Back

Loss of Neurodevelopmental Gene CASK Disrupts Neural Connectivity in Human Cortical Excitatory Neurons

McSweeney, D.; Gabriel, R.; Jin, K.; Pang, Z. P.; Aronow, B. J.; Pak, C.

2022-03-28 neuroscience
10.1101/2022.02.14.480404 bioRxiv
Show abstract

Loss-of-function (LOF) mutations in CASK cause severe developmental phenotypes, including microcephaly with pontine and cerebellar hypoplasia, X-linked intellectual disability, and autism. Unraveling the pathogenesis of CASK-related disorders has been challenging due to limited human cellular models to study the dynamic roles of this molecule during neuronal and synapse development. Here, we generated CASK knockout (KO) isogenic cell lines from human embryonic stem cells (hESCs) using CRISPR/Cas9 and examined gene expression, morphometrics, and synaptic function of induced neuronal cells during development. While young (immature) CASK KO neurons show robust neuronal outgrowth, mature CASK KO neurons displayed severe defects in synaptic transmission and synchronized burst activity without compromising neuronal morphology and synapse numbers. In developing human cortical neurons, CASK functioned to promote both structural integrity and establishment of cortical excitatory neuronal networks. These results lay the foundation for future studies to identify suppressors of such phenotypes relevant to human patients. HighlightsO_LICASK LOF mutations increase neuronal complexity in immature developing neurons C_LIO_LICASK LOF does not alter synapse formation and neurite complexity in mature neurons C_LIO_LISynaptic transmission and network synchronicity are compromised in CASK KO neurons C_LIO_LIDifferential gene expression analysis reveals enrichment of synaptic gene networks in mature CASK KO neurons C_LI

Matching journals

The top 8 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.