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  • Autor
    • Hackl, Hubert
    • Burkard, Thomas
    • Sturn, Alexander
    • Rubio, Renee
    • Schleiffer, Alexander
    • Tian, Sun
    • Quackenbush, John
    • Eisenhaber, Frank
    • Trajanoski, Zlatko
  • TitelMolecular processes during fat cell development revealed by gene expression profiling and functional annotation
  • Volltext
  • DOI10.1186/gb-2005-6-13-r108
  • Persistent Identifier
  • Erschienen inGenome Biology
  • Band6
  • Erscheinungsjahr2005
  • Heft13
  • LicenceCC-BY
  • ISSN1465-6906
  • ZugriffsrechteCC-BY
  • Download Statistik104
  • Peer ReviewNein
  • AbstractBACKGROUND:Large-scale transcription profiling of cell models and model organisms can identify novel molecular components involved in fat cell development. Detailed characterization of the sequences of identified gene products has not been done and global mechanisms have not been investigated. We evaluated the extent to which molecular processes can be revealed by expression profiling and functional annotation of genes that are differentially expressed during fat cell development.RESULTS:Mouse microarrays with more than 27,000 elements were developed, and transcriptional profiles of 3T3-L1 cells (pre-adipocyte cells) were monitored during differentiation. In total, 780 differentially expressed expressed sequence tags (ESTs) were subjected to in-depth bioinformatics analyses. The analysis of 3'-untranslated region sequences from 395 ESTs showed that 71% of the differentially expressed genes could be regulated by microRNAs. A molecular atlas of fat cell development was then constructed by de novo functional annotation on a sequence segment/domain-wise basis of 659 protein sequences, and subsequent mapping onto known pathways, possible cellular roles, and subcellular localizations. Key enzymes in 27 out of 36 investigated metabolic pathways were regulated at the transcriptional level, typically at the rate-limiting steps in these pathways. Also, coexpressed genes rarely shared consensus transcription-factor binding sites, and were typically not clustered in adjacent chromosomal regions, but were instead widely dispersed throughout the genome.CONCLUSIONS:Large-scale transcription profiling in conjunction with sophisticated bioinformatics analyses can provide not only a list of novel players in a particular setting but also a global view on biological processes and molecular networks.