Identification of 76 novel B1 metallo-beta-lactamases through large-scale screening of genomic and metagenomic data
Journal article, 2017
Background: Metallo-beta-lactamases are bacterial enzymes that provide resistance to carbapenems, the most potent class of antibiotics. These enzymes are commonly encoded on mobile genetic elements, which, together with their broad substrate spectrum and lack of clinically useful inhibitors, make them a particularly problematic class of antibiotic resistance determinants. We hypothesized that there is a large and unexplored reservoir of unknown metallo-beta-lactamases, some of which may spread to pathogens, thereby threatening public health. The aim of this study was to identify novel metallo-beta-lactamases of class B1, the most clinically important subclass of these enzymes. Results: Based on a new computational method using an optimized hidden Markov model, we analyzed over 10,000 bacterial genomes and plasmids together with more than 5 terabases of metagenomic data to identify novel metallo-beta-lactamase genes. In total, 76 novel genes were predicted, forming 59 previously undescribed metallo-beta-lactamase gene families. The ability to hydrolyze imipenem in an Escherichia coli host was experimentally confirmed for 18 of the 21 tested genes. Two of the novel B1 metallo-beta-lactamase genes contained atypical zinc-binding motifs in their active sites, which were previously undescribed for metallo-beta-lactamases. Phylogenetic analysis showed that B1 metallo-beta-lactamases could be divided into five major groups based on their evolutionary origin. Our results also show that, except for one, all of the previously characterized mobile B1 beta-lactamases are likely to have originated from chromosomal genes present in Shewanella spp. and other Proteobacterial species. Conclusions: This study more than doubles the number of known B1 metallo-beta-lactamases. The findings have further elucidated the diversity and evolutionary history of this important class of antibiotic resistance genes and prepare us for some of the challenges that may be faced in clinics in the future.
Hidden Markov model