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Screening of candidate genes in fibroblasts derived from patients with Dupuytren’s contracture using bioinformatics analysis

  • Original Article - Genes and Disease
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Abstract

Our study aimed to identify candidate genes associated with Dupuytren’s contracture (DC) and elucidate their roles in DC development. The microarray data of GSE21221 were downloaded from Gene Expression Omnibus database, including six samples from carpal tunnel-derived fibroblasts and six samples from DC-derived fibroblasts. The differentially expressed genes (DEGs) in DC samples were screened using limma package. GO annotation and KEGG pathway analyses were performed by DAVID online tool. Protein–protein interaction network and expression correlation network were constructed to identify crucial relationships between DEGs. Finally, candidate DC-associated genes were predicted based on comparative toxicogenomics database. A total of 529 DEGs (138 up- and 391 down-regulated) in DC-derived fibroblasts were screened and compared with carpal tunnel-derived fibroblasts. Only ten DC-associated genes, such as neurotrophin 3 (NTF3) and protein kinase C, epsilon (PRKCE), were further screened. In addition, NTF3 was significantly enriched in MAPK signaling pathway, in which other DEGs, such as nuclear receptor subfamily 4, group A, member 1 (NR4A1), fibroblast growth factor 22 (FGF22) and BDNF, were enriched. Besides, NTF3 could co-express with fibrillin 2 (FBN2), and PRKCE could co-express with zinc finger protein 516 (ZNF516), solute carrier organic anion transporter family, member 2A1 (SLCO2A1), chromosome 10 open reading frame 10 (C10orf10) and Kelch domain containing 7A (KLHDC7A). Our study indicates that these DEGs, including NTF3, FBN2, NR4A1, FGF22, BDNF, PRKCE, ZNF516, SLCO2A1, C10orf10 and KLHDC7A, may play important roles in DC development and serve as candidate molecular targets for treating DC.

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References

  1. Wilbrand S, Flodmark C, Ekbom A, Gerdin B (2003) Activation markers of connective tissue in Dupuytren’s contracture: relation to postoperative outcome. Scand J Plast Reconstr Surg Hand Surg 37:283–292

    Article  PubMed  Google Scholar 

  2. Beldner S (2013) Dupuytren’s contracture. Orthopedics 36:929–930. doi:10.3928/01477447-20131120-05

    Article  PubMed  Google Scholar 

  3. Ryhanen J, Forsman M (2012) Dupuytren’s contracture. Duodecim 128:421–429

    PubMed  Google Scholar 

  4. Krüger-Sayn M, Porzberg G, Paschmeyer H (1998) Does the open palm technique for surgery of Dupuytren’s contracture extend treatment and disability duration? A retrospective study. [Handchirurgie, Mikrochirurgie, plastische Chirurgie: Organ der Deutschsprachigen Arbeitsgemeinschaft fur Handchirurgie: Organ der Deutschsprachigen Arbeitsgemeinschaft fur Mikrochirurgie der Peripheren Nerven und Gefasse]. Organ der V 30:269–271

    Google Scholar 

  5. Shaw RB Jr, Chong AK, Zhang A, Hentz VR, Chang J (2007) Dupuytren’s disease: history, diagnosis, and treatment. Plast Reconstr Surg 120:44e–54e

    Article  PubMed  Google Scholar 

  6. Satish L, LaFramboise WA, O’Gorman DB et al (2008) Identification of differentially expressed genes in fibroblasts derived from patients with Dupuytren’s contracture. BMC Med Genomics 1:10. doi:10.1186/1755-8794-1-10

    Article  PubMed Central  PubMed  Google Scholar 

  7. Pan D, Watson HK, Swigart C, Thomson JG, Honig SC, Narayan D (2003) Microarray gene analysis and expression profiles of Dupuytren’s contracture. Ann Plast Surg 50:618–622

    Article  PubMed  Google Scholar 

  8. Bayat A, Walter J, Lambe H, Watson JS, Stanley JK, Marino M, Ferguson MW, Ollier WE (2005) Identification of a novel mitochondrial mutation in Dupuytren’s disease using multiplex DHPLC. Plast Reconstr Surg 115:134–141

    CAS  PubMed  Google Scholar 

  9. Hu F, Nystrom A, Ahmed A et al (2005) Mapping of an autosomal dominant gene for Dupuytren’s contracture to chromosome 16q in a Swedish family. Clin Genet 68:424–429

    Article  CAS  PubMed  Google Scholar 

  10. Shih B, Wijeratne D, Armstrong DJ, Lindau T, Day P, Bayat A (2009) Identification of biomarkers in Dupuytren’s disease by comparative analysis of fibroblasts versus tissue biopsies in disease-specific phenotypes. J Hand Surg Am 34:124–136

    Article  PubMed  Google Scholar 

  11. Kraljevic Pavelic S, Sedic M, Hock K et al (2009) An integrated proteomics approach for studying the molecular pathogenesis of Dupuytren’s disease. J Pathol 217:524–533

