ABSTRACT
The antiviral innate immunity is the first line of host defense against viral infection. Mitochondrial antiviral signaling protein (MAVS, also named Cardif/IPS-1/VISA) is a critical protein in RNA virus-induced antiviral signaling pathways. Our previous research suggested that E3 ubiquitin-protein ligases RING-finger protein (RNF90) negatively regulate cellular antiviral responses by targeting STING for degradation, though its role in RNA virus infection remains unknown. This study demonstrated that RNF90 negatively regulated RNA virus-triggered antiviral innate immune responses in RNF90-silenced PMA-THP1 cells, RNF90-deficient cells (including HaCaTs, MEFs, and BMDMs), and RNF90-deficient mice. However, RNF90 regulated RNA virus-triggered antiviral innate immune responses independent of STING. RNF90 promoted K48-linked ubiquitination of MAVS and its proteasome-dependent degradation, leading to the inhibition of innate immune responses. Altogether, our findings suggested a novel function and mechanism of RNF90 in antiviral innate immunity.
Subject(s)
Adaptor Proteins, Signal Transducing/metabolism , Immunity, Innate , Tripartite Motif Proteins/metabolism , Ubiquitin-Protein Ligases/metabolism , Vesicular Stomatitis/metabolism , Vesiculovirus/immunology , Adaptor Proteins, Signal Transducing/genetics , Adaptor Proteins, Signal Transducing/immunology , Animals , Chlorocebus aethiops , Cytokines/genetics , Cytokines/immunology , Cytokines/metabolism , HEK293 Cells , HaCaT Cells , Host-Pathogen Interactions , Humans , Mice, Knockout , Proteasome Endopeptidase Complex/metabolism , Proteolysis , Signal Transduction , THP-1 Cells , Tripartite Motif Proteins/genetics , Tripartite Motif Proteins/immunology , Ubiquitin-Protein Ligases/genetics , Ubiquitin-Protein Ligases/immunology , Ubiquitination , Vero Cells , Vesicular Stomatitis/genetics , Vesicular Stomatitis/immunology , Vesicular Stomatitis/virology , Vesiculovirus/pathogenicityABSTRACT
In a recent study, it was shown that, with adversarial training of an attentive generative network, it is possible to convert a raindrop degraded image into a relatively clean one. However, in real world, raindrop appearance is not only formed by individual raindrops, but also by the distant raindrops accumulation and the atmospheric veiling, namely haze. Current methods are limited in extracting accurate features from a raindrop degraded image with background scene, the blurred raindrop regions, and the haze. In this paper, we propose a new model for an image corrupted by the raindrops and the haze, and introduce an integrated multi-task algorithm to address the joint raindrop and haze removal (JRHR) problem by combining an improved estimate of the atmospheric light, a modified transmission map, a generative adversarial network (GAN) and an optimized visual attention network. The proposed algorithm can extract more accurate features for both sky and non-sky regions. Experimental evaluation has been conducted to show that the proposed algorithm significantly outperforms state-of-the-art algorithms on both synthetic and real-world images in terms of both qualitative and quantitative measures.