Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17827
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRavindranath, Aakash Chavan-
dc.contributor.authorPERUALILA, Nolen Joy-
dc.contributor.authorKASIM, Adetayo-
dc.contributor.authorDrakakis, Georgios-
dc.contributor.authorLiggi, Sonia-
dc.contributor.authorBrewerton, Suzanne-
dc.contributor.authorMason, Daniel-
dc.contributor.authorBodkin, Michael-
dc.contributor.authorBhagwat, Aditya-
dc.contributor.authorTALLOEN, Willem-
dc.contributor.authorGohlmann, Hinrich-
dc.contributor.authorQstar Consortium-
dc.contributor.authorSHKEDY, Ziv-
dc.contributor.authorBender, Andreas-
dc.date.accessioned2014-11-21T11:40:28Z-
dc.date.available2014-11-21T11:40:28Z-
dc.date.issued2014-
dc.identifier.citationMolecular BioSystems, 11 (1), p 86-96-
dc.identifier.issn1742-206X-
dc.identifier.urihttp://hdl.handle.net/1942/17827-
dc.description.abstractIntegrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.-
dc.description.sponsorshipWe would like to gratefully acknowledge the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT) for providing us with the O&O grant 100988: QSTAR - Quantitative structure transcriptional activity relationship. We would like to thank IWT and Janssen Pharmaceutica NV for jointly funding PhD projects of Aakash Chavan Ravindranath and Nolen Perualila-Tan. Ziv Shkedy and Nolen Perualila-Tan gratefully acknowledge the support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy). Georgios Drakakis thanks Lilly and EPSRC for funding his PhD. Sonia Liggi and Daniel Mason thanks Unilever for funding. Dr Andreas Bender thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013-StG-336159 MIXTURE).-
dc.language.isoen-
dc.rightsThis journal is © The Royal Society of Chemistry 2014.-
dc.titleConnecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis-
dc.typeJournal Contribution-
dc.identifier.epage96-
dc.identifier.issue1-
dc.identifier.spage86-
dc.identifier.volume11-
local.format.pages11-
local.bibliographicCitation.jcatA1-
dc.description.notesShkedy, Z (reprint author), Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat, Agoralaan 1, B-3590 Diepenbeek, Belgium. ab454@cam.ac.uk-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1039/C4MB00328D-
dc.identifier.isi000345897600008-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
item.contributorQstar Consortium-
item.contributorTALLOEN, Willem-
item.contributorPERUALILA, Nolen Joy-
item.contributorSHKEDY, Ziv-
item.contributorDrakakis, Georgios-
item.contributorKASIM, Adetayo-
item.contributorRavindranath, Aakash Chavan-
item.contributorGohlmann, Hinrich-
item.contributorBender, Andreas-
item.contributorBrewerton, Suzanne-
item.contributorBodkin, Michael-
item.contributorLiggi, Sonia-
item.contributorBhagwat, Aditya-
item.contributorMason, Daniel-
item.fullcitationRavindranath, Aakash Chavan; PERUALILA, Nolen Joy; KASIM, Adetayo; Drakakis, Georgios; Liggi, Sonia; Brewerton, Suzanne; Mason, Daniel; Bodkin, Michael; Bhagwat, Aditya; TALLOEN, Willem; Gohlmann, Hinrich; Qstar Consortium; SHKEDY, Ziv & Bender, Andreas (2014) Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis. In: Molecular BioSystems, 11 (1), p 86-96.-
item.validationecoom 2016-
crisitem.journal.issn1742-206X-
Appears in Collections:Research publications
Files in This Item:
File Description SizeFormat 
c4mb00328d.pdfpublished version2.42 MBAdobe PDFView/Open
Show simple item record

SCOPUSTM   
Citations

18
checked on Sep 5, 2020

WEB OF SCIENCETM
Citations

20
checked on Jun 29, 2022

Page view(s)

230
checked on Jul 3, 2022

Download(s)

266
checked on Jul 3, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.