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Volume 10, Issue 2

Compensatory mutations are associated with increased growth in resistant clinical samples of Open Access

Abstract

Mutations in associated with resistance to antibiotics often come with a fitness cost for the bacteria. Resistance to the first-line drug rifampicin leads to lower competitive fitness of populations when compared to susceptible populations. This fitness cost, introduced by resistance mutations in the RNA polymerase, can be alleviated by compensatory mutations (CMs) in other regions of the affected protein. CMs are of particular interest clinically since they could lock in resistance mutations, encouraging the spread of resistant strains worldwide. Here, we report the statistical inference of a comprehensive set of CMs in the RNA polymerase of , using over 70 000 . genomes that were collated as part of the CRyPTIC project. The unprecedented size of this data set gave the statistical tests more power to investigate the association of putative CMs with resistance-conferring mutations. Overall, we propose 51 high-confidence CMs by means of statistical association testing and suggest hypotheses for how they exert their compensatory mechanism by mapping them onto the protein structure. In addition, we were able to show an association of CMs with higher growth densities, and hence presumably with higher fitness, in resistant samples in the more virulent lineage 2. Our results suggest the association of CM presence with significantly higher growth than for wild-type samples, although this association is confounded with lineage and sub-lineage affiliation. Our findings emphasize the integral role of CMs and lineage affiliation in resistance spread and increases the urgency of antibiotic stewardship, which implies accurate, cheap and widely accessible diagnostics for infections to not only improve patient outcomes but also prevent the spread of resistant strains.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): antimicrobial resistance , compensatory mutations , fitness cost and tuberculosis
Funding
This study was supported by the:
  • NIHR Oxford Biomedical Research Centre (Award BB/T008784/1)
    • Principle Award Recipient: NotApplicable
  • National Institute for Health Research Health Protection Research Unit (Award NIHR200915)
    • Principle Award Recipient: NotApplicable
© 2024 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2024-02-05
2024-04-27
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References

  1. Ladner J. The CRyPTIC Consortium A data compendium associating the genomes of 12,289 Mycobacterium tuberculosis isolates with quantitative resistance phenotypes to 13 antibiotics. PLoS Biol 2022; 20:e3001721 [View Article] [PubMed]
     [Google Scholar]
  2. Brunner V, Fowler PW. Accompanying code to reproduce analysis and figures. n.d https://github.com/fowler-lab/tb-rnap-compensation
  3. Ikuta KS, Swetschinski LR, Robles Aguilar G, Sharara F, Mestrovic T. Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2022; 400:2221–2248 [View Article] [PubMed]
     [Google Scholar]
  4. Smith PA, Romesberg FE. Combating bacteria and drug resistance by inhibiting mechanisms of persistence and adaptation. Nat Chem Biol 2007; 3:549–556 [View Article] [PubMed]
     [Google Scholar]
  5. Nikaido H. Multidrug resistance in bacteria. Annu Rev Biochem 2009; 78:119–146 [View Article] [PubMed]
     [Google Scholar]
  6. Iskandar K, Murugaiyan J, Hammoudi Halat D, Hage SE, Chibabhai V et al. Antibiotic discovery and resistance: the chase and the race. Antibiotics 2022; 11:182 [View Article] [PubMed]
     [Google Scholar]
