Untargeted strategy for identification of Toxic Chemicals in OSPW

Transcription

1 Untargeted strategy for identification of Toxic Chemicals in OSPW ---- oxidative stress and PPARG agonistic activity in OSPW Jianxian Sun, Hui Peng, Hattan Al Harbi, John P. Giesy, Steve Wiseman Oct 6, 2015, University of Saskatchewan

2 What is OSPW Oil Sands Process-Affected Water (OSPW) is produced during the surface mining of oil sands in Alberta, The volume of OSPW is currently stored in tailings is greater than 1 billion m 3 PNAS, 2010, Alberta Energy. Alberta Government

3 OSPW: A Complex Mixture of Dissolved Organics Complex mixture of chemicals Uncharacterized compound Unknown mechanism of toxicity How to Identify causative chemicals in OPSW? O x +/- SO x +/- NO x +/- Chemicals Thousands of dissolved organic compounds? Toxicity Acute toxicity; Deformity; Nuclear receptors; Endocrine disruption; Immune toxicity; Oxidative stress

4 Methods to Identify Causative Chemicals Unknown protein target Known protein Target Effect-Directed Analysis (EDA) Pull down system Fractionation of Active Fractions Chemical Analysis Compare Effects in Bioassay Identification of Active Chemicals

5 Case 1: Oxidative Stress (no protein target) Water Research, 2012, Comparative Biochemistry and Physiology, 2013, Oxidative stress is the production and accumulation of free radicals. Induction of oxidative stress in fathead minnow by OSPW have been reported There are multiple pathways to induce ROS with no certain protein targets.

6 EDA: Chemical Analysis and Bioassay Chemical Analysis: High-Resolution Mass Spectrometry Bioassay: NRF2 Luciferase Reporter System (High Throughput)

7 1 st Dimension Fractionation OSPW HLB HLB 100% Hex Hex/DCM (5:1) Hex/DCM (1:1) 100% DCM 100% MeOH F 1 F 2 F 3 F 4 F 5 Polarity Dissolved organic phase of was separated into 5 fractions based on polarity by use of HLB columns. Total extract created by pooling of samples.

8 NRF2 Activity HLB 4 *** F 1 F 2 F 3 F 4 F 5 TE 3 *** *** Fold Change 2 ** * ** *** *** ** Ctrl tbhq Oxidative stress responses of these five fractions were validated in vivo (medaka)

9 Reduced Responses by Co-exposure with GSH Fold-change *** *** ** tbhq tbhq+gsh F2 F2+GSH Fold-change *** *** *** tbhq tbhq+gsh TE TE+GSH 0 Ctrl tbhq Ctrl tbhq GSH is an anti-oxidant; Organic chemicals in OSPW cause ROS production

10 Peaks Untargeted Mass Spec Analysis positive negative Total peaks: in positive, in negative F1 F2 F3 F4 F5 Greater than 10,000 peaks detected in 5 fractions Many peaks were specifically eluted in F2, which may cause its greater potency. Potential causative chemicals were narrowed in 1,000 chemicals from 10,000 compounds ratio to max positive F1 F2 F3 F4 F5 ratio to max negative F4 F1 F5 F2 F peak ID peak ID

11 2 nd Dimension HPLC Fractionation OSPW HLB HLB 100% Hex Hex/DCM (5:1) Hex/DCM (1:1) 100% DCM 100% MeOH F 1 F 2 F 3 F 4 F 5 HPLC F2-1, F2-2, F2-3 F2-60 Orbitrap NRF2 System Effective Chemicals

12 NRF2 Activity of 60 HPLC fractions * ** * ** Fold Change 1.2 * * * Ctrl Working on chemical analysis

13 Summary of EDA method Effect-Directed Analysis (EDA) Chemical Analysis Fractionation of Active Fractions Compare Effects in Bioassay Identification of Active Chemicals EDA is useful to narrow down causative chemicals and to identify active chemicals; Needs a lot of fractionation and chemical analysis work. It is tricky to identify the causative chemicals from complex samples

14 Case 2: PPARɣ Activation (known protein target) PPARs regulate intracellular lipid flux and adipocyte proliferation and differentiation. PPARγ ligands may promote development of obesity. Activated by structurally diverse ligands

15 80 PPARɣ Activation - Luciferase Reporter Gene Assay Human PPAR-G cell reporter system TE PPAR response Rosiglitazone (nm) Fractions

16 PPARɣ mediated adipogenesis in 3T3-L1 At low concentrations of OSPW (0.1, 0.3 ), accumulation of lipids was observed. At higher concentrations (1, 3 ), lipid droplet size was reduced but level was increased.

17 Ligand Identification: PPARɣ Pull-Down Assay Negative control OSPW Pull-down Competitive pull-down His-PPARɣ Putative ligands Nonspecific binding Rosiglitazone Interfering compounds Magnetic beads intensity mz

18 Positive control: MTX-DHFR Ratio 10 MTX H 2 N N NH 2 N N N Peak ID N H O N OH O O OH

19 Profile of Compounds from Different Groups

20 Compounds Detected as PPARɣ Ligands Negative ion mode Positive ion mode 20 compounds in negative ion mode and 44 compounds in positive ion mode were detected as potential PPARɣ ligands

21 Conclusions and Perspectives Pull down system is a useful method for identifying causative chemicals from complex environmental matrix OSPW can cause oxidative stress, we are still working on identification of causative chemicals PPARɣ is sensitive target of OSPW organic chemicals, and multiple ligands were identified

22 Acknowledgements

23 Thank you!

24 Validation in 3T3-L1 Cell Line The mouse embryonic fibroblast cell line 3T3-L1 is a favored model for metabolism and obesity research, because the cells can be chemically induced to differentiate into adipocytes. Induce for 2 days 10 ug/ml insulin 1 um DEX 0.5 mm MIX Exposure for 8 days 10 ug/ml insulin OSPW Red Oil O staining of lipids

25 Keap 1-NRF2 Under normal conditions, Nrf2 is constantly ubiquitinated through Keap1 and degraded in the proteasome. Following Exposure to electrophiles or oxidative stress, Keap1 is inactivated. Stabilized Nrf2 accumulates in the nucleus and activates many cytoprotective genes. Yoichiro et al., Frontiers in Oncology

26 in vivo: Medaka larvae Gene Expression Fold-change Lipid Hydroperoxide (LPO) ** ** 0 Ctrl tbhq F1 F2 F5 Oxidative stress related genes were induced in F2 group; F2 exposure significant increased concentrations of LPO

27 Wnt-pathway

28 PPARG-Wnt pathway

29 Peak Detection and Match Relative Abundance Computation: rt: min Intensity: 1.16E6 NL: 1.16E Relative Abundance NL: 4.28E4 Computation: rt: 2.42 min Intensity: 4.28E4 Peaks Time (min) positive negative Total peaks: in positive, in negative Time (min) Intensity cutoff: 1E4 Greater than 10,000 peaks detected in 5 fractions Rank order of peak abundance F1 > F3 > F4 > F2 > F5 0 F1 F2 F3 F4 F5