环境科学  2021, Vol. 42 Issue (7): 3366-3374   PDF    
基于宏基因组技术分析MBR膜清洗后污泥中抗性基因
杜彩丽1,2, 李中浤1,3, 李晓光1, 张列宇1, 陈素华3, 黎佳茜1, 李曹乐1     
1. 中国环境科学研究院国家环境保护地下水污染模拟与控制重点实验室, 北京 100012;
2. 同济大学环境科学与工程学院, 上海 200092;
3. 南昌航空大学江西省持久性污染物控制与资源循环利用重点实验室, 南昌 330063
摘要: 污水处理厂作为抗生素抗性基因(antibiotic resistance genes,ARGs)的重要储存库,是自然界ARGs的主要来源之一.膜生物反应器(membrane bioreactor,MBR)被认为是一种能够有效去除污水处理厂中ARGs的技术工艺.MBR膜截留的废水中胶体、颗粒物、悬浮物及微生物代谢物中存在着大量的病原菌与抗性基因,而目前关于膜清洗后污泥中抗性基因的分布特征和规律尚不明确.本文采用宏基因组技术对MBR膜清洗后污泥中抗性基因进行了分析.结果显示,膜清洗后污泥中共检测出39门,其中优势菌门为Proteobacteria、Nitrospirae和Actinobacteria,优势菌属为NitrospiraPseudomonasBradyrhizobium.污泥样品含有的病原菌属占所有菌属的10.54%,其中Pseudomonas属相对丰度最高,占到所有菌属的3.94%.样品中共注释出17类ARGs和16类金属抗性基因(metal resistance genes,MRGs,15类单金属抗性基因和1类多重金属抗性基因).其中,多药类抗生素抗性基因相对丰度最高,占49.08%.金属抗性基因中多重金属类抗性基因相对丰度最高,占该污泥样品的34.58%,单金属抗性基因中对铜的抗性基因数量最多,占19.99%.该膜清洗后污泥中微生物群落最主要的功能通路为代谢相关,并存在大量与人类疾病相关的代谢通路相关基因,其中涉及细菌耐药和细菌传染疾病的基因数量最多,分别为占人类疾病相关的代谢通路已注释序列的34.50%和16.62%.由此可见,膜清洗后污泥中蕴藏着丰富的ARGs、MRGs以及病原菌属,具有潜在的环境健康风险,需要加强对膜清洗后污泥中ARGs、MRGs以及病原菌的管控.本文为选择合适的技术工艺有效去除膜清洗后污泥中ARGs、MRGs以及病原菌提供指导.
关键词: 膜生物反应器(MBR)      污泥      抗生素抗性基因(ARGs)      金属抗性基因(MRGs)      宏基因组学     
Metagenomic Analysis of Resistance Genes in Membrane Cleaning Sludge
DU Cai-li1,2 , LI Zhong-hong1,3 , LI Xiao-guang1 , ZHANG Lie-yu1 , CHEN Su-hua3 , LI Jia-xi1 , LI Cao-le1     
1. State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution, Chinese Research Academy of Environmental Sciences, Beijing 100012, China;
2. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;
3. Key Laboratory of Jiangxi Province for Persistant Pollutants Control and Resources Recycle, Nanchang Hangkong University, Nanchang 330063, China
Abstract: Wastewater treatment plants (WWTPs) are considered important reservoirs of antibiotic resistance genes (ARGs) and function as the main sources of ARGs in the environment. Membrane bioreactors (MBRs) have been recognized as effective tools for removing ARGs in WWTPs.There are a large number of pathogens and resistance genes in colloids, particulate matter, suspended matter, and microbial metabolites in intercepted wastewater by MBR. However, the distribution characteristics of resistance genes in membrane cleaning sludge remains unclear. In this study, resistance genes of membrane cleaning sludge were analyzed using a metagenomic technique. The results showed that there were 39 phyla in the membrane cleaning sludge. Proteobacteria, Nitrospirae, and Actinobacteria were the dominant phyla. The dominant genera were Nitrospira, Pseudomonas, and Bradyrhizobium. The pathogens accounted for 10.54% of all bacteria in the sample, among which Pseudomonas had the highest abundance, accounting for 3.94%. A total of 17 types of antibiotic resistance genes and 16 types of metal resistance genes (MRGs) (15 types of single metal resistance genes and 1 types of multi-heavy metal resistance gene) were identified. Multidrug resistance genes had the highest abundance, accounting for 49.08%. Multi-heavy metal resistance genes were the most abundant, accounting for 34.58%. The copper resistance genes were the most abundant of the single metal resistance genes, accounting for 19.99%. The most important functional pathway of microbial community in the membrane cleaning sludge was metabolic related, and many genes identified were related to human diseases. The numbers of genes related to bacterial resistance and bacterial infectious diseases were the largest, accounting for 34.50% and 16.62%, respectively. These results indicate that there were abundant ARGs, MRGs, and pathogens in the membrane cleaning sludge, which has potential environmental health risks. It is necessary to strengthen the control of ARGs, MRGs, and pathogens in membrane cleaning sludge to provide guidance for selecting appropriate technologies for effectively removing ARGs, MRGs, and pathogens.
Key words: membrane bioreactor (MBR)      sludge      antibiotic resistance genes (ARGs)      metal resistance genes (MRGs)      metagenomic technique     

近年来, 抗生素在人类生活、畜牧业养殖和农业种植等领域广泛使用, 导致抗生素在废水、河流、湖泊及土壤等不同环境介质中出现不同程度的残留, 致使抗生素耐药细菌(antibiotic resistance bacteria, ARB)和抗生素抗性基因(antibiotic resistance genes, ARGs)迅速繁殖和增加[1].有研究表明, ARGs不会随着宿主细胞的死亡而消失, 其能够在脱氧核糖核酸酶的保护下继续生存, 可以整合到可移动遗传元件(mobile genetic elements)中, 通过基因水平转移(horizontal gene transfer)在物种间传播耐药性[2, 3].ARGs丰度的增加会降低用于疾病治疗的抗生素效力, 对人类和动物健康造成了极大威胁[4].抗生素耐药性的广泛传播和不断进化已成为一个全球关注的健康问题.

