Reconstruction of gene association network reveals a transmembrane protein required for adipogenesis and targeted by PPARγ
We have developed a method for reconstructing gene association networks and have applied this method to gene profiles from 3T3-L1 cells. Priorization of the candidate genes pinpointed a transcript annotated as APMAP (adipocyte plasma membrane-associated protein). Functional studies showed that APMAP is upregulated in murine and human adipogenic cell models as well as in a genetic mouse model of obesity. Silencing APMAP in 3T3-L1 cells strongly impaired the differentiation into adipocytes. Moreover, APMAP expression was strongly induced by the PPARγ ligand rosiglitazone in adipocytes in vitro and in vivo in adipose tissue. Using ChIP-qPCR and luciferase reporter assays, we show a functional PPARγ binding site. In addition, we provide evidence that the extracellular C-terminal domain of APMAP is required for the function of APMAP in adipocyte differentiation. Finally, we demonstrate that APMAP translocates from the endoplasmatic reticulum to the plasma membrane during adipocyte differentiation.
KeywordsAdipogenesis APMAP PPARγ Transcriptional regulation Gene expression Gene networks
This work was supported by the Austrian Ministry for Science and Research (GEN-AU projects GOLD and BIN) and the Austrian Science Fund SFB (Project Lipotoxicity). PPARγ-MEFs were a gift from Dr. E. Rosen. OP9 cells were kindly provided by B. Pickel and SGBS cells by Novo Department of Pediatrics and Adolescent Medicine, University of Ulm. We thank David J. Steger and Mitch A. Lazar for providing ChIP material. The authors acknowledge the technical assistance provided by Stephan Seifriedsberger, Florian Stoeger, Martina Schweiger and Marie Loh.
- 10.Nielsen R, Pedersen TA, Hagenbeek D, Moulos P, Siersbaek R, Megens E, Denissov S, Borgesen M, Francoijs KJ, Mandrup S, Stunnenberg HG (2008) Genome-wide profiling of PPARgamma:RXR and RNA polymerase II occupancy reveals temporal activation of distinct metabolic pathways and changes in RXR dimer composition during adipogenesis. Genes Dev 22:2953–2967CrossRefPubMedGoogle Scholar
- 13.Liang S, Fuhrman S, Somogyi R (1998) Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput 18–29Google Scholar
- 15.Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel AE, Wingender E (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 34:D108–D110CrossRefPubMedGoogle Scholar
- 22.Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29CrossRefPubMedGoogle Scholar
- 25.Schneider G, Neuberger G, Wildpaner M, Tian S, Berezovsky I, Eisenhaber F (2006) Application of a sensitive collection heuristic for very large protein families: evolutionary relationship between adipose triglyceride lipase (ATGL) and classic mammalian lipases. BMC Bioinformatics 7:164CrossRefPubMedGoogle Scholar
- 29.Harel M, Aharoni A, Gaidukov L, Brumshtein B, Khersonsky O, Meged R, Dvir H, Ravelli RB, McCarthy A, Toker L, Silman I, Sussman JL, Tawfik DS (2004) Structure and evolution of the serum paraoxonase family of detoxifying and anti-atherosclerotic enzymes. Nat Struct Mol Biol 11:412–419CrossRefPubMedGoogle Scholar
- 35.Scheideler M, Elabd C, Zaragosi LE, Chiellini C, Hackl H, Sanchez-Cabo F, Yadav S, Duszka K, Friedl G, Papak C, Prokesch A, Windhager R, Ailhaud G, Dani C, Amri EZ, Trajanoski Z (2008) Comparative transcriptomics of human multipotent stem cells during adipogenesis and osteoblastogenesis. BMC Genomics 9:340CrossRefPubMedGoogle Scholar