Effect of fertilizer types on antibiotic resistance genes and bacterial community in vegetable fields
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摘要: 农田土壤生态系统是抗生素抗性基因(ARGs)的源与汇, 畜禽粪便施用是土壤中ARGs的重要来源。畜禽粪便在蔬菜地土壤中的大量施用, 加剧了蔬菜地土壤ARGs的污染, 对人类健康造成潜在危害。本文采集了河北省不同施肥类型(施用鲜鸡粪、鲜羊粪、鲜牛粪、商品有机肥以及单施化肥)的蔬菜地表层土壤(0~20 cm)样品, 采用定量PCR技术和高通量测序技术对蔬菜地土壤ARGs和细菌群落结构开展了研究, 旨在探究不同施肥类型蔬菜地土壤中ARGs的分布特征及其影响因素。结果表明, 不同施肥类型蔬菜地土壤中均检测到较高丰度的四环素类ARGs (tetA、tetC、tetG、tetL、tetO、tetM、tetW、tetQ)、磺胺类ARGs (sul1、sul2)以及Ⅰ类整合酶基因(intI1), 其中所有施肥处理土壤中磺胺类ARGs总绝对丰度高达9.96×109 copies∙g−1(干土), 且显著高于四环素类ARGs总丰度[1.07×109 copies∙g−1(干土)]。畜禽粪肥和化肥的施用都显著增加了土壤中ARGs丰度, 其中高化肥施加量土壤中ARGs检出丰度最高[6.34×109 copies∙g−1(干土)], 商品有机肥土壤中ARGs检出丰度最低[3.09×108 copies∙g−1(干土)]。施畜禽粪肥土壤中细菌群落的Shannon指数和Chao1指数显著高于高化肥施加量土壤, 但与低化肥施加量土壤差异不显著, 说明畜禽粪肥施用显著提高了土壤细菌群落的α多样性。Pearson相关性分析结果表明, 细菌群落结构是影响ARGs分布的重要因素。IntI1基因与sul2、tetG、tetQ以及tetW基因呈显著正相关(P<0.05), 说明intI1基因在ARGs的传播和扩散中也起着重要作用。本研究结果表明高化肥施用量能显著增加蔬菜地土壤ARGs的丰度, 商品有机肥的施用对土壤ARGs丰度影响最小。本研究为评估不同施肥类型蔬菜地土壤中ARGs的污染现状提供了相应的数据参考。Abstract: Farmland ecosystems are essential sources and sinks of antibiotic resistance genes (ARGs), and the application of livestock manure is a major contributor to ARGs in soil. The massive application of livestock manure to vegetable fields has intensified the pollution caused by ARGs in soil. Raw consumption of edible vegetables is one of the most direct ways to introduce ARGs from the soil–plant system to humans, which poses a potential threat to human health. However, few studies have investigated the effects of different fertilizer types on ARGs and bacterial communities in vegetable fields. In this study, 21 soil samples (0–20 cm) were collected from vegetable fields in Hebei Province using different fertilizer types (fresh fowl manure, fresh sheep manure, fresh cattle manure, commercial organic fertilizer, and chemical fertilizer). The distributions and characteristics of ARGs and bacterial communities in vegetable fields were investigated using real-time quantitative polymerase chain reaction (PCR) and high-throughput sequencing techniques. Eight tetracycline resistance genes (tetA, tetC, tetG, tetL, tetO, tetM, tetW, and tetQ), two sulfonamide resistance genes (sul1 and sul2), and one intI1 gene were detected in all vegetable fields. The absolute abundance of sulfonamide resistance genes (9.96×109 copies·g−1 in dry soil) was significantly higher than that of tetracycline resistance genes (1.07×109 copies·g−1 in dry soil). The application of livestock manure and chemical fertilizer both significantly increased the abundance of ARGs in vegetable fields. The highest abundance of ARGs (6.34×109 copies∙g−1 in dry soil) was found in vegetable fields with higher chemical fertilizer amendment, while the lowest abundance of ARGs (3.09×108 copies∙g−1 in dry soil) was found in vegetable soil with commercial organic fertilizer. In addition, the Shannon and Chao1 indices, representing the α diversity of the soil bacterial community, were significantly higher in soil fertilized with livestock manure compared to high-chemical fertilizer application but not in low-chemical fertilization soil, indicating that livestock manure application significantly increased the abundance and diversity of the soil bacterial community. Pearson’s correlation analysis showed that soil bacterial community structure was an important factor influencing the distribution of ARGs. Proteobacteriota, Bacteroidota, Actinobacteriota, and Firmicutes were the dominant potential hosts of ARGs and were significantly correlated with sulfonamide and tetracycline resistance genes (P<0.05). The distribution of ARGs was also affected by soil organic matter and total nitrogen content. The intI1 gene had significant and positive correlations with the sul2, tetG, tetQ, and tetW genes, suggesting its crucial role in ARGs dissemination. In the present study, the use of higher concentrations of chemical fertilizers led to a significantly increased abundance of ARGs in the soil of vegetable fields, whereas the application of commercial organic fertilizers had the least effect on ARGs abundance. This study serves as a guide for evaluating the status of ARGs pollution in vegetable fields with different fertilizer types.
