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Items: 1 to 50 of 213

1.

Publisher Correction: Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma.

Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, Tian T, Wei Z, Madan S, Sullivan RJ, Boland G, Flaherty K, Herlyn M, Ruppin E.

Nat Med. 2018 Oct 17. doi: 10.1038/s41591-018-0247-8. [Epub ahead of print]

PMID:
30333558
2.

Targeting the Warburg effect via LDHA inhibition engages ATF4 signaling for cancer cell survival.

Pathria G, Scott DA, Feng Y, Sang Lee J, Fujita Y, Zhang G, Sahu AD, Ruppin E, Herlyn M, Osterman AL, Ronai ZA.

EMBO J. 2018 Oct 15;37(20). pii: e99735. doi: 10.15252/embj.201899735. Epub 2018 Sep 12.

PMID:
30209241
3.

Combined Analysis of Antigen Presentation and T-cell Recognition Reveals Restricted Immune Responses in Melanoma.

Kalaora S, Wolf Y, Feferman T, Barnea E, Greenstein E, Reshef D, Tirosh I, Reuben A, Patkar S, Levy R, Quinkhardt J, Omokoko T, Qutob N, Golani O, Zhang J, Mao X, Song X, Bernatchez C, Haymaker C, Forget MA, Creasy C, Greenberg P, Carter BW, Cooper ZA, Rosenberg SA, Lotem M, Sahin U, Shakhar G, Ruppin E, Wargo JA, Friedman N, Admon A, Samuels Y.

Cancer Discov. 2018 Sep 12. doi: 10.1158/2159-8290.CD-17-1418. [Epub ahead of print]

PMID:
30209080
4.

CAPN1 is a novel binding partner and regulator of the tumor suppressor NF1 in melanoma.

Alon M, Arafeh R, Lee JS, Madan S, Kalaora S, Nagler A, Abgarian T, Greenberg P, Ruppin E, Samuels Y.

Oncotarget. 2018 Jul 27;9(58):31264-31277. doi: 10.18632/oncotarget.25805. eCollection 2018 Jul 27.

5.

Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma.

Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, Tian T, Wei Z, Madan S, Sullivan RJ, Boland G, Flaherty K, Herlyn M, Ruppin E.

Nat Med. 2018 Oct;24(10):1545-1549. doi: 10.1038/s41591-018-0157-9. Epub 2018 Aug 20. Erratum in: Nat Med. 2018 Oct 17;:.

PMID:
30127394
6.

Urea Cycle Dysregulation Generates Clinically Relevant Genomic and Biochemical Signatures.

Lee JS, Adler L, Karathia H, Carmel N, Rabinovich S, Auslander N, Keshet R, Stettner N, Silberman A, Agemy L, Helbling D, Eilam R, Sun Q, Brandis A, Malitsky S, Itkin M, Weiss H, Pinto S, Kalaora S, Levy R, Barnea E, Admon A, Dimmock D, Stern-Ginossar N, Scherz A, Nagamani SCS, Unda M, Wilson DM 3rd, Elhasid R, Carracedo A, Samuels Y, Hannenhalli S, Ruppin E, Erez A.

Cell. 2018 Sep 6;174(6):1559-1570.e22. doi: 10.1016/j.cell.2018.07.019. Epub 2018 Aug 9.

PMID:
30100185
7.

Systems analysis of intracellular pH vulnerabilities for cancer therapy.

Persi E, Duran-Frigola M, Damaghi M, Roush WR, Aloy P, Cleveland JL, Gillies RJ, Ruppin E.

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

8.

Harnessing synthetic lethality to predict the response to cancer treatment.

Lee JS, Das A, Jerby-Arnon L, Arafeh R, Auslander N, Davidson M, McGarry L, James D, Amzallag A, Park SG, Cheng K, Robinson W, Atias D, Stossel C, Buzhor E, Stein G, Waterfall JJ, Meltzer PS, Golan T, Hannenhalli S, Gottlieb E, Benes CH, Samuels Y, Shanks E, Ruppin E.

Nat Commun. 2018 Jun 29;9(1):2546. doi: 10.1038/s41467-018-04647-1.

9.

