Contact us
닫기
Article Search
닫기

Microbiology and Biotechnology Letters

Research Article(보문)

View PDF

Molecular and Cellular Microbiology  |  Host-Microbe Interaction and Pathogenesis

Microbiol. Biotechnol. Lett. 2024; 52(4): 470-478

https://doi.org/10.48022/mbl.2407.07020

Received: July 30, 2024; Revised: November 7, 2024; Accepted: November 12, 2024

Analysis of Drug Susceptibility Testing and Fluoroquinolone-Resistant Mutations in Mycobacterium tuberculosis Complex

Tae Soung Kim1†, Ga Yeon Kim 2†, and Jae Kyung Kim3*

1Department of Clinical Pathology, Graduate School of Dankook University, Cheonan 31116, Republic of Korea
2Department of Dental Hygiene, College of Health Science, Dankook University, Cheonan 31116, Republic of Korea
3Department of Biomedical Laboratory Science, College of Health Sciences, Dankook University Cheonan 31116, Republic of Korea

Correspondence to :
Jae Kyung Kim,      nerowolf@naver.com

†These authors contributed equally to this work.

Abstract

Go to

Fluoroquinolones (FQs) are important drugs used to treat tuberculosis (TB), particularly multidrug-resistant (MDR) TB, whose management remains challenging. Herein, we aimed to analyze the susceptibility of TB to anti-TB agents and identify gene mutations associated with FQ resistance. Between 2017 and 2022, acid-fast bacilli drug susceptibility testing (DST) was performed in 11,748 cases, whereas between 2020 and 2023, TB quinolone rapid susceptibility testing was performed in 817 cases. Our analyses revealed that 7.3 and 11.4% of cases exhibited isoniazid (INH) resistance at doses of 1.0 and 0.2 μg/ml, respectively. In comparison, rifampin (RIF) resistance was observed in 3.3 and 2.2% of cases at doses of 1.0 and 0.5 μg/ml, respectively. Quinolone resistance-determining region tests identified mutations in gyrA (2.0% in D94G and 1.2% in A90V) and gyrB (0.4% in N499T and 0.4% in D461N), which corresponded to the prevalence of MDR-TB. In this study, DST was conducted for the anti-TB drugs INH, RIF, and FQ to assess resistance patterns in Mycobacterium tuberculosis strains. Various diagnostic methods, including cell culture and DST, are needed to accurately diagnose TB and identify gene mutations associated with FQ resistance, which will help in MDR-TB treatment.

Keywords: Mycobacterium tuberculosis complex, drug susceptibility testing, multidrug-resistant tuberculosis, fluoroquinolones, quinolone resistance determining regions, rifampin-resistant tuberculosis

Graphical Abstract


Introduction

Go to

Fluoroquinolones (FQs) are among the most important anti-tuberculosis (TB) drugs. The most effective secondline drugs and later-generation FQs, such as levofloxacin (LEV) and moxifloxacin (MOX), are used to treat drugresistant TB (DR-TB) [1, 2]. This highlights the need to target various TB bacteria when selecting anti-TB drugs to address TB bacterial heterogeneity [3]. Quinolones are among the most important classes of broad-spectrum antibiotics used as first-line agents against several types of infections; however, the recent emergence of drugresistant bacterial strains has affected the clinical utility of these drugs. Such resistance poses a major public health challenge; thus, considerable efforts have been made to develop new and effective anti-TB drug candidates [4]. DNA gyrase is a major target of quinolone drugs. The most common and clinically relevant resistance occurs due to mutations in enzyme targets [4]. The most prominent drug targets that have recently attracted attention are gyrA and gyrB [5].

Despite being preventable and treatable, TB remains a major global health challenge, with more than 10 million people affected annually [6]. TB continues to cause a considerable number of deaths, highlighting the urgent need for improved diagnostic and treatment strategies. DR-TB encompasses various forms of TB resistance, including isoniazid (INH)-resistant TB, rifampin (RIF)- resistant TB, multi-DR (MDR) TB, extensively drugresistant (XDR)-TB, and pre-XDR-TB. MDR-TB is an ongoing problem for TB control and prevention. Molecular diagnostic methods that detect specific gene mutations associated with anti-TB drug resistance have been recognized as efficient and effective approaches to address this challenge [7]. Pre-XDR-TB refers to various TB strains resistant to RIF or FQs. XDR-TB refers to TB resistant to either RIF or FQs and to at least one bedaquiline or linezolid. In 2022, 73% (2.9 of 4.0 million) of patients diagnosed with bacteriologically confirmed pulmonary TB were evaluated for RIF resistance. Of these, 4.4% had MDR/RIF-resistant-TB, pre-XDR-TB, or XDR-TB [6].

In the Republic of Korea, cases of mycobacterial infections have increased among individuals aged >61 years [8]. Additionally, the detection of acid-fast bacteria (mycobacteria) has increased in clinical samples (such as sputum or bronchoalveolar lavage) from individuals aged >61 years. Latent TB detected using the interferon-gamma release assay also showed an increased incidence [9, 10]. The RIF resistance rate, determined using the standard concentration of RIF resistance standards, was 0.19%, with an 8.57% increase in the number of detections [11]. FQ-resistant cases accounted for 16.56% of multidrugresistant TB isolates, whereas 4.02% exhibited INH resistance. This characteristic distribution resulted in approximately 17.2% of FQ resistance events accompanied by relevant gyrA mutations observed in MDR-TB isolates. This highlights the increased risk posed by increasing FQ resistance among RIF-resistant and INHresistant TB cases, which comprises the effectiveness of newly endorsed MDR-TB regimens [12]. Managing DRTB is increasingly challenging and less promising, perpetuating the relentlessness of the TB pandemic [5].

