Integrating analytical resolutions in non-targeted wine metabolomics

Roullier-Gall, Chloé; Witting, Michael; Tziotis, Dimitrios; Ruf, Alexander; Gougeon, Régis D.; Schmitt‐Kopplin, Philippe

doi:10.1016/j.tet.2015.02.054

Cited by 40 publications

(39 citation statements)

References 31 publications

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“…We found 16 (4.7%), 8 (3.1%), 79 (13.8%), 14 (13.7%) and 17 (7.2%) possible metabolite markers for the Sc, LT, SB1, SB2, and Mp sample, respectively (Supplementary table 1). The low percentage of annotated markers illustrates the extent of the unknown composition of wine where at best for FT-ICR-MS data less than 20% of detected features can find hits in databases 48,49 .…”

Section: Resultsmentioning

confidence: 99%

“…Out of the 134 markers reported above, in total 40 compounds could also be detected by LC-MS/MS. The lower coverage in LC-MS/MS compared to FT-ICR-MS is because of the lower sensitivity of the Q-ToF instrument 49,50 . MS/MS spectra obtained were manually extracted and compared to known or predicted MS/MS spectra from Metlin, Metfrag and HMDB databases.…”

Section: Resultsmentioning

confidence: 99%

“…Ultrahigh-resolution FT-ICR-MS were acquired with a 12 T Bruker Solarix mass spectrometer (Bruker Daltonics, Bremen, Germany) equipped with an APOLLO II electrospray source in negative ionization mode 48 . For MS analysis, samples were diluted 1:100 (v/v) in methanol (LC-MS grade, Fluka, Germany) 7,49 . The diluted samples were infused into the electrospray ion source with a flow rate of 120 μL.h −1 48,49 .…”

Section: Direct Infusion Ft-icr-msmentioning

confidence: 99%

“…A resolving power of 400,000 at m/z 300 was achieved 48,49 . processing of ft-icR-MS data. Raw spectra were post-processed by Compass DataAnalysis 4.2 (Bruker Daltonics, Bremen, Germany) and peaks with a signal-to-noise ratio (S/N) of at least 6 were exported to mass lists 49 . All exported m/z features were aligned into a matrix containing averaged m/z values (peak alignment window width: ±1 ppm) and corresponding peak intensities of all analyzed samples 48 .…”

Section: Direct Infusion Ft-icr-msmentioning

confidence: 99%

“…Samples were directly injected into the LC-MS/MS system without dilution. Metabolites were separated using a Waters Acquity UPLC system coupled to a Bruker maXis UHR-ToF-MS. A reversed-phase (RP) separation method was employed which separates middle to non-polar metabolites using a BEH C18 column (100 × 2.1 mm ID, 1.7 μm; (Waters, City, Country) 49 . The column temperature was set to 40 °C.…”

Section: Uhplc-qtof-ms/ms Experimentsmentioning

confidence: 99%

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Exploring yeast interactions through metabolic profiling

Roullier-Gall

David

Hemmler

et al. 2020

Sci Rep

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As a complex microbial ecosystem, wine is a particularly interesting model for studying interactions between microorganisms as fermentation can be done by microbial consortia, a unique strain or mixed culture. The effect of a specific yeast strain on its environments is unique and characterized by its metabolites and their concentration. With its great resolution and excellent mass accuracy, ultrahigh resolution mass spectrometry (uHRMS) is the perfect tool to analyze the yeast metabolome at the end of alcoholic fermentation. this work reports the change in wine chemical composition from pure and mixed culture fermentation with Lachancea thermotolerans, Starmerella bacillaris, Metschnikowia pulcherrima and S. cerevisiae. We could clearly differentiate wines according to the yeast strain used in single cultures and markers, which reflect important differences between the yeast species, were extracted and annotated. Moreover, uHRMS revealed underlining intra species metabolomics differences, showing differences at the strain level between the two Starmerella bacillaris. non volatile metabolomics analysis of single and sequential fermentations confirmed that mixed fermentations lead to a different composition. Distinct metabolites appeared in wines from sequential fermentation compared to single fermentation. this suggests that interactions between yeasts are not neutral.Microorganisms coexist in most environments and interact with each other. These interactions happen in nearly every niche on the planet and in numerous processes such as bioremediation of pollutants, farming, biotechnology, medicine or food-processing 1,2 . Under oenological conditions, when yeasts grow simultaneously during alcoholic fermentation, they often do not coexist passively, and in most cases, physiological and metabolic interactions are established between them. The interactions between the different strains of Saccharomyces and non-Saccharomyces yeast can be direct or indirect, through the physicochemical changes in the environment caused by one strain reacting to the other. In oenology, the effect of these interactions is characterized as being positive, negative or neutral 3 . Genomics and proteomics provide an understanding of the interaction 3-5 but none of them could yet provide significant progress on the microbial interaction mechanisms. Recently, high resolution mass spectrometry has been used to elucidate interactions between yeasts and bacteria in the malolactic fermentation by comparison of extracellular metabolic profiles 6 . Even more recently, ultrahigh resolution mass spectrometry (uHRMS) confirmed that cell-cell contact influences the metabolism of L. thermotolerans and S. cerevisiae 7 . Therefore, metabolomics seems to be a suitable tool to better understand the microbial interactome in order to control fermentation by multi-starters. Metabolomics is defined as the study of all metabolites given in a biological system under particular physiological conditions 8 . The analytical techniques developed for metabolomics studi...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Direct Infusion Ft-icr-msmentioning

