Pathogenic microorganisms pose a significant threat to food security and in the spread of infectious diseases, particularly where the transmission of antibiotic resistant organisms (ARO) remains a critical concern1. The drivers for the selection or maintenance of ARO’s within the environment represents an important route for the dissemination of these organisms via the food chain. As such, the occurrence and presence of ARO’s in the environment can arise simply as a result of selection by endogenous antibiotics, or the application of antibiotic or antibiotic-like compounds within the environment1. As such, recent data reveals that the genetic factors which contribute to pesticide resistance also confer antibiotic resistance2. Despite this evidence, little is known about the impact of pesticide exposure and the potential for the development of antimicrobial resistance in common microorganisms found within the foodchain. In this application, our overarching aim is to establish a mechanistic basis for pesticide exposure and the transfer of AMR organisms from the environment into the food supply; where the specific aims are to (1) develop and validate a relevant model system to evaluate the selection and transmission of ARO’s (2) determine using an “omics” approach the impact of commonly used agricides in the generation of mutations linked to both pesticide and antibiotic resistance (3) Using in-vivo model if microbial transmission and persistence is enhanced in pesticide-exposed plants.
Aim 1: To establish a “colonisation & pesticide resistance” plant model system, we will use a genetically tractable model, which will exploit a relevant edible horticultural species (lettuce) subject to common pesticide treatment (glyphosate), inoculated with two microbial species associated with antimicrobial resistance (Kpn) and foodborne transmission (Eco O157:H7). This model system will be used to parameterise the conditions of in vivo colonisation such as bacterial colonisation versus time, pesticide concentrations to elicit adaptive genetic changes.
Aim 2: To establish the mechanistic basis of pesticide resistance, wholescale adaptive changes after pesticide exposure (Aim 1) will be determined from the optimised model system, by both whole genome sequence analysis and transcriptome (RNA-seq) profiling. This will establish both, which genes represent mutational hot-spots and regulatory networks are altered, thus facilitating an integrated network of genetic changes to be mapped and linked to the antibiotic/pesticide resistance phenotype.
Aim 3: Determine if pesticide exposure accelerates the acquisition of antimicrobial and pestcide resistance. Using the in-vivo model, the genetic and phenotypic properties of the focal species e.g, Kpn or Eco O157:H7 will be established in the presence or absence of pesticides and exogenous AMR bacteria e.g. animal manure. This will establish whether adaptation to pesticides can accelerate the acquisition or horizontal transfer of resistance elements from exogenous sources.
The expected outcomes will be the validation and implementation of an in-vivo plantmicrobe colonisation model to assess the contributions of pesticide use in promoting microorganism and ARO transfer.
The supervisors have combined expertise and will provide training in molecular microbiology, plant biology for the laboratory work, as well as bioinformatics analyses. Additional training will be provided for postgraduate translational skills and in computational science.