Article(id=1242119547344453883, tenantId=1146029695717560320, journalId=1192105938417971205, issueId=1242119544966283483, articleNumber=null, orderNo=null, doi=10.13343/j.cnki.wsxb.20240340, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1717171200000, receivedDateStr=2024-06-01, revisedDate=null, revisedDateStr=null, acceptedDate=1724256000000, acceptedDateStr=2024-08-22, onlineDate=1774073977551, onlineDateStr=2026-03-21, pubDate=1724342400000, pubDateStr=2024-08-23, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1774073977551, onlineIssueDateStr=2026-03-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774073977551, creator=13701087609, updateTime=1774073977551, updator=13701087609, issue=Issue{id=1242119544966283483, tenantId=1146029695717560320, journalId=1192105938417971205, year='2024', volume='64', issue='11', pageStart='4011', pageEnd='4465', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1774073976985, creator=13701087609, updateTime=1774074072279, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1242119944725397854, tenantId=1146029695717560320, journalId=1192105938417971205, issueId=1242119544966283483, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1242119944725397855, tenantId=1146029695717560320, journalId=1192105938417971205, issueId=1242119544966283483, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4338, endPage=4357, ext={EN=ArticleExt(id=1242119547814215952, articleId=1242119547344453883, tenantId=1146029695717560320, journalId=1192105938417971205, language=EN, title=Characterization of bacterial communities in freshwater and seawater of the Yellow River estuary by 16S rRNA gene high-throughput absolute abundance quantification, columnId=1241045257748533520, journalTitle=Acta Microbiologica Sinica, columnName=Research Articles, runingTitle=null, highlight=null, articleAbstract=

[Objective] The Yellow River estuary located at the confluence of the Yellow River, land, and ocean is an area with mixed freshwater and seawater and a diverse and productive estuary ecosystem. This study aims to characterize the bacterial communities in freshwater and seawater of the Yellow River estuary. [Methods] High-throughput absolute abundance quantification was adopted to measure the absolute abundance of bacterial communities. The dominant taxa, α and β diversity, co-occurrence network, assembly mechanisms, and potential functions were compared between the bacterial communities in freshwater and seawater. The correlations between dominant taxa and environmental factors were explored. [Results] The absolute abundance of bacteria in freshwater was 2.61×106 copies/mL, which was 1.8 times of that in seawater. The common dominant phyla in freshwater and seawater were Actinomycetota, Pseudomonadota, Cyanobacteriota, and Bacteroidota, with significant differences in absolute abundance. The abundance of Actinomycetota ranked first in freshwater, which was approximately equal to the sum of all dominant phyla in seawater. The abundance of Pseudomonadota was the highest in seawater. The alpha diversity of bacteria in freshwater was higher than that in seawater. There were significant differences in the bacterial community structure between freshwater and seawater, mainly due to the differences in the abundance of the dominant taxa. The bacterial co-occurrence network in freshwater was more complex and stable than that in seawater, and stochastic processes dominated the bacterial community assembly in both freshwater and seawater. The bacterial communities in freshwater and seawater presented different functions, while they shared some common functions. Metabolism was the most abundant function, with higher relative abundance in freshwater than in seawater. Five environmental factors ((pH, oxidation-reduction potential (ORP), electrical conductivity (EC), total organic carbon (TOC), and total nitrogen (TN)) correlated with the dominant bacterial taxa to different extent. There were collinear relationships among the four environmental factors except EC. The dominant genera showing positive correlations with pH, TOC and TN were all negatively correlated with ORP, and vice versa. Actinomycetota and Pseudomonadota were positively and negatively correlated with pH, respectively. [Conclusion] The bacterial communities showed great differences between freshwater and seawater in the Yellow River estuary. The differences were mainly reflected in the abundance, diversity, functional structure, and co-occurrence network. The bacterial communities in freshwater and seawater had similar dominant taxa and assembly mechanisms. The results provide data support for studying the microbial ecology and exploiting microbial resources in the Yellow River estuary.

, correspAuthors=Wen ZHANG, authorNote=null, correspAuthorsNote=
*ZHANG Wen, E-mail:
, copyrightStatement=Copyright ©2024 Acta Microbiologica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Wenxiu BAO, Ming CHEN, Wen ZHANG, Jiahao TANG, Yuting LI, Peng GU, Yuan LU), CN=ArticleExt(id=1242119551240962468, articleId=1242119547344453883, tenantId=1146029695717560320, journalId=1192105938417971205, language=CN, title=基于16S rRNA基因高通量绝对定量解析黄河入海口淡水与海水水体细菌群落特征, columnId=1192149544164012138, journalTitle=微生物学报, columnName=研究报告, runingTitle=null, highlight=null, articleAbstract=

【目的】黄河入海口地处河流-陆地-海洋的交汇地带,是淡水水体与海水水体相互作用的混合区域,也是多样化和生产力较强的河口生态系统。本研究以黄河入海口为研究对象,探究其淡水与海水水体细菌群落特征。【方法】采用高通量绝对定量技术,获得细菌群落绝对丰度信息,在此基础上比较分析2种水体细菌群落优势物种组成、α和β多样性、网络共现模式、构建机制及潜在功能,并探究优势物种与水体环境因子的相关性。【结果】淡水水体细菌的绝对拷贝数为2.61×106 copies/mL,是海水的1.8倍。2种水体共同优势菌门为放线菌门、假单胞菌门、蓝细菌门和拟杆菌门等,其各自绝对丰度有明显差异。淡水中放线菌门数量最高,约等于海水中所有优势菌门数量之和,而海水中假单胞菌门数量最高。淡水水体细菌群落α多样性高于海水,2种水体细菌群落结构存在较明显的差异,主要源于各自优势物种丰度的差异。淡水水体细菌共现网络较海水更复杂和稳定,随机性过程主导2种水体细菌群落构建机制。2种水体细菌群落功能结构存在差异,但拥有共性功能。新陈代谢是2种水体细菌群落丰度最高的功能,其在淡水中的相对丰度显著高于海水。5种环境因子[pH、氧化还原电位(oxidation-reduction potential, ORP)、电导率(electrical conductivity, EC)、总有机碳(total organic carbon, TOC)和总氮(total nitrogen, TN)]与水体优势物种分别具有不同程度的相关性。除EC外的4种环境因子间均存在共线性关系,与pH、TOC和TN呈正相关关系的优势菌属均与ORP呈负相关,反之亦然。放线菌门和假单胞菌门分别与pH呈正相关和负相关关系。【结论】黄河入海口淡水和海水水体细菌群落特征存在较大差异,主要体现在细菌数量、多样性、功能结构和共现网络上,但2种水体具有相似的优势物种组成和群落构建机制。本研究结果可为黄河入海口水体微生物生态学研究及开发利用微生物资源提供数据支持。