污水处理厂是ARGs的重要储存库, 也是环境中ARGs的重要传播源.目前, 已在污水处理厂中检测到氨基糖甙类、β-内酰胺酶和喹诺酮类等多种ARGs[5].而且污水处理厂也存在着高浓度土著微生物和抗生素, 持续性对ARGs和ARB造成选择性压力[6], 导致ARGs在细菌间不断交换和重组.同时, 污水处理厂中的微生物群落亦携带着对宿主具有重要功能的多种基因质粒[7], 如抗生素抗性、重金属抗性[8]、生物降解[9]和毒力因子[10].除此之外, ARGs与污水处理厂中重金属的含量显著相关, 较低浓度的重金属有助于ARGs的扩散[11], 重金属亦会对重金属抗性基因(metal resistance genes, MRGs)造成选择性压力, 增加其扩散和传播的风险[12].由此可见, 污水处理厂是向自然环境排放ARGs的重要途径[13], 亦是阻止ARGs传播的一个关键因素[14].

污水处理厂可以通过多种污水处理工艺过程去除ARGs[15].膜生物反应器(membrane bioreactor, MBR)是一种由膜分离单元与生物处理单元相结合的新型处理技术, 具有占地面积小, 污泥产量低等特点, 被广泛应用于城市污水和工业废水处理中[16].与传统污水处理工艺相比, MBR可更有效地降低污水处理厂中的ARGs[17, 18].Li等[17]的研究发现MBR能够有效地去除ermBsul1int1(106.39~107.79 copies·mL-1)等ARGs, 其去除值在1.5~7.3个数量级, 比传统工艺高出0.8~3.4个数量级.但随着MBR的长期运行, 膜过滤过程中会截留污水中的胶体、颗粒物、悬浮物及微生物代谢物等污染物, 导致膜分离过滤效率下降[19], 造成MBR膜中大量病原菌和抗性基因的积累, 必须对MBR膜进行定期清理.目前, 关于MBR膜清洗后污泥上富集的微生物、病原菌以及抗性基因的分布特征鲜见报道.本研究采用宏基因组技术, 系统开展膜清洗后污泥中微生物群落结构组成和抗性基因分析, 以期为膜处理工艺后污泥中抗性基因的管控和去除提供理论和数据支持.

1 材料与方法 1.1 样品采集

本实验污泥样品取自北京市某生活污水处理厂, 该厂污水处理量为10万t·d-1, 主体工艺为A2O-MBR, 废水经过处理后用于中水回用.取膜组件高压水枪清洗过程中脱离污泥, 用50 mL无菌离心管分装后于-4℃低温冷藏, 当日即进行基因组DNA提取.

1.2 基因组提取

采用FastDNATM SPIN Kit for Soil(MP Biomedicals, CA, USA)试剂盒完成污泥基因组DNA抽提, 而后利用1%琼脂糖凝胶电泳检测抽提基因组DNA的质量与完整性.采用Qubit 2.0荧光光谱仪(Thermo Fisher Scientific, MA, USA)检测PCR扩增后DNA的产量和纯度.基因组DNA样品用干冰保藏并立即送往上海美吉生物医药科技有限公司进行测序.

1.3 生物信息学分析

采用Illumina HiSeq 4000平台进行PE150双端测序, 使用Trimmonatic[20]对原始测序数据进行质量控制, 采用Kraken2[21]法对污泥样品在门(phylum)、纲(class)和属(genus)这3个层级开展微生物群落组成分析.应用USEARCH(accel≤0.5、e-value≤10-5)和BlastX(alignment length 25 aa、amino acids ≥80%和evalue 1e-5)与SARG抗生素抗性基因数据库进行对比注释[22], 对注释后的数据按照SARG抗生素抗性基因数据库分类成不同的ARGs类型以及亚型.采用BacMet对宏基因组测序中MRGs进行注释[23].ARGs和MRGs的相对丰度采用每百万条reads中ARGs和MRGs reads数量进行标准化.

采用基于De Bruijn graph的SOAPdenovo(version 1.06)[24]软件, 组装质控后的reads, 仅将大于500 bp的scaftig片段用于下一步分析; 采用MetaGene[25]对组装后的序列预测开放阅读框(ORFs); 采用CD-HIT[26]来获得非冗余基因; 采用SOAPaligner软件将保留的clean read与基因库进行比对, 以计算匹配的数目.通过去除含有少于3个mapped reads映射片段的基因类别来获得unigene, 采用DIAMOND软件(blastp, evalue ≤10-5)将unigene对应的蛋白序列在KEGG[27]数据库中进行比对, 分析该样本宏基因组序列的基因功能.采用Origin 2018进行数据绘图.

