Department of Biological Sciences, North Carolina State University, Raleigh, NC, 27695, USA.
Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA.
Sci Rep. 2018 Nov 5;8(1):16334. doi: 10.1038/s41598-018-34642-x.
We use shotgun proteomics to identify biomarkers of diagnostic and prognostic value in individuals diagnosed with amyotrophic lateral sclerosis. Matched cerebrospinal and plasma fluids were subjected to abundant protein depletion and analyzed by nano-flow liquid chromatography high resolution tandem mass spectrometry. Label free quantitation was used to identify differential proteins between individuals with ALS (n = 33) and healthy controls (n = 30) in both fluids. In CSF, 118 (p-value < 0.05) and 27 proteins (q-value < 0.05) were identified as significantly altered between ALS and controls. In plasma, 20 (p-value < 0.05) and 0 (q-value < 0.05) proteins were identified as significantly altered between ALS and controls. Proteins involved in complement activation, acute phase response and retinoid signaling pathways were significantly enriched in the CSF from ALS patients. Subsequently various machine learning methods were evaluated for disease classification using a repeated Monte Carlo cross-validation approach. A linear discriminant analysis model achieved a median area under the receiver operating characteristic curve of 0.94 with an interquartile range of 0.88-1.0. Three proteins composed a prognostic model (p = 5e-4) that explained 49% of the variation in the ALS-FRS scores. Finally we investigated the specificity of two promising proteins from our discovery data set, chitinase-3 like 1 protein and alpha-1-antichymotrypsin, using targeted proteomics in a separate set of CSF samples derived from individuals diagnosed with ALS (n = 11) and other neurological diseases (n = 15). These results demonstrate the potential of a panel of targeted proteins for objective measurements of clinical value in ALS.
我们使用鸟枪法蛋白质组学来鉴定诊断和预后价值的生物标志物，这些标志物存在于被诊断为肌萎缩侧索硬化症（ALS）的个体中。将配对的脑脊液和血浆样品进行大量蛋白质耗尽处理，并通过纳流液相色谱-高分辨串联质谱法进行分析。无标记定量法用于鉴定两种液体中 ALS 患者（n=33）和健康对照者（n=30）之间的差异蛋白。在 CSF 中，有 118 个蛋白（p 值<0.05）和 27 个蛋白（q 值<0.05）被鉴定为 ALS 患者和对照组之间差异明显。在血浆中，有 20 个蛋白（p 值<0.05）和 0 个蛋白（q 值<0.05）被鉴定为 ALS 患者和对照组之间差异明显。补体激活、急性期反应和视黄醇信号通路相关的蛋白在 ALS 患者的 CSF 中显著富集。随后，使用重复蒙特卡罗交叉验证方法评估了各种机器学习方法用于疾病分类。线性判别分析模型的中位数接收器工作特征曲线下面积为 0.94，四分位距为 0.88-1.0。一个由三个蛋白组成的预后模型（p=5e-4）解释了 ALS-FRS 评分中 49%的变化。最后，我们使用靶向蛋白质组学在一组来自被诊断为 ALS 的个体（n=11）和其他神经疾病的 CSF 样本（n=15）中，对我们发现数据集中的两个有前途的蛋白，几丁质酶 3 样蛋白 1 和α-1-抗胰蛋白酶，进行了特异性研究。这些结果证明了一组靶向蛋白在 ALS 中具有客观测量临床价值的潜力。
