The Role of Functional Polymorphisms in the Extracellular Matrix Modulation-Related Genes on Dupuytren’s Contracture
by
Gediminas Samulenas
1, 
Ruta Insodaite
2,*,
Edita Kunceviciene
2,
Roberta Poceviciute
2,
Lorena Masionyte
2,
Urte Zitkeviciute
3,
Loreta Pilipaityte
1 and
Alina Smalinskiene
2 1
Department of Plastic and Reconstructive Surgery, Lithuanian University of Health Sciences, LT 50009 Kaunas, Lithuania
2
Institute of Biology Systems and Genetics Research, Lithuanian University of Health Sciences, LT 50103 Kaunas, Lithuania
3
Faculty of Medicine, Lithuanian University of Health Sciences, LT 44307 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Genes 2022, 13(5), 743; https://doi.org/10.3390/genes13050743
Submission received: 26 March 2022
/
Revised: 21 April 2022
/
Accepted: 22 April 2022
/
Published: 23 April 2022
(This article belongs to the Special Issue Genetic and Epigenetic Factors and Their Interactions in the Susceptibility to Multifactorial Diseases)
Abstract
:(1) Background: genetic variations, localized in the functional regions of the extracellular matrix (ECM) modulation-related genes, may alter the transcription process and impact the Dupuytren’s contracture (DC). The present study investigated the association of single nucleotide polymorphisms (SNPs), localized in the functional regions of the MMP8, MMP14, and CHST6 genes, with DC risk. (2) Methods: we enrolled 219 genomic DNA samples, which were extracted from 116 patients with DC and 103 healthy controls. Genotyping of selected SNPs was performed using TaqMan single nucleotide polymorphisms genotyping assay. Three polymorphisms (MMP8 rs11225395, MMP14 rs1042704, and CHST6 rs977987) were analyzed. All studied SNPs were in Hardy–Weinberg equilibrium. (3) Results: significant associations of the studied SNPs with the previous onset of the disease were observed between the CHST6 rs977987 minor T allele (p = 0.036) and the MMP14 rs1042704 mutant AA genotype (p = 0.024). Significant associations with the previous onset of the disease were also observed with a positive family history of the DC (p = 0.035). Moreover, risk factor analysis revealed that a combination of major disease risk factors (smoking and manual labor) and the MMP14 minor A allele increases the risk of DC development by fourteen times (p = 0.010). (4) Conclusions: our findings suggest that CHST6 rs977987, MMP14 rs1042704, and positive family history are associated with the previous onset of Dupuytren’s contracture. In addition, the combination of the MMP14 minor A allele and additional risk factors increase the likelihood of the manifestation of the DC.
1. Introduction
Dupuytren’s contracture manifests as a chronic hand condition, causing fibroproliferative alterations in the structure of the palmar aponeurosis, resulting in progressive flexion contractures of finger joints [1,2,3]. Disease prevalence rates range from 0.6% to 31.6% in different population groups. Significantly higher rates of DC are observed among middle-aged or older males of Northern European descent [4,5]. Epidemiological studies have suggested a correlation with alcoholism, smoking, increasing age, male gender, diabetes, and epilepsy [6]. Also, genetic predisposition plays a major role in the development of this condition. Studies show that genetic factors, and especially heredity, account for 80% of the factors involved in causing this disease [7]. A genome-wide association study identified 26 risk alleles associated with DC [8,9]. Despite this, the pathogenesis of DC disease has not been fully explained.
In Luck classification, Dupuytren’s tissue has been reported to contain several histological zones [10]. The proliferative or active zone is characterized by the presence of a large number of (myo)fibroblasts and the development of a nodular lesion. The involution zone contains large amounts of collagens and myofibroblasts. Cells in the involution zone align themselves to lines of stress. The final stage or the residual zone leaves scar-like cord tissue. The growing nodules and the arrangement of newly formatted fibers entail tissue reorganization coupled with degradation of the surrounding ECM [11,12].
The major enzymes implicated in ECM degradation are considered matrix metalloproteinases (MMP) or matrixsins. MMPs are calcium-dependent, zinc-containing endopeptidases involved in tissue remodeling by interfering with the cell-cell and cell-extracellular matrix interactions [13]. In general, MMPs degrade different kinds of ECM components, including collagens, elastin, gelatin, matrix glycoproteins, proteoglycan, other ECM molecules, and soluble proteins [14]. Different MMPs (MMP2, MMP9, MMP14) and their tissue inhibitors (TIMP1 and TIMP2) expression were studied in DC tissue, but only a few have been prognostically significant [15,16]. Ulrich and coauthors show a significant correlation between collagenase MMP2 expression changes and DC [12]. Additionally, some studies show that MMP14 is over-expressed in DD nodules, and knockdown of MMP14 in DC-derived cells reduced both contraction and MMP2 activation in vitro [15,17]. Another potential biomarker for DC, due to its ability to degrade type I collagen three times more patently compared to other collagenases (MMP1, MMP13, and MMP18), could be MMP8 [18]. According to biochemical analysis, the DC cord demonstrated the abundance of type I collagen [19,20]. These findings can be explained by MMP8 and other collagenases’ expression changes in DC pathogenesis; however, the association between these MMPs and this pathology is poorly investigated.
