title: Large-scale analysis of expression signatures reveals hidden links among diverse cellular processes.
abstract: BACKGROUND
  Cells must respond to various perturbations using their limited available gene repertoires. In order to study how cells coordinate various responses, we conducted a comprehensive comparison of 1,186 gene expression signatures (gene lists) associated with various genetic and chemical perturbations.
  
  
  RESULTS
  We identified 7,419 statistically significant overlaps between various published gene lists. Most (80%) of the overlaps can be represented by a highly connected network, a "molecular signature map," that highlights the correlation of various expression signatures. By dissecting this network, we identified sub-networks that define clusters of gene sets related to common biological processes (cell cycle, immune response, etc). Examination of these sub-networks has confirmed relationships among various pathways and also generated new hypotheses. For example, our result suggests that glutamine deficiency might suppress cellular growth by inhibiting the MYC pathway. Interestingly, we also observed 1,369 significant overlaps between a set of genes upregulated by factor X and a set of genes downregulated by factor Y, suggesting a repressive interaction between X and Y factors.
  
  
  CONCLUSIONS
  Our results suggest that molecular-level responses to diverse chemical and genetic perturbations are heavily interconnected in a modular fashion. Also, shared molecular pathways can be identified by comparing newly defined gene expression signatures with databases of previously published gene expression signatures.
authors.affiliation: Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57006, USA. gexijin@gmail.com
authors.forename: Steven X
authors.initials: SX
authors.lastname: Ge
mesh_terms: D000818:Animals; D002453:Cell Cycle; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D005973:Glutamine / Q000378:metabolism; D006801:Humans; D007107:Immune System; D051379:Mice; D008954:Models, Biological; D008957:Models, Genetic; D016271:Proto-Oncogene Proteins c-myc / Q000378:metabolism; D049490:Systems Biology / Q000379:methods*
chemical_list: D016271:Proto-Oncogene Proteins c-myc; D005973:Glutamine