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Tuberculosis drug discovery in the CRISPR era


Autoři: Jeremy Rock aff001
Působiště autorů: Laboratory of Host-Pathogen Biology, The Rockefeller University, New York, New York, United States of America aff001
Vyšlo v časopise: Tuberculosis drug discovery in the CRISPR era. PLoS Pathog 15(9): e32767. doi:10.1371/journal.ppat.1007975
Kategorie: Pearls
doi: https://doi.org/10.1371/journal.ppat.1007975

Souhrn

Stewart Cole and colleagues determined the complete genome sequence of Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis (TB), in 1998 [1]. This was a landmark achievement that heralded a new age in TB drug discovery. With the genome sequence in hand, drug discoverers suddenly had thousands of new potential targets to explore. But the excitement has since faded [2]. It is unquestioned that genomics has transformed our understanding of the biology of this pathogen. However, the expectation that the Mtb genome sequence would rapidly lead to new therapeutic interventions remains unfulfilled [3]. One of the (many) reasons for this unrealized potential is that our tools to systematically interrogate the Mtb genome and its drug targets—so-called functional genomics—have been limited. In this Pearl, I argue that the recent development of robust CRISPR-based genetics in Mtb [4] overcomes many prior limitations and holds the potential to close the gap between genomics and TB drug discovery.

Klíčová slova:

Medicine and health sciences – Pharmacology – Drug research and development – Drug discovery – Tuberculosis drug discovery – Drugs – Antimicrobial resistance – Antibiotic resistance – Infectious diseases – Bacterial diseases – Tuberculosis – Extensively drug-resistant tuberculosis – Tropical diseases – Biology and life sciences – Organisms – Bacteria – Actinobacteria – Mycobacterium tuberculosis – Microbiology – Microbial control – Antimicrobials – Antibiotics


Zdroje

1. 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. doi: 10.1038/31159 9634230

2. Payne DJ, Gwynn MN, Holmes DJ, Pompliano DL. Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat Rev Drug Discov. 2007;6: 29–40. doi: 10.1038/nrd2201 17159923

3. Lechartier B, Rybniker J, Zumla A, Cole ST. Tuberculosis drug discovery in the post-post-genomic era. EMBO Mol Med. 2014;6: 158–168. doi: 10.1002/emmm.201201772 24401837

4. Rock JM, Hopkins FF, Chavez A, Diallo M, Chase MR, Gerrick ER, et al. Programmable transcriptional repression in mycobacteria using an orthogonal CRISPR interference platform. Nature Microbiology. 2017;2: 16274. doi: 10.1038/nmicrobiol.2016.274 28165460

5. World Health Organization. Global tuberculosis report 2018. 2018.

6. Eder J, Sedrani R, Wiesmann C. The discovery of first-in-class drugs: origins and evolution. Nat Rev Drug Discov. 2014;13: 577–587. doi: 10.1038/nrd4336 25033734

7. Schnappinger D. Genetic Approaches to Facilitate Antibacterial Drug Development. Cold Spring Harb Perspect Med. 2015;5: a021139. doi: 10.1101/cshperspect.a021139 25680982

8. Pethe K, Sequeira PC, Agarwalla S, Rhee K, Kuhen K, Phong WY, et al. A chemical genetic screen in Mycobacterium tuberculosis identifies carbon-source-dependent growth inhibitors devoid of in vivo efficacy. Nat Commun. 2010;1: 57. doi: 10.1038/ncomms1060 20975714

9. Qi LS, Larson MH, Gilbert LA, Doudna JA, Weissman JS, Arkin AP, et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell. 2013;152: 1173–1183. doi: 10.1016/j.cell.2013.02.022 23452860

10. Bikard D, Jiang W, Samai P, Hochschild A, Zhang F, Marraffini LA. Programmable repression and activation of bacterial gene expression using an engineered CRISPR-Cas system. Nucleic Acids Res. 2013;41: 7429–7437. doi: 10.1093/nar/gkt520 23761437

