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MLPA identification of dystrophin mutations and in silico evaluation of the predicted protein in dystrophinopathy cases from India

Overview of attention for article published in BMC Medical Genomics, June 2017
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Title
MLPA identification of dystrophin mutations and in silico evaluation of the predicted protein in dystrophinopathy cases from India
Published in
BMC Medical Genomics, June 2017
DOI 10.1186/s12881-017-0431-6
Pubmed ID
Authors

Sekar Deepha, Seena Vengalil, Veeramani Preethish-Kumar, Kiran Polavarapu, Atchayaram Nalini, Narayanappa Gayathri, Meera Purushottam

Abstract

Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) are X-linked recessive disorders caused by mutations in the DMD gene. The aim of this study was to predict the effect of gene mutations on the dystrophin protein and study its impact on clinical phenotype. In this study, 415 clinically diagnosed patients were tested for mutations by Multiplex ligation dependent probe amplification (MLPA). Muscle biopsy was performed in 34 patients with negative MLPA. Phenotype-genotype correlation was done using PROVEAN, hydrophobicity and eDystrophin analysis. We have utilized bioinformatics tools in order to evaluate the observed mutations both at the level of primary as well as secondary structure. Mutations were identified in 75.42% cases, of which there were deletions in 91.6% and duplications in 8.30%. As per the reading frame rule, 84.6% out-of frame and 15.3% in-frame mutations were noted. Exon 50 was the most frequently deleted exon and the exon 45-52 region was the hot-spot for deletions in this cohort. There was no correlation noted between age of onset or creatine kinase (CK) values with extent of gene mutation. The PROVEAN analysis showed a deleterious effect in 94.5% cases and a neutral effect in 5.09% cases. Mutations in exon 45-54 (out of frame) and exon 46-54 (in-frame) regions in the central rod domain of dystrophin showed more negative scores compared to other domains in the present study. Hydrophobicity profile analysis showed that the hydrophobic regions I & III were equally affected. Analysis of deletions in hinge III hydrophobic region by the eDystrophin programme also predicted a hybrid repeat seen to be associated with a BMD like disease progression, thus making the hinge III region relatively tolerant to mutations. We found that, while the predictions made by the software utilized might have overall significance, the results were not convincing on a case by case basis. This reflects the inadequacy of the currently available tools and also underlines the possible inadequacy of MLPA to detect other minor mutations that might enhance or suppress the effect of the primary mutation in this large gene. Next Generation Sequencing or targeted Sanger sequencing on a case by case basis might improve phenotype- genotype correlation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 20%
Researcher 7 20%
Student > Ph. D. Student 5 14%
Other 3 9%
Student > Master 3 9%
Other 4 11%
Unknown 6 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 31%
Medicine and Dentistry 7 20%
Agricultural and Biological Sciences 2 6%
Social Sciences 2 6%
Psychology 1 3%
Other 3 9%
Unknown 9 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 July 2017.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from BMC Medical Genomics
#2,010
of 2,444 outputs
Outputs of similar age
#289,899
of 331,711 outputs
Outputs of similar age from BMC Medical Genomics
#36
of 44 outputs
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