Quorum-sensing regulation of phenazine production heightens Pseudomonas aeruginosa resistance to ciprofloxacin.

Quorum sensing is a type of cell-cell communication that modulates various biological activities of bacteria. Previous studies indicate that quorum sensing contributes to the evolution of bacterial resistance to antibiotics, but the underlying mechanisms are not fully understood. In this study, we grew Pseudomonas aeruginosa in the presence of sub-lethal concentrations of ciprofloxacin, resulting in a large increase in ciprofloxacin minimal inhibitory concentration. We discovered that quorum sensing-mediated phenazine biosynthesis was significantly enhanced in the resistant isolates, where the quinolone circuit was the predominant contributor to this phenomenon. We found that production of pyocyanin changed carbon flux and showed that the effect can be partially inhibited by the addition of pyruvate to cultures. This study illustrates the role of quorum sensing-mediated phenotypic resistance and suggests a strategy for its prevention.

Proteome analysis develops novel plasma proteins classifier in predicting the mortality of COVID-19.

COVID-19 has been a global concern for 3 years, however, consecutive plasma protein changes in the disease course are currently unclear. Setting the mortality within 28 days of admission as the main clinical outcome, plasma samples were collected from patients in discovery and independent validation groups at different time points during the disease course. The whole patients were divided into death and survival groups according to their clinical outcomes. Proteomics and pathway/network analyses were used to find the differentially expressed proteins and pathways. Then, we used machine learning to develop a protein classifier which can predict the clinical outcomes of the patients with COVID-19 and help identify the high-risk patients. Finally, a classifier including C-reactive protein, extracellular matrix protein 1, insulin-like growth factor-binding protein complex acid labile subunit, E3 ubiquitin-protein ligase HECW1 and phosphatidylcholine-sterol acyltransferase was determined. The prediction value of the model was verified with an independent patient cohort. This novel model can realize early prediction of 28-day mortality of patients with COVID-19, with the area under curve 0.88 in discovery group and 0.80 in validation group, superior to 4C mortality and E-CURB65 scores. In total, this work revealed a potential protein classifier which can assist in predicting the outcomes of COVID-19 patients and providing new diagnostic directions.