Background Duchenne muscular dystrophy (DMD) is a progressive X-linked disorder causing muscle degeneration and multisystem involvement, requiring precise genetic diagnosis for timely intervention and treatment.
Objective To investigate the genetic landscape of DMD using a two-tiered diagnostic approach combining MLPA and WES, and to correlate genetic findings with clinical outcomes for improved management.
Materials and methods A cross-sectional study of 80 male DMD patients was conducted using a sequential genetic approach, combining MLPA and WES, with bioinformatics and statistical analyses to explore genotype-phenotype correlations.
Results Pathogenic variants were identified in 65 cases (81.2 %), with deletions (67.5 %) being the most common, followed by duplications (6.3 %), splice-site (3.8 %), and nonsense variants (3.8 %). WES identified additional pathogenic variants in MLPA-negative cases, including novel mutations, expanding the known genetic spectrum of DMD. The combined MLPA-WES approach significantly improved diagnostic yield (χ² = 12.90, p < 0.001). Functional analysis revealed disruptions in glycogen metabolism (46 %), calcium transport (24 %), and mitochondrial function (12 %), with dystrophin-associated proteins (DAG1, SGCD) critically involved in muscle stability. Out-of-frame deletions were significantly associated with early disease onset (χ² = 49.03, p < 0.001) and severe phenotypes (χ² = 47.04, p < 0.001), supporting exon-skipping therapy. In-frame deletions correlated with milder progression, while nonsense variants posed a 2.5-fold increased risk of early cardiomyopathy (p = 0.002), emphasizing the need for early intervention.
Conclusion Combining MLPA and WES enhances DMD diagnostic accuracy, enabling timely clinical interventions. Integrating functional analysis with genotype-phenotype correlations supports personalized therapeutic strategies, improving patient outcomes.
Ethical clearance
Obtained from the Office of the Ethics Committee, S.M.S. Medical College and Attached Hospitals, Jaipur, dated 18th November 2021 and number- 1025MC/EC/2021.
Declaration of Competing Interest
None.
Acknowledgement
This study utilized the DAVID Bioinformatics Resource for GO enrichment analysis. The authors acknowledge the developers and maintainers of DAVID and have cited the relevant publications as per guidelines (Sherman et al., 2022; Huang et al., 2009). Protein-protein interactions were analyzed using the STRING database (v11.5), a resource for known and predicted protein interactions, as described by Szklarczyk et al. (2021).
Appendix A. Supplementary material
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.gmg.2025.100038.
| [1] |
S. Crisafulli, J. Sultana, A. Fontana, F. Salvo, S. Messina, et al., Global epidemiology of Duchenne muscular dystrophy: an updated systematic review and meta- analysis, Orphanet J. Rare Dis. 15 (1) (2020) 141, https://doi.org/10.1186/s13023-020-01430-8.
|
| [2] |
A.L. Beaudet, A.B. Boggs, A.C. Chinault, C.M. Eng, D. Gaudio, et al., Molecular diagnosis of Duchenne/Becker muscular dystrophy: Enhanced detection of dystrophin gene rearrangements by oligonucleotide array-comparative genomic hybridization, Hum. Mutat. 29 (9) (2008) 1100-1107.
|
| [3] |
E.P. Hoffman, R.H. Brown, L.M. Kunkel, Dystrophin: the protein product of the Duchenne muscular dystrophy locus, Cell 51 (6) (1987) 919-928.
|
| [4] |
S. Kohli, R. Saxena, E. Thomas, J. Singh, I.C. Verma, Gene changes in Duchenne muscular dystrophy: comparison of multiplex PCR and multiplex ligation- dependent probe amplification techniques, Neurol. India 58 (6) (2010) 852-856, https://doi.org/10.4103/0028-3886.73744.
|
| [5] |
A. Aartman-Rus, I. Fokkema, J. Verschuuren, I. Ginjaar, J. van Deutekom, G.J. van Ommen, et al., Theoretic applicability of antisense-mediated exon skipping for Duchenne muscular dystrophy pathogenic variants, Hum. Mutat. 30 (2) (2009) 293-299.
|
| [6] |
D.G. Allen, N.P. Whitehead, S.C. Froehner, Absence of dystrophin disrupts skeletal muscle signaling: roles of Ca2+, reactive oxygen species, and nitric oxide in the development of muscular dystrophy, Physiol. Rev. 96 (1) (2016) 253-305, https://doi.org/10.1152/physrev.00007.2015.
