Investigation into the underlying molecular mechanisms of white adipose tissue through comparative transcriptome analysis of multiple tissues

Zhang,Song; Wang,Li; Zan,Linsen

doi:10.3892/mmr.2018.9740

Investigation into the underlying molecular mechanisms of white adipose tissue through comparative transcriptome analysis of multiple tissues

Authors:
- Song Zhang
- Li Wang
- Linsen Zan
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Affiliations: Key Laboratory of Animal Biotechnology, College of Animal Science and Technology, Northwest A&F University, Xianyang, Shaanxi 712100, P.R. China
Published online on: December 11, 2018 https://doi.org/10.3892/mmr.2018.9740
Pages: 959-966
Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Adipose tissue has a primary role in lipid and glucose metabolism as a storage site for fatty acids, and also functions as an endocrine organ, producing large numbers of hormones and cytokines. Adipose dysfunction triggers a number of obesity‑associated health problems. The aim of the present study was, therefore, to investigate the molecular mechanisms of white adipose tissue (WAT). The GSE9954 microarray data were downloaded from the Gene Expression Omnibus. Adipose‑specific genes were identified through limma package analysis, based on samples of WAT and 17 other types of non‑adipose tissue obtained from mice. Process and pathway enrichment analyses were performed for these genes. Finally, protein‑protein interaction (PPI) and co‑expression networks were constructed and analyzed. In total, 202 adipose‑specific genes were identified, which were involved in key biological processes (including fat cell differentiation and lipid metabolic process) and one key pathway [namely, the adenine monophosphate‑activated protein kinase (AMPK) signaling pathway]. Construction of the PPI network and further molecular complex detection revealed the presence of 17 key genes, including acetyl‑CoA carboxylase α, peroxisome proliferator‑activated receptor (PPAR) γ and leptin, that were involved in the AMPK, PPAR and insulin signaling pathways. In addition, amine oxidase copper containing 3 and adrenoceptor beta 3 were communication hubs in the co‑expression network of adipose‑specific genes. In conclusion, the present study promotes our understanding of the underlying molecular mechanisms of WAT, and may offer an insight into the prevention and treatment of obesity‑associated diseases caused by adipose dysfunction.