  7. Tacconelli E, Carrara E, Savoldi A, Harbarth S, Mendelson M et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect Dis 2018; 18:318–327 [View Article] [PubMed]
     [Google Scholar]
  8. Almeida Da Silva PEA, Palomino JC. Molecular basis and mechanisms of drug resistance in Mycobacterium tuberculosis: classical and new drugs. J Antimicrob Chemother 2011; 66:1417–1430 [View Article] [PubMed]
     [Google Scholar]
  9. Cambau E, Drancourt M. Steps towards the discovery of Mycobacterium tuberculosis by Robert Koch, 1882. Clin Microbiol Infect 2014; 20:196–201 [View Article] [PubMed]
     [Google Scholar]
  10. Bagcchi S. WHO’s Global Tuberculosis Report 2022. Lancet Microbe 2023; 4:e20 [View Article] [PubMed]
     [Google Scholar]
  11. Chan ED, Iseman MD. Multidrug-resistant and extensively drug-resistant tuberculosis: a review. Curr Opin Infect Dis 2008; 21:587–595 [View Article] [PubMed]
     [Google Scholar]
  12. Merker M, Blin C, Mona S, Duforet-Frebourg N, Lecher S et al. Evolutionary history and global spread of the Mycobacterium tuberculosis Beijing lineage. Nat Genet 2015; 47:242–249 [View Article] [PubMed]
     [Google Scholar]
  13. Bortoluzzi A, Muskett FW, Waters LC, Addis PW, Rieck B et al. Mycobacterium tuberculosis RNA polymerase-binding protein A (RbpA) and its interactions with sigma factors. J Biol Chem 2013; 288:14438–14450 [View Article] [PubMed]
     [Google Scholar]
  14. Lin W, Mandal S, Degen D, Liu Y, Ebright YW et al. Structural basis of Mycobacterium tuberculosis transcription and transcription ihibition. Mol Cell 2017; 66:169–179 [View Article] [PubMed]
     [Google Scholar]
  15. Gygli SM, Borrell S, Trauner A, Gagneux S. Antimicrobial resistance in Mycobacterium tuberculosis: mechanistic and evolutionary perspectives. FEMS Microbiol Rev 2017; 41:354–373 [View Article] [PubMed]
     [Google Scholar]
  16. Yuen LKW, Leslie D, Coloe PJ. Bacteriological and molecular analysis of rifampin-resistant Mycobacterium tuberculosis strains isolated in Australia. J Clin Microbiol 1999; 37:3844–3850 [View Article]
     [Google Scholar]
  17. Aristoff PA, Garcia GA, Kirchhoff PD, Showalter HD. Rifamycins--obstacles and opportunities. Tuberculosis 2010; 90:94–118 [View Article] [PubMed]
     [Google Scholar]
  18. Rothstein DM. Rifamycins, alone and in combination. Cold Spring Harb Perspect Med 2016; 6:a027011 [View Article]
     [Google Scholar]
  19. Billington OJ, McHugh TD, Gillespie SH. Physiological cost of rifampin resistance induced in vitro in Mycobacterium tuberculosis. Antimicrob Agents Chemother 1999; 43:1866–1869 [View Article] [PubMed]
     [Google Scholar]
  20. Gagneux S, Long CD, Small PM, Van T, Schoolnik GK et al. The competitive cost of antibiotic resistance in Mycobacterium tuberculosis. Science 2006; 312:1944–1946 [View Article] [PubMed]
     [Google Scholar]
  21. Stefan MA, Ugur FS, Garcia GA. Source of the fitness defect in rifamycin-resistant Mycobacterium tuberculosis RNA polymerase and the mechanism of compensation by mutations in the β’ subunit. Antimicrob Agents Chemother 2018; 62:e00164-18 [View Article] [PubMed]
     [Google Scholar]
  22. Maisnier-Patin S, Andersson DI. Adaptation to the deleterious effects of antimicrobial drug resistance mutations by compensatory evolution. Res Microbiol 2004; 155:360–369 [View Article] [PubMed]
     [Google Scholar]
  23. Brandis G, Wrande M, Liljas L, Hughes D. Fitness-compensatory mutations in rifampicin-resistant RNA polymerase. Mol Microbiol 2012; 85:142–151 [View Article] [PubMed]
     [Google Scholar]
  24. Alame Emane AK, Guo X, Takiff HE, Liu S. Drug resistance, fitness and compensatory mutations in Mycobacterium tuberculosis. Tuberculosis 2021; 129:102091 [View Article] [PubMed]
     [Google Scholar]
