Mutations in gidB Confer Low-Level Streptomycin Resistance in Mycobacterium tuberculosis

Sharon Y Wong; Jong Seok Lee; Hyun Kyung Kwak; Laura E Via; Helena I M Boshoff; Clifton E Barry, III

doi:10.1128/AAC.01814-10

. 2011 Jun;55(6):2515–2522. doi: 10.1128/AAC.01814-10

Mutations in gidB Confer Low-Level Streptomycin Resistance in Mycobacterium tuberculosis^{^▿}^,^†

Sharon Y Wong ¹, Jong Seok Lee ², Hyun Kyung Kwak ², Laura E Via ¹, Helena I M Boshoff ¹, Clifton E Barry III ^1,^*

PMCID: PMC3101441 PMID: 21444711

Abstract

The global threat posed by drug-resistant strains of Mycobacterium tuberculosis demands a greater understanding of the genetic basis and molecular mechanisms that govern how such strains develop resistance against various antituberculous drugs. In this report, we examine a new genetic basis for resistance to one of the oldest and most widely used second-line drugs employed in tuberculosis therapy, streptomycin (SM). This marker for SM resistance was first discovered on the basis of genomic data obtained from drug-resistant M. tuberculosis strains collected in Japan, wherein an association was observed between SM resistance and a mutation in gidB, a putative 16S rRNA methyltransferase. By evaluating an isogenic ΔgidB mutant strain constructed from strain H37Rv, we demonstrate the causal role of gidB in conferring a low-level SM-resistant phenotype in M. tuberculosis with a 16-fold increase in the MIC over the parent strain. Among clinical isolates, the modest increase in SM resistance conferred by a gidB mutation leads to an MIC distribution of gidB mutation-containing strains that spans the recommended SM breakpoint concentration currently used in drug susceptibility testing protocols. As such, some gidB mutation-containing isolates are found to be SM sensitive, while others are SM resistant. On the basis of a pharmacodynamic analysis and Monte Carlo simulation, those isolates that are found to be SM sensitive should still respond favorably to SM treatment, while nearly half of those found to be SM resistant will likely respond poorly. This report provides the first microbiological evidence for the contribution of gidB in streptomycin resistance and examines the clinical implications of mutations in the gidB gene.

INTRODUCTION

The World Health Organization (WHO) 2010 report estimates that 440,000 cases of multidrug-resistant (MDR) tuberculosis (TB) emerged in 2008, with 5.4% of these cases being extensively drug resistant (XDR) TB (36). The strains responsible for producing these forms of TB incur far greater mortality and morbidity to TB patients than drug-sensitive strains (12, 13). If left undetected, these drug-resistant strains can further propagate throughout a community with the potential to cause an epidemic of untreatable, drug-resistant TB. Yet if the patients receive a proper diagnosis and are properly cared for, the success of treatment of MDR-TB can be as high as 60% (36). Therefore, there is an urgent need for faster, more accurate diagnostic alternatives to the current, culture-based drug susceptibility test (DST) methods, which require at least 6 to 9 weeks to determine the resistance pattern of the organism. Fortunately, molecular-based diagnostic methods have already begun to emerge as a viable alternative (2, 23, 38). The most successful of these are based on detecting genotypic polymorphisms associated with drug resistance. In order for these improved methods to supplant culture-based methods, the molecular mechanisms and genetic bases that underlie drug resistance must be understood.

In many parts of the world, streptomycin (SM) is the first of the second-line, aminoglycoside drugs used to treat TB treatment failures. Patients who fail their first course of front-line therapy are given a retreatment regimen recommended by the WHO that adds SM to the three front-line agents isoniazid, rifampin, and ethambutol for 8 months (35). The primary mode of action through which SM exerts its toxic effects involves binding to the 16S rRNA and disrupting protein translation (9, 28). Mutations in the rrs gene, which encodes the 16S rRNA, in either the 530 stem-loop or 915 nucleoside regions, both of which are situated close to the SM-binding site, confer moderate levels of SM resistance. A mutation at lysine residue 42 or lysine residue 87 of the S12 ribosomal protein, which interacts intimately with the 16S rRNA near the SM-binding site, confers high-level resistance to the drug (3, 7, 15, 31). Approximately 85% of SM-resistant Mycobacterium tuberculosis isolates observed throughout the world contain one of these genetic markers, leaving roughly 15% of SM-resistant isolates with a yet-to-be-discovered marker for resistance (29, 32).

Recently, we and others have reported an association between M. tuberculosis clinical isolates harboring a variety of mutations in the gidB gene (Rv3919c) and a low-level SM-resistant phenotype (22, 30, 33). However, in all reports, mutations were present in both SM-sensitive and SM-resistant isolates. In this study, we sought to determine definitively the role of gidB in conferring SM resistance in M. tuberculosis. A knockout mutant constructed from strain H37Rv exhibited a low-level SM resistance phenotype and a growth rate comparable to that of the parent strain. We further examined the clinical implications of a gidB mutation and show that the MIC distribution of a population of gidB mutation-containing clinical isolates straddles the recommended breakpoint concentration of SM, resulting in some gidB mutation-containing isolates being classified as SM sensitive and others being classified as SM resistant. Finally, we present a pharmacodynamic Monte Carlo simulation to address the efficacy of SM therapy on those gidB mutation-containing clinical isolates that are classified as SM sensitive.

MATERIALS AND METHODS

Bacterial strains, culture conditions, gidB-knockout mutant generation, and complementation of knockout mutant.

The laboratory strains, plasmids, and primers used in this study are summarized in Tables 1 and 2. One Shot Top10 Escherichia coli (Invitrogen) cells were used for all cloning. M. tuberculosis H37Rv was grown in Middlebrook 7H9 broth (Becton Dickinson, NJ) supplemented with 10% albumin-dextrose-catalase, 0.05% Tween 80, and 0.2% glycerol. All antibiotics used in this study were purchased from Sigma-Aldrich (St. Louis, MO) and used at the following concentrations: kanamycin at 50 μg/ml for both E. coli and M. tuberculosis and hygromycin at 200 μg/ml for E. coli and 50 μg/ml for M. tuberculosis.

Table 1.

