Identifying and exploiting genes that potentiate the evolution of antibiotic resistance

Nature Ecology and Evolution

Volumn 2, Issue 6, 2018, Pages 1033-1039

  Gifford, Danna R ^a   Furió, Victoria ^a   Papkou, Andrei ^a   Vogwill, Tom ^a   Oliver, Antonio ^b   MacLean, R Craig ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b HEALTH RESEARCH INSTITUTE OF THE BALEARIC ISLANDS IDISBA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIINFECTIVE AGENT; AVIBACTAM; AZABICYCLO DERIVATIVE; BETA LACTAMASE INHIBITOR; CEFTAZIDIME;

ANTIBIOTIC RESISTANCE; BACTERIAL GENE; DRUG EFFECT; GENETICS; MOLECULAR EVOLUTION; PSEUDOMONAS AERUGINOSA;

ANTI-BACTERIAL AGENTS; AZABICYCLO COMPOUNDS; BETA-LACTAMASE INHIBITORS; CEFTAZIDIME; DRUG RESISTANCE, MICROBIAL; EVOLUTION, MOLECULAR; GENES, BACTERIAL; PSEUDOMONAS AERUGINOSA;

EID: 85045845796     PISSN: None     EISSN: 2397334X     Source Type: Journal    
DOI: 10.1038/s41559-018-0547-x     Document Type: Article

References (50)