Reverting the molecular fingerprint of tumor dormancy as a therapeutic strategy for glioblastoma.

Tiram G, Ferber S, Ofek P, Eldar-Boock A, Ben-Shushan D, Yeini E, Krivitsky A, Blatt R, Almog N, Henkin J, Amsalem O, Yavin E, Cohen G, Lazarovici P, Lee JS, Ruppin E, Milyavsky M, Grossman R, Ram Z, Calderón M, Haag R, Satchi-Fainaro R.

FASEB J. 2018 Jun 1:fj201701568R. doi: 10.1096/fj.201701568R. [Epub ahead of print]

PMID:
29856660
10.

Transcriptomic alterations during ageing reflect the shift from cancer to degenerative diseases in the elderly.

Aramillo Irizar P, Schäuble S, Esser D, Groth M, Frahm C, Priebe S, Baumgart M, Hartmann N, Marthandan S, Menzel U, Müller J, Schmidt S, Ast V, Caliebe A, König R, Krawczak M, Ristow M, Schuster S, Cellerino A, Diekmann S, Englert C, Hemmerich P, Sühnel J, Guthke R, Witte OW, Platzer M, Ruppin E, Kaleta C.

Nat Commun. 2018 Jan 30;9(1):327. doi: 10.1038/s41467-017-02395-2.

11.

Putative functional genes in idiopathic dilated cardiomyopathy.

Nair NU, Das A, Amit U, Robinson W, Park SG, Basu M, Lugo A, Leor J, Ruppin E, Hannenhalli S.

Sci Rep. 2018 Jan 8;8(1):66. doi: 10.1038/s41598-017-18524-2.

12.

Amphiphilic nanocarrier-induced modulation of PLK1 and miR-34a leads to improved therapeutic response in pancreatic cancer.

Gibori H, Eliyahu S, Krivitsky A, Ben-Shushan D, Epshtein Y, Tiram G, Blau R, Ofek P, Lee JS, Ruppin E, Landsman L, Barshack I, Golan T, Merquiol E, Blum G, Satchi-Fainaro R.

Nat Commun. 2018 Jan 2;9(1):16. doi: 10.1038/s41467-017-02283-9.

13.

An integrated computational and experimental study uncovers FUT9 as a metabolic driver of colorectal cancer.

Auslander N, Cunningham CE, Toosi BM, McEwen EJ, Yizhak K, Vizeacoumar FS, Parameswaran S, Gonen N, Freywald T, Bhanumathy KK, Freywald A, Vizeacoumar FJ, Ruppin E.

Mol Syst Biol. 2017 Dec 1;13(12):956. doi: 10.15252/msb.20177739.

14.

Co-targeting the tumor endothelium and P-selectin-expressing glioblastoma cells leads to a remarkable therapeutic outcome.

Ferber S, Tiram G, Sousa-Herves A, Eldar-Boock A, Krivitsky A, Scomparin A, Yeini E, Ofek P, Ben-Shushan D, Vossen LI, Licha K, Grossman R, Ram Z, Henkin J, Ruppin E, Auslander N, Haag R, Calderón M, Satchi-Fainaro R.

Elife. 2017 Oct 4;6. pii: e25281. doi: 10.7554/eLife.25281.

15.

Prediction and Subtyping of Hypertension from Pan-Tissue Transcriptomic and Genetic Analyses.

Basu M, Sharmin M, Das A, Nair NU, Wang K, Lee JS, Chang YC, Ruppin E, Hannenhalli S.

Genetics. 2017 Nov;207(3):1121-1134. doi: 10.1534/genetics.117.300280. Epub 2017 Sep 12.

PMID:
28899996
16.

Precision Oncology: The Road Ahead.

Senft D, Leiserson MDM, Ruppin E, Ronai ZA.

Trends Mol Med. 2017 Oct;23(10):874-898. doi: 10.1016/j.molmed.2017.08.003. Epub 2017 Sep 5. Review.

17.

Detecting similar binding pockets to enable systems polypharmacology.

Duran-Frigola M, Siragusa L, Ruppin E, Barril X, Cruciani G, Aloy P.

PLoS Comput Biol. 2017 Jun 29;13(6):e1005522. doi: 10.1371/journal.pcbi.1005522. eCollection 2017 Jun.