DR-TB remains a global crisis due to the rising incidence of DR forms of the disease, gaps in detection and prevention, care models, and limited treatment options [13]. Rapid and precise diagnosis of MDR-TB, along with enhancements in the utilization of new drugs, can effectively treat patients with TB and curb bacterial transmission [14, 15]. Both the genotypic and phenotypic characteristics of bacterial strains provide invaluable insights into treatment decisions and facilitate surveillance for emerging resistance. Consequently, diagnostic strategies for DR-TB must be customized to accommodate local disease epidemiology, laboratory capabilities, availability of diagnostic tools and technical assistance, shifts in therapeutic approaches, and the complexities posed by HIV/TB co-infection, multimorbidity, and rising antimicrobial resistance. Addressing these challenges constitutes an essential priority for future research and implementation [16].

The detection of drug resistance may require bacteriological confirmation of TB and drug resistance testing (DST) using rapid molecular diagnostic tests, culture methods, or sequencing techniques. Therefore, we aimed to analyze the susceptibility of patients with TB to anti- TB drugs. We also aimed to identify gene mutations associated with FQ resistance in MDR-TB, pre-XDR-TB, and XDR-TB.

Materials and Methods

Go to

Between 2017 and 2022, 11,748 patients underwent acid-fast bacilli (AFB) DST. Sputum samples were collected from these patients for analysis. The samples were obtained from various hospitals across the country, encompassing primary hospitals with fewer than 30 beds, secondary hospitals with 30−300 beds, and tertiary hospitals with over 300 beds. These samples were then analyzed at the Clinical Laboratory in Yongin City. The presence of Mycobacterium tuberculosis (MTB) was confirmed through AFB culture and polymerase chain reaction (PCR) tests. This analysis focused exclusively on confirmed TB strains, without determining the active status of TB in these cases. Between 2020 and 2023, the results of quinolone resistance-determining region (QRDR) tests were gathered for 817 patients. TB samples were collected from various hospitals nationwide, ranging from primary hospitals with < 30 beds to tertiary hospitals with > 300 beds. The AFB culture test utilized sputum samples treated with 3% N-acetyl-L-cysteine-NaOH to eliminate contaminants. These treated samples were then inoculated onto both solid (3% Ogawa, Shinyang Chemical, Republic of Korea) and liquid (BD BBL Mycobacteria Growth Indicator Tube, BD Biosciences, USA) media. The solid media were subjected to an eight-week culture period, while the liquid media were cultured for six weeks. Upon culturing AFB, TB was identified using TB Ag MPT64 (Abbott, Republic of Korea), and MTB real-time PCR was conducted using an AdvanSure Nucleic Acid D kit (LG Cham, Republic of Korea) to detect MTB. AFB DST was conducted using the agar proportion method with Middlebrook 7H10 medium (BD Biosciences). The critical concentration for RIF resistance was tested for susceptibility at 1.0 and 0.5 μg/ml. For INH resistance, susceptibility was evaluated at critical concentrations of 1.0 and 0.2 μg/ml. Ofloxacin (OFL), a fluoroquinolone (FQ) antibiotic, has a resistance critical concentration of 2.0 μg/ml. In contrast, LEV and MOX have resistance critical concentrations of 1.0 and 0.5 μg/ml, respectively (Table 1).

Table 1 . Distribution of tuberculosis drug susceptibility test results.

Category (CC [μg/ml])SusceptibleResistant
n%n%
INH (0.2)9,42888.61,21011.4
INH (1.0)9,86692.77727.3
RIF (0.5)2,71697.8622.2
RIF (1.0)10,29096.73483.3
SM (2.0)9,90693.17326.9
EMB (5.0)10,31597.32882.7
ETH (5.0)10,59599.6430.4
CAP (10.0)10,62299.8160.2
KM (5.0)10,59899.6400.4
LZD (1.0)2,778100.0--
PAS (2.0)10,57399.4650.6
OFL (2.0)10,50698.81321.2
RBT (0.5)10,37497.52642.5
AMK (4.0)10,61299.8260.2
CYC (30.0)5,960100.0--
LEV (1.0)10,50998.81291.2
MOX (0.5)10,50798.81311.2

Abbreviations: AMK, amikacin; CAP, capreomycin; CC, critical concentration; CYC, cycloserine; EMB, ethambutol; ETH, ethionamide; INH, isoniazid; KM, kanamycin; LEV, levofloxacin; LZD, Linezolid; MOX, moxifloxacin; OFL, ofloxacin; PAS, para-aminosalicylic acid; RBT, rifabutin; RIF, rifampin; SM, streptomycin

For the TB quinolone rapid susceptibility test, nucleic acid extraction was performed using an E3 system (AdvanSure, Republic of Korea). PCR was performed using an ABI Proflex (ABI, Singapore). Denaturation was performed for 30 s at 94℃, annealing was repeated 35 times at 65–66℃ for 40 s, extension was repeated 35 times for 5 min at 72℃, and a final extension was performed at 72℃. The reaction was allowed to proceed for 5 min. Primers (5' to 3') for gyrA_F, gyrA_R, gyrB_F, and gyrB_R were used (Table 5). A total of 11,748 strains were initially collected for DST. However, given limitations in sample availability and varying experimental conditions at different time points, the number of strains tested for each antibiotic differed. For certain antibiotics, such as RIF at 0.5 μg/ml, fewer strains were included, reflecting the limited availability of samples at this concentration.

