Convergent evolution of Enterobacteriaceae in epidemiological networks with high antimicrobial use

Lead Research Organisation: University of Liverpool
Department Name: Institute of Integrative Biology

Abstract

Disease-causing bacteria that are resistant to antimicrobials are a global health concern. While there are many research programs looking into the nature and spread of antimicrobial resistance (AMR), little is known about the evolutionary 'stepping stones' that precede the emergence of resistance. Understanding these could help with identifying early warning signs and targeting public health interventions.

We will investigate the underlying evolutionary mechanisms that give rise to antimicrobial resistance in bacteria. Unlike other studies, that are done solely in the laboratory, our novel approach will do this using a unique, real-world situation of bacterial populations transmitting among a high antimicrobial use community. We will focus on enteric bacteria, which cause diarrhoeal disease and are pathogens with the most worrying AMR.

We will study bacterial genome sequences from real infections and identify genetic changes that happen alongside the development of AMR using the latest bioinformatic methods. We will then analyse hundreds of bacterial strains in the laboratory looking at behaviours that might help the bacteria develop AMR and associate these behaviours with our genetic changes, developing models to quantify which of these signatures is most important. Finally, we will confirm that these genetic changes contribute to the development of AMR by evolving bacteria with and without the genetic change in the presence of antimicrobials.

This work will increase our knowledge of the critical problem of AMR by identifying genetic changes underlying the emergence of AMR in an important bacterial group. Because we are finding the genetic changes in a real-world setting and testing our hypotheses across hundreds of different bacterial strains, we know our findings will be important and could help us prevent AMR emergence by, for example, helping design new laboratory testing for use in AMR surveillance.

Technical Summary

AMR emergence is a global concern which we must work to prevent. Our proposal will identify novel genetic and phenotypic signatures that precede and promote the emergence of AMR in enteric organisms. We work with a recently emerged, highly relevant real-world epidemiological scenario; the parallel emergence of multiple highly AMR enteric organisms sexually transmitting among UK men who have sex with men. Our pilot work on a subset of Shigella epidemics revealed previously uncharacterised genetic signatures associated with the emergence of AMR in this setting.

We will expand this pilot work with extensive routinely generated data from an up-to-date collection of phylogenetically diverse Enterobacteriaceae and employ multiple GWAS methodologies to comprehensively identify genetic signatures that accompany AMR emergence in this setting. We will then statistically associate these genetic signatures with AMR-associated phenotypes (e.g. tolerance, persistence) using our novel translatable approach 'bulk phenotyping of epidemiological replicates' and quantitate the contribution of the genetic signatures to AMR emergence. To validate the causal role of these candidates in AMR emergence, we will combine molecular microbiology with experimental evolution, reconstructing the genetic signatures in novel and model backgrounds and compare their adaptation to a high antimicrobial environment, allowing for AMR development by both de novo mutation and horizontal gene transfer.

This work will increase our knowledge of the critical problem of AMR by identifying genetic signatures underlying its emergence in an important pathogen group. Our approach of drawing the genetic signatures from a real epidemiological scenario, and supporting them through phenotyping of hundreds of clinical isolates mean our findings will be highly translatable to real-world applications such as enhancing phenotypic and genotypic surveillance programs for enteric bacteria in public health.
 
Title A tale of two plasmids: contributions of plasmid associated phenotypes to epidemiological success among Shigella 
Description Dissemination of antimicrobial resistance (AMR) genes by horizontal gene transfer (HGT) mediated through plasmids is a major global concern. Genomic epidemiology studies have shown varying success of different AMR plasmids during outbreaks, but the underlying reasons for these differences are unclear. Here, we investigated two Shigella plasmids (pKSR100 and pAPR100) that circulated in the same transmission network but had starkly contrasting epidemiological outcomes to identify plasmid features that may have contributed to the differences. We used plasmid comparative genomics to reveal divergence between the two plasmids in genes encoding AMR, SOS response alleviation, and conjugation. Experimental analyses revealed that these genomic differences corresponded with reduced conjugation efficiencies for the epidemiologically successful pKSR100, but more extensive AMR, reduced fitness costs, and a reduced SOS response in the presence of antimicrobials, compared with the less successful pAPR100. The discrepant phenotypes between the two plasmids are consistent with the hypothesis that plasmid associated phenotypes contribute to determining the epidemiological outcome of AMR HGT and suggest that phenotypes relevant in responding to antimicrobial pressure and fitness impact may be more important than those around conjugation in this setting. Plasmid phenotypes could thus be valuable tools in conjunction with genomic epidemiology for predicting AMR dissemination. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL http://datadryad.org/stash/dataset/doi:10.5061/dryad.sxksn0363
 
Title Escherichia albertii tree files 
Description These are two newick format tree files used in the Escherichia albertii publication. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://figshare.com/articles/dataset/Escherichia_albertii_tree_files/20894854
 
Title Escherichia albertii tree files 
Description These are two newick format tree files used in the Escherichia albertii publication. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://figshare.com/articles/dataset/Escherichia_albertii_tree_files/20894854/1
 
Title colicin_database 
Description A database of over 10,000 colicins from over 50 species of bacteria were collated from the ENA as well as including some isolates from previously published sources. A multi-FASTA file containing the collated colicin sequences was utilised to generate a custom database via the prepareref command of ARIBA v2.14.6 where prepareref removes erroneous data and runs cd-hit to cluster the sequences based on a user-defined similarity threshold (90% in our case). ARIBA was then run with the FASTQ files of all isolates and the colicin database to report which sequences were observed in each isolate. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://figshare.com/articles/dataset/colicin_database/20768260/1
 
Title colicin_database 
Description A database of over 10,000 colicins from over 50 species of bacteria were collated from the ENA as well as including some isolates from previously published sources. A multi-FASTA file containing the collated colicin sequences was utilised to generate a custom database via the prepareref command of ARIBA v2.14.6 where prepareref removes erroneous data and runs cd-hit to cluster the sequences based on a user-defined similarity threshold (90% in our case). ARIBA was then run with the FASTQ files of all isolates and the colicin database to report which sequences were observed in each isolate. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://figshare.com/articles/dataset/colicin_database/20768260
 
Description Collaboration across Universities Bath, Bristol, and Liverpool 
Organisation University of Bath
Country United Kingdom 
Sector Academic/University 
PI Contribution We consult and advise on data and bioinformatic processing
Collaborator Contribution Bioinformatic analysis
Impact It is ongoing - results and not yet consolidated and reported anywhere.
Start Year 2022
 
Description Working with Benoit Marteyn at University of Strausbourg on functional microbiology 
Organisation University of Strasbourg
Country France 
Sector Academic/University 
PI Contribution We have identified a novel adhesion in our early GWAS analyses and are collaborating with Benoit to get the function tested in the labroratory
Collaborator Contribution He will be testing mutant constructs of the variants in the lab
Impact None yet, except to expand the network
Start Year 2022