2022, Vol. 43
2. 北京大学环境科学与工程学院, 北京 100871;
3. 浙江省海洋生态环境监测中心, 舟山 316021
2. College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China;
3. Zhejiang Marine Ecology and Environment Monitoring Center, Zhoushan 316021, China
近年来, 抗生素在医疗和养殖等领域的大量使用甚至滥用, 导致环境中的抗生素污染问题日益严重[1].尤其是抗生素会诱导产生抗生素抗性基因(antibiotic resistance genes, ARGs), 使其在各种环境介质中广泛分布, 大大增加了环境细菌获得耐药性的可能性, 从而使耐药细菌感染成为威胁人类健康的全球性问题[2, 3].
作为连接人类活动和自然环境的纽带, 污水处理厂接收了各种来源的污水, 其丰富的营养物质和高密度的细菌为ARGs的繁殖提供了适宜的环境, 因此污水处理厂被视作ARGs的储存库[4, 5].然而现有的污水处理技术对ARGs的去除作用有限, 污水处理厂出水中仍含有较高丰度的ARGs[6, 7], 是环境中ARGs的重要来源[8].目前污水处理厂尾水通常直接排放到河流和近海等水环境中, 这一长期排放行为对受纳水体中ARGs组成和分布的影响还不甚清楚.ARGs在环境中的传播扩散受到多种因素的影响.转座子、整合子和质粒等可移动遗传元件(mobile genetic elements, MGEs)介导的水平基因转移(horizontal gene transfer, HGT)是ARGs在不同细菌间广泛传播的主要途径[9].此外, 环境中ARGs的传播扩散还易受到抗生素、重金属、营养盐和微生物群落等因素的影响[10, 11].尤其是微生物群落之间复杂的相互关系, 可能对ARGs的水平转移产生促进或抑制的作用, 因此对微生物群落与ARGs共存关系的研究近年来备受关注[12, 13].有研究表明, 废水排放能够引起受纳水体中微生物群落结构的显著变化[14].为深入探究废水排放对受纳水体中ARGs的影响, 进一步解析环境样本中MGEs和微生物群落的组成至关重要.
沿海地区人口密度高、工业发达, 沿岸市政污水处理厂和工业废水处理厂众多, 持续的废水排放活动使得近岸海域污染已成为全球范围内的环境问题.为探究废水排放对近海环境中ARGs和微生物群落的影响, 本文选择我国受陆源废水排放负荷最重和污染形势最严峻的杭州湾地区为研究区域, 采用高通量荧光定量PCR (HT-qPCR)和高通量16S rRNA扩增子测序技术对杭州湾中典型近岸纳污区和远岸湾区中表层沉积物的ARGs、MGEs和微生物群落进行分析, 并识别近海沉积物中ARGs的潜在宿主.
1 材料与方法 1.1 样品采集选取我国东海杭州湾中两个典型的污水处理厂排放区及远离排放区的海湾作为研究区域.采样点SY1、SY2、JX1和JX2位于上虞(SY)和嘉兴(JX)纳污区(effluent receiving areas, ERAs, 图 1).其中, SY1和JX1位于污水处理厂排放口, 长期接收污水厂尾水的直接干扰.杭州湾的样点HB1~HB6与SY、JX污水处理厂的距离分别为32.1~216.9 km和18.0~209.0 km, 远离污水处理厂排放的干扰. 用抓泥斗(Van Veen, Germany)在各样点重复采集3份表层(0~5 cm)沉积物样品, 存放于无菌塑料袋中, 并立刻运回实验室, 置于-80℃冰箱中保存.
|
图 1 采样点分布示意 Fig. 1 Location of sampling sites |
称取0.25 g冻干后的沉积物, 根据标准方法使用Power Soil DNA分离试剂盒(MoBio Laboratories, USA)提取沉积物样品DNA.并使用NanoDrop分光光度计(Thermo Fisher, USA)检测提取的DNA的浓度与纯度.
