In silico analysis of the molecular mechanism of postmenopausal osteoporosis

Liu,Yanqing; Wang,Yueqiu; Yang,Nailong; Wu,Suning; Lv,Yanhua; Xu,Lili

doi:10.3892/mmr.2015.4283

In silico analysis of the molecular mechanism of postmenopausal osteoporosis

Authors:
- Yanqing Liu
- Yueqiu Wang
- Nailong Yang
- Suning Wu
- Yanhua Lv
- Lili Xu
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Affiliations: Department of Geriatric Medicine, Jining No. 1 People's Hospital, Jining, Shandong 272011, P.R. China, Department of Joint Brach, Jining No. 2 People's Hospital, Jining, Shandong 272000, P.R. China, Department of Endocrinology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266003, P.R. China, Department of Obstertrics and Gynecology, The Affiliated Hospital of Jining Medical College, Jining, Shandong 272000, P.R. China
Published online on: September 2, 2015 https://doi.org/10.3892/mmr.2015.4283
Pages: 6584-6590
Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Postmenopausal osteoporosis (PO) is a common disease in females >50 years of age worldwide and is becoming an increasing burden to society. The present study aimed to assess the molecular mechanism of PO using bioinformatic methods. The gene expression data from patients with PO and normal controls were downloaded from the ArrayExpress database provided by European Bioinformatics Institute. Following the screening of the differentially expressed genes (DEGs) using the Limma package in R language, Kyoto Encyclopedia of Genes and Genomes pathways enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery online tools. Sequentially, modulators of the DEGs, including transcription factors (TFs) and microRNAs, were predicted by the ChIP Enrichment Analysis databases and WEB‑based GEne SeT AnaLysis Toolkit system, respectively. In addition, the protein‑protein interaction network of DEGs was constructed via the search tool for the retrieval of interacting genes and then the functional modules were further analyzed via the clusterMaker package and The Biological Networks Gene Ontology package within the Cytoscape software. A total of 482 DEGs, including 279 upregulated and 203 downregulated DEGs, were screened out. DEGs were predominantly enriched in the pathways of fatty acid metabolism, cardiac muscle contraction and DNA replication. TFs, including SMAD4, in addition to microRNAs, including the microRNA‑125 (miR‑125) family, miR‑331 and miR‑24, may be the modulators of the DEGs in PO. In addition, the five largest modules were identified with TTN, L1G1, ACADM, UQCRC2 and TRIM63 as the hub proteins, and they were associated with the biological processes of muscle contraction, DNA replication initiation, lipid modification, generation of precursor metabolites and energy, and regulation of acetyl‑CoA biosynthetic process, respectively. SMAD4, CACNG1 and TRIM63 are suggested to be important factors in the molecular mechanisms of PO, and miR‑331 may be novel potential biomarker for PO.