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抗生素在保障人类健康、预防和治疗动物传染性疾病、促进动物生长以及提高饲料转化率等方面起到了重要作用[1]。长期过量、不规范使用抗生素使环境中残留的抗生素日益增多, 对环境微生物造成选择性压力[2], 加剧了抗生素抗性细菌(ARB)和抗生素抗性基因(ARGs)的污染程度。目前, ARGs在环境中的传播、扩散已成为全球性的污染问题[3]。土壤生态系统作为ARGs的源与汇, 因与人类健康密切相关而备受关注[4]。我国是畜禽养殖大国, 兽用抗生素的使用量逐年递增, 2020年兽用抗生素使用总量高达32 776.30 t[5], 动物体内摄入的抗生素大部分会以抗生素原型或代谢产物的形式随粪尿排出体外[1,6], 使得畜禽粪便成为抗生素和ARGs的重要储蓄库[7], 而畜禽粪便的资源化利用又加剧了农田土壤生态系统中ARGs的污染程度。
畜禽粪便是农田土壤ARGs的主要来源。农作物种植过程中会施用大量畜禽粪肥来提高土壤肥力, 保障作物产量[8-9]。在集约化养殖过程中, 由于畜禽种类(主要包括猪、羊、鸡、牛)、抗生素使用种类及使用量的不同, 导致畜禽粪便中ARGs多样性和丰度也存在显著差异。四环素和磺胺作为广谱抗生素在畜牧业中广泛使用。Ji等[10]在猪粪、家禽粪便和牛粪中检测到高丰度的磺胺和四环素ARGs, 其相对丰度分别为2.37×10−5~4.23×10−2 copies·copies−1(16S rRNA)和2.23×10−8~3.96×10−3 copies·copies−1(16S rRNA)。McKinney等[11]在长期施用牛粪的土壤中检测到较高丰度的sul1、tetW和tetX基因, 丰度范围为10−4~10−2 copies·copies−1(16S rRNA)。Heuer等[12]研究发现, 连续施用含有抗生素的猪粪显著增加了土壤中sul1和sul2基因丰度。此外, Cheng等[13]发现, 施用不同类型畜禽粪肥均会提高土壤中ARGs的污染水平, 但施畜禽粪便土壤中ARGs的多样性和丰度具有显著差异。畜禽粪便施用增加了土壤中ARGs的丰度和种类, 加剧了土壤中ARGs的传播风险。此外, ARGs能够通过质粒、转座子、整合子等可移动遗传元件以水平基因转移的方式在同一物种成员之间以及不同属的细菌之间转移[6]。其中, intI1基因(Ⅰ型整合子)与ARGs关系密切, 可以将不同的ARGs以位点特异性重组的方式进行重排, 并通过接合型质粒传播[14], 促进ARGs在土壤中的扩散[15]。Mu等[16]调查发现鸡、猪和牛饲养场附近土壤中磺胺类ARGs浓度最高, 占ARGs总丰度的71.1%~80.2%, 而intI1基因在促进磺胺类ARGs的传播扩散中发挥了重要作用。此外, 畜禽粪肥施用影响了土壤环境和生态过程, 进而影响了土壤细菌群落。土壤微生物作为ARGs的宿主, 其群落结构的变化直接影响了ARGs的分布。Li等[17]研究发现施用化肥和鸡粪土壤中ARGs的变化与细菌群落之间存在密切联系, 细菌群落结构的演替性在ARGs的传播扩散中起着不可替代的作用。
近年来, 随着农业种植结构的深入调整, 蔬菜种植面积不断扩大。我国蔬菜种植普遍采用粪肥作为底肥, 由此引起的蔬菜地土壤ARGs污染问题日趋严重[18-19]。此外, 农田土壤生态系统是抗生素和ARGs暴露的重要环境, 农产品的食用, 尤其是可生食的蔬菜, 是土壤-植物系统中ARGs进入人体最直接的途径之一, 是环境ARGs人群暴露的主要来源[20]。目前, 蔬菜地施肥类型多样, 为进一步探究施用不同畜禽粪肥对蔬菜地土壤中ARGs传播扩散的影响, 本研究采集了河北省保定市周边县区施用不同畜禽粪肥的蔬菜地表层土壤(0~20 cm), 采用定量PCR方法探究施用不同类型肥料蔬菜地土壤ARGs的污染特征, 采用高通量测序技术解析土壤细菌群落结构组成, 结合土壤环境因子指标探明影响蔬菜地土壤中ARGs分布的主控因子, 以期为评估蔬菜地土壤中ARGs污染现状提供相应的数据参考。
1. 材料与方法
1.1 样品采集
本研究在河北省保定市清苑区、安新县、定州市、涞水县采集施用商品有机肥、鲜羊粪、鲜牛粪、鲜鸡粪和化肥的蔬菜地土壤样品。蔬菜地种植作物主要包括: 西红柿(Solanum lycopersicum L.)、韭菜(Allium tuberosum Rottler ex Sprengle)、生菜(Lactuca sativa var. ramosa Hort.)、芹菜(Apium graveolens L.)和白菜(Brassica rapa var. glabra Regel)。采样点信息见表1。2021年4月大棚蔬菜成熟期采用五点采样法采集表层0~20 cm土壤, 每个采样地采集3个重复, 共采集21个土壤样品, 土壤样品置于冰上运送至实验室。将采集的土壤样品过2 mm筛除去植物根茎、砂砾、石块等。一部分土壤样品风干后, 用于土壤环境因子(pH、有机质、总氮)的分析, 其余部分置于−80 ℃冰箱内保存, 用于土壤微生物组DNA的提取。
表 1 采样点信息Table 1. Details of soil sampling sites处理
Treatment采样地
Sampling site经纬度
Longitude and latitude种植面积
Planting area (hm2)蔬菜种类
Vegetable type种植年限
Planting years (a)施肥量
Fertilizer application rate (t∙hm−2)施肥种类
Fertilizer typeOF 定州市
Dingzhou City115°13′N, 38°33′E 0.09 西红柿
Tomato6 30 商品有机肥
Commercial organic fertilizerSM 定州市
Dingzhou City114°95′N, 38°63′E 0.10 韭菜
Leek1 75 鲜羊粪
Fresh sheep manureCM1 涞水县
Laishui County115°71′N, 39°33′E 0.07 生菜
Romaine lettuce6 45 鲜牛粪
Fresh cattle manureCM2 安新县
Anxin County115°92′N, 38°87′E 0.10 白菜
Chinese cabbage5 84 鲜牛粪
Fresh cattle manureFM 清苑区
Qingyuan District115°57′N, 38°86′E 0.03 芹菜
Celery20 52.5 鲜鸡粪
Fresh fowl manureCF1 清苑区
Qingyuan District115°33′N, 38°48′E 0.20 芹菜
Celery10 0.825 化肥
Chemical fertilizerCF2 安新县
Anxin County115°93′N, 38°87′E 0.08 西红柿
Tomato6 3 化肥
Chemical fertilizer1.2 土壤环境因子分析测定
土壤理化指标测定主要参考《土壤农化分析》[21]。采用酸度计电位法测定pH [ 土壤与水的比值为1∶2.5 (质量∶体积)]; 采用烘干称重法测定土壤含水率(SWC); 采用低温外热重铬酸钾氧化法, 利用分光光度计(UV-6100S, Shanghai)测定土壤有机质(SOM); 采用凯氏定氮方法, 利用FOSS-凯氏定氮仪(Kjeltec 8400, Denmark)测定土壤总氮(TN), 结果见表2。
表 2 不同施肥类型下土壤环境因子分析Table 2. Physical and chemical properties of soil under different fertilization treatments处理
Treatment土壤含水量
Soil water content (%)土壤有机质
Soil organic matter content (g·kg−1)pH 土壤总氮
