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BACKGROUND: Any reduction in myocardial oxygen delivery relative to its demands can impair cardiac contractile performance. Understanding the mitochondrial metabolic response to hypoxia is key to understanding ischemia tolerance in the myocardium. We used a novel combination of 2 genome-scale methods to study key processes underlying human myocardial hypoxia tolerance. In particular, we hypothesized that computational modeling and evolution would identify similar genes as critical to human myocardial hypoxia tolerance. METHODS AND RESULTS: We analyzed a reconstruction of the cardiac mitochondrial metabolic network using constraint-based methods, under conditions of simulated hypoxia. We used flux balance analysis, random sampling, and principal component analysis to explore feasible steady-state solutions. Hypoxia blunted maximal ATP (-17%) and heme (-75%) synthesis and shrank the feasible solution space. Tricarboxylic acid and urea cycle fluxes were also reduced in hypoxia, but phospholipid synthesis was increased. Using mathematical optimization methods, we identified reactions that would be critical to hypoxia tolerance in the human heart. We used data regarding single-nucleotide polymorphism frequency and distribution in the genomes of Tibetans (whose ancestors have resided in persistent high-altitude hypoxia for several millennia). Six reactions were identified by both methods as being critical to mitochondrial ATP production in hypoxia: phosphofructokinase, phosphoglucokinase, complex II, complex IV, aconitase, and fumarase. CONCLUSIONS: Mathematical optimization and evolution converged on similar genes as critical to human myocardial hypoxia tolerance. Our approach is unique and completely novel and demonstrates that genome-scale modeling and genomics can be used in tandem to provide new insights into cardiovascular genetics.

Original publication




Journal article


Circ Cardiovasc Genet

Publication Date





407 - 415


computational biology, genomics, mitochondria, systems biology, Adenosine Triphosphate, Genome, Human, Heme, Humans, Hypoxia, Metabolic Networks and Pathways, Mitochondria, Models, Biological, Myocytes, Cardiac, Phospholipids, Polymorphism, Single Nucleotide, Principal Component Analysis, Proteomics, Transcriptome