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Aquat Toxicol. 2005 Jan 18;71(1):13-23. Epub 2004 Dec 8.

Contaminants as viral cofactors: assessing indirect population effects.

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  • 1Columbia Basin Research, School of Fisheries, University of Washington, Puget Sound Plaza, 1325 4th Avenue, Suite 1820, Seattle, WA 98101-2509, USA.


Current toxicological methods often miss contaminant effects, particularly when immune suppression is involved. The failure to recognize and evaluate indirect and sublethal effects severely limits the applicability of those methods at the population level. In this study, the Vitality model is used to evaluate the population level effects of a contaminant exerting only indirect, sublethal effects at the individual level. Juvenile rainbow trout (Oncorhynchus mykiss) were injected with 2.5 or 10.0 mg/kg doses of the model CYP1A inducer, beta-naphthoflavone (BNF) as a pre-stressor, then exposed to a challenge dose of 10(2) or 10(4) pfu/fish of infectious hematopoietic necrosis virus (IHNV), an important viral pathogen of salmonids in North America. At the end of the 28-d challenge, the mortality data were processed according to the Vitality model which indicated that the correlation between the average rate of vitality loss and the pre-stressor dose was strong: R2=0.9944. Average time to death and cumulative mortality were dependent on the BNF dose, while no significant difference between the two viral dosages was shown, implying that the history of the organism at the time of stressor exposure is an important factor in determining the virulence or toxicity of the stressor. The conceptual framework of this model permits a smoother transfer of results to a more complex stratum, namely the population level, which allows the immunosuppressive results generated by doses of a CYP1A inducer that more accurately represent the effects elicited by environmentally-relevant contaminant concentrations to be extrapolated to target populations. The indirect effects of other environmental contaminants with similar biotransformation pathways, such as polycyclic aromatic hydrocarbons (PAH), could be assessed and quantified with this model and the results applied to a more complex biological hierarchy.

[PubMed - indexed for MEDLINE]
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