On the (un)predictability of a large intragenic fitness landscape

Proceedings of the National Academy of Sciences of the United States of America

Volumn 113, Issue 49, 2016, Pages 14085-14090

  Bank, Claudia ^a,b,c   Matuszewski, Sebastian ^b,c   Hietpas, Ryan T ^d,e   Jensen, Jeffrey D ^f  

^a INSTITUTO GULBENKIAN DE CIÊNCIA   (Portugal)

^b EPFL   (Switzerland)

^c SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

^d ELI LILLY AND COMPANY   (United States)

^e UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL   (United States)

^f ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Adaptation; Epistasis; Evolution; Fitness landscape; Mutagenesis

Indexed keywords

AMINO ACID; HEAT SHOCK PROTEIN 90; HSP82 PROTEIN, S CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEIN;

ARTICLE; EPISTASIS; FUNGUS MUTANT; GENE MUTATION; INTRAGENIC FITNESS LANDSCAPE; MOLECULAR GENETICS; NONHUMAN; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SACCHAROMYCES CEREVISIAE; SALINITY; ADAPTATION; EVOLUTION; GENETICS; MISSENSE MUTATION; REPRODUCTIVE FITNESS;

ADAPTATION, BIOLOGICAL; BIOLOGICAL EVOLUTION; EPISTASIS, GENETIC; GENETIC FITNESS; HSP90 HEAT-SHOCK PROTEINS; MUTATION, MISSENSE; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS; SALINITY;

EID: 85002156867     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1612676113     Document Type: Article

References (58)