Total nitrogen (g·kg−1)OF 18.70±0.42c 15.74±0.02cd 7.11±0.07c 0.07±0.01cd SM 7.98±1.48e 20.01±0.68bc 7.39±0.05ab 0.11±0.03bc CM1 14.44±1.84d 24.99±0.44b 7.28±0.05b 0.12±0.02b CM2 21.37±1.35b 25.25±0.16b 7.49±0.05a 0.14±0.01b FM 20.81±1.17bc 23.42±0.32b 7.28±0.06b 0.13±0.02b CF1 13.00±2.02d 18.45±0.32bd 7.48±0.01a 0.10±0.01bd CF2 26.83±0.41a 33.99±0.59a 7.06±0.11c 0.22±0.05a 不同处理详情见表1。不同小写字母代表各处理间差异显著(P<0.05)。Details of each treatment can be seen in Table 1. Different lowercase letters indicate significant differences among treatments (P<0.05). 1.3 DNA提取和定量PCR
土壤微生物DNA采用FastDNA SPIN Kit (MP Biomedicals, Santa Ana, CA, United States)按制造商说明提取。使用超微量分光光度计(NanoDropTM One, Thermo Fisher Scientific, United States)测定提取的DNA浓度, DNA样品于−20 ℃下保存备用。采用SYBR Green染料法定量2个磺胺类ARGs (sul1、sul2)、8个四环素类ARGs (tetA、tetC、tetG、tetL、tetO、tetM、tetW、tetQ)以及Ⅰ类整合酶基因(intI1)[20,22]。PCR反应体系为25 μL, 包括12.5 μL SYBR Premix Ex Taq Ⅱ (Takara Biotech, Dalian, China), 前后引物各0.5 μL (10 μmol·L−1), DNA 1 μL (20~30 ng·μL−1), ddH2O 10.5 μL。扩增程序如下: 95 ℃ 3 min, 95 ℃ 30 s, 退火温度30 s (sul1: 55.9 ℃; sul2: 60.8 ℃; tetA: 55 ℃; tetC: 55 ℃; tetG: 55 ℃; tetL: 55 ℃; tetO: 45 ℃; tetM: 45 ℃; tetW: 60 ℃; tetQ: 55 ℃; intI1: 60 ℃), 72 ℃ 30 s, 40个循环, 72 ℃延伸3 min, 通过溶解度曲线分析(每次读取0.5 ℃, 10 s)确定扩增产物特异性。采用TaqMan探针法定量16S rRNA基因丰度[23]。使用定量PCR仪CFX Connect™ (Bio-Rad, United States)对所有样品已检出的ARGs、intI1基因和16S rRNA基因进行定量分析。
1.4 细菌16S rRNA基因高通量测序
使用515F/806R引物对16S rRNA基因V4区进行扩增[24], 探究不同施肥类型下土壤细菌群落结构组成。利用Illumina Miseq平台(Illumina, San Diego, CA, United States)对扩增产物进行测序。使用QIIME2 2019.7版本(Quantitative Insights Into Microbial Ecology)对测序数据进行分析[25]。利用DADA2对序列进行去噪、拼接和过滤后将序列以99%的相似度进行聚类, 最终生成特征序列。使用QIIME2的classify-sklearn算法(基于Silva数据库)对特征序列进行注释[26-27]。通过q2-diversity插件计算α和β多样性。
1.5 数据分析
使用IBM SPSS 22.0进行数据统计分析。采用单因素方差分析(ANOVA)和最小显著性差异法(LSD)探讨不同土壤样品中pH、SOM、SWC、TN、细菌群落组成和ARGs丰度是否存在显著差异。采用Pearson相关性分析探究土壤环境因子对ARGs分布的影响, 以及土壤中主要细菌门与ARGs之间的相互关系。使用R 4.1.1中的“vegan”包利用冗余分析(RDA)分析ARGs与土壤环境因子之间的相关性; 使用“pheatmap”包绘制ARGs丰度热图。采用OriginPro 2018软件绘制箱线图以揭示不同施肥处理下细菌α多样性的差异, 采用非度量多维尺度(NMDS) (基于Bray-Curtis距离)探究细菌β多样性。
2. 结果与分析
2.1 土壤中抗性基因和intI1基因的分布特征
定量PCR结果表明, 在不同土壤样品中磺胺类ARGs (sul1和sul2)、四环素类ARGs (tetA、tetC、tetG、tetL、tetO、tetM、tetW、tetQ)以及整合酶基因(intI1)丰度差异较大(图1a)。磺胺类ARGs sul1和sul2平均丰度分别为8.47×108 copies∙g−1(干土)和5.76×108 copies∙g−1(干土), 四环素类ARGs平均丰度范围为4.58×105 (tetM)~1.21×108 (tetL) copies∙g−1(干土), 磺胺类ARGs的总绝对丰度[9.96×109 copies∙g−1(干土)]高于四环素类ARGs [1.07×109copies∙g−1 (干土)]。intI1基因的绝对丰度范围为2.11×105 (OF)~5.48×107 (CF2) copies∙g−1(干土)。tetL [8.45×108 copies∙g−1(干土)]和tetW [1.31×108 copies∙g−1(干土)]绝对丰度较高, 而tetA、tetC、tetG、tetO、tetM、tetQ绝对丰度相对较低, 平均值范围为4.58×105~7.81×106 copies∙g−1(干土)。对比不同施肥类型蔬菜地土壤中的ARGs, CF2土壤中的ARGs总绝对丰度最高[6.34×109 copies∙g−1(干土)], 而CF1 [5.64×108 copies∙g−1(干土)]与施畜禽粪肥土壤中ARGs平均丰度 [8.24×108 copies∙g−1(干土)]相比差异较小。对比不同粪肥处理土壤中ARGs总绝对丰度, FM [1.23×109 copies∙g−1(干土)]>CM [1.06×109 copies∙g−1(干土)]>SM [4.67×108 copies∙g−1(干土)]>OF [3.09×108 copies∙g−1(干土)]。其中, FM土壤中磺胺类ARGs的绝对丰度[9.61×108 copies∙g−1(干土)]是OF [3.05×108 copies∙g−1(干土)]的3倍。
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图 1 不同施肥类型蔬菜地土壤中sul1、sul2、tetA、tetC、tetG、tetL、tetO、tetM、tetW、tetQ和intI1基因的绝对丰度(a)及土壤环境因子与抗性基因丰度间的冗余分析(b)基因绝对丰度[copies·g−1(干土)]数据经过lg转换。图1b蓝色箭头表示土壤环境因子。SWC表示含水率, SOM表示土壤有机质, TN表示总氮, RDA表示冗余分析。不同处理详情见表1。**和***分别表示P<0.01和P<0.001水平显著相关。The data is a lg scale of gene absolute abundance. In Fig. 1b, soil properties are marked with blue arrows. SWC is soil water content, SOM is soil organic matter, TN is total nitrogen, and RDA is redundancy analysis. Details of each treatment can be seen in Table 1. ** and *** indicate significant correlation at P<0.01 and P<0.001 levels, respctively.Figure 1. Absolute abundances of sul1, sul2, tetA, tetC, tetG, tetL, tetO, tetM, tetW, tetQ and intI1 genes in soils under different fertilization treatments (a) and redundancy analysis of absolute abundance of antibiotic resistance genes and soil environmental factors (b)2.2 抗性基因与土壤环境因子的相关性分析
