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. 2022 Oct 21;2022:7318325. doi: 10.1155/2022/7318325

Assessment of Antibiotic Resistance and Efflux Pump Gene Expression in Neisseria Gonorrhoeae Isolates from South Africa by Quantitative Real-Time PCR and Regression Analysis

Nireshni Mitchev 1,✉, Ravesh Singh 1,2, Veron Ramsuran 1, Arshad Ismail 3,4, Mushal Allam 3,5, Stanford Kwenda 3, Florah Mnyameni 3, Nigel Garrett 6,7, Khine Swe Swe-Han 1,2, Abraham J Niehaus 1, Koleka P Mlisana 1,6,8
PMCID: PMC9616671  PMID: 36312786

Abstract

Introduction

Treatment of gonorrhoea infection is limited by the increasing prevalence of multidrug-resistant strains. Cost-effective molecular diagnostic tests can guide effective antimicrobial stewardship. The aim of this study was to correlate mRNA expression levels in Neisseria gonorrhoeae antibiotic target genes and efflux pump genes to antibiotic resistance in our population.

Methods

This study investigated the expression profile of antibiotic resistance-associated genes (penA, ponA, pilQ, mtrR, mtrA, mtrF, gyrA, parC, parE, rpsJ, 16S rRNA, and 23S rRNA) and efflux pump genes (macAB, norM, and mtrCDE), by quantitative real-time PCR, in clinical isolates from KwaZulu-Natal, South Africa. Whole-genome sequencing was used to determine the presence or absence of mutations.

Results

N. gonorrhoeae isolates, from female and male patients presenting for care at clinics in KwaZulu-Natal, South Africa, were analysed. As determined by binomial regression and ROC analysis, the most significant (p ≤ 0.05) markers for resistance prediction in this population, and their cutoff values, were determined to be mtrC (p = 0.024; cutoff <0.089), gyrA (p = 0.027; cutoff <0.0518), parE (p = 0.036; cutoff <0.0033), rpsJ (p = 0.047; cutoff <0.0012), and 23S rRNA (p = 0.042; cutoff >7.754).

Conclusion

Antimicrobial stewardship includes exploring options to conserve currently available drugs for gonorrhoea treatment. There is the potential to predict an isolate as either susceptible or nonsusceptible based on the mRNA expression level of specific candidate markers, to inform patient management. This real-time qPCR approach, with few targets, can be further investigated for use as a potentially cost-effective diagnostic tool to detect resistance.

1. Introduction

Increasing antimicrobial resistance (AMR) to Neisseria gonorrhoeae is now a public health priority [1, 2] as it threatens the current World Health Organization (WHO) recommended dual therapy (ceftriaxone and azithromycin) [1–3]. Molecular mechanisms of drug resistance have been well characterized [4, 5] and are mainly due to mutational alterations of the drug target, plasmids, and efflux pumps [6, 7].

Globally, 87 million new cases of the sexually transmitted infection (STI) gonorrhoea occur annually, where the highest prevalence has been reported in the WHO Africa region [8]. The estimated prevalence of gonorrhoea in African countries was reported to be 1.4%–15.2%, with higher prevalence in high-risk groups (sex-workers and participants recruited from venues considered to have a higher probability for acquiring infection, e.g., bars) [9]. South Africa has an estimated prevalence of ∼5% [9, 10]. N. gonorrhoeae infections are usually localized to the mucosal surfaces of the hosts initial exposure to the organism [11, 12]. Infection of the male urethra causes urethritis (inflammation of the urethra), the symptoms of which, include purulent discharge and dysuria [13]. While male urethritis commonly produces symptoms, gonorrhoea in women is often asymptomatic [14]. The sequelae of untreated gonorrhoea includes acute urethritis, cervicitis, pelvic inflammatory disease (PID), infertility, abortion, ectopic pregnancy, maternal death, and neonatal blindness [15–19].

In Africa and other resource-limited settings, syndromic management of patients remains the main STI management model. Syndromic and empiric treatment leads to overtreatment [20] and contributes to the development of resistance to currently recommended drugs in many parts of the world [21–24]. There is no vaccine for gonorrhoea yet; thus, its prevention and control depends on an accurate diagnosis and appropriate antimicrobial therapy [25]. Currently, treatment options are few and antimicrobial stewardship programmes can reduce antibiotic resistance [26]. There is an urgent need for rapid diagnostic tools to direct therapy [27, 28].

