Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Pairwise and higher-order genetic interactions during the evolution of a tRNA

Pairwise and higher-order genetic interactions during the evolution of a tRNA A central question in genetics and evolution is the extent to which the outcomes of mutations change depending on the genetic context in which they occur 1–3 . Pairwise interactions between mutations have been systematically mapped within 4–18 and between 19 genes, and have been shown to contribute substantially to phenotypic variation among individuals 20 . However, the extent to which genetic interactions themselves are stable or dynamic across genotypes is unclear 21, 22 . Here we quantify more than 45,000 genetic interactions between the same 87 pairs of mutations across more than 500 closely related genotypes of a yeast tRNA. Notably, all pairs of mutations interacted in at least 9% of genetic backgrounds and all pairs switched from interacting positively to interacting negatively in different genotypes (false discovery rate < 0.1). Higher-order interactions are also abundant and dynamic across genotypes. The epistasis in this tRNA means that all individual mutations switch from detrimental to beneficial, even in closely related genotypes. As a consequence, accurate genetic prediction requires mutation effects to be measured across different genetic backgrounds and the use of  higher-order epistatic terms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Springer Journals

Pairwise and higher-order genetic interactions during the evolution of a tRNA

Domingo, Júlia; Diss, Guillaume; Lehner, Ben
Nature , Volume 558 (7708) – May 30, 2018
Download PDF
26 pages

Loading next page...
 
/lp/springer_journal/pairwise-and-higher-order-genetic-interactions-during-the-evolution-of-3XIXHx4mZE
Publisher
Springer Journals
Copyright
Copyright © 2018 by Macmillan Publishers Ltd., part of Springer Nature
Subject
Science, Humanities and Social Sciences, multidisciplinary; Science, Humanities and Social Sciences, multidisciplinary; Science, multidisciplinary
ISSN
0028-0836
eISSN
1476-4687
DOI
10.1038/s41586-018-0170-7
pmid
29849145
Publisher site
See Article on Publisher Site

Abstract

A central question in genetics and evolution is the extent to which the outcomes of mutations change depending on the genetic context in which they occur 1–3 . Pairwise interactions between mutations have been systematically mapped within 4–18 and between 19 genes, and have been shown to contribute substantially to phenotypic variation among individuals 20 . However, the extent to which genetic interactions themselves are stable or dynamic across genotypes is unclear 21, 22 . Here we quantify more than 45,000 genetic interactions between the same 87 pairs of mutations across more than 500 closely related genotypes of a yeast tRNA. Notably, all pairs of mutations interacted in at least 9% of genetic backgrounds and all pairs switched from interacting positively to interacting negatively in different genotypes (false discovery rate < 0.1). Higher-order interactions are also abundant and dynamic across genotypes. The epistasis in this tRNA means that all individual mutations switch from detrimental to beneficial, even in closely related genotypes. As a consequence, accurate genetic prediction requires mutation effects to be measured across different genetic backgrounds and the use of  higher-order epistatic terms.

