Antimicrobial resistance in Escherichia coli and Staphylococcus aureus at human-animal interfaces on Chongming Island, Shanghai: A One Health perspective

Antimicrobial resistance (AMR) is a significant concern within the One Health framework due to its ability to spread across multiple interfaces. Phenotypic data remains the primary type for AMR surveillance, but exploring association across multiple interfaces poses certain challenges. In this study, AMR phenotypic data of clinical and food animal <i>E. coli</i> and <i>S. aureus</i> from Chongming Island over the past five years were analyzed to determine key characteristics of AMR and explore its association at the human-animal interface. The clinical <i>E. coli</i> isolates showed significant resistance to penicillins (83.92 %), cephems (63.05 %), fluoroquinolones (62.21 %), and tetracyclines (57.77 %), while <i>S. aureus</i> exhibited high resistance to penicillinase-labile penicillins (90.89 %), macrolides (51.51 %), penicillinase-stable penicillins (43.96 %), and lincosamides (43.55 %). Extended-spectrum β-lactamase (ESBL)-producing <i>E. coli</i> isolates accounted for 53.26 % (1398/2526), while methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) prevalence was 43.81 % (435/993). Notably, there has been an increase in the proportion of <i>E. coli</i> isolates resistant to 8 to 12 antimicrobial classes, and in the proportion of <i>S. aureus</i> isolates resistant to 5 to 9 classes. Certain multi-drug resistance (MDR) phenotypes were first identified in food animal isolates and later emerged in clinical settings. Meanwhile, several MDR phenotypes were shared between the two interfaces, with 44 identified in <i>E. coli</i> and 12 in <i>S. aureus</i>. Further co-occurrence analysis in <i>E. coli</i> and <i>S. aureus</i> identified several co-occurrence phenotypic pairs or clusters, potentially mediated by a single plasmid or multiple plasmids within a bacterium, indicating potential associations at the human-animal interface. To summarize, a heightened prevalence of MDR in clinical <i>E. coli</i> and <i>S. aureus</i> has been observed, with some MDR profiles appearing in food animals before emerging in clinical settings. The co-occurrence of phenotypic pairs or clusters underscores the potential for AMR association and transmission between humans and food animals. Within the One Health framework, integrating genomic data into AMR monitoring is a crucial next step.

Authors

Institutions

Publication Details

Journal
One Health
Published
2024-10-05
DOI
https://doi.org/10.1016/j.onehlt.2024.100910
Citations
9
Primary Topic
Salmonella and Campylobacter epidemiology
Type
article
Field-Weighted Citation Impact
3.14

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Antimicrobial resistance in Escherichia coli and Staphylococcus aureus at human-animal interfaces on Chongming Island, Shanghai: A One Health perspective

Yongzhang Zhu, Nan Zhou, Zile Cheng, Chao Lv et al.
9 citations
One Health
Salmonella and Campylobacter epidemiology
3.14
article

Antimicrobial resistance in Escherichia coli and Staphylococcus aureus at human-animal interfaces on Chongming Island, Shanghai: A One Health perspective

Yongzhang Zhu, Nan Zhou, Zile Cheng, Chao Lv, Bingqing Sun, Jun Leng, Min Li, Li Zhang, Yiwen Chen, Jun Shang, Xiaokui Guo, Minjian Qian, HuiPing Ye
article en
9 citations

Abstract

Antimicrobial resistance (AMR) is a significant concern within the One Health framework due to its ability to spread across multiple interfaces. Phenotypic data remains the primary type for AMR surveillance, but exploring association across multiple interfaces poses certain challenges. In this study, AMR phenotypic data of clinical and food animal <i>E. coli</i> and <i>S. aureus</i> from Chongming Island over the past five years were analyzed to determine key characteristics of AMR and explore its association at the human-animal interface. The clinical <i>E. coli</i> isolates showed significant resistance to penicillins (83.92 %), cephems (63.05 %), fluoroquinolones (62.21 %), and tetracyclines (57.77 %), while <i>S. aureus</i> exhibited high resistance to penicillinase-labile penicillins (90.89 %), macrolides (51.51 %), penicillinase-stable penicillins (43.96 %), and lincosamides (43.55 %). Extended-spectrum β-lactamase (ESBL)-producing <i>E. coli</i> isolates accounted for 53.26 % (1398/2526), while methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) prevalence was 43.81 % (435/993). Notably, there has been an increase in the proportion of <i>E. coli</i> isolates resistant to 8 to 12 antimicrobial classes, and in the proportion of <i>S. aureus</i> isolates resistant to 5 to 9 classes. Certain multi-drug resistance (MDR) phenotypes were first identified in food animal isolates and later emerged in clinical settings. Meanwhile, several MDR phenotypes were shared between the two interfaces, with 44 identified in <i>E. coli</i> and 12 in <i>S. aureus</i>. Further co-occurrence analysis in <i>E. coli</i> and <i>S. aureus</i> identified several co-occurrence phenotypic pairs or clusters, potentially mediated by a single plasmid or multiple plasmids within a bacterium, indicating potential associations at the human-animal interface. To summarize, a heightened prevalence of MDR in clinical <i>E. coli</i> and <i>S. aureus</i> has been observed, with some MDR profiles appearing in food animals before emerging in clinical settings. The co-occurrence of phenotypic pairs or clusters underscores the potential for AMR association and transmission between humans and food animals. Within the One Health framework, integrating genomic data into AMR monitoring is a crucial next step.

One HealthVol. 19
National Institute for Parasitic Diseases (CN), Shanghai Jiao Tong University (CN), China Animal Disease Control Center (CN), Feed Control (Norway) (NO), Shanghai University of Medicine and Health Sciences (CN), Shanghai Municipal Center For Disease Control Prevention (CN)
National Natural Science Foundation of China
Life below water
Openalex Percentile: Top 9%
Salmonella and Campylobacter epidemiology
3.14
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.