Aeromonas species are ubiquitous inhabitants of freshwater environments, and are responsible for fish motile aeromonad septicemia (MAS). A. hydrophila is implicated as the primary etiologic agent of MAS. Here, we analysed MAS epidemiological data for cyprinid fish in southern China, and found that A. veronii infections dominated. Consistent with this observation, A. veronii isolates were generally more virulent than A. hydrophila isolates when infecting germ-free zebrafish larvae via continuous immersion challenge. Through in vivo screening of the transposon library of the A. veronii strain Hm091, aerolysin was identified as the key virulence factor. Further results indicated that A. veronii Hm091 aerolysin disrupts the intestinal barrier of zebrafish, enabling systematic invasion by not only A. veronii Hm091 in a mono-infection, but also A. hydrophila NJ-1 in a mixed infection. Moreover, the differences in aerolysin expression and activity were the major contributor to the observed differences between the A. veronii and A. hydrophila strains regarding invasion efficacy via intestine. Together, our results provide new insights into the aetiology and pathogenesis of Aeromonas infections, and highlight the importance of A. veronii-targeted treatments in future efforts against MAS.
The PA-altered microbiota indirectly induced ER stress and liver damage in zebrafish. Moreover, the PA microbiota promoted the absorption of PA, leading to enhanced PA overflow into the liver and aggravated hepatotoxicity of PA in zebrafish.
Identifying protein complexes in static protein-protein interaction (PPI) networks is essential for understanding the underlying mechanism of biological processes. Proteins in a complex are co-localized at the same place and co-expressed at the same time. We propose a novel method to identify protein complexes with the features of joint co-localization and joint co-expression in static PPI networks. To achieve this goal, we define a joint localization vector to construct a joint co-localization criterion of a protein group, and define a joint gene expression to construct a joint co-expression criterion of a gene group. Moreover, the functional similarity of proteins in a complex is an important characteristic. Thus, we use the CC-based, MF-based, and BP-based protein similarities to devise functional similarity criterion to determine whether a protein is functionally similar to a protein cluster. Based on the core-attachment structure and following to seed expanding strategy, we use four types of biological data including PPI data with reliability score, protein localization data, gene expression data, and gene ontology annotations, to identify protein complexes. The experimental results on yeast data show that comparing with existing methods our proposed method can efficiently and exactly identify more protein complexes, especially more protein complexes of sizes from 2 to 6. Furthermore, the enrichment analysis demonstrates that the protein complexes identified by our method have significant biological meaning.
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