TY - JOUR
T1 - Computational evaluation of cellular metabolic costssuccessfully predicts genes whose expression is deleterious
AU - Wagner, Allon
AU - Zarecki, Raphy
AU - Reshef, Leah
AU - Gochev, Camelia
AU - Sorek, Rotem
AU - Gophna, Uri
AU - Ruppin, Eytan
N1 - Israeli Centers of Research Excellence program of the Israeli Planning and Budgeting Committee; Israel Science Foundation [41/11]; European Union; Edmond J. Safra Center for Bioinformatics at Tel-Aviv University; National Institutes of Health [R01AI082376-01]; Israeli Science Foundation [1303/12]; European Research Council [260432]We thank Livnat Jerby, Martin Kupiec, Nathan E. Lewis, and Roded Sharan for many fruitful discussions. This research was supported by the Israeli Centers of Research Excellence program of the Israeli Planning and Budgeting Committee and the Israel Science Foundation (Grant 41/11) and by the Seventh Framework Programme of the European Union Microme and Infect Projects. A. W. was supported, in part, by a fellowship from the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University. R. S. was supported, in part, by National Institutes of Health Grant R01AI082376-01, Israeli Science Foundation Grant 1303/12, and European Research Council Starting Grants program Grant 260432.
PY - 2013/11/19
Y1 - 2013/11/19
N2 - 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 downregulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGEbased 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.
AB - 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 downregulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGEbased 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.
UR - http://www.scopus.com/inward/record.url?scp=84888122057&partnerID=8YFLogxK
U2 - https://doi.org/10.1073/pnas.1312361110
DO - https://doi.org/10.1073/pnas.1312361110
M3 - Article
C2 - 24198337
SN - 0027-8424
VL - 110
SP - 19166
EP - 19171
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 47
ER -