Large-scale genomic analysis of antimicrobial resistance in the zoonotic pathogen Streptococcus suis

Background Antimicrobial resistance (AMR) is among the gravest threats to human health and food security worldwide. The use of antimicrobials in livestock production can lead to emergence of AMR, which can have direct effects on humans through spread of zoonotic disease. Pigs pose a particular risk as they are a source of zoonotic diseases and receive more antimicrobials than most other livestock. Here we use a large-scale genomic approach to characterise AMR in Streptococcus suis, a commensal found in most pigs, but which can also cause serious disease in both pigs and humans. Results We obtained replicated measures of Minimum Inhibitory Concentration (MIC) for 16 antibiotics, across a panel of 678 isolates, from the major pig-producing regions of the world. For several drugs, there was no natural separation into ‘resistant’ and ‘susceptible’, highlighting the need to treat MIC as a quantitative trait. We found differences in MICs between countries, consistent with their patterns of antimicrobial usage. AMR levels were high even for drugs not used to treat S. suis, with many multidrug-resistant isolates. Similar levels of resistance were found in pigs and humans from regions associated with zoonotic transmission. We next used whole genome sequences for each isolate to identify 43 candidate resistance determinants, 22 of which were novel in S. suis. The presence of these determinants explained most of the variation in MIC. But there were also interesting complications, including epistatic interactions, where known resistance alleles had no effect in some genetic backgrounds. Beta-lactam resistance involved many core genome variants of small effect, appearing in a characteristic order. Conclusions We present a large dataset allowing the analysis of the multiple contributing factors to AMR in S. suis. The high levels of AMR in S. suis that we observe are reflected by antibiotic usage patterns but our results confirm the potential for genomic data to aid in the fight against AMR.