CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.
CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.
Bioinformatics. 2015 Jun 1;31(11):1771-9. doi: 10.1093/bioinformatics/btv036. Epub 2015 Jan 23.
With the advent of meta-'omics' data, the use of metabolic networks for the functional analysis of microbial communities became possible. However, while network-based methods are widely developed for single organisms, their application to bacterial communities is currently limited.
Herein, we provide a novel, context-specific reconstruction procedure based on metaproteomic and taxonomic data. Without previous knowledge of a high-quality, genome-scale metabolic networks for each different member in a bacterial community, we propose a meta-network approach, where the expression levels and taxonomic assignments of proteins are used as the most relevant clues for inferring an active set of reactions. Our approach was applied to draft the context-specific metabolic networks of two different naphthalene-enriched communities derived from an anthropogenically influenced, polyaromatic hydrocarbon contaminated soil, with (CN2) or without (CN1) bio-stimulation. We were able to capture the overall functional differences between the two conditions at the metabolic level and predict an important activity for the fluorobenzoate degradation pathway in CN1 and for geraniol metabolism in CN2. Experimental validation was conducted, and good agreement with our computational predictions was observed. We also hypothesize different pathway organizations at the organismal level, which is relevant to disentangle the role of each member in the communities. The approach presented here can be easily transferred to the analysis of genomic, transcriptomic and metabolomic data.
随着元组学数据的出现,代谢网络开始被用于微生物群落的功能分析。然而,尽管基于网络的方法已广泛应用于单个生物体,但将其应用于细菌群落目前还受到限制。
本文提供了一种新颖的、基于元蛋白质组学和分类学数据的特定上下文重建程序。在没有每个细菌群落成员的高质量、基因组规模代谢网络的先验知识的情况下,我们提出了一种元网络方法,其中蛋白质的表达水平和分类学分配被用作推断一组活跃反应的最相关线索。我们的方法应用于从人为影响的多环芳烃污染土壤中提取的两个不同萘富集群落的特定上下文代谢网络的草图绘制,分别为有(CN2)和没有(CN1)生物刺激。我们能够在代谢水平上捕捉到两种条件之间的整体功能差异,并预测 CN1 中氟苯甲酸降解途径和 CN2 中香叶醇代谢的重要活性。进行了实验验证,观察到与我们的计算预测有很好的一致性。我们还假设在生物体水平上存在不同的途径组织,这对于分解群落中每个成员的作用很重要。这里提出的方法可以很容易地转移到基因组、转录组和代谢组数据的分析中。