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Volume 7, Issue 7

spp. genetic adaptation in cystic fibrosis Open Access

Abstract

spp. are emerging pathogens in patients with cystic fibrosis (CF) and spp. caused infections are associated with more severe disease outcomes and high intrinsic antibiotic resistance. While conventional CF pathogens are studied extensively, little is known about the genetic determinants leading to antibiotic resistance and the genetic adaptation in spp. infections. Here, we analysed 101 spp. genomes from 51 patients with CF isolated during the course of up to 20 years of infection to identify within-host adaptation, mutational signatures and genetic variation associated with increased antibiotic resistance. We found that the same regulatory and inorganic ion transport genes were frequently mutated in persisting clone types within and between species, indicating convergent genetic adaptation. Genome-wide association study of six antibiotic resistance phenotypes revealed the enrichment of associated genes involved in inorganic ion transport, transcription gene enrichment in β-lactams, and energy production and translation gene enrichment in the trimethoprim/sulfonamide group. Overall, we provide insights into the pathogenomics of spp. infections in patients with CF airways. Since emerging pathogens are increasingly recognized as an important healthcare issue, our findings on evolution of antibiotic resistance and genetic adaptation can facilitate better understanding of disease progression and how mutational changes have implications for patients with CF.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): Achromobacter , cystic fibrosis airway infection , genomic adaptation , GWAS , host-pathogen interaction , microbial genomics and within-host evolution
Funding
This study was supported by the:
  • RegionH Rammebevilling (Award R144-A5287)
    • Principle Award Recipient: HelleK. Johansen
  • Savvaerksejer Jeppe Juhl og Hustru Ovita Juhls Mindelegat
    • Principle Award Recipient: HelleK. Johansen
  • Danmarks Frie Forskningsfond (Award FTP-4183-00051)
    • Principle Award Recipient: HelleK. Johansen
  • Novo Nordisk Fonden (Award NNF15OC0017444)
    • Principle Award Recipient: HelleK. Johansen
  • Lundbeckfonden (Award R167-2013-15229)
    • Principle Award Recipient: HelleK. Johansen
  • Rigshospitalets Rammebevilling (Award 2015-17 (R88-A3537))
    • Principle Award Recipient: HelleK. Johansen
  • Novo Nordisk Fonden (Award NNF12OC1015920)
    • Principle Award Recipient: HelleK. Johansen
  • Danmarks Grundforskningsfond (Award 126)
    • Principle Award Recipient: NotApplicable
  • Danish Cystic Fibrosis Association (Cystisk Fibrose Foreningen)
    • Principle Award Recipient: MigleGabrielaite
© 2021 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2021-07-07
2024-05-14
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References

  1. Ridderberg W, Nielsen SM, Nørskov-Lauritsen N. Genetic adaptation of Achromobacter sp. during persistence in the lungs of cystic fibrosis patients. PLoS One 2015; 10:e0136790 [View Article] [PubMed]
     [Google Scholar]
  2. Ciofu O, Hansen CR, Høiby N. Respiratory bacterial infections in cystic fibrosis. Curr Opin Pulm Med 2013; 19:251–258 [View Article] [PubMed]
     [Google Scholar]
  3. Lambiase A, Catania MR, Del Pezzo M, Rossano F, Terlizzi V et al. Achromobacter xylosoxidans respiratory tract infection in cystic fibrosis patients. Eur J Clin Microbiol Infect Dis 2011; 30:973–980 [View Article] [PubMed]
     [Google Scholar]
  4. Rønne Hansen C, Pressler T, Høiby N, Gormsen M. Chronic infection with Achromobacter xylosoxidans in cystic fibrosis patients; a retrospective case control study. J Cyst Fibros 2006; 5:245–251 [View Article] [PubMed]
     [Google Scholar]
  5. Hansen CR, Pressler T, Nielsen KG, Jensen PØ, Bjarnsholt T et al. Inflammation in Achromobacter xylosoxidans infected cystic fibrosis patients. J Cyst Fibros 2010; 9:51–58 [View Article] [PubMed]
     [Google Scholar]
