Arabidopsis katanin binds microtubules using a multimeric microtubule-binding domain.

Katanin is a heterodimeric protein that mediates ATP-dependent destabilization of microtubules in animal cells. In plants, the catalytic subunit of Arabidopsis thaliana katanin (AtKSS, Arabidopsis thaliana Katanin Small Subunit) has been identified and its microtubule-severing activity has been demonstrated in vitro. In vivo, plant katanin plays a role in the organization of cortical microtubules, but the way it achieves this function is unknown. To go further in our understanding of the mechanisms by which katanin severs microtubules, we analyzed the functional domains of Arabidopsis katanin. We characterized the microtubule-binding domain of katanin both in vitro and in vivo. It corresponds to a poorly conserved sequence between plant and animal katanins that is located in the N-terminus of the protein. This domain interacts with cortical microtubules in vivo and has a low affinity for microtubules in vitro. We also observed that katanin microtubule-binding domain oligomerizes into trimers. These results show that, besides being involved in the interaction of katanin with microtubules, the microtubule-binding domain may also participate in the oligomerization of katanin. At the structural level, we observed that AtKSS forms ring-shaped oligomers

Functional evidence for in vitro microtubule severing by the plant katanin homologue.

Temporal and spatial assembly of microtubules in plant cells depends mainly on the activity of microtubule-interacting proteins, which either stabilize, destabilize or translocate microtubules. Recent data have revealed that the thale cress (Arabidopsis thaliana) contains a protein related to the p60 catalytic subunit of animal katanin, a microtubule-severing protein. However, effects of the plant p60 on microtubule assembly are not known. We report the first functional evidence that the recombinant A. thaliana p60 katanin subunit, Atp60, binds to microtubules and severs them in an ATP-dependent manner in vitro. ATPase activity of Atp60 is stimulated by low tubulin/katanin ratios, and is inhibited at higher ratios. Considering its properties in vitro, several functions of Atp60 in vivo are discussed

Adenomatous Polyposis Coli as a Scaffold for Microtubule End-Binding Proteins

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Plant katanin, a microtubule severing protein

DOI: 10.1016/S1065-6995(02)00324-4International audienc

Classification contrainte de signaux, application à l'étude de la protéine neuronale tau

International audienceThis work is motivated by an application in neuroscience, in particular by the study of the (dys)functioning of a protein called Tau. The objective is to establish a classification of intensity profiles, according to the presence or absence of the protein and its monomer or dimer proportion. For this, we propose here a Gaussian mixture model with a fixed number of groups whose mean parameters are constrained and shared by the groups. The inference of this model is done via the classical EM algorithm. The performance of the method will be evaluated via simulation studies and an application on real data will be done.Ce travail est motivé par une application en neuroscience, en particulier par l'étude du (dys)fonctionnement d'une protéine appelée Tau. L'objectif est d'établir une classification de profils d'intensité, selon la présence ou pas de la protéine et sa proportion monomère ou dimère. Pour cela, nous proposons ici un modèle de mélange gaussienne en un nombre fixé de groupes dont les paramètres de moyennes sont contraints et partagés par les groupes. L'inférence de ce modèle est faite via l'algorithme classique EM. La méthode proposée sera évaluée via des études de simulations et une application sur des données réelles sera effectuée. Mots-clés. Données fonctionnelles, Classification automatique, Modèle de mélange, paramètres contraints, algorithme ECM

Plant and mouse EB1 proteins have opposite intrinsic properties on the dynamic instability of microtubules

International audienceObjective: Most eukaryotic cells contain microtubule filaments, which play central roles in intra-cellular organization. However, microtubule networks have a wide variety of architectures from one cell type and organism to another. Nonetheless, the sequences of tubulins, of Microtubule Associated proteins (MAPs) and the structure of microtubules are usually well conserved throughout the evolution. MAPs being known to be responsible for regulating microtubule organization and dynamics, this raises the question of the conservation of their intrinsic properties. Indeed, knowing how the intrinsic properties of individual MAPs differ between organisms might enlighten our understanding of how distinct microtubule networks are built. End-Binding protein 1 (EB1), first described as a MAP in yeast, is conserved in plants and mammals. The intrinsic properties of the mammalian and the yeast EB1 proteins have been well described in the literature but, to our knowledge, the intrinsic properties of EB1 from plant and mammals have not been compared thus far. Results: Here, using an in vitro assay, we discovered that plant and mammalian EB1 purified proteins have different intrinsic properties on microtubule dynamics. Indeed, the mammalian EB1 protein increases microtubules dynamic while the plant EB1 protein stabilizes them

Classification contrainte de signaux, application à l'étude de la protéine neuronale tau

International audienceThis work is motivated by an application in neuroscience, in particular by the study of the (dys)functioning of a protein called Tau. The objective is to establish a classification of intensity profiles, according to the presence or absence of the protein and its monomer or dimer proportion. For this, we propose here a Gaussian mixture model with a fixed number of groups whose mean parameters are constrained and shared by the groups. The inference of this model is done via the classical EM algorithm. The performance of the method will be evaluated via simulation studies and an application on real data will be done.Ce travail est motivé par une application en neuroscience, en particulier par l'étude du (dys)fonctionnement d'une protéine appelée Tau. L'objectif est d'établir une classification de profils d'intensité, selon la présence ou pas de la protéine et sa proportion monomère ou dimère. Pour cela, nous proposons ici un modèle de mélange gaussienne en un nombre fixé de groupes dont les paramètres de moyennes sont contraints et partagés par les groupes. L'inférence de ce modèle est faite via l'algorithme classique EM. La méthode proposée sera évaluée via des études de simulations et une application sur des données réelles sera effectuée. Mots-clés. Données fonctionnelles, Classification automatique, Modèle de mélange, paramètres contraints, algorithme ECM

Classification contrainte de signaux, application à l'étude de la protéine neuronale tau

International audienceThis work is motivated by an application in neuroscience, in particular by the study of the (dys)functioning of a protein called Tau. The objective is to establish a classification of intensity profiles, according to the presence or absence of the protein and its monomer or dimer proportion. For this, we propose here a Gaussian mixture model with a fixed number of groups whose mean parameters are constrained and shared by the groups. The inference of this model is done via the classical EM algorithm. The performance of the method will be evaluated via simulation studies and an application on real data will be done.Ce travail est motivé par une application en neuroscience, en particulier par l'étude du (dys)fonctionnement d'une protéine appelée Tau. L'objectif est d'établir une classification de profils d'intensité, selon la présence ou pas de la protéine et sa proportion monomère ou dimère. Pour cela, nous proposons ici un modèle de mélange gaussienne en un nombre fixé de groupes dont les paramètres de moyennes sont contraints et partagés par les groupes. L'inférence de ce modèle est faite via l'algorithme classique EM. La méthode proposée sera évaluée via des études de simulations et une application sur des données réelles sera effectuée. Mots-clés. Données fonctionnelles, Classification automatique, Modèle de mélange, paramètres contraints, algorithme ECM

Interactions of tobacco microtubule-associated protein MAP65-1b with microtubules

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