1. O'Neill, J. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations (Wellcome Trust & HM Government, 2016).
2. Martinez, J. L., Baquero, F. & Andersson, D. I. Predicting antibiotic resistance. Nat. Rev. Microbiol. 5, 958-965 (2007).
3. Baym, M., Stone, L. K. & Kishony, R. Multidrug evolutionary strategies to reverse antibiotic resistance. Science 351, aad3292 (2016).
4. Palmer, A. C. & Kishony, R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat. Rev. Genet. 14, 243-248 (2013).
5. MacLean, R. C., Hall, A. R., Perron, G. G. & Buckling, A. The population genetics of antibiotic resistance: integrating molecular mechanisms and treatment contexts. Nat. Rev. Genet. 11, 405-414 (2010).
6. Vogwill, T., Kojadinovic, M., Furió, V. & MacLean, R. C. Testing the role of genetic background in parallel evolution using the comparative experimental evolution of antibiotic resistance. Mol. Biol. Evol. 31, 3314-3323 (2014).
7. Loman, N. J. & Pallen, M. J. Twenty years of bacterial genome sequencing. Nat. Rev. Microbiol. 13, 787-794 (2015).
8. Van Opijnen, T., Dedrick, S. & Bento, J. Strain dependent genetic networks for antibiotic-sensitivity in a bacterial pathogen with a large pan-genome. PLoS Pathog. 12, e1005869 (2016).
9. Blount, Z. D., Barrick, J. E., Davidson, C. J. & Lenski, R. E. Genomic analysis of a key innovation in an experimental Escherichia coli population. Nature 489, 513-518 (2012).
10. Lind, P. A., Farr, A. D. & Rainey, P. B. Experimental evolution reveals hidden diversity in evolutionary pathways. eLife 4, e07074 (2015).
11. San Millan, A., Escudero, J. A., Gifford, D. R., Mazel, D. & MacLean, R. C. Multicopy plasmids potentiate the evolution of antibiotic resistance in bacteria. Nat. Ecol. Evol. 1, 0010 (2016).
12. Vogwill, T., Kojadinovic, M. & MacLean, R. C. Epistasis between antibiotic resistance mutations and genetic background shape the fitness effect of resistance across species of Pseudomonas. Proc. Biol. Sci. 283, 20160151 (2016).
13. Giamarellou, H. & Kanellakopoulou, K. Current therapies for Pseudomonas aeruginosa. Crit. Care Clin. 24, 261-278 (2008).
14. Castanheira, M., Mills, J. C., Farrell, D. J. & Jones, R. N. Mutation-driven beta-lactam resistance mechanisms among contemporary ceftazidime-nonsusceptible Pseudomonas aeruginosa isolates from U.S. hospitals. Antimicrob. Agents Chemother. 58, 6844-6850 (2014).
15. Ropy, A. et al. Role of Pseudomonas aeruginosa low-molecular-mass penicillin-binding proteins in AmpC expression, beta-lactam resistance, and peptidoglycan structure. Antimicrob. Agents Chemother. 59, 3925-3934 (2015).
16. Cabot, G. et al. Evolution of Pseudomonas aeruginosa antimicrobial resistance and fitness under low and high mutation rates. Antimicrob. Agents Chemother. 60, 1767-1778 (2016).
17. Berrazeg, M. et al. Mutations in β-lactamase AmpC increase resistance of Pseudomonas aeruginosa isolates to antipseudomonal cephalosporins. Antimicrob. Agents Chemother. 59, 6248-6255 (2015).
18. Mark, B. L., Vocadlo, D. J. & Oliver, A. Providing beta-lactams a helping hand: Targeting the AmpC beta-lactamase induction pathway. Future Microbiol. 6, 1415-1427 (2011).
19. Moya, B. et al. Beta-lactam resistance response triggered by inactivation of a nonessential penicillin-binding protein. PLoS Pathog. 5, e1000353 (2009).
20. Moya, B., Juan, C., Albertí, S., Pérez, J. L. & Oliver, A. Benefit of having multiple ampD genes for acquiring β-lactam resistance without losing fitness and virulence in Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 52, 3694-3700 (2008).
21. Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-Adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394-407 (2007).
22. Vadlamani, G. et al. The beta-lactamase gene regulator AmpR is a tetramer that recognizes and binds the D-Ala-D-Ala motif of its repressor UDP-N-Acetylmuramic acid (MurNAc)-pentapeptide. J. Biol. Chem. 290, 2630-2643 (2015).
23. Balasubramanian, D. et al. Deep sequencing analyses expands the Pseudomonas aeruginosa AmpR regulon to include small RNA-mediated regulation of iron acquisition, heat shock and oxidative stress response. Nucleic Acids Res. 42, 979-998 (2014).
24. Kong, K. F. et al. Pseudomonas aeruginosa AmpR is a global transcriptional factor that regulates expression of AmpC and PoxB beta-lactamases, proteases, quorum sensing, and other virulence factors. Antimicrob. Agents Chemother. 49, 4567-4575 (2005).
25. Kumari, H., Balasubramanian, D., Zincke, D. & Mathee, K. Role of Pseudomonas aeruginosa AmpR on β-lactam and non-β-lactam transient cross-resistance upon pre-exposure to subinhibitory concentrations of antibiotics. J. Med. Microbiol. 63, 544-555 (2014).
26. Campbell, J. I. A., Ciofu, O. & Høiby, N. Pseudomonas aeruginosa isolates from patients with cystic fibrosis have different β-lactamase expression phenotypes but are homogeneous in the ampC-AmpR genetic region. Antimicrob. Agents Chemother. 41, 1380-1384 (1997).
27. Jacoby, G. A. AmpC β-lactamases. Clin. Microbiol. Rev. 22, 161-182 (2009).
28. Lahiri, S. D. et al. Avibactam and class C beta-lactamases: mechanism of inhibition, conservation of the binding pocket, and implications for resistance. Antimicrob. Agents Chemother. 58, 5704-5713 (2014).
29. Goering, R. V., Sanders, C. C., Sanders, W. E. Jr, Guay, R. & Guerin, S. Heterogeneity in ampR-AmpC gene interaction in Enterobacter cloacae. Rev. Infect. Dis. 10, 786-792 (1988).
30. Laabei, M. et al. Predicting the virulence of MRSA from its genome sequence. Genome Res. 24, 839-849 (2014).
31. Earle, S. G. et al. Identifying lineage effects when controlling for population structure improves power in bacterial association studies. Nat. Microbiol. 1, 16041 (2016).
32. Luria, S. E. & Delbrück, M. Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28, 491-511 (1943).
33. Moya, B. et al. Beta-lactam resistance response triggered by inactivation of a nonessential penicillin-binding protein. PLoS Pathog. 5, e1000353 (2009).
34. San Millan, A. S. et al. Positive selection and compensatory adaptation interact to stabilize non-Transmissible plasmids. Nat. Commun. 5, 5208 (2014).
35. Patel, R. K. & Jain, M. NGS QC Toolkit: A toolkit for quality control of next generation sequencing data. PLoS ONE 7, e30619 (2012).
36. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-1760 (2009).
37. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491-498 (2011).
38. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-2079 (2009).
39. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156-2158 (2011).
40. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491-498 (2011).
41. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80-92 (2012).
42. Chen, K. et al. BreakDancer: An algorithm for high-resolution mapping of genomic structural variation. Nat. Methods 6, 677-681 (2009).
43. Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: A pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865-2871 (2009).
44. Boeva, V. et al. Control-FREEC: A tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics 28, 423-425 (2012).
45. Marco-Sola, S., Sammeth, M., Guigo, R. & Ribeca, P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat. Methods 9, 1185-1188 (2012).
46. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353-D361 (2017).
47. Hahne, F. et al. flowCore: A Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10, 106 (2009).
48. Sarkar, D., Le Meur, N. & Gentleman, R. Using flowViz to visualize flow cytometry data. Bioinformatics 24, 878-879 (2008).
49. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).
50. Vogwill, T., Kojadinovic, M. & MacLean, R. C. Epistasis between antibiotic resistance mutations and genetic background shape the fitness effect of resistance across species of Pseudomonas. Proc. Biol. Sci. 283, 20160151 (2016).