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Defining Proteomic Signatures to Predict Multidrug Persistence in Pseudomonas aeruginosa
Pablo Manfredi1, Isabella Santi1, Enea Maffei1
1Biozentrum, University of Basel, Basel, Switzerland.
Abstract:
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.
Insights
Bacterial persisters, small subpopulations surviving antibiotics, are hard to study. This new proteomic method analyzes whole populations to identify bacterial persistence signatures, aiding in antimicrobial resistance diagnostics.
Area of Science:
- Microbiology
- Proteomics
- Machine Learning
Background:
- Bacterial persisters are subpopulations that survive antibiotic treatment, posing a significant challenge in infectious disease management.
- Studying persisters is difficult due to their small numbers and the experimental biases introduced by isolation methods.
- Understanding the physiological state of persisters is crucial for developing effective eradication strategies.
Purpose of the Study:
- To develop and validate a methodology for generating proteomic signatures of bacterial persisters in Pseudomonas aeruginosa.
- To establish a proteomic approach that avoids biases associated with persister cell sorting and enrichment.
- To create a diagnostic tool for assessing antimicrobial persistence in clinical isolates.
Main Methods:
- Proteome sample preparation from entire bacterial populations.
- Mass spectrometry analysis for high-throughput proteomic profiling.
- An adaptable machine learning regression pipeline for data analysis.
Main Results:
- The methodology successfully generated proteomic signatures for P. aeruginosa isolates with varying persister fractions.
- A common proteomic signature of persistence was identified across different P. aeruginosa hyper-persister mutants.
- The approach demonstrated the potential for analyzing entire populations to understand persistence.
Conclusions:
- Proteome analysis of whole bacterial populations offers a sensitive and reproducible method to study bacterial persistence.
- The developed methodology can identify common proteomic signatures associated with persistence.
- This approach serves as a potential diagnostic tool for evaluating the antimicrobial persistence of clinical isolates.