2 结果与讨论 2.1 膜清洗后污泥中微生物群落结构

污泥样品测序数据经过质量控制后, 得到5.01 GB数据, 获得82 276 572条reads.用Kraken2软件进行注释, 统计样本在门水平、纲水平及属水平上的微生物群落组成(图 1).研究结果表明, 细菌域是膜清洗后污泥中主要微生物类群, 占比99.27%, 古细菌域占比0.73%.污泥样品中共检测到39个门, 丰度前十的优势菌门分别为Proteobacteria(69.48%)、Nitrospirae(13.73%)、Actinobacteria(10.94%)、Firmicutes(1.31%)、Bacteroidetes(1.15%)、Planctomycetes(0.77%)、Euryarchaeota(0.70%)、Chloroflexi(0.39%)、Cyanobacteria(0.27%)和Acidobacteria(0.26%).其中, 变形菌门(Proteobacteria)是最优势菌门, 在污水处理厂有机物和营养物去除中发挥着重要作用, 这与其他学者研究结果一致[28].硝化螺旋菌门(Nitrospirae)为活性污泥中的主要细菌类群, 能够有效氧化亚硝酸盐[29]; 放线菌门(Actinobacteria)是一类与污泥膨胀有关的细菌[30], 丰度达到10.94%; 上述细菌菌门在污水处理厂中均发挥着重要作用, 其中变形菌门和放线菌门是多种ARGs的主要宿主[1, 31, 32], 其他7个优势菌群丰度在0.2%~1.5%之间.

图 1 膜清洗后污泥在门、纲和属水平上微生物群落结构 Fig. 1 Microbial community structure in the membrane cleaning sludge at the phylum, class, and genus level

污泥样品中共检测到79个纲, 丰度前十的优势纲分别为β-Proteobacteria(31.51%)、α-Proteobacteria(20.51%)、γ-Proteobacteria(14.82%)、Nitrospirae(13.73%)、Actinobacteria(10.27%)、Deltaproteobacteria(2.46%)、Planctomycetia(0.72%)、Bacilli(0.69%)、Methanomicrobia(0.53%)和Clostridia(0.47%).其中β-Proteobacteria、α-Proteobacteria和γ-Proteobacteria均属于变形菌门, 是主要的耐药菌, 能够降解硝酸盐和亚硝酸盐[33], β-Proteobacteria是一类与氮循环显著相关的细菌, 在医药废水处理厂中数量最多[34], γ-Proteobacteria在反硝化和除磷中起着重要作用[35].

污泥样品中共检测到1 143种属, 丰度前十的优势属分别为Nitrospira(13.73%)、Pseudomonas(3.93%)、Bradyrhizobium(3.42%)、Thauera(3.05%)、Acidovorax(2.64%)、Burkholderia(2.22%)、Streptomyces(1.97%)、Cupriavidus(1.60%)、Stenotrophomonas(1.12%)和Dechloromonas(1.08%).Nitrospira是废水处理过程中常见的亚硝酸盐氧化菌(nitrite oxidizing bacteria)[36]. Lücker等[37]的研究发现从活性污泥中富集的Nitrospira携带着包含四环素在内的多种ARGs.Liu等[38]的研究结果也表明Nitrospira与四环素耐药基因呈正相关, 因此Nitrospira可能会促进废水处理过程中四环素类抗性基因的增加.

32种膜清洗后污泥中病原菌属(VFDB数据库收录)相对丰度取对数后如图 2所示.除Shigella属外, 其他病原菌属均在该样本中被检测出, 病原菌属占检测到的所有菌属的10.54%, 其中Pseudomonas属、Burkholderia属和Bordetella属是该污泥样品中的主要病原菌属, 占比分别达到3.94%、2.22%和1.04%.Pseudomonas属广泛分布在水和土壤等环境介质中[39], 且含有临床上重要的病原菌Pseudomonas aeruginosa.该菌是一种典型的革兰氏阴性条件致病菌, Ringen和Drake调查结果发现在土壤样本中Pseudomonas aeruginosa含量仅有3%, 而在污水中含量达到90%, 表明人的粪便和污水是Pseudomonas aeruginosa的主要栖息地[40].该病原菌可以通过人类接触引起血液、肺部(肺炎)或身体其他部位的感染, 是医院感染的重要源头之一.

图 2 基于样本中病原菌属相对丰度取log10的热图 Fig. 2 Heat map based on the log10 of the relative abundance of pathogenic genera in samples

2.2 膜清洗后污泥中抗生素抗性基因种类及丰度

污水处理厂接收来自城市居民、屠宰场和医院等各种废水, 这些废水均可能含有各种抗生素.如Li等[41]在污水处理厂水样中检测出高浓度的磺胺类和四环素类抗生素.环境中高浓度的抗生素可促进抗生素耐药性[42].并且长期暴露于低浓度抗生素及中间产物也会促进ARB和ARGs的产生和传播[43].在此过程中, 由于抗生素选择性压力持续存在, 进而刺激了细菌代谢和ARB的扩散[44, 45].本样品经生物信息学分析共注释出17类ARGs[包括unclassified, 图 3(a)]和174种ARGs, 包括多药类(multidrug, 49.08%, 46种亚型)、杆菌肽类(bacitracin, 11.72%, 1种亚型)、大环内酯类(macrolide-lincosamide-streptogramin, 10.89%, 18种亚型)、磺胺类(sulfonamide, 10.29%, 4种亚型)、膦胺霉素类(Fosmidomycin, 4.26%, 2种亚型)、氨基糖苷类(aminoglycoside, 3.45%, 19种亚型)、四环素类(tetracycline, 2.98%, 16种亚型)、unclassified(2.45%, 3种亚型)、β-内酰胺类(β-lactam, 2.14%, 41种亚型)、万古霉素类(vancomycin, 0.85%, 5种亚型)、利福霉素(rifamycin, 0.70%, 1种亚型)、氯霉素类(chloramphenicol, 0.49%, 7种亚型)、多粘菌素类(polymyxin, 0.25%, 2种亚型)、喹诺酮类(quinolone, 0.22%, 2种亚型)、甲氧苄啶类(trimethoprim, 0.20%, 5种亚型)、嘌呤霉素类(puromycin, 0.02%, 1种亚型)和春雷霉素类(kasugamycin, 0.01%, 1种亚型).多药类抗生素抗性基因相对丰度最高, 达到44.78×10-3‰, 其中杆菌肽类抗生素抗性基因bacA相对丰度最高, 达到了11.41×10-3‰.

