Background: The growth cone interprets cues in its environment in order to reach its target. We want to identify molecules that regulate growth cone behaviour in the developing embryo. We investigated the role of A disintegrin and metalloproteinase 10 (ADAM10) in axon guidance in the developing visual system of African frog, Xenopus laevis.
Methods: We first examined the expression patterns of adam10 mRNA by in situ hybridization. We then exposed the developing optic tract to an ADAM10 inhibitor, GI254023X, in vivo. Lastly, we inhibited ADAM10 function in diencephalic neuroepithelial cells (through which retinal ganglion cell (RGC) axons extend) or RGCs by electroporating or transfecting an ADAM10 dominant negative (dn-adam10).
Results: We show that adam10 mRNA is expressed in the dorsal neuroepithelium over the time RGC axons extend towards their target, the optic tectum. Second, pharmacological inhibition of ADAM10 in an in vivo exposed brain preparation causes the failure of RGC axons to recognize their target at low concentrations (0.5, 1 μM), and the failure of the axons to make a caudal turn in the mid-diencephalon at higher concentration (5 μM). Thus, ADAM10 function is required for RGC axon guidance at two key guidance decisions. Finally, molecular inhibition of ADAM10 function by electroporating dn-adam10 in the brain neuroepithelium causes defects in RGC axon target recognition (57%) and/or defects in caudal turn (12%), as seen with the pharmacological inhibitor. In contrast, molecular inhibition of ADAM10 within the RGC axons has no effect. Conclusions: These data argue strongly that ADAM10 acts cell non-autonomously within the neuroepithelium to regulate the guidance of RGC axons. This study shows for the first time that a metalloproteinase acts in a cell non-autonomous fashion to direct vertebrate axon growth. It will provide important insights into candidate molecules that could be used to reform nerve connections if destroyed because of injury or disease.
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Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification
