Case Studies: Towards Aquatic Monitoring of Emerging...

Case Studies: Towards Aquatic Monitoring of Emerging Contaminants

Tara Sabo-Attwood and Nancy Denslow

University of Florida Department of Environmental Global Health and Physiological Sciences

Center for Environmental and Human Toxicology Emerging Pathogens Institute

Utility of In Vitro Assays in Aquatic Monitoring

Focus on aquatic scenarios State-of-the-science through 2 case studies

- Monitoring throughout a WWTP pipeline - Linking in vitro responses to in vivo effects

Challenges and forward thinking for discussion

- How might assays be integrated into monitoring programs? - What are gaps in predictability measures of in vivo effects?

Adverse Outcome Pathways (AOPs)

linkage between a molecular initiating event (MIE) and adverse outcome (AO) that occurs at a level of biological organization considered relevant to regulatory decision-making

AOP provides a toxicological

tool for selection/use of bioanalytical assays in the context of hazard assessment

Epa.gov

Wittwher et al., 2017

In Vitro Assay Bio-suite

Escher et al., Environ. Sci. Technol., 2014, 48 (3), pp 1940–1956

• High-throughput screening using cell-based and cell-free bioassays

• Used to - Elucidate MOA - Prioritize chemical testing - Develop predictive models

• Not meant to replace regulatory in vivo tests but provide hazard information for chemical screening and prioritization

• Movement towards testing complex environmental samples of unknown composition

• AOPs have evolved to include metabolism, quantification, computational models

Nuclear Receptors/transcription factors Level of activity Nuclear Receptors/transcription factors

Level of activity

AhR Aryl Hydrocarbon receptor ++++ PPARa Peroxisome proliferator-activated receptor ++++ AP1 Activator protein 1 + PPARd1 Peroxisome proliferator-activated receptor + AR Androgen receptor + PPARg Peroxisome proliferator-activated receptor ++ CAR Constitutive androstane receptor +++ PXR Pregnane-X-receptor + ERa Estrogen receptor alpha ++++ RARa Retinoic Acid receptor, alpha ++++++ ERb Estrogen receptor beta +++ RARb Retinoic Acid receptor, beta +++++ ERRg Estrogen receptor related gamma ++ RARg Retinoic Acid receptor, gamma ++++++ FXR Farnesoid X Receptr + RORb Retinoid related orphan receptor beta ++++ GR Glucocorticoid receptor ++ RXRa Retinoic-X receptor, alpha + HNF4a Hepatocyte Nuclear factor 4 alpha + RXRb Retinoic-X receptor, beta +++ LXR Liver X receptor + VDR Vitamin D receptor + NRF2 Nuclear factor erythroid 2-related factor 2 +++

Attagene Assays - FACTORIAL™ technology that enables quantitative assessment of activities of multiple NRs/TFs within cell. 73 receptors/TFs tested; 23 total hits with 11 strong hits

Case Study 1: Monitoring of a WWTP pipeline

(Nancy Denslow)

A= Effluent 2 B= Effluent 1 C= Ozonation D= Storm water E= Membrane F= RO G= River Water H = AO J= Blank K= Drinking water CA= SCCWRP proj

ER alpha

GeneBlazer – individual reporter assays

A B C D E F G H J K CA


(Nancy Denslow)

A= Effluent 2 B= Effluent 1 C= Ozonation D= Storm water E= Membrane F= RO G= River Water H = AO J= Blank K= Drinking water CA= SCCWRP proj

ER alpha

GeneBlazer – individual reporter assays

A B C D E F G H J K CA


(Nancy Denslow)

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Data obtained from bioanalytical assays: - Indicator of overall water extract activity - With maximum effect – benchmark effect concentrations can be quantified - Useful to calculate response thresholds (EC50, EC10, BEQs, PTEC) - Linear dilution response increases confidence

