INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1; CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université d'Évry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire d'Etude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France.
Bioinformatics. 2014 Jan 1;30(1):61-70. doi: 10.1093/bioinformatics/btt597. Epub 2013 Oct 27.
The increasing availability of metabolomics data enables to better understand the metabolic processes involved in the immediate response of an organism to environmental changes and stress. The data usually come in the form of a list of metabolites whose concentrations significantly changed under some conditions, and are thus not easy to interpret without being able to precisely visualize how such metabolites are interconnected.
We present a method that enables to organize the data from any metabolomics experiment into metabolic stories. Each story corresponds to a possible scenario explaining the flow of matter between the metabolites of interest. These scenarios may then be ranked in different ways depending on which interpretation one wishes to emphasize for the causal link between two affected metabolites: enzyme activation, enzyme inhibition or domino effect on the concentration changes of substrates and products. Equally probable stories under any selected ranking scheme can be further grouped into a single anthology that summarizes, in a unique subnetwork, all equivalently plausible alternative stories. An anthology is simply a union of such stories. We detail an application of the method to the response of yeast to cadmium exposure. We use this system as a proof of concept for our method, and we show that we are able to find a story that reproduces very well the current knowledge about the yeast response to cadmium. We further show that this response is mostly based on enzyme activation. We also provide a framework for exploring the alternative pathways or side effects this local response is expected to have in the rest of the network. We discuss several interpretations for the changes we see, and we suggest hypotheses that could in principle be experimentally tested. Noticeably, our method requires simple input data and could be used in a wide variety of applications.
The code for the method presented in this article is available at http://gobbolino.gforge.inria.fr.
代谢组学数据的可用性不断提高，使我们能够更好地理解生物体对环境变化和压力的即时反应所涉及的代谢过程。这些数据通常以代谢物浓度显著变化的列表形式出现，如果不能精确地可视化这些代谢物是如何相互关联的，就很难解释这些数据。
我们提出了一种方法，能够将任何代谢组学实验的数据组织成代谢故事。每个故事对应于一种可能的情景，解释了感兴趣的代谢物之间物质的流动。然后，可以根据希望强调两个受影响代谢物之间因果关系的哪种解释（酶激活、酶抑制或底物和产物浓度变化的级联效应），以不同的方式对这些情景进行排名。在任何选定的排名方案下，同样可能的故事可以进一步分组到一个单一的选集，该选集在一个独特的子网络中总结了所有同样合理的替代故事。选集只是这些故事的并集。我们详细介绍了该方法在酵母对镉暴露的反应中的应用。我们将该系统用作我们方法的概念验证，并表明我们能够找到一个很好地再现当前关于酵母对镉反应的知识的故事。我们进一步表明，这种反应主要基于酶的激活。我们还提供了一个框架，用于探索局部反应在网络其余部分预期产生的替代途径或副作用。我们讨论了我们看到的变化的几种解释，并提出了原则上可以通过实验测试的假设。值得注意的是，我们的方法只需要简单的输入数据，可以应用于各种不同的应用。
本文中提出的方法的代码可在 http://gobbolino.gforge.inria.fr 获得。