RDA分析结果表明, 土壤环境因子SOM、SWC、TN和pH显著影响了土壤中ARGs的分布, RDA1和RDA2共解释了总变量的79.20% (图1b)。SOM和TN分别与CM1和CF2中的ARGs呈显著正相关(P<0.01), 与OF、SM和CF1中的ARGs呈显著负相关(P<0.01) (图1b)。Pearson相关性分析结果表明(表3), SOM和TN分别与sul2、tetG、tetQ及tetW基因呈显著正相关(P<0.05, P<0.01), pH与ARGs的相关性不显著(P>0.05), intI1基因与TN、SOM呈显著正相关(P<0.01)。intI1基因与sul2、tetG、tetQ、tetW基因呈显著正相关(P<0.01), 这表明intI1基因在ARGs的传播扩散中起着重要的作用。编码不同抗生素类型的ARGs之间也存在一定的相关性, 例如tetQ和tetW基因均与sul1 (P<0.05)、sul2 (P<0.01)基因呈显著正相关。
表 3 抗性基因、intI1基因和土壤环境因子的Pearson相关性分析Table 3. Pearson’s correlation analysis among antibiotic resistance genes, intI1 gene and soil environmental factorssul1 sul2 tetA tetC tetG tetL tetM tetO tetQ tetW intI1 SOM SWC pH TN sul2 0.84* tetA 0.31 0.19 tetC −0.35 −0.36 0.77* tetG 0.50 0.74 0.53 0.23 tetL 0.40 0.43 0.59 0.34 0.53 tetM 0.10 0.21 0.73 0.68 0.56 0.90** tetO −0.44 −0.09 0.07 0.40 0.50 0.07 0.32 tetQ 0.77* 0.96** 0.19 −0.29 0.81* 0.38 0.22 0.07 tetW 0.79* 0.94** 0.12 −0.38 0.67 0.62 0.34 −0.08 0.89** intI1 0.65 0.93** 0.32 −0.09 0.90** 0.55 0.45 0.23 0.93** 0.88** SOM 0.52 0.82* 0.18 −0.11 0.83* 0.67 0.54 0.33 0.86* 0.90** 0.92** SWC 0.25 0.23 0.83* 0.65 0.55 0.17 0.40 0.11 0.31 0.01 0.35 0.12 pH −0.75 −0.62 −0.16 0.31 −0.53 0.07 0.23 0.05 −0.69 −0.46 −0.47 −0.33 −0.34 TN 0.60 0.89** 0.26 −0.12 0.83* 0.69 0.55 0.22 0.87* 0.93** 0.96** 0.97** 0.16 −0.32 SWC表示含水率; SOM表示土壤有机质; TN表示总氮。*和**分别表示在P<0.05和P<0.01水平显著相关。SWC is soil water content, SOM is soil organic matter content, TN is total nitrogen content. * and ** indicate significant correlation at P<0.05 and P<0.01 levels, respectively. 2.3 土壤中的细菌群落
2.3.1 细菌群落结构组成
本研究对21个土壤样品进行了16S rRNA基因测序分析, 质量筛选后共得到了2 341 622条特征序列, 平均每个土壤样品111 505条特征序列, 共检测到43个细菌门, 其中变形菌门(Proteobacteriota)、拟杆菌门(Bacteroidota)、酸杆菌门(Acidobacteriota)、放线菌门(Actinobacteriota)、绿弯菌门(Chloroflexi)、浮霉菌门(Planctomycetota)、厚壁菌门(Firmicutes)和芽单胞菌门(Gemmatimonadota)占土壤总细菌相对丰度的85.97%~94.82% (图2a)。不同施肥类型蔬菜地土壤中细菌群落组成存在显著差异。例如, 变形菌门在CM1和FM处理中的相对丰度分别为34.75%和34.68%, 而在CF1和CF2处理中的相对丰度分别为26.94%和24.03%。拟杆菌门的相对丰度在CF2处理中高达43.49%, 在CF1处理为12.59%, 在OF处理仅占6.70%。不同施肥类型土壤中酸杆菌门与放线菌门相对丰度也较高, 其在SM处理比重最高, 分别占细菌群落的20.89%和16.42%, CF2处理相对丰度最低, 仅分别占5.00%和7.80%。不同处理间绿弯菌门的相对丰度差异最小, 丰度范围为4.66%~7.00%, 平均丰度为5.74%。此外, CF2处理中除拟杆菌门外, 其余细菌门类丰度都较有机肥处理组低, 而CF1处理中各细菌门丰度相较于有机肥处理组差异较小。
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图 2 不同施肥类型蔬菜地土壤中细菌优势门(相对丰度>1%)的相对丰度(a)和基于Bray-Curtis距离(NMDS)的细菌群落结构组成(b)Figure 2. Relative abundance of bacterial dominance phyla (relative abundance > 1%) in soils (a) and structural composition of the bacterial community based on Bray-Curtis distance (NMDS) (b) under different fertilization treatments2.3.2 细菌群落多样性
NMDS结果表明(图2b), 不同有机肥施用处理间的细菌群落结构无显著差异, 与低化肥施加量处理(CF1)的细菌群落结构较为相似, 而与高化肥施加量处理(CF2)的细菌群落结构差异显著(P<0.05)。不同的施肥类型影响土壤细菌群落多样性(图3)。例如, 高化肥施加量(CF2)的Shannon和Chao1指数显著低于各畜禽粪肥处理组; 低化肥施加量(CF1)的Shannon和Chao1指数与有机肥施用处理无显著差异。此外, 不同有机肥施用处理间的Shannon和Chao1指数也无显著差异。
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2.3.3 土壤细菌群落与土壤环境因子和抗性基因的关系
土壤细菌群落组成与土壤环境因子密切相关。土壤样品中16S rRNA基因的绝对丰度范围为1.67×109 ~1.41×1010 copies∙g−1(干土)。Pearson相关性分析结果表明(图4a), 16S rRNA基因绝对丰度与Chao1、Shannon、SWC、SOM、pH和TN均不存在显著相关性, 而Chao1、Shannon与SWC、SOM、TN呈显著负相关(P<0.05, P<0.01), 与pH呈显著正相关(P<0.01)。
