Associate Professor The University of Texas at Austin, United States
Introduction: Cancer is metabolically heterogeneous, with adaptations driven by nutrient availability in the tissue microenvironment. As cancer is an adaptive process, this variation in metabolism influences the competition for nutrients and disease progression. However, the interplay between the nutrient microenvironment and metabolic competition is not well understood. This project aims to characterize the metabolic phenotypes of different breast cancer cells by evaluating their transcriptomic profiles and clonal selection under various nutrient availability conditions. We also utilize consumer resource modeling to evaluate metabolic phenotypes and predict competitive interactions in these altered conditions, generating insight into tumor development and evolution.
Materials and
Methods: This study includes an interdisciplinary approach that combines transcriptomics and clonal methods with mathematical modeling. To evaluate metabolic phenotype, MDA-MB-231 breast adenocarcinoma cells (ATCC) were labeled with the ClonMapper expressed barcode system. Cells were cultured in specialized media conditions that included the presence of low (1g/L) and high (4.5g/L) of glucose both with and without added dietary fatty acids. Cells were harvested after two weeks to evaluate changes in transcriptomic expression by RNA sequencing and to measure clonal dynamics by targeted sequencing of the ClonMapper barcodes.
Results, Conclusions, and Discussions:
Results:
Transcriptomic results suggest an upregulation in both known and unknown fatty acid metabolism related genes after the addition of fatty acids. This is exacerbated in the case of high glucose availability as opposed to low glucose availability (Fig. 1). Early modeling work also suggests that consumer-resource modeling can provide predictions of competition based on resource functionality, such as whether two resources are perfectly substitutable, complimentary, or essential. Using the transcriptomic data, we identify such relationships to generate hypothesis-driven models and evaluate the amount of resource presence needed in an environment for exclusion or coexistence between cells with different metabolic phenotypes.
Figure 1: Early transcriptomics indicate that the addition of dietary fatty acids result in upregulation of both known and unknown fatty acid genes. This appears to be further exacerbated in the presence of high glucose, suggesting resource relationship may be complimentary.
This study seeks to unify mathematical modeling with genomic and transcriptomic experiments to understand metabolic phenotype and cellular competition in cancer. Early results from this study have demonstrated that metabolic phenotype is influenced by nutrient availability. This data, along with other developing results, will be used to characterize the cellular phenotype and develop a method of predicting the outcome of competitive interactions between cells. Such an approach has the potential to further understanding of tumor development and metabolic selection. This understanding could provide insight to new methods of metabolic perturbation and ecological exploitation of the tumor.
