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Frontiers of Environmental Science & Engineering

Front. Environ. Sci. Eng.    2020, Vol. 14 Issue (1) : 1     https://doi.org/10.1007/s11783-019-1180-x
RESEARCH ARTICLE
Increasing prevalence of antibiotic resistance genes in manured agricultural soils in northern China
Nan Wu1, Weiyu Zhang1, Shiyu Xie1, Ming Zeng2(), Haixue Liu3, Jinghui Yang4, Xinyuan Liu1, Fan Yang1
1. College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384, China
2. College of Marine and Environmental Sciences, Tianjin University of Science & Technology, Tianjin 300457, China
3. Laboratory of Agricultural Analysis, Tianjin Agricultural University, Tianjin 300384, China
4. College of Horticulture and Landscape, Tianjin Agricultural University, Tianjin 300384, China
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Abstract

• Manure application increased the abundances of ARGs and MGEs in agricultural soils.

• Five classes of ARGs and two MGEs were prevalent in manured and unfertilized soils.

• Genera Pseudomonas and Bacteroidetes might be the dominant hosts of intI1 and ermF.

• The abundances of ARGs positively correlated with TC, TN, OM, Cu, Zn, Pb and MGEs.

Land application of manure tends to result in the dissemination of antibiotic resistance in the environment. In this study, the influence of long-term manure application on the enrichment of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in agricultural soils was investigated. All the analyzed eight ARGs (tetA, tetW, tetX, sulI, sulII, ermF, aac(6’)-Ib-cr and blaTEM) and two MGEs (intI1 and Tn916/1545) were detected in both the manured and control soils, with relative abundances ranging from 10-6 to 10-2. Compared with the control soil, the relative abundances of ARGs and MGEs in manured soils were enriched 1.0–18.1 fold and 0.6–69.1 fold, respectively. High-throughput sequencing analysis suggested that at the phylum level, the bacteria carrying intI1 and ermF might be mainly affiliated with Proteobacteria and Bacteroides, respectively. The dominant genera carrying intI1 and ermF could be Pseudomonas and Bacteroides, independent of manure application. Correlation analysis revealed that ARGs had strong links with soil physicochemical properties (TC, TN, and OM), heavy metals (Cu, Zn and Pb) and MGEs, indicating that the profile and spread of ARGs might be driven by the combined impacts of multiple factors. In contrast, soil pH and C/N exhibited no significant relationships with ARGs. Our findings provide evidence that long-term manure application could enhance the prevalence and stimulate the propagation of antibiotic resistance in agricultural soils.

