Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China.
Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China.
Sci Total Environ. 2021 Apr 1;763:144509. doi: 10.1016/j.scitotenv.2020.144509. Epub 2020 Dec 16.
Understanding microbial interactions in the methanogenesis system through quorum sensing (QS) is very important for system optimization. Known QS genes were collected and classified into seven groups based on the signal molecules, which were used for constructing a hierarchical quorum sensing database (QSDB). QSDB containing 39,981 QS genes of seven QS groups was constructed and QS genes were analyzed with QSDB. Methanogen genomes were aligned with QSDB and acyl-homoserine lactones (AHLs) system was predicted as the most probable QS system. This database was further applied to analyze QS in methanogens from two upflow anaerobic sludge blanket-anaerobic filter hybrid reactors with conductive filter (CFB) and nonconductive filter (SEP), and a control without filter (CON). The maximum COD degradation rates in CFB (722.2 ± 10.1 mg/L·h) was elevated by 42.9% compared to CON (505.4 ± 5.98 mg/L·h). Metagenomic sequencing revealed Methanosaeta, Methanobacterium, and Methanosarcina were dominant, and the abundances was 4.3 times higher in the sludge of CFB compared to CON. The overall abundance of QS genes was CFB > SEP > CON, and AHLs were the most abundant group of QS genes. The filI/filR system, a luxI/luxR homolog, was firstly detected in methanogens, showing a high abundance in the CFB (0.085%) compared to in the CON (0.058%). The concentration of AHL molecules in CFB biofilms (0.04%) was about four times that in the CON (0.01%). Syntrophobacter and Smithella were the two major syntrophic bacteria of methanogens, and their abundances were positively correlated with methanogens. In addition, Syntrophobacter and Smithella harbored QS RpfB (component of the diffusible signal factor system) and PDE (component of cyclic di-GMP system). This study provides useful guidance for deeply understanding of QS in anaerobic digestion systems.
通过群体感应 (QS) 了解产甲烷系统中的微生物相互作用对于系统优化非常重要。收集了已知的 QS 基因,并根据信号分子将其分为七组,用于构建层次 QS 数据库 (QSDB)。构建了包含七个 QS 组的 39981 个 QS 基因的 QSDB,并使用 QSDB 对 QS 基因进行了分析。将产甲烷菌基因组与 QSDB 进行比对,预测出酰基高丝氨酸内酯 (AHL) 系统是最有可能的 QS 系统。该数据库进一步应用于分析来自两个上流式厌氧污泥床-厌氧滤池混合反应器(带有导电滤池 (CFB) 和非导电滤池 (SEP))和一个无滤池 (CON) 的产甲烷菌中的 QS。CFB 的最大 COD 降解速率(722.2±10.1mg/L·h)比 CON(505.4±5.98mg/L·h)提高了 42.9%。宏基因组测序显示,Methanosaeta、Methanobacterium 和 Methanosarcina 是优势菌,并且 CFB 中的污泥丰度比 CON 高 4.3 倍。QS 基因的总丰度为 CFB>SEP>CON,其中 AHLs 是最丰富的 QS 基因组。首次在产甲烷菌中检测到 filI/filR 系统,该系统是 luxI/luxR 同源物,在 CFB 中的丰度(0.085%)比 CON(0.058%)高。CFB 生物膜中的 AHL 分子浓度(0.04%)约为 CON(0.01%)的四倍。Syntrophobacter 和 Smithella 是产甲烷菌的两种主要共生菌,它们的丰度与产甲烷菌呈正相关。此外,Syntrophobacter 和 Smithella 携带 QS RpfB(可扩散信号因子系统的组成部分)和 PDE(环二鸟苷酸系统的组成部分)。本研究为深入了解厌氧消化系统中的 QS 提供了有用的指导。