摘要
Background:Diagnosing latent tuberculosis(TB)infection(LTBI)and active TB(ATB)is crucial for preventing disease progression and transmission.However,current diagnostic tests have limitations in terms of accuracy and sensitivity,making it challenging to diagnose these different infection states.Therefore,this study intends to develop a promising biomarker for LTBI and ATB diagnosis to overcome the limitations of the current diagnostic tests.Methods:We developed a novelmultiepitope-based diagnostic biomarker(MEBDB)fromLTBI region of differentiation antigens using bioinformatics and immunoinformatics.Immune responses induced byMEBDM were detected using enzyme-linked immunosorbent spot and cytometric bead assays.This study was conducted from April 2022 to December 2022 in the SeniorDepartment of Tuberculosis at the 8thMedical Center of PLA General Hospital,China.Blood samples were collected from participants with ATB,individuals with LTBI,and healthy controls(HCs).The diagnostic efficacy of MEBDB was evaluated using receiver operating characteristic curves.Results:A novel MEBDB,designated as CP19128P,was generated.CP19128P comprises 19 helper T lymphocyte epitopes,12 cytotoxic T lymphocyte epitopes,and 8 B-cell epitopes.In silico simulations demonstrated that CP19128P possesses strong affinity for Toll-like receptors and elicits robust innate and adaptive immune responses.CP19128P generated significantly higher levels of tumor necrosis factor(TNF-α),interleukin 4(IL-4),and IL-10 in ATB patients(n=7)and LTBI(n=8)individuals compared with HCs(n=62)(P<0.001).Moreover,CP19128P-induced specific cytokines could be used to discriminate LTBI and ATB from healthy subjects with high sensitivity and specificity.Combining IL-2 with IL-4 or TNF-α could differentiate LTBI from HCs(the area under the receiver operating characteristic curve[AUC],0.976[95% confidence interval[CI],0.934-1.000]or 0.986[0.956-1.000]),whereas combining IL-4 with IL-17A or TNF-α could differentiate ATB from HCs(AUC,0.887[0.782-0.993]or 0.984[0.958-1.00
基金
supported by the BeijingMunicipal Science&Technology Commission(7212103)
the Eighth Medical Center of PLA General Hospital(MS202211002).
