JIP3 Activates Kinesin-1 Motility to Promote Axon Elongation.

Kinesin-1 is a molecular motor responsible for cargo transport along microtubules and plays critical roles in polarized cells, such as neurons. Kinesin-1 can function as a dimer of two kinesin heavy chains (KHC), which harbor the motor domain, or as a tetramer in combination with two accessory light chains (KLC). To ensure proper cargo distribution, kinesin-1 activity is precisely regulated. Both KLC and KHC subunits bind cargoes or regulatory proteins to engage the motor for movement along microtubules. We previously showed that the scaffolding protein JIP3 interacts directly with KHC in addition to its interaction with KLC and positively regulates dimeric KHC motility. Here we determined the stoichiometry of JIP3-KHC complexes and observed approximately four JIP3 molecules binding per KHC dimer. We then determined whether JIP3 activates tetrameric kinesin-1 motility. Using an in vitro motility assay, we show that JIP3 binding to KLC engages kinesin-1 with microtubules and that JIP3 binding to KHC promotes kinesin-1 motility along microtubules. We tested the in vivo relevance of these findings using axon elongation as a model for kinesin-1-dependent cellular function. We demonstrate that JIP3 binding to KHC, but not KLC, is essential for axon elongation in hippocampal neurons as well as axon regeneration in sensory neurons. These findings reveal that JIP3 regulation of kinesin-1 motility is critical for axon elongation and regeneration.

Tubulin-tyrosine Ligase (TTL)-mediated Increase in Tyrosinated α-Tubulin in Injured Axons Is Required for Retrograde Injury Signaling and Axon Regeneration.

Injured peripheral neurons successfully activate a pro-regenerative program to enable axon regeneration and functional recovery. The microtubule-dependent retrograde transport of injury signals from the lesion site in the axon back to the cell soma stimulates the increased growth capacity of injured neurons. However, the mechanisms initiating this retrograde transport remain poorly understood. Here we show that tubulin-tyrosine ligase (TTL) is required to increase the levels of tyrosinated α-tubulin at the axon injury site and plays an important role in injury signaling. Preventing the injury-induced increase in tyrosinated α-tubulin by knocking down TTL impairs retrograde organelle transport and delays activation of the pro-regenerative transcription factor c-Jun. In the absence of TTL, axon regeneration is reduced severely. We propose a model in which TTL increases the levels of tyrosinated α-tubulin locally at the injury site to facilitate the retrograde transport of injury signals that are required to activate a pro-regenerative program.

Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms.

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation.