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1.
Int J Mol Sci ; 25(3)2024 Feb 02.
Artículo en Inglés | MEDLINE | ID: mdl-38339085

RESUMEN

Systemic acquired resistance (SAR) is a plant defense mechanism that provides protection against a broad spectrum of pathogens in distal tissues. Recent studies have revealed a concerted function of salicylic acid (SA) and N-hydroxypipecolic acid (NHP) in the establishment of SAR against bacterial pathogens, but it remains unknown whether NHP is also involved in SAR against viruses. We found that the local application of acibenzolar-S-methyl (ASM), a synthetic analog of SA, suppressed plantago asiatica mosaic virus (PlAMV) infection in the distal leaves of Arabidopsis thaliana. This suppression of infection in untreated distal leaves was observed at 1 day, but not at 3 days, after application. ASM application significantly increased the expression of SAR-related genes, including PR1, SID2, and ALD1 after 1 day of application. Viral suppression in distal leaves after local ASM application was not observed in the sid2-2 mutant, which is defective in isochorismate synthase 1 (ICS1), which is involved in salicylic acid synthesis; or in the fmo1 mutant, which is defective in the synthesis of NHP; or in the SA receptor npr1-1 mutant. Finally, we found that the local application of NHP suppressed PlAMV infection in the distal leaves. These results indicate that the local application of ASM induces antiviral SAR against PlAMV through a mechanism involving NHP.


Asunto(s)
Proteínas de Arabidopsis , Arabidopsis , Tiadiazoles , Arabidopsis/metabolismo , Tiadiazoles/farmacología , Proteínas de Arabidopsis/genética , Proteínas de Arabidopsis/metabolismo , Ácido Salicílico/farmacología , Ácido Salicílico/metabolismo , Antivirales/farmacología , Antivirales/metabolismo , Regulación de la Expresión Génica de las Plantas , Enfermedades de las Plantas/genética , Enfermedades de las Plantas/microbiología
2.
Mitochondrion ; 62: 176-180, 2022 01.
Artículo en Inglés | MEDLINE | ID: mdl-34906750

RESUMEN

7-Nitro-2,1,3-benzoxadiazole (NBD) is an environmentally responsive fluorophore. We have reported that GIF2114 and GIF2115, anti-ferroptotic N,N-dimethylaniline-compounds, localize to lysosome when they are visualized by NBD. Here we show that the NBD fluorescence of GIF2259, a hybrid derivative of GIF2114 and GIF2115, was quenched in aqueous buffer. However, the fluorescence was recovered when GIF2259 was localized on lysosomes. Although the dimethylamine group of GIF2259 is not essential for the lysosome localization, it contributes to a high specific/nonspecific ratio of fluorescence. Under a normal condition, the lysosomal signal visualized by GIF2259 did not overlap with mitochondria, while, under starved or depolarization conditions, it overlapped with mitochondria, suggesting that GIF2259 could be used as a simple tool for monitoring lysosomal metabolism and mitochondrial turnover, that is mitophagy.


Asunto(s)
Bencilaminas/farmacología , Mitocondrias/fisiología , Mitofagia/fisiología , Bencilaminas/química , Colorantes Fluorescentes , Células HEK293 , Humanos , Estructura Molecular , Coloración y Etiquetado
3.
Sensors (Basel) ; 20(20)2020 Oct 12.
Artículo en Inglés | MEDLINE | ID: mdl-33053692

RESUMEN

This paper proposes a novel 3D representation, namely, a latent 3D volume, for joint depth estimation and semantic segmentation. Most previous studies encoded an input scene (typically given as a 2D image) into a set of feature vectors arranged over a 2D plane. However, considering the real world is three-dimensional, this 2D arrangement reduces one dimension and may limit the capacity of feature representation. In contrast, we examine the idea of arranging the feature vectors in 3D space rather than in a 2D plane. We refer to this 3D volumetric arrangement as a latent 3D volume. We will show that the latent 3D volume is beneficial to the tasks of depth estimation and semantic segmentation because these tasks require an understanding of the 3D structure of the scene. Our network first constructs an initial 3D volume using image features and then generates latent 3D volume by passing the initial 3D volume through several 3D convolutional layers. We apply depth regression and semantic segmentation by projecting the latent 3D volume onto a 2D plane. The evaluation results show that our method outperforms previous approaches on the NYU Depth v2 dataset.

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