    Article  PubMed  Google Scholar 

  12. Casalone R, Mazzola D, Meroni E et al (1997) Cytogenetic and interphase cytogenetic analyses reveal chromosome instability but no clonal trisomy 8 in Dupuytren contracture. Cancer Genet Cytogenet 99:73–76

    Article  CAS  PubMed  Google Scholar 

  13. Bayat A, Watson JS, Stanley JK, Ferguson MW, Ollier WE (2003) Genetic susceptibility to dupuytren disease: association of Zf9 transcription factor gene. Plast Reconstr Surg 111:2133–2139

    Article  PubMed  Google Scholar 

  14. Augoff K, Kula J, Gosk J, Rutowski R (2005) Epidermal growth factor in Dupuytren’s disease. Plast Reconstr Surg 115:128–133

    CAS  PubMed  Google Scholar 

  15. Hinz B, Gabbiani G (2012) The role of the myofibroblast in Dupuytren’s disease: fundamental aspects of contraction and therapeutic perspectives. In: Dupuytren’s disease and related hyperproliferative disorders. Springer, Basel, Switzerland, pp 53–60

  16. Ji X, Tian F, Tian L (2015) Identification and function analysis of contrary genes in Dupuytren’s contracture. Mol Med Rep 12:482–488

    PubMed  Google Scholar 

  17. Smyth GK (2005) Limma: linear models for microarray data. In: Bioinformatics and computational biology solutions using R and bioconductor. Springer, New York, pp 397–420

  18. Ashburner M, Ball CA, Blake JA et al (2000) Gene Ontology: tool for the unification of biology. Nat Genet 25:25–29

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Alvord G, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8:R183

    Article  PubMed Central  PubMed  Google Scholar 

  21. Franceschini A, Szklarczyk D, Frankild S et al (2013) STRING v9. 1: protein–protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41:D808–D815

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Nepusz T, Yu H, Paccanaro A (2012) Detecting overlapping protein complexes in protein–protein interaction networks. Nat Methods 9:471–472

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Janaki C, Joshi RR (2004) Motif detection in arabidopsis: correlation with gene expression data. In Silico Biol 4:149–161

    CAS  PubMed  Google Scholar 

  25. Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21:3448–3449

    Article  CAS  PubMed  Google Scholar 

  26. Davis AP, King BL, Mockus S, Murphy CG, Saraceni-Richards C, Rosenstein M, Wiegers T, Mattingly CJ (2011) The comparative toxicogenomics database: update 2011. Nucleic Acids Res 39:D1067–D1072

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Vaughan MB, Howard EW, Tomasek JJ (2000) Transforming growth factor-β1 promotes the morphological and functional differentiation of the myofibroblast. Exp Cell Res 257:180–189

    Article  CAS  PubMed  Google Scholar 

  28. Al-Qattan MM (2006) Factors in the pathogenesis of Dupuytren’s contracture. J Hand Surg Am 31:1527–1534

    Article  PubMed  Google Scholar 

  29. Petrov VV, Fagard RH, Lijnen PJ (2002) Stimulation of collagen production by transforming growth factor-β1 during differentiation of cardiac fibroblasts to myofibroblasts. Hypertension 39:258–263

    Article  CAS  PubMed  Google Scholar 

  30. Kuwahara F, Kai H, Tokuda K, Kai M, Takeshita A, Egashira K, Imaizumi T (2002) Transforming growth factor-β function blocking prevents myocardial fibrosis and diastolic dysfunction in pressure-overloaded rats. Circulation 106:130–135

    Article  CAS  PubMed  Google Scholar 

  31. Horiguchi M, Ota M, Rifkin DB (2012) Matrix control of transforming growth factor-β function. J Biochem 152:321–329

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Wagenseil JE, Mecham RP (2007) New insights into elastic fiber assembly. Birth Defects Res C Embryo Today 81:229–240

    Article  CAS  PubMed  Google Scholar 

  33. Forrester HB, Li J, Leong T, McKay MJ, Sprung CN (2014) Identification of a radiation sensitivity gene expression profile in primary fibroblasts derived from patients who developed radiotherapy-induced fibrosis. Radiother Oncol 111:186–193

    Article  CAS  PubMed  Google Scholar 

  34. Nishimura A, Sakai H, Ikegawa S et al (2007) FBN2, FBN1, TGFBR1, and TGFBR2 analyses in congenital contractural arachnodactyly. Am J Med Genet A 143:694–698

    Article  Google Scholar 

  35. Durany N, Michel T, Zöchling R, Boissl KW, Cruz-Sánchez FF, Riederer P, Thome J (2001) Brain-derived neurotrophic factor and neurotrophin 3 in schizophrenic psychoses. Schizophr Res 52:79–86

    Article  CAS  PubMed  Google Scholar 

  36. Howe EN, Cochrane DR, Cittelly DM, Richer JK (2012) miR-200c targets a NF-κB up-regulated TrkB/NTF3 autocrine signaling loop to enhance anoikis sensitivity in triple negative breast cancer. PLoS ONE 7:e49987

