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Review

Comparison on Major Gene Mutations Related to Rifampicin and Isoniazid Resistance between Beijing and Non-Beijing Strains of Mycobacterium tuberculosis: A Systematic Review and Bayesian Meta-Analysis

by
Shengqiong Guo
1,2,*,
Virasakdi Chongsuvivatwong
2 and
Shiguang Lei
1
1
Guizhou Provincial Center for Disease Prevention and Control, Guiyang 550004, China
2
Department of Epidemiology, Faculty of Medicine, Prince of Songkla University, Hat Yai 90110, Thailand
*
Author to whom correspondence should be addressed.
Genes 2022, 13(10), 1849; https://doi.org/10.3390/genes13101849
Submission received: 6 September 2022 / Revised: 7 October 2022 / Accepted: 8 October 2022 / Published: 13 October 2022
(This article belongs to the Special Issue Epigenetics and Cell-to-Cell Communication)
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Abstract

:
Objective: The Beijing strain of Mycobacterium tuberculosis (MTB) is controversially presented as the predominant genotype and is more drug resistant to rifampicin and isoniazid compared to the non-Beijing strain. We aimed to compare the major gene mutations related to rifampicin and isoniazid drug resistance between Beijing and non-Beijing genotypes, and to extract the best evidence using the evidence-based methods for improving the service of TB control programs based on genetics of MTB. Method: Literature was searched in Google Scholar, PubMed and CNKI Database. Data analysis was conducted in R software. The conventional and Bayesian random-effects models were employed for meta-analysis, combining the examinations of publication bias and sensitivity. Results: Of the 8785 strains in the pooled studies, 5225 were identified as Beijing strains and 3560 as non-Beijing strains. The maximum and minimum strain sizes were 876 and 55, respectively. The mutations prevalence of rpoB, katG, inhA and oxyR-ahpC in Beijing strains was 52.40% (2738/5225), 57.88% (2781/4805), 12.75% (454/3562) and 6.26% (108/1724), respectively, and that in non-Beijing strains was 26.12% (930/3560), 28.65% (834/2911), 10.67% (157/1472) and 7.21% (33/458), separately. The pooled posterior value of OR for the mutations of rpoB was 2.72 ((95% confidence interval (CI): 1.90, 3.94) times higher in Beijing than in non-Beijing strains. That value for katG was 3.22 (95% CI: 2.12, 4.90) times. The estimate for inhA was 1.41 (95% CI: 0.97, 2.08) times higher in the non-Beijing than in Beijing strains. That for oxyR-ahpC was 1.46 (95% CI: 0.87, 2.48) times. The principal patterns of the variants for the mutations of the four genes were rpoB S531L, katG S315T, inhA-15C > T and oxyR-ahpC intergenic region. Conclusion: The mutations in rpoB and katG genes in Beijing are significantly more common than that in non-Beijing strains of MTB. We do not have sufficient evidence to support that the prevalence of mutations of inhA and oxyR-ahpC is higher in non-Beijing than in Beijing strains, which provides a reference basis for clinical medication selection.

1. Introduction

Tuberculosis (TB) is one of the deadliest transmissible diseases that cause death worldwide. However, only 10% of people infected with Mycobacterium tuberculosis (MTB) develop TB disease [1], indicating that either the host or the pathogen’s genetic factors may play a critical role in determining the occurrence of TB disease. The Beijing strain of MTB is presented as the predominant strain. It plays a vital role in many countries, such as Bangladesh [2], Upper Myanmar [3] and China [4,5], with the Beijing strain accounting for 26.8%, 71.4% and 81.7%, respectively. The latter country, China, holds the second highest tuberculosis (TB) burden, presenting 8.5% of case notifications worldwide [6].
The Beijing strain of MTB is reported to be more virulent, more pathogenic, and faster-growing, with more histopathological changes and drug resistance, especially multidrug-resistance TB (MDR-TB) tendencies, than other strains, leading to a higher mortality rate [7]. The rate of treatment success in MDR-TB remains low, reaching only 47–62.7% [2,8]. MDR TB is not only a severe clinical and epidemiological problem but also entails substantial economic costs of management.
Thus, treating patients with resistance to the main anti-TB agents, such as rifampicin (RIF) and isoniazid (INH), may be many times more expensive compared to treatment costs incurred by the management of TB susceptible to the main medication panel [9]. MDR-TB poses a significant threat not only to the individual faced with diminished chances of cure compared with non-MDR-TB, but also to the community, as outbreaks of MDR-TB have been shown to have devastating consequences [10].
Furthermore, some studies have suggested an association between drug resistance and some MTB genotypes [11,12,13]. Resistance to anti-TB drugs in MTB mainly arises from genomic mutations in genes encoding either the drug target or enzymes involved in drug activation [14,15]. Even some efflux pump genes, such as drrA, drrB, efpA, Rv2459, Rv1634, and Rv1250 [16], were also reported to be related to the resistance of MDR; however, some previous studies suggested the more common candidate genes’ mutations to be related to MDR [10,17,18,19], such as the rpoB gene is associated with rifampicin, and katG, inhA, and ahpC genes are related to isoniazid resistance [10]. Other genes mutations related to drug resistance are also reported, such as rpsL K43R to streptomycin, embB M306V to Ethambutol, pncA promoter T (-11) C to pyrazinamide, gyrA A90V to fluoroquinolones, RRS A1401G to second-line injection drug, and fabGl_promoter C(-15) T to Ethionamide) [20].
It is addressed that 95% of rifampicin resistance (RR) is associated with the mutation in the 81 bp rifampicin resistance determining region (RRDR) [21]. Resistance mutations in RRDR of the rpoB gene were found to be associated with phenotypic RIF resistance. [22]. The rpoB gene codes the β-subunit of DNA-dependent RNA-polymerase, which acts as a major target for RIF, and up to 95–98% of RIF-resistance strains exhibit mutations in the rpoB gene, whereas 90–95% of these mutations are located in RRDR [8,23].
INH resistance is associated with mutations in multiple loci, such as the catalase-peroxidase gene (katG), the enoyl-ACP reductase gene (inhA) and its promoter, the alkyl hydroperoxide reductase gene (ahpC), and the intergenic region between the oxyR and ahpC (oxyR-ahpC) genes, which is distinguished from that of RIF [24,25,26]. One specific KatG variant, S315T, is found in 94% of INH-resistance clinical isolates. Around 15 mutations in inhA have been identified in INH-resistance clinical isolates, although two of them were also found in INH-sensitive strains. In this regard, the analysis of gene expression profiling of the Beijing strain of MTB can give us a snapshot of actively expressed genes under various conditions, even though some other researchers hold the opposite issue [27].
Due to the discrepancies between studies possibly resulting from the small sample sizes and variant detection methods of genes in different areas, pooled evidence is needed to provide better evidence that inform policymakers’ decisions for controlling TB. Bayesian meta-analysis (BMA) is reported that it harbors more robustness [28] than the conventional meta-analysis (MA) and is not limited to the premise of classical statistical methods, which can be combined with a priori information, sample information and general information, can obtain the posterior distribution easily and is based on its effect quantity variance between the mergers of the values, research, other parameters, and 95% CI, e.g., the shrinkage estimation values with the consideration of the potential publication biases. It is believed that Bayesian statistical methods will be more widely used in evidence-based medicine/meta-analyses [28].
This systematic review focused on combining the results about genes relevant to MDR with the concepts of the classifications of Beijing and non-Beijing using conventional meta-analysis MA and BMA. We aimed to compare the major gene mutations related to RIF and INH resistance between Beijing and non-Beijing genotypes and extract the best evidence using evidence-based methods for improving the TB control program’s service based on the genetics of MTB.

2. Methods

2.1. Study Design

This systematic review and Bayesian meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (PRISMA, http://links.lww.com/SLA/C529, accessed on 5 September 2022) (Supplementary File S1 PRISMA checklist) [29] and the meta-analyses of observational studies in epidemiology guidelines [30,31]. Bayesian meta-analysis is performed using Bayesian methods, which provide a profitable opportunity for flexible modeling of inter-study heterogeneity by mildly regularizing priors to obtain a stable estimation, which frequency models prove impossible to calculate [32,33].

