title: Function-based discovery of significant transcriptional temporal patterns in insulin stimulated muscle cells.
abstract: BACKGROUND
  Insulin action on protein synthesis (translation of transcripts) and post-translational modifications, especially of those involving the reversible modifications such as phosphorylation of various signaling proteins, are extensively studied but insulin effect on transcription of genes, especially of transcriptional temporal patterns remains to be fully defined.
  
  
  RESULTS
  To identify significant transcriptional temporal patterns we utilized primary differentiated rat skeletal muscle myotubes which were treated with insulin and samples were collected every 20 min for 8 hours. Pooled samples at every hour were analyzed by gene array approach to measure transcript levels. The patterns of transcript levels were analyzed based on a novel method that integrates selection, clustering, and functional annotation to find the main temporal patterns associated to functional groups of differentially expressed genes. 326 genes were found to be differentially expressed in response to in vitro insulin administration in skeletal muscle myotubes. Approximately 20% of the genes that were differentially expressed were identified as belonging to the insulin signaling pathway. Characteristic transcriptional temporal patterns include: (a) a slow and gradual decrease in gene expression, (b) a gradual increase in gene expression reaching a peak at about 5 hours and then reaching a plateau or an initial decrease and other different variable pattern of increase in gene expression over time.
  
  
  CONCLUSIONS
  The new method allows identifying characteristic dynamic responses to insulin stimulus, common to a number of genes and associated to the same functional group. The results demonstrate that insulin treatment elicited different clusters of gene transcript profile supporting a temporal regulation of gene expression by insulin in skeletal muscle cells.
authors.affiliation: Information Engineering Department, University of Padova, Padova, Italy.
authors.forename: Barbara
authors.initials: B
authors.lastname: Di Camillo
authors.forename: Brian A
authors.initials: BA
authors.lastname: Irving
authors.forename: Jill
authors.initials: J
authors.lastname: Schimke
authors.forename: Tiziana
authors.initials: T
authors.lastname: Sanavia
authors.forename: Gianna
authors.initials: G
authors.lastname: Toffolo
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
authors.forename: K Sreekumaran
authors.initials: KS
authors.lastname: Nair
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D005947:Glucose / Q000378:metabolism; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007328:Insulin / Q000378:metabolism*; D015233:Models, Statistical; D032342:Muscle Cells / Q000378:metabolism*; D018482:Muscle, Skeletal / Q000166:cytology / Q000378:metabolism; D009132:Muscles / Q000166:cytology / Q000378:metabolism*; D032446:Myoblasts / Q000166:cytology; D020411:Oligonucleotide Array Sequence Analysis; D010766:Phosphorylation; D011499:Protein Processing, Post-Translational; D015398:Signal Transduction; D013997:Time Factors; D014158:Transcription, Genetic
chemical_list: D007328:Insulin; D005947:Glucose