Assessment of global health risk of antibiotic resistance genes

• Published in: Nature communications Vol. 13; no. 1; pp. 1553 - 11
• Main Authors: Zhang, Zhenyan, Zhang, Qi, Wang, Tingzhang, Xu, Nuohan, Lu, Tao, Hong, Wenjie, Penuelas, Josep, Gillings, Michael, Wang, Meixia, Gao, Wenwen, Qian, Haifeng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.03.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/158; 631/326/107; 631/326/171; 631/326/2565/2142; 692/499; Accessibility; Animal health; Animals; Anti-Bacterial Agents - pharmacology; Antibiotic resistance; Antibiotics; Aquatic habitats; Availability; Drug resistance; Drug Resistance, Microbial - genetics; Evaluation; Genes; Genes, Bacterial; Global Health; Habitats; Health risk assessment; Health risks; Humanities and Social Sciences; Learning algorithms; Machine learning; Metagenome; Metagenomics; Microorganisms; Mobility; multidisciplinary; Multidrug resistance; Pathogenicity; Pathogens; Public health; Risk assessment; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-29283-8

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.