Research Article
Error compensation of tRNA misacylation by codon–anticodon mismatch prevents translational amino acid misinsertion

https://doi.org/10.1016/j.compbiolchem.2011.03.001Get rights and content

Abstract

Codon–anticodon mismatches and tRNA misloadings cause translational amino acid misinsertions, producing dysfunctional proteins. Here I explore the original hypothesis whether mismatches tend to compensate misacylation, so as to insert the amino acid coded by the codon. This error compensation is promoted by the fact that codon–anticodon mismatch stabilities increase with tRNA misacylation potentials (predicted by ‘tfam’) by non-cognate amino acids coded by the mismatched codons for most tRNAs examined. Error compensation is independent of preferential misacylation by non-cognate amino acids physico-chemically similar to cognate amino acids, a phenomenon that decreases misinsertion impacts. Error compensation correlates negatively with (a) codon/anticodon abundance (in human mitochondria and Escherichia coli); (b) developmental instability (estimated by fluctuating asymmetry in bilateral counts of subdigital lamellae, in each of two lizard genera, Anolis and Sceloporus); and (c) pathogenicity of human mitochondrial tRNA polymorphisms. Patterns described here suggest that tRNA misacylation is sometimes compensated by codon–anticodon mismatches. Hence translation inserts the amino acid coded by the mismatched codon, despite mismatch and misloading. Results suggest that this phenomenon is sufficiently important to affect whole organism phenotypes, as shown by correlations with pathologies and morphological estimates of developmental stability.

Highlights

► For a majority of tRNAs, non-cognate amino acids with the greatest probability of being misloaded, match the codons with the greatest probability of being mismatched by the tRNA's anticodon. ► This property of error compensation is more stronger for regular polymorphisms than pathogenic ones. ► It is stronger in lizards with low developmental instability. ► It is stronger for rarely used codons and anticodons.

Introduction

The genetic code is optimal in relation to several properties important for coding and translation. This suggests that the genetic code is not frozen (Sella and Ardell, 2006), but evolves towards a multi-functional optimum (Bollenbach et al., 2007). For example, the genetic code might have minimized codon length (Baranov et al., 2009), and seems to minimise mutation impacts (Freeland and Hurst, 1998, Freeland et al., 2000, Gilis et al., 2001, Sella and Ardell, 2002) as well as costs of accidental ribosomal frameshifts during protein synthesis (Seligmann and Pollock, 2004, Seligmann, 2007), while maximizing the potential for secondary structure formation (Itzkovitz and Alon, 2007).

Presumably, the evolution of the genetic code involved codon reassignments (Osawa and Jukes, 1989, Knight et al., 2001), and created alternative genetic codes (Santos et al., 2004), perhaps because needs for optimization of different properties differ among organisms (as for optimizing numbers of off frame stops (Singh and Pardasani, 2009)). Early observations that physico-chemical properties of amino acids correlate with properties of codons, and especially anticodons (Jungck, 1978), suggest that the structure of the genetic code coevolved with properties of tRNAs (Chechetkin, 2006) and of the tRNA synthetases that aminoacylate the tRNAs (Jestin and Soulé, 2007). Indeed, the two major groups of tRNA synthetases, class I and II, seem to minimize impacts of misinserted amino acids in protein sequences by tRNAs that were misloaded by these tRNA synthetases (Cavalcanti et al., 2000, Torabi et al., 2007).

The tRNA synthetases frequently ‘edit’ (correct) tRNA misacylations (Schimmel and Ribas de Pouplana, 2001, Ling et al., 2009), either by pre- or post-transfer editing, two major, non-exclusive mechanisms depending on catalytic sites that differ from the aminoacylation site (Splan et al., 2008, Yadavalli et al., 2008, Martinis and Boniecki, 2010). Editing of tRNAs apparently even occurs for tRNAs already very advanced in the translational pathway (Ling et al., 2009). Some tRNA synthetases seem to have very complex editing capacities which might reflect the multifunctionality of early tRNA synthetases, at the origins of the genetic code and the translational machinery (Zhu et al., 2007). These editing properties also probably affect tendencies for developing mitochondrial diseases (Zhou and Wang, 2008).