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图 4 土壤细菌α多样性与土壤环境因子Pearson相关性(a)和主要细菌门与抗性基因的Pearson相关性(b)Chao1表示Chao1指数; Shannon表示Shannon指数; 16S表示16S rRNA的绝对丰度; SWC表示土壤含水率; SOM表示土壤有机质; TN表示土壤总氮。*和**分别表示在P<0.05和P<0.01水平显著相关。Chao1 refers to Chao1 index; Shannon refers to Shannon index; 16S refers to absolute abundance of 16S rRNA; SWC refers to soil water content; SOM refers to soil organic matter; TN refers to soil total nitrogen. * and ** indicate significant correlation at P<0.05 and P<0.01 levels, respectively.Figure 4. Pearson’s correlation of soil bacterial α diversity and environmental factors (a) and Pearson’s correlation of major bacterial phyla with antibiotic resistance genes (b)Pearson相关性分析结果表明, 细菌门水平丰度与ARGs丰度存在显著相关性(图4b)。例如 sul1 和 sul2 基因丰度与拟杆菌门丰度呈显著正相关(P<0.01), 与酸杆菌门、粘球菌门(Myxococcota)、疣微菌门(Verrucomicrobiota)、硝化螺旋菌门(Nitrospirota)、芽单胞菌门丰度呈显著负相关(P<0.01, P<0.05)。tetA、tetC、tetG、tetL、tetM 基因丰度与放线菌门丰度呈显著负相关(P<0.01, P<0.05), tetG、tetL、tetQ、tetW 基因丰度与拟杆菌门丰度呈显著正相关(P<0.01, P<0.05), 与粘球菌门丰度呈显著负相关(P<0.01, P<0.05)。tetW 基因丰度与疣微菌门、硝化螺旋菌门、甲基肌酐门(Methylomirabilota)丰度呈显著负相关(P<0.01), tetO 基因丰度与变形菌门丰度呈显著正相关(P<0.05)。intI1基因与拟杆菌门丰度呈显著正相关(P<0.05), 和酸杆菌门、甲基肌酐门丰度呈显著负相关(P<0.05)。
3. 讨论和结论
本研究在不同施肥类型蔬菜地土壤中都检测到较高丰度的磺胺类ARGs (sul1、sul2)和四环素类ARGs (tetA、tetC、tetG、tetL、tetO、tetM、tetW、tetQ), 表明农田土壤中施用畜禽粪肥或化肥均会造成不同程度的ARGs污染。在低化肥施加量(CF1) [5.64×108 copies∙g−1(干土)]和高化肥施加量(CF2)[6.34×109 copies∙g−1(干土)]处理中检测到高丰度的ARGs。有研究发现施加化肥促进了作物生长, 提高了土壤有机碳和细菌群落丰度, 同时可能会增加某些ARGs的丰度[28]。此外, CF2处理中ARGs丰度显著高于CF1处理, CF2处理的施肥量约是CF1处理的4倍, 施肥量的较大差异也可能是造成ARGs丰度差异的主要原因。
许多研究表明, 动物粪便是ARB和ARGs的重要储存库[29-32], 施用粪肥可以显著提高土壤中ARGs丰度[28,33]。研究发现, 在施加畜禽粪肥土壤中检测到较高丰度的ARGs, 总绝对丰度为3.09×108~1.23×109 copies∙g−1(干土)。不同畜禽粪肥土壤中的ARGs丰度对比发现, 鸡粪>牛粪>羊粪>商品有机肥, 施加鲜鸡粪土壤中的ARGs丰度最高, 这可能是由于鸡的养殖密度大、销售期短, 养殖期抗生素的使用剂量较高。钱勋[34]在鸡粪中检出134种ARGs, 种类和总相对丰度均大于猪粪和牛粪。本研究通过对比已检测到的ARGs丰度发现施用不同类型肥料蔬菜地土壤中磺胺类ARGs丰度高于四环素类ARGs, 且施加鸡粪、羊粪和牛粪土壤中磺胺类ARGs相对丰度均显著高于四环素类ARGs。Zhao等[35]利用qPCR技术分析了长期施用鸡粪的农田土壤ARGs的污染程度, 结果表明土壤中磺胺类抗性基因(sul1和sul2)的相对丰度均高于四环素类。Mu等[16]检测牛场土壤中的ARGs发现, 磺胺类ARGs和部分喹诺酮类ARGs的污染程度高于四环素类。有研究指出, sul1基因通常位于更容易传播的第Ⅰ类整合子上, 而sul2基因存在于小的非结合抗性质粒和大的可传播多抗性质粒中[36], 因此土壤中更容易残留较高丰度的sul1和sul2基因。
土壤中ARGs分布特征与细菌群落结构和土壤环境因子密切相关[37]。施肥引起土壤营养条件和土壤环境条件的改变, 直接或间接影响土壤微生物区系组成[38], 不同施肥类型会对土壤细菌群落结构产生影响。研究发现, 施畜禽粪便蔬菜地土壤细菌α多样性显著高于高化肥施加量的土壤。施用畜禽粪肥可以显著提升土壤氮、磷和有机碳含量[39-40]以及微生物活性、多样性和丰富度[41-42]。长期过量施加化肥会引起土壤酸化、氮淋失、板结、有机质减少等问题[43], 同时还会降低土壤细菌的丰富度和多样性[44-45]。细菌群落结构变化是影响ARGs分布的重要因素[46-47]。在本研究中, 土壤中主要的细菌门类是变形菌门、拟杆菌门、酸杆菌门以及放线菌门, 已有研究表明这些细菌门类是ARGs的重要潜在宿主[48-49], 与四环素类ARGs显著相关(P<0.05)。本研究还发现一些丰度较低的细菌门类, 例如粘球菌门、疣微菌门、硝化螺旋菌门、甲基肌酐门, 均与磺胺类和四环素类ARGs呈显著相关(P<0.05), 说明低丰度细菌门类的变化对ARGs分布也有一定影响。此外, intI1基因在一定程度上也影响ARGs的分布。研究发现, intI1基因与sul2、tetG、tetQ和tetW基因呈显著正相关(P<0.01)。intI1基因常存在于各种可移动遗传元件和染色体上, 与ARGs在环境中的传播、扩散联系十分紧密[20,50]。不同类型的ARGs也存在一定的相关性, 如编码四环素类抗生素抗性的tetQ与编码磺胺类抗生素抗性的sul1和sul2以及编码四环素类抗生素抗性的tetG和tetW呈显著正相关(P<0.05, P<0.01)。Wang等[51]研究发现环境中一种抗生素的选择性压力可能会导致多种类型的ARGs丰度增加。
综上所述, 施用畜禽粪肥增加了蔬菜地土壤中ARGs丰度和多样性, 其中施用鲜鸡粪蔬菜地土壤中ARGs检出丰度最高, 施用商品有机肥蔬菜地土壤中ARGs丰度低于施用鲜畜禽粪肥。相比施用畜禽粪肥, 高化肥施用量显著增加了蔬菜地土壤中ARGs丰度。不同施肥类型蔬菜地土壤中检出的磺胺类ARGs丰度均显著高于四环素类ARGs。intI1基因在蔬菜地土壤ARGs的传播和扩散中起着重要作用。此外, 土壤环境因子与细菌群落结构均会影响土壤ARGs分布。本研究结果为评估施用不同类型畜禽粪肥蔬菜地土壤中ARGs污染现状提供了相应的数据参考。后续应进一步开展蔬菜地土壤中畜禽粪肥来源ARGs在土壤-蔬菜系统中传播扩散途径的研究, 特别关注畜禽粪肥中ARGs能否通过食物链转移到病原体宿主菌中, 及其对人体健康造成的潜在危害。
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图 1 不同施肥类型蔬菜地土壤中sul1、sul2、tetA、tetC、tetG、tetL、tetO、tetM、tetW、tetQ和intI1基因的绝对丰度(a)及土壤环境因子与抗性基因丰度间的冗余分析(b)
基因绝对丰度[copies·g−1(干土)]数据经过lg转换。图1b蓝色箭头表示土壤环境因子。SWC表示含水率, SOM表示土壤有机质, TN表示总氮, RDA表示冗余分析。不同处理详情见表1。**和***分别表示P<0.01和P<0.001水平显著相关。The data is a lg scale of gene absolute abundance. In Fig. 1b, soil properties are marked with blue arrows. SWC is soil water content, SOM is soil organic matter, TN is total nitrogen, and RDA is redundancy analysis. Details of each treatment can be seen in Table 1. ** and *** indicate significant correlation at P<0.01 and P<0.001 levels, respctively.
Figure 1. Absolute abundances of sul1, sul2, tetA, tetC, tetG, tetL, tetO, tetM, tetW, tetQ and intI1 genes in soils under different fertilization treatments (a) and redundancy analysis of absolute abundance of antibiotic resistance genes and soil environmental factors (b)
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图 2 不同施肥类型蔬菜地土壤中细菌优势门(相对丰度>1%)的相对丰度(a)和基于Bray-Curtis距离(NMDS)的细菌群落结构组成(b)
不同处理详情见表1。Details of each treatment can be seen in Table 1.
Figure 2. Relative abundance of bacterial dominance phyla (relative abundance > 1%) in soils (a) and structural composition of the bacterial community based on Bray-Curtis distance (NMDS) (b) under different fertilization treatments
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图 4 土壤细菌α多样性与土壤环境因子Pearson相关性(a)和主要细菌门与抗性基因的Pearson相关性(b)
Chao1表示Chao1指数; Shannon表示Shannon指数; 16S表示16S rRNA的绝对丰度; SWC表示土壤含水率; SOM表示土壤有机质; TN表示土壤总氮。*和**分别表示在P<0.05和P<0.01水平显著相关。Chao1 refers to Chao1 index; Shannon refers to Shannon index; 16S refers to absolute abundance of 16S rRNA; SWC refers to soil water content; SOM refers to soil organic matter; TN refers to soil total nitrogen. * and ** indicate significant correlation at P<0.05 and P<0.01 levels, respectively.
Figure 4. Pearson’s correlation of soil bacterial α diversity and environmental factors (a) and Pearson’s correlation of major bacterial phyla with antibiotic resistance genes (b)
表 1 采样点信息
Table 1 Details of soil sampling sites
处理
Treatment采样地
Sampling site经纬度
Longitude and latitude种植面积
Planting area (hm2)蔬菜种类
Vegetable type种植年限
Planting years (a)施肥量
Fertilizer application rate (t∙hm−2)施肥种类
Fertilizer typeOF 定州市
Dingzhou City115°13′N, 38°33′E 0.09 西红柿
Tomato6 30 商品有机肥
Commercial organic fertilizerSM 定州市
Dingzhou City114°95′N, 38°63′E 0.10 韭菜
Leek1 75 鲜羊粪
Fresh sheep manureCM1 涞水县
Laishui County115°71′N, 39°33′E 0.07 生菜
Romaine lettuce6 45 鲜牛粪
Fresh cattle manureCM2 安新县
Anxin County115°92′N, 38°87′E 0.10 白菜
Chinese cabbage5 84 鲜牛粪
Fresh cattle manureFM 清苑区
Qingyuan District115°57′N, 38°86′E 0.03 芹菜
Celery20 52.5 鲜鸡粪
Fresh fowl manureCF1 清苑区
Qingyuan District115°33′N, 38°48′E 0.20 芹菜
Celery10 0.825 化肥
Chemical fertilizerCF2 安新县
Anxin County115°93′N, 38°87′E 0.08 西红柿
Tomato6 3 化肥
Chemical fertilizer
下载: 导出CSV
表 2 不同施肥类型下土壤环境因子分析
Table 2 Physical and chemical properties of soil under different fertilization treatments
处理
Treatment土壤含水量
Soil water content (%)土壤有机质
Soil organic matter content (g·kg−1)pH 土壤总氮
Total nitrogen (g·kg−1)OF 18.70±0.42c 15.74±0.02cd 7.11±0.07c 0.07±0.01cd SM 7.98±1.48e 20.01±0.68bc 7.39±0.05ab 0.11±0.03bc CM1 14.44±1.84d 24.99±0.44b 7.28±0.05b 0.12±0.02b CM2 21.37±1.35b 25.25±0.16b 7.49±0.05a 0.14±0.01b FM 20.81±1.17bc 23.42±0.32b 7.28±0.06b 0.13±0.02b CF1 13.00±2.02d 18.45±0.32bd 7.48±0.01a 0.10±0.01bd CF2 26.83±0.41a 33.99±0.59a 7.06±0.11c 0.22±0.05a 不同处理详情见表1。不同小写字母代表各处理间差异显著(P<0.05)。Details of each treatment can be seen in Table 1. Different lowercase letters indicate significant differences among treatments (P<0.05).
下载: 导出CSV
表 3 抗性基因、intI1基因和土壤环境因子的Pearson相关性分析
Table 3 Pearson’s correlation analysis among antibiotic resistance genes, intI1 gene and soil environmental factors
sul1 sul2 tetA tetC tetG tetL tetM tetO tetQ tetW intI1 SOM SWC pH TN sul2 0.84* tetA 0.31 0.19 tetC −0.35 −0.36 0.77* tetG 0.50 0.74 0.53 0.23 tetL 0.40 0.43 0.59 0.34 0.53 tetM 0.10 0.21 0.73 0.68 0.56 0.90** tetO −0.44 −0.09 0.07 0.40 0.50 0.07 0.32 tetQ 0.77* 0.96** 0.19 −0.29 0.81* 0.38 0.22 0.07 tetW 0.79* 0.94** 0.12 −0.38 0.67 0.62 0.34 −0.08 0.89** intI1 0.65 0.93** 0.32 −0.09 0.90** 0.55 0.45 0.23 0.93** 0.88** SOM 0.52 0.82* 0.18 −0.11 0.83* 0.67 0.54 0.33 0.86* 0.90** 0.92** SWC 0.25 0.23 0.83* 0.65 0.55 0.17 0.40 0.11 0.31 0.01 0.35 0.12 pH −0.75 −0.62 −0.16 0.31 −0.53 0.07 0.23 0.05 −0.69 −0.46 −0.47 −0.33 −0.34 TN 0.60 0.89** 0.26 −0.12 0.83* 0.69 0.55 0.22 0.87* 0.93** 0.96** 0.97** 0.16 −0.32 SWC表示含水率; SOM表示土壤有机质; TN表示总氮。*和**分别表示在P<0.05和P<0.01水平显著相关。SWC is soil water content, SOM is soil organic matter content, TN is total nitrogen content. * and ** indicate significant correlation at P<0.05 and P<0.01 levels, respectively.