  25. Song T, Park Y, Shamputa IC, Seo S, Lee SY et al. Fitness costs of rifampicin resistance in Mycobacterium tuberculosis are amplified under conditions of nutrient starvation and compensated by mutation in the β’ subunit of RNA polymerase. Mol Microbiol 2014; 91:1106–1119 [View Article] [PubMed]
     [Google Scholar]
  26. Comas I, Borrell S, Roetzer A, Rose G, Malla B et al. Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes. Nat Genet 2012; 44:106–110 [View Article] [PubMed]
     [Google Scholar]
  27. de Vos M, Müller B, Borrell S, Black PA, van Helden PD et al. Putative compensatory mutations in the rpoC gene of rifampin-resistant Mycobacterium tuberculosis are associated with ongoing transmission. Antimicrob Agents Chemother 2013; 57:827–832 [View Article] [PubMed]
     [Google Scholar]
  28. Li Q-J, Jiao W-W, Yin Q-Q, Xu F, Li J-Q et al. Compensatory mutations of rifampin resistance are associated with transmission of multidrug-resistant Mycobacterium tuberculosis Beijing genotype strains in China. Antimicrob Agents Chemother 2016; 60:2807–2812 [View Article] [PubMed]
     [Google Scholar]
  29. Casali N, Nikolayevskyy V, Balabanova Y, Ignatyeva O, Kontsevaya I et al. Microevolution of extensively drug-resistant tuberculosis in Russia. Genome Res 2012; 22:735–745 [View Article] [PubMed]
     [Google Scholar]
  30. Ali A, Hasan Z, McNerney R, Mallard K, Hill-Cawthorne G et al. Whole genome sequencing based characterization of extensively drug-resistant Mycobacterium tuberculosis isolates from Pakistan. PLoS One 2015; 10:e0117771 [View Article] [PubMed]
     [Google Scholar]
  31. Ma P, Luo T, Ge L, Chen Z, Wang X et al. Compensatory effects of M. tuberculosis rpoB mutations outside the rifampicin resistance-determining region. Emerg Microbes Infect 2021; 10:743–752 [View Article] [PubMed]
     [Google Scholar]
  32. Vargas AP, Rios AA, Grandjean L, Kirwan DE, Gilman RH et al. Determination of potentially novel compensatory mutations in rpoc associated with rifampin resistance and rpob mutations in Mycobacterium tuberculosis Clinical isolates from peru. Int J Mycobacteriol 2020; 9:121–137 [View Article] [PubMed]
     [Google Scholar]
  33. Fowler PW, Gibertoni Cruz AL, Hoosdally SJ, Jarrett L, Borroni E et al. Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis. Microbiology 2018; 164:1522–1530 [View Article] [PubMed]
     [Google Scholar]
  34. The CRyPTIC Consortium CRyPTIC data. n.d https://ftp.ebi.ac.uk/pub/databases/cryptic/release_june2022/
  35. Allix-Béguec C, Arandjelovic I. The CRyPTIC Consortium and the 100,000 Genomes Project Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing. N Engl J Med 2018; 379:1403–1415 [View Article]
     [Google Scholar]
  36. Rancoita PMV, Cugnata F, Gibertoni Cruz AL, Borroni E, Hoosdally SJ et al. Validating a 14-drug microtiter plate containing Bedaquiline and Delamanid for large-scale research susceptibility testing of Mycobacterium tuberculosis. Antimicrob Agents Chemother 2018; 62:e00344-18 [View Article] [PubMed]
     [Google Scholar]
  37. CRyPTIC Consortium Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis. Eur Respir J 2022; 60:2200239 [View Article] [PubMed]
     [Google Scholar]
  38. Malone KM, Brankin AE. CRyPTIC phylogenetic tree. n.d https://github.com/kerrimalone/Brankin_Malone_2022
  39. Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res 2021; 49:W293–W296 [View Article] [PubMed]
     [Google Scholar]
  40. Earle SG, Wu C-H, Charlesworth J, Stoesser N, Gordon NC et al. Identifying lineage effects when controlling for population structure improves power in bacterial association studies. Nat Microbiol 2016; 1:1–8 [View Article] [PubMed]
     [Google Scholar]