Keywords Antibiotic resistance      Mobile genetic elements      Soil      Manure      Heavy metals     
Corresponding Author(s): Ming Zeng   
Issue Date: 22 October 2019
 Cite this article:   
Nan Wu,Weiyu Zhang,Shiyu Xie, et al. Increasing prevalence of antibiotic resistance genes in manured agricultural soils in northern China[J]. Front. Environ. Sci. Eng., 2020, 14(1): 1.
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http://journal.hep.com.cn/fese/EN/10.1007/s11783-019-1180-x
http://journal.hep.com.cn/fese/EN/Y2020/V14/I1/1
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Nan Wu
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Haixue Liu
Jinghui Yang
Xinyuan Liu
Fan Yang
Target Genes Primer Sequences (5′—3′) Amplicon size (bp) Annealing temp (°C) Ref.
tetA tetA-FW GCTACATCCTGCTTGCCTTC 210 55 Ng et al. (2001)
tetA-RV CATAGATCGCCGTGAAGAGG
tetW tetW-FW GAGAGCCTGCTATATGCCAGC 168 60 Aminov et al. (2001)
tetW-RV GGGCGTATCCACAATGTTAAC
tetX tetX-FW CAATAATTGGTGGTGGACCC 468 58 Ng et al. (2001)
tetX-RV TTCTTACCTTGGACATCCCG
sulI sulI-FW CGCACCGGAAACATCGCTGCAC 163 56 Negreanu et al. (2012)
sulI-RV TGAAGTTCCGCCGCAAGGCTCG
sulII sulII-FW TCCGGTGGAGGCCGGTATCTGG 191 60 Negreanu et al. (2012)
sulII-RV CGGGAATGCCATCTGCCTTGAG
ermF ermF-FW CGACACAGCTTTGGTTGAAC 309 56 Chen et al. (2007)
ermF-RV GGACCTACCTCATAGACAAG
acc(6’)-Ib-cr acc(6’)-FW TTGCGATGCTCTATGAGTGGCTA 482 55 Zhang et al. (2016)
acc(6’)-RV CTCGAATGCCTGGCGTGTTT
blaTEM blaTEM-FW ATCAGCAATAAACCAGC 516 55 Zhang et al. (2016)
blaTEM-RV CCCCGAAGAACGTTTTC
intI1 HS463a CTGGATTTCGATCACGGCACG 473 55 Hardwick et al. (2008)
HS464 GGWTACCTTGTTACGACTT
Tn916/1545 Tn916/1545-FW GACAGTATTAAGCCATCAGAC 142 41 Zhang et al. (2016)
Tn916/1545-RV TCTTCCGAACACAATCATCT
16S rRNA 1369F CGGTGAATACGTTCYCGG 123 56 Suzuki et al. (2000)
1492R GGWTACCTTGTTACGACTT
Tab.1  Primer sequences of the ARGs, MGEs, and 16S rRNA
Target
Genes
Primer Sequences (5′—3′) Amplicon size (bp) Annealing temp (℃) Ref.
tetA tetA-FW GCTACATCCTGCTTGCCTTC 210 55 Ng et al. (2001)
tetA-RV CATAGATCGCCGTGAAGAGG
tetW tetW-FW GAGAGCCTGCTATATGCCAGC 168 60 Aminov et al. (2001)
tetW-RV GGGCGTATCCACAATGTTAAC
tetX tetX-FW CAATAATTGGTGGTGGACCC 468 58 Ng et al. (2001)
tetX-RV TTCTTACCTTGGACATCCCG
sulI sulI-FW CGCACCGGAAACATCGCTGCAC 163 56 Negreanu et al. (2012)
sulI-RV TGAAGTTCCGCCGCAAGGCTCG
sulII sulII-FW TCCGGTGGAGGCCGGTATCTGG 191 60 Negreanu et al. (2012)
sulII-RV CGGGAATGCCATCTGCCTTGAG
ermF ermF-FW CGACACAGCTTTGGTTGAAC 309 56 Chen et al. (2007)
ermF-RV GGACCTACCTCATAGACAAG
aac(6’)-Ib-cr acc(6’)-FW TTGCGATGCTCTATGAGTGGCTA 482 55 Zhang et al. (2016)
acc(6’)-RV CTCGAATGCCTGGCGTGTTT
blaTEM blaTEM-FW ATCAGCAATAAACCAGC 516 55 Zhang et al. (2016)
blaTEM-RV CCCCGAAGAACGTTTTC
intI1 HS463a CTGGATTTCGATCACGGCACG 473 55 Hardwick et al. (2008)
HS464 GGWTACCTTGTTACGACTT
Tn916/1545 Tn916/1545-FW GACAGTATTAAGCCATCAGAC 142 41 Zhang et al. (2016)
Tn916/1545-RV TCTTCCGAACACAATCATCT
16S rRNA 1369F CGGTGAATACGTTCYCGG 123 56 Suzuki et al. (2000)
1492R GGWTACCTTGTTACGACTT
Tab.2  Primer sequences of the ARGs, MGEs, and 16S rRNA
Samples Cr Ni Cu Zn As Pb Cd
