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

QSTAR Consortium, Aakash Chavan Ravindranath, Nolen Perualila-Tan, Adetayo Kasim, Georgios Drakakis, Sonia Liggi, Suzanne C. Brewerton, Daniel Mason, Michael J. Bodkin, David A. Evans, Aditya Bhagwat, Willem Talloen, Hinrich W.H. Göhlmann, Ziv Shkedy, Andreas Bender

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)86-96
Number of pages11
JournalMolecular BioSystems
Issue number1
Publication statusPublished - 1 Jan 2015
Externally publishedYes


  • Algorithms
  • Anti-Inflammatory Agents/pharmacology
  • Antineoplastic Agents/pharmacology
  • Antipsychotic Agents/pharmacology
  • Cell Line, Tumor
  • Cluster Analysis
  • Computational Biology/methods
  • Computer Simulation
  • Databases, Genetic
  • Drug Discovery/methods
  • Gene Expression Regulation/drug effects
  • Gene Regulatory Networks
  • Humans
  • Hypoglycemic Agents/pharmacology
  • Signal Transduction
  • Transcriptome


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