The AMR mechanisms via which N. gonorrhoeae has developed resistance has been thoroughly reviewed [4, 29]. These include antimicrobial inactivation, alteration of target sites, increased export via efflux pumps, and decreased uptake via porins [29]. Resistance-to-penicillin and extended-spectrum cephalosporins (ESC) have been associated with modifications and recombination within penA, porB, ponA [30], and the presence of blaTEM plasmid (penicillin) [4, 31]. Modifications in penA result in decreased affinity for penicillin, and recombination with penA genes from commensal Neisseria species has led to the development of mosaic penA alleles which causes resistance-to-penicillin, cefixime, and ceftriaxone [4, 32, 33]. The mutation L421P in ponA reduces the rate of acylation with penicillin [34]. Mutations in porB, which encode porinB, reduce the porin permeability, which then reduces penicillin influx [4]. The blaTEM-1 gene is responsible for plasmid-mediated resistance-to-penicillin, and a previous study from South Africa showed a prevalence of 66% in nonsusceptible isolates [35]. Mutations in mtrR, as well as its promoter region, can cause overexpression of the MtrCDE efflux pump, which has been associated with resistance-to-hydrophobic agents (penicillin, cefixime, ceftriaxone, and azithromycin) [36]. Pore formation in the outer membrane is encoded for by the pilQ gene, mutations in this gene result in reduced antibiotic influx and high-level resistance-to-penicillin [4, 12, 37–39]. When treating patients with an antibiotic, low-level resistance means that an increased dose of the antibiotic can still overcome the resistance to it and clear the infection. High-level resistance, however, means that even an increased dose will not be able to clear the infection i.e., the antibiotic should not be used.

Resistance to tetracycline has been associated with the presence of tetM and mutations in rpsJ, mtrR, and porB [36]. The tetM gene confers high-level plasmid-mediated resistance-to-tetracycline by binding to the 30S ribosomal subunit, thus, releasing the tetracycline molecule and protein synthesis continues [40]. Chromosomally mediated resistance is due to the ribosomal subunit being modified, thus, increasing the efflux and decreasing the influx of tetracycline [4]. The rpsJ mutation V57M, alters the binding site, thus, reducing binding affinity of tetracycline for the ribosome [41]. As described for penicillin, modifications in mtrR and porB, which result in reduced drug accumulation, also contribute to resistance-to-tetracycline [4, 12, 42].

Resistance to ciprofloxacin is due to mutations in gyrA and parC [36], and mutations in the norM promoter results in overexpression of NorM efflux pump, which increases ciprofloxacin MICs [4, 7, 43]. Quinolones inhibit DNA gyrase (encoded by gyrA and gyrB) and topoisomerase IV (encoded by parC and parE), which are essential for DNA metabolism, resulting in bactericidal activity [4]. Mutations in these genes alter quinolone recognition of the enzymes and result in resistance [4, 29]. Although many mutations have been identified in gyrA and parC, the key mutations responsible for quinolone resistance include gyrA_S91F, gyrA_D95N, parC_S88P, and parC_E91K [4, 5, 44]. Mutations in the gyrB and parE genes did not significantly impact resistance-to-ciprofloxacin. Mutation in the norM promoter results in overexpression of the NorM efflux pump, which decreases ciprofloxacin susceptibility MICs [4, 45].

High-level resistance-to-spectinomycin is due to the mutation C1192U in 16S rRNA by reducing target affinity [42]. Resistance-to-azithromycin is often due to mutations in 23S rRNA, namely C2611T (low-level resistance) or A2059 (high-level resistance) [4, 42]. Mutation in the promoter regions of MacAB and mef-encoded efflux pumps result in overexpression contributing to resistance-to-macrolides [4, 7, 42, 43, 46].

A range of molecular diagnostic approaches have been evaluated, each with its strengths and limitations [42, 47–51]. While most methods target specific mutations to infer resistance, our approach targets the gene and its expression levels to infer resistance. Genomics prediction tools and equations have been extremely effective in characterizing antimicrobial resistance mechanisms [52, 53]. At present, ResistancePlus® GC (SpeeDx Pty Ltd, Sydney, Australia), which detects resistance-to-ciprofloxacin, is the only commercially available genotypic resistance testing assay for N. gonorrhoeae. The assay uses real-time PCR to detect species-specific porA and opa genes for identification and differentiates the gyrA_S91 wild type from the gyrA_S91F mutant to determine susceptibility or resistance-to-ciprofloxacin. In populations where the resistance-to-ciprofloxacin is high, assays which predict resistance to additional drugs would be beneficial.

The aim of this study was to correlate mRNA expression levels in N. gonorrhoeae antibiotic target genes and efflux pump genes to antibiotic resistance in our population using real-time qPCR, a cost-effective alternative to WGS. A secondary objective was to determine from isolates, if any genes are expressed more in either gender.

2. Materials and Methods

2.1. Source of Isolates

All 110 N. gonorrhoeae isolates in this study were stored and analysed at the University of KwaZulu-Natal Department of Medical Microbiology. The specimens were collected between 2013 and 2016 from male and female patients attending KZN public healthcare clinics for STI care during ethics approved studies. Ethical approval for this study was granted by the Biomedical Research Ethics Committee of the University of KwaZulu-Natal BREC/00000097/2019.

