutamine secretion [6]. Another study showed that while lactate dehydrogenase A is important for breast carcinoma, neuroblastoma, and B-cell tumor cells, it is dispensable for MYC-induced lymphomagenesis [7]. Similar results were observed for phosphoglycerate dehyrogenease in breast cancer and melanoma [8,9] versus MYC-induced lymphomagenesis [7]. Importantly, cancer metabolism in general also operates in unique environmental and signaling contexts compared to normal physiology and metabolic diseases such as obesity and diabetes [4].

The context in which a metabolic network operates can be viewed at multiple scales, all of which can be dependent on one another. The broadest level typically associated with metabolic models is genomic context—i.e., the full enzymatic capability encoded in the genome. Since the genome is the starting point from which to construct any generic organismal model, we will not consider it further here. A more critical contextual consideration for genome-scale models in higher organisms—especially in human tissues—is the subset of metabolic enzymes that are being expressed (e.g., represented in the transcriptome) at a given time. The transcriptional regulatory state governs which subset of metabolic enzymes and pathways are active, and manifests as either (i) the specific expression program for a tissue or cell type; or (ii) the tissue or cellular response to intracellular or environmental conditions. The ideal strategy for modeling such contextual differences would be the integration of a generic, genomescale model (e.g., Human Recon 1 [1]) with a detailed, context-specific transcriptional regulatory network (TRN), including signaling events that relay cues from the cellular microenvironment. However, as these TRNs 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 [10-19].

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