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使用 T7 噬菌体展示文库检测囊性纤维化的血清生物标志物。

Detection of Cystic Fibrosis Serological Biomarkers Using a T7 Phage Display Library.

机构信息

Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Wayne State University School of Medicine and Detroit Medical Center, Detroit, MI, 48201, USA.

Department of Computer Science, Wayne State University, 540 E, Canfield, Detroit, MI, 48201, USA.

出版信息

Sci Rep. 2017 Dec 18;7(1):17745. doi: 10.1038/s41598-017-18041-2.

Abstract

Cystic fibrosis (CF) is an autosomal recessive disorder affecting the cystic fibrosis transmembrane conductance regulator (CFTR). CF is characterized by repeated lung infections leading to respiratory failure. Using a high-throughput method, we developed a T7 phage display cDNA library derived from mRNA isolated from bronchoalveolar lavage (BAL) cells and leukocytes of sarcoidosis patients. This library was biopanned to obtain 1070 potential antigens. A microarray platform was constructed and immunoscreened with sera from healthy (n = 49), lung cancer (LC) (n = 31) and CF (n = 31) subjects. We built 1,000 naïve Bayes models on the training sets. We selected the top 20 frequently significant clones ranked with student t-test discriminating CF antigens from healthy controls and LC at a False Discovery Rate (FDR) < 0.01. The performances of the models were validated on an independent validation set. The mean of the area under the receiver operating characteristic (ROC) curve for the classifiers was 0.973 with a sensitivity of 0.999 and specificity of 0.959. Finally, we identified CF specific clones that correlate highly with sweat chloride test, BMI, and FEV1% predicted values. For the first time, we show that CF specific serological biomarkers can be identified through immunocreenings of a T7 phage display library with high accuracy, which may have utility in development of molecular therapy.

摘要

囊性纤维化 (CF) 是一种常染色体隐性遗传病,影响囊性纤维化跨膜电导调节因子 (CFTR)。CF 的特征是反复肺部感染导致呼吸衰竭。我们使用高通量方法,从支气管肺泡灌洗液 (BAL) 细胞和结节病患者的白细胞中分离的 mRNA 中开发了 T7 噬菌体展示 cDNA 文库。该文库经过生物淘选,获得了 1070 种潜在抗原。构建了一个微阵列平台,并与来自健康个体 (n=49)、肺癌 (LC) (n=31) 和 CF (n=31) 的血清进行免疫筛选。我们在训练集上构建了 1000 个朴素贝叶斯模型。我们使用学生 t 检验选择前 20 个经常显著的克隆,以区分 CF 抗原与健康对照和 LC,假发现率 (FDR) < 0.01。模型的性能在独立验证集上进行了验证。分类器的受试者工作特征 (ROC) 曲线下面积的平均值为 0.973,灵敏度为 0.999,特异性为 0.959。最后,我们确定了与汗液氯化物试验、BMI 和 FEV1%预测值高度相关的 CF 特异性克隆。我们首次表明,通过 T7 噬菌体展示文库的免疫筛选,可以以高精度识别 CF 特异性血清生物标志物,这可能对分子治疗的发展具有实用价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73d/5735098/b7a4139fb99e/41598_2017_18041_Fig1_HTML.jpg

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