#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Bacterial Adaptation through Loss of Function


The metabolic capabilities and regulatory networks of bacteria have been optimized by evolution in response to selective pressures present in each species' native ecological niche. In a new environment, however, the same bacteria may grow poorly due to regulatory constraints or biochemical deficiencies. Adaptation to such conditions can proceed through the acquisition of new cellular functionality due to gain of function mutations or via modulation of cellular networks. Using selection experiments on transposon-mutagenized libraries of bacteria, we illustrate that even under conditions of extreme nutrient limitation, substantial adaptation can be achieved solely through loss of function mutations, which rewire the metabolism of the cell without gain of enzymatic or sensory function. A systematic analysis of similar experiments under more than 100 conditions reveals that adaptive loss of function mutations exist for many environmental challenges. Drawing on a wealth of examples from published articles, we detail the range of mechanisms through which loss-of-function mutations can generate such beneficial regulatory changes, without the need for rare, specific mutations to fine-tune enzymatic activities or network connections. The high rate at which loss-of-function mutations occur suggests that null mutations play an underappreciated role in the early stages of adaption of bacterial populations to new environments.


Vyšlo v časopise: Bacterial Adaptation through Loss of Function. PLoS Genet 9(7): e32767. doi:10.1371/journal.pgen.1003617
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003617

Souhrn

The metabolic capabilities and regulatory networks of bacteria have been optimized by evolution in response to selective pressures present in each species' native ecological niche. In a new environment, however, the same bacteria may grow poorly due to regulatory constraints or biochemical deficiencies. Adaptation to such conditions can proceed through the acquisition of new cellular functionality due to gain of function mutations or via modulation of cellular networks. Using selection experiments on transposon-mutagenized libraries of bacteria, we illustrate that even under conditions of extreme nutrient limitation, substantial adaptation can be achieved solely through loss of function mutations, which rewire the metabolism of the cell without gain of enzymatic or sensory function. A systematic analysis of similar experiments under more than 100 conditions reveals that adaptive loss of function mutations exist for many environmental challenges. Drawing on a wealth of examples from published articles, we detail the range of mechanisms through which loss-of-function mutations can generate such beneficial regulatory changes, without the need for rare, specific mutations to fine-tune enzymatic activities or network connections. The high rate at which loss-of-function mutations occur suggests that null mutations play an underappreciated role in the early stages of adaption of bacterial populations to new environments.


Zdroje

1. FreddolinoPL, TavazoieS (2012) Beyond Homeostasis: A Predictive-Dynamic Framework for Understanding Cellular Behavior. Annu Rev Cell Dev Biol 28: 363–84.

2. TagkopoulosI, LiuYC, TavazoieS (2008) Predictive behavior within microbial genetic networks. Science 320: 1313–1317.

3. HoekstraHE, CoyneJA (2007) The locus of evolution: evo devo and the genetics of adaptation. Evolution 61: 995–1016.

4. JacobF (1977) Evolution and tinkering. Science 196: 1161–1166.

5. WrayGA (2007) The evolutionary significance of cis-regulatory mutations. Nat Rev Genet 8: 206–216.

6. WiedenbeckJ, CohanFM (2011) Origins of bacterial diversity through horizontal genetic transfer and adaptation to new ecological niches. FEMS Microbiol Rev 35: 957–976.

7. AnderssonDI, HughesD (2009) Gene amplification and adaptive evolution in bacteria. Annu Rev Genet 43: 167–195.

8. BlountZD, BarrickJE, DavidsonCJ, LenskiRE (2012) Genomic analysis of a key innovation in an experimental Escherichia coli population. Nature 489: 513–518.

9. LeratE, OchmanH (2005) Recognizing the pseudogenes in bacterial genomes. Nucleic Acids Res 33: 3125–3132.

10. AnderssonSG, KurlandCG (1998) Reductive evolution of resident genomes. Trends Microbiol 6: 263–268.

11. HerronMD, DoebeliM (2013) Parallel Evolutionary Dynamics of Adaptive Diversification in Escherichia coli. PLoS Biol 11: e1001490.

12. KhanAI, DinhDM, SchneiderD, LenskiRE, CooperTF (2011) Negative epistasis between beneficial mutations in an evolving bacterial population. Science 332: 1193–1196.

13. WoodsRJ, BarrickJE, CooperTF, ShresthaU, KauthMR, et al. (2011) Second-order selection for evolvability in a large Escherichia coli population. Science 331: 1433–1436.

14. NicholsRJ, SenS, ChooYJ, BeltraoP, ZietekM, et al. (2011) Phenotypic landscape of a bacterial cell. Cell 144: 143–156.