, correspAuthors=张闻, authorNote=null, correspAuthorsNote=null, copyrightStatement=版权所有©《微生物学报》编辑部2024, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=RiYmfJJ/Yu76juV7jepx0w==, magXml=sB92YILc9I+KGUILOX+sEQ==, pdfUrl=null, pdf=XNfUnkzcquSIdhOTVd3QCw==, pdfFileSize=1682854, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=gShfb9Gayo+tDJR7lspnYA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=PoJpnymIunSKgUF6v5PI/g==, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=鲍文秀, 陈明, 张闻, 汤佳豪, 李瑜婷, 古鹏, 卢媛)}, authors=[Author(id=1243291005114495599, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, 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A: Absolute abundance at the phylum level. B: Relative abundance at the phylum level. C: Absolute abundance at the order level. D: Relative abundance at the order level. R: Freshwater; S: Seawater., figureFileSmall=sJSOPBS+04cgwdMtIiuB2g==, figureFileBig=Rm2MRwUTvPNnN0Nivc+b/g==, tableContent=null), ArticleFig(id=1243291009824699322, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=CN, label=图1, caption=淡水和海水水体前12优势物种绝对及相对丰度

A:门水平绝对丰度. B:门水平相对丰度. C:目水平绝对丰度. D:目水平相对丰度. R:淡水水体;S:海水水体

, figureFileSmall=sJSOPBS+04cgwdMtIiuB2g==, figureFileBig=Rm2MRwUTvPNnN0Nivc+b/g==, tableContent=null), ArticleFig(id=1243291010067968968, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=EN, label=Figure 2, caption=Clustering heatmap of absolute abundances of top 30 phyla in freshwater and seawater. R: Freshwater; S: Seawater., figureFileSmall=8i/fzGXNRzzxRjTWtFh0mg==, figureFileBig=WL6XfGjInWfoD6nYIhYqRw==, tableContent=null), ArticleFig(id=1243291010210575317, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=CN, label=图2, caption=淡水和海水水体绝对丰度排名前30菌门聚类热图

R:淡水水体;S:海水水体

, figureFileSmall=8i/fzGXNRzzxRjTWtFh0mg==, figureFileBig=WL6XfGjInWfoD6nYIhYqRw==, tableContent=null), ArticleFig(id=1243291010311238619, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=EN, label=Figure 3, caption=Principal co-ordinates analysis (PCoA) (A) and non-metric multidimensional scaling (NMDS) analysis (B) of beta diversity of bacterial communities. R: Freshwater; S: Seawater., figureFileSmall=jLgjGTjnHae4WvOar5phCQ==, figureFileBig=WaqAWJc19xbybUKenVM0yg==, tableContent=null), ArticleFig(id=1243291010445456351, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=CN, label=图3, caption=细菌群落β多样性主坐标分析(A)和非度量多维尺度分析(B)

R:淡水水体;S:海水水体

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R:淡水水体;S:海水水体

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A:混合水体. B:淡水水体. C:海水水体

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R:淡水水体;S:海水水体

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R:淡水水体;S:海水水体

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R:淡水水体;S:海水水体

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Physical and chemical properties of water samples

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GroupsT/℃pHORP (mV)EC (μs/cm)S (‰)TOC (mg/L)TN (mg/L)
R27.87.971339880.543.733.59
S22.37.5715342 83328.133.310.72
), ArticleFig(id=1243291012303532091, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=CN, label=表1, caption=

水样物化性质

, figureFileSmall=null, figureFileBig=null, tableContent=
GroupsT/℃pHORP (mV)EC (μs/cm)S (‰)TOC (mg/L)TN (mg/L)
R27.87.971339880.543.733.59
S22.37.5715342 83328.133.310.72
), ArticleFig(id=1243291012425166915, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=EN, label=Table 2, caption=

The alpha diversity index of the bacterial community

, figureFileSmall=null, figureFileBig=null, tableContent=
GroupsGoods_coverag indexObserved species indexShannon indexSimpson indexChao1 indexACE index
Different lowercase letters in the same column indicate significant differences between groups (P < 0.05).
R0.992 4b2 607a4.452a0.038 2a3 369.928a3 536.930a
S0.997 7a1 117b4.057b0.044 7a1 218.904b1 265.850b
), ArticleFig(id=1243291012571967566, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=CN, label=表2, caption=

细菌群落的α多样性指数

, figureFileSmall=null, figureFileBig=null, tableContent=
GroupsGoods_coverag indexObserved species indexShannon indexSimpson indexChao1 indexACE index
Different lowercase letters in the same column indicate significant differences between groups (P < 0.05).
R0.992 4b2 607a4.452a0.038 2a3 369.928a3 536.930a
S0.997 7a1 117b4.057b0.044 7a1 218.904b1 265.850b
), ArticleFig(id=1243291012697796694, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=EN, label=Table 3, caption=

Topological properties of bacterial communities co-occurrence networks

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Groups节点
Node

Edge
平均度
Average degree
图密度
Graph density
模块化指数
Modularity index
R+S1872 18923.410.130.72
R1473 74550.950.350.58
S1313 31750.640.390.43
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细菌群落共现网络拓扑参数

, figureFileSmall=null, figureFileBig=null, tableContent=
Groups节点
Node

Edge
平均度
Average degree
图密度
Graph density
模块化指数
Modularity index
R+S1872 18923.410.130.72
R1473 74550.950.350.58
S1313 31750.640.390.43
), ArticleFig(id=1243291012962037858, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=EN, label=Table 4, caption=

Contributions of different ecological processes of bacterial communities in water

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Groups同质选择
Homogeneous selection (%)
异质选择
Heterogeneous selection (%)
同质扩散
Homogenizing dispersal (%)
扩散限制
Dispersal limitation (%)
漂变
Drift (%)
R0.142.973.980.2892.63
S1.464.790.570.2792.91
), ArticleFig(id=1243291013075284072, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=CN, label=表4, caption=

不同生态过程对水体细菌群落构建过程的相对贡献

, figureFileSmall=null, figureFileBig=null, tableContent=
Groups同质选择
Homogeneous selection (%)
异质选择
Heterogeneous selection (%)
同质扩散
Homogenizing dispersal (%)
扩散限制
Dispersal limitation (%)
漂变
Drift (%)
R0.142.973.980.2892.63
S1.464.790.570.2792.91
), ArticleFig(id=1243291013196918897, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=EN, label=Table 5, caption=

Functional prediction of bacterial communities (level 2)

