Real time PCR assays to detect common mutations in the biotinidase gene and application of mutational analysis to newborn screening for biotinidase deficiency.

摘要

Biotinidase deficiency is an autosomal recessive disorder of biotin metabolism caused by defects in the biotinidase gene. Symptoms of biotinidase deficiency are resolved or prevented with oral biotin supplementation and as such newborn screening is performed to prospectively identify affected individuals prior to the onset of symptoms. Biotinidase deficiency is detected by determining the activity of the biotinidase enzyme utilizing the newborn dried blood spot and colorimetric end point analysis. While newborn screening by enzyme analysis is effective, external factors may compromise results of the enzyme analysis and difficulty is encountered in distinguishing between complete and partial enzyme deficiencies. In the United States, the four mutations most commonly associated with complete biotinidase deficiency are c98:d7i3, Q456H, R538C, and the double mutation D444H:A171T. Partial biotinidase deficiency is almost universally attributed to the D444H mutation. To more effectively distinguish between profound and partial biotinidase deficiency, a panel of assays utilizing real time PCR and melting curve analysis using Light Cycler technology was developed. Employing DNA extracted from the original dried blood specimens from newborns identified through prospective newborn screening as presumptive positive for biotinidase deficiency, the specimens were analyzed for the presence of the five common mutations. Using this approach it was possible to separate newborns with partial and complete deficiency from each other as well as from many of those with false positive results. In most cases it was also possible to correlate the genotype with the degree of residual enzyme activity present. In newborn screening for biotinidase deficiency, we have shown that the analysis of common mutations is useful in distinguishing between partial and complete enzyme deficiency as well as improving specificity. Combining biotinidase enzyme analysis with genotypic data also increases the sensitivity of screening for biotinidase deficiency and provides information useful to clinicians earlier than would otherwise be possible.