Original Article
Wnt Signaling and Dupuytren's Disease
N Engl J Med 2011; 365:307-317July 28, 2011DOI: 10.1056/NEJMoa1101029
- Abstract
Background
Dupuytren's disease is a benign fibromatosis of the hands and fingers that leads to flexion contractures. We hypothesized that multiple genetic and environmental factors influence susceptibility to this disease and sought to identify susceptibility genes to better understand its pathogenesis.
Methods
We conducted a genomewide association study of 960 Dutch persons with Dupuytren's disease and 3117 controls (the discovery set) to test for association between the disease and genetic markers. We tested the 35 single-nucleotide polymorphisms (SNPs) most strongly associated with Dupuytren's disease (P<1×10−4) in the discovery set in three additional, independent case series comprising a total of 1365 affected persons and 8445 controls from Germany, the United Kingdom, and the Netherlands.
Results
Initially, we observed a significant genomewide association between Dupuytren's disease and 8 SNPs at three loci. Tests of replication and joint analysis of all data from 2325 patients with Dupuytren's disease and 11,562 controls yielded an association with 11 SNPs from nine different loci (P<5.0×10−8). Six of these loci contain genes known to be involved in the Wnt-signaling pathway: WNT4 (rs7524102) (P=2.8×10−9; odds ratio, 1.28), SFRP4 (rs16879765) (P=5.6×10−39; odds ratio, 1.98), WNT2 (rs4730775) (P=3.0×10−8; odds ratio, 0.83), RSPO2 (rs611744) (P=7.9×10−15; odds ratio, 0.75), SULF1 (rs2912522) (P=2.0×10−13; odds ratio, 0.72), and WNT7B (rs6519955) (P=3.2×10−33; odds ratio, 1.54).
Conclusions
This study implicates nine different loci involved in genetic susceptibility to Dupuytren's disease. The fact that six of these nine loci harbor genes encoding proteins in the Wnt-signaling pathway suggests that aberrations in this pathway are key to the process of fibromatosis in Dupuytren's disease.
Media in This Article
Figure 1
Results of the Genomewide Association Study in Dupuytren's Disease.Figure 2
Signaling Pathways of Wnt and β-Catenin.Article Activity
- Article
Dupuytren's disease is a benign fibromatosis of the hands and fingers, giving rise to the formation of nodules and cords and often leading to flexion contractures (Fig. 1 in the Supplementary Appendix, available with the full text of this article at NEJM.org). The prevalence of Dupuytren's disease is reported to be between 0.2% and 56.0%.1 Men are more often affected than women, but by the ninth decade of life, the incidence in women is the same as that in men.2,3 Standard treatment consists of surgical excision or transection of pathologic nodules and cords, but other methods of treatment are emerging.4,5 There is no cure for the disease, however, and reported recurrence rates range from 8% to 66%, depending on the treatment.6,7 The pathogenesis of Dupuytren's disease is not fully understood.
The clustering of cases of Dupuytren's disease in families suggests a genetic influence on the onset of disease; however, it is probably a complex condition in which several genetic and environmental risk factors are involved, each contributing in small part to susceptibility to the disease. To date, a limited number of small candidate-gene association studies have been performed,8,9 but no causal genes have been identified. To identify common genetic variants associated with this disease, we carried out a genomewide association study involving 960 persons with Dupuytren's disease and 3117 controls, all from the Netherlands and of European descent.
