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系统发生预测仅能预测少数化学物质在水生动物中的敏感性。

Phylogeny predicts sensitivity in aquatic animals for only a minority of chemicals.

机构信息

University of Southern California Department of Biological Sciences, Los Angeles, CA, USA.

出版信息

Ecotoxicology. 2024 Oct;33(8):921-936. doi: 10.1007/s10646-024-02791-7. Epub 2024 Jul 22.

Abstract

There are substantial gaps in our empirical knowledge of the effects of chemical exposure on aquatic life that are unlikely to be filled by traditional laboratory toxicity testing alone. One possible alternative of generating new toxicity data is cross-species extrapolation (CSE), a statistical approach in which existing data are used to predict the effect of a chemical on untested species. Some CSE models use relatedness as a predictor of chemical sensitivity, but relatively little is known about how strongly shared evolutionary history influences sensitivity across all chemicals. To address this question, we conducted a survey of phylogenetic signal in the toxicity data from aquatic animal species for a large set of chemicals using a phylogeny inferred from taxonomy. Strong phylogenetic signal was present in just nine of thirty-six toxicity datasets, and there were no clear shared properties among those datasets with strong signal. Strong signal was rare even among chemicals specifically developed to target insects, meaning that these chemicals may be equally lethal to non-target taxa, including chordates. When signal was strong, distinct patterns of sensitivity were evident in the data, which may be informative when assembling toxicity datasets for regulatory use. Although strong signal does not appear to manifest in aquatic toxicity data for most chemicals, we encourage additional phylogenetic evaluations of toxicity data in order to guide the selection of CSE tools and as a means to explore the patterns of chemical sensitivity across the broad diversity of life.

摘要

我们对化学物质暴露对水生生物影响的实证知识存在很大的差距,仅通过传统的实验室毒性测试是不可能填补这些差距的。生成新毒性数据的一种可能替代方法是跨物种外推(CSE),这是一种统计方法,其中使用现有数据来预测化学物质对未经测试的物种的影响。一些 CSE 模型使用亲缘关系作为化学敏感性的预测指标,但关于共同进化历史在多大程度上影响所有化学物质的敏感性,我们知之甚少。为了解决这个问题,我们使用从分类学推断出的系统发育,对大量化学物质的水生动物物种毒性数据进行了亲缘关系信号的调查。在 36 个毒性数据集的 9 个数据集中存在强烈的亲缘关系信号,而具有强烈信号的数据集之间没有明显的共同属性。即使是针对昆虫专门开发的化学物质,强烈的信号也很少见,这意味着这些化学物质可能对非目标类群(包括脊索动物)同样具有致命性。当信号强烈时,数据中明显存在敏感性的差异模式,这在为监管用途组装毒性数据集时可能具有信息性。虽然对于大多数化学物质来说,强烈的信号似乎没有在水生毒性数据中表现出来,但我们鼓励对毒性数据进行更多的系统发育评估,以指导 CSE 工具的选择,并探索广泛的生命多样性中的化学敏感性模式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12b9/11399186/c99097a42ed8/10646_2024_2791_Fig1_HTML.jpg

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