A genome-wide association study identifies another with ECM changes in DC-related gene carbohydrate sulfotransferase 6 (CHST6), which is involved in the biosynthesis of keratan sulfate [8]. Keratan sulfate is a carbohydrate moiety of the proteoglycans found especially in the corneal stroma, extracellular matrix, cartilage, and bone [21]. Studies showed that mutations in CHST6 cause a keratan sulfate metabolism change, resulting in the deposition of an unsulfated proteoglycan, both within the intracellular and also in the extracellular space, and disturbance of the structural integrity of the tissues. Most studies analyzed the association between CHST6 mutations, their caused expression changes, and macular corneal dystrophy, but to our knowledge, the relation between this gene and DC was analyzed in only one study [22].
Single nucleotide polymorphisms in the genes related to ECM changes may play a role in the development of DC. Three functional SNPs in MMP14 (rs1042704), MMP8 (rs11225395), and CHST6 (rs977987) were selected. MMP14 gene polymorphism rs1042704 results in missense mutation that changes both the sequence and the function of the MMP14. Studies have shown that the altered protein exhibited significantly less collagen degradation activity compared to the wild-type enzyme [23]. Another polymorphism, rs11225395, is localized in the MMP8 gene promoter region causing changes in protein expression levels [24,25,26]. The third selected polymorphism is localized in the 3′-untranslated region of the CHST6 gene and may cause protein expression changes due to modifications in the miRNA binding site [27]. Genome-wide association study showed possible links between selected MMP14 and CHST6 gene polymorphisms and DC [8]. However, to the best of our knowledge, there is no data in the literature about the possible links between MMP8 gene polymorphisms and DC development.
Therefore, the present study aimed to investigate the contributions of MMP8, MMP14, and CHST6 gene functional polymorphisms to the development of DC.
2. Materials and Methods
2.1. Ethics Statement
Permission to undertake the study was obtained from the Kaunas Regional Biomedical Research Ethics Committee (Number No. BE-2-21, 08 March 2019) and from the Department of Bioethics, LUHS (BEC-MF-63. 23 October 2017). Before the study, the procedure and purpose of the study were explained, and informed consent was obtained from all participants.
2.2. Study Population
We collected peripheral blood samples of 219 day-case patients (116 DC patients and 103 healthy controls), receiving surgery in the Department of Plastic and Reconstructive Surgery, Hospital of LUHS (Table 1). The DC group included patients with diagnosis of Dupuytren’s contracture. The criteria for the control group were no clinical signs or previous diagnosis/treatment of DC or stenosing tenosynovitis and no family history of Dupuytren’s contracture. Patients younger than 30 years were excluded from the study. All patients were indigenous Lithuanians. Data on patients’ general health and concomitant conditions were acquired from clinical examination and medical records.
2.3. Candidate Polymorphisms
The genes and polymorphisms known to participate in ECM modulation were selected. The selection criteria included: (i) functional SNPs in MMP8, MMP14, and CHST6 genes; (ii) SNPs relevant to outcomes in other settings; and (iii) SNPs with minor allele frequency greater than 10% in the study population. We selected three functional SNPs: the MMP14 gene rs1042704; the MMP8 gene rs11225395; and the CHST6 gene rs977987.
2.4. DNA Extraction and Genotyping
Peripheral blood samples were collected from each individual in ethylenediaminetetraacetic (EDTA) tubes for DNA extraction. DNA was extracted from leukocytes using a reagent kit (NucleoSpin Blood L Kit; Macherey & Nagel, Düren, Germany) according to the manufacturer’s recommendations.
Four SNPs of the MMP8, CHST6, and MMP14 genes were assessed using commercial genotyping kits (C_175744753_10, C___2848998_10, C__11436237_20, Applied Biosystems, Foster City, CA, USA). The Applied Biosystems 7900HT Real-Time Polymerase Chain Reaction System (Applied Biosystems, Foster City, CA, USA) was used for detecting the SNPs. The cycling program started with heating for 10 min at 95 °C, followed by 40 cycles of 15 s at 95 °C and 1 min at 60 °C. Allelic discrimination was performed using the SDS 2.3 software (Applied Biosystems, Foster City, CA, USA). For negative control, nuclease-free ddH2O was used, while for positive control, the DNA of the known genotype was used. Each sample genotyping was repeated twice for accuracy.
2.5. Statistical Analysis
For each SNP, a Hardy–Weinberg equilibrium was assessed by using Pearson’s chi-square and Fisher’s exact test. Age between groups was compared using Student’s t-test. Allele and genotype frequencies between DC and control groups were compared by Pearson’s chi-square test. The association between the MMP8, CHST6, and MMP14 polymorphisms and DC was estimated by computing the odds ratios (ORs) and then 95% confidence intervals (CI) from logistic regression in genotype and allele models. The ORs and CI from Pearson’s chi-square test were calculated to evaluate the effects of risk factors on DC. Results were statistically significant when p was less than 0.05. Statistical analysis was performed using SPSS v20.0 software (Released 2011; IBM Corp, Armonk, NY, USA).
