植物学预测水生动物对少数化学物质的敏感性
Alice L Coleman1, Suzanne Edmands2
1University of Southern California Department of Biological Sciences, Los Angeles, CA, USA. alicecol@usc.edu.
Ecotoxicology (London, England)
|July 22, 2024
概括
遗传学信号,或共同的进化历史,很少预测水生动物的化学敏感性. 这表明,对于大多数毒性数据,跨物种推断 (CSE) 可能不可靠,需要对化学敏感性模式进行进一步研究.
科学领域:
- 生态毒理学 生态毒理学
- 进化生物学 进化生物学
- 计算生物学 计算生物学
背景情况:
- 传统的实验室毒性测试在产生足够的关于化学物质对水生生物影响的经验数据方面存在局限性.
- 跨物种推断 (CSE) 提供了一个统计替代方法来预测未经测试的物种中的化学毒性,通常使用进化相关性.
- 共同的进化历史 (家族遗传信号) 对各种水生物种化学敏感性的影响仍然不太清楚.
研究的目的:
- 调查一系列化学品的水生动物毒性数据中的基因信号的流行程度和特征.
- 评估使用进化相关性作为水生物种化学敏感性预测指标的可靠性.
- 确定植物遗传信号是否可以为监管毒性评估的CSE的应用提供信息.
主要方法:
- 编制和分析了各种水生动物物种中许多化学品的毒性数据集.
- 使用分类学数据来表示进化关系的推断物种类型.
- 在毒性数据中的量化基因信号,以确定共享敏感性的模式.
主要成果:
- 在36个分析的毒性数据集中,只有9个中检测到强烈的遗传学信号.
- 在表现出强烈的家族遗传信号的数据集中没有发现一致的属性.
- 即使是针对昆虫特有的毒素,强烈的基因信号也是不常见的,这表明对非标生物体 (如带动物) 存在潜在风险.
- 当植物遗传信号强烈时,出现了明显的敏感性模式,提供了潜在的监管见解.
结论:
- 在水生毒性数据中,植物遗传信号不是化学敏感性的广泛预测因素.
- 遗传学信号的有限流行程度表明,在多种水生物种中应用标准CSE模型时要谨慎.
- 建议对毒性数据进行进一步的遗传学分析,以改进CSE工具并了解水生生物多样性的化学敏感性.
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