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Table 1_Shotgun metagenomics reveals the prevalence and mobility of antibiotic resistance genes in the West Bay of the human-impacted Laguna Lake.xlsx

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NIAID Data Ecosystem2026-05-10 收录
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https://figshare.com/articles/dataset/Table_1_Shotgun_metagenomics_reveals_the_prevalence_and_mobility_of_antibiotic_resistance_genes_in_the_West_Bay_of_the_human-impacted_Laguna_Lake_xlsx/31122211
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Laguna Lake, the largest freshwater lake in the Philippines, has been reported to harbor antibiotic-resistant bacteria, posing health risks to the millions who depend on it. However, limited knowledge of antibiotic resistance genes (ARGs) in the lake highlights the need for a comprehensive assessment of its resistome. In line with this, we characterized ARGs in the West Bay of Laguna Lake using shotgun metagenomic sequencing based on six metagenomes collected from three stations across two sampling months at a single depth. ARGs were quantified from short reads, and assembled contigs containing these genes—antibiotic-resistant contigs (ARCs)—were analyzed to assess mobility through associations with plasmids and mobile genetic elements (MGEs). β-lactam resistance genes (0.023–0.048 copies per cell) were the most prevalent, corroborating previous reports. Meanwhile, the detection of bacitracin (0.013–0.028 cpc) and polymyxin (0.009–0.011 cpc) resistance genes raises new concerns, as resistance to these antibiotic classes has not been previously reported in the lake. Furthermore, 44.8 and 30.4% of ARCs were associated with plasmids and MGEs, respectively. ARCs carrying genes for resistance to β-lactams, chloramphenicol, and tetracyclines were frequently identified as mobile, indicating a high potential for horizontal gene transfer and suggesting possible antibiotic contamination in the lake. Overall, this study provides the first metagenomic insight into the resistome of Laguna Lake using short-read sequencing and highlights its role as an environmental reservoir of mobile ARGs. The findings underscore the need for expanded ARG surveillance to improve antimicrobial resistance risk prediction.
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2026-01-22
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