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4 June 2025

Whole-Genome Sequence Analysis to Assess Mutations in Efflux Pumps in Mycobacterium tuberculosis: The Influence in Drug Resistance

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1
Health Sciences Doctoral Program, Instituto de Ciencias de la Salud, Universidad Veracruzana, Veracruz 91190, Mexico
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Instituto de Salud Pública, Universidad Veracruzana, Av. Luis Castelazo Ayala s/n, A.P. 57 Col. Industrial Animas, Veracruz 91190, Mexico
3
Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Monterrey 64849, Mexico
4
Red Multidisciplinaria de Investigación en Tuberculosis, Ensenada 22890, Mexico
Microorganisms2025, 13(6), 1306;https://doi.org/10.3390/microorganisms13061306
This article belongs to the Special Issue Prevention, Treatment and Diagnosis of Tuberculosis, 2nd Edition
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Abstract

Efflux pumps are proteins related to the transport of molecules in bacteria, and some of them have been recently reported to be involved in drug resistance (DR) in Mycobacterium tuberculosis. In addition, the association with type 2 diabetes mellitus (T2DM) has been considered a factor favoring the development of drug resistance. Therefore, the aim of this study was to characterize, by analysis of M. tuberculosis genomes, the variants in efflux pump genes and to determine the level of association with T2DM and DR. Nearly 400 Mtb genomes from individuals with and without T2DM and with and without DR were recovered. Of the 164 efflux pump genes analyzed, 10 lack any variant, while 154 genes presented from 3 to 19 variants. The variant S217P in mmpL13a (Rv1145) was the most abundant, found in 98 (25%) isolates. A significant association was observed between 19 variants and DR, and between 20 variants and T2DM (p ≤ 0.005). Although preliminary, the results show a tendency for certain variants to appear in tuberculosis isolates from individuals with DR and T2DM, demonstrating the possible influence of the host in the evolution of tuberculosis. Further studies are necessary to confirm the participation of these variants in the efflux pump function in tuberculosis.

1. Introduction

With more than 10 million cases and 1.5 million deaths annually, after COVID-19, tuberculosis (TB) remains the infectious disease with the greatest global impact on human health [1]. Human Immunodeficiency Virus and type 2 diabetes mellitus (T2DM) are comorbidities frequently observed in TB, with a significant increase in recent years promoting development of drug-resistant (DR) tuberculosis [1,2].
T2DM increases the risk of developing TB three-fold and also promotes reactivation of latent infection, rapid progression from latent to active infection, and increased risk of TB treatment failure, relapse, and death [3,4,5]. In addition, individuals with binomial TB-T2DM have a five-fold increased risk of developing monoresistance and a four-fold increased risk of multidrug-resistant tuberculosis (MDR-TB) [5,6,7,8].
Drug resistance in Mycobacterium tuberculosis (Mtb) is not only the result of a mutation in the canonical genes. Recently, new physiological processes have been described as promoting Mtb survival against antimicrobials, including DNA damage repair mechanisms and efflux pumps (EPs) [9,10]. These EPs are mostly transmembrane proteins that transport a wide variety of substrates into and out of Mtb cells. The drug resistance conferred by EPs is mainly the result of dysregulation of gene expression, or alteration of protein function in response to antibiotic treatment, that promotes the extrusion of an important collection of molecules, including antimicrobial compounds, thus maintaining a sub-lethal concentration within the Mtb cell, driving a drug-resistant phenotype [11,12,13].
Efflux pumps are divided into five super families based on sequence homology, protein domain structures and function: (1) the ATP-binding cassette (ABC) superfamily; (2) the major facilitator superfamily (MFS); (3) the multidrug and toxic compound extrusion superfamily (MATE); (4) the resistance-nodulation-cell-division superfamily (RND); and (5) the small multidrug resistance superfamily (SMR). All of these families include pumps that are implicated in resistance to several antibiotics in Mtb [11].
Although the population structure of Mtb is monomorphic, (this mean low levels of genome diversity and apparently absence of genetic exchange), it has been demonstrated that the host environment modulates genetic variations, enhancing the evolution and adaptation of Mtb to its environment [14,15,16]. However, it is unclear how T2DM influences the development of Mtb and promotes the increase in DR. It was recently shown that T2DM does not have an impact on the development of new or specific polymorphisms in genes directly related to first-line DR in Mtb [17] but does influence the development of polymorphisms in genes related to DNA damage repair, some of which are related to DR [18]. This study aims to characterize efflux pump gene (EPG) variants in Mtb genomes from patients diagnosed with and without T2DM, and with and without drug resistance, and to determine their association with drug resistance.

