International Journal of Antimicrobial Agents最新文献

Acinetobacter baumannii is a significant pathogen responsible for healthcare-acquired infections (HAIs), posing challenges due to its rising resistance to multiple antibiotics. Traditional diagnostic methods like bacterial cultures and antibiotic susceptibility testing (AST) are slow, necessitating the development of faster alternatives. This study aimed to create a predictive model for antibiotic resistance phenotypes of A. baumannii using clinical MALDI-TOF mass spectrometry (MS) spectra combined with machine learning techniques. A total of 3644 A. baumannii strains were analyzed for resistance patterns to nine antibiotic classes. Eight machine learning models were trained and evaluated, with the best model showing over 83% accuracy in predicting resistance to carbapenems, penicillins, and quinolones. The model performed especially well for imipenem and ceftazidime, with accuracies of 84.90% and 84.64%, respectively. Multi-center validation confirmed the model's robustness, achieving 81.68% and 80.72% accuracy for imipenem and ceftazidime. These findings demonstrate the potential of integrating machine learning with MALDI-TOF MS for rapid, accurate profiling of A. baumannii's antimicrobial resistance in clinical settings.

Background: Cefiderocol (CFDC) is a novel and potent antimicrobial agent; however, the emergence of CFDC resistance is increasingly concerning. This study investigated the mechanisms underlying CFDC resistance in ST11 carbapenem-resistant Klebsiella pneumoniae (CRKP) and explored potential therapeutic strategies against CFDC-resistant strains.

Methods: Two isogenic ST11 CRKP isolates, KP399 (CFDC-susceptible) and KP400 (CFDC-resistant), were obtained from a single patient. Whole-genome sequencing, genetic knockout, plasmid curing, complementation assays, real-time quantitative PCR, and antimicrobial susceptibility testing (AST) were performed to elucidate the resistance mechanisms. AST of CFDC in combination with β-lactamase inhibitors (BLIs), along with computational structural modeling, was conducted to evaluate BLI activity against SHV-12.

Results: An ISKpn74 insertion sequence in the cirA promoter significantly reduced cirA transcription and was associated with decreased CFDC susceptibility. SHV-12 was identified as the predominant β-lactamase conferring CFDC resistance. High-level resistance required the combined effects of reduced cirA expression (<0.5 relative units) and SHV-12 production. In silico analyses predicted that relebactam exhibits the strongest binding affinity to SHV-12, attributable to its bulky C2-linked piperidine ring that enables additional interactions with the Asp104 residue, together with its slow deacylation kinetics. Consistently, AST showed that combining CFDC with relebactam markedly enhanced antimicrobial activity against SHV-12-producing CRKP compared with other BLIs.

Conclusions: This study identifies a novel CFDC resistance mechanism and underscores the importance of assessing regulatory regions when evaluating resistance determinants. It also supports the potential use of relebactam as an effective adjunct to CFDC against SHV-12-producing, CFDC-resistant CRKP.

Objectives: The Microbial Evolution and Growth Arena (MEGA-plate) is a spatially structured experimental system that enables direct visualization of microbial adaptation under antimicrobial pressure. Unlike traditional well-mixed models, the MEGA-plate captures spatial dynamics and evolutionary bottlenecks in real time, offering unique insights into resistance development.

Methods: Here, we review advances in the application of the MEGA-plate across diverse contexts, integrating findings from bacterial, fungal, and veterinary domains. We also consider the MEGA-plate alongside established experimental evolution systems (e.g., serial passaging, continuous culture, and feedback-controlled selection devices).

Results: Since its introduction, the MEGA-plate has been applied across diverse contexts, from education and evolutionary theory to clinically relevant investigations of antibiotic resistance. Published studies show that the platform can reveal mutational trajectories, multidrug co-selection, and cross-resistance to critical antibiotic classes such as β-lactams and fluoroquinolones, as well as tolerance phenomena in fungal pathogens.

Conclusions: These advances have important implications for antimicrobial stewardship, resistance surveillance, and therapeutic strategy. By integrating findings across bacterial, fungal, and veterinary domains, the MEGA-plate highlights translational potential as a tool to anticipate resistance pathways and inform clinical microbiology. Importantly, positioning the MEGA-plate alongside established experimental evolution systems clarifies where spatial drug landscapes provide unique mechanistic value and where alternative approaches remain essential for fastidious organisms and community-level questions.

Objective: Phage therapy is emerging as an alternative therapy for difficult-to-treat bacterial infections. Flow cytometry (FCM) can rapidly screen for infective phages, reducing the time taken for screening infective phages to deliver a timely and effective phage-based therapy. Phage infection increases reactive oxygen species in bacteria, which cause oxidative damage such as protein carbonylation-an irreversible addition of carbonyls to amino acids-causing loss of function in proteins. Here, we aimed to assess carbonylation for predicting the bacterial response to phage infection. We hypothesised that the extent of protein carbonylation corresponds to bacterial phage susceptibility.

Methods: Fourteen Pseudomonas aeruginosa clinical isolates were exposed to bacteriophage at a bacteriophage:bacteria ratio of 100:1. Bacteria were then stained with cascade blue hydrazine, which labels carbonylation, prior to FCM. The cascade blue hydrazine fluorescence intensities obtained from the FCM analyses were used to construct polar plots illustrating both the extent of carbonylation and the heterogeneity of isolates exhibiting carbonylation.

Results: Increased carbonylation was observed at 2 h post-phage infection (hppi) in clinical isolates determined to be phage susceptible by double-agar overlay assay. FCM further revealed decreased carbonylation in several isolates at 4-hppi from 2-hppi. A phasor approach was adopted to estimate when the isolate would exhibit decreased carbonylation, and our estimations were accurate in 11 of 14 isolates assessed (accuracy: 78.6%).

Conclusions: Compared to traditional methods requiring overnight incubation, assessing carbonylation by FCM is a more rapid method to determine phage infectivity. As carbonylation often causes irreversible loss of function in proteins, we postulate that the onset of subsequent decreased carbonylation indicates bacterial attempts to survive by degrading carbonylated proteins.