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1	World Health Organization. Obesity and overweight. http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweightMarch 8–2018
2	Hajer GR, van Haeften TW and Visseren FL: Adipose tissue dysfunction in obesity, diabetes, and vascular diseases. Eur Heart J. 29:2959–2971. 2008. View Article : Google Scholar : PubMed/NCBI
3	Oda E: Historical perspectives of the metabolic syndrome. Clin Dermatol. 36:3–8. 2018. View Article : Google Scholar : PubMed/NCBI
4	Moreno-Navarrete JM and Fernández-Real JM: Adipocyte differentiation. Adipose tissue biology. ‘Vol’. Springer; pp. 69–90. 2017, View Article : Google Scholar
5	Ali AT, Hochfeld WE, Myburgh R and Pepper MS: Adipocyte and adipogenesis. Eur J Cell Biol. 92:229–236. 2013. View Article : Google Scholar : PubMed/NCBI
6	Spalding KL, Arner E, Westermark PO, Bernard S, Buchholz BA, Bergmann O, Blomqvist L, Hoffstedt J, Näslund E, et al: Dynamics of fat cell turnover in humans. Nature. 453:783–787. 2008. View Article : Google Scholar : PubMed/NCBI
7	Gustafson B, Hedjazifar S, Gogg S, Hammarstedt A and Smith U: Insulin resistance and impaired adipogenesis. Trends Endocrin Metab. 26:193–200. 2015. View Article : Google Scholar
8	Hirsch J and Batchelor B: Adipose tissue cellularity in human obesity. Clin Endocrinol Metab. 5:299–311. 1976. View Article : Google Scholar : PubMed/NCBI
9	Ahima RS and Flier JS: Adipose tissue as an endocrine organ. Trends Endocrinol Metab. 11:327–332. 2000. View Article : Google Scholar : PubMed/NCBI
10	Ducharme NA and Bickel PE: Lipid droplets in lipogenesis and lipolysis. Endocrinology. 149:942–949. 2008. View Article : Google Scholar : PubMed/NCBI
11	Gregoire FM, Smas CM and Sul HS: Understanding adipocyte differentiation. Physiol Rev. 78:783–809. 1998. View Article : Google Scholar : PubMed/NCBI
12	Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, Rudnev D, Lash AE, Fujibuchi W and Edgar R: NCBI GEO: Mining millions of expression profiles-database and tools. Nucleic Acids Res. 33:(Database Issue). D562–D566. 2005. View Article : Google Scholar : PubMed/NCBI
13	Kaplan S, Boztepe S and Arikoglu H: The potential of microarray databases to identify tissue specific genes. Kafkas Univ Vet Fak. 22:29–35. 2016.
14	Ullah M, Stich S, Häupl T, Eucker J, Sittinger M and Ringe J: Reverse differentiation as a gene filtering tool in genome expression profiling of adipogenesis for fat marker gene selection and their analysis. PLoS One. 8:e697542013. View Article : Google Scholar : PubMed/NCBI
15	Fujioka S, Matsuzawa Y, Tokunaga K, Kawamoto T, Kobatake T, Keno Y, Kotani K, Yoshida S and Tarui S: Improvement of glucose and lipid metabolism associated with selective reduction of intra-abdominal visceral fat in premenopausal women with visceral fat obesity. Int J Obes. 15:853–859. 1991.PubMed/NCBI
16	Thorrez L, Van Deun K, Tranchevent LC, Van Lommel L, Engelen K, Marchal K, Moreau Y, Van Mechelen I and Schuit F: Using ribosomal protein genes as reference: A tale of caution. PLoS One. 3:e18542008. View Article : Google Scholar : PubMed/NCBI
17	Hastie T, Tibshirani R, Narasimhan B and Chu G: Impute: Imputation for microarray data. Bioinformatics. 17:520–525. 2001.PubMed/NCBI
18	Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI
19	Tripathi S, Pohl MO, Zhou Y, Rodriguez-Frandsen A, Wang G, Stein DA, Moulton HM, DeJesus P, Che J, Mulder LC, et al: Meta- and orthogonal integration of influenza ‘OMICs’ data defines a role for UBR4 in virus budding. Cell Host Microbe. 18:723–735. 2015. View Article : Google Scholar : PubMed/NCBI
20	Wu J, Mao X, Cai T, Luo J and Wei L: KOBAS server: A web-based platform for automated annotation and pathway identification. Nucleic Acids Res. 34:W720–W724. 2006. View Article : Google Scholar : PubMed/NCBI
21	Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, Kong L, Gao G, Li CY and Wei L: KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39:W316–W322. 2011. View Article : Google Scholar : PubMed/NCBI
22	Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C and Jensen LJ: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41:(Database Issue). D808–D815. 2013. View Article : Google Scholar : PubMed/NCBI
23	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
24	Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI
25	Langfelder P and Horvath S: WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI
26	Vázquez-Vela ME, Torres N and Tovar AR: White adipose tissue as endocrine organ and its role in obesity. Arch Med Res. 39:715–728. 2008. View Article : Google Scholar : PubMed/NCBI