1.3 ARGs和MGEs的高通量检测使用WaferGen SmartChip Real-time PCR (Warfergen Inc, USA)技术平台, 对样品中ARGs和MGEs进行高通量qPCR分析[15].使用296对引物检测目的基因, 包括285对ARGs、10对MGEs和1对16S rRNA基因引物[16].HT-qPCR体系包括1×LightCycler 480 SYBR Gree I Master, 500nmol·L-1引物以及2 ng·μL-1 DNA模板(100 nL).反应程序为预变性95℃, 10 min; 95℃, 30 s和60℃, 30 s (40个循环); 最终采用熔融曲线估算扩增反应产物, 扩增效率在1.8~2.2之间[17].检测阈值(CT)设为31.如果目的基因在大于或等于两个重复样本中均被检出, 且具有较小的偏差(< 20%)和较好的拟合度, 将被视为阳性检出.根据CT值计算目的基因的相对丰度[式(1)][18].使用实时荧光定量PCR (qPCR)测定16S rRNA的绝对丰度[11].根据16S rRNA的绝对丰度与ΔCT值[式(2)]计算ARGs和MGEs的绝对丰度.
|
(1) |
|
(2) |
使用上游引物515F(GTGCCAGCMGCCGCG GTAA)和下游引物806R(GGACTACHVGGGT WTCTAAT)对16S rRNA基因高变区(V4)进行扩增.纯化后的扩增产物在Illumina MiSeq 2500平台(MAGIGENE, China)进行测序.使用Trimmomatic (v0.33, http://www.usadellab.org/cms/?page=trimmomatic)对原始序列进行质控, 获得双端干净序列.采用FLASH合并后(v1.2.11, https://ccb.jhu.edu/software/FLASH/), 使用UCHIME对嵌合序列进行检查和过滤[19].根据97%的相似度, 通过UPARSE将获得的序列聚类为相同的操作分类单元(OTUs)[20].最后, 通过核糖体数据库(ribosomal database poject, RDP)分类器(https://sourceforge.net/projects/rdp-classifier/)以80%阈值对OTUs进行分类[21].
1.5 数据分析ARGs、MGEs和微生物群落分布柱状图以及ARGs和MGEs相关性由Excel绘制.ARGs和MGEs丰度热图由R语言(v1.4, https://www.r-project.org/)中的phatmap软件包完成.使用R语言中的vegan软件包计算微生物群落的α多样性指数(Chao1、ACE、Richness、Shannon、Simpson和PD指数). ARGs、MGEs和微生物群落分布的主坐标分析(PCoA)由R语言中的vegan软件包和OmicStudio工具(https://www.omicstudio.cn/tool)共同完成.使用MENA(https://ieg4.rccc.ou.edu/mena/)和Gephi (v0.9.2, https://gephi.org/)实现ARGs、MGEs和细菌属的共现网络分析(cutoff=0.8, P < 0.05)[22, 23].
2 结果与讨论 2.1 近海环境中ARGs的多样性和组成 2.1.1 ARGs的多样性如图 2所示, 所有沉积物样品共检出氨基糖苷类、β-内酰胺类、氯霉素类、磺胺类、四环素类、万古霉素类、MLSB类(大环内酯类、林可胺类、链阳菌素类)、多重耐药类和其它类这9种ARGs类型.其中, SY纳污区中SY1样点(检出83种ARGs亚型)的ARGs多样性最高, 说明长期的尾水排放可能导致环境中ARGs多样性增加.除了HB4 (1种ARGs亚型)和HB5 (9种ARGs亚型)外, 其他样点中ARGs多样性水平相似, 检出ARGs介于34 (HB6)~42 (HB2)之间.
|
图 2 近海环境中ARGs和MGEs多样性分布 Fig. 2 Diversity of ARGs and MGEs in the coastal environment |
沉积物中ARGs的相对丰度为8.07×10-4 (HB5)~5.96×10-2 (SY2) copies·cell-1, 绝对丰度为4.52×106(HB5)~1.23×1010 (JX1) copies·g-1 (图 3).SY和JX纳污区沉积物中的ARGs丰度明显高于杭州湾沉积物, 尤其是位于污水处理厂排放口的样点(SY1和JX1).说明长期的污水处理厂排放行为不仅增加了近岸环境中ARGs的多样性, 而且提高了ARGs的丰度水平.实际上, 污水处理厂的处理工艺、污水来源和污泥絮凝及沉淀等特性是尾水释放ARGs的关键[24, 25].因此, 以上特定因素可能是导致SY和JX纳污区ARGs组成差异的原因.
|
图 3 近海环境中ARGs和MGEs的丰度 Fig. 3 Abundances of ARGs and MGEs in the coastal environment |
所有样点中, 多重耐药类ARGs的多样性和丰度最高[平均值分别为2.24×10-2copies·cell-1(相对丰度, 下同)和2.03×109copies·g-1 (绝对丰度, 下同)], 其次是氨基糖苷类(1.93×10-3copies·cell-1和2.80×108copies·g-1)、MLSB类(1.27×10-3copies·cell-1和1.34×108copies·g-1)和β-内酰胺类ARGs (1.13×10-3copies·cell-1和1.24×108copies·g-1).多重耐药类ARGs能够编码对多种抗生素的耐药性, 因此对人类健康构成的威胁可能更大[26, 27].有研究表明, 多重耐药细菌更多是由人为污染造成的[28].环杭州湾地区频繁的人类活动可能是其环境中多重耐药类ARGs丰度水平高的原因, 尤其是在近岸纳污区环境.
所有样点中共检出122种ARGs亚型, 图 4为杭州湾沉积物中占优势地位的ARGs.其中, mexF [平均值分别为1.66×10-2 copies·cell-1(相对丰度, 下同)和1.52×109copies·g-1 (绝对丰度, 下同)]、pncA (3.16×10-4copies·cell-1和4.08×107copies·g-1)、qacEdelta1-01 (3.92×10-4 copies·cell-1和1.55×108copies·g-1)、sul2 (1.18×10-4 copies·cell-1和1.28×108copies·g-1)、oprD (3.17×10-4 copies·cell-1和9.73×106copies·g-1)和mphA-01 (4.52×10-4copies·cell-1和3.59×107copies·g-1)的丰度最高, 比其他ARGs亚型高2~4个数量级.这些ARGs亚型在垃圾填埋场、养鸡场和污水处理系统等不同的人为环境被频繁检出[29~31].
|
图 4 近海环境中ARGs和MGEs亚型的组成和分布 Fig. 4 Compositions and distributions of ARGs and MGEs in the coastal environment |
在所有样品中共检测到9个MGEs, 包括7个转座子(tnpA-01、tnpA-02、tnpA-03、tnpA-04、tnpA-05、tnpA-06、tnpA-07)和2个整合子(cIntI-1、intI-1) (图 2和图 3).其中, tnpA-05 [平均值分别为1.02×10-2 copies·cell-1(相对丰度, 下同)和2.42×109copies·g-1 (绝对丰度, 下同)]的丰度最高, tnpA-07 (5.72×10-6 copies·cell-1和1.31×106copies·g-1)的丰度最低.与ARGs分布类似, MGEs在纳污区中的丰度高于远岸湾区(图 3).尤其是SY1样点, MGEs总丰度(1.54×10-1 copies·cell-1和3.63×1010copies·g-1)高于其他取样点1~4个数量级.这表明污水处理厂也是MGEs的热点, 且能够将MGEs释放到环境中[32, 33].更重要的是, 纳污区和远岸湾区沉积物中的ARGs与MGEs呈显著正相关(图 5).高丰度的MGEs能够驱动ARGs在微生物群落中发生水平转移, 从而加快了ARGs在环境中的传播扩散[34].
|
图 5 近海环境中ARGs和MGEs的相关性 Fig. 5 Correlation between ARGs and MGEs in the coastal environment |
对所有样品的序列进行聚类, 共得到19 931个OTUs.各样点的覆盖度均为0.99, 表明测序结果能够较好地反映沉积物样品中微生物群落的真实情况(表 1).各个多样性指数表明JX纳污区表层沉积物中微生物群落的物种丰富度和多样性均高于SY纳污区和远岸湾区, 尾水排放可能释放了大量人为源的微生物, 从而影响其受纳环境中微生物群落[35].值得注意的是, 杭州湾沉积物中微生物群落物种丰富度与多样性沿湾内至湾外逐渐降低.这表明环杭州湾区域废水排放等人类活动剧烈, 杭州湾海水水质一直处于富营养状态[36].这为微生物生长提供了营养和能量, 从而提升杭州湾沉积物中微生物群落的多样性[37, 38].