11. Peters JM, Colavin A, Shi H, Czarny TL, Larson MH, Wong S, et al. A Comprehensive, CRISPR-based Functional Analysis of Essential Genes in Bacteria. Cell. 2016;165: 1493–1506. doi: 10.1016/j.cell.2016.05.003 27238023

12. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 2012;337: 816–821. doi: 10.1126/science.1225829 22745249

13. Mojica FJM, Díez-Villaseñor C, García-Martínez J, Almendros C. Short motif sequences determine the targets of the prokaryotic CRISPR defence system. Microbiology. 2009;155: 733–740. doi: 10.1099/mic.0.023960-0 19246744

14. Sternberg SH, Redding S, Jinek M, Greene EC, Doudna JA. DNA interrogation by the CRISPR RNA-guided endonuclease Cas9. Nature. 2014;507: 62–67. doi: 10.1038/nature13011 24476820

15. Gandotra S, Schnappinger D, Monteleone M, Hillen W, Ehrt S. In vivo gene silencing identifies the Mycobacterium tuberculosis proteasome as essential for the bacteria to persist in mice. Nat Med. 2007;13: 1515–1520. doi: 10.1038/nm1683 18059281

16. Sakamoto KM, Kim KB, Kumagai A, Mercurio F, Crews CM, Deshaies RJ. Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation. Proc Natl Acad Sci USA. 2001;98: 8554–8559. doi: 10.1073/pnas.141230798 11438690

17. Sassetti CM, Boyd DH, Rubin EJ. Genes required for mycobacterial growth defined by high density mutagenesis. Molecular Microbiology. 2003;48: 77–84. 12657046

18. Wei J-R, Krishnamoorthy V, Murphy K, Kim J-H, Schnappinger D, Alber T, et al. Depletion of antibiotic targets has widely varying effects on growth. Proc Natl Acad Sci USA. 2011;108: 4176–4181. doi: 10.1073/pnas.1018301108 21368134

19. Carey AF, Rock JM, Krieger IV, Chase MR, Fernandez-Suarez M, Gagneux S, et al. TnSeq of Mycobacterium tuberculosis clinical isolates reveals strain-specific antibiotic liabilities. PLoS Pathog. 2018;14: e1006939. doi: 10.1371/journal.ppat.1006939 29505613

20. Comas I, Coscolla M, Luo T, Borrell S, Holt KE, Kato-Maeda M, et al. Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat Genet. 2013;45: 1176–1182. doi: 10.1038/ng.2744 23995134

21. Abrahams GL, Kumar A, Savvi S, Hung AW, Wen S, Abell C, et al. Pathway-selective sensitization of Mycobacterium tuberculosis for target-based whole-cell screening. Chem Biol. 2012;19: 844–854. doi: 10.1016/j.chembiol.2012.05.020 22840772

22. Ballell L, Bates RH, Young RJ, Alvarez-Gomez D, Alvarez-Ruiz E, Barroso V, et al. Fueling open-source drug discovery: 177 small-molecule leads against tuberculosis. ChemMedChem. 2013;8: 313–321. doi: 10.1002/cmdc.201200428 23307663

23. Evans JC, Trujillo C, Wang Z, Eoh H, Ehrt S, Schnappinger D, et al. Validation of CoaBC as a Bactericidal Target in the Coenzyme A Pathway of Mycobacterium tuberculosis. ACS Infect Dis. 2016;2: 958–968. doi: 10.1021/acsinfecdis.6b00150 27676316

24. Wang J, Soisson SM, Young K, Shoop W, Kodali S, Galgoci A, et al. Platensimycin is a selective FabF inhibitor with potent antibiotic properties. Nature. 2006;441: 358–361. doi: 10.1038/nature04784 16710421

25. Banerjee A, Dubnau E, Quemard A, Balasubramanian V, Um KS, Wilson T, et al. inhA, a gene encoding a target for isoniazid and ethionamide in Mycobacterium tuberculosis. Science. 1994;263: 227–230. doi: 10.1126/science.8284673 8284673

26. Johnson EO, LaVerriere E, Office E, Stanley M, Meyer E, Kawate T, et al. Large-scale chemical-genetics yields new M. tuberculosis inhibitor classes. Nature. 2019;571: 72–78. doi: 10.1038/s41586-019-1315-z 31217586