Despite tRNA editing, tRNA misloading occurs, and typically results in amino acid misinsertion. This means that the amino acid that is added to the elongating peptide is not the one that is coded by the mRNA's codon. Misinsertion does not only result from misloading. Correct insertion depends on that the tRNA is loaded by the amino acid that matches the tRNA's anticodon, and that the anticodon complements a codon that codes for the tRNA's cognate. Therefore, each tRNA misloading and codon–anticodon mismatch cause misinsertions.

Even misloaded tRNAs can still, occasionally, transfer the amino acid to the ‘right’ position, and that is when the anticodon of that misloaded tRNA is mismatched with a codon that codes for the misloaded, non-cognate amino acid. The original working hypothesis of this study is that tRNA potentials for amino acid misloading correspond to potentials for mismatching codons coding for the misloaded amino acid. If this is the case, part of the translational activity by misloaded tRNAs does not result in misinsertions. The process is termed here error compensation. Error compensation occurs by increasing the frequency of adequate combinations of mismatches and misloadings. This optimization of the genetic code and of the translational machinery would minimize the frequency of translational errors. This mechanism has to be contrasted with existing evidence for the genetic code's optimization to minimize the effects of replicational and translational errors (termed here misinsertion impact), as suggested earlier (Sonneborn, 1965, Woese, 1965a, Woese, 1965b, Massey, 2008).

In this context, it is important to note that two different processes result in misloading: either the tRNA synthetase that is adequate for the tRNA loads an amino acid that is not its cognate, hence errors result from similarities between cognate and non-cognate amino acids; or a tRNA synthetase that is not the adequate one for the tRNA loads its cognate amino acid to the tRNA's acceptor stem, and errors result from similarities between tRNAs. Previous analyses about optimization of the genetic code and the structure of the translational machinery (Torabi et al., 2007) address only the mechanism by which amino acid similarities cause misacylations. This mechanism is a major component of the phenomenon that minimizes misinsertion impacts and hence yields identical predictions with the hypothesis of error compensation. Part of the analyses presented here account for amino acid similarities, and hence specifically test the error compensation hypothesis in the context of the mechanism where tRNA synthetases confuse tRNAs, not amino acids. These analyses also make sure that the phenomenon described is due to error compensation, and is not an indirect result of the known phenomenon that minimizes misinsertion impacts.

The working hypothesis produces several testable predictions, some tested below in 4 independent, different datasets. (1) Are mismatches and misacylation correlated (tested for the most frequent (modal) tRNA sequences from human mitochondrial genomes and Escherichia coli tRNAs)? (2) Is error compensation weaker in pathogenic human polymorphisms of these mitochondrial tRNAs than in unpathogenic tRNAs? (3) Does error compensation of mitochondrial tRNAs increase developmental stability in lizards (two independent tests, for iguanid genera Anolis and Sceloporus, expecting more developmental stability in species with high error compensation)? Mitochondrial genomes were chosen because ample comparative data is available (within a single species), for human tRNA mutation data, making comparisons between pathogenic and unpathogenic tRNA mutations possible, and because of availability of sequence data corresponding to lizard species for which data on developmental stability is also available (Seligmann, 1998, Seligmann, 2000, Seligmann, 2006, Seligmann et al., 2003a, Seligmann et al., 2003b, Seligmann et al., 2008). Analyses confirm that error compensation occurs in a wide majority of tRNAs and codons, but mainly in rare ones, and that this property affects whole organism properties: error compensation is weaker in tRNA mutations that cause pathologies, and in species with high developmental instability.

Section snippets

Materials and methods

I explored the working hypothesis for the 22 mitochondrial human tRNAs and their polymorphisms, using tRNA sequences from the appendix in Seligmann (2008), which was updated using Mitomap (as accessed in early 2009, for pathogenic polymorphisms) (Ruiz-Pesini et al., 2007), and mtDB http://www.genpat.uu.se/mtDB/ for unpathogenic polymorphisms (Ingman and Gyllensten, 2006). The stability (ΔG) of RNA duplexes formed by each of the 22 mitochondrial anticodons and all 64 codons was predicted by the

Misacylation potentials of mitochondrial tRNAs

The code for aminoacylation specificity is not yet well