Physiological%20Responses%20of%20the%20Eastern%20Oyster%20Crassostrea%20virginica%20Exposed%20to%20Mixtures%20of%20Copper,%20Cadmium%20and%20Zinc

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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

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Biological Response Networks
Environmental changes
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(No Transcript)
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Can we generate a predictive model that links
physiological responses to environmental change?
Physiological responses
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Environmental changeexposure to multiple metals

• 216 C. virginica
• 27 combinations
• Cu (0 200 ppb)
• Cd (0 50 ppb)
• Zn (0 200 ppb)
• 0 27 days exposure

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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

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

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Cu content of tissues did not change with
exposure to Cu
Patterns of metal accumulation are complex and
interdependent
Metal exposure uMdays
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Zn content of tissues did not changewith
exposure to Zn
Tissue ?Gill ?Hepatopancreas
Metal exposure uMdays
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Cd content of tissues increasedwith exposure
to Cd
Tissue ?Gill ?Hepatopancreas
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Physiological Responses Correlated with Metal
Exposure
NONE
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Physiological Responses Correlatedwith Metal
Contents of Gill
Correlation Coefficient
LPx
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Physiological Responses Correlated with Metal
Contents of Hepatopancreas
Correlation Coefficient
LPx
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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.

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Systems Modeling
LPx
Can we find a model that better predicts the
relationship between oxidative damage and metal
content?
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Artificial Neural Networks

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

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Artificial Neural Networks
Tissue metals Cu Zn Cd
LPx or GSH
Hemolymph pH PO2 CO2
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Artificial Neural Networks(contd)

• 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

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Artificial Neural NetworksResults

• Poor prediction of GSH

Hepatopancreas Average nodes 7.2667 Average R2
0.0726
Gill Average nodes 6.3000 Average R2 0.1480

• Stronger prediction of LPx

Hepatopancreas Average nodes 6.4333 Average R2
0.3462
Gill Average nodes 5.8000 Average R2 0.5002
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Sensitivity Analysis for Gill - LPXbest-fit
model
nodes 7 R2 0.6465
Contribution to observed variance in LPx
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Sensitivity Analysis for Gill - LPxbest-fit
models
Hepatopancreas LPx
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Sensitivity Analysis forHepatopancreas -
LPxbest-fit model
nodes 8 R2 0.4818
Contribution to observed variance in LPx
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Sensitivity Analysis forHepatopancreas -
LPxbest-fit models
Gill LPx
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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.

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Thanks