Journal

Nature – Springer Journals

Published: May 30, 2018

References

  • Genotype to phenotype: lessons from model organisms for human genetics
    Lehner, B
  • Empirical fitness landscapes and the predictability of evolution
    Visser, JA; Krug, J
  • Epistasis in protein evolution
    Starr, TN; Thornton, JW
  • High-resolution mapping of protein sequence–function relationships
    Fowler, DM
  • A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function
    Araya, CL
  • The genetic landscape of a physical interaction
    Diss, G; Lehner, B
  • Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein
    Melamed, D; Young, DL; Gamble, CE; Miller, CR; Fields, S
  • Stability-mediated epistasis constrains the evolution of an influenza protein
    Gong, LI; Suchard, MA; Bloom, JD
  • A comprehensive biophysical description of pairwise epistasis throughout an entire protein domain
    Olson, CA; Wu, NC; Sun, R
  • Epistatically interacting substitutions are enriched during adaptive protein evolution
    Gong, LI; Bloom, JD
  • A systematic survey of an intragenic epistatic landscape
    Bank, C; Hietpas, RT; Jensen, JD; Bolon, DN
  • Intramolecular phenotypic capacitance in a modular RNA molecule
    Hayden, EJ; Bendixsen, DP; Wagner, A
  • On the (un)predictability of a large intragenic fitness landscape
    Bank, C; Matuszewski, S; Hietpas, RT; Jensen, JD
  • Network of epistatic interactions within a yeast snoRNA
    Puchta, O
  • The fitness landscape of a tRNA gene
    Li, C; Qian, W; Maclean, CJ; Zhang, J
  • The complete local genotype–phenotype landscape for the alternative splicing of a human exon
    Julien, P; Miñana, B; Baeza-Centurion, P; Valcárcel, J; Lehner, B
  • Local fitness landscape of the green fluorescent protein
    Sarkisyan, KS
  • Identification of the determinants of tRNA function and susceptibility to rapid tRNA decay by high-throughput in vivo analysis
    Guy, MP
  • Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast
    Forsberg, SK; Bloom, JS; Sadhu, MJ; Kruglyak, L; Carlborg, Ö
  • Evolutionary plasticity of genetic interaction networks
    Tischler, J; Lehner, B; Fraser, AG
  • Should evolutionary geneticists worry about higher-order epistasis?
    Weinreich, DM; Lan, Y; Wylie, CS; Heckendorn, RB
  • Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes
    Palmer, AC
  • Detecting high-order epistasis in nonlinear genotype-phenotype maps
    Sailer, ZR; Harms, MJ
  • Adaptation in protein fitness landscapes is facilitated by indirect paths
    Wu, NC; Dai, L; Olson, CA; Lloyd-Smith, JO; Sun, R
  • Beyond the whole-genome duplication: phylogenetic evidence for an ancient interspecies hybridization in the baker’s yeast lineage
    Marcet-Houben, M; Gabaldón, T
  • Mutation effects predicted from sequence co-variation
    Hopf, TA
  • Measuring epistasis in fitness landscapes: The correlation of fitness effects of mutations
    Ferretti, L
  • Perspective: Sign epistasis and genetic constraint on evolutionary trajectories
    Weinreich, DM; Watson, RA; Chao, L
  • GtRNAdb: a database of transfer RNA genes detected in genomic sequence
    Chan, PP; Lowe, TM
  • Basic local alignment search tool
    Altschul, SF; Gish, W; Miller, W; Myers, EW; Lipman, DJ
  • Analysis tool web services from the EMBL-EBI
    McWilliam, H
  • A system of shuttle vectors and yeast host strains designed for efficient manipulation of DNA in Saccharomyces cerevisiae
    Sikorski, RS; Hieter, P
  • A statistical guide to the design of deep mutational scanning experiments
    Matuszewski, S; Hildebrandt, ME; Ghenu, AH; Jensen, JD; Bank, C
  • Cutadapt removes adapter sequences from high-throughput sequencing reads
    Martin, M
  • PEAR: a fast and accurate Illumina Paired-End reAd mergeR
    Zhang, J; Kobert, K; Flouri, T; Stamatakis, A
  • The R Book.
    Crawley, MJ
  • A statistical framework for analyzing deep mutational scanning data
    Rubin, AF
  • The context-dependence of mutations: a linkage of formalisms
    Poelwijk, FJ; Krishna, V; Ranganathan, R
  • Controlling the false discovery rate: a practical and powerful approach to multiple testing
    Benjamini, Y; Hochberg, Y
  • Empirical fitness landscapes reveal accessible evolutionary paths
    Poelwijk, FJ; Kiviet, DJ; Weinreich, DM; Tans, SJ
  • Quantitative analyses of empirical fitness landscapes
    Szendro, IG; Schenk, MF; Franke, J; Krug, J; Visser, JA