Assessment of global health risk of antibiotic resistance genes
By Zhenyan Zhang, Qi Zhang, Tingzhang Wang, Nuohan Xu, Tao Lu, Wenjie Hong, J. Penuelas, M. Gillings, Meixia Wang, Wenwen Gao and 1 more. Nature Communications, 2022
811 citations recorded by the source corpus.
Antibiotic resistance genes (ARGs) have accelerated microbial threats to human health in the last decade. Many genes can confer resistance, but evaluating the relative health risks of ARGs is complex. Factors such as the abundance, propensity for lateral transmission and ability of ARGs to be expressed in pathogens are all important. Here, an analysis at the metagenomic level from various habitats (6 types of habitats, 4572 samples) detects 2561 ARGs that collectively conferred resistance to 24 classes of antibiotics. We quantitatively evaluate the health risk to humans, defined as the risk that ARGs will confound the clinical treatment for pathogens, of these 2561 ARGs by integrating human accessibility, mobility, pathogenicity and clinical availability. Our results demonstrate that 23.78% of the ARGs pose a health risk, especially those which confer multidrug resistance. We also calculate the antibiotic resistance risks of all samples in four main habitats, and with machine learning, successfully map the antibiotic resistance threats in global marine habitats with over 75% accuracy. Our novel method for quantitatively surveilling the health risk of ARGs will help to manage one of the most important threats to human and animal health. Antibiotic resistance genes (ARGs) have accelerated microbial threats to human health. Here, Zhang et al. analyze 4572 metagenomic samples to illustrate the global patterns of ARG distribution in diverse habitats. They quantitatively evaluate the health risk to humans of 2561 ARGs by integrating human accessibility, mobility, pathogenicity and clinical availability. With the machine learning, they map the antibiotic resistance threats in global marine habitats.
Subfields: Molecular Biology, Molecular Medicine, Pollution
View Paper: https://www.semanticscholar.org/paper/da65ea68f16d44bcc5cba710862bad99d1d4420b
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Citations in the corpus
- CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database (2022, background): “However, the complexity and global burden of the AMR crisis (6) continues to expand with the continuous discovery of novel antibiotic resistance genes (ARGs), including those using novel mechanisms such as MCR-mediated colistin resistance via change in the charge of the cell membrane (7) or ejection”
- Emerging contaminants: A One Health perspective (2024, neutral): “256 Anthropogenic activities play a key role in selecting genes from environmental and cellular sources, facilitating their subsequent co-option to confer antibiotic resistance.”