Methods
Study Participants
Participants provided written informed consent, and we obtained approval from an institutional review board to carry out the study. Between 2007 and 2010, we recruited 960 patients with Dupuytren's disease through the outpatient clinics of the plastic surgery departments of six hospitals in the Netherlands. All 3117 controls for the discovery set were drawn from LifeLines, a large, population-based cohort study being conducted in the northern Netherlands.10 We obtained blood samples for replication studies from 189 Dutch patients with Dupuytren's disease, as well as from 561 Dutch controls who were newly enrolled in LifeLines and for whom genotyping data were already available; from 711 British patients with Dupuytren's disease, as well as from 5984 controls from the Wellcome Trust Case Control Consortium 2 (WTCCC, 1958 British Birth Cohort, and U.K. National Blood Service) for whom genotyping data were already available11; and from 465 German patients with Dupuytren's disease, as well as from 1900 controls, for 1618 of whom genotyping data were already available (1164 from the PopGen study at the University of Kiel and 454 from KORA [Cooperative Health Research in the Region of Augsburg] at the Helmholtz Center Munich in Neuherberg) (Table 1Table 1
Sample Collections and Genotyping Platforms for the Genomewide Association Study (GWAS) and Replications in Patients with Dupuytren's Disease and Control Subjects.). (A detailed description of case patients and controls is provided in the Supplementary Appendix.) Participants reported ancestry by means of a multiple-choice questionnaire, the choices being Dutch, European (specifying country), and other (specifying country). DNA samples were obtained from either blood samples (in the Dutch and German case series) or saliva (in the U.K. case series).Tests for Association
We genotyped the Dutch samples (discovery set) and control samples (LifeLines) with Illumina HumanCytoSNP-12 arrays, comprising 301,232 SNPs, and called SNPs with the use of the Illumina algorithm (Genome Studio, version 2.10.1). To test for replication of association, we selected SNPs that showed an association in the discovery set with a P value of less than 10−4. We confirmed the integrity of these associations by manually inspecting genotype clusters and selected two SNPs to represent each independent locus (see the Supplementary Appendix). We genotyped the SNPs selected for tests of replication in the Dutch and British persons by means of KASP by Design assays (KBioscience). We used Human SNP Array 6.0 (Affymetrix) to genotype these SNPs in the German persons. To test for replication of SNPs for which no direct or tag SNPs were present on the Affymetrix 6.0 platform, we genotyped the selected SNPs in both the German persons affected with Dupuytren's disease and a separate control series, using GenomeLab SNPstream (Beckman Coulter).
Statistical Analysis
We excluded from the analysis specific SNPs and data from specific samples, as described previously.12,13 SNPs with call rates of less than 95%, a minor-allele frequency of less than 0.01, or deviation from Hardy–Weinberg equilibrium (P<0.0001) were excluded, as were samples with call rates below 99% or with a discrepancy between recorded sex and genotype-inferred sex. We also excluded relatives and ethnic outliers (see the Supplementary Appendix). Because genomewide association studies are performed in patients and controls in the same ethnic group, we used multidimensional scaling in the study participants and persons in HapMap to identify nonwhites in our study population (i.e., ethnic outliers). We compared genotype prevalence in cases and controls with the use of a basic chi-square allelic test with 1 degree of freedom and calculated the overdispersion factor of association test statistics (genomic control inflation factor, λgc) with the use of observed versus expected values for all SNPs by means of the PLINK software package (version 1.07). Principal-component analysis was performed with the use of EIGENSTRAT software to control for population stratification. We conducted conditional analysis with the use of SNPTEST, version 2, when more than one SNP with a significant genomewide association clustered at a certain region.
We excluded SNPs from tests of replication if they had a call rate below 98% or deviated from Hardy–Weinberg equilibrium (P<0.0001), and we excluded samples with call rates below 95%. We carried out a joint analysis of the discovery and replication phases with the use of Cochran–Mantel–Haenszel stratification. In the U.K. control series, not all SNPs selected for replication were available on the Illumina 1.2M and Affymetrix 6.0 genotyping platforms, which were used by WTCCC; in some cases, we used tag SNPs, and for four replication SNPs, we used imputed WTCCC control data (Table 1 in the Supplementary Appendix) generated with BEAGLE Genetic Analysis Software Package 3.2 and based on the HapMap phase 2 reference of Centre d'Etude du Polymorphisme Humain (CEPH) persons of European ancestry (CEU). In the German series, several SNPs selected for replication were not available on the Affymetrix 6.0 platform; in these cases, tag SNPs were used as well (Table 1 in the Supplementary Appendix). We used multiple genotyping platforms for the control cohorts in the replication phase. Since the replication signals were in the same direction and of the same magnitude as the discovery results, it is unlikely that the confirmatory results were due to biased genotyping. We checked for interplatform reproducibility by comparing genotypes of the same samples between different platforms and found concordance rates of more than 99.99% (see the Supplementary Appendix).