图 3 膜清洗后污泥中抗生素抗性基因相对丰度 Fig. 3 Relative abundance of different ARGs in membrane cleaning sludge

污水处理厂常见的ARGs包括磺胺类、四环素类、万古霉素类、喹诺酮类、甲氧苄啶类、和大环内酯类[46, 47], 在膜清洗后污泥中均检测到上述ARGs[图 3(b)3(c)3(d)].其中磺胺类中60%的抗性基因是sul1, 相对丰度达到6.00×10-3‰.四环素类抗性基因共检测出16类, tetC相对丰度最高, 达到8.4×10-4‰, 占比31%.万古霉素类抗性基因中vanR相对丰度最高, 为5.8×10-4‰.喹诺酮类共检测出2种抗性基因, 分别是qepAabaQ, 相对丰度分别是2.1×10-4‰和1×10-5‰.甲氧苄啶类相对丰度均较低, 其中dfrB2最高, 为7×10-5‰.大环内酯类共检测出18种亚型, 其中macB相对丰度最高, 占比69%.Wang等[48]的研究表明MBR可有效降解废水中61.8%~77.5%的抗生素, 其中22.5%~38.2%的抗生素被MBR中污泥吸附.因此, 膜清洗后污泥中含有较高浓度的抗生素, 促进ARGs的产生与传播.此外, 病原体中普遍存在和传播的ARGs, 特别是多药类抗生素抗性基因, 可能对人类生命健康及环境微生物群落进化造成严重后果[49].而且在当前的医疗手段中由于病原体引起的感染, 如multidrug-resistant Klebsiella pneumoniae和Acinetobacter baumannii, 是无法使用抗生素进行治疗的[50].虽然经过MBR工艺处理废水后, ARGs相对丰度有所下降, 但只是从水体转移到污泥中, 导致膜清洗后脱落污泥中的ARGs在不断富积和繁殖[18].因此MBR膜清洗后污泥中蕴藏着丰富的ARGs, 存在潜在的环境健康风险, 需要对膜清洗后污泥加强管控.

2.3 膜清洗后污泥中金属抗性基因

该污泥样品质控后的宏基因组数据经过BacMet软件与金属抗性数据库进行比对, BacMet数据库包含23种金属类型, 470个经实验证实的抗性基因, 此外还包含从公共序列库收集的25 477个潜在抗性基因.膜清洗污泥中MRGs类别及相对丰度如图 4所示.该污泥样品中共检测出16类MRGs和156种MRGs, 其中包括1类多重金属抗性基因(As-Sb、Cd-Zn、Cd-Zn-Co、Cd-Zn-Hg、Cd-Zn-Ni、Cr-Te-Se、Co-Cd-Ni、Co-Mg、Co-Ni、Co-Ni-Fe、Cu-Co、Cu-Au、Cu-Ni-Zn、Cu-Ag、Cu-Zn、Fe-Ga、Fe-Mn、Fe-Ni、Pb-Cd-Zn、Mg-Co-Ni-Mn、Mn-Cd、Mn-Fe-Cd-Co-Zn、Mn-Fe-Co-Zn-Ni-Cu-Cd-Ga、Mn-Mg、Mn-Zn、Mo-W-V、Ni-Cd-Pb、Ni-Cd-Zn-Co、Ni-Co、Ni-Co-Cd、Ni-Co-Zn、Ni-Zn、Te-Cu、W-Mo、Zn-Cd、Zn-Fe-Co-Ni-Cu-Cd、Zn-Pb、Zn-Mn-Fe、Zn-Hg和Zn-Te)和15类单金属抗性基因(Al、As、Cd、Cr、Co、Cu、Au、Fe、Pb、Hg、Ni、Ag、Te、W和Zn).

图 4 膜清洗污泥中金属抗性基因类别及相对丰度 Fig. 4 Types and relative abundance of metal resistance gene in membrane cleaning sludge

多重金属类抗性基因在16类金属抗性基因中相对丰度最高, 相对丰度达到了171.55×10-3‰, 占该样本总金属抗性基因的34.58%.在156种MRGs中, 多重金属类抗性基因ruvB(Cr-Te-Se)相对丰度最高, 达到43.07×10-3‰.其次为Cu金属抗性基因copF和Fe金属抗性基因acn, 相对丰度分别达到了39.72×10-3‰和34.88×10-3‰, 分别占样本金属抗性基因的8.68%和8.01%.

单金属抗性基因中Cu和As的相对丰度最高, 分别达到了99.18×10-3‰和79.15×10-3‰, 分别占样本金属抗性基因的19.99%和15.95%.其中Cu的抗性基因共有25种, 包括copFcop-unnamedcopBcopRcutOctpVcopScopGctpGcorRcopCcopPmmcOcopZmctBricRcsoRcopY/tcrYtcrBpcoAcopDtcrAcopJcopMcorS; As的抗性基因共有16种, 包括arrAacr3arsMpstBarsTaioA/aoxBarsCpstCarsHaioB/aoxApstAarrBaioEaioR/aoxRpstSarsD; Au(golS)的单金属抗性基因相对丰度最低, 仅为2.4×10-5‰.

在所有检测到的MRGs中, 由于Zn、Cu和Co等金属离子是微生物所必需的营养物质, 因此这些离子的抗性基因在环境中广泛传播.但当其浓度高时具有毒性[51], 会对环境以及人类造成严重危害.同时Cu、As、Zn和Cr等重金属也具有抗菌和促生长作用[52, 53], 常常会被添加到动物的饲料中, 以提高养殖动物的抵抗力[54], 但动物对这些金属添加剂吸收率极低, 其中95%通过动物粪便和尿液排泄, 造成养殖废水中重金属含量较高[55].而重金属的广泛使用会导致废水中MRGs丰度的增加, 特别是在处理养殖废水的污水处理厂[56].除此之外, 化工、冶金和钢铁等工业废水中也残留着高浓度的重金属.有研究表明, 重金属会对ARGs的丰度有显著影响, 并且ARGs和MRGs会编码在细菌类群上相同的转座子和/或质粒上[57].ARGs和MRGs的共生也表明当有重金属存在时会促进ARGs的传播和繁殖, 而且在废水中金属浓度通常要比抗生素高出2~3个数量级, 可能极大地促进了抗生素耐药性的传播[58]. 有细菌门类如Firmicutes、Proteobacteria和Actinobacteria也与不同种类ARGs和MRGs呈正相关, 而这些细菌极可能是ARGs和MRGs的潜在宿主[59].

2.4 膜清洗后污泥中微生物群落功能注释

采用DIAMOND软件与获得的unigene与KEGG数据库进行比对, 基于KEGG数据库注释的膜清洗后污泥微生物群落功能基因分布如图 5所示.结果显示, 膜清洗后污泥中微生物群落功能基因共分为6个大类和46个小类.在6个大类中, 与微生物菌群的功能通路相关基因数量依次为代谢>环境信息系统>细胞过程>遗传信息处理>人类疾病>生物系统.其中, 代谢相关是最重要的功能通路, 占到注释序列的67.87%, 其次为环境信息系统和细胞过程, 分别占已注释序列的9.84%和7.68%.在代谢类别的功能分类中, 丰度高低顺序依次为氨基酸代谢>其他次生代谢物生物合成>碳水化合物代谢>能量代谢>多糖生物合成与代谢>脂质代谢>辅助因子和维生素代谢>其他氨基酸代谢>萜类和聚酮化合物代谢>核苷酸代谢>异生素生物降解与新陈代谢.其中, 氨基酸代谢是代谢通路中的主要途径, 占注释序列比重最大, 为11.77%.其次是其他次生代谢物生物合成和碳水化合物代谢, 分别占注释序列比重的10.80%和10.59%.环境信息系统类别的功能分类中微生物群落丰度依次为:膜转运>信号转导>信号分子与相互作用, 其中膜转运是最主要的部分, 占已注释序列的5.16%, 信号分子与相互作用相对较少, 仅占已注释序列的0.01%.

图 5 基于KEGG数据库注释的膜清洗后污泥微生物群落功能基因分布 Fig. 5 Distribution of genes according to functional classes annotated with KEGG database in membrane cleaning sludge

人类疾病类别功能通路相关基因的相对丰度为4.70%, 在与人类疾病相关的代谢通路中, 涉及细菌耐药和细菌传染疾病的基因数量最多, 分别为占人类疾病相关的代谢通路已注释序列的34.50%和16.62%.膜清洗后污泥中细菌耐药的通路涉及到3类, 相对丰度依次为β-内酰胺类耐药>阳离子抗菌肽耐药>万古霉素耐药, β-内酰胺类耐药基因相对丰度最高, 达到45.42%; 细菌传染疾病的通路涉及到10类, 相对丰度依次为结核病>军团杆菌病>百日咳>幽门螺杆菌感染中的上皮细胞信号转导>沙门氏菌感染>上皮细胞的细菌入侵>金黄色葡萄球菌感染>霍乱弧菌感染>志贺氏菌病>致病性大肠杆菌感染.其中, 最主要的传染病为结核病, 占细菌传染疾病通路的36.93%, 其次为军团杆菌病和百日咳, 相对丰度分别为35.73%和10.63%; 其余涉及细菌传染病的通路, 如幽门螺杆菌感染中的上皮细胞信号转导、沙门氏菌感染、上皮细胞的细菌入侵、金黄色葡萄球菌感染、霍乱弧菌感染、志贺氏菌病和致病性大肠杆菌感染的相对丰度均在10%以下(图 6).因此根据功能注释结果, 膜清洗后污泥中最主要的功能通路为代谢相关, 该结果与其他研究报道相符[59~61].如Lin等[60]通过使用PICRUSt软件预测在不同重金属(Pb、Hg和As)胁迫下微生物群落功能通路, 结果表明代谢是最主要的功能通路, 占比约42.61%~46.35%, 并且与对照组相比, 重金属胁迫下其相对丰度有所上升.Wang等[59]的研究结果也表明在两个不同处理工艺的污水处理厂, 代谢是主要的功能通路, 占52.52%~59.37%.此外在本研究中微生物还存在着细菌耐药和细菌感染性疾病等多种与人类疾病相关的基因, 因此污水处理厂特别是通过膜处理清洗后污泥会富集多种病原菌属, 可以通过食物链进入到人体, 进而对人类健康造成威胁.

