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

Abstract

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.

One Day in Denmark: Comparison of Phenotypic and Genotypic Antimicrobial Susceptibility Testing in Bacterial Isolates From Clinical Settings

“…This approach will elucidate which species-antimicrobial combinations should be further investigated regarding currently unknown genetic determinants of AMR. For these combinations, analyzing large datasets of MIC distributions and comparing those with the genome sequences can reveal new ARGs or PMs of interest, and their contribution to phenotypic resistance profiles can be then confirmed in vitro through, for example, transformation experiments (Hadjirin et al, 2021). This procedure can also be used to confirm if specific genetic determinants that apparently do not confer phenotypic resistance can be removed from bioinformatics databases, despite previous literature suggesting that association.…”

Effect of Dextranase and Dextranase-and-Nisin-Containing Mouthwashes on Oral Microbial Community of Healthy Adults—A Pilot Study

“…Current methods for identifying and quantifying bacteria in dental biofilms, including genome sequencing, have shown a considerably more complicated ecology than previously thought [61,62]. The genera Catonella, Centipeda, Fretibacterium, Rhizobium, Ochrobactrum, Mogibacterium, Actinomyces, Streptococcus, Rothia, Selenomonas, and Veillonella were detected as major dental caries pathogens [63,64].…”

Antimicrobial Susceptibility Testing of Porcine Bacterial Pathogens: Investigating the Prospect of Testing a Representative Drug for Each Antimicrobial Family

“…S. suis has three key pbp genes (pbp1A, pbp2B and pbp2X). Single point mutations in pbp2X alone have small effects on the MIC value, whereas additional mutations in the rest of pbp, taking place in a set order, may explain the high MIC value observed for some antimicrobials belonging to this family [42]. On the other hand, studies have shown that cefotaxime, a third-generation cephalosporin like ceftiofur, selectively inactivates pbp2X but not pbp2B [43].…”