Longitudinal whole genome analysis of pre and post drug treatment Mycobacterium tuberculosis isolates reveals progressive steps to drug resistance

Tuberculosis

Volumn 98, Issue , 2016, Pages 50-55

  Datta, Gargi ^a,b   Nieto, Luisa M ^c   Davidson, Rebecca M ^a   Mehaffy, Carolina ^c   Pederson, Caroline ^d   Dobos, Karen M ^c   Strong, Michael ^a,b  

^a DEPARTMENT OF MEDICINE   (United States)

^b UNIVERSITY OF COLORADO SCHOOL OF MEDICINE   (United States)

^c Veterinary Dentistry and Oral Surgery   (United States)

^d ATCC   (United States)

Author keywords

Drug resistance progression; Isoniazid; Mycobacterium tuberculosis; Rifampin; Single nucleotide polymorphism; Whole genome sequencing

Indexed keywords

ETHAMBUTOL; ISONIAZID; KATG PROTEIN; PYRAZINAMIDE; RIFAMPICIN; RNA POLYMERASE BETA SUBUNIT; BACTERIAL PROTEIN; BACTERIAL TOXIN; CATALASE; KATG PROTEIN, MYCOBACTERIUM TUBERCULOSIS; TUBERCULOSTATIC AGENT;

ANTIBIOTIC RESISTANCE; ANTIBIOTIC THERAPY; ARTICLE; BACTERIAL GENOME; BACTERIUM ISOLATE; GENE INSERTION SEQUENCE; GENE MUTATION; GENOME ANALYSIS; MYCOBACTERIUM TUBERCULOSIS; POLYMERASE CHAIN REACTION; PRIORITY JOURNAL; RESTRICTION FRAGMENT LENGTH POLYMORPHISM; SINGLE NUCLEOTIDE POLYMORPHISM; START CODON; CHEMISTRY; DNA MUTATIONAL ANALYSIS; DRUG EFFECTS; GENETICS; GENOTYPE; HUMAN; MICROBIAL SENSITIVITY TEST; MICROBIOLOGY; MOLECULAR MODEL; MULTIDRUG RESISTANCE; MUTATION; PATHOGENICITY; PHENOTYPE; PROTEIN CONFORMATION; STRUCTURE ACTIVITY RELATION; TIME FACTOR; TREATMENT OUTCOME; TUBERCULOSIS;

ANTITUBERCULAR AGENTS; BACTERIAL PROTEINS; BACTERIAL TOXINS; CATALASE; DNA MUTATIONAL ANALYSIS; DRUG RESISTANCE, MULTIPLE, BACTERIAL; GENOME, BACTERIAL; GENOTYPE; HUMANS; ISONIAZID; MICROBIAL SENSITIVITY TESTS; MODELS, MOLECULAR; MUTATION; MYCOBACTERIUM TUBERCULOSIS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; PROTEIN CONFORMATION; RIFAMPIN; STRUCTURE-ACTIVITY RELATIONSHIP; TIME FACTORS; TREATMENT OUTCOME; TUBERCULOSIS;

EID: 84962533693     PISSN: 14729792     EISSN: 1873281X     Source Type: Journal    
DOI: 10.1016/j.tube.2016.02.004     Document Type: Article

References (40)

1. WHO|Global Tuberculosis Report World Health Organization n.d. 2015
2. T.C. Rodwell, F. Valafar, J. Douglas, L. Qian, R.S. Garfein, A. Chawla, and et al. Predicting extensively drug-resistant Mycobacterium tuberculosis phenotypes with genetic mutations J Clin Microbiol 52 2014 781 789 10.1128/JCM.02701-13
3. A. Koul, E. Arnoult, N. Lounis, J. Guillemont, and K. Andries The challenge of new drug discovery for tuberculosis Nature 469 2011 483 490 10.1038/nature09657
4. G.P. Bisson, C. Mehaffy, C. Broeckling, J. Prenni, D. Rifat, D.S. Lun, and et al. Upregulation of the phthiocerol dimycocerosate biosynthetic pathway by rifampin-resistant, rpoB mutant Mycobacterium tuberculosis J Bacteriol 194 2012 6441 6452 10.1128/JB.01013-12
5. T. Cohen, M.C. Becerra, and M.B. Murray Isoniazid resistance and the future of drug-resistant tuberculosis Microb Drug Resist 10 2004 280 285 10.1089/mdr.2004.10.280
6. C.B. Ford, P.L. Lin, M.R. Chase, R.R. Shah, O. Iartchouk, J. Galagan, and et al. Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection Nat Genet 43 2011 482 486 10.1038/ng.811
7. S.M. Fortune The surprising diversity of Mycobacterium tuberculosis: change you can believe in J Infect Dis 206 2012 1642 1644 10.1093/infdis/jis603
8. K.E. Niehaus, T.M. Walker, D.W. Crook, T.E.A. Peto, and D.A. Clifton Machine learning for the prediction of antibacterial susceptibility in Mycobacterium tuberculosis IEEE-EMBS Int. Conf. Biomed. Heal. Informatics, IEEE 2014 618 621 10.1109/BHI.2014.6864440
9. M. Wozniak, J. Tiuryn, and L. Wong An approach to identifying drug resistance associated mutations in bacterial strains BMC Genomics 13 Suppl. 7 2012 S23 10.1186/1471-2164-13-S7-S23
10. C.B. Ford, R.R. Shah, M.K. Maeda, S. Gagneux, M.B. Murray, T. Cohen, and et al. Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis Nat Genet 45 2013 784 790 10.1038/ng.2656
11. G. Sun, T. Luo, C. Yang, X. Dong, J. Li, Y. Zhu, and et al. Dynamic population changes in Mycobacterium tuberculosis during acquisition and fixation of drug resistance in patients J Infect Dis 206 2012 1724 1733 10.1093/infdis/jis601
12. M. Merker, T.A. Kohl, A. Roetzer, L. Truebe, E. Richter, S. Rüsch-Gerdes, and et al. Whole genome sequencing reveals complex evolution patterns of multidrug-resistant Mycobacterium tuberculosis Beijing strains in patients PLoS One 8 2013 e82551 10.1371/journal.pone.0082551
13. V. Eldholm, J. Monteserin, A. Rieux, B. Lopez, B. Sobkowiak, V. Ritacco, and et al. Four decades of transmission of a multidrug-resistant Mycobacterium tuberculosis outbreak strain Nat Commun 6 2015 7119 10.1038/ncomms8119
14. A.A. Witney, K.A. Gould, A. Arnold, D. Coleman, R. Delgado, J. Dhillon, and et al. Clinical application of whole-genome sequencing to inform treatment for multidrug-resistant tuberculosis cases J Clin Microbiol 53 2015 1473 1483 10.1128/JCM.02993-14
15. J.D. van Embden, M.D. Cave, J.T. Crawford, J.W. Dale, K.D. Eisenach, B. Gicquel, and et al. Strain identification of Mycobacterium tuberculosis by DNA fingerprinting: recommendations for a standardized methodology J Clin Microbiol 31 1993 406 409
16. J.T. Belisle, and M.G. Sonnenberg Isolation of genomic DNA from mycobacteria Methods Mol Biol 101 1998 31 44 10.1385/0-89603-471-2:31
17. Applied Biosystems SOLiD™ 3 System Instrument Operation Guide, n.d. http://tools.lifetechnologies.com/content/sfs/manuals/4407430b.pdf [accessed 07.06.15.].
18. LifeScope Genomic Analysis Software, n.d. http://www.lifetechnologies.com/us/en/home/technical-resources/software-downloads/lifescope-genomic-analysis-software.html [accessed 05.05.15.].
19. R.M. Davidson, N.A. Hasan, P.R. Reynolds, S. Totten, B. Garcia, A. Levin, and et al. Genome sequencing of Mycobacterium abscessus isolates from patients in the united states and comparisons to globally diverse clinical strains J Clin Microbiol 52 2014 3573 3582 10.1128/JCM.01144-14