2.2. Literature Search Strategy

To ensure that a piece of relevant contemporary information was obtained [31], limits were applied to years 1960 onward and MTB genetics or clinic research related to MDR, or RIF/INH drug resistance. Eventually, a retrieval of literature relating the genetics from 1 January 1960 to the present was performed.
Search engines: Google Scholar, PubMed, ResearchGate, ResearchGate, Cochrane Library and Chinese National Knowledge Infrastructure (CNKI) Database.
Search terms: MTB AND Beijing AND non-Beijing AND gene mutation AND MDR, or RIF, or INH drug resistance; rpoB mutation AND Beijing AND non-Beijing AND MDR, or RIF; katG mutation AND Beijing AND non-Beijing AND MDR, or INH; inhA mutation AND Beijing AND non-Beijing AND MDR, or INH; oxyR-ahpC mutation AND Beijing AND non-Beijing AND MDR, or INH.

2.3. Study Selection Criteria

Inclusion criteria: (1) Full article, abstract, letter presenting the major gene mutations related to MDR of MTB classified as Beijing and non-Beijing strains written in English or Chinese; and (2) Gray literature related to the first point above, which is a kind of information produced outside of traditional publishing and distribution channels, and can include reports, policy literature, working papers, newsletters, government documents, speeches, white papers, urban plans, and so on, written in English or Chinese [34].
Exclusion criteria: (1) Studies only related to the genes of MTB produced by the contacts of the studied subjects or produced by the same subject but obtained through follow-up; (2) studies with drug susceptibility test (DST) involving rifampicin and/or isoniazid only related to children analyzed; and (3) studies conducted in unique sites, such as prisons and asylums.

2.4. Data Extraction

Screening of studies and all essential data from the included studies meeting the inclusion criteria were extracted by the investigators (S.G. and V.C.). The principal mutations of the four genes, rpoB, katG, inhA and oxyR-ahpC, of MTB related to RIF and INH were input into a predesigned Excel sheet. The results were compared electronically according to the two classification variables, Beijing and not-Beijing strains. The place with a supposed gene absence was labeled with “NA” in the Excel sheet.
The study content recorded the data related to the surname of the first author, country of the subjects, date of publication, study design, sample size, and frequency in the relevant sheet (Supplementary File S2).
Any records with discrepancies were resolved by referring to the source articles. Discrepancies between the two reviewers were resolved by consensus involving all the authors. The R package metagear [35,36] was performed for the initial screening articles for the literature review.

2.5. Data Synthesis and Statistical Analysis

All analyses were conducted using R software (version 3.6.3) with the following packages, epicalc, medorator and Bayesmeta [28]. The Bayesian random-effects model was used for Bayesian meta-analysis [28,37]. Significant heterogeneity between studies would be considered the presence of heterogeneity when the p value is less than 0.05 or I2 is greater than 50%.
The leave-one-out [38] and influence sensitivity analyses were also employed by iteratively removing one study at a time while recalculating the odds ratio (OR) to assess the robustness of the pooled values to explore potential sources of inter-study heterogeneity and to further determine the influence of each study, from which the preprint studies had been excluded.
Subgroup analysis was employed for the three groups according to the regions, East Asia, South/Southeast/West/Central Asia and East/North/Central Europe. Potential publication bias was also assessed by the funnel plot, tests of Egger’s liner weighted regression [39] and Begg [40]. Asymmetry of the collected studies’ distribution by visual inspection or p value is less than 0.05 was considered as statistically significant [41], indicating the presence of a publication bias evaluated by weight-function. Duval and Tweedie’s trim and fill method’s assumption was considered to reduce the bias in the pooled estimates [42]. To make it more profitable to interpret, logarithms were converted into corresponding constants where appropriate.

3. Results

3.1. Literature Search Results

In the initial literature search, 1733 relevant articles were identified. After removing 871 duplicates and 573 articles from primary screening, 198 full-text articles were assessed for eligibility in the meta-analysis. Of these, 91 were excluded due to a paucity of sufficient data. Eventually, a total of 134 articles published between 1 January 1960 and 5 March 2022 were included in the quality review part and 31 in the Bayesian meta-analysis part (Figure 1).

3.2. Characteristics of Studies Included

As described in Table 1, a total of 31 studies were included in the final Bayesian meta-analysis. The literature has a relatively wide global range covering Asia (China, Korea, Japan, Thailand, Indonesia, Kyrgyzstan, Bangladesh, India, Iran and Turkey) and Europe (Germany, Latvia, Russia, Ukraine and Sweden). Notably, the proportion of studies conducted in China accounted for the majority.
All included studies described critical elements of study design, including study setting, data source, inclusion criteria, participant selection and statistical methods. No studies explained the solution to the missing values, mentioned sample size calculation, or conducted subgroup analysis based on region (Table 1).

