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Structural dynamics insights into the M306L, M306V, and D1024N mutations in Mycobacterium tuberculosis inducing resistance to ethambutol

  • Yustinus Maladan (Eijkman Research Center for Molecular Biology, The National Research and Innovation Agency) ;
  • Dodi Safari (Eijkman Research Center for Molecular Biology, The National Research and Innovation Agency) ;
  • Arli Aditya Parikesit (Department of Bioinformatics, School of Life Sciences, Indonesia International Institute for Life Sciences (I3L))
  • Received : 2023.03.27
  • Accepted : 2023.08.07
  • Published : 2023.09.30

Abstract

Resistance to anti-tuberculosis drugs, especially ethambutol (EMB), has been widely reported worldwide. EMB resistance is caused by mutations in the embB gene, which encodes the arabinosyl transferase enzyme. This study aimed to detect mutations in the embB gene of Mycobacterium tuberculosis from Papua and to evaluate their impact on the effectiveness of EMB. We analyzed 20 samples of M. tuberculosis culture that had undergone whole-genome sequencing, of which 19 samples were of sufficient quality for further bioinformatics analysis. Mutation analysis was performed using TBProfiler, which identified M306L, M306V, D1024N, and E378A mutations. In sample TB035, the M306L mutation was present along with E378A. The binding affinity of EMB to arabinosyl transferase was calculated using AutoDock Vina. The molecular docking results revealed that all mutants demonstrated an increased binding affinity to EMB compared to the native protein (-0.948 kcal/mol). The presence of the M306L mutation, when coexisting with E378A, resulted in a slight increase in binding affinity compared to the M306L mutation alone. The molecular dynamics simulation results indicated that the M306L, M306L + E378A, M306V, and E378A mutants decreased protein stability. Conversely, the D1024N mutant exhibited stability comparable to the native protein. In conclusion, this study suggests that the M306L, M306L + E378A, M306V, and E378A mutations may contribute to EMB resistance, while the D1024N mutation may be consistent with continued susceptibility to EMB.

Keywords

Acknowledgement

We would like to thank the Head of the Papua Health Research and Development Center, who provided access to a high-performance computer for computational data analysis.

