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Environmental selection and vertical inheritance shape antibiotic resistance in cryospheric bacteria on the Tibetan Plateau

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Abstract

Antibiotic resistance genes (ARGs) occur even in remote cryospheric regions, yet their environmental selection mechanisms, distribution, and risks remain unclear. Here, 45% of 920 bacterial strains from Tibetan Plateau ice cores, cryoconites, snow, and lakes carry ARGs, with prevalence and diversity varying across habitats, reflecting distinct environmental pressures. Snow-derived ARGs reflect atmospheric deposition, whereas ice cores preserve resistance via genome recycling. Organic- and heavy metal-rich cryoconites promote ARG acquisition and persistence, while lakes reduce ARG abundance through natural attenuation. Strains of the same species exhibit consistent ARG profiles, and only 6.4% of ARGs are associated with mobile genetic elements, indicating that vertical inheritance predominates. Thirty-three high-risk ARG subtypes were identified, including several in potentially pathogenic genera. Tibetan strains carry fewer ARGs than those from human-impacted sites. This large-scale, culture-based resistome survey identifies the Tibetan Plateau as a natural baseline and highlights potential ARG mobilization risks under environmental change.

Data availability

The genome assembly data for the 920 strains reported in this study have been deposited in the Genome Warehouse of the National Genomics Data Center under BioProject ID PRJCA038636102. Accession numbers for each bacterial strain are listed in Supplementary Data 4. Supplementary Data 1, 2, 3, and 4 have been uploaded to Figshare (https://doi.org/10.6084/m9.figshare.31868527).

References

  1. Hernando-Amado, S., Coque, T. M., Baquero, F. & Martínez, J. L. Defining and combating antibiotic resistance from One Health and Global Health perspectives. Nat. Microbiol. 4, 1432–1442 (2019).

    Google Scholar 

  2. Serwecińska, L. Antimicrobials and antibiotic-resistant bacteria: a risk to the environment and to public health. Water 12, 3313 (2020).

    Google Scholar 

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

    Google Scholar 

  4. Davies, J. & Davies, D. Origins and evolution of antibiotic resistance. Microbiol. Mol. Biol. Rev. 74, 417–433 (2010).

    Google Scholar 

  5. Baquero, F. et al. Evolutionary pathways and trajectories in antibiotic resistance. Clin. Microbiol. Rev. 34, e00050–00019 (2021).

    Google Scholar 

  6. Smith, W. P., Wucher, B. R., Nadell, C. D. & Foster, K. R. Bacterial defences: mechanisms, evolution and antimicrobial resistance. Nat. Rev. Microbiol. 21, 519–534 (2023).

    Google Scholar 

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

    Google Scholar 

  8. Depta, J. & Niedźwiedzka-Rystwej, P. The phenomenon of antibiotic resistance in the polar regions: an overview of the global problem. Infect. Drug Resist. 16, 1979–1995 (2023).

    Google Scholar 

  9. Van Goethem, M. W. et al. A reservoir of ‘historical’ antibiotic resistance genes in remote pristine Antarctic soils. Microbiome 6, 40 (2018).

    Google Scholar 

  10. Tan, L. et al. Arctic antibiotic resistance gene contamination, a result of anthropogenic activities and natural origin. Sci. Total Environ. 621, 1176–1184 (2018).

    Google Scholar 

  11. Ying, H., Zhang, Y., Hu, W., Wu, W. & Mao, G. Glaciers as reservoirs of antibiotic resistance genes: hidden risks to human and ecosystem health in a warming world. Biocontaminant 1, e021 (2025).

    Google Scholar 

  12. Wu, Z. et al. Profiles and risk assessment of antibiotic resistome between Qinghai-Xizang Plateau and Polar Regions. Geogr. Sustainability 6, 100342 (2025).

    Google Scholar 

  13. Li, X. et al. Climate change threatens terrestrial water storage over the Tibetan Plateau. Nat. Clim. Change 12, 801–807 (2022).

