Genomic and transcriptomic analysis to dissect the role of the intrinsic resistance factor RarA in carbapenem resistance in Klebsiella pneumoniae

Project Summary Antibiotic resistance is a rapidly escalating global health crisis and is predicted to cause 10 million deaths per year by 2050 without sufficient intervention.1 We have made strides in the arms race against antibiotic resistance through the availability and feasibility of bacterial whole genome sequencing (WGS), which has enabled us to connect bacterial genotypes to resistance phenotypes; however, many gaps in knowledge remain about the genetics of bacterial resistance.2 In this proposal, I focus on carbapenem resistance (CR) in Klebsiella pneumoniae (Kp), which is difficult to treat and leads to high rates of morbidity and mortality. Previous work has shown that most CR in Kp is explained by a combination of enzymes that hydrolyze antibiotics and mutations in porins, decreasing cellular permeability to antibiotics.3,4 However, we find that levels of CR are often higher than expected based on the strain's genotype. We have observed for the first time that overexpression of the transcription factor RarA is associated with unexplained, high-level CR. However, RarA's cellular function and its role in antibiotic resistance is poorly understood. The goal of the proposed research project is to investigate the genetic pathways through which RarA modulates CR and identify natural genetic variation associated with high-level CR. To address this goal, I will leverage a unique collection of Kp clinical isolates with associated whole genome sequencing data and antibiograms collected by our group. I propose two specific aims: (1) define the RarA regulon and its role in CR by using RNA-Seq on RarA deletion and overexpression strains to identify downstream effectors, followed by perturbation of these effectors to assess their contributions to CR; and (2) elucidate how natural variation in the RarA regulon interacts with known resistance determinants to contribute to CR. We will use regression models to quantify the relationship between genetic variations in RarA and its repressor OqxR, and their effects on RarA expression, and apply statistical models to understand how these variations, along with known resistance determinants, contribute to CR. This research will provide insights into the role of RarA in CR, improving our capacity for diagnostics, treatment, and surveillance of CRKp.