A Novel Pathway of Platelet Activation in ACS Mediated by LL-37 Immunoglobulin G Autoantibody Immune Complexes.

The cathelicidin antimicrobial peptide LL-37 is a self-antigen in neutrophil extracellular traps that provokes autoantibody responses in autoimmune/autoinflammatory conditions. LL-37 immunoglobulin (Ig) G autoantibody levels were measured in subjects with and without atherosclerotic cardiovascular disease assessed using the coronary artery calcium score, in patients who had a future myocardial infarction and in a cohort of acute coronary syndrome (ACS) patients. LL-37 IgG levels were not associated with coronary artery calcium score, but future myocardial infarction patients had significantly higher LL-37 IgG at baseline. Reduced LL-37 IgG in ACS was associated with increased LL-37 IgG-immune complex. ACS plasma increased activated CD62P+ platelets from healthy donors mediated in part by LL-37 IgG-immune complexes and platelet Fc gamma receptor 2a.

Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures.

BACKGROUND: Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. METHODS: This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. RESULTS: Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p < 0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. CONCLUSIONS: We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.