Person:

Kunkel, Louis

Congenital muscular dystrophy (CMD) is a clinically and genetically heterogeneous group of inherited muscle disorders. In patients, muscle weakness is usually present at or shortly after birth and is progressive in nature. Merosin deficient congenital muscular dystrophy (MDC1A) is a form of CMD caused by a defect in the laminin-α2 gene (LAMA2). Laminin-α2 is an extracellular matrix protein that interacts with the dystrophin-dystroglycan (DGC) complex in membranes providing stability to muscle fibers. In an N-ethyl-N-nitrosourea mutagenesis screen to develop zebrafish models of neuromuscular diseases, we identified a mutant fish that exhibits severe muscular dystrophy early in development. Genetic mapping identified a splice site mutation in the lama2 gene. This splice site is highly conserved in humans and this mutation results in mis-splicing of RNA and a loss of protein function. Homozygous lama2 mutant zebrafish, designated lama2cl501/cl501, exhibited reduced motor function and progressive degeneration of skeletal muscles and died at 8–15 days post fertilization. The skeletal muscles exhibited damaged myosepta and detachment of myofibers in the affected fish. Laminin-α2 deficiency also resulted in growth defects in the brain and eye of the mutant fish. This laminin-α2 deficient mutant fish represents a novel disease model to develop therapies for modulating splicing defects in congenital muscular dystrophies and to restore the muscle function in human patients with CMD.

Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62–0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65–0.82), but not for female samples (AUC 0.51, 95% CI 0.36–0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58–0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified.