Mutations in PA2491 (mexS) Promote MexT-Dependent mexEF-oprN Expression and Multidrug Resistance in a Clinical Strain of Pseudomonas aeruginosa

Disruption of the PA2491 gene in a mini-Tn5-tet insertion mutant of a clinical isolate of Pseudomonas aeruginosa increased expression of the mexEF-oprN multidrug efflux genes and decreased production of outer membrane protein OprD, concomitant with enhanced resistance to chloramphenicol, quinolones, and imipenem, which was reminiscent of previously described nfxC mutants. PA2491 encodes a probable oxidoreductase previously shown to be positively regulated by the MexT positive regulator of mexEF-oprN expression (T. Köhler, S. F. Epp, L. K. Curty, and J. C. Pechére, J. Bacteriol. 181:6300-6305, 1999). Spontaneous multidrug-resistant mutants of the P. aeruginosa clinical isolate hyperexpressing mexEF-oprN and showing reduced production of OprD were readily selected in vitro, and all of them were shown to carry mutations in PA2491, highlighting the probable significance of such mutations as determinants of MexEF-OprN-mediated multidrug resistance in vivo

Mutations in PA3574 (nalD) Lead to Increased MexAB-OprM Expression and Multidrug Resistance in Laboratory and Clinical Isolates of Pseudomonas aeruginosa

Mutations in genes mexR and nalC have previously been shown to drive overexpression of the MexAB-OprM multidrug efflux system in Pseudomonas aeruginosa. A transposon insertion multidrug-resistant mutant of P. aeruginosa overproducing MexAB-OprM was disrupted in yet a third gene, PA3574, encoding a probable repressor of the TetR/AcrR family that we have dubbed NalD. Clinical strains overexpressing MexAB-OprM but lacking mutations in mexR or nalC were also shown to carry mutations in nalD. Moreover, the cloned nalD gene reduced the multidrug resistance and MexAB-OprM expression of the transposon mutant and clinical isolates, highlighting the significance of the nalD mutations vis-à-vis MexAB-OprM overexpression in these isolates

Induction of the MexXY Efflux Pump in Pseudomonas aeruginosa Is Dependent on Drug-Ribosome Interaction

MexXY is an inducible efflux system that contributes to the natural resistance of Pseudomonas aeruginosa to antibiotics. Experiments involving real-time PCR after reverse transcription in reference strain PAO1 showed concentration-dependent induction of gene mexY by various ribosome inhibitors (e.g., chloramphenicol, tetracycline, macrolides, and aminoglycosides) but not by antibiotics acting on other cellular targets (e.g., β-lactams, fluoroquinolones). Confirming a functional link between the efflux system and the translational machinery, ribosome protection by plasmid-encoded proteins TetO and ErmBP increased the resistance of a ΔmexAB-oprM mutant of PAO1 to tetracycline and erythromycin, respectively, as well as the concentrations of both drugs required to induce mexY. Furthermore, spontaneous mutations resulting in specific resistance to dihydrostreptomycin or spectinomycin also raised the minimal drug concentration for mexXY induction in strain PAO1. While strongly upregulated in a PAO1 mutant defective in gene mexZ (which codes for a putative repressor of operon mexXY), gene mexY remained inducible by agents such as tetracycline, chloramphenicol, and spectinomycin, suggesting additional regulatory loci for mexXY. Altogether, these data demonstrate physiological interplays between MexXY and the ribosome and are suggestive of an alternative function for MexXY beyond antibiotic efflux

Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study

Abstract Background Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. Methods An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. Results The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. Conclusions We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19