Axotrophin/MARCH7 acts as an E3 ubiquitin ligase and ubiquitinates tau protein in vitro impairing microtubule binding.

Tau is the major microtubule-associated protein in neurons involved in microtubule stabilization in the axonal compartment. Changes in tau gene expression, alternative splicing and posttranslational modification regulate tau function and in tauopathies can result in tau mislocalization and dysfunction, causing tau aggregation and cell death. To uncover proteins involved in the development of tauopathies, a yeast two-hybrid system was used to screen for tau-interacting proteins. We show that axotrophin/MARCH7, a RING-variant domain containing protein with similarity to E3 ubiquitin ligases interacts with tau. We defined the tau binding domain to amino acids 552-682 of axotrophin comprising the RING-variant domain. Co-immunoprecipitation and co-localization confirmed the specificity of the interaction. Intracellular localization of axotrophin is determined by an N-terminal nuclear targeting signal and a C-terminal nuclear export signal. In AD brain nuclear localization is lost and axotrophin is rather associated with neurofibrillary tangles. We find here that tau becomes mono-ubiquitinated by recombinant tau-interacting RING-variant domain, which diminishes its microtubule-binding. In vitro ubiquitination of four-repeat tau results in incorporation of up to four ubiquitin molecules compared to two molecules in three-repeat tau. In summary, we present a novel tau modification occurring preferentially on 4-repeat tau protein which modifies microtubule-binding and may impact on the pathogenesis of tauopathies.

Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.

Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.