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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 Laboratory. Talk Overview:. Biomarkers: Definition Traits Advantages / Strengths Drawbacks / Difficulties
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Biomarkers in ActionExamining the Effects of Dormant-Season Pesticide Runoff on Resident Fish Species ><> Andrew Whitehead <>< UC Davis, Bodega Marine Laboratory
Talk Overview: • Biomarkers: • Definition • Traits • Advantages / Strengths • Drawbacks / Difficulties • Biomarkers in Action: Pesticides Project • Goals • Experimental Design • Data
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
Definition (cont.) • 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 • Biomarkers of Effect: • - exposure has exceeded organism’s ability to accommodate • tissue necrosis • DNA mutations • AChE inhibition • developmental abnormalities • eggshell thinning • demasculinization, feminization • neoplasia, tumor formation
Biomarkers: Traits • Variability • Sensitivity • Selectivity • Clarity of Interpretation • Biological Significance • Duration of Response • Ease of use, Cost, Labor
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
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?”
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
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
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
Experimental Design: Exposure • 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
Field Caging Design Cage 1 OUT 3 Cages IN Cage 2 OUT River Flow Cage 3 OUT Rain Rain 11 12 13 14 15 16 17 18 19 20 21 22 23 Date (February, 2000)
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
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
DATA: AChE Activity - Field San Joaquin R.
DATA: DNA Strand Breaks - Field San Joaquin R.
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
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
A Day in the Life... 4 X 4 ? Catch anything? Speed, anyone? Hmm...
Population-LevelBiomarker 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 Pop’n 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