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BMC Neurology

, 16:141

Movement disorders


BackgroundSpinal muscular atrophy SMA is the most common autosomal recessive disease in children, and the diagnosis is complicated and difficult, especially at early stage. Early diagnosis of SMA is able to improve the outcome of SMA patients. In our study, Real-time PCR was developed to measure the gene mutation or deletion of key genes for SMA and to further analyse genotype-phenotype correlation.

MethodsThe multiple real-time PCR for detecting the mutations of survival of motor neuron SMN, apoptosis inhibitory protein NAIP and general transcription factor IIH, polypeptide 2 gene GTF2H2 was established and confirmed by DNA sequencing and multiplex ligation-dependent probe amplification MLPA. The diagnosis and prognosis of 141 hospitalized children, 100 normal children and further 2000 cases of dry blood spot DBS samples were analysed by this multiple real-time PCR.

ResultsThe multiple real-time PCR was established and the accuracy of it to detect the mutations of SMN, NAIP and GTF2H2 was at least 98.8 % comparing with DNA sequencing and MLPA. Among 141 limb movement disorders children, 75 cases were SMA. 71 cases of SMA 94.67 % were with SMN c.840 mutation, 9 cases 12 % with NAIP deletion and 3 cases 4 % with GTF2H2 deletion. The multiple real-time PCR was able to diagnose and predict the prognosis of SMA patients. Simultaneously, the real-time PCR was applied to detect trace DNA from DBS and able to make an early diagnosis of SMA.

ConclusionThe clinical and molecular characteristics of SMA in Southwest of China were presented. Our work provides a novel way for detecting SMA in children by using real-time PCR and the potential usage in newborn screening for early diagnosis of SMA.

KeywordsSpinal muscular atrophy Multiplex real-time PCR Newborn screening Electronic supplementary materialThe online version of this article doi:10.1186-s12883-016-0651-y contains supplementary material, which is available to authorized users.

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Autor: Zhidai Liu - Penghui Zhang - Xiaoyan He - Shan Liu - Shi Tang - Rong Zhang - Xinbin Wang - Junjie Tan - Bin Peng - Li Ji


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