Improved molecular diagnostics for patients with respiratory chain complex deficiency

Aim: Mitochondrial diseases (incidence of 1: 8 000 in children) are attributed to isolated or combined respiratory chain complex (RCC) deficiencies. With around 25%, isolated RCC I-deficiency is the most common form. Clinically the patients present a heterogeneous spectrum which can be multi systemic (e.g. neonatal lactic acidosis, Leigh syndrome) or with distinct symptoms (e.g. ataxia, myopathy). RCC I is composed of 45 different subunits which are encoded by nuclear DNA (n=38) and mitochondrial DNA (n=7). The high number of genes involved hampers the search for the molecular basis of RCC I deficiency. Since in routine diagnostics only part of the mtDNA is analyzed, causal mutations can be identified in less than 10% of the pediatric patients.

Methods: A high-throughput screen for molecular genetic scanning of the complex I encoding genes has been established to improve the diagnostics. Within this research study 92 patients with isolated RCC I-deficiency and previous exclusion of common mtDNA mutations were investigated by DNA melting profile analysis using an Idaho LightScanner. The samples were analyzed in parallel followed by direct sequencing of those PCR products that displayed divergent melting profiles. To analyze genotype-phenotype correlations a clinical questionnaire was developed based on the 'Guidelines issued by the Working Group on Pediatric Metabolic Disorders'.

Results: 59 genes coding for subunits and assembly factors of RCC I were screened. Causative mutations have been identified in 16% of the patients which were inconspicuous in routine diagnostics. A single variant was identified in 30% of additional samples while in 54% of samples no mutations have been found yet. The analysis shows typical clinical patterns correlated with mutations in specific genes.

Conclusion: The molecular genetic diagnostics was improved and prenatal diagnostics can be offered. Genotype-phenotype correlations will enable more efficient diagnostics and allow predictions on disease course.