Post on 02-Aug-2020
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
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
Case Study 1: Monitoring of a WWTP pipeline
(Nancy Denslow)
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Case Study 1: Monitoring of a WWTP pipeline
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
Case Study 2: Linking in vitro responses to in vivo effects
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
Case Study 2: Linking in vitro responses to in vivo effects
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
* * **
Case Study 2: Linking in vitro responses to in vivo effects
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
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
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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Dilution 1Dilution 2Dilution 3Dilution 4
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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
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
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)
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)
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
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)
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