Paris Descartes University

About: Paris Descartes University is a government organization based out in Paris, France. It is known for research contribution in the topics: Population & Transplantation. The organization has 20987 authors who have published 37456 publications receiving 1206222 citations. The organization is also known as: Université Paris V-Descartes & Université de Paris V.

Identification of a low–molecular weight TrkB antagonist with anxiolytic and antidepressant activity in mice

Abstract: The neurotrophin brain-derived neurotrophic factor (BDNF) and its receptor tropomyosin-related kinase B (TrkB) have emerged as key mediators in the pathophysiology of several mood disorders, including anxiety and depression. However, therapeutic compounds that interact with TrkB receptors have been difficult to develop. Using a combination of structure-based in silico screening and high-capacity functional assays in recombinant and neuronal cells, we identified a low-molecular weight TrkB ligand (ANA-12) that prevented activation of the receptor by BDNF with a high potency. ANA-12 showed direct and selective binding to TrkB and inhibited processes downstream of TrkB without altering TrkA and TrkC functions. KIRA-ELISA analysis demonstrated that systemic administration of ANA-12 to adult mice decreased TrkB activity in the brain without affecting neuronal survival. Mice administered ANA-12 demonstrated reduced anxiety- and depression-related behaviors on a variety of tests predictive of anxiolytic and antidepressant properties in humans. This study demonstrates that structure-based virtual screening strategy can be an efficient method for discovering potent TrkB-selective ligands that are active in vivo. We further propose that ANA-12 may be a valuable tool for studying BDNF/TrkB signaling and may constitute a lead compound for developing the next generation of therapeutic agents for the treatment of mood disorders.

Learning in the recurrent random neural network

Abstract: The capacity to learn from examples is one of the most desirable features of neural network models. We present a learning algorithm for the recurrent random network model (Gelenbe 1989, 1990) using gradient descent of a quadratic error function. The analytical properties of the model lead to a "backpropagation" type algorithm that requires the solution of a system of n linear and n nonlinear equations each time the n-neuron network "learns" a new input-output pair.

Type I interferon-mediated monogenic autoinflammation: The type I interferonopathies, a conceptual overview.

Abstract: Type I interferon is a potent substance. As such, the induction, transmission, and resolution of the type I interferon–mediated immune response are tightly regulated. As defined, the type I interferonopathies represent discrete examples of a disturbance of the homeostatic control of this system caused by Mendelian mutations. Considering the complexity of the interferon response, the identification of further monogenic diseases belonging to this disease grouping seems likely, with the recognition of type I interferonopathies becoming of increasing clinical importance as treatment options are developed based on an understanding of disease pathology and innate immune signaling. Definition of the type I interferonopathies indicates that autoinflammation can be both interferon and noninterferon related, and that a primary disturbance of the innate immune system can “spill over” into autoimmunity in some cases. Indeed, that several non-Mendelian disorders, most particularly systemic lupus erythematosus and dermatomyositis, are also characterized by an up-regulation of type I interferon signaling suggests the possibility that insights derived from this work will have relevance to a broader field of clinical medicine.