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Nat Commun. 2018 Jul 31;9(1):2997. doi: 10.1038/s41467-018-05261-x.

Systems analysis of intracellular pH vulnerabilities for cancer therapy.

Author information

School of Physics and Astronomy, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, 69978, Tel-Aviv, Israel.
Center for Bioinformatics and Computational Biology, Institute of Advanced Computer Studies, Department of Computer Science, University of Maryland, College Park, MD, 20742, USA.
Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, 08028, Catalonia, Spain.
Department of Cancer Physiology, Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, 33612, FL, USA.
Department of Chemistry, The Scripps Research Institute, 110 Scripps Way, Jupiter, 33458, USA.
Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, 08010, Catalonia, Spain.
Department of Tumor Biology, Moffitt Cancer Center & Research Institute, Tampa, FL, 33612, USA.
Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, 20894, USA.


A reverse pH gradient is a hallmark of cancer metabolism, manifested by extracellular acidosis and intracellular alkalization. While consequences of extracellular acidosis are known, the roles of intracellular alkalization are incompletely understood. By reconstructing and integrating enzymatic pH-dependent activity profiles into cell-specific genome-scale metabolic models, we develop a computational methodology that explores how intracellular pH (pHi) can modulate metabolism. We show that in silico, alkaline pHi maximizes cancer cell proliferation coupled to increased glycolysis and adaptation to hypoxia (i.e., the Warburg effect), whereas acidic pHi disables these adaptations and compromises tumor cell growth. We then systematically identify metabolic targets (GAPDH and GPI) with predicted amplified anti-cancer effects at acidic pHi, forming a novel therapeutic strategy. Experimental testing of this strategy in breast cancer cells reveals that it is particularly effective against aggressive phenotypes. Hence, this study suggests essential roles of pHi in cancer metabolism and provides a conceptual and computational framework for exploring pHi roles in other biomedical domains.

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