Pseudomonas aeruginosa is a remarkably versatile environmental bacterium with an extraordinary capacity to infect the cystic fibrosis (CF) lung. Infection with P. aeruginosa occurs early, and although eradication can be achieved following early detection, chronic infection occurs in over 60% of adults with CF. Chronic infection is associated with accelerated disease progression and increased mortality. Extensive research has revealed complex mechanisms by which P. aeruginosa adapts to and persists within the CF airway. Yet knowledge gaps remain, and prevention and treatment strategies are limited by the lack of sensitive detection methods and by a narrow armoury of antibiotics. Further developments in this field are urgently needed in order to improve morbidity and mortality in people with CF. Here, we summarize current knowledge of pathophysiological mechanisms underlying P. aeruginosa infection in CF. Established treatments are discussed, and an overview is offered of novel detection methods and therapeutic strategies in development.

Pseudomonas aeruginosa opportunistically infects the airways of patients with cystic fibrosis and causes significant morbidity and mortality. Initial infection can often be eradicated though requires prompt detection and adequate treatment. Intermittent and then chronic infection occurs in the majority of patients. Better detection of P. aeruginosa infection using biomarkers may enable more successful eradication before chronic infection is established. In chronic infection P. aeruginosa adapts to avoid immune clearance and resist antibiotics via efflux pumps, β-lactamase expression, reduced porins and switching to a biofilm lifestyle. The optimal treatment strategies for P. aeruginosa infection are still being established, and new antibiotic formulations such as liposomal amikacin, fosfomycin in combination with tobramycin and inhaled levofloxacin are being explored. Novel agents such as the alginate oligosaccharide OligoG, cysteamine, bacteriophage, nitric oxide, garlic oil and gallium may be useful as anti-pseudomonal strategies, and immunotherapy to prevent infection may have a role in the future. New treatments that target the primary defect in cystic fibrosis, recently licensed for use, have been associated with a fall in P. aeruginosa infection prevalence. Understanding the mechanisms for this could add further strategies for treating P. aeruginosa in future.

Rapid evaporative ionisation mass spectrometry (REIMS) is a novel technique for the real-time analysis of biological material. It works by conducting an electrical current through a sample, causing it to rapidly heat and evaporate, with the analyte containing vapour channelled to a mass spectrometer. It was used to characterise the metabolome of 45 Pseudomonas aeruginosa (P. aeruginosa) isolates from cystic fibrosis (CF) patients and compared to 80 non-CF P. aeruginosa. Phospholipids gave the highest signal intensity; 17 rhamnolipids and 18 quorum sensing molecules were detected, demonstrating that REIMS has potential for the study of virulence-related metabolites. P. aeruginosa isolates obtained from respiratory samples showed a higher diversity, which was attributed to the chronic nature of most respiratory infections. The analytical sensitivity of REIMS allowed the detection of a metabolome that could be used to classify individual P. aeruginosa isolates after repeated culturing with 81% accuracy, and an average 83% concordance with multilocus sequence typing. This study underpins the capacities of REIMS as a tool with clinical applications, such as metabolic phenotyping of the important CF pathogen P. aeruginosa, and highlights the potential of metabolic fingerprinting for fine scale characterisation at a sub-species level.

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Thin film microextraction to sample VOCs from the apical side of an air–liquid interface culture model. After S. aureus infection, infected and uninfected cultures were distinguished using an untargeted metabolomics approach.

BackgroundAlthough rapid screening for and diagnosis of coronavirus disease 2019 (COVID-19) are still urgently needed, most current testing methods are long, costly or poorly specific. The objective of the present study was to determine whether or not artificial-intelligence-enhanced real-time mass spectrometry breath analysis is a reliable, safe, rapid means of screening ambulatory patients for COVID-19.MethodsIn two prospective, open, interventional studies in a single university hospital, we used real-time, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of exhaled breath from adults requiring screening for COVID-19. Artificial intelligence and machine learning techniques were used to build mathematical models based on breath analysis data either alone or combined with patient metadata.ResultsWe obtained breath samples from 173 participants, of whom 67 had proven COVID-19. After using machine learning algorithms to process breath analysis data and further enhancing the model using patient metadata, our method was able to differentiate between COVID-19-positive and -negative participants with a sensitivity of 98%, a specificity of 74%, a negative predictive value of 98%, a positive predictive value of 72% and an area under the receiver operating characteristic curve of 0.961. The predictive performance was similar for asymptomatic, weakly symptomatic and symptomatic participants and was not biased by COVID-19 vaccination status.ConclusionsReal-time, noninvasive, artificial-intelligence-enhanced mass spectrometry breath analysis might be a reliable, safe, rapid, cost-effective, high-throughput method for COVID-19 screening.

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Background: Volatilomics is the branch of metabolomics dedicated to the analysis of volatile organic compounds (VOCs) in exhaled breath for medical diagnostic or therapeutic monitoring purposes. Real-time mass spectrometry technologies such as proton transfer reaction mass spectrometry (PTR-MS) are commonly used, and data normalisation is an important step to discard unwanted variation from non-biological sources, as batch effects and loss of sensitivity over time may be observed. As normalisation methods for real-time breath analysis have been poorly investigated, we aimed to benchmark known metabolomic data normalisation methods and apply them to PTR-MS data analysis. Methods: We compared seven normalisation methods, five statistically based and two using multiple standard metabolites, on two datasets from clinical trials for COVID-19 diagnosis in patients from the emergency department or intensive care unit. We evaluated different means of feature selection to select the standard metabolites, as well as the use of multiple repeat measurements of ambient air to train the normalisation methods. Results: We show that the normalisation tools can correct for time-dependent drift. The methods that provided the best corrections for both cohorts were Probabilistic Quotient Normalisation and Normalisation using Optimal Selection of Multiple Internal Standards. Normalisation also improved the diagnostic performance of the machine learning models, significantly increasing sensitivity, specificity and area under the ROC curve for the diagnosis of COVID-19. Conclusions: Our results highlight the importance of adding an appropriate normalisation step during the processing of PTR-MS data, which allows significant improvements in the predictive performance of statistical models.