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A growing number of chemicals are being introduced into commerce, yet the universe of chemicals makes the characterization of toxicological profiles of all chemicals exceedingly difficult. This task was further complicated by the presence of unknown substances in environmental matrices, and mounting data from the use of bioassays to assess mixtures for specific measurement endpoints has revealed that most biological activities in human environments are driven by unknown chemicals. Targeted monitoring and assessment of risks posed by single chemicals of known pollutants might underestimate risks to human health. To more completely understand the potential risks and toxic components of environmental mixtures, our group proposed a hybrid platform to deconvolute environmental mixtures, by the combination of chemical and biological strategies. 1) Build prioritized compounds list by untargeted chemical analysis

(DIPIC-Frag method). Untargeted chemical analysis was used to identify unknown halogenated compounds in sediment, house dust and drinking water samples.

2) In situ identification of toxic components in environmental mixtures. Use oil sands process-affected water (OSPW) as a case study, we developed a pull-down system combined with untargeted chemical analysis (PUCA), for in situ identification of PPARγ ligands from OSPW extracts.

Methodology

Results

Acknowledgements

Collection of Samples: Sediment samples were collected from Lake Michigan in September 2010. OSPW samples were collected from the West-In-Pit tailings pond (Syncrude Canada Ltd, Fort McMurray, AB, Canada), in December 2012. Dust samples were collected in Saskatoon, Canada from May to August, 2013. Drinking water samples were collected in Saskatoon, Canada, October 2015 and April 2016.

Conclusions This study developed a hybrid platform for deconvolution of environmental mixtures, by the combination of chemical and biological strategies. 1) An untargeted chemical analysis method (DIPIC-Frag) was

developed for identification of halogenated compounds in environmental mixtures. This method showed good sensitivity, dynamic range and specificity.

2) Numerous previously unknown halogenated compounds were discovered in the environment: >2000 brominated compounds detected in sediment; >4000 iodinated compounds detected in sediment; >500 brominated compounds detected in dust; >1000 DBPs detected in drinking water.

3) A PUCA assay was developed for in situ identification of toxic components from OSPW, which showed better sensitivity, specificity than classic effect-directed analysis assays.

References 1. Peng, H.; Chen, C. L.; Saunders, D. M. V.; Sun, J. X.; Tang, S.; Codling, G.; Hecker, M.; Wiseman, S.; Jones, P. D.; Li, A.; Rockne, K. J.; Giesy, J. P., Untargeted identification of organo-bromine compounds in lake sediments by ultrahigh-resolution mass spectrometry with the data-independent precursor isolation and characteristic fragment method. Anal. Chem. 2015, 87 (20), 10237-10246. 2. Peng, H.; Chen, C. L.; Cantin, J.; Saunders, D. M. V.; Sun, J. X.; Tang, S.; Codling, G.; Hecker, M.; Wiseman, S.; Jones, P. D.; Li, A.; Rockne, K. J.; Sturchio, N. C.; Giesy, J. P. Untargeted Screening and Distribution of Organo-Bromine Compounds in Sediments of Lake Michigan. Environ. Sci. Technol. 2016 (1), 321-330. 3. Peng, H.; Saunders, D. M. V.; Sun, J. X.; Jones, P. D.; Wong C. K. C.; Liu, H. L.; Giesy, J. P. Mutagenic azo dyes, rather than flame retardants, are predominant brominated compounds in house dust. Environ. Sci. Technol. in press. 4. Peng, H.; Sun, J. X.; Alharbi, H. A.; Jones, P. D.; Giesy, J. P.; Wiseman, S. Peroxisome proliferator-activated receptor γ is a sensitive target for oil sands process-affected water: effects on adipogenesis and identification of ligands. Environ. Sci. Technol. 2016, 50 (14), 7816-7824.

