Untargeted LC-MS Metabolomics Differentiates Between Virulent and Avirulent Clinical Strains of Pseudomonas aeruginosa.
Tobias DepkeJanne Gesine ThömingAdrian KordesSusanne HäußlerMark BrönstrupPublished in: Biomolecules (2020)
Pseudomonas aeruginosa is a facultative pathogen that can cause, inter alia, acute or chronic pneumonia in predisposed individuals. The gram-negative bacterium displays considerable genomic and phenotypic diversity that is also shaped by small molecule secondary metabolites. The discrimination of virulence phenotypes is highly relevant to the diagnosis and prognosis of P. aeruginosa infections. In order to discover small molecule metabolites that distinguish different virulence phenotypes of P. aeruginosa, 35 clinical strains were cultivated under standard conditions, characterized in terms of virulence and biofilm phenotype, and their metabolomes were investigated by untargeted liquid chromatography-mass spectrometry. The data was both mined for individual candidate markers as well as used to construct statistical models to infer the virulence phenotype from metabolomics data. We found that clinical strains that differed in their virulence and biofilm phenotype also had pronounced divergence in their metabolomes, as underlined by 332 features that were significantly differentially abundant with fold changes greater than 1.5 in both directions. Important virulence-associated secondary metabolites like rhamnolipids, alkyl quinolones or phenazines were found to be strongly upregulated in virulent strains. In contrast, we observed little change in primary metabolism. A hitherto novel cationic metabolite with a sum formula of C12H15N2 could be identified as a candidate biomarker. A random forest model was able to classify strains according to their virulence and biofilm phenotype with an area under the Receiver Operation Characteristics curve of 0.84. These findings demonstrate that untargeted metabolomics is a valuable tool to characterize P. aeruginosa virulence, and to explore interrelations between clinically important phenotypic traits and the bacterial metabolome.
Keyphrases
- pseudomonas aeruginosa
- mass spectrometry
- escherichia coli
- biofilm formation
- liquid chromatography
- staphylococcus aureus
- cystic fibrosis
- small molecule
- acinetobacter baumannii
- antimicrobial resistance
- gram negative
- multidrug resistant
- candida albicans
- ms ms
- high performance liquid chromatography
- climate change
- magnetic resonance
- gas chromatography
- electronic health record
- computed tomography
- dna methylation
- big data
- machine learning
- gas chromatography mass spectrometry
- artificial intelligence
- genome wide
- deep learning
- high speed