, figureFileSmall=null, figureFileBig=null, tableContent=
第二层级功能
Functions at level 2
R (%)S (%)所属第一层级功能
Belonging to the functions at level 1
Relative abundance greater than 2.00% of functions at level 2 are listed in the table. Different letters in the same row indicate significant differences (P < 0.05).
全球和概览图
Global and overview maps
41.07a40.54bMetabolism
碳水化合物代谢
Carbohydrate metabolism
8.58a8.40b
氨基酸代谢
Amino acid metabolism
7.83a8.21b
能量代谢Energy metabolism4.93a4.72b
辅助因子和维生素代谢Metabolism of cofactors and vitamins4.41a4.42a
核苷酸代谢
Nucleotide metabolism
2.58a2.38b
翻译Translation3.26a3.00bGenetic information processing
复制和修复Replication and repair2.70a2.41b
膜运输Membrane transport2.95a2.86aEnvironmental information processing
信号传导Signal transduction2.17a2.13a
细胞群落-原核生物Cellular community-prokaryotes2.20a2.14bCellular processes
), ArticleFig(id=1243291013339525239, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1242119547344453883, language=CN, label=表5, caption=

细菌群落功能预测(第二层级)

, figureFileSmall=null, figureFileBig=null, tableContent=
第二层级功能
Functions at level 2
R (%)S (%)所属第一层级功能
Belonging to the functions at level 1
Relative abundance greater than 2.00% of functions at level 2 are listed in the table. Different letters in the same row indicate significant differences (P < 0.05).
全球和概览图
Global and overview maps
41.07a40.54bMetabolism
碳水化合物代谢
Carbohydrate metabolism
8.58a8.40b
氨基酸代谢
Amino acid metabolism
7.83a8.21b
能量代谢Energy metabolism4.93a4.72b
辅助因子和维生素代谢Metabolism of cofactors and vitamins4.41a4.42a
核苷酸代谢
Nucleotide metabolism
2.58a2.38b
翻译Translation3.26a3.00bGenetic information processing
复制和修复Replication and repair2.70a2.41b
膜运输Membrane transport2.95a2.86aEnvironmental information processing
信号传导Signal transduction2.17a2.13a
细胞群落-原核生物Cellular community-prokaryotes2.20a2.14bCellular processes
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基于16S rRNA基因高通量绝对定量解析黄河入海口淡水与海水水体细菌群落特征
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鲍文秀 1 , 陈明 1 , 张闻 1, * , 汤佳豪 1 , 李瑜婷 1, 2 , 古鹏 1, 3 , 卢媛 2
微生物学报 | 研究报告 2024,64(11): 4338-4357
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微生物学报 | 研究报告 2024, 64(11): 4338-4357
基于16S rRNA基因高通量绝对定量解析黄河入海口淡水与海水水体细菌群落特征
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鲍文秀1, 陈明1, 张闻1, * , 汤佳豪1, 李瑜婷1, 2, 古鹏1, 3, 卢媛2
作者信息
  • 1 齐鲁工业大学(山东省科学院), 山东省科学院生态研究所, 山东省应用微生物重点实验室, 山东 济南 250103
  • 2 南开大学 环境科学与工程学院, 天津 300350
  • 3 日照市环境保护科学研究所有限公司, 山东 日照 276800
Characterization of bacterial communities in freshwater and seawater of the Yellow River estuary by 16S rRNA gene high-throughput absolute abundance quantification
Wenxiu BAO1, Ming CHEN1, Wen ZHANG1, * , Jiahao TANG1, Yuting LI1, 2, Peng GU1, 3, Yuan LU2
Affiliations
  • 1 Shandong Provincial Key Laboratory of Applied Microbiology, Ecology Institute of Shandong Academy of Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250103, Shandong, China
  • 2 College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
  • 3 Rizhao Institute of Environmental Science Co., Ltd., Rizhao 276800, Shandong, China
出版时间: 2024-08-23 doi: 10.13343/j.cnki.wsxb.20240340
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【目的】黄河入海口地处河流-陆地-海洋的交汇地带,是淡水水体与海水水体相互作用的混合区域,也是多样化和生产力较强的河口生态系统。本研究以黄河入海口为研究对象,探究其淡水与海水水体细菌群落特征。【方法】采用高通量绝对定量技术,获得细菌群落绝对丰度信息,在此基础上比较分析2种水体细菌群落优势物种组成、α和β多样性、网络共现模式、构建机制及潜在功能,并探究优势物种与水体环境因子的相关性。【结果】淡水水体细菌的绝对拷贝数为2.61×106 copies/mL,是海水的1.8倍。2种水体共同优势菌门为放线菌门、假单胞菌门、蓝细菌门和拟杆菌门等,其各自绝对丰度有明显差异。淡水中放线菌门数量最高,约等于海水中所有优势菌门数量之和,而海水中假单胞菌门数量最高。淡水水体细菌群落α多样性高于海水,2种水体细菌群落结构存在较明显的差异,主要源于各自优势物种丰度的差异。淡水水体细菌共现网络较海水更复杂和稳定,随机性过程主导2种水体细菌群落构建机制。2种水体细菌群落功能结构存在差异,但拥有共性功能。新陈代谢是2种水体细菌群落丰度最高的功能,其在淡水中的相对丰度显著高于海水。5种环境因子[pH、氧化还原电位(oxidation-reduction potential, ORP)、电导率(electrical conductivity, EC)、总有机碳(total organic carbon, TOC)和总氮(total nitrogen, TN)]与水体优势物种分别具有不同程度的相关性。除EC外的4种环境因子间均存在共线性关系,与pH、TOC和TN呈正相关关系的优势菌属均与ORP呈负相关,反之亦然。放线菌门和假单胞菌门分别与pH呈正相关和负相关关系。【结论】黄河入海口淡水和海水水体细菌群落特征存在较大差异,主要体现在细菌数量、多样性、功能结构和共现网络上,但2种水体具有相似的优势物种组成和群落构建机制。本研究结果可为黄河入海口水体微生物生态学研究及开发利用微生物资源提供数据支持。

