NDLI: Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Ravindranath, Aakash Chavan Perualila-Tan, Nolen Kasim, Adetayo Drakakis, Georgios Liggi, Sonia Brewerton, Suzanne C. Mason, Daniel Bodkin, Michael J. Evans, David A. Bhagwat, Aditya Talloen, Willem Göhlmann, Hinrich W. H. Shkedy, Ziv Bender, Andreas
Organization	QSTAR Consortium
Description	Country affiliation: United kingdom Author Affiliation: Ravindranath AC ( Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK. ab454@cam.ac.uk.)
Abstract	Integrating 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.
File Format	HTM / HTML
ISSN	1742206X
Issue Number	1
Volume Number	11
e-ISSN	17422051
Journal	Molecular BioSystems
Language	English
Publisher	Royal Society of Chemistry
Publisher Date	2015-01-01
Publisher Place	Great Britain (UK)
Access Restriction	Subscribed
Subject Keyword	Discipline Molecular Biology Discipline Biochemistry Computational Biology Methods Computer Simulation Drug Discovery Gene Expression Regulation Gene Regulatory Networks Transcriptome Algorithms Anti-inflammatory Agents Pharmacology Antineoplastic Agents Antipsychotic Agents Cell Line, Tumor Cluster Analysis Databases, Genetic Drug Effects Humans Hypoglycemic Agents Signal Transduction Journal Article Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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