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Tick-vectored mobilization of antibiotic resistance genes: transboundary dissemination across wildlife-livestock-vector-environment interfaces


Abstract

Antibiotic resistance genes (ARGs) are emerging as critical environmental contaminants across diverse ecological interfaces. To dissect evidence of microbiome and resistome in the different interconnected interfaces of ecotone, we conducted a field investigation of the microbiome and resistome of marmots, along with coexisting domestic sheep, ticks and their cave soils within the same ecological habitat. We used shotgun metagenomics with metagenome-assembled genomes (MAGs), species-resolved binning, ARG identification, source-tracker analyses, and horizontal gene transfer (HGT) network analysis to examine potential cross-interface dissemination. The composition of the mammalian gut microbiome was primarily comprised of Firmicutes, while ticks and soils exhibited distinct clusters that were predominantly dominated by Proteobacteria. The observed resistance mechanisms manifested niche-specific patterns, with target alteration predominating in mammals, whereas ticks exhibited elevated antibiotic inactivation/efflux strategies, and soils prioritized efflux mechanisms. Metagenomic assembly from these four groups yielded 5339 metagenome-assembled genomes (MAGs), of which 1481 met medium- or high-quality standards. Ticks exhibited 72% species similarity and 52% ARG concordance with marmots, while soils conserved 32% ARGs and >86% toxin genes with mammals. Our findings demonstrate that the transboundary dissemination of ARGs across different ecological interfaces, necessitates integrated surveillance of antimicrobial resistance at ecological boundaries to mitigate public health risks.

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Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes

Population-level impacts of antibiotic usage on the human gut microbiome

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock

Data availability

The sequence data in this study have been deposited into the CNGB Sequence Archive of China National GeneBank Database (CNGBdb) with accession number CNP0009025.

References

  1. World Health Organization. Global Antibiotic Resistance Surveillance Report 2025: Summary (WHO, 2025).

  2. GBD 2021 Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. Lancet 404, 1199–1226 (2024).

  3. Lin, H. et al. Epigenetic modifications and metabolic gene mutations drive resistance evolution in response to stimulatory antibiotics. Mol. Syst. Biol. 21, 294–314 (2025).

    Google Scholar 

  4. Durão, P., Balbontín, R. & Gordo, I. Evolutionary mechanisms shaping the maintenance of antibiotic resistance. Trends Microbiol. 26, 677–691 (2018).

    Google Scholar 

  5. Arnold, B. J., Huang, I. T. & Hanage, W. P. Horizontal gene transfer and adaptive evolution in bacteria. Nat. Rev. Microbiol. 20, 206–218 (2022).

    Google Scholar 

  6. Larsson, D. G. J. & Flach, C.-F. Antibiotic resistance in the environment. Nat. Rev. Microbiol. 20, 257–269 (2022).

    Google Scholar 

  7. Lugagne, J. B. & Dunlop, M. J. Anticipating antibiotic resistance. Science 375, 818–819 (2022).

    Google Scholar 

  8. Groussin, M. et al. Elevated rates of horizontal gene transfer in the industrialized human microbiome. Cell 184, 2053–2067.e2018 (2021).

    Google Scholar 

  9. Shi, X., Xia, Y., Wei, W. & Ni, B. J. Accelerated spread of antibiotic resistance genes (ARGs) induced by non-antibiotic conditions: roles and mechanisms. Water Res. 224, 119060 (2022).

    Google Scholar 

  10. Hassell, J. M. et al. Clinically relevant antimicrobial resistance at the wildlife-livestock-human interface in Nairobi: an epidemiological study. Lancet Planet. Health 3, e259–e269 (2019).

    Google Scholar 

  11. Hassell, J. M. et al. Socio-ecological drivers of vertebrate biodiversity and human-animal interfaces across an urban landscape. Glob. Change Biol. 27, 781–792 (2021).

    Google Scholar 

  12. Chavarría-Bencomo, I. V., Nevárez-Moorillón, G. V., Espino-Solís, G. P. & Adame-Gallegos, J. R. Antibiotic resistance in tick-borne bacteria: a one health approach perspective. J. Infect. Public Health 16, 153–162 (2023).

    Google Scholar 

  13. Wei, N., Lu, J., Dong, Y. & Li, S. Profiles of microbial community and antibiotic resistome in wild tick species. mSystems 7, e0003722 (2022).

    Google Scholar 

  14. Winkler, A. S. et al. The Lancet One Health Commission: harnessing our interconnectedness for equitable, sustainable, and healthy socioecological systems. Lancet 406, 501–570 (2025).