    Google Scholar 

  14. Chen, H. et al. Carbon and nitrogen cycling on the Qinghai-Tibetan Plateau. Nat. Rev. Earth Environ. 3, 701–716 (2022).

    Google Scholar 

  15. Liu, K. et al. Bacteria in the lakes of the Tibetan Plateau and polar regions. Sci. Total Environ. 754, 142248 (2021).

    Google Scholar 

  16. Czatzkowska, M., Wolak, I., Harnisz, M. & Korzeniewska, E. Impact of anthropogenic activities on the dissemination of ARGs in the environment—a review. Int. J. Environ. Res. Public Health 19, 12853 (2022).

    Google Scholar 

  17. Wang, Y. et al. Metagenomic analysis of antibiotic-resistance genes and viruses released from glaciers into downstream habitats. Sci. Total Environ. 908, 168310 (2024).

    Google Scholar 

  18. Zhang, B. et al. From glacier forelands to human settlements: Patterns, environmental drivers, and risks of antibiotic resistance genes. J. Hazard. Mater. 494, 138455 (2025).

    Google Scholar 

  19. Klümper, U. et al. Towards the integration of antibiotic resistance gene mobility into environmental surveillance and risk assessment. npj Antimicrob. Resist. 3, 81 (2025).

    Google Scholar 

  20. Zhang, Z. et al. Assessment of global health risk of antibiotic resistance genes. Nat. Commun. 13, 1–11 (2022).

    Google Scholar 

  21. 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 

  22. Sudlow, K., Tremblay, S. S. & Vinebrooke, R. D. Glacial stream ecosystems and epilithic algal communities under a warming climate. Environ. Rev. 31, 471–483 (2023).

    Google Scholar 

  23. Qu, B., Zhang, Y., Kang, S. & Sillanpää, M. Water quality in the Tibetan Plateau: major ions and trace elements in rivers of the “Water Tower of Asia”. Sci. Total Environ. 649, 571–581 (2019).

    Google Scholar 

  24. Liu, Y. et al. Bacterial diversity in the snow over Tibetan Plateau Glaciers. Extremophiles 13, 411–423 (2009).

    Google Scholar 

  25. Ren, Z. & Gao, H. Antibiotic resistance genes in integrated surface ice, cryoconite, and glacier-fed stream in a mountain glacier in Central Asia. Environ. Int. 184, 108482 (2024).

    Google Scholar 

  26. Mao, G. et al. Monsoon affects the distribution of antibiotic resistome in Tibetan glaciers. Environ. Pollut. 317, 120809 (2023).

    Google Scholar 

  27. Li, L. et al. Short-and long-read metagenomics insight into the genetic contexts and hosts of mobile antibiotic resistome in Chinese swine farms. Sci. Total Environ. 827, 154352 (2022).

    Google Scholar 

  28. Pereira-Marques, J. et al. Impact of host DNA and sequencing depth on the taxonomic resolution of whole metagenome sequencing for microbiome analysis. Front. Microbiol. 10, 1277 (2019).

    Google Scholar 

  29. Zhang, X., Yao, T., Tian, L., Xu, S. & An, L. Phylogenetic and physiological diversity of bacteria isolated from Puruogangri ice core. Microb. Ecol. 55, 476–488 (2008).

    Google Scholar 

  30. Sajjad, W. et al. High prevalence of antibiotic-resistant and metal-tolerant cultivable bacteria in remote glacier environment. Environ. Res. 239, 117444 (2023).

    Google Scholar 

  31. Bilen, M. et al. The contribution of culturomics to the repertoire of isolated human bacterial and archaeal species. Microbiome 6, 94 (2018).

    Google Scholar 

  32. Rodrigue, S. et al. Whole genome amplification and de novo assembly of single bacterial cells. PLoS ONE 4, e6864 (2009).

    Google Scholar 

  33. Makowska, N. et al. Occurrence of integrons and antibiotic resistance genes in cryoconite and ice of Svalbard, Greenland, and the Caucasus glaciers. Sci. Total Environ. 716, 137022 (2020).

    Google Scholar 

  34. Zhu, G. et al. Air pollution could drive global dissemination of antibiotic resistance genes. ISME J 15, 270–281 (2021).

    Google Scholar 

  35. Harding, T., Jungblut, A. D., Lovejoy, C. & Vincent, W. F. Microbes in high arctic snow and implications for the cold biosphere. Appl. Environ. Microbiol. 77, 3234–3243 (2011).

    Google Scholar 

  36. Rogers, S. O., Starmer, W. T. & Castello, J. D. Recycling of pathogenic microbes through survival in ice. Med. Hypotheses 63, 773–777 (2004).

    Google Scholar 

  37. Priscu, J. C., Christner, B. C., Foreman, C. M. & Royston-Bishop, G. Biological material in ice cores. Encycl. Quat. Sci. 2, 1156–1166 (2007).

    Google Scholar 

  38. Rozwalak, P. et al. Cryoconite – From minerals and organic matter to bioengineered sediments on glacier’s surfaces. Sci. Total Environ. 807, 150874 (2022).

    Google Scholar 

  39. Nizamutdinov, T., Mavlyudov, B., Polyakov, V. & Abakumov, E. Sediments from cryoconite holes and dirt cones on the surface of Svalbard glaciers: main chemical and physicochemical properties. Acta Geochim. 42, 346–359 (2023).

    Google Scholar 

  40. Vats, P., Kaur, U. J. & Rishi, P. Heavy metal-induced selection and proliferation of antibiotic resistance: A review. J. Appl. Microbiol. 132, 4058–4076 (2022).

    Google Scholar 

  41. Xu, W. H., Zhang, G., Wai, O. W., Zou, S. C. & Li, X. D. Transport and adsorption of antibiotics by marine sediments in a dynamic environment. J. Soils Sediments 9, 364–373 (2009).

    Google Scholar 

  42. Zang, L. et al. Salinity as a key factor affecting viral activity and life strategies in alpine lakes. Limnol. Oceanogr. 69, 961–975 (2024).

    Google Scholar 

  43. Tan, L. et al. Antibiotic resistance genes attenuated with salt accumulation in saline soil. J. Hazard. Mater. 374, 35–42 (2019).

    Google Scholar 

  44. Liang, H. et al. Metagenomics analysis revealing the occurrence of antibiotic resistome in salt lakes. Sci. Total Environ. 790, 148262 (2021).

    Google Scholar 

  45. Sun, J. et al. Comparison of fecal microbial composition and antibiotic resistance genes from swine, farm workers and the surrounding villagers. Sci. Rep. 7, 4965 (2017).

    Google Scholar 

  46. Tang, M. et al. Exploring diversity patterns and driving mechanisms of the antibiotic resistome and microbiome in saline groundwater. J. Hazard. Mater. 446, 130734 (2023).

    Google Scholar 

  47. Jeong, H., Arif, B., Caetano-Anollés, G., Kim, K. M. & Nasir, A. Horizontal gene transfer in human-associated microorganisms inferred by phylogenetic reconstruction and reconciliation. Sci. Rep. 9, 5953 (2019).

    Google Scholar 

  48. Willems, R. J., Hanage, W. P., Bessen, D. E. & Feil, E. J. Population biology of Gram-positive pathogens: high-risk clones for dissemination of antibiotic resistance. FEMS Microbiol. Rev. 35, 872–900 (2011).

    Google Scholar 

  49. Cai, M. et al. Tracking intra-species and inter-genus transmission of KPC through global plasmids mining. Cell Rep. 43, 114351 (2024).

    Google Scholar 

  50. Wang, C. et al. Distribution of antibiotic resistance genes on chromosomes, plasmids and phages in aerobic biofilm microbiota under antibiotic pressure. J. Environ. Sci. 156, 647–659 (2024).