  6. Folkesson A, Jelsbak L, Yang L, Johansen HK, Ciofu O et al. Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: An evolutionary perspective. Nat Rev Microbiol 2012; 10:841–851 [View Article] [PubMed]
     [Google Scholar]
  7. Baishya J, Wakeman CA. Selective pressures during chronic infection drive microbial competition and cooperation. NPJ Biofilms Microbiomes 2019; 5:16
     [Google Scholar]
  8. Callaghan M, McClean S. Bacterial host interactions in cystic fibrosis. Curr Opin Microbiol 2012; 15:71–77 [View Article] [PubMed]
     [Google Scholar]
  9. McGuigan L, Callaghan M. The evolving dynamics of the microbial community in the cystic fibrosis lung. Environ Microbiol 2015; 17:16–28 [View Article] [PubMed]
     [Google Scholar]
  10. Gade SS, Nørskov-Lauritsen N, Ridderberg W. Prevalence and species distribution of Achromobacter sp. cultured from cystic fibrosis patients attending the Aarhus centre in Denmark. J Med Microbiol 2017; 66:686–689 [View Article]
     [Google Scholar]
  11. Gordon NC, Price JR, Cole K, Everitt R, Morgan M et al. Prediction of Staphylococcus aureus antimicrobial resistance by whole-genome sequencing. J Clin Microbiol 2014; 52:1182–1191 [View Article]
     [Google Scholar]
  12. Nguyen M, Long SW, McDermott PF, Olsen RJ, Olson R et al. Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal salmonella. J Clin Microbiol 2019; 57: [View Article]
     [Google Scholar]
  13. Su M, Satola SW, Read TD. Genome-based prediction of bacterial antibiotic resistance. J Clin Microbiol 2019; 57:
     [Google Scholar]
  14. Gabrielaite M, Bartell JA, Nørskov-Lauritsen N, Pressler T, Nielsen FC et al. Transmission and antibiotic resistance of Achromobacter in cystic fibrosis. J Clin Microbiol 2021; 59: [View Article]
     [Google Scholar]
  15. Gabrielaite M, Marvig RL. Pactyper: Snakemake pipeline for continuous clone type prediction for WGS sequenced bacterial isolates based on their core genome. Zenodo 2020 [View Article]
     [Google Scholar]
  16. Gabrielaite M, Marvig RL. GenAPI: a tool for gene absence-presence identification in fragmented bacterial genome sequences. BMC Bioinformatics 2020; 21:320 [View Article]
     [Google Scholar]
  17. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012; 19:455–477 [View Article]
     [Google Scholar]
  18. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D et al. Reference sequence (Refseq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 2016; 44:D733–45 [View Article]
     [Google Scholar]
  19. Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 2018; 9:5114 [View Article]
     [Google Scholar]
  20. R The R Project for Statistical Computing [Internet]. https://www.r-project.org/ accessed 10 Feb 2020
  21. CRAN Package Pheatmap [Internet. https://cran.r-project.org/web/packages/pheatmap/index.html accessed 10 Feb 2020
  22. Gabrielaite M, Misiakou MA, Marvig RL. BACDIST: Snakemake pipeline for bacterial SNP distance and phylogeny analysis. Zenodo 2020 [View Article]
     [Google Scholar]
  23. Seemann T Snippy: Rapid haploid variant calling and core genome alignment [Internet]; 2018 https://github.com/tseemann/snippy accessed 10 Feb 2020
  24. Barrick JE, Lenski RE. Genome dynamics during experimental evolution. Nat Rev Genet 2013; 14:827–839 [View Article]
     [Google Scholar]
  25. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H et al. Beast 2: A software platform for bayesian evolutionary analysis. PLoS Comput Biol 2014; 10:e1003537 [View Article]
     [Google Scholar]
  26. Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst Biol 2018; 67:901–904 [View Article]
     [Google Scholar]
  27. Seemann T Abricate: Mass screening of contigs for antimicrobial and virulence genes [Internet; 2018 https://github.com/tseemann/abricate accessed 10 Feb 2020
  28. Chen L, Zheng D, Liu B, Yang J, Jin Q. VFDB 2016: hierarchical and refined dataset for big data analysis—10 years on. Nucleic Acids Res 2016; 44:D694–D697 [View Article]
     [Google Scholar]
  29. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S et al. Resfinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 2020; 75:3491–3500 [View Article]
     [Google Scholar]