3.3. Mutations Prevalence for Mutations of Genes

Globally, 8785 pooled MTB isolates were tested to identify MDR-TB, RIF and INH resistance patterns, with 5225 identified as Beijing strains and 3560 as non-Beijing strains. The maximum sample size was 876 strains, and the minimum one was 55 isolates. The prevalence of mutations for rpoB, katG, inhA and oxyR-ahpC in Beijing strains was 52.40% (2738/5225), 57.88% (2781/4805), 12.75% (454/3562) and 6.26% (108/1724), respectively; and that in non-Beijing strains was 26.12% (930/3560), 28.65% (834/2911), 10.67% (157/1472) and 7.21% (33/458), separately. The principal variants for the four genes were rpoB Ser531Leu, katG S315T, inhA-15C > T and oxyR-ahpC intergenic region, respectively (Table 2).

3.4. Publication Bias and Sensitivity Analyses

The symmetrical distributions of the funnel plots were detected when the publication biases were evaluated for all the mutations of rpoB, katG, inhA and oxyR-ahpC among Beijing and non-Beijing strains, paralleled with the p > 0.05 of both Egger and Begg tests, indicating the absence of the publication biases. The robustness was detected after sensitivity analysis using leave-one-out and influence tests (Figure 2A–D).

3.5. Mutations of Major Genes in Beijing and Non-Beijing Strains

Of the 31 studies, 31 studies were evaluated for the mutations of rpoB, 27 studies for the mutations of katG, 18 studies for the mutations of inhA and 9 studies for that of oxyR-ahpC [5,10,15,17,20,21,22,24,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69]. The subgroup analysis was conducted for the mutations of rpoB, katG and inhA instead of the mutations of oxyR-ahpC because only a few studies were included to be analyzed for the latter. All the ORs were assessed using the Bayesian meta-analysis as well.
The pooled posterior value of OR for the mutations of rpoB (100% mutated in locus rpoB S531L) was [exp (log1.00)] = 2.72 ((95% confidence interval (CI): 1.90, 3.94) times higher in Beijing than in non-Beijing strains for all 31 studies included that evaluated the mutations of rpoB, analogous with the value of the conventional pooled OR (2.76), with a statistical significance being found in east subgroup analysis. Meanwhile, the combined heterogeneity was detected (I2 = 83.5%), and a prediction interval for the effect as [exp (log1.00)] = 2.72 (95% CI: 0.50, 15.03), meaning there would be an OR of 2.72 for the same indicators for the 32nd (θk+1) study in the future [28] (Figure 3 and Figure 4).
The converged posterior value of OR for the mutations of katG (100% mutated in locus katG S531T) was [exp (log1.17)] = 3.22 (95% CI: 2.12, 4.90) times higher in Beijing than in non-Beijing strains for all the 27 studies included. The mutations of katG, comparable to the value of the pooled OR (3.26) obtained through the traditional meta-analysis, with a significant difference were found in each subgroup analysis. Simultaneously, the combined heterogeneity was detected (I2 = 90.8%), and a prediction interval for effect as [exp (log1.17)] = 3.22 (95% CI: 0.42, 24.53) was found for the 28th study in the future (Figure 5 and Figure 6).
The summarized posterior value of OR for the mutations of inhA (100% mutated in inhA -15 C > T) was [1/exp (log-0.34)] = 1.41 (95% CI: 1/exp (log0.03) = 0.97, 1/exp (log-0.73) = 2.08; the following algorithm is the same) times higher in the non-Beijing than in the Beijing strains for all 18 studies included that evaluated the mutations of inhA, with a significant difference found in the East/North/Central Europe group. Although the pooled posterior value of the OR between BMA and MA are close (1/0.71 vs. 1/0.70), the values of the 95% CIs of both diverted with the marginal significance, which was more obvious rather in the BMA compared to that of the MA (OR = 1.43 (95% CI: 0.95, 2.13)). Furthermore, a combined heterogeneity was detected (I2 = 63.3%), and a prediction interval for effect as 1.41 (95% CI: 0.41, 4.90) was found for the 19th study in the future (Figure 7 and Figure 8).
The pooled posterior value of OR for the mutations of oxyR-ahpC (100% mutated in oxyR-ahpC intergenic region) was [1/exp (log-0.38)] = 1.46 (95% CI: 1/exp (log0.14) = 0.87, 1/exp (log-0.91) = 2.48) times higher in the non-Beijing than in the Beijing strains for all nine studies included that evaluated the mutations of oxyR-ahpC, without any statistical significances found, neither in BMA nor in MA (OR = 1.45, 95% CI: 0.94, 2.22). A homogeneity (I2 = 0.0%) and a prediction interval for the effect as 1.46 (95% CI: 0.59, 3.71) were found for the seventh in a future study were identified (Figure 9 and Figure 10).

