Increase in antioxidant capacity associated with the successful subclone of hypervirulent carbapenem-resistant Klebsiella pneumoniae ST11-KL64.

The acquisition of exogenous mobile genetic material imposes an adaptive burden on bacteria, whereas the adaptational evolution of virulence plasmids upon entry into carbapenem-resistant Klebsiella pneumoniae (CRKP) and its impact remains unclear. To better understand the virulence in CRKP, we characterize virulence plasmids utilizing a large genomic data containing 1219 K. pneumoniae from our long-term surveillance and publicly accessible databases. Phylogenetic evaluation unveils associations between distinct virulence plasmids and serotypes. The sub-lineage ST11-KL64 CRKP acquires a pK2044-like virulence plasmid from ST23-KL1 hypervirulent K. pneumoniae, with a 2698 bp region deletion in all ST11-KL64. The deletion is observed to regulate methionine metabolism, enhance antioxidant capacity, and further improve survival of hypervirulent CRKP in macrophages. The pK2044-like virulence plasmid discards certain sequences to enhance survival of ST11-KL64, thereby conferring an evolutionary advantage. This work contributes to multifaceted understanding of virulence and provides insight into potential causes behind low fitness costs observed in bacteria.

mleS in Staphylococcus aureus Contributes to Microaerobic Metabolic Activity, Abscess Formation, and Survival in Macrophages.

Staphylococcus aureus is subdivided into lineages termed sequence types (STs), infections of which necessitate the expression of virulence factors and metabolic adaptation to the host niche. Given that mechanisms underlying the dynamic replacement of sequence types in S. aureus populations have yet to be sufficiently determined, we investigated the role of metabolic determinants in epidemic clones. mleS, encoding the NAD+-dependent malolactic enzyme, was found to be carried by the epidemic clones ST59 and ST398, although not by ST239 and ST5. The genomic location of mleS in the metabolism-associated region flanked by the thiol-specific redox system and glycolysis operon implies that it plays significant roles in metabolism and pathogenesis. Mouse skin abscess caused by the BS19-mleS mutant strain (isogenic mleS mutant in an ST59 isolate) was significantly attenuated and associated with reductions in interleukin-6 (IL-6) and lactic acid production. mleS deletion also impaired S. aureus biofilm formation and survival in RAW264.7 cells. The BS19-mleS-mutant was also characterized by reduced ATP and lactic acid production under microaerobic conditions; however, NAD+/NADH levels remained unaffected. mleS is thus identified as an epidemiological marker that plays an important role in the microaerobic metabolism and pathogenesis of epidemic S. aureus clones. IMPORTANCE Given the importance of metabolic adaptation during infection, new insights are required regarding the pathogenesis of S. aureus, particularly for epidemic clones. We accordingly investigated the role of metabolic determinants that are unique to the epidemic clones ST59 and ST398. Our results provide evidence that the NAD+-dependent malolactic enzyme-coding gene mleS is an epidemiological marker that plays an important role in the microaerobic metabolism and pathogenesis of epidemic S. aureus clones.

Klebsiella quasipneumoniae in intestine damages bile acid metabolism in hematopoietic stem cell transplantation patients with bloodstream infection.

BACKGROUND: Bloodstream infection (BSI) is a serious hematopoietic stem cell transplantation (HSCT) complication. The intestinal microbiome regulates host metabolism and maintains intestinal homeostasis. Thus, the impact of microbiome on HSCT patients with BSI is essential. METHODS: Stool and serum specimens of HSCT patients were prospectively collected from the pretransplant conditioning period till 4 months after transplantation. Specimens of 16 patients without BSI and 21 patients before BSI onset were screened for omics study using 16S rRNA gene sequencing and untargeted metabolomics. The predictive infection model was constructed using LASSO and the logistic regression algorithm. The correlation and influence of microbiome and metabolism were examined in mouse and Caco-2 cell monolayer models. RESULTS: The microbial diversity and abundance of Lactobacillaceae were remarkably reduced, but the abundance of Enterobacteriaceae (especially Klebsiella quasipneumoniae) was significantly increased in the BSI group before onset, compared with the non-BSI group. The family score of microbiome features (Enterobacteriaceae and Butyricicoccaceae) could highly predict BSI (AUC = 0.879). The serum metabolomic analysis showed that 16 differential metabolites were mainly enriched in the primary bile acid biosynthesis pathway, and the level of chenodeoxycholic acid (CDCA) was positively correlated with the abundance of K. quasipneumoniae (R = 0.406, P = 0.006). The results of mouse experiments confirmed that three serum primary bile acids levels (cholic acid, isoCDCA and ursocholic acid), the mRNA expression levels of bile acid farnesol X receptor gene and apical sodium-dependent bile acid transporter gene in K. quasipneumoniae colonized mice were significantly higher than those in non-colonized mice. The intestinal villus height, crypt depth, and the mRNA expression level of tight junction protein claudin-1 gene in K. quasipneumoniae intestinal colonized mice were significantly lower than those in non-colonized mice. In vitro, K. quasipneumoniae increased the clearance of FITC-dextran by Caco-2 cell monolayer. CONCLUSIONS: This study demonstrated that the intestinal opportunistic pathogen, K. quasipneumoniae, was increased in HSCT patients before BSI onset, causing increased serum primary bile acids. The colonization of K. quasipneumoniae in mice intestines could lead to mucosal integrity damage. The intestinal microbiome features of HSCT patients were highly predictive of BSI and could be further used as potential biomarkers.

Machine learning and feature extraction for rapid antimicrobial resistance prediction of Acinetobacter baumannii from whole-genome sequencing data.

Background: Whole-genome sequencing (WGS) has contributed significantly to advancements in machine learning methods for predicting antimicrobial resistance (AMR). However, the comparisons of different methods for AMR prediction without requiring prior knowledge of resistance remains to be conducted. Methods: We aimed to predict the minimum inhibitory concentrations (MICs) of 13 antimicrobial agents against Acinetobacter baumannii using three machine learning algorithms (random forest, support vector machine, and XGBoost) combined with k-mer features extracted from WGS data. Results: A cohort of 339 isolates was used for model construction. The average essential agreement and category agreement of the best models exceeded 90.90% (95%CI, 89.03-92.77%) and 95.29% (95%CI, 94.91-95.67%), respectively; the exceptions being levofloxacin, minocycline and imipenem. The very major error rates ranged from 0.0 to 5.71%. We applied feature selection pipelines to extract the top-ranked 11-mers to optimise training time and computing resources. This approach slightly improved the prediction performance and enabled us to obtain prediction results within 10 min. Notably, when employing these top-ranked 11-mers in an independent test dataset (120 isolates), we achieved an average accuracy of 0.96. Conclusion: Our study is the first to demonstrate that AMR prediction for A. baumannii using machine learning methods based on k-mer features has competitive performance over traditional workflows; hence, sequence-based AMR prediction and its application could be further promoted. The k-mer-based workflow developed in this study demonstrated high recall/sensitivity and specificity, making it a dependable tool for MIC prediction in clinical settings.