2024, Vol. 45
2. 中建八局发展建设有限公司, 青岛 266061
, WANG Xu-yi1 , SUI Xin1 , HUI Xiao-liang1 , WANG Zhen-xing2 , JIANG Bo1 , ZHANG Zhan-peng1 , LI Xin-long1
2. Development Construction Co., Ltd., China Construction Eighth Engineering Division, Qingdao 266061, China
抗生素滥用引发的细菌耐药性已经成为不容忽视的全球性问题, 严重威胁人类健康和社会经济的持续发展[1, 2]. 预计到2050年, 全球每年因抗性细菌感染所导致的死亡人数将达到1 000万, 远高于每年因糖尿病和癌症死亡的总人数[3, 4]. 为积极应对抗生素抗性带来的威胁, 我国国家卫健委等13部门联合印发了《遏制微生物耐药国家行动计划（2022-2025年）》.
作为水污染控制及水质保障的重要基础设施, 污水处理厂也是抗生素抗性基因（antibiotic resistance genes, ARGs）和抗生素抗性致病菌（pathogenic antibiotic resistance bacteria, PARB）的源和汇之一[5~8]. 而在分离、搅拌、曝气和污泥脱水等污水处理过程中, 混合液悬浮固体会突破水气界面形成生物气溶胶[9, 10]. 生物气溶胶是指空气动力学直径0.001~100 μm的固态或液态所构成的胶体体系, 其中包含细菌、真菌、古菌、病毒等生物组分和花粉、动植物碎屑等其他物质[11, 12]. 近年来, 有研究也表明ARGs和PARB可负载于生物气溶胶自污水处理厂向周边空气传播[13, 14]. 目前, 基于可培养方法或实时荧光定量多聚核苷酸链式反应（q-PCR）, 已证实了特定或有限的ARGs与PARB在污水处理厂生物气溶胶中的存在[15~18]. Zhang等[16]对污水处理厂生物气溶胶进行分离培养, 通过抗生素敏感性试验发现超过45%可培养的细菌对两种及以上抗生素具有抗性；一些可培养的致病菌, 包括不动杆菌属、肠杆菌属和假单胞菌属等对β-内酰胺类、氨基糖苷类、喹诺酮类、呋喃类和磺胺类抗生素具有抗性. Wang等[18]通过q-PCR技术检测到β-内酰胺类、磺胺类、四环素类、大环内酯类和喹诺酮类抗生素抗性基因在污水处理厂生物气溶胶的富集. 尽管如此, 关于污水处理厂生物气溶胶中ARGs和PARB的整体赋存, 及污水处理过程对生物气溶胶负载抗生素抗性基因组释放的贡献尚缺乏系统性研究.
因此, 本研究利用宏基因组测序与组装技术对山东省某污水处理厂厂界内及周边生物气溶胶样本、污水样本抗生素抗性进行检测, 并分析污水处理厂生物气溶胶负载的ARGs和PARB的来源及其气溶胶化潜力, 以期为污水处理厂生物气溶胶抗生素抗性风险评估与防控提供理论依据.
1 材料与方法 1.1 采样点描述以山东省某一座采用改良厌氧-缺氧-好氧工艺的污水处理厂为研究对象. 该污水处理厂主要处理生活污水, 处理规模25万m3·d-1. 如图 1所示, 在厂界内共设置4个采样点, 包括细格栅、缺氧池、好氧池和污泥脱水间；同时, 沿着风向, 在厂界外各设置上风向和下风向采样点, 其距离厂界均大于500 m. 采样高度为距地面高1.5 m处[19], 采样时间为2022年8月24~25日.
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图 1 采样点示意 Fig. 1 Schematic diagram of sampling sites |
生物气溶胶样品通过中流量总悬浮颗粒物采样器（TH-150, 天虹, 中国）在6个采样点同时进行采集. 收集载体为直径90 mm的无菌石英膜, 无菌石英膜在500 ℃条件下预焙5 h制得[4]. 采样流量为100 L·min-1, 收集时间为23.5 h, 每7.5 h收集一次富集膜并更换新膜, 中间换膜的延迟时间为0.5 h. 每个采样点采集的3个气溶胶样品均在-20 ℃冷藏保存. 同一个样本点富集生物气溶胶的石英膜经过剪碎、旋转和磷酸缓冲液冲洗混合后在4 ℃、转速546 r·min-1条件下离心3 h, 悬浮液经抽吸过滤富集至0.22 μm的聚醚砜膜, 以备进行DNA的提取. 具体的富集膜的预处理过程参见文献[20]. 同时, 每7.5 h从对应污水处理单元收集污水, 将每个污水处理单元收集到的3个污水样本混合成一个样本. 将得到的4个污水混合样本分别经0.22 μm的聚醚砜膜再富集, 以备用于DNA提取. 本研究所使用的其他实验器具经过75%酒精擦拭消毒. 采样期间, 温度和相对湿度通过温湿度计（WD 35612, Oakton Instruments, Germany）测定；风速和紫外线辐射强度分别利用风速仪（HD2303, Delta OHM, Italy）和紫外辐照计（ZDZ-1, Grows Instrument, China）测定；总悬浮颗粒物浓度通过文献[20]所述的方法进行测定. 气象条件结果如表 1所示.
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表 1 采样期间气象条件 Table 1 Meteorological condition during sampling period |
1.3 DNA提取、宏基因组测序及组装
使用MO-BIO Power Soil DNA Isolation Kit（Mobio Laboratories, Carlsbad, CA, USA）试剂盒进行DNA提取, 采用微量紫外分光光度计（Nanodrop ND 200, USA）检验其纯度和浓度. 合格后的DNA样品委托武汉百奥维凡生物科技有限公司进行宏基因组测序. 利用Illumina NovaSeq PE150平台（Illumina, USA）进行测序. 使用Trimmomatic（v 0.39）[21]软件对原始测序数据进行质控以去除接头及低质量的Reads. 质控合格的Reads通过MEGAHIT（v 1.1.1）[22]软件按照De-Brujin graph原理进行拼接组装, 筛选出≥ 500 bp的contigs作为最终组装的结果. 所有样本组共产生74.10 Gb的高质量数据. 通过Prodigal（v 1.1.1）[23]软件对contigs预测开放式阅读框（ORF）, 并构建非冗余基因集. 随后通过BLASTX软件将非冗余基因集与SARG数据库[24]的核苷酸序列进行比对（设置阈值：e-value ≤ 10-10, 相似度≥ 80%, 平均覆盖度≥ 70%）, 获得ARGs注释信息[4, 25, 26]. 同时使用Centrifuge（v1.0.4）[27]软件比对Non-Redundant Protein Sequence Database获得物种注释信息. 通过比对已发表的致病菌数据集[4, 28]得到PARB的组成. ARGs与PARB的丰度单位为TPM（transcripts per million）, 其含义为：每千个碱基的转录每百万映射读取的transcripts[29].
1.4 数据统计分析所有数据通过Excel 2019进行统计分析. 柱形图与雷达图通过origin 2021进行绘制. 韦恩图和热图通过R语言中“UpSetR”[30]和“pheatmap”[31]程序包进行绘制, 热图使用的数据均进行lg（X+1）转化. 利用R语言中“sourcetracker”软件包[32]对污水处理厂生物气溶胶负载的ARGs和PARB进行溯源, 探究上风向空气、污水和其他来源对污水处理厂生物气溶胶中ARGs和PARB的贡献.
为进一步探究ARGs和PARB从污水处理厂逸散到大气环境的能力, 选用气溶胶化因子（AF）这一指标进行评估, 其含义为对特定ARGs或PARB在某一处理单元生物气溶胶与上风向空气生物气溶胶丰度的差值与其在该处理单元污水中丰度的比值[33]. 若AF值> 1, 说明该ARGs或PARB极易被气溶胶化；反之, 说明其不易在空气中富集.
2 结果与讨论 2.1 ARGs赋存所测定生物气溶胶及污水样本ARGs总丰度及亚型种类数如图 2所示. 上风向、污水处理厂和下风向生物气溶胶样本中ARGs总丰度为2 504.6、15 600.92~23 975.51和22 510.5 TPM. 上风向检测到ARGs亚型种类数为260种, 而细格栅、缺氧池、好氧池、污泥脱水间和下风向分别为432、379、443、436和431种ARGs亚型. 相比于上风向, 污水处理厂厂界内及下风向生物气溶胶样本检测到更高的ARGs总丰度和ARGs亚型种类数. 不同样本组间共有的ARGs亚型种类数如图 3所示. 所有样本中共检测到509种ARGs亚型, 其中465种（占比为91.36%）为污水处理厂生物气溶胶与污水所共有. 在污水处理厂生物气溶胶样本中共检测到501种ARGs亚型, 分别260种（占比为51.90%）和427种（占比为85.23%）为其与上风向和下风向生物气溶胶样本所共有. 以上结果在一定程度上反映了污水处理厂生物气溶胶的释放对厂界内及周边大气环境抗生素抗性的污染.
