Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection.
Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection.
复制标题
无标签定量蛋白质组学鉴定出区分肺结核和潜伏感染的新型血浆生物标志物。
DOI：
10.3389/fmicb.2018.01267
复制
发表时间：
2018
影响因子：
5.2
通讯作者：
Zhang Z
中科院分区：
文献类型：
--
作者：
Sun H;Pan L;Jia H;Zhang Z;Gao M;Huang M;Wang J;Sun Q;Wei R;Du B;Xing A;Zhang Z
活动性结核病（TB）和潜伏性感染（LTBI）缺乏有效的鉴别诊断方法仍然是结核病控制的一个障碍。此外，LTBI发展为活动性结核病的分子机制尚未阐明。因此，我们进行了无标记的定量蛋白质组学，以鉴定区分肺结核（PTB）和LTBI的血浆生物标志物。与LTBI个体（n = 15）和健康对照（hc, n = 15）相比，PTB患者（n = 15）共鉴定出31个表达水平有显著差异的重叠蛋白。western blot分析验证了8个差异表达蛋白，与蛋白质组学结果100%一致。在训练集中（n = 240），采用ELISA进一步验证PTB组与LTBI组和HC组6种蛋白的差异有统计学意义。采用分类回归树（CART）分析确定区分PTB与LTBI和HC的理想蛋白组合。建立了由α -1抗凝乳胰蛋白酶（ACT）、α -1酸性糖蛋白1 （AGP1）和E-cadherin （CDH1）组成的诊断模型，鉴别PTB和LTBI的敏感性为81.2%(69/85)，特异性为95.2%(80/84)，鉴别PTB和hc的敏感性为81.2%(69/85)，特异性为90.1%（64/81）。通过在盲检测集（n = 113）中评估诊断模型进行进一步验证，PTB对LTBI的敏感性为75.0%(21/28)，特异性为96.1% (25/26)，PTB对hc的敏感性为75.0%（21/28）和92.3% (24/26)，PTB对肺癌（LC）的敏感性分别为75.0%（21/28）和81.8%（27/33）。本研究获得了不同结核分枝杆菌感染状态的血浆蛋白质组学特征，有助于更好地了解结核分枝杆菌从潜伏感染到结核活化的发病机制，并为区分结核分枝杆菌和结核分枝杆菌感染提供新的潜在的诊断生物标志物。
The lack of effective differential diagnostic methods for active tuberculosis (TB) and latent infection (LTBI) is still an obstacle for TB control. Furthermore, the molecular mechanism behind the progression from LTBI to active TB has been not elucidated. Therefore, we performed label-free quantitative proteomics to identify plasma biomarkers for discriminating pulmonary TB (PTB) from LTBI. A total of 31 overlapping proteins with significant difference in expression level were identified in PTB patients (n = 15), compared with LTBI individuals (n = 15) and healthy controls (HCs, n = 15). Eight differentially expressed proteins were verified using western blot analysis, which was 100% consistent with the proteomics results. Statistically significant differences of six proteins were further validated in the PTB group compared with the LTBI and HC groups in the training set (n = 240), using ELISA. Classification and regression tree (CART) analysis was employed to determine the ideal protein combination for discriminating PTB from LTBI and HC. A diagnostic model consisting of alpha-1-antichymotrypsin (ACT), alpha-1-acid glycoprotein 1 (AGP1), and E-cadherin (CDH1) was established and presented a sensitivity of 81.2% (69/85) and a specificity of 95.2% (80/84) in discriminating PTB from LTBI, and a sensitivity of 81.2% (69/85) and a specificity of 90.1% (64/81) in discriminating PTB from HCs. Additional validation was performed by evaluating the diagnostic model in blind testing set (n = 113), which yielded a sensitivity of 75.0% (21/28) and specificity of 96.1% (25/26) in PTB vs. LTBI, 75.0% (21/28) and 92.3% (24/26) in PTB vs. HCs, and 75.0% (21/28) and 81.8% (27/33) in PTB vs. lung cancer (LC), respectively. This study obtained the plasma proteomic profiles of different M.TB infection statuses, which contribute to a better understanding of the pathogenesis involved in the transition from latent infection to TB activation and provide new potential diagnostic biomarkers for distinguishing PTB and LTBI.
登录
查看更多内容
通过外周血单核细胞基因表达谱鉴定区分活动性结核病和潜伏感染的新型生物标志物
影响因子：
3.7
作者：
Lu C;Wu J;Wang H;Wang S;Diao N;Wang F;Gao Y;Chen J;Shao L;Weng X;Zhang Y;Zhang W
通讯作者：
Zhang W
影响因子：
2.4
作者：
Kim C;Kim DG;Park SH;Hwang YI;Jang SH;Kim CH;Jung KS;Min K;Lee JW;Jang YS
通讯作者：
Jang YS
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
--
作者：
Fan NJ;Gao JL;Liu Y;Song W;Zhang ZY;Gao CF
通讯作者：
Gao CF
影响因子：
3.7
作者：
Jensen AV;Jensen L;Faurholt-Jepsen D;Aabye MG;Praygod G;Kidola J;Faurholt-Jepsen M;Changalucha J;Range N;Krarup H;Friis H;Andersen AB
通讯作者：
Andersen AB