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Physiological Responses of the Eastern Oyster Crassostrea virginica Exposed to Mixtures of Copper, Cadmium and Zinc

Physiological Responses of the Eastern Oyster Crassostrea virginica Exposed to Mixtures of Copper, Cadmium and Zinc. Brett Macey, Matthew Jenny, Lindy Thibodeaux,

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Physiological Responses of the Eastern Oyster Crassostrea virginica Exposed to Mixtures of Copper, Cadmium and Zinc

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  1. Physiological Responses of theEastern Oyster Crassostrea virginicaExposed to Mixtures ofCopper, Cadmium and Zinc Brett Macey, Matthew Jenny, Lindy Thibodeaux, Heidi Williams, Jennifer Ikerd, Marion Beal, Jonas Almeida, Charles Cunningham, AnnaLaura Mancia, Gregory Warr, Erin Burge, Fred Holland, Paul Gross, Sonomi Hikima, Karen Burnett, Louis Burnett, and Robert Chapman

  2. Physiological responses Immune responses Genomic and proteomic responses Biological Response Networks Environmental changes

  3. Environmental changes Can we generate a predictive model that links physiological responses to environmental change? Physiological responses

  4. Environmental change:exposure to multiple metals 216 C. virginica 27 combinations: Cu (0 – 200 ppb) Cd (0 – 50 ppb) Zn (0 – 200 ppb) 0 – 27 days exposure

  5. Physiological Responses Physical • weight, width, length • accumulated metals Respiratory/acid-base/ redox status • hemolymph Po2, pH, & total CO2 • gill & hepatopancreas glutathione (GSH) • gill & hepatopancreas lipid peroxidation (LPx) Immune response • culturable bacteria • culturable Vibrio spp. • hemocyte count

  6. Glutathione (GSH) Oxidative Damage (e.g. Lipid peroxidation)

  7. What We Learned • metal accumulation in tissues • physiological responses to mixed metal exposure • linear analysis • modelling interactions of metals to predict physiological effects • Non-linear analysis (Artificial Neural Networks)

  8. Cu++ content of tissues did not change with exposure to Cu++ Patterns of metal accumulation are complex and interdependent Metal exposure [uM*days]

  9. Zn++ content of tissues did not changewith exposure to Zn++ Tissue ●…Gill □…Hepatopancreas Metal exposure [uM*days]

  10. Cd++ content of tissues increasedwith exposure to Cd++ Tissue ●…Gill □…Hepatopancreas

  11. Physiological Responses Correlated with Metal Exposure NONE

  12. Physiological Responses Correlatedwith Metal Contents of Gill Correlation Coefficient LPx

  13. Physiological Responses Correlated with Metal Contents of Hepatopancreas Correlation Coefficient LPx

  14. Conclusions of Linear Analyses • Lipid Peroxidation (Oxidative Damage) was the most reliable marker for metal tissue content across tissue and treatments. • General Linear Models showed significant interaction between measured Cu and Zn in predicting oxidative damage.

  15. Environmental changes Cu, Zn, Cd Systems Modeling LPx Can we find a model that better predicts the relationship between oxidative damage and metal content?

  16. Artificial Neural Networks • non-linear statistical data modeling tools • used to model complex relationships • - between inputs and outputs • - find patterns in data

  17. Artificial Neural Networks Tissue metals Cu Zn Cd LPx or GSH Hemolymph pH PO2 CO2

  18. Artificial Neural Networks(cont’d) • Generated 30 ANNs for each tissue and each output (LPx or GSH). • Looked for models with • high R2 • cross-validation with high R2 • low variance among models

  19. Artificial Neural NetworksResults • Poor prediction of GSH Gill Average #nodes = 6.3000 Average R2 = 0.1480 Hepatopancreas Average #nodes = 7.2667 Average R2 = 0.0726 • Stronger prediction of LPx Gill Average #nodes = 5.8000 Average R2 = 0.5002 Hepatopancreas Average #nodes = 6.4333 Average R2 = 0.3462

  20. Sensitivity Analysis for Gill - LPX:best-fit model # nodes = 7 R2 = 0.6465 % Contribution to observed variance in LPx

  21. Sensitivity Analysis for Gill - LPx:best-fit models Hepatopancreas LPx

  22. Sensitivity Analysis forHepatopancreas - LPx:best-fit model # nodes = 8 R2 = 0.4818 % Contribution to observed variance in LPx

  23. Sensitivity Analysis forHepatopancreas - LPx:best-fit models Gill LPx

  24. Importance of these findings • Oxidative damage, measured by LPx, is a broad-based biomarker for metal-induced toxicity in oysters. • ANNs incorporating markers of oxidative damage (e.g. LPx) along with markers of redox status (hemolymph pH, Po2, Pco2) provide powerful predictive models for the complex relationships between mixed metal exposure and oxidative damage in whole oysters.

  25. Thanks

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