Table 5 . Primers for gyrA and gyrB were used for fluoroquinolone sequencing in this study.

LocusPrimer sequence (5ʹ to 3ʹ)Annealing temperature of primers
gyrA_FAGCGCAGCTACATCGACTATGCGgyrA: 65℃, gyrB: 66℃ (35 cycles for 40 s)
gyrA_RCTTCGGTGTACCTCATCGCCGCC
gyrB_FTCGGCGCAAGCCCGTATCGCGGC
gyrB_RCATCAGCACGATCTTGTGGTAGC

H37Rv: M. tuberculosis H37Rv complete genome (NCBI Reference Sequence: NC_000962.3); gyrA: NC_000962.3:7302-9818 Mycobacterium tuberculosis H37Rv, complete genome; gyrB: NC_000962.3:5240- 7267 Mycobacterium tuberculosis H37Rv, complete genome

Before nucleotide sequence analysis, the alcohol prep method was used; this involved the addition of 100% ethanol to the sequencing PCR product, followed by centrifugation at 900 ×g for 30 min. Subsequently, 70% ethanol was added, followed by centrifugation at 2,000 ×g for 5 min. Highly deionized formamide was then added, and denaturation was performed at 95℃ for 2 min. DNA sequencing was performed using the ABI 3500Xl sequencer to identify mutations in gyrA and gyrB. The sequencing results were analyzed and confirmed through secondary data analysis using the mutations detected to correlate with the observed drug resistance patterns in MTB strains. The critical concentrations for each drug were determined based on the guidelines provided by the Clinical and Laboratory Standards Institute and the European Committee on Antimicrobial Susceptibility Testing. These references were used to ensure that the drug concentrations applied in this study reflected internationally recognized standards for TB DST. Secondary data analysis was performed using IBM SPSS Statistics (version 29.0; IBM Corp., USA), and the associations among age, sex, DST, and FQ resistance mutations in MTB complex (MTBC) were investigated. The Institutional Review Board (IRB) of Dankook University (IRB No. DKU 2023-11-024) approved this study. This study was conducted in accordance with the principles of the Declaration of Helsinki. The requirement for informed consent was renounced by the IRB of Dankook University because the data were retrospectively analyzed, and no personal information of the patients was used.

Results

Go to

Mycobacterium tuberculosis complex DST

A total of 11,748 DSTs were conducted for MTBC. Among individuals in their 20 s, 545 were subjected to DST, including 4.6% males and 2.5% females. In the 30 s age group, 703 (6%) tests were administered, comprising 3.3% males and 2.7% females. Among those in their 40 s, 1,199 (10.2%) tests were conducted, with 7.1% males and 3.1% females. A total of 1,904 (16.2%) tests were administered to individuals in their 50 s, comprising 13.1% males and 3.1% females. For individuals in their 60 s, 1,760 (15%) tests were administered, comprising 11.4% males and 3.6% females. Additionally, 2,500 (21.3%) tests were administered to individuals in their 70 s, and 2,580 (22.0%) tests were administered to those in their 80 s. Participants in their 90 s underwent 460 tests (1.5% for males and 2.4% for females). Overall, 62.6% (7,354 cases) males and 37.4% (4.394 cases) females were tested. In the DST results, a total of 11,748 strains were initially collected; however, the number of strains tested for each antibiotic differed due to variations in sample availability across testing periods. Specifically, for RIF at 0.5 μg/ml, the number of strains analyzed was limited owing to constraints in sample collection during certain intervals. This variation in sample size across antibiotics is detailed in Tables 1 and 2. The testing rate was higher among individuals in their 70 s (21.3%) and 80 s (22.0%) than among those in other age groups (Fig. 1).

Table 2 . Comparison between males and females in tuberculosis drug susceptibility testing.

Category (CC [μg/ml])MalesFemales
SusceptibleResistantSusceptibleResistant
n%n%n%n%
INH (0.2)5,91388.577211.53,51588.943811.1
INH (1.0)6,19092.64957.43,67693.02777.0
RIF (0.5)1,68297.6412.41,03498.0212.0
RIF (1.0)6,44596.42403.63,84597.31082.7
SM (2.0)6,19892.74877.33,70893.82456.2
EMB (5.0)6,47697.21862.83,83997.41022.6
ETH (5.0)6,65499.5310.53,94199.7120.3
CAP (10.0)6,67699.990.13,94699.870.2
KM (5.0)6,66099.6250.43,93899.6150.4
LZD (1.0)1,723100.0--1,055100.0--
PAS (2.0)6,63499.2510.83,93999.6140.4
OFL (2.0)6,59898.7871.33,90898.9451.1
RBT (0.5)6,50297.31832.73,87298.0812.0
AMK (4.0)6,67099.8150.23,94299.7110.3
CYC (30.0)3,754100.0--2,206100.0--
LEV (1.0)6,60198.7841.33,90898.9451.1
MOX (0.5)6,59998.7861.33,90898.9451.1

Abbreviations: AMK, amikacin; CAP, capreomycin; CC, critical concentration; CYC, cycloserine; EMB, ethambutol; ETH, ethionamide; INH, isoniazid; KM, kanamycin; LZD, Linezolid; LEV, levofloxacin; MOX, moxifloxacin; OFL, ofloxacin; PAS, para-aminosalicylic acid; RBT, rifabutin; RIF, rifampin; SM, streptomycin

Figure 1.Number of patients who underwent Mycobacterium tuberculosis complex drug susceptibility tests stratified by age.

