The commensal microbiota of the mosquito gut plays a complex role in determining the vector competence for arboviruses. In this study, we identified a bacterium from the gut of fieldAedes albopictusmosquitoes namedRosenbergiellasp. YN46 (Rosenbergiella_YN46) that rendered mosquitoes refractory to infection with dengue and Zika viruses. Inoculation of 1.6 × 10³colony forming units (CFUs) ofRosenbergiella_YN46 intoA. albopictusmosquitoes effectively prevents viral infection. Mechanistically, this bacterium secretes glucose dehydrogenase (RyGDH), which acidifies the gut lumen of fed mosquitoes, causing irreversible conformational changes in the flavivirus envelope protein that prevent viral entry into cells. In semifield conditions,Rosenbergiella_YN46 exhibits effective transstadial transmission in field mosquitoes, which blocks transmission of dengue virus by newly emerged adult mosquitoes. The prevalence ofRosenbergiella_YN46 is greater in mosquitoes from low-dengue areas (52.9 to ~91.7%) than in those from dengue-endemic regions (0 to ~6.7%).Rosenbergiella_YN46 may offer an effective and safe lead for flavivirus biocontrol.

Members of the leucine-rich repeat (LRR) superfamily play critical roles in multiple biological processes. As the LRR unit sequence is highly variable, accurately predicting the number and location of LRR units in proteins is a highly challenging task in the field of bioinformatics. Existing methods still need to be improved, especially when it comes to similarity-based methods. We introduce our DeepLRR method based on a convolutional neural network (CNN) model and LRR features to predict the number and location of LRR units in proteins. We compared DeepLRR with six existing methods using a dataset containing 572 LRR proteins and it outperformed all of them when it comes to overall F1 score. In addition, DeepLRR has integrated identifying plant disease-resistance proteins (NLR, LRR-RLK, LRR-RLP) and non-canonical domains. With DeepLRR, 223, 191 and 183 LRR-RLK genes in Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa ssp. Japonica) and tomato (Solanum lycopersicum) genomes were re-annotated, respectively. Chromosome mapping and gene cluster analysis revealed that 24.2% (54/223), 29.8% (57/191) and 16.9% (31/183) of LRR-RLK genes formed gene cluster structures in Arabidopsis, rice and tomato, respectively. Finally, we explored the evolutionary relationship and domain composition of LRR-RLK genes in each plant and distributions of known receptor and co-receptor pairs. This provides a new perspective for the identification of potential receptors and co-receptors.

Small mammals harbor a diverse array of potentially zoonotic pathogens. To date, however, metagenomic surveys of these species have primarily focused on viruses, with limited attention directed to bacterial and eukaryotic pathogens. Additionally, the ecological determinants of pathogen diversity within these mammals have not been systematically examined. Herein, we employed a metatranscriptomics approach to survey the pathogen infectome—defined as the set of microorganisms infecting the host—across 2408 individual samples, representing lung, spleen, and gut from 858 animals in Guangdong province, China, considering the impact of host species, tissue, season, and geographic location on pathogen diversity. We identified 76 potential pathogen species, comprising 29 RNA viruses, 12 DNA viruses, five bacteria, and 30 eukaryotic pathogens, including 33 that are newly discovered. Distinct tissue tropisms were identified, suggesting varied transmission routes. Individual animals carried an average of one pathogen, with 10 pathogens widely distributed among mammalian orders. Total pathogen richness was largely influenced by geographic region, followed by host species and season, while zoonotic pathogen richness was primarily driven by host species. Collectively, these data provide insights into the structure of the pathogen infectome and the drivers of pathogen diversity and transmission in these key mammalian disease reservoirs.