Application of SWATH Mass Spectrometry and Machine Learning in Diagnosis of Inflammatory Bowel Disease Based on Stool Proteome
Ontology highlight
ABSTRACT:
INSTRUMENT(S): TripleTOF 5600
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: 
Elmira Shajari
LAB HEAD: Jean-Francois Beaulieu
PROVIDER: PXD047585 | Pride | 2024-02-01
REPOSITORIES: Pride
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Project description:Rationale There is a need for new and better biomarkers for hypertrophic cardiomyopathy (HCM) which correlate more closely with disease progression as determined by clinical imaging and biohumoral information. We have used a combination of heart tissue and plasma proteomics to identify potential biomarkers for HCM and developed them into an exploratory targeted proteomic assay. Objective To identify informative staging biomarkers for HCM and develop them into a blood test. The test using 10 µl of plasma, was developed into a 10 min liquid chromatography-tandem/mass spectrometry (LC-MS/MS) assay to analyze multiple candidate biomarkers and evaluate their association with clinical phenotypes in patients with HCM. Methods and Results Myocardial tissue and plasma samples from patients with HCM and healthy volunteers (controls) were screened using a combined gel- and nano-LC quadrupole time of flight MS approach. Twenty-six potential biomarkers were identified from the proteomics screens and developed into a multiplexed targeted proteomic assay. Their association with clinical phenotypes was tested in plasma samples collected from 207 prospectively recruited participants: 110 patients with HCM (50.1 ± 15.0 years, 70% male; 48 [44%] with identified genetic mutations) and 97 controls (49.6 ± 13.4 years, 58% male), randomly split into training (80 HCM, 67 controls) and validation datasets (30 HCM, 30 controls). Six markers (Aldolase Fructose-Bisphosphate A, Complement C3, Glutathione S-Transferase Omega 1, Ras Suppressor Protein 1, Talin 1, and Thrombospondin 1) were significantly increased (P<0.006) in the plasma of HCM patients compared to controls in the training dataset. These markers correlated with left ventricular (LV) wall thickness, LV mass and % myocardial scar on cardiovascular magnetic resonance imaging. Using supervized machine learning (ML) this panel differentiated HCM from controls (area under the curve: 0.89 in the training dataset, sensitivity 96%, 95% confidence interval [CI] 77–93; specificity 87%, 95%CI 77–94; and 0.87 in the validation dataset, sensitivity 97%, 95%CI 83–100; specificity 77%, 95%CI 58–90). Four of the biomarkers as well as the composite ML score of the plasma proteome correlated with the presence of nonsustained ventricular tachycardia and the estimated 5-year risk of sudden cardiac death. Conclusion By developing a high-throughput, multiplex, and targeted proteomic plasma assay we identified 6 biomarkers that correlate with the presence of disease and with clinical risk score for sudden cardiac death.
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