吕锡斌,吴云成,陈良强,刘明庆,杨帆,王蒙蒙,田伟,王莉.赤水河流域浮游细菌群落特征及其与水质的关系[J].环境科学学报,2021,41(11):4596-4605
赤水河流域浮游细菌群落特征及其与水质的关系
- Characteristics of the bacterioplankton community and their relationships with water quality in Chishui River Basin
- 基金项目：中央级公益性科研院所基本科研业务专项（No.GYZX20022202）
- 吕锡斌
- 贵州茅台酒股份有限公司, 仁怀 564501
- 吴云成
- 生态环境部南京环境科学研究所, 南京 210042
- 陈良强
- 贵州茅台酒股份有限公司, 仁怀 564501
- 刘明庆
- 生态环境部南京环境科学研究所, 南京 210042
- 王蒙蒙
- 生态环境部南京环境科学研究所, 南京 210042
- 田伟
- 生态环境部南京环境科学研究所, 南京 210042
- 摘要：赤水河是茅台酒酿造用水的水源地，其环境承载能力和水质质量与该流域微生物的群落息息相关，而目前赤水河浮游细菌群落组成、功能及其与水质之间的关系研究开展较少.本研究以茅台酒厂采水点为中心，在其上中下游设置了W1~W6共6个采样点，采用16S rDNA Miseq高通量测序技术研究了赤水河浮游细菌群落的组成及其功能.结果表明浮游细菌群落主要由55门、167纲、415目、706科、1431属组成，假单胞菌属（Pseudomonas）和马赛菌属（Massilia）是相对优势种群.此外，W1和W3采样点样品与其他采样点样品相比，群落组成差异较大.冗余分析表明CODMn、COD和DO是影响群落组成的显著因素（p<0.05），其与NH3-N、pH、TN、Novosphingobium、Stenotrophomona和Pontibacter等参数是该流域浮游细菌群落网络的重要节点.使用PICRUSt2软件对该水源地微生物群落的功能进行预测，结果显示其功能主要涉及代谢（metabolism）、环境信息处理（environmental information processing）、遗传信息处理（genetic information processing）等6类生物代谢通路和碳水化合物代谢（carbohydrate metabolism）、氨基酸代谢（amino acid metabolism）、能量代谢（energy metabolism）、辅助因子和维生素的代谢（metabolism of cofactors and vitamins）等46个子功能.本研究探明了赤水河浮游细菌群落组成和功能及其与环境因子的相互联系，丰富了赤水河地区的第一手研究资料，为改善其水域环境提供了参考.
- Abstract：The Chishui River is the main water source for Moutai liquor, and its environmental carrying capacity and water quality are closely related to the microbial community. However, few studies have been carried out on the composition and function of the bacterioplanktonic community and their relationship with the water quality until now. Here, the Moutai distillery was taken for the center of the study, and six locations (W1~W6) distributed in the upper and down streams of the river were set for sample collection. 16S rDNA Miseq high-throughput sequencing technology was used to study the composition and function of the bacterioplanktonic community in Chishui River. The results showed that the bacterioplanktonic community was comprised of 55 phyla, 167 classes, 418 orders, 706 families, and 1431 genera, among which, Pseudomonas and Massilia were the dominant genera. Moreover, the community compositions of W1 and W3 samples were significantly different from those of the other samples (p<0.05). Redundancy analysis (RDA) showed that water CODMn, COD and DO were the significant factors that could affect the composition of the bacterioplanktonic community. The three environmental factors and NH3-N, pH, TN, Novosphingobium, Stenotrophomonas, and Pontibacter were the key nodes of the bacterioplanktonic community network in the river. PICRUSt2 software was used to predict the function of the bacterioplankton communities, and the results revealed that these functions were primarily associated with six biological metabolic pathway categories (e.g., metabolism, environmental information processing, genetic information processing) and 46 subfunctions (e.g., carbohydrate metabolism, amino acid metabolism, energy metabolism, metabolism of cofactors and vitamins). The result will enrich the research data of Chishui River and provided reference for improving its water environment.
