(41b) Metabolic Modeling to Explore the Landscape of Pancreatic Ductal Adenocarcinoma Cells in Diverse Physiological Conditions

Pancreatic ductal adenocarcinoma (PDAC), with poor prognosis, resistance to radio- and chemotherapy, and a five-year survival rate of only 8.2%¹, is the most prevalent form of pancreatic cancer and the third-leading cause of cancer-related morbidity in the USA. Its poor prognosis can be attributed to both the lack of early diagnostic markers as well as its ability to quickly metastasize to surrounding organs. Additionally, high rates of glycolysis and lactate secretion are observed in PDAC cells and highly acidic extracellular environment is observed due to limited proton transport into tumor cells², fulfilling the biosynthetic demands for rapid tumor growth. Since the treatment methods against PDAC are palliative at best and there is an increasing risk of the cases rising by almost 100% in the next decade, there is desperate need to understand the complex biology of PDAC carcinogenesis in order to identify more effective therapeutic approaches and improve patient survival.

To understand the PDAC-associated metabolic reprogramming involving changes in the metabolic reaction fluxes and metabolite concentrations, a genome-scale metabolic model of the human pancreas encompassing the genes, metabolites, and reactions was reconstructed. Transcriptomic data was obtained on 32 healthy and cancerous patients across varied demographic from The Cancer Genome Atlas (TCGA) database [https://www.cancer.gov/tcga]. A genome-scale metabolic model of a pancreatic cell describing reaction stoichiometry, directionality, and gene-protein-reaction (GPR) association was constructed by mapping these transcriptomic datasets to the latest global human metabolic model, Human1³ using the Integrative Metabolic Analysis Tool (iMAT)⁴. The preliminary model generated contained 3,628 genes, catalyzing 6,076 reactions, across 123 pathways, involving 4,415 metabolites. The pathways involving the largest number of reactions include leukotriene metabolism, exchange/demand reactions, fatty acid oxidation, and peptide metabolism. The model was curated through the design-build-test-refine cycle to generate a model that most accurately reflects the metabolic capabilities of a PDAC cell. Specifically, every reaction in the model was checked and corrected for elementary and charge balance if necessary and the thermodynamic infeasibility issues were minimized. After the model had been refined by rectifying reaction imbalances and fixing thermodynamically infeasible cycles, the final model contains 2299 genes, catalyzing 5478 reactions, involving 4412 metabolites. The reactions are distributed across 123 different pathways, the largest of which include transport reactions, exchange/demand reactions, fatty acid oxidation, and peptide metabolism.

Incorporation of the transcriptomic dataset with the curated genome-scale metabolic model revealed genome-wide changes in metabolic flux distribution and pathway functionalities. The fatty acid beta-oxidation pathways were upregulated in mitochondria, but these pathways were completely downregulated in peroxisome. This is indicative that very long chain fatty acids are not oxidized in tumor cells at a rate comparable to healthy cells⁵. Also, the cells probably upregulate the mitochondrial pathways to satisfy the increased ATP demand by the rapidly growing tumor. The reduced H₂O₂ production due to downregulated peroxisomal fatty acid beta-oxidation can lead to reduced oxidative stress on the cell⁶, which can explain the fitness of PDAC. Since upregulated mitochondrial fatty acid beta-oxidation requires carnitine acyltransferase I and II, the Carnitine shuttle pathway also shows significant upregulation in the model simulations. These results lead us to pinpoint several metabolic conversions as the distinct signature of PDAC metabolism and has the potential to guide us in developing novel treatment methods against pancreatic cancer.

References:

1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA: a cancer journal for clinicians. 2021/01/01 2021;71(1):7-33.
2. Behrendorff N, Floetenmeyer M, Schwiening C, Thorn P. Protons released during pancreatic acinar cell secretion acidify the lumen and contribute to pancreatitis in mice. Gastroenterology. Nov 2010;139(5):1711-1720, 1720 e1711-1715.
3. Robinson JL, Kocabas P, Wang H, et al. An atlas of human metabolism. Science signaling. Mar 24 2020;13(624).
4. Zur H, Ruppin E, Shlomi T. iMAT: an integrative metabolic analysis tool. Bioinformatics. Dec 15 2010;26(24):3140-3142.
5. Singh I. Biochemistry of peroxisomes in health and disease. Mol Cell Biochem. Feb 1997;167(1-2):1-29.
6. Wanders RJA, Waterham HR, Ferdinandusse S. Metabolic Interplay between Peroxisomes and Other Subcellular Organelles Including Mitochondria and the Endoplasmic Reticulum. Frontiers in Cell and Developmental Biology. 2016-January-28 2016;3(83).