3. Results
3.1. Sample Characteristics
A total of 116 patients with DC were included in the current analysis. All 103 persons from the control group were also included. The DC patients and reference groups were matched by age (p > 0.05). Detailed group demographics and characteristics are summarized in Table 1.
All subjects were genotyped for a panel of three SNPs: the MMP8 gene rs11225395; the CHST6 rs977987; and the MMP14 rs1042704. The genotypes were found to be in Hardy–Weinberg equilibrium in the four SNPs.
3.2. Case-Control Analysis
The distribution of MMP8, CHST6, and MMP14 genotypes and alleles in DC and control groups are presented in Table 2. No statistically significant results were observed in genotype and allele distribution between patients and control groups. Statistically significant results were also not obtained in the logistic regression analysis where the effect of MMP8, CHST6, and MMP14 SNPs on DC development was analyzed (Table 3).
To analyze the associations of MMP8, CHST6, and MMP14 polymorphisms with the previous onset of the disease, we divided the DC patients into groups (>56 and ≤56) according to the age when the first symptoms appeared. The analysis results on the association between analyzed SNPs and the previous onset of the DC are shown in Table 4 and Table 5. A significant link between the CHST6 rs977987 mutant TT genotype and the previous onset of the DC (OR 1.691; 95% CI: 1.465–3.737; p = 0.044) was revealed. The allelic model showed that the major allele G of this SNP is associated with the subsequent onset of the disease (OR 0.591; 95% CI: 0.268–0.831; p = 0.029), in contrast to minor T allele (OR 1.404; 95% CI: 1.571–3.453; p = 0.036). Additionally, MMP14 polymorphism rs1042704 in the genotype model was linked to the previous onset of the DC. Specifically, 16.8% of DC patients carrying the MMP14 rs1042704 AA genotype first symptoms of DC were observed at less than 56 years of age, compared to 3.2% of noncarriers (OR 2.160; 95% CI: 2.160–7.824; p = 0.024).
3.3. The Previous Onset of the Disease and Positive Family History
The relationship between the incidence of DC cases among relatives of DC patients and the previous onset of the disease was evaluated. In the DC group, 33 out of 116 cases were hereditary DCs. We found that a family history of DCs significantly associated with the previous onset of the disease (OR: 2439; 95% CI: 1.055–5.642; p = 0.035) (Table 6). The relationships between our examined polymorphisms and heritability of DC did not exhibit statistically significant differences.
3.4. Risk Factor Analysis
Risk analysis was also conducted to examine the impact of anthropometric lifestyle factors and their combination with analyzed gene polymorphisms on Dupuytren’s contracture (Table 7). Compared with non-smokers, persons who are smokers were 2.615 times (95% CI: 1.114–3.898; p = 0.020) more likely to develop DC. Manual labor was associated with 2.615 times increased risk of DC. Compared with non-smokers and non-manual laborers, persons who are smokers and manual laborers were 13.174 times (95% CI: 3.029–57.290; p < 0.001) more likely to develop Dupuytren’s contracture. Finally, the most high-risk factor (OR: 14.00) of DC development was for persons who have a combination of smoking, manual labor, and MMP14 rs1042704 minor A allele (95% CI: 1.807–108.450; p = 0.010).
4. Discussion
A genome-wide association study (GWAS) has identified five harbor loci genes associated with DC that are known to interact with and modulate the ECM. The researchers report that not all the links between genes and DC identified by GWAS may be clear and, therefore, require experimental verification [8]. We investigated two SNPs of the rs1042704 in MMP14 and the rs977987 in the CHST6 genes from GWAS and one SNP of rs11225395 in MMP8, which were of interest primarily because of the lack of literature on the MMP8 gene and DC.
Many studies are being conducted on the balance of MMPs and TIMPs inhibitors for diseases involving tissue destruction. It is already known that the high expression of MMPs can cause increased synthesis and deposition of collagen, leading to palmar fibromatosis [12]. Previous studies have shown that the MMP14 gene polymorphism rs1042704 causes a missense mutation that alters the MMP14 sequence. Altered expression of the MMP14 gene alters a protein with lower collagen degradation activity [23]. Our study also suggests that 2.2 times more frequent occurrence of the A minor allele of the SNP rs1042704 in the MMP14 gene in the DC group may be related to an unsuccessful attempt to reduce collagen deposition.
The genome-wide association study has shown possible links between the selected MMP14 and CHST6 gene polymorphisms and DC [8]. In the current study, subjects with the mutant TT genotype of SNP rs977987 in the CHST6 gene were 1.7 times more likely to develop DC. Therefore, our study complements the GWA study and confirms that MMP14 and CHST6 are significant for DC development.
The third polymorphism we studied, SNP rs11225395 in MMP8, is localized in the promoter region of the MMP8 gene, and changes in the sequence of the MMP8 gene are known to cause changes in protein expression [24]. However, our studies did not show significant associations between the SNP rs11225395 MMP8 gene and DC. No further publications related to MMP8 gene studies and DC were found.