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1.上风向, 2.细格栅, 3.缺氧池, 4.好氧池, 5.污泥脱水间, 6.下风向 图 2 ARGs总丰度及亚型种类数 Fig. 2 Total abundance and subtype number of ARGs |
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图 3 不同样本间共有的ARGs亚型数目 Fig. 3 Number of ARG subtypes shared among different samples |
对于ARGs主型, 如图 4（a）所示, 在所有样本中共检测到23种ARGs主型. 所有样本中主导的ARGs主型均为多药类ARGs, 丰度范围为4 618.31~8 485.21 TPM（占比为23.46%~33.88%）. 多药类ARGs也被发现在饮用水[34]、海水[35]和湖泊[36]环境中是主导的, 这可能与多种抗生素的选择压力和原核生物中多药耐药外排泵的普遍性有关[36]. 其次为大环内酯类-林肯酰胺类-链阳性菌素（MLS）类、万古霉素类、杆菌肽类、β-内酰胺类和四环素类抗性基因. 以上主导的ARGs主型也与畜牧业与临床治疗所经常使用的抗生素密切相关[37]. 对于ARGs亚型, macB（属于MLS类）在所有样本中均具有最高的丰度[图 4（b）], 具体地, 在上风向、细格栅、缺氧池、好氧池、污泥脱水间和下风向的丰度分别为2 650.50、3 043.99、1 695.24、3 214.14、3 309.91和3 271.73 TPM. 对应处理单元污水样本中macB的丰度为3 486.76、3 271.45、3 247.89和3 091.49 TPM. 以往研究已证实macB可能与促进ARGs扩散的人为因素有关, 在一定程度上能够促使微生物承受人类活动造成的抗生素选择压力[38].
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（a）ARGs主型, a1.氨基糖苷类, a2.杆菌肽类, a3.β-内酰胺类, a4.博来霉素类, a5.碳霉素类, a6.氯霉素类, a7.磷霉素类, a8.膦氨霉素类, a9.甾体类, a10.春雷霉素类, a11.MLS类, a12.多药类, a13.多粘菌素类, a14.嘌呤霉素类, a15.喹诺酮类, a16.利福霉素类, a17.壮观霉素类, a18.磺胺类, a19.特曲霉素类, a20.四环素类, a21.甲氧苄啶类, a22.未定义类, a23.万古霉素类；（b）前30ARGs亚型, b1.macB, b2.multidrug_ABC_transporter, b3.vanR, b4.bcrA, b5.vanS, b6.truncated_putative_response_regulator_ArlR, b7.transcriptional_regulatory_protein_CpxR_cpxR, b8.vanH, b9.multidrug_transporter, b10.carA, b11.mexF, b12.PBP-1A, b13.oleB, b14.mexW, b15.penA, b16.bacterial_regulatory_protein_LuxR, b17.bacA, b18.mdtB, b19.macA, b20.tlcC, b21.mexE, b22.major_facilitator_superfamily_transporter, b23.tetP, b24.mexD, b25.EmrB-QacA_family_major_facilitator_transporter, b26.sul4, b27.mdtC, b28.tetA, b29.mdtA, b30.lsa；ARGs丰度经log（x+1）转化 图 4 不同样本中ARGs的种类及丰度 Fig. 4 Richness and abundance of ARGs in different samples |
通过宏基因组组装技术得到了具有抗生素抗性致病菌的注释信息, 如图 5所示, 污水处理厂、下风向和上风向生物气溶胶中PARB的丰度分别为204.77、49.25和26.56 TPM. 相比于上风向, 污水处理厂及下风向生物气溶胶中PARB具有更高的丰度. 不同采样点生物气溶胶样本中主导的PARB类型是不同的. 例如, 细格栅、好氧池和污泥脱水间生物气溶胶中主导的PARB分别为Aliarcobacter cryaerophilus、产碱假单胞菌（Pseudomonas alcaligenes）和木糖氧化无色杆菌（Achromobacter xylosoxidans）, 对应的丰度分别为305.06、1.61和12.46 TPM. 在所有样本中共检测到65种PARB. 其中37种PARB携带至少两种及以上的ARGs主型, 表现出多重耐药性. 例如, 铜绿假单胞菌（Pseudomonas aeruginosa）是一种典型的条件致病菌, 其与人类内分泌系统疾病的增加紧密联系[14], 携带高达47种ARGs亚型（隶属8种ARGs主型）. 大部分检测到的PARB与人类呼吸系统、心血管系统和消化系统感染密切相关. 例如, 奥斯陆莫拉菌（Moraxella osloensis）能够引起外伤感染、肺炎和菌血症等疾病[39]；猪链球菌（Streptococcus suis）是一种在猪体内的革兰氏阳性细菌病原体, 可引起人类严重感染, 包括脑膜炎和败血症等疾病[40]；大肠埃希氏菌（Escherichia coli）能引起严重腹泻等胃肠道疾病[41]. 多重耐药致病菌的存在会增大抗生素治愈疾病的成本与难度, 对污水处理厂工人及周边居民构成严重的健康威胁[42]. 所检测到的PARB携带最多的ARGs类型为多药类抗性基因, 其次是MLS类、万古霉素类和β-内酰胺类ARGs等. 多药类ARGs最易被抗生素抗性细菌或PARB携带, 也已在土壤[43]、学校大气环境[44]和河流[45]中被证实.
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1.Streptococcus suis, 2.Aliarcobacter cryaerophilus, 3.Klebsiella pneumoniae, 4.Moraxella osloensis, 5.Citrobacter freundii, 6.Pseudomonas aeruginosa, 7.Pseudomonas alcaligenes, 8.Achromobacter xylosoxidans, 9.Escherichia coli, 10.Aliarcobacter butzleri, 11.Stenotrophomonas maltophilia, 12.Lactobacillus delbrueckii, 13.Acinetobacter junii, 14.Bacteroides stercoris, 15.Chromobacterium violaceum, 16.Corynebacterium xerosis, 17.Acinetobacter johnsonii, 18.Aeromonas veronii, 19.Pseudomonas stutzeri, 20.Aeromonas hydrophila, 21.Bacteroides fragilis, 22.Streptococcus salivarius, 23.Brevundimonas diminuta, 24.Aeromonas caviae, 25.Acinetobacter baumannii, 26.Streptococcus agalactiae, 27.Mycobacterium tuberculosis, 28.Eubacterium rectale CAG：36, 29.Neisseria weaveri, 30.Cutibacterium acnes, 31.Mycobacterium gordonae, 32.Collinsella aerofaciens, 33.Neisseria elongata, 34.Gordonia bronchialis, 35.Orientia tsutsugamushi, 36.Pseudonocardia autotrophica, 37.Delftia acidovorans, 38.Paeniclostridium sordellii, 39.Sutterella wadsworthensis, 40.Acinetobacter pittii, 41.Parabacteroides merdae, 42.Yersinia rohdei, 43.Moraxella atlantae, 44.Mycobacterium haemophilum, 45.Streptococcus equinus, 46.Actinomyces israelii, 47.Finegoldia magna, 48.Enterobacter hormaechei, 49.Bacteroides uniformis, 50.Listeria monocytogenes, 51.Sebaldella termitidis, 52.Ralstonia pickettii, 53.Enterococcus faecium, 54.Enterobacter asburiae, 55.Saccharopolyspora rectivirgula, 56.Bacillus licheniformis, 57.Streptococcus pneumoniae, 58.Eikenella corrodens, 59.Staphylococcus aureus, 60.Kingella kingae, 61.Enterobacter cloacae, 62.Neisseria lactamica, 63.Mycobacterium kansasii, 64.Streptococcus gordonii, 65.Gordonia terrae；PARB丰度经log（x+1）转化 图 5 PARB丰度及其携带不同种类的ARGs Fig. 5 Abundance of PARB carrying various types of ARGs |
污水处理厂生物气溶胶中ARGs和PARB的溯源结果如图 6所示. 对于ARGs, 污水和背景空气是最主要的贡献来源, 在细格栅、缺氧池、好氧池和污泥脱水间生物气溶胶中68.23%、55.64%、63.88%和72.94%的ARGs来源于污水, 而22.44%、36.19%、29.98%和20.76%的ARGs来源于背景空气. 两者总的源的贡献大于90.67%；同时, 污水相比背景空气具有更高源的贡献. 相比于其他处理单元, 污泥脱水间的污水对于生物气溶胶中ARGs具有最高的源的贡献. 主要由于污泥脱水间处于室内, 受外界背景空气的影响较小. 而对于PARB, 在细格栅、缺氧池、好氧池和污泥脱水间生物气溶胶中66.53%、18.23%、59.89%和77.04%的PARB来源于污水, 而8.60%、14.75%、10.97%和10.41%的PARB来源于背景空气. 除缺氧池外, 污水和背景空气是最主要的来源, 两者总的源贡献大于70.86%. 而预缺氧池生物气溶胶PARB中, 67.03%源于未知来源, 以上来源可能包括植物材料和土壤等[46].
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（a）ARGs, （b）PARB, 1.细格栅, 2.缺氧池, 3.好氧池, 4.污泥脱水间 图 6 污水处理厂生物气溶胶中ARGs与PARB的来源 Fig. 6 Source of ARGs and PARB in bioaerosols from this wastewater treatment plant |
在本研究中, 共检测到39种ARGs亚型（9种ARGs主型）在至少3个污水处理单元极易气溶胶化[AF值> 1, 图 7（a）]. 在细格栅、缺氧池、好氧池和污泥脱水间最易被气溶胶化的ARGs亚型分别为IND-4、tetD、tetX6和gadX, 其AF值分别为32.56、57.24、240.66和10.68. 共检测到铜绿假单胞菌（Pseudomonas aeruginosa）和大肠埃希氏菌（Escherichia coli）等32种PARB在至少1个污水处理单元极易气溶胶化[AF > 1, 图 7（b）]. 在细格栅、好氧池和污泥脱水间最易被气溶胶化的PARB分别为维氏气单胞菌（Aeromonas veronii）、嗜血分枝杆菌（Mycobacterium haemophilum）和木糖氧化无色杆菌（Achromobacter xylosoxidans）, 对应的AF值分别为21.78、25.12和4.55. 不同类型ARGs或PARB在不同的处理单元具有不同的气溶胶化潜力. 一方面, 由于分离、搅拌、曝气和污泥脱水等不同的污水处理过程对生物气溶胶颗粒逸散的驱动作用不同[33]. 另一方面, 研究也表明不同ARGs可能会引起的微生物表型变, 例如ARGs宿主微生物胞外聚合物的结构或质量发生变化, 进而削弱细胞表面的粘附能力和细胞间作用力, 从而使ARGs和PARB更易负载于生物气溶胶溢出[31, 47, 48].
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图 7 污水处理厂生物气溶胶中ARGs与PARB的气溶胶化潜力 Fig. 7 Aerosolization potential of ARGs and PARB in bioaerosols from this wastewater treatment plant |
（1）在污水处理厂生物气溶胶样本中共检测到501种ARGs亚型和65种PARB.
（2）多药类ARGs是最易被致病菌携带的ARGs类型；铜绿假单胞菌（Pseudomonas aeruginosa）携带的ARGs亚型数最多, 为47种.
（3）细格栅、好氧池和污泥脱水间的生物气溶胶抗生素抗性基因组的主要来源是污水和背景空气, 两者源的贡献大于70.86%.
（4）最易在细格栅、缺氧池、好氧池和污泥脱水间气溶胶化的ARGs亚型分别是IND-4、tetD、tetX6和gadX, 其AF值分别为32.56、57.24、240.66和10.68.
（5）最易在细格栅、好氧池和污泥脱水间气溶胶化的PARB分别是维氏气单胞菌（Aeromonas veronii）、嗜血分枝杆菌（Mycobacterium haemophilum）和木糖氧化无色杆菌（Achromobacter xylosoxidans）, 对应的AF值分别为21.78、25.12和4.55.
| [1] |
张丹, 彭双, 王丹青, 等. 鸡粪和猪粪生物发酵过程中抗生素抗性基因的动态变化[J]. 环境科学, 2023, 44(3): 1780-1791. Zhang D, Peng S, Wang D Q, et al. Dynamic changes in antibiotic resistance genes during biological fermentation of chicken manure and pig manure[J]. Environmental Science, 2023, 44(3): 1780-1791. |
| [2] |
高敏, 仇天雷, 秦玉成, 等. 养鸡场空气中抗性基因和条件致病菌污染特征[J]. 环境科学, 2017, 38(2): 510-516. Gao M, Qiu T L, Qin Y C, et al. Sources and pollution characteristics of antibiotic resistance genes and conditional pathogenic bacteria in concentrated poultry feeding operations[J]. Environmental Science, 2017, 38(2): 510-516. |
| [3] |
张越, 胡雪莹, 王旭明. 噬菌体编码的抗生素抗性基因研究进展[J]. 中国环境科学, 2022, 42(5): 2315-2320. Zhang Y, Hu X Y, Wang X M. Recent advances on antibiotic resistance genes encoded by bacteriophages[J]. China Environmental Science, 2022, 42(5): 2315-2320. DOI:10.3969/j.issn.1000-6923.2022.05.038 |
| [4] | Xie J W, Jin L, Wu D, et al. Inhalable antibiotic resistome from wastewater treatment plants to urban areas: bacterial hosts, dissemination risks, and source contributions[J]. Environmental Science & Technology, 2022, 56(11): 7040-7051. |
| [5] |
毛秋燕, 赵栩宁, 苏宇傲, 等. 不同预处理方式对剩余污泥中活菌菌群及ARGs的影响[J]. 中国环境科学, 2020, 40(6): 2537-2545. Mao Q Y, Zhao X N, Su Y A, et al. Impact of different pretreatments on ARGs and live microbial communities in excess sludge[J]. China Environmental Science, 2020, 40(6): 2537-2545. DOI:10.3969/j.issn.1000-6923.2020.06.024 |
| [6] |
罗晓, 袁立霞, 张文丽, 等. 制药废水厂抗性基因和微生物群落相关性研究[J]. 中国环境科学, 2019, 39(2): 831-838. Luo X, Yuan L X, Zhang W L, et al. Correlation study between resistance genes and microbial communities in pharmaceutical wastewater treatment plants[J]. China Environmental Science, 2019, 39(2): 831-838. DOI:10.3969/j.issn.1000-6923.2019.02.048 |
| [7] | Wang Y, Han Y P, Li L, et al. Distribution, sources, and potential risks of antibiotic resistance genes in wastewater treatment plant: a review[J]. Environmental Pollution, 2022, 310. DOI:10.1016/j.envpol.2022.119870 |
| [8] | Wang J Q, Xu S Q, Zhao K, et al. Risk control of antibiotics, antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB) during sewage sludge treatment and disposal: a review[J]. Science of the Total Environment, 2023, 877. DOI:10.1016/j.scitotenv.2023.162772 |
| [9] | Yang T, Jiang L, Han Y P, et al. Linking aerosol characteristics of size distributions, core potential pathogens and toxic metal(loid)s to wastewater treatment process[J]. Environmental Pollution, 2020, 264. DOI:10.1016/j.envpol.2020.114741 |
| [10] | Yang K X, Li L, Wang Y J, et al. Airborne bacteria in a wastewater treatment plant: emission characterization, source analysis and health risk assessment[J]. Water Research, 2019, 149: 596-606. DOI:10.1016/j.watres.2018.11.027 |
| [11] |
谢雯文, 路瑞, 慕飞飞, 等. 西安市秋冬季市区与山区微生物气溶胶组成特征及来源[J]. 环境科学, 2020, 41(5): 2044-2049. Xie W W, Lu R, Mu F F, et al. Characteristics and sources of microbial aerosols in urban and mountainous areas in autumn and winter in Xi'an, China[J]. Environmental Science, 2020, 41(5): 2044-2049. |
| [12] |
胡嘉琳. 污水处理厂空气中菌群、ARGs、MGEs的时空分布特征及其相关性研究[D]. 南京: 南京大学, 2017. Hu J L. Correlations and distribution patterns of ARGs, MGEs and airborne microbial communities in wastewater treatment plant[D]. Nanjing: Nanjing University, 2017. |
| [13] |
杨唐, 惠晓亮, 王振兴, 等. 污水处理厂生物气溶胶抗生素抗性污染特征[J]. 中国环境科学, 2022, 42(12): 5626-5632. Yang T, Hui X L, Wang Z X, et al. Pollution characteristics of antibiotics resistance associated with bioaerosols from a wastewater treatment plant[J]. China Environmental Science, 2022, 42(12): 5626-5632. DOI:10.3969/j.issn.1000-6923.2022.12.019 |
| [14] | Yang T, Jiang L, Bi X J, et al. Submicron aerosols share potential pathogens and antibiotic resistomes with wastewater or sludge[J]. Science of the Total Environment, 2022, 821. DOI:10.1016/j.scitotenv.2022.153521 |