We were not able to correct for population stratification in the samples used to test for replication because we genotyped only a limited number of SNPs in this phase. Meta-analysis of the discovery and replication data was also performed with PLINK software. We performed an analysis with the use of the Gene Relationships across Implicated Loci (GRAIL) statistical strategy involving hg18 and PubMed data sets (December 2006), with the 11 SNPs that had a significant genomewide association as query regions.14
Results
Genomewide Associations
Data obtained through genomewide genotyping of affected persons and controls are stored at the European Genome–Phenome Archive (accession number, EGAS00000000043). We excluded 66,293 SNP genotypes because they did not meet quality-control criteria, leaving 234,939 SNPs typed in 856 patients with Dupuytren's disease and in 2836 controls (Table 1). The call rate for the remaining SNPs was 99.9%. There was moderate evidence for inflation in the test statistic (λgc=1.21). Adjustment for differential population stratification with the use of the first five components on the basis of a principal-component analysis of uncorrelated SNPs reduced the inflation to λgc=1.19. Figure 2 in the Supplementary Appendix shows that the case and control groups were well matched for population stratification after correction for these components. We found that the inflation was caused by genetic heterogeneity between persons in the north and south in the Netherlands and noted differences in case patients between the clinics (Fig. 3 in the Supplementary Appendix). After exclusion of case patients from the most southern and eastern hospitals in the Netherlands, the inflation decreased to 1.07 (Figure 4 and Figure 5 in the Supplementary Appendix). There were no signs of differences in SNP call rates between case patients and controls. After correction for the inflation factor, the quantile–quantile plots of the logarithms of our genomewide P values showed 83 data points that were above the expected diagonal line (Figure 1AFigure 1
Results of the Genomewide Association Study in Dupuytren's Disease.).In the genomewide association study, we identified eight SNPs at three loci that showed significant association (P<5×10−8) (Figure 1B). On chromosome 7, we identified a locus with four significantly associated SNPs. Association was strongest at rs16879765, which lies within the gene encoding ependymin-related protein 1 (EPDR1) (P=1.9×10−16; odds ratio, 1.94). The three other associated SNPs were in linkage disequilibrium with the top SNP: rs1668357 (r2=0.57), rs1668347 (r2=0.59), and rs952368 (r2=0.44). Similarly, three significantly associated SNPs were identified at a single locus on chromosome 22. The most significant SNP on 22q, rs6519955 (P=2.8×10−13; odds ratio, 1.56), is located between wingless-type mammary-tumor virus integration site family member 7B (WNT7B) and LOC100271722, a hypothetical noncoding RNA gene. The two other associated SNPs on chromosome 22 were in linkage disequilibrium with the top SNP: rs8140558 (r2=0.96) and rs4072455 (r2=0.75). A conditional analysis to adjust for the top SNPs for these two loci showed no independent signals, suggesting that there is one pivotal genetic variation that drives the association of the neighboring SNPs. One additional putative Dupuytren's disease–associated locus was identified on chromosome 19, with a single significant SNP (rs11672517) (P=2.8×10−8; odds ratio, 1.46).