Strains and plasmids used in this study

Strain or plasmid	Description	Reference or source
Strains
H37Rv	Laboratory strain of M. tuberculosis H37Rv	ATCC 27294
ΔgidB	In-frame ΔgidB mutant of H37Rv	This work
ΔgidB-pMVgidB	ΔgidB mutant complemented with pMV261-gidB	This work
Plasmids
pCR2.1	Cloning vector, Km^r Amp^r	Invitrogen
p2NIL	Cloning vector, Km^r	24
pMV261	Complementation vector, Hyg^r	10
pMV261-gidB	Complementing construct containing gidB cloned into pMV261	This work
pGOAL17	Plasmid carrying sacB and lacZ genes on a PacI cassette, Amp^r	24
p2NILgidBKO	p2NIL vector carrying upstream and downstream regions of gidB, PacI cassette from pGOAL17, Km^r	This work

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Table 2.

Oligonucleotides used in this study

Purpose and oligonucleotide	Forward primer sequence (5′–3′)	Reverse primer sequence (5′–3′)	Probe sequence
Construction of mutant strain
gidB upstream region	`GGATCCGCCGGCTCGTGAGGTCTG`	`AGCGGCCGCCTTGTTCGGCGCTCC`
gidB downstream region	`AAGCTTGCCTCGATGGCGCTCGGTGGG`	`GGGATCCCTCACGACGAAGTACGGGAG`
gidB	`AAGCTTTCACGCCGTCCCTCCACTCG`	`GGGATCCATGTCTCCGATCGAGCCC`
qRT-PCR analysis
gidB	`AATGGAGCATGCCGTTGATAC`	`GAGCCCGCTCGCCTTT`	`CGAACGGGCGAATG`
parA	`AAGTGTTGCGGACGGTGATT`	`CCGGCGCTTCGGAAA`	`CACGCAGCGTCAAG`
parB	`TCATTGGCTGGATCCCAAA`	`CCGCTCCCCCATCACTATCT`	`CGGCGTGCGCTAC`

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The ΔgidB mutant strain was generated from H37Rv by homologous recombination. Upstream and downstream regions flanking gidB were amplified by PCR and cloned into p2NIL at the HindIII and NotI sites (24). The lacZ- and sacB-containing PacI fragment from pGOAL17 was cloned into the PacI site of p2NIL. The ΔgidB mutant was generated following a previously reported protocol (24). Successful gene disruption was confirmed by direct sequencing and PCR using primers within and flanking the deleted region.

For complementation of ΔgidB, PCR-amplified gidB was cloned into pMV261 at the HindIII and BamHI sites (10). Successful transformants were selected on Middlebrook 7H11 agar plates supplemented with 10% oleic acid-albumin-dextrose-catalase, 0.5% glycerol, and 50 μg/ml hygromycin.

RNA extraction and quantitative real-time PCR analysis.

Three independent biological cultures of H37Rv and ΔgidB were grown in supplemented Middlebrook 7H9 medium to optical densities at 650 nm of 0.25 (early log stage), 1.0 (log stage), and 2.8 (late log stage). Cells were harvested by centrifugation, and the pellets resuspended in 900 μl Trizol reagent (Invitrogen). The cell suspension was transferred to a 1.5-ml screw-cap microtube containing approximately 0.2 ml of 0.1-mm zirconium/silica beads (Biospec Products, Bartlesville, OK). Cells were disrupted by three, 30-s pulses at a speed setting of 6,000 in a Roche MagNA Lyser bead beater and with a 5-min ice incubation after each pulse. Samples were centrifuged at 9,500 × g for 45 s, and the supernatants were transferred to a fresh microcentrifuge tube. A 1/10 volume of 1-bromo-3-chloropropane (Molecular Research Center, Cincinnati, OH) was added to the supernatant, the tube was inverted by hand, and the mixtures were left to separate on ice for 10 min. The samples were centrifuged at 9,500 × g for 10 min at room temperature. The aqueous phase was transferred to a fresh microcentrifuge tube, an equal volume of 70% ethanol was added, and the mixture was inverted by hand. The solution was applied onto Qiagen RNeasy minicolumns, and the RNA was purified according to the manufacturer's protocol, including that for the on-column DNase digestion.

First-strand cDNA synthesis was carried out using Superscript III reverse transcriptase (Invitrogen, Carlsbad, CA). Quantitative real-time PCR (qRT-PCR) was performed using TaqMan Universal PCR master mix with AmpErase uracil-N-glycosylase reagents (Applied Biosystems, Carlsbad, CA), according to the manufacturer's protocol, on a 7500 real-time PCR system (Applied Biosystems, Carlsbad, CA). Primer and probe sequences are given in Table 2. The absolute quantitation method was employed, where a standard curve ranging from 1.5 pg to 15 ng genomic DNA was used for quantification for each primer-probe set. Quantified mRNA levels for gidB, parA, and parB were normalized to the quantified mRNA levels of the housekeeping gene sigA. Each RNA sample was independently reverse transcribed and tested in duplicate for each primer-probe set. Control samples with no reverse transcriptase added during cDNA synthesis were also evaluated in duplicate for all samples to confirm the absence of DNA contamination.

Clinical strains.

Characteristics of the patients from whom clinical isolates were obtained are detailed elsewhere (11, 33). Eighteen gidB mutation-containing isolates and 14 gidB mutation-free isolates were evaluated in this study. Strains were cultured in supplemented Middlebrook 7H9 medium. The resistance and mutation profiles of the clinical isolates used in this study are detailed in Table 3. Strain H37Rv and four clinical isolates containing the SM resistance-associated mutation rpsL A128G (K42R) were used as controls.

Table 3.

Resistance profile, mutation profile in rrs, rpsL, and gidB, and SM MICs for all clinical isolates evaluated in this study^e

