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一种结合生物医学知识的蛋白质组非标记定量分析方法及其应用
A Mass Spectrometry-based Label-free Quantitative Approach Coupled With Complex Proteome Functional Analysis
投稿时间:2014-07-04  最后修改时间:2014-10-21
中文关键词:  蛋白质组学,非标记定量分析,生物质谱,生物医学知识
英文关键词:proteomics, label-free quantitative approach, mass spectrometry, biomedical knowledge
基金项目:国家自然科学基金(31100592),国家高技术研究发展计划(863)(2012AA02A601, 2012AA02A602, 2012AA020201),国家科技重大专项(2013ZX03005012)
作者单位E-mail
潘超 浙江大学生物医学工程与仪器科学学院生物医学工程教育部重点实验室杭州 310027 panchao0517@gmail.com 
苏运聪 浙江大学生物医学工程与仪器科学学院生物医学工程教育部重点实验室杭州 310027  
杨睿 浙江大学生物医学工程与仪器科学学院生物医学工程教育部重点实验室杭州 310027  
段会龙 浙江大学生物医学工程与仪器科学学院生物医学工程教育部重点实验室杭州 310027  
邓宁 浙江大学生物医学工程与仪器科学学院生物医学工程教育部重点实验室杭州 310027 zju.dengning@gmail.com 
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中文摘要:
      基于质谱的非标记定量方法能够对复杂蛋白质组进行规模化分析,同时,在定量分析的基础上理解和解释蛋白质组的功能和相互作用关系更有意义.这需要建立一种有效的兼容定量和定性分析结果的方法.针对这一需求,本文首先借鉴了NSAF(normalized spectral abundance factor)算法采用肽段计数对蛋白质组数据进行定量,进一步结合共享肽对该方法进行优化.以此为基础,通过g:Profiler获取海量蛋白质组的功能注释信息,在定量分析的过程中,同步实现了对蛋白质组数据的功能性分析.本文选择来自人心脏、小鼠心脏、小鼠肝脏的三组线粒体蛋白质组数据对该方法进行验证,按照功能性分析将三组数据划分为若干功能组或信号通路,并进行相关性、功能聚类以及电子传递链分析.结果表明,结合共享肽的优化算法克服了对低丰度蛋白质的错误估计,提高了非标记定量的准确性.同时,结合生物医学知识的分析方法解释了蛋白质组的功能和相互作用关系,为差异比较蛋白质组学、疾病蛋白质组学以及功能蛋白质组学等组学研究提供了新的方法.
英文摘要:
      Label-free quantitative approach based mass spectrometry was used for analysis of complex proteomes, meanwhile, a method based on quantitative analysis which is used for explaining functions and interactions in a large-scale manner is of great importance. To systematically overcome this challenge, we should build a method combing with quantitation and qualification. We used Normalized Spectral Abundance Factor (NSAF) based peptide count as starting point for our analysis and proposed a new method with shared peptides to accurately evaluate abundance of Isoforms for complex proteomes. In addition, large-scale functional annotations of complex proteomes were extracted by g:Profiler and analyzed in the process of quantitation. In this paper, three groups of mitochondrial proteins including mouse heart mitochondrial proteins, mouse liver mitochondrial proteins and human heart mitochondrial proteins were selected for analysis. All MS/MS spectra t were searched against the IPI mouse database and IPI human database using the pFind software kit. Detailed search parameters were performed using as follows: partial tryptic digest allowing two missed cleavages; fixed modification of cysteine with carbamidomethylation (57.021 Da) and variable modification of methionine with oxidation (15.995 Da), the precursor and fragment mass tolerances were set up at 1.5 and 0.5 Da, respectively. Peptides matching the following criteria were used for protein identification: DeltaCN≥0.1; FDR≤1.0%; peptide mass was 600.0~6000.0; peptide length was 6~60. According to the biochemical properties of mitochondrial proteins, all functional annotations were assigned to various signaling pathway or functional clusters, such as apoptosis, DNA/RNA/protein synthesis, metabolism, oxidative phosphorylation, protein binding/folding, proteolysis, redox, signal transduction, structure, transport, cell adhesion and cell cycle, and analyzed by correlation analysis, functional clustering and electron transport chain analysis. We found that proteins which rank have enormous variation between NSAF and the new method even came from a same family, such as proteins belonging to acyl-CoA dehydrogenase family. Proteins in the family play an important role in life event due to their biochemical properties of fatty acid metabolism and lipid metabolism searched using the online database analysis tool available through UniProt (www.uniprot.org). From the global perspective of the three groups of mitochondrial proteins, the correlation of mouse heart mitochondrial proteins and mouse liver mitochondrial proteins shows highest, while the correlation of human heart mitochondrial proteins and mouse liver mitochondrial proteins shows lowest, it denotes that the correlation of simple species and different organs shows highest. On the aspect of functional clustering, metabolic proteins have highest abundance in mouse liver mitochondrial dataset, while oxidative phosphorylation proteins show highest abundance in cardiac mitochondrial dataset. This explains that liver plays an important role in metabolic process including nutrients synthesis, transformation and decomposition, however, heart promotes blood flowing to provide adequate blood to the organs or tissues, supply oxygen or various nutrients and take metabolic products away. We concluded that the strategy with shared peptides overcame inaccurate and overestimated results to improve accuracy, and label-free quantitative approach coupled with complex proteome functional analysis can thoroughly explore protein functions or relationship and provide a new method for large-scale comparative or diseased proteomics.
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