代謝體學是研究所有包含在生物體內的代謝物分子的所有組成內容，在過去十年當中，代謝體學的研究正蓬勃發展，而液相層析串聯質譜儀則是一重要的分析工具去找尋在生物體樣本中的可能代謝物訊號，由於經過液相層析串聯質譜儀分析會產生大量的數據，因此如何從中篩選出可能的代謝物訊號將是一大要點。故此，本篇研究的目標是比較及找尋最有效率的鑑定代謝物策略。此外，鄰苯二甲酸二異壬酯 (DINP) 在工業上為一個廣泛被使用的塑化劑之一，且鄰苯二甲酸二異壬酯可能會造成人類健康上的危害。因此本研究即利用鄰苯二甲酸二異壬酯為例進行體外代謝以鑑定出可能的代謝物訊號，接著以餵食鄰苯二甲酸二異壬酯的大鼠尿液進行代謝物訊號的驗證。所使用的三項代謝物鑑定的策略分別為: 同位素追蹤法 (Signal Mining Algorithm with Isotope Tracing, SMAIT) 、質量虧損過濾法 (Mass Defect Filter, MDF) 以及XCMS。在體外代謝部分，SMAIT成功篩選出16個鄰苯二甲酸二異壬酯 代謝物訊號、MDF篩選出11個以及XCMS篩選出134個可能代謝物訊號，由於XCMS所篩選出的代謝物訊號數量過多，在驗證代謝物訊號的步驟是困難的，因此，在體外代謝階段，XCMS不是一個有效率的篩選代謝物訊號的策略。在體內代謝驗證的部分，SMAIT所篩選出的代謝物有14個代謝物訊號被驗證，MDF則有9個代謝物訊號被驗證，從此結果可得，SMAIT相較於MDF可以篩選出更多鄰苯二甲酸二異壬酯代謝物訊號，且SMAIT是三種策略中能最有效率的鑑定出代謝物訊號的策略。此外，高解析質譜儀的數據顯示我們找尋到和文獻所發表不一樣的代謝物，針對此代謝物，我們進行了結構驗證，由結果顯示，我們找尋到一個新的氫氧基的鄰苯二甲酸二異壬酯代謝物。

Metabolomics includes the study of small molecules in a biological sample. Over the last decade, the study of metabolomics grows rapidly and liquid chromatography coupled with mass spectrometry (LC–MS) profiling is an important approach for the identification of metabolites from complex biological samples. Due to the large amount of data produced in LC–MS profiling experiment, there is a great need to filter useful metabolite signals in biological samples. Hence, the aim of this study is to evaluate the efficiency of metabolite identification strategy. On the other hand, di-isononyl phthalate (DINP) is a widely used plasticizer in industry and may have potentially adverse health effects on humans. Here, we used DINP as in vitro metabolism model to identify probable DINP metabolites and validate these metabolites in rat urine. Then, we used three strategies to identify probable DINP metabolite signals including Signal Mining Algorithm with Isotope Tracing (SMAIT), Mass Defect Filter (MDF), and XCMS. In in vitro stage, SMAIT filtered out 16 metabolite signals, MDF filtered out 11 probable metabolite signals, and XCMS filtered out 134 metabolite signals. Because of the large amount of metabolite signals which XCMS filtered, it’s hard to validate each metabolite. Consequently, XCMS is not an efficient strategy in in vitro stage. In in vivo stage, 14 metabolites were validated by SMAIT and 9 metabolites were validated by MDF in rat urine. As the results, SMAIT can filter more DINP metabolites than MDF. According to above results, SMAIT is the most efficient strategy among these three strategies. Specifically, the high resolution LC-MS data showed that we found new DINP metabolites differ with literature. Based on this result, we did structure confirmation to check the structure of m/z 293.139. The results showed that we found a new hydroxyl metabolite of DINP.

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