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Review Articles

Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery

, , , , , , & show all
Pages 109-118 | Received 15 Oct 2018, Accepted 09 Dec 2018, Published online: 16 Jan 2019

References

  • Shinnick T, Good R. Mycobacterial taxonomy. Eur J Clin Microbiol Infect Dis. 1994;13:884–901.
  • Forbes BA. Mycobacterial taxonomy. J Clin Microbiol. 2017;55:380.
  • WHO. Global tuberculosis report 2017; 2017.
  • Prevots DR, Loddenkemper R, Sotgiu G, et al. Nontuberculous mycobacterial pulmonary disease: an increasing burden with substantial costs. Eur Respir J. 2017;49:1700374.
  • Stout JE, Koh WJ, Yew WW. Update on pulmonary disease due to non-tuberculous mycobacteria. Int J Infect Dis. 2016;45:123–134.
  • Smith M, Efthimiou J, Hodson ME, et al. Mycobacterial isolations in young adults with cystic fibrosis. Thorax. 1984;39:369–375.
  • Rodman DM, Polis JM, Heltshe SL, et al. Late diagnosis defines a unique population of long-term survivors of cystic fibrosis. Am J Respir Crit Care Med. 2005;171:621–626.
  • Silver LL. Challenges of antibacterial discovery. Clin Microbiol Rev. 2011;24:71–109.
  • D’Ambrosio L, Centis R, Sotgiu G, et al. New anti-tuberculosis drugs and regimens: 2015 update. ERJ Open Res. 2015;1:00010-2015.
  • Gygli SM, Borrell S, Trauner A, et al. Antimicrobial resistance in Mycobacterium tuberculosis: mechanistic and evolutionary perspectives. FEMS Microbiol Rev. 2017;41:354–373.
  • Dookie N, Rambaran S, Padayatchi N, et al. Evolution of drug resistance in Mycobacterium tuberculosis: a review on the molecular determinants of resistance and implications for personalized care. J Antimicrob Chemother. 2018;73:1138–1151.
  • Nessar R, Cambau E, Reyrat JM, et al. Mycobacterium abscessus: a new antibiotic nightmare. J Antimicrob Chemother. 2012;67:810–818.
  • Saunderson PR. Drug-resistant M leprae. Clin Dermatol. 2016;34:79–81.
  • Davies J, Davies D. Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev. 2010;74:417–433.
  • Variava E, Martinson N. Drug-resistant tuberculosis: the rise of the monos. Lancet Infect Dis. 2018;18:705–706.
  • Lange C, Chesov D, Heyckendorf J, et al. Drug-resistant tuberculosis: An update on disease burden, diagnosis and treatment. Respirology. 2018;23:656–673.
  • Liu W, Li B, Chu H, et al. Rapid detection of mutations in erm(41) and rrl associated with clarithromycin resistance in Mycobacterium abscessus complex by denaturing gradient gel electrophoresis. J Microbiol Methods. 2017;143:87–93.
  • Bell G, MacLean C. The search for ‘evolution-proof’ antibiotics. Trends Microbiol. 2017;26:471–483.
  • Pucci MJ. Use of genomics to select antibacterial targets. Biochem Pharmacol. 2006;71:1066–1072.
  • Mills SD. When will the genomics investment pay off for antibacterial discovery? Biochem Pharmacol. 2006;71:1096–1102.
  • Cole S, Brosch R, Parkhill J, et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature. 1998;393:537.
  • Brosch R, Gordon SV, Billault A, et al. Use of a Mycobacterium tuberculosis H37Rv bacterial artificial chromosome library for genome mapping, sequencing, and comparative genomics. Infect Immun. 1998;66:2221–2229.
  • Cole S, Eiglmeier K, Parkhill J, et al. Massive gene decay in the leprosy bacillus. Nature. 2001;409:1007.
  • Ripoll F, Pasek S, Schenowitz C, et al. Non mycobacterial virulence genes in the genome of the emerging pathogen Mycobacterium abscessus. PloS One. 2009;4:e5660.
  • Wee WY, Dutta A, Choo SW. Comparative genome analyses of mycobacteria give better insights into their evolution. PloS One. 2017;12:e0172831.
  • Schürch AC, van Soolingen D. Dna fingerprinting of Mycobacterium tuberculosis: from phage typing to whole-genome sequencing. Infect Genet Evol. 2012;12:602–609.
  • Roetzer A, Diel R, Kohl TA, et al. Whole genome sequencing versus traditional genotyping for investigation of a Mycobacterium tuberculosis outbreak: a longitudinal molecular epidemiological study. PLoS Med. 2013;10:e1001387.