The tested concentrations for TB DST, comprising INH (0.2 μg/ml), INH (1.0 μg/ml), RIF (0.5 μg/ml), and RIF (1.0 μg/ml), demonstrated susceptibility at 88.6, 92.7, 97.8, and 96.7%, respectively. Furthermore, susceptibility was 98.8% for each of the tested concentrations of the FQ drugs OFL (2.0 μg/ml), LEV (1.0 μg/ml), and MOX (0.5 μg/ml).

Among the tested samples, the resistance rates for each antibiotic were analyzed. INH resistance was observed in 11.4% of cases at 0.2 μg/ml and 7.3% of cases at 1.0 μg/ml. RIF resistance was found in 2.2% of cases at 0.5 μg/ml and 3.3% of cases at 1.0 μg/ml. Fluoroquinolone resistance, including OFL, LEV, and MOX, was observed in 1.2% of cases. These findings underscore the varying levels of resistance across different antibiotics, providing insight into resistance patterns in MTBC (Table 1). RIF resistance was observed in 2.0% of the samples at 0.5 μg/ml and in 2.7% of the samples at 1.0 μg/ml. Additionally, 1.1% of the samples were resistant to OFL at 2.0 μg/ml, LEV at 1.0 μg/ml, and MOX at 0.5 μg/ml.

Conversely, susceptibility was noted as follows: In male samples, resistance rates were as follows: 11.5% for INH at 0.2 μg/ml, 7.4% for INH at 1.0 μg/ml, 2.4% for RIF at 0.5 μg/ml, and 3.6% for RIF at 1.0 μg/ml. In female samples, resistance rates were 11.1% for INH at 0.2 μg/ml, 7.0% for INH at 1.0 μg/ml, 2.0% for RIF at 0.5 μg/ml, and 2.7% for RIF at 1.0 μg/ml. Fluoroquinolone resistance rates were 1.3% in males and 1.1% in females for OFL, LEV, and MOX. Among females, susceptibility to INH was observed in 88.9 and 93.0% of the samples at 0.2 and 1.0 μg/ml, respectively. RIF susceptibility was observed in 98.0 and 97.3% of samples at 0.5 and 1.0 μg/ml, respectively. Moreover, the susceptibility to OFL, LEV, and MOX was 98.9% among the female samples. Overall, these data indicate that males demonstrated significantly higher resistance to drugs than females (p < 0.05; chi-square test), who exhibited higher susceptibility rates (Table 2).

MDR-TB and gyrA and gyrB QRDR

Mutations in gyrA (six cases of D94G, six cases of S91P, one case of D94N, and one case of A90V) and gyrB (D461A, N448M, and N499T) correlated with elevated resistance to LEV and MOX, particularly in multidrug-resistant cases. RIF resistance and FQ QRDR mutations were detected in gyrA (five cases of D94G, one case of S91P, one case of D94N, one case of A90V, and one case of D90V); mutations in gyrB (one case of E501D and two cases of N499T) were also detected. We also detected INH- and RIF-susceptible and FQ QRDR mutations in gyrA (one case of D94A and one case of D89G). Finally, INH and RIF resistance and FQ QRDR mutations were detected in gyrA (five cases of D94G, one case of S91P, one case of D94N, and one case of A90V) and gyrB (N499T in two cases). The most frequently detected FQ QRDR mutations were D94G in gyrA (Table 3).

Table 3 . INH and RIF drug susceptibility testing and fluoroquinolone resistance-associated mutation detection.

Drugs (n)Fluoroquinolone resistance-associated mutations
Gene
gyrAgyrB
A90VD94AD89GD94GD94ND90VS91PD461AE501DN448MN499T
INH resistant (196)1616112
RIF resistant (97)1511112
INH and RIF susceptible (227)11
INH and RIF resistant (76)15112

Abbreviations: INH, isoniazid; RIF, rifampin

For MTBC, the mutation and MDR-TB (%) results of FQ DNA sequencing showed 2.0% mutations in D94G in gyrA, 0.6% mutations in MDR-TB, 1.2% mutations in A90V, 0.1% in MDR-TB, 0.4% mutations in N499T in gyrB, 0.2% in MDR-TB, and 0.4% mutations in D461N in gyrB. The D94G mutation in gyrA was the most common (2.0 %), followed by the A90V mutation (1.2%; Table 4).

Table 4 . Detection of drug-resistant mutation sites in the Mycobacterium tuberculosis complex against fluoroquinolones using DNA sequencing.

GeneVariant (common name)Number of mutationsMutations (%)Number of MDR-TB (%)
gyrAA90V101.21 (0.1)
D89G10.1
D90V20.2
D94A30.4
D94G162.05 (0.6)
D94N50.61 (0.1)
D94H10.1
D94Y30.4
S91P70.91 (0.1)
gyrBA504V10.1
D461A20.2
D461N30.4
E501D20.2
N499T30.42 (0.2)
T448M10.1

Note: Total: 817 cases. MDR-TB; multidrug-resistant tuberculosis (Isoniazid, Rifampin); gyrA; DNA gyrase subunit A, gyrB; DNA gyrase subunit B Interpretation of results: Catalog of mutations in Mycobacterium tuberculosis complex and their association with drug resistance World Health Organization (2018, 2023) [1, 18].

Result analysis: National Library of Medicine (BLAST®; https://blast.ncbi.nlm.nih.gov/Blast.cg).

Discussion

Go to

Enhancing the efficacy of antibacterial agents against resistant bacterial strains and exploring novel antibacterial drug candidates will help to overcome challenges associated with antimicrobial resistance. Additionally, the development of a new generation of quinolones with heightened efficacy against resistant bacterial strains can significantly bolster the effective treatment of bacterial infections [4]. The primary goal is to enhance the diagnosis and treatment of DR-TB [19].

RIF resistance was identified in 2.19 and 2.38% of cases at concentrations of 1.0 and 0.5 μg/ml, respectively. In contrast, INH resistance was reported in 9.9% of cases [11]. Resistance to anti-TB drugs has become widespread, with the estimated pooled prevalence of INH and RIF monoresistance rates being significantly elevated compared to previous levels. Additionally, MDR-TB continues to be prevalent among newly diagnosed cases [20]. The geographically heterogeneous distribution of uncommon mutations evidently confers resistance to the most crucial first-line anti-TB drugs [21, 22].

In this study, resistance rates varied significantly among different antibiotics, with INH and RIF showing higher resistance rates than fluoroquinolones. These results align with those of previous studies [21] on resistance patterns in MTB and suggest that monitoring resistance trends for these key drugs is essential for effective TB management. Furthermore, the observed resistance patterns underline the importance of developing diagnostic methods that can rapidly detect such resistance, enabling timely and appropriate treatment decisions. Future studies should investigate additional genetic factors that could contribute to resistance mechanisms.

Among all resistant TB cases, 65.8% were INH monoresistant, 8.4% were MDR, and 5.8% were XDR [15]. Among the MTBC isolates resistant to INH and RIF, 56.9 and 42.7% were found in males and females, respectively. Approximately 89% of MTBC strains were classified as MDR, of which 23% were pre-XDR- and FQresistant, whereas 4% were XDR- and FQ-resistant [23]. Additionally, 72.8% of patients with MDR-TB were males, and the average age of the patients was 39 years [2]. The prevalence of resistance to one or more anti-TB drugs was 23.5%. Among all patients, 9.8% were diagnosed with MDR-TB, 1.2% were newly diagnosed, and 25.5% had a history of TB treatment. Notably, INH resistance was the most prevalent among the mono-resistant TB strains (9.8%). Among patients with MDR-TB, 6.5% were diagnosed with DR-TB, and 3.2% were identified as pre-symptomatic cases of XDR-TB [24]. In a study conducted in the Amhara region, a history of TB treatment and TB/HIV co-infection emerged as determinants of MDR-TB among individuals aged 26–45 years. To effectively prevent and control MDR-TB, special attention should be directed toward individuals aged 26–45 years, those with prior TB treatment experience, and those with TB/HIV co-infection [25].

In a previous study, males showed higher rates of drug resistance than females [23]. Similarly, in the present study, male patients showed a significantly higher rate of antibiotic resistance than female patients, suggesting that differences in resistance rates among sexes may have implications for resistance management and treatment strategies. Specifically, resistance management programs should consider the higher potential risk of antibiotic resistance in male patients, and further research investigating the underlying causes could support the development of tailored treatment approaches.

In a retrospective study investigating the impact of pre-XDR/XDR-TB resistance on MDR-TB treatment outcomes and safety in France, 68.8% of the cases were MDR-TB susceptible to FQs, and 31.2% were pre-XDR. Compared with MDR-TB, which is susceptible to FQs, pre-XDR/XDR-TB is more associated with cavitary lung lesions and bilateral diseases. Overall, there was no significant difference between pre-XDR/XDR-TB (67.7%) and FQ-susceptible MDR-TB (67.8%) [26].

Whole-genome sequencing can furnish precise and comprehensive drug resistance data for MDR- and XDRTB. Such data can facilitate personalized treatment strategies aimed at optimizing treatment outcomes. High levels of FQ resistance due to mutations in gyrA 94AAC and 94GGC are correlated with unfavorable treatment outcomes [27]. The characteristic distribution leads to approximately 17.2% of FQ resistance events and relevant marker gyrA mutations in MDR-TB isolates [12].

