Avoiding the Misuse of Pathway Analysis Tools in Environmental Metabolomics.
Avoiding the Misuse of Pathway Analysis Tools in Environmental Metabolomics.
复制标题
避免环境代谢组学中途径分析工具的误用。
DOI：
10.1021/acs.est.2c05588
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发表时间：
2022-10-18
影响因子：
11.4
通讯作者：
Ebbels, Timothy M. D.
中科院分区：
文献类型：
--
作者：
Wieder, Cecilia;Bundy, Jacob G.;Frainay, Clement;Poupin, Nathalie;Rodriguez-Mier, Pablo;Vinson, Florence;Cooke, Juliette;Lai, Rachel P. J.;Jourdan, Fabien;Ebbels, Timothy M. D.
关键词：
在过去的 20 年里，代谢组学已经从环境科学领域一项令人兴奋的创新变成了几乎司空见惯的事情。它可以被认为是生成代谢生物标志物的一种手段，尽管也值得注意的是生态毒理学领域对环境生物标志物方法的中肯批评：简而言之，生物标志物是具有群体水平影响的宏观表型（例如，生存、繁殖和行为）的替代品，并且直接测量这些终点通常更直接和更有意义。 1 相反，一些研究强调获得潜在相关机制信息的能力，即使对于非模式生物也是如此，尤其是作为多组学方法的一部分时。 2 改进的生物学理解通常隐含或明确地成为将代谢组学纳入研究的理由的一部分。到目前为止，一切都很好，但存在一个问题：没有简单的、普遍接受的方法从代谢组数据中逆向工程机械理解，即使对于模式生物也是如此，对于非模式物种来说，问题甚至更加复杂。最接近标准方法的是途径分析（PA），即利用现有的生化知识。 PA 有多种方法，但我们只关注一种，即过度代表性分析 (ORA)。（注意，ORA 一词经常不被使用，许多作者一般指“通路富集”方法。）不过，应该清楚地理解，ORA 当然不是分析代谢组学数据的唯一方法。审查可用选项超出了本工作的范围，但我们将引导感兴趣的读者查看最近的评论。 3, 4 ORA 使用直观的方法来识别代谢物生物标志物“命中”，并将其与特定途径中的代谢物数量进行比较，以确定命中是否比偶然预期的多或少。因此，它具有计算简单和易于理解的双重优点。但它确实有缺点。所有依赖预定途径定义的方法都存在一个潜在的局限性：传统途径通常是在生化网络上施加秩序的主观和启发式方法。 5 虽然这很重要
Within the past 20 years, metabolomics has moved from an exciting innovation within the environmental sciences to something that is almost routine. It can be considered as a means to generate metabolite biomarkers, although it is also important to note the cogent criticisms of the environmental biomarker approach that have been made within ecotoxicology: briefly, that biomarkers are surrogates for macro phenotypes (eg, survival, reproduction, and behavior) that have population-level effects and that it is generally more straightforward and meaningful to measure these end points directly. 1 Some studies have emphasized instead the ability to gain potentially relevant mechanistic information, even for nonmodel organisms, especially when used as part of a multiomic approach. 2 An improved biological understanding is often implicitly or explicitly part of the justification of including metabolomics in a study.So far, so good, but there is a problem: there is no simple, universally accepted way of reverse engineering mechanistic understanding from metabolomic data, even for model organisms, and the problem is even more complicated for nonmodel species. The closest thing to a standard approach is pathway analysis (PA), ie, making use of existing biochemical knowledge. There are multiple approaches to PA, but we will focus on just one, over-representation analysis (ORA).(NB that the term ORA is often not used, and many authors refer generically to “pathway enrichment” methods.) It should clearly be understood, though, that ORA is certainly not the only approach to analyzing metabolomics data. It is beyond the scope of this work to review the options available, but we direct the interested reader to recent reviews. 3, 4 ORA uses the intuitive approach of identifying metabolite biomarker “hits” and comparing them to the numbers of metabolites in specific pathways, to determine if there are either more or fewer hits than one would expect by chance. It therefore has the twin advantages of being simple to calculate and simple to understand. It does, though, have disadvantages. One potential limitation is shared with all methods that rely on predetermined pathway definitions: traditional pathways are, generally, subjective and heuristic approaches to imposing order on a biochemical network. 5 While this is an important
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影响因子：
5.4
作者：
Bundy, Jacob G.;Sidhu, Jasmin K.;Rana, Faisal;Spurgeon, David J.;Svendsen, Claus;Wren, Jodie F.;Sturzenbaum, Stephen R.;Morgan, A. John;Kille, Peter
通讯作者：
Kille, Peter
影响因子：
4.1
作者：
Forbes, VE;Palmqvist, A;Bach, L
通讯作者：
Bach, L
影响因子：
14.9
作者：
Pang Z;Chong J;Zhou G;de Lima Morais DA;Chang L;Barrette M;Gauthier C;Jacques PÉ;Li S;Xia J
通讯作者：
Xia J
影响因子：
3
作者：
Marco-Ramell A;Palau-Rodriguez M;Alay A;Tulipani S;Urpi-Sarda M;Sanchez-Pla A;Andres-Lacueva C
通讯作者：
Andres-Lacueva C
影响因子：
4.3
作者：
Wieder C;Frainay C;Poupin N;Rodríguez-Mier P;Vinson F;Cooke J;Lai RP;Bundy JG;Jourdan F;Ebbels T
通讯作者：
Ebbels T