Dataset Information

Discovery and Longitudinal Evaluation of Candidate Biomarkers for Ischaemic Stroke by Mass Spectrometry-Based Proteomics.

ABSTRACT: Application of acute therapies such as thrombolysis for ischaemic stroke (IS) is constrained because of diagnostic uncertainty and the dynamic nature of stroke biology. To investigate changes in blood proteins after stroke and as a result of thrombolysis treatment we performed label-free quantitative proteomics on serum samples using high-resolution mass spectrometry and long high-performance liquid chromatography gradient (5 hours) combined with a 50-cm column to optimise the peptide separation. We identified (false discovery rate [FDR]: 1%) and quantified a total of 574 protein groups from a total of 92 samples from 30 patients. Ten patients were treated by thrombolysis as part of a randomised placebo-controlled trial and up to 5 samples were collected from each individual at different time points after stroke. We identified 26 proteins differently expressed by treatment group (FDR: 5%) and significant changes of expression over time for 23 proteins (FDR: 10%). Molecules such as fibrinogen and C-reactive protein showed expression profiles with a high-potential clinical utility in the acute stroke setting. Protein expression profiles vary acutely in the blood after stroke and have the potential to allow the construction of a stroke clock and to have an impact on IS treatment decision making.

SUBMITTER: Dagonnier M

PROVIDER: S-EPMC5751906 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Discovery and Longitudinal Evaluation of Candidate Biomarkers for Ischaemic Stroke by Mass Spectrometry-Based Proteomics.

Dagonnier Marie M Cooke Ira Robin IR Faou Pierre P Sidon Tara Kate TK Dewey Helen Margaret HM Donnan Geoffrey Alan GA Howells David William DW

Biomarker insights 20171220

Application of acute therapies such as thrombolysis for ischaemic stroke (IS) is constrained because of diagnostic uncertainty and the dynamic nature of stroke biology. To investigate changes in blood proteins after stroke and as a result of thrombolysis treatment we performed label-free quantitative proteomics on serum samples using high-resolution mass spectrometry and long high-performance liquid chromatography gradient (5 hours) combined with a 50-cm column to optimise the peptide separation ...[more]

PMID: 29308009

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Project description:BackgroundOsteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers.MethodsSerum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography-tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis.ResultsIn total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064).ConclusionOur study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

Dataset Information

Discovery and Longitudinal Evaluation of Candidate Biomarkers for Ischaemic Stroke by Mass Spectrometry-Based Proteomics.

Publications

Discovery and Longitudinal Evaluation of Candidate Biomarkers for Ischaemic Stroke by Mass Spectrometry-Based Proteomics.

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