Back

SHANK3-CAMSAP2 interaction links synapses to dendritic microtubule organisation in PV neurons

Hacker, D.; Lee, S.; Hecht-Bucher, M.; Lee, S.; Lamborelle, C.; Doil, A. N.; Fanutza, T.; Brueckner, A. M.; Yoo, T.; Ramirez-Rios, S.; Smaczniak, C.; Kaufmann, K.; Dekkers, D. H. W.; Demmers, J. A. A.; Moutin, M.-J.; Kim, E.; Mikhaylova, M.

2026-02-11 neuroscience
10.64898/2026.02.10.705051 bioRxiv
Show abstract

The microtubule cytoskeleton plays an essential role in establishing and maintaining neuronal polarity. In neurons, microtubules are initially generated at the centrosome which gradually loses this role in development. In mature neurons, microtubules are stabilised by the microtubule minus end-binding protein CAMSAP2. How and where microtubule minus ends are anchored within dendrites of mammalian neurons remains an open question. Here, we show that microtubules are directly attached to the postsynaptic density of excitatory synapses through an interaction of CAMSAP2 with the scaffolding protein SHANK3. This process is particularly relevant in parvalbumin-positive GABAergic neurons, in which excitatory synapses are predominantly located directly on the dendritic shaft in direct proximity to microtubules. Notably, this association is strongly enhanced by the Autism Spectrum Disorder (ASD)-associated SHANK3L68P mutation, leading to an increase of synaptic levels of CAMSAP2. This promotes an increased microtubule association with the synapse and alters microtubule dynamics. Downregulation of CAMSAP2 in SHANK3L68P parvalbumin neurons tips the balance between site-specific microtubule stabilisation and dynamics, reshaping dendritic architecture, connecting SHANK3-CAMSAP2 dependent microtubule regulation to synaptic ASD pathology. TeaserInteraction between synapses and microtubules is enhanced in parvalbumin neurons bearing the ASD-associated SHANK3 L68P mutation.

Matching journals

The top 9 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.