高通量绝对定量  /  黄河入海口  /  淡水  /  海水  /  细菌群落

[Objective] The Yellow River estuary located at the confluence of the Yellow River, land, and ocean is an area with mixed freshwater and seawater and a diverse and productive estuary ecosystem. This study aims to characterize the bacterial communities in freshwater and seawater of the Yellow River estuary. [Methods] High-throughput absolute abundance quantification was adopted to measure the absolute abundance of bacterial communities. The dominant taxa, α and β diversity, co-occurrence network, assembly mechanisms, and potential functions were compared between the bacterial communities in freshwater and seawater. The correlations between dominant taxa and environmental factors were explored. [Results] The absolute abundance of bacteria in freshwater was 2.61×106 copies/mL, which was 1.8 times of that in seawater. The common dominant phyla in freshwater and seawater were Actinomycetota, Pseudomonadota, Cyanobacteriota, and Bacteroidota, with significant differences in absolute abundance. The abundance of Actinomycetota ranked first in freshwater, which was approximately equal to the sum of all dominant phyla in seawater. The abundance of Pseudomonadota was the highest in seawater. The alpha diversity of bacteria in freshwater was higher than that in seawater. There were significant differences in the bacterial community structure between freshwater and seawater, mainly due to the differences in the abundance of the dominant taxa. The bacterial co-occurrence network in freshwater was more complex and stable than that in seawater, and stochastic processes dominated the bacterial community assembly in both freshwater and seawater. The bacterial communities in freshwater and seawater presented different functions, while they shared some common functions. Metabolism was the most abundant function, with higher relative abundance in freshwater than in seawater. Five environmental factors ((pH, oxidation-reduction potential (ORP), electrical conductivity (EC), total organic carbon (TOC), and total nitrogen (TN)) correlated with the dominant bacterial taxa to different extent. There were collinear relationships among the four environmental factors except EC. The dominant genera showing positive correlations with pH, TOC and TN were all negatively correlated with ORP, and vice versa. Actinomycetota and Pseudomonadota were positively and negatively correlated with pH, respectively. [Conclusion] The bacterial communities showed great differences between freshwater and seawater in the Yellow River estuary. The differences were mainly reflected in the abundance, diversity, functional structure, and co-occurrence network. The bacterial communities in freshwater and seawater had similar dominant taxa and assembly mechanisms. The results provide data support for studying the microbial ecology and exploiting microbial resources in the Yellow River estuary.

