Pao-Chi Liao (Presenter)
National Cheng Kung University
Authorship: Pao-Chi Liao(1), Jing-Fang Hsu(1), Jen-Yi Hsu(1),Yet-Ran Chen(2), Yi-Jen Chen(1), Yi-Sheng Hsu(1)
(1) National Cheng Kung University, 138 Sheng-Li Road, Tainan 704, Taiwan (2) Academia Sinica, Taipei 11529, Taiwan
| Short Abstract Phthalates, including DINP, DPHP, and DINCH, are used as plasticizers and could cause undesired health impacts, such as endocrine disrupting effects. Workers and general population are exposed to them, so assessment of their exposure is a public health issue. Therefore, exposure biomarker discovery for toxic phthalates is of great interest in occupational and environmental health. We used HRMS with metabolomics approaches, signal mining algorithm with isotope tracing (SMAIT), MDF, and XCMS, to filter out meaningful metabolite signals from complex LC-HRMS data. They were further verified as markers of toxic phthalate exposure using animal models and tested in general population. |
Long Abstract
Introduction
Di-isononyl phthalates (DINPs) are widely used in plastics, building materials, toys, and personal care products. DINPs could have undesired effects on reproductive outcomes, development of the male reproductive tract. [1] A high molecular weight phthalate, Di(2-propylheptyl) phthalate (DPHP), is primarily used as a plasticizer in polyvinyl chloride and vinyl chloride copolymers for technical applications, as a substitute for other phthalates currently being scrutinized because of endocrine disrupting effects. [2] Di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH), a structurally-related compound to phthalates, was first introduced commercially in 2002 as a substitute for phthalate plasticizers in many polyvinyl chloride (PVC) products.[3] Workers in specific sectors and general population are exposed to these toxic plasticizers, Assessment of their exposure is a public health issue because of its potential adverse health impacts. For this reason, exposure biomarker discovery for toxic phthalate plasticizers is of great research interest in occupational and environmental health.
Mass spectrometry plays a key role in metabolomics research, especially in metabolite identification in a complex matrix. The development of high-resolution mass spectrometry (HRMS) instrumentation, such as Orbitrap, has improved the efficiency and accuracy of metabolite identification. Various methods have been proposed and developed to perform objective filtering of accurate-mass-based LC-MS data to facilitate metabolite detection, such as stable-isotope tracing, mass defect filtering (MDF), XCMS and background subtraction. However, no single analytical approach can be suited for all metabolomics study. Therefore, a combination of different analytical technologies and data mining tools is necessary to efficiently identify metabolites for discovering the biomarkers of exposure. We used high resolution mass spectrometry coupled with three data processing methods, including signal mining algorithm with isotope tracing (SMAIT), MDF, and XCMS to filter out phthalate metabolite signals. Furthermore, these metabolite signals were validated as exposure markers using a rat model.
Methods
We used LTQ/Orbitrap high resolution mass spectrometry coupled with three data processing methods, including SMAIT, MDF, and XCMS, to perform mass spectral signal filtering in a complex LC-HRMS data obtained from liver enzyme incubation for phthalate metabolites. For the SMAIT procedure, the metabolites were generated after incubating S9 from the human liver and the precursor compounds with varying isotope concentration ratios of D0- phthalate to D4-phthalate (3:7, 4:6, 5:5, 6:4, and 7:3). For the MDF and XCMS procedures, the phthalate metabolites were generated after incubating S9 from human liver and the D0-phthalate precursor compounds. A rat model was used to validate the probable DINP metabolite signals. The rat model of DINP exposure was used to generate the DINP metabolite in vivo. The rats were individually administered commercial DINP II (Dose: 0 and 300 mg/kg) orally by gavage. Rat urine samples were collected 24 h after DINP administration. In addition, we proposed the list of probable metabolites from known pathways of phthalate metabolism for comparison.
Results
Three data processing methods, including SMAIT, MDF, and XCMS were used. Taking DINP as an example, there were 16 probable DINP metabolite signals were filtered by SMAIT procedure. Using the MDF, 84 probable metabolite signals were filtered out. There were 139 probable metabolite signals were filtered out by XCMS and included all the above 16 probable metabolite signals filtered either by the SMAIT procedure. A rat model was used to validate the 14 probable metabolite signals filtered by SMAIT, MDF and XCMS as DINP exposure markers. The rat urine samples were collected from the rats administered with 0 and 300 mg/kg DINP. Using the MS/MS data of the 14 probable metabolite signals obtained from liver enzyme incubation, these metabolites signals were detected in rat urine samples. In these 14 metabolite signals, 13 signals were validated as exposure marker because of the dose-relationship and tested in the urine samples collected from general population. For discovery of DINCH and DPHP exposure biomarker, more than thirty probable metabolite signals for both were detected and proposed to be potential exposure markers. Compared with these possible metabolites suggested by the known metabolism pathways of phthalate, most of the discovered exposure markers appeared to be reasonably expected. However, several novel metabolites were discovered and elucidated for their chemical structures.
Conclusions
In this study, we demonstrated three metabolomics approaches for processing mass spectral data in filtering out phthalate metabolite signals. The SMAIT provided an efficient procedure to effectively and systematically sieve for meaningful metabolite signals from complex LC-HRMS data. These probable metabolite signals were subsequently verified as markers of toxic phthalate exposure using animal models and tested in general population.
References & Acknowledgements:
[1] L.E. Gray, J. Ostby, J. Furr, M. Price, D.N.R. Veeramachaneni, L. Parks, Perinatal exposure to the phthalates DEHP, BBP, and DINP, but not DEP, DMP, or DOTP, alters sexual differentiation of the male rat, Toxicological Sciences, 58 (2000) 350-365.
[2] Leng G, Koch HM, Gries W, Schütze A, Langsch A, Brüning T, Otter R, Urinary metabolite excretion after oral dosage of bis(2-propylheptyl) phthalate (DPHP) to five male volunteers--characterization of suitable biomarkers for human biomonitoring. Toxicol Lett. 231(2014) 282-8.
[3] Silva MJ, Furr J, Preau JL Jr, Samandar E, Gray LE, Calafat AM, Identification of potential biomarkers of exposure to di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH), an alternative for phthalate plasticizers, J Expo Sci Environ Epidemiol, 22 (2012) 204-11
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