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Lee, W. C.

Publications and source records attributed to Lee, W. C..

2 recordsLinked to original sources

Common ALS/FTD risk variants in UNC13A exacerbate its cryptic splicing and loss upon TDP-43 mislocalization

Variants within the UNC13A gene have long been known to increase risk of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), two related neurodegenerative diseases defined by mislocalization of the RNA-binding protein TDP-43. Here, we show that TDP-43 depletion induces robust inclusion of a cryptic exon (CE) within UNC13A, a critical synaptic gene, resulting in nonsense-mediated decay and protein loss. Strikingly, two common polymorphisms strongly associated with ALS/FTD risk directly alter TDP-43 binding within the CE or downstream intron, increasing CE inclusion in cultured cells and in patient brains. Our findings, which are the first to demonstrate a genetic link specifically between loss of TDP-43 nuclear function and disease, reveal both the mechanism by which UNC13A variants exacerbate the effects of decreased nuclear TDP-43 function, and provide a promising therapeutic target for TDP-43 proteinopathies. One-Sentence SummaryShared ALS/FTD risk variants increase the sensitivity of a cryptic exon in the synaptic gene UNC13A to TDP-43 depletion.

neuroscience

NMJ-Analyser: high-throughput morphological screening of neuromuscular junctions identifies subtle changes in mouse neuromuscular disease models

The neuromuscular junction (NMJ) is the peripheral synapse formed between a motor neuron axon terminal and a muscle fibre. NMJs are thought to be the primary site of peripheral pathology in many neuromuscular diseases, but innervation/denervation status is often assessed qualitatively with poor systematic criteria across studies, and separately from 3D morphological structure. Here, we describe the development of NMJ-Analyser, to comprehensively screen the morphology of NMJs and their corresponding innervation status automatically. NMJ-Analyser generates 29 biologically relevant features to quantitatively define healthy and aberrant neuromuscular synapses and applies machine learning to diagnose NMJ degeneration. We validated this framework in longitudinal analyses of wildtype mice, as well as in four different neuromuscular disease models: three for amyotrophic lateral sclerosis (ALS) and one for peripheral neuropathy. We showed that structural changes at the NMJ initially occur in the nerve terminal of mutant TDP43 and FUS ALS models. Using a machine learning algorithm, healthy and aberrant neuromuscular synapses are identified with 95% accuracy, with 88% sensitivity and 97% specificity. Our results validate NMJ-Analyser as a robust platform for systematic and structural screening of NMJs, and pave the way for transferrable, and cross-comparison and high-throughput studies in neuromuscular diseases.

neuroscience