图 6 膜清洗后污泥中细菌感染疾病相关功能基因分布 Fig. 6 Distribution of genes related to bacterial infectious diseases in membrane cleaning sludge

3 结论

(1) 膜清洗后污泥中微生物群落结构中细菌域是最主要的微生物类群, 优势菌门分别为Proteobacteria、Nitrospirae和Actinobacteria, 优势菌纲分别为β-Proteobacteria、α-Proteobacteria和γ-Proteobacteria, 优势菌属分别为NitrospiraPseudomonasBradyrhizobium.污泥样本含有的病原菌属占所有菌属的10.54%, 其中Pseudomonas属相对丰度最高, 占到所有菌属的3.94%.

(2) 膜清洗后污泥中共注释出17类ARGs和174种ARGs, 其中多药类抗生素抗性基因丰度最高, 达到44.78×10-3‰.在174种抗生素抗性基因中杆菌肽类抗生素抗性基因bacA相对丰度最高, 为11.41×10-3‰.

(3) 膜清洗后污泥中共注释出1类多重金属抗性基因和15类单金属抗性基因, 其中多重金属类抗性基因相对丰度最高, 达到了171.55×10-3‰.单金属抗性基因中对Cu相对丰度最高, 达到了99.18×10-3‰.

(4) 膜清洗后污泥中微生物群落最主要的功能通路为代谢相关, 且存在细菌耐药和细菌感染性疾病等多种与人类疾病相关的基因.

