Quantifying environmental adaptation of metabolic pathways in metagenomics
Quantifying environmental adaptation of metabolic pathways in metagenomics
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DOI：
10.1073/pnas.0808022106
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发表时间：
2009-02-03
影响因子：
11.1
通讯作者：
Gerstein, Mark B.
中科院分区：
文献类型：
--
作者：
Gianoulis, Tara A.;Raes, Jeroen;Gerstein, Mark B.
最近，已经开发出方法来对异质环境的遗传内容进行采样（宏基因组学）。然而，这些序列是如何将不同的环境条件与特定的生物过程联系起来的，目前还不清楚。因此，一个主要的挑战是特定途径和子网络的使用如何反映微生物群落在环境和生境中的适应性，即，网络动态与环境特征的关系。以前的研究将环境视为离散的，有些简化的类（e。例如，在一个实施例中，陆地对海洋），并寻找它们之间明显的代谢差异（即，将分析视为典型的分类问题）。然而，环境差异是许多因素综合作用的结果，而这些因素往往只有微小的差异。因此，我们介绍了一种方法，采用相关性和回归，涉及多个，连续变化的因素，定义一个环境的程度上，特定的微生物途径存在于一个地理位置。此外，而不是只看个人的相关性（一对一），我们采用典型相关分析和相关技术来定义一个加权途径的合奏，最大限度地与环境变量的组合（多对多），我们称之为代谢足迹。应用到现有的水生生物数据集，我们确定了足迹预测他们的环境，可能被用作生物传感器。例如，我们展示了一个社区的能量转换策略和多个环境梯度之间的强多变量相关性。例如，在一个实施例中，温度）。此外，我们确定了氨基酸转运和辅因子合成的协变，这表明，有限的辅因子可以（部分）解释增加进口的氨基酸在营养有限的条件下。
Recently, approaches have been developed to sample the genetic content of heterogeneous environments (metagenomics). However, by what means these sequences link distinct environmental conditions with specific biological processes is not well understood. Thus, a major challenge is how the usage of particular pathways and subnetworks reflects the adaptation of microbial communities across environments and habitats-i.e., how network dynamics relates to environmental features. Previous research has treated environments as discrete, somewhat simplified classes (e. g., terrestrial vs. marine), and searched for obvious metabolic differences among them (i.e., treating the analysis as a typical classification problem). However, environmental differences result from combinations of many factors, which often vary only slightly. Therefore, we introduce an approach that employs correlation and regression to relate multiple, continuously varying factors defining an environment to the extent of particular microbial pathways present in a geographic site. Moreover, rather than looking only at individual correlations (one-to-one), we adapted canonical correlation analysis and related techniques to define an ensemble of weighted pathways that maximally covaries with a combination of environmental variables (many-to-many), which we term a metabolic footprint. Applied to available aquatic datasets, we identified footprints predictive of their environment that can potentially be used as biosensors. For example, we show a strong multivariate correlation between the energy-conversion strategies of a community and multiple environmental gradients (e. g., temperature). Moreover, we identified covariation in amino acid transport and cofactor synthesis, suggesting that limiting amounts of cofactor can (partially) explain increased import of amino acids in nutrient-limited conditions.