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Shotgun proteomics data on the impact of hyperglycaemia on platelet protein acetylation by aspirin.

ABSTRACT: This data article associated with the manuscript "A high glucose levels is associated with decreased aspirin-mediated acetylation of platelet cyclooxygenase (COX)-1 at serine 529: a pilot study" (Finamore et al., 2018) refers to the shotgun proteomics approach carried out on platelet protein extracts from diabetic patients and healthy controls. Platelet proteins were in vitro incubated with 500?µM aspirin for 30?min at 37?°C to enhance the acetylation process. After protein digestion with trypsin, DDA data were acquired on a Thermo QExactive plus using 3 technical replicate injections per sample. Here, we were able to elucidate the preferential sites of aspirin-induced acetylation on a significant fraction of the platelet proteome and to quantify the impact of diabetes on the effect of aspirin on several platelet proteins. Data are available via ProteomeXchange with identifier PXD011582.

SUBMITTER: Finamore F

PROVIDER: S-EPMC6288982 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Shotgun proteomics data on the impact of hyperglycaemia on platelet protein acetylation by aspirin.

Finamore Francesco F Reny Jean-Luc JL Malacarne Sarah S Fontana Pierre P Sanchez Jean-Charles JC

Data in brief 20181123

This data article associated with the manuscript "A high glucose levels is associated with decreased aspirin-mediated acetylation of platelet cyclooxygenase (COX)-1 at serine 529: a pilot study" (Finamore et al., 2018) refers to the shotgun proteomics approach carried out on platelet protein extracts from diabetic patients and healthy controls. Platelet proteins were <i>in vitro</i> incubated with 500 µM aspirin for 30 min at 37 °C to enhance the acetylation process. After protein digestion with ...[more]

PMID: 30560156

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Project description:Label-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification ("FFId"), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between "internal" and "external" (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the "uncertain" feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known ground-truth. The algorithm reached almost complete (>99%) quantification coverage for the full set of peptides identified at 1% FDR (PSM level). Compared with other software solutions for label-free quantification, this is an outstanding result, which was achieved at competitive quantification accuracy and reproducibility across replicates. The FDR for the feature selection was estimated at a low 1.5% on average per sample (3% for features inferred from external peptide IDs). The FFId software is open-source and freely available as part of OpenMS ( www.openms.org ).

Dataset Information

Shotgun proteomics data on the impact of hyperglycaemia on platelet protein acetylation by aspirin.

Publications

Shotgun proteomics data on the impact of hyperglycaemia on platelet protein acetylation by aspirin.

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