20. H. Li, B. Handsaker, A. Wysoker, T. Fennell, J. Ruan, N. Homer, and et al. The sequence alignment/map format and SAMtools Bioinformatics 25 2009 2078 2079 10.1093/bioinformatics/btp352
21. H. Li A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data Bioinformatics 27 2011 2987 2993 10.1093/bioinformatics/btr509
22. K. Wang, M. Li, and H. Hakonarson ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data Nucleic Acids Res 38 2010 e164 10.1093/nar/gkq603
23. A. Sandgren, M. Strong, P. Muthukrishnan, B.K. Weiner, G.M. Church, and M.B. Murray Tuberculosis drug resistance mutation database PLoS Med 6 2009 e2 10.1371/journal.pmed.1000002
24. J.E. Galagan, P. Sisk, C. Stolte, B. Weiner, M. Koehrsen, F. Wymore, and et al. TB database 2010: overview and update Tuberculosis (Edinb) 90 2010 225 235 10.1016/j.tube.2010.03.010
25. T.B.K. Reddy, R. Riley, F. Wymore, P. Montgomery, D. DeCaprio, R. Engels, and et al. TB database: an integrated platform for tuberculosis research Nucleic Acids Res 37 2009 D499 D508 10.1093/nar/gkn652
26. L.P. Miller, J.T. Crawford, and T.M. Shinnick The rpoB gene of Mycobacterium tuberculosis Antimicrob Agents Chemother 38 1994 805 811 10.1128/AAC.38.4.805
27. D.H. Mariam, Y. Mengistu, S.E. Hoffner, and D.I. Andersson Effect of society 48 2004 1289 1294 10.1128/AAC.48.4.1289
28. A. Sala, P. Bordes, and P. Genevaux Multiple toxin-antitoxin systems in Mycobacterium tuberculosis Toxins (Basel) 6 2014 1002 1020 10.3390/toxins6031002
29. B. Heym, P.M. Alzari, N. Honoré, and S.T. Cole Missense mutations in the catalase-peroxidase gene, katG, are associated with isoniazid resistance in Mycobacterium tuberculosis Mol Microbiol 15 1995 235 245
30. M. Ballif, P. Harino, S. Ley, M. Coscolla, S. Niemann, R. Carter, and et al. Drug resistance-conferring mutations in Mycobacterium tuberculosis from Madang, Papua New Guinea BMC Microbiol 12 2012 191 10.1186/1471-2180-12-191
31. I. Comas, S. Borrell, A. Roetzer, G. Rose, B. Malla, M. Kato-Maeda, and et al. Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes Nat Genet 44 2012 106 110 10.1038/ng.1038
32. T.R. Ioerger, Y. Feng, X. Chen, K.M. Dobos, T.C. Victor, E.M. Streicher, and et al. The non-clonality of drug resistance in Beijing-genotype isolates of Mycobacterium tuberculosis from the Western Cape of South Africa BMC Genomics 11 2010 670 10.1186/1471-2164-11-670
33. E.A. Campbell, N. Korzheva, A. Mustaev, K. Murakami, S. Nair, A. Goldfarb, and et al. Structural mechanism for rifampicin inhibition of bacterial rna polymerase Cell 104 2001 901 912
34. J.A. DeVito, and S. Morris Exploring the structure and function of the mycobacterial KatG protein using trans-dominant mutants Antimicrob Agents Chemother 47 2003 188 195
35. M. Wilming, and K. Johnsson Inter- and intramolecular domain interactions of the catalase-peroxidase KatG from M. tuberculosis FEBS Lett 509 2001 272 276
36. P. Vidossich, P.C. Loewen, X. Carpena, G. Fiorin, I. Fita, and C. Rovira Binding of the antitubercular pro-drug isoniazid in the heme access channel of catalase-peroxidase (KatG). A combined structural and metadynamics investigation J Phys Chem B 118 2014 2924 2931 10.1021/jp4123425
37. C.E. Cade, A.C. Dlouhy, K.F. Medzihradszky, S.P. Salas-Castillo, and R.A. Ghiladi Isoniazid-resistance conferring mutations in Mycobacterium tuberculosis KatG: catalase, peroxidase, and INH-NADH adduct formation activities Protein Sci 19 2010 458 474 10.1002/pro.324
38. T. Bertrand, N.A.J. Eady, J.N. Jones, Jesmin, J.M. Nagy, B. Jamart-Grégoire, and et al. Crystal structure of Mycobacterium tuberculosis catalase-peroxidase J Biol Chem 279 2004 38991 38999 10.1074/jbc.M402382200
39. Y. Yamaguchi, J.-H. Park, and M. Inouye Toxin-antitoxin systems in bacteria and archaea Annu Rev Genet 45 2011 61 79 10.1146/annurev-genet-110410-132412
40. K. Georgiades, and D. Raoult Genomes of the most dangerous epidemic bacteria have a virulence repertoire characterized by fewer genes but more toxin-antitoxin modules PLoS One 6 2011 e17962 10.1371/journal.pone.0017962