Identification of combinatorial mutations associated with colistin resistance in Shewanella algae

Abstract

Colistin is a last-resort antibiotic used to treat infections caused by drug-resistant gram-negative bacteria. However, the genetic mechanisms underlying colistin resistance in Shewanella algae are not well understood. In this study, we sequenced and compared the genomes of 23 mcr-negative colistin-resistant and sensitive S. algae samples from various sources. We applied a computational approach to identify combinatorial mutations associated with colistin resistance. Our analysis revealed a combination of three mutations (PmrB 451, PmrE168, PmrH292) that were strongly associated with colistin resistance in S. algae. This study provides insights into the genetic mechanisms of colistin resistance in S. algae and demonstrates the utility of a computational approach for identifying epistatic interactions among mutations. Identifying the genetic mutations responsible for colistin resistance in S. algae can inform the development of new treatments or strategies to combat infections caused by this emerging pathogen.

Keywords: Drug-resistant gram-negative bacteria; Machine learning; Whole genome sequencing.