Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance

PLoS Biology

Volumn 13, Issue 11, 2015, Pages

  Chevereau, Guillaume ^a,c   Dravecká, Marta ^a   Batur, Tugce ^b,d   Guvenek, Aysegul ^b,e   Ayhan, Dilay Hazal ^b,f   Toprak, Erdal ^b,g   Bollenbach, Tobias ^a  

^a INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA   (Austria)

^b SABANCI UNIVERSITY   (Turkey)

^c INSA DE STRASBOURG   (France)

^d DOKUZ EYLUL UNIVERSITY   (Turkey)

^e RUTGERS UNIVERSITY   (United States)

^f UNIVERSITY OF MASSACHUSETTS   (United States)

^g UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CEFOXITIN; CHLORAMPHENICOL; CIPROFLOXACIN; MECILLINAM; NITROFURANTOIN; TETRACYCLINE; TRIMETHOPRIM; ANTIINFECTIVE AGENT; ESCHERICHIA COLI PROTEIN; MUTAGENIC AGENT;

ANTIBIOTIC RESISTANCE; ARTICLE; CONTROLLED STUDY; DISTRIBUTION OF FITNESS EFFECTS; DOSE RESPONSE; EFFECTIVE CONCENTRATION; ESCHERICHIA COLI; EVOLUTION; GENETIC PARAMETERS; GENETIC VARIABILITY; GROWTH RATE; HIGH THROUGHPUT SEQUENCING; IC50; LOSS OF FUNCTION MUTATION; MATHEMATICAL MODEL; NONHUMAN; POPULATION GENETICS; ALGORITHM; BIOLOGICAL MODEL; COMPARATIVE STUDY; DRUG EFFECTS; ESCHERICHIA COLI K 12; GENE DELETION; GENETICS; GROWTH, DEVELOPMENT AND AGING; METABOLISM; MICROBIAL SENSITIVITY TEST; MOLECULAR EVOLUTION; MULTIDRUG RESISTANCE; MUTATION; MUTATION RATE; REPRODUCIBILITY; REPRODUCTIVE FITNESS;

ALGORITHMS; ANTI-BACTERIAL AGENTS; DRUG RESISTANCE, BACTERIAL; DRUG RESISTANCE, MULTIPLE, BACTERIAL; ESCHERICHIA COLI; ESCHERICHIA COLI K12; ESCHERICHIA COLI PROTEINS; EVOLUTION, MOLECULAR; GENE DELETION; GENETIC FITNESS; MICROBIAL SENSITIVITY TESTS; MODELS, GENETIC; MUTAGENS; MUTATION; MUTATION RATE; NITROFURANTOIN; REPRODUCIBILITY OF RESULTS;

EID: 84949478882     PISSN: 15449173     EISSN: 15457885     Source Type: Journal    
DOI: 10.1371/journal.pbio.1002299     Document Type: Article

References (59)