4. Discussion

Heterogeneities were identified by both BMA and MA in most mutations of the genes, and no publication biases were detected. Mutations of rpoB and katG related to RIF and INH were significantly more common in Beijing than in non-Beijing strains, which were not identified in the mutations of inhA and oxyR-ahpC. There was not enough evidence to demonstrate that the mutations of inhA and oxyR-ahpC were higher in the non-Beijing than in the Beijing strains.
RRDR, the so-called “hot” locus of the rpoB gene (81-b.p., codon 507–533) harbors around 98% of gene mutations related to RIF drug resistance [8,23]. Compared to the mutations of katG, which were more prevalent in European countries, combined with the evidence exhibited in the Beijing and non-Beijing strains in this study, the mutations of rpoB were more common in Asian countries. This is equivalent to the finding of Anwaiejiang (isolates collected in China). Despite such miscellaneous mutation locations, most of them are harbored in three rpoB codons: 531, 526 and 516 [9]. In this current meta-analysis, 100% of the mutations of rpoB were presented in the pattern of S531L. This is slightly different from a survey with isolates collected from Japan, Korea, and China [43], in which although the most prevalent mutations were similar, only Asp-516 was found with a higher mutation rate in Beijing than in non-Beijing isolates, different from the study results in the Kyrgyz Republic [9] and Korea [10] that displayed a lower rate in rpoB mutation in Beijing vs. in non-Beijing [8,65,66].
However, it might not be comprehensive to use the rpoB gene mutation to represent genes with mutation-conferred resistance to RIF to illustrate the drug resistance of the Beijing strain since some new variants have been found in Beijing strains. According to a previous study, the functional consequence of nonsynonymous Rv2629 as one of the members of the dosR dormancy regulons was found to be upregulated under dormancy conditions in Beijing genotype strains and in a phenotype that might confer a selective advantage under microaerophilic and anaerobic conditions in Beijing strains [70].
The current review demonstrated a prevalence of 100% for the katG315 mutation related to INH-resistance, higher than in some previous studies [10,43,71]. The katG mutations in Beijing strains of MTB manifested a significantly higher rate than that in non-Beijing isolates (57.88% vs. 28.65%). The rate was higher than that of a study in Southern Xinjiang, China (30.6%; 95% CI, 25.8–35.5%, unclassified by lineages). The prevalence of the inhA promoter region mutation in MTB relevant to INH-resistance in Beijing was lower than that of non-Beijing strains in the East/North/Central Europe group, with a significance detected. It might be because the strains of Beijing family strains are not the predominant ones currently [22]. Notably, according to the previous study, some mutations of inhA are also found in drug pan-susceptible strains [71]. The drug resistance rate of oxyR-ahpC of Beijing strains was lower than that of non-Beijing strains (6.26% vs. 7.21%) without significance in MTB relevant to INH-resistance, although both were higher than that in a study of Isakova et al. (1.7%). Similar to the way of the katG gene mutation presented as katG S315T, almost all the mutations related to oxyR-ahpC happened in the oxyR-ahpC intergenic region (100%) [9].
This systematic review and Bayesian meta-analysis focused on combining the results of the principal gene mutations of MTB relevant to RIF/INH with the concepts of the classifications of Beijing and non-Beijing strains. It provides a snapshot of the active genes’ mutations of the circulating MTB and informs policymakers to make feasible decisions for TB control programs. Furthermore, the pooled data harbors a kind of comprehensive information that the individual study lacks, releasing the clinical practitioners with MTB genetics information for reasonable selections of the anti-TB drugs.
However, entirely relying on genetic methods is not that comprehensive and unreasonable since some potential genes’ mutations might have been discovered. Combining considerations based on the merging data of genetics, clinical and epidemiological concerns might be a promising exploration.

