Michael J. Knauer

RATIONALE: The 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, or statins, are important drugs used in the treatment and prevention of cardiovascular disease. Although statins are well tolerated, many patients develop myopathy manifesting as muscle aches and pain. Rhabdomyolysis is a rare but severe toxicity of statins. Interindividual differences in the activities of hepatic membrane drug transporters and metabolic enzymes are known to influence statin plasma pharmacokinetics and risk for myopathy. Interestingly, little is known regarding the molecular determinants of statin distribution into skeletal muscle and its relevance to toxicity. OBJECTIVE: We sought to identify statin transporters in human skeletal muscle and determine their impact on statin toxicity in vitro. METHODS AND RESULTS: We demonstrate that the uptake transporter OATP2B1 (human organic anion transporting polypeptide 2B1) and the efflux transporters, multidrug resistance-associated protein (MRP)1, MRP4, and MRP5 are expressed on the sarcolemmal membrane of human skeletal muscle fibers and that atorvastatin and rosuvastatin are substrates of these transporters when assessed using a heterologous expression system. In an in vitro model of differentiated, primary human skeletal muscle myoblast cells, we demonstrate basal membrane expression and drug efflux activity of MRP1, which contributes to reducing intracellular statin accumulation. Furthermore, we show that expression of human OATP2B1 in human skeletal muscle myoblast cells by adenoviral vectors increases intracellular accumulation and toxicity of statins and such effects were abrogated when cells overexpressed MRP1. CONCLUSIONS: These results identify key membrane transporters as modulators of skeletal muscle statin exposure and toxicity.

Abstract Background Long-COVID is characterized by prolonged, diffuse symptoms months after acute COVID-19. Accurate diagnosis and targeted therapies for Long-COVID are lacking. We investigated vascular transformation biomarkers in Long-COVID patients. Methods A case–control study utilizing Long-COVID patients, one to six months (median 98.5 days) post-infection, with multiplex immunoassay measurement of sixteen blood biomarkers of vascular transformation, including ANG-1, P-SEL, MMP-1, VE-Cad, Syn-1, Endoglin, PECAM-1, VEGF-A, ICAM-1, VLA-4, E-SEL, thrombomodulin, VEGF-R2, VEGF-R3, VCAM-1 and VEGF-D. Results Fourteen vasculature transformation blood biomarkers were significantly elevated in Long-COVID outpatients, versus acutely ill COVID-19 inpatients and healthy controls subjects (P < 0.05). A unique two biomarker profile consisting of ANG-1/P-SEL was developed with machine learning, providing a classification accuracy for Long-COVID status of 96%. Individually, ANG-1 and P-SEL had excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, P < 0.0001; validated in a secondary cohort). Specific to Long-COVID, ANG-1 levels were associated with female sex and a lack of disease interventions at follow-up (P < 0.05). Conclusions Long-COVID patients suffer prolonged, diffuse symptoms and poorer health. Vascular transformation blood biomarkers were significantly elevated in Long-COVID, with angiogenesis markers (ANG-1/P-SEL) providing classification accuracy of 96%. Vascular transformation blood biomarkers hold potential for diagnostics, and modulators of angiogenesis may have therapeutic efficacy.

AIMS: It is thought that clopidogrel bioactivation and antiplatelet response are related to cytochrome P450 2C19 (CYP2C19). However, a recent study challenged this notion by proposing CYP2C19 as wholly irrelevant, while identifying paraoxonase-1 (PON1) and its Q192R polymorphism as the major driver of clopidogrel bioactivation and efficacy. The aim of this study was to systematically elucidate the mechanism and relative contribution of PON1 in comparison to CYP2C19 to clopidogrel bioactivation and antiplatelet response. METHODS AND RESULTS: First, the influence of CYP2C19 and PON1 polymorphisms and plasma paraoxonase activity on clopidogrel active metabolite (H4) levels and antiplatelet response was assessed in a cohort of healthy subjects (n = 21) after administration of a single 75 mg dose of clopidogrel. There was a remarkably good correlation between H4 AUC (0-8 h) and antiplatelet response (r2 = 0.78). Furthermore, CYP2C19 but not PON1 genotype was predictive of H4 levels and antiplatelet response. There was no correlation between plasma paraoxonase activity and H4 levels. Secondly, metabolic profiling of clopidogrel in vitro confirmed the role of CYP2C19 in bioactivating clopidogrel to H4. However, heterologous expression of PON1 in cell-based systems revealed that PON1 cannot generate H4, but mediates the formation of another thiol metabolite, termed Endo. Importantly, Endo plasma levels in humans are nearly 20-fold lower than H4 and was not associated with any antiplatelet response. CONCLUSION: Our results demonstrate that PON1 does not mediate clopidogrel active metabolite formation or antiplatelet action, while CYP2C19 activity and genotype remains a predictor of clopidogrel pharmacokinetics and antiplatelet response.

BACKGROUND The role of humoral immunity in COVID-19 is not fully understood, owing, in large part, to the complexity of antibodies produced in response to the SARS-CoV-2 infection. There is a pressing need for serology tests to assess patient-specific antibody response and predict clinical outcome. METHODS Using SARS-CoV-2 proteome and peptide microarrays, we screened 146 COVID-19 patients’ plasma samples to identify antigens and epitopes. This enabled us to develop a master epitope array and an epitope-specific agglutination assay to gauge antibody responses systematically and with high resolution. RESULTS We identified linear epitopes from the spike (S) and nucleocapsid (N) proteins and showed that the epitopes enabled higher resolution antibody profiling than the S or N protein antigen. Specifically, we found that antibody responses to the S-811–825, S-881–895, and N-156–170 epitopes negatively or positively correlated with clinical severity or patient survival. Moreover, we found that the P681H and S235F mutations associated with the coronavirus variant of concern B.1.1.7 altered the specificity of the corresponding epitopes. CONCLUSION Epitope-resolved antibody testing not only affords a high-resolution alternative to conventional immunoassays to delineate the complex humoral immunity to SARS-CoV-2 and differentiate between neutralizing and non-neutralizing antibodies, but it also may potentially be used to predict clinical outcome. The epitope peptides can be readily modified to detect antibodies against variants of concern in both the peptide array and latex agglutination formats. FUNDING Ontario Research Fund (ORF) COVID-19 Rapid Research Fund, Toronto COVID-19 Action Fund, Western University, Lawson Health Research Institute, London Health Sciences Foundation, and Academic Medical Organization of Southwestern Ontario (AMOSO) Innovation Fund.

BACKGROUND: Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as "Long-COVID". A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID. METHODS: A case-control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase. RESULTS: Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID. CONCLUSIONS: Proteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics.