Deng Ye, Zhang Ping, Qin Yujia, Tu Qichao, Yang Yunfeng, He Zhili, Schadt Christopher Warren, Zhou Jizhong
CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (CAS), Beijing, China.
Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA.
Environ Microbiol. 2016 Jan;18(1):205-18. doi: 10.1111/1462-2920.12981. Epub 2015 Aug 11.
Discerning network interactions among different species/populations in microbial communities has evoked substantial interests in recent years, but little information is available about temporal dynamics of microbial network interactions in response to environmental perturbations. Here, we modified the random matrix theory-based network approach to discern network succession in groundwater microbial communities in response to emulsified vegetable oil (EVO) amendment for uranium bioremediation. Groundwater microbial communities from one control and seven monitor wells were analysed with a functional gene array (GeoChip 3.0), and functional molecular ecological networks (fMENs) at different time points were reconstructed. Our results showed that the network interactions were dramatically altered by EVO amendment. Dynamic and resilient succession was evident: fairly simple at the initial stage (Day 0), increasingly complex at the middle period (Days 4, 17, 31), most complex at Day 80, and then decreasingly complex at a later stage (140-269 days). Unlike previous studies in other habitats, negative interactions predominated in a time-series fMEN, suggesting strong competition among different microbial species in the groundwater systems after EVO injection. Particularly, several keystone sulfate-reducing bacteria showed strong negative interactions with their network neighbours. These results provide mechanistic understanding of the decreased phylogenetic diversity during environmental perturbations.
近年来,识别微生物群落中不同物种/种群之间的网络相互作用引发了广泛关注,但关于微生物网络相互作用响应环境扰动的时间动态的信息却很少。在此,我们改进了基于随机矩阵理论的网络方法,以识别地下水微生物群落中响应乳化植物油(EVO)添加进行铀生物修复的网络演替。使用功能基因芯片(GeoChip 3.0)分析了来自一口对照井和七口监测井的地下水微生物群落,并重建了不同时间点的功能分子生态网络(fMEN)。我们的结果表明,EVO添加显著改变了网络相互作用。动态且有弹性的演替很明显:初始阶段(第0天)相当简单,中期(第4、17、31天)越来越复杂,第80天最复杂,随后后期(140 - 269天)逐渐变得不那么复杂。与之前在其他生境中的研究不同,负相互作用在时间序列fMEN中占主导,表明EVO注入后地下水系统中不同微生物物种之间存在强烈竞争。特别是,几种关键的硫酸盐还原菌与其网络邻居表现出强烈的负相互作用。这些结果为环境扰动期间系统发育多样性降低提供了机制性理解。