# Bayesian network models to assess antimicrobial resistance patterns of Streptococcus suis isolated from swine production systems in the United States between 2014–2021

## TL;DR

This study uses Bayesian networks to analyze antibiotic resistance patterns in Streptococcus suis from U.S. swine farms, revealing complex multidrug resistance relationships.

## Contribution

The study introduces Bayesian network analysis to uncover statistical dependencies among antimicrobial resistance patterns in S. suis isolates.

## Key findings

- 95.6% of isolates were resistant to more than one antimicrobial drug.
- Penicillin, tiamulin, and tilmicosin showed the most central resistance associations.
- Conditional dependencies suggest mechanisms like cross-resistance and co-resistance.

## 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.

Streptococcus suis is a bacterial pathogen that affects pigs and can also cause serious infections in humans. Antibiotics are commonly used to prevent and treat S. suis infections in pigs, but the growing prevalence of antimicrobial resistance and multidrug resistance has challenged the current control efforts. This presents a significant challenge to both animal and public health. In our study, we aimed to better understand how S. suis develops resistance to multiple antibiotics. We analyzed 259 S. suis isolates collected from pig farms across the United States, assessing their resistance to 13 commonly used antibiotics. Nearly all isolates were resistant to more than one antibiotic, and many showed resistance across several antibiotic classes. To explore how resistance to one drug might be linked to resistance to others, we used Bayesian network analysis. This approach revealed several statistically significant resistance patterns. Penicillin, a widely used and effective antibiotic, tiamulin, and tilmicosin were linked to resistance to more antibiotics than any other drugs. Our findings highlight the complex nature of antibiotic resistance in S. suis. Recognizing these resistance patterns can support more informed treatment decisions, reduce the misuse of antibiotics, and help slow the spread of resistance in animals and people.