RESUMO
Introduction: Metagenomic next-generation sequencing (mNGS) has emerged as a powerful tool for rapid pathogen identification in clinical practice. However, the parameters used to interpret mNGS data, such as read count, genus rank, and coverage, lack explicit performance evaluation. In this study, the developed indicators as well as novel parameters were assessed for their performance in bacterium detection. Methods: We developed several relevant parameters, including 10M normalized reads, double-discard reads, Genus Rank Ratio, King Genus Rank Ratio, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank. These parameters, together with frequently used read indicators including raw reads, reads per million mapped reads (RPM), transcript per kilobase per million mapped reads (TPM), Genus Rank, and coverage were analyzed for their diagnostic efficiency in bronchoalveolar lavage fluid (BALF), a common source for detecting eight bacterium pathogens: Acinetobacter baumannii, Klebsiella pneumoniae, Streptococcus pneumoniae, Staphylococcus aureus, Hemophilus influenzae, Stenotrophomonas maltophilia, Pseudomonas aeruginosa, and Aspergillus fumigatus. Results: The results demonstrated that these indicators exhibited good diagnostic efficacy for the eight pathogens. The AUC values of all indicators were almost greater than 0.9, and the corresponding sensitivity and specificity values were almost greater than 0.8, excepted coverage. The negative predictive value of all indicators was greater than 0.9. The results showed that the use of double-discarded reads, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank exhibited better diagnostic efficiency than that of raw reads, RPM, TPM, and in Genus Rank. These parameters can serve as a reference for interpreting mNGS data of BALF. Moreover, precision filters integrating our novel parameters were built to detect the eight bacterium pathogens in BALF samples through machine learning. Summary: In this study, we developed a set of novel parameters for pathogen identification in clinical mNGS based on reads and ranking. These parameters were found to be more effective in diagnosing pathogens than traditional approaches. The findings provide valuable insights for improving the interpretation of mNGS reports in clinical settings, specifically in BALF analysis.
RESUMO
Streptococcus suis (SS), an important pathogen for pigs, is not only considered as a zoonotic agent for humans, but is also recognized as a major reservoir of antimicrobial resistance contributing to the spread of resistance genes to other pathogenic Streptococcus species. In addition to serotype 2 (SS2), serotype 9 (SS9) is another prevalent serotype isolated from diseased pigs. Although many SS strains have been sequenced, the complete genome of a non-SS2 virulent strain has been unavailable to date. Here, we report the complete genome of GZ0565, a virulent strain of SS9, isolated from a pig with meningitis. Comparative genomic analysis revealed five new putative virulence or antimicrobial resistance-associated genes in strain GZ0565 but not in SS2 virulent strains. These five genes encode a putative triacylglycerol lipase, a TipAS antibiotic-recognition domain protein, a putative TetR family transcriptional repressor, a protein containing a LPXTG domain and a G5 domain, and a type VII secretion system (T7SS) putative substrate (EsxA), respectively. Western blot analysis showed that strain GZ0565 can secrete EsxA. We generated an esxA deletion mutant and showed that EsxA contributes to SS virulence in a mouse infection model. Additionally, the antibiotic resistance gene vanZSS was identified and expression of vanZSS conferred resistance to teicoplanin and dalbavancin in Streptococcus agalactiae. We believe this is the first experimental demonstration of the existence of the T7SS putative substrate EsxA and its contribution to bacterial virulence in SS. Together, our results contribute to further understanding of the virulence and antimicrobial resistance characteristics of SS.
