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研究生: 彭麗文
Peng, Li-Wen
論文名稱: 利用大鼠肝臟酵素進行體外代謝結合液相層析質譜儀法鑑定DiNP代謝產物做為曝露指標
Identification of DiNP metabolites for exposure marker discovery using in vitro metabolism by rat liver enzymes and liquid chromatography-mass spectrometry
指導教授: 廖寶琦
Liao, Pao-Chi
學位類別: 碩士
Master
系所名稱: 醫學院 - 環境醫學研究所
Department of Environmental and Occupational Health
論文出版年: 2011
畢業學年度: 99
語文別: 英文
論文頁數: 106
中文關鍵詞: 鄰苯二甲酸二異壬脂代謝物鑑定驗證曝露指標
外文關鍵詞: Di-isononyl phthalate, metabolites, identification, validation, exposure marker
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  • 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,它是由一群支鏈含有九個碳的同分異構物所構成的,先前有許多動物實驗指出,當鄰苯二甲酸二異壬酯進入到生物體時會造成肝臟和腎臟的毒性效應以及內分泌系統上的干擾,由於DiNP可能會造成人類健康上的危害,因此本研究的目的為利用體外代謝的方式鑑定出DiNP的可能的代謝物訊號並在公鼠的尿液中作一驗證。由於DiNP是由多種同分異構物組成的關係,因此如選用DiNP做為前驅物質,會造成分析上的複雜度,因此本實驗選擇以DiNP主要的isomer form-(Mono-methyl octyl phthalate)來作為體外代謝的前驅物質,並利用同位素標定追蹤法結合液相層析質譜儀(LC-MS)的方式去追蹤可能的代謝物訊號。將五個不同比例未經標定和經重氫標定的前驅物質(7/3, 6/4, 5/5, 4/6, 3/7) 經過大鼠肝臟酵素代謝後產生出DiNP的代謝物,其樣本經由固相萃取作一淨化和濃縮後送入液相層析質譜儀(LC-MS)分析,隨後將所得LC-MS的數據利用MES (Metabolomics Export Script)轉檔成訊號峰值列表後,送入SMAIT (Signal Metabolite Algorithm by Isotope Tracing) 的軟體來進行代謝物訊號的篩選和分析。我們由三重複的數據經由SMAIT統計步驟篩選後,由LC-MS的13490個訊號峰中有效率的篩出八個可能的DiNP代謝物訊號,隨後以LC-MS/MS的方式將篩出的八個可能的DiNP代謝物訊號做碎片離子的分析,其結果顯示全部都和前驅物質產生相似的特徵碎片。在公鼠尿液分析部分,我們分別餵食公鼠玉米油和300mg/kg DiNP2並收集餵食後二十四小時的尿液作分析,其結果顯示,我們的研究找到七個可能的代謝物訊號是在控制組和曝露組之間有明顯差異的( p-value < 0.05, Mann-Whitney test ); 此外,我們探討了餵食五個不同劑量的DiNP2與其代謝物的關係,根據結果顯示,有七個訊號接會隨著劑量上升呈現正相關的趨勢。最後總結我們成功地利用SMAIT分析策略找到八個可能的DiNP代謝物訊號,並在公鼠尿液中驗證其七個訊號是有差異的,未來將建議將這七個訊號m/z 279.1, 293.1, 305.1, 307.1, 321.1,365.1, 375.1做更深入的探討並做為DiNP曝露指標的評估。

