Pengcheng Dong

(1) Background: methicillin‑resistant Staphylococcus aureus (MRSA) is a zoonotic pathogen, and its hemolysins serve as key virulence factors. Functional membrane microdomains (FMMs) are protein-enriching platforms and regulate diverse physiological functions by recruiting and assembling various proteins. However, whether and how FMMs regulate the hemolytic ability of MRSA remains unclear. This study aimed to investigate FMM-mediated regulation of MRSA hemolysins and the underlying mechanisms. (2) Methods: Homologous recombination was employed to generate FMM-disrupted (N315ΔfloA) and complemented (N315ΔfloA::floA) strains from the MRSA N315 wild-type strain (N315 WT). The three strains were compared with respect to hemolytic activity, transcript levels of key virulence and regulatory genes, and in vivo virulence. (3) Results: Disruption of FMMs significantly enhanced hemolytic activity compared with N315 WT and complemented strains. Meanwhile, FMM disruption repressed the two-component system genes (vraS and vraR), while activating the agr operon (agrB, agrD, agrC and agrA) and its effector molecule RNAIII, leading to upregulation of hemolysin genes (hla, hlb, hld). In vivo, N315ΔfloA infection markedly increased mortality in G. mellonella larvae and BALB/c mice, with significantly elevated pro-inflammatory factors (TNF-α, IL-6, and IL-1β) in mouse plasma. All these phenotypes were effectively reversed in N315ΔfloA::floA. (4) Conclusions: Disruption of FMMs potentiates both hemolytic activity and overall virulence in MRSA, with the potential underlying mechanism involving the VraS/R-Agr regulatory axis that drives transcriptional upregulation of hemolysin-encoding genes.

The objective of this study was to investigate the relationships between ruminal microbial communities and carcass traits associated with adipose accumulation in two Tibetan sheep breeds—Gannan and Tianzhu. A total of twenty Tibetan sheep (ten from each breed) were slaughtered, and samples of ruminal contents along with carcass trait data were collected for analysis. Ruminal microbial DNA was analyzed by 16S rRNA gene sequencing, and correlations between microbial composition and carcass traits were examined using correlation analysis and one-way ANOVA. The results showed that marbling score (p = 0.001) and longissimus lipid content (p = 0.007) were positively correlated with the Chao1 richness index, indicating that individuals with higher intramuscular fat content had greater ruminal microbial species richness. At the phylum level, Rikenellaceae RC9 gut group, Ruminococcaceae NK4A214 group were negatively correlated (p ≤ 0.05) with the above fat traits, whereas the abundance of the bacterial family Ruminococcus 1 was positively correlated with marbling score (p = 0.002). Stratified analysis by marbling grade further revealed associations with microbial richness (p ≤ 0.063), diversity (p = 0.044), and Ruminococcus 1 abundance (p < 0.001). However, microbial metabolic pathway prediction showed no significant differences (p ≥ 0.05) between the high- and low-marbling groups. In addition, several microbial taxa were positively correlated (p ≤ 0.05) with rib fat thickness and yield grade. In summary, ruminal microbial composition was closely associated with variations in carcass fat traits. Notably, most of the bacterial taxa associated with intramuscular and subcutaneous fat deposition did not overlap, suggesting that microbial metabolites may regulate fat deposition by influencing distinct adipogenic pathways in the host.

Introduction: This study aimed to systematically evaluate the diagnostic performance of radiomics-based models in predicting KRAS gene mutations in lung cancer and quantitatively analyze the methodological quality and reporting standardization of related studies. Methods: Original studies evaluating radiomics models for predicting KRAS mutation status in lung cancer patients were identified through systematic searches of databases including PubMed, Embase, China National Knowledge Infrastructure (CNKI), Web of Science, and the Cochrane Library (from inception to June 2025). The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess diagnostic bias risk, the Radiomics Quality Score (RQS, comprising 16 items with a total score of 36) was employed to quantify methodological quality, and the METRICS (10 criteria, 100-point scale) was applied to evaluate reporting standardization. A single-arm meta-analysis was conducted on 20 eligible studies (total sample size: 4,953 cases) to calculate pooled sensitivity, specificity, and the area under the summary receiver operating characteristic curve (SROC AUC). External validation was performed using validation cohorts from 12 studies. Results: The mean RQS score of included studies was 9.86 ± 3.7 (range: 4–15, representing 27.4% ± 10.3% of the maximum score), with a mean METRICS score of 59.95 ± 13.5%. The primary analysis revealed pooled sensitivity of 0.80 (95% CI: 0.76–0.83), specificity of 0.78 (95% CI: 0.75–0.82), and AUC of 0.85 (95% CI: 0.82–0.88). Validation cohort results were consistent: sensitivity 0.79 (95% CI: 0.73–0.84), specificity 0.77 (95% CI: 0.71–0.82), and AUC 0.85 (95% CI: 0.81–0.88). Significant heterogeneity was observed among studies, but meta-regression and subgroup analyses (based on key methodological variables such as modeling algorithms, imaging modalities, RQS scores, and validation methods) confirmed stable results across subgroups, demonstrating clinical applicability. Conclusion: Radiomics models exhibit moderate diagnostic performance in predicting KRAS mutations in lung cancer. Future efforts should strictly adhere to relevant guidelines, strengthen model validation, and standardize workflows to enhance the practical value of radiomics in precision oncology. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251148699 , identifier CRD420251148699.