Dataset Information

Metabolic modeling-based drug repurposing in Glioblastoma.

ABSTRACT: The manifestation of intra- and inter-tumor heterogeneity hinders the development of ubiquitous cancer treatments, thus requiring a tailored therapy for each cancer type. Specifically, the reprogramming of cellular metabolism has been identified as a source of potential drug targets. Drug discovery is a long and resource-demanding process aiming at identifying and testing compounds early in the drug development pipeline. While drug repurposing efforts (i.e., inspecting readily available approved drugs) can be supported by a mechanistic rationale, strategies to further reduce and prioritize the list of potential candidates are still needed to facilitate feasible studies. Although a variety of 'omics' data are widely gathered, a standard integration method with modeling approaches is lacking. For instance, flux balance analysis is a metabolic modeling technique that mainly relies on the stoichiometry of the metabolic network. However, exploring the network's topology typically neglects biologically relevant information. Here we introduce Transcriptomics-Informed Stoichiometric Modelling And Network analysis (TISMAN) in a recombinant innovation manner, allowing identification and validation of genes as targets for drug repurposing using glioblastoma as an exemplar.

SUBMITTER: Tomi-Andrino C

PROVIDER: S-EPMC9249780 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Metabolic modeling-based drug repurposing in Glioblastoma.

Tomi-Andrino Claudio C Pandele Alina A Winzer Klaus K King John J Rahman Ruman R Kim Dong-Hyun DH

Scientific reports 20220701 1

The manifestation of intra- and inter-tumor heterogeneity hinders the development of ubiquitous cancer treatments, thus requiring a tailored therapy for each cancer type. Specifically, the reprogramming of cellular metabolism has been identified as a source of potential drug targets. Drug discovery is a long and resource-demanding process aiming at identifying and testing compounds early in the drug development pipeline. While drug repurposing efforts (i.e., inspecting readily available approved ...[more]

PMID: 35778411

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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

Metabolic modeling-based drug repurposing in Glioblastoma.

Publications

Metabolic modeling-based drug repurposing in Glioblastoma.

Similar Datasets

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