18.

Altered protein glycosylation predicts Alzheimer's disease and modulates its pathology in disease model Drosophila.

Frenkel-Pinter M, Stempler S, Tal-Mazaki S, Losev Y, Singh-Anand A, Escobar-Álvarez D, Lezmy J, Gazit E, Ruppin E, Segal D.

Neurobiol Aging. 2017 Aug;56:159-171. doi: 10.1016/j.neurobiolaging.2017.04.020. Epub 2017 May 3.

PMID:
28552182
19.

New Role for Interleukin-13 Receptor α1 in Myocardial Homeostasis and Heart Failure.

Amit U, Kain D, Wagner A, Sahu A, Nevo-Caspi Y, Gonen N, Molotski N, Konfino T, Landa N, Naftali-Shani N, Blum G, Merquiol E, Karo-Atar D, Kanfi Y, Paret G, Munitz A, Cohen HY, Ruppin E, Hannenhalli S, Leor J.

J Am Heart Assoc. 2017 May 20;6(5). pii: e005108. doi: 10.1161/JAHA.116.005108.

20.

Insulin-like growth factor 1 receptor activation promotes mammary gland tumor development by increasing glycolysis and promoting biomass production.

Ter Braak B, Siezen CL, Lee JS, Rao P, Voorhoeve C, Ruppin E, van der Laan JW, van de Water B.

Breast Cancer Res. 2017 Feb 7;19(1):14. doi: 10.1186/s13058-017-0802-0.

21.

Essential Genes Embody Increased Mutational Robustness to Compensate for the Lack of Backup Genetic Redundancy.

Cohen O, Oberhardt M, Yizhak K, Ruppin E.

PLoS One. 2016 Dec 20;11(12):e0168444. doi: 10.1371/journal.pone.0168444. eCollection 2016.

22.

Diet-induced changes of redox potential underlie compositional shifts in the rumen archaeal community.

Friedman N, Shriker E, Gold B, Durman T, Zarecki R, Ruppin E, Mizrahi I.

Environ Microbiol. 2017 Jan;19(1):174-184. doi: 10.1111/1462-2920.13551. Epub 2016 Dec 12.

PMID:
27696646
23.

Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction.

Auslander N, Wagner A, Oberhardt M, Ruppin E.

PLoS Comput Biol. 2016 Sep 27;12(9):e1005125. doi: 10.1371/journal.pcbi.1005125. eCollection 2016 Sep.

24.

Therapeutic relevance of the protein phosphatase 2A in cancer.

Cunningham CE, Li S, Vizeacoumar FS, Bhanumathy KK, Lee JS, Parameswaran S, Furber L, Abuhussein O, Paul JM, McDonald M, Templeton SD, Shukla H, El Zawily AM, Boyd F, Alli N, Mousseau DD, Geyer R, Bonham K, Anderson DH, Yan J, Yu-Lee LY, Weaver BA, Uppalapati M, Ruppin E, Sablina A, Freywald A, Vizeacoumar FJ.

Oncotarget. 2016 Sep 20;7(38):61544-61561. doi: 10.18632/oncotarget.11399.

25.

The Role of Temporal Trends in Growing Networks.

Mokryn O, Wagner A, Blattner M, Ruppin E, Shavitt Y.

PLoS One. 2016 Aug 3;11(8):e0156505. doi: 10.1371/journal.pone.0156505. eCollection 2016.

26.

A joint analysis of transcriptomic and metabolomic data uncovers enhanced enzyme-metabolite coupling in breast cancer.

Auslander N, Yizhak K, Weinstock A, Budhu A, Tang W, Wang XW, Ambs S, Ruppin E.

Sci Rep. 2016 Jul 13;6:29662. doi: 10.1038/srep29662.

27.

Metabolic Network Prediction of Drug Side Effects.

Shaked I, Oberhardt MA, Atias N, Sharan R, Ruppin E.

Cell Syst. 2016 Mar 23;2(3):209-13. doi: 10.1016/j.cels.2016.03.001. Epub 2016 Mar 23.

28.