15. QianW, MaD, XiaoC, WangZ, ZhangJ (2012) The genomic landscape and evolutionary resolution of antagonistic pleiotropy in yeast. Cell Rep 2: 1399–1410.

16. ThanassiDG, SuhGS, NikaidoH (1995) Role of outer membrane barrier in efflux-mediated tetracycline resistance of Escherichia coli. J Bacteriol 177: 998–1007.

17. GoodarziH, BennettBD, AminiS, ReavesML, HottesAK, et al. (2010) Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli. Mol Syst Biol 6: 378.

18. GirgisHS, HottesAK, TavazoieS (2009) Genetic architecture of intrinsic antibiotic susceptibility. PLoS One 4: e5629.

19. KohanskiMA, DwyerDJ, HayeteB, LawrenceCA, CollinsJJ (2007) A common mechanism of cellular death induced by bactericidal antibiotics. Cell 130: 797–810.

20. SchurekKN, MarrAK, TaylorPK, WiegandI, SemenecL, et al. (2008) Novel genetic determinants of low-level aminoglycoside resistance in Pseudomonas aeruginosa. Antimicrob Agents Chemother 52: 4213–4219.

21. GirgisHS, LiuY, RyuWS, TavazoieS (2007) A comprehensive genetic characterization of bacterial motility. PLoS Genet 3: 1644–1660.

22. CrozatE, PhilippeN, LenskiRE, GeiselmannJ, SchneiderD (2005) Long-term experimental evolution in Escherichia coli. XII. DNA topology as a key target of selection. Genetics 169: 523–532.

23. FreddolinoPL, GoodarziH, TavazoieS (2012) Fitness Landscape Transformation through a Single Amino Acid Change in the Rho Terminator. PLoS Genet 8: e1002744.

24. GoodarziH, ElementoO, TavazoieS (2009) Revealing global regulatory perturbations across human cancers. Mol Cell 36: 900–911.

25. EdwardsJS, PalssonBO (1998) How will bioinformatics influence metabolic engineering? Biotechnol Bioeng 58: 162–169.

26. BeckerSA, FeistAM, MoML, HannumG, PalssonBO, et al. (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2: 727–738.

27. FeistAM, HenryCS, ReedJL, KrummenackerM, JoyceAR, et al. (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3: 121.

28. FinkelSE (2006) Long-term survival during stationary phase: evolution and the GASP phenotype. Nat Rev Microbiol 4: 113–120.

29. ZinserER, KolterR (2000) Prolonged stationary-phase incubation selects for lrp mutations in Escherichia coli K-12. J Bacteriol 182: 4361–4365.

30. ZambranoMM, SiegeleDA, AlmironM, TormoA, KolterR (1993) Microbial competition: Escherichia coli mutants that take over stationary phase cultures. Science 259: 1757–1760.

31. FarrellMJ, FinkelSE (2003) The growth advantage in stationary-phase phenotype conferred by rpoS mutations is dependent on the pH and nutrient environment. J Bacteriol 185: 7044–7052.

32. SchneiderJ, WendischVF (2010) Putrescine production by engineered Corynebacterium glutamicum. Appl Microbiol Biotechnol 88: 859–868.

33. SinghA, Cher SohK, HatzimanikatisV, GillRT (2011) Manipulating redox and ATP balancing for improved production of succinate in E. coli. Metab Eng 13: 76–81.

34. BollenbachT, QuanS, ChaitR, KishonyR (2009) Nonoptimal microbial response to antibiotics underlies suppressive drug interactions. Cell 139: 707–718.

35. BaqueroF (2001) Low-level antibacterial resistance: a gateway to clinical resistance. Drug Resist Updat 4: 93–105.

36. LeeH, PopodiE, TangH, FosterPL (2012) Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing. Proc Natl Acad Sci U S A 109: E2774–E2783.

37. PonderRG, FonvilleNC, RosenbergSM (2005) A switch from high-fidelity to error-prone DNA double-strand break repair underlies stress-induced mutation. Mol Cell 19: 791–804.

38. RosenbergSM (2001) Evolving responsively: adaptive mutation. Nat Rev Genet 2: 504–515.

39. CooperVS, SchneiderD, BlotM, LenskiRE (2001) Mechanisms causing rapid and parallel losses of ribose catabolism in evolving populations of Escherichia coli B. J Bacteriol 183: 2834–2841.

40. PerfeitoL, FernandesL, MotaC, GordoI (2007) Adaptive mutations in bacteria: high rate and small effects. Science 317: 813–815.

41. AgaisseH, GominetM, OkstadOA, KolstoAB, LereclusD (1999) PlcR is a pleiotropic regulator of extracellular virulence factor gene expression in Bacillus thuringiensis. Mol Microbiol 32: 1043–1053.

