Bacterial persisters are difficult to eradicate because of their ability to survive prolonged exposure to a range of different antibiotics. Because they often represent small subpopulations of otherwise drug-sensitive bacterial populations, studying their physiological state and antibiotic stress response remains challenging. Sorting and enrichment procedures of persister fractions introduce experimental biases limiting the significance of follow-up molecular analyses. In contrast, proteome analysis of entire bacterial populations is highly sensitive and reproducible and can be employed to explore the persistence potential of a given strain or isolate. Here, we summarize methodology to generate proteomic signatures of persistent Pseudomonas aeruginosa isolates with variable fractions of persisters. This includes proteome sample preparation, mass spectrometry analysis, and an adaptable machine learning regression pipeline. We show that this generic method can determine a common proteomic signature of persistence among different P. aeruginosa hyper-persister mutants. We propose that this approach can be used as diagnostic tool to gauge antimicrobial persistence of clinical isolates.
Keywords: Machine learning regression; Mass spectrometry; Persistence; Proteomic signature; Pseudomonas aeruginosa.
© 2021. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.