Burnett Karen G.

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Burnett
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Karen G.
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  • Preprint
    Fundulus as the premier teleost model in environmental biology : opportunities for new insights using genomics
    ( 2007-09-01) Burnett, Karen G. ; Bain, Lisa J. ; Baldwin, William S. ; Callard, Gloria V. ; Cohen, Sarah ; Di Giulio, Richard T. ; Evans, David H. ; Gomez-Chiarri, Marta ; Hahn, Mark E. ; Hoover, Cindi A. ; Karchner, Sibel I. ; Katoh, Fumi ; MacLatchy, Deborah L. ; Marshall, William S. ; Meyer, Joel N. ; Nacci, Diane E. ; Oleksiak, Marjorie F. ; Rees, Bernard B. ; Singer, Thomas D. ; Stegeman, John J. ; Towle, David W. ; Van Veld, Peter A. ; Vogelbein, Wolfgang K. ; Whitehead, Andrew ; Winn, Richard N. ; Crawford, Douglas L.
    A strong foundation of basic and applied research documents that the estuarine fish Fundulus heteroclitus and related species are unique laboratory and field models for understanding how individuals and populations interact with their environment. In this paper we summarize an extensive body of work examining the adaptive responses of Fundulus species to environmental conditions, and describe how this research has contributed importantly to our understanding of physiology, gene regulation, toxicology, and ecological and evolutionary genetics of teleosts and other vertebrates. These explorations have reached a critical juncture at which advancement is hindered by the lack of genomic resources for these species. We suggest that a more complete genomics toolbox for F. heteroclitus and related species will permit researchers to exploit the power of this model organism to rapidly advance our understanding of fundamental biological and pathological mechanisms among vertebrates, as well as ecological strategies and evolutionary processes common to all living organisms.
  • Preprint
    Modelling interactions of acid–base balance and respiratory status in the toxicity of metal mixtures in the American oyster Crassostrea virginica
    ( 2009-11-12) Macey, Brett M. ; Jenny, Matthew J. ; Williams, Heidi R. ; Thibodeaux, Lindy K. ; Beal, Marion ; Almeida, Jonas S. ; Cunningham, Charles ; Mancia, Annalaura ; Warr, Gregory W. ; Burge, Erin J. ; Holland, A. Fredrick ; Gross, Paul S. ; Hikima, Sonomi ; Burnett, Karen G. ; Burnett, Louis ; Chapman, Robert W.
    Heavy metals, such as copper, zinc and cadmium, represent some of the most common and serious pollutants in coastal estuaries. In the present study, we used a combination of linear and artificial neural network (ANN) modelling to detect and explore interactions among low-dose mixtures of these heavy metals and their impacts on fundamental physiological processes in tissues of the Eastern oyster, Crassostrea virginica. Animals were exposed to Cd (0.001 – 0.400 μM), Zn (0.001 – 3.059 μM) or Cu (0.002 – 0.787 μM), either alone or in combination for 1 to 27 days. We measured indicators of acid-base balance (hemolymph pH and total CO2), gas exchange (Po2), immunocompetence (total hemocyte counts, numbers of invasive bacteria), antioxidant status (glutathione, GSH), oxidative damage (lipid peroxidation; LPx), and metal accumulation in the gill and the hepatopancreas. Linear analysis showed that oxidative membrane damage from tissue accumulation of environmental metals was correlated with impaired acid-base balance in oysters. ANN analysis revealed interactions of metals with hemolymph acid-base chemistry in predicting oxidative damage that were not evident from linear analyses. These results highlight the usefulness of machine learning approaches, such as ANNs, for improving our ability to recognize and understand the effects of sub-acute exposure to contaminant mixtures.