Back

Genetic Rescue of a Lethal Wasting Mutation (Syb1lew/lew) by Neuron-Specific Expression of ECFP-Syb2

Lin, W.; Liu, Y.; Ye, Q.

2026-05-26 neuroscience
10.64898/2026.05.22.727260 bioRxiv
Show abstract

Lethal-wasting (lew) is a spontaneous null mutation in the Syb1/Vamp1 gene encoding the vesicular SNARE protein synaptobrevin 1 (SYB1/VAMP1). Homozygous Syb1lew/lew mice exhibit profound impairment in neuromuscular transmission and die around three weeks after birth. Pathogenic variants in human SYB1 are associated with a variety of disorders such as hereditary spastic ataxia and congenital myasthenic syndrome. Although Syb1 expression is highly enriched in neurons, it has also been reported in non-neuronal tissues, raising the possibility that non-neuronal defects contribute to the lethal phenotype. Here, we tested whether neuronal dysfunction is the primary cause of death in Syb1lew/lew mice and whether the closely related isoform synaptobrevin 2 (Syb2/VAMP2) can substitute for Syb1functin in vivo. We show that neuron-specific expression of ECFP-Syb2 in Syb1lew/lew mice fully rescues lethality in Syb1lew/lewmice, restoring normal growth and motor function. Electrophysiological analyses demonstrate complete recovery of neuromuscular synaptic transmission, including spontaneous and evoked release as well as short-term plasticity. These findings establish that neuronal expression of ECFP-Syb2 is sufficient to prevent the lethal phenotype associated with Syb1 deficiency and demonstrate that Syb2 can functionally replace Syb1 at motor nerve terminals in vivo. Significance StatementNeuron-specific expression of Syb2 fully rescues survival, motor function, and neuromuscular transmission in Syb1lew/lew mice. These findings establish presynaptic Syb1 dysfunction as the primary cause of the lethal phenotype and demonstrate that Syb2 can fully substitute for Syb1 in vivo.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.