PLoS Genetics | 2021

A novel type of colistin resistance genes selected from random sequence space

 
 
 
 
 
 

Abstract


Antibiotic resistance is a rapidly increasing medical problem that severely limits the success of antibiotic treatments, and the identification of resistance determinants is key for surveillance and control of resistance dissemination. Horizontal transfer is the dominant mechanism for spread of resistance genes between bacteria but little is known about the original emergence of resistance genes. Here, we examined experimentally if random sequences can generate novel antibiotic resistance determinants de novo. By utilizing highly diverse expression libraries encoding random sequences to select for open reading frames that confer resistance to the last-resort antibiotic colistin in Escherichia coli, six de novo colistin resistance conferring peptides (Dcr) were identified. The peptides act via direct interactions with the sensor kinase PmrB (also termed BasS in E. coli), causing an activation of the PmrAB two-component system (TCS), modification of the lipid A domain of lipopolysaccharide and subsequent colistin resistance. This kinase-activation was extended to other TCS by generation of chimeric sensor kinases. Our results demonstrate that peptides with novel activities mediated via specific peptide-protein interactions in the transmembrane domain of a sensory transducer can be selected de novo, suggesting that the origination of such peptides from non-coding regions is conceivable. In addition, we identified a novel class of resistance determinants for a key antibiotic that is used as a last resort treatment for several significant pathogens. The high-level resistance provided at low expression levels, absence of significant growth defects and the functionality of Dcr peptides across different genera suggest that this class of peptides could potentially evolve as bona fide resistance determinants in natura.

Volume 17
Pages None
DOI 10.1371/journal.pgen.1009227
Language English
Journal PLoS Genetics

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