Using constraint-based metabolic modeling to elucidate drug-induced metabolic changes in a cancer cell line
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PROVIDER: PRJNA1204965 | ENA |
REPOSITORIES: ENA
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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.
2025-09-03 | GSE285616 | GEO
Project description:Rhodotorula toruloides is a non-conventional, oleaginous yeast able to naturally accumulate high amounts of microbial lipids. Constraint-based modeling of R. toruloides has been mainly focused on the comparison of experimentally measured and model predicted growth rates, while the analysis of intracellular flux patterns has been superficial. Hence, the intrinsic metabolic properties of R. toruloides that make lipid synthesis possible are not thoroughly understood. At the same time, the lack of diverse physiological data sets has often been the bottleneck to predict accurate fluxes. In this study, we collected detailed physiology data sets of R. toruloides while growing on glucose, xylose and acetate as the sole carbon source in mineral medium. Regardless of the carbon source, the growth was divided into two phases from which proteomic and lipidomic data were collected. Complemental physiological parameters were collected in these two phases and altogether implemented into metabolic models. Simulated intracellular flux patterns acknowledged the role of phosphoketolase in the generation of acetyl-CoA, one of the main precursors during lipid biosynthesis, while the role of ATP citrate lyase was not confirmed. Metabolic modeling on xylose as a carbon substrate was greatly improved by the detection of chirality of D-arabinitol, which together with D-ribulose were involved in an alternative xylose assimilation pathway. Further, flux patterns pointed to a metabolic trade-offs associated with NADPH allocation between nitrogen assimilation and lipid biosynthetic pathways, which was linked to large-scale differences in protein and lipid content. This work includes the first extensive multi-condition analysis of R. toruloides using enzyme-constrained models and quantitative proteomics. Further, more precise kcat values should extend the application of the newly developed enzyme-constrained models that are publicly available for future studies.
2023-03-13 | PXD037281 | Pride
Project description:Aging is the predominant cause of morbidity and mortality in industrialized countries. The specific molecular mechanisms that drive aging are poorly understood, especially the contribution of the microbiota in these processes. Here, we combined multi-omics with metabolic modeling in mice to comprehensively characterize host–microbiome interactions and how they are affected by aging. Our findings reveal a complex dependency of host metabolism on microbial functions, including previously known as well as novel interactions. We observed a pronounced reduction in metabolic activity within the aging microbiome, which we attribute to reduced beneficial interactions in the microbial community and a reduction in the metabolic output of the microbiome. These microbial changes coincided with a corresponding downregulation of key host pathways predicted by our model that are crucial for maintaining intestinal barrier function, cellular replication, and homeostasis. Our results elucidate potential microbiome–host interactions that may influence host aging processes, focusing on microbial nucleotide metabolism as a pivotal factor in aging dynamics.
2024-03-27 | GSE262290 | GEO
Project description:Mangrove-derived Streptomyces xiamenensis 318, with a relatively compact genome and simpler secondary metabolism, is used as model organism in our investigation. We performed integrated studies of metabolic dynamical modeling, transcriptome level measurements, and metabolic profiling experiments on this strain. To explore the relationship between primary and secondary metabolism, the global gene expression levels of strain 318 from early stationary phase to late stationary phase were compared by RNAseq analysis at 16 hour, 24 hour, 36 hour and 72 hour after batch culture started.
2019-02-10 | E-MTAB-7314 | biostudies-arrayexpress
Project description:Aging is the predominant cause of morbidity and mortality in industrialized countries, yet the molecular mechanisms driving aging and especially the contribution by the microbiome remain unclear. We combined multi-omics with metabolic modeling to comprehensively characterize host–microbiome interactions during aging in mice. Our findings reveal a complex dependency of host metabolism on known and novel microbial interactions. We observed a pronounced reduction in metabolic activity within the aging microbiome accompanied by reduced beneficial interactions between bacterial species. These microbial changes coincided with increased inflammaging as well as a corresponding downregulation of key host pathways, predicted by our model to be microbiome-dependent, that are crucial for maintaining intestinal barrier function, cellular replication, and homeostasis. Our results elucidate microbiome–host interactions that potentially influence host aging processes, focusing on microbial nucleotide metabolism as a pivotal factor in aging dynamics. These pathways could serve as future targets for the development of microbiome-based anti-aging therapies.
2024-10-15 | GSE278548 | GEO
Project description:Metabolic modeling reveals the aging-associated decline of host–microbiome metabolic interactions in mice
| PRJNA1090973 | ENA
Project description:Metabolic modeling reveals the aging-associated decline of host-microbiome metabolic interactions in mice
| PRJNA1167759 | ENA
Project description:Metabolic state determines drug response in liver cancer
| PRJNA507425 | ENA
Project description:Little is known about the metabolic state of Mycobacterium tuberculosis (Mtb) inside the phagosome, a compartment inside phagocytes for killing pathogens and other foreign substances. We have developed a combined model of Mtb and human metabolism, sMtb-RECON and used this model to predict the metabolic state of Mtb during infection of the host. Amino acids are predicted to be used for energy production as well as biomass formation. Subsequently we assessed the effect of increasing dosages of drugs targeting metabolism on the metabolic state of the pathogen and predict resulting metabolic adaptations and flux rerouting through various pathways. In particular, the TCA cycle becomes more important upon drug application, as well as alanine, aspartate, glutamate, proline, arginine and porphyrin metabolism, while glycine, serine, and threonine metabolism become less important. We modeled the effect of 11 metabolically active drugs. Notably, the effect of eight could be recreated and two major profiles of the metabolic state were predicted. The profiles of the metabolic states of Mtb affected by the drugs BTZ043, cycloserine and its derivative terizidone, ethambutol, ethionamide, propionamide, and isoniazid were very similar, while TMC207 is predicted to have quite a different effect on metabolism as it inhibits ATP synthase and therefore indirectly interferes with a multitude of metabolic pathways.
| S-EPMC6519342 | biostudies-literature
Project description:Breaking constraint of mammalian axial formulae
| PRJNA660363 | ENA