CK 77.50±1.68 43.15±0.47 39.39±0.79 32.93±0.86 17.47±0.27 23.84±0.12 ND
JX 123.18±11.49** 47.46±1.36** 25.03±1.32 13.61±1.01 8.33±0.04 10.29±0.63 ND
XQ 82.28±3.54 47.89±0.40** 74.59±1.06** 104.78±0.95** 19.86±0.17** 24.23±0.49 0.10±0.01
WQ 62.02±2.14 42.28±0.21 36.61±1.22 39.20±0.46** 13.45±0.22 23.43±0.31 0.10±0.01
NH 79.94±2.52 37.63±0.53 147.13±3.15** 205.51±1.42** 10.42±0.13 40.15±0.89** 0.35±0.02
DG 60.22±1.63 38.15±0.33 36.52±0.34 28.17±0.25 11.82±0.09 17.29±0.50 ND
JH 97.59±1.24* 45.98±0.52* 26.70±0.26 10.39±0.41 8.36±0.20 10.75±0.07 ND
BD 129.47±8.01** 53.63±0.77** 46.49±1.40** 20.98±0.43 13.69±0.09 21.86±0.26 ND
Tab.3  Heavy metal concentrations in surface soils from different sites (mg/kg DW)
Fig.1  Absolute abundances of ARGs, MGEs and 16S rRNA in soils from different sites. Plotted values are log10-transformed numbers of gene copies per g dry soil.
Fig.2  Relative abundances of ARGs and MGEs in soils from different sites.
Samples Tags OTUs Chao1 Coverage Shannon
intI1 ermF intI1 ermF intI1 ermF intI1 ermF intI1 ermF
CK 36726 97845 339 230 340 236 0.9960 0.9996 2.89 3.87
JX 14516 62790 441 233 466 244 0.9940 0.9994 1.82 0.85
XQ 20182 64383 309 48 334 59 0.9929 0.9998 1.86 0.37
WQ 23260 58540 250 187 319 200 0.9919 0.9994 1.26 0.44
NH 33810 56807 336 22 359 29 0.9908 0.9998 1.12 0.10
DG 16180 60648 152 261 179 265 0.9963 0.9997 1.92 1.48
JH 32667 65265 591 129 586 235 0.9918 0.9990 5.06 1.79
BD 36797 61476 432 17 419 17 0.9955 0.9999 6.18 1.43
Tab.4  Bacterial diversity index values of intI1 and ermF in soil samples
Fig.3  The host bacteria composition for (a) intI1 and (b) ermF at the phylum level (Phyla with relative abundances less than 0.5% are pooled into the category “others”).
Fig.4  The host bacteria composition for intI1 at the genus level (top 20 genera).
Fig.5  The host bacteria composition for ermF at the genus level (top 20 genera).
Genes pH TC OM TN C/N 16S rRNA Cr Ni Cu Zn As Pb intI1 Tn916/1545 MGEs
tetA -0.619 0.809* 0.673 0.798* 0.103 0.712* -0.277 -0.292 0.876** 0.927** 0.275 0.737* 0.706 0.730* 0.711*
tetW -0.541 0.557 0.662 0.416 0.367 0.402 0.257 -0.110 0.464 0.503 -0.242 0.175 0.476 0.436 0.469
tetX -0.305 0.773* 0.654 0.840** 0.009 0.913** -0.469 -0.671 0.634 0.657 -0.253 0.678 0.778* 0.746* 0.772*
sulI -0.591 0.775* 0.824* 0.683 0.152 0.946** -0.143 -0.521 0.927** 0.899** -0.244 0.799* 0.998** 0.990** 0.997**
sulII -0.588 0.775* 0.822* 0.685 0.149 0.949** -0.148 -0.525 0.925** 0.897** -0.245 0.802* 0.998** 0.990** 0.998**
ermF -0.579 0.791* 0.820* 0.716* 0.135 0.968** -0.182 -0.541 0.920** 0.897** -0.240 0.814* 0.999** 0.989** 0.998**
aac(6’)-Ib-cr -0.404 0.844** 0.767* 0.867** 0.134 0.845** -0.317 -0.619 0.621 0.671 -0.324 0.525 0.745* 0.696 0.736*
blaTEM -0.101 -0.097 0.057 -0.188 0.245 -0.241 0.503 0.235 -0.336 -0.298 -0.451 -0.544 -0.206 -0.263 -0.217
ARGs -0.583 0.809* 0.835** 0.735* 0.144 0.970** -0.185 -0.552 0.920** 0.901** -0.248 0.804* 0.998** 0.986** 0.997**
Tab.5  Correlation analysis among ARGs, MGEs, soil properties and heavy metals
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