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

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

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

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

Physiological Responses Physical weight, width, length accumulated metals Immune response culturable bacteria culturable Vibrio spp. hemocyte count Respiratory/acid-base/ redox status hemolymph Po 2, pH, & total CO 2 gill & hepatopancreas glutathione (GSH) gill & hepatopancreas lipid peroxidation (LPx)

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

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)

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

Zn ++ content of tissues did not change with exposure to Zn ++ Tissue ● … Gill □ … Hepatopancreas

Cd ++ content of tissues increased with exposure to Cd ++ Tissue ● … Gill □ … Hepatopancreas

Physiological Responses Correlated with Metal Exposure NONE

Physiological Responses Correlated with Metal Contents of Gill Correlation Coefficient LPx

Physiological Responses Correlated with Metal Contents of Hepatopancreas Correlation Coefficient LPx

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.

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

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

Artificial Neural Networks LPx or GSH Tissue metals Cu Zn Cd Hemolymph pH PO 2 CO 2

Artificial Neural Networks (cont’d) Generated 30 ANNs for each tissue and each output (LPx or GSH). Looked for models with high R 2 cross-validation with high R 2 low variance among models

Artificial Neural Networks Results Poor prediction of GSH Hepatopancreas Average #nodes = Average R 2 = Gill Average #nodes = Average R 2 = Stronger prediction of LPx Hepatopancreas Average #nodes = Average R 2 = Gill Average #nodes = Average R 2 =

Sensitivity Analysis for Gill - LPX: best-fit model % Contribution to observed variance in LPx # nodes = 7 R 2 =

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

Sensitivity Analysis for Hepatopancreas - LPx: best-fit model % Contribution to observed variance in LPx # nodes = 8 R 2 =

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

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, Po 2, Pco 2 ) provide powerful predictive models for the complex relationships between mixed metal exposure and oxidative damage in whole oysters.

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