Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.
Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.
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DOI：
10.1039/d1np00023c
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发表时间：
2021-11-17
影响因子：
11.9
通讯作者：
van der Hooft JJJ
中科院分区：
文献类型：
--
作者：
Beniddir MA;Kang KB;Genta-Jouve G;Huber F;Rogers S;van der Hooft JJJ
覆盖：最近推出的计算代谢组挖掘工具已开始对非靶向代谢组学分析的化学和生物学解释产生积极影响。我们认为，这些目前的进展使人们有可能开始分解复杂的代谢物混合物的子结构和化学类信息，从而支持代谢组学分析的关键任务，包括代谢物注释，代谢谱的比较和网络分析。在本次审查中，我们强调并解释了涵盖2015年至2020年底的关键工具和新兴战略。这些工具中的大多数旨在处理和分析与质谱裂解数据耦合的液相色谱。我们首先定义什么是亚结构，它们如何与分子指纹相关，以及识别它们如何有助于分解复杂的混合物。我们继续基于特定分子支架和/或官能团的存在或不存在的化学类别，因此与子结构内在相关。我们讨论了新的工具来挖掘子结构，注释化学化合物类，并从代谢组学数据创建质谱网络，并使用两个案例研究来演示它们。我们还审查和推测的机会，NMR光谱为基础的代谢物挖掘复杂的代谢物混合物提供了发现子结构和化学类。最后，我们将描述依赖于它们的当前工具和策略的主要优点和局限性，以及我们对这个令人兴奋的领域如何发展到仓库规模的代谢组学分析的愿景。还讨论了来自基因组学分析和精心策划的分类记录的结构信息的补充来源。天然产物发现、药代动力学和药物代谢研究以及环境代谢组学等许多研究领域越来越依赖非靶向代谢组学来获得生化和生物学见解。这里描述的技术进步将有利于所有这些代谢组学学科的光谱数据转化为知识，可以回答生物学问题。这篇综述强调了代谢物注释的关键计算工具和新兴策略，并讨论了这些进展将如何使综合大规模分析加速天然产物的发现。
Covering: up to the end of 2020 Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions. This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.
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影响因子：
4.3
作者：
da Silva RR;Wang M;Nothias LF;van der Hooft JJJ;Caraballo-Rodríguez AM;Fox E;Balunas MJ;Klassen JL;Lopes NP;Dorrestein PC
通讯作者：
Dorrestein PC
影响因子：
7.4
作者：
Das S;Edison AS;Merz KM Jr
通讯作者：
Merz KM Jr
影响因子：
14.9
作者：
Allen F;Pon A;Wilson M;Greiner R;Wishart D
通讯作者：
Wishart D
影响因子：
5.9
作者：
Del Carratore，Francesco;Zych，Konrad;Breitling，Rainer
通讯作者：
Breitling，Rainer
影响因子：
7.4
作者：
Bruguiere, Antoine;Derbre, Severine;Richomme, Pascal
通讯作者：
Richomme, Pascal