Identification of key genes associated with Schmid-type metaphyseal chondrodysplasia based on microarray data

Wang,Bing; He,Li; Miao,Wusheng; Wu,Ge; Jiang,Hai; Wu,Yongtao; Qu,Jining; Li,Min

doi:10.3892/ijmm.2017.2954

Identification of key genes associated with Schmid-type metaphyseal chondrodysplasia based on microarray data

Authors:
- Bing Wang
- Li He
- Wusheng Miao
- Ge Wu
- Hai Jiang
- Yongtao Wu
- Jining Qu
- Min Li
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Affiliations: Department of Pediatric Orthopedics, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710054, P.R. China, Department of Child Health Care, Xi'an Children's Hospital, Xi'an, Shaanxi 710003, P.R. China
Published online on: April 19, 2017 https://doi.org/10.3892/ijmm.2017.2954
Pages: 1428-1436
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

This study aimed to gain a better understanding of the molecular circuitry of Schmid-type metaphyseal chondrodysplasia (SMCD), and to identify more potential genes associated with the pathogenesis of SMCD. Microarray data from GSE72261 were downloaded from the NCBI GEO database, including collagen X p.Asn617Lys knock-in mutation (ColXN617K), ablated XBP1 activity (Xbp1CartΔEx2), compound mutant (C/X), and wild-type (WT) specimens. Differentially expressed genes (DEGs) were screened in Xbp1 vs. WT, Col vs. WT and CX vs. WT, respectively. Pathway enrichment analysis of these DEGs was performed. Transcription factors (TFs) of the overlapping DEGs were identified. Weighted correlation network analysis (WGCNA) was performed to find modules of DEGs with high correlations, followed by gene function analysis and a protein-protein interaction network construction. In total, 481, 1,530 and 1,214 DEGs were identified in Xbp1 vs. WT, Col vs. WT and CX vs. WT, respectively. These DEGs were enriched in different pathways, such as extracellular matrix (ECM)-receptor interaction and metabolism-related pathways. A total of 7 TFs were found to regulate 19 common upregulated genes, and 4 TFs were identified to regulate 21 common downregulated genes. Two significant gene co-expression modules were enriched and DEGs in the 2 modules were mainly enriched in different biological processes, such as ribosome biogenesis. Moreover, Kras (downregulated), Col5a1 (upregulated) and Furin (upregulated) were both identified in the regulatory networks and protein-protein interaction (PPI) network. On the whole, our findings indicate that the Kras, Col5a1 and Furin genes may play essential roles in the molecular mechanisms of SMCD, which warrants further investigation.