Tests of Replication
To test for replication of our initial findings, we selected 35 SNPs from 24 independent loci that met the significance threshold of P<1×10−4 in the discovery phase. We collected genotype data for the 35 SNPs in three different populations of case patients and controls, from the Netherlands, the United Kingdom, and Germany (1365 case patients with Dupuytren's disease and 8445 controls before quality control). One SNP (rs10809642) failed on genotyping in the Dutch and U.K. replication series and four SNPs (rs1123148, rs2179367, rs638791, and rs12372139) failed genotyping because of a laboratory error in the German series. One SNP (rs1668357) was out of Hardy–Weinberg equilibrium (P<0.0001) in all three replication series and was therefore excluded from further analysis. Eleven SNPs from nine different regions showed clear evidence of replication after correction for the 35 tested SNPs (P<0.0014 and association with the same allele and in the same direction as in the discovery phase) and reached genomewide significance in a meta-analysis (P<5.0×10−8) (Table 2Table 2
The 35 Single-Nucleotide Polymorphisms (SNPs) Selected for Replication., and the Supplementary Appendix). All loci that showed significant associations in the discovery set also showed significant associations in the replication set: rs16879765 (P=5.6×10−39; odds ratio, 1.98) on 7p14.1, rs6519955 (P=3.2×10−33; odds ratio, 1.54) and rs8140558 (P=1.2×10−22; odds ratio, 1.39) on 22q13, and rs11672517 (P=6.8×10−14; odds ratio, 1.34) on 19q13.4. Two SNPs of borderline significance in the discovery set showed significant associations in the tests of replication: rs2912522 (P=2.0×10−13; odds ratio, 0.72) on 8q13 and rs8124695 ((P=7.6×10−10; odds ratio, 1.48) on 20q11.2–q13.1. Four additional SNPs also reached genomewide significance: rs611744 (P=7.9×10−15; odds ratio, 0.75) on 8q23.1, rs10809650 (P=6.2×10−9; odds ratio, 0.80) and rs10809642 (P=1.2×10−8; odds ratio, 1.35) on 9p24.3, and rs7524102 (P=2.8×10−9; odds ratio, 1.28) on 1p36.23–p35.1.For 2 of the 11 SNPs with a significant genomewide association, we used tag SNPs with less than complete linkage disequilibrium or imputed SNPs in the meta-analysis (Table 1 in the Supplementary Appendix). We genotyped one of these SNPs, rs611744, on the Immunochip platform in 8274 U.K. controls (Table 1) and observed an association (P=1.8×10−14) on meta-analysis. The other SNP, rs8140558, is one of two SNPs at the WNT7B locus. In addition, a meta-analysis for this SNP that excluded the data from the U.K. and German series (since these data were only indirectly genotyped [Table 1 in the Supplementary Appendix]) showed a significant genomewide association (P=4.8×10−16) (Table 3 in the Supplementary Appendix).
Regional plots of the nine Dupuytren's disease risk loci are shown in Figure 6 in the Supplementary Appendix. To gain insight into the biologic mechanisms and to find genes functionally related at these regions, we applied GRAIL analysis. The 11 SNPs at the nine regions that had a significant genomewide association were used as query regions, resulting in the analysis of 22 unique genes. We found a total of seven associations with SNPs (P<0.05), including four SNPs implicating four WNT genes (P<0.0001 for each) (Table 4 in the Supplementary Appendix). When these results were corrected for multiple testing (22 tests), the associations with SNPs implicating the four WNT genes (rs7524102-WNT4, rs4730775-WNT2, rs6519955-WNT7B, and rs611744-RSPO2) remained significant (P<0.003). We observed no association between the identified SNPs with a significant genomewide association and gene expression in six quantitative-trait-locus data sets (see the Supplementary Appendix).
Discussion
We identified nine chromosomal loci associated with susceptibility to Dupuytren's disease. Very little is known about the heritability of this disease, since there are only a few reports from family and twin studies.15-17 Our findings suggest that common genetic variants have an important causative role in Dupuytren's disease in Northern European populations.
A GRAIL analysis showed that four different Dupuytren's disease risk loci contain genes that encode proteins in the Wnt-signaling pathway: 1p36.23–p35.1, containing WNT4 (rs7524102); 7q31.2, containing WNT2 (rs4730775); 22q13, containing WNT7B (rs6519955); and 8q23.1, containing RSPO2 (rs611744). Three other associated loci also contain WNT genes, although they were not implicated on GRAIL analysis: 7p14.1, containing SFRP4 (rs16879765); 8q13, containing SULF1 (rs2912522); and 6q25.1, containing TAB2 (rs2179367). However, the last of these three did not reach genomewide significance (Pmeta=2.5×10−7).
The WNT gene family consists of structurally related genes that encode glycoproteins, extracellular signaling molecules. Abnormal Wnt signaling is linked to a range of diseases, especially cancer. The best-understood Wnt-signaling pathway is the canonical pathway, which activates the nuclear functions of β-catenin, leading to changes in gene expression that influence cell proliferation and survival.18 Abnormal proliferation of fibroblasts is a key feature in the early development of Dupuytren's disease. The disease can be divided into three histologic stages: stage 1, proliferation of fibroblasts; stage 2, differentiation of fibroblasts into myofibroblasts; and stage 3, formation of mature type 1 collagen.19,20 Wnt signaling is known to regulate the proliferation and differentiation of fibroblasts in both cancer and fibromatosis.21 Most of our knowledge of Wnt signaling is derived from studies of cancer. In colon cancer, up-regulation of Wnt signaling causes intestinal crypt cells to proliferate for longer than usual before they migrate and differentiate.22 This prolonged proliferation phase results in the formation of polyps and confers a predisposition to cancer.
