פרופ' איתן רופין

Computational Analysis of Metabolic Alterations in Cancer and Aging

Our research focuses on computational biology with an emphasis on metabolic modeling. Our lab is currently working on the development and study of large-scale models of metabolism in a variety of human tissues, in both healthy and disease states.

 

Our efforts are focused on two main subjecst:

1. We have generated the first model of cancer metabolism. This development has paved the way for the first large-scale computational search for new and selective metabolic drug targets in cancer (Nature/ MSB 2011) – some which are already under various stages of further experimental testing and validation (Nature 2011).
2. We have recently developed a new approach for inferring drug target for extending life span in humans (anti-aging), which are currently under experimental investigation. Taken together, these studies and others ongoing in the lab offer new ways for harnessing computers to advance our understanding of metabolically-related human disorders, and further our ability to diagnose and treat them in a rationale-designed manner.

 

L. Zheng, S. Cardaci, L. Jerby, E.D. MacKenzie, M. Sciacovelli, T. Isaac Johnson, E. Gaude, A. King, J.D.G. Leach, R. Edrada-Ebel, A. Hedly, N.A.
Morrice, G. Kalna, K. Blyth, E. Ruppin, C. Frezza, E. Gottlieb. Fumarate induces redox-dependent senescenceaby modifying glutathione metabolism.
Nature Communications, 6, 6001, 2015.

 

K. Yizhak*, E. Guade*, S. E. Devedec, Y.Y. Waldman, G.Y. Stein, B. van de Water#, C. Frezza#, E. Ruppin#, (first author equal contribution *, last author equal contribution #). Phenotype-based cell specific modeling reveals metabolic liabilities of cancer eLife, 3, e03641, 2014. 

 

S. Stempler, K. Yizhak, E. Ruppin. Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer’s disease.
PLoS One, 9; e105383, 2014.

 

R.A. Notebaart, B. Szappanos, B. Kinteses, F. Pal, A. Gyorkei, B. Bogos, V. Lazar, R. Spohn, A. Wagner, E. Ruppin, C. Pal, B. Papp. Network-level architecture and the evolutionary potential of underground metabolism. Proceedings of the National Academy of Sciences (PNAS), 111: 11762-11767, 2014.

 

K. Yizhak*, S. E. Devedec*, V.M. Rogkoti, F. Baenke, V.C. de Boer, C. Frezza, A. Schulze, B. van de Water#, E. Ruppin#. A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration (first author equal contribution *, last author equal contribution #). Molecular Systems Biology (MSB), 10:744, 2014.

 

O. Eilam, R. Zarecky, M. Oberhardt, L.K. Ursell, M. Kupiec, R. Knight, U. Gophna, E. Ruppin. Glycan Degradation (GlyDer) analysis predicts mammalian gut microbiota abundance and host diet-specific adaptations mBio, 5; e01526-14, 2014.

 

L. Jerby-Arnon, N. Pfetzer, Y.Y. Yaldman, L. McGarry, D. James, E. Shanks, B. Seashore-Ludlow, A. Weinstock, T. Geiger, P.A. Clemons, E. Gottlieb, E. Ruppin. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell, 158, 1199-1209, 2014.

 

R. Zarecki, M. Oberhardt, L. Reshef, U. Gophna, E. Ruppin. A novel nutritional predictor links microbial fastidiousness wth lowered ubiquity, growth rate and cooperativeness. PLoS Computational Biology, 10: e1003726, 2014.

 

R. Zarecki, M. Oberhardt, K. Yizhak, A. Wagner, E. Shiftman Segal, C.S. Henry, U. Gophna, E. Ruppin. Maximal sum of metabolic exchange fluxes outperforms biomass yield as a predictor of growth rate of microorganisms. PLoS One, 9: e98372, 2014.

 

Goldstein I, Yizhak K, Madar S, Goldfinger N, Ruppin E, Rotter V. p53 promotes the expression of gluconeogenesis-related genes and enhances hepatic glucose production. Cancer Metab.1:9, 2013.

 

Wagner A, Zarecki R, Reshef L, Gochev C, Sorek R, Gophna U, Ruppin E. Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious. Proc Natl Acad Sci USA. 110:19166-71, 2013

 

Y. Waldman, T. Geiger, E. Ruppin. A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells. PLoS Genet, 9:e1003806, 2013

 

K. Yizhak, O. Gabay, H. Cohen, E. Ruppin. Modelbased identification of drug targets that revert disrupted metabolism and its application to aging. Nature Comm, 4:2632, 2013.