high-throughput absolute abundance quantification  /  Yellow River estuary  /  freshwater  /  seawater  /  bacterial community
鲍文秀, 陈明, 张闻, 汤佳豪, 李瑜婷, 古鹏, 卢媛. 基于16S rRNA基因高通量绝对定量解析黄河入海口淡水与海水水体细菌群落特征. 微生物学报, 2024 , 64 (11) : 4338 -4357 . DOI: 10.13343/j.cnki.wsxb.20240340
Wenxiu BAO, Ming CHEN, Wen ZHANG, Jiahao TANG, Yuting LI, Peng GU, Yuan LU. Characterization of bacterial communities in freshwater and seawater of the Yellow River estuary by 16S rRNA gene high-throughput absolute abundance quantification[J]. Acta Microbiologica Sinica, 2024 , 64 (11) : 4338 -4357 . DOI: 10.13343/j.cnki.wsxb.20240340
黄河是中国第二大河流,也是全球含沙量最高的河流。它发源于青藏高原巴颜喀拉山北麓,流经9省区,最终汇入渤海。黄河入海口地处河流-陆地-海洋的交汇地带,是淡水水体与海水水体相互作用的混合区域[1-2]。河口是全球最多样化和生产力最强的生态系统之一。微生物群落作为其中不可或缺的组成部分,不仅在生态系统碳、氮、硫等物质循环中扮演着重要的角色,在维持水环境稳定中也发挥着关键作用[3-4],其结构特征直接关系着水体生态环境安全,有必要对其进行关注。
科学家围绕黄河入海口湿地、水体及沉积物等环境基质中的微生物群落特征已开展了系列研究,取得了一定进展。黄河口湿地沿自然盐度梯度形成了特定的微生物功能区,土壤盐度对微生物群落有显著影响[5-6]。黄河淡水水体与沉积物中细菌和古菌群落的丰度、多样性及组成显示,水体和沉积物间存在着不同的群落分布模式[7]。黄河口海域水体中细菌群落结构受黄河及环境因子影响呈现不同的空间分布特征[8];近岸沉积物中微生物群落组成和功能基因与近海沉积物相比组成相似,但信号强度更高[9]
然而,之前研究多数基于高通量扩增子测序得到的相对丰度数据分析微生物群落特征,不同样本微生物总丰度的差异可能使相对定量无法完全反映样本真实情况。研究表明菌群相对丰度的变化与绝对丰度变化不一定一致[10-11],而且基于相对定量和绝对定量的微生物组成、功能群落演变及环境驱动因素存在显著差异[12]。因此,有必要在黄河入海口微生物群落特征研究中考虑纳入微生物绝对丰度。目前获得微生物绝对丰度较常见的技术是实时定量聚合酶链式反应(qPCR),但特定物种qPCR需要设计特定引物,对引物特异性要求较高,复杂环境基质会干扰引物的特异性从而影响其定量的准确性。高通量绝对定量技术通过向样品DNA中添加一定量人工合成内参标准品序列,进行扩增子文库构建及高通量测序,再根据内参标准品测序序列数及其绝对拷贝数绘制标准曲线,最终计算出样品中操作分类单元(operational taxonomic unit, OTU)序列对应微生物物种的绝对拷贝数[13-14]。通过高通量绝对定量技术可获得样本中总菌、优势菌的绝对定量数据,客观还原菌群结构及丰度比例,而且内标法避免了qPCR等定量实验遇到的引物设计和优化难题,排除了样本基质干扰,较qPCR法特异性、灵敏度和一致性更高,能够实现环境微生物群落特征与微生物绝对丰度的关联。
本研究采用高通量绝对定量技术,对黄河入海口淡水与海水水体细菌群落进行对比研究,基于OTU绝对丰度数据,分析水体细菌群落优势物种组成、群落α和β多样性、群落网络共现模式、构建机制及潜在功能,并探究优势物种与水体环境因子的相关性。研究结果可为黄河入海口水体微生物生态学研究及开发利用微生物资源提供数据支持。
分别在黄河入海口黄河段(37°47′16″N,119°2′17″E,记作R)和入海口临近海域(37°54′8″N,119°5′48″E,记作S)设置采样点,分别采集淡水和海水水样。用排空式采集器采集水样,在无菌容器中混合均匀后分装到容积为1 L的高密度聚乙烯瓶(内含聚四氟乙烯层)中,装满密封后立即4 ℃冷藏并暗处保存运回实验室进行处理,水样保存运输依据文献[15],每个采样点至少采集3个样品重复(共3 L)。采用便携式多参数水质检测仪测定现场理化指标,如温度(temperature, T)、pH、氧化还原电位(oxidation-reduction potential, ORP)、电导率(electrical conductivity, EC)和盐度(salinity, S),运回的水样用总有机碳/总氮分析仪(Analytik Jena公司)测定水样总有机碳(total organic carbon, TOC)和总氮(total nitrogen, TN)含量,结果见表1。用0.22 μm孔径的水系微孔滤膜对水样进行抽滤,每250 mL换1张滤膜,将抽滤后的滤膜装入灭菌的15 mL离心管内,置于−80 ℃超低温冰箱保存,用于后续DNA的提取和16S rRNA基因高通量绝对定量测序。
将装有滤膜的离心管置于干冰中运送至上海美吉生物医药科技有限公司,进行水体微生物DNA提取和16S rRNA基因高通量绝对定量测序。采用MagAtrract Power Soil Pro DNA Kit (Hilden公司)抽提水体微生物群落总基因组DNA,使用1%的琼脂糖凝胶电泳检测基因组DNA质量,使用Qubit® 3.0 Fluorometer (Invitrogen公司)检测基因组DNA浓度,确保各样本的DNA浓度满足上机要求。将已知浓度的12条不同Spike-in DNA序列添加到样品DNA中,Spike-in DNA序列由天然16S rRNA基因保守区和人工可变区组成,作为内标用于样本的绝对定量。
以上述混合后的DNA为模板,使用携带Barcode序列的上游引物338F (5′-ACTCCTACG GGAGGCAGCAG-3′)和下游引物806R (5′-GGACTACHVGGGTWTCTAAT-3′)[16]对16S rRNA基因V3−V4可变区进行PCR扩增。PCR反应体系(20 μL):2×Pro Taq HS PCR Master Mix (艾科瑞生物公司) 10 µL,上、下游引物(5 µmol/L)各0.8 µL,DNA模板(10 ng/µL) 2 µL,ddH2O 6.4 µL。PCR反应条件:95 ℃预变性3 min;95 ℃变性30 s,55 ℃退火30 s,72 ℃延伸45 s,25个循环;72 ℃稳定延伸10 min。使用2%琼脂糖凝胶回收PCR产物,利用AxyPrep DNA凝胶回收试剂盒(AXYGEN公司)进行回收产物纯化,并用QuantiFluor™-ST蓝色荧光定量系统(Promega公司)对回收产物进行检测定量,之后对纯化后的PCR产物进行文库构建。利用Illumina PE250平台进行高通量测序。对测序得到的双端原始序列进行质控、拼接和过滤,得到优化序列。使用UPARSE v7.1[17-18]软件,按照97%的相似度对质控拼接后的序列进行操作分类单元OTU聚类并剔除嵌合体。识别并提取Spike-in OTU,根据每个样本Spike-in DNA测序序列数制作标准曲线方程,计算每个样本中OTU的绝对拷贝数,利用rrnDB数据库[19]进行16S rRNA基因拷贝数矫正,获得样本环境DNA中OTU的绝对丰度。由于Spike-in DNA为人工添加物,后续的数据分析基于过滤Spike-in序列后的OTU绝对丰度进行分析。
利用RDP Classifier[20]比对Silva 16S rRNA基因数据库,按照70%的置信度进行物种分类学注释,得到样本的绝对丰度组成和相对丰度组成信息。采用mothur[21]软件计算α多样性指数(Goods_coverage、Observed species、Shannon、Simpson、Chao1和ACE指数)。使用基于Bray-Curtis距离算法的主坐标分析(principal co-ordinates analysis, PCoA)和非度量多维尺度分析(non-metric multidimensional scaling analysis, NMDS)比较不同组间细菌群落β多样性。用线性判别分析法(linear discriminant analysis effect size, LEfSe) (LDA阈值=4,P < 0.05)确定组间各分类学水平上丰度显著差异的细菌类群。在相对丰度大于0.05%的OTUs中筛选Spearman相关系数|r| > 0.95且显著性水平P < 0.01的OTUs构建细菌群落共现网络[22-23]使用Gephi (v0.10.1)交互式软件对共现网络进行可视化。使用infer community assembly mechanisms by phylogrnrtic-bin-based null model analysis (iCAMP)生态模型量化同质选择、异质选择、同位扩散、扩散限制和漂变,分别对不同水体细菌群落构建过程的相对贡献,解析群落构建受随机和确定性过程的影响程度。用PICRUSt2软件(v2.2.0)对细菌群落进行功能预测,基于京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)数据库进行细菌功能注释,采用基于Bray-Curtis距离的主成分分析(principal component analysis, PCA)探究组间功能基因组成的差异。通过db冗余分析(distance- based redundancy analysis, db-RDA)研究优势菌门、样本分布与环境因子之间的关系,通过相关性热图分析比较相对丰度前50的菌属与不同环境因子的Spearman等级相关系数所有数据分析均借助R软件(v4.3.3)完成。
淡水和海水水体中OTU数量分别为3 545和1 711,淡水中的OTU数量远高于海水,大约是海水的2倍。淡水和海水水体共有OTU数是384,分别占各水样总OTU数的10.8% (淡水)和22.4% (海水),淡水独有的OTU数为3 161,海水为1 327,说明两种水体差异OTU较共有OTU数量更多,即水体微生物群落差异性较共性更大。
对每个OTU进行物种注释,基于注释结果并计算得到各分类水平上的物种绝对和相对丰度。淡水水体中细菌的绝对拷贝数为2.61×106 copies/mL,海水中细菌的绝对拷贝数为1.45×106 copies/mL,淡水中细菌的绝对拷贝数是海水中的1.8倍。门水平上丰度居前12的物种组成如图1A1B所示。淡水和海水水体共同优势菌门为放线菌门(Actinomycetota)、假单胞菌门(Pseudomonadota)、蓝细菌门(Cyanobacteriota)、拟杆菌门(Bacteroidota)、髌骨菌门(Patescibacteriota)、疣微菌门(Verrucomicrobiota)、绿弯菌门(Chloroflexota)、浮霉菌门(Planctomycetota)、酸杆菌门(Acidobacteriota)、蛭弧菌门(Bdellovibrionota)、芽孢杆菌门(Bacillota)和脱硫杆状菌门(Desulfobacterota),合计相对丰度占总体的98.6% (淡水)和99.4% (海水)。淡水和海水水体中优势菌门绝对丰度存在明显差异,淡水中仅放线菌门一门的数量约等于海水所有优势菌门数量之和。淡水中相对丰度居前3的菌门分别为放线菌门(55.3%)、假单胞菌门(23.3%)和蓝细菌门(8.9%);海水中分别为假单胞菌门(43.5%)、蓝细菌门(18.7%)和拟杆菌门(17.5%)。
淡水和海水水体前12优势菌目的绝对和相对丰度如图1C1D所示。共同优势菌目为弗兰克氏菌目(Frankiales)、伯克霍尔德氏菌目(Burkholderiales)、p_Cyanobacteriota_c_ Cyanobacteriia_o_Chloroplast (简写为Chloroplast)、微球菌目(Microtrichales)、黄杆菌目(Flavobacteriales)、微球菌目(Micrococcales)、红细菌目(Rhodobacterales)、SAR11_clade、紫螺旋菌目(Puniceispirillales)、大洋螺菌目(Oceanospirillales)、噬几丁质菌目(Chitinophagales)和噬纤维菌目(Cytophagales)。不同水体优势菌目数量存在明显差异。淡水中数量最多的菌目是弗兰克氏菌目,数量为1.09×106 copies/mL,相对丰度占比41.8%,而其在海水中数量甚微(3.10×102 copies/mL);数量居第2、3位的菌目分别是伯克霍尔德氏菌目(4.26×105 copies/mL,占比16.3%)和Microtrichales (3.22×105 copies/mL,占比12.3%)。海水水体排名前3的菌目与淡水完全不同,数量最多的菌目是Chloroplast,拷贝数为2.68×105 copies/mL,相对丰度占比18.5%,其次为黄杆菌目(2.40×105 copies/mL,占比16.6%)和微球菌目(2.38×105 copies/mL,占比16.4%),前3优势菌目间数量差别不大,除此之外,在海水中还发现数量高出淡水10余倍的红细菌目。
为了反映2种水体的物种组成相似性和差异性,根据优势菌门绝对丰度和样本间距离绘制了聚类热图(图2)。图2清晰地揭示了2种水体间的优势菌门丰存在不同程度的差异,尤其是MargulisbacteriaArmatimonadotaMethylomirabilota在海水中绝对丰度远低于淡水。
比较了黄河入海口2个采样点淡水和海水水体细菌群落α多样性指数,结果见表2。2种水体Goods_coverage指数均在0.99以上,说明样本测序深度足够,数据可以真实有效地反映样本环境的细菌多样性。淡水和海水水体Observed species分别为2 607和1 117,淡水约为海水的2倍,与两者OTU数量比例一致。同时,Chao1指数和ACE指数也存在同样规律,表明2种水体物种丰富度差异显著,淡水水体细菌群落丰富度更高。淡水水体Shannon指数大于海水,而Simpson指数小于海水。由于Shannon指数数值越大,Simpson指数越小代表细菌群落多样性越高,因此本研究结果表明淡水水体细菌群落多样性高于海水水体。
比较了2个采样点淡水和海水水体细菌群落β多样性,结果见图3图3A为基于细菌属水平的绝对丰度,使用Bray-Curtis距离算法的PCoA分析结果,PCoA1轴和PCoA2轴的贡献率分别为96.97%和2.27%;淡水水体样本点细菌群落集中分布于一、四象限,海水样本点分布于二、三象限,说明2种水体细菌群落结构存在较明显的差异。图3B为基于Bray-Curtis距离的NMDS结果,2种水体细菌群落样本点成簇分开且距离较远,佐证了PCoA分析结果;Stress < 0.1,反映了2种水体细菌群落结构在秩次上的差距。
采用LEfSe分析确定2种水体从门到种各分类学水平绝对丰度差异显著的细菌类群,结果如图4所示。2种水体相比较共得到69个差异物种,包括4门6纲11目13科17属18种,其中25个物种在淡水中丰度显著高于海水,44个物种在海水中丰度显著高于淡水。在门水平上:假单胞菌门、拟杆菌门、蓝细菌门和放线菌门是2种水体间的差异物种,其中假单胞菌门、拟杆菌门和蓝细菌门在海水显著富集,而放线菌门在淡水中显著富集。在纲水平上:γ-变形菌纲(Gammaproteobacteria)、酸微菌纲(Acidimicrobiia)和放线杆菌纲(Actinobacteria)在淡水中丰度显著高于海水,α-变形菌纲(Alphaproteobacteria)、拟杆菌纲(Bacteroidia)和Cyanobacteriia则正相反。在目水平上:噬几丁质菌目、伯克霍尔德氏菌目、Microtrichales和弗兰克氏菌目是淡水水体中的差异物种,而微球菌目、黄杆菌目、红细菌目、SAR11_cladeChloroplastPuniceispirillales和大洋螺菌目则是海水水体中的差异物种。