| [15] | Kowalski M, Wolany J, Pastuszka J S, et al. Characteristics of airborne bacteria and fungi in some Polish wastewater treatment plants[J]. International Journal of Environmental Science and Technology, 2017, 14(10): 2181-2192. DOI:10.1007/s13762-017-1314-2 |
| [16] | Zhang M Y, Zuo J N, Yu X, et al. Quantification of multi-antibiotic resistant opportunistic pathogenic bacteria in bioaerosols in and around a pharmaceutical wastewater treatment plant[J]. Journal of Environmental Sciences, 2018, 72: 53-63. DOI:10.1016/j.jes.2017.12.011 |
| [17] | Gaviria-Figueroa A, Preisner E C, Hoque S, et al. Emission and dispersal of antibiotic resistance genes through bioaerosols generated during the treatment of municipal sewage[J]. Science of the Total Environment, 2019, 686: 402-412. DOI:10.1016/j.scitotenv.2019.05.454 |
| [18] | Wang Y Z, Wang C, Song L. Distribution of antibiotic resistance genes and bacteria from six atmospheric environments: exposure risk to human[J]. Science of the Total Environment, 2019, 694. DOI:10.1016/j.scitotenv.2019.133750 |
| [19] | Niazi S, Hassanvand M S, Mahvi A H, et al. Assessment of bioaerosol contamination (bacteria and fungi) in the largest urban wastewater treatment plant in the Middle East[J]. Environmental Science and Pollution Research, 2015, 22(20): 16014-16021. DOI:10.1007/s11356-015-4793-z |
| [20] | Cao C, Jiang W J, Wang B Y, et al. Inhalable microorganisms in Beijing's PM2.5 and PM10 pollutants during a severe smog event[J]. Environmental Science & Technology, 2014, 48(3): 1499-1507. |
| [21] | Bolger A M, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data[J]. Bioinformatics, 2014, 30(15): 2114-2120. DOI:10.1093/bioinformatics/btu170 |
| [22] | Li D H, Liu C M, Luo R B, et al. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph[J]. Bioinformatics, 2015, 31(10): 1674-1676. DOI:10.1093/bioinformatics/btv033 |
| [23] | Hyatt D, Chen G L, LoCascio P F, et al. Prodigal: prokaryotic gene recognition and translation initiation site identification[J]. BMC Bioinformatics, 2010, 11. DOI:10.1186/1471-2105-11-119 |
| [24] | Yin X L, Jiang X T, Chai B L, et al. ARGs-OAP v2.0 with an expanded SARG database and hidden Markov models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes[J]. Bioinformatics, 2018, 34(13): 2263-2270. DOI:10.1093/bioinformatics/bty053 |
| [25] | Ma L P, Xia Y, Li B, et al. Metagenomic assembly reveals hosts of antibiotic resistance genes and the shared resistome in pig, chicken, and human feces[J]. Environmental Science & Technology, 2016, 50(1): 420-427. |
| [26] | Yang T, Wang X Y, Hui X L, et al. Antibiotic resistome associated with inhalable bioaerosols from wastewater to atmosphere: mobility, bacterial hosts, source contributions and resistome risk[J]. Water Research, 2023, 243. DOI:10.1016/j.watres.2023.120403 |
| [27] | Kim D, Song L, Breitwieser F P, et al. Centrifuge: rapid and sensitive classification of metagenomic sequences[J]. Genome Research, 2016, 26(12): 1721-1729. DOI:10.1101/gr.210641.116 |
| [28] | Li B, Ju F, Cai L, et al. Profile and fate of bacterial pathogens in sewage treatment plants revealed by high-throughput metagenomic approach[J]. Environmental Science & Technology, 2015, 49(17): 10492-10502. |
| [29] | Bray N L, Pimentel H, Melsted P, et al. Near-optimal probabilistic RNA-seq quantification[J]. Nature Biotechnology, 2016, 34(8): 525-527. |
| [30] | Conway J R, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties[J]. Bioinformatics, 2017, 33(18): 2938-2940. DOI:10.1093/bioinformatics/btx364 |
| [31] | Shi B, Zhao R X, Su G J, et al. Metagenomic surveillance of antibiotic resistome in influent and effluent of wastewater treatment plants located on the Qinghai-Tibetan Plateau[J]. Science of the Total Environment, 2023, 870. DOI:10.1016/j.scitotenv.2023.162031 |
| [32] | Wu D, Jin L, Xie J W, et al. Inhalable antibiotic resistomes emitted from hospitals: metagenomic insights into bacterial hosts, clinical relevance, and environmental risks[J]. Microbiome, 2022, 10(1). DOI:10.1186/s40168-021-01197-5 |
