Beijing Engineering Research Center of Environmental Material for Water Purification, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
Beijing Engineering Research Center of Environmental Material for Water Purification, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Guo Dian Fu Tong Science and Technology Development Co., Ltd., Beijing 100070.
Water Res. 2021 Jan 1;188:116540. doi: 10.1016/j.watres.2020.116540. Epub 2020 Oct 20.
Response of microbial interactions to environmental perturbations has been a central issue in wastewater treatment system. However, the interactions among anammox microbial community under salt perturbation is still unclear. Here, we used random matrix theory (RMT)-based network analysis to investigate the dynamics of networks under elevated salinity in an anammox system. Results showed that high salinity (20 and 30 g/L NaCl) inhibited anammox performance. Salinity led to closer and more complex networks for the overall network and subnetwork of Planctomycetes and Proteobacteria, especially under low salinity (5 g/L NaCl), which could serve as a strategy to survive under salt perturbation. Planctomycetes, most dominant phylum and playing crucial roles in anammox, possessed higher proportion of competitive relationships (64.3%) under 30 g/L NaCl. OTU 109 (closely related to Ignavibacterium), the only network hub detected in the anammox system, also had larger amount of competitive relationships (27.3%) than the control (0%) under 30 g/L NaCl. Similar result was found for the most abundant keystone bacteria Candidatus Kuenenia. These increasing competitions at different taxa level could be responsible for the deterioration of nitrogen removal. Besides, all the network topological features tended to reach the values of the original network, which showed the network of microbial community could gradually adapt to the elevated salinity. Microbial network analysis adds a different dimension for our understanding of the response in microbial community to elevated salinity.
微生物相互作用对环境胁迫的响应一直是废水处理系统的核心问题。然而,盐胁迫下厌氧氨氧化微生物群落的相互作用仍不清楚。在这里,我们使用基于随机矩阵理论(RMT)的网络分析来研究盐度升高对厌氧氨氧化系统中网络的动态影响。结果表明,高盐度(20 和 30 g/L NaCl)抑制了厌氧氨氧化性能。盐度导致整体网络和浮霉菌门和变形菌门子网更加紧密和复杂,特别是在低盐度(5 g/L NaCl)下,这可能是一种在盐胁迫下生存的策略。浮霉菌门,最主要的门,在厌氧氨氧化中起着至关重要的作用,在 30 g/L NaCl 下具有更高比例的竞争关系(64.3%)。在厌氧氨氧化系统中检测到的唯一网络枢纽 OTU 109(与 Ignavibacterium 密切相关),在 30 g/L NaCl 下的竞争关系(27.3%)也比对照(0%)大。丰度最高的关键细菌 Candidatus Kuenenia 也有类似的结果。不同分类群水平上竞争的增加可能是氮去除恶化的原因。此外,所有网络拓扑特征都趋于达到原始网络的值,这表明微生物群落的网络可以逐渐适应升高的盐度。微生物网络分析为我们理解微生物群落对升高的盐度的响应提供了一个不同的维度。