Holistic understanding of trimethoprim resistance in Streptococcus pneumoniae using an integrative approach of genome-wide association study, resistance reconstruction and machine learning

Antimicrobial resistance (AMR) is a current growing public health threat worldwide. The next generation sequencing (NGS) revolution has opened unprecedented opportunities to accelerate AMR mechanism discovery and diagnostic. Here we present an integrative approach to investigate trimethoprim (TMP) resistance in the key pathogen Streptococcus pneumoniae. We explored a collection of 662 S. pneumoniae genomes by conducting a genome-wide association study (GWAS), followed by functional validation using resistance reconstruction experiments, combined with machine learning (ML) approaches to predict TMP minimum inhibitory concentration (MIC). Our study showed that multiple additive mutations in the folA and sulA loci are responsible for TMP non-susceptibility in S. pneumoniae and can be used as key features to build ML models for digital MIC prediction, reaching an average accuracy within 1 two-fold dilution factor of 86.3 percent. Our framework setting of in silico analysis and wet-lab validation for diagnostic purposes could be adapted to explore AMR in other combinations of bacteria and antibiotic.