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Biomarkers in Action Examining the Effects of Dormant- Season Pesticide Runoff on Resident Fish Species ><> Andrew Whitehead <>< UC Davis, Bodega Marine.

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Presentation on theme: "Biomarkers in Action Examining the Effects of Dormant- Season Pesticide Runoff on Resident Fish Species ><> Andrew Whitehead <>< UC Davis, Bodega Marine."— Presentation transcript:

1 Biomarkers in Action Examining the Effects of Dormant- Season Pesticide Runoff on Resident Fish Species ><> Andrew Whitehead <>< UC Davis, Bodega Marine Laboratory

2 Talk Overview: Biomarkers: Definition Traits Advantages / Strengths Drawbacks / Difficulties Biomarkers in Action: Pesticides Project Goals Experimental Design Data

3 Biomarkers: Definition Physiological / biochemical response of an organism that is mechanistically / functionally related to xenobiotic exposure Principle: Xenobiotics interact with molecular targets through defined biochemical pathways which result in predictable physiological effects

4 Definition ( cont.) Biomarkers of Effect: - exposure has exceeded organisms ability to accommodate tissue necrosis DNA mutations AChE inhibition developmental abnormalities eggshell thinning demasculinization, feminization neoplasia, tumor formation Biomarkers of Exposure: - induction of accommodation responses metallothionein induction P450 induction DNA adducts heat shock protein induction increase in plasma cortisol levels induction of immune system measurement of metabolites serum leukocyte levels, antibody production

5 Biomarkers: Traits Variability Sensitivity Selectivity Clarity of Interpretation Biological Significance Duration of Response Ease of use, Cost, Labor

6 Biomarkers vs. Other Approaches H2O Chemistry Monitoring: Unequivocal demonstration of presence/absence Snapshot in time/space, partitioning, exposure pathways, linkage to biological responses... Body Burden Analysis: Multiple exposure pathways Metabolism, sequestration Bioassays: Biological consequences Lab setting, standard test species

7 Biomarkers: Advantages/Strengths So What? Linking Exposure to Effects Integrated Information - Spatial - Temporal - Additive effects Lab and Field experiments Resident / Native organisms Complex Field Evaluations: Do Contaminants Play a Role?

8 Biomarkers: Drawbacks/Difficulties Interpretation - Inferring causes - Scaling to meaningful effects - Timecourse of response Understanding components of variation Choice of biomarkers: What to measure? - Use tiered approach - Use other tools (chemistry) to focus choice

9 Examining the Effects of Dormant- Season Pesticide Runoff on Resident Fish Species PI: Dr. Susan Anderson – UC Davis, Bodega Marine Laboratory Coinvestigators: Dr. Bernie May – UC Davis Dr. Kathryn Kuivila – USGS Dr. David Hinton – Duke U Dr. Barry Wilson – UC Davis Graduate Student: Andrew Whitehead – UC Davis, Bodega Marine Laboratory Funding: EPA Star Grant, 1998

10 Project Goals: Overall: Examine biological effects of landscape-scale pesticide contamination on native fish at the individual and population levels. Characterize Exposure: GIS mapping of pesticide use databases Water chemistry Examine Effects on Individuals: Acetylcholinesterase (AChE) inhibition assay DNA strand break (comet) assay Examine Effects on Populations: DNA fingerprinting / population genetic analysis using AFLP and microsatellites

11 Field-Caging Approach : Cage suckers at 1 reference, 2 impacted sites Retrieve cages at multiple timepoints, in order to: A) Capture pesticide peak B) Examine recovery time Environmentally realistic Risky, chance of catastrophe Water and sediment exposure Lab Exposure to Field-collected water approach : collect field water in SS milk cans, transport to BML, expose fish - 6 d. Safe back-up Less environmentally realistic Can examine more sites Minimal sediment exposure Experimental Design: Exposure

12 Field Caging Design 11 12 13 14 15 16 17 18 19 20 21 22 23 Date (February, 2000) River Flow Rain Cage 1 OUT Cage 3 OUT 3 Cages IN Cage 2 OUT

13 San Joaquin River @ Vernalis

14 Orestimba Creek @ River Road

15 Orestimba Creek @ Orestimba Road

16 Lab Exposure Design Composite samples collected in 35-L stainless steel milk cans 6-day laboratory exposure to Sacramento sucker Multiple tissues excised and archived for biomarker analysis (Brain, muscle, liver, gill, blood) Sites: Feather R. upstream of ag. Feather R. downstream Orestimba Ck. upstream Orestimba Ck. downstream San Joaquin R. downstream Laboratory control

17 Experimental Design: Effects AChE Activity: Indicator of exposure to and/or effects from specific class of xenobiotics with same mechanism of action = Organophosphate and carbamate pesticides DNA Strand Breaks: Comet Assay Indicator of exposure to and/or effects from variety of stressors. = dormant-spray pesticides? Mutagenicity: Ames Assay Cytochrome P450 Activity

18 DATA: AChE Activity - Field San Joaquin R.

19 DATA: AChE Activity - Lab

20 DATA: DNA Strand Breaks - Field San Joaquin R.

21 DATA: DNA Strand Breaks - Lab

22 Summary: Project Suite of indicators, coupled with chemistry, has been a strong approach for assessing effects in the field, and in lab, on relevant species AChE Data: - As hypothesized, dormant-season pesticides are affecting resident fish - Would not have expected effects based on chemistry alone DNA Strand Break Data: - Indicates importance of chemicals other than pesticides Ongoing/Future Work: - Other indicators: Mutagenicity assay, P450 activity, more chemistry - Population genetic approach

23 Overall Summary For simple problems, use simple tools Complex problems demand more sophisticated approaches Biomarker information So What? Focus - what are the real problems? Integrated information Relevant organisms Field and lab evaluations

24 A Day in the Life... 4 X 4 ? Speed, anyone? Hmm... Catch anything?

25 Population-Level Biomarker Approach Working H: Long-term exposure to contaminants can alter gene pools of exposed populations. Rationale: Population genetic structure = historical record Record of environmental influences on previous generations Challenges: Distinguish natural variation from induced genetic change (field design) Step from correlation to attribution (test for mechanisms) Hypotheses of Mechanisms that may Drive Popn Genetic Change: A) Natural Selection: Loss of sensitive individuals B) Mutation: Accumulation of rare mutations over generations C) Random Genetic Drift: Bottleneck Erosion of genetic diversity

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