    Google Scholar 

  51. Fresia, P. et al. Urban metagenomics uncover antibiotic resistance reservoirs in coastal beach and sewage waters. Microbiome 7, 1–9 (2019).

    Google Scholar 

  52. Tao, S., Chen, H., Li, N., Wang, T. & Liang, W. The spread of antibiotic resistance genes in vivo model. Can. J. Infect. Dis. Med. 2022, 3348695 (2022).

    Google Scholar 

  53. Curtsinger, H. D., Martínez-Absalón, S., Liu, Y. & Lopatkin, A. J. The metabolic burden associated with plasmid acquisition: An assessment of the unrecognized benefits to host cells. BioEssays 47, 2400164 (2024).

    Google Scholar 

  54. Liu, Y. et al. Transposon insertion sequencing reveals T4SS as the major genetic trait for conjugation transfer of multi-drug resistance pEIB202 from Edwardsiella. BMC Microbiol. 17, 1–15 (2017).

    Google Scholar 

  55. Bonnin, R. A., Nordmann, P., Carattoli, A. & Poirel, L. Comparative genomics of IncL/M-type plasmids: evolution by acquisition of resistance genes and insertion sequences. Antimicrob. Agents Chemother. 57, 674–676 (2013).

    Google Scholar 

  56. De, R. Mobile genetic elements of Vibrio cholerae and the evolution of its antimicrobial resistance. Front. Trop. Dis. 2, 691604 (2021).

    Google Scholar 

  57. Chong, Y. et al. Prevalence of blaZ gene types and the cefazolin inoculum effect among methicillin-susceptible Staphylococcus aureus blood isolates and their association with multilocus sequence types and clinical outcome. Eur. J. Clin. Microbiol. Infect. Dis. 34, 349–355 (2015).

    Google Scholar 

  58. Liu, S., Wang, P., Wang, C., Wang, X. & Chen, J. Anthropogenic disturbances on antibiotic resistome along the Yarlung Tsangpo River on the Tibetan Plateau: ecological dissemination mechanisms of antibiotic resistance genes to bacterial pathogens. Water Res. 202, 117447 (2021).

    Google Scholar 

  59. Miller, W. R. & Arias, C. A. ESKAPE pathogens: antimicrobial resistance, epidemiology, clinical impact and therapeutics. Nat. Rev. Microbiol. 22, 598–616 (2024).

    Google Scholar 

  60. Robinson, L. A., Collins, A. C., Murphy, R. A., Davies, J. C. & Allsopp, L. P. Diversity and prevalence of type VI secretion system effectors in clinical Pseudomonas aeruginosa isolates. Front. Microbiol. 13, 1042505 (2023).

    Google Scholar 

  61. Drake, D. & Montie, T. C. Flagella, motility and invasive virulence of Pseudomonas aeruginosa. Microbiology 134, 43–52 (1988).

    Google Scholar 

  62. Turner, K. H., Everett, J., Trivedi, U., Rumbaugh, K. P. & Whiteley, M. Requirements for Pseudomonas aeruginosa acute burn and chronic surgical wound infection. PLoS Genet. 10, e1004518 (2014).

    Google Scholar 

  63. Newman, J. W., Floyd, R. V. & Fothergill, J. L. The contribution of Pseudomonas aeruginosa virulence factors and host factors in the establishment of urinary tract infections. FEMS Microbiol. Lett. 364, fnx124 (2017).

    Google Scholar 

  64. Bravo, Z., Chapartegui-Gonzalez, I., Lazaro-Diez, M. & Ramos-Vivas, J. Acinetobacter pittii biofilm formation on inanimate surfaces after long-term desiccation. J. Hosp. Infect. 98, 74–82 (2018).

    Google Scholar 

  65. Singh, R. P., Sinha, A., Deb, S. & Kumari, K. First report on in-depth genome and comparative genome analysis of a metal-resistant bacterium Acinetobacter pittii S-30, isolated from environmental sample. Front. Microbiol. 15, 1351161 (2024).