System-wide Clinical Proteomics of Breast Cancer Reveals Global Remodeling of Tissue Homeostasis.

Pozniak Y, Balint-Lahat N, Rudolph JD, Lindskog C, Katzir R, Avivi C, Pontén F, Ruppin E, Barshack I, Geiger T.

Cell Syst. 2016 Mar 23;2(3):172-84. doi: 10.1016/j.cels.2016.02.001. Epub 2016 Mar 3.

29.

Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease.

Hyötyläinen T, Jerby L, Petäjä EM, Mattila I, Jäntti S, Auvinen P, Gastaldelli A, Yki-Järvinen H, Ruppin E, Orešič M.

Nat Commun. 2016 Feb 3;7:8994. doi: 10.1038/ncomms9994.

30.

Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli.

Oberhardt MA, Zarecki R, Reshef L, Xia F, Duran-Frigola M, Schreiber R, Henry CS, Ben-Tal N, Dwyer DJ, Gophna U, Ruppin E.

PLoS Comput Biol. 2016 Jan 28;12(1):e1004705. doi: 10.1371/journal.pcbi.1004705. eCollection 2016 Jan.

31.

Functional Alignment of Metabolic Networks.

Mazza A, Wagner A, Ruppin E, Sharan R.

J Comput Biol. 2016 May;23(5):390-9. doi: 10.1089/cmb.2015.0203. Epub 2016 Jan 13.

PMID:
26759932
32.

Glutamine synthetase activity fuels nucleotide biosynthesis and supports growth of glutamine-restricted glioblastoma.

Tardito S, Oudin A, Ahmed SU, Fack F, Keunen O, Zheng L, Miletic H, Sakariassen PØ, Weinstock A, Wagner A, Lindsay SL, Hock AK, Barnett SC, Ruppin E, Mørkve SH, Lund-Johansen M, Chalmers AJ, Bjerkvig R, Niclou SP, Gottlieb E.

Nat Cell Biol. 2015 Dec;17(12):1556-68. doi: 10.1038/ncb3272. Epub 2015 Nov 23.

33.

Diversion of aspartate in ASS1-deficient tumours fosters de novo pyrimidine synthesis.

Rabinovich S, Adler L, Yizhak K, Sarver A, Silberman A, Agron S, Stettner N, Sun Q, Brandis A, Helbling D, Korman S, Itzkovitz S, Dimmock D, Ulitsky I, Nagamani SC, Ruppin E, Erez A.

Nature. 2015 Nov 19;527(7578):379-383. doi: 10.1038/nature15529. Epub 2015 Nov 11.

34.

Harnessing the landscape of microbial culture media to predict new organism-media pairings.

Oberhardt MA, Zarecki R, Gronow S, Lang E, Klenk HP, Gophna U, Ruppin E.

Nat Commun. 2015 Oct 13;6:8493. doi: 10.1038/ncomms9493.

35.

Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.

Megchelenbrink W, Katzir R, Lu X, Ruppin E, Notebaart RA.

Proc Natl Acad Sci U S A. 2015 Sep 29;112(39):12217-22. doi: 10.1073/pnas.1508573112. Epub 2015 Sep 14.

36.

The role of branched chain amino acid and tryptophan metabolism in rat's behavioral diversity: Intertwined peripheral and brain effects.

Asor E, Stempler S, Avital A, Klein E, Ruppin E, Ben-Shachar D.

Eur Neuropsychopharmacol. 2015 Oct;25(10):1695-705. doi: 10.1016/j.euroneuro.2015.07.009. Epub 2015 Jul 31.

PMID:
26271721
37.

Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia.

Wagner A, Cohen N, Kelder T, Amit U, Liebman E, Steinberg DM, Radonjic M, Ruppin E.

Mol Syst Biol. 2015 Mar;11(3):791.

38.

Modeling cancer metabolism on a genome scale.

Yizhak K, Chaneton B, Gottlieb E, Ruppin E.

Mol Syst Biol. 2015 Jun 30;11(6):817. doi: 10.15252/msb.20145307. Review.

39.

Evolutionary Conservation of Bacterial Essential Metabolic Genes across All Bacterial Culture Media.

Ish-Am O, Kristensen DM, Ruppin E.