References

  1. Global tuberculosis report 2020. Geneva: World Health Organization, 2020. Accessed 2023 May 27. Available from: https://apps.who.int/iris/bitstream/handle/10665/336069/97892400 13131-eng.pdf.
  2. Global tuberculosis report 2021. Geneva: World Health Organization, 2021. Accessed 2023 May 27. Available from: https://www.who.int/publications/i/item/9789240037021.
  3. Global leprosy strategy 2016-2020: accelerating towards a leprosy-free world. Vol. 1, Weekly epidemiological record. Geneva: World Health Organization, 2016. Accessed 2023 May 27. Available from: http://apps.who.int/iris/bitstream/10665/205149/1/B5233.pdf?ua=1.
  4. Brown AC, Bryant JM, Einer-Jensen K, Holdstock J, Houniet DT, Chan JZ, et al. Rapid whole-genome sequencing of Mycobacterium tuberculosis isolates directly from clinical samples. J Clin Microbiol 2015;53:2230-2237. https://doi.org/10.1128/JCM.00486-15
  5. Dixit A, Freschi L, Vargas R, Calderon R, Sacchettini J, Drobniewski F, et al. Whole genome sequencing identifies bacterial factors affecting transmission of multidrug-resistant tuberculosis in a high-prevalence setting. Sci Rep 2019;9:5602.
  6. World Health Organization. The Use of Next-Generation Sequencing Technologies for the Detection of Mutations Associated with Drug Resistance in Mycobacterium tuberculosis Complex: Technical Guide. Geneva: World Health Organization, 2018.
  7. Coll F, McNerney R, Preston MD, Guerra-Assuncao JA, Warry A, Hill-Cawthorne G, et al. Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences. Genome Med 2015;7:51.
  8. Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 1998;393:537-544. https://doi.org/10.1038/31159
  9. Maladan Y, Krismawati H, Wahyuni T, Tanjung R, Awaludin K, Audah KA, et al. The whole-genome sequencing in predicting Mycobacterium tuberculosis drug susceptibility and resistance in Papua, Indonesia. BMC Genomics 2021;22:844.
  10. Phelan JE, O'Sullivan DM, Machado D, Ramos J, Oppong YE, Campino S, et al. Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. Genome Med 2019;11:41.
  11. Ruesen C, Riza AL, Florescu A, Chaidir L, Editoiu C, Aalders N, et al. Linking minimum inhibitory concentrations to whole genome sequence-predicted drug resistance in Mycobacterium tuberculosis strains from Romania. Sci Rep 2018;8:9676.
  12. Hazbon MH, Bobadilla del Valle M, Guerrero MI, Varma-Basil M, Filliol I, Cavatore M, et al. Role of embB codon 306 mutations in Mycobacterium tuberculosis revisited: a novel association with broad drug resistance and IS6110 clustering rather than ethambutol resistance. Antimicrob Agents Chemother 2005;49:3794-3802. https://doi.org/10.1128/AAC.49.9.3794-3802.2005
  13. Bakula Z, Napiorkowska A, Bielecki J, Augustynowicz-Kopec E, Zwolska Z, Jagielski T. Mutations in the embB gene and their association with ethambutol resistance in multidrug-resistant Mycobacterium tuberculosis clinical isolates from Poland. Biomed Res Int 2013;2013:167954.
  14. Li MC, Chen R, Lin SQ, Lu Y, Liu HC, Li GL, et al. Detecting ethambutol resistance in Mycobacterium tuberculosis isolates in China: a comparison between phenotypic drug susceptibility testing methods and DNA sequencing of embAB. Front Microbiol 2020;11:781.
  15. Sekiguchi J, Miyoshi-Akiyama T, Augustynowicz-Kopec E, Zwolska Z, Kirikae F, Toyota E, et al. Detection of multidrug resistance in Mycobacterium tuberculosis. J Clin Microbiol 2007;45:179-192. https://doi.org/10.1128/JCM.00750-06
  16. Sreevatsan S, Stockbauer KE, Pan X, Kreiswirth BN, Moghazeh SL, Jacobs WR Jr, et al. Ethambutol resistance in Mycobacterium tuberculosis: critical role of embB mutations. Antimicrob Agents Chemother 1997;41:1677-1681. https://doi.org/10.1128/AAC.41.8.1677
  17. Lee AS, Othman SN, Ho YM, Wong SY. Novel mutations within the embB gene in ethambutol-susceptible clinical isolates of Mycobacterium tuberculosis. Antimicrob Agents Chemother 2004;48:4447-4449. https://doi.org/10.1128/AAC.48.11.4447-4449.2004
  18. Kumar S, Jena L. Understanding rifampicin resistance in tuberculosis through a computational approach. Genomics Inform 2014;12:276-282. https://doi.org/10.5808/GI.2014.12.4.276
  19. Alatawi EA, Alshabrmi FM. Structural and dynamic insights into the W68L, L85P, and T87A mutations of Mycobacterium tuberculosis inducing resistance to pyrazinamide. Int J Environ Res Public Health 2022;19:1615.
  20. Kumar V, Sobhia ME. Molecular dynamics assisted mechanistic study of isoniazid-resistance against Mycobacterium tuberculosis InhA. PLoS One 2015;10:e0144635.
  21. Schwengers O, Hoek A, Fritzenwanker M, Falgenhauer L, Hain T, Chakraborty T, et al. ASA3P: an automatic and scalable pipeline for the assembly, annotation and higher-level analysis of closely related bacterial isolates. PLoS Comput Biol 2020;16:e1007134.
  22. Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. The FoldX web server: an online force field. Nucleic Acids Res 2005;33:W382-W388. https://doi.org/10.1093/nar/gki387
  23. Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform 2012;4:17.
  24. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004;47:1739-1749.
  25. Jo S, Kim T, Iyer VG, Im W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 2008;29:1859-1865. https://doi.org/10.1002/jcc.20945
  26. Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, et al. CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 2016;12:405-413. https://doi.org/10.1021/acs.jctc.5b00935
  27. Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, de Groot BL, et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods 2017;14:71-73. https://doi.org/10.1038/nmeth.4067
  28. Abraham MJ, Murtola T, Schulz R, Pall S, Smith JC, Hess B, et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015;1-2:19-25. https://doi.org/10.1016/j.softx.2015.06.001
  29. Ali A, Hasan Z, McNerney R, Mallard K, Hill-Cawthorne G, Coll F, et al. Whole genome sequencing based characterization of extensively drug-resistant Mycobacterium tuberculosis isolates from Pakistan. PLoS One 2015;10:e0117771.
  30. World Health Organization. Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance. Geneva: World Health Organization, 2021.
  31. Eldholm V, Norheim G, von der Lippe B, Kinander W, Dahle UR, Caugant DA, et al. Evolution of extensively drug-resistant Mycobacterium tuberculosis from a susceptible ancestor in a single patient. Genome Biol 2014;15:490.
  32. Mokrousov I, Otten T, Vyshnevskiy B, Narvskaya O. Detection of embB306 mutations in ethambutol-susceptible clinical isolates of Mycobacterium tuberculosis from Northwestern Russia: implications for genotypic resistance testing. J Clin Microbiol 2002;40:3810-3813. https://doi.org/10.1128/JCM.40.10.3810-3813.2002
  33. Senghore M, Diarra B, Gehre F, Otu J, Worwui A, Muhammad AK, et al. Evolution of Mycobacterium tuberculosis complex lineages and their role in an emerging threat of multidrug resistant tuberculosis in Bamako, Mali. Sci Rep 2020;10:327.
  34. Gagneux S. Host-pathogen coevolution in human tuberculosis. Philos Trans R Soc Lond B Biol Sci 2012;367:850-859. https://doi.org/10.1098/rstb.2011.0316
  35. Coscolla M, Gagneux S. Consequences of genomic diversity in Mycobacterium tuberculosis. Semin Immunol 2014;26:431-444. https://doi.org/10.1016/j.smim.2014.09.012
  36. Kato-Maeda M, Shanley CA, Ackart D, Jarlsberg LG, Shang S, Obregon-Henao A, et al. Beijing sublineages of Mycobacterium tuberculosis differ in pathogenicity in the guinea pig. Clin Vaccine Immunol 2012;19:1227-1237. https://doi.org/10.1128/CVI.00250-12
  37. Abdelhaleem A, Hershan A, Agarwal P, Farasani A, Omar SV, Ismail A, et al. Whole-genome sequencing of a Mycobacterium tuberculosis strain belonging to lineage 1 (Indo-Oceanic) and the East African Indian spoligotype, isolated in Jazan, Saudi Arabia. Microbiol Resour Announc 2020;9:e00717-20. https://doi.org/10.1128/MRA.00717-20
  38. Huang Y, Zhang X, Suo H. Interaction between beta-lactoglobulin and EGCG under high-pressure by molecular dynamics simulation. PLoS One 2021;16:e0255866.
  39. Krebs BB, De Mesquita JF. Amyotrophic lateral sclerosis type 20: in silico analysis and molecular dynamics simulation of hnRNPA1. PLoS One 2016;11:e0158939.