Mouse and human neutrophils induce anaphylaxis

International audienceAnaphylaxis is a life-threatening hyperacute immediate hypersensitivity reaction. Classically, it depends on IgE, FcεRI, mast cells, and histamine. However, anaphylaxis can also be induced by IgG antibodies, and an IgG1-induced passive type of systemic anaphylaxis has been reported to depend on basophils. In addition, it was found that neither mast cells nor basophils were required in mouse models of active systemic anaphylaxis. Therefore, we investigated what antibodies, receptors, and cells are involved in active systemic anaphylaxis in mice. We found that IgG antibodies, FcγRIIIA and FcγRIV, platelet-activating factor, neutrophils, and, to a lesser extent, basophils were involved. Neutrophil activation could be monitored in vivo during anaphylaxis. Neutrophil depletion inhibited active, and also passive, systemic anaphylaxis. Importantly, mouse and human neutrophils each restored anaphylaxis in anaphylaxis-resistant mice, demonstrating that neutrophils are sufficient to induce anaphylaxis in mice and suggesting that neutrophils can contribute to anaphylaxis in humans. Our results therefore reveal an unexpected role for IgG, IgG receptors, and neutrophils in anaphylaxis in mice. These molecules and cells could be potential new targets for the development of anaphylaxis therapeutics if the same mechanism is responsible for anaphylaxis in humans

Das Eleu rokardiograiam "bei angeTborenen Herzfehle ren.

OPLADEN-RUG0

Abstract

A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex dependencies among the latter. In recent years, much attention has been devoted to the development of algorithms for learning parameters, and in some cases structure, in the presence of hidden variables. In this paper, we address the related problem of detecting hidden variables that interact with the observed variables. This problem is of interest both for improving our understanding of the domain and as a preliminary step that guides the learning procedure towards promising models. A very natural approach is to search for “structural signatures ” of hidden variables — substructures in the learned network that tend to suggest the presence of a hidden variable. We make this basic idea concrete, and show how to integrate it with structure-search algorithms. We evaluate this method on several synthetic and real-life datasets, and show that it performs surprisingly well.