Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans.
Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans.
复制标题
使用秀丽隐杆线虫自动表型分析对化学品毒性进行分类和预测
DOI：
10.1186/s40360-018-0208-3
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发表时间：
2018-04-18
影响因子：
2.9
通讯作者：
Li G
中科院分区：
文献类型：
--
作者：
Gao S;Chen W;Zeng Y;Jing H;Zhang N;Flavel M;Jois M;Han JJ;Xian B;Li G
背景：传统的毒理学研究在很大程度上依赖于各种动物模型来了解各种化合物在生物学背景下的作用。考虑到使用高阶生物的实验所涉及的巨大成本、复杂性和时间。研究人员一直在探索避免这些缺点的替代模型。这种模型的一个例子是线虫秀丽隐杆线虫。C语言有很多优点。方法：由于这些优点使大规模的研究相对容易地开始，如何有效地捕获，组织和分析所产生的大量数据的问题必须得到解决。我们发展了一种利用C.优雅的。结果：不同毒性的化合物对C.在不同的和可检测的模式。我们发现，表型分析揭示了保守的功能，分类和预测不同的化学品的毒性。在不同的化学环境下。
Background:Traditional toxicological studies have relied heavily on various animal models to understand the effect of various compounds in a biological context. Considering the great cost, complexity and time involved in experiments using higher order organisms. Researchers have been exploring alternative models that avoid these disadvantages. One example of such a model is the nematode Caenorhabditis elegans. There are some advantages of C. elegans, such as small size, short life cycle, well defined genome, ease of maintenance and efficient reproduction.Methods:As these benefits allow large scale studies to be initiated with relative ease, the problem of how to efficiently capture, organize and analyze the resulting large volumes of data must be addressed. We have developed a new method for quantitative screening of chemicals using C. elegans. 33 features were identified for each chemical treatment.Results:The compounds with different toxicities were shown to alter the phenotypes of C. elegans in distinct and detectable patterns. We found that phenotypic profiling revealed conserved functions to classify and predict the toxicity of different chemicals.Conclusions:Our results demonstrate the power of phenotypic profiling in C. elegans under different chemical environments.
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影响因子：
3.8
作者：
Boyd WA;McBride SJ;Rice JR;Snyder DW;Freedman JH
通讯作者：
Freedman JH
影响因子：
3.4
作者：
Becker, Richard A.;Borgert, Christopher J.;Stott, William T.
通讯作者：
Stott, William T.
影响因子：
2.1
作者：
Li, Yu;Gao, Shan;Li, Guojun
通讯作者：
Li, Guojun
DOI：
10.1016/j.vascn.2004.04.002
发表时间：
2004-11-01
影响因子：
1.9
作者：
Dengg, Marlene;van Meel, Jacques C. A.
通讯作者：
van Meel, Jacques C. A.
影响因子：
3.7
作者：
Boyd WA;McBride SJ;Freedman JH
通讯作者：
Freedman JH