Combination of UHPLC-MS/MS-molecular networking approach and FTICR-MS for the metabolic profiling of Saccharomyces cerevisiae

Lipidome of Acinetobacter baumannii antibiotic persister cells

Persister cells constitute a bacterial subpopulation able to survive to high concentrations of antibiotics. This phenotype is temporary and reversible, and thus could be involved in the recurrence of infections and emergence of antibiotic resistance. To better understand how persister cells survive to such high antibiotic concentration, we examined changes in their lipid composition. We thus compared the lipidome of Acinetobacter baumannii ATCC 19606T persister cells formed under ciprofloxacin treatment with the lipidome of control cells grown without antibiotic. Using matrix assisted laser desorption ionisation-Fourier transform ion cyclotron resonance mass spectrometry, we observed a higher abundance of short chains and secondary chains without hydroxylation for lipid A in persister cells. Using liquid chromatography-tandem mass spectrometry, we found that persister cells produced particular phosphatidylglycerols, as LPAGPE and PAGPE, but also lipids with particular acyl chains containing additional hydroxyl group or uncommon di-unsaturation on C18 and C16 acyl chains. In order to determine the impact of these multiple lipidome modifications on membrane fluidity, fluorescence anisotropy assays were performed. They showed an increase of rigidity for the membrane of persister cells, inducing likely a decrease membrane permeability to protect cells during dormancy. Finally, we highlighted that A. baumannii persister cells also produced particular wax esters, composed of two fatty acids and a fatty diol. These uncommon storage lipids are key metabolites allowing a rapid bacterial regrow when antibiotic pressure disappears. These overall changes in persister lipidome may constitute new therapeutic targets to combat these particular dormant cells.

An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics

We used the Exploris 240 mass spectrometer for non-targeted metabolomics on Saccharomyces cerevisiae strain BY4741 and tested AcquireX software for increasing the number of detectable compounds and Compound Discoverer 3.3 software for identifying compounds by MS2 spectral library matching. AcquireX increased the number of potentially identifiable compounds by 50% through six iterations of MS2 acquisition. On the basis of high-scoring MS2 matches made by Compound Discoverer, there were 483 compounds putatively identified from nearly 8000 candidate spectra. Comparisons to 20 amino acid standards, however, revealed instances whereby compound matches could be incorrect despite strong scores. Situations included the candidate with the top score not being the correct compound, matching the same compound at two different chromatographic peaks, assigning the highest score to a library compound much heavier than the mass for the parent ion, and grouping MS2 isomers to a single parent ion. Because the software does not calculate false positive and false discovery rates at these multiple levels where such errors can propagate, we conclude that manual examination of findings will be required post software analysis. These results will interest scientists who may use this platform for metabolomics research in diverse disciplines including medical science, environmental science, and agriculture.

Alternating in-source fragmentation with single-stage high-resolution mass spectrometry with high annotation confidence in non-targeted metabolomics

Non-targeted metabolomics is increasingly applied in various applications for understanding biological processes and finding novel biomarkers in living organisms. However, high-confidence identity confirmation of metabolites in complex biological samples is still a significant bottleneck, especially when using single-stage mass analysers. In the current study, a complete workflow for alternating in-source fragmentation on a time-of-flight mass spectrometry (TOFMS) instrument for non-targeted metabolomics is presented. Hydrophilic interaction liquid chromatography (HILIC) was employed to assess polar metabolites in yeast following ESI parameter optimization using experimental design principles, which revealed the key influence of fragmentor voltage for this application. Datasets from alternating in-source fragmentation high resolution mass spectrometry (HRMS) were evaluated using open-source data processing tools combined with public reference mass spectral databases. The significant influence of the selected fragmentor voltages on the abundance of the primary analyte ion of interest and the extent of in-source fragmentation allowed an optimum selection of qualifier fragments for the different metabolites. The new acquisition and evaluation workflow was implemented for the non-targeted analysis of yeast extract samples whereby more than 130 metabolites were putatively annotated with more than 40% considered to be of high confidence. The presented workflow contains a fully elaborated acquisition and evaluation methodology using alternating in-source fragmentor voltages suitable for peak annotation and metabolite identity confirmation for non-targeted metabolomics applications performed on a single-stage HRMS platform.

Metabolome Exploration by High-Resolution Mass Spectrometry Methodologies of Two New Yeast Species: Starmerella reginensis and Starmerella kourouensis

Nature is harnessed since ancient times to fulfill human needs, and yeast culture has been mastered for bakery, brewery, or the preparation of beverages. In this context, the two recently discovered yeast species Starmerella reginensis and Starmerella kourouensis, belonging to a genus related to fermentative activities in the literature, were explored via untargeted metabolomics approaches. Ultrahigh-performance liquid chromatography hyphenated with tandem mass spectrometry and a deep investigation of molecular networks and spectral data allowed the annotation of, respectively, 439 and 513 metabolites for S. reginensis and S. kourouensis, with approximatively 30% compound annotations and 40% chemical class annotations for both yeast strains. These analyses and Fourier transform ion cyclotron resonance mass spectrometry accurate metabolic profiles unveiled a rich content of alkaloids, lipids, amino acids, and terpenoids for S. reginensis. S. kourouensis presents a similar profile with more sulfated compounds. In short, these results enrich the current knowledge about Starmerella yeast secondary metabolites and reveal their significant structural diversity of small molecules.