    Google Scholar 

  66. Medina, E. & Pieper, D. H. Tackling threats and future problems of multidrug-resistant bacteria. How to overcome the antibiotic crisis: facts, challenges, technologies and future perspectives 398, https://doi.org/10.1007/82_2016_492 (2016).

  67. Rakitin, A. L. et al. Genome analysis of Acinetobacter lwoffii strains isolated from permafrost soils aged from 15 thousand to 1.8 million years revealed their close relationships with present-day environmental and clinical isolates. Biology 10, 871 (2021).

    Google Scholar 

  68. Bourdonnais, E., Colcanap, D., Le Bris, C. & Brauge, T. Occurrence of indicator genes of antimicrobial resistance contamination in the English Channel and North Sea sectors and interactions with environmental variables. Front. Microbiol. 13, 883081 (2022).

    Google Scholar 

  69. Gunjyal, N., Singh, G. & Ojha, C. S. P. Elevated levels of anthropogenic antibiotic resistance gene marker, sul1, linked with extreme fecal contamination and poor water quality in wastewater-receiving ponds. J. Environ. Qual. 52, 652–664 (2023).

    Google Scholar 

  70. Adelowo, O. O. et al. High abundances of class 1 integrase and sulfonamide resistance genes, and characterisation of class 1 integron gene cassettes in four urban wetlands in Nigeria. PLoS ONE 13, e0208269 (2018).

    Google Scholar 

  71. Che, Y. et al. High-resolution genomic surveillance elucidates a multilayered hierarchical transfer of resistance between WWTP-and human/animal-associated bacteria. Microbiome 10, 16 (2022).

    Google Scholar 

  72. Wang, Q. et al. Rainfall facilitates the transmission and proliferation of antibiotic resistance genes from ambient air to soil. Sci. Total Environ. 799, 149260 (2021).

    Google Scholar 

  73. Rizzo, C. et al. Cultivable bacterial communities in brines from perennially ice-covered and pristine Antarctic lakes: Ecological and biotechnological implications. Microorganisms 8, 819 (2020).

    Google Scholar 

  74. Morozova, O. V. et al. Antibiotic resistance and cold-adaptive enzymes of antarctic culturable bacteria from King George Island. Polar Sci. 31, 100756 (2022).

    Google Scholar 

  75. Liu, Y. et al. Culturable bacteria isolated from seven high-altitude ice cores on the Tibetan Plateau. J. Glaciol. 65, 29–38 (2019).

    Google Scholar 

  76. Nawaz, S. et al. Prevalence and abundance of antibiotic-resistant genes in culturable bacteria inhabiting a non-polar passu glacier, karakorum mountains range, Pakistan. World J. Microb. Biot. 39, 94 (2023).

    Google Scholar 

  77. Shen, L. et al. Variation of culturable bacteria along depth in the East Rongbuk ice core. Mt. Everest. Geosci. Front. 3, 327–334 (2012).

    Google Scholar 

  78. Miteva, V. I. & Brenchley, J. E. Detection and isolation of ultrasmall microorganisms from a 120,000-year-old Greenland glacier ice core. Appl. Environ. Microbiol. 71, 7806–7818 (2005).

    Google Scholar 

  79. Liu, Y. et al. A genome and gene catalog of glacier microbiomes. Nat. Biotechnol. 40, 1341–1348 (2022).

    Google Scholar 

  80. Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Google Scholar 

  81. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    Google Scholar 

  82. Alcock, B. P. et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 48, D517–D525 (2020).

    Google Scholar 

  83. Rooney, A. M. et al. Performance characteristics of next-generation sequencing for the detection of antimicrobial resistance determinants in Escherichia coli genomes and metagenomes. MSystems 7, e00022–e00022 (2022).

    Google Scholar 

  84. Cooper, A. L. et al. Systematic evaluation of whole genome sequence-based predictions of Salmonella serotype and antimicrobial resistance. Front. Microbiol. 11, 549 (2020).

    Google Scholar 

  85. Naas, T. et al. Beta-lactamase database (BLDB)–structure and function. J. Enzyme Inhib. Med. Chem. 32, 917–919 (2017).

    Google Scholar 

  86. Liu, B., Zheng, D., Zhou, S., Chen, L. & Yang, J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res. 50, D912–D917 (2022).

    Google Scholar 

  87. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinf. 10, 1–9 (2009).

    Google Scholar 

  88. Krawczyk, P. S., Lipinski, L. & Dziembowski, A. PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res. 46, e35–e35 (2018).

    Google Scholar 

  89. Carattoli, A. & Hasman, H. PlasmidFinder and In Silico pMLST: Identification and Typing of Plasmid Replicons in Whole-Genome Sequencing (WGS). in Horizontal gene transfer: methods in molecular biology. 2075, https://doi.org/10.1007/978-1-4939-9877-7_20 (2020).

  90. Molano, L.-A. G., Hirsch, P., Hannig, M., Müller, R. & Keller, A. The PLSDB 2025 update: enhanced annotations and improved functionality for comprehensive plasmid research. Nucleic Acids Res. 53, D189–D196 (2025).

    Google Scholar 

  91. Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A. & Sun, F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome 5, 69 (2017).

    Google Scholar 

  92. Camargo, A. P. et al. Identification of mobile genetic elements with geNomad. Nat. Biotechnol. 42, 1303–1312 (2024).

    Google Scholar 

  93. Siguier, P., Pérochon, J., Lestrade, L., Mahillon, J. & Chandler, M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res. 34, D32–D36 (2006).

    Google Scholar 

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

    Google Scholar 

  95. Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).

    Google Scholar 

  96. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Google Scholar 

  97. Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). in Wiley statsref: statistics reference online, https://doi.org/10.1002/9781118445112.stat07841 (2014).

  98. Stine, R. An introduction to bootstrap methods: examples and ideas. Sociol. Methods Res. 18, 243–291 (1989).

    Google Scholar 

  99. Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).

    Google Scholar 

  100. Chen, C. et al. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol. Plant 13, 1194–1202 (2020).

    Google Scholar 

  101. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).

    Google Scholar 

  102. Ma, Y. et al. The updated genome warehouse: enhancing data value, security, and usability to address data expansion. Genom. Proteom. Bioinform. 23, qzaf010 (2025).

    Google Scholar 

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Acknowledgements

This study was supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 42421001) and the National Natural Science Foundation of China (Grant Nos. 92451301 and 42101128). Sampling permissions for this study were obtained through the Second Tibetan Plateau Scientific Expedition and Research Program, which facilitated access to the sampling sites. We thank Namita Paudel Adhikari for language editing and constructive suggestions on the manuscript.

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  1. These authors contributed equally: Guannan Mao, Qingqing Ma.

Authors and Affiliations

  1. Center for the Pan-third Pole Environment, Lanzhou University, Lanzhou, China

    Guannan Mao, Qingqing Ma, Zhihao Zhang & Yongqin Liu

  2. Chayu Integrated Observation and Research Station of The Xizang Autonomous Region, Chayu, China

    Guannan Mao & Yongqin Liu

  3. School of Life Sciences, Lanzhou University, Lanzhou, China

    Qingqing Ma & Yongqin Liu

  4. State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Yongqin Liu

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Y. L. and G. M. designed the study. G. M. and Q. M. wrote the main manuscript text. G. M., Q. M., and Z. Z. performed the analyses. All authors read and approved the final manuscript.

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Mao, G., Ma, Q., Zhang, Z. et al. Environmental selection and vertical inheritance shape antibiotic resistance in cryospheric bacteria on the Tibetan Plateau.
Commun Earth Environ (2026). https://doi.org/10.1038/s43247-026-03490-3

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