参考文献
[1] 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
[2] Zhang J, Lin H, Ma J W, et al. Compost-bulking agents reduce the reservoir of antibiotics and antibiotic resistance genes in manures by modifying bacterial microbiota[J]. Science of the Total Environment, 2019, 649: 396-404. DOI:10.1016/j.scitotenv.2018.08.212
[3] Yang Y Y, Li Z, Song W J, et al. Metagenomic insights into the abundance and composition of resistance genes in aquatic environments: Influence of stratification and geography[J]. Environment International, 2019, 127: 371-380. DOI:10.1016/j.envint.2019.03.062
[4] Berendonk T U, Manaia C M, Merlin C, et al. Tackling antibiotic resistance: the environmental framework[J]. Nature Reviews Microbiology, 2015, 13(5): 310-317. DOI:10.1038/nrmicro3439
[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] Di Cesare A, Eckert E M, D'Urso S, et al. Co-occurrence of integrase 1, antibiotic and heavy metal resistance genes in municipal wastewater treatment plants[J]. Water Research, 2016, 94: 208-214. DOI:10.1016/j.watres.2016.02.049
[7] Schlüter A, Szczepanowski R, Kurz N, et al. Erythromycin resistance-conferring plasmid pRSB105, isolated from a sewage treatment plant, harbors a new macrolide resistance determinant, an integron-containing Tn402-like element, and a large region of unknown function[J]. Applied and Environmental Microbiology, 2007, 73(6): 1952-1960. DOI:10.1128/AEM.02159-06
[8] Yang Y, Jiang X T, Zhang T. Evaluation of a hybrid approach using UBLAST and BLASTX for metagenomic sequences annotation of specific functional genes[J]. PLoS One, 2014, 9(10). DOI:10.1371/journal.pone.0110947
[9] Ma J Y, Quan X C, Yang Z F, et al. Biodegradation of a mixture of 2, 4-dichlorophenoxyacetic acid and multiple chlorophenols by aerobic granules cultivated through plasmid pJP4 mediated bioaugmentation[J]. Chemical Engineering Journal, 2012, 181-182: 144-151. DOI:10.1016/j.cej.2011.11.041
[10] Mnif B, Harhour H, Jdidi J, et al. Molecular epidemiology of extended-spectrum beta-lactamase-producing Escherichia coli in Tunisia and characterization of their virulence factors and plasmid addiction systems[J]. BMC Microbiology, 2013, 13(1). DOI:10.1186/1471-2180-13-147
[11] Knapp C W, McCluskey S M, Singh B K, et al. Antibiotic resistance gene abundances correlate with metal and geochemical conditions in archived Scottish soils[J]. PLoS One, 2011, 6(11). DOI:10.1371/journal.pone.0027300
[12] Selvam A, Xu D L, Zhao Z Y, et al. Fate of tetracycline, sulfonamide and fluoroquinolone resistance genes and the changes in bacterial diversity during composting of swine manure[J]. Bioresource Technology, 2012, 126: 383-390. DOI:10.1016/j.biortech.2012.03.045
[13] Makowska N, Koczura R, Mokracka J. Class 1 integrase, sulfonamide and tetracycline resistance genes in wastewater treatment plant and surface water[J]. Chemosphere, 2016, 144: 1665-1673. DOI:10.1016/j.chemosphere.2015.10.044
[14] Breazeal M V R, Novak J T, Vikesland P J, et al. Effect of wastewater colloids on membrane removal of antibiotic resistance genes[J]. Water Research, 2013, 47(1): 130-140. DOI:10.1016/j.watres.2012.09.044
[15] Yang Y, Li B, Ju F, et al. Exploring variation of antibiotic resistance genes in activated sludge over a four-year period through a metagenomic approach[J]. Environmental Science & Technology, 2013, 47(18): 10197-10205.
[16] Rehman Z U, Fortunato L, Cheng T Y, et al. Metagenomic analysis of sludge and early-stage biofilm communities of a submerged membrane bioreactor[J]. Science of the Total Environment, 2020, 701. DOI:10.1016/j.scitotenv.2019.134682
[17] Li B, Qiu Y, Li J, et al. Removal of antibiotic resistance genes in four full-scale membrane bioreactors[J]. Science of the Total Environment, 2019, 653: 112-119. DOI:10.1016/j.scitotenv.2018.10.305
[18] 姚鹏城, 陈嘉瑜, 张永明, 等. 抗生素抗性基因在生活及工业混合废水处理系统中的分布和去除[J]. 生态毒理学报, 2020, 15(1): 201-208.
Yao P C, Chen J Y, Zhang Y M, et al. Distribution and removal of antibiotic resistance genes in municipal and industrial mixed wastewater treatment systems[J]. Asian Journal of Ecotoxicology, 2020, 15(1): 201-208.
[19] 付翠彦, 张光辉, 顾平. 膜生物反应器在污水处理中的研究应用进展[J]. 水处理技术, 2009, 35(5): 1-6.
Fu C Y, Zhang G H, Gu P. Development of membrane bioreactors application in wasterwater treatment[J]. Technology of Water Treatment, 2009, 35(5): 1-6.
[20] 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
[21] Wood D E, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2[J]. Genome Biology, 2019, 20(1). DOI:10.1186/s13059-019-1891-0
[22] 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
[23] Pal C, Bengtsson-Palme J, Rensing C, et al. BacMet: antibacterial biocide and metal resistance genes database[J]. Nucleic Acids Research, 2014, 42(D1): D737-D743. DOI:10.1093/nar/gkt1252
[24] Luo R B, Liu B H, Xie Y L, et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler[J]. GigaScience, 2012, 1(1). DOI:10.1186/2047-217X-1-18
[25] Noguchi H, Park J, Takagi T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences[J]. Nucleic Acids Research, 2006, 34(19): 5623-5630. DOI:10.1093/nar/gkl723
[26] Li W Z, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences[J]. Bioinformatics, 2006, 22(13): 1658-1659. DOI:10.1093/bioinformatics/btl158
[27] Mitra S, Rupek P, Richter D C, et al. Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG[J]. BMC Bioinformatics, 2011, 12(1). DOI:10.1186/1471-2105-12-S1-S21
[28] Vasiliadou I A, Molina R, Martinez F, et al. Toxicity assessment of pharmaceutical compounds on mixed culture from activated sludge using respirometric technique: The role of microbial community structure[J]. Science of the Total Environment, 2018, 630: 809-819. DOI:10.1016/j.scitotenv.2018.02.095
[29] Gilbert E M, Agrawal S, Brunner F, et al. Response of different Nitrospira species to anoxic periods depends on operational DO[J]. Environmental Science & Technology, 2014, 48(5): 2934-2941.
[30] Kragelund C, Remesova Z, Nielsen J L, et al. Ecophysiology of mycolic acid-containing Actinobacteria (Mycolata) in activated sludge foams[J]. FEMS Microbiology Ecology, 2007, 61(1): 174-184. DOI:10.1111/j.1574-6941.2007.00324.x
[31] Ye L, Zhang T, Wang T T, et al. Microbial structures, functions, and metabolic pathways in wastewater treatment bioreactors revealed using high-throughput sequencing[J]. Environmental Science & Technology, 2012, 46(24): 13244-13252.