The involvement of the Wnt-signaling pathway in the pathogenesis of Dupuytren's disease is consistent with features of the disease and with established aspects of Wnt signaling. An imbalance of Wnt signaling in Dupuytren's disease could cause fibroblasts in the fascia of the hand to proliferate and form nodules. Indeed, increased levels of β-catenin have been observed in primary cell cultures in vitro,23 suggesting that the Wnt-signaling pathway is overstimulated in Dupuytren's disease.
The Wnt proteins Wnt2, Wnt4, and Wnt7B, which were identified on GRAIL analysis, bind to frizzled receptors, leading to a cascade of events that eventually result in a decrease in the rate of β-catenin degradation18 (Figure 2Figure 2
Signaling Pathways of Wnt and β-Catenin.). Secreted frizzled-related proteins, such as SFRP4, antagonize the Wnt-signaling pathway by binding to either Wnts or frizzled receptors, thereby affecting receptor occupancy. In the absence of active Wnt, β-catenin is degraded, and potential target genes will not be activated.Another Dupuytren's disease risk locus contains RSPO2, encoding an R-spondin; members of the R-spondin family interact with frizzled receptors and LRP5/6 to induce β-catenin signaling. Furthermore, R-spondins induce canonical Wnt signaling by competing with the dickkopf (DKK) protein for binding to LRP5/6. The DKK protein is an inhibitor of Wnt signaling; it hinders the formation of a complex among Wnt, frizzled receptor, and LRP5/6 (Figure 2).25 SULF1, a heparan sulfate 6-O-endosulfatase, is known to influence canonical Wnt signaling. How it does so is not clear, but 6-O-desulfation of heparan sulfate proteoglycans may alter the binding of Wnt to its frizzled receptor.26,27 Increased activity of these WNT and R-spondin genes or decreased activity of SFRP could stimulate Wnt signaling and reduce intracellular β-catenin degradation. This mechanism could trigger fibroblasts to proliferate, leading to the development of Dupuytren's disease.
Also supporting a role for Wnt signaling in Dupuytren's disease is the microRNA (miRNA) expression profiles of fibroblasts and palmar fascia in persons with this disease, as compared with those in healthy controls. These miRNAs regulate genes related to the β-catenin pathway: WNT5A, ZIC1, and TGFB1.28 The three remaining significant loci lack an obvious connection to the Wnt pathway. An interesting candidate gene from these regions is MAFB. The RNA of MAFB has been shown to be up-regulated in the excised cord tissue from persons with Dupuytren's disease, as compared with fascia of the hand in healthy controls.29 Maf proteins are known for their role in fibrosarcoma and are believed to influence tissue development and cellular differentiation.30 MAFB can transform primary fibroblasts in vitro.31 The gene might therefore be involved in stage 2 of Dupuytren's disease (the differentiation of fibroblasts into myofibroblasts). The miRNA expression profile associated with Dupuytren's disease implicated some miRNAs in influencing the expression of MAFB as well.28
The results of our study indicate that genetic factors have a major role in the development of Dupuytren's disease. Associations with variations in genes that encode proteins in the Wnt-signaling pathway suggest that aberrations in this pathway confer susceptibility to the disease. Further genetic and basic research is required to fully unravel the pathogenesis of Dupuytren's disease.
