Chicken is the first sequenced avian that has a crucial role in human life for its meat and egg production. Because of various metabolic disorders, study the metabolism of chicken cell is important. Herein, the first genome-scale metabolic model of a chicken cell named iES1300, consists of 2427 reactions, 2569 metabolites, and 1300 genes, was reconstructed manually based on KEGG, BiGG, CHEBI, UNIPROT, REACTOME, and MetaNetX databases. Interactions of metabolic genes for growth were examined for E. coli, S. cerevisiae, human, and chicken metabolic models. The results indicated robustness to genetic manipulation for iES1300 similar to the results for human. iES1300 was integrated with transcriptomics data using algorithms and Principal Component Analysis was applied to compare context-specific models of the normal, tumor, lean and fat cell lines. It was found that the normal model has notable metabolic flexibility in the utilization of various metabolic pathways, especially in metabolic pathways of the carbohydrate metabolism, compared to the others. It was also concluded that the fat and tumor models have similar growth metabolisms and the lean chicken model has a more active lipid and carbohydrate metabolism.

Hepatocellular carcinoma is the third leading cause of cancer related mortality worldwide. Often this hepatic cancer is associated with fatty liver disease and insulin resistance with genetic predisposition are its major driver. Genome-scale metabolic modeling (GEM) is a promising approach to understand cancer metabolism and to identify new drug targets. Here, we used TRFBA-CORE, an algorithm generating a model using key growth-correlated reactions. Speci cally, we generated a HepG2 cell-speci c GEM by integrating this cell line transcriptomic data with a generic human metabolic model to predict potential drug targets for hepatocellular carcinoma (HCC).A total of 108 essential genes for growth were predicted by TRFBA-CORE. These genes were enriched for metabolic pathways involved in cholesterol, sterols and steroids biosynthesis. Furthermore, we silenced a predicted essential gene, 11-beta dehydrogenase hydroxysteroid type 2 (HSD11B2), in HepG2 cells resulting in a reduction in cell viability. To further identify novel potential drug targets in HCC, we examined the effect of 9 drugs targeting the essential genes, and observed that most drugs inhibited the growth of HepG2 cells. Interestingly, some of these drugs in this model performed better than Sorafenib, the rst line therapeutic against HCC.

Hepatocellular carcinoma is the third leading cause of cancer related mortality worldwide. Often this hepatic cancer is associated with fatty liver disease and insulin resistance with genetic predisposition are its major driver. Genome-scale metabolic modeling (GEM) is a promising approach to understand cancer metabolism and to identify new drug targets. Here, we used TRFBA-CORE, an algorithm generating a model using key growth-correlated reactions. Specifically, we generated a HepG2 cell-specific GEM by integrating this cell line transcriptomic data with a generic human metabolic model to predict potential drug targets for hepatocellular carcinoma (HCC). A total of 108 essential genes for growth were predicted by TRFBA-CORE. These genes were enriched for metabolic pathways involved in cholesterol, sterols and steroids biosynthesis. Furthermore, we silenced a predicted essential gene, 11-beta dehydrogenase hydroxysteroid type 2 (HSD11B2), in HepG2 cells resulting in a reduction in cell viability. To further identify novel potential drug targets in HCC, we examined the effect of 9 drugs targeting the essential genes, and observed that most drugs inhibited the growth of HepG2 cells. Interestingly, some of these drugs in this model performed better than Sorafenib, the first line therapeutic against HCC.

Chicken is the first sequenced avian that has a crucial role in human life for its meat and egg production. Because of various metabolic disorders, study the metabolism of chicken cell is important. Herein, the first genome-scale metabolic model of a chicken cell named iES1300, consists of 2427 reactions, 2569 metabolites, and 1300 genes, was reconstructed manually based on databases. Interactions of metabolic genes for growth were examined for E. coli , S. cerevisiae , human, and chicken metabolic models. The results indicated robustness to genetic manipulation for iES1300 similar to the results for human. iES1300 was integrated with transcriptomics data using algorithms and Principal Component Analysis was applied to compare context-specific models of the normal, tumor, lean and fat cell lines. It was found that the normal model has notable metabolic flexibility in the utilization of various metabolic pathways, especially in metabolic pathways of the carbohydrate metabolism, compared to the others. It was also concluded that the fat and tumor models have similar growth metabolisms and the lean chicken model has a more active lipid and carbohydrate metabolism.