Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a major research focus due to its poor therapy response and dismal prognosis. PDAC cells adapt their metabolism efficiently to the environment to which they are exposed, often relying on diverse fuel sources depending on availability. Since traditional experimental techniques appear exhaustive in the search for a viable therapeutic strategy against PDAC, in this study, a highly curated and omics-informed genome-scale metabolic model of PDAC was reconstructed using patient-specific transcriptomic data. From the analysis of the model-predicted metabolic changes, several new metabolic functions were explored as potential therapeutic targets against PDAC in addition to the already known metabolic hallmarks of pancreatic cancer. Significant downregulation in the peroxisomal fatty acid beta oxidation pathway reactions, flux modulation in the carnitine shuttle system, and upregulation in the reactive oxygen species detoxification pathway reactions were observed. These unique metabolic traits of PDAC were then correlated with potential drug combinations that can be repurposed for targeting genes with poor prognosis in PDAC. Overall, these studies provide a better understanding of the metabolic vulnerabilities in PDAC and will lead to novel effective therapeutic strategies.

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models

“…GSMMs have been widely used to simulate the metabolic phenotypes of cells and metabolic interactions between groups of cells [1][2][3][4]. GSMMs have been used in contexts as varied as predicting novel drug targets for various human diseases [5][6][7][8][9][10][11][12][13], characterizing the metabolic differences between different human cell (sub)types [14][15][16][17][18][19][20], and helping engineer microbes to produce commercially and/or medically valuable compounds [3,[21][22][23][24][25]. GSMMs represent metabolic networks as stoichiometric matrices, where each row in the matrix corresponds to a single metabolite, each column represents a reaction, and each entry contains the stoichiometric coefficient of that row's metabolite in that column's reaction.…”