    Google Scholar 

  15. D’Costa, V. M. et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).

    Google Scholar 

  16. Arnold, K. E., Williams, N. J. & Bennett, M. ‘Disperse abroad in the land’: the role of wildlife in the dissemination of antimicrobial resistance. Biology Lett. https://doi.org/10.1098/rsbl.2016.0137 (2016).

  17. Torres, R. T. et al. Wild boar as a reservoir of antimicrobial resistance. Sci. Total Environ. 717, 135001 (2020).

    Google Scholar 

  18. Brealey, J. C., Leitão, H. G., Hofstede, T., Kalthoff, D. C. & Guschanski, K. The oral microbiota of wild bears in Sweden reflects the history of antibiotic use by humans. Curr. Biol. 31, 4650–4658.e4656 (2021).

    Google Scholar 

  19. Branck, T. et al. Comprehensive profile of the companion animal gut microbiome integrating reference-based and reference-free methods. ISME J. https://doi.org/10.1093/ismejo/wrae201 (2024).

  20. Yang, Y. et al. Pet cats may shape the antibiotic resistome of their owner’s gut and living environment. Microbiome 11, 235 (2023).

    Google Scholar 

  21. Taş, N. et al. Metagenomic tools in microbial ecology research. Curr. Opin. Biotechnol. 67, 184–191 (2021).

    Google Scholar 

  22. Upadhayay, A. et al. Resistance-proof antimicrobial drug discovery to combat global antimicrobial resistance threat. Drug Resist. Updat. 66, 100890 (2023).

    Google Scholar 

  23. Tecon, R. & Or, D. Biophysical processes supporting the diversity of microbial life in soil. FEMS Microbiol. Rev. 41, 599–623 (2017).

    Google Scholar 

  24. Ma, J. et al. The interaction among gut microbes, the intestinal barrier and short chain fatty acids. Anim. Nutr. 9, 159–174 (2022).

    Google Scholar 

  25. Gregor, R. et al. Mammalian gut metabolomes mirror microbiome composition and host phylogeny. ISME J. 16, 1262–1274 (2022).

    Google Scholar 

  26. Buysse, M., Binetruy, F., Leibson, R., Gottlieb, Y. & Duron, O. Ecological contacts and host specificity promote replacement of nutritional endosymbionts in ticks. Microb. Ecol. 83, 776–788 (2022).

    Google Scholar 

  27. Douglas, S. S. Jr, Hunter, W. B., Qureshi, J. A. & Cano, L. M. Transcriptomic characterization of Wolbachia endosymbiont from Leuronota fagarae (Hemiptera: Psylloidae). Microbiome Res. Rep. 4, 19 (2025).

    Google Scholar 

  28. da Silva Pereira, M. et al. Microbial Rumen proteome analysis suggests Firmicutes and Bacteroidetes as key producers of lignocellulolytic enzymes and carbohydrate-binding modules. Braz. J. Microbiol. 30, 241–253 (2025).

    Google Scholar 

  29. Mathur, H., Beresford, T. P. & Cotter, P. D. Health benefits of lactic acid bacteria (LAB) fermentates. Nutrients. https://doi.org/10.1016/j.crmeth.2025.101005 (2020).

  30. Kengmo Tchoupa, A., Eijkelkamp, B. A. & Peschel, A. Bacterial adaptation strategies to host-derived fatty acids. Trends Microbiol. 30, 241–253 (2022).

    Google Scholar 

  31. Allen, P. E. & Martinez, J. J. Modulation of host lipid pathways by pathogenic intracellular bacteria. Pathogens. https://doi.org/10.1016/j.tim.2021.06.002 (2020).

  32. Nolan, S. J., Romano, J. D. & Coppens, I. Host lipid droplets: an important source of lipids salvaged by the intracellular parasite Toxoplasma gondii. PLoS Pathogens 13, e1006362 (2017).

    Google Scholar 

  33. Dos Santos, P. T., Larsen, P. T., Menendez-Gil, P., Lillebæk, E. M. S. & Kallipolitis, B. H. Listeria monocytogenes relies on the heme-regulated transporter hrtAB to resist heme toxicity and uses heme as a signal to induce transcription of lmo1634, encoding listeria adhesion protein. Front. Microbiol. 9, 3090 (2018).

    Google Scholar 

  34. Song, J. et al. Bacterial necromass as the main source of organic matter in saline soils. J. Environ. Manag. 371, 123130 (2024).