Isolate group and identifier	Resistance profile^d	Mutation			SM MIC (μg/ml)
Isolate group and identifier	Resistance profile^d	rrs	rpsL	gidB^a	7H10 medium	LJ medium
Clinical isolates with a mutation in gidB
1	HRZSRb	NM	NM	T133C	2	4
2	HRESX	NM	NM	C413T	1	8^f
3	HRKPThO	NM	NM	T341C	0.5	2
4	HRESX	NM	NM	115ΔC	16	16
5	HZ	NM	NM	A254G	2	16
6	HREZPThPO	NM	NM	102ΔG	0.5	1
7	HRSPO	NM	NM	C413T	2	8
8^b^,^c	HREKO	T17G	NM	G169T	16	128
9	HRZEO	NM	NM	C278A	2	4
10	HRES	A1338C	NM	193ΔG	2	8
11	HREP	NM	NM	T341C	1	4
12	HRZSEKPThO	A1401G	NM	T230C	2	64
13	HRZSEKPThO	A1401G	NM	102ΔG	4	8
14	HRZKPThO	A1401G	NM	C286T	1	1
15	HRZKOPTh	A1401G	NM	A254G	4	8
16	HRZSKPThO	A1401G	NM	102ΔG	2	8
17	HRZESKPThO	A1401G	NM	102ΔG	2	2
18	HRZEK	A1401G, G149T	NM	C142G, G560A	NA	2
Clinical isolates with no mutation in gidB
19	Drug sensitive	−98 insG	NM	NM	1	1
20	HRE	NM	NM	NM	0.25	4
21	Drug sensitive	NM	NM	NM	0.5	4
22	H	NM	NM	NM	0.5	1
23^c	H	A-10C	NM	NM	1	1
24	Drug sensitive	NM	NM	NM	0.25	1
25	Drug sensitive	NM	NM	NM	0.5	2
26	Drug sensitive	NM	NM	NM	1	8
27	HRZ	NM	NM	NM	0.5	1
28	HRSO	NM	NM	NM	0.5	2
29	HREPTh	NM	NM	NM	NA	1
30	HRZEKPThO	A1401G	NM	NM	NA	2
31	HRKCAZ	A1401G	NM	NM	0.25	1
32	HRZKO	A1401G	NM	NM	0.5	1
Clinical isolates with RpsL K42R mutation (control)
33	HRSPTh	NM	128 A–>G	NM	>256	>256
34	HSPTh	NM	128 A–>G	NM	>256	>256
35	RS	NM	128 A–>G	NM	>256	>256
36	HRS	NM	128 A–>G	NM	>256	>256

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Unless otherwise noted, all clinical isolates also harbored the following additional single nucleotide polymorphisms in gidB: 615A → G, 276A → C. Due to the prevalence of these single nucleotide polymorphisms among the clinical isolates, they were not considered to be associated with streptomycin resistance.

Isolate without gidB mutation 615A → G.

Isolate without gidB mutation 276A → C.

Resistance profiles determined in LJ medium by the proportion method (4).

H, isoniazid; R, rifampin; E, ethambutol; Z, pyrazinamide; PTh, prothionamide; P, para-aminosalicylic acid; T, thiacetazone; C, cycloserine; K, kanamycin; S, streptomycin; O, ofloxacin; Lf, levofloxacin; Am, amikacin; Rb, rifabutin; Cl, clarithromycin; A, amoxicillin-clavulanic acid (Augmentin); NM, no mutation; NA, clinical isolates that were omitted from the 7H10 medium MIC analysis due to contamination.

LJ medium MICs are in boldface for isolates that were classified as SM resistant by LJ medium-based DST but SM sensitive by 7H10 medium-based DST.

Drug susceptibility testing.

The MIC values of strain H37Rv and ΔgidB isolates were determined using the broth microdilution method. Strains were grown in supplemented Middlebrook 7H9 medium to an optical density at 650 nm of 0.25 and subsequently diluted with supplemented Middlebrook 7H9 to prepare a 10⁻³ dilution for use in the assay. Each drug of interest was added to the first column of a round-bottom, 96-well plate in triplicate. A 2-fold serial dilution of the drug was created down the remaining columns with supplemented Middlebrook 7H9 medium as the diluent. An equal volume of the 1,000-fold-diluted cell suspension was added to each drug-containing well for a final volume in each well of 100 μl. Plates were incubated at 37°C for 14 days, after which cell growth was visually inspected. The MIC value was defined as the lowest drug concentration that resulted in <1% growth compared to the growth in no-drug control wells.

The MIC values of all clinical isolates were determined using the proportion method on supplemented Middlebrook 7H10 agar and Lowenstein-Jensen (LJ) medium following the method outlined in the National Committee for Clinical Laboratory Standards (NCCLS) and the WHO protocol for DST of second-line antituberculosis drugs (4, 18, 34). Each clinical isolate was evaluated on medium containing 2-fold serial dilutions from 256 to 0.125 μg/ml of streptomycin. Frozen stock cultures of clinical strains were cultured in supplemented Middlebrook 7H9 medium, grown to an optical density at 650 nm of 0.2, and subsequently diluted with supplemented Middlebrook 7H9 to prepare 10⁻² and 10⁻⁴ dilutions for use in the 7H10 agar MIC assay and 10⁰ and 10⁻³ dilutions for use in the LJ medium MIC assay. On 7H10 agar plates, 100 μl of the 10⁻² dilution was inoculated in 3 drops at different points on each quadrant. The same was done for the 10⁻⁴ dilution on separate agar plates. The inoculum was allowed to absorb into the agar inside a biosafety cabinet, before it was incubated inverted at 37°C. After 3 weeks, the numbers of CFU were counted. One hundred microliters of each dilution was inoculated on a separate LJ slant. The inoculum was allowed to absorb into the agar in a slanted position inside the biosafety cabinet before incubation at 37°C with the screw caps slightly loosened to allow evaporation of the inoculum. After 24 h, the screw caps were tightened. Slants were read for MIC determination after 40 days of growth. The MIC value in both media was defined as the lowest drug concentration that resulted in <1% growth compared to the growth on no-drug control plates/slants.

Lowenstein-Jensen medium and Middlebrook 7H10 agar medium.

Streptomycin was incorporated into the LJ medium prior to inspissation and dispensed in 10-ml volumes into screw-cap bottles (28 ml). The media were inspissated at 85°C for 45 min. All drug-loaded media were stored at 4°C and used within 2 weeks. Streptomycin-loaded 7H10 agar plates were prepared according to NCCLS guidelines. Plates were stored at 4°C and used within 1 week.

Statistical analysis.

The MIC distributions of the gidB mutation-containing and gidB mutation-free clinical isolates were evaluated for normalcy using the D'Agostino-Pearson test. The normalcy test, F-test comparison of variances, and t-test comparison of the means were performed using either Microsoft Excel or GraphPad Prism (version 5) software.

Monte Carlo simulation.

