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16种谷物的蛋白质组分析以及靶向蛋白质组学在检测小麦污染中的应用。

Proteomic profiling of 16 cereal grains and the application of targeted proteomics to detect wheat contamination.

作者信息

Colgrave Michelle L, Goswami Hareshwar, Byrne Keren, Blundell Malcolm, Howitt Crispin A, Tanner Gregory J

机构信息

†CSIRO Agriculture Flagship, 306 Carmody Road, St. Lucia, Queensland 4067, Australia.

‡CSIRO Agriculture Flagship, GPO Box 1600, Canberra, Australian Capital Territory 2601, Australia.

出版信息

J Proteome Res. 2015 Jun 5;14(6):2659-68. doi: 10.1021/acs.jproteome.5b00187. Epub 2015 Apr 27.

Abstract

Global proteomic analysis utilizing SDS-PAGE, Western blotting and LC-MS/MS of total protein and gluten-enriched extracts derived from 16 economically important cereals was undertaken, providing a foundation for the development of MS-based quantitative methodologies that would enable the detection of wheat contamination in foods. The number of proteins identified in each grain correlated with the number of entries in publicly available databases, highlighting the importance of continued advances in genome sequencing to facilitate accurate protein identification. Subsequently, candidate wheat-specific peptide markers were evaluated by multiple-reaction monitoring MS. The selected markers were unique to wheat, yet present in a wide range of wheat varieties that represent up to 80% of the bread wheat genome. The final analytical method was rapid (15 min) and robust (CV < 10%), showed linearity (R(2) > 0.98) spanning over 3 orders of magnitude, and was highly selective and sensitive with detection down to 15 mg/kg in intentionally contaminated soy flour. Furthermore, application of this technology revealed wheat contamination in commercially sourced flours, including rye, millet, oats, sorghum, buckwheat and three varieties of soy.

摘要

利用SDS-PAGE、蛋白质免疫印迹法以及液相色谱-串联质谱法,对16种具有重要经济价值的谷物中的总蛋白和富含面筋的提取物进行了全蛋白质组分析,为基于质谱的定量方法的开发奠定了基础,该方法能够检测食品中的小麦污染。每种谷物中鉴定出的蛋白质数量与公开数据库中的条目数量相关,这突出了基因组测序持续进展对于促进准确蛋白质鉴定的重要性。随后,通过多反应监测质谱法对候选小麦特异性肽标记物进行了评估。所选标记物是小麦特有的,但存在于代表高达80%面包小麦基因组的多种小麦品种中。最终的分析方法快速(15分钟)且稳健(变异系数<10%),在超过3个数量级的范围内呈线性(R²>0.98),并且具有高度的选择性和灵敏度,在故意污染的大豆粉中检测限低至15毫克/千克。此外,这项技术的应用揭示了商业来源面粉中的小麦污染情况,这些面粉包括黑麦、小米、燕麦、高粱、荞麦以及三种大豆品种。

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