Surveillance of drug-resistant bacteria in resource-limited settings: a focus on cost-effective long-read sequencing of eyedrop-associated P. aeruginosa and locally sourced metagenomic samples

Project Abstract: Surveillance of drug-resistant bacteria in resource-limited settings: a focus on cost- effective long-read sequencing of eyedrop-associated P. aeruginosa and locally sourced metagenomic samples Antimicrobial resistance (AMR) poses a growing threat to global public health, rendering antibiotics

increasingly ineffective against drug-resistant bacteria. Recent episodes involving the contamination of preservative-free eyedrops with extensively drug-resistant (XDR) Pseudomonas aeruginosa have resulted in hundreds of infection cases and three fatalities. This outbreak underscores that superbug occurrences are no

longer confined to healthcare-associated settings, and we need a community-wide surveillance system that monitors local infection outbreaks efficiently in terms of time, accuracy, and cost. Such a system is essential to mitigate the adverse consequences of superbugs, encompassing a broad spectrum of sample types including

medicinal and agricultural products. To achieve this long-term goal, this project first aims to test a decentralized AMR surveillance system in resource-limited settings by assessing the detection of drug- resistant bacteria and antimicrobial bacteriophages with cost-efficient long-read sequencing. We will use

Flongle platforms that enable the sequencing of smaller assays (