We did not find any significant interfaces between SNP rs11225395 in the MMP8 gene and DC. The view of other researchers that not all MMPs show signs of tissue-degrading enzymes should probably be accepted. Other publications’ description that MMP8 has more anti-inflammatory effects in the treatment of anti-inflammatory cytokines and chemokines should probably be shared [28,29].
In this study, we examined several potential DC risk factors, such as age, gender, smoking, manual labor, and their combinations with SNP polymorphisms of the MMP8, MMP14, and CHST6 genes.
We found that SNP rs1042704 AA in MMP14 and CHST6 rs977987 mutant TT genotype were significantly associated with the early onset of DC.
There is another study that proves that older age is not associated with the efficacy of MMP14. Only a few MMPs have been identified in this study whose activity is increased in long-lived individuals compared to younger ones, one of them being MMP2 [30].
The occurrence of DC is most common in populations of Northern European/Scandinavian origin and uncommon in populations in Southern Europe and South America [31]. Men suffer from DC at a 2:1 ratio compared to women, and men also suffer worse [32]. Our sample of subjects who reported DC onset also had a higher proportion of men (83.6%) than women (16.4%). This suggests that the male gender is a very important risk factor for the onset of DC.
Cigarette smoking is statistically associated with Dupuytren’s disease [32]. We found that smokers were statistically 2.084 times more likely to develop DC than non-smokers. Other researchers have also found that DC is 1.3 to 2.8 times more common in smokers than in non-smokers [33,34].
Although DC is not considered an occupational disease, in a study of workers, 3.76% were affected by DC, especially younger workers. It is also estimated that it takes 10 years of hard manual labor to develop [35]. Our study revealed that manual laborers are 2.6 times more likely to develop DC than non-manual laborers. We also found that a combination of strong risk factors, smoking and manual labor, resulted in a 13.2-fold increase in DC incidence compared to non-smokers and non-manual laborers. The combination of these risk factors has not been described in previous publications. However, our study revealed that these risk factors significantly determine the occurrence of DC. Finally, we found an even greater propensity for DC development when smoking, manual labor, and the MMP14 rs1042704 A allele interact together. The possibility of DC development increased 14 times. Riesmeijer et al. conducted an analysis of risk alleles in DCs with SNPs in African, East Asian, European, Hispanic, and South Asian populations and found that the frequency of alleles varies across populations [36]. Therefore, DC genetic testing in different populations can help predict the onset of the disease and pre-select preventive measures, such as risk factor reduction (smoking, manual labor), which, as our research shows, increases the occurrence of DC by several times.
5. Conclusions
Single nucleotide polymorphisms in CHST6 rs977987, MMP14 rs1042704, and positive family history are associated with the previous onset of Dupuytren’s contracture: CHST6 rs977987 minor allele T increase the chances by 1.404-fold, MMP14 rs1042704 mutant genotype AA—2.160-fold, and positive family history by two and a half times. Our study also showed that a combination of the MMP14 minor A allele and additional risk factors such as smoking and manual labor appears to increase the likelihood of the manifestation of DC by fourteen times.
Author Contributions
Conceptualization, A.S.; methodology, A.S., R.P. and L.M.; investigation, R.P. and L.M.; data curation, R.I. and U.Z.; writing—original draft preparation, G.S., R.I. and E.K.; writing—review and editing, A.S. and L.P.; visualization, R.I. and U.Z.; supervision, A.S. and L.P. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by Kaunas Regional Biomedical Research Ethics Committee (No. BE-2-21, 8 March 2019), and the Department of Bioethics, LUHS (BEC-MF-63, 23 October 2017).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.
Data Availability Statement
Data supporting the reported results are available directly from the main author upon request.
Acknowledgments
We thank all volunteers who agreed to participate in this project.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Gabbiani, G.; Majno, G. Dupuytren’s Contracture: Fibroblast Contraction? An Ultrastructural Study. Am. J. Pathol. 1972, 66, 131–146. [Google Scholar] [PubMed]
- Zhang, A.Y.; Kargel, J.S. The Basic Science of Dupuytren Disease. Hand Clin. 2018, 34, 301–305. [Google Scholar] [CrossRef] [PubMed]
- Tripoli, M.; Cordova, A.; Moschella, F. Update on the Role of Molecular Factors and Fibroblasts in the Pathogenesis of Dupuytren’s Disease. J. Cell Commun. Signal. 2016, 10, 315–330. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hindocha, S. Risk Factors, Disease Associations, and Dupuytren Diathesis. Hand Clin. 2018, 34, 307–314. [Google Scholar] [CrossRef]
- DiBenedetti, D.B.; Nguyen, D.; Zografos, L.; Ziemiecki, R.; Zhou, X. Prevalence, Incidence, and Treatments of Dupuytren’s Disease in the United States: Results from a Population-Based Study. Hand 2011, 6, 149–158. [Google Scholar] [CrossRef] [Green Version]
- Lanting, R.; Van Den Heuvel, E.R.; Westerink, B.; Werker, P.M.N. Prevalence of Dupuytren Disease in the Netherlands. Plast. Reconstr. Surg. 2013, 132, 394–403. [Google Scholar] [CrossRef]
- Becker, K.; Tinschert, S.; Lienert, A.; Bleuler, P.E.; Staub, F.; Meinel, A.; Rößler, J.; Wach, W.; Hoffmann, R.; Kühnel, F.; et al. The Importance of Genetic Susceptibility in Dupuytren’s Disease. Clin. Genet. 2015, 87, 483–487. [Google Scholar] [CrossRef]
- Ng, M.; Thakkar, D.; Southam, L.; Werker, P.; Ophoff, R.; Becker, K.; Nothnagel, M.; Franke, A.; Nürnberg, P.; Espirito-Santo, A.I.; et al. A Genome-Wide Association Study of Dupuytren Disease Reveals 17 Additional Variants Implicated in Fibrosis. Am. J. Hum. Genet. 2017, 101, 417–427. [Google Scholar] [CrossRef] [Green Version]
- Dolmans, G.H.; Werker, P.M.; Hennies, H.C.; Furniss, D.; Festen, E.A.; Franke, L.; Becker, K.; Van Der Vlies, P.; Wolffenbuttel, B.H.; Tinschert, S.; et al. Wnt Signaling and Dupuytren’s Disease. N. Engl. J. Med. 2011, 365, 307–317. [Google Scholar] [CrossRef] [Green Version]
- Luck, J.V. Dupuytren’s Contracture; a New Concept of the Pathogenesis Correlated with Surgical Management. J. Bone Jt. Surg. Am. 1959, 41-A, 635–664. [Google Scholar] [CrossRef]
- van Beuge, M.M.; ten Dam, E.J.P.M.; Werker, P.M.N.; Bank, R.A. Matrix and Cell Phenotype Differences in Dupuytren’s Disease. Fibrogenes. Tissue Repair 2016, 9, 9. [Google Scholar] [CrossRef] [Green Version]
- Ulrich, D.; Ulrich, F.; Piatkowski, A.; Pallua, N. Expression of Matrix Metalloproteinases and Their Inhibitors in Cords and Nodules of Patients with Dupuytren’s Disease. Arch. Orthop. Trauma Surg. 2009, 129, 1453–1459. [Google Scholar] [CrossRef] [PubMed]
- Verma, R.P.; Hansch, C. Matrix Metalloproteinases (MMPs): Chemical-Biological Functions and (Q)SARs. Bioorg. Med. Chem. 2007, 15, 2223–2268. [Google Scholar] [CrossRef] [PubMed]
- Kapoor, C.; Vaidya, S.; Wadhwan, V.; Hitesh; Kaur, G.; Pathak, A. Seesaw of Matrix Metalloproteinases (MMPs). J. Cancer Res. Ther. 2016, 12, 28–35. [Google Scholar] [CrossRef] [PubMed]
- Wilkinson, J.M.; Davidson, R.K.; Swingler, T.E.; Jones, E.R.; Corps, A.N.; Johnston, P.; Riley, G.P.; Chojnowski, A.J.; Clark, I.M. MMP-14 and MMP-2 Are Key Metalloproteases in Dupuytren’s Disease Fibroblast-Mediated Contraction. Biochim. Biophys. Acta-Mol. Basis Dis. 2012, 1822, 897–905. [Google Scholar] [CrossRef] [PubMed]
- Ratajczak-Wielgomas, K.; Gosk, J.; Rabczyński, J.; Augoff, K.; Podhorska-Okołów, M.; Gamian, A.; Rutowski, R. Expression of MMP-2, TIMP-2, TGF-Β1, and Decorin in Dupuytren’s Contracture. Connect. Tissue Res. 2012, 53, 469–477. [Google Scholar] [CrossRef] [PubMed]
- Johnston, P.; Bchir, M.B.; Chojnowski, A.J.; Bchir, M.B.; Davidson, R.K.; Riley, G.P.; Donell, S.T.; Clark, I.M. A Complete Expression Profile of Matrix-Degrading Metalloproteinases in Dupuytren’s Disease. J. Hand Surg. Am. 2007, 32, 343–351. [Google Scholar] [CrossRef] [Green Version]
- Alipour, H.; Raz, A.; Zakeri, S.; Dinparast Djadid, N. Therapeutic Applications of Collagenase (Metalloproteases): A Review. Asian Pac. J. Trop. Biomed. 2016, 6, 975–981. [Google Scholar] [CrossRef] [Green Version]
- Watt, A.J.; Hentz, V.R. Collagenase Clostridium Histolyticum: A Novel Nonoperative Treatment for Dupuytren’s Disease. Int. J. Clin. Rheumtol. 2011, 6, 123–133. [Google Scholar] [CrossRef]
