Dataset Information

Zakhartsev2022 - Constraint-based model of metabolism of Atlantic salmon (Salmo salar)

ABSTRACT: SALARECON links the Atlantic salmon genome to metabolic fluxes and growth, focusing on energy, amino acid, and nucleotide metabolism.

SUBMITTER: Jon Olav Vik

PROVIDER: MODEL2207070001 | BioModels | 2023-01-11

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MODEL2207070001?filename=salarecon.xml	Xml

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Exposure to hypoxia disrupts energy metabolism and induces inflammation. However, the pathways and mechanisms underlying energy metabolism disorders caused by hypoxic conditions remain unclear. In this study, we constructed a hypoxic animal model and applied transcriptomic and non-targeted metabolomics techniques to further investigate the pathways and mechanisms of hypoxia exposure that disrupt energy metabolism. Transcriptome results showed that 3007 genes were significantly differentially expressed under hypoxic exposure, and Gene Ontology (GO) annotation analysis and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis showed that the differentially expressed genes (DEGs) were mainly involved in energy metabolism and were significantly enriched in the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS) pathway. Differential genes in the TCA cycle (IDH3A, SUCLA2, and MDH2) and OXPHOS pathway (NDUFA3, NDUFS7, UQCRC1, CYC1, and UQCRFS1) were validated using mRNA and protein expression, and the results showed downregulation. The results of non-targeted metabolomics showed that 365 significant differential metabolites were identified under plateau hypoxia stress. KEGG enrichment analysis showed that the differential metabolites were mainly enriched in metabolic processes, such as energy metabolism, nucleotide metabolism, and amino acid metabolism. Hypoxia exposure disrupted the TCA cycle and reduced the synthesis of amino acids and nucleotides by decreasing the concentrations of cis-aconitate, α-ketoglutarate, NADH, NADPH, most amino acids, purines, and pyrimidines. Bioinformatics analysis was used to identify inflammatory genes related to hypoxia exposure, and some inflammatory genes were selected for verification. We found that the mRNA and protein expression levels of IL1B, IL12B, S100A8, and S100A9 in kidney tissues were upregulated under hypoxic exposure. Our results suggest that hypoxia exposure inhibits the TCA cycle and OXPHOS signalling pathway by inhibiting IDH3A, SUCLA2, MDH2, NDUFFA3, NDUFS7, UQCRC1, CYC1, and UQCRFS1, thereby suppressing energy metabolism, inducing amino acid and nucleotide deficiency, and promoting inflammation, ultimately leading to kidney damage.

Project description:Since the discovery of the Warburg effect, metabolism has become a crucial cancer hallmark, for cancer cells change the activity of metabolic pathways to satisfy the demand of biomolecules and energy required to sustain continuous growth. Specially, gastric cancer (GC) still remains a growing burden of society, and often require the development of targeted and personalized interventions, including the use of combinatorial drug therapies that allow for synergistic interactions. However, the mechanism underlying synergistic interactions between multiple drugs remain poorly understood. The objective of this study is to use genome-scale metabolic models (GEMs) and transcriptomic data to investigate potential synergistic mechanisms in the GC cell line AGS following treatment with three kinase inhibitors and their combinations. To explore the metabolic phenotype of AGS cells, we curated and used a set of descriptions of metabolic functions (metabolic tasks) based on the latest iteration of the Human-GEM, and we used the Task Inferred from Differential Expression (TIDE) framework to infer the metabolic changes in AGS cells. Additionally, we expanded and complemented the TIDE framework by integrating essential genes from metabolic tasks, allowing for a more robust analysis. The results revealed a complex metabolic response in AGS cells after kinase inhibitor treatments, particularly in amino acid and nucleotide metabolism, and characterized by the down-regulation of metabolic functions. Using two definitions of synergistic effects, we identified significant metabolic shifts in combinatorial treatments, including a combination-specific increase in ornithine levels, potentially linked to an antiproliferative effect on AGS cell growth. These findings highlight the potential of using transcriptomic data to infer the metabolic phenotype of AGS cells and the role of metabolic alterations in cancer progression. In addition, we developed a Python library for Metabolic Task Enrichment Analysis, making the TIDE frameworks accessible to other researchers.

Dataset Information

Zakhartsev2022 - Constraint-based model of metabolism of Atlantic salmon (Salmo salar)

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets