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1.
Elife ; 102021 06 11.
Artigo em Inglês | MEDLINE | ID: mdl-34114563

RESUMO

During HIV-1 particle formation, the requisite plasma membrane curvature is thought to be solely driven by the retroviral Gag protein. Here, we reveal that the cellular I-BAR protein IRSp53 is required for the progression of HIV-1 membrane curvature to complete particle assembly. siRNA-mediated knockdown of IRSp53 gene expression induces a decrease in viral particle production and a viral bud arrest at half completion. Single-molecule localization microscopy at the cell plasma membrane shows a preferential localization of IRSp53 around HIV-1 Gag assembly sites. In addition, we observe the presence of IRSp53 in purified HIV-1 particles. Finally, HIV-1 Gag protein preferentially localizes to curved membranes induced by IRSp53 I-BAR domain on giant unilamellar vesicles. Overall, our data reveal a strong interplay between IRSp53 I-BAR and Gag at membranes during virus assembly. This highlights IRSp53 as a crucial host factor in HIV-1 membrane curvature and its requirement for full HIV-1 particle assembly.


Assuntos
HIV-1/metabolismo , Proteínas do Tecido Nervoso/metabolismo , Vírion/metabolismo , Montagem de Vírus , Produtos do Gene gag do Vírus da Imunodeficiência Humana/metabolismo , Membrana Celular/metabolismo , Células HEK293 , HIV-1/fisiologia , Humanos , Células Jurkat , Imagem Individual de Molécula/métodos
2.
Sensors (Basel) ; 18(11)2018 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-30463240

RESUMO

This article presents a method for step detection from accelerometer and gyrometer signals recorded with Inertial Measurement Units (IMUs). The principle of our step detection algorithm is to recognize the start and end times of the steps in the signal thanks to a predefined library of templates. The algorithm is tested on a database of 1020 recordings, composed of healthy subjects and patients with various neurological or orthopedic troubles. Simulations on more than 40,000 steps show that the template-based method achieves remarkable results with a 98% recall and a 98% precision. The method adapts well to pathological subjects and can be used in a medical context for robust step estimation and gait characterization.

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