    Google Scholar 

  35. Katz, L. & Baltz, R. H. Natural product discovery: past, present, and future. J. Ind. Microbiol. Biotechnol. 43, 155–176 (2016).

    Google Scholar 

  36. Núñez-Montero, K. et al. Advances in Antarctic Research for Antimicrobial Discovery: a comprehensive narrative review of bacteria from Antarctic environments as potential sources of novel antibiotic compounds against human pathogens and microorganisms of industrial importance. Antibiotics 7, 123130 (2018).

    Google Scholar 

  37. Zhang, L. et al. Nitrifiers drive successions of particulate organic matter and microbial community composition in a starved macrocosm. Environ. Int. 157, 106776 (2021).

    Google Scholar 

  38. Elazhari, M. et al. Characterization of fusidic acid-resistant Staphylococcus aureus isolates in the community of Casablanca (Morocco). Int. J. Med. Microbiol. IJMM 302, 96–100 (2012).

    Google Scholar 

  39. Yu, F. et al. Dissemination of fusidic acid resistance among Staphylococcus aureus clinical isolates. BMC Microbiol. 15, 210 (2015).

    Google Scholar 

  40. Tang, T., Chen, Y., Du, Y., Yao, B. & Liu, M. Effects of functional modules and bacterial clusters response on transmission performance of antibiotic resistance genes under antibiotic stress during anaerobic digestion of livestock wastewater. J. Hazard. Mater. 441, 129870 (2023).

    Google Scholar 

  41. Wee, B. A., Muloi, D. M. & van Bunnik, B. A. D. Quantifying the transmission of antimicrobial resistance at the human and livestock interface with genomics. Clin. Microbiol. Infect. 26, 1612–1616 (2020).

    Google Scholar 

  42. Wang, F. et al. Neglected drivers of antibiotic resistance: survival of extended-spectrum β-lactamase-producing pathogenic Escherichia coli from livestock waste through dormancy and release of transformable extracellular antibiotic resistance genes under heat treatment. Environ. Sci. Technol. 57, 9955–9964 (2023).

    Google Scholar 

  43. Hulse, S. V., Antonovics, J., Hood, M. E. & Bruns, E. L. Host-pathogen coevolution promotes the evolution of general, broad-spectrum resistance and reduces foreign pathogen spillover risk. Evol. Lett. 7, 467–477 (2023).

    Google Scholar 

  44. Chatterjee, D., Sivashanmugam, K., Delgado, L. F. & Andersson, A. F. Immunomodulatory peptides: new therapeutic horizons for emerging and re-emerging infectious diseases. Front. Microbiol. 15, 1505571 (2024).

    Google Scholar 

  45. Graça-Souza, A. V. et al. Adaptations against heme toxicity in blood-feeding arthropods. Insect Biochem. Mol. Biol. 36, 322–335 (2006).

    Google Scholar 

  46. Ekiert, D. C. et al. Architectures of lipid transport systems for the bacterial outer membrane. Cell 169, 273–285.e217 (2017).

    Google Scholar 

  47. Chung, B. C. et al. Crystal structure of MraY, an essential membrane enzyme for bacterial cell wall synthesis. Science 341, 1012–1016 (2013).

    Google Scholar 

  48. Whitfield, C., Wear, S. S. & Sande, C. Assembly of bacterial capsular polysaccharides and exopolysaccharides. Annu. Rev. Microbiol. 74, 521–543 (2020).

    Google Scholar 

  49. Molin, S. & Tolker-Nielsen, T. Gene transfer occurs with enhanced efficiency in biofilms and induces enhanced stabilisation of the biofilm structure. Curr. Opin. Biotechnol. 14, 255–261 (2003).

    Google Scholar 

  50. Di Iorio, E. et al. Environmental implications of interaction between humic substances and iron oxide nanoparticles: a review. Chemosphere 303, 135172 (2022).

    Google Scholar 

  51. Nachmias, N. et al. Systematic discovery of antibacterial and antifungal bacterial toxins. Nat. Microbiol. 9, 3041–3058 (2024).

    Google Scholar 

  52. Hu, J. et al. Animal production predominantly contributes to antibiotic profiles in the Yangtze River. Water Res. 242, 120214 (2023).

    Google Scholar 

  53. Yan, K. et al. Diffusion and enrichment of high-risk antibiotic resistance genes (ARGs) via the transmission chain (mulberry leave, guts and feces of silkworm, and soil) in an ecological restoration area of manganese mining, China: Role of heavy metals. Environ. Res. 225, 115616 (2023).