2.2. Identification of Neisseria gonorrhoeae

Stored N. gonorrhoeae isolates (vaginal and urethral specimens) were revived on nonselective Thayer Martin media (supplemented with 1% Vitox, excluding antibiotic supplements) for 18–24 hours in a 37°C 5% CO2 incubator. Identification was confirmed (supplementary Table 1 and supplementary Figure 1) using bright field microscopy (N. gonorrhoeae is a Gram-negative diplococcus), Bactident® Oxidase rapid test (Merck, Germany) (N. gonorrhoeae is oxidase positive), and Phadebact® Monoclonal GC test (Pharmacia, Sweden) (a coagglutination technique used for the definitive identification of N. gonorrhoeae) [11, 13, 54]. In addition, a real-time PCR assay, N. gonorrhoeae TaqMan® probe Ba046466252 (Thermo Scientific) was used for molecular identification. A subset of 61 male and female isolates with similar antibiotic profiles were selected for WGS. This data confirmed the identification of N. gonorrhoeae using Kraken [55] and Pathogenwatch [56].

2.3. Phenotypic Antibiotic Susceptibility Testing

Antimicrobial susceptibility testing was performed, using Etest® (bioMérieux, Marcy l'Etoile, France), for all isolates, using GC agar base medium (used for the isolation and cultivation of N. gonorrhoeae) supplemented with 1% Vitox (Oxoid) [57–59]. The minimum inhibitory concentration (MIC) was determined as the lowest concentration of the drug to visually inhibit the growth of the organism. The drugs and concentration ranges were as follows; penicillin (0.016–256 μg/mL), ciprofloxacin (0.002–32 μg/mL), ceftriaxone (0.002–32 μg/mL), cefixime (0.016–256 μg/mL), spectinomycin (0.064–1024 μg/mL), tetracycline (0.016–256 μg/mL), and azithromycin (0.016–256 μg/mL). Susceptibility was interpreted as per the European Committee on Antimicrobial Susceptibility Testing (EUCAST) guidelines [60]. Nitrocefin (a chromogenic cephalosporin substrate) was used to detect β-lactamase production, which indicates resistance to beta-lactam antibiotics such as penicillin [61]. The 2016 WHO gonococcal reference strains (F, G, K, L, M, N, O, P, U, V, W, X, Y, Z) [62] and ATCC 49226 were used as controls in this study.

2.4. Whole-Genome Sequencing and Assembly

DNA was extracted using the PureLink™ Microbiome DNA Purification Kit (ThermoFisher Scientific) as per the manufacturer's instructions. Paired-end libraries were prepared using the Nextera DNA Prep kit, followed by sequencing (2 × 75 bp) on a NextSeq platform (Illumina, Inc., USA). Raw paired-end (PE) reads were initially run through the Jekesa pipeline v1.0 [63] for WGS bacterial typing. Briefly, Trim Galore v0.6.2 [64]; was used to filter the PE reads (Q > 30 and length >50 bp). De novo assembly and polishing of assemblies were performed using SPAdes v.3.13 [65] and Shovill v1.1.0 [66], respectively. Assembly metrics were calculated using QUAST v5.0.2 [67]. AMR markers were identified using PointFinder [68] and confirmed using Pathogenwatch [69] and Clustal Omega [70]. Whole-genome sequence data is available in DDBJ/ENA/GenBank with the BioProject number PRJNA681740.

2.5. RNA Extraction and cDNA Synthesis

RNA was extracted from the N. gonorrhoeae isolates using TRIzol™ reagent (Invitrogen) with the PureLink™ RNA Mini Kit (ThermoFisher Scientific) and PureLink™ DNase (ThermoFisher Scientific) as per the manufacturer's instructions. The total RNA concentration was quantified using a nanodrop spectrophotometer, and samples were used only if the optical density at 260 nm (OD260/280) was ∼2.0. RNA integrity was confirmed using a bleach gel method [71]. One microgram of total RNA from each sample was reversed transcribed using the iScript™ Reverse Transcription Supermix for RT-qPCR (Bio-Rad) as per the manufacturer's instruction and reaction protocol. The total cDNA concentration was quantified, and samples were used only if the optical density at 260 nm (OD260/280) was >1.8.

2.6. RNA Quantification by Real-Time PCR

Quantitative real-time PCR was performed for the primer sequences listed in Table 1 cDNA was diluted using a 1 : 10 ratio for real-time PCR analysis. Each PCR mixture (5 μl total volume) consisted of the respective primers (0.5 pmol/μl for farB and mtrD; 0.7 pmol/μl for 16S rRNA, penA and macA; 0.3 pmol/μl for all other primers), 2.5 μl PowerUp™ SYBR™ Green Master Mix (ThermoFisher Scientific, USA), 1 μg cDNA and nuclease-free water. Reactions were run in duplicate on the Quant Studio 5 (ThermoFisher, CA, USA) (1 cycle at 95°C, 2 min.), followed by 40 cycles consisting of denaturation at 95°C (15 sec.), annealing at 60°C (15 s), extension at 72°C (1 min). Followed by a melt curve stage (95°C, 15°s) ramp rate 1.6°C/s, 60°C (1 min) ramp rate 1.6°C/s, and 95°C (15 s) ramp rate 0.15°C/s. Amplification specificity was confirmed using melting curve analysis and gel electrophoresis. Serial dilutions of cDNA from total RNA (control strain WHO F) were performed for each target. These served as standard curves for quantitative analysis. The Quant Studio 5 (ThermoFisher, CA, USA) analysis software version 3.3 was used for quantitative analysis. The expression levels were calculated after normalization to a housekeeping gene (16S rRNA).

Table 1.

Primers used for real-time qPCR.

Gene Locus tag Primer sequence (5′-3′) Amplicon size (bp) Description
penA NGO1542 penAF_ACCGAAAGACATCGTCGCCT 172 Penicillin-binding protein
penAR_CGTCGGCACAAGCAAACTGT
ponA NGO0099 ponAF_GGAGTGGGTCTGGTTGCCAT 201 Penicillin-binding protein 1A
ponAR_GGCAATAACCGCATTCCGCA
pilQ NGO0094 pilQF_ACGAGGCTTTGGATTGCGAG 234 Type IV pilus secretin PilQ.
pilQR_TTATGCTTTTTGCCGCGACCG
rpsJ NGO1841 rpsJF_CCATCAGGCGCAAATGGGTG 179 30S ribosomal protein S10
rpsJR_CGCCCTGATTGACCGTTCTG
16S rRNA NGO_r03 16SrRNAF_AGCCGTAACACAGGTGCTGC 209 16S ribosomal RNA
NGO_r06
NGO_r09 16S rRNAR_GACCATTGTATGACGTGTGAAGCC
NGO_r12
gyrA NGO0629 gyrAF_TTGTGAGAAGCTGGATGACGG 185 DNA gyrase subunit A
gyrAR_TGGACGAAGGCGAAACCTTG
parC NGO1259 parCF_GGTTGCCGTCTATGCCTCCT 213 DNA topoisomerase IV subunit A
parCR_CGCCTGCCTTCGCTTTCAAT
parE NGO1333 parEF_GCCTTCGCGTTCCATCCAAG 166 DNA topoisomerase IV subunit B
parER_GATGAACCCCGACCAGCTCA
23S rRNA NGO_r02 23SF_TGCTTCCAAGCCTTCCAC 171 23S ribosomal RNA
NGO_r05 23SR_GAATGGCGTAACGATGGC
NGO_r08
NGO_r11
mtrR NGO1366 mtrRF_CGTTGGACGGGCTGATTTGG 118 HTH-type transcriptional regulator MtrR
mtrRR_CGCAGGCAGGGATGGTTTTC
mtrA NGO1250 mtrAF_GTGCCTTTTGGGCGGACAAT 173 Transcriptional activator of mtrCDE
mtrAR_TCCGTCGTGGCTCAACACAT
mtrC NGO1365 mtrCF_TCCACAACCACCTTGTCCCC 136 Cation/multidrug efflux protein
mtrCR_GCGGTGCGAAAGATACCGTG
mtrD NGO1364 mtrDF_CGTATTGCTGGACGGTTGCC 242 Cation/multidrug efflux protein
mtrDR_GCACGCCATTTATCCGGGTG
mtrE NGO1363 mtrEF_AGACGGCATTTGTTTGCCCG 165 Multidrug transporter
mtrER_ATTTGCTCGATGCGGAACGC
mtrF NGO1368 mtrFF_ACAGTCGAATGGCTGGGCAA 99 Integral membrane protein. Newly described efflux pump
mtrFR_GAAATACGCACCGACGGCAG
macA NGO1440 macAF_TTCACGGTCAGCGACGGAAT 115 Macrolide transport protein MacA
macAR_CCCGTTCGTTTGTGCCGAAT
macB NGO1439 macBF_ATCTGCCTGATGCTGTCGCT 199 Macrolide ABC transporter ATP-binding protein/permease
macBR_CCGACGTGCTGATGCTTTGG
norM NGO0395 norMF_ATCGAAACGGTAGGCGAGCA 140 Multidrug efflux protein
norMR_AACCGGCAGACTTCACCCAA
farA NGO1683 farAF_GCGGATTGCCCGAGGATTTC 183 Multidrug resistance protein
farAR_GCTGAACCGCGAAGATGTGG
farB NGO1682 farBF_TGTTGCGGAATAGGGCGTGA 170 EmrB/QacA subfamily multidrug transporter
farBR_CACTGTCGCACATGAAGGGC
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2.7. Statistical Analysis

Nonparametric statistical analysis and correlations were performed using GraphPad Prism v5.0 (Graphpad Software Inc. CA, USA) and IBM® SPSS Statistics v27. The differences between the groups (susceptible/nonsusceptible, mutations/no mutations, and males/females) were evaluated using t-tests. The regression analysis was performed to determine the relationship of susceptibility as the dependant variable to mRNA levels of antibiotic resistance-associated genes as the independent variables. For each drug, the regression model included all independent variables. A p -value <0.05 was considered to denote statistical significance. Using a receiver operating characteristic (ROC) analysis (which assesses the accuracy of model predictions), cutoff values were calculated for genes associated with resistance for each drug.

