Abstract
Antimicrobial use in livestock production is linked to the emergence and spread of antimicrobial resistance (AMR), but large-scale studies on AMR changes in livestock isolates remain scarce. Here we applied whole-genome sequence analysis to 982 animal-derived Escherichia coli samples collected in China from the 1970s to 2019, finding that the number of AMR genes (ARGs) per isolate doubled—including those conferring resistance to critically important agents for both veterinary (florfenicol and norfloxacin) and human medicine (colistin, cephalosporins and meropenem). Plasmids of incompatibility groups IncC, IncHI2, IncK, IncI and IncX increased distinctly in the past 50 years, acting as highly effective vehicles for ARG spread. Using antimicrobials of the same class, or even unrelated classes, may co-select for mobile genetic elements carrying multiple co-existing ARGs. Prohibiting or strictly curtailing antimicrobial use in livestock is therefore urgently needed to reduce the growing threat from AMR.
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Data availability
All data analysed in this study are publicly available. The whole-genome sequencing data downloaded from previous projects are listed in Supplementary Table 6, including the accession numbers, collection times and sampling preferences. The genomes of the newly sequenced isolates have been deposited in NCBI as well (BioProject accession number: PRJNA718052). The phylogenetic tree of all 982 genomes along with all metadata is available on the interactive online platform Microreact (https://microreact.org/project/9ozGFW62hE7LmgkVZ9Uouh/449bbe4b). The map we used for generating Fig. 1 was from an open-source database and free for use. Source data are provided with this paper.
References
O’Neill, J. Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations. The Review on Antimicrobial Resistance (2014).
Van Boeckel, T. P. et al. Global antibiotic consumption 2000 to 2010: an analysis of national pharmaceutical sales data. Lancet Infect. Dis. 14, 742–750 (2014).
O’Neill, J. Antimicrobials in agriculture and the environment: reducing unnecessary use and waste. The Review on Antimicrobial Resistance. (2015).
Aarestrup, F. M., Kruse, H., Tast, E., Hammerum, A. M. & Jensen, L. B. Associations between the use of antimicrobial agents for growth promotion and the occurrence of resistance among Enterococcus faecium from broilers and pigs in Denmark, Finland, and Norway. Microb. Drug Resist. 6, 63–70 (2000).
Carattoli, A. Plasmids and the spread of resistance. Int. J. Med. Microbiol. 303, 298–304 (2013).
Hernando-Amado, S., Coque, T. M., Baquero, F. & Martinez, J. L. Defining and combating antibiotic resistance from One Health and Global Health perspectives. Nat. Microbiol. 4, 1432–1442 (2019).
OIE List of Antimicrobial Agents of Veterinary Importance (OIE, 2018); https://www.oie.int/fileadmin/Home/eng/Our_scientific_expertise/docs/pdf/AMR/A_OIE_List_antimicrobials_July2019.pdf
Critically Important Antimicrobials for Human Medicine (WHO, 2019); https://apps.who.int/iris/bitstream/handle/10665/312266/9789241515528-eng.pdf?ua=1
Arredondo-Alonso, S. et al. mlplasmids: a user-friendly tool to predict plasmid- and chromosome-derived sequences for single species. Microb. Genom. https://doi.org/10.1099/mgen.0.000224 (2018).
Zhang, K. J. Review of veterinary traditional Chinese medicine and formulae in the 20th century. J. Chin. Vet. Med. 4, 31–33 (1999).
Van Boeckel, T. P. et al. Global trends in antimicrobial use in food animals. Proc. Natl Acad. Sci. USA 112, 5649–5654 (2015).
Yuan, Z., Zhang, M., Dai, M. & Huang, L. Analysis of veterinary antimicrobial resistance and suggestions for prevention and control. Chin. J. Vet. Med. 46, 7–11 (2012).
Tang, K. L. et al. Restricting the use of antibiotics in food-producing animals and its associations with antibiotic resistance in food-producing animals and human beings: a systematic review and meta-analysis. Lancet Planet. Health 1, e316–e327 (2017).
Wang, Y. et al. Changes in colistin resistance and mcr-1 abundance in Escherichia coli of animal and human origins following the ban of colistin-positive additives in China: an epidemiological comparative study. Lancet Infect. Dis. 20, 1161–1171 (2020).
Wang, R., Dorp, L. V., Shaw, L. P., Bradley, P. & Balloux, F. The global distribution and spread of the mobilized colistin resistance gene mcr-1. Nat. Commun. 9, 1179 (2018).
Yang, Q. et al. Balancing mcr-1 expression and bacterial survival is a delicate equilibrium between essential cellular defence mechanisms. Nat. Commun. 8, 1–12 (2017).