Supported by grants from the University Medical Center Groningen and the University of Groningen, the Netherlands, and in part by grants from the German Ministry of Education and Research through the German National Genome Research Network (NGFN-2 and NGFNplus) and the German Research Foundation Cluster of Excellence Inflammation at Interfaces; the Academy of Medical Sciences, Wellcome Trust Starter Grants for Clinical Lecturers, and the Oxford University Medical Research Fund (MRF/2009/HT/1932) (all to Dr. Furniss); data generated by the LifeLines Cohort Study, the KORA research platform, and the Wellcome Trust Case Control Consortium (WTCCC); a grant to the LifeLines Cohort Study from the Netherlands Organization of Scientific Research (NWO 175.010.2007.006); the Dutch government's Economic Structure Enhancing Fund; the Ministries of Economic Affairs, Education, Culture and Science, and Health, Welfare and Sports; the Northern Netherlands Collaboration of Provinces; the Province of Groningen; the Dutch Kidney Foundation; the Dutch Diabetes Research Foundation; the KORA research platform, initiated and financed by the Helmholtz Center Munich and the German Research Center for Environmental Health and supported by the Munich Center of Health Sciences as part of LMUinnovativ; and the WTCCC (awards 076113 and 085475).
Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.
Drs. Ophoff and Wijmenga contributed equally to this article.
This article (10.1056/NEJMoa1101029) was published on July 6, 2011, at NEJM.org.
We thank the WTCCC for providing access to the control data for SNP rs611744 and the Wellcome Trust Sanger Institute for generating the Immunochip data; Gosia Trynka for help with the data analysis; Rik Korswagen for critically reviewing the manuscript; Nirvana Kornmann for her administrative work; Ron Booij, Mathieu Platteel, Soesma Jankipersadsing, Ramona Casper, and Janine Kurtenbach for their assistance in preparing samples and genotyping; Evert-Jan ten Dam for his assistance with earlier versions of the figures; Bert Tebbes for providing the photographs of Dupuytren's disease; Jackie Senior for editing an earlier version of the manuscript; the staff of the Lifelines Cohort Study, including Behrooz Alizadeh, Annemieke Boesjes, Marcel Bruinenberg, Ilja Nolte, and Mitra Valimohammadi for their help in creating the GWAS database, and Rob Bieringa, Joost Keers, René Oostergo, Rosalie Visser, and Judith Vonk for their work related to data collection and validation; the staff of the Medical Biobank Northern Netherlands; all the participating general practitioners and pharmacists; Wolfgang Wach and the patient support group of the Dupuytren Society for continued support; and all the patients with Dupuytren's disease and the controls for participating in this study.
Source Information
The authors' affiliations are listed in the Appendix.
Address reprint requests to Dr. Wijmenga at the Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, the Netherlands, or at cisca.wijmenga@med.umcg.nl; or to Dr. Ophoff at ophoff@ucla.edu.
Additional members of the Dutch Dupuytren Study Group, the German Dupuytren Study Group, and the British Society for Surgery of the Hand–Genetic Origin of Dupuytren's Disease (BSSH–GODD) Consortium are listed in the Supplementary Appendix, available at NEJM.org.
Appendix
The authors' affiliations are as follows: Departments of Plastic Surgery (G.H.D., P.M.W.), Genetics (G.H.D., E.A.F., L.F., P.V., C.W.), Gastroenterology (E.A.F.), and Endocrinology (B.H.W.), University Medical Center Groningen and University of Groningen, Groningen; and the Department of Medical Genetics and Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht (R.A.O.) — all in the Netherlands; the Cologne Center for Genomics, Division of Dermatogenetics (H.C.H., K.B., M.R.T., P.N.), the Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (H.C.H., K.B., P.N.), and the Center for Molecular Medicine (H.C.H., P.N.), University of Cologne, Cologne; the Institute of Clinical Genetics, Faculty of Medicine Carl Gustav Carus, Dresden University of Technology, Dresden (S.T.); the Institute of Medical Informatics and Statistics (M.N.) and the Institute of Clinical Molecular Biology (A.F.), Christian-Albrechts-Universität, Kiel; the Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg (N.K., H.-E.W.); and the Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität and Klinikum Grosshadern, Munich (H.-E.W.) — all in Germany; the Nuffield Department of Surgical Sciences, University of Oxford, and the Department of Plastic and Reconstructive Surgery, West Wing, John Radcliffe Hospital, Oxford, United Kingdom (D.F., H.G.); and the Center for Neurobehavioral Genetics, University of California, Los Angeles (R.A.O.)