3. Results

Resistance-to-penicillin, tetracycline, and ciprofloxacin were high in our isolates (supplementary Figure 2), also described in our previous paper [35]. All isolates were susceptible to spectinomycin, azithromycin, ceftriaxone, and cefixime. A total of 110 isolates were analysed to determine differences in mRNA levels between susceptible and nonsusceptible isolates. A subset of 61 isolates with similar MIC values (30 vaginal swabs and 31 urethral swabs) with similar MIC values were analysed to determine differences in mRNA levels between males and females and for differences in mRNA levels between isolates with and without resistance-associated mutations.

3.1. Comparison of Gene Expression Levels between Susceptible and Nonsusceptible Isolates

We found that for all drugs tested, expression levels between the two groups (susceptible and nonsusceptible) were significantly different (supplementary Table 2). For penicillin (penA, ponA, pilQ, mtrR, mtrC, mtrD, mtrE, mtrA, and mtrF) the p values ranged from 0.01–0.04 (except mtrF, p value 0.17). For ciprofloxacin (gyrA, parC, parE, and norM) the p values were ≤0.001. For tetracycline (rpsJ, mtrR, mtrC, mtrD, mtrE, mtrA, and mtrF) the p values ranged from 0.001–0.04. For azithromycin (23S rRNA, macA, macB, mtrR, mtrC, mtrD, mtrE, mtrA, and mtrF) the p values ranged from ≤0.001–0.01 (except mtrC, p value 0.1). For spectinomycin, the p value for 16S rRNA was ≤0.001. For ESC (penA, mtrR, mtrC, mtrD, mtrE, mtrA, and mtrF) the p values ranged from ≤0.001–0.05.

3.2. Comparison of Gene Expression Levels between Isolates with No Mutations Vs. Isolates with Mutations

The median expressions of antimicrobial and efflux pump genes (farA, farB, gyrA, macA, macB, mtrA, mtrC, mtrD, mtrE, mtrF, mtrR, norM, parC, parE, penA, ponA, pilQ, rpsJ, 16S rRNA, and 23S rRNA) associated with resistance were examined to determine any differences between isolates with mutations in the resistance-associated genes compared to isolates without mutations (supplementary Table 3). We found significant differences between the wildtype genes and isolates genes with mutations mtrF_V213I (p=0.02), gyrA_S91F (p=0.02), gyrA_D95G (p=0.026), parC_S87N (p=0.023), parC_V384I (p ≤ 0.001), and macA_A8S (p=0.01).

3.3. Comparison of Gene Expression Levels between Males and Females

The median expressions of antimicrobial target genes and efflux pump genes (farA, farB, gyrA, macA, macB, mtrA, mtrC, mtrD, mtrE, mtrF, mtrR, norM, parC, parE, penA, ponA, pilQ, rpsJ, 16S rRNA, and 23S rRNA) associated with resistance were examined to determine any differences between isolates from males and females. We found no significant difference between the expression levels of isolates from males compared to females. This was confirmed when we subdivided groups into male susceptible, male nonsusceptible, female susceptible, and female nonsusceptible.

3.4. Correlation of mRNA Expression Levels with Resistance

penA, ponA, pilQ, mtrR, mtrC, mtrD, mtrE, mtrA, mtrF, gyrA, parC, parE and norM, rpsJ, 16S, 23S, macA, and macB mRNA expression levels were determined for an association with resistance status using a logistic regression analysis (Table 2).

Table 2.

Statistically significant logistic regression models of genes associated with AMR as determined by EUCAST MIC interpretation.

Drug Model Beta Std. error Wald Df Sig.
PEN penA −477.444 480.180 0.989 1 0.320
pilQ −45.427 409.199 0.012 1 0.912
ponA 48.974 161.466 0.092 1 0.762
mtrR 15.605 22.818 0.468 1 0.494
mtrC −21.799 9.640 5.113 1 0.024
mtrD 40.099 207.809 0.037 1 0.847
mtrE 38.026 94.177 0.163 1 0.686
mtrA 6.592 7.251 0.827 1 0.363
mtrF 7.687 13.078 0.345 1 0.557
Constant 2.351 0.422 31.058 1 ≤0.001
CIP gyrA 22.691 10.286 4.867 1 0.027
parC 92.723 155.843 0.354 1 0.552
parE −457.577 218.360 4.391 1 0.036
norM −95.040 97.960 0.941 1 0.332
Constant 0.769 0.243 9.994 1 0.002
TET rpsJ −1431.586 734.579 3.798 1 0.047
mtrR −58.549 101.811 0.331 1 .565
mtrC −23.590 40.055 0.347 1 .556
mtrD 415.541 449.943 0.853 1 .356
mtrE −179.181 257.407 0.485 1 .486
mtrA 33.872 44.609 0.577 1 .448
mtrF 24.805 38.481 0.415 1 .519
Constant 4.886 1.182 17.075 1 ≤.001
AZ 23S 0.212 0.104 4.140 1 0.042
macA −104.209 68.197 2.335 1 0.126
macB 429.998 317.542 1.834 1 0.176
mtrR −51.528 171.359 .090 1 0.764
mtrC 43.816 30.898 2.011 1 0.156
mtrD −767.506 1316.124 .340 1 0.560
mtrE −2304.401 1446.997 2.536 1 0.111
mtrA −47.677 65.918 .523 1 0.470
mtrF 162.321 126.611 1.644 1 0.200
Constant −3.426 1.545 4.916 1 0.027
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Bold = significant resistance-associated marker for prediction of antimicrobial resistance in this setting. ∗ The regression models for spectinomycin and ESC were not statistically significant due to a low number of data points in the resistant group, and therefore not included in this table.

Binomial logistic regression was performed to ascertain the effects of penA, ponA, pilQ, mtrR, mtrC, mtrD, mtrE, mtrA, and mtrF on the likelihood that isolates were resistant to penicillin. The model explained 28.6% (Nagelkerke R2) of the variance in resistance-to-penicillin and correctly classified 81.0% of cases. Sensitivity was 96.3% and specificity was 29.2%. Of the predictor variables, mtrC was statistically significant (p=0.024). The discrimination of this model, as determined by ROC curve analysis, is acceptable (AUC 0.8). The logistic regression model was statistically significant, p < 0.009. The regression analysis was used to ascertain the effects of gyrA, parC, parE, and norM on the likelihood that isolates were resistant-to-ciprofloxacin. The model explained 19% (Nagelkerke R2) of the variance in resistance-to-ciprofloxacin and correctly classified 65.8% of cases. Sensitivity was 90.1% and specificity was 30.6%. Of the predictor variables, gyrA and parE were statistically significant, p=0.027 and 0.036, respectively. The discrimination of this model, as determined by the ROC curve analysis, is acceptable (AUC 0.7). The logistic regression model was statistically significant, p=0.001.

Regression analysis was used to ascertain the effects of 23S rRNA, macA, macB, mtrR, mtrC, mtrD, mtrE, mtrA, and mtrF on the likelihood that isolates were resistant to azithromycin. The model explained 67.9% (Nagelkerke R2) of the variance in resistance-to-azithromycin and correctly classified 94.9% of cases. Sensitivity was 42.9% and specificity was 98.2%. Of the predictor variables, 23S rRNA was statistically significant (p=0.042). The discrimination of this model, as determined by the ROC curve analysis, is outstanding (AUC 0.98). The logistic regression model was statistically significant, p ≤ 0.001.

For tetracycline, spectinomycin, and ESC, the discrimination of the models was excellent (AUC >9) and correctly classified >98% of cases. However, due to a low number of data points in either the susceptible or resistant groups, the regression models for these drugs were not statistically significant.

3.5. ROC Analysis of qPCR Data

To determine the threshold (cutoff) of individual genes determined as drug-resistant, a ROC (receiver operating characteristic) analysis was performed. Using the AUC, cutoff, sensitivity, and specificity results (listed, respectively), we evaluated the qPCR assays as a tool to predict resistance to each drug (Table 3). Multiple resistance-associated genes for each antibiotic showed high sensitivities (82%–100%). The performance characteristics of the significant markers, as determined by regression analysis, were as follows: mtrC (0.63; <0.0890; 84%; 44%), gyrA (0.66; <0.0518; 83%; 33%), parE (0.67; <0.0033; 88%; 33%), rpsJ (0.72; <0.0012; 91%; 33%), 16S rRNA (0.96; <0.0454; 100%; 96%), and 23S rRNA (0.99; >7.754; 86%; 99%).

Table 3.

Diagnostic performance of AMR-associated genes to detect antibiotic resistance using cutoff values determined by ROC analysis.