1. Ling LL, Schneider T, Peoples AJ, Spoering AL, Engels I, Conlon BP, et al. A new antibiotic kills pathogens without detectable resistance. Nature. 2015;517: 455–9. doi: 10.1038/nature14098 25561178.
2. Bush K, Courvalin P, Dantas G, Davies J, Eisenstein B, Huovinen P, et al. Tackling antibiotic resistance. Nat Rev Microbiol. 2011;9: 894–6. doi: 10.1038/nrmicro2693 22048738
3. Palmer AC, Kishony R, Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat Rev Genet. 2013;14: 243–8. doi: 10.1038/nrg3351 23419278
4. Andersson DI, Hughes D, Microbiological effects of sublethal levels of antibiotics. Nat Rev Microbiol. 2014;12: 465–478. doi: 10.1038/nrmicro3270 24861036
5. Drlica K, The mutant selection window and antimicrobial resistance. J Antimicrob Chemother. 2003;52: 11–7. doi: 10.1093/jac/dkg269 12805267
6. Imamovic L, Sommer MO, Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci Transl Med. 2013;5: 204ra132. doi: 10.1126/scitranslmed.3006609 24068739
7. Oz T, Guvenek A, Yildiz S, Karaboga E, Tamer YT, Mumcuyan N, et al. Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol Biol Evol. 2014;31: 2387–401. doi: 10.1093/molbev/msu191 24962091.
8. Kim S, Lieberman TD, Kishony R, Alternating antibiotic treatments constrain evolutionary paths to multidrug resistance. Proc Natl Acad Sci U S A. 2014;111: 14494–9. doi: 10.1073/pnas.1409800111 25246554.
9. Munck C, Gumpert HK, Wallin AIN, Wang HH, Sommer MO, Prediction of resistance development against drug combinations by collateral responses to component drugs. Sci Transl Med. 2014;6: 262ra156. doi: 10.1126/scitranslmed.3009940 25391482
10. Martínez JL, Baquero F, Andersson DI, Predicting antibiotic resistance. Nat Rev Microbiol. 2007;5: 958–65. doi: 10.1038/nrmicro1796 18007678
11. Toprak E, Veres A, Michel J-B, Chait R, Hartl DL, Kishony R, Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat Genet. 2012;44: 101–5. doi: 10.1038/ng.1034 22179135
12. Pena-Miller R, Laehnemann D, Jansen G, Fuentes-Hernandez A, Rosenstiel P, Schulenburg H, et al. When the most potent combination of antibiotics selects for the greatest bacterial load: the smile-frown transition. PLoS Biol. 2013;11: e1001540. doi: 10.1371/journal.pbio.1001540 23630452
13. Lázár V, Nagy I, Spohn R, Csörgő B, Györkei Á, Nyerges Á, et al. Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network. Nat Commun. 2014;5: 4352. doi: 10.1038/ncomms5352 25000950.
14. Lázár V, Pal Singh G, Spohn R, Nagy I, Horváth B, Hrtyan M, et al. Bacterial evolution of antibiotic hypersensitivity. Mol Syst Biol. 2013;9: 700. doi: 10.1038/msb.2013.57 24169403
15. Cirz RT, Chin JK, Andes DR, de Crécy-Lagard V, Craig WA, Romesberg FE, Inhibition of mutation and combating the evolution of antibiotic resistance. PLoS Biol. 2005;3: e176. doi: 10.1371/journal.pbio.0030176 15869329
16. Kohanski MA, DePristo MA, Collins JJ, Sublethal antibiotic treatment leads to multidrug resistance via radical-induced mutagenesis. Mol Cell. 2010;37: 311–20. doi: 10.1016/j.molcel.2010.01.003 20159551
17. Petrosino JF, Galhardo RS, Morales LD, Rosenberg SM, Stress-induced beta-lactam antibiotic resistance mutation and sequences of stationary-phase mutations in the Escherichia coli chromosome. J Bacteriol. 2009;191: 5881–9. doi: 10.1128/JB.00732-09 19648247
18. Hermsen R, Deris JB, Hwa T, On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient. Proc Natl Acad Sci U S A. 2012;109: 10775–80. doi: 10.1073/pnas.1117716109 22711808
19. Zhang Q, Lambert G, Liao D, Kim H, Robin K, Tung C, et al. Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments. Science. 2011;333: 1764–7. doi: 10.1126/science.1208747 21940899
20. Greulich P, Waclaw B, Allen RJ, Mutational Pathway Determines Whether Drug Gradients Accelerate Evolution of Drug-Resistant Cells. Phys Rev Lett. 2012;109: 088101. doi: 10.1103/PhysRevLett.109.088101 23002776
