Back

TRAF6 controls excitatory spinogenesis and excitation-inhibition balance thought binding neuroplastin

Kumar Vemula, S.; Malci, A.; Junge, L.; Lehmann, A.-C.; Rama, R.; Hradsky, J.; Matute, R. A.; Naumann, M.; Kreutz, M. R.; Seidenbecher, C. I.; Gundelfinger, E. D.; Herrera-Molina, R.

2019-09-14 cell biology
10.1101/768341 bioRxiv
Show abstract

Synaptogenic mechanisms and their relevance to achieve a correct synapse density and activity in mature neurons are poorly understood. Here, we show that the tumor necrosis factor receptor-associated factor 6 (TRAF6) controls early spinogenesis by binding the cell adhesion molecule neuroplastin which is has been related to synapse formation in vivo. TRAF6-neuroplastin co-precipitations from brain samples and co-transfected HEK cells is explained by direct interaction of the proteins based on three-dimensional modelling and biochemical identification of intracellular amino acids of neuroplastin binding the TRAF-C domain of TRAF6 with micromolar affinity. TRAF6 was not only required for normal spinogenesis but also was strictly necessary to restore failed spinogenesis in neuroplastin-deficient neurons. Independently from neuroplastins extracellular adhesive properties or interaction with another known partner i.e. the plasma membrane Ca2+ ATPases, TRAF6 mediated formation of new postsynapses by neuroplastin overexpression in rat hippocampal neurons. Furthermore, TRAF6-controlled spinogenesis was required for the establishment of a correct synapse density as well as proper synaptic activity and intrinsic neuronal activity as demonstrated with intracellular and extracellular electrophysiological recordings. These findings provide a novel mechanism for early synapse formation that shapes connectivity and functioning of hippocampal neurons.

Matching journals

The top 5 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.