Genomic and Proteomic Characterization of the Extended-Spectrum β-Lactamase (ESBL)-Producing Escherichia coli Strain CCUG 73778: A Virulent, Nosocomial Outbreak Strain

Abstract

Escherichia coli strain CCUG 78773 is a virulent extended-spectrum β-lactamase (ESBL)-producing ST131-O25b type strain isolated during an outbreak at a regional university hospital. The complete and closed genome sequence, comprising one chromosome (5,076,638 bp) and six plasmids (1718–161,372 bp), is presented. Characterization of the genomic features detected the presence of 59 potential antibiotic resistance factors, including three prevalent β-lactamases. Several virulence associated elements were determined, mainly related with adherence, invasion, biofilm formation and antiphagocytosis. Twenty-eight putative type II toxin-antitoxin systems were found. The plasmids were characterized, through in silico analyses, confirming the two β-lactamase-encoding plasmids to be conjugative, while the remaining plasmids were mobilizable. BLAST analysis of the plasmid sequences showed high similarity with plasmids in E. coli from around the world. Expression of many of the described virulence and AMR factors was confirmed by proteomic analyses, using bottom-up, liquid chromatography-tandem mass spectrometry (LC-MS/MS). The detailed characterization of E. coli strain CCUG 78773 provides a reference for the relevance of genetic elements, as well as the characterization of antibiotic resistance and the spread of bacteria harboring ESBL genes in the hospital environment.

Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics

“…Therefore, it remains elusive what AMR determinants are actually detectable without secondary antibiotic cultivation. It has already been shown previously that many AMR determinants are detectable on the protein level by lateral flow assays and targeted mass spectrometry without induction by antibiotics. ,,− Furthermore, several mechanism of action (MoA) studies provided evidence for high correlation of the expression of AMR-related proteins analyzed by MS and the expression of corresponding transcripts analyzed by either qPCR or transcriptomics. ,,,,− This study confirms these findings in an untargeted manner, which is a big advantage, in comparison to targeted protein detection methods. The induced expression of Van ligases relies on its regulatory system.…”

“…However, no such unbiased LC-MS/MS workflow exists so far. Instead, the majority of AMR-related proteomics studies rely on targeted analysis of selected peptides representing AMR determinants. − Moreover, feasibility of shotgun proteomics for AMR detection has been reported for certain predefined AMR determinants in samples with known species information. − The results of these studies are quite promising, since they prove that sensitive and specific detection for at least some of the AMR phenotypes based on proteins is possible. On the other hand, untargeted proteomics has not been evaluated systematically for unbiased AMR prediction, most probably because of major technical limitations …”

Spread and antibiotic resistance profile of pathogens isolated from human and hospital wastewater in Ouagadougou

“…These resistances (MDR, PDR and XDR) in isolates circulating in the cities of Burkina Faso could be due by the expression of resistance genes of the bla and mecA types carried by mobile and transferable genetic elements between cells of the same or different species described previously by several authors [11,13,14,37,38]. The transfer of resistance genes between isolates depends on the type of ecosystem and the mode of transfer [39][40][41][42][43].…”

Good microbes, bad genes? The dissemination of antimicrobial resistance in the human microbiome

“…In addition to identifying whether an ARG was horizontally acquired in retrospect, the ability to predict future ARG transmissions is a key goal in translating resistome research. Global surveillance of antimicrobial resistance in potentially high-risk environments, such as hospitals, 107 animal sectors, 108 wastewater, 109 and rural communities 18 can play a critical role in identifying ARGs and pathogens with recently acquired ARGs that may be poised for future epidemic spread. Genomics, isolate screens, and functional metagenomics can all be used for ARG surveillance.…”