Untargeted LC-MS Metabolomics Differentiates Between Virulent and Avirulent Clinical Strains of Pseudomonas aeruginosa

Abstract

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.

Mining Public Mass Spectrometry Data to Characterize the Diversity and Ubiquity of P. aeruginosa Specialized Metabolites

“…Thus, the length of the FA chain could have a significant effect on the molecules' diffusive properties during infections and in the environment. Importantly, Depke et al [16] recently showed that the metabolite profiles of P. aeruginosa can predict virulence in model systems, further highlighting the importance of small chemical variations of specialized metabolites, such as those described here, as important for virulence. The different FA chain lengths may induce quorum sensing signaling differently depending on the environment.…”

The Peptide Chain Release Factor Methyltransferase PrmC Influences the Pseudomonas aeruginosa PA14 Endo- and Exometabolome

“…Unfortunately, pyoverdine was not detected by our analytical methods, which complicates the interpretation of elevated pyochelin levels. Earlier research has associated elevated pyochelin levels with a virulent phenotype [ 15 , 21 , 22 ], and proteome and transcriptome data show the pchD transcript as being upregulated in tn prmC [ 9 ].…”

“…Among the non-annotated features, it is particularly noteworthy that M187T7, a metabolite with an m/z of 187.1230 and the putative sum formula C 12 H 15 N 2 , displays a significantly differential abundance with approximately 5-fold lower levels in tn prmC and a corrected p -value of 0.0001 in the cellular metabolomics data (fold change ‒2.8 and corrected p -value 0.016 in the exometabolome). The ‘unknown’ metabolite has been identified as a putative biomarker for virulent phenotypes in clinical P. aeruginosa strains in a previous study [ 15 ]. Though its identity and function are unassigned, this finding emphasises the potential importance of the metabolite for pseudomonal virulence.…”

“…Pooled samples containing aliquots of all samples were generated for quality control purposes. LC–MS analysis, pre-processing using XCMSonline [ 14 ], metabolite identification and statistical analysis were performed as described previously [ 11 , 15 ]. In brief, the metabolite extracts were separated by ultra-high perfomance liquid chromatography on a C18 reversed phase column by gradient elution with H 2 O plus 0.1% formic acid and acetonitrile plus 0.1% formic acid.…”

“…Pooled samples were additionally subjected to collision-induced dissociation by Bruker’s data-dependent auto-MS/MS functionality to record tandem mass spectra for metabolite identification by comparison to authentic chemical standards and/or metabolite databases. Data pre-processing by XCMS used the same parameters as an earlier study [ 15 ]. Further processing involved exclusion of features eluting in the first 0.8 min or after more than 20 min, features with a standard deviation below 20% over all samples and features with an intesity below 10,000 counts.…”