PLoS One. 2015 Apr 20;10(4):e0123785. doi: 10.1371/journal.pone.0123785. eCollection 2015.

40.

Moving ahead on harnessing synthetic lethality to fight cancer.

Jerby-Arnon L, Ruppin E.

Mol Cell Oncol. 2015 Feb 25;2(2):e977150. doi: 10.4161/23723556.2014.977150. eCollection 2015 Apr-Jun.

41.

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm.

Seaver SM, Bradbury LM, Frelin O, Zarecki R, Ruppin E, Hanson AD, Henry CS.

Front Plant Sci. 2015 Mar 10;6:142. doi: 10.3389/fpls.2015.00142. eCollection 2015.

42.

Fumarate induces redox-dependent senescence by modifying glutathione metabolism.

Zheng L, Cardaci S, Jerby L, MacKenzie ED, Sciacovelli M, Johnson TI, Gaude E, King A, Leach JD, Edrada-Ebel R, Hedley A, Morrice NA, Kalna G, Blyth K, Ruppin E, Frezza C, Gottlieb E.

Nat Commun. 2015 Jan 23;6:6001. doi: 10.1038/ncomms7001.

43.

Proteomics-based metabolic modeling reveals that fatty acid oxidation (FAO) controls endothelial cell (EC) permeability.

Patella F, Schug ZT, Persi E, Neilson LJ, Erami Z, Avanzato D, Maione F, Hernandez-Fernaud JR, Mackay G, Zheng L, Reid S, Frezza C, Giraudo E, Fiorio Pla A, Anderson K, Ruppin E, Gottlieb E, Zanivan S.

Mol Cell Proteomics. 2015 Mar;14(3):621-34. doi: 10.1074/mcp.M114.045575. Epub 2015 Jan 8.

44.

The effects of telomere shortening on cancer cells: a network model of proteomic and microRNA analysis.

Uziel O, Yosef N, Sharan R, Ruppin E, Kupiec M, Kushnir M, Beery E, Cohen-Diker T, Nordenberg J, Lahav M.

Genomics. 2015 Jan;105(1):5-16. doi: 10.1016/j.ygeno.2014.10.013. Epub 2014 Nov 13.

45.

Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.

Yizhak K, Gaude E, Le Dévédec S, Waldman YY, Stein GY, van de Water B, Frezza C, Ruppin E.

Elife. 2014 Nov 21;3. doi: 10.7554/eLife.03641.

46.

Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality.

Jerby-Arnon L, Pfetzer N, Waldman YY, McGarry L, James D, Shanks E, Seashore-Ludlow B, Weinstock A, Geiger T, Clemons PA, Gottlieb E, Ruppin E.

Cell. 2014 Aug 28;158(5):1199-1209. doi: 10.1016/j.cell.2014.07.027.

47.

Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease.

Stempler S, Yizhak K, Ruppin E.

PLoS One. 2014 Aug 15;9(8):e105383. doi: 10.1371/journal.pone.0105383. eCollection 2014.

48.

Glycan degradation (GlyDeR) analysis predicts mammalian gut microbiota abundance and host diet-specific adaptations.

Eilam O, Zarecki R, Oberhardt M, Ursell LK, Kupiec M, Knight R, Gophna U, Ruppin E.

MBio. 2014 Aug 12;5(4). pii: e01526-14. doi: 10.1128/mBio.01526-14.

49.

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration.

Yizhak K, Le Dévédec SE, Rogkoti VM, Baenke F, de Boer VC, Frezza C, Schulze A, van de Water B, Ruppin E.

Mol Syst Biol. 2014 Aug 1;10:744. doi: 10.15252/msb.20134993. Erratum in: Mol Syst Biol. 2014 Nov;10(11):765.

50.

Network-level architecture and the evolutionary potential of underground metabolism.

Notebaart RA, Szappanos B, Kintses B, Pál F, Györkei Á, Bogos B, Lázár V, Spohn R, Csörgő B, Wagner A, Ruppin E, Pál C, Papp B.

Proc Natl Acad Sci U S A. 2014 Aug 12;111(32):11762-7. doi: 10.1073/pnas.1406102111. Epub 2014 Jul 28.

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