1. Wright S (1932) The roles of mutation, inbreeding, crossbreeding, and selection in evolution. Proceedings of the Sixth International Congress of Genetics, ed Jones DF (Brooklyn Botanic Garden, Menasha, WI), pp 356-366.
2. Coyne JA, Barton NH, Turelli M (1997) Perspective: A critique of Sewall Wright's shifting balance theory of evolution. Evolution 51(3):643-671.
3. Gavrilets S (2004) Fitness Landscapes and the Origin of Species. (Princeton Univ Press, Princeton).
4. de Visser JAGM, Krug J (2014) Empirical fitness landscapes and the predictability of evolution. Nat Rev Genet 15(7):480-490.
5. Costanzo M, et al. (2010) The genetic landscape of a cell. Science 327(5964):425-431.
6. Jiménez JI, Xulvi-Brunet R, Campbell GW, Turk-Macleod R, Chen IA (2013) Comprehensive experimental fitness landscape and evolutionary network for small RNA. Proc Natl Acad Sci USA 110(37):14984-14989.
7. Huang S (2013) Genetic and non-genetic instability in tumor progression: Link between the fitness landscape and the epigenetic landscape of cancer cells. Cancer Metastasis Rev 32(3-4):423-448.
8. Mann JK, et al. (2014) The fitness landscape of HIV-1 gag: Advanced modeling approaches and validation of model predictions by in vitro testing. PLoS Comput Biol 10(8):e1003776.
9. Liu G, Rancati G (2016) Adaptive evolution: Don't fix what's broken. Curr Biol 26(4):R169-R171.
10. Szendro IG, Schenk MF, Franke J, Krug J (2013) Quantitative analyses of empirical fitness landscapes. J Stat Mech 2013:P01005.
11. Aita T, Iwakura M, Husimi Y (2001) A cross-section of the fitness landscape of dihydrofolate reductase. Protein Eng 14(9):633-638.
12. Franke J, Klö zer A, de Visser JAGM, Krug J (2011) Evolutionary accessibility of mutational pathways. PLoS Comput. Biol. 7(8):e1002134.
13. Schenk MF, Szendro IG, Salverda MLM, Krug J, de Visser JAGM (2013) Patterns of Epistasis between beneficial mutations in an antibiotic resistance gene. Mol Biol Evol 30(8):1779-1787.
14. Ferretti L, et al. (2016) Measuring epistasis in fitness landscapes: The correlation of fitness effects of mutations. J Theor Biol 396:132-143.
15. Kryazhimskiy S, Rice DP, Jerison ER, Desai MM (2014) Microbial evolution. Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344(6191):1519-1522.
16. Khan AI, Dinh DM, Schneider D, Lenski RE, Cooper TF (2011) Negative epistasis between beneficial mutations in an evolving bacterial population. Science 332(6034):1193-1196.
17. Schoustra SE, Bataillon T, Gifford DR, Kassen R (2009) The properties of adaptive walks in evolving populations of fungus. PLoS Biol 7(11):e1000250.
18. Weinreich D, Delaney N, DePristo M (2006) Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312(5770):111-114.
19. Dickinson WJ (2008) Synergistic fitness interactions and a high frequency of beneficial changes among mutations accumulated under relaxed selection in Saccharomyces cerevisiae. Genetics 178(3):1571-1578.
20. Bank C, Hietpas RT, Jensen JD, Bolon DNA (2015) A systematic survey of an intragenic epistatic landscape. Mol Biol Evol 32(1):229-238.
21. MacLean RC, Hall AR, Perron GG, Buckling A (2010) The population genetics of antibiotic resistance: Integrating molecular mechanisms and treatment contexts. Nat Rev Genet 11(6):405-414.
22. Jasmin JN, Lenormand T (2016) Accelerating mutational load is not due to Synergistic epistasis or mutator alleles in mutation accumulation lines of yeast. Genetics 202(2):751-763.
23. Blanquart F, Achaz G, Bataillon T, Tenaillon O (2014) Properties of selected mutations and genotypic landscapes under Fisher's geometric model. Evolution 68(12): 3537-3554.
24. Hietpas RT, Jensen JD, Bolon DNA (2011) Experimental illumination of a fitness landscape. Proc Natl Acad Sci USA 108(19):7896-7901.
25. Hietpas R, Roscoe B, Jiang L, Bolon DNA (2012) Fitness analyses of all possible point mutations for regions of genes in yeast. Nat Protoc 7(7):1382-1396.
26. Hietpas RT, Bank C, Jensen JD, Bolon DNA (2013) Shifting fitness landscapes in response to altered environments. Evolution 67(12):3512-3522.
27. Bank C, Hietpas RT, Wong A, Bolon DN, Jensen JD (2014) A bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: Uncovering the potential for adaptive walks in challenging environments. Genetics 196(3):841-852.
28. Weinreich DM, Knies JL (2013) Fisher's geometric model of adaptation meets the functional synthesis: Data on pairwise epistasis for fitness yields insights into the shape and size of phenotype space. Evolution 67(10):2957-2972.
29. Kimura M, Maruyama T (1966) Mutational load with epistatic gene interactions in fitness. Genetics 54(6):1337-1351.
30. Soskine M, Tawfik DS (2010) Mutational effects and the evolution of new protein functions. Nat Rev Genet 11(8):572-582.
31. Trindade S, et al. (2009) Positive epistasis drives the acquisition of multidrug resistance. PLoS Genet 5(7):e1000578.
32. de Visser JA, Hoekstra RF, van den Ende H (1997) An experimental test for synergistic epistasis and its application in Chlamydomonas. Genetics 145(3):815-819.
33. Draghi JA, Plotkin JB (2013) Selection biases the prevalence and type of epistasis along adaptive trajectories. Evolution 67(11):3120-3131.
34. Gillespie JH (1984) Molecular evolution over the mutational landscape. Evolution 38(5):1116-1129.
35. Weinreich DM, Watson RA, Chao L (2005) Perspective: Sign epistasis and genetic constraint on evolutionary trajectories. Evolution 59(6):1165-1174.
36. Kauffman S, Levin S (1987) Towards a general theory of adaptive walks on rugged landscapes. J Theor Biol 128(1):11-45.
37. Kauffman SA (1993) The Origins of Order: Self Organization and Selection in Evolution. (Oxford Univ Press, New York).
38. Schmiegelt B, Krug J (2014) Evolutionary accessibility of modular fitness landscapes. J Stat Phys 154(1):334-355.
39. Neidhart J, Szendro IG, Krug J (2014) Adaptation in tunably rugged fitness landscapes: The Rough Mount Fuji model. Genetics 198(2):699-721.
40. Aita T, et al. (2000) Analysis of a local fitness landscape with a model of the rough Mt. Fuji-type landscape: Application to prolyl endopeptidase and thermolysin. Biopolymers 54(1):64-79.
41. Kingman JFC (1978) A simple model for the balance between selection and mutation. J Appl Probab 15(1):1-12.
42. Geisser S (1993) Predictive Inference. (CRC Press, New York), Vol 55.
43. Bajaj K, et al. (2007) Stereochemical criteria for prediction of the effects of proline mutations on protein stability. PLoS Comput Biol 3(12):e241.
44. Wylie CS, Shakhnovich EI (2011) A biophysical protein folding model accounts for most mutational fitness effects in viruses. Proc Natl Acad Sci USA 108(24): 9916-9921.
45. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci USA 89(22):10915-10919.
46. Wang S, Ma J, Peng J, Xu J (2013) Protein structure alignment beyond spatial proximity. Sci Rep 3:1448.
47. Gietz DR, Woods RA (2002) Transformation of yeast by lithium acetate/singlestranded carrier DNA/polyethylene glycol method. Methods Enzymol 350: 87-96.
48. Ewing B, Green P (1998) Base-calling of automated sequencer traces using Phred. II. Error probabilities. Genome Res 8:186-194.
49. Ewing B, Hillier L, Wendl MC, Green P (1998) Base-calling of automated sequencer traces using Phred. I. Accuracy assessment. Genome Res 8:175-185.
50. Boone EL, Merrick JRW, Krachey MJ (2012) A Hellinger distance approach to MCMC diagnostics. J Stat Comput Simulat 84(4):833-849.
51. Gillespie JH (1983) A simple stochastic gene substitution model. Theor Popul Biol 23(2):202-215.
52. Fisher RA (1930) The Genetical Theory of Natural Selection. (Clarendon Press, Oxford, UK).
53. Wright S (1931) Evolution in Mendelian populations. Genetics 16(2):97.
54. McCandlish DM (2011) Visualizing fitness landscapes. Evolution 65(6):1544-1558.
55. Kemeny JG, Snell JL (1960) Finite Markov Chains (van Nostrand, Princeton).
56. Poelwijk FJ, Kiviet DJ, Weinreich DM, Tans SJ (2007) Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445(7126):383-386.
57. Fisher RA (1918) The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinburgh 52:399-433.
58. Brouillet S, Annoni H, Ferretti L, Achaz G (2015) MAGELLAN: A tool to explore small fitness landscapes. bioRxiv:031583.