Defining Proteomic Signatures to Predict Multidrug Persistence in Pseudomonas aeruginosa

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 responseStress responses remains challenging. Sorting and enrichmentEnrichmentsprocedures of persister fractions introduce experimental biases limiting the significance of follow-up molecular analyses. In contrast, proteomeProteomesanalysis 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 signaturesProteomic signatures of persistent Pseudomonas aeruginosaPseudomonas aeruginosa (P. aeruginosa)isolates with variable fractions of persisters. This includes proteomeProteomessample preparation, mass spectrometryMass spectrometry analysis, and an adaptable machine learning regressionMachine learning regression pipeline. We show that this generic method can determine a common proteomic signatureProteomic signatures of persistence among different P. aeruginosaPseudomonas aeruginosa (P. aeruginosa)hyper-persister mutants. We propose that this approach can be used as diagnostic tool to gauge antimicrobial persistence of clinical isolates.