21. Weinreich DM, Delaney NF, Depristo MA, Hartl DL, Darwinian evolution can follow only very few mutational paths to fitter proteins. Science. 2006;312: 111–4. doi: 10.1126/science.1123539 16601193
22. Eyre-Walker A, Keightley PD, The distribution of fitness effects of new mutations. Nat Rev Genet. 2007;8: 610–8. doi: 10.1038/nrg2146 17637733
23. Fisher RA, The Genetical Theory of Natural Selection. Oxford.: Clarendon Press; 1930.
24. Sousa A, Magalhães S, Gordo I, Cost of antibiotic resistance and the geometry of adaptation. Mol Biol Evol. 2012;29: 1417–28. doi: 10.1093/molbev/msr302 22144641
25. Kishony R, Leibler S, Environmental stresses can alleviate the average deleterious effect of mutations. J Biol. 2003;2: 14. doi: 10.1186/1475-4924-2-14 12775217
26. Trindade S, Sousa A, Gordo I, Antibiotic resistance and stress in the light of Fisher’s model. Evolution. 2012;66: 3815–24. doi: 10.1111/j.1558-5646.2012.01722.x 23206139
27. Elena SF, Ekunwe L, Hajela N, Oden SA, Lenski RE, Distribution of fitness effects caused by random insertion mutations in Escherichia coli. Genetica. 1998;102–103: 349–58. http://link.springer.com/article/10.1023/A:1017031008316 9720287
28. Burch CL, Guyader S, Samarov D, Shen H, Experimental estimate of the abundance and effects of nearly neutral mutations in the RNA virus phi 6. Genetics. 2007;176: 467–76. doi: 10.1534/genetics.106.067199 17339206
29. Lind PA, Berg OG, Andersson DI, Mutational robustness of ribosomal protein genes. Science. 2010;330: 825–7. doi: 10.1126/science.1194617 21051637
30. MacLean RC, Buckling A, The distribution of fitness effects of beneficial mutations in Pseudomonas aeruginosa. PLoS Genet. 2009;5: e1000406. doi: 10.1371/journal.pgen.1000406 19266075
31. Kassen R, Bataillon T, Distribution of fitness effects among beneficial mutations before selection in experimental populations of bacteria. Nat Genet. 2006;38: 484–8. doi: 10.1038/ng1751 16550173
32. McDonald MJ, Cooper TF, Beaumont HJE, Rainey PB, The distribution of fitness effects of new beneficial mutations in Pseudomonas fluorescens. Biol Lett. 2011;7: 98–100. doi: 10.1098/rsbl.2010.0547 20659918
33. Sanjuán R, Moya A, Elena SF, The distribution of fitness effects caused by single-nucleotide substitutions in an RNA virus. Proc Natl Acad Sci U S A. 2004;101: 8396–8401. doi: 10.1073/pnas.0400146101 15159545
34. Schoustra SE, Bataillon T, Gifford DR, Kassen R, The properties of adaptive walks in evolving populations of fungus. PLoS Biol. 2009;7: e1000250. doi: 10.1371/journal.pbio.1000250 19956798.
35. Nichols RJ, Sen S, Choo YJ, Beltrao P, Zietek M, Chaba R, et al. Phenotypic landscape of a bacterial cell. Cell. 2011;144: 143–56. doi: 10.1016/j.cell.2010.11.052 21185072
36. Liu A, Tran L, Becket E, Lee K, Chinn L, Park E, et al. Antibiotic sensitivity profiles determined with an Escherichia coli gene knockout collection: Generating an antibiotic bar code. Antimicrob Agents Chemother. 2010;54: 1393–1403. doi: 10.1128/AAC.00906-09 20065048
37. Girgis HS, Hottes AK, Tavazoie S, Genetic architecture of intrinsic antibiotic susceptibility. PLoS One. 2009;4: e5629. doi: 10.1371/journal.pone.0005629 19462005
38. Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M, et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol. 2006;2: 2006.0008. doi: 10.1038/msb4100050 16738554.
39. Pache RA, Babu MM, Aloy P, Exploiting gene deletion fitness effects in yeast to understand the modular architecture of protein complexes under different growth conditions. BMC Syst Biol. 2009;3: 74. doi: 10.1186/1752-0509-3-74 19615085
40. Martin G, Lenormand T, The fitness effect of mutations across environments: a survey in light of fitness landscape models. Evolution (N Y). 2006;60: 2413–2427. http://onlinelibrary.wiley.com/doi/10.1111/j.0014-3820.2006.tb01878.x/pdf 17263105
