Human biomonitoring is the assessment of actual internal contamination of chemicals by measuring exposure markers, chemicals or their metabolites, in human urine, blood, serum, and other body fluids. However, the metabolism of chemicals within an organism is extremely complex. Therefore, the identification of metabolites is often difficult and laborious. Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification. In this study, three metabolomics data processing approaches were used for chemical exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset; di-isononyl phthalate (DINP) was used as an example. The data processing techniques included the SMAIT, mass defect filtering (MDF), and XCMS. Fourteen probable metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS. Among them, 13 probable metabolite signals were validated as exposure-related markers in a rat model. With the use of Orbitrap, we found three metabolites (m/z 279.123, 293.139 and 321.170) with different m/z values than those that were reported in the literature (m/z 279.087, 293.103 and 321.134, respectively). The m/z values derived from the three hydroxyl-metabolites are only slightly different (0.036 u) from those containing carboxyl or carbonyl groups reported in the literature. The finding also raises subtle implications on quantitation of urinary exposure markers by mass spectrometers with a diverse mass accuracy being used by a wide range of analytical laboratories. Triple quadrupole mass spectrometers (TQMS) are frequently used for routine quantitative analyses for urinary exposure markers. However, they may not be able to differentiate metabolites with a small mass variance. While HRMS, such as TOF and Orbitrap, are gaining popularity in quantitating urinary exposure markers, they may not measure correctly the concentrations if an incorrect chemical structure is assumed and the targeted m/z value might be missed by a small margin.

Novel metabolites of food contaminant phthalates revealed by HRMS and their implications in quantitating urinary toxicant exposure markers

253rd National Meeting (2017)

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