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Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

Significance Biologists frequently overexpress genes to learn about their cellular functions, and biotechnologists do so to construct novel metabolic pathways that produce valuable chemical compounds. However, gene overexpression often leads to deleterious consequences whose cause is unclear. Here, we present a computational method named Expression-Dependent Gene Effects (EDGE) that can successfully predict the deleterious effects resulting from overexpression of either native or foreign (originating in another species) metabolic genes. EDGE relies on genome-scale metabolic models, an emerging computational paradigm for studying metabolism in silico. Beyond its biotechnological significance, gene overexpression also plays an important role in human disease. We show EDGE’s applicability in the latter case by demonstrating its ability to detect toxic genes whose expression tends to be suppressed in cancer cells. Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE’s predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically down-regulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGE-based analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering.