Skip to main content
Download PDF

The mutation rate of rpoB gene showed an upward trend with the increase of MIRU10, MIRU39 and QUB4156 repetitive number

BMC Genomics volume 24, Article number: 26 (2023) Cite this article

Abstract

Background

Mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) is a frequently used typing method for identifying the Beijing genotype of Mycobacterium tuberculosis (Mtb), which is easily transformed into rifampicin (RIF) resistance. The RIF resistance of Mtb is considered to be highly related with the mutation of rpoB gene. Therefore, this study aimed to analyze the relationship between the repetitive number of MIRU loci and the mutation of rpoB gene.

Methods

An open-source whole-genome sequencing data of Mtb was used to detect the mutation of rpoB gene and the repetitive number of MIRU loci by bioinformatics methods. Cochran-Armitage analysis was performed to analyze the trend of the rpoB gene mutation rate and the repetitive number of MIRU loci.

Results

Among 357 rifampicin-resistant tuberculosis (RR-TB), 304 strains with mutated rpoB genes were detected, and 6 of 67 rifampicin susceptible strains were detected mutations. The rpoB gene mutational rate showed an upward trend with the increase of MIRU10, MIRU39, QUB4156 and MIRU16 repetitive number, but only the repetitive number of MIRU10, MRIU39 and QUB4156 were risk factors for rpoB gene mutation. The Hunter-Gaston discriminatory index (HGDI) of MIRU10 (0.65) and QUB4156 (0.62) was high in the overall sample, while MIRU39 (0.39) and MIRU16 (0.43) showed a moderate discriminatory Power.

Conclusion

The mutation rate of rpoB gene increases with the addition of repetitive numbers of MIRU10, QUB4156 and MIRU39 loci.

Peer Review reports

Introduction

In recent years, the global tuberculosis (TB) epidemic continues to be serious. Drug-resistant tuberculosis, especially those resistant to rifampicin (RR-TB), has become one of the major obstacles to achieve the goal of TB elimination [1]. According to the Global Tuberculosis Report 2020 of World Health Organization (WHO), there were an estimated 465,000 (range, 400,000–535,000) incident cases of RR-TB, and China accounts for 14% of them [2].

More than 95% of rifampicin resistance is associated with mutations in the rpoB gene of Mycobacterium tuberculosis (Mtb), with 97% of mutations occurring within the 81 bp rifampicin-resistant determining region (RRDR) of this gene [3]. Besides, it has been proved that sequence mutation out of RRDR may be involved in the formation of rifampicin cross resistance [4]. In China, Beijing genotype tuberculosis occupies a dominant position of Mtb, Uddin MKM et al. [5] had proved the mutation of rpoB gene was a risk factor of rifampicin resistance for Beijing genotype TB.

In recent years, mycobacterial interspersed repetitive units-variable number tandem repeats (MIRU-VNTR) had been widely used in the typing of TB. Combined with Spoligotyping, MIRU-VNTR typing can distinguish Beijing family genotype with other genotype strains by cluster analysis [6]. Besides, different MIRU loci showed different discriminatory power for Beijing and non-Beijing genotype strains and significant differences were found in mutation of the rpoB gene between two genotype [7, 8]. On this basis, we hypothesized that there may be a correlation between the mutation of rpoB gene and the repetitive number of MIRU loci.

With the popularization of whole-genome sequencing technology, the mutation of known drug resistance genes and MIRU-VNTR information can be obtained based on the analysis of Mtb Illumina, Pacific Biosciences or Oxford Nanopore sequencing data [9, 10]. Therefore, we conducted this study to explore the relationship of rpoB gene mutation and MIRU loci with sequencing data.

Methods

Data source

Sample information was acquired from one study of the Chinese Center for Disease Control and Prevention (Chinese CDC) [11], including the phenotypic drug resistance of each strain, type of patient from which the strain originated, etc. Whole-genome sequencing raw data were deposited at NCBI Sequence Read Archive (SRP134826) and Genome Sequence Archive (CRA000786) (https://ngdc.cncb.ac.cn/search/?dbId=gsa&q = CRA000786)

RpoB gene mutation determination

In the first step, the sequencing data was submitted to remove linker and low-quality base treatment (filtering the bases with Phred < 20) using Fastp (https://github.com/OpenGene/fastp) software. Secondly, BWA (http://bio-bwa.sourceforge.net/bwa.shtml) software was used to compare the above sequence data with the genome template sequence of Mycobacterium tuberculosis standard strain (H37Rv) (obtained from the gene sequence database GenBank access: NC 000962.3 maintained by the National Institutes of Health). In the third step, according to the comparison results, sequencing data samples were screened that the sequencing depth is more than 10× and the genome coverage is more than 95%. Finally, SNPs of each strain compared with H37Rv were identified using Samtools (https://github.com/samtools/samtools/issues), and the lowest value of comparison quality was set to 30. Then VarScan 2 (http://varscan.sourceforge.net) software was used to further identify and screen SNP fixed mutations with a frequency of more than 75% and supported by at least 10 sequences. The whole genome SNPs detected in this study were compared with known rpoB gene mutations (obtained from GenBank gene database) to obtain the mutation information of rpoB gene of each strain [12], and only the non-synonymous mutations were recorded.

MIRU loci repetitive number determination

The sequencing data outputted from Fastp were assessed by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) to guarantee good reads quality. Spades (https://github.com/ablab/spades) was carried to assemble second generation sequencing data to long sequence, the finally assembled data were assessed by QUAST (http://bioinf.spbau.ru/quast) and BUSCO (https://busco.ezlab.org). MIRUReader (https://github.com/phglab/MIRUReader) was used to get the repetitive number of 24 MIRU loci (MIRU02, MTUB04, ETRC, MIRU04, MIRU40, MIRU10, MIRU16, MTUB21, MIRU20, QUB11B, ETRA, Mtub29, Mtub30, ETRB, MIRU23, MIRU24, MIRU26, MIRU27, Mtub34, MIRU31, Mtub39, QUB26, QUB4156, MIRU39) directly from long sequence reads [9].

HGDI calculation

$$HGDI=1-\frac{1}{N\left(N-1\right)}{\sum}_{j=1}^s nj\left(j-1\right)$$

N stands for the total number of strains, nj is the number of strains with the jth genotype, and s is the number of different genotypes at the MIRU-VNTR loci.

Statistical analysis

IBM SPSS 18.0 and GraphPad 7 were implemented for statistical analysis. Chi-square test or t-test was conducted to compare the differences in variables of general characteristics between TB groups with mutational and non-mutational rpoB gene. All variables with a P-value < 0.10 on Chi-Square test and t-test were included in a multivariate conditional logistic regression model to investigate the relationship of the mutation of rpoB gene and the repetitive number of the MIRU loci. Besides, Cochran-Armitage analysis was conducted to determine the trend of rpoB gene mutation rate and the repetitive number of MIRU loci.

Results

The rpoB mutation results of the study samples

There were 424 TB samples included in our study, 357 (84.2%) strains extracted from them were RR-TB, and 67 (15.8%) were rifampicin sensitive strains. Among RR-TB, rpoB genes of 304 strains were detected mutations, and 6 of 67 susceptible strains were detected mutations. RpoB gene mutational rate between strains of retreated cases (83.41%) and new cases (62.32%) showed a significant difference (χ2 = 24.0 P < 0.05).

Relation between rpoB gene and 24-loci MIRU-VNTR

The mutation rate of rpoB gene showed an upward trend with the increase of MIRU10, MIRU39, QUB4156 and MIRU16 repetitive number after the Cochran-Armitage analysis (Fig. 1). However, only the repetitive number of MIRU10, MRIU39 and QUB4156 were risk factors for rpoB gene mutation after adjusted by category (retreated or new cases) and MIRU23 (Table 1).

Fig. 1
figure 1

Cochran-Armitage analysis of rpoB gene mutation rate and MIRU repeated numbers. The abscissa of the black dot on the broken line represents the repetitive number of different MIRU loci, and the ordinate represents the corresponding rpoB gene mutation rate. The broken line trend reflects whether the rpoB mutation rate increases or decreases with the increase of MIRU loci repetitive number. When P < 0.05, there was a significant overall trend between them

Full size image
Table 1 Logistic regression analysis of the rpoB gene mutation and the repetitive number of 24 MIRU loci
Full size table

Allelic diversity of the MIRU loci

As shown in the Table 2, two loci (MIRU10, QUB4156) were highly discriminative (Hunter-Gaston discriminatory index, HGDI> 0.6), two loci (MIRU39, MIRU16) were moderately discriminative (HGDI> 0.3) among all 24 loci studied. The allelic diversity of the 4 loci were different between the rpoB gene mutational strains and non-mutational strains. It was worth noting that MIRU39 showed a moderately discriminablility in rpoB gene mutational strains, while a low discriminablility in rpoB gene non-mutational strains.

Table 2 Allelic diversity of four loci in rpoB gene mutation and non-mutation isolates
Full size table

Discussion

In this study, we assessed the associated risk factors for rpoB gene mutation in data sourced areas. The rpoB gene mutation rate of retreated TB patients (83.41%) was higher than that of new cases (62.32%), it has been proved that the RIF resistance rate of retreated tuberculosis is higher than that of new cases in previous studies [13, 14], the higher rate may since that patients with retreated pulmonary tuberculosis often fail in the initial treatment due to unreasonable or irregular anti-tuberculosis treatment, resulting in the dominant growth of drug-resistant tuberculosis bacteria, and it’s drug resistance mechanism is related to the mutation of rpoB gene which coding RNA polymerase β-subunit [3]. Notably, we found no rpoB gene mutation in partial RR-TB strains, but mutations in sensitive strains, the inconsistency between gene resistance and phenotype resistance may be caused by heterogeneity of Mtb. The presence of low-frequency RR-TB and the predominance of sensitive Mtb in the specimen may result in ineffective extraction of drug-resistant DNA if the specimen is not handled properly, while the proportional method of drug sensitivity suggested that it was RR-TB [15]. Patients may have been treated with multiple anti-tuberculosis drugs before sputum specimens were sent for testing, resulting in multiple Mtb states in sputum specimens, which can also lead to this result [16]. And mutations in the rpoB gene leading to low levels of rifampicin resistance may be the reason that these strains with mutations in the rpoB gene were detected as sensitive [17].

Different VNTR loci always has different discrimination ability between Beijing and non-Beijing genotype Mtb [18]. In our study, MIRU10, MIRU39, QUB4156 and MIRU16 all showed a difference in allellic diversity between the Beijing and non-Beijing genotype strains, but only MIRU39 showed remarkable difference (△HGDI > 0.2).

VNTR is a highly polymorphic and highly repetitive DNA fragment, which is characterized by variety and wide distribution. The distribution of VNTR in Mtb showed high individual specificity [19]. In recent years, MIRU-VNTR had been widely used in the typing of tuberculosis, some loci, such as MIRU10, MIRU39 and QUB4156 could genotype Mtb with high discriminatory power [20,21,22]. In this study, we found that strains with high MIRU10, MIRU39 QUB4156 or MIRU16 repetitive numbers may often have a high rpoB gene mutation rate, but only the repetitive number of MIRU10, MRIU39 and QUB4156 were risk factors for rpoB gene mutation after adjusting by category (retreated or new cases) and MIRU23.

MIRU loci are located in the spacer of DNA coding genes, and their specific functions are not clear. Some scholars [23,24,25] believed that the difference in the copy number of MIRU sites upstream of the coding gene will lead to the difference in the number of ribosomal binding sites (RBS), thus affecting the transcription and expression level of the gene. The coding product encoded by the fadB gene downstream of MIRU10 is an oxidoreductase that binds to flavin adenine dinucleotide (FAD), the oxidative stress response induced by this gene may be one of the mechanisms of anti-tuberculosis drugs killing bacteria [26]. With the increase of MIRU10 loci repetitive number, it may increase the inhibition of fadB gene expression [27], finally resulting in RIF resistance. EccCa1 gene which downstream of MIRU39 is part of the ESX-1 specialized secretion system, which delivers several virulence factors to host cells during infection, including the key virulence factors ESAT-6 and CFP-10 [28, 29]. The increase of MIRU39 repetitive number may target up-regulation of eccCa1 gene expression, resulting in increased bacterial virulence. The coding product encoded by the murT gene downstream of QUB4156 is involved in the pathway peptidoglycan biosynthesis, which is part of cell wall biogenesis [30]. The increase of QUB4156 repetitive number may enhance the virulence of Mtb by promoting the synthesis of cell wall.

The mutation rate of rpoB gene increased with the addition of MIRU10, MRIU39 and QUB4156 repetitive numbers, we speculated that these MIRU loci caused the RIF resistance in Mtb respectively through different ways. Repetitive numbers of MIRU loci are relatively easy to detect in the laboratory [31]. We hope that this upward trend can deepen the understanding of the function of MIRU10, QUB4156 and MIRU39 loci and the mechanism of RIF resistance. However, this experiment is limited to the research characteristics of molecular epidemiology, which need to be further verified by experimental research.

Conclusion

The mutation rate of rpoB gene increased with the addition of the number of repeats at MIRU10, QUB4156 and MIRU39 loci.

Availability of data and materials

The sequencing datasets used and/or analyzed during the current study are available from NCBI Sequence Read Archive (SRP134826) and Genome Sequence Archive (CRA000786) (https://ngdc.cncb.ac.cn/search/?dbId=gsa&q=CRA000786).

References

  1. Caminero Luna JA, Pérez Mendoza G, Rodríguez de Castro F. Multi-drug resistant tuberculosis, ten years later. Tuberculosis multirresistentediez años después. Med Clin (Barc). 2021;156(8):393–401. https://doi.org/10.1016/j.medcli.2020.08.018.

    Article  CAS  Google Scholar 

  2. WHO, Global Tuberculosis Report 2020. (https://apps.who.int/iris/bitstream/handle/10665/336069/9789240013131-eng.pdf)，20220603.

  3. Zaw MT, Emran NA, Lin Z. Mutations inside rifampicin-resistance determining region of rpoB gene associated with rifampicin-resistance in mycobacterium tuberculosis. J Infect Public Health. 2018;11(5):605–10. https://doi.org/10.1016/j.jiph.2018.04.005.

    Article  Google Scholar 

  4. Portelli S, Myung Y, Furnham N, Vedithi SC, Pires DEV, Ascher DB. Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches. Sci Rep. 2020;10(1):18120. https://doi.org/10.1038/s41598-020-74648-y.

    Article  CAS  Google Scholar 

  5. Uddin MKM, Rahman A, Ather MF, et al. Distribution and frequency of rpoB mutations detected by Xpert MTB/RIF assay among Beijing and non-Beijing rifampicin resistant mycobacterium tuberculosis isolates in Bangladesh. Infect Drug Resist. 2020;10(13):789–97. https://doi.org/10.2147/IDR.S240408.

    Article  Google Scholar 

  6. Chee CBE, Lim LKY, Ong RTH, et al. Whole genome sequencing analysis of multidrug-resistant tuberculosis in Singapore, 2006-2018. Eur J Clin Microbiol Infect Dis. 2021;40(5):1079–83. https://doi.org/10.1007/s10096-020-04100-6.

    Article  CAS  Google Scholar 

  7. Shi J, Zheng D, Zhu Y, et al. Role of MIRU-VNTR and spoligotyping in assessing the genetic diversity of mycobacterium tuberculosis in Henan Province, China. BMC Infect Dis. 2018;18(1):447. https://doi.org/10.1186/s12879-018-3351-y.

    Article  CAS  Google Scholar 

  8. Gupta A, Sinha P, Nema V, et al. Detection of Beijing strains of MDR M. tuberculosis and their association with drug resistance mutations in katG, rpoB, and embB genes. BMC Infect Dis. 2020;20(1):752. https://doi.org/10.1186/s12879-020-05479-5.

    Article  CAS  Google Scholar 

  9. Tang CY, Ong RT. MIRUReader: MIRU-VNTR typing directly from long sequencing reads. Bioinformatics. 2020;36(5):1625–6. https://doi.org/10.1093/bioinformatics/btz771.

    Article  CAS  Google Scholar 

  10. Maladan Y, Krismawati H, Wahyuni T, et al. The whole-genome sequencing in predicting mycobacterium tuberculosis drug susceptibility and resistance in Papua, Indonesia. BMC Genomics. 2021;22(1):844. https://doi.org/10.1186/s12864-021-08139-3.

    Article  CAS  Google Scholar 

  11. Huang H, Ding N, Yang T, et al. Cross-sectional whole-genome sequencing and epidemiological study of multidrug-resistant mycobacterium tuberculosis in China. Clin Infect Dis. 2019;69(3):405–13. https://doi.org/10.1093/cid/ciy883.

    Article  CAS  Google Scholar 

  12. Walker TM, Kohl TA, Omar SV, et al. Whole-genome sequencing for prediction of mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study. Lancet Infect Dis. 2015;15(10):1193–202. https://doi.org/10.1016/S1473-3099(15)00062-6.

    Article  CAS  Google Scholar 

  13. Liu L, Zhao X, Wu X, et al. Prevalence and molecular characteristics of drug-resistant mycobacterium tuberculosis in Hainan, China: from 2014 to 2019. BMC Microbiol. 2021;21(1):185. https://doi.org/10.1186/s12866-021-02246-7.

    Article  CAS  Google Scholar 

  14. Li D, Song Y, Yang P, Li X, Zhang AM, Xia X. Genetic diversity and drug resistance of mycobacterium tuberculosis in Yunnan, China. J Clin Lab Anal. 2019;33(5):e22884. https://doi.org/10.1002/jcla.22884.

    Article  CAS  Google Scholar 

  15. Zheng C, Li S, Luo Z, et al. Mixed infections and rifampin Heteroresistance among mycobacterium tuberculosis clinical isolates. J Clin Microbiol. 2015;53(7):2138–47. https://doi.org/10.1128/JCM.03507-14.

    Article  CAS  Google Scholar 

  16. Sun G, Luo T, Yang C, Dong X, Li J, Zhu Y, et al. Dynamic population changes in mycobacterium tuberculosis during acquisition and fixation of drug resistance in patients. J Infect Dis. 2012;206(11):1724–33. https://doi.org/10.1093/infdis/jis601.

    Article  CAS  Google Scholar 

  17. Gopie FA, Commiesie E, Baldi S, et al. Should treatment of low-level rifampicin mono-resistant tuberculosis be different? J Clin Tuberc Other Mycobact Dis. 2021;23:100222. https://doi.org/10.1016/j.jctube.2021.100222.

    Article  CAS  Google Scholar 

  18. Li Y, Pang Y, Zhang T, Xian X, Yang J, Wang R, et al. Genotypes of mycobacterium tuberculosis isolates circulating in Shaanxi Province, China. PLoS One. 2020;15(12):e0242971. https://doi.org/10.1371/journal.pone.0242971.

    Article  CAS  Google Scholar 

  19. Al-Mahrouqi S, Ahmed R, Al-Azri S, Al-Hamidhi S, Balkhair AA, Al-Jardani A, et al. Dynamics of mycobacterium tuberculosis lineages in Oman, 2009 to 2018. Pathogens. 2022;11(5):541. https://doi.org/10.3390/pathogens11050541.

    Article  Google Scholar 

  20. Elsayed MSAE. Applicability of using 15 MIRU-VNTR loci for genotyping of Mycobacterium avium subsp. paratuberculosis from two cattle farms in Egypt. Mol Biol Rep. 2019;46(6):6253–62. https://doi.org/10.1007/s11033-019-05065-2.

    Article  CAS  Google Scholar 

  21. Antonenko PB, Kresyun VI, Antonenko KO. Clusters of mycobacterium tuberculosis genotypes in Odesa region. Mikrobiol Z. 2016;78(2):103–10. Russian 30141603.

    Article  CAS  Google Scholar 

  22. Ghavidel M, Tadayon K, Mosavari N, et al. Introducing the best six loci in mycobacterial interspersed repetitive unit-variable-number tandem repeat (MIRU-VNTR) typing for mycobacterium tuberculosis genotyping. Rep Biochem Mol Biol. 2019;8(3):335–46. https://doi.org/10.1186/s12879-020-05479-5.

    Article  CAS  Google Scholar 

  23. Refaya AK, Sivakumar S, Sundararaman B, Narayanan S. Polymorphism in the RD1 locus and its effect on downstream genes among south Indian clinical isolates of mycobacterium tuberculosis. J Med Microbiol. 2012;61(Pt 10):1352–9. https://doi.org/10.1099/jmm.0.044453-0.

    Article  CAS  Google Scholar 

  24. Yu-Feng W, Chao J, Xian-Feng C. Drug-resistant tuberculosis can be predicted by mycobacterial interspersed repetitive unit locus. Front Microbiol. 2015;6:147. https://doi.org/10.3389/fmicb.2015.00147.

    Article  Google Scholar 

  25. Cheng XF, Jiang C, Zhang M, et al. Mycobacterial interspersed repetitive unit can predict drug resistance of mycobacterium tuberculosis in China. Front Microbiol. 2016;7:378. https://doi.org/10.3389/fmicb.2016.00378.

    Article  Google Scholar 

  26. Cox JAG, Taylor RC, Brown AK, Attoe S, Besra GS, Fütterer K. Crystal structure of mycobacterium tuberculosis FadB2 implicated in mycobacterial β-oxidation. Acta Crystallogr D Struct Biol. 2019;75(Pt 1):101–8. https://doi.org/10.1107/S2059798318017242.

    Article  CAS  Google Scholar 

  27. Pérez-Lago L, Navarro Y, Herranz M, Bouza E, García-de-Viedma D. Differences in gene expression between clonal variants of mycobacterium tuberculosis emerging as a result of microevolution. Int J Med Microbiol. 2013;303(8):674–7. https://doi.org/10.1016/j.ijmm.2013.09.010.

    Article  CAS  Google Scholar 

  28. Clemmensen HS, Knudsen NPH, Rasmussen EM, et al. An attenuated mycobacterium tuberculosis clinical strain with a defect in ESX-1 secretion induces minimal host immune responses and pathology. Sci Rep. 2017;7:46666. https://doi.org/10.1038/srep46666.

    Article  Google Scholar 

  29. Soler-Arnedo P, Sala C, Zhang M, Cole ST, Piton J. Polarly localized EccE1 is required for ESX-1 function and stabilization of ESX-1 membrane proteins in mycobacterium tuberculosis. J Bacteriol. 2020;202(5):e00662–19. https://doi.org/10.1128/JB.00662-19.

    Article  CAS  Google Scholar 

  30. Maitra A, Nukala S, Dickman R, et al. Characterization of the MurT/GatD complex in mycobacterium tuberculosis towards validating a novel anti-tubercular drug target. JAC Antimicrob Resist. 2021;3(1):dlab028. https://doi.org/10.1093/jacamr/dlab028.

    Article  Google Scholar 

  31. Shafipour M, Shirzad-Aski H, Ghaemi EA, et al. Mycobacterium tuberculosis typing using allele-specific oligonucleotide multiplex PCR (ASO-PCR) method. Curr Microbiol. 2021;78(12):4009–13. https://doi.org/10.1007/s00284-021-02659-7.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Thanks are due to China CDC for sharing their sequencing data in Genome Sequence Archive.

Funding

This work was supported by Teaching Reform and Quality Improvement Plan of Education Department of Anhui Province-Famous Teacher (2019jxms066), and Public Health Collaborative Innovation Project of Provincial Undergraduate Medical Colleges (GXXT-2020-22).

Author information

Author notes

  1. Fan Su and Lei Cao contributed equally to this work.

Authors and Affiliations

  1. School of Public Health, Wannan Medical College, Wuhu, Anhui Province, China

    Fan Su, Lei Cao, Xia Ren, Jian Hu, Grace Tavengana, Yumei Zhou, Yuhan Fu & Yufeng Wen

  2. School of Laboratory Medicine, Wannan Medical College, Wuhu, Anhui Province, China

    Huan Wu

  3. School of Clinical Medicine, Wannan Medical College, Wuhu, Anhui Province, China

    Mingfei Jiang

Authors
  1. Fan Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xia Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jian Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Grace Tavengana
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yumei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yuhan Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mingfei Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yufeng Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yufeng Wen designed the research. Fan Su and Lei Cao participated in data analysis and drafted the manuscript. Yuhan Fu, Yumei Zhou, Grace Tavengana, Mingfei Jiang, Huan Wu, Jian Hu and Xia Ren helped analyze data and manuscript development. Yufeng Wen provided research funding and software support. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yufeng Wen.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

None declared.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Su, F., Cao, L., Ren, X. et al. The mutation rate of rpoB gene showed an upward trend with the increase of MIRU10, MIRU39 and QUB4156 repetitive number. BMC Genomics 24, 26 (2023). https://doi.org/10.1186/s12864-023-09120-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-023-09120-y

Keywords

  • RpoB gene
  • Mutation
  • MIRU10
  • QUB4156
  • MIRU39
  • Repetitive number

BMC Genomics

ISSN: 1471-2164

Contact us