Dataset Information

Mass Spectrometry-Based Proteomic Profiling of a Silvaner White Wine.

ABSTRACT: The comprehensive identification of the proteome content from a white wine (cv. Silvaner) is described here for the first time. The wine protein composition isolated from a representative wine sample (250 L) was identified via mass spectrometry (MS)-based proteomics following in-solution and in-gel digestion methods after being submitted to size exclusion chromatographic (SEC) fractionation to gain a comprehensive insight into proteins that survive the vinification processes. In total, we identified 154 characterized (with described functional information) or so far uncharacterized proteins, mainly from Vitis vinifera L. and Saccharomyces cerevisiae. With the complementarity of the two-step purification, the digestion techniques and the high-resolution (HR)-MS analyses provided a high-score identification of proteins from low to high abundance. These proteins can be valuable for future authentication of wines by tracing proteins derived from a specific cultivar or winemaking process. The proteomics approach presented herein may also be generally helpful to understand which proteins are important for the organoleptic properties and stability of wines.

SUBMITTER: Albuquerque W

PROVIDER: S-EPMC10136162 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Mass Spectrometry-Based Proteomic Profiling of a Silvaner White Wine.

Albuquerque Wendell W Ghezellou Parviz P Seidel Leif L Burkert Johannes J Will Frank F Schweiggert Ralf R Spengler Bernhard B Zorn Holger H Gand Martin M

Biomolecules 20230404 4

The comprehensive identification of the proteome content from a white wine (cv. Silvaner) is described here for the first time. The wine protein composition isolated from a representative wine sample (250 L) was identified via mass spectrometry (MS)-based proteomics following <i>in-solution</i> and <i>in-gel</i> digestion methods after being submitted to size exclusion chromatographic (SEC) fractionation to gain a comprehensive insight into proteins that survive the vinification processes. In to ...[more]

PMID: 37189397

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Project description:Proteomic profiling using mass spectrometry (MS) is one of the most promising methods for the analysis of complex biological samples such as urine, serum and tissue for biomarker discovery. Such experiments are often conducted using MALDI-TOF (matrix-assisted laser desorption/ionisation time-of-flight) and SELDI-TOF (surface-enhanced laser desorption/ionisation time-of-flight) MS. Using such profiling methods it is possible to identify changes in protein expression that differentiate disease states and individual proteins or patterns that may be useful as potential biomarkers. However, the incorporation of quality control (QC) processes that allow the identification of low quality spectra reliably and hence allow the removal of such data before further analysis is often overlooked. In this paper we describe rigorous methods for the assessment of quality of spectral data. These procedures are presented in a user-friendly, web-based program. The data obtained post-QC is then examined using variance components analysis to quantify the amount of variance due to some of the factors in the experimental design.Using data from a SELDI profiling study of serum from patients with different levels of renal function, we show how the algorithms described in this paper may be used to detect systematic variability within and between sample replicates, pooled samples and SELDI chips and spots. Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms. Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.Using the techniques described in this paper it is possible to reliably detect poor quality data within proteomic profiling experiments undertaken by MS. The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

Dataset Information

Mass Spectrometry-Based Proteomic Profiling of a Silvaner White Wine.

Publications

Mass Spectrometry-Based Proteomic Profiling of a Silvaner White Wine.

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