| [33] | Yang T, Jiang L, Cheng L H, et al. Characteristics of size-segregated aerosols emitted from an aerobic moving bed biofilm reactor at a full-scale wastewater treatment plant[J]. Journal of Hazardous Materials, 2021, 416. DOI:10.1016/j.jhazmat.2021.125833 |
| [34] | Zhou H, Beltran J F, Brito I L. Functions predict horizontal gene transfer and the emergence of antibiotic resistance[J]. Science Advances, 2021, 7(43). DOI:10.1126/sciadv.abj5056 |
| [35] | Guo F, Li B, Yang Y, et al. Impacts of human activities on distribution of sulfate-reducing prokaryotes and antibiotic resistance genes in marine coastal sediments of Hong Kong[J]. FEMS Microbiology Ecology, 2016, 92(9). DOI:10.1093/femsec/fiw128 |
| [36] | Han M Z, Zhang L, Zhang N, et al. Antibiotic resistome in a large urban-lake drinking water source in middle China: dissemination mechanisms and risk assessment[J]. Journal of Hazardous Materials, 2022, 424. DOI:10.1016/j.jhazmat.2021.127745 |
| [37] | Sabri N A, Schmitt H, Van Der Zaan B, et al. Prevalence of antibiotics and antibiotic resistance genes in a wastewater effluent-receiving river in the Netherlands[J]. Journal of Environmental Chemical Engineering, 2020, 8(1). DOI:10.1016/j.jece.2018.03.004 |
| [38] | Zhou L, Xu P, Gong J Y, et al. Metagenomic profiles of the resistome in subtropical estuaries: co-occurrence patterns, indicative genes, and driving factors[J]. Science of The Total Environment, 2022, 810. DOI:10.1016/j.scitotenv.2021.152263 |
| [39] | Koleri J, Petkar H M, Husain A A M, et al. Moraxella osloensis bacteremia, a case series and review of the literature[J]. IDCases, 2022, 27. DOI:10.1016/j.idcr.2022.e01450 |
| [40] | Rayanakorn A, Goh B H, Lee L H, et al. Risk factors for Streptococcus suis infection: a systematic review and meta-analysis[J]. Scientific Reports, 2018, 8(1). DOI:10.1038/s41598-018-31598-w |
| [41] | Leekitcharoenphon P, Johansson M H K, Munk P, et al. Genomic evolution of antimicrobial resistance in Escherichia coli[J]. Scientific Reports, 2021, 11(1). DOI:10.1038/s41598-021-93970-7 |
| [42] |
胡静. 城市污水处理厂中抗生素抗性细菌和抗生素抗性基因的污染特征研究[D]. 青岛: 青岛理工大学, 2021. Hu J. Study on the contamination characteristic of antibiotic bacteria and antibiotic resistant genes in municipal wastewater treatment plants[D]. Qingdao: Qingdao University of Technology, 2021. |
| [43] | Furlan J P R, Gallo I F L, Stehling E G. Genomic characterization of multidrug-resistant extraintestinal pathogenic Escherichia coli isolated from grain culture soils[J]. Pedosphere, 2022, 32(3): 495-502. DOI:10.1016/S1002-0160(21)60089-9 |
| [44] | Hu J M, Li Z Y, Li L, et al. Detection of multidrug resistant pathogenic bacteria and novel complex class 1 integrons in campus atmospheric particulate matters[J]. Science of the Total Environment, 2023, 856. DOI:10.1016/j.scitotenv.2022.158976 |
| [45] | Ho J Y, Jong M C, Acharya K, et al. Multidrug-resistant bacteria and microbial communities in a river estuary with fragmented suburban waste management[J]. Journal of Hazardous Materials, 2021, 405. DOI:10.1016/j.jhazmat.2020.124687 |
| [46] | Uhrbrand K, Schultz A C, Koivisto A J, et al. Assessment of airborne bacteria and noroviruses in air emission from a new highly-advanced hospital wastewater treatment plant[J]. Water Research, 2017, 112: 110-119. DOI:10.1016/j.watres.2017.01.046 |
| [47] | Li Z W, Wan C L, Liu X, et al. Understanding of the mechanism of extracellular polymeric substances of aerobic granular sludge against tetracycline from the perspective of fluorescence properties[J]. Science of the Total Environment, 2021, 756. DOI:10.1016/j.scitotenv.2020.144054 |
| [48] | Wang X L, Yang F X, Zhao J, et al. Bacterial exposure to ZnO nanoparticles facilitates horizontal transfer of antibiotic resistance genes[J]. NanoImpact, 2018, 10: 61-67. DOI:10.1016/j.impact.2017.11.006 |