42. KolstoAB, TourasseNJ, OkstadOA (2009) What sets Bacillus anthracis apart from other Bacillus species? Annu Rev Microbiol 63: 451–476.

43. MignotT, MockM, RobichonD, LandierA, LereclusD, et al. (2001) The incompatibility between the PlcR- and AtxA-controlled regulons may have selected a nonsense mutation in Bacillus anthracis. Mol Microbiol 42: 1189–1198.

44. SastallaI, MalteseLM, PomerantsevaOM, PomerantsevAP, Keane-MyersA, et al. (2010) Activation of the latent PlcR regulon in Bacillus anthracis. Microbiology 156: 2982–2993.

45. ProssedaG, Di MartinoML, CampilongoR, FioravantiR, MicheliG, et al. (2012) Shedding of genes that interfere with the pathogenic lifestyle: The Shigella model. Res Microbiol 163: 399–406.

46. BlivenKA, MaurelliAT (2012) Antivirulence Genes: Insights into pathogen evolution through gene loss. Infect Immun 80: 4061–4070.

47. MaurelliAT, FernándezRE, BlochCA, RodeCK, FasanoA (1998) “Black holes” and bacterial pathogenicity: A large genomic deletion that enhances the virulence of Shigella spp. and enteroinvasive Escherichia coli. Proc Natl Acad Sci U S A 95: 3943–3948.

48. McCormickBA, FernandezMI, SiberAM, MaurelliAT (1999) Inhibition of Shigella flexneri-induced transepithelial migration of polymorphonuclear leucocytes by cadaverine. Cell Microbiol 1: 143–155.

49. PrunierA-L, SchuchR, FernandezRE, MumyKL, KohlerH, et al. (2007) nadA and nadB of Shigella flexneri 5a are antivirulence loci responsible for the synthesis of quinolinate, a small molecule inhibitor of Shigella pathogenicity. Microbiology 153: 2363–2372.

50. BarbagalloM, Di MartinoML, MarcocciL, PietrangeliP, De CarolisE, et al. (2011) A New Piece of the Shigella Pathogenicity Puzzle: Spermidine Accumulation by Silencing of the speG Gene. PLoS One 6: e27226.

51. HogardtM, HeesemannJ (2010) Adaptation of Pseudomonas aeruginosa during persistence in the cystic fibrosis lung. Int J Med Microbiol 300: 557–562.

52. BoucherJC, YuH, MuddMH, DereticV (1997) Mucoid Pseudomonas aeruginosa in cystic fibrosis: characterization of muc mutations in clinical isolates and analysis of clearance in a mouse model of respiratory infection. Infect Immun 65: 3838–3846.

53. SmithEE, BuckleyDG, WuZ, SaenphimmachakC, HoffmanLR, et al. (2006) Genetic adaptation by Pseudomonas aeruginosa to the airways of cystic fibrosis patients. Proc Natl Acad Sci U S A 103: 8487–8492.

54. StewartEJ (2012) Growing unculturable bacteria. J Bacteriol 194: 4151–4160.

55. Ausubel FM, Brent R, Kingston RE, Moore DD, Seidman JG, et al.. (1994) Current protocols in molecular biology. New York, NY: Wiley Interscience.

56. NeidhardtFC, BlochPL, SmithDF (1974) Culture medium for enterobacteria. J Bacteriol 119: 736–747.

57. BabaT, AraT, HasegawaM, TakaiY, OkumuraY, et al. (2006) Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol 2: 2006 0008.

58. Silhavy TJ, Berman ML, Enquist LW (1984) Experiments with gene fusions. Plainview, NY : Cold Spring Harbor Press.

59. DatsenkoKA, WannerBL (2000) One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc Natl Acad Sci U S A 97: 6640–6645.

60. HottesAK, TavazoieS (2011) Microarray-based genetic footprinting strategy to identify strain improvement genes after competitive selection of transposon libraries. Methods Mol Biol 765: 83–97.

61. Cleveland WS, Grosse E, Shyu WM (1992) Local regression models. In: Chambers JM, Hastie TJ, editors. Statistical Models in S. Pacific Grove, California: Wadsworth & Brooks/Cole.

62. KatoJ-i, HashimotoM (2007) Construction of consecutive deletions of the Escherichia coli chromosome. Mol Syst Biol 3: 132.

63. ZaslaverA, BrenA, RonenM, ItzkovitzS, KikoinI, et al. (2006) A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat Methods 3: 623–628.

64. AminiS, GoodarziH, TavazoieS (2009) Genetic dissection of an exogenously induced biofilm in laboratory and clinical isolates of E. coli. PLoS Pathog 5: e1000432.

65. BenjaminiY, HochbergY (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B 57: 289–300.

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2013 Číslo 7
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#