41. Wood KB, Wood KC, Nishida S, Cluzel P, Uncovering Scaling Laws to Infer Multidrug Response of Resistant Microbes and Cancer Cells. Cell Rep. 2014;6: 1073–84. doi: 10.1016/j.celrep.2014.02.007 24613352.
42. Regoes RR, Wiuff C, Zappala RM, Garner KN, Baquero F, Levin BR, Pharmacodynamic Functions: a Multiparameter Approach to the Design of Antibiotic Treatment Regimens. Antimicrob Agents Chemother. 2004;48: 3670–3676. doi: 10.1128/AAC.48.10.3670-3676.2004 15388418
43. Deris JB, Kim M, Zhang Z, Okano H, Hermsen R, Groisman A, et al. The innate growth bistability and fitness landscapes of antibiotic-resistant bacteria. Science. 2013;342: 1237435. doi: 10.1126/science.1237435 24288338
44. Breeze AS, Obaseiki-Ebor EE, Mutations to nitrofurantoin and nitrofurazone resistance in Escherichia coli K12. J Gen Microbiol. 1983;129: 99–103. doi: 10.1099/00221287-129-1-99 6339681
45. Keseler IM, Collado-Vides J, Gama-Castro S, Ingraham J, Paley S, Paulsen IT, et al. EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res. 2005;33: D334–337. doi: 10.1093/nar/gki108 15608210
46. McCalla DR, Reuvers A, Kaiser C, Mode of action of nitrofurazone. J Bacteriol. 1970;104: 1126–1134. http://jb.asm.org/content/104/3/1126.full.pdf 16559085
47. Okumus B, Yildiz S, Toprak E, Fluidic and microfluidic tools for quantitative systems biology. Curr Opin Biotechnol. 2014;25: 30–8. doi: 10.1016/j.copbio.2013.08.016 24484878
48. Bean D, Krahe D, Wareham D, Antimicrobial resistance in community and nosocomial Escherichia coli urinary tract isolates, London 2005–2006. Ann Clin Microbiol Antimicrob. 2008;7: 13. doi: 10.1186/1476-0711-7-13 18564430
49. Zhanel GG, Hisanaga TL, Laing NM, DeCorby MR, Nichol KA, Palatnik LP, et al. Antibiotic resistance in outpatient urinary isolates: final results from the North American Urinary Tract Infection Collaborative Alliance (NAUTICA). Int J Antimicrob Agents. 2005;26: 380–388. doi: 10.1016/j.ijantimicag.2005.08.003 16243229
50. Zaslaver A, Bren A, Ronen M, Itzkovitz S, Kikoin I, Shavit S, et al. A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat Methods. 2006;3: 623–628. doi: 10.1038/nmeth895 16862137
51. Chevereau G, Bollenbach T, Systematic discovery of drug interaction mechanisms. Mol Syst Biol. 2015;11: 807. doi: 10.15252/msb.20156098 25924924
52. Bollenbach T, Quan S, Chait R, Kishony R, Nonoptimal microbial response to antibiotics underlies suppressive drug interactions. Cell. 2009;139: 707–18. doi: 10.1016/j.cell.2009.10.025 19914165
53. Yeh P, Tschumi AI, Kishony R, Functional classification of drugs by properties of their pairwise interactions. Nat Genet. 2006;38: 489–94. doi: 10.1038/ng1755 16550172
54. Baltzer B, Lund F, Rastrup-Andersen N, Degradation of mecillinam in aqueous solution. J Pharm Sci. 1979;68: 1207–1215. doi: 10.1002/jps.2600681005 41925
55. Hegreness M, Shoresh N, Hartl D, Kishony R, An equivalence principle for the incorporation of favorable mutations in asexual populations. Science. 2006;311: 1615–7. doi: 10.1126/science.1122469 16543462
56. Fogle CA, Nagle JL, Desai MM, Clonal interference, multiple mutations and adaptation in large asexual populations. Genetics. 2008;180: 2163–73. doi: 10.1534/genetics.108.090019 18832359
57. Barrick JE, Colburn G, Deatherage DE, Traverse CC, Strand MD, Borges JJ, et al. Identifying structural variation in haploid microbial genomes from short-read resequencing data using breseq. BMC Genomics. 2014;15: 1039. doi: 10.1186/1471-2164-15-1039 25432719
58. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28: 1647–9. doi: 10.1093/bioinformatics/bts199 22543367
59. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM, Systematic determination of genetic network architecture. Nat Genet. 1999;22: 281–5. doi: 10.1038/10343 10391217