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[1] QIAO M, YING G G, SINGER A C, et al. Review of antibiotic resistance in China and its environment[J]. Environment International, 2018, 110: 160−172 doi: 10.1016/j.envint.2017.10.016
[2] MARTINEZ J L. Environmental pollution by antibiotics and by antibiotic resistance determinants[J]. Environmental Pollution, 2009, 157(11): 2893−2902 doi: 10.1016/j.envpol.2009.05.051
[3] 周启星, 罗义, 王美娥. 抗生素的环境残留、生态毒性及抗性基因污染[J]. 生态毒理学报, 2007, 2(3): 243−251 ZHOU Q X, LUO Y, WANG M E. Environmental residues and ecotoxicity of antibiotics and their resistance gene pollution: a review[J]. Asian Journal of Ecotoxicology, 2007, 2(3): 243−251
[4] 安新丽, 苏建强. 土壤抗生素抗性组: 来源、扩散和驱动因子[J]. 科技导报, 2022, 40(3): 64−74 AN X L, SU J Q. The soil resistome: origin, dissemination and driving factor[J]. Science & Technology Review, 2022, 40(3): 64−74
[5] 2020年中国兽用抗菌药使用情况报告[N]. 北京: 中国畜牧兽医报, 2021-11-14 (003) Report on the use of veterinary antibiotics in China in 2020[N]. Beijing: Chinese Animal Husbandry and Veterinary News, 2021-11-14 (003)
[6] CHEE-SANFORD J C, MACKIE R I, KOIKE S, et al. Fate and transport of antibiotic residues and antibiotic resistance genes following land application of manure waste[J]. Journal of Environmental Quality, 2009, 38(3): 1086−1108 doi: 10.2134/jeq2008.0128
[7] 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
[8] HEUER H, SCHMITT H, SMALLA K. Antibiotic resistance gene spread due to manure application on agricultural fields[J]. Current Opinion in Microbiology, 2011, 14(3): 236−243 doi: 10.1016/j.mib.2011.04.009
[9] FRANZ E, VAN BRUGGEN A H C. Ecology of E. coli O157: H7 and Salmonella enterica in the primary vegetable production chain[J]. Critical Reviews in Microbiology, 2008, 34(3/4): 143−161
[10] JI X L, SHEN Q H, LIU F, et al. Antibiotic resistance gene abundances associated with antibiotics and heavy metals in animal manures and agricultural soils adjacent to feedlots in Shanghai, China[J]. Journal of Hazardous Materials, 2012, 235: 178−185
[11] MCKINNEY C W, DUNGAN R S, MOORE A, et al. Occurrence and abundance of antibiotic resistance genes in agricultural soil receiving dairy manure[J]. FEMS Microbiology Ecology, 2018, 94(3): fiy010
[12] HEUER H, SOLEHATI Q, ZIMMERLING U, et al. Accumulation of sulfonamide resistance genes in arable soils due to repeated application of manure containing sulfadiazine[J]. Applied and Environmental Microbiology, 2011, 77(7): 2527−2530 doi: 10.1128/AEM.02577-10
[13] CHENG J H, TANG X Y, CUI J F. Distinct aggregate stratification of antibiotic resistome in farmland soil with long-term manure application[J]. Science of the Total Environment, 2022, 833: 155088 doi: 10.1016/j.scitotenv.2022.155088
[14] 苏志国, 张衍, 代天娇, 等. 环境中抗生素抗性基因与Ⅰ型整合子的研究进展[J]. 微生物学通报, 2018, 45(10): 2217−2233 SU Z G, ZHANG Y, DAI T J, et al. Antibiotic resistance genes and class Ⅰ integron in the environment: research progress[J]. Microbiology China, 2018, 45(10): 2217−2233
[15] 沈怡雯, 黄智婷, 谢冰. 抗生素及其抗性基因在环境中的污染、降解和去除研究进展[J]. 应用与环境生物学报, 2015, 21(2): 181−187 SHEN Y W, HUANG Z T, XIE B. Advances in research of pollution, degradation and removal of antibiotics and antibiotic resistance genes in the environment[J]. Chinese Journal of Applied and Environmental Biology, 2015, 21(2): 181−187
[16] MU Q H, LI J, SUN Y X, et al. Occurrence of sulfonamide-, tetracycline-, plasmid-mediated quinolone- and macrolide-resistance genes in livestock feedlots in Northern China[J]. Environmental Science and Pollution Research, 2015, 22(9): 6932−6940 doi: 10.1007/s11356-014-3905-5
[17] LI T T, LI R C, CAO Y F, et al. Soil antibiotic abatement associates with the manipulation of soil microbiome via long-term fertilizer application[J]. Journal of Hazardous Materials, 2022, 439: 129704 doi: 10.1016/j.jhazmat.2022.129704
[18] WANG F H, SUN R B, HU H W, et al. The overlap of soil and vegetable microbes drives the transfer of antibiotic resistance genes from manure-amended soil to vegetables[J]. Science of the Total Environment, 2022, 828: 154463 doi: 10.1016/j.scitotenv.2022.154463
[19] PU Q, ZHAO L X, LI Y T, et al. Manure fertilization increase antibiotic resistance in soils from typical greenhouse vegetable production bases, China[J]. Journal of Hazardous Materials, 2020, 391: 122267 doi: 10.1016/j.jhazmat.2020.122267
[20] WANG F H, QIAO M, CHEN Z, et al. Antibiotic resistance genes in manure-amended soil and vegetables at harvest[J]. Journal of Hazardous Materials, 2015, 299: 215−221 doi: 10.1016/j.jhazmat.2015.05.028
[21] 鲍士旦. 土壤农化分析[M]. 3版. 北京: 中国农业出版社, 2000 BAO S D. Soil and Agricultural Chemistry Analysis[M]. 3rd ed. Beijing: China Agriculture Press, 2000
[22] AMINOV R I, GARRIGUES-JEANJEAN N, MACKIE R I. Molecular ecology of tetracycline resistance: development and validation of primers for detection of tetracycline resistance genes encoding ribosomal protection proteins[J]. Applied and Environmental Microbiology, 2001, 67(1): 22−32 doi: 10.1128/AEM.67.1.22-32.2001
[23] SUZUKI M T, TAYLOR L T, DELONG E F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5’-nuclease assays[J]. Applied and Environmental Microbiology, 2000, 66(11): 4605−4614 doi: 10.1128/AEM.66.11.4605-4614.2000
[24] WALTERS W, HYDE E R, BERG-LYONS D, et al. Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys[J]. mSystems, 2016, 1(1): e00009−e00015
[25] BOLYEN E, RIDEOUT J R, DILLON M R, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2[J]. Nature Biotechnology, 2019, 37(9): 1091
[26] GLÖCKNER F O, YILMAZ P, QUAST C, et al. 25 years of serving the community with ribosomal RNA gene reference databases and tools[J]. Journal of Biotechnology, 2017, 261: 169−176 doi: 10.1016/j.jbiotec.2017.06.1198
[27] BOKULICH N A, KAEHLER B D, RIDEOUT J R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin[J]. Microbiome, 2018, 6: 90 doi: 10.1186/s40168-018-0470-z