在科水平上,嗜甲基菌科(Methylophilaceae)、丛毛单胞菌科(Comamonadaceae)、萤光杆菌科(Ilumatobacteraceae)和鱼孢菌科(Sporichthyaceae)是淡水中显著富集的细菌类群,微杆菌科(Microbacteriaceae)、红细菌科(Rhodobacteraceae)、Chloroplast-norank、黄杆菌科(Flavobacteriaceae)、SAR116_cladeClade_I、冷形菌科(Cryomorphaceae)、栖海岸菌科(Litoricolaceae)和藏红花线菌科(Crocinitomicaceae)在海水中显著富集。在属水平上,淡水中存在5个差异物种,分别是hgcI_cladeSporichthyaceae_norank、CL500_29_ marine_group、栖湖菌属(Limnohabitans)和未分类的嗜甲基菌科(Methylophilaceae_unclassified),而海水中有12个差异物种,分别是未分类的冷形菌科(Cryomorphaceae_unclassified)、OM43_cladeRS62_marine_groupCandidatus_Puniceispirillum、栖海岸菌属(Litoricola)、SAR116_clade_norank、NS5_marine_groupClade_IaCandidatus_Aquiluna、未分类的微杆菌科(Microbacteriaceae_unclassified)、海洋浮游菌属(Planktomarina)和Chloroplast_norank
细菌群落内菌株的相互关系在维持生态系统功能和结构稳定方面起关键作用,为探究水体细菌之间的关系,在OTU水平上对混合水体、淡水和海水水体分别构建了细菌共现网络(图5),其拓扑特性相关参数见表3。混合水体网络共得到187个节点,98.94%的节点与优势菌门相关,主要是假单胞菌门、放线菌门、拟杆菌门和蓝细菌门;得到2 189个边,正向和负向相关的边所占比例分别为99.45%和0.55%,正向相关边比例较高,表明物种间协同作用较强;模块化指数为0.72 (> 0.40,说明网络具有模块化结构[24]),具有模块化结构。淡水与海水水体细菌群落共现网络存在明显差异,淡水网络由147个节点和3 745条边组成,平均度为50.95,海水网络由131个节点和3 317条边组成,平均度为50.64。淡水网络的节点、边和平均度数值均高于海水,表明淡水水体细菌间较海水具有更复杂的互联关系和网络结构。淡水细菌群落共现网络负相关的边比例为14.21%,高于海水水体(10.49%),说明淡水水体物种之间的竞争更激烈。淡水和海水水体细菌群落共现网络模式的模块化指数分别为0.58和0.43 (> 0.40),表明2种水体中细菌均具有模块化结构,但淡水水体细菌共现网络的模块化程度大于海水水体。
使用iCAMP量化不同生态过程对水体细菌群落构建过程的相对贡献率,结果如表4所示。随机性过程(同质扩散、扩散限制和漂变之和)对淡水和海水水体细菌群落构建的相对贡献率分别为96.89%和93.75%,确定性过程(同质选择和异质选择之和)的相对贡献率分别为3.11%和6.25%,表明2种水体细菌群落构建机制中随机性过程占主导作用。相比于海水水体,淡水水体细菌群落的随机性过程贡献更高,即淡水受各种随机性过程的影响更突出。
使用PCA分析2种水体细菌群落功能,结果见图6。PCA1轴和PCA2轴的贡献率分别为98.71%和0.73%。淡水和海水水体样本点分别分布在PCA1轴的正、负方向,说明2种水体细菌群落功能结构存在差异。在2种水体细菌群落中均预测出第一层级的6个主要功能,其相对丰度排序为新陈代谢(metabolism) > 遗传信息处理(genetic information processing) > 环境信息处理(environmental information processing) > 细胞过程(cellular processes)及人类疾病(human diseases) > 有机系统(organismal systems),其中前3类功能丰度之和为90.85% (淡水)和89.72% (海水)。2种水体细菌群落间相对丰度存在显著差异的功能(图7),新陈代谢和遗传信息处理功能在淡水水体中的相对丰度显著高于海水,人类疾病和有机系统功能在海水水体中的丰度显著高于淡水。在第二层级预测出46个功能,其中相对丰度大于2.00%的功能有11个(表5)。归属于新陈代谢功能的第二层级功能有6个,分别为全球和概览图、碳水化合物代谢、能量代谢、氨基酸代谢、辅助因子和维生素代谢和核苷酸代谢。其中,全球和概览图占比最高,在淡水水体群落功能中所占比例显著高于海水,分别为41.07%和40.54%;此外,碳水化合物代谢、能量代谢在淡水中的丰度也显著高于海水,而氨基酸代谢在淡水中的丰度显著低于海水。
水体pH、ORP、EC、TOC和TN等环境因子是驱动水体细菌群落多样性的重要因素[25-26]。优势菌门、样本分布与上述环境因子相关性的db-RDA分析结果如图8所示。db-RDA内置模型判断pH、ORP、TOC和TN这4个环境因子存在共线性关系,因此以pH作为代表,与EC共同用于db-RDA分析。排序轴db-RDA1轴和db-RDA2轴对样本变化的解释率为73.89%和13.03%,共解释86.92%的差异信息。环境因子之间的夹角反映因子之间的相关性,pH与EC之间为钝角,说明两者存在负相关关系。环境因子箭头的长短代表其对物种的影响程度,pH与EC长度相近,说明两者与前10优势菌门丰度相关程度相似。环境因子箭头与原点到物种连线之间的夹角反映物种与环境因子之间的相关性。前10优势物种中,只有放线菌门与pH之间的夹角为锐角,呈正相关,与EC呈负相关;假单胞菌门、蓝细菌门和拟杆菌门与EC呈正相关关系,与pH呈负相关;其余物种分布在原点区域,与pH和EC的相关性较小。从样本点向环境因子箭头做投影,投影点距离原点的距离代表环境因子对样本群落分布影响的大小,点与箭头的方向是否一致代表正、负相关性。pH对淡水细菌群落分布呈正相关,对海水细菌群落分布呈负相关;EC影响则正相反,二者影响程度相近。
将丰度排名前50的菌属与环境因子进行相关性热图分析,结果如图9所示。存在共线性关系的4个环境因子pH、ORP、TOC和TN,与46个菌属均显著相关。其中,与pH、TOC和TN呈正相关关系的菌属有24个,与ORP均呈负相关关系,包含GKS98_freshwater_group、鞘氨醇杆状菌属(Sphingorhabdus)、噬冷菌属(Algoriphagus)、Candidatus_Methylopumilus、hgcI_clade、假单胞菌属(Pseudomonas)、莱茵海默氏菌属(Rheinheimera)等;剩余22个菌属如橙色杆菌属(Aurantivirga)、嗜光菌属(Luminiphilus)、沿海水杆菌属(Aquibacter)、Candidatus_Actinomarina、慢生杆菌属(Lentibacter)、RS62_marine_group、海洋浮游菌属(Planktomarina)等,与pH、TOC和TN呈负相关关系,与ORP呈正相关。与EC显著相关的菌属有33个,少于另4个环境因子影响的菌属数量。不同细菌受不同环境因子影响的程度存在差异。
通过对黄河入海口淡水和海水水体细菌群落进行16S rRNA基因高通量绝对定量分析,揭示该地区2种水体细菌群落的特征。
淡水水体OTU数量远高于海水,二者共有OTU分别占各自总OTU数的10.8% (淡水)和22.4% (海水),差异OTU各占89.2% (淡水)和77.6% (海水)。2种水体差异OTU较共有OTU数量更多,表明2种水体细菌群落存在较大差异。对细菌群落α和β多样性的分析结果显示,淡水水体细菌群落α多样性高于海水水体,2种水体细菌群落结构存在较明显的差异,这与OTU结果一致。水体中颗粒物含量和盐度的不同,可能是导致2种水体细菌群落存在显著差异的原因。淡水水体采样点位于黄河下游,黄河水体含泥沙量高,颗粒物多,为颗粒附着型细菌提供了较大的附着面积[27],颗粒物表面吸附了水体中的营养物质可为细菌提供适宜其生长的微环境;海水中颗粒物相对较少,不利于颗粒附着型细菌的生长,这可能导致了细菌群落间的差异。另外,研究报道盐度是影响入海口水体细菌群落结构及多样性的关键因子[28-30]。Campbell等[29]通过对沿入海口盐度梯度的水体细菌群落进行研究,指出低盐度(< 5‰)水体细菌多样性明显高于中等盐度(5‰−30‰)水体;本研究中淡水的盐度为0.54‰,海水的盐度为28.13‰,其多样性规律与本研究结果一致。
