MKLN1 splicing defect in dogs with lethal acrodermatitis

Lethal acrodermatitis (LAD) is a genodermatosis with monogenic autosomal recessive inheritance in Bull Terriers and Miniature Bull Terriers. The LAD phenotype is characterized by poor growth, immune deficiency, and skin lesions, especially at the paws. Utilizing a combination of genome wide association study and haplotype analysis, we mapped the LAD locus to a critical interval of similar to 1.11 Mb on chromosome 14. Whole genome sequencing of an LAD affected dog revealed a splice region variant in the MKLN1 gene that was not present in 191 control genomes (chr14:5,731,405T>G or MKLN/:c.400+3A>C). This variant showed perfect association in a larger combined Bull Terrier/Miniature Bull Terrier cohort of 46 cases and 294 controls. The variant was absent from 462 genetically diverse control dogs of 62 other dog breeds. RT-PCR analysis of skin RNA from an affected and a control dog demonstrated skipping of exon 4 in the MKLN1 transcripts of the LAD affected dog, which leads to a shift in the MKLN1 reading frame. MKLN1 encodes the widely expressed intracellular protein muskelin 1, for which diverse functions in cell adhesion, morphology, spreading, and intracellular transport processes are discussed. While the pathogenesis of LAD remains unclear, our data facilitate genetic testing of Bull Terriers and Miniature Bull Terriers to prevent the unintentional production of LAD affected dogs. This study may provide a starting point to further clarify the elusive physiological role of muskelin 1 in vivo.Peer reviewe

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision