August 16, 2012
Genes Act As Early Warning Indicators Of Environmental Risks
Though no systemic studies have been performed, it is generally believed that genes are the most sensitive toxicological endpoints for pollutants, and are thus desirable early warning indicators of environmental risks. A recent study, however, unexpectedly found that the sensitivity of the gene expression effect for cadmium was significantly lower than the individual level chronic toxicity indicators (such as the no observed effect concentration, NOEC). Therefore, the gene expression effect may not be the most sensitive toxicological endpoint. Dr Yan Zhenguang, Prof. Liu Zhengtao and their colleagues from the State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences further investigated this problem using more pollutants and more data. They found that genes might be the most sensitive endpoints for copper, but were only moderately sensitive for cadmium and zinc, because of existing different data quantities and qualities. The work, entitled "Preliminary analysis of species sensitivity distribution based on gene expression effect", was published in SCIENCE CHINA Earth Sciences, 2012, Vol 55(6).
Environmental pollutants have been widely distributed for a long time, and extensive ecotoxicological studies at the molecular, cellular, tissue, individual, population, community or ecosystem levels have been carried out, generating a large amount of ecotoxicological data. Since the 1990s, the "omics" technologies have been used in the field of ecotoxicological studies, including "ecotoxicogenomics" characterizing the toxic effects of environmental pollutants or chemical substances on living organisms at the gene level, which has become a hot topic in research. It is generally believed that the impact of environmental toxicants on gene expression is sensitive to the effect indicators at the individual level and that the stress response to environmental toxicants at the gene expression level should be apparent much earlier than survival indicators of individual organisms and other indicators. The gene expression effect under the pollutant stress has become an important subject of study for early warning of environmental risks.
To evaluate the use of the gene expression effect data in risk assessment, Fedorenkova et al. (in Environ Sci & Technol, 2010, Vol 44(11)) selected cadmium as an example and carried out a comparative analysis of individual level acute and chronic toxicity data, and the gene expression effect data. The data used in that work was collected from the e-toxBase database and published literature. Unexpectedly, the sensitivity of the gene expression effect for cadmium was found to be significantly lower than the individual level chronic toxicity indicators. Therefore, the authors proposed that the gene expression effect at the molecular level is not the most sensitive toxicological endpoint. Thus, there was doubt as to whether the gene expression effect could be used as an early warning indicator of ecological risks. However, the quantity of selected pollutant and ecotoxicological data were both limited in that study; therefore, further study of this subject with more pollutants and more data is required before a reliable conclusion can be drawn.
In the present work, based on more abundant data collected from the ECOTOX, TOXNET, GEO, and CNKI databases, and published literature, a re-analysis of the sensitivity of the individual level acute and chronic toxicity data, and gene expression effect data, for cadmium was performed. At the same time, two other heavy metals, copper and zinc, were used to verify the findings for cadmium. To compare the sensitivity difference, distribution fitting was carried out on the individual level acute and chronic toxicity data, and the gene expression effect data, for the three heavy metals (see Figure 1). The results showed that different heavy metals produced different results. The sensitivity rank of ecotoxicological data for cadmium was chronic> gene> acute. The gene effect data of copper and zinc were not as sufficient as that of cadmium, but trends of species sensitivity were apparent. Based on these trends, the species sensitivity rank of zinc was similar to that of cadmium. For copper, the sensitivity of the gene effect data was greater than that of individual level acute and chronic toxicity data. For all three heavy metals, the individual level acute toxicity data was the least sensitive.
For quantitative comparison of sensitivity of the individual level acute toxicity, chronic toxicity and the gene expression effect, the HC5 with 95% protection ratio of aquatic organisms was calculated for each of the three metals. The HC5 value is an important measure for calculating water quality criteria. The calculated results indicated that the gene expression effect indicators of cadmium were seven times more sensitive than the individual level acute toxicity effect indicators and three times less sensitive than the individual level chronic toxicity indicators. For copper and zinc, there were only seven and three gene expression effect data, respectively. The SSD curve for copper was generated to analyze the sensitivity trend (Figure 1). The results showed that the gene expression effect was more sensitive than the chronic toxicity and acute toxicity effects. The data for zinc are shown in Figure 1; however, the corresponding SSD curve was not established because of the scarcity of the gene effect data.
Thus, in this work, more pollutants and biological toxicity data were used to analyze the differences in sensitivity of the individual level acute toxicity, chronic toxicity and the gene expression effect. Copper and zinc had relatively abundant gene effect data and were selected to verify the conclusions from the cadmium data. The results of cadmium toxicity data analysis agreed with those of Fedorenkova et al.: the sensitivity order was: "chronic> gene> acute". However, the gene expression effect data of copper were more sensitive than the chronic toxicity data, especially for the low concentration range, which is more important for environmental management. Thus, the gene expression effect could represent early warning indicators of ecological risk. Nevertheless, the existing data may not support its wider application. First, the concentration of the pollutants used in the current studies was not low enough, and the sensitivity of the gene expression effect would be improved if lower exposure concentrations were used. Second, there are thousands of target genes in vivo and the selected stress response genes from the references were probably not the genes that first respond to pollutants. Thus, only through high-throughput screening of target genes is it possible to identify the most sensitive response genes and properly evaluate the sensitivity of the gene expression effect. In addition, distribution logic makes it possible for the gene expression effect data to be used as an endpoint in studies of water quality criteria, although they still require further validation.
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