27	Rosen ED, Walkey CJ, Puigserver P and Spiegelman BM: Transcriptional regulation of adipogenesis. Genes Dev. 14:1293–1307. 2000.PubMed/NCBI
28	Fernández-Galilea M, Pérez-Matute P, Prieto-Hontoria PL, Sáinz N, López-Yoldi M, Houssier M, Martínez JA, Langin D and Moreno-Aliaga MJ: α-lipoic acid reduces fatty acid esterification and lipogenesis in adipocytes from overweight/obese subjects. Obesity (Silver Spring). 22:2210–2215. 2014. View Article : Google Scholar : PubMed/NCBI
29	Minokoshi Y, Kim YB, Peroni OD, Fryer LG, Müller C, Carling D and Kahn BB: Leptin stimulates fatty-acid oxidation by activating AMP-activated protein kinase. Nature. 415:339–343. 2002. View Article : Google Scholar : PubMed/NCBI
30	Rosen ED and MacDougald OA: Adipocyte differentiation from the inside out. Nat Rev Mol Cell Biol. 7:885–896. 2006. View Article : Google Scholar : PubMed/NCBI
31	Song NJ, Kim S, Jang BH, Chang SH, Yun UJ, Park KM, Waki H, Li DY, Tontonoz P and Park KW: Small molecule-induced complement factor D (Adipsin) promotes lipid accumulation and adipocyte differentiation. PLoS One. 11:e1622282016.
32	Meier U and Gressner AM: Endocrine regulation of energy metabolism: Review of pathobiochemical and clinical chemical aspects of leptin, ghrelin, adiponectin, and resistin. Clin Chem. 50:1511–1525. 2004. View Article : Google Scholar : PubMed/NCBI
33	Damen MSMA, Dos Santos JC, Hermsen R, Adam van der Vliet J, Netea MG, Riksen NP, Dinarello CA, Joosten LAB and Heinhuis B: Interleukin-32 upregulates the expression of ABCA1 and ABCG1 resulting in reduced intracellular lipid concentrations in primary human hepatocytes. Atherosclerosis. 271:193–202. 2018. View Article : Google Scholar : PubMed/NCBI
34	Tanos R, Murray IA, Smith PB, Patterson A and Perdew GH: Role of the Ah receptor in homeostatic control of fatty acid synthesis in the liver. Toxicol Sci. 129:372–379. 2012. View Article : Google Scholar : PubMed/NCBI
35	Stahl A: A current review of fatty acid transport proteins (SLC27). Pflugers Arch. 447:722–727. 2004. View Article : Google Scholar : PubMed/NCBI
36	Hertzel AV and Bernlohr DA: The mammalian fatty acid-binding protein multigene family: Molecular and genetic insights into function. Trends Endocrinol Metab. 11:175–180. 2000. View Article : Google Scholar : PubMed/NCBI
37	Smith SJ, Cases S, Jensen DR, Chen HC, Sande E, Tow B, Sanan DA, Raber J, Eckel RH and Farese RV Jr: Obesity resistance and multiple mechanisms of triglyceride synthesis in mice lacking Dgat. Nat Genet. 25:87–90. 2000. View Article : Google Scholar : PubMed/NCBI
38	Takeuchi K and Reue K: Biochemistry, physiology, and genetics of GPAT, AGPAT, and lipin enzymes in triglyceride synthesis. Am J Physiol Endocrinol Metab. 296:E1195–E1209. 2009. View Article : Google Scholar : PubMed/NCBI
39	Li Y, Kang H, Chu Y, Jin Y, Zhang L, Yang R, Zhang Z, Zhao S and Zhou L: Cidec differentially regulates lipid deposition and secretion through two tissue-specific isoforms. Gene. 641:265–271. 2018. View Article : Google Scholar : PubMed/NCBI
40	Reilich P, Horvath R, Krause S, Schramm N, Turnbull DM, Trenell M, Hollingsworth KG, Gorman GS, Hans VH, Reimann J, et al: The phenotypic spectrum of neutral lipid storage myopathy due to mutations in the PNPLA2 gene. J Neurol. 258:1987–1997. 2011. View Article : Google Scholar : PubMed/NCBI
41	Hernandez-Guillamon M, Solé M, Delgado P, García- Bonilla L, Giralt D, Boada C, Penalba A, García S, Flores A, Ribó M, et al: VAP-1/SSAO plasma activity and brain expression in human hemorrhagic stroke. Cerebrovasc Dis. 33:55–63. 2012. View Article : Google Scholar : PubMed/NCBI
42	Schilter HC, Collison A, Russo RC, Foot JS, Yow TT, Vieira AT, Tavares LD, Mattes J, Teixeira MM and Jarolimek W: Effects of an anti-inflammatory VAP-1/SSAO inhibitor, PXS-4728A, on pulmonary neutrophil migration. Resp Res. 16:422015. View Article : Google Scholar
43	Bour S, Daviaud D, Gres S, Lefort C, Prévot D, Zorzano A, Wabitsch M, Saulnier-Blache J, Valet P and Carpéné C: Adipogenesis-related increase of semicarbazide-sensitive amine oxidase and monoamine oxidase in human adipocytes. Biochimie. 89:916–925. 2007. View Article : Google Scholar : PubMed/NCBI
44	Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, et al: Finding the missing heritability of complex diseases. Nature. 461:747–753. 2009. View Article : Google Scholar : PubMed/NCBI
45	Nakashima H, Omae K, Nomiyama T, Yamano Y, Takebayashi T and Sakurai Y: Beta-3-adrenergic receptor Trp64Arg polymorphism: Does it modulate the relationship between exercise and percentage of body fat in young adult Japanese males? Environ Health Prev Med. 18:323–329. 2013. View Article : Google Scholar : PubMed/NCBI