Abstract
Bacterial infections often involve virulence factors that play a crucial role in the pathogenicity of bacteria. Accurate detection of virulence factor genes (VFGs) is essential for precise treatment and prognostic management of hypervirulent bacterial infections. However, there is a lack of rapid and accurate methods for VFG identification from the metagenomic data of clinical samples. Here, we developed a Reads-based Virulence Factors Scanner (RVFScan), an innovative user-friendly online tool that integrates a comprehensive VFG database with similarity matrix-based criteria for VFG prediction and annotation using metagenomic data without the need for assembly. RVFScan demonstrated superior performance compared to previous assembly-based and read-based VFG predictors, achieving a sensitivity of 97%, specificity of 98% and accuracy of 98%. We also conducted a large-scale analysis of 2425 clinical metagenomic datasets to investigate the utility of RVFScan, the species-specific VFG profiles and associations between VFGs and virulence phenotypes for 24 important pathogens were analyzed. By combining genomic comparisons and network analysis, we identified 53 VFGs with significantly higher abundances in hypervirulent Klebsiella pneumoniae (hvKp) than in classical K. pneumoniae. Furthermore, a cohort of 1256 samples suspected of K. pneumoniae infection demonstrated that RVFScan could identify hvKp with a sensitivity of 90%, specificity of 100% and accuracy of 98.73%, with 90% of hvKp samples consistent with clinical diagnosis (Cohen's kappa, 0.94). RVFScan has the potential to detect VFGs in low-biomass and high-complexity clinical samples using metagenomic reads without assembly. This capability facilitates the rapid identification and targeted treatment of hvKp infections and holds promise for application to other hypervirulent pathogens.