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Molecular processes during fat cell development revealed by gene expression profiling and functional annotation

Hubert Hackl1, Thomas Rainer Burkard12, Alexander Sturn1, Renee Rubio3, Alexander Schleiffer2, Sun Tian2, John Quackenbush3, Frank Eisenhaber2 and Zlatko Trajanoski1*

Author Affiliations

1 Institute for Genomics and Bioinformatics and Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria

2 Research Institute of Molecular Pathology, Dr Bohr-Gasse 7, 1030 Vienna, Austria

3 Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology, 44 Binney Street, Boston, MA 02115

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Genome Biology 2005, 6:R108  doi:10.1186/gb-2005-6-13-r108

Published: 19 December 2005

Abstract

Background

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.