2. Materials and Methods

2.1. M. tuberculosis Genome and Database Construction

A search for Mtb genomes was performed in the following public repositories: GenBank (https://www.ncbi.nlm.nih.gov/genome/, accessed on September 2020), TB Portals-NIH (https://tbportals.niaid.nih.gov/), ENA (https://www.ebi.ac.uk/ena/browser/home), and PATRIC (https://www.bv-brc.org/). Inclusion of the genomes in the analysis was conducted according to the metadata information, considering the following criteria: (1) individuals subjected to testing for T2DM; (2) a detailed description of the genotypic profile of sensibility condition and resistance to first-line drugs (rifampicin, isoniazid, pyrazinamide, and ethambutol), considering resistance to only one of the first-line drugs (Mono-TB), poly-resistant (Poly-TB), resistant to two or more drugs except for INH and RIF, and multidrug-resistant (MDR-TB), with simultaneous resistance to INH and RIF; (3) coverage values > 99% and depth > 100×; and (4) belonging exclusively to L4, due to its global distribution and its lack of inherent tendency to develop resistance [19]. Based on host characteristics, the genomes were organized into four groups: (1) individuals with sensitive TB, (2) with drug-resistant TB, (3) with sensitive TB and T2DM, and (4) with drug-resistant TB and T2DM.

2.2. Bioinformatics Analysis

Genome sequences were analyzed following Perez Martinez et al [18]. Low-quality ends in the sequences (<30) were trimmed using Fastp [20]; then, Kraken V.2 [21] was used to filter reads and to avoid false variants as a result of DNA contamination. The reads were aligned with the BWA program using Mtb H37Rv as a reference sequence (Gen Bank ID: ASM19595v2) and considering the default parameters [19]. Variant calling (SNPs and INDELS) was performed following a previously described and validated pipeline [22]. Variants present in at least 20 reads and at ≥90% frequency within each isolate were used to detect phylogenetic mutations and confirm belonging to L4. The L4 sublineages were identified by matching fixed SNPs (those with a frequency ≥90%) to specific phylogenetic positions, as described in a previous report [23]. Those variants in at least 10 reads with a frequency of >10% to <90% were termed non-fixed SNPs and used to predict the first- and second-line drug-resistance profile. Those variants with an allelic frequency >10% in the coding regions of the 175 related efflux pump genes were selected (Supplementary Table S1), and a database was then constructed including the resistance/sensitivity profiles and the presence/absence of DMT2 in the host.

2.3. Statistical Analysis and Identification of Specific Variations in the Groups

Inter-group variant differences were analyzed by the Fisher exact test using IBM SPSS V21 [24] (95% confidence level), excluding those SNPs strongly related to sublineages and those present in >99% of the sample. A p value of <0.05 was considered statistically significant.

3. Results

3.1. Characteristics of the Genomes Included in This Study

From the databases consulted, 399 Mtb genomes were recovered; these were mostly from ten countries, including Georgia (33.6%), Moldova (13.8%), Indonesia (12.8%), México (12%), and Peru (7%), among others. Considering the characteristics of the population, the mean age of the individuals was 42.5 years (15.1), and 255 (61.4%) were male. According to the resistant patterns, 223 strains (55.9%) were sensitive, and 176 (44.1%) were mono- poly-, multi-, pre-, and extremely DR. The most common resistance observed was to isoniazid in 35% (139 isolates), followed by rifampicin in 32% (127 isolates), and ethambutol in 21% (83 isolates).
According to the occurrence of T2DM, 175 genomes were from individuals with T2DM, of which 100 (57%) were drug-sensitive TB and 75 (43%) were DR-TB. The remaining 224 genomes belonged to patients without T2DM, of which 123 (55%) were drug-sensitive TB and 101 (45%) showed DR-TB.

3.2. Sublineages Identified

All genomes were confirmed as belonging to L4, also known as Euro-American, and were distributed among seven sublineages. The sublineage 4.10, known as T and globally described, was observed in 101 (25%) isolates. Sublineage 4.6.1 (Cameron) was found in one isolate (0.2%), and sublineage 4.5 (H3,H4,T1) was observed in three (0.7%) strains.
Two branches of sublineage 4.4 (S,T1,T2) were observed in thirteen isolates: 4.4.1.1 (S type) with ten strains (2.5%) and 4.4.1.2 (T1) with three (0.7%).
Sublineage 4,3, also termed LAM, and widely distributed in Europe and Latin America, was identified in 111 isolates and distributed in five branches: 4.3.1 with 14 (3.5%) strains, 4.3.2 with 9 (2.2%), 4.3.3 with 71 (18%), 4.3.4.1 with 6 (1.4%), and 4.3.4.2 with 11 (2.7%) strains.
Sublineage 4.2 was observed in 48 strains and presented in two branches: 4.2.1 (H3,H4) with 47 (12%) strains and 4.2.2 (Lam7-TUR,T1) with one isolate. Sublineages 4.1, described as Harlem and widely distributed in Europe and Latin America, was observed in 109 isolates and included in four branches: 4.1.1 (X-family) with 7 (1.7%) isolates, 4.1.2 (T1,H1) with 8 (2%), 4.1.2.1 (T1,H1) with 81 (20%), and 4.1.1.3 (X3,X1) with 13 (3.2%) isolates.

3.3. Characterization of Variants and SNPs in the EPGs

A total of 994 non-synonymous SNPs were identified in the 173 efflux pump genes analyzed. No variants were observed in 10 efflux pump genes (Rv0820, Rv1238, Rv1858, Rv2060, Rv2399c, Rv2937, Rv3501c, Rv3665c, Rv3666c, and Rv3756c), while 9 efflux pump genes showed between 1 and 3 variants (Rv0168, Rv0934, Rv0936, Rv1281c, Rv1680, Rv1964, Rv1965, Rv2832c, and Rv2835c), and 154 efflux pump genes showed between 3 and 19 variants (Supplementary Table S1). Table 1 shows the loci/genes with the highest number of changes, as well as the position and change in the most frequent of these. The loci Rv0194 and Rv2059 showed the highest number of variants, with 15 and 14, respectively. The most frequent non-synonymous mutations were S217P (position 1273071) at Rv1145 (mmpL13a) observed in 98 isolates, and M544I (position 1648620) at Rv1461, recorded in 78 isolates.
Table 1. EPGs with a high frequency of variants and SNPs in M. tuberculosis.

3.4. Characterization of Variants and SNPs in Efflux Pump Genes Associated with Sublineages

Table 2 shows the 29 changes found exclusively in the sublineages identified. Sublineage 4.2.1 showed seven mutations at six loci/genes, while 4.4.1 and 4.4.2 had six changes at five loci/genes, and 4.3.4.1 and 4.3.4.2 presented five changes at five loci/genes. Given the strong relationship between these variants and the sublineages, no further consideration of these was made in the subsequent comparative analysis, in order to avoid any bias.
Table 2. SNPs in EPGs restricted to sublineages.

3.5. Efflux Pump Genes Variants in TB Genomes from Individuals with TB Drug Resistance and T2DM

Table 3 shows the 19 changes with the highest frequency of occurrence observed most frequently in isolates with DR, and the eight changes observed in eight genes found mainly in sensitive isolates, both of statistical significance. Six changes were observed in more than 20% of the resistant isolates, N105K (Rv0073), R189Q (Rv0173, lprK), V207L (Rv0411c, glnH), V77A (Rv0450c, mmpL4), Q334E (Rv1967, mce3B), and P161S (Rv1966, mce3A), while the change S217P (Rv1145) was reported in 29% of the sensitive drug isolates.
Table 3. EPG SNPs in sensitive and resistant M. tuberculosis genomes from individuals with TB and TB-T2DM.
Table 4 shows the 40 changes of statistical significance present in the efflux pump genes in individuals with TB and T2DM. The frequencies of changes observed in individuals with T2DM were mainly low, with the most abundant being R140C (Rv3041c) found in 6% of isolates and M787V (Rv2339, narK1) in 5%. In TB patients, four changes presented frequencies higher than 19%: S217P (Rv1145, mmpL13a) in 32% of isolates, A291V (Rv1557, mmpL6) in 23.7%, P161S (Rv1966, mce3A) in 19%, and S220P (Rv2994) in 23.7%.
Table 4. Efflux pump genes variants with significant differences in hosts with TB and TB-T2DM.

4. Discussion

Several reports describe a modification in the expression of efflux pump genes in the presence of certain drugs and the occurrence of specific variants associated with first- and second-line TB-DR in tuberculosis [12,25,26,27,28,29]. However, to our knowledge, this is the first report to characterize the variants in all of the efflux pump genes described to date in Mtb and to relate these in the context of possible association with DR and T2DM. The results evidence the occurrence of variants specifically associated with L4 sublineages and with the conditions TB-DR and T2DM.
Eleven genes showed the highest frequency of variants. The Rv1145 gene (mmpL13a) included the polymorphism S217P observed in 98 (25%) of the genomes analyzed. This gene has also been described as being overexpressed in the presence of anti-TB drugs, contributing to the overall resistance phenotype [26], and participating in the mechanism of resistance to pyrazinamide [30], even though this variant was observed mainly in sensitive isolates (Table 4). The efflux pumps Rv1967 (mce3B) and Rv0173 (lprK) also included mutations with a high frequency of occurrence. These genes are members of the Mce family and present a strong association mainly with virulence in TB [31]. The mutation S220P in Rv2994 was recorded in 75 (19%) strains. This gene encoded a transporter of the MFS family, which has been found to be highly expressed in MDR-TB isolates [32]. On the other hand, Rv0194 has been associated with the development of resistance against rifampicin [27,33]. Other variants found in efflux pump genes at high frequencies were M544I in the Rv1461 gene, found in 78 strains; variant A291V in Rv1557 (mmpL6), found in 75 strains; and variant R745Q in Rv0202c (mmpL11), observed in 57 isolates. Nevertheless, despite the high frequencies observed in our dataset, no function or association with DR has been described previously in these genes. These data show the great diversity of functions related to the genes carrying a significant number of variants, which could exert an influence at different levels of development in Mtb.
In contrast, 19 efflux pump genes showed less than three SNPs or no changes at all. All of these genes belong to the ABC transporter family and are involved in the transport of different molecules, such as (i) inorganic ions (Rv0936 (pstA2), Rv1858 (modB), Rv2060 (Znub), Rv3666c (dppA), and Rv2399c (cyst)); (ii) organic molecules (Rv0820 (PhoT), Rv0934 (pstS1), Rv1281c (oppD), Rv1238 (sugC), Rv2832c (ugpC), Rv2835c (ugpA), Rv2937 (drrB), Rv3665c (DppB), and Rv3756c (proZ) (PZA)); and (iii) participating as integral membrane proteins (Rv3501c (MlaE), Rv0168 (yrbE1B), Rv1964 (yrbE3A), and Rv1965 (yrbE3B)). Only Rv3756c (proZ) was previously reported as being associated with resistance against pyrazinamide [28]. The low frequency or absence of variants in these genes suggests the importance of these pumps in the maintenance of the mycobacterial membrane function, so they could be considered as target for the design of new molecules to block their function, opening a whole new line of anti-tuberculosis adjunctive therapies.
To the best of our knowledge, this is the first description of 29 non-synonymous SNPs in efflux pump genes strongly related to 12 sublineages of L4 in Mtb. While the limited number of genomes analyzed here is acknowledged, along with the low representativeness of some sublineages, the clustering found, using those SNPs and the sublineages detected, raises new questions regarding the co-evolution of these mutations and the biological significance of these associations. It will undoubtedly be necessary to increase the number of isolates to determine the degree of conservation and coadaptation of these polymorphisms with sublineages, as well as their participation in the biology of mycobacteria.
Among the 19 genes that presented polymorphisms strongly associated with drug-resistant isolates, three families were observed: (i) the ABC family, which included five genes (Rv0073, Rv2038c, Rv3758c (proV), Rv0411c (glnH) and Rv0587, of which only Rv2038c has been described as related to MDR-TB [34]); (ii) the Mce family, which included Rv0171, (mce1C), Rv0173 (lprK), Rv1966 (mce3A), and Rv1967 (mce3B), all of which are associated mainly with virulence [31]; (iii) the Mycobacterial membrane proteins large family (MmpL), in which five genes were found (Rv0206c (mmpL3), Rv0450c (mmpL4), Rv0676c (mmpL5), Rv1522c (mmpL12), and Rv2942 (mmpL7)). Of these latter genes, Rv0450c and Rv2942 have been described as associated with DR [29,35], and Rv0676c has recently been related to resistance against bedaquiline [36]. Identification of these variants in DR isolates raises the need for further testing and developing additional assays to confirm their involvement in drug resistance.
The 19 polymorphic genes with the highest frequency in the TB-T2DM group were placed in three families: (1) the MmpLs, which have been described as necessary for the growth, resistance, nodulation, division, and survival of Mtb. These genes were Rv2339 (narK1), Rv0206c (mmpL3), Rv1146 (mmpL13b), Rv2942 (mmpL7), and Rv3823c (mmpL8), of which Rv1146 and Rv3823c have been found overexpressed in MDR-TB isolates [26] and those with high levels of resistance to isoniazid [37]. In addition, Rv0206 (MmpL3) is the only MmpL considered essential for the replication and viability of mycobacterial cells; (2) the ABC transporter family, comprising Rv0265c, Rv2041c, Rv3041c, Rv0986, Rv1273c, and Rv0587 (yrbE2A), of which only Rv1273c has been described as overexpressed in isoniazid-resistant isolates [37]; (3) the Mce family, Rv0170 (mce1B) and Rv0171 (mce1C), which has mainly been associated with Mtb virulence [27]. Finally, of the remaining six genes of no clear family (Rv0783c (emrB), Rv1146 (mmpL13b), Rv1619, Rv1667c, Rv1843c (guaB1), and Rv2265), only Rv1667c has been described as overexpressed in pyrazinamide-resistant isolates [28]. This extensive number of variant and pump families, with multiple functions, could be the result of the response of the bacteria to altered metabolic conditions in the host presenting T2DM. However, further studies are necessary to fully explore this possibility, including the potential effect of compensatory mutations.
The major limitations of this study were as follows; Firstly, the reduced number of genomes and thus of the individuals included in this study generated an unmatched sample distribution in some of the groups. This was due to the absence of information in the metadata associated with the genomes and related to the clinical and epidemiological information of the hosts, including the occurrence of comorbidities such as T2DM, pharmacological treatment for glycemia control, glycemia tests, time elapsed with the T2DM condition, or substance addictions, among others; in this context, metadata is becoming a fundamental variable to guide the development of research, where it is intended to find associations between genomic variants and clinical variables, so it should be considered as an important element for the deposition of genomes in the repositories. The second limitation was the absence of information related to the phenotypic determination against all the drug resistances in the isolates whose genomes were included in this study; this information could address with more clarity the occurrence of some of the identified variants; however, this is difficult to obtain considering the diverse contexts in which these isolates were recovered and analyzed experimentally. The last limitation was that non-coding DNA associated with genetic regulation of efflux pump genes was not included in the analysis, and an important number of reports are associated with variants in these regions that modify the expression of these pumps. An additional limitation is related to the restriction of lineage 4, limiting the generalization of the results to other lineages such as lineage 1, widely recognized as a lineage with a significant tendency to develop drug resistance, and mainly distributed in countries from China and India, also important contributors of T2DM.

