Optimizing parameters of SMAIT MDF and XCMS for toxicant exposure marker discovery using mass spectrometry-based metabolomics approaches

Abstract

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

Date of Award	2015 Feb 5
Original language	English
Supervisor	Pao-Chi Liao (Supervisor)