 

G. Romano, Y. Harari, T. Yehuda, A. Podhorzer, L. Rubinstein, R. Shamir, A. Gottlieb, Y. Silberberg, D. Pe’er, E. Ruppin, R. Sharan, M. Kupiec. Environmental stresses disrupt telomere length homoestasis. PLoS Genet, 9:e1003721, 2013

 

A. Gottlieb, G.Y. Stein, E. Ruppin, R.B. Altman, R. Sharan. A method for inferring medical diagnoses from patients similarities. BMC Med, 2013, 11:194.

 

T. Tuller, S. Atar, E. Ruppin, M. Gurevich, A. Achiron. Common and specific signatures of gene expression and protein-protein interactions in autoimmune diseases. Genes Immunity, 14, 67-82 (2013).

 

I. Goldstein, K. Yizhak, S. Madar, N. Goldfinger, E. Ruppin, V. Rotter. p53 promotes the expression of gluconeogenesis-related genes and enhances hepatic glucose production. Cancer Metabol 2013, 1:9 (2013)

 

L. Jerby, L. Wolf, C. Denkert, G.Y. Stein, M. Hilvo, M. Oresic, T. Geiger, and E. Ruppin. Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. Cancer Res, 72, 5712-5720 (2012)

 

L Jerby, and E. Ruppin. Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clin Cancer Res, 18, 5572-5584 (2012)

 

S. Stempler, E. Ruppin. Analyzing gene expression from whole tissue vs. different cell types reveals the central role of neurons in predicting severity of Alzheimer’s Disease. PLoS One, 7: e45879 (2012)

 

G.Y. Stein, N. Yosef, H. Reichman, J. Horev, A. Laser-Azogui, A. Berens, J. Resau, E. Ruppin, R. Sharan, and I. Tsarfaty. Met kinetic signature derived from the response to HGF/SF in a cellular model predicts breast cancer patient survival. PLoS One, 7: e45969 (2012)

 

L. Lobel, S. Nadejda, I. Borovok, E. Ruppin, and A. Hershkovitz. Integrative genomic analysis identifies isoleucine and cody as regulators of Listeria monocytogenes virulence. PLoS Genet, 8: e1002887 (2012)

 

O. Magger, Y.Y. Waldman, E. Ruppin, and R. Sharan. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. PLoS Comp Biol 8:e1002690 (2012)

 

S. Stempler, Y.Y Waldman, L. Wolf, and E. Ruppin. Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer’s disease progression. Neurobiol Aging, 33, 2230.e13 (2012)

 

A. Gottlieb, G.Y. Stein, Y. Oron, E. Ruppin, and R. Sharan. INDI: a computational framework for inferring drug interactions and their associated recommendations. Mol Syst Biol, 8, 592 (2012)

 

L. Vardi, E. Ruppin, and R. Sharan: A linearized constraint-based approach for modeling signaling networks. J Comp Biol 19: 232-40 (2012).

 

Y. Silberberg, A. Gottlieb, M. Kupiec E. Ruppin, and R. Sharan: Large-scale elucidation of drug response pathways in humans. J Comp Biol 19: 163-74 (2012).

 

T. Ben-Shitrit , N. Yosef, K. Shemesh, R. Sharan, E. Ruppin and M. Kupiec: Systematic identification of gene annotation errors in the widely used yeast mutation collections. Nature Meth 9: 373-U82 (2012).

 

S. Mintz-Oron, S. Meir, S. Malitsky, E. Ruppin, A. Aharoni and T. Shlomi: Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proc Natl Acad Sci USA 109: 339-44 (2012)

 

 

Review
L. Jerby-Arnon, E. Ruppin. Moving ahead on harnessing synthetic lethality to fight cancer. Molecular & Cellular Oncology, in press.

 

M. Oberhardt*, K. Yizhak*, E. Ruppin. Metabolically re-modeling the drug pipeline. Curr. Opin. in Pharmacology, http://dx.doi.org/10.1016/j. coph.2013.05.006, 2013

 

 

Grants

• 2011-2015   US-Israeli Binational Science Foundation (BSF) for studying human host-pathogen metabolic interactions in the gut