|
表 1 微生物群落α多样性指数 Table 1 The α-diversity of microbial community |
2.3.2 微生物群落的物种组成
本研究共识别到细菌的76个门、214个纲、471个目、971个科和1 102个属.门水平上, 11个细菌门的相对丰度均超过0.1%[图 6(a)].绝对优势菌门包括变形菌门(Proteobacteria, 44.9%)、厚壁菌门(Firmicutes, 27.9%)、拟杆菌门(Bacteroidetes, 12.1%)、绿弯菌门(Chloroflexi, 3.0%)、酸杆菌门(Acidobacteria, 2.2%)、放线菌门(Actinobacteria, 1.7%)和浮霉菌门(Planctomycetes, 1.6%), 相对丰度均超过1%.之前的研究发现, 变形菌门和拟杆菌门等细菌在污水处理厂污泥中高度富集[39].SY、JX纳污区样点高丰度的拟杆菌门(21.5%)有可能是长期废水排放行为导致的.而厚壁菌门在杭州湾远岸湾区沉积物中所占比例(35.9%) 大于近岸纳污区沉积物(16%).这与Dai等[40]对杭州湾沉积物微生物群落的研究结果一致, 厚壁菌门在杭州湾沉积物中占有很大的比例.
|
(a)门水平; (b)属水平 图 6 微生物群落相对丰度 Fig. 6 Relative abundance of microbial community |
属水平上[图 6(b)], 共有13个菌属的丰度在所有样点中高于0.1%, 其余201个菌属的丰度均小于0.1%, 被归类为其它类菌属.其中, 优势菌属(相对丰度大于1%)为: 芽孢杆菌属(Paenisporosarcina, 17.6%)、假单胞菌属(Pseudomonas, 15.4%)和嗜冷杆菌属(Psychrobacter, 4.7%).芽孢杆菌属和假单胞菌属在杭州湾沉积物(22.0%和15.9%)中所占比例高于SY和JX纳污区(11.1%和15.2%).而201个其它类菌属(相对丰度小于0.1%)在近岸纳污区沉积物(41.1%)中所占比例远远高于杭州湾远岸湾区(18.8%).
2.4 杭州湾近岸纳污区与远岸湾区中ARGs、MGEs和微生物群落的分布差异如图 7(a)所示, SY和JX纳污区样点(SY1、SY2、JX1和JX2)与远岸湾区(HB) 样点(HB1~HB6)分别聚集, 说明杭州湾近岸纳污区与远岸湾区沉积物中ARGs和MGEs分布差异显著(R=0.444 8, P=0.001).有研究发现污水处理厂能够影响环境中ARGs的分布. Marti等[41]通过对污水处理厂下游河流中ARGs分布的调查发现, 污水处理厂排放行为可能将ARGs释放到环境. Su等[42]发现废水排放还能够驱动ARGs在近海环境中的传播扩散.
|
图 7 基于Bray-Curtis距离的主坐标分析(PCoA) Fig. 7 Principal coordinate analysis (PCoA) based on the Bray-Curtis distance |
此外, SY和JX纳污区与远岸湾区沉积物中微生物群落分布与其ARGs和MGEs分布模式相似[图 7(b)], 即纳污区沉积物与杭州湾沉积物中微生物群落具有显著差异(R=0.298 4, P=0.001).有研究发现, 废水排放对微生物群落有直接或间接的影响[43].一方面, 污水处理厂可以直接将人为源的微生物释放到环境中[44].另一方面, 污水处理厂通过输入有机或无机污染物改变微生物群落的生存环境[45].因此, 污水处理厂的排放行为不仅对近海环境ARGs和MGEs的分布有显著影响, 而且对环境中微生物群落也能产生作用.
