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dc.contributor.authorMiraldi, Emily Rae
dc.contributor.authorSharfi, Hadar
dc.contributor.authorFriedline, Randall H.
dc.contributor.authorJohnson, Hannah
dc.contributor.authorZhang, Tejia
dc.contributor.authorLau, Ken S.
dc.contributor.authorKo, Hwi Jin
dc.contributor.authorCurran, Timothy G.
dc.contributor.authorHaigis, Kevin M.
dc.contributor.authorYaffe, Michael B.
dc.contributor.authorBonneau, Richard
dc.contributor.authorLauffenburger, Douglas A.
dc.contributor.authorKahn, Barbara B.
dc.contributor.authorKim, Jason K.
dc.contributor.authorNeel, Benjamin G.
dc.contributor.authorSaghatelian, Alan
dc.contributor.authorWhite, Forest M.
dc.date.accessioned2014-09-04T18:50:36Z
dc.date.available2014-09-04T18:50:36Z
dc.date.issued2013-05
dc.date.submitted2013-01
dc.identifier.issn1757-9694
dc.identifier.issn1757-9708
dc.identifier.urihttp://hdl.handle.net/1721.1/89179
dc.description.abstractMetabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b[superscript −/−]) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantify protein-phosphotyrosine network changes in L-PTP1b[superscript −/−] mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b[superscript −/−] mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation–reduction in altered PUFA and triglyceride metabolism.en_US
dc.description.sponsorshipPfizer Inc. (grant)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant 5R24DK090963)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant U54-CA112967)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant CA49152 R37)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant R01-DK080756)en_US
dc.description.sponsorshipNational Mouse Metabolic Phenotyping Center at UMASS (Grant (U24-DK093000))en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Graduate Research Fellowship)en_US
dc.language.isoen_US
dc.publisherRoyal Society of Chemistryen_US
dc.relation.isversionofhttp://dx.doi.org/10.1039/c3ib40013aen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleMolecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion miceen_US
dc.typeArticleen_US
dc.identifier.citationMiraldi, Emily R., Hadar Sharfi, Randall H. Friedline, Hannah Johnson, Tejia Zhang, Ken S. Lau, Hwi Jin Ko, et al. “Molecular Network Analysis of Phosphotyrosine and Lipid Metabolism in Hepatic PTP1b Deletion Mice.” Integr. Biol. 5, no. 7 (2013): 940.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Programen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.contributor.departmentKoch Institute for Integrative Cancer Research at MITen_US
dc.contributor.mitauthorMiraldi, Emily Raeen_US
dc.contributor.mitauthorSharfi, Hadaren_US
dc.contributor.mitauthorJohnson, Hannahen_US
dc.contributor.mitauthorLau, Ken S.en_US
dc.contributor.mitauthorCurran, Timothy G.en_US
dc.contributor.mitauthorYaffe, Michael B.en_US
dc.contributor.mitauthorLauffenburger, Douglas A.en_US
dc.contributor.mitauthorWhite, Forest M.en_US
dc.relation.journalIntegrative Biologyen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsMiraldi, Emily R.; Sharfi, Hadar; Friedline, Randall H.; Johnson, Hannah; Zhang, Tejia; Lau, Ken S.; Ko, Hwi Jin; Curran, Timothy G.; Haigis, Kevin M.; Yaffe, Michael B.; Bonneau, Richard; Lauffenburger, Douglas A.; Kahn, Barbara B.; Kim, Jason K.; Neel, Benjamin G.; Saghatelian, Alan; White, Forest M.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1545-1651
dc.identifier.orcidhttps://orcid.org/0000-0002-9547-3251
dspace.mitauthor.errortrue
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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