Background: Infections caused by linezolid-resistant enterococci (LRE) are clinically difficult to treat and threaten patient health. However, there is a lack of studies on long time-span LRE strains in China. For this reason, our study comprehensively revealed the resistance mechanisms of LRE strains collected in a Chinese tertiary care hospital from 2011 to 2022. Methods: Enterococcal strains were screened and verified after retrospective analysis of microbial data. Subsequently, 65 LRE strains (61 Enterococcus faecalis and 4 Enterococcus faecium, MIC ≥ 8 µg/ml), 1 linezolid-intermediate Enterococcus faecium (MIC = 4 µg/ml) and 1 linezolid-susceptible Enterococcus faecium (MIC = 1.5 µg/ml) were submitted for whole-genome sequencing (WGS) analysis and bioinformatics analysis. Results: The optrA gene was found to be the most common linezolid resistance mechanism in our study. We identified the wild-type OptrA and various OptrA variants in 98.5% of LRE strains (61 Enterococcus faecalis and 3 Enterococcus faecium). We also found one linezolid-resistant Enterococcus faecium strain carried both optrA and cfr(D) gene, while one linezolid-resistant Enterococcus faecium only harbored the poxtA gene. Most optrA genes (55/64) were located on plasmids, with impB-fexA-optrA, impB-fexA-optrA-erm(A), fexA-optrA-erm(A), and fexA-optrA segments. A minority of optrA genes (9/64) were found on chromosomes with the Tn6674-like platform. Besides, other possible linezolid resistance-associated mechanisms (mutations in the rplC and rplD genes) were also found in 26 enterococcal strains. Conclusions: Our study suggested that multiple mechanisms of linezolid resistance exist among clinical LRE strains in China.

The intestinal mycobiome is closely related to human health. There have been several reports investigating the association between the gut fungi and disease, but there is still a lack of overall assessment of the human gut mycobiome. Here, we performed a meta-analysis based on 2372 ITS (Internal Transcribed Spacer) data collected publicly online. We found that the mycobiome diversity of human gut fungi varies significantly across diseases by using EasyAmplicon, and these fungi are mainly composed of three genera, Saccharomyces, Candida, and Aspergillus. In addition, we performed the construction of disease prediction models based on ITS data by using the random forest model and verified the generalization ability of the models. We hope that our results will provide strong support for subsequent studies of the intestinal mycobiome.

Background. Mycoplasma pneumoniae ( MP ) is a common causative pathogen of community-acquired pneumonia in children, with clinical presentations ranging in severity. Early stratification and timely intervention are essential for improving patient outcomes. However, a major clinical challenge lies in the limited ability to accurately distinguish between mild and severe cases based solely on early clinical indicators. Methods. This prospective real-world study investigated the differences in microbiome and metabolomics between mild and severe MP pneumonia (MPP) in children. Bronchoalveolar lavage fluid samples were collected from 153 children and subjected to metagenomic sequencing and non-targeted metabolomic analysis. Meanwhile, to enhance early diagnostic accuracy, this study developed a machine learning classification model and validated it using a third-party validation set. Results. The results revealed significant alterations in the abundance of specific bacterial communities in the severe group, most notably the coexistence of MP and Alphainfluenzavirus influenzae , which may contribute to disease exacerbation through synergistic pathogenic mechanisms. Furthermore, the macrolide resistant rate of MP in the severe group exceeded 80%, emphasizing the importance of appropriate antibiotic selection. Metabolomic analysis showed a significant enrichment of metabolites related to cellular energy metabolism and immune regulation in severe cases. The model demonstrated exceptional predictive performance, achieving an area under the curve ranging from 0.909 to 0.991, which significantly outperformed conventional clinical stratification methods. Conclusions. These findings elucidate the distinct pathophysiological mechanisms underlying both mild and severe MP infections and provide a promising framework for improving early diagnosis and personalized treatment strategies in paediatric MPP.