Project description:Rheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or inform development of near-patient diagnostics. We performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to screen protein expression in 215 African patients with severe RHD, and 230 controls. A machine learning (ML) approach was applied to feature selection among the 366 proteins quantifiable in at least 40% of samples, using the Boruta wrapper algorithm. The case-control differences and contribution to AUC of the ROC for each of the 56 proteins identified by the Boruta algorithm were calculated by Logistic Regression adjusted for age, sex and BMI. Adiponectin, complement component C7 and fibulin-1, a component of heart valve matrix, were each higher in cases when compared with controls. Ficolin-3, a protein with calcium-independent lectin activity that activates the complement pathway, was lower in cases than controls. The top six biomarkers from the Boruta analyses conferred an AUC of 0.90 indicating excellent discriminatory capacity between RHD cases and controls.
Project description:FAM134B is a reticulon-homology domain (RHD)-containing protein that participates in membrane-shaping of the endoplasmic reticulum (ER)8 13. It also functions as a mammalian ER-phagy receptor, mediating the fragmentation and selective degradation of ER sheets in multiple cell types8. However, little is known about the molecular and biophysical mechanisms that control and/or switch between these two FAM134B functions.