5. Limitations

There are several limitations in our study. First, due to the local practical conditions, not all the methods used in the included studies followed the World Health Organization criteria, leading to some potential heterogeneities, although corrected technically, which was not as good as it never happening. Second, although the high concern concentrations are focused on the gene mutations of anti-MTB drug resistance, not so many studies are available with the forms meeting the requirements of both the four names of gene mutation related to and the lineages of MTB as well [72], which might lead to some selection biases on the original studies interpretable for the geographical distribution. Third, we included only the major common mutations of MTB genes related to RIF and INH instead of all genes’ mutations because of the length limitation of the paper, which might not well interpret the difference in the gene mutations of the anti-TB drug resistance and the polymorphisms relevant to the two lineages of MTB.

6. Conclusions

The mutations in rpoB and katG genes in Beijing are significantly more common than those in non-Beijing strains of MTB. We do not have sufficient evidence to support that the prevalence of mutations of inhA and oxyR-ahpC is higher in non-Beijing than in Beijing strains, which provides a reference basis for clinical medication selection.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes13101849/s1. File S2 Data of Studies included.

Author Contributions

S.G. contributed to the study design, literature review, data extraction and analysis, manuscript draft, and revision. S.L. and V.C. contributed to reach a consensus when there was disagreement on the results of the literature search. All authors have read and agreed to the published version of the manuscript.

Funding

1. The Guizhou Science and Technology project (S.G., grant number (2020)1Y355); 2. The Post-subsidy Fund Project of National Natural Science Foundation of China in 2019: Special project for the cultivation of novel academic seedlings and innovative exploration of Guizhou Provincial Center for Disease Control and Prevention 2019 (S.L., grant number 2019); 3. Thailand’s Education Hub for ASEAN Countries (TEH-AC) Scholarship (S.G., grant number TEH-AC 054/2017); 4. The research reported in this publication was partially supported by the Fogarty International Center and the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (V.C., grant number D43 TW009522). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

This study did not need ethical and consent to participate approval because it is a systematic review and Bayesian meta-analysis.

Data Availability Statement

The data and materials that support the findings of this study are included in the article and within the supporting information.

Acknowledgments

The authors acknowledge the original authors, the participants, and any contributors of the included studies.

Conflicts of Interest

The authors declare that they have no competing interests.