    Google Scholar 

  54. Chai, J., Zhuang, Y., Cui, K., Bi, Y. & Zhang, N. Metagenomics reveals the temporal dynamics of the rumen resistome and microbiome in goat kids. Microbiome 12, 14 (2024).

    Google Scholar 

  55. Feng, Y. et al. Regional antimicrobial resistance gene flow among the One Health sectors in China. Microbiome 13, 3 (2025).

    Google Scholar 

  56. Wise, M. G. et al. Global trends in carbapenem- and difficult-to-treat-resistance among World Health Organization priority bacterial pathogens: ATLAS surveillance program 2018-2022. J. Glob. Antimicrob. Resist. 37, 168–175 (2024).

    Google Scholar 

  57. Rubiola, S. et al. Shotgun metagenomic sequencing of bulk tank milk filters reveals the role of Moraxellaceae and Enterobacteriaceae as carriers of antimicrobial resistance genes. Food Res. Int. 158, 111579 (2022).

    Google Scholar 

  58. Kocher, T. D. et al. Dynamics of mitochondrial DNA evolution in animals: amplification and sequencing with conserved primers. Proc. Natl. Acad. Sci. USA 86, 6196–6200 (1989).

    Google Scholar 

  59. Yu, D. W. et al. Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods Ecol. Evol. 3, 613–623 (2012).

    Google Scholar 

  60. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).

    Google Scholar 

  61. Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, e104 (2017).

    Google Scholar 

  62. de Nies, L. et al. PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data. Microbiome 9, 49 (2021).

    Google Scholar 

  63. Arango-Argoty, G. et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6, 23 (2018).

    Google Scholar 

  64. Alcock, B. P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the comprehensive antibiotic resistance database. Nucleic Acids Res. 51, D690–d699 (2023).

    Google Scholar 

  65. Knights, D. et al. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods 8, 761–763 (2011).

    Google Scholar 

  66. McGhee, J. J. et al. Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics. PeerJ 8, e8783 (2020).

    Google Scholar 

  67. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Google Scholar 

  68. Pan, S., Zhu, C., Zhao, X.-M. & Coelho, L. P. SemiBin: incorporating information from reference genomes with semi-supervised deep learning leads to better metagenomic assembled genomes (MAGs). bioRxiv https://doi.org/10.1101/2021.08.16.456517 (2021).

  69. Chaumeil, P. A. et al. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics 36, 1925–1927 (2019).

    Google Scholar 

  70. Goris, J. et al. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int. J. Syst. Evolut. Microbiol. 57, 81–91 (2007).

    Google Scholar 

  71. Butler, G. et al. Evolution of pathogenicity and sexual reproduction in eight Candida genomes. Nature 459, 657–662 (2009).

    Google Scholar 

  72. Segata, N., Börnigen, D., Morgan, X. C. & Huttenhower, C. PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes. Nat. Commun. 4, 2304 (2013).

    Google Scholar 

  73. Song, W., Wemheuer, B., Zhang, S., Steensen, K. & Thomas, T. MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches. Microbiome 7, 36 (2019).

    Google Scholar 

  74. Zhang, A. N. et al. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nat. Commun. 12, 4765 (2021).

    Google Scholar 

  75. Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Biol. Evol. 38, 5825–5829 (2021).

    Google Scholar 

  76. Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–d314 (2019).

    Google Scholar 

  77. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–w259 (2019).

    Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 82273689 and 82473689), Key Research and Development Program of Shaanxi (grant no. 2024SF-YBXM-289), and Wuwei City Science and Technology Plan Project (No. WW25Z01SF027).

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Zhenhua Lu: writing—original draft, software, methodology, investigation, formal analysis, and visualization. Ruishan Li: investigation, data curation, and writing—original draft. Kaichun Zhou, Shiyu Li, Shijie Chen, and Shiwei Sun: methodology and investigation. Jiacheng Liu and Lele Zhao: methodology. Xiang Yuan: resources, investigation, and supervision. Kun Liu and Zhongjun Shao: writing—review and editing, project administration, and funding acquisition.

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Kun Liu, Xiang Yuan or Zhongjun Shao.

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Lu, Z., Li, R., Zhou, K. et al. Tick-vectored mobilization of antibiotic resistance genes: transboundary dissemination across wildlife-livestock-vector-environment interfaces.
npj Biofilms Microbiomes (2026). https://doi.org/10.1038/s41522-026-00986-w

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