Drug Gene AUC p value Cutoff Sens % 95% CI Spec % 95% CI Likelihood ratio
PEN penA 0.63 0.027 <0.0018 84 74 to 91 38 21 to 56 1.35
ponA 0.63 0.037 <0.0021 82 72 to 89 36 19 to 56 1.27
mtrR 0.62 0.052 <0.0137 80 70 to 88 40 23 to 59 1.33
mtrC 0.63 0.027 <0.0890 84 75 to 91 44 26 to 62 1.49
mtrD 0.63 0.033 <0.0025 83 74 to 90 41 24 to 59 1.4
mtrE 0.65 0.017 <0.0081 84 75 to 91 34 18 to 54 1.28
mtrA 0.62 0.039 <0.1003 86 77 to 93 25 11 to 43 1.15
CIP gyrA 0.66 0.003 <0.0518 83 73 to 91 33 20 to 48 1.24
parC 0.65 0.004 <0.0032 85 74 to 92 29 17 to 43 1.19
parE 0.67 0.001 <0.0033 88 78 to 94 33 20 to 48 1.3
norM 0.65 0.006 <0.0084 93 85 to 98 29 17 to 43 1.3
TET rpsJ 0.72 0.068 <0.0012 91 84 to 96 33 4 to 78 1.37
mtrR 0.71 0.040 <0.0431 96 91 to 99 33 7 to 70 1.45
mtrC 0.76 0.009 <0.1376 90 83 to 95 44 14 to 79 1.62
mtrA 0.72 0.027 <0.1024 87 80 to 93 44 1 to 79 1.57
mtrF 0.75 0.020 <0.1217 97 92 to 99 38 9 to 76 1.56
SPT 16S 0.96 0.115 <0.0454 100 2.5 to 100 96 91 to 99 24
AZ 23S 0.99 ≤0.001 >7.7540 86 42 to 99 99 95 to 99 92.57
macA 0.86 0.003 <0.0628 100 54 to 100 42 32 to 51 1.71
macB 0.78 0.013 <0.0281 100 5 to 100 31 22 to 40 1.44
mtrR 0.78 0.011 <0.0035 86 42 to 100 60 51 to 69 2.15
mtrC 0.69 0.094 <0.0435 86 42 to 100 47 37 to 57 1.61
mtrD 0.83 0.003 <0.0010 86 42 to 100 47 37 to 57 1.61
mtrE 0.81 0.006 <0.0011 86 42 to 100 68 58 to 76 2.67
mtrA 0.80 0.007 <0.0146 86 42 to 100 72 62 to 80 3.03
mtrF 0.80 0.009 <0.0070 86 42 to 100 64 5 to 73 2.36
ESC penA 1.0 0.086 <7.650e − 005 100 2.5 to 100 99 95 to 99 114
mtrR 0.89 0.023 <0.0014 100 29 to 100 80 71 to 87 4.91
mtrC 0.87 0.028 <0.0170 100 29 to 100 78 69 to 85 4.56
mtrD 0.96 0.007 ≤0.001 100 29 to 100 91 84 to 96 11.4
mtrE 0.94 0.01 ≤0.001 100 29 to 100 87 79 to 92 7.53
mtrA 0.92 0.014 <0.0070 100 29 to 100 89 81 to 94 8.77
mtrF 0.92 0.013 <0.0031 100 29 to 100 87 79 to 92 7.53
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Bold = significant resistance-associated marker for prediction of antimicrobial resistance in this setting.

4. Discussion

DNA-based diagnostic approaches which detect resistance-associated single nucleotide polymorphisms (SNPs) are commonly investigated for use in N. gonorrhoeae AMR diagnosis. These approaches require the detection of multiple known mutations to infer resistance to a particular drug. In this study, we considered the whole gene rather than SNPs, and investigated the expression of known antibiotic target genes and efflux pump genes and correlated gene expression with AMR. To determine if sex-specific environments contribute to the transcription of AMR genes, we compared expression levels from isolates with similar susceptibility profiles from males and females. Regression analysis was used to determine the strongest predictors of drug resistance in our population, and using a ROC analysis; we estimated cutoff values.

The antibiotic target genes in N. gonorrhoeae have been widely described [4, 12]. Alterations in antibiotic target genes are associated with increased MICs and resistance. Our analysis shows that expression levels of antibiotic target genes are significantly higher in susceptible isolates compared to nonsusceptible isolates. A recent RNA-based study identified candidate markers from the transcriptomes which were highly expressed in the susceptible isolates only [51]. The mtrR gene, which represses the MtrCDE efflux pump, was higher in the susceptible group. This emphasizes the role of mtrR in AMR. MtrR has a more diverse regulatory role than only the MtrCDE efflux pump. It has also been shown to regulate MtrF (inner membrane accessory protein for efficient drug efflux) and FarR (repressor of the farAB efflux pump) [72]. MtrR has also been described to play a role in the expression of two other genes involved in susceptibility, ponA, and pilQ. By increasing the expression of ponA (encodes penicillin binding protein) and repressing the expression of pilQ (channel for entry for penicillin) [72].