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Genes 13 01849 g001 550
Figure 1. Flow diagram of literature screening and identification strategy.
Figure 1. Flow diagram of literature screening and identification strategy.
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Genes 13 01849 g002 550
Figure 2. Funnel plots for publication bias tests ((A), rpoB; (B), katG; (C), inhA; (D), oxyR−ahpC).
Figure 2. Funnel plots for publication bias tests ((A), rpoB; (B), katG; (C), inhA; (D), oxyR−ahpC).
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Genes 13 01849 g003 550
Figure 3. BMA for the mutations of rpoB in Beijing and Non-Beijing Strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain).
Figure 3. BMA for the mutations of rpoB in Beijing and Non-Beijing Strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain).
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Genes 13 01849 g004 550
Figure 4. MA for the mutations of rpoB in Beijing and non-Beijing Strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain).
Figure 4. MA for the mutations of rpoB in Beijing and non-Beijing Strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain).
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Figure 5. BMA for the mutations of katG in Beijing and non-Beijing strains.
Figure 5. BMA for the mutations of katG in Beijing and non-Beijing strains.
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Genes 13 01849 g006 550
Figure 6. MA for the mutations of katG in Beijing and non-Beijing Strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain).
Figure 6. MA for the mutations of katG in Beijing and non-Beijing Strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain).
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Figure 7. BMA for the mutations of inhA in Beijing and non-Beijing strains.
Figure 7. BMA for the mutations of inhA in Beijing and non-Beijing strains.
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Figure 8. MA for the mutations of inhA in Beijing and non-Beijing strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain).
Figure 8. MA for the mutations of inhA in Beijing and non-Beijing strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain).
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Figure 9. BM for the mutations of oxyR-ahpC in Beijing and non-Beijing strains.
Figure 9. BM for the mutations of oxyR-ahpC in Beijing and non-Beijing strains.
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Figure 10. BMA&MA for the mutations of oxyR-ahpC in Beijing and non-Beijing strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain; OxyR.C: oxyR-ahpC).
Figure 10. BMA&MA for the mutations of oxyR-ahpC in Beijing and non-Beijing strains (CI: confidence interval; BJ: Beijing strain; Non-BJ: non-Beijing strain; OxyR.C: oxyR-ahpC).
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Table
Table 1. Characteristics of studies included in the systematic review and Bayesian meta-analysis.
Table 1. Characteristics of studies included in the systematic review and Bayesian meta-analysis.
No.AuthorCountryYearIsolate Sample SizeSample Size by GenotypesrpoB-RifkatG-INHinhA-INHoxyR-ahpC-INH
BeijingNon-BeijingBeijingNon-BeijingBeijingNon-BeijingBeijingNon-BeijingBeijingNon-Beijing
1QianAsian Countries20026650164514NANANANANANA
2TracevskaLatvia2003109634661416346NANANANA
3ToungoussovaRussia20045524311620NANANANANANA
4ParkKorea20057435691742145025048622686
5HillemannGermany20051036241624159311202
6Nikolayevskyysouthern Ukraine20072258913643315247922NANA
7CheunoyThailand20097650262111321743NANA
8ParwatiIndonesia20098182735452948NANANANANANA
9HuChina20103512431085497110NANANANA
10MäkinenRussia201143918425583229121NANANANA
11LiChina20161761562013261317382211
12MaChina20113512431084294619NANANANA
13YuChina2013857875144231040
14MokrousovKyrgyzstan2013103624117317824NANA
15ZhangChina2015376261115258981735845223216
16ZhaoChina2015584414441331617233
17VyazovayaRussia20151078027603717135NANA
18KisaTurkey201295689436436NANANANA
19HongChina2020447378692164130141234NANA
20WangChina20182762562013261317382211
21FigueroaRussia201817913049902299232719NANA
22LiuChina20201731571613816989333NANA
23UddinBangladesh202020584121841217288NANANANA
24WanChina20201831414214110139721820
25GuptaIndia20203817630529494373NANANANA
26VyazovayaRussia20201307357405NANA33NANA
27GaoChina2020876749127437725608210415NANA
28LuoChina202172140931255336551NANANANA
29LuoChina201918215725120219018245174
30GhebremichaelSweden201053670466165944598NANANA
31KhosraviIran20141608152416612NANANANA
Table
Table 2. Mutations prevalence for mutations of rpoB, katG, inhA and oxyR-ahpC.
Table 2. Mutations prevalence for mutations of rpoB, katG, inhA and oxyR-ahpC.
GenesSample SizeMutation IsolatesMutation RatesPrincipal Mutations Pattern
TotalBeijingNon-BeijingBeijingNon-BeijingBeijingNon-Beijing
rpoB878552253560273893052.4026.12rpoB Ser531Leu
katG771648052911278183457.8828.65katG S315T
inhA50343562147245415712.7510.67inhA -15 C > T, promoter region of inhA
oxyR-ahpC21821724458108336.267.21oxyR-ahpC intergenic region
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Guo, S.; Chongsuvivatwong, V.; Lei, S. Comparison on Major Gene Mutations Related to Rifampicin and Isoniazid Resistance between Beijing and Non-Beijing Strains of Mycobacterium tuberculosis: A Systematic Review and Bayesian Meta-Analysis. Genes 2022, 13, 1849. https://doi.org/10.3390/genes13101849

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Guo S, Chongsuvivatwong V, Lei S. Comparison on Major Gene Mutations Related to Rifampicin and Isoniazid Resistance between Beijing and Non-Beijing Strains of Mycobacterium tuberculosis: A Systematic Review and Bayesian Meta-Analysis. Genes. 2022; 13(10):1849. https://doi.org/10.3390/genes13101849

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Guo, Shengqiong, Virasakdi Chongsuvivatwong, and Shiguang Lei. 2022. "Comparison on Major Gene Mutations Related to Rifampicin and Isoniazid Resistance between Beijing and Non-Beijing Strains of Mycobacterium tuberculosis: A Systematic Review and Bayesian Meta-Analysis" Genes 13, no. 10: 1849. https://doi.org/10.3390/genes13101849

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