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MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression

ABSTRACT: During tumor progression, cancer cells rewire their metabolism to face their bioenergetic demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that inhibit the translation and stability of crucial mRNAs, some of them causing direct metabolic alterations in cancer. In this study, we investigated the relationship between miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned regulation is diversified depending on the tumor stage. To do so, we implemented a paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7). The cell line was cultured in multicellular tumor spheroid (MCTS) and monoculture conditions. For MCTS, we selected two-time points during their development to recapitulate a proliferative and quiescent stage and contrast their miRNA and mRNA expression patterns associated with metabolism. As a result, we identified a set of new direct putative regulatory interactions between miRNAs and metabolic mRNAs representative for proliferative and quiescent stages. Notably, our study allows us to suggest that miR-3143 regulates the carbon metabolism by targeting hexokinase-2. Also, we found that the overexpression of several miRNAs could directly overturn the expression of mRNAs that control glycerophospholipid and N-Glycan metabolism. While this set of miRNAs downregulates their expression in the quiescent stage, the same set is upregulated in proliferative stages. This last finding suggests an additional metabolic switch of the above mentioned metabolic pathways between the quiescent and proliferative stages. Our results contribute to a better understanding of how miRNAs modulate the metabolic landscape in breast cancer MCTS, which eventually will help to design new strategies to mitigate cancer phenotype.

SUBMITTER: Mucino-Olmos E

PROVIDER: S-EPMC7793838 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Project description:Mathematical modeling provides the predictive ability to understand the metabolic reprogramming and complex pathways that mediate cancer cells' proliferation. We present a mathematical model using a multiscale, multicellular approach to simulate avascular tumor growth, applied to pancreatic cancer. The model spans three distinct spatial and temporal scales. At the extracellular level, reaction diffusion equations describe nutrient concentrations over a span of seconds. At the cellular level, a lattice-based energy driven stochastic approach describes cellular phenomena including adhesion, proliferation, viability and cell state transitions, occurring on the timescale of hours. At the sub-cellular level, we incorporate a detailed kinetic model of intracellular metabolite dynamics on the timescale of minutes, which enables the cells to uptake and excrete metabolites and use the metabolites to generate energy and building blocks for cell growth. This is a particularly novel aspect of the model. Certain defined criteria for the concentrations of intracellular metabolites lead to cancer cell growth, proliferation or death. Overall, we model the evolution of the tumor in both time and space. Starting with a cluster of tumor cells, the model produces an avascular tumor that quantitatively and qualitatively mimics experimental measurements of multicellular tumor spheroids. Through our model simulations, we can investigate the response of individual intracellular species under a metabolic perturbation and investigate how that response contributes to the response of the tumor as a whole. The predicted response of intracellular metabolites under various targeted strategies are difficult to resolve with experimental techniques. Thus, the model can give novel predictions as to the response of the tumor as a whole, identifies potential therapies to impede tumor growth, and predicts the effects of those therapeutic strategies. In particular, the model provides quantitative insight into the dynamic reprogramming of tumor cells at the intracellular level in response to specific metabolic perturbations. Overall, the model is a useful framework to study targeted metabolic strategies for inhibiting tumor growth.

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MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression

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