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Bayesian network models to assess antimicrobial resistance patterns of Streptococcus suis isolated from swine
Ruwini Rupasinghe1, Brittany L Morgan Bustamante1,2, Rebecca C Robbins3
1Center for Animal Disease Modeling and Surveillance, Department of Medicine and Epidemiology, Weill School of Veterinary Medicine, University of California, Davis, Davis, California, United States of America.
Abstract:
Multidrug resistance (MDR) is frequently evident in Streptococcus suis, generating distinct antimicrobial resistance (AMR) profiles, which limits the effective antimicrobial drug (AMD) options against S. suis in pigs and humans. Despite its significance, there is a lack of studies and pertinent methodologies that uncover complex interactions among AMDs and associated resistance patterns. This study aimed to identify associations between phenotypic resistance patterns of S. suis isolates from swine production systems in the United States against common AMDs using Bayesian network analysis (BNA). Data from 259 unique S. suis isolates collected from 91 farms were included. Phenotypic susceptibility interpretations (resistance vs susceptible) of minimum inhibitory concentrations (MICs) were evaluated for 13 commonly used AMDs: ceftiofur (CEF), penicillin (PEN), enrofloxacin (ENR), gentamicin (GEN), neomycin (NEO), spectinomycin (SPC), sulfadimethoxine (SUL), tiamulin (TIA), tilmicosin (TIL), clindamycin (CLN), chlortetracycline (CHL), oxytetracycline (OXY), and tetracycline (TET). BNA was conducted using the R package bnlearn to identify joint resistance patterns and estimate conditional dependencies among resistance outcomes. Results revealed a high prevalence of MDR: 248 isolates (95.6%) were resistant to more than one AMD, and 209 isolates (80.7%) were resistant to at least one AMD in three or more classes. The Bayesian network comprised of 11 edges connecting 13 AMD nodes, highlighting statistical dependencies between AMDs resistances. PEN, TIA, and TIL were the most central nodes, with PEN connected to SUL, TIA, GEN, and CEF; TIA to PEN, SPC, TIL, and CLN; and TIL to SUL, TIA, CLN, and OXY. Other associations included CEF-SPC, TET-CLN, CEF-ENR, and OXY-CHL. These relationships implicate systematic dependencies between AMDs and may have resulted from mechanisms like cross-resistance and co-resistance. While these relationships are statistically derived and hypothesis-generating, they underscore the importance of understanding AMR patterns in guiding more effective AMD use. This approach can help prevent overuse, reduce treatment failures, and support AMR mitigation efforts for improved animal and public health outcomes.
Insights
Multidrug resistance in Streptococcus suis is widespread, with Bayesian network analysis revealing key associations between antimicrobial drugs. Understanding these links is crucial for effective antimicrobial use and mitigating resistance in animal and public health.
Area of Science:
- Veterinary Microbiology
- Antimicrobial Resistance
- Computational Biology
Background:
- Multidrug resistance (MDR) in Streptococcus suis poses a significant threat to swine and human health due to limited effective antimicrobial drug (AMD) options.
- Complex interactions among AMDs and their associated resistance patterns in S. suis are not well-understood, hindering effective treatment strategies.
- There is a need for advanced methodologies to elucidate these intricate relationships and inform antimicrobial stewardship.
Purpose of the Study:
- To identify associations between phenotypic resistance patterns of Streptococcus suis isolates against common AMDs using Bayesian network analysis (BNA).
- To uncover complex interactions and dependencies among AMD resistance in S. suis from US swine production systems.
- To provide data-driven insights for optimizing AMD usage and mitigating antimicrobial resistance (AMR).
Main Methods:
- Phenotypic susceptibility testing (MICs) was performed on 259 S. suis isolates against 13 common AMDs.
- Bayesian network analysis (BNA) was employed using the R package bnlearn to model conditional dependencies between AMD resistance outcomes.
- Analysis focused on identifying joint resistance patterns and central AMD nodes within the network.
Main Results:
- A high prevalence of MDR was observed, with 95.6% of isolates resistant to more than one AMD and 80.7% resistant to AMDs in three or more classes.
- The BNA identified 11 significant statistical dependencies (edges) among the 13 AMD resistance nodes.
- Penicillin (PEN), tiamulin (TIA), and tilmicosin (TIL) emerged as the most central AMDs, showing significant associations with resistance to other drugs, suggesting potential cross-resistance or co-resistance mechanisms.
Conclusions:
- Statistically derived associations between AMD resistances in S. suis highlight systematic dependencies that can inform antimicrobial stewardship.
- Understanding these resistance patterns is essential for preventing AMD overuse, reducing treatment failures, and improving animal and public health outcomes.
- The BNA approach offers a valuable tool for hypothesis generation and guiding more effective AMD use in veterinary medicine.
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