Monitoring longitudinal antimicrobial resistance trends of Staphylococcus aureus strains worldwide over the past 100 years to decipher its evolution and transmission
Staphylococcus aureus inhabits diverse habitats including food waste and wastewater treatment plants. Cases of S. aureus-induced infection are commonly reported worldwide. The emergence of antimicrobial resistance (AMR) of S. aureus is a growing public health threat worldwide. Here, we longitudinall...
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| Published in | Journal of hazardous materials Vol. 465; p. 133136 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
05.03.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0304-3894 1873-3336 1873-3336 |
| DOI | 10.1016/j.jhazmat.2023.133136 |
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| Abstract | Staphylococcus aureus inhabits diverse habitats including food waste and wastewater treatment plants. Cases of S. aureus-induced infection are commonly reported worldwide. The emergence of antimicrobial resistance (AMR) of S. aureus is a growing public health threat worldwide. Here, we longitudinally monitored global trends in antibiotic resistance genes (ARGs) of 586 S. aureus strains, isolated between 1884 and 2022. The ARGs in S. aureus exhibited a significant increase over time (P < 0.0001). Mobile genetic elements play a crucial role in the transfer of ARGs in S. aureus strains. The structural equation model results revealed a significant correlation between the human development index and rising antibiotic consumption, which subsequently leads to an indirect escalation of AMR in S. aureus strains. Lastly, a machine learning algorithm successfully predicted the AMR risk of global terrestrial S. aureus with over 70% accuracy. Overall, these findings provided valuable insights for managing AMR in S. aureus.
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•Distinct increase in AMR among S. aureus during recent decades.•The human development index drives the consumption of antibiotics.•Antibiotic consumption promotes the increase of AMR in S. aureus.•MGEs are important contributors to ARGs in S. aureus.•AMR risk map in terrestrial habitats was constructed with over 70% accuracy. |
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| AbstractList | Staphylococcus aureus inhabits diverse habitats including food waste and wastewater treatment plants. Cases of S. aureus-induced infection are commonly reported worldwide. The emergence of antimicrobial resistance (AMR) of S. aureus is a growing public health threat worldwide. Here, we longitudinally monitored global trends in antibiotic resistance genes (ARGs) of 586 S. aureus strains, isolated between 1884 and 2022. The ARGs in S. aureus exhibited a significant increase over time (P < 0.0001). Mobile genetic elements play a crucial role in the transfer of ARGs in S. aureus strains. The structural equation model results revealed a significant correlation between the human development index and rising antibiotic consumption, which subsequently leads to an indirect escalation of AMR in S. aureus strains. Lastly, a machine learning algorithm successfully predicted the AMR risk of global terrestrial S. aureus with over 70% accuracy. Overall, these findings provided valuable insights for managing AMR in S. aureus.Staphylococcus aureus inhabits diverse habitats including food waste and wastewater treatment plants. Cases of S. aureus-induced infection are commonly reported worldwide. The emergence of antimicrobial resistance (AMR) of S. aureus is a growing public health threat worldwide. Here, we longitudinally monitored global trends in antibiotic resistance genes (ARGs) of 586 S. aureus strains, isolated between 1884 and 2022. The ARGs in S. aureus exhibited a significant increase over time (P < 0.0001). Mobile genetic elements play a crucial role in the transfer of ARGs in S. aureus strains. The structural equation model results revealed a significant correlation between the human development index and rising antibiotic consumption, which subsequently leads to an indirect escalation of AMR in S. aureus strains. Lastly, a machine learning algorithm successfully predicted the AMR risk of global terrestrial S. aureus with over 70% accuracy. Overall, these findings provided valuable insights for managing AMR in S. aureus. Staphylococcus aureus inhabits diverse habitats including food waste and wastewater treatment plants. Cases of S. aureus-induced infection are commonly reported worldwide. The emergence of antimicrobial resistance (AMR) of S. aureus is a growing public health threat worldwide. Here, we longitudinally monitored global trends in antibiotic resistance genes (ARGs) of 586 S. aureus strains, isolated between 1884 and 2022. The ARGs in S. aureus exhibited a significant increase over time (P < 0.0001). Mobile genetic elements play a crucial role in the transfer of ARGs in S. aureus strains. The structural equation model results revealed a significant correlation between the human development index and rising antibiotic consumption, which subsequently leads to an indirect escalation of AMR in S. aureus strains. Lastly, a machine learning algorithm successfully predicted the AMR risk of global terrestrial S. aureus with over 70% accuracy. Overall, these findings provided valuable insights for managing AMR in S. aureus. Staphylococcus aureus inhabits diverse habitats including food waste and wastewater treatment plants. Cases of S. aureus-induced infection are commonly reported worldwide. The emergence of antimicrobial resistance (AMR) of S. aureus is a growing public health threat worldwide. Here, we longitudinally monitored global trends in antibiotic resistance genes (ARGs) of 586 S. aureus strains, isolated between 1884 and 2022. The ARGs in S. aureus exhibited a significant increase over time (P < 0.0001). Mobile genetic elements play a crucial role in the transfer of ARGs in S. aureus strains. The structural equation model results revealed a significant correlation between the human development index and rising antibiotic consumption, which subsequently leads to an indirect escalation of AMR in S. aureus strains. Lastly, a machine learning algorithm successfully predicted the AMR risk of global terrestrial S. aureus with over 70% accuracy. Overall, these findings provided valuable insights for managing AMR in S. aureus. Staphylococcus aureus inhabits diverse habitats including food waste and wastewater treatment plants. Cases of S. aureus-induced infection are commonly reported worldwide. The emergence of antimicrobial resistance (AMR) of S. aureus is a growing public health threat worldwide. Here, we longitudinally monitored global trends in antibiotic resistance genes (ARGs) of 586 S. aureus strains, isolated between 1884 and 2022. The ARGs in S. aureus exhibited a significant increase over time (P < 0.0001). Mobile genetic elements play a crucial role in the transfer of ARGs in S. aureus strains. The structural equation model results revealed a significant correlation between the human development index and rising antibiotic consumption, which subsequently leads to an indirect escalation of AMR in S. aureus strains. Lastly, a machine learning algorithm successfully predicted the AMR risk of global terrestrial S. aureus with over 70% accuracy. Overall, these findings provided valuable insights for managing AMR in S. aureus. [Display omitted] •Distinct increase in AMR among S. aureus during recent decades.•The human development index drives the consumption of antibiotics.•Antibiotic consumption promotes the increase of AMR in S. aureus.•MGEs are important contributors to ARGs in S. aureus.•AMR risk map in terrestrial habitats was constructed with over 70% accuracy. |
| ArticleNumber | 133136 |
| Author | Mu, Xiao-Jing Jiang, Yu-Jian Liu, Xing-Quan Fang, Guan-Yu Wu, Feng-Hua |
| Author_xml | – sequence: 1 givenname: Guan-Yu surname: Fang fullname: Fang, Guan-Yu email: fangguanyu@zafu.edu.cn organization: College of Food and Health, Zhejiang A&F University, Hangzhou 311300, PR China – sequence: 2 givenname: Feng-Hua surname: Wu fullname: Wu, Feng-Hua organization: College of Food and Health, Zhejiang A&F University, Hangzhou 311300, PR China – sequence: 3 givenname: Xiao-Jing surname: Mu fullname: Mu, Xiao-Jing organization: School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, PR China – sequence: 4 givenname: Yu-Jian surname: Jiang fullname: Jiang, Yu-Jian organization: School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, PR China – sequence: 5 givenname: Xing-Quan surname: Liu fullname: Liu, Xing-Quan email: liuxq@zafu.edu.cn organization: College of Food and Health, Zhejiang A&F University, Hangzhou 311300, PR China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38056258$$D View this record in MEDLINE/PubMed |
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| Keywords | Genomic analysis Antimicrobial resistance Staphylococcus aureus Mobile genetic elements Machine learning |
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| SubjectTerms | algorithms Anti-Bacterial Agents - pharmacology antibiotic resistance antibiotics Antimicrobial resistance Drug Resistance, Bacterial evolution Food food waste Genomic analysis human development Humans Machine learning Mobile genetic elements public health Refuse Disposal risk Staphylococcal Infections Staphylococcus aureus structural equation modeling wastewater treatment |
| Title | Monitoring longitudinal antimicrobial resistance trends of Staphylococcus aureus strains worldwide over the past 100 years to decipher its evolution and transmission |
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