Deconvolution of environmental mixture: chemical and biological strategies Hui Peng1, Jianxian Sun1, David M.V. Saunders1, Song Tang1, Markus Hecker1, Steve Wiseman1, Paul D. Jones1, An Li3,

Karl J. Rockne3, Neil C. Sturchio,4 John. P. Giesy1

1 Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada; 2 School of Public Health, University of Illinois at Chicago, Chicago, United States; 3 Department of Civil and Materials Engineering, University of Illinois at Chicago, Chicago, United States;

4.Department of Geological Science, University of Delaware, Newark, United States

Background

Contact Hui Peng, PhD

Post-Doctoral Fellow Email: huisci@gmail.com

Objectives

Fig 2. Distribution of 2520 brominated compound peaks identified in Lake Michigan sediments by DIPIC-Frag method. (a) Distribution of intensities of 2520 brominated compounds. (b) Distribution of intensities of brominated compounds with different ranges of m/z values. (c) Distribution of brominated compounds by retention time and m/z values. The colors of dots represent numbers of bromines. Red represents numbers of bromines, and gray represents those precursor ions whose formulas could not be identified. All brominated compounds were determined in a surficial sediment sample from Lake Michigan.1-2

LC-Q Exactive Data Acquisition. Aliquots of extracts were analyzed using a Q Exactive mass spectrometry equipped with a DionexTM UltiMateTM 3000 UHPLC system. Separation of NSOHCs was achieved with Hypersil GOLDTM C18 column (3µm; 2.1mm×50mm). Data were acquired in data-independent acquisition (DIA) mode. Parameters for DIA were one full MS1 scan (150-2000 m/z) recorded at resolution R=70000 with a maximum of 3×106 ions collected within 100ms, followed by six DIA MS/MS scans recorded at a resolution R=35000 with maximum of 1×105 ions collected within 60ms. DIA data were collected using 5-m/z-wide isolation windows per MS/MS scan. All data analysis was conducted by in-house R program.

Sample pretreatment: Sediment, and dust samples were extracted by ASE or liquid-liquid extraction. The extract was loaded onto a Florisil cartridge for cleanup. OSPW samples were concentrated by EVO-LUTE ABN cartridges. Drinking water samples were extracted by HLB, C18 and WAX cartridges.

Fig 1. Workflow of the data independent precursor isolation and characteristic fragment (DIPIC-Frag) method. 180 successive 5-m/z width precursor isolation windows were used. A novel chemometric strategy which combined chromatographic profiles, isotopic peaks, precursor isolation window information, and intensities was used to identify precursor ions and chemical formulas. Specificity of the method was achieved by detection of bromine fragment. Dynamic range of the method was expanded by using 180 narrow DIA windows, which efficiently deconvolute co-eluted bromine compounds with different m/z values.1

Days (dpf)

A

• J.P. Giesy and M. Hecker are supported by the Canada Research Chairs Program and Discovery Grants form the Natural Sciences and Engineering Research Council (NSERC) of Canada.

• An equipment grant from Western Economic Diversification • Aquatic Toxicology Research Facility at the University of Saskatchewan.

Ongoing and Future Research 1) Application of DIPIC-Frag method to other halogenated compounds and environmental samples (dust, biota and drinking water samples). 2) Investigate the source and potential toxicities of these novel halogenated compounds. 3) Investigation of the bioactive ligands in other environmental samples.

Fig 3. Distributions of identified brominated compounds in house dust. Distributions of brominated compounds. Brominated compounds from Group I and II (black) shown different distribution patterns from Group III (blue).3

Fig 4. (A) Heatmap and hierarchical clustering of brominated compounds identified in drinking water. (B) Precision of different sample pretreatment methods.

Fig 5. Workflow of the PUCA assay. (A) In step 1, two different reactions were performed. In group I (negative control), BML-OSPW and BSA were mixed. In group II (PPARγ), BML-OSPW, BSA and His-PPARγ were mixed. (B) In step 2, magnetic beads with an antibody against the His-tag protein were used to “pull out” the complex of His-PPARγ and ligands. (C) In step 3, chemicals in the extracts from His-PPARγ and negative control were identified by use of ultrahigh resolution mass spectrometry. (D) In step 4, a computation program was used to identify the differentiated peaks among groups, and only those chemical peaks exhibiting higher abundance in group II than group I were considered to be potential ligands.4