Mutations in gyrA A90V, D94G, D94N, and D94Y were significantly associated with an increased risk of LEV resistance. Additionally, mutations in gyrA G88C, A90V, D94G, D94H, D94N, and D94Y were significantly associated with an increased risk of MOX resistance [28]. gyrA D94G mutations conferring resistance to FQ are evident in 1.8% of cases [29]. Of the 130 TB strains analyzed in other regional studies, 68.5% were RIF-resistant, and two strains with gyrA mutations (D94G and S91P) were resistant to FQs [30]. Low-level resistance to MOX is particularly difficult to detect phenotypically when gyrA A90V subpopulations are few and exist only at low frequencies [31]. In a study conducted in Shanghai, whole-genome sequencing helped predict 82.07% of phenotypically drug-resistant strains of drug-resistant MTB isolates obtained from Shanghai and Russia. The predicted sensitivities for RIF, INH, and OFL were 79.7, 86.3, and 83.3%, respectively. The MDR and XDR prediction sensitivities were 92.1 and 92.8%, respectively [32]. XDR-TB was identified in the strain, including resistance to INH, RIF, MOX (gyrA D94G), bedaquiline, and clofazimine, which confer resistance to RIF, INH, FQ, and linezolid. The gyrA D94G mutation is associated with high resistance [1, 18, 33].

The introduction of short-course therapy for MDR-TB and the transition to injection-free therapy have marked notable advancements in DR-TB treatment [13]. However, understanding the molecular mechanisms underlying drug resistance in MTB is crucial for developing enhanced diagnostic tools and effective strategies to combat MDR-TB [7]. Our study reinforces the significant role of gyrA and gyrB mutations, specifically D94G and S91P in gyrA and N499T and D461N in gyrB, as primary contributors to FQ resistance. These mutations serve as important markers for resistance and are consistent with those reported in previous studies [35]. Although these mutations are key contributors, further research on additional genes, such as parC and parE, may provide further insights into resistance mechanisms. In addition to gyrA and gyrB mutations, mutations in other genes, such as parC and parE, may also contribute to FQ resistance through mechanisms related to DNA repair. Although this study focused on gyrA and gyrB mutations, future research could benefit from investigating these and other associated genes to develop a more comprehensive understanding of FQ resistance in MTB.

Investigating gyrA and gyrB mutations, which underlie the resistance mechanisms of TB strains, provides valuable insights for TB treatment strategies. In the current study, mutations associated with INH and RIF resistance and FQ QRDR mutations were identified. INH-resistant mutations included D94G, S91P, D94N, and A90V in gyrA, and D461A, N448M, and N499T in gyrB. RIF resistance and FQ QRDR mutations included gyrA (D94G, S91P, D94N, A90V, and D90V) and gyrB (E501D and N499T) mutations. Mutations associated with susceptibility to INH and RIF included D94A and D89G, respectively, in gyrA. The most detected FQ QRDR mutation was D94G in gyrA, which was correlated with INH and RIF resistance. However, it is crucial to acknowledge that the analysis was constrained because it relied solely on the sample results provided by the inspection agency. Additionally, the rates of specific mutations were determined. The mutation rate of D94G in gyrA was 2.0%, with a corresponding rate of 0.6% for MDR-TB, whereas that of A90V was 1.2%, with an MDR-TB rate of 0.1%. Additionally, the mutation rate of N499T in gyrB was 0.4%, with rates of MDR-TB and D461N of 0.2% and 0.4%, respectively.

In conclusion, the detection of drug resistance necessitated bacteriological confirmation of TB and DST using rapid molecular diagnostic tests, culture methods, and sequencing techniques. Consequently, susceptibility to TB drugs was analyzed, and gene mutations for the FQclass of antibiotics were identified, encompassing MDRTB, pre-XDR-TB, and XDR-TB. The agar proportion method detects RIF resistance at a lower rate than that of molecular testing [34]. Our results indicated that resistance to INH and RIF surpasses resistance to other anti-TB drugs, underscoring the need for additional and varied analyses and research on MDR-TB. Given the variations in resistance detection rates among testing methods, it is essential to conduct culture, INH, RIF DST, and FQ QRDR to effectively detect and diagnose TB. Further research is imperative to develop rapid and accurate tests that will aid public health efforts and deter resistance to crucial anti-TB drugs.

Acknowledgments

Go to

We thank the employees at our testing center in Yongin, South Korea, for helping us access the MTB data. The data used in this study were obtained from Seoul Clinical Laboratories (https://www.scllab.co.kr).

Author Contributions

Go to

Conceptualization, T.S.K. and G.Y.K.; methodology, T.S.K. and G.Y.K.; software, T.S.K.; validation, T.S.K., G.Y.K. and J.K.K.; formal analysis, T.S.K.; investigation, T.S.K.; resources, T.S.K.; data curation, T.S.K.; writing— original draft preparation, T.S.K.; writing—review and editing, T.S.K., G.Y.K. and J.K.K.; visualization, T.S.K. and J.K.K.; supervision, J.K.K.; project administration, J.K.K. All authors have read and agreed to the published version of the manuscript.

Abbreviations

Go to

AFB, acid-fast bacilli; DR-TB, drug-resistant tuberculosis; DST, drug susceptibility testing; FQ, fluoroquinolone; INH, isoniazid; IRB, Institutional Review Board; LEV, levofloxacin; MDR, multidrug-resistant; MOX, moxifloxacin; MTB, Mycobacterium tuberculosis; MTBC, Mycobacterium tuberculosis complex; OFL, ofloxacin; PCR, polymerase chain reaction; QRDR, quinolone resistance-determining region; RIF, rifampin; TB, tuberculosis; XDR, extensively drug-resistant.

