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在与啤酒糟混合的储存污泥生物干化过程中细菌微生物群落演替的研究:对其生物多样性、结构、关联和功能的研究。

Insights into the succession of the bacterial microbiota during biodrying of storage sludge mixed with beer lees: Studies on its biodiversity, structure, associations, and functionality.

机构信息

School of Environment, Harbin Institute of Technology, Harbin 150090, PR China.

School of Environment, Harbin Institute of Technology, Harbin 150090, PR China.

出版信息

Sci Total Environ. 2018 Dec 10;644:1088-1100. doi: 10.1016/j.scitotenv.2018.06.298. Epub 2018 Jul 11.

Abstract

Biodrying was first used for post-treatment of storage sludge mixed with beer lees. In this study, dynamic changes in dissolved organic matter (DOM), bacterial community structure, bacterial associations as well as metabolic functions were investigated using Excitation-Emission Matrix (EEM) spectra, high-throughput sequencing, network and correlation matrix analyses, and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). Furthermore, a hypothetical model was proposed to better understand the biodrying process. The results showed that desired performance was obtained and DOM variations revealed that biodrying can increase biostability of the matrix. The bacterial communities differed among different stages of the biodrying. At the phylum level, the dominant phyla were Proteobacteria and Bacteroidetes in the mesophilic and cooling phases, whereas Firmicutes became the most dominant phylum in the thermophilic phase. At the genus level, the dominant bacteria in the mesophilic and cooling phases were not obvious, while Ureibacillus and Bacillus were the dominant genera in the thermophilic phase. Network and correlation matrix analyses were useful tools for insights into the bacterial interactions. PICRUSt metagenome inference indicated that metabolism, genetic information processing, and environmental information processing were the primary metabolic pathways. These results allowed us to advance a hypothetical model explaining how succession in bacterial associations regulates the dynamics of a biodrying system.

摘要

生物干化最初用于储存污泥与啤酒糟混合后的后处理。本研究采用激发-发射矩阵(EEM)光谱、高通量测序、网络和相关矩阵分析以及群落不可培养状态重建的系统发育推断(PICRUSt),研究了溶解有机物(DOM)、细菌群落结构、细菌关联以及代谢功能的动态变化。此外,还提出了一个假设模型,以更好地理解生物干化过程。结果表明,生物干化取得了预期的效果,DOM 的变化表明生物干化可以提高基质的生物稳定性。细菌群落在生物干化的不同阶段存在差异。在门水平上,在中温和冷却阶段,优势门为变形菌门和拟杆菌门,而在高温阶段,厚壁菌门成为最主要的门。在属水平上,中温和冷却阶段的优势菌不明显,而高温阶段的优势菌为尿芽孢杆菌属和芽孢杆菌属。网络和相关矩阵分析是深入了解细菌相互作用的有用工具。PICRUSt 宏基因组推断表明,代谢、遗传信息处理和环境信息处理是主要的代谢途径。这些结果使我们能够提出一个假设模型,解释细菌关联的演替如何调节生物干化系统的动态。

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