Movatterモバイル変換

[0]ホーム
URL:

Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
Thehttps:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

NIH NLM Logo
Log inShow account info
Access keysNCBI HomepageMyNCBI HomepageMain ContentMain Navigation
pubmed logo
Advanced Clipboard
User Guide

Full text links

Atypon full text link Atypon Free PMC article
Full text links

Actions

.2011 Jan 18;108(3):1082-7.
doi: 10.1073/pnas.1012918108. Epub 2011 Jan 3.

Evolution of molecular error rates and the consequences for evolvability

Affiliations

Evolution of molecular error rates and the consequences for evolvability

Etienne Rajon et al. Proc Natl Acad Sci U S A..

Abstract

Making genes into gene products is subject to predictable errors, each with a phenotypic effect that depends on a normally cryptic sequence. Many cryptic sequences have strongly deleterious effects, for example when they cause protein misfolding. Strongly deleterious effects can be avoided globally by avoiding making errors (e.g., via proofreading machinery) or locally by ensuring that each error has a relatively benign effect. The local solution requires powerful selection acting on every cryptic site and so evolves only in large populations. Small populations with less effective selection evolve global solutions. Here we show that for a large range of realistic intermediate population sizes, the evolutionary dynamics are bistable and either solution may result. The local solution facilitates the genetic assimilation of cryptic genetic variation and therefore substantially increases evolvability.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Evolutionary dynamics of the read-through error rate ρ. (A) Coevolution of ρ and of the number of cryptic sequences that are deleterious,Ldel. The optimal value of ρ as a function ofLdel, calculated as the value maximizing Eq.2 (Methods), is represented by a thick gray line, and ρ evolves toward this line whenLdel is fixed. The equilibrium values ofLdel as functions of log10(ρ) are represented by thin lines, for population sizesN = 103 andN = 105.Ldel evolves toward these lines when ρ is fixed. The values of ρ andLdel obtained by simulation are represented by solid crosses (centered on the mean, ±2 SD across replicate populations), each matching the intersection of the thick and one thin line.Movie S1 shows the simulated coevolutionary dynamics. (B) The evolved error rate ρ is low in small populations, high in large populations, and bistable for intermediate populations. Solid and open symbols (error bars ±2 SD;x values were slightly changed to avoid superposition) represent the results of simulations starting with (log10(ρ),Ldel) = (−5, 400) and (−2, 0), respectively. (C) The ranges ofN at which ρ is low or bistable increase with the total number of loci,Ltot. Bistable outcomes were identified by comparing the results of simulations obtained with the same initial conditions as inB, using at test with critical value 0.005. Parameter values: γ = 0.01,pdel = 0.4,p−del = 0.1, δ = 10−2.5, μ = 10−8, andLtot = 500 (A andB).
Fig. 2.
Fig. 2.
The mean population trait value,x, changes faster after an environmental change with the high -ρ (log10(ρ) = −1.71) than with the low-ρ attractor (log10(ρ) = −2.96). (A) One simulation in which the population with the high-ρ attractor (dashed line) reaches the new optimum before the low-ρ attractor (solid line). A change in the optimal trait value, from −1.5 to 1.5, is simulated att = 20,000. (B) The time before the population reaches a mean trait value near the optimum (1.4) after an environmental change (time to adaptation) increases with the number of loci coding for the trait,K. The time to adaptation is consistently lower for the high-ρ attractor whenK > 5. The size of mutations decreases withK (see text), which explains that the time to fixation of advantageous mutants increases. Keeping the size of mutations independent ofK leads to a decreasing time to adaptation withK (Fig. S7), but leads to the sameK > 5 condition for high-ρ attractor advantage. The error bars represent the first and third quartiles in the distribution of the time to adaptation, calculated from 20 simulations for each set of parameter values. The two attractors are those shown in Fig. 1A andB forN = 105. Parameter values:N = 105, σm = 0.5,a = 750; other values are the same as in Fig. 1A andB.
See this image and copyright information in PMC

References

    1. Drummond DA, Wilke CO. The evolutionary consequences of erroneous protein synthesis. Nat Rev Genet. 2009;10:715–724. - PMC - PubMed
    1. Eyre-Walker A, Keightley PD. The distribution of fitness effects of new mutations. Nat Rev Genet. 2007;8:610–618. - PubMed
    1. Bloom JD, et al. Thermodynamic prediction of protein neutrality. Proc Natl Acad Sci USA. 2005;102:606–611. - PMC - PubMed
    1. Andersson DI, van Verseveld HW, Stouthamer AH, Kurland CG. Suboptimal growth with hyper-accurate ribosomes. Arch Microbiol. 1986;144:96–101. - PubMed
    1. Youngman EM, McDonald ME, Green R. Peptide release on the ribosome: Mechanism and implications for translational control. Annu Rev Microbiol. 2008;62:353–373. - PubMed

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full text links
Atypon full text link Atypon Free PMC article
Cite
Send To

NCBI Literature Resources

MeSHPMCBookshelfDisclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

[8]ページ先頭
©2009-2026 Movatter.jp