利用 LC-HCD-PRM-MS 大规模筛查和定量分析早期阿尔茨海默氏症患者血清中的特定位点 N-糖肽

Journal of proteomics & bioinformatics Pub Date : 2022-01-01 Epub Date: 2022-06-27
Lingyun Pan, Yu Lin, Jianhui Zhu, Jie Zhang, Zhijing Tan, David M Lubman
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摘要

质谱法糖肽分析为发现生物标记物提供了重要机会,有助于早期检测阿尔茨海默病(AD)。在这项研究中,我们利用纳米液相色谱分离-HCD-DDA-MS/MS平台和纳米液相色谱分离-HCD-PRM-MS平台大规模筛选和定量分析了新型N-糖肽生物标记物,用于早期检测患者血清中的AD。经过胰蛋白酶消化、糖肽富集、分馏、NanoLC-Stepped-HCD-DDA-MS/MS 或 NanoLC-Stepped-HCD-PRM-MS 分析,分别从轻度认知障碍(MCI,AD 的前驱期)患者和正常对照组的 10 μL 血清中提取了 N-糖肽。通过结合使用 Byonic、Byologic 和 Skyline 软件,我们完成了 MCI 和正常对照组之间特定位点 N-糖肽的鉴定和无标记定量。Byologic的差异定量分析显示,与正常对照组相比,MCI患者中来自16种糖蛋白的29种N-糖肽发生了显著变化。此外,对所选 N-糖肽候选物进行的 HCD-PRM-MS 定量分析证实,来自 CERU 的 EHEGAIYPDN138TTDFQR_HexNAc(4)Hex(5)-Fuc(2)NeuAc(1) 和来自 AHSG 的 VCQDCPLLAPLN156DTR_HexNAc(4)Hex(5)NeuAc(2) 可显著区分 MCI 和正常对照组。这两种糖肽的接收者操作特征曲线下面积(AUC)分别为 0.850(95% CI,0.66-1.0)和 0.867(95% CI,0.68-1.0)(p
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Large Scale Screening and Quantitative Analysis of Site-Specific N-Glycopeptides from Human Serum in Early Alzheimer's Disease Using LC-HCD-PRM-MS.

Glycopeptide analysis by mass spectrometry may provide an important opportunity in discovery of biomarkers to aid in early detection of Alzheimer's Disease (AD). In this work, we have used a NanoLC-Stepped-HCD-DDA-MS/MS platform and a NanoLC-Stepped-HCD-PRM-MS platform for large-scale screening and quantification of novel N-glycopeptide biomarkers for early detection of AD in patient serum. N-glycopeptides were retrieved from 10 μL of serum in patients with mild cognitive impairment (MCI, a prodromal phase of AD) and normal controls, respectively, after trypsin digestion, glycopeptide enrichment, fractionation, and NanoLC-Stepped-HCD-DDA-MS/MS or NanoLC-Stepped-HCD-PRM-MS analysis. Using a combination of Byonic, Byologic and Skyline softwares, we were able to accomplish both identification and label-free quantitation of site-specific N-glycopeptides between MCI and normal controls. Differential quantitation analysis by Byologic showed that 29 N-glycopeptides derived from 16 glycoproteins were significantly changed in MCI compared to normal controls. Further, HCD-PRM-MS quantitative analysis of the selected N-glycopeptide candidates confirmed that EHEGAIYPDN138TTDFQR_HexNAc(4)Hex(5)-Fuc(2)NeuAc(1) from CERU, and VCQDCPLLAPLN156DTR_HexNAc(4)Hex(5)NeuAc(2) from AHSG can significantly discriminate MCI from normal controls. These two glycopeptides had the area under the receiver operating characteristic curve (AUC) of 0.850 (95% CI, 0.66-1.0) and 0.867 (95% CI, 0.68-1.0), respectively (p<0.05). The result demonstrates that changes in the expression level of the N-glycopeptides provide potential serum biomarkers for detection of AD at a very early stage.

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