[32] Sun Y M, Shen Y X, Liang P, et al. Multiple antibiotic resistance genes distribution in ten large-scale membrane bioreactors for municipal wastewater treatment[J]. Bioresource Technology, 2016, 222: 100-106. DOI:10.1016/j.biortech.2016.09.117
[33] Lin Y, Zhang T. Bacterial communities in different sections of a municipal wastewater treatment plant revealed by 16S rDNA 454 pyrosequencing[J]. Applied Microbiology and Biotechnology, 2013, 97(6): 2681-2690. DOI:10.1007/s00253-012-4082-4
[34] Ahn Y, Choi J. Bacterial communities and antibiotic resistance communities in a full-scale hospital wastewater treatment plant by high-throughput pyrosequencing[J]. Water, 2016, 8(12). DOI:10.3390/w8120580
[35] Yoshie S, Makino H, Hirosawa H, et al. Molecular analysis of halophilic bacterial community for high-rate denitrification of saline industrial wastewater[J]. Applied Microbiology and Biotechnology, 2006, 72(1): 182-189. DOI:10.1007/s00253-005-0235-z
[36] 李慧莉, 武彩云, 唐安平, 等. 不同污泥在微波预处理-厌氧消化过程中抗性基因分布及菌群结构演替[J]. 环境科学, 2021, 42(1): 323-332.
Li H L, Wu C Y, Tang A P, et al. Occurrence of antibiotic resistance genes and bacterial community structure of different sludge samples during microwave pretreatment-anaerobic digestion[J]. Environmental Science, 2021, 42(1): 323-332.
[37] Lücker S, Wagner M, Maixner F, et al. A Nitrospira metagenome illuminates the physiology and evolution of globally important nitrite-oxidizing bacteria[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(30): 13479-13484. DOI:10.1073/pnas.1003860107
[38] Liu H, Sun H F, Zhang M, et al. Dynamics of microbial community and tetracycline resistance genes in biological nutrient removal process[J]. Journal of Environmental Management, 2019, 238: 84-91.
[39] Crone S, Vives-Flórez M, Kvich L, et al. The environmental occurrence of Pseudomonas aeruginosa[J]. APMIS, 2020, 128(3): 220-231. DOI:10.1111/apm.13010
[40] Ringen L M, Drake C H. A study of the incidence of Pseudomonas aeruginosa from various natural sources[J]. Journal of Bacteriology, 1952, 64(6): 841-845. DOI:10.1128/jb.64.6.841-845.1952
[41] Li J N, Cheng W X, Xu L K, et al. Occurrence and removal of antibiotics and the corresponding resistance genes in wastewater treatment plants: effluents' influence to downstream water environment[J]. Environmental Science and Pollution Research, 2016, 23(7): 6826-6835. DOI:10.1007/s11356-015-5916-2
[42] Kristiansson E, Fick J, Janzon A, et al. Pyrosequencing of antibiotic-contaminated river sediments reveals high levels of resistance and gene transfer elements[J]. PLoS One, 2011, 6(2). DOI:10.1371/journal.pone.0017038
[43] Brown K D, Kulis J, Thomson B, et al. Occurrence of antibiotics in hospital, residential, and dairy effluent, municipal wastewater, and the Rio Grande in New Mexico[J]. Science of the Total Environment, 2006, 366(2-3): 772-783. DOI:10.1016/j.scitotenv.2005.10.007
[44] 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
[45] Gullberg E, Cao S, Berg O G, et al. Selection of resistant bacteria at very low antibiotic concentrations[J]. PLoS Pathogens, 2011, 7(7). DOI:10.1371/journal.ppat.1002158
[46] Pazda M, Kumirska J, Stepnowski P, et al. Antibiotic resistance genes identified in wastewater treatment plant systems-a review[J]. Science of the Total Environment, 2019, 697. DOI:10.1016/j.scitotenv.2019.134023
[47] 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.
[48] Wang S, Ma X X, Liu Y L, et al. Fate of antibiotics, antibiotic-resistant bacteria, and cell-free antibiotic-resistant genes in full-scale membrane bioreactor wastewater treatment plants[J]. Bioresource Technology, 2020, 302. DOI:10.1016/j.biortech.2020.122825
[49] Levy S B, Marshall B. Antibacterial resistance worldwide: causes, challenges and responses[J]. Nature Medicine, 2004, 10(12): S122-S129.
[50] McGowan Jr J E. Resistance in nonfermenting gram-negative bacteria: multidrug resistance to the maximum[J]. American Journal of Infection Control, 2006, 34(5 Suppl 1): S29-S37.
[51] Ji G Y, Silver S. Bacterial resistance mechanisms for heavy metals of environmental concern[J]. Journal of Industrial Microbiology, 1995, 14(2): 61-75. DOI:10.1007/BF01569887
[52] Hölzel C S, Müller C, Harms K S, et al. Heavy metals in liquid pig manure in light of bacterial antimicrobial resistance[J]. Environmental Research, 2012, 113: 21-27. DOI:10.1016/j.envres.2012.01.002
[53] Deka R S, Mani V, Kumar M, et al. Chromium supplements in the feed for lactating Murrah buffaloes (Bubalus bubalis): influence on nutrient utilization, lactation performance, and metabolic responses[J]. Biological Trace Element Research, 2015, 168(2): 362-371. DOI:10.1007/s12011-015-0372-x
[54] Zhang F S, Li Y, Yang M, et al. Content of heavy metals in animal feeds and manures from farms of different scales in northeast China[J]. International Journal of Environmental Research and Public Health, 2012, 9(8): 2658-2668. DOI:10.3390/ijerph9082658
[55] Xiong W G, Zeng Z L, Zhang Y N, et al. Fate of metal resistance genes in arable soil after manure application in a microcosm study[J]. Ecotoxicology and Environmental Safety, 2015, 113: 59-63. DOI:10.1016/j.ecoenv.2014.11.026
[56] Li A D, Li L G, Zhang T. Exploring antibiotic resistance genes and metal resistance genes in plasmid metagenomes from wastewater treatment plants[J]. Frontiers in Microbiology, 2015, 6. DOI:10.3389/fmicb.2015.01025
[57] Summers A O. Generally overlooked fundamentals of bacterial genetics and ecology[J]. Clinical Infectious Diseases, 2002, 34(S3): S85-S92. DOI:10.1086/340245
[58] Stepanauskas R, Glenn T C, Jagoe C H, et al. Coselection for microbial resistance to metals and antibiotics in freshwater microcosms[J]. Environmental Microbiology, 2006, 8(9): 1510-1514. DOI:10.1111/j.1462-2920.2006.01091.x
[59] Wang J B, Li X, Zhou Z W, et al. Bacterial communities, metabolic functions and resistance genes to antibiotics and metals in two saline seafood wastewater treatment systems[J]. Bioresource Technology, 2019, 287. DOI:10.1016/j.biortech.2019.121460
[60] Lin H, Jiang L T, Li B, et al. Screening and evaluation of heavy metals facilitating antibiotic resistance gene transfer in a sludge bacterial community[J]. Science of the Total Environment, 2019, 695. DOI:10.1016/j.scitotenv.2019.133862
[61] 陈红玲, 张兴桃, 王晴, 等. 宏基因组方法分析医药化工废水厂中抗生素耐药菌及耐性基因[J]. 环境科学, 2020, 41(1): 313-320.
Chen H L, Zhang X T, Wang Q, et al. Metagenomic analysis of antibiotic resistant bacteria and resistance genes in a pharmaceutical and chemical wastewater treatment plant[J]. Environmental Science, 2020, 41(1): 313-320.