  30. Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol Biol Evol 2017; 34:2115–2122 [View Article]
     [Google Scholar]
  31. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012; 28:3150–3152 [View Article]
     [Google Scholar]
  32. Jaillard M, Lima L, Tournoud M, Mahé P, van Belkum A et al. A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events. PLoS Genet 2018; 14:e1007758 [View Article]
     [Google Scholar]
  33. The UniProt Consortium UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 2019; 47:D506–D515 [View Article]
     [Google Scholar]
  34. Ridderberg W, Jensen Handberg K, Nørskov-Lauritsen N. Prevalence of hypermutator isolates of Achromobacter spp. from cystic fibrosis patients. Int J Med Microbiol. 2020; 151393:
     [Google Scholar]
  35. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M et al. Card 2020: Antibiotic resistome surveillance with the comprehensive antibiotic Resistance database. Nucleic Acids Res 2020; 48:D517–525
     [Google Scholar]
  36. Bador J, Amoureux L, Blanc E, Neuwirth C. Innate aminoglycoside resistance of Achromobacter xylosoxidans is due to AxyXY-OprZ, an RND-type multidrug efflux pump. Antimicrob Agents Chemother 2013; 57:603–605 [View Article]
     [Google Scholar]
  37. Bador J, Neuwirth C, Grangier N, Muniz M, Germé L et al. Role of Axyz transcriptional regulator in overproduction of Axyxy-oprz multidrug efflux system in achromobacter species mutants selected by tobramycin. Antimicrob Agents Chemother 2017; 61: [View Article]
     [Google Scholar]
  38. Li X, Hu Y, Gong J, Zhang L, Wang G. Comparative genome characterization of Achromobacter members reveals potential genetic determinants facilitating the adaptation to a pathogenic lifestyle. Appl Microbiol Biotechnol 2013; 97:6413–6425 [View Article]
     [Google Scholar]
  39. Didelot X, Walker AS, Peto TE, Crook DW, Wilson DJ. Within-host evolution of bacterial pathogens. Nat Rev Microbiol 2016; 14:150–162 [View Article]
     [Google Scholar]
  40. Marvig RL, Johansen HK, Molin S, Jelsbak L. Genome analysis of a transmissible lineage of Pseudomonas aeruginosa reveals pathoadaptive mutations and distinct evolutionary paths of hypermutators. PLoS Genet 2013; 9:e1003741 [View Article]
     [Google Scholar]
  41. Holt KE, Baker S, Weill F-X, Holmes EC, Kitchen A et al. Shigella sonnei genome sequencing and phylogenetic analysis indicate recent global dissemination from Europe. Nat Genet 2012; 44:1056–1059 [View Article]
     [Google Scholar]
  42. Reeves PR, Liu B, Zhou Z, Li D, Guo D et al. Rates of mutation and host transmission for an Escherichia coli clone over 3 years. PLoS One 2011; 6:e26907 [View Article]
     [Google Scholar]
  43. Young BC, Golubchik T, Batty EM, Fung R, Larner-Svensson H et al. Evolutionary dynamics of staphylococcus aureus during progression from carriage to disease. Proc Natl Acad Sci U S A 2012; 109:4550–4555 [View Article]
     [Google Scholar]
  44. Mathers AJ, Stoesser N, Sheppard AE, Pankhurst L, Giess A et al. Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae at a single institution: Insights into endemicity from whole-genome sequencing. Antimicrob Agents Chemother 2015; 59:1656–1663 [View Article]
     [Google Scholar]
  45. Turton JF, Mustafa N, Shah J, Hampton CV, Pike R et al. Identification of Achromobacter xylosoxidans by detection of the bla(OXA-114-like) gene intrinsic in this species. Diagn Microbiol Infect Dis 2011; 70:408–411 [View Article]
     [Google Scholar]
  46. Papalia M, Almuzara M, Cejas D, Traglia G, Ramírez MS et al. OXA-258 from Achromobacter ruhlandii: A species-specific marker. J Clin Microbiol 2013; 51:1602–1605 [View Article]
     [Google Scholar]
  47. Traglia G, Papalia M, Almuzara M, Gutkind G, Centrón D et al. Presence of oxa-type enzymes in Achromobacter insuavis and A. dolens . Curr Microbiol 2014; 69:501–506 [View Article]
     [Google Scholar]
  48. Fernandez RC, Weiss AA. Cloning and sequencing of a Bordetella pertussis serum resistance locus. Infect Immun 1994; 62:4727–4738 [View Article]
     [Google Scholar]