Mutations can interrupt cellular processes and often hold the key to understanding gene function. Mutations in target genes are associated with resistance; however, for most mutations, we did not see any significant difference in gene expression between isolates with mutations and isolates without mutations. We found significant differences between the wildtype genes and isolates genes with mutations mtrF_V213I (higher expression), gyrA_S91F (lower), gyrA_D95G (lower), parC_S87N (lower), parC_V384I (higher), and macA_A8S (lower). A combination of mutations and other factors contribute to increased MICs; however, in our cohort, the majority of resistance to penicillin and tetracycline is attributed to blaTEM and tetM, respectively [35].

It was previously reported that N. gonorrhoeae transcriptional responses to infection differed in genital specimens from men and women, and AMR gene expression was increased in men, with a higher expression of MtrCDE efflux pump-related genes, suggesting that the expression of AMR genes is driven by sex-specific environments [49]. While overall gene expression signatures may be sex-specific, we found that in our cohort of South African patients, there were no significant differences in expression of resistance-associated targets between isolates from men and women. In addition, the farA and farB genes which encode the FarAB efflux pump (export host-derived antimicrobials, including cationic antimicrobial peptides and long-chain fatty acids) [4], were similarly expressed. Based on this outcome, we found that while therapeutic strategies could be based on gender, when using a diagnostic assay, there was no need to streamline the gene target profile based on gender, and that the same targets can be used for AMR detection for specimens from males and females in our setting.

In this study, we determined thresholds of mRNA levels, which could be used for resistance prediction within a South African population. Using regression analysis, we then determined the strongest predictors associated with AMR status. N. gonorrhoeae AMR is associated with numerous resistance mechanisms. We found the most significant markers for AMR status prediction in this population to be mtrC, gyrA, parE, rpsJ, and 23S rRNA. Our approach provides thresholds with high sensitivity for each of the strongest predictors using ROC analysis and can be used as a rule-in test for resistance prediction.

A limitation of the study and proposed models was the lack of clinical isolates with resistance to spectinomycin, azithromycin, cefixime, and ceftriaxone. Future studies will include a larger data set. Another limitation is that this needs to be evaluated on clinical specimens direct from patients to establish sensitivity as well as time-to-result. Also, the strongest predictors for the antibiotic resistance detection were limited to the isolates data used to generate the regression equations and is valid for the local setting in which these strains were isolated. This enforces the need for continuous local surveillance of isolates. Similar regression analysis can be used to identify candidate markers for resistance prediction in different geographic areas.

5. Conclusion

Using real-time qPCR, we have identified that mRNA levels of potential candidate markers of resistance can be used for AMR testing of N. gonorrhoeae. Together with the ROC cutoff values, these can be explored further as a set of genetic markers of antimicrobial resistance in our setting. A larger-scale validation is required, and evaluation directly from clinical specimens. Identifying local candidate markers has the potential to be used as a near-patient test in addition to NAATs identification.

Acknowledgments

This study was funded by the DST-NRF Centre of Excellence (CoE) in HIV Prevention grant (96354) and the National Health Laboratory Service Research Trust grant (19762). The funders had no role in study design, data collection, and interpretation or the decision to submit for publication. The authors acknowledge Prof P. Moodley for donating a subset of isolates, V. Maseko and the staff at National Health Laboratory Service Microbiology Department (Durban).

Data Availability

Whole-genome sequence data is available in DDBJ/ENA/GenBank with the BioProject number PRJNA681740.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Supplementary Materials

Supplementary Materials

Supplementary Figure 1. Confirmation of Neisseria gonorrhoeae isolates using real-time PCR. Supplementary Figure 2. Antibiotic susceptibility profile of Neisseria gonorrhoeae isolated from South Africa between 2013 and 2017. Supplementary Table 1. Results of presumptive and confirmatory identification tests for Neisseria gonorrhoeae. Supplementary Table 2. Comparison of mRNA expression levels between antibiotic susceptible and nonsusceptible N. gonorrhoeae isolates from South Africa. Supplementary Table 3. Comparison of mRNA expression levels between South African N. gonorrhoeae isolates with mutations and with no mutations.

Click here for additional data file. (339.7KB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Materials

Supplementary Figure 1. Confirmation of Neisseria gonorrhoeae isolates using real-time PCR. Supplementary Figure 2. Antibiotic susceptibility profile of Neisseria gonorrhoeae isolated from South Africa between 2013 and 2017. Supplementary Table 1. Results of presumptive and confirmatory identification tests for Neisseria gonorrhoeae. Supplementary Table 2. Comparison of mRNA expression levels between antibiotic susceptible and nonsusceptible N. gonorrhoeae isolates from South Africa. Supplementary Table 3. Comparison of mRNA expression levels between South African N. gonorrhoeae isolates with mutations and with no mutations.

Click here for additional data file. (339.7KB, docx)

Data Availability Statement

Whole-genome sequence data is available in DDBJ/ENA/GenBank with the BioProject number PRJNA681740.

Articles from International Journal of Microbiology are provided here courtesy of Wiley

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