ABSTRACT
Objectives To develop a multi-stage and multi-epitope vaccine, which consists of epitopes from the early secretory and latency-associated antigens of Mycobacterium tuberculosis (MTB). Methods The B-cell, cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes of 12 proteins were predicted using an immunoinformatics. The epitopes with antigenicity, without cytotoxicity and sensitization, were further screened to construct the multi-epitope vaccine. Furthermore, the proposed vaccine underwent physicochemical properties analysis and secondary structure prediction as well as 3D structure modeling, refinement and validation. Then the refined model was docked with TLR4. Finally, an immune simulation of the vaccine was carried out. Results The proposed vaccine, which consists of 12 B-cell, 11 CTL and 12 HTL epitopes, had a flexible and stable globular conformation as well as a thermostable and hydrophilic structure. A stable interaction of the vaccine with TLR4 was confirmed by molecular docking. The efficiency of the candidate vaccine to trigger effective cellular and humoral immune responses was assessed by immune simulation. Conclusion A multi-stage multi-epitope MTB vaccine construction strategy based on immunoinformatics is proposed, which is expected to prevent both active and latent MTB infection.
Subject(s)
Mycobacterium tuberculosis/metabolism , Molecular Docking Simulation , Toll-Like Receptor 4 , Epitopes, T-Lymphocyte/chemistry , Epitopes, B-Lymphocyte/chemistry , Vaccines, Subunit/chemistry , Computational Biology/methodsABSTRACT
Objective:To investigate the predictive value of serum C-type lectin-like receptor 2 (CLEC-2) combined with insulin resistance in the outcome of patients with acute ischemic stroke (AIS) after intravenous thrombolysis.Methods:Patients with AIS received alteplase intravenous thrombolytic therapy in the Department of Neurology, the Second Affiliated Hospital of Nantong University from October 2019 to March 2021 were enrolled retrospectively. According to the modified Rankin Scale score at 90 d after onset, they were divided into good outcome group (0-2) and poor outcome group (>2). Homeostasis model assessment of insulin resistance (HOMA-IR) was used to evaluate insulin resistance. Person correlation analysis was used to determine the correlation between CLEC-2 and HOMA-IR. Multivariate logistic regression analysis was used to determine the correlation between serum CELC-2, HOMA-IR and the outcome after intravenous thrombolysis. Receiver operating characteristic (ROC) curve was used to determine the predictive value of serum CLEC-2 combined with HOMA-IR for poor outcome after intravenous thrombolysis. Results:A total of 100 patients were enrolled (56 males, 56.0%; aged 70.6±10.86 years, range 49-83 years). The baseline National Institutes of Health Stroke Scale (NIHSS) score was 10.00±6.36. Senenty-four patients (74.0%) had a good outcome and 26 (26.0%) had a poor outcome. Person correlation analysis showed that there was a significant positive correlation between serum CLEC-2 and HOMA-IR ( r=0.523; P<0.001). Multivariate logistic regression analysis showed that after adjusting for confounding factors (C-reactive protein, baseline NIHSS score, onset-to-needle time), the highest quartile of serum CLEC-2 (compared with the lowest quartile: odds ratio [ OR] 4.836, 95% confidence interval [ CI] 1.105-21.169; P=0.036) and the highest quartile of HOMA-IR (compared with the quartile 1-3: OR 15, 95% CI 2.647-30.722; P=0.002) were the independent risk factors for the poor outcome in patients with AIS after intravenous thrombolysis. ROC curve analysis showed that the area under the curve for serum CLEC-2 combined with HOMA-IR to predict poor outcome was 0.785 (95% CI 0.688-0.883; P<0.001), the optimal cut-off value was 0.72, and the sensitivity and specificity were 76.0% and 95.0%, respectively. Conclusion:CLEC-2 combined with insulin resistance has a certain predictive value for the poor outcome of patients with AIS after intravenous thrombolysis.