5. Conclusions

The results show that the occurrence of 19 variants in EPG are apparently associated with DR, while 19 are possibly influenced by the presence of T2DM. These data support the possibility that the environment of a patient with T2DM could influence the generation of polymorphisms in M. tuberculosis. However, it is necessary to increase the number of genomes including other lineages and, importantly, to develop experimental studies to confirm the influence of the variants here identified to confirm their real participation and the influence of the DM2 in the induction of variants in M. tuberculosis in efflux pumps.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms13061306/s1. Table S1: Information of the TB genomes used in this study.

Author Contributions

M.C.-M.: conceptualization, methodology, investigation, writing—original draft preparation. D.E.P.-M.: conceptualization, data curation, methodology, software, investigation, writing—original draft preparation. G.A.B.H.: data curation, methodology, software. P.M.M.-P.: data curation, methodology, investigation, writing—reviewing and editing. C.L.-C.: data curation, methodology, writing—reviewing and editing, resources. R.M.-S.: methodology, writing—reviewing and editing. H.M.: methodology, writing—reviewing and editing. R.Z.-C.: supervision, methodology, writing—reviewing and editing, conceptualization, resources. All authors have read and agreed to the published version of the manuscript.

Funding

R. Zenteno-Cuevas was financed with resources from Secretary for Science, Humanities, Technology and Innovation-CONACYT-Basic Sciences Fund: Influence of type 2 diabetes mellitus in the development of mutations associated with multidrug resistance Tuberculosis, A1-S-22956. Cuauhtémoc Licona-Cassani was financed by Proyecto Conacyt Fronteras de la Ciencia No. 319590. Damian E. Pérez-Martínez was CONACyT fellow No. 411155, Gustavo Bermúdez, No. 710466 and Paulina M. Mejía-Ponce No. 595484.

Institutional Review Board Statement

This work was approved by the research and the ethical research committees under registration numbers 002-2020 and D074-2020 respectively.

Data Availability Statement

Code information of TB genomes used in this study are listed in Supplementary Materials, and can be freely accessed using the code assigned by the respective genome database.

Acknowledgments

We are grateful to the Secretary for Science, Humanities, Technology and Innovation (SECIHTI) for providing some of the samples used in this study.

Conflicts of Interest

The authors declare no conflicts of interest and the funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Global Tuberculosis Report 2021. Available online: https://iris.who.int/bitstream/handle/10665/346387/9789240037021-eng.pdf?sequence=1 (accessed on 30 May 2025).
  2. Restrepo, B.I. Diabetes and tuberculosis. Microbiol. Spectr. 2016, 4, TNMI7-0023-2016. [Google Scholar] [CrossRef] [PubMed]
  3. Jeon, C.Y.; Murray, M.B. Diabetes Mellitus Increases the Risk of Active Tuberculosis: A Systematic Review of 13 Observational Studies. PLoS Med. 2008, 5, 1091–1101. [Google Scholar] [CrossRef]
  4. Pérez, L.M.; Restrepo, B.I.; Fuentes-Domínguez, F.J.; Duggirala, R.; Morales-Romero, J.; López-Alvarenga, J.C.; Comas, I.; Zenteno-Cuevas, R. The effect size of type 2 diabetes mellitus on tuberculosis drug resistance and adverse treatment outcomes. Tuberculosis 2017, 103, 83–91. [Google Scholar] [CrossRef] [PubMed]
  5. Restrepo, B.I.; Schlesinger, L.S. Impact of diabetes on the natural history of tuberculosis. Diabetes Res. Clin. Prac. 2014, 106, 191–199. [Google Scholar] [CrossRef]
  6. Pérez-Navarro, L.M.; Fuentes-Domínguez, F.J.; Zenteno-Cuevas, R. Type 2 diabetes mellitus and its influence in the development of multidrug resistance tuberculosis in patients from southeastern Mexico. J. Diabetes Complicat. 2015, 29, 77–82. [Google Scholar] [CrossRef]
  7. Cornejo-Báez, A.A.; Zenteno-Cuevas, R.; Luna-Herrera, J. Association Between Diabetes Mellitus-Tuberculosis and the Generation of Drug Resistance. Microorganisms 2024, 12, 2649. [Google Scholar] [CrossRef]
  8. Dasan, B.; Rajamanickam, A.; Pandiarajan, A.N.; Shanmugam, S.; Nott, S.; Babu, S. Immunological mechanisms of tuberculosis susceptibility in TB-infected individuals with type 2 diabetes mellitus: Insights from mycobacterial growth inhibition assay and cytokine analysis. Microbiol. Spectr. 2024, 13, e0144524. [Google Scholar] [CrossRef]
  9. Sander, P.; Papavinasasundaram, K.G.; Dick, T.; Stavropoulos, E.; Ellrott, K.; Springer, B.; Colston, M.J.; Bo, E.C. Mycobacterium bovis BCG recA deletion mutant shows increased susceptibility to DNA-damaging agents but wild-type survival in a mouse infection model. Infect. Immun. 2001, 69, 3562–3568. [Google Scholar] [CrossRef]
  10. Singh, A. Guardians of the mycobacterial genome: A review on DNA repair systems in Mycobacterium tuberculosis. Microbiology 2017, 163, 1740–1758. [Google Scholar] [CrossRef]
  11. Laws, M.; Jin, P.; Rahman, K.M. Efflux pumps in Mycobacterium tuberculosis and their inhibition to tackle antimicrobial resistance. Trends Microbiol. 2021, 30, 57–68. [Google Scholar] [CrossRef]
  12. Wang, K.; Pei, H.; Huang, B.; Zhu, X.; Zhang, J.; Zhou, B.; Zhu, L.; Zhang, Y.; Zhou, F.-F. The expression of ABC efflux pump, Rv1217c-Rv1218c, and its association with multidrug resistance of Mycobacterium tuberculosis in China. Curr. Microbiol. 2013, 66, 222–226. [Google Scholar] [CrossRef] [PubMed]
  13. Rodrigues, L.; Machado, D.; Couto, I.; Amaral, L.; Viveiros, M. Contribution of efflux activity to isoniazid resistance in the Mycobacterium tuberculosis complex. Infect. Genet. Evol. 2012, 12, 695–700. [Google Scholar] [CrossRef] [PubMed]
  14. Pérez-Lago, L.; Comas, I.; Navarro, Y.; González-Candelas, F.; Herranz, M.; Bouza, E.; García-de-Viedma, D. Whole Genome Sequencing Analysis of Intrapatient Microevolution in Mycobacterium tuberculosis: Potential Impact on the Inference of Tuberculosis Transmission. J. Infect. Dis. 2014, 209, 98–108. [Google Scholar] [CrossRef]
  15. Nimmo, C.; Brien, K.; Millard, J.; Grant, A.D.; Padayatchi, N.; Pym, A.S.; Max, O’Donnell; Goldstein, R.; Breuer, J.; Balloux, F.; et al. Dynamics of within-host Mycobacterium tuberculosis diversity and heteroresistance during treatment. EBioMedicine 2020, 55, 102747. [Google Scholar] [CrossRef]
  16. Liu, Q.; Wei, J.; Li, Y.; Wang, M.; Su, J.; Lu, Y.; López, M.G.; Qian, X.; Zhu, Z.; Wang, H. Mycobacterium tuberculosis clinical isolates carry mutational signatures of host immune environments. Sci. Adv. 2020, 6, eaba4901. [Google Scholar] [CrossRef]
  17. Bermudez-Hernández, G.A.; Pérez-Martínez, D.E.; Madrazo-Moya, C.F.; Cancino-Muñoz, I.; Comas, I.; Zenteno-Cuevas, R. Whole genome sequencing analysis to evaluate the influence of T2DM on polymorphisms associated with drug resistance in M. tuberculosis. BMC Genom. 2022, 23, 465. [Google Scholar] [CrossRef] [PubMed]
  18. Pérez-Martínez, D.E.; Bermúdez-Hernández, G.A.; Madrazo-Moya, C.F.; Cancino-Muñoz, I.; Montero, H.; Licona-Cassani, C.; Muñiz-Salazar, R.; Comas, I.; Zenteno-Cuevas, R. SNPs in Genes Related to DNA Damage Repair in Mycobacterium Tuberculosis: Their Association with Type 2 Diabetes Mellitus and Drug Resistance. Genes 2022, 13, 609. [Google Scholar] [CrossRef]
  19. Picard Tools—By Broad Institute. Available online: https://broadinstitute.github.io/picard/ (accessed on 29 March 2021).
  20. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef] [PubMed]
  21. Wood, D.E.; Salzberg, S.L. Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014, 15, R46. [Google Scholar] [CrossRef]
  22. Comas, I.; Coscolla, M.; Luo, T.; Borrell, S.; Holt, K.E.; Kato-Maeda, M.; Parkhill, J.; Malla, B.; Berg, S.; Thwaites, G.; et al. Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat. Genet. 2013, 45, 1176–1182. [Google Scholar] [CrossRef]
  23. Coll, F.; McNerney, R.; Guerra-Assunção, J.A.; Glynn, J.R.; Perdigão, J.; Viveiros, M.; Portugal, I.; Pain, A.; Martin, N. Clark A robust SNP barcode for typing Mycobacterium tuberculosis complex strains. Nat. Commun. 2014, 5, 4812. [Google Scholar] [CrossRef] [PubMed]
  24. IBM Corp. IBM SPSS Statistics for Windows 2020, version 27; IBM Corp.: Armonk, NY, USA, 2020. [Google Scholar]
  25. Rodrigues, L.; Baptista, P.; Veigas, B.; Amaral, L.; Viveiros, M. Contribution of Efflux to the Emergence of Isoniazid and Multidrug Resistance in Mycobacterium tuberculosis. PLoS ONE 2012, 7, e34538. [Google Scholar] [CrossRef]
  26. Ghajavand, H.; Kargarpour Kamakoli, M.; Khanipour, S.; Pourazar Dizaji, S.; Masoumi, M.; Rahimi Jamnani, F.; Fateh, A.; Yaseri, M.; Siadat, S.D. Scrutinizing the drug resistance mechanism of multi- and extensively-drug resistant Mycobacterium tuberculosis: Mutations versus efflux pumps. Antimicrob. Resist. Infect. Control 2019, 8, 70. [Google Scholar] [CrossRef] [PubMed]
  27. Narang, A.; Garima, K.; Porwal, S.; Bhandekar, A.; Shrivastava, K.; Giri, A.; Sharma, N.K.; Bose, M.; Varma-Basil, M. Potential impact of efflux pump genes in mediating rifampicin resistance in clinical isolates of Mycobacterium tuberculosis from India. PLoS ONE 2019, 14, e0223163. [Google Scholar] [CrossRef]
  28. Zhang, Y.; Zhang, J.; Cui, P.; Zhang, Y.; Zhang, W. Identification of Novel Efflux Proteins Rv0191, Rv3756c, Rv3008, and Rv1667c Involved in Pyrazinamide Resistance in Mycobacterium tuberculosis. Antimicrob. Agents Chemother. 2017, 61, e00940-17. [Google Scholar] [CrossRef]
  29. Shahi, F.; Khosravi, A.D.; Tabandeh, M.R.; Salmanzadeh, S. Investigation of the Rv3065, Rv2942, Rv1258c, Rv1410c, and Rv2459 efflux pump genes expression among multidrug-resistant Mycobacterium tuberculosis clinical isolates. Heliyon 2021, 7, e07566. [Google Scholar] [CrossRef]
  30. Wang, X.; Ding, B.; Li, B. Biomimetic electrospun nanofibrous structures for tissue engineering. Mater. Today 2013, 16, 229–241. [Google Scholar] [CrossRef]
  31. Klepp, L.I.; y Garcia, J.S.; FabianaBigi. Mycobacterial MCE proteins as transporters that control lipid homeostasis of the cell wall. Tuberculosis 2022, 132, 102162. [Google Scholar] [CrossRef]
  32. Li, G.; Zhang, J.; Guo, Q.; Jiang, Y.; Wei, J.; Zhao, L.L.; Zhao, X.; Lu, J.; Wan, K. Efflux pump gene expression in multidrug-resistant Mycobacterium tuberculosis clinical isolates. PLoS ONE 2015, 10, e0119013. [Google Scholar] [CrossRef]
  33. Kanji, A.; Hasan, R.; Ali, A.; Zaver, A.; Zhang, Y.; Imtiaz, K.; Shi, W.; Clark, T.G.; McNerney, R.; Phelan, J.; et al. Single nucleotide polymorphisms in efflux pumps genes in extensively drug resistant Mycobacterium tuberculosis isolates from Pakistan. Tuberculosis 2017, 107, 20–30. [Google Scholar] [CrossRef]
  34. Lee, J.J.; Kang, H.Y.; Lee, W.I.; Cho, S.Y.; Kim, Y.J.; Lee, H.J. Efflux pump gene expression study using RNA-seq in multidrug-resistant TB. Int. J. Tuberc. Lung Dis. 2021, 25, 974–981. [Google Scholar] [CrossRef] [PubMed]
  35. De Keijzer, J.; De Haas, P.E.; De Ru, A.H.; Van Veelen, P.A.; Van Soolingen, D. Disclosure of selective advantages in the “modern” sublineage of the Mycobacterium tuberculosis Beijing genotype family by quantitative proteomics. Mol. Cell Proteom. 2014, 13, 2632–2645. [Google Scholar] [CrossRef] [PubMed]
  36. Rivière, E.; Verboven, L.; Dippenaar, A.; Goossens, S.; De Vos, E.; Streicher, E.; Cuypers, B.; Laukens, K.; Ben-Rached, F.; Rodwell, T.C.; et al. Variants in Bedaquiline-Candidate-Resistance Genes: Prevalence in Bedaquiline-Naive Patients, Effect on MIC, and Association with Mycobacterium tuberculosis Lineage. Antimicrob Agents Chemother 2022, 66, e00322-22. [Google Scholar] [CrossRef] [PubMed]
  37. Narang, A.; Giri, A.; Gupta, S.; Garima, K.; Bose, M.; Varma-Basil, M. Contribution of putative efflux pump genes to isoniazid resistance in clinical isolates of Mycobacterium tuberculosis. Int. J. Mycobacteriology 2017, 6, 177–183. [Google Scholar] [CrossRef]
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