Genomics & Informatics

Korea Genome Organization (한국유전체학회)
권호 표지
DOI QR코드

DOI QR Code

Understanding Rifampicin Resistance in Tuberculosis through a Computational Approach

  • Kumar, Satish (Bioinformatics Centre and Biochemistry, Mahatma Gandhi Institute of Medical Sciences) ;
  • Jena, Lingaraja (Bioinformatics Centre and Biochemistry, Mahatma Gandhi Institute of Medical Sciences)
  • Received : 2014.10.07
  • Accepted : 2014.11.15
  • Published : 2014.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The disease tuberculosis, caused by Mycobacterium tuberculosis (MTB), remains a major cause of morbidity and mortality in developing countries. The evolution of drug-resistant tuberculosis causes a foremost threat to global health. Most drug-resistant MTB clinical strains are showing resistance to isoniazid and rifampicin (RIF), the frontline anti-tuberculosis drugs. Mutation in rpoB, the beta subunit of DNA-directed RNA polymerase of MTB, is reported to be a major cause of RIF resistance. Amongst mutations in the well-defined 81-base-pair central region of the rpoB gene, mutation at codon 450 (S450L) and 445 (H445Y) is mainly associated with RIF resistance. In this study, we modeled two resistant mutants of rpoB (S450L and H445Y) using Modeller9v10 and performed a docking analysis with RIF using AutoDock4.2 and compared the docking results of these mutants with the wild-type rpoB. The docking results revealed that RIF more effectively inhibited the wild-type rpoB with low binding energy than rpoB mutants. The rpoB mutants interacted with RIF with positive binding energy, revealing the incapableness of RIF inhibition and thus showing resistance. Subsequently, this was verified by molecular dynamics simulations. This in silico evidence may help us understand RIF resistance in rpoB mutant strains.

Keywords

References

  1. World Health Organization. Global Tuberculosis Report 2013. Geneva: World Health Organization, 2013.
  2. Raviglione MC, Snider DE Jr, Kochi A. Global epidemiology of tuberculosis: morbidity and mortality of a worldwide epidemic. JAMA 1995;273:220-226. https://doi.org/10.1001/jama.1995.03520270054031
  3. Shah NS, Wright A, Bai GH, Barrera L, Boulahbal F, Martin-Casabona N, et al. Worldwide emergence of extensively drug-resistant tuberculosis. Emerg Infect Dis 2007;13:380-387. https://doi.org/10.3201/eid1303.061400
  4. Marrakchi H, Laneelle G, Quemard A. InhA, a target of the antituberculous drug isoniazid, is involved in a mycobacterial fatty acid elongation system, FAS-II. Microbiology 2000;146(Pt 2):289-296. https://doi.org/10.1099/00221287-146-2-289
  5. Mitra PP. Drug discovery in tuberculosis: a molecular approach. Indian J Tuberc 2012;59:194-206.
  6. Calvori C, Frontali L, Leoni L, Tecce G. Effect of rifamycin on protein synthesis. Nature 1965;207:417-418. https://doi.org/10.1038/207417a0
  7. Yang B, Koga H, Ohno H, Ogawa K, Fukuda M, Hirakata Y, et al. Relationship between antimycobacterial activities of rifampicin, rifabutin and KRM-1648 and rpoB mutations of Mycobacterium tuberculosis. J Antimicrob Chemother 1998;42:621-628. https://doi.org/10.1093/jac/42.5.621
  8. Kolyva AS, Karakousis PC. Old and new TB drugs: mechanisms of action and resistance: In: Understanding Tuberculosis: New Approaches to Fighting against Drug Resistance (Cardona JP, ed.). Rijeka: InTech, 2011. pp. 209-232.
  9. Wade MM, Zhang Y. Mechanisms of drug resistance in Mycobacterium tuberculosis. Front Biosci 2004;9:975-994. https://doi.org/10.2741/1289
  10. Somasundaram S, Ram A, Sankaranarayanan L. Isoniazid and rifampicin as therapeutic regimen in the current era: a review. J Tuberc Res 2014;2:40-51. https://doi.org/10.4236/jtr.2014.21005
  11. Telenti A, Imboden P, Marchesi F, Lowrie D, Cole S, Colston MJ, et al. Detection of rifampicin-resistance mutations in Mycobacterium tuberculosis. Lancet 1993;341:647-650. https://doi.org/10.1016/0140-6736(93)90417-F
  12. Ramaswamy S, Musser JM. Molecular genetic basis of antimicrobial agent resistance in Mycobacterium tuberculosis: 1998 update. Tuberc Lung Dis 1998;79:3-29. https://doi.org/10.1054/tuld.1998.0002
  13. Zhang Y, Telenti A. Genetics of drug resistance in Mycobacterium tuberculosis. In: Molecular Genetics of Mycobacteria (Hatfull GF, Jacobs WR Jr, eds.). Washington DC: ASM Press, 2000. pp. 235-254.
  14. Rahmo A, Hamdar Z, Kasaa I, Dabboussi F, Hamze M. Genotypic detection of rifampicin-resistant M. tuberculosis strains in Syrian and Lebanese patients. J Infect Public Health 2012;5:381-387. https://doi.org/10.1016/j.jiph.2012.07.004
  15. Brandis G, Hughes D. Genetic characterization of compensatory evolution in strains carrying rpoB Ser531Leu, the rifampicin resistance mutation most frequently found in clinical isolates. J Antimicrob Chemother 2013;68:2493-2497. https://doi.org/10.1093/jac/dkt224
  16. Tang K, Sun H, Zhao Y, Guo J, Zhang C, Feng Q, et al. Characterization of rifampin-resistant isolates of Mycobacterium tuberculosis from Sichuan in China. Tuberculosis (Edinb) 2013;93:89-95. https://doi.org/10.1016/j.tube.2012.10.009
  17. Kapur V, Li LL, Iordanescu S, Hamrick MR, Wanger A, Kreiswirth BN, et al. Characterization by automated DNA sequencing of mutations in the gene (rpoB) encoding the RNA polymerase beta subunit in rifampin-resistant Mycobacterium tuberculosis strains from New York City and Texas. J Clin Microbiol 1994;32:1095-1098.
  18. Kelley LA, Sternberg MJ. Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc 2009;4:363-371. https://doi.org/10.1038/nprot.2009.2
  19. Eswar N, Eramian D, Webb B, Shen MY, Sali A. Protein structure modeling with MODELLER. Methods Mol Biol 2008;426:145-159. https://doi.org/10.1007/978-1-60327-058-8_8
  20. Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, et al. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins 2009;77 Suppl 9:114-122. https://doi.org/10.1002/prot.22570
  21. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 1993;26:283-291. https://doi.org/10.1107/S0021889892009944
  22. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007;35:W407-W410. https://doi.org/10.1093/nar/gkm290
  23. Wallner B, Elofsson A. Can correct protein models be identified? Protein Sci 2003;12:1073-1086. https://doi.org/10.1110/ps.0236803
  24. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009;37:W623-W633. https://doi.org/10.1093/nar/gkp456
  25. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009;30:2785-2791. https://doi.org/10.1002/jcc.21256
  26. Hess B, Kutzner C, van Der Spoel D, Lindahl E. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 2008;4:435-447. https://doi.org/10.1021/ct700301q
  27. Schuttelkopf AW, van Aalten DM. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D Biol Crystallogr 2004;60:1355-1363. https://doi.org/10.1107/S0907444904011679
  28. Berendsen HJ, Grigera JR, Straatsma TP. The missing term in effective pair potentials. J Phys Chem 1987;91:6269-6271. https://doi.org/10.1021/j100308a038
  29. Hess B, Bekker H, Berendsen HJ, Fraaije JG. LINCS: a linear constraint solver for molecular simulations. J Comput Chem 1997;18:1463-1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H
  30. Bussi G, Donadio D, Parrinello M. Canonical sampling through velocity rescaling. J Chem Phys 2007;126:014101. https://doi.org/10.1063/1.2408420
  31. Parrinello M, Rahman A. Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 1981;52:7182-7190. https://doi.org/10.1063/1.328693
  32. Darden T, York D, Pedersen L. Particle mesh Ewald: an N.log(N) method for Ewald sums in large systems. J Chem Phys 1993;98:10089-10092. https://doi.org/10.1063/1.464397
  33. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, et al. Clustal W and Clustal X version 2.0. Bioinformatics 2007;23:2947-2948. https://doi.org/10.1093/bioinformatics/btm404
  34. Opalka N, Brown J, Lane WJ, Twist KA, Landick R, Asturias FJ, et al. Complete structural model of Escherichia coli RNA polymerase from a hybrid approach. PLoS Biol 2010;8:e1000483. https://doi.org/10.1371/journal.pbio.1000483

Cited by

  1. Mycobacterium tuberculosis whole genome sequencing and protein structure modelling provides insights into anti-tuberculosis drug resistance vol.14, pp.1, 2016, https://doi.org/10.1186/s12916-016-0575-9
  2. vol.118, pp.12, 2017, https://doi.org/10.1002/jcb.26124
  3. Clinical Strains vol.62, pp.10, 2018, https://doi.org/10.1128/AAC.01093-18
  4. The Prediction Factors of Pre-XDR and XDR-TB among MDR-TB Patients in Northern Thailand vol.06, pp.01, 2018, https://doi.org/10.4236/jtr.2018.61004
  5. Isolates in the Capital of Iran, Revealed by Whole-Genome Sequencing vol.57, pp.1, 2018, https://doi.org/10.1128/JCM.01477-18