[28] XIE W Y, YUAN S T, XU M G, et al. Long-term effects of manure and chemical fertilizers on soil antibiotic resistome[J]. Soil Biology and Biochemistry, 2018, 122: 111−119 doi: 10.1016/j.soilbio.2018.04.009
[29] MARTI R, SCOTT A, TIEN Y C, et al. Impact of manure fertilization on the abundance of antibiotic-resistant bacteria and frequency of detection of antibiotic resistance genes in soil and on vegetables at harvest[J]. Applied and Environmental Microbiology, 2013, 79(18): 5701−5709 doi: 10.1128/AEM.01682-13
[30] ZHAO L, DONG Y H, WANG H. Residues of veterinary antibiotics in manures from feedlot livestock in eight provinces of China[J]. Science of the Total Environment, 2010, 408(5): 1069−1075 doi: 10.1016/j.scitotenv.2009.11.014
[31] LI J J, XIN Z H, ZHANG Y Z, et al. Long-term manure application increased the levels of antibiotics and antibiotic resistance genes in a greenhouse soil[J]. Applied Soil Ecology, 2017, 121: 193−200 doi: 10.1016/j.apsoil.2017.10.007
[32] QIAN X, GU J, SUN W, et al. Diversity, abundance, and persistence of antibiotic resistance genes in various types of animal manure following industrial composting[J]. Journal of Hazardous Materials, 2018, 344: 716−722 doi: 10.1016/j.jhazmat.2017.11.020
[33] WU N, ZHANG W Y, XIE S Y, et al. Increasing prevalence of antibiotic resistance genes in manured agricultural soils in northern China[J]. Frontiers of Environmental Science & Engineering, 2020, 14(1): 1
[34] 钱勋. 好氧堆肥对畜禽粪便中抗生素抗性基因的削减条件探索及影响机理研究[D]. 杨凌: 西北农林科技大学, 2016 QIAN X. Study on the conditions of reducing antibiotic resistance genes in livestock manure by aerobic composting and its influencing mechanism[D]. Yangling: Northwest A & F University, 2016
[35] ZHAO X, WANG J H, ZHU L S, et al. Environmental analysis of typical antibiotic-resistant bacteria and ARGs in farmland soil chronically fertilized with chicken manure[J]. Science of the Total Environment, 2017, 593: 10−17
[36] SUNDSTRÖM L, RÅDSTRÖM P, SWEDBERG G, et al. Site-specific recombination promotes linkage between trimethoprim resistance and sulfonamide resistance genes. Sequence characterization of dhfrV and sulI and a recombination active locus of Tn21[J]. Molecular and General Genetics MGG, 1988, 213(2/3): 191−201
[37] 韩婉雪, 王凤花, 柏兆海, 等. 畜禽粪便堆放地土壤中抗生素抗性基因和细菌群落的垂直分布特征[J]. 中国生态农业学报(中英文), 2022, 30(2): 268−275 HAN W X, WANG F H, BAI Z H, et al. Vertical distribution of antibiotic resistance genes and bacterial communities in soil of livestock manure stacking site[J]. Chinese Journal of Eco-Agriculture, 2022, 30(2): 268−275
[38] 张宇亭. 长期施肥对土壤微生物多样性和抗生素抗性基因累积的影响[D]. 重庆: 西南大学, 2017 ZHANG Y T. Effects of long-term fertilization on soil microbial diversity and antibiotic resistance gene accumulation[D]. Chongqing: Southwest University, 2017
[39] ANGERS D A, CHANTIGNY M H, MACDONALD J D, et al. Differential retention of carbon, nitrogen and phosphorus in grassland soil profiles with long-term manure application[J]. Nutrient Cycling in Agroecosystems, 2010, 86(2): 225−229 doi: 10.1007/s10705-009-9286-3
[40] MAILLARD É, ANGERS D A. Animal manure application and soil organic carbon stocks: a meta-analysis[J]. Global Change Biology, 2014, 20(2): 666−679 doi: 10.1111/gcb.12438
[41] ZHONG W H, GU T, WANG W, et al. The effects of mineral fertilizer and organic manure on soil microbial community and diversity[J]. Plant and Soil, 2010, 326(1/2): 511−522
[42] CHINNADURAI C, GOPALASWAMY G, BALACHANDAR D. Long term effects of nutrient management regimes on abundance of bacterial genes and soil biochemical processes for fertility sustainability in a semi-arid tropical Alfisol[J]. Geoderma, 2014, 232: 563−572
[43] SUN R B, ZHANG X X, GUO X S, et al. Bacterial diversity in soils subjected to long-term chemical fertilization can be more stably maintained with the addition of livestock manure than wheat straw[J]. Soil Biology and Biochemistry, 2015, 88: 9−18 doi: 10.1016/j.soilbio.2015.05.007
[44] KUMAR U, NAYAK A K, SHAHID M, et al. Continuous application of inorganic and organic fertilizers over 47 years in paddy soil alters the bacterial community structure and its influence on rice production[J]. Agriculture, Ecosystems & Environment, 2018, 262: 65−75
[45] WANG Q F, JIANG X, GUAN D W, et al. Long-term fertilization changes bacterial diversity and bacterial communities in the maize rhizosphere of Chinese Mollisols[J]. Applied Soil Ecology, 2018, 125: 88−96 doi: 10.1016/j.apsoil.2017.12.007
[46] ZHOU Z C, ZHENG J, WEI Y Y, et al. Antibiotic resistance genes in an urban river as impacted by bacterial community and physicochemical parameters[J]. Environmental Science and Pollution Research, 2017, 24(30): 23753−23762 doi: 10.1007/s11356-017-0032-0
[47] WANG F H, HAN W X, CHEN S M, et al. Fifteen-year application of manure and chemical fertilizers differently impacts soil ARGs and microbial community structure[J]. Frontiers in Microbiology, 2020, 11: 62 doi: 10.3389/fmicb.2020.00062
[48] AWASTHI M K, LIU T, CHEN H Y, et al. The behavior of antibiotic resistance genes and their associations with bacterial community during poultry manure composting[J]. Bioresource Technology, 2019, 280: 70−78 doi: 10.1016/j.biortech.2019.02.030
[49] ZHANG R M, LIU X, WANG S L, et al. Distribution patterns of antibiotic resistance genes and their bacterial hosts in pig farm wastewater treatment systems and soil fertilized with pig manure[J]. Science of the Total Environment, 2021, 758: 143654 doi: 10.1016/j.scitotenv.2020.143654
[50] MA L P, LI A D, YIN X L, et al. The prevalence of integrons as the carrier of antibiotic resistance genes in natural and man-made environments[J]. Environmental Science & Technology, 2017, 51(10): 5721−5728
[51] WANG F H, QIAO M, LV Z E, et al. Impact of reclaimed water irrigation on antibiotic resistance in public parks, Beijing, China[J]. Environmental Pollution, 2014, 184: 247−253 doi: 10.1016/j.envpol.2013.08.038
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