  49. Jeukens J, Freschi L, Vincent AT, Emond-Rheault J-G, Kukavica-Ibrulj I et al. A pan-genomic approach to understand the basis of host adaptation in Achromobacter. Genome Biol Evol 2017; 9:1030–1046 [View Article]
     [Google Scholar]
  50. Marvig RL, Sommer LM, Molin S, Johansen HK. Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis. Nat Genet 2015; 47:57–64 [View Article]
     [Google Scholar]
  51. Guénard S, Muller C, Monlezun L, Benas P, Broutin I et al. Multiple mutations lead to MexXY-OprM-dependent aminoglycoside resistance in clinical strains of Pseudomonas aeruginosa . Antimicrob Agents Chemother 2014; 58:221–228 [View Article]
     [Google Scholar]
  52. Frimodt-Møller J, Rossi E, Haagensen JAJ, Falcone M, Molin S et al. Mutations causing low level antibiotic resistance ensure bacterial survival in antibiotic-treated hosts. Sci Rep 2018; 8:12512 [View Article]
     [Google Scholar]
  53. Marvig RL, Sommer LM, Jelsbak L, Molin S, Johansen HK. Evolutionary insight from whole-genome sequencing of Pseudomonas aeruginosa from cystic fibrosis patients. Future Microbiol 2015; 10:599–611 [View Article]
     [Google Scholar]
  54. Gabrielaite M, Johansen HK, Molin S, Nielsen FC, Marvig RL. Gene loss and acquisition in lineages of bacteria evolving in a human host environment. BioRxiv 2020
     [Google Scholar]
  55. Marvig RL, Damkiær S, Khademi SMH, Markussen TM, Molin S et al. Within-host evolution of Pseudomonas aeruginosa reveals adaptation toward iron acquisition from hemoglobin. mBio 2014; 5:e00966–14 [View Article]
     [Google Scholar]
  56. Tyrrell J, Whelan N, Wright C, Sá-Correia I, McClean S et al. Investigation of the multifaceted iron acquisition strategies of Burkholderia cenocepacia . BioMetals 2015; 28:367–380 [View Article]
     [Google Scholar]
  57. Conn GL, Bavro VN, Davies C. Editorial: bacterial mechanisms of antibiotic resistance: a structural perspective. Front Mol Biosci 2019; 6:71 [View Article]
     [Google Scholar]
  58. Galperin MY, Kristensen DM, Makarova KS, Wolf YI, Koonin EV. Microbial genome analysis: the COG approach. Brief Bioinform 2019; 20:1063–1070 [View Article]
     [Google Scholar]
  59. Hu Y, Zhu Y, Ma Y, Liu F, Lu N et al. Genomic insights into intrinsic and acquired drug resistance mechanisms in Achromobacter xylosoxidans . Antimicrob Agents Chemother 2015; 59:1152–1161 [View Article]
     [Google Scholar]
  60. Sommer LM, Johansen HK, Molin S. Antibiotic resistance in Pseudomonas aeruginosa and adaptation to complex dynamic environments. Microb Genom 2020; 6: [View Article]
     [Google Scholar]
  61. Vranakis I, Goniotakis I, Psaroulaki A, Sandalakis V, Tselentis Y et al. Proteome studies of bacterial antibiotic resistance mechanisms. J Proteomics 2014; 97:88–99 [View Article]
     [Google Scholar]
  62. Harms A, Maisonneuve E, Gerdes K. Mechanisms of bacterial persistence during stress and antibiotic exposure. Science 2016; 354:aaf4268 [View Article]
     [Google Scholar]
  63. Lopatkin AJ, Bening SC, Manson AL, Stokes JM, Kohanski MA et al. Clinically relevant mutations in core metabolic genes confer antibiotic resistance. Science 2021; 371:eaba0862 [View Article]
     [Google Scholar]
  64. Lieberman TD, Flett KB, Yelin I, Martin TR, McAdam AJ et al. Genetic variation of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective pressures. Nat Genet 2014; 46:82–87 [View Article]
     [Google Scholar]
  65. Lieberman TD, Wilson D, Misra R, Xiong LL, Moodley P et al. Genomic diversity in autopsy samples reveals within-host dissemination of hiv-associated mycobacterium tuberculosis. Nat Med 2016; 22:1470–1474 [View Article]
     [Google Scholar]
  66. Microreact Phylogenetic tree of clinical Achromobacter isolates and Achromobacter genomes available on Refseq database [Internet. https://microreact.org/project/XBjPPEXWl accessed 23 Jun 2020
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Achromobacter spp. genetic adaptation in cystic fibrosis
M Gen 7, 000582 (2021); https://doi.org/10.1099/mgen.0.000582
/content/journal/mgen/10.1099/mgen.0.000582
/content/journal/mgen/10.1099/mgen.0.000582
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