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1	Park H, Hong S, Cho SI, Cho TJ, Choi IH, Jin DK, Sohn YB, Park SW, Cho HH, Cheon JE, et al: Case of mild Schmid-type metaphyseal chondrodysplasia with novel sequence variation involving an unusual mutational site of the COL10A1 gene. Eur J Med Genet. 58:175–179. 2015. View Article : Google Scholar
2	Bateman JF, Freddi S, Nattrass G and Savarirayan R: Tissue-specific RNA surveillance? Nonsense-mediated mRNA decay causes collagen X haploinsufficiency in Schmid metaphyseal chondrodysplasia cartilage. Hum Mol Genet. 12:217–225. 2003. View Article : Google Scholar : PubMed/NCBI
3	Patterson SE and Dealy CN: Mechanisms and models of endoplasmic reticulum stress in chondrodysplasia. Dev Dyn. 243:875–893. 2014. View Article : Google Scholar : PubMed/NCBI
4	Bogin O, Kvansakul M, Rom E, Singer J, Yayon A and Hohenester E: Insight into Schmid metaphyseal chondrodysplasia from the crystal structure of the collagen X NC1 domain trimer. Structure. 10:165–173. 2002. View Article : Google Scholar : PubMed/NCBI
5	Woelfle JV, Brenner RE, Zabel B, Reichel H and Nelitz M: Schmid-type metaphyseal chondrodysplasia as the result of a collagen type X defect due to a novel COL10A1 nonsense mutation: A case report of a novel COL10A1 mutation. J Orthop Sci. 16:245–249. 2011. View Article : Google Scholar : PubMed/NCBI
6	Warman ML, Cormier-Daire V, Hall C, Krakow D, Lachman R, LeMerrer M, Mortier G, Mundlos S, Nishimura G, Rimoin DL, et al: Nosology and classification of genetic skeletal disorders: 2010 revision. Am J Med Genet A. 155A:943–968. 2011. View Article : Google Scholar : PubMed/NCBI
7	Arnold WV and Fertala A: Skeletal diseases caused by mutations that affect collagen structure and function. Int J Biochem Cell Biol. 45:1556–1567. 2013. View Article : Google Scholar : PubMed/NCBI
8	Hetz C: The unfolded protein response: Controlling cell fate decisions under ER stress and beyond. Nat Rev Mol Cell Biol. 13:89–102. 2012.PubMed/NCBI
9	Rajpar MH, McDermott B, Kung L, Eardley R, Knowles L, Heeran M, Thornton DJ, Wilson R, Bateman JF and Poulsom R: Targeted induction of endoplasmic reticulum stress induces cartilage pathology. PLoS Genet. 5:e10006912009. View Article : Google Scholar : PubMed/NCBI
10	Yoshida H, Matsui T, Yamamoto A, Okada T and Mori K: XBP1 mRNA is induced by ATF6 and spliced by IRE1 in response to ER stress to produce a highly active transcription factor. Cell. 107:881–891. 2001. View Article : Google Scholar
11	Cameron TL, Bell KM, Gresshoff IL, Sampurno L, Mullan L, Ermann J, Glimcher LH, Boot-Handford RP and Bateman JF: XBP1-Independent UPR Pathways Suppress C/EBP-β Mediated Chondrocyte Differentiation in ER-Stress Related Skeletal Disease. PLoS Genet. 11:e10055052015. View Article : Google Scholar
12	Bolstad BM: preprocessCore: A collection of pre-processing functions. R package version 1. 2013, http://www.bioconductor.org/packages/release/bioc/html/preprocessCore.html. Accessed March 27, 2013.
13	Gardeux V, Arslan AD, Achour I, Ho TT, Beck WT and Lussier YA: Concordance of deregulated mechanisms unveiled in underpowered experiments: TBP1 knockdown case study. BMC Med Genomics. 7(Suppl 1): S12014. View Article : Google Scholar :
14	Hoeksema MA, Scicluna BP, Boshuizen MC, van der Velden S, Neele AE, Van den Bossche J, Matlung HL, van den Berg TK, Goossens P and de Winther MP: IFN-γ priming of macrophages represses a part of the inflammatory program and attenuates neutrophil recruitment. J Immunol. 194:3909–3916. 2015. View Article : Google Scholar : PubMed/NCBI
15	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
16	Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, Lumley T, Maechler M, Magnusson A and Moeller S: gplots: Various R programming tools for plotting data. R package version 2.12.1. 2013, http://cran.r-project.org/web/packages/gplots/index.html.
17	Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:32003. View Article : Google Scholar
18	Mao X, Cai T, Olyarchuk JG and Wei L: Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics. 21:3787–3793. 2005. View Article : Google Scholar : PubMed/NCBI
19	Janky R, Verfaillie A, Imrichová H, Van de Sande B, Standaert L, Christiaens V, Hulselmans G, Herten K, Naval Sanchez M and Potier D: iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol. 10:e10037312014. View Article : Google Scholar : PubMed/NCBI
20	Smoot ME, Ono K, Ruscheinski J, Wang P-L and Ideker T: Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar :
21	Langfelder P and Horvath S: WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics. 9:5592008. 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, et al: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41:D808–D815. 2013. View Article : Google Scholar :
23	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 :
24	Spinelli L, Gambette P, Chapple CE, Robisson B, Baudot A, Garreta H, Tichit L, Guénoche A and Brun C: Clust&See: A Cytoscape plugin for the identification, visualization and manipulation of network clusters. Biosystems. 113:91–95. 2013. View Article : Google Scholar : PubMed/NCBI
25	Vakiani E and Solit DB: KRAS and BRAF: Drug targets and predictive biomarkers. J Pathol. 223:219–229. 2011. View Article : Google Scholar
26	Bryant KL, Mancias JD, Kimmelman AC and Der CJ: KRAS: Feeding pancreatic cancer proliferation. Trends Biochem Sci. 39:91–100. 2014. View Article : Google Scholar : PubMed/NCBI
27	Chan D, Ho MS and Cheah KS: Aberrant signal peptide cleavage of collagen X in Schmid metaphyseal chondrodysplasia. Implications for the molecular basis of the disease. J Biol Chem. 276:7992–7997. 2001. View Article : Google Scholar
28	Raleigh SM, van der Merwe L, Ribbans WJ, Smith RK, Schwellnus MP and Collins M: Variants within the MMP3 gene are associated with Achilles tendinopathy: Possible interaction with the COL5A1 gene. Br J Sports Med. 43:514–520. 2009. View Article : Google Scholar
29	Kahai S, Vary CP, Gao Y and Seth A: Collagen, type V, α1 (COL5A1) is regulated by TGF-β in osteoblasts. Matrix Biol. 23:445–455. 2004. View Article : Google Scholar : PubMed/NCBI
30	Dubois CM, Blanchette F, Laprise MH, Leduc R, Grondin F and Seidah NG: Evidence that furin is an authentic transforming growth factor-β1-converting enzyme. Am J Pathol. 158:305–316. 2001. View Article : Google Scholar : PubMed/NCBI
31	Yang M, Wang X, Zhang L, Yu C, Zhang B, Cole W, Cavey G, Davidson P and Gibson G: Demonstration of the interaction of transforming growth factor beta 2 and type X collagen using a modified tandem affinity purification tag. J Chromatogr B Analyt Technol Biomed Life Sci. 875:493–501. 2008. View Article : Google Scholar : PubMed/NCBI