4.1 Article

Sensitive quantitative detection of somatic mosaic mutation in double cortex syndrome

期刊

EPILEPTIC DISORDERS
卷 19, 期 4, 页码 450-455

出版社

JOHN LIBBEY EUROTEXT LTD
DOI: 10.1684/epd.2017.0944

关键词

LIS1 gene; somatic mosaic mutation; subcortical band heterotopia; Double Cortex

资金

  1. National Health and Medical Research Council Program [1091593, 1129054, 1079058]
  2. RD Wright Career Development Fellowship [1063799]
  3. Jansen Cilag
  4. UCB Pharma
  5. Sanofi-Aventis
  6. Novartis Pharmaceuticals
  7. National Health and Medical Research Council of Australia [1129054, 1079058] Funding Source: NHMRC

向作者/读者索取更多资源

Aims. Somatic mutation of the I issencephaly-1 gene is a cause of subcortical band heterotopia (double cortex). The severity of the phenotype depends on the level of mutation in brain tissue. Detecting and quantifying low-level somatic mosaic mutations is challenging. Here, we utilized droplet digital PCR, a sensitive method to detect low-level mutation.& para;& para;Methods. Droplet digital PCR was used in concert with classic genotyping techniques (SNaPshot assays and pyrosequencing) to detect and characterize the tissue mosaicism of a somatic mutation (LIS1 c.190A>T; p.K64X) in a patient with posterior bilateral SBH and refractory epilepsy.& para;& para;Results. The high sensitivity of droplet digital PCR and the ability to target individual DNA molecules allowed us to detect the mutation at low level in the brain, despite the low quality of the DNA sample derived from formalin-fixed paraffin-embedded tissue. This low mutation frequency in the brain was consistent with the relatively subtle malformation resolved by magnetic resonance imaging. The presence of the mutation in other tissues from the patient permitted us to predict the timing of mutagenesis.& para;& para;Conclusion. This sensitive methodology will have utility for a variety of other brain malformation syndromes associated with epilepsy for which historical pathological specimens are available and specific somatic mosaic mutations are predicted.

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