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1	Bouillon R, Burckhardt P, Christiansen C, et al: Consensus development conference: Prophylaxis and treatment of osteoporosis. Am J Med. 90:107–110. 1991. View Article : Google Scholar
2	Marcus R: Post-menopausal osteoporosis. Best Pract Res Clin Obstet Gynaecol. 16:309–327. 2002. View Article : Google Scholar : PubMed/NCBI
3	Michaëlsson K, Melhus H, Ferm H, Ahlbom A and Pedersen NL: Genetic liability to fractures in the elderly. Arch Intern Med. 165:1825–1830. 2005. View Article : Google Scholar : PubMed/NCBI
4	Mullin BH, Prince RL, Dick IM, Hart DJ, Spector TD, Dudbridge F and Wilson SG: Identification of a role for the ARHGEF3 gene in postmenopausal osteoporosis. Am J Hum Genet. 82:1262–1269. 2008. View Article : Google Scholar : PubMed/NCBI
5	Capulli M, Olstad OK, Önnerfjord P, Tillgren V, Muraca M, Gautvik KM, Heinegård D, Rucci N and Teti A: The C-terminal domain of chondroadherin: A new regulator of osteoclast motility counteracting bone loss. J Bone Miner Res. 29:1833–1846. 2014. View Article : Google Scholar : PubMed/NCBI
6	Reppe S, Refvem H, Gautvik VT, Olstad OK, Høvring PI, Reinholt FP, Holden M, Frigessi A, Jemtland R and Gautvik KM: Eight genes are highly associated with BMD variation in postmenopausal Caucasian women. Bone. 46:604–612. 2010. View Article : Google Scholar
7	Jemtland R, Holden M, Reppe S, Olstad OK, Reinholt FP, Gautvik VT, Refvem H, Frigessi A, Houston B and Gautvik KM: Molecular disease map of bone characterizing the postmenopausal osteoporosis phenotype. J Bone Miner Res. 26:1793–1801. 2011. View Article : Google Scholar : PubMed/NCBI
8	Brazma A, Parkinson H, Sarkans U, Shojatalab M, Vilo J, Abeygunawardena N, Holloway E, Kapushesky M, Kemmeren P, Lara GG, et al: ArrayExpress-a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31:68–71. 2003. View Article : Google Scholar : PubMed/NCBI
9	Bolstad BM, Irizarry RA, Astrand M and Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 19:185–193. 2003. View Article : Google Scholar : PubMed/NCBI
10	Smyth GK: Limma: Linear models for microarray data. Bioinformatics and computational biology solutions using R and Bioconductor. Springer; pp. 397–420. 2005, View Article : Google Scholar
11	Benjamini Y and Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 289–300. 1995.
12	Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
13	Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar
14	Lachmann A, Xu H, Krishnan J, Berger SI, Mazloom AR and Ma'ayan A: ChEA: Transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics. 26:2438–2444. 2010. View Article : Google Scholar : PubMed/NCBI
15	Wang J, Duncan D, Shi Z and Zhang B: WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): Update 2013. Nucleic Acids Res. 41:4392013.
16	Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, et al: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39:D561–D568. 2011. View Article : Google Scholar :
17	Smoot ME, Ono K, Ruscheinski J, Wang PL and Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar :
18	Enright AJ, Van Dongen S and Ouzounis CA: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30:1575–1584. 2002. View Article : Google Scholar : PubMed/NCBI
19	Morris JH, Apeltsin L, Newman AM, Baumbach J, Wittkop T, Su G, Bader GD and Ferrin TE: ClusterMaker: A multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics. 12:4362011. View Article : Google Scholar : PubMed/NCBI
20	Maere S, Heymans K and Kuiper M: BiNGO: A Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 21:3448–3449. 2005. View Article : Google Scholar : PubMed/NCBI
21	Li B: Bone morphogenetic protein-Smad pathway as drug targets for osteoporosis and cancer therapy. Endocr Metab Immune Disord Drug Targets. 8:208–219. 2008. View Article : Google Scholar : PubMed/NCBI
22	David L, Mallet C, Mazerbourg S, Feige JJ and Bailly S: Identification of BMP9 and BMP10 as functional activators of the orphan activin receptor-like kinase 1 (ALK1) in endothelial cells. Blood. 109:1953–1961. 2007. View Article : Google Scholar
23	Li B: Bone morphogenetic protein-Smad pathway as drug targets for osteoporosis and cancer therapy. Endocr Metab Immune Disord Drug Targets. 8:208–219. 2008. View Article : Google Scholar : PubMed/NCBI
24	Wu L, Wu Y, Gathings B, Wan M, Li X, Grizzle W, Liu Z, Lu C, Mao Z and Cao X: Smad4 as a transcription corepressor for estrogen receptor alpha. J Biol Chem. 278:15192–15200. 2003. View Article : Google Scholar : PubMed/NCBI
25	Genant HK, Lucas J, Weiss S, Akin M, Emkey R, McNaney-Flint H, Downs R, Mortola J, Watts N, Yang HM, et al: Low-dose esterified estrogen therapy: Effects on bone, plasma estradiol concentrations, endometrium and lipid levels. Estratab/Osteoporosis Study Group. Arch Intern Med. 157:2609–2615. 1997. View Article : Google Scholar
26	Cummings SR, Ensrud K, Delmas PD, LaCroix AZ, Vukicevic S, Reid DM, Goldstein S, Sriram U, Lee A, Thompson J, et al: Lasofoxifene in postmenopausal women with osteoporosis. N Engl J Med. 362:686–696. 2010. View Article : Google Scholar : PubMed/NCBI
27	Caro JJ, Ishak KJ, Huybrechts KF, Raggio G and Naujoks C: The impact of compliance with osteoporosis therapy on fracture rates in actual practice. Osteoporos Int. 15:1003–1008. 2004. View Article : Google Scholar : PubMed/NCBI
28	Cheng P, Chen C, He HB, Hu R, Zhou HD, Xie H, Zhu W, Dai RC, Wu XP, Liao EY, et al: miR-148a regulates osteoclastogenesis by targeting V-maf musculoaponeurotic fibrosarcoma oncogene homolog B. J Bone Miner Res. 28:1180–1190. 2013. View Article : Google Scholar
29	Wang Y, Li L, Moore BT, Peng XH, Fang X, Lappe JM, Recker RR and Xiao P: MiR-133a in human circulating monocytes: a potential biomarker associated with postmenopausal osteoporosis. PLoS One. 7:e346412012. View Article : Google Scholar : PubMed/NCBI
30	Cao Z, Moore BT, Wang Y, Peng XH, Lappe JM, Recker RR and Xiao P: MiR-422a as a potential cellular microRNA biomarker for postmenopausal osteoporosis. Plos One. 9:e970982014. View Article : Google Scholar : PubMed/NCBI
31	Seeliger C, Karpinski K, Haug AT, Vester H, Schmitt A, Bauer JS and van Griensven M: Five freely circulating miRNAs and bone tissue miRNAs are associated with osteoporotic fractures. J Bone Miner Res. 29:1718–1728. 2014. View Article : Google Scholar : PubMed/NCBI
32	Di Vizio D, Freeman MR and Morello M: Large oncosomes in human tumors and in circulation in patients with cancer. U.S. Patent Application 13/975,059. 2013 8 23
33	Burgess DL, Gefrides LA, Foreman PJ and Noebels JL: A cluster of three novel Ca2+ channel gamma subunit genes on chromosome 19q13. 4: evolution and expression profile of the gamma subunit gene family. Genomics. 71:339–350. 2001. View Article : Google Scholar : PubMed/NCBI
34	Giannini S, Nobile M, Dalle Carbonare L, Lodetti MG, Sella S, Vittadello G, Minicuci N and Crepaldi G: Hypercalciuria is a common and important finding in postmenopausal women with osteoporosis. Eur J Endocrinol. 149:209–213. 2003. View Article : Google Scholar : PubMed/NCBI
35	Azuma K, Urano T, Ouchi Y and Inoue S: Glucocorticoid-induced gene tripartite motif-containing 63 (TRIM63) promotes differentiation of osteoblastic cells. Endocr J. 57:455–462. 2009. View Article : Google Scholar
36	Kondo H, Ezura Y, Nakamoto T, Hayata T, Notomi T, Sorimachi H, Takeda S and Noda M: MURF1 deficiency suppresses unloading-induced effects on osteoblasts and osteoclasts to lead to bone loss. J Cell Biochem. 112:3525–3530. 2011. View Article : Google Scholar : PubMed/NCBI