Back

Unrestrained growth of correctly oriented microtubules instructs axonal microtubule orientation

Jakobs, M. A.; Zemel, A.; Franze, K.

2021-08-06 cell biology
10.1101/2021.06.03.446862 bioRxiv
Show abstract

In many eukaryotic cells, directed molecular transport occurs along microtubules. Within neuronal axons, transport over vast distances particularly relies on uniformly oriented microtubules, whose +-ends point towards the distal axon tip (+end out). However, axonal microtubules initially have mixed orientations, and how they orient during development is not yet fully understood. Using live imaging of primary Drosophila melanogaster neurons and physical modelling, we found that +end out microtubules are less likely to undergo catastrophe near the advancing axon tip, leading to their persistent long-term growth. In contrast, oppositely oriented microtubules remain short. Using chemical and physical perturbations of microtubule growth and genetic perturbations of the anti -catastrophe factor p150, which was enriched in the distal axon tip, we confirmed that the enhanced growth of +end out microtubules is critical for achieving uniform microtubule orientation. Computer simulations of axon development mimicking the enhanced +end out microtubule growth identified here along with previously proposed mechanisms correctly predicted the long-term evolution of axonal microtubule orientation as found in our experiments, highlighting the importance of the reduced catastrophe rate of +end out microtubules near the advancing axon tip in establishing uniform microtubule polarity. Our study thus leads to a holistic explanation of how axonal microtubules orient uniformly, a prerequisite for efficient long-range transport essential for neuronal functioning.

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.