Ideal battery of bioassays proposed for WWTE: - Induction of xenobiotic metabolism - Endocrine disruption - Reactive modes of action (genotoxicity) - Adaptive stress response (Ox stress) - Cytotoxicity and systemic response

Case Study 2: Linking in vitro responses to in vivo effects

• Establish quantitative linkages between in vitro assays and in vivo traditional endpoints of adversity

• Focus on existing AOP knowledge in fish; activation of estrogen receptors and subsequent induction of egg transcripts (vitellogenin and choriogenins), sex ratio

• Two life stages of inland silverside (Menidia beryllina), established EPA model for estuarine toxicity

• Water exposures to chemicals with known estrogenic activity; estradiol, estrone, nonylphenol • GeneBLAzer® estrogen receptor alpha transactivation assay

Mehinto et al., Environ. Sci. Technol., 2017, In Press


7-day exposure 20 larvae x 4 replicates Endpoints: growth (biomass),

survival, molecular changes (qPCR)

28-day exposure 15 fish x 4 replicates Endpoints: growth, gonad sex

differentiation, molecular changes (qPCR)

Larvae (10-17 dph) Juveniles (30-58 dph)

Chemical treatments:

Estradiol: 20, 200, 2,000 ng/L Nonylphenol: 20, 70, 200 µg/L

Estradiol: 2, 20, 200, 500 ng/L Estrone: 10, 30, 100, 300 ng/L Nonylphenol: 10, 20, 50, 70 µg/L

Mehinto et al., Environ. Sci. Technol., 2017, In Press


Chemical EC50 (M)

Concentration ng/L

E2 7E-11 20 E1 2.52E-10 68 4-NP 8.57E-08 18,900

Effective dose > EC50 for in vitro assay

Vtg – E2

Vtg - NP

* * **

Challenges and Recommendations for Discussion

Establish thresholds for tier-based monitoring of complex samples - Standardization of thresholds: EC50/EC10 values? Dose additivity models? - Linkage of environmental mixture in vitro screening to in vivo effects - Tailored bioanalytical suites that are commercially available

- Occurrence to PNEC - Consideration of unknown unknowns

Allen et al 2016




A role for tandem analysis with non-target chemical micropollutant detection technology

- Define relationship between chemical mixtures and bioactivity - Help in expansion of relevant assays

- ID unknown unknowns - Effects directed analysis – testing of complex fractions - Inclusion of new assays (neuro, immune)

A comprehensive workflow for organic micropollutant identification

1. Molecular feature detection 2. Molecular formula prediction 3. Postulate structure

• PubChem formula query 4. Holistic structure scoring

• Fragment prediction • Literature and patent data • Similarity searching

(Lee Ferguson)

Water Reuse in Turf-Management: Kiawah Island, SC

P5 P25

P43

WWTP

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Sample BEQ using EC10 values (nM) P5 106.3 P25 28.7 P43 93.5 WWTP 45.1 C ND

Sample BEQ using EC10 values (nM) P5 0.9 P25 0.3 P43 0.6 WWTP 2.1 C ND

Estrogen and Progesterone Activity of Kiawah Waters

WWTE > P5 > P43 > P25 P5 > P25 > P43 > WWTE

Targeted Micropollutant Analysis Compound quantity (ng/POCIS sampler)