27. Bollenbach T. Antimicrobial interactions: mechanisms and implications for drug discovery and resistance evolution. Curr Opin Microbiol. 2015;27: 1–9. doi: 10.1016/j.mib.2015.05.008 26042389

28. Kerantzas CA, Jacobs WR. Origins of Combination Therapy for Tuberculosis: Lessons for Future Antimicrobial Development and Application. mBio. 2017;8. doi: 10.1128/mBio.01586-16 28292983

29. Gomez JE, McKinney JD. M. tuberculosis persistence, latency, and drug tolerance. Tuberculosis. 2004;84: 29–44. doi: 10.1016/j.tube.2003.08.003 14670344

30. Zhang Y, Yew WW, Barer MR. Targeting persisters for tuberculosis control. Antimicrobial Agents and Chemotherapy. 2012;56: 2223–2230. doi: 10.1128/AAC.06288-11 22391538

31. Adams KN, Takaki K, Connolly LE, Wiedenhoft H, Winglee K, Humbert O, et al. Drug tolerance in replicating mycobacteria mediated by a macrophage-induced efflux mechanism. Cell. 2011;145: 39–53. doi: 10.1016/j.cell.2011.02.022 21376383

32. Wakamoto Y, Dhar N, Chait R, Schneider K, Signorino-Gelo F, Leibler S, et al. Dynamic persistence of antibiotic-stressed mycobacteria. Science. 2013;339: 91–95. doi: 10.1126/science.1229858 23288538

33. Prideaux B, Via LE, Zimmerman MD, Eum S, Sarathy J, O'Brien P, et al. The association between sterilizing activity and drug distribution into tuberculosis lesions. Nat Med. 2015;21: 1223–1227. doi: 10.1038/nm.3937 26343800

34. Mitchison DA, Davies GR. Assessment of the Efficacy of New Anti-Tuberculosis Drugs. Open Infect Dis J. 2008;2: 59–76. doi: 10.2174/1874279300802010059 23814629

35. Zhang Y, Shi W, Zhang W, Mitchison D. Mechanisms of Pyrazinamide Action and Resistance. Microbiol Spectr. 2014;2: MGM2–0023–2013. doi: 10.1128/microbiolspec.MGM2-0023-2013 25530919

36. Blanc L, Sarathy JP, Alvarez Cabrera N, O'Brien P, Dias-Freedman I, Mina M, et al. Impact of immunopathology on the antituberculous activity of pyrazinamide. J Exp Med. 2018;215: 1975–1986. doi: 10.1084/jem.20180518 30018074

37. Colangeli R, Jedrey H, Kim S, Connell R, Ma S, Chippada Venkata UD, et al. Bacterial Factors That Predict Relapse after Tuberculosis Therapy. N Engl J Med. 2018;379: 823–833. doi: 10.1056/NEJMoa1715849 30157391

38. Cokol M, Kuru N, Bicak E, Larkins-Ford J, Aldridge BB. Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis. Sci Adv. 2017;3: e1701881. doi: 10.1126/sciadv.1701881 29026882

39. Lee B-Y, Clemens DL, Silva A, Dillon BJ, Masleša-Galić S, Nava S, et al. Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time. Nat Commun. 2017;8: 14183. doi: 10.1038/ncomms14183 28117835

40. Katzir I, Cokol M, Aldridge BB, Alon U. Prediction of ultra-high-order antibiotic combinations based on pairwise interactions. PLoS Comput Biol. 2019;15: e1006774. doi: 10.1371/journal.pcbi.1006774 30699106

41. Knight ZA, Shokat KM. Chemical genetics: where genetics and pharmacology meet. Cell. 2007;128: 425–430. doi: 10.1016/j.cell.2007.01.021 17289560

42. Jensen KJ, Moyer CB, Janes KA. Network Architecture Predisposes an Enzyme to Either Pharmacologic or Genetic Targeting. Cell Syst. 2016;2: 112–121. doi: 10.1016/j.cels.2016.01.012 26942229

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Hygiena a epidemiologie Infekční lékařství Laboratoř

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