Moura, A., Pereira, C., Henriques, I. & Correia, A. Novel gene cassettes and integrons in antibiotic-resistant bacteria isolated from urban wastewaters. Res. Microbiol. 163, 92–100 (2012).
Duijkeren, E. v. et al. Mechanisms of Bacterial Resistance to Antimicrobial Agents. Microbiology Spectrum. https://doi.org/10.1128/microbiolspec.ARBA-0019-2017 (2018).
Pribis, J. P. et al. Gamblers: an antibiotic-induced evolvable cell subpopulation differentiated by reactive-oxygen-induced general stress response. Mol. Cell 74, 785–800 (2019).
Bennett, P. M. Plasmid encoded antibiotic resistance: acquisition and transfer of antibiotic resistance genes in bacteria. Br. J. Pharmacol. 153, S347–S357 (2008).
Carattoli, A. Resistance plasmid families in Enterobacteriaceae. Antimicrob. Agents Chemother. 53, 2227–2238 (2009).
Partridge, S. R., Kwong, S. M., Firth, N. & Jensen, S. O. Mobile genetic elements associated with antimicrobial resistance. Clin. Microbiol. Rev. https://doi.org/10.1128/CMR.00088-17 (2018).
Kopotsa, K., Osei Sekyere, J. & Mbelle, N. M. Plasmid evolution in carbapenemase-producing Enterobacteriaceae: a review. Ann. N. Y. Acad. Sci. 1457, 61–91 (2019).
Matamoros, S. et al. Global phylogenetic analysis of Escherichia coli and plasmids carrying the mcr-1 gene indicates bacterial diversity but plasmid restriction. Sci. Rep. 7, 15364 (2017).
Li, S. et al. Investigation of integrons/cassettes in antimicrobial-resistant Escherichia coli isolated from food animals in China. Sci. China 53, 613–619 (2010).
Li, L. et al. Characterization of antimicrobial resistance and molecular determinants of beta-lactamase in Escherichia coli isolated from chickens in China during 1970–2007. Vet. Microbiol. 144, 505–510 (2010).
Dai, L. et al. Characterization of antimicrobial resistance among Escherichia coli isolates from chickens in China between 2001 and 2006. FEMS Microbiol. Lett. 286, 178–183 (2008).
Shen, Z., Wang, Y., Shen, Y., Shen, J. & Wu, C. Early emergence of mcr-1 in Escherichia coli from food-producing animals. Lancet Infect. Dis. 16, 293 (2016).
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595 (2017).
CLSI M100-ED30:2020 Performance Standards for Antimicrobial Susceptibility Testing 30th edn (CLSI, 2020); http://em100.edaptivedocs.net/
Breakpoint Tables for Interpretation of MICs and Zone Diameters Version 10.0 (EUCAST, 2020); http://www.eucast.org
Treangen, T. J., Ondov, B. D., Koren, S. & Phillippy, A. M. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biol. 15, 524 (2014).
Cheng, L., Connor, T. R., Sirén, J., Aanensen, D. M. & Corander, J. Hierarchical and spatially explicit clustering of DNA sequences with BAPS software. Mol. Biol. Evol. 30, 1224–1228 (2013).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Croucher, N. J. et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res. 43, e15 (2015).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
Maiden, M. C. et al. Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms. Proc. Natl Acad. Sci. USA 95, 3140–3145 (1998).
Gupta, S. K. et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob. Agents Chemother. 58, 212–220 (2014).
Carattoli, A. et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob. Agents Chemother. 58, 3895–3903 (2014).
Inouye, M. et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 6, 90 (2014).
Page, A. J. et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31, 3691–3693 (2015).
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).
Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. in Proceedings of the international AAAI conference on web and social media 3, 361–362 (2009).
Canisius, S., Martens, J. W. M. & Wessels, L. F. A. A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome Biol. 17, 261 (2016).
Acknowledgements
This work was supported in part by grants from the Laboratory of Lingnan Modern Agriculture Project (no. NT2021006 to Y.W., Z.S. and J.S.), the National Natural Science Foundation of China (no. 81991535 to C.W.) and the UK Medical Research Council (project DETER-XDR-China-HUB, grant no. MR/S013768/1 to T.R.W.).
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The study was conceived and supervised by J.S., Z.S. and Y.W., and designed by L.Y., Y.S., B.S. and C.W. L.Y., J.J. and X.W. completed the wet lab experiments. L.Y. and D.S. analysed the data under the guidance of M.M.C.L. and K.E.H. L.Y. and Y.W. drafted the majority of the manuscript, and T.R.W., S.S. and Z.S. also contributed to the text. All authors contributed to the review of the manuscript before submission for publication and approved the final version.