比较淡水和海水水体细菌绝对丰度发现,淡水水体中细菌的绝对拷贝数为2.61×106 copies/mL,是海水中的1.8倍(绝对拷贝数为1.45×106 copies/mL)。目前关于细菌绝对丰度的研究多围绕土壤基质[31-33],关于水体的报道相对较少。Han等[34]对渤海缺氧海水微生物进行了16S rRNA基因绝对定量分析,得到海水中细菌总丰度为2.5×106 copies/mL,与本研究黄河入海口海水水体细菌数量基本一致。薛银刚等[35]采用qPCR技术检测了蓝藻暴发期间太湖竺山湾中的细菌数量为107−108 copies/mL,高于黄河入海口淡水水体细菌数量,推测可能是蓝藻暴发期间太湖中营养物质过多导致的。Yang等[36]利用流式细胞仪检测了不同季节胶州湾大沽河口邻近海域的细菌丰度,范围在5.91×104−6.94×107 cells/mL之间。
两种水体存在共同优势菌门,其各自绝对丰度有明显差异,淡水中放线菌门的数量最高,仅其一门的数量约等于海水中所有优势菌门数量之和。淡水水体中主要优势菌门排序为放线菌门(55.3%) > 假单胞菌门(23.3%) > 蓝细菌门(8.9%);海水中为假单胞菌门(43.5%) > 蓝细菌门(18.7%) > 拟杆菌门(17.5%)。放线菌门在自然界分布广泛,主要存在于土壤和淡水环境中[37-38],彭飞等报道淮河中下游水体中放线菌门为第一优势菌门,其相对丰度为30.1%−48.5%[39],与本研究淡水水体相对丰度接近。假单胞菌门和蓝细菌门是排名丰度前3的共有菌门,假单胞菌门具有最大的系统发育组成以及高度多样的形态和生理功能,该菌门下不同的变形菌纲可适应不同盐度的水体,使其在复杂多样的生态位(如土壤、淡水和海水)方面保持竞争优势[40-41]。蓝细菌门分布极其广泛,淡水和海水环境中均有分布,极端环境中也能生长[42]
淡水水体排名前3的菌目与海水完全不同,其中丰度最高的菌目是弗兰克氏菌目,其次是伯克霍尔德氏菌目和Microtrichales;而海水水体中丰度最高的菌目是Chloroplast,其次是黄杆菌目和微球菌目。在海水中还发现数量高出淡水10余倍的红细菌目,有报道红细菌目为海州湾海域表层水中主要优势菌目[43]。上述7种优势菌目均为2种水体通过LEfSe分析得到的差异物种。2种水体从门到种共存在69个丰度差异显著的物种,将其中门、纲、目、科和属水平上共51个差异物种分别与各分类学水平上丰度前12优势物种进行比对(种水平不作分析),发现淡水水体17个差异物种中15个为优势物种,海水水体34个差异物种中25个为优势物种,说明2种水体间细菌群落结构差异主要源于优势物种丰度的差异。
通过共现网络分析发现,黄河入海口水体细菌群落均具有明显模块化结构,而且细菌之间以正相关关系为主,这表明群落内部更容易形成互利的网络结构[44]。共现网络节点、边、平均度和模块化指数越高,网络越复杂和稳定[45-46]。本研究中淡水水体细菌群落共现网络上述各指数均高于海水,说明淡水水体细菌共现网络较海水更复杂和稳定。更多的负相关连线数可以通过限制正相关连线数和削弱生态相互作用来促进生态网络的稳定性[47],相比于海水水体,淡水水体细菌群落共现网络负相关的边比例更高,表明淡水水体细菌群落网络稳定性高于海水水体。
使用iCAMP模型研究2种水体细菌群落构建机制,发现随机性过程主导2种水体细菌的群落构建。黄河河口地处河流-陆地-海洋的交汇地带,作为淡水水体与海水水体相互作用的混合区域,淡水及海水水体流动性均较高。流动性高的水体生态系统水文水动力条件导致水体细菌群落易受随机性过程的驱动。这是因为在高度流动的水生态系统中,水体不断变动和混合,导致细菌群落中的不同物种更容易随机定殖到不同地点,而不受空间位置和资源分布的限制,物种之间随机相遇的机会增加[48]
PICRUSt2功能预测可为研究水体细菌生态功能提供有力支持。2种水体细菌群落功能结构存在差异,但存在共性功能。新陈代谢是2种水体细菌群落的主要共同功能,其中第二层级上丰度前4位的功能是全球和概览图、碳水化合物代谢、氨基酸代谢和能量代谢。淡水水体细菌群落碳水化合物代谢功能相对丰度显著高于海水,可能是由于水土流失使得土壤和植物凋落物在黄河淡水水体积累,给异养细菌提供了利用碳水化合物代谢途径分解有机物的可能性。氨基酸是有机氮的主要形式[49-50],海水水体细菌群落氨基酸代谢功能相对丰度高于淡水,预示着海水具有更高的氮循环潜力。
本文研究了5种环境因子(pH、ORP、EC、TOC和TN)与水体优势物种的相关性。pH与ORP、TOC和TN具有共线性关系,而且与pH、TOC和TN呈正相关关系的优势菌属均与ORP呈负相关,反之亦然。研究指出,水体pH与ORP这2个环境因子呈负相关关系[51-52],可以间接解释本研究结果。放线菌门和假单胞菌门作为淡水和海水水体丰度最高的菌门,与pH分别呈正相关和负相关关系。Ouyang等[53]研究发现放线菌门丰度与水体pH呈正相关关系,假单胞菌门丰度则相反,与本研究结果一致。
(1) 淡水水体细菌的绝对拷贝数为2.61×106 copies/mL,是海水的1.8倍。2种水体共同优势菌门为放线菌门、假单胞菌门、蓝细菌门和拟杆菌门等,其绝对丰度有明显差异。淡水中放线菌门数量最高,约等于海水中所有优势菌门数量之和;海水中假单胞菌门数量最高。淡水丰度排名前3的菌目是弗兰克氏菌目、伯克霍尔德氏菌目和Microtrichales,而海水中是Chloroplast、黄杆菌目和微球菌目。
(2) 淡水水体细菌群落α多样性高于海水水体,2种水体细菌群落结构存在较明显的差异,从门到种共存在69个丰度差异显著的物种,细菌群落结构差异主要源于优势物种丰度的差异。2种水体细菌群落均具有明显模块化结构,淡水水体细菌共现网络较海水更复杂和稳定。随机性过程主导2种水体细菌群落构建机制。2种水体细菌群落功能结构存在差异,但拥有共性功能,新陈代谢是2种水体细菌群落丰度最高的功能,其在淡水中的相对丰度显著高于海水。
(3) 5种环境因子(pH、ORP、EC、TOC和TN)与水体优势物种分别具有不同程度的相关性。pH、ORP、TOC和TN之间存在共线性关系,与pH、TOC和TN呈正相关关系的24个优势菌属均与ORP呈负相关,与pH、TOC和TN呈负相关关系的22个菌属均与ORP呈正相关。与EC显著相关的菌属数量少于其他环境因子。淡水和海水水体丰度最高的菌门为放线菌门和假单胞菌门,分别与pH呈正相关和负相关关系。
  • 国家重点研发计划(2023YFC3709004)
  • 国家自然科学基金(U22A20615)
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2024年第64卷第11期
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doi: 10.13343/j.cnki.wsxb.20240340
  • 接收时间:2024-06-01
  • 首发时间:2026-03-21
  • 出版时间:2024-08-23
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  • 收稿日期:2024-06-01
  • 录用日期:2024-08-22
基金
National Key Research and Development Program of China(2023YFC3709004)
国家重点研发计划(2023YFC3709004)
National Natural Science Foundation of China(U22A20615)
国家自然科学基金(U22A20615)
作者信息
    1 齐鲁工业大学(山东省科学院), 山东省科学院生态研究所, 山东省应用微生物重点实验室, 山东 济南 250103
    2 南开大学 环境科学与工程学院, 天津 300350
    3 日照市环境保护科学研究所有限公司, 山东 日照 276800

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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