Antibiotic resistome in the glacier forelands of polar regions

Abstract

Antibiotic resistance genes (ARGs) pose a significant threat, exacerbated by climate change impacts on polar regions, particularly melting glaciers and permafrost. While ancient antibiotic resistance exists in the environments, the release and dissemination of ARGs remain poorly understood. This study investigated ARG composition and distribution in 43 metagenomes from Arctic and Antarctic glacier forelands. We identified 154 ARGs, predominantly bacitracin resistance, followed by rifamycin, fosfomycin, vancomycin, tetracycline, and beta-lactam resistance genes. Significant correlations were observed between ARGs and mobile genetic elements (MGEs), with 20 ARGs associated with tnpA MGEs. Actinomycetota and Pseudomonadota were the primary ARG-carrying phyla. Metagenome-assembled genomes revealed Mycobacterium sp., Pseudomonas sp., and Tatlockia sp. as major ARG-harboring pathogens in the glacier forelands. Evolutionarily adapted, distinct ancient ARGs were abundant in the polar environments, varying between different geographic regions. The environmental parameters such as pH and total organic carbon significantly influenced the ARG distribution in the Arctic and Antarctic glacier forelands. This study provides crucial baseline data on antimicrobial resistance, highlighting potential risks associated with rapid environmental change in the regions.

Importance: Antibiotic resistance poses a significant global health threat, exacerbated by the release of antibiotic resistance genes from melting glaciers and permafrost due to climate change. This study provides crucial baseline data on the composition and distribution of antibiotic resistance genes in these vulnerable polar environments, which is essential for understanding and mitigating the risks associated with their release. The findings have far-reaching implications for global health security and emphasize the need for further research to address this emerging threat.

Keywords: ARGs; MGEs; Mycobacterium; antimicrobial resistance; glacier forelands; polar regions.

Fig 1

Distribution and abundance of ARGs. (a) The percentage of the number of unique ARGs within each ARG type found in the GFs. (b) The percentage of abundance of unique ARGs within each ARG type. Source data are provided in Table S3.

Fig 2

Heatmap showing the abundance of ARGs in the Antarctic (Mackay Glacier region, Antarctica) and Arctic GFs (Storglaciären Glacier, Sweden; Russell Glacier, Greenland; Midtre Lovérnbreen Glacier, Svalbard). The row represents the ARGs grouped into ARG class and resistance mechanisms. The ARG class normalized as abundance based on the z-score value.

Fig 3

Comparison of antimicrobial resistome between the Arctic and Antarctic GFs. (a) Comparison of the abundance of the total ARGs between Arctic (n = 27) and Antarctic (n = 16) (**P < 0.05). (b) Comparison of the evenness index (Shannon) of ARGs between Arctic (n = 27) and Antarctica (n = 16) GFs (**P < 0.05). (c) Comparison of the richness (number) index of ARGs between the Arctic (n = 27) and Antarctic (n = 16) (**P < 0.05) GFs. (d) Bray-Curtis distance-based NMDS analysis of the ARG in the Arctic (n = 27) and Antarctic (n = 16) (**P < 0.05) GFs. The source data are provided in Table S6.

Fig 4

Details of mobile genetic elements (MGEs) in Arctic and Antarctic GFs. (a) Percentage relative abundance of MGEs in the GFs in the Arctic and Antarctic. (b) The abundance of the total MGEs between Arctic (n = 27) and Antarctic (n = 16). (c) Shannon index of MGEs between Arctic (n = 27) and Antarctica glaciers (n = 16) (**P < 0.05). (d) Richness index of MGEs between the Arctic (n = 27) and Antarctic (n = 16) glacier foreland (**P < 0.05). (e) Bray-Curtis distance-based NMDS analysis of the ARGs in the Arctic (n = 27) and Antarctic (n = 16) GFs.

Fig 5

Relationships between ARGs and MGEs. (a) Correlation between the richness of MGEs and ARGs. (b) Correlation between the richness of MGEs and Shannon index of ARGs. (c) Network of the co-abundance relationship between ARGs and MGEs. The connections represent significant correlations (correlation coefficients, Spearman ≥ 0.5 and P-values < 0.05). Nodes with larger text sizes indicate genes with more connections to other genes. The colors of the nodes reflect the different types of ARGs or MGEs connection, indicating the modularity. Notably, the tnpA13 family, which includes 20 ARGs associated with five aminoglycoside, four each of beta-lactam and multidrug, and two quinolone resistance. Various modules and respective colors were provided in Table S14.

Fig 6

Relationship of ARGs with the microbiome of GFs. (a) Correlation between the richness of microbiomes in the glacier foreland and the richness of ARGs. (b) Correlation between the richness of microbiome in the GFs and the Shannon index of ARGs. (c) Dominant ARGs (five) containing host bacteria in the GFs at different taxonomic levels.

Fig 7

MAG contains ARGs, VFGs, and MGEs in the GFs. (a) Association between the ARGs and MGEs. The close linkage between tnpA in the MAG and ARGs such as OXA-29, vano, and rpoB2 in the GFs. The x-axis represents the locations of the genes in the MAG. Green arrows indicate the ARGs, and orange arrows indicate the MGEs. The y-axis indicates the MAG details. (b) Close linkage of ARGs and VFGs in the GFs. The violet color indicates the VFG genes in the GFs. (c) Association between MGEs and VFGs in the GFs.

Fig 8

Redundancy analysis (RDA) explaining variation in ARG composition as a function of environmental variables such as pH and TOC variables and elevations of the sampling site. The ellipse highlights the relative position of the sampling location in the Arctic (blue) and Antarctic (red) regions. The pH and TOC significantly varied in the locations and influenced the ARG abundance (P < 0.05).