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Comparative Study
Correlation between deletion patterns of SMN and NAIP genes and the clinical features of spinal muscular atrophy in Indian patients.
- Rashna S Dastur, Pradnya S Gaitonde, Satish V Khadilkar, Vrajesh P Udani, and Jayshree J Nadkarni.
- Department of Neuropathology and Applied Biology, Medical Research Centre, Bombay Hospital, Mumbai, India.
- Neurol India. 2006 Sep 1;54(3):255-9.
BackgroundSpinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disorder involving degeneration of anterior horn cells of spinal cord resulting in progressive muscle weakness and atrophy.AimsThe molecular analysis of two marker genes for spinal muscular atrophy (SMA) i.e, the survival motor neuron gene (SMN) and the neuronal apoptosis inhibitory protein gene (NAIP) was conducted in 39 Indian patients with clinical symptoms of SMA. Out of these, 28 showed homozygous deletions and the phenotypic features of these SMA patients were compared with the corresponding genotypes.SettingsA tertiary care teaching Hospital.DesignThis is a prospective hospital based study.Materials And MethodsPolymerase chain reaction (PCR) combined with restriction fragment length polymorphism (RFLP) was used to detect the deletion of exon 7 and exon 8 of SMN1 gene, as well as multiplex PCR for exon 5 and 13 of NAIP gene.ResultsExons 7 and 8 of SMN and NAIP (exon 5) were homozygously deleted in 73% of SMA I and 27% of SMA II patients. SMN exon 7 and 8 deletions without NAIP deletions were seen in 27% of type I SMA and 46% of SMA type II patients. Two patients of type III SMA showed single deletion of SMN exon 7 along with 27% of SMA type II patients.ConclusionWith the advent of molecular biology techniques, SMN gene deletion studies have become the first line of investigation for confirmation of a clinical diagnosis of SMA. The findings of homozygous deletions of exons 7 and/or 8 of SMN1 gene confirms the diagnosis of SMA, even in patients with atypical clinical features. Deletions of NAIP gene were mainly seen in severely affected patients, hence is useful for predicting the prognosis.
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