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Dolcet X, Llobet D, Pallares J, Matias-Guiu X (2005) NF-kB in development and progression of human cancer. Virchows Arch 446:475–482

    Article  CAS  PubMed  Google Scholar 

  38. Bonni A, Brunet A, West AE, Datta SR, Takasu MA, Greenberg ME (1999) Cell survival promoted by the Ras-MAPK signaling pathway by transcription-dependent and-independent mechanisms. Science 286:1358–1362

    Article  CAS  PubMed  Google Scholar 

  39. Hirano S, Rees RS, Gilmont RR (2002) MAP kinase pathways involving hsp27 regulate fibroblast-mediated wound contraction. J Surg Res 102:77–84

    Article  CAS  PubMed  Google Scholar 

  40. Krause C, Kloen P, ten Dijke P (2011) Elevated transforming growth factor B and mitogen-activated protein kinase pathways mediate fibrotic traits of Dupuytren’s disease fibroblasts. Fibrogenesis Tissue Repair 4:14

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Smith AG, Lim W, Pearen M, Muscat GE, Sturm RA (2011) Regulation of NR4A nuclear receptor expression by oncogenic BRAF in melanoma cells. Pigment Cell Melanoma Res 24:551–563

    Article  CAS  PubMed  Google Scholar 

  42. Palumbo-Zerr K, Zerr P, Distler A et al (2015) Orphan nuclear receptor NR4A1 regulates transforming growth factor-[beta] signaling and fibrosis. Nat Med 21:150–158

    Article  CAS  PubMed  Google Scholar 

  43. Mulhall JP, Thom J, Lubrano T, Shankey TV (2001) Basic fibroblast growth factor expression in Peyronie’s disease. J Urol 165:419–423

    Article  CAS  PubMed  Google Scholar 

  44. IJzer J, Roskams T, Molenbeek RF, Ultee T, Penning LC, Rothuizen J, Van den Ingh TS (2006) Morphological characterisation of portal myofibroblasts and hepatic stellate cells in the normal dog liver. Comp Hepatol 5:1–9

    Article  Google Scholar 

  45. Nguyen N, Lee SB, Lee YS, Lee K-H, Ahn J-Y (2009) Neuroprotection by NGF and BDNF against neurotoxin-exerted apoptotic death in neural stem cells are mediated through Trk receptors, activating PI3-kinase and MAPK pathways. Neurochem Res 34:942–951

    Article  CAS  PubMed  Google Scholar 

  46. Stawowy P, Margeta C, Blaschke F, Lindschau C, Spencer-Hänsch C, Leitges M, Biagini G, Fleck E, Graf K (2005) Protein kinase C epsilon mediates angiotensin II-induced activation of β1-integrins in cardiac fibroblasts. Cardiovasc Res 67:50–59

    Article  CAS  PubMed  Google Scholar 

  47. Bogatkevich GS, Gustilo E, Oates JC, Feghali-Bostwick C, Harley RA, Silver RM, Ludwicka-Bradley A (2005) Distinct PKC isoforms mediate cell survival and DNA synthesis in thrombin-induced myofibroblasts. Am J Physiol Lung Cell Mol Physiol 288:L190–L201

    Article  CAS  PubMed  Google Scholar 

  48. Ribas J, Michailidi C, Perez J, Soudry E, Tapiaz O, Guzman P, Munoz S (2014) Genome-wide methylation profiling reveals Zinc finger protein 516 (ZNF516) and FK-506-binding protein 6 (FKBP6) promoters frequently methylated in cervical neoplasia, associated with HPV status and ethnicity in a Chilean population. Epigenetics 9:1–10

    Article  Google Scholar 

  49. Zhang Z, Xia W, He J et al (2012) Exome sequencing identifies SLCO2A1 mutations as a cause of primary hypertrophic osteoarthropathy. Am J Hum Genet 90:125–132

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  50. Wang J, Robinson JF, O’Neil CH, Edwards JY, Williams CM, Huff MW, Pickering JG, Hegele RA (2006) Ankyrin G overexpression in Hutchinson–Gilford progeria syndrome fibroblasts identified through biological filtering of expression profiles. J Hum Genet 51:934–942

    Article  CAS  PubMed  Google Scholar 

  51. Vidhya G, Anusha B (2014) Diaretinopathy database—a gene database for diabetic retinopathy. Bioinformation 10:235

    Article  PubMed Central  PubMed  Google Scholar 

  52. Townley W, Baker R, Sheppard N, Grobbelaar A (2006) Dupuytren’s contracture unfolded. BMJ 332:397–400

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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Correspondence to Zhuang Wei.

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Haoyu Liu and Weitian Yin have contributed equally to this work.

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Liu, H., Yin, W., Liu, B. et al. Screening of candidate genes in fibroblasts derived from patients with Dupuytren’s contracture using bioinformatics analysis. Rheumatol Int 35, 1343–1350 (2015). https://doi.org/10.1007/s00296-015-3276-3

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  • DOI: https://doi.org/10.1007/s00296-015-3276-3

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