2.5 ARGs、MGEs和微生物群落的相关性在环境中, ARGs可被某些细菌所携带, 因此ARGs的分布在一定程度上会受微生物群落的影响[46].ARGs、MGEs和细菌属的共现网络如图 8所示.其中, 与ARGs或MGEs呈强烈正相关的细菌被认为是其潜在宿主. SY和JX纳污区及远岸湾区沉积物中共识别出12种菌属与34个ARGs、MGEs存在显著正相关.例如, 其它类菌属(others)与aacC4、cphA-02和tetA-02等14种ARGs亚型呈显著正相关; 嗜冷杆菌属(Psychrobacter)与aacC4、acrA-01和sulA等8种ARGs亚型呈显著正相关; 假单胞菌属(Pseudomonas)与aadA5-02、blaOXY和emrD等7种ARGs亚型呈显著正相关; 亚硫酸杆菌属(Sulfitobacter)与aacC4、blaOXY和sulA等6种ARGs亚型呈显著正相关; 假交替单胞菌属(Pseudoalteromonas)与blaCMY2-02和vanB-01等5种ARGs亚型呈显著正相关.这些潜在的ARGs宿主, 如嗜冷杆菌属和假交替单胞菌属等菌属被证明与sul2等其他ARGs亚型也存在显著正相关[47, 48].而假单胞菌属宿主是潜在致病菌, 进一步增加了近海环境中ARGs传播扩散的健康风险[49]. 9种ARGs类型中, 多重耐药类和β-内酰胺类ARGs的潜在宿主最多, 这可能是这两类ARGs在杭州湾沉积物中广泛分布的原因之一.而MGEs的潜在宿主远远少于ARGs, 仅有tnpA-05与其它类菌属存在显著正相关.
|
图 8 ARGs、MGEs和细菌属的共现网络分析 Fig. 8 Co-occurrence network among ARGs, MGEs, and microbial community |
(1) 多重耐药类ARGs是杭州湾沉积物中多样性和丰度最高的ARGs类型, 其次是氨基糖苷类、MLSB类和β-内酰胺类ARGs. 芽孢杆菌属、假单胞菌属和嗜冷杆菌属是杭州湾沉积物中的优势菌属.
(2) 污水处理厂长期的排放行为可能影响了近海环境中ARGs、MGEs和微生物群落的组成：纳污区沉积物中ARGs和MGEs的多样性和丰度远远高于远岸湾区中不受排水干扰的沉积物, 尤其是位于废水排放口的样点(SY1和JX1); 纳污区与远岸湾区沉积物中ARGs、MGEs和微生物群落分布存在显著差异.
(3) MGEs和微生物群落是ARGs丰度和组成的主要影响因素：MGEs丰度和ARGs丰度呈显著正相关, 12种菌属与ARGs、MGEs呈显著正相关.多重耐药类、β-内酰胺类ARGs和细菌属的关联最多.
| [1] | Zhao R X, Feng J, Liu J, et al. Deciphering of microbial community and antibiotic resistance genes in activated sludge reactors under high selective pressure of different antibiotics[J]. Water Research, 2019, 151: 388-402. DOI:10.1016/j.watres.2018.12.034 |
| [2] | Zheng D S, Yin G Y, Liu M, et al. A systematic review of antibiotics and antibiotic resistance genes in estuarine and coastal environments[J]. Science of the Total Environment, 2021, 777. DOI:10.1016/j.scitotenv.2021.146009 |
| [3] | Aslam B, Wang W, Arshad M I, et al. Antibiotic resistance: A rundown of a global crisis[J]. Infection and Drug Resistance, 2018, 11: 1645-1658. DOI:10.2147/IDR.S173867 |
| [4] | Liu Z B, Klümper U, Liu Y, et al. Metagenomic and metatranscriptomic analyses reveal activity and hosts of antibiotic resistance genes in activated sludge[J]. Environment International, 2019, 129: 208-220. DOI:10.1016/j.envint.2019.05.036 |
| [5] | Rizzo L, Manaia C, Merlin C, et al. Urban wastewater treatment plants as hotspots for antibiotic resistant bacteria and genes spread into the environment: a review[J]. Science of the Total Environment, 2013, 447: 345-360. DOI:10.1016/j.scitotenv.2013.01.032 |
| [6] | Munir M, Wong K, Xagoraraki I. Release of antibiotic resistant bacteria and genes in the effluent and biosolids of five wastewater utilities in Michigan[J]. Water Research, 2011, 45(2): 681-693. DOI:10.1016/j.watres.2010.08.033 |
| [7] | Wang J L, Chu L B, Wojnárovits L, et al. Occurrence and fate of antibiotics, antibiotic resistant genes (ARGs) and antibiotic resistant bacteria (ARB) in municipal wastewater treatment plant: An overview[J]. Science of the Total Environment, 2020, 744. DOI:10.1016/j.scitotenv.2020.140997 |
| [8] |
颉亚玮, 於驰晟, 李菲菲, 等. 某市污水厂抗生素和抗生素抗性基因的分布特征[J]. 环境科学, 2021, 42(1): 315-322. Xie Y W, Yu C S, Li F F, et al. Distribution characteristics of antibiotics and antibiotic resistance genes in wastewater treatment plants[J]. Environmental Science, 2021, 42(1): 315-322. |