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Extended data
Extended Data Fig. 1 Phylogenetic tree of E. coli isolates from food-animal in China.
a. Phylogenetic tree of 982 E. coli isolates from food-animal in China. The midpoint-rooted tree was constructed using n=50672 core-genome SNPs, and the tree along with all these metadata have been uploaded to microreact (https://microreact.org/project/9ozGFW62hE7LmgkVZ9Uouh/449bbe4b). b. Phylogenetic tree of the two clades C1 and C2 highlighted in Fig. 1a and annotated by host, time group and gene content. The tips are colored by the host. Columns are as follows: sampling time, and presence or absence of ARGs (blue) and plasmid replicons (red).
Extended Data Fig. 2 Minimum spanning tree of the whole data set by multi-locus sequence typing.
The time group of all isolates is indicated by different colors.
Extended Data Fig. 3 Prevalence of some specific STs with a rising (red) or declining (blue) trend over time and the ARG containing numbers in these STs.
The average numbers were noted by ‘x’, and discrete numbers were showed as dots. For some groups like ST46, most of the isolates (n = 17) contain same n umber of ARGs (n = 9, including colistin resistance gene mcr-1), which were overlapped on the ‘x’.
Extended Data Fig. 4 The distribution of the number of plasmid replicon genes and virulence genes per isolate in different time group.
The mean ARG count for each group is noted above. The ‘*’ on the right represent p-values. *: p < 0.05; **: p < 0.01; ***: p < 0.001.
Extended Data Fig. 5 The prevalence of plasmid replicon genes and P-values between different groups.
a. The prevalence of plasmid replicon genes grouped by time and host. b. The P-values of pairwise comparisons of prevalence of time-groups.
Extended Data Fig. 6 Comparison of AMR genotype and phenotype of different antimicrobial agents.
The bars labeled name of antimicrobial agents represent the resistance rate. And the label with ‘lab’, ‘NCBI’ and ‘overall’ represent the prevalence of corresponding resistant genes in lab-preserved strains (n=450), NCBI-downloaded strains (n=532) and the overall dataset (n=982), respectively.
Extended Data Fig. 7 The value of antimicrobial production with phenotype and genotype changes over the time.
a. The dark grey histogram shows the mean value of production of norfloxacin per year in different time groups. The light grey histogram shows the mean value of production of other quinolones used in animals (ciprofloxacin, ofloxacin, levofloxacin, lomefloxacin, pefloxacin and pipemidic acid) per year, and data were only available from 2000s. The black line represents the prevalence of norfloxacin resistance and the orange lines represent the prevalence of quinolone-resistant genes. b. The histogram shows the mean value of production of colistin in different time groups. The black line represents the prevalence of colistin resistance and the green line represent the prevalence of colistin-resistant gene mcr-1. Production data before 2000 were unavailable.
Extended Data Fig. 8 The correlation of mean counts of ARGs, plasmid replicons, and virulence genes detected from genomes.
a. The correlation between ARG and virulence genes counts. b. The correlation between plasmid replicon and ARG counts. c. The correlation between plasmid replicons and virulence gene counts.
Extended Data Fig. 9 The co-occurrence of ARGs and plasmid replicons in different time groups.
The first two of the groups (1970s-80s and 1990s) were merged as ‘before 2000’, due to the smaller quantities of isolates. We define the false discovery rate (FDR) threshold as 0.05 in this algorithm; significant pairwise values from 1 (blue) to -1 (red) (that is co-occurrence and mutual exclusivity) are shown. The blue box labelled ‘ARG-ARG’ shows the pairwise co-occurrence and mutual exclusivity of ARGs, and the red box labelled ‘plasmid-plasmid’ shows the pairwise co-occurrence and mutual exclusivity of plasmid replicons. The gold boxes labelled with numbers indicate the co-occurrence and mutual exclusivity between ARGs and plasmid replicons, and these are annotated on the bottom left.
Extended Data Fig. 10 Per Capita Consumption of food and population size in China.
a. Per Capita Consumption of pork and chicken in rural area, urban area and nationwide. b. Population size in China.
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Yang, L., Shen, Y., Jiang, J. et al. Distinct increase in antimicrobial resistance genes among Escherichia coli during 50 years of antimicrobial use in livestock production in China. Nat Food 3, 197–205 (2022). https://doi.org/10.1038/s43016-022-00470-6
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DOI: https://doi.org/10.1038/s43016-022-00470-6
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