A global approach to analysis and interpretation of metabolic data for plant natural product discovery.
A global approach to analysis and interpretation of metabolic data for plant natural product discovery.
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DOI：
10.1039/c3np20111b
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发表时间：
2013-04
影响因子：
11.9
通讯作者：
Wurtele ES
中科院分区：
文献类型：
--
作者：
Hur M;Campbell AA;Almeida-de-Macedo M;Li L;Ransom N;Jose A;Crispin M;Nikolau BJ;Wurtele ES
发现分子组分及其功能是发展关于代谢网络组织和调节的假说的关键。这些假设的迭代实验测试是最终能够实现精确计算建模和预测代谢结果的轨迹。这些信息对于理解天然产物的生物学特别重要，因为其代谢本身往往定义不清。在这里，我们描述的因素，必须到位，以优化预测生物学中的代谢组学的使用。实现这一愿景的关键是收集准确的时间分辨和空间界定的代谢物丰度数据和相关元数据。与代谢物谱分析相关的一个巨大挑战是与全面确定生物体的代谢组相关的复杂性和分析限制。此外，为了使代谢组学数据被研究界有效地利用，它必须在医学上可用的代谢组学数据库中进行管理。这些数据库需要清晰、一致的格式、易于访问的数据和元数据、数据下载以及可访问的计算工具来整合基因组系统规模的数据集。虽然转录组学和蛋白质组学整合了基因组的线性预测能力，但代谢组学代表了基因组的非线性最终生化产物，其产生于调控基因组表达的复杂系统。例如，代谢组学数据与代谢网络的关系被代谢物和基因产物之间的冗余连接所混淆。然而，代谢物之间的联系是可以通过化学规则预测的。因此，增强将代谢组与转录组和蛋白质组中的锚点整合的能力将增强基因组学数据的预测能力。我们详细介绍了代谢组学，工具和方法的代谢组学数据的统计分析，并将这些数据集与转录组学数据，以创建有关专门的代谢，产生天然产物化学的多样性的假设方法的公共数据库。我们讨论了生物学家，化学家，计算机科学家和统计学家之间的密切合作的重要性，在整个开发这样的综合代谢为中心的数据库和软件。
Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publically available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these dataset with transcriptomic data to create hypotheses concerning specialized metabolism that generates the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.