Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models
Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models
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DOI：
10.1371/journal.pone.0034670
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发表时间：
2012-04-16
期刊：
影响因子：
3.7
通讯作者：
Reed, Jennifer L.
中科院分区：
文献类型：
--
作者：
Hamilton, Joshua J.;Reed, Jennifer L.
基因组规模的网络重建是了解细胞代谢的有用工具，这种重建的比较可以提供生物体之间代谢差异的见解。最近的努力，比较基因组规模的模型主要集中在对齐代谢网络的反应水平，然后在反应和基因内容的差异和相似之处。然而，这些反应比较方法是耗时的，并且不能识别网络差异对网络功能状态的影响。我们已经开发了一种双层混合整数规划方法，CONGA，通过比较在基因水平上对齐的网络重建来识别代谢网络之间的功能差异。我们首先在两个重建中识别orthopathic基因，然后使用CONGA来识别基因含量差异引起代谢能力差异的条件。通过寻找基因，其在一个或两个模型中的缺失不成比例地改变了通过选择反应的通量（e.例如，在一个实施例中，生长或副产物分泌），我们能够识别结构代谢网络差异，从而实现独特的代谢能力。使用CONGA，我们探索大肠杆菌的两个代谢重建之间的功能差异，并确定一组负责两种模型之间的化学生产差异的反应。我们还使用这种方法来帮助开发聚球藻PCC 7002的基因组规模模型。最后，我们提出了结核分枝杆菌和金黄色葡萄球菌的代谢能力的差异的基础上潜在的抗菌目标。通过这些例子，我们证明了以基因为中心的方法来比较代谢网络，可以在功能水平上快速比较代谢模型。使用CONGA，我们可以识别反应和基因含量的差异，从而产生不同的功能预测。由于CONGA提供了一个通用的框架，因此它可以应用于发现模型和生物系统之间的功能差异。
Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e. g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.