| [9] | Wang H J, Hou L Y, Liu Y Q, et al. Horizontal and vertical gene transfer drive sediment antibiotic resistome in an urban lagoon system[J]. Journal of Environmental Sciences, 2021, 102: 11-23. DOI:10.1016/j.jes.2020.09.004 |
| [10] | Li D, Zeng S Y, He M, et al. Water disinfection byproducts induce antibiotic resistance-role of environmental pollutants in resistance phenomena[J]. Environmental Science & Technology, 2016, 50(6): 3193-3201. |
| [11] | Chen J Y, Su Z G, Dai T J, et al. Occurrence and distribution of antibiotic resistance genes in the sediments of the East China Sea bays[J]. Journal of Environmental Sciences, 2019, 81: 156-167. DOI:10.1016/j.jes.2019.01.016 |
| [12] | Qiu W H, Sun J, Fang M J, et al. Occurrence of antibiotics in the main rivers of Shenzhen, China: association with antibiotic resistance genes and microbial community[J]. Science of the Total Environment, 2019, 653: 334-341. DOI:10.1016/j.scitotenv.2018.10.398 |
| [13] | Zhang R R, Gu J, Wang X J, et al. Contributions of the microbial community and environmental variables to antibiotic resistance genes during co-composting with swine manure and cotton stalks[J]. Journal of Hazardous Materials, 2018, 358: 82-91. DOI:10.1016/j.jhazmat.2018.06.052 |
| [14] | Tao Y L, Dai T J, Huang B, et al. The impact of wastewater treatment effluent on microbial biomasses and diversities in coastal sediment microcosms of Hangzhou Bay[J]. Marine Pollution Bulletin, 2017, 114(1): 355-363. DOI:10.1016/j.marpolbul.2016.09.047 |
| [15] | An X L, Su J Q, Li B, et al. Tracking antibiotic resistome during wastewater treatment using high throughput quantitative PCR[J]. Environment International, 2018, 117: 146-153. DOI:10.1016/j.envint.2018.05.011 |
| [16] | Zhu Y G, Johnson T A, Su J Q, et al. Diverse and abundant antibiotic resistance genes in Chinese swine farms[J]. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(9): 3435-3440. DOI:10.1073/pnas.1222743110 |
| [17] | Chen Q L, An X L, Li H, et al. Do manure-borne or indigenous soil microorganisms influence the spread of antibiotic resistance genes in manured soil?[J]. Soil Biology and Biochemistry, 2017, 114: 229-237. DOI:10.1016/j.soilbio.2017.07.022 |
| [18] | Looft T, Johnson T A, Allen H K, et al. In-feed antibiotic effects on the swine intestinal microbiome[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(5): 1691-1696. DOI:10.1073/pnas.1120238109 |
| [19] | Edgar R C, Haas B J, Clemente J C, et al. UCHIME improves sensitivity and speed of chimera detection[J]. Bioinformatics, 2011, 27(16): 2194-2200. DOI:10.1093/bioinformatics/btr381 |
| [20] | Edgar R C. UPARSE: highly accurate OTU sequences from microbial amplicon reads[J]. Nature Methods, 2013, 10(10): 996-998. DOI:10.1038/nmeth.2604 |
| [21] | Dai T J, Zhao Y N, Ning D L, et al. Dynamics of coastal bacterial community average ribosomal RNA operon copy number reflect its response and sensitivity to ammonium and phosphate[J]. Environmental Pollution, 2020, 260. DOI:10.1016/j.envpol.2020.113971 |
| [22] | Li B, Yang Y, Ma L P, et al. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes[J]. The ISME Journal, 2015, 9(11): 2490-2502. DOI:10.1038/ismej.2015.59 |
| [23] | Bard J. Analysis of biological networks[J]. Journal of Anatomy, 2009, 215(4): 473-473. DOI:10.1111/j.1469-7580.2009.01132.x |
| [24] | Quintela-Baluja M, Abouelnaga M, Romalde J, et al. Spatial ecology of a wastewater network defines the antibiotic resistance genes in downstream receiving waters[J]. Water Research, 2019, 162: 347-357. DOI:10.1016/j.watres.2019.06.075 |
| [25] | Pallares-Vega R, Blaak H, van der Plaats R, et al. Determinants of presence and removal of antibiotic resistance genes during WWTP treatment: a cross-sectional study[J]. Water Research, 2019, 161: 319-328. DOI:10.1016/j.watres.2019.05.100 |
| [26] | Cazares A, Moore M P, Hall J P J, et al. A megaplasmid family driving dissemination of multidrug resistance in Pseudomonas[J]. Nature Communications, 2020, 11(1): 1370. DOI:10.1038/s41467-020-15081-7 |
| [27] | Tanwar J, Das S, Fatima Z, et al. Multidrug resistance: an emerging crisis[J]. Interdisciplinary Perspectives on Infectious Diseases, 2014, 2014. DOI:10.1155/2014/541340 |
| [28] | Czekalski N, Berthold T, Caucci S, et al. Increased levels of multiresistant bacteria and resistance genes after wastewater treatment and their dissemination into Lake Geneva, Switzerland[J]. Frontiers in Microbiology, 2012, 3. DOI:10.3389/fmicb.2012.00106 |
| [29] | Xu S, Lu W J, Qasim M Z. High-throughput characterization of the expressed antibiotic resistance genes in sewage sludge with transcriptional analysis[J]. Ecotoxicology and Environmental Safety, 2020, 205. DOI:10.1016/j.ecoenv.2020.111377 |
| [30] | Wang P L, Wu D, You X X, et al. Distribution of antibiotics, metals and antibiotic resistance genes during landfilling process in major municipal solid waste landfills[J]. Environmental Pollution, 2019, 255. DOI:10.1016/j.envpol.2019.113222 |
| [31] | Yang F, Gao Y L, Zhao H C, et al. Revealing the distribution characteristics of antibiotic resistance genes and bacterial communities in animal-aerosol-human in a chicken farm: From One-Health perspective[J]. Ecotoxicology and Environmental Safety, 2021, 224. DOI:10.1016/j.ecoenv.2021.112687 |
| [32] | Marano R B M, Zolti A, Jurkevitch E, et al. Antibiotic resistance and class 1 integron gene dynamics along effluent, reclaimed wastewater irrigated soil, crop continua: elucidating potential risks and ecological constraints[J]. Water Research, 2019, 164. DOI:10.1016/j.watres.2019.114906 |
| [33] | Guo J H, Li J, Chen H, et al. Metagenomic analysis reveals wastewater treatment plants as hotspots of antibiotic resistance genes and mobile genetic elements[J]. Water Research, 2017, 123: 468-478. DOI:10.1016/j.watres.2017.07.002 |
| [34] |
黄福义, 朱永官, 苏建强. 尾矿库水体环境抗生素抗性基因的分布特征[J]. 环境科学, 2021, 42(2): 761-765. Huang F Y, Zhu Y G, Su J Q. Diversity and abundance of antibiotic resistance genes in tailings ponds[J]. Environmental Science, 2021, 42(2): 761-765. |
| [35] | Saarenheimo J, Aalto S L, Rissanen A J, et al. Microbial community response on wastewater discharge in boreal lake sediments[J]. Frontiers in Microbiology, 2017, 8. DOI:10.3389/fmicb.2017.00750 |
| [36] | Zhu G H, Noman M A, Narale D D, et al. Evaluation of ecosystem health and potential human health hazards in the Hangzhou Bay and Qiantang Estuary region through multiple assessment approaches[J]. Environmental Pollution, 2020, 264. DOI:10.1016/j.envpol.2020.114791 |
