Johnson David R, Helbling Damian E, Men Yujie, Fenner Kathrin
Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland.
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA.
Environ Sci (Camb). 2015 May 1;1(3):272-278. doi: 10.1039/C5EW00016E. Epub 2015 Mar 25.
There is increasing interest in using meta-omics association studies to investigate contaminant biotransformations. The general strategy is to characterize the complete set of genes, transcripts, or enzymes from environmental communities and use the abundances of particular genes, transcripts, or enzymes to establish associations with the communities' potential to biotransform one or more contaminants. The associations can then be used to generate hypotheses about the underlying biological causes of particular biotransformations. While meta-omics association studies are undoubtedly powerful, they have a tendency to generate large numbers of non-causal associations, making it potentially difficult to identify the genes, transcripts, or enzymes that cause or promote a particular biotransformation. In this perspective, we describe general scenarios that could lead to pervasive non-causal associations or conceal causal associations. We next explore our own published data for evidence of pervasive non-causal associations. Finally, we evaluate whether causal associations could be identified despite the discussed limitations. Analysis of our own published data suggests that, despite their limitations, meta-omics association studies might still be useful for improving our understanding and predicting the contaminant biotransformation capacities of microbial communities.
利用元组学关联研究来调查污染物生物转化的兴趣与日俱增。一般策略是对环境群落中的全套基因、转录本或酶进行表征,并利用特定基因、转录本或酶的丰度来建立与群落生物转化一种或多种污染物潜力的关联。然后,这些关联可用于生成有关特定生物转化潜在生物学原因的假设。虽然元组学关联研究无疑很强大,但它们往往会产生大量非因果关联,这可能使得识别导致或促进特定生物转化的基因、转录本或酶变得困难。从这个角度来看,我们描述了可能导致普遍非因果关联或掩盖因果关联的一般情况。接下来,我们探索我们自己已发表的数据,以寻找普遍非因果关联的证据。最后,我们评估尽管存在上述局限性,是否仍能识别因果关联。对我们自己已发表数据的分析表明,尽管存在局限性,但元组学关联研究可能仍有助于增进我们对微生物群落污染物生物转化能力的理解并进行预测。