  • Walker TM, Ip CL, Harrell RH, et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect Dis. 2013;13:137–146.
  • Niemann S, Supply P. Diversity and evolution of Mycobacterium tuberculosis: moving to whole-genome-based approaches. Cold Spring Harb Perspect Med. 2014;4:a021188.
  • Sassi M, Drancourt M. Genome analysis reveals three genomospecies in Mycobacterium abscessus. BMC Genomics. 2014;15:359.
  • Benjak A, Avanzi C, Singh P, et al. Phylogenomics and antimicrobial resistance of the leprosy bacillus Mycobacterium leprae. Nat Commun. 2018;9:352.
  • Borrell S, Trauner A. Strain variation in the Mycobacterium tuberculosis complex: its role in biology, epidemiology and control. In: Gagneux S, editor. Advances in experimental medicine and biology, Vol. 1019, Chapter 14. Cham: Springer; 2017. p. 263–279.
  • Aminov RI, Mackie RI. Evolution and ecology of antibiotic resistance genes. FEMS Microbiol Lett. 2007;271:147–161.
  • Cohen KA, Abeel T, Manson McGuire A, et al. Evolution of extensively drug-resistant tuberculosis over four decades: whole genome sequencing and dating analysis of Mycobacterium tuberculosis isolates from KwaZulu-Natal. PLoS Med. 2015;12:e1001880.
  • Roycroft E, O’Toole RF, Fitzgibbon MM, et al. Molecular epidemiology of multi-and extensively-drug-resistant Mycobacterium tuberculosis in Ireland, 2001–2014. J Infect. 2018;76:55–67.
  • Merker M, Kohl TA, Roetzer A, et al. Whole genome sequencing reveals complex evolution patterns of multidrug-resistant Mycobacterium tuberculosis Beijing strains in patients. PloS One. 2013;8:e82551.
  • De Beer J, Kodmon C, van der Werf M, et al. Molecular surveillance of multi-and extensively drug-resistant tuberculosis transmission in the European Union from 2003 to 2011. Euro Surveill. 2014;19:pii: 20742.
  • Tan JL, Ng KP, Ong CS, et al. Genomic comparisons reveal microevolutionary differences in Mycobacterium abscessus subspecies. Front Microbiol. 2017;8:2042.
  • Sapriel G, Konjek J, Orgeur M, et al. Genome-wide mosaicism within Mycobacterium abscessus: evolutionary and epidemiological implications. BMC Genomics. 2016;17:118.
  • Davidson RM, Hasan NA, Reynolds PR, et al. Genome sequencing of Mycobacterium abscessus isolates from patients in the United States and comparisons to globally diverse clinical strains. J Clin Microbiol. 2014;52:3573–82.
  • Bryant JM, Grogono DM, Rodriguez-Rincon D, et al. Population-level genomics identifies the emergence and global spread of a human transmissible multidrug-resistant nontuberculous mycobacterium. Science. 2016;354:751–757.
  • Monot M, Honoré N, Garnier T, et al. Comparative genomic and phylogeographic analysis of Mycobacterium leprae. Nat Genet. 2009;41:1282.
  • Perdigão J, Clemente S, Ramos J, et al. Genetic diversity, transmission dynamics and drug resistance of Mycobacterium tuberculosis in Angola. Sci Rep. 2017;7:42814.
  • Malhotra S, Vedithi SC, Blundell TL. Decoding the similarities and differences among Mycobacterial species. PLoS Negl Trop Dis. 2017;11:e0005883.
  • Kavvas ES, Seif Y, Yurkovich JT, et al. Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions. BMC Syst Biol. 2018;12:25.
  • Caspi R, Billington R, Ferrer L, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2016;44:D471–D480.
  • Kanehisa M. ‘In silico’ simulation of biological processes: Novartis foundation symposium 247.Wiley Online Library; 2002. p. 91–103.
  • Shanmugham B, Pan A. Identification and characterization of potential therapeutic candidates in emerging human pathogen Mycobacterium abscessus: a novel hierarchical in silico approach. PloS One. 2013;8:e59126.
  • Singh P, Cole ST. Mycobacterium leprae: genes, pseudogenes and genetic diversity. Future Microbiol. 2011;6:57–71.
  • Griffin JE, Gawronski JD, DeJesus MA, et al. High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism. PLoS Pathog. 2011;7:e1002251.
  • Sassetti CM, Boyd DH, Rubin EJ. Genes required for mycobacterial growth defined by high density mutagenesis. Mol Microbiol. 2003;48:77–84.
  • DeJesus MA, Gerrick ER, Xu W, et al. Comprehensive essentiality analysis of the Mycobacterium tuberculosis genome via saturating transposon mutagenesis. MBio. 2017;8:e02133-16.
  • Peng C, Lin Y, Luo H, et al. A comprehensive overview of online resources to identify and predict bacterial essential genes. Front Microbiol. 2017;8:2331.
  • Zhang R, Ou HY, Zhang CT. Deg: a database of essential genes. Nucleic Acids Res. 2004;32:271D–D272.
  • Chen W-H, Lu G, Chen X, et al. Ogee v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines. Nucleic Acids Res. 2017;45(D1):D940–D944.
  • Uddin R, Azam SS, Wadood A, et al. Computational identification of potential drug targets against Mycobacterium leprae. Med Chem Res. 2016;25:473–481.
  • Ghosh S, Baloni P, Mukherjee S, et al. A multi-level multi-scale approach to study essential genes in Mycobacterium tuberculosis. BMC Syst Biol. 2013;7:132.
  • Kaur D, Kutum R, Dash D, et al. Data intensive genome level analysis for identifying novel, non-toxic drug targets for multi drug resistant Mycobacterium tuberculosis. Sci Rep. 2017;7:46595.
  • Farhat MR, Shapiro BJ, Kieser KJ, et al. Genomic analysis identifies targets of convergent positive selection in drug-resistant Mycobacterium tuberculosis. Nat Genet. 2013;45:1183.
  • Gladki A, Kaczanowski S, Szczesny P, et al. The evolutionary rate of antibacterial drug targets. BMC Bioinformatics. 2013;14:36.
  • Nguta JM, Appiah-Opong R, Nyarko AK, et al. Current perspectives in drug discovery against tuberculosis from natural products. Int J Mycobacteriol. 2015;4:165–183.
  • Sibanda B, Pearl L, Hemmings A, et al. Acta crystallographica section A C42-C42. Copenhagen: Munksgaard.
  • Blundell TL. Structure-based drug design. Nature. 1996;384:23–26.
  • Blundell TL. Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry. IUCrJ. 2017;4:308–321.
  • Blundell TL, Jhoti H, Abell C. High-throughput crystallography for lead discovery in drug design. Nat Rev Drug Discov. 2002;1:45–54.
  • Mendes V, Blundell TL. Targeting tuberculosis using structure-guided fragment-based drug design. Drug Discov Today. 2017;22:546–554.
  • Malhotra S, Thomas SE, Montano BO, et al. Structure-guided, target-based drug discovery–exploiting genome information from HIV to mycobacterial infections. Postepy Biochem. 2016;62:262–272.
  • Thomas SE, Mendes V, Kim SY, et al. Structural biology and the design of new therapeutics: from HIV and cancer to mycobacterial infections: a paper dedicated to John Kendrew. J Mol Biol. 2017;429:2677–2693.
  • Šali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol. 1993;234:779–815.
  • Ochoa-Montaño B, Mohan N, Blundell TL. Chopin: a web resource for the structural and functional proteome of Mycobacterium tuberculosis. Database. 2015; 2015. pii:bav026.
  • Radoux CJ, Olsson TS, Pitt WR, et al. Identifying interactions that determine fragment binding at protein hotspots. J Med Chem. 2016;59:4314–4325.
  • Hung AW, Silvestre HL, Wen S, et al. Optimization of inhibitors of Mycobacterium tuberculosis pantothenate synthetase based on group efficiency analysis. Chem Med Chem. 2016;11:38–42.
  • Nikiforov PO, Blaszczyk M, Surade S, et al. Fragment-sized EthR inhibitors exhibit exceptionally strong ethionamide boosting effect in whole-cell Mycobacterium tuberculosis assays. ACS Chem Biol. 2017;12:1390–1396.
  • Kavanagh ME, Coyne AG, McLean KJ, et al. Fragment-based approaches to the development of Mycobacterium tuberculosis CYP121 inhibitors. J Med Chem. 2016;59:3272–3302.
  • Naik M, Raichurkar A, Bandodkar BS, et al. Structure guided lead generation for M. tuberculosis thymidylate kinase (Mtb TMK): discovery of 3-cyanopyridone and 1, 6-naphthyridin-2-one as potent inhibitors. J Med Chem. 2015;58:753–766.
  • Huang H-L, Krieger IV, Parai MK, et al. Mycobacterium tuberculosis malate synthase structures with fragments reveal a portal for substrate/product exchange. J Biol Chem. 2016;M116:750877.
  • Poyraz OM, Jeankumar VU, Saxena S, et al. Structure-guided design of novel thiazolidine inhibitors of O-acetyl serine sulfhydrylase from Mycobacterium tuberculosis. J Med Chem. 2013;56:6457–6466.
