臺灣博碩士論文加值系統

鄰苯二甲酸酯被廣泛的使用在許多產品且被視為內分泌干擾物，其中鄰苯二甲酸二異壬酯 (Di-isononyl phthalate, DINP) 可能引起許多健康問題，因此毒物暴露指標之尋找成為重要的議題。代謝體學是研究代謝物的學問，而液相層析串聯質譜儀能幫助代謝物的鑑定。被鑑定出的代謝物一旦能在生物樣本裡被驗證，它們就可做為暴露指標。由於液相層析串聯質譜儀產生非常大量的數據，許多方法如同位素追蹤法 (Signal mining algorithm with isotope tracing, SMAIT)、質量虧損過濾法 (Mass defect filter, MDF)及XCMS被用來從數據中篩選出可能的代謝物訊號。在此研究中，為了找尋毒物暴露指標，我們使用已被驗證的14個暴露指標來做SMAIT、MDF及XCMS這三種方法的參數最佳化。除了14個暴露指標之外，以這三種方法篩選出的其它訊號被定義為偽陽性暴露指標。我們調整SMAIT、MDF與XCMS的參數來探討有多少暴露指標包含在結果內，當最多暴露指標從高效液相層析質譜數據中被篩選出且包含最少的偽陽性暴露指標時，我們能得到SMAIT、MDF與XCMS的最佳化參數。SMAIT的最佳化的參數為在同位素配對尋找步驟中質量偏移設在0.004 Da、同位素配對訊號強度反應中同位素配對之質量偏移設為0.003 Da；在MDF方法中，當選擇訊噪比大於等於3之訊號時能得到最佳的結果；另外在XCMS的最佳化參數為profstep設為1、mzwid設為0.01、minfrac設為0.5與bw設為6。SMAIT、MDF與XCMS之最佳化參數能應用在未來尋找毒物暴露指標之研究中。

Phthalates are widely used in many products and regarded as endocrine disrupters. Di-isononyl phthalate (DINP) is one of phthalates may induce many health problems. Due to this reason, toxicant exposure marker discovery becomes an important issue. Metabolomics is the study of metabolite and liquid chromatography coupled with mass spectrometry (LC-MS) can develop the identification of metabolites. Once these metabolites are validated in biological samples, they are considered exposure markers. Owing to a large number of data generated from LC-MS, many methods such as signal mining algorithm with isotope tracing (SMAIT), mass defect filter (MDF) and XCMS are used in processing data to select out probable metabolite signals. Here, we used 14 validated exposure markers to optimize parameters of three methods, SMAIT, MDF and XCMS for toxicant exposure marker discovery. Except for these 14 exposure markers, the other signals filtered by these three methods were defined as false-positive hits. We adjusted parameters of SMAIT, MDF and XCMS to investigate how many of these 14 exposure markers covered in the results. The optimized parameters of SMAIT, MDF and XCMS were obtained when the maximized number of these 14 exposure markers was filtered out in an HPLC-MS dataset with the least number of false-positive hits. The optimized parameters of SMAIT were 0.004 Da set at mass shift in isotopic pair (IP) finding step, and 0.003 Da at mass shift between IPs in IP response ratio analysis. MDF method yielded optimal results when all signals with S/N ≥ 3 were included for consideration. The optimized parameters of XCMS were 1 profstep, 0.01 at mzwid, 0.5 at minfrac and 6 at bw. These optimized parameters of SMAIT, MDF and XCMS can be applied in the future investigations for toxicant exposure marker discovery.

摘要 II
Abstract III
致謝 V
Content VI
List of Tables VII
List of Figures VIII
Chapter 1 Research background 1
1-1 Metabolomics 1
1-1-1 Definition 1
1-1-2 Metabolite detection and identification by mass spectrometry 1
1-1-3 Signal mining algorithm with isotope tracing (SMAIT) 3
1-1-4 Mass defect filter (MDF) 4
1-1-5 XCMS 4
1-2 Phthalate 5
1-3 Di-isononyl phthalate (DINP) 7
1-3-1 Exposure routes 7
1-3-2 Di-isononyl phthalate toxicity 8
1-3-3 Di-isononyl phthalate metabolism 8
Chapter 2 Objectives 11
Chapter 3 Materials and methods 12
3-1 Research scheme 12
3-2 Experimental data 13
3-2-1 MINP selection 13
3-2-2 MINP in vitro metabolism 15
3-2-3 LC-MS analysis 17
3-3 Data processing in SMAIT, MDF and XCMS 19
3-3-1 Method of SMAIT to select out probable DINP metabolite signals 19
3-3-2 Method of MDF to select out probable DINP metabolite signals 22
3-3-3 Methods of XCMS to select out probable metabolite signals 23
3-4 Method to optimize parameters of SMAIT, MDF and XCMS 26
Chapter 4 Results and discussion 27
4-1 The result of exposure markers discovery with different parameters of SMAIT 27
4-2 The result of exposure markers discovery after MDF processing 34
4-3 The result of exposure markers discovery with different parameters of XCMS 38
4-4 Comparison the result of exposure marker discovery in SMAIT, MDF and XCMS 39
Chapter 5 Conclusion 41
Chapter 6 References 43

List of Tables
Table 1. Classification and main usage of phthalates 6
Table 2. LC gradient 19
Table 3. 14 validated exposure markers in previous study 26
Table 4. Signals filtered out from SMAIT with parametric combination 1 27
Table 5. Sensitivity and specificity for discovering exposure marker with different cut-off value for R 29
Table 6. Sensitivity and specificity for discovering exposure marker with different cut-off value for R when SMAIT parameter set on narrowed RT shift 31
Table 7. Sensitivity and specificity for discovering exposure marker with narrowed mass shift in isotopic pair finding step 31
Table 8. Sensitivity and specificity for discovering exposure marker with narrowed mass shift in IPRR analysis 32
Table 9 Different number of exposure marker can be filtered out with changing response ratio of D0 and D4. 34
Table 10 Comparison of different criteria for selecting signals in MDF 36
Table 11 Results of exposure marker discovering with different mass defect shift. 36
Table 12.Results for discovering exposure markers with different parameters in XCMS 39

List of Figures
Figure 1. Consumption of plasticizer 7
Figure 2. Metabolic pathways of phthalates 9
Figure 3. Suggested mechanism of DINP metabolism 10
Figure 4. Study design 13
Figure 5. Chemical structures of the precursor standards 14
Figure 6. Workflow of in vitro metabolism 17
Figure 7. Schematic diagrams of SMAIT processing and parameter settings. 22
Figure 8. Workflow of MDF processing 23
Figure 9. Parameter setting of XCMS 25
Figure 10. Sensitivity and specificity for discovering exposure marker with different cut-off value for R 30
Figure 11.Sensitivity for discovering exposure marker with different mass shift 33
Figure 12 The TIC after MDF processing 35
Figure 13 Different number of exposure markers can be filtered out with different mass defect shift. 37
Figure 14. Efficiencies of exposure markers discovery by using different methods. 40

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