DNA-based prenatal diagnosis for severe and variant forms of multiple acyl-CoA dehydrogenation deficiency

Rikke K J Olsen, Brage S Andresen, Ernst Christensen, Hanna Mandel, Flemming Skovby, Jens Peter Nielsen, Inga Knudsen, Christine Vianey-Saban, Henrik Simonsen, Niels Gregersen

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

OBJECTIVES: Multiple acyl-CoA dehydrogenation deficiency (MADD) is a clinically heterogeneous disorder of mitochondrial fatty acid, amino acid, and choline oxidation due to mutations in the genes encoding electron transfer flavoprotein (ETF) or ETF ubiquinone oxidoreductase (ETFQO). So far, prenatal diagnosis of MADD has relied mostly on second-trimester biochemical analyses of amniotic fluid or cultured amniocytes. We report here on an alternative DNA-based approach for prenatal diagnosis in pregnancies at risk of MADD.

METHODS: We used our knowledge of the mutational status in three unrelated families with a history of MADD to perform direct sequencing for the familial mutations using genomic DNA isolated from chorionic villus samples (CVS) at gestational week 10 to 11.

RESULTS: Within two days, we were able to carry out accurate DNA-based prenatal testing in one pregnancy at risk of severe MADD, and in two pregnancies at risk of variant forms of MADD.

CONCLUSION: This is the first report of DNA-based prenatal diagnosis of MADD. Our molecular approach is suitable for fast and reliable first-trimester prenatal diagnosis in pregnancies at risk of severe and variant forms of MADD.

Original languageEnglish
JournalPrenatal Diagnosis
Volume25
Issue number1
Pages (from-to)60-64
ISSN0197-3851
DOIs
Publication statusPublished - Jan 2005
Externally publishedYes

Keywords

  • Acyl-CoA Dehydrogenase
  • Adult
  • Cells, Cultured
  • Chorionic Villi Sampling
  • DNA
  • Electron-Transferring Flavoproteins
  • Female
  • Humans
  • Iron-Sulfur Proteins
  • Mutation
  • Oxidoreductases Acting on CH-NH Group Donors
  • Pregnancy
  • Pregnancy Trimester, First
  • Reproducibility of Results

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