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dc.contributor.authorAurich, Maike K
dc.contributor.authorThiele, Ines
dc.date.accessioned2015-06-05T13:43:26Zen
dc.date.available2015-06-05T13:43:26Zen
dc.date.issued2012en
dc.identifier.citationPLoS ONE 2012, 7 (12):e49978en
dc.identifier.issn1932-6203en
dc.identifier.pmid23236359en
dc.identifier.doi10.1371/journal.pone.0049978en
dc.identifier.urihttp://hdl.handle.net/2336/556460en
dc.description.abstractInnate immunity is the first line of defense against invasion of pathogens. Toll-like receptor (TLR) signaling is involved in a variety of human diseases extending far beyond immune system-related diseases, affecting a number of different tissues and cell-types. Computational models often do not account for cell-type specific differences in signaling networks. Investigation of these differences and its phenotypic implications could increase understanding of cell signaling and processes such as inflammation. The wealth of knowledge for TLR signaling has been recently summarized in a stoichiometric signaling network applicable for constraint-based modeling and analysis (COBRA). COBRA methods have been applied to investigate tissue-specific metabolism using omics data integration. Comparable approaches have not been conducted using signaling networks. In this study, we present ihsTLRv2, an updated TLR signaling network accounting for the association of 314 genes with 558 network reactions. We present a mapping procedure for transcriptomic data onto signaling networks and demonstrate the generation of a monocyte-specific TLR network. The generated monocyte network is characterized through expression of a specific set of isozymes rather than reduction of pathway contents. While further tailoring the network to a specific stimulation condition, we observed that the quantitative changes in gene expression due to LPS stimulation affected the tightly connected set of genes. Differential expression influenced about one third of the entire TLR signaling network, in particular, NF-κB activation. Thus, a cell-type and condition-specific signaling network can provide functional insight into signaling cascades. Furthermore, we demonstrate the energy dependence of TLR signaling pathways in monocytes.
dc.description.sponsorshipEuropean Research Council Advanced Grant N 232816 info:eu-repo/grantAgreement/EC/FP7/ 249261en
dc.language.isoenen
dc.publisherPublic Library Scienceen
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/249261en
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/ 249261en
dc.relation.urlhttp://dx.doi.org/10.1371/journal.pone.0049978en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3516512/en
dc.rightsopenAccessen
dc.subject.meshHumansen
dc.subject.meshImmunity, Innateen
dc.subject.meshModels, Biologicalen
dc.subject.meshMonocytesen
dc.subject.meshSignal Transductionen
dc.subject.meshToll-Like Receptorsen
dc.subject.meshTranscriptomeen
dc.titleContextualization procedure and modeling of monocyte specific TLR signaling.en
dc.typearticleen
dc.contributor.department[ 1 ] Univ Iceland, Ctr Syst Biol, Reykjavik, Iceland, Univ Iceland, Fac Ind Engn Mech Engn & Comp Sci, Reykjavik, Icelanden
dc.identifier.journalPloS oneen
dc.rights.accessOpen Access - Opinn aðganguren
refterms.dateFOA2018-09-12T15:15:06Z
html.description.abstractInnate immunity is the first line of defense against invasion of pathogens. Toll-like receptor (TLR) signaling is involved in a variety of human diseases extending far beyond immune system-related diseases, affecting a number of different tissues and cell-types. Computational models often do not account for cell-type specific differences in signaling networks. Investigation of these differences and its phenotypic implications could increase understanding of cell signaling and processes such as inflammation. The wealth of knowledge for TLR signaling has been recently summarized in a stoichiometric signaling network applicable for constraint-based modeling and analysis (COBRA). COBRA methods have been applied to investigate tissue-specific metabolism using omics data integration. Comparable approaches have not been conducted using signaling networks. In this study, we present ihsTLRv2, an updated TLR signaling network accounting for the association of 314 genes with 558 network reactions. We present a mapping procedure for transcriptomic data onto signaling networks and demonstrate the generation of a monocyte-specific TLR network. The generated monocyte network is characterized through expression of a specific set of isozymes rather than reduction of pathway contents. While further tailoring the network to a specific stimulation condition, we observed that the quantitative changes in gene expression due to LPS stimulation affected the tightly connected set of genes. Differential expression influenced about one third of the entire TLR signaling network, in particular, NF-κB activation. Thus, a cell-type and condition-specific signaling network can provide functional insight into signaling cascades. Furthermore, we demonstrate the energy dependence of TLR signaling pathways in monocytes.


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