Compound name Class Control WWTP lagoon Pond 5 Pond 25 Pond 43

Azoxystrobin Fungicide < 1.0 15.7 < 1.0 < 1.0 20.0

Fenarimol (Bloc) Fungicide < 1.0 < 1.0 2.4 4.8 4.5

Flutolanil Fungicide < 1.0 5.3 1.3 6.5 6.8

Propiconazole Fungicide < 1.0 < 1.0 45.0 100 778

Fipronil Insecticide < 1.0 8.3 < 1.0 1.2 1.4

Fipronil-desulfinyl Insecticide metabolite < 1.0 129 < 1.0 < 1.0 60.3

Fipronil-sulfone Insecticide metabolite < 1.0 149 15.8 68.5 138

Fipronil-sulfide Insecticide metabolite < 1.0 496 6.0 20.8 107

Acephate Insecticide < 2.0 < 2.0 < 2.0 < 2.0 < 2.0

Atrazine* Herbicide < 10.0 156 437 121 1,150

Atrazine-desisopropyl Herbicide metabolite < 10.0 < 10.0 < 10.0 < 10.0 < 10.0

Atrazine-desethyl Herbicide metabolite < 10.0 < 10.0 < 10.0 < 10.0 < 10.0

Oxadiazon Herbicide < 1.0 52.0 101 75.3 784

Pronamide Herbicide < 10.0 < 10.0 < 10.0 < 10.0 < 10.0

4-Nonylphenol (branched)* Surfactant metabolite 17.3 1,180 56.2 42.4 24.8

4-tert-Octylphenol Surfactant metabolite 3.4 69.4 2.3 11.8 15.7

Bisphenol A* Industrial chemical < 1.0 66.2 < 1.0 < 1.0 3.5

α-Ethynylestradiol* Synthetic estrogen < 1.0 < 1.0 < 1.0 < 1.0 < 1.0

β -Estradiol* Natural estrogen < 1.0 < 1.0 < 1.0 < 1.0 < 1.0

Estrone* Natural estrogen < 1.0 10.9 21.5 25.9 31.3

Estriol* Natural estrogen < 1.0 < 1.0 < 1.0 < 1.0 < 1.0

Not detected: Thiophanate-methyl, Metalaxyl, Iprodione, Acephate, Fenamiphos, Bromoxynil, Pronamide, Dicamba, 2,4-D

Targeted Micropollutant Analysis

4-Nonylphenol (branched)* 17.3 1,180 56.2 42.4 24.8

4-tert-Octylphenol 3.4 69.4 2.3 11.8 15.7

Bisphenol A* < 1.0 66.2 < 1.0 < 1.0 3.5

α-Ethynylestradiol* < 1.0 < 1.0 < 1.0 < 1.0 < 1.0

β-Estradiol* < 1.0 < 1.0 < 1.0 < 1.0 < 1.0

Estrone* < 1.0 10.9 21.5 25.9 31.3

Estriol* < 1.0 < 1.0 < 1.0 < 1.0 < 1.0

C WWTP P5 P20 P43

(ng/pocis)

Non-Targeted Micropollutant Analysis

2,997 features

Name Detected

previously Classification

valsartan yes high blood pressure

certirizine yes antihistamine

citalopram yes antidepresant

bupropion yes antidepresant N4-acetylsulfa- methoxazole yes antibiotic

fluconazole no antifungal

losartan no high blood pressure

irebesartan no high blood pressure

lamictal no anticonvulsant







Using ‘omic’ data as a link to sensitive MIE (e.g. transcriptomics, (phosphoproteomics)

Emerging ‘omics’ in Ecotox Brain phosphoproteome of minnows

exposed to EE2 and LNG

(Sabo-Attwood and Denslow, in prep)

Emerging ‘omics’ in Ecotox Brain phosphoproteome of minnows

exposed to EE2 and LNG

Identified MIEs: Receptor-ligand interaction DNA binding Covalent protein binding Protein oxidation Phosphorylation events

(Sabo-Attwood and Denslow, in prep)







Using ‘omic’ data as a link to sensitive MIE (e.g. transcriptomics, (phosphoproteomics)

Acknowledgements

University of Florida Joseph Bisesi, PhD

Alvina Mehinto, PhD Cody Smith, PhD

Candice Lavelle, PhD Gustavo Dominguez, PhD

Sean McGee, PhD Sumith Jayasinghe, PhD

Kevin Kroll, MS Sarah Robinson, MS

Jessica Clark, PhD

Duke University Gordon Getzinger, PhD

Audry Bone, PhD

University of Nebraska Alan Kolok, PhD

Kiawah Island Utility

and Lakes Division Norm Shea and Becky Dennis

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