  • Devi PB, Samala G, Sridevi JP, et al. Structure-guided design of thiazolidine derivatives as Mycobacterium tuberculosis pantothenate synthetase inhibitors. Chem Med Chem. 2014;9:2538–2547.
  • Luciani R, Saxena P, Surade S, et al. Virtual screening and X-ray crystallography identify non-substrate analog inhibitors of flavin-dependent thymidylate synthase. J Med Chem. 2016;59:9269–9275.
  • Zuniga ES, Early J, Parish T. The future for early-stage tuberculosis drug discovery. Future Microbiol. 2015;10:217–29.
  • Abrahams GL, Kumar A, Savvi S, et al. Pathway-selective sensitization of Mycobacterium tuberculosis for target-based whole-cell screening. Chem Biol . 2012;19:844–854.
  • Aggarwal A, Parai MK, Shetty N, et al. Development of a novel lead that targets M. tuberculosis polyketide synthase 13. Cell. 2017;170:249–259.e25.
  • Singh V, Donini S, Pacitto A, et al. The inosine monophosphate dehydrogenase, GuaB2, is a vulnerable new bactericidal drug target for tuberculosis. ACS Infect Dis. 2017;3:5–17.
  • Boshoff HI, Myers TG, Copp BR, et al. The transcriptional responses of Mycobacterium tuberculosis to inhibitors of metabolism novel insights into drug mechanisms of action. J Biol Chem. 2004;279:40174–40184.
  • Prosser GA, de Carvalho LP. Metabolomics reveal d-alanine: d-alanine ligase as the target of d-cycloserine in Mycobacterium tuberculosis. ACS Med Chem Lett. 2013;4:1233–1237.
  • Mugumbate G, Mendes V, Blaszczyk M, et al. Target identification of mycobacterium tuberculosis phenotypic hits using a concerted chemogenomic, biophysical, and structural approach. Front Pharmacol. 2017;8:681.
  • Shi J, Blundell TL, Mizuguchi K. Fugue: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J Mol Biol. 2001;310:243–257.
  • Dorn M, e Silva MB, Buriol LS, et al. Three-dimensional protein structure prediction: methods and computational strategies. Comput Biol Chem. 2014;53:251–276.
  • Song Y, DiMaio F, Wang RY-R, et al. High-resolution comparative modeling with RosettaCM. Structure. 2013;21:1735–1742.
  • Moult J, Fidelis K, Kryshtafovych A, et al. Critical assessment of methods of protein structure prediction (CASP)—round XII. Proteins: Struct, Funct, Bioinf. 2018;86:7–15.
  • Hassan SS, Tiwari S, Guimarães LC, et al. Proteome scale comparative modeling for conserved drug and vaccine targets identification in Corynebacterium pseudotuberculosis. BMC Genomics. 2014;15(S3).
  • Worth CL, Preissner R, Blundell TL. Sdm—a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Res. 2011;39:W215–W222.
  • Pires DE, Ascher DB, Blundell TL. Mcsm: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics. 2014;30:335–342.
  • Pires DE, Blundell TL, Ascher DB. mCSM-lig: quantifying the effects of mutations on protein-small molecule affinity in genetic disease and emergence of drug resistance. Sci Rep. 2016;6:29575.
  • Frappier V, Chartier M, Najmanovich RJ. Encom server: exploring protein conformational space and the effect of mutations on protein function and stability. Nucleic Acids Res. 2015;43:W395–W400.
  • Schymkowitz J, Borg J, Stricher F, et al. The FoldX web server: an online force field. Nucleic Acids Res. 2005;33:W382–W388.
  • Vedithi SC, Malhotra S, Das M, et al. Structural implications of mutations conferring rifampin resistance in mycobacterium leprae. Sci Rep. 2018;8:5016.
  • Pires DE, Chen J, Blundell TL, et al. In silico functional dissection of saturation mutagenesis: Interpreting the relationship between phenotypes and changes in protein stability, interactions and activity. Sci Rep. 2016;6:19848.
  • Forman JR, Worth CL, Bickerton GRJ, et al. Structural bioinformatics mutation analysis reveals genotype–phenotype correlations in von Hippel-Lindau disease and suggests molecular mechanisms of tumorigenesis. Proteins: Struct, Funct, Bioinf. 2009;77:84–96.
  • Pandurangan AP, Ascher DB, Thomas SE, et al. Genomes, structural biology and drug discovery: combating the impacts of mutations in genetic disease and antibiotic resistance. Biochem Soc Trans. 2017;45:303–311.