confidence: 99%

Section: Direct Infusion Ft-icr-msmentioning

confidence: 99%

Section: Uhplc-qtof-ms/ms Experimentsmentioning

confidence: 99%

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Exploring yeast interactions through metabolic profiling

Roullier-Gall

David

Hemmler

et al. 2020

Sci Rep

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Tandem HILIC‐RP liquid chromatography for increased polarity coverage in food analysis

et al. 2018

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Comprehensive non-targeted analysis of food products normally requires two complementary chromatographic runs to achieve maximum compound coverage. In this study, we present a sensitive tandem-LC method, which combines RP and HILIC separation in a single run. The setup consists of a C18 trap column and two subsequently coupled analytical columns (HILIC and C18) which are operated in parallel. First, hydrophobic compounds are retained on the RP trap column while rather hydrophilic compounds are directly transferred onto a HILIC phase. Next, the pre-fractionated sample composition is analyzed by HILIC or RP chromatography, respectively. The presented setup allows individual and independent gradient elution as well as interfacing with mass spectrometry. The performance of the method has been proven by means of food relevant standards and analysis of complex food samples (e.g. red wine, meat extract). The simple and robust setup provides high flexibility in the selection of column combinations and does not require sophisticated instrumental setups or software. The method significantly increases the covered polarity range compared to classical one-dimensional chromatography. Our results indicate that tandem LC is a valuable and universal tool in the non-targeted screening of various types of complex food samples.

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Comprehensive Analysis of the Alternaria Mycobolome Using Mass Spectrometry Based Metabolomics

Gotthardt

Kanawati

Schmidt

et al. 2020

Molecular Nutrition Food Res

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Scope: Alternaria fungi are widely distributed plant pathogens infecting grains and vegetables and causing major harvest losses in the field and during postharvest storage. Besides, consumers are endangered by the formation of toxic secondary metabolites. Some of these secondary metabolites are chemically characterized as mycotoxins, but the majority of the Alternaria mycobolome still remains unknown. Methods and results: Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) and LC-MS/MS are combined for the non-targeted and targeted analysis of the metabolome of three A. alternata isolates and one A. solani isolate. Due to the ultra-high resolution of FTICR-MS, unique molecular formulae are assigned to measured m/z signals. The molecular formulae are matched to entries of the databases Antibase and Kyoto Encyclopedia of Genes and Genomes. The non-targeted analysis of the fungal extracts reveals variations in the secondary metabolite profile of A. alternata and A. solani. Differences in the biosynthesis of dibenzo--pyrones, perylene quinones, tentoxin, and tenuazonic acid of the A. alternata and A. solani isolates are determined applying targeted LC-MS/MS. Conclusion: FTICR-MS analyses reveal clear differences in the metabolic profile of the A. solani and the A. alternata isolates.

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Integrating analytical resolutions in non-targeted wine metabolomics

Cited by 40 publications

References 31 publications

Exploring yeast interactions through metabolic profiling

Exploring yeast interactions through metabolic profiling

Tandem HILIC‐RP liquid chromatography for increased polarity coverage in food analysis

Comprehensive Analysis of the Alternaria Mycobolome Using Mass Spectrometry Based Metabolomics

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