Fidgetin interacting with microtubule end binding protein EB3 affects axonal regrowth in spinal cord injury

Fidgetin,a microtubule-severing enzyme,regulates neurite outgrowth,axonal regeneration,and cell migration by trimming off the labile domain of microtubule polymers.Because maintenance of the microtubule labile domain is essential for axon initiation,elongation,and navigation,it is of interest to determine whether augmenting the microtubule labile domain via depletion of fidgetin serves as a therapeutic approach to promote axonal regrowth in spinal cord injury.In this study,we constructed rat models of spinal cord injury and sciatic nerve injury.Compared with spinal cord injury,we found that expression level of tyrosinated microtubules in the labile portion of microtubules continuously increased,whereas fidgetin decreased after peripheral nerve injury.Depletion of fidgetin enhanced axon regeneration after spinal cord injury,whereas expression level of end binding protein 3(EB3)markedly increased.Next,we performed RNA interference to knockdown EB3 or fidgetin.We found that deletion of EB3 did not change fidgetin expression.Conversely,deletion of fidgetin markedly increased expression of tyrosinated microtubules and EB3.Deletion of fidgetin increased the amount of EB3 at the end of neurites and thereby increased the level of tyrosinated microtubules.Finally,we deleted EB3 and overexpressed fidgetin.We found that fidgetin trimmed tyrosinated tubulins by interacting with EB3.When fidgetin was deleted,the labile portion of microtubules was elongated,and as a result the length of axons and number of axon branches were increased.These findings suggest that fidgetin can be used as a novel therapeutic target to promote axonal regeneration after spinal cord injury.Furthermore,they reveal an innovative mechanism by which fidgetin preferentially severs labile microtubules.

The DrugPattern tool for drug set enrichment analysis and its prediction for beneficial effects of oxLDL on type 2 diabetes

Enrichment analysis methods, e.g., gene set enrichment analysis, represent one class of important bio- informatical resources for mining patterns in biomedical datasets. However, tools for inferring patterns and rules of a list of drugs are limited. In this study, we developed a web-based tool, DrugPattern, for drug set enrichment analysis. We first collected and curated 7019 drug sets, including indications, adverse reactions, targets, pathways, etc. from public databases. For a list of interested drugs, DrugPat- tern then evaluates the significance of the enrichment of these drugs in each of the 7019 drug sets. To validate DrugPattern, we employed it for the prediction of the effects of oxidized low-density lipoprotein （oxLDL）, a factor expected to be deleterious. We predicted that oxLDL has beneficial effects on some diseases, most of which were supported by evidence in the literature. Because DrugPattern predicted the potential beneficial effects of oxLDL in type 2 diabetes （T2D）, animal experiments were then performed to further verify this prediction. As a result, the experimental evidences validated the DrugPattern prediction that oxLDL indeed has beneficial effects on T2D in the case of energy restriction. These data confirmed the prediction accuracy of our approach and revealed unexpected protective roles for oxLDL in various diseases. This study provides a tool to infer patterns and rules in biomedical datasets based on drug set enrichment analysis.