- Al-Qattan, M.M. Factors in the Pathogenesis of Dupuytren’s Contracture. J. Hand Surg. Am. 2006, 31, 1527–1534. [Google Scholar] [CrossRef]
- Melrose, J. Keratan Sulfate (KS)-Proteoglycans and Neuronal Regulation in Health and Disease: The Importance of KS-Glycodynamics and Interactive Capability with Neuroregulatory Ligands. J. Neurochem. 2019, 149, 170–194. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huo, Y.N.; Yao, Y.F.; Yu, P. Pathogenic Mutations of TGFBI and CHST6 Genes in Chinese Patients with Avellino, Lattice, and Macular Corneal Dystrophies. J. Zhejiang Univ. Sci. B 2011, 12, 687–693. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Itoh, Y.; Ng, M.; Wiberg, A.; Inoue, K.; Hirata, N.; Paiva, K.B.S.; Ito, N.; Dzobo, K.; Sato, N.; Gifford, V.; et al. A Common SNP Risk Variant MT1-MMP Causative for Dupuytren’s Disease Has a Specific Defect in Collagenolytic Activity. Matrix Biol. 2021, 97, 20–39. [Google Scholar] [CrossRef] [PubMed]
- Tai, J.; Sun, D.; Wang, X.; Kang, Z. Matrix Metalloproteinase-8 Rs11225395 Polymorphism Correlates with Colorectal Cancer Risk and Survival in a Chinese Han Population: A Case-Control Study. Aging (Albany. NY). 2020, 12, 19618–19627. [Google Scholar] [CrossRef] [PubMed]
- Feng, J.; Chen, Y.; Hua, W.; Sun, X.; Chen, Y.; Liu, Y.; Fan, J.; Zhao, Y.; Zhao, L.; Xu, X.; et al. The MMP-8 Rs11225395 Promoter Polymorphism Increases Cancer Risk of Non-Asian Populations: Evidence from a Meta-Analysis. Biomolecules 2019, 9, 570. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, X.Z.; Du, X.M.; Jing, Q.M.; Li, X.X.; Gu, R.X.; Wang, J.; Han, Y.L. Impact of Matrix Metalloproteinase-8 Gene Variations on the Risk of Thoracic Aortic Dissection in a Chinese Han Population. Mol. Biol. Rep. 2013, 40, 5953–5958. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sherry, S.T.; Ward, M.H.; Kholodov, M.; Baker, J.; Phan, L.; Smigielski, E.M.; Sirotkin, K. dbSNP: The NCBI database of genetic variation. Nucleic Acids Res. 2001, 29, 308–311. [Google Scholar] [CrossRef] [Green Version]
- Owen, C.A.; Hu, Z.; Lopez-Otin, C.; Shapiro, S.D. Membrane-Bound Matrix Metalloproteinase-8 on Activated Polymorphonuclear Cells Is a Potent, Tissue Inhibitor of Metalloproteinase-Resistant Collagenase and Serpinase. J. Immunol. 2004, 172, 7791–7803. [Google Scholar] [CrossRef] [Green Version]
- Gueders, M.M.; Balbin, M.; Rocks, N.; Foidart, J.M.; Gosset, P.; Louis, R.; Shapiro, S.; Lopez-Otin, C.; Noël, A.; Cataldo, D.D. Matrix Metalloproteinase-8 Deficiency Promotes Granulocytic Allergen-Induced Airway Inflammation. J. Immunol. 2005, 175, 2589–2597. [Google Scholar] [CrossRef] [Green Version]
- Cancemi, P.; Aiello, A.; Accardi, G.; Caldarella, R.; Candore, G.; Caruso, C.; Ciaccio, M.; Cristaldi, L.; Di Gaudio, F.; Siino, V.; et al. The Role of Matrix Metalloproteinases (MMP-2 and MMP-9) in Ageing and Longevity: Focus on Sicilian Long-Living Individuals (LLIs). Mediat. Inflamm. 2020, 2020, 8635158. [Google Scholar] [CrossRef]
- Benson, L.S.; Williams, C.S.; Kahle, M. Dupuytren’s Contracture. J. Am. Acad. Orthop. Surg. 1998, 6, 24–35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- An, H.S.; Southworth, S.R.; Thomas Jackson, W.; Russ, B. Cigarette Smoking and Dupuytren’s Contracture of the Hand. J. Hand Surg. Am. 1988, 13, 872–874. [Google Scholar] [CrossRef]
- Burge, P.; Hoy, G.; Regan, P.; Milne, R. Smoking, Alcohol and the Risk of Dupuytren’s Contracture. J. Bone Jt. Surg. Br. 1997, 79, 206–210. [Google Scholar] [CrossRef]
- Burke, F.D.; Proud, G.; Lawson, I.J.; McGeoch, K.L.; Miles, J.N.V. An Assessment of the Effects of Exposure to Vibration, Smoking, Alcohol and Diabetes on the Prevalence of Dupuytren’s Disease in 97,537 Miners. J. Hand Surg. Eur. Vol. 2007, 32, 400–406. [Google Scholar] [CrossRef]
- de la Caffinière, J.Y.; Wagner, R.; Etscheid, J.; Metzger, F. Manual labor and Dupuytren disease. The results of a computerized survey in the field of iron metallurgy. Ann. Chir. Main Organe Off. Soc. Chir. Main 1983, 2, 66–72. [Google Scholar] [CrossRef]
- Riesmeijer, S.A.; Werker, P.M.N.; Nolte, I.M. Ethnic Differences in Prevalence of Dupuytren Disease Can Partly Be Explained by Known Genetic Risk Variants. Eur. J. Hum. Genet. 2019, 27, 1876–1884. [Google Scholar] [CrossRef] [Green Version]
Table 1.