    Di-isononyl phthalate (DiNP) is a plasticizer which is widely used in industry. It contains various isomeric nine-carbon branch chain dialkyl phthalates. Previously, several animal experiments have indicated that DiNP can exhibit toxic effects on liver and kidney and interfere in the endocrine system when they are incorporated into the organisms. Due to the human adverse health effect of DiNP, the purpose of this study aims to use in vitro metabolism for identifying DiNP potential metabolite signals. The isomer form of DiNP, mono-methyl octyl phthalate, was chosen as a precursor, and we used in vitro metabolism and stable isotope-labeled method combined with liquid chromatography- mass spectrometry (LC-MS) to trace the signals of its probable metabolites. Five mixtures of native and stable-isotope labeled precursors with different ratio (7/3, 6/4, 5/5, 4/6, 3/7) were used to generate DiNP metabolites by liver enzyme incubation. Samples were cleaned-up and concentrated by solid phase extraction. Subsequently, LC-MS analysis was performed to get the LC-MS data. The raw peak list of LC-MS data was obtained by using Metabolomics Export Script (MES) software. Next, the signals from the raw peak list were analyzed by in-house software, Signal Mining Algorithm with Isotope Tracing (SMAIT), to mining metabolite signals. The statistical procedure effectively filtered nineteen potential DiNP metabolite signals from 13490 signals by triplicate LC-MS data. Followed by LC-MS/MS analysis to get the fragment patterns, eight potential metabolites are all structurally relative to the fragment pattern of the precursor. For rats’ urine analysis, we gavaged corn oil and 300 mg/kg DiNP2 to rats respectively and collected their urine after 24hr gavaged. As the result, we found that seven of them were significantly differential (p-value < 0.05, Mann-Whitney test) between case and control group. Furthermore, we analyzed the correlation of five varied doses with different metabolites. According to the results, the trends of those seven metabolites had positive
    VI
    correlation when increasing administrated doses. At the conclusion, the integrated approach, SMAIT, provided an efficient method which can efficiently filter nineteen probable metabolite signals from a complex LC-MS data for toxicant exposure marker discovery. We successfully identified eight potential DiNP metabolites signals from SMAIT, and seven of them were significantly differential (p-value < 0.05, Mann-Whitney test) between case and control group in rat’s urine. Further evaluation by in vitro/in vivo metabolism, we suggest that the seven probable DiNP metabolite signals of m/z 279.1, 293.1, 305.1, 307.1, 321.1, 365.1, and 375.1 are potential exposure markers for DiNP exposure assessment and should been further investigated.

    致謝 ....................................... II 摘要 ......................................... III Abstract ........................................ V Contents .......................................... VII Table list .......................................... X Figure list ........................................ XI Abbreviations .............................. XIV Chapter 1 Overview of the research ................ 1 1-1 Background ............................... 1 1-2 Objectives ................................... 3 Chapter 2 Literature review ....................... 4 2-1 Phthalate esters .................. 4 2-2 Toxicity of phthalate esters ..................... 6 2-3 Di-isononyl phthalate ................... 7 2-3-1 Physical and chemical properties ............ 7 2-3-2 DiNPs composition .......................... 8 2-3-3 Usage ................................... 9 2-3-4 Exposure routes ........................ 9 2-3-5 DiNP toxicity ........................... 10 2-3-6 DiNP metabolism ............................ 10 2-4 Metabolite detection and identification ......... 12 2-4-1 Definitions .................. 12 2-4-2 Techniques for metabolite detection and identification .. 12 Chapter 3 Material and methods ................. 16 3-1 Research scheme ............................ 16 3-2 Research materials .......................... 18 3-2-1 MiNP precursor selection .....................18 3-2-2 Mono-4-methyl octyl phthalate and D4-mono-4-methyl octyl phthalate selection ...................... 18 3-2-3 S9 liver enzyme extraction .................19 3-2-4 MiNP in vitro metabolism ................... 20 3-2-5 LC-MS analysis ........................... 24 3-2-6 Tracing probable DiNP metabolites signals ...... 26 3-2-7 Identification of probable DiNP metabolites signals by LC-MS/MS .......30 3-2-8 In vivo rat urine sample collection ......... 33 3-2-9 Multiple reaction monitoring for rat urine ..... 35 Chapter 4 Results and discussion ................. 36 4-1 Synthesize Mono-4-methyl octyl phthalate and D4-mono-4-methyl octyl phthalate ......................... 36 4-2 Purity of mono-4-methyl octyl phthalate .......38 4-3 Purity of D4-mono-4-methyl octyl phthalate .........40 4-4 LC-MS results traced from SMAIT ............... 42 4-4-1 LC-MS result derived from five varied ratio of mono-4-methyl octyl phthalate and D4-mono-4-methyl octyl phthalate. .................. 42 4-4-2 Probable DiNP metabolite signals tracing from SMAIT ....42 4-4-3 Results of blank control and negative control .... 50 4-5 LC-MS/MS results for MiNP .................... 52 4-5-1 Criteria for selecting DiNP metabolites .....52 4-5-2 LC-MS/MS results for MiNP ................... 53 4-5-3 Isotopic signals traced from SMAIT ......... 70 4-5-4 High resolution LC-MS/MS results for MiNP ...... 72 4-6 Rat urine results ..............................91 4-6-1 Case-control study results from rat urine .........91 4-6-2 Correlation between varied dose and metabolite response from rat urine ........................... 93 4-7 Summaries of in-vitro and in-vivo results ........ 95 Chapter 5 Conclusions ...........................101 Chapter 6 References ................................... 102

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