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出 处:《分析测试学报》2017年第5期633-639,共7页Journal of Instrumental Analysis
基 金:公益性行业(农业)科研专项经费项目(201303025)
摘 要:对农作物重要病害灰霉病的致病菌——灰葡萄孢菌进行了代谢组提取溶剂、衍生化时间和温度等前处理条件以及内标物筛选优化。结果表明,以水杨苷作为内标物,试验条件下以甲醇-水(80∶20)为提取溶剂,按照4.0 m L/0.1 g菌丝的剂量进行提取,采用N,O-双(三甲基硅烷基)乙酰胺和甲氧基胺盐酸盐在37℃和6 h条件下进行衍生化,可实现多种代谢物衍生产物收率和稳定性的最优化。灰葡萄孢菌丝代谢组的检测获得了210个峰,其中50种代谢物与NIST 2008的匹配度达到80%以上,主要为氨基酸类、醇类、有机酸类、糖类等代谢物,并通过标准品对部分代谢物进行了定性。典型的17种代谢物在1.0~100μg/m L范围内线性关系良好,相关系数均大于0.98,检出限为0.02~10.0 ng/m L,定量下限为0.1~34.0 ng/m L。方法精密度分析结果显示89.05%代谢物的相对标准偏差为0.01%~16.8%。所建立的方法适合于丝状真菌的代谢组检测,可为农业和医学领域开展微生物代谢组研究提供参考。The pretreatment conditions, including extraction solvent, derivatization time and temperature, as well as internal standard were optimized for metabolome analysis of Botrytis cinerea (B. cinerea) , an important pathogenic fungus that could induce a severe grey mold on plants. The results showed that, using saliein as the internal standard, a mixture of methanol and water ( 80 : 20) with a dosage of 4.0 mL for 0. 1 g of myeelium as extraction reagent, a silylation of N, O- his(trimethylsilyl) trifluoroacetamide with methoxylamine for 6 h of derivation at 37 ~C was the optimal for most kinds of metabolites in derivatives yield and stability. In B. cinerea hypha metabolome, 210 peaks were detected by GC - MS, and the metabolome was identified as a group of metabolites including mainly amino acids, alcohols, organic acids, sugars and others. Among the metabolites, 50 of them were identified by NIST 2008 with a match quality of 80% or more, and part of them by standards. As a result, typical 17 metabolites obtained good linearities in the range of 1.0 - 100 μg/mL with correlation coefficients more than 0. 98. The limits of detection and limits of quantitation were in the range of 0. 02 - 10.0 ng/mL and 0. 1 - 34.0 ng/mL, respectively. Investigation on the repro- dueibility of the optimal method suggested that the RSD for 89.05% metabolites detected were in the range of 0.01% - 16. 8% . The good reproducibility implied that the method established was suitable for the metabolome detection of myeelial fungi, and could provide a good reference for microorganism metabolomics research in the field of agricultural and medical science.
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