Characteristics of study groups.
| Characteristic. | DC (n = 116) | Control (n = 103) | p-Value |
|---|---|---|---|
| Age (years) | 60.24 (SD 10.665) | 58.85 (SD 14.012) | 0.415 1 |
| Gender: | <0.001 2 | ||
| Male | 97 (83.6%) | 65 (63.1%) | |
| Female | 19 (16.4%) | 38 (36.9%) |
1 Student’s t-test, for independent samples. 2 Significant differences, chi-squared comparison of patients’ gender between study groups. DC: Dupuytren’s contracture. SD: standard deviation.
Table 2.
Distribution of genotypes and allele frequencies for polymorphisms at MMP8, CHST6, and MMP14 genes in Dupuytren’s contracture (n = 116) and control (n = 103) groups.
Table 2.
Distribution of genotypes and allele frequencies for polymorphisms at MMP8, CHST6, and MMP14 genes in Dupuytren’s contracture (n = 116) and control (n = 103) groups.
| Gene | SNP; Position | Group | Genotype Frequency | p-Value | MAF | p-Value | ||
|---|---|---|---|---|---|---|---|---|
| MMP8 | rs11225395 | AA | AG | GG | 0.496 | A | 0.477 | |
| G > A | DC | 25 (21.6%) | 60 (51.7%) | 31 (26.7%) | 47.4 | |||
| Chr.11:102725749 | Control | 19 (18.4%) | 49 (47.6%) | 35 (34.0%) | 42.2 | |||
| CHST6 | rs977987 | TT | GT | GG | 0.708 | T | 0.568 | |
| G > T | DC | 25 (21.6%) | 55 (47.4%) | 36 (31.0%) | 45.3 | |||
| Chr.16:75472695 | Control | 19 (18.4%) | 47 (45.6%) | 37 (35.9%) | 41.3 | |||
| MMP14 | rs1042704 | AA | AG | GG | 0.422 | A | 0.635 | |
| G > A | DC | 11 (9.5%) | 46 (39.7%) | 59 (50.9%) | 29.3 | |||
| Chr.14:22843385 | Control | 5 (4.9%) | 43 (41.7%) | 55 (53.4%) | 25.7 | |||
DC: Dupuytren’s contracture. MAF: minor allele frequency.
Table 3.
Association between MMP8, CHST6, and MMP14 polymorphisms in allele and genotype models and Dupuytren’s contracture development.
Table 3.
Association between MMP8, CHST6, and MMP14 polymorphisms in allele and genotype models and Dupuytren’s contracture development.
| Gene | SNP | Model | OR | 95% CI | p-Value |
|---|---|---|---|---|---|
| MMP8 | rs11225395 G > A | Genotype model: | |||
| GG vs. AG and AA | 1.411 | 0.791–2.518 | 0.244 | ||
| AA vs. AG and GG | 0.823 | 0.423–1.603 | 0.567 | ||
| AG vs. GG and AA | 0.847 | 0.498–1.441 | 0.540 | ||
| Allelic model: | |||||
| G carrier vs. G noncarrier | 0.709 | 0.397–1.264 | 0.244 | ||
| A carrier vs. A noncarrier | 1.215 | 0.624–2.365 | 0.567 | ||
| CHST6 | rs977987 G > T | Genotype model: | |||
| GG vs. TG and TT | 0.825 | 0.423–1.603 | 0.567 | ||
| TT vs. TG and GG | 1.246 | 0.710–2.187 | 0.444 | ||
| TG vs. GG and TT | 0.931 | 0.547–1.585 | 0.792 | ||
| Allelic model: | |||||
| G carrier vs. G noncarrier | 0.803 | 0.457–1.409 | 0.444 | ||
| T carrier vs. T noncarrier | 1.215 | 0.625–2.365 | 0.567 | ||
| MMP14 | rs1042704 G > A | Genotype model: | |||
| GG vs. AG and AA | 1.107 | 0.651–1.883 | 0.708 | ||
| AA vs. AG and GG | 0.487 | 0.163–1.452 | 0.197 | ||
| AG vs. GG and AA | 1.091 | 0.635–1.872 | 0.753 | ||
| Allelic model: | |||||
| G carrier vs. G noncarrier | 0.903 | 0.531–1.537 | 0.708 | ||
| A carrier vs. A noncarrier | 2.053 | 0.689–6.122 | 0.197 |
OR: Odds ratio. CI: Confidence intervals.
Table 4.
Distribution of genotype and allele frequencies for polymorphisms at MMP8, CHST6, and MMP14 genes in Dupuytren’s contracture patients according to the age when first symptoms appeared.
Table 4.