| [37] | Chase J M, Leibold M A. Spatial scale dictates the productivity-biodiversity relationship[J]. Nature, 2002, 416(6879): 427-430. DOI:10.1038/416427a |
| [38] | Sawall Y, Richter C, Ramette A. Effects of eutrophication, seasonality and macrofouling on the diversity of bacterial biofilms in equatorial coral reefs[J]. PLoS One, 2012, 7(7). DOI:10.1371/journal.pone.0039951 |
| [39] |
彭永臻, 钱雯婷, 王琦, 等. 基于宏基因组的城市污水处理厂生物脱氮污泥菌群结构分析[J]. 北京工业大学学报, 2019, 45(1): 95-102. Peng Y Z, Qian W T, Wang Q, et al. Unraveling microbial structure of activated sludge in a full-scale nitrogen removal plant using metagenomic Sequencing[J]. Journal of Beijing University of Technology, 2019, 45(1): 95-102. |
| [40] | Dai T J, Zhang Y, Tang Y S, et al. Identifying the key taxonomic categories that characterize microbial community diversity using full-scale classification: a case study of microbial communities in the sediments of Hangzhou Bay[J]. FEMS Microbiology Ecology, 2016, 93(10). DOI:10.1093/femsec/fiw150 |
| [41] | Marti E, Jofre J, Balcazar J L, et al. Prevalence of antibiotic resistance genes and bacterial community composition in a river influenced by a wastewater treatment plant[J]. PLoS One, 2013, 8(10). DOI:10.1371/journal.pone.0078906 |
| [42] | Su Z G, Li A L, Chen J Y, et al. Wastewater discharge drives ARGs spread in the coastal area: a case study in Hangzhou Bay, China[J]. Marine Pollution Bulletin, 2020, 151. DOI:10.1016/j.marpolbul.2019.110856 |
| [43] | Zhang Y, Chen L J, Sun R H, et al. Effect of wastewater disposal on the bacterial and archaeal community of sea sediment in an industrial area in China[J]. FEMS Microbiology Ecology, 2014, 88(2): 320-332. DOI:10.1111/1574-6941.12298 |
| [44] | Kim S, Aga D S. Potential ecological and human health impacts of antibiotics and antibiotic-resistant bacteria from wastewater treatment plants[J]. Journal of Toxicology and Environmental Health, Part B, 2007, 10(8): 559-573. DOI:10.1080/15287390600975137 |
| [45] | Yu M D, Liu S J, Li G W, et al. Municipal wastewater effluent influences dissolved organic matter quality and microbial community composition in an urbanized stream[J]. Science of the Total Environment, 2020, 705. DOI:10.1016/j.scitotenv.2019.135952 |
| [46] | Su Z G, Huang B, Mu Q L, et al. Evaluating the potential antibiotic resistance status in environment based on the trait of microbial community[J]. Frontiers in Microbiology, 2020, 11. DOI:10.3389/fmicb.2020.575707 |
| [47] | Zhang L, Gu J, Wang X J, et al. Behavior of antibiotic resistance genes during co-composting of swine manure with Chinese medicinal herbal residues[J]. Bioresource Technology, 2017, 244. |
| [48] | Wu J J, Su Y L, Deng Y Q, et al. Prevalence and distribution of antibiotic resistance in marine fish farming areas in Hainan, China[J]. Science of the Total Environment, 2019, 653: 605-611. DOI:10.1016/j.scitotenv.2018.10.251 |
| [49] | Guo W, Huang C H, Xi B D, et al. The maturity period is the main stage of antibiotic resistance genes reduction in aerobic composting process of swine manure in sub-scale farms[J]. Bioresource Technology, 2021, 319. DOI:10.1016/j.biortech.2020.124139 |