Genome-Wide Identification of Genes Conferring Energy Related Resistance to a Synthetic Antimicrobial Peptide (Bac8c)

Abstract

A fundamental issue in the design and development of antimicrobials is the lack of understanding of complex modes of action and how this complexity affects potential pathways for resistance evolution. Bac8c (RIWVIWRR-NH2) is an 8 amino acid antimicrobial peptide (AMP) that has been shown to have enhanced activity against a range of pathogenic Gram-positive and Gram-negative bacteria, as well as yeast. We have previously demonstrated that Bac8c appears to interfere with multiple targets, at least in part through the disruption of cytoplasmic membrane related functions, and that resistance to this peptide does not easily develop using standard laboratory methods. Here, we applied a genomics approach, SCalar Analysis of Library Enrichement (SCALEs), to map the effect of gene overexpression onto Bac8c resistance in parallel for all genes and gene combinations (up to ∼ 10 adjacent genes) in the E. coli genome (a total of ∼ 500,000 individual clones were mapped). Our efforts identified an elaborate network of genes for which overexpression leads to low-level resistance to Bac8c (including biofilm formation, multi-drug transporters, etc). This data was analyzed to provide insights into the complex relationships between mechanisms of action and potential routes by which resistance to this synthetic AMP can develop.

The intrinsic resistome of bacterial pathogens

“…However, it does not allow analyzing the effect of mutations that do not fully inactivate a given determinant but changes its activity. Alternatively, the use of plasmid libraries containing each open reading frame of a given genome allows to establish the contribution to resistance of each gene when is overexpressed or when it is transferred to a heterologous host (Soo et al, 2011; Spindler et al, 2013). This method is only useful for analyzing acquired resistance, but not for studying genes which inactivation alters the bacterial susceptibility to antimicrobials.…”

“…These methodologies can be used for comparing populations grown in the absence and in the presence of antibiotics. These populations can be formed by the type of libraries above mentioned (transposon-tagged of expression libraries), in which case the enrichment of mutants or clones in the presence of antibiotics allow defining genes contributing to resistance (Girgis et al, 2009; Zhang et al, 2012; Spindler et al, 2013). While these methods are faster and cheaper than methods based in analyzing the susceptibility of each single mutant/clone, their main drawback is that among all potential determinants that contribute to resistance, only those with lower fitness costs will be enriched and several of the elements that may be detected by classical susceptibility tests would not be detected using enrichment-based technologies.…”

Genome-Wide Mapping of Furfural Tolerance Genes in Escherichia coli

“…This method produces genome-wide fitness data at approximately 125 nucleotide resolution, thus allowing for precise mapping of the genetic basis of high fitness clones. The SCALEs method has previously been used to map genotype-to-phenotype relationships in a variety of applications, including: engineering tolerance to anti-metabolites [42] , solvents [8] , [9] , [43] , organic acids [7] , [44] , [45] , antibiotics [46] , [47] , as well as identifying genes restoring redox balance [48] . Here, we applied the SCALEs method to simultaneously map furfural related fitness effects resulting from overexpression of all E. coli genes (a total of >10 5 individual clones were evaluated).…”

Comparison of genome‐wide selection strategies to identify furfural tolerance genes in Escherichia coli

“…In Escherichia coli , construction and mapping tools include a knockout collection (Baba et al, ; Datsenko and Wanner, ), a collection of all E. coli open reading frames cloned into overexpression plasmids (Kitagawa et al, ), methods for parallel mapping of plasmid‐based overexpression libraries (Cho et al, ; Gill et al, ; Lynch et al, ), phage‐based recombineering technologies like multiplex automated genome engineering (Wang et al, ), a genome‐wide barcoded promoter mutation library (trackable multiplex recombineering, or TRMR, (Warner et al, )), among others (Alper and Stephanopoulos, ; Jiang et al, ). These tools have been broadly applied to improve understanding of metabolic pathway optimization, chemical tolerance, antimicrobial resistance, and the mechanisms of laboratory evolution, to name a few (Alper et al, ; Bonomo et al, ; Dunlop et al, ; Gall et al, ; Glebes et al, ; Goodarzi et al ; Sandoval et al, ; Sandoval et al, ; Singh et al, ; Spindler et al, ; Wang et al, ; Warnecke et al, ; Warnecke et al, ; Woodruff et al, ; Woodruff et al, ).…”