Evaluation of Various Metabolomics Approaches for the Elucidation of in-vivo di-isononyl phthalate Metabolites using Liquid Chromatography - High Resolution Mass Spectrometry

Abstract

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) pro?ling is an important approach for the identi?cation of metabolites from complex biological samples Due to the large amount of data produced in LC–MS pro?ling 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

Date of Award	2014 Jun 27
Original language	English
Supervisor	Pao-Chi Liao (Supervisor)