Applications of SWATH-MS technology in proteomic and metabolomic studies of rice, soybean, rat and tea

博士 === 國立中興大學 === 分子生物學研究所 === 107 === In recent years, omic studies have become a popular trend, among them, proteomics and metabolomics are related to biological traits, so many studies focus on these areas. A novel quantitative technology-Sequential Window Acquisition of All THeoretical fragment...

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Bibliographic Details
Main Authors: Han-Ju Chien, 簡涵如
Other Authors: 賴建成
Format: Others
Language:zh-TW
Published: 2019
Online Access:http://ndltd.ncl.edu.tw/handle/ys4a5h
Description
Summary:博士 === 國立中興大學 === 分子生物學研究所 === 107 === In recent years, omic studies have become a popular trend, among them, proteomics and metabolomics are related to biological traits, so many studies focus on these areas. A novel quantitative technology-Sequential Window Acquisition of All THeoretical fragment ion spectra (SWATH) is used as analytical tool in this study. SWATH is a type of data-independant acquisition mode, it can extract most of all analyte signals from a limited sample and obtain massive quantitative information. SWATH technology is applied on a series of biological samples in this study, including rice, soybeans, rats and tea. First, the team of Professor Wang Chang-Sheng from National Chung Hsing University cultivated the yellow endosperm mutant AZ1180 by chemical mutagenesis from IR64, but the mechanism of causing yellow matter and forming the phenotype were not clear. Therefore, this study use SWATH technology to analyze the proteome and targeted metabolome of the yellow endosperm of rice mutant AZ1180, and found that the yellow substance is most likely succinate dehydrogenase flavoprotein subunit 1, 6,7-dimethyl-8-ribityllumazine and riboflavin co-contribution; in addition to, the overexpression of 13kDa prolamin may be the main reason of tremendous different traits between AZ1180 and IR64. Second, isoflavones are the main bioactive compounds in soybeans and have many benefits for humans. In order to help breeders quickly screen out high levels of isoflavones in soybeans, an analytical method for rapidly determining soy isoflavones is needed urgently. However, the time of current analytical methods are not short and only a few method use mass spectrometers with high specificity. Therefore, this study developes SWATH technology to rapidly measure isoflavones in soybeans to accelerate the implementation of soybean breeding. This platform has good linear regression (r2 > 0.99), precision and accuracy (CV% < 15%), and it can do relative quantification of other compounds in soybeans by backtracking analysis. Third, some Taiwanese unscrupulous people added modified starch containing maleic acid to foods, which led to the outbreak of toxic starch events in 2013. According to some experimental animal model studies, maleic acid caused damage to the renal tubules, and in 2016, Xue Yu-Ting''s master thesis used SWATH technology to investigate the effect of maleic acid on the proteome of kidney in rats, and found 12 related pathways. It was speculated that these proteins reduced the efficiency of energy biosynthesis, blocked renal tubular reabsorption and hydrogen ion regulation, and finally caused renal tubular acidification. However, the verification of protein content has not been completed, so this study verified the relative content of protein with sMRMHR technology, and finally successfully verified it. The last, in recent years, the unscrupulous vendor mixed or forged tea and sold it at high prices to make huge profits, which affected the tea industry in Taiwan tremendously. Even though the Ministry of Economic Affairs’ Intellectual Property Office has a certificate of origin to protect the quality and origin of tea, the way of commenting is subjective, if there is objective scientific data to prove, it can further protect consumers'' rights and food safety. Therefore, in 2013, Chien Han-Ju’s master thesis has proposed the use of proteomic mass spectrometry technology with bioinformatics to establish a platform of tea origins with an accuracy of 89.4%. However, in order to improve the accuracy, this article begin to improve the bioinformatics, and successfully find 20 feature proteins that can distinguish the origin of Oolong tea, and the plateform has an accuracy of 95.5%.