Mutations in KLHL40 are a frequent cause of severe autosomal-recessive nemaline myopathy.

Nemaline myopathy (NEM) is a common congenital myopathy. At the very severe end of the NEM clinical spectrum are genetically unresolved cases of autosomal-recessive fetal akinesia sequence. We studied a multinational cohort of 143 severe-NEM-affected families lacking genetic diagnosis. We performed whole-exome sequencing of six families and targeted gene sequencing of additional families. We identified 19 mutations in KLHL40 (kelch-like family member 40) in 28 apparently unrelated NEM kindreds of various ethnicities. Accounting for up to 28% of the tested individuals in the Japanese cohort, KLHL40 mutations were found to be the most common cause of this severe form of NEM. Clinical features of affected individuals were severe and distinctive and included fetal akinesia or hypokinesia and contractures, fractures, respiratory failure, and swallowing difficulties at birth. Molecular modeling suggested that the missense substitutions would destabilize the protein. Protein studies showed that KLHL40 is a striated-muscle-specific protein that is absent in KLHL40-associated NEM skeletal muscle. In zebrafish, klhl40a and klhl40b expression is largely confined to the myotome and skeletal muscle, and knockdown of these isoforms results in disruption of muscle structure and loss of movement. We identified KLHL40 mutations as a frequent cause of severe autosomal-recessive NEM and showed that it plays a key role in muscle development and function. Screening of KLHL40 should be a priority in individuals who are affected by autosomal-recessive NEM and who present with prenatal symptoms and/or contractures and in all Japanese individuals with severe NEM.

Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study.

BACKGROUND: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings. METHODS: The CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time-frequency decomposition of the movement trajectories of the infant's body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9-15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement assessment (GMA) and neonatal imaging. RESULTS: The CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%, p = 0.02) compared with those with ambulatory CP (median: 72.7%). CONCLUSION: The CIMA model may be a clinically feasible alternative to observational GMA.