Large-scale characterization of the macrolide resistome reveals high diversity and several new pathogen-associated genes. Issue 1 (27th January 2022)

• Record Type: Journal Article
• Authors: Lund, David; Kieffer, Nicolas; Parras-Moltó, Marcos; Ebmeyer, Stefan; Berglund, Fanny; Johnning, Anna; Larsson, D. G. Joakim; Kristiansson, Erik
• Is Part Of: Microbial genomics. Volume 8:Issue 1(2022)
• Publication Date: 2022-01-27
• Subjects: antimicrobial resistance -- HMM -- microbiome -- phylogenetics -- horizontal gene transfer; Microbial genomics -- Periodicals; 572.8629
• DOI: 10.1099/mgen.0.000770
• Languages: English
• ISSNs: 2057-5858

Abstract

Abstract : Macrolides are broad-spectrum antibiotics used to treat a range of infections. Resistance to macrolides is often conferred by mobile resistance genes encoding Erm methyltransferases or Mph phosphotransferases. New erm and mph genes keep being discovered in clinical settings but their origins remain unknown, as is the type of macrolide resistance genes that will appear in the future. In this study, we used optimized hidden Markov models to characterize the macrolide resistome. Over 16 terabases of genomic and metagenomic data, representing a large taxonomic diversity (11 030 species) and diverse environments (1944 metagenomic samples), were searched for the presence of erm and mph genes. From this data, we predicted 28 340 macrolide resistance genes encoding 2892 unique protein sequences, which were clustered into 663 gene families (<70 % amino acid identity), of which 619 (94 %) were previously uncharacterized. This included six new resistance gene families, which were located on mobile genetic elements in pathogens. The function of ten predicted new resistance genes were experimentally validated in Escherichia coli using a growth assay. Among the ten tested genes, seven conferred increased resistance to erythromycin, with five genes additionally conferring increased resistance to azithromycin, showing that our models can be used to predict new functional resistance genes. Our analysis also showed that macrolide resistance genes have diverse origins and have transferred horizontally over large phylogenetic distances into human pathogens. This study expands the known macrolide resistome more than ten-fold, provides insights into its evolution, and demonstrates how computational screening can identify new resistance genes before they become a significant clinical problem.