210 research outputs found

    A Novel Viral Strategy for Host Factor Recruitment: The Co-Opted Proteasomal Rpn11 Protein Interaction Hub in Cooperation with Subverted Actin Filaments Are Targeted to Deliver Cytosolic Host Factors for Viral Replication

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    Positive-strand (+)RNA viruses take advantage of the host cells by subverting a long list of host protein factors and transport vesicles and cellular organelles to build membranous viral replication organelles (VROs) that support robust RNA replication. How RNA viruses accomplish major recruitment tasks of a large number of cellular proteins are intensively studied. In case of tomato bushy stunt virus (TBSV), a single viral replication protein, named p33, carries out most of the recruitment duties. Yet, it is currently unknown how the viral p33 replication protein, which is membrane associated, is capable of the rapid and efficient recruitment of numerous cytosolic host proteins to facilitate the formation of large VROs. In this paper, we show that, TBSV p33 molecules do not recruit each cytosolic host factor one-by-one into VROs, but p33 targets a cytosolic protein interaction hub, namely Rpn11, which interacts with numerous other cytosolic proteins. The highly conserved Rpn11, called POH1 in humans, is the metalloprotease subunit of the proteasome, which couples deubiquitination and degradation of proteasome substrates. However, TBSV takes advantage of a noncanonical function of Rpn11 by exploiting Rpn11’s interaction with highly abundant cytosolic proteins and the actin network. We provide supporting evidence that the co-opted Rpn11 in coordination with the subverted actin network is used for delivering cytosolic proteins, such as glycolytic and fermentation enzymes, which are readily subverted into VROs to produce ATP locally in support of VRO formation, viral replicase complex assembly and viral RNA replication. Using several approaches, including knockdown of Rpn11 level, sequestering Rpn11 from the cytosol into the nucleus in plants or temperature-sensitive mutation in Rpn11 in yeast, we show the inhibition of recruitment of glycolytic and fermentation enzymes into VROs. The Rpn11-assisted recruitment of the cytosolic enzymes by p33, however, also requires the combined and coordinated role of the subverted actin network. Accordingly, stabilization of the actin filaments by expression of the Legionella VipA effector in yeast and plant, or via a mutation of ACT1 in yeast resulted in more efficient and rapid recruitment of Rpn11 and the selected glycolytic and fermentation enzymes into VROs. On the contrary, destruction of the actin filaments via expression of the Legionella RavK effector led to poor recruitment of Rpn11 and glycolytic and fermentation enzymes. Finally, we confirmed the key roles of Rpn11 and the actin filaments in situ ATP production within TBSV VROs via using a FRET-based ATP-biosensor. The novel emerging theme is that TBSV targets Rpn11 cytosolic protein interaction hub driven by the p33 replication protein and aided by the subverted actin filaments to deliver several co-opted cytosolic pro-viral factors for robust replication within VROs

    Dynamic Interplay between the Co-Opted Fis1 Mitochondrial Fission Protein and Membrane Contact Site Proteins in Supporting Tombusvirus Replication

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    Plus-stranded RNA viruses have limited coding capacity and have to co-opt numerous pro-viral host factors to support their replication. Many of the co-opted host factors support the biogenesis of the viral replication compartments and the formation of viral replicase complexes on subverted subcellular membrane surfaces. Tomato bushy stunt virus (TBSV) exploits peroxisomal membranes, whereas the closely-related carnation Italian ringspot virus (CIRV) hijacks the outer membranes of mitochondria. How these organellar membranes can be recruited into pro-viral roles is not completely understood. Here, we show that the highly conserved Fis1 mitochondrial fission protein is co-opted by both TBSV and CIRV via direct interactions with the p33/p36 replication proteins. Deletion of FIS1 in yeast or knockdown of the homologous Fis1 in plants inhibits tombusvirus replication. Instead of the canonical function in mitochondrial fission and peroxisome division, the tethering function of Fis1 is exploited by tombusviruses to facilitate the subversion of membrane contact site (MCS) proteins and peroxisomal/mitochondrial membranes for the biogenesis of the replication compartment. We propose that the dynamic interactions of Fis1 with MCS proteins, such as the ER resident VAP tethering proteins, Sac1 PI4P phosphatase and the cytosolic OSBP-like oxysterol-binding proteins, promote the formation and facilitate the stabilization of virus-induced vMCSs, which enrich sterols within the replication compartment. We show that this novel function of Fis1 is exploited by tombusviruses to build nuclease-insensitive viral replication compartment

    Graph Interpolation via Fast Fused-Gromovization

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    Graph data augmentation has proven to be effective in enhancing the generalizability and robustness of graph neural networks (GNNs) for graph-level classifications. However, existing methods mainly focus on augmenting the graph signal space and the graph structure space independently, overlooking their joint interaction. This paper addresses this limitation by formulating the problem as an optimal transport problem that aims to find an optimal strategy for matching nodes between graphs considering the interactions between graph structures and signals. To tackle this problem, we propose a novel graph mixup algorithm dubbed FGWMixup, which leverages the Fused Gromov-Wasserstein (FGW) metric space to identify a "midpoint" of the source graphs. To improve the scalability of our approach, we introduce a relaxed FGW solver that accelerates FGWMixup by enhancing the convergence rate from O(t−1)\mathcal{O}(t^{-1}) to O(t−2)\mathcal{O}(t^{-2}). Extensive experiments conducted on five datasets, utilizing both classic (MPNNs) and advanced (Graphormers) GNN backbones, demonstrate the effectiveness of FGWMixup in improving the generalizability and robustness of GNNs
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