A Novel Platform Using RNA Signatures To Accelerate Antimicrobial Susceptibility Testing in Neisseria gonorrhoeae

Abstract

The rise of antimicrobial-resistant pathogens can be attributed to the lack of a rapid pathogen identification (ID) or antimicrobial susceptibility testing (AST), resulting in delayed therapeutic decisions at the point of care. Gonorrhea is usually empirically treated, with no AST results available before treatment, thus contributing to the rapid rise in drug resistance. Here, we present a rapid AST platform using RNA signatures for Neisseria gonorrhoeae Transcriptome sequencing (RNA-seq) followed by bioinformatic tools was applied to explore potential markers in the transcriptome profile of N. gonorrhoeae upon minutes of azithromycin exposure. Validation of candidate markers using quantitative real-time PCR (qRT-PCR) showed that two markers (arsR [NGO1562] and rpsO) can deliver accurate AST results across 14 tested isolates. Further validation of our susceptibility threshold in comparison to MIC across 64 more isolates confirmed the reliability of our platform. Our RNA markers combined with emerging molecular point-of-care systems has the potential to greatly accelerate both ID and AST to inform treatment.

Keywords: Neisseria gonorrhoeae; RNA markers; RNA-seq; antimicrobial susceptibility testing; azithromycin.

FIG 1

Summarized workflow for generation of candidate RNA markers of the proposed molecular AST. N. gonorrhoeae was cultured and exposed to azithromycin for 10 and 60 min along with a control without azithromycin. Samples were collected for the RNA-seq library and sequencing. Data analysis was conducted to find differentially expressed genes, followed by marker selection steps. Selected markers were validated by qRT-PCR and ΔCT calculation. This figure was created with https://biorender.com/ and PowerPoint.

FIG 2

MDS plot to display differences in the azithromycin-induced gene expression profile at 10 and 60 min. Dissimilarity in the expression profiles between the replicates, strains, and conditions was calculated and plotted. CS, control susceptible strain; AziS, azithromycin-treated susceptible strain; CR, control resistant strain; AziR, azithromycin-treated resistant strain.

FIG 3

(A) Volcano plot to show that the gene expression profile induced by azithromycin varied significantly at 10 and 60 min between the treated and untreated susceptible strains. Colored points indicate genes that are up- or downregulated in the SvC as well as SvR comparisons at each time point. (B) Heat map for differentially expressed genes (P value < 0.05, with an FC of ≥1) in SvC and SvR induced by 10 min of azithromycin exposure.

FIG 4

Validation of 6 RNA markers across 14 N. gonorrhoeae isolates upon 10 min of azithromycin exposure using qRT-PCR. Six RNA markers were selected based on the RNA-seq results and then validated using qRT-PCR, including ABC, bolA, arsR, and rpsO, which were upregulated, and dinD and acoT, which were downregulated. Black, susceptible isolates (SPL4 and S1 to S11); gray, resistant isolates (SPJ and R1).

FIG 5

(A) Determination of susceptibility threshold by correlation of MIC and our AST platform using arsR and rpsO across 14 tested N. gonorrhoeae isolates. (B) Linear fitting for arsR and rpsO to show how transcriptional response is associated with the MIC across 14 tested N. gonorrhoeae isolates (12 susceptible and 2 resistant).

FIG 6

Proposed workflow for combined ID and AST using novel molecular point-of-care systems such as a magnetofluidic device (38). Clinical samples are collected and exposed to antimicrobial for 10 min. Control and antimicrobial-treated samples are loaded into the magnetofluidic device for further ID and AST. This figure was created with https://biorender.com/ and PowerPoint.