  41. Chen PE, Shapiro BJ. Classic genome-wide association methods are unlikely to identify causal variants in strongly clonal microbial populations. Genomics 2021 [View Article]
     [Google Scholar]
  42. Karmakar M, Trauer JM, Ascher DB, Denholm JT. Hyper transmission of Beijing lineage Mycobacterium tuberculosis: systematic review and meta-analysis. J Infect 2019; 79:572–581 [View Article] [PubMed]
     [Google Scholar]
  43. UK Health Security Agency Mycobacterium Tuberculosis Whole-Genome Sequencing and Cluster Investigation Handbook 2022
     [Google Scholar]
  44. Ribeiro SCM, Gomes LL, Amaral EP, Andrade MRM, Almeida FM et al. Mycobacterium tuberculosis strains of the modern sublineage of the Beijing family are more likely to display increased virulence than strains of the ancient sublineage. J Clin Microbiol 2014; 52:2615–2624 [View Article] [PubMed]
     [Google Scholar]
  45. Ford CB, Shah RR, Maeda MK, Gagneux S, Murray MB et al. Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis. Nat Genet 2013; 45:784–790 [View Article] [PubMed]
     [Google Scholar]
  46. Nickels BE, Hochschild A. Regulation of RNA polymerase through the secondary channel. Cell 2004; 118:281–284 [View Article] [PubMed]
     [Google Scholar]
  47. Loiseau C, Windels EM, Gygli SM, Jugheli L, Maghradze N et al. The relative transmission fitness of multidrug-resistant Mycobacterium tuberculosis in a drug resistance hotspot. In Nat Communications vol 14 Nature Publishing Group; 2023 p 1988 [View Article] [PubMed]
     [Google Scholar]
  48. Gygli SM, Loiseau C, Jugheli L, Adamia N, Trauner A et al. Prisons as ecological drivers of fitness-compensated multidrug-resistant Mycobacterium tuberculosis. Nat Med 2021; 27:1171–1177 [View Article] [PubMed]
     [Google Scholar]
  49. Wang S, Zhou Y, Zhao B, Ou X, Xia H et al. Characteristics of compensatory mutations in the rpoC gene and their association with compensated transmission of Mycobacterium tuberculosis. Front Med 2020; 14:51–59 [View Article] [PubMed]
     [Google Scholar]
  50. Merker M, Barbier M, Cox H, Rasigade J-P, Feuerriegel S et al. Compensatory evolution drives multidrug-resistant tuberculosis in Central Asia. eLife 2018; 7:e38200 [View Article] [PubMed]
     [Google Scholar]
  51. Chen Y, Liu Q, Takiff HE, Gao Q. Comprehensive genomic analysis of Mycobacterium tuberculosis reveals limited impact of high-fitness genotypes on MDR-TB transmission. J Infect 2022; 85:49–56 [View Article] [PubMed]
     [Google Scholar]
  52. Liu Q, Zuo T, Xu P, Jiang Q, Wu J et al. Have compensatory mutations facilitated the current epidemic of multidrug-resistant tuberculosis?. Emerg Microbes Infect 2018; 7:98 [View Article] [PubMed]
     [Google Scholar]
  53. Goig GA, Menardo F, Salaam-Dreyer Z, Dippenaar A, Streicher EM et al. Effect of compensatory evolution in the emergence and transmission of rifampicin-resistant Mycobacterium tuberculosis in Cape Town, South Africa: a genomic epidemiology study. Lancet Microbe 2023; 4:e506–e515 [View Article] [PubMed]
     [Google Scholar]
  54. Napier G, Campino S, Phelan JE, Clark TG. Large-scale genomic analysis of Mycobacterium tuberculosis reveals extent of target and compensatory mutations linked to multi-drug resistant tuberculosis. Sci Rep 2023; 13:623 [View Article] [PubMed]
     [Google Scholar]
  55. Reynolds MG. Compensatory evolution in rifampin-resistant Escherichia coli. Genetics 2000; 156:1471–1481 [View Article] [PubMed]
     [Google Scholar]
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Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis
M Gen 10, 001187 (2024); https://doi.org/10.1099/mgen.0.001187
/content/journal/mgen/10.1099/mgen.0.001187
/content/journal/mgen/10.1099/mgen.0.001187
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