References

Go to
  1. World Health Organization. The Use of Next-Generation Sequencing Technologies for the Detection of Mutations Associated with Drug Resistance in Mycobacterium tuberculosis Complex [Technical guide]. WHO/CDS/TB/2018.19. Available from https://iris.who.int/bitstream/handle/10665/274443/WHO-CDS-TB-2018.19-eng.pdf?sequence=1. Accessed Jan. 3, 2024.
  2. Li M, Zhang Y, Wu Z, Jiang Y, Sun R, Yang J, et al. 2024. Transmission of fluoroquinolones resistance among multidrug-resistant tuberculosis in Shanghai, China: a retrospective populationbased genomic epidemiology study. Emerg. Microbes Infect. 13: 2302837.
    Pubmed KoreaMed CrossRef
  3. Perveen S, Negi A, Saini S, Gangwar A, Sharma R. 2024. Identification of chemical scaffolds targeting drug-resistant and latent Mycobacterium tuberculosis through high-throughput whole-cell screening. ACS Infect. Dis. 10: 513-526.
    Pubmed CrossRef
  4. Spencer AC, Panda SS. 2023. DNA gyrase as a target for quinolones. Biomedicines 11: 371.
    Pubmed KoreaMed CrossRef
  5. Alsayed SSR, Gunosewoyo H. 2023. Tuberculosis: Pathogenesis, current treatment regimens and new drug targets. Int. J. Mol. Sci. 24: 5202.
    Pubmed KoreaMed CrossRef
  6. World Health Organization. 2023. Global Tuberculosis Report 2023, World Health Organization, Geneva.
    Available from: https://iris.who.int/bitstream/handle/10665/373828.
  7. Traore AN, Rikhotso MC, Mphaphuli MA, Patel SM, Mahamud HA, Kachienga LO, et al. 2023. Isoniazid and rifampicin resistanceconferring mutations in Mycobacterium tuberculosis isolates from South Africa. Pathogens 12: 1015.
    Pubmed KoreaMed CrossRef
  8. Kim TS, Kim GY, Lee YK, Kim JK. 2023. Isolation rate and species distribution of non-tuberculous mycobacteria From 2014-2021. Am. J. Biochem. Biotechnol. 19: 321-326.
    CrossRef
  9. Kim TS, Kim GY, Lee YK, Kim JK. 2023. Analysis of interferongamma release assay results for latent tuberculosis infection diagnosis at a referral clinical laboratory in South Korea. Am. J. Biochem. Biotechnol. 19: 292-297.
    CrossRef
  10. Kim TS, Kim GY, Lee YK, Kim JK. 2023. Trends in the isolation rates and species distribution of Mycobacteria from 2014 to 2021 at referral clinical laboratories in South Korea. Int. J. Adv. Cult. Technol. 11: 260-267.
  11. Kim C-K, Huh HJ, Park JS, Kim T, Sohn JH. 2023. Comparative evaluation of critical concentrations for detecting borderline rifampin resistance in Mycobacterium tuberculosis. Ann. Clin. Microbiol. 26: 139-145.
    CrossRef
  12. Ramachandra V, Brammacharry U, Muralidhar A, Muthukumar A, Mani R, Muthaiah M, et al. 2023. Assess the diagnostic accuracy of GeneXpert to detect Mycobacterium tuberculosis and rifampicinresistant tuberculosis among presumptive tuberculosis and presumptive drug-resistant tuberculosis patients. Microbiol. Res. 15: 91-108.
    CrossRef
  13. Dookie N, Ngema SL, Perumal R, Naicker N, Padayatchi N, Naidoo K. 2022. The changing paradigm of drug-resistant tuberculosis treatment: Successes, pitfalls, and future perspectives. Clin. Microbiol. Rev. 35: e0018019.
    Pubmed KoreaMed CrossRef
  14. Sanchini A, Lanni A, Giannoni F, Mustazzolu A. 2024. Exploring diagnostic methods for drug-resistant tuberculosis: A comprehensive overview. Tuberculosis (Edinb) 148: 102522.
    Pubmed CrossRef
  15. Dorji T, Horan K, Sherry NL, Tay EL, Globan M, Viberg L, et al. 2024. Whole genome sequencing of drug-resistant Mycobacterium tuberculosis isolates in Victoria, Australia. Int. J. Infect. Dis. 138: 46-53.
    Pubmed CrossRef
  16. Naidoo K, Perumal R, Ngema SL, Shunmugam L, Somboro AM. 2023. Rapid diagnosis of drug-resistant tuberculosis-opportunities and challenges. Pathogens 13: 27.
    Pubmed KoreaMed CrossRef
  17. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10: 421.
    Pubmed KoreaMed CrossRef
  18. World Health Organization. 2023. Catalogue of Mutations in Mycobacterium tuberculosis Complex and Their Association with Drug Resistance. Available from https://iris.who.int/bitstream/handle/10665/374061/9789240082410-eng.pdf?sequence=1.
  19. Viney K, Linh NN, Gegia M, Zignol M, Glaziou P, Ismail N, et al. 2021. New definitions of pre-extensively and extensively drugresistant tuberculosis: update from the World Health Organization. Eur. Respir. J. 57: 2100361.
    Pubmed CrossRef
  20. Reta MA, Tamene BA, Abate BB, Mensah E, Maningi NE, Fourie PB. 2022. Mycobacterium tuberculosis drug resistance in Ethiopia: An updated systematic review and meta-analysis. Trop. Med. Infect. Dis. 7: 300.