The probability that strains classified as either SM sensitive or SM resistant would respond to SM therapy was estimated using a Monte Carlo simulation. The algorithm entailed computationally choosing an MIC value from the distribution of MICs obtained for the gidB mutation-containing isolates. Each value was chosen with a normally distributed probability. The maximum concentrations in serum (C_max) were also computationally chosen with a normally distributed probability based on C_max data obtained from a population of TB patients who received an intravenous dosage of 12 to 15 mg SM/kg of body weight (39). The mean C_max and standard deviation values for this population of patients were obtained through personal communication with Chuck Peloquin. The C_max/MIC value was subsequently calculated and monitored if the value exceeded 10. The process of computationally choosing an MIC and C_max and calculating the corresponding C_max/MIC value was performed 10,000 times for the gidB mutation-containing isolates to generate a distribution of C_max/MIC values and to calculate the probability of exceeding a C_max/MIC value of 10. The same recursive algorithm was performed for the gidB mutation-free isolates. Microsoft Excel software was employed for the Monte Carlo simulation using the NORMINV(rand(),mean,stdev) function.

RESULTS

Growth rate comparison.

To assess the importance of gidB mutations in conferring SM resistance, we constructed an isogenic deletion mutant by homologous recombination in laboratory strain H37Rv. Successful construction of this deletion was confirmed by direct sequencing and PCR using primers within and flanking the deleted region (data not shown). To evaluate the potential fitness cost associated with a gidB deletion, we quantified the number of colonies formed as a function of time. We observed comparable growth rates between the two strains, shown in Fig. 1, indicating no apparent cost in overall fitness associated with the gene deletion.

Fig. 1. — Growth of H37Rv parent and Δ*gidB* mutant strains. Data shown are mean values and standard deviations of the numbers of CFU per ml that grew with respect to time for three independent biological samples.

Expression profile of gidB, parA, and parB.

By simple inspection of the genetic arrangement of gidB within the M. tuberculosis chromosome, one might expect its deletion to induce polar effects; as it seemingly belongs to an operon with the critical, chromosomal partitioning genes, parA and parB (see Fig. S1 in the supplemental material). Casart et al. had previously reported coexpression of gidB with parA and parA with parB in Mycobacterium bovis BCG, a slow-growing mycobacterium closely related to M. tuberculosis whose genetic arrangement of gidB, parA, and parB is nearly identical to that of M. tuberculosis (6). However, their study also showed the presence of at least one distinct transcription start site upstream of each of the three genes, suggesting that their respective transcription profiles may be independent of one another to a certain extent. To determine whether polar effects may have been induced by a gidB deletion, the expression profiles of parA, parB, and gidB were compared between the mutant and parent strains at the early log, log, and late log stages of growth using molecular beacons and qRT-PCR to measure transcript levels. As shown in Fig. 2, expression levels of parA and parB were not affected by the deletion of the upstream gidB gene, consistent with the comparable growth rates observed between mutant and parent strains.

Fig. 2. — Relative expression of *gidB*, *parA*, and *parB* genes in H37Rv parent and Δ*gidB* mutant strains. Expression levels were quantified by qRT-PCR and normalized against *sigA* expression levels. Data shown are mean values and standard errors obtained from three independent biological samples that were each tested in duplicate with each primer-probe set.

Drug susceptibility testing.

The drug susceptibility profiles of the ΔgidB mutant and H37Rv parent strains are shown in Fig. 3. The streptomycin MIC of the ΔgidB mutant was 16-fold higher than that of the H37Rv parent strain, resulting in a low-level SM-resistant phenotype. Complementation of the ΔgidB mutant with a wild-type copy of gidB restored MIC levels back to near wild-type levels. To evaluate for cross-resistance conferred by the gidB deletion, drug susceptibility testing was also performed on other drugs that interact directly with the bacterial ribosome and drugs with targets other than the ribosome (Table 4). No difference in MIC levels was observed between the mutant and parent strains for any of these other drugs.

Table 4.

Drugs evaluated for cross-resistance

Drug class	Drug	MIC (μg/ml)
16S rRNA-inhibiting drugs	Kanamycin	1.25
	Amikacin	0.5
	Capreomycin	1.25
	Paromomycin	0.5
	Hygromycin	4
	Geneticin	4
	Spectinomycin	32
	Kasugamycin	>128
	Tetracycline	16
23S rRNA-inhibiting drugs	Linezolid	1
	Chloramphenicol	8
	Clindamycin	>64
	Clarithromycin	32
	Thiostrepton	1
Non-ribosome-inhibiting drugs	Rifampin	0.25
	Ethambutol	0.5
	Isoniazid	0.08
	Clinafloxacin	0.125
	para-Aminosalicyclic acid	0.0625

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Clinical relevance of gidB mutation.

Since the MIC value of the ΔgidB mutant was higher than that of parent strain H37Rv, we hypothesized that the distribution of MIC levels of a population of clinical isolates with a gidB mutation would accordingly trend higher than that of a population of isolates without a mutation in gidB. Moreover, the slight upward shift in the MIC distribution of gidB mutation-containing isolates would span the breakpoint concentration of SM specified in standard DST protocols, such that some isolates, despite harboring a gidB mutation, would have an MIC less than or equal to the breakpoint concentration and thus be classified SM sensitive, while other isolates, also harboring a gidB mutation, would have an MIC higher than the breakpoint concentration and be classified SM resistant.

To evaluate the effects of alterations in gidB in clinical isolates, we compared the aggregated MIC levels of 18 gidB mutation-containing isolates to those of 14 gidB mutation-free isolates. MIC levels were determined in both 7H10 agar and LJ media following NCCLS- and WHO-recommended protocols to replicate the DST conditions in use throughout the world (18, 34). Due to no growth or contamination in the 7H10 agar DSTs, however, two gidB mutation-free isolates and one gidB mutation-containing isolate had to be omitted from the analysis. The identities of the isolates omitted from analysis on 7H10 agar and all MIC values are provided in Table 3. The MIC levels of isolates containing a gidB mutation were, on average, 4-fold higher in both 7H10 and LJ media than their gidB mutation-free counterparts, thereby confirming our experimental results obtained with laboratory strains (Fig. 4; P = 0.00001 for 7H10 medium; P = 0.0003 for LJ medium). The clinical breakpoint concentration suggested for both methods is indicated by an arrow on both Fig. 4A and B (37). As a result of the upward shift in MIC distributions, part of the population of gidB mutation-containing isolates has an MIC less than or equal to the SM breakpoint concentration and thus was classified as SM sensitive, while the other part of the population has an MIC greater than the breakpoint concentration and thus was classified as SM resistant. These results reconcile the ambiguity surrounding the classification of some clinical isolates as SM resistant and others as SM sensitive, despite the presence of a gidB mutation in both groups.