- References
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Christina Raykha, Justin Crawford, Bing Siang Gan, Ping Fu, Leon A. Bach, David B. O'Gorman. (2013) IGF-II and IGFBP-6 regulate cellular contractility and proliferation in Dupuytren's disease. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 1832:10, 1511-1519
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Elisa Cinotti, Giulio Ferrero, Francesco Paparo, Marina Papadia, Francesca Faravelli, Franco Rongioletti, Carlo Traverso, Emilio Di Maria. (2013) Arthropathy, osteolysis, keloids, relapsing conjunctival pannus and gingival overgrowth: A variant of polyfibromatosis?. American Journal of Medical Genetics Part A 161:6, 1214-1220
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L. S. Verjee, J. S. N. Verhoekx, J. K. K. Chan, T. Krausgruber, V. Nicolaidou, D. Izadi, D. Davidson, M. Feldmann, K. S. Midwood, J. Nanchahal. (2013) PNAS Plus: Unraveling the signaling pathways promoting fibrosis in Dupuytren's disease reveals TNF as a therapeutic target. Proceedings of the National Academy of Sciences 110:10, E928-E937
4
L. B. Dahlin, C. Bainbridge, C. Leclercq, R. A. Gerber, D. Guerin, J. C. Cappelleri, P. P. Szczypa, J. Dias. (2013) Dupuytren's disease presentation, referral pathways and resource utilisation in Europe: regional analysis of a surgeon survey and patient chart review. International Journal of Clinical Practice 67:3, 261-270
5
D. In: Ferri's Clinical Advisor 2013. Elsevier, 2013:297-342.
6
Mahmood F. Bhutta, Lindsey Hobson, Jane Lambie, Elizabeth S. H. Scaman, Martin J. Burton, Henk Giele, Sarra E. Jamieson, Dominic Furniss. (2013) Alternative Recruitment Strategies Influence Saliva Sample Return Rates in Community-Based Genetic Association Studies. Annals of Human Geneticsn/a-n/a
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Yong-Ri Jin, Jeong Kyo Yoon. (2012) The R-spondin family of proteins: Emerging regulators of WNT signaling. The International Journal of Biochemistry & Cell Biology 44:12, 2278-2287
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Karsten Knobloch. (2012) Die Operation ist nicht mehr die einzige therapeutische Option. MMW - Fortschritte der Medizin 154:19, 36-37
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Patrick Herr, George Hausmann, Konrad Basler. (2012) WNT secretion and signalling in human disease. Trends in Molecular Medicine 18:8, 483-493
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Janine M. Wilkinson, Rose K. Davidson, Tracey E. Swingler, Eleanor R. Jones, Anthony N. Corps, Phillip Johnston, Graham P. Riley, Adrian J. Chojnowski, Ian M. Clark. (2012) MMP-14 and MMP-2 are key metalloproteases in Dupuytren's disease fibroblast-mediated contraction. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 1822:6, 897-905
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Johann Beaudreuil. (2012) La maladie de Dupuytren en 2012. Revue du Rhumatisme Monographies 79:3, 126-132
12
Guido H. Dolmans, Paul M. Werker, Igle J. de Jong, Rien J. Nijman, , Cisca Wijmenga, Roel A. Ophoff. (2012) WNT2 Locus Is Involved in Genetic Susceptibility of Peyronie's Disease. The Journal of Sexual Medicine 9:5, 1430-1434
13
C. Krause, P. Kloen. (2012) Concurrent inhibition of TGF-β and mitogen driven signaling cascades in Dupuytren’s disease – Non-surgical treatment strategies from a signaling point of view. Medical Hypotheses 78:3, 385-388
14
Jeong Kyo Yoon, Jin-Seon Lee. (2012) Cellular signaling and biological functions of R-spondins. Cellular Signalling 24:2, 369-377
15
Ardeshir Bayat. (2011) Connective tissue diseases: Unpicking Dupruyten Disease etiology—is Wnt the way?. Nature Reviews Rheumatology 8:1, 5-6
16
(2011) Wnt Signaling and Dupuytren's Disease. New England Journal of Medicine 365:18, 1740-1740
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Serge Ferrari, Ego Seeman, Philippe Clézardin, David Karasik, David G Little, Toshio Matsumoto. (2011) Clinical and basic research papers – August 2011. IBMS BoneKEy 8:8, 352-361
18
Mark A Jobling. (2011) The Baron's complaint. Investigative Genetics 2:1, 18
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