Identification of differentially expressed genes in a porcine in vivo model of adipogenesis using suppression subtractive hybridization |
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Authors: | Benoît Labrecque Olivier Mathieu Vilceu Bordignon Bruce D. Murphy Marie-France Palin |
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Affiliation: | 1. Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm University, SE-10691 Stockholm, Sweden;2. Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Max-von-Laue-Straße 7, D-60438 Frankfurt am Main, Germany;3. Science for Life Laboratory, Stockholm University, SE-17177 Solna, Sweden;1. Department of Health and Medical Sciences, Graduate School of Medicine, Shinshu University, Japan;2. Department of Laboratory Medicine, Shinshu University Hospital, Japan |
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Abstract: | Although they provide valuable information, in vitro models of adipocyte development often require high doses of hormones and growth factors, which may influence gene expression and adipocyte differentiation patterns. To overcome these problems, a novel in vivo model of adipose tissue development was used to characterize genes involved in adipogenesis. The suppression subtractive hybridization technique was used to identify genes showing differential expression between the adipose tissue of a day 90 gestating sow, which is enriched in adipocytes, and day 90 fetal adipose tissue, which is enriched in preadipocytes. A total of 149 expressed sequence tags corresponding to identified genes and tentative consensus sequences emerged. Thirty-seven clones matched expressed sequence tags or genomic DNA sequences and six novel sequences were also identified. Adipogenesis-related genes were identified, many of which have never been reported to be expressed in mammalian adipose tissue, and may play a role in regulation of adipose tissue differentiation. Validation of differentially expressed genes was confirmed for perilipin, monocyte to macrophage differentiation-associated, myocilin, paraoxonase 3, stearoyl-CoA desaturase, angiotensinogen and adiponectin genes using real-time RT-PCR. |
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