Fig. 4. — Distribution of MICs of *gidB* mutation-free and *gidB* mutation-containing clinical isolates in Middlebrook 7H10 agar (A) and Lowenstein-Jensen egg-based medium (B). Frequency is represented on a semilog plot, and the MIC values are shown in parentheses. Fourteen *gidB* mutation-free isolates and 18 *gidB* mutation-containing isolates were initially evaluated in this study. However, for 7H10 medium drug susceptibility testing, two *gidB* mutation-free isolates and one *gidB* mutation-containing isolate were lost to contamination. The identity of the omitted 7H10 medium isolates and all MIC values are provided in Table 3. The recommended SM breakpoint concentration is indicated by an arrow (2 μg/ml in 7H10 medium; 4 μg/ml in LJ medium). Isolates with an MIC less than or equal to the SM breakpoint concentration are classified as SM sensitive, while those with an MIC greater than the SM breakpoint concentration are classified as SM resistant. The MIC levels of isolates containing a *gidB* mutation were, on average, 4-fold higher in 7H10 medium and LJ medium than those of their *gidB* mutation-free counterparts (P = 0.00001 for 7H10 medium; P = 0.0003 for LJ medium).

Pharmacodynamic analysis and Monte Carlo simulation of the efficacy of streptomycin therapy for patients with gidB mutation-containing strains.

Given that some strains harboring a gidB mutation will be classified as SM sensitive and patients will potentially be prescribed a course of SM therapy, we sought to explore the therapeutic efficacy of SM therapy on such strains. An initial attempt to compare clinical outcomes between patients harboring a gidB mutation-containing isolate and patients harboring a gidB mutation-free isolate was inconclusive, since most drug-resistant cases were prescribed a multidrug treatment that confounded the effects of SM therapy. As such, we adopted a pharmacodynamic approach coupled with a Monte Carlo simulation to evaluate the therapeutic efficacy of SM therapy for TB patients harboring gidB mutation-containing M. tuberculosis strains.

For aminoglycosides, which exhibit concentration-dependent killing, C_max/MIC is the pharmacodynamic parameter that best correlates with bactericidal activity and clinical response (8, 21, 25). Although a C_max/MIC target has not been established specifically for M. tuberculosis, a C_max/MIC value of 10 is a generally accepted therapeutic target for the aminoglycosides (14, 16, 21). A simulation of 10,000 trials was performed to determine the distribution of calculated C_max/MIC values for gidB mutation-containing isolates and for gidB mutation-free isolates. Each of the 10,000 C_maxs used in the calculation was computationally chosen from a distribution of clinically determined C_maxs (39). The MIC values used in this analysis were those determined in 7H10 medium, since previous studies that examined the correlation between pharmacodynamic indices and clinical response employed MIC values that were determined in agar-based media (1, 16, 17).

On the basis of a 10,000-trial simulation, 74.3% (7,431/10,000) of isolates that harbored a gidB mutation achieved a C_max/MIC value of at least 10, compared to 99.5% (9,953/10,000) of isolates without a gidB mutation. Of the gidB mutation-containing strains, 48.1% (4,812/10,000) had an MIC less than or equal to the SM breakpoint concentration of 2 μg/ml (i.e., SM sensitive) and 51.9% (5,188/10,000) had an MIC greater than the SM breakpoint concentration (i.e., SM resistant). Of the 48.1% SM-sensitive, gidB mutation-containing isolates, 98.5% (4,738/4,812) achieved a C_max/MIC value of at least 10 and should therefore respond favorably to SM therapy. In contrast, of the 51.9% SM-resistant, gidB mutation-containing isolates, only 51.9% (2,693/5,188) were estimated to respond favorably to SM therapy. These statistics stand in sharp contrast to those for isolates that contain no mutation in gidB. From a 10,000-trial simulation of the gidB mutation-free isolates, 99.6% (9,963/10,000) had an MIC less than or equal to the SM breakpoint concentration, with 99.6% (9,920/9,963) of those isolates achieving a C_max/MIC value of at least 10, while 89.2% (33/37) of the remaining 0.4% (37/10,000) SM-resistant, gidB mutation-free isolates achieved a C_max/MIC value of at least 10.

DISCUSSION

In recent years, a series of articles by Ochi and colleagues reported the role of GidB in conferring low-level SM resistance in E. coli, Streptomyces coelicolor, and Bacillus subtilis (19, 20, 22). The first of these reports further extended their study to clinical isolates of M. tuberculosis, whereby an association was observed between isolates harboring a gidB mutation and a low-level SM-resistant phenotype (22). Indeed, a recent study reported by our group also revealed an association between harboring a gidB mutation and a low-level SM-resistant phenotype in clinical isolates (33). However, in both studies, some isolates harboring a gidB mutation were found to be SM sensitive, while other isolates also harboring a gidB mutation—the same mutation in some cases—-were found to be SM resistant. In this study, we demonstrated by gene knockout that in M. tuberculosis, gidB definitively plays in role in conferring low-level SM resistance. Similarly, clinical isolates harboring one of a variety of gidB mutations also exhibited a low-level SM-resistant phenotype.

GidB functions as an S-adenosylmethionine-dependent, 16S rRNA methyltransferase (22). Its methylation target in E. coli is the guanine base at nucleoside position 527 of the 16S rRNA (22). The corresponding nucleoside in M. tuberculosis is G518, with which SM directly interacts and which also lies in the functionally critical, 530 stem-loop pseudoknot region of the 16S rRNA A site (5, 26). Given that most of the clinically observed gidB mutations occurred in residues that are highly conserved in both Gram-positive and Gram-negative bacteria, it is likely that GidB's methyltransferase function is altered to some degree (see Fig. S2 in the supplemental material). We speculate that the altered methyltransferase function changes the methylation status of the 16S rRNA at position 518G, thereby interfering with SM binding and consequently producing the observed SM-resistant phenotype. Although this mechanistic role of GidB-induced SM resistance is only conjecture at this point, it offers a biologically plausible mechanism that is consistent with the resistance mechanisms of the known markers of SM resistance, namely, a mutation at RpsL K42 and mutations in various nucleosides within rrs at positions 491 to 536, which demarcate the 530 stem-loop region. Like the altered GidB methylation site that we speculate, these known markers also reside in or interact with the 530 stem-loop region of the A site and modify the binding affinity of SM to the 16S rRNA, thereby diminishing its toxic effects (5, 9, 27).

