title: Network-based analysis of transcriptional profiles from chemical perturbations experiments.
abstract: BACKGROUND
  Methods for inference and comparison of biological networks are emerging as powerful tools for the identification of groups of tightly connected genes whose activity may be altered during disease progression or due to chemical perturbations. Connectivity-based comparisons help identify aggregate changes that would be difficult to detect with differential analysis methods comparing individual genes.
  
  
  METHODS
  In this study, we describe a pipeline for network comparison and its application to the analysis of gene expression datasets from chemical perturbation experiments, with the goal of elucidating the modes of actions of the profiled perturbations. We apply our pipeline to the analysis of the DrugMatrix and the TG-GATEs, two of the largest toxicogenomics resources available, containing gene expression measurements for model organisms exposed to hundreds of chemical compounds with varying carcinogenicity and genotoxicity.
  
  
  RESULTS
  Starting from chemical-specific transcriptional networks inferred from these data, we show that the proposed comparative analysis of their associated networks identifies groups of chemicals with similar functions and similar carcinogenicity/genotoxicity profiles. We also show that the in-silico annotation by pathway enrichment analysis of the gene modules with a significant gain or loss of connectivity for specific groups of compounds can reveal molecular pathways significantly associated with the chemical perturbations and their likely modes of action.
  
  
  CONCLUSIONS
  The proposed pipeline for transcriptional network inference and comparison is highly reproducible and allows grouping chemicals with similar functions and carcinogenicity/genotoxicity profiles. In the context of drug discovery or drug repositioning, the methods presented here could help assign new functions to novel or existing drugs, based on the similarity of their associated network with those built for other known compounds. Additionally, the method has broad applicability beyond the uses here described and could be used as an alternative or as a complement to standard approaches of differential gene expression analysis.
authors.affiliation: Division of Computational Biomedicine, Boston University School of Medicine, 72 E Concord St, Boston, 02118, USA.
authors.forename: Francesca
authors.initials: F
authors.lastname: Mulas
authors.affiliation: Division of Computational Biomedicine, Boston University School of Medicine, 72 E Concord St, Boston, 02118, USA.
authors.forename: Amy
authors.initials: A
authors.lastname: Li
authors.affiliation: Department of Environmental Health, Boston University School of Public Health, 72 E Concord St, Boston, 02118, USA.
authors.forename: David H
authors.initials: DH
authors.lastname: Sherr
authors.affiliation: Division of Computational Biomedicine, Boston University School of Medicine, 72 E Concord St, Boston, 02118, USA. smonti@bu.edu.
authors.forename: Stefano
authors.initials: S
authors.lastname: Monti
mesh_terms: D000818:Animals; D002273:Carcinogens / Q000494:pharmacology / Q000633:toxicity*; D003198:Computer Simulation; D053263:Gene Regulatory Networks / Q000187:drug effects*; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000187:drug effects; D009153:Mutagens / Q000494:pharmacology / Q000633:toxicity*; D043922:Toxicogenetics / Q000379:methods*; D059467:Transcriptome / Q000187:drug effects*
chemical_list: D002273:Carcinogens; D009153:Mutagens
keywords: Chemical perturbations; Comparative analysis; Compounds similarity; Correlation networks; Gene expression; Toxicogenomics