|
| [7] |
K. Tallapaka, P. Ranganath, A. Ramachandran, M.S. Uppin, S. Perala, S. Aggarwal, et al., Molecular and histopathological characterization of patients presenting with the Duchenne muscular dystrophy phenotype in a tertiary care center in southern India, Indian Pedia 56 (7) (2019) 556-559.
|
| [8] |
M. Michalak, M. Opas, Duchenne Muscular Dystrophy, John Wiley & Sons Ltd, 2001.
|
| [9] |
C.F. Wright, D.R. FitzPatrick, H.V. Firth, Paediatric genomics: diagnosing rare disease in children, Nat. Rev. Genet 19 (1) (2018) 25-40.
|
| [10] |
K. Zhang, X. Yang, G. Lin, Y. Han, J. Li, Molecular genetic testing and diagnosis strategies for dystrophinopathies in the era of next-generation sequencing, Clin. Chim. Acta 491 (2019) 66-73.
|
| [11] |
C. Happi Mbakam, G. Lamothe, J.P. Tremblay, Therapeutic strategies for dystrophin replacement in Duchenne muscular dystrophy, Front Med 9 (2022) 1-19.
|
| [12] |
C. Pisani, G. Strimpakos, F. Gabanella, M.G. Di Certo, A. Onori, C. Severini, et al., Utrophin up-regulation by artificial transcription factors induces muscle rescue and impacts the neuromuscular junction in mdx mice, Biochim. Biophys. Acta Mol. Basis Dis. 1864 (2018) 1172-1182.
|
| [13] |
D.R. Rodríguez-Rodríguez, R. Ramírez-Solís, M.A. Garza-Elizondo, M. De Lourdes Garza-Rodríguez, H.A. Barrera-Saldaña, Genome editing: a perspective on the application of CRISPR/Cas9 to study human diseases, Int. J. Mol. Med. 43 (4) (2019) 1559-1574.
|
| [14] |
D. Szklarczyk, A.L. Gable, K.C. Nastou, D. Lyon, R. Kirsch, S. Pyysalo, et al., The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets, Nucleic Acids Res. 49 (D1) (2021) D605-D612.
|
| [15] |
B.T. Sherman, M. Hao, J. Qiu, X. Jiao, M.W. Baseler, H.C. Lane, et al., DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update), Nucleic Acids Res (2022).
|
| [16] |
D.W. Huang, B.T. Sherman, R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources, Nat. Protoc. 4 (1) (2009) 44-57.
|
| [17] |
M. Goyal, A. Gupta, K. Agarwal, S. Kapoor, S. Kumar, Duchenne muscular dystrophy: genetic and clinical profile in the population of Rajasthan, India, Ann. Indian Acad. Neurol. 24 (3) (2021) 255-261.
|
| [18] |
L.C. Werneck, R.H. Scola, G.H.B. Maegawa, M.C.M. Werneck, Comparative analysis of PCR-deletion detection and immunohistochemistry in Brazilian Duchenne and Becker muscular dystrophy patients, Am. J. Med Genet 103 (2) (2001) 115-120.
|
| [19] |
M. Orso, A. Migliore, B. Polistena, E. Russo, F. Gatto, M. Monterubbianesi, et al., Duchenne muscular dystrophy in Italy: A systematic review of epidemiology, quality of life, treatment adherence, and economic impact, PLoS One 18 (6) (2023) e0287774.
|
| [20] |
J. Juan-Mateu, L. González-Quereda, M.J. Rodríguez, M. Baena, E. Verdura, A. Nascimento, et al., DMD variant in 576 dystrophinopathy families: A step forward in genotype-phenotype correlations, PLoS One 10 (8) (2015) e0135189, https://doi.org/10.1371/journal.pone.0135189.
|
| [21] |
D. Sekar, S. Vengalil, V. Preethish-Kumar, K. Polavarapu, A. Nalini, N. Gayathri, et al., MLPA identification of dystrophin variant and in silico evaluation of the predicted protein in dystrophinopathy cases from India, BMC Med. Genet. 18 (2017) 67.
|
| [22] |
M. Falzarano, C. Scotton, C. Passarelli, A. Ferlini, Duchenne muscular dystrophy: From diagnosis to therapy, Molecules 20 (10) (2015) 18168-18184.
|
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