On the basis of the results of our pharmacodynamic analysis and Monte Carlo simulation, almost half of gidB mutation-containing isolates that are determined to be SM resistant by the 7H10 medium-based DST protocol may respond poorly to SM therapy. This analysis demonstrates the power of current 7H10 medium-based DST methods to differentiate between those gidB mutation-containing strains that will respond favorably to SM therapy and those that may not. However, because the pharmacodynamic analysis and simulation were limited to the 7H10 medium-derived MIC results, it is uncertain whether the current LJ medium-based DST protocol affords the same level of differentiation. Given that LJ medium-based DSTs are still widely performed throughout the world, further clinical studies are needed to correlate the clinical outcome with pharmacodynamic indices that are based on LJ medium-derived MIC data to determine whether the current LJ medium-based DST protocol is adequate in identifying gidB mutation-containing isolates that may respond poorly to SM therapy.

In support of the need for more correlation studies between clinical outcome and LJ medium-based pharmacodynamic indices is the question of whether the breakpoint concentration of LJ medium-based DST protocols is set appropriately. In this study, all of the isolates that were classified as SM resistant in 7H10 medium were also classified as SM resistant in LJ medium. But an additional six gidB mutation-containing isolates and one gidB mutation-free isolate were classified as SM resistant in LJ medium, while they were classified as SM sensitive in 7H10 medium (Table 3). This discordance suggests that the SM breakpoint concentration in LJ medium may be set too low, resulting in the classification of some isolates as SM resistant when they are actually SM susceptible. These isolates would represent unfortunate missed opportunities for which one more drug (i.e., SM) would be erroneously considered unsuitable for use in treatment. Without studies that correlate clinical outcome with pharmacodynamic indices that are based on LJ medium-derived data, it would be misguided for us to suggest an alternative breakpoint concentration for LJ medium-based DST protocols with just the LJ medium-derived MIC data alone. The marked discordance in SM susceptibility between the 7H10 medium- and LJ medium-based DST results was also observed in our previous study (33). Research efforts are also needed to understand the basis for this seemingly SM-specific discordance in culture-based SM DST methods in order to either modify existing culture-based protocols or develop alternative ways to accurately determine the SM susceptibility profile of clinical strains.

The pharmacodynamic analysis and Monte Carlo simulation also highlight the importance of identifying those gidB mutation-containing strains that may respond poorly to SM therapy. Such isolates may account for up to 15% of the SM-resistant clinical strains for which a genetic marker had yet, until now, to be identified. Identifying such strains would preclude their potentially ineffective treatment with SM and instead direct the use of other drugs to which gidB mutation-containing strains are still susceptible (e.g., kanamycin, amikacin, and capreomycin) (Table 4).

As DSTs move from culture-based methods toward molecular-based diagnostics and because clinical strains may harbor a variety of mutations in gidB, the challenge in developing means to quickly and accurately differentiate strains that harbor one of many mutations in a given gene remains. Our results suggest that for gidB this may remain problematic both because the number of mutations required to accurately assess gidB status is large and also because the impact of specific mutations in gidB on the resistance level of the isolate remains unclear. Thus, at least for this gene, phenotypic DST will for the moment remain the most reliable way of predicting therapeutic response to treatment with streptomycin.

Supplementary Material

[Supplemental material]

supp_55_6_2515__index.html^{(1KB, html)}

ACKNOWLEDGMENTS

We thank Gerry Davies for helpful discussions and suggesting the Monte Carlo simulation work and Chuck Peloquin for providing the pharmacokinetic data necessary to run the Monte Carlo simulation.

Funding for this work was provided by the Intramural Research Program of the NIH, NIAID; the Ministry for Health, Welfare and Family Affairs of the Republic of Korea; and the Korea Foundation for International Cooperation for Science and Technology through a grant provided by the Ministry of Education and Science and Technology of the Republic of Korea.

Footnotes

^†

Supplemental material for this article may be found at http://aac.asm.org/.

^▿

Published ahead of print on 28 March 2011.

REFERENCES

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22. Okamoto S., et al. 2007. Loss of a conserved 7-methylguanosine modification in 16S rRNA confers low-level streptomycin resistance in bacteria. Mol. Microbiol. 63:1096–1106 [DOI] [PubMed] [Google Scholar]
23. Palomino J. C. 2009. Molecular detection, identification and drug resistance detection in Mycobacterium tuberculosis. FEMS Immunol. Med. Microbiol. 56:103–111 [DOI] [PubMed] [Google Scholar]
24. Parish T., Stoker N. G. 2000. Use of a flexible cassette method to generate a double unmarked Mycobacterium tuberculosis tlyA plcABC mutant by gene replacement. Microbiology 146(Pt 8):1969–1975 [DOI] [PubMed] [Google Scholar]
25. Peloquin C. A. 2001. Pharmacological issues in the treatment of tuberculosis. Ann. N. Y. Acad. Sci. 953:157–164 [DOI] [PubMed] [Google Scholar]
26. Powers T., Noller H. F. 1991. A functional pseudoknot in 16S ribosomal RNA. EMBO J. 10:2203–2214 [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Ruusala T., Andersson D., Ehrenberg M., Kurland C. G. 1984. Hyper-accurate ribosomes inhibit growth. EMBO J. 3:2575–2580 [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Ruusala T., Kurland C. G. 1984. Streptomycin preferentially perturbs ribosomal proofreading. Mol. Gen. Genet. 198:100–104 [DOI] [PubMed] [Google Scholar]
29. Shi R. R., Zhang J. Y., Li C. Y., Kazumi Y., Sugawara I. 2007. Detection of streptomycin resistance in Mycobacterium tuberculosis clinical isolates from China as determined by denaturing HPLC analysis and DNA sequencing. Microbes Infect. 9:1538–1544 [DOI] [PubMed] [Google Scholar]
30. Spies F. S., da Silva P. E., Ribeiro M. O., Rossetti M. L., Zaha A. 2008. Identification of mutations related to streptomycin resistance in clinical isolates of Mycobacterium tuberculosis and possible involvement of efflux mechanism. Antimicrob. Agents Chemother. 52:2947–2949 [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Sreevatsan S., et al. 1996. Characterization of rpsL and rrs mutations in streptomycin-resistant Mycobacterium tuberculosis isolates from diverse geographic localities. Antimicrob. Agents Chemother. 40:1024–1026 [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Tracevska T., et al. 2004. Characterisation of rpsL, rrs and embB mutations associated with streptomycin and ethambutol resistance in Mycobacterium tuberculosis. Res. Microbiol. 155:830–834 [DOI] [PubMed] [Google Scholar]
33. Via L. E., et al. 2010. Polymorphisms associated with resistance and cross-resistance to aminoglycosides and capreomycin in Mycobacterium tuberculosis isolates from South Korean patients with drug-resistant tuberculosis. J. Clin. Microbiol. 48:402–411 [DOI] [PMC free article] [PubMed] [Google Scholar]
34. World Health Organization 2001. Guidelines for drug susceptibility testing for second-line anti-tuberculosis drugs for DOTS-Plus. World Health Organization, Geneva, Switzerland [Google Scholar]
35. World Health Organization 1997. Guidelines for the management of drug-resistant tuberculosis. World Health Organization, Geneva, Switzerland [Google Scholar]
36. World Health Organization 2010. Multidrug and extensively drug-resistant TB (M/XDR-TB): 2010 global report on surveillance and response. World Health Organization, Geneva, Switzerland [Google Scholar]
37. World Health Organization 2008. Policy guidance on drug-susceptibility testing (DST) of second-line antituberculosis drugs. World Health Organization, Geneva, Switzerland: [PubMed] [Google Scholar]
38. Young D. B., Perkins M. D., Duncan K., Barry C. E., 3rd 2008. Confronting the scientific obstacles to global control of tuberculosis. J. Clin. Invest. 118:1255–1265 [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Zhu M., et al. 2001. Population pharmacokinetics of intravenous and intramuscular streptomycin in patients with tuberculosis. Pharmacotherapy 21:1037–1045 [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

[Supplemental material]

supp_55_6_2515__index.html^{(1KB, html)}

supp_55_6_2515__1.pdf^{(558.8KB, pdf)}

[B1] 1. Anderson E. T., Young L. S., Hewitt W. L. 1976. Simultaneous antibiotic levels in “breakthrough” gram-negative rod bacteremia. Am. J. Med. 61:493–497 [DOI] [PubMed] [Google Scholar]

[B2] 2. Boehme C. C., et al. 2010. Rapid molecular detection of tuberculosis and rifampin resistance. N. Engl. J. Med. 363:1005–1015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B3] 3. Bottger E. C., Springer B. 2008. Tuberculosis: drug resistance, fitness, and strategies for global control. Eur. J. Pediatr. 167:141–148 [DOI] [PubMed] [Google Scholar]

[B4] 4. Canetti G., et al. 1969. Advances in techniques of testing mycobacterial drug sensitivity, and the use of sensitivity tests in tuberculosis control programmes. Bull. World Health Organ. 41:21–43 [PMC free article] [PubMed] [Google Scholar]

[B5] 5. Carter A. P., et al. 2000. Functional insights from the structure of the 30S ribosomal subunit and its interactions with antibiotics. Nature 407:340–348 [DOI] [PubMed] [Google Scholar]

[B6] 6. Casart Y., Gamero E., Rivera-Gutierrez S., Gonzalez-Y-Merchand J. A., Salazar L. 2008. par genes in Mycobacterium bovis and Mycobacterium smegmatis are arranged in an operon transcribed from “SigGC” promoters. BMC Microbiol. 8:51. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B7] 7. Cole S. T., Telenti A. 1995. Drug resistance in Mycobacterium tuberculosis. Eur. Respir. J. Suppl. 20:701s–713s [PubMed] [Google Scholar]

[B8] 8. Craig W. A. 1998. Pharmacokinetic/pharmacodynamic parameters: rationale for antibacterial dosing of mice and men. Clin. Infect. Dis. 26:1–10 [DOI] [PubMed] [Google Scholar]

[B9] 9. Finken M., Kirschner P., Meier A., Wrede A., Bottger E. C. 1993. Molecular basis of streptomycin resistance in Mycobacterium tuberculosis: alterations of the ribosomal protein S12 gene and point mutations within a functional 16S ribosomal RNA pseudoknot. Mol. Microbiol. 9:1239–1246 [DOI] [PubMed] [Google Scholar]

[B10] 10. George K. M., Yuan Y., Sherman D. R., Barry C. E., III 1995. The biosynthesis of cyclopropanated mycolic acids in Mycobacterium tuberculosis. Identification and functional analysis of CMAS-2. J. Biol. Chem. 270:27292–27298 [DOI] [PubMed] [Google Scholar]

[B11] 11. Jeon C. Y., et al. 2008. Extensively drug-resistant tuberculosis in South Korea: risk factors and treatment outcomes among patients at a tertiary referral hospital. Clin. Infect. Dis. 46:42–49 [DOI] [PubMed] [Google Scholar]

[B12] 12. Jeon D. S., et al. 2009. Survival and predictors of outcomes in non-HIV-infected patients with extensively drug-resistant tuberculosis. Int. J. Tuberc. Lung Dis. 13:594–600 [PubMed] [Google Scholar]

[B13] 13. Kim D. H., et al. 2010. Treatment outcomes and survival based on drug resistance patterns in multidrug-resistant tuberculosis. Am. J. Respir. Crit. Care Med. 182:113–119 [DOI] [PubMed] [Google Scholar]

[B14] 14. Lacy M. K., Nicolau D. P., Nightingale C. H., Quintiliani R. 1998. The pharmacodynamics of aminoglycosides. Clin. Infect. Dis. 27:23–27 [DOI] [PubMed] [Google Scholar]

[B15] 15. Meier A., Sander P., Schaper K. J., Scholz M., Bottger E. C. 1996. Correlation of molecular resistance mechanisms and phenotypic resistance levels in streptomycin-resistant Mycobacterium tuberculosis. Antimicrob. Agents Chemother. 40:2452–2454 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B16] 16. Moore R. D., Lietman P. S., Smith C. R. 1987. Clinical response to aminoglycoside therapy: importance of the ratio of peak concentration to minimal inhibitory concentration. J. Infect. Dis. 155:93–99 [DOI] [PubMed] [Google Scholar]

[B17] 17. Moore R. D., Smith C. R., Lietman P. S. 1984. The association of aminoglycoside plasma levels with mortality in patients with gram-negative bacteremia. J. Infect. Dis. 149:443–448 [DOI] [PubMed] [Google Scholar]

[B18] 18. National Committee for Clinical Laboratory Standards 2003. Susceptibility testing of mycobacteria, norcardiae, and other aerobic actinomycetes. Approved standard M24-A. National Committee for Clinical Laboratory Standards, Wayne, PA: [PubMed] [Google Scholar]

