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引用次数: 0
摘要
动机:由于技术上的巨大差异和患者间的异质性导致统计能力有限,基于质谱的蛋白质组学的临床转化一直面临挑战。自下而上的蛋白质组学通过消化肽对蛋白质进行间接测量。结果我们提出了一种名为肽集测试的新方法,它能检测源自同一蛋白质的肽表达的协调变化,并将其与肽组的其他部分进行比较。与基于聚集的方法相比,将我们的方法应用于已发表的尖峰实验数据和模拟实验,结果表明在不影响精度的前提下提高了灵敏度。此外,应用肽集检验比较对他莫昔芬敏感和对他莫昔芬耐药的乳腺癌患者的肿瘤蛋白质组发现,胶原蛋白 XII 的肽水平发生了显著变化,这表明胶原蛋白 XII 介导的基质重组与他莫昔芬耐药之间存在关联。我们的研究证明肽集测试是一种强大的以肽为中心的策略,可用于推断蛋白质组学研究中的差异表达。AVAILABILITY肽集测试(PepSetTest)可在 https://github.com/JmWangBio/PepSetTest.SUPPLEMENTARY 上公开获取信息补充数据可在 Bioinformatics online 上获取。
Peptide Set Test: a Peptide-Centric Strategy to Infer Differentially Expressed Proteins.
MOTIVATION
The clinical translation of mass spectrometry-based proteomics has been challenging due to limited statistical power caused by large technical variability and inter-patient heterogeneity. Bottom-up proteomics provides an indirect measurement of proteins through digested peptides. This raises the question whether peptide measurements can be used directly to better distinguish differentially expressed proteins.
RESULTS
We present a novel method called the peptide set test, which detects coordinated changes in the expression of peptides originating from the same protein and compares them to the rest of the peptidome. Applying our method to data from a published spike-in experiment and simulations demonstrates improved sensitivity without compromising precision, compared to aggregation-based approaches. Additionally, applying the peptide set test to compare the tumor proteomes of tamoxifen-sensitive and tamoxifen-resistant breast cancer patients reveals significant alterations in peptide levels of collagen XII, suggesting an association between collagen XII-mediated matrix reassembly and tamoxifen resistance. Our study establishes the peptide set test as a powerful peptide-centric strategy to infer differential expression in proteomics studies.
AVAILABILITY
Peptide Set Test (PepSetTest) is publicly available at https://github.com/JmWangBio/PepSetTest.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
期刊介绍:
The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.