Distribution of genotype and allele frequencies for polymorphisms at MMP8, CHST6, and MMP14 genes in Dupuytren’s contracture patients according to the age when first symptoms appeared.
| Gene | SNP | Age group | Genotypes | p-Value | ||
|---|---|---|---|---|---|---|
| MMP8 | rs11225395 | AA | AG | GG | 0.834 | |
| ≤56 | 12 (22.2%) | 29 (53.7%) | 13 (24.1%) | |||
| >57 | 13 (21.0%) | 31 (50.0%) | 18 (29.0%) | |||
| CHST6 | rs977987 | AA | AG | GG | 0.024 1 | |
| ≤56 | 24 (44.4%) | 22 (40.7%) | 8 (14.8%) | |||
| >57 | 12 (19.3%) | 33 (53.2%) | 17 (27.4%) | |||
| MMP14 | rs1042704 | AA | AG | GG | 0.040 1 | |
| ≤56 | 9 (16.8%) | 19 (35.1%) | 26 (48.1%) | |||
| >57 | 2 (3.2%) | 27 (43.5%) | 33 (53.3%) | |||
1 Significant difference.
Table 5.
The associations of CHST6 and MMP14 polymorphisms with the previous onset of the Dupuytren’s contracture.
Table 5.
The associations of CHST6 and MMP14 polymorphisms with the previous onset of the Dupuytren’s contracture.
| Gene | SNP | Model | OR | 95% CI | p-Value |
|---|---|---|---|---|---|
| CHST6 | rs977987 | Genotype model: | |||
| GG vs. GT and TT | 0.712 | 0.290–1.751 | 0.459 | ||
| TT vs. GT and GG | 1.691 | 1.465–3.737 | 0.044 1 | ||
| GT vs. TT and GG | 0.800 | 0.385–1.664 | 0.550 | ||
| Allelic model: | |||||
| G carrier vs. G noncarrier | 0.591 | 0.268–0.831 | 0.029 1 | ||
| T carrier vs. T noncarrier | 1.404 | 1.571–3.453 | 0.036 1 | ||
| MMP14 | rs1042704 | Genotype model: | |||
| GG vs. GA and AA | 1.077 | 0.519–2.235 | 0.842 | ||
| AA vs. GA and GG | 2.160 | 2.160–7.824 | 0.024 1 | ||
| GA vs. GG and AA | 0.704 | 0.332–1.491 | 0.359 | ||
| Allelic model: | |||||
| G carrier vs. G noncarrier | 0.463 | 0.278–0.678 | 0.024 1 | ||
| A carrier vs. A noncarrier | 0.929 | 0.448–1.927 | 0.842 | ||
1 Significant difference. OR: Odds ratio. CI: Confidence intervals.
Table 6.
The association between the previous onset of the Dupuytren’s contracture and positive family history of the disease.
Table 6.
The association between the previous onset of the Dupuytren’s contracture and positive family history of the disease.
| Age Group | Family History in DC | OR | 95 % CI | p-Value | |
|---|---|---|---|---|---|
| Positive (n = 33) | Negative (n = 83) | ||||
| ≤56 >57 | 21 (38.9%) 12 (19.4%) | 33 (61.1%) 50 (80.6%) | 2439 | 1.055–5.642 | 0.035 1 |
1 Significant difference. OR: Odds ratio. CI: Confidence intervals. DC: Dupuytren’s contracture.
Table 7.
The odds ratio for risk factors in Dupuytren’s contracture.
| Parameters | Reference Values | Odds Ratio | 95% CI | p-Value |
|---|---|---|---|---|
| Smoking | Non-smoking | 2.084 | 1.114–3.898 | 0.020 1 |
| Manual labor | Non-manual labor | 2.615 | 1.482–4.615 | 0.001 1 |
| Smoking + manual labor | Non-smoking + non-manual labor | 13.174 | 3.029–57.290 | <0.001 1 |
| MMP14 rs1042704 A allele + smoking + manual labor | MMP14 rs1042704 G allele + non-smoking + non-manual labor | 14.000 | 1.807–108.450 | 0.010 1 |
1 Significant difference. OR: Odds ratio. CI: Confidence intervals.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Samulenas, G.; Insodaite, R.; Kunceviciene, E.; Poceviciute, R.; Masionyte, L.; Zitkeviciute, U.; Pilipaityte, L.; Smalinskiene, A. The Role of Functional Polymorphisms in the Extracellular Matrix Modulation-Related Genes on Dupuytren’s Contracture. Genes 2022, 13, 743. https://doi.org/10.3390/genes13050743
AMA Style
Samulenas G, Insodaite R, Kunceviciene E, Poceviciute R, Masionyte L, Zitkeviciute U, Pilipaityte L, Smalinskiene A. The Role of Functional Polymorphisms in the Extracellular Matrix Modulation-Related Genes on Dupuytren’s Contracture. Genes. 2022; 13(5):743. https://doi.org/10.3390/genes13050743
Chicago/Turabian StyleSamulenas, Gediminas, Ruta Insodaite, Edita Kunceviciene, Roberta Poceviciute, Lorena Masionyte, Urte Zitkeviciute, Loreta Pilipaityte, and Alina Smalinskiene. 2022. "The Role of Functional Polymorphisms in the Extracellular Matrix Modulation-Related Genes on Dupuytren’s Contracture" Genes 13, no. 5: 743. https://doi.org/10.3390/genes13050743
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