[B19] 19. Nishimura K., Hosaka T., Tokuyama S., Okamoto S., Ochi K. 2007. Mutations in rsmG, encoding a 16S rRNA methyltransferase, result in low-level streptomycin resistance and antibiotic overproduction in Streptomyces coelicolor A3(2). J. Bacteriol. 189:3876–3883 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B20] 20. Nishimura K., et al. 2007. Identification of the RsmG methyltransferase target as 16S rRNA nucleotide G527 and characterization of Bacillus subtilis rsmG mutants. J. Bacteriol. 189:6068–6073 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B21] 21. Nuermberger E., Grosset J. 2004. Pharmacokinetic and pharmacodynamic issues in the treatment of mycobacterial infections. Eur. J. Clin. Microbiol. Infect. Dis. 23:243–255 [DOI] [PubMed] [Google Scholar]

[B22] 22. Okamoto S., et al. 2007. Loss of a conserved 7-methylguanosine modification in 16S rRNA confers low-level streptomycin resistance in bacteria. Mol. Microbiol. 63:1096–1106 [DOI] [PubMed] [Google Scholar]

[B23] 23. Palomino J. C. 2009. Molecular detection, identification and drug resistance detection in Mycobacterium tuberculosis. FEMS Immunol. Med. Microbiol. 56:103–111 [DOI] [PubMed] [Google Scholar]

[B24] 24. Parish T., Stoker N. G. 2000. Use of a flexible cassette method to generate a double unmarked Mycobacterium tuberculosis tlyA plcABC mutant by gene replacement. Microbiology 146(Pt 8):1969–1975 [DOI] [PubMed] [Google Scholar]

[B25] 25. Peloquin C. A. 2001. Pharmacological issues in the treatment of tuberculosis. Ann. N. Y. Acad. Sci. 953:157–164 [DOI] [PubMed] [Google Scholar]

[B26] 26. Powers T., Noller H. F. 1991. A functional pseudoknot in 16S ribosomal RNA. EMBO J. 10:2203–2214 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B27] 27. Ruusala T., Andersson D., Ehrenberg M., Kurland C. G. 1984. Hyper-accurate ribosomes inhibit growth. EMBO J. 3:2575–2580 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B28] 28. Ruusala T., Kurland C. G. 1984. Streptomycin preferentially perturbs ribosomal proofreading. Mol. Gen. Genet. 198:100–104 [DOI] [PubMed] [Google Scholar]

[B29] 29. Shi R. R., Zhang J. Y., Li C. Y., Kazumi Y., Sugawara I. 2007. Detection of streptomycin resistance in Mycobacterium tuberculosis clinical isolates from China as determined by denaturing HPLC analysis and DNA sequencing. Microbes Infect. 9:1538–1544 [DOI] [PubMed] [Google Scholar]

[B30] 30. Spies F. S., da Silva P. E., Ribeiro M. O., Rossetti M. L., Zaha A. 2008. Identification of mutations related to streptomycin resistance in clinical isolates of Mycobacterium tuberculosis and possible involvement of efflux mechanism. Antimicrob. Agents Chemother. 52:2947–2949 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B31] 31. Sreevatsan S., et al. 1996. Characterization of rpsL and rrs mutations in streptomycin-resistant Mycobacterium tuberculosis isolates from diverse geographic localities. Antimicrob. Agents Chemother. 40:1024–1026 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B32] 32. Tracevska T., et al. 2004. Characterisation of rpsL, rrs and embB mutations associated with streptomycin and ethambutol resistance in Mycobacterium tuberculosis. Res. Microbiol. 155:830–834 [DOI] [PubMed] [Google Scholar]

[B33] 33. Via L. E., et al. 2010. Polymorphisms associated with resistance and cross-resistance to aminoglycosides and capreomycin in Mycobacterium tuberculosis isolates from South Korean patients with drug-resistant tuberculosis. J. Clin. Microbiol. 48:402–411 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B34] 34. World Health Organization 2001. Guidelines for drug susceptibility testing for second-line anti-tuberculosis drugs for DOTS-Plus. World Health Organization, Geneva, Switzerland [Google Scholar]

[B35] 35. World Health Organization 1997. Guidelines for the management of drug-resistant tuberculosis. World Health Organization, Geneva, Switzerland [Google Scholar]

[B36] 36. World Health Organization 2010. Multidrug and extensively drug-resistant TB (M/XDR-TB): 2010 global report on surveillance and response. World Health Organization, Geneva, Switzerland [Google Scholar]

[B37] 37. World Health Organization 2008. Policy guidance on drug-susceptibility testing (DST) of second-line antituberculosis drugs. World Health Organization, Geneva, Switzerland: [PubMed] [Google Scholar]

[B38] 38. Young D. B., Perkins M. D., Duncan K., Barry C. E., 3rd 2008. Confronting the scientific obstacles to global control of tuberculosis. J. Clin. Invest. 118:1255–1265 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B39] 39. Zhu M., et al. 2001. Population pharmacokinetics of intravenous and intramuscular streptomycin in patients with tuberculosis. Pharmacotherapy 21:1037–1045 [DOI] [PubMed] [Google Scholar]

PERMALINK

Mutations in gidB Confer Low-Level Streptomycin Resistance in Mycobacterium tuberculosis▿,†

Sharon Y Wong

Jong Seok Lee

Hyun Kyung Kwak

Laura E Via

Helena I M Boshoff

Clifton E Barry III

Abstract

INTRODUCTION

MATERIALS AND METHODS

Bacterial strains, culture conditions, gidB-knockout mutant generation, and complementation of knockout mutant.

Table 1.

Table 2.

RNA extraction and quantitative real-time PCR analysis.

Clinical strains.

Table 3.

Drug susceptibility testing.

Lowenstein-Jensen medium and Middlebrook 7H10 agar medium.

Statistical analysis.

Monte Carlo simulation.

RESULTS

Growth rate comparison.

Fig. 1.

Expression profile of gidB, parA, and parB.

Fig. 2.

Drug susceptibility testing.

Fig. 3.

Table 4.

Clinical relevance of gidB mutation.

Fig. 4.

Pharmacodynamic analysis and Monte Carlo simulation of the efficacy of streptomycin therapy for patients with gidB mutation-containing strains.

DISCUSSION

Supplementary Material

ACKNOWLEDGMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Cited by other articles

Links to NCBI Databases

Mutations in gidB Confer Low-Level Streptomycin Resistance in Mycobacterium tuberculosis^{^▿}^,^†