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Comparison of Small Biomolecule Ionization and Fragmentation in Pseudomonas aeruginosa Using Common MALDI Matrices.

ABSTRACT: Different bacterial cell surface associated biomolecules can be analyzed by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry and coupled with collision induced dissociation (CID) for identification. Pseudomonas aeruginosa is an opportunistic, Gram-negative bacterium that causes acute or chronic biofilm infections. Cells of P. aeruginosa communicate through a system of signaling biomolecules known as quorum sensing (QS). The QS system can result in the production of biosurfactant rhamnolipids known to associate and alter the cellular membrane. MALDI-TOF utilizes a variety of matrices that can interact differently with biomolecules for selective ionization. We examined six common matrices to determine the optimal matrix specific to different molecule classes in P. aeruginosa associated with cell surfaces. Three major molecule classes (quinolones, rhamnolipids, and phospholipids) were observed to ionize selectively with the different matrices tested. Sodiated and protonated adducts differed between matrices utilized in our study. Isobaric ions were identified as different molecule classes depending on the matrix used. We highlight the role of matrix selection in MALDI-TOF identification of molecules within a complex biological mixture.

SUBMITTER: Wamer NC

PROVIDER: S-EPMC9983012 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Comparison of Small Biomolecule Ionization and Fragmentation in Pseudomonas aeruginosa Using Common MALDI Matrices.

Wamer Nathan C NC Morse Chase N CN Gadient Jennifer N JN Dodson Taylor A TA Carlson Eric A EA Prestwich Erin G EG

Journal of the American Society for Mass Spectrometry 20230125 3

Different bacterial cell surface associated biomolecules can be analyzed by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry and coupled with collision induced dissociation (CID) for identification. Pseudomonas aeruginosa is an opportunistic, Gram-negative bacterium that causes acute or chronic biofilm infections. Cells of P. aeruginosa communicate through a system of signaling biomolecules known as quorum sensing (QS). The QS system can resu ...[more]

PMID: 36696681

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Project description:Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in Pseudomonas aeruginosa, a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern β-lactam/β-lactamase inhibitor drugs, namely, ceftazidime/avibactam and ceftolozane/tazobactam. For these drugs, the model demonstrated area under the receiver operating characteristic curve (AUROC) of 0.869 and 0.856, specificity of 0.925 and 0.897, and sensitivity of 0.731 and 0.714, respectively. As part of this work, we developed dynamic binning, a feature engineering technique that effectively reduces the high-dimensional feature set and has wide-ranging applicability to MALDI-TOF MS data. Compared to conventional feature engineering approaches, the dynamic binning method yielded highest performance in 7 of 10 antimicrobials. Moreover, we showcased the efficacy of transfer learning in enhancing the AUROC performance for 8 of 11 antimicrobials. By assessing the contribution of features to the model's prediction, we identified proteins that may contribute to AMR mechanisms. Our findings demonstrate the potential of combining AI with MALDI-TOF MS as a rapid AMR diagnostic tool for Pseudomonas aeruginosa.IMPORTANCEPseudomonas aeruginosa is a key bacterial pathogen that causes significant global morbidity and mortality. Antimicrobial resistance (AMR) emerges rapidly in P. aeruginosa and is driven by complex mechanisms. Drug-resistant P. aeruginosa is a major challenge in clinical settings due to limited treatment options. Early detection of AMR can guide antibiotic choices, improve patient outcomes, and avoid unnecessary antibiotic use. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid species identification in clinical microbiology. In this study, we repurposed mass spectra generated by MALDI-TOF and used them as inputs for artificial intelligence approaches to successfully predict AMR in P. aeruginosa for multiple key antibiotic classes. This work represents an important advance toward using MALDI-TOF as a rapid AMR diagnostic for P. aeruginosa in clinical settings.

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Comparison of Small Biomolecule Ionization and Fragmentation in Pseudomonas aeruginosa Using Common MALDI Matrices.

Publications

Comparison of Small Biomolecule Ionization and Fragmentation in <i>Pseudomonas aeruginosa</i> Using Common MALDI Matrices.

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