    Pubmed KoreaMed CrossRef
  21. Nadarajan D, Hillemann D, Kamara R, Foray L, Conteh OS, Merker M, et al. 2021. Evaluation of the Roche cobas MTB and MTB-RIF/INH assays in samples from Germany and Sierra Leone. J. Clin. Microbiol. 59: 10.1128/jcm.02983-20.
    Pubmed KoreaMed CrossRef
  22. Bartfai Z, Somoskovi A, Kodmon C, Szabo N, Puskas E, Kosztolanyi L, et al. 2001. Molecular characterization of rifampin-resistant isolates of Mycobacterium tuberculosis from Hungary by DNA sequencing and the line probe assay. J. Clin. Microbiol. 39: 3736-3739.
    Pubmed KoreaMed CrossRef
  23. Barilar I, Fernando T, Utpatel C, Abujate C, Madeira CM, Jose B, et al. 2024. Emergence of bedaquiline-resistant tuberculosis and of multidrug-resistant and extensively drug-resistant Mycobacterium tuberculosis strains with rpoB Ile491Phe mutation not detected by Xpert MTB/RIF in Mozambique: a retrospective observational study. Lancet Infect. Dis. 24: 297-307.
    Pubmed CrossRef
  24. Monde N, Munyeme M, Chongwe G, Wensman JJ, Zulu M, Siziya S, et al. 2023. First and second-line anti-tuberculosis drug-resistance patterns in pulmonary tuberculosis patients in Zambia. Antibiotics (Basel) 12: 166.
    Pubmed KoreaMed CrossRef
  25. Ambaye GY, Tsegaye GW. 2021. Factors associated with multidrug resistant tuberculosis among TB patients in selected treatment centers of Amhara Region: A case-control study. Ethiop. J. Health Sci. 31: 25-34.
    Pubmed KoreaMed CrossRef
  26. Kherabi Y, Frechet-Jachym M, Rioux C, Yazdanpanah Y, Mechai F, Pourcher V, et al. 2022. Revised definitions of tuberculosis resistance and treatment outcomes, France, 2006-2019. Emerg. Infect. Dis. 28: 1796-1804.
    Pubmed KoreaMed CrossRef
  27. Hazra D, Lam C, Chawla K, Sintchenko V, Dhyani VS, Venkatesh BT. 2023. Impact of whole-genome sequencing of Mycobacterium tuberculosis on treatment outcomes for MDR-TB/XDR-TB: A systematic review. Pharmaceutics 15: 2782.
    Pubmed KoreaMed CrossRef
  28. An Q, Lin R, Yang Q, Wang C, Wang D. 2023. Evaluation of genetic mutations associated with phenotypic resistance to fluoroquinolones, bedaquiline, and linezolid in clinical Mycobacterium tuberculosis: A systematic review and meta-analysis. J. Glob. Antimicrob. Resist. 34: 214-226.
    Pubmed CrossRef
  29. Prommi A, Wongjarit K, Petsong S, Somsukpiroh U, Faksri K, Kawkitinarong K, et al. 2024. Co-resistance to isoniazid and second-line anti-tuberculosis drugs in isoniazid-resistant tuberculosis at a tertiary care hospital in Thailand. Microbiol. Spectr. 12: e0346223.
    Pubmed KoreaMed CrossRef
  30. Ejo M, Torrea G, Diro E, Abebe A, Kassa M, Girma Y, et al. 2023. Strain diversity and gene mutations associated with presumptive multidrug-resistant Mycobacterium tuberculosis complex isolates in Northwest Ethiopia. J. Glob. Antimicrob. Resist. 32: 167-175.
    Pubmed CrossRef
  31. Feuerriegel S, Kohl TA, Utpatel C, Andres S, Maurer FP, Heyckendorf J, et al. 2021. Rapid genomic first- and second-line drug resistance prediction from clinical Mycobacterium tuberculosis specimens using Deeplex-MycTB. Eur. Respir. J. 57: 2001796.
    Pubmed CrossRef
  32. Wang L, Yang J, Chen L, Wang W, Yu F, Xiong H. 2022. Wholegenome sequencing of Mycobacterium tuberculosis for prediction of drug resistance. Epidemiol. Infect. 150: e22.
    Pubmed KoreaMed CrossRef
  33. Kabahita JM, Kabugo J, Kakooza F, Adam I, Guido O, Byabajungu H, et al. 2022. First report of whole-genome analysis of an extensively drug-resistant Mycobacterium tuberculosis clinical isolate with bedaquiline, linezolid and clofazimine resistance from Uganda. Antimicrob. Resist. Infect. Control 11: 68.
    Pubmed KoreaMed CrossRef
  34. Danchuk SN, Solomon OE, Kohl TA, Dreyer V, Barilar I, Utpatel C, et al. 2024. Challenging the gold standard: the limitations of molecular assays for detection of Mycobacterium tuberculosis heteroresistance. Thorax. 79: 670-675.
    Pubmed KoreaMed CrossRef
  35. Naidoo K, Perumal R, Ngema SL, Shunmugam L, Somboro AM. 2023. Rapid diagnosis of drug-resistant tuberculosis-opportunities and challenges. Pathogens 13: 27.
    Pubmed KoreaMed CrossRef

Article Tools

Starts of Metrics

Share this article on :

Related articles in MBL

Most Searched Keywords ?

What is Most Searched Keywords?

  • It is most registrated keyword in articles at this journal during for 2 years.