Letarte Simon, Brusniak Mi-Youn, Campbell David, Eddes James, Kemp Christopher J, Lau Hollis, Mueller Lukas, Schmidt Alexander, Shannon Paul, Kelly-Spratt Karen S, Vitek Olga, Zhang Hui, Aebersold Ruedi, Watts Julian D
Institute for Systems Biology, 1441 North 34th Street, Seattle, WA, 98103.
Clin Proteomics. 2008 Dec 1;4(3-4):105. doi: 10.1007/s12014-008-9018-8.
A proof-of-concept demonstration of the use of label-free quantitative glycoproteomics for biomarker discovery workflow is presented here, using a mouse model for skin cancer as an example. Blood plasma was collected from 10 control mice, and 10 mice having a mutation in the p19(ARF) gene, conferring them high propensity to develop skin cancer after carcinogen exposure. We enriched for N-glycosylated plasma proteins, ultimately generating deglycosylated forms of the modified tryptic peptides for liquid chromatography mass spectrometry (LC-MS) analyses. LC-MS runs for each sample were then performed with a view to identifying proteins that were differentially abundant between the two mouse populations. We then used a recently developed computational framework, Corra, to perform peak picking and alignment, and to compute the statistical significance of any observed changes in individual peptide abundances. Once determined, the most discriminating peptide features were then fragmented and identified by tandem mass spectrometry with the use of inclusion lists. We next assessed the identified proteins to see if there were sets of proteins indicative of specific biological processes that correlate with the presence of disease, and specifically cancer, according to their functional annotations. As expected for such sick animals, many of the proteins identified were related to host immune response. However, a significant number of proteins also directly associated with processes linked to cancer development, including proteins related to the cell cycle, localisation, trasport, and cell death. Additional analysis of the same samples in profiling mode, and in triplicate, confirmed that replicate MS analysis of the same plasma sample generated less variation than that observed between plasma samples from different individuals, demonstrating that the reproducibility of the LC-MS platform was sufficient for this application. These results thus show that an LC-MS-based workflow can be a useful tool for the generation of candidate proteins of interest as part of a disease biomarker discovery effort.
本文展示了一个概念验证演示，即使用无标记定量糖蛋白质组学进行生物标志物发现工作流程，以皮肤癌小鼠模型为例。从10只对照小鼠和10只p19(ARF)基因发生突变的小鼠中采集血浆，这些突变小鼠在接触致癌物后具有很高的患皮肤癌倾向。我们富集了N-糖基化血浆蛋白，最终生成修饰胰蛋白酶肽的去糖基化形式用于液相色谱质谱（LC-MS）分析。然后对每个样品进行LC-MS运行，以鉴定两个小鼠群体之间差异丰富的蛋白质。然后我们使用最近开发的计算框架Corra进行峰挑选和比对，并计算单个肽丰度中任何观察到的变化的统计显著性。一旦确定，最具区分性的肽特征随后通过使用包含列表的串联质谱进行碎片化和鉴定。接下来，我们根据功能注释评估鉴定出的蛋白质，看是否有指示与疾病（特别是癌症）存在相关的特定生物学过程的蛋白质组。正如对这类患病动物所预期的那样，鉴定出的许多蛋白质与宿主免疫反应有关。然而，也有大量蛋白质直接与癌症发展相关的过程有关，包括与细胞周期、定位、运输和细胞死亡相关的蛋白质。对相同样品进行三次平行的分析模式进一步分析，证实对相同血浆样品进行重复MS分析产生的变异比不同个体血浆样品之间观察到的变异小，表明LC-MS平台的重现性足以用于此应用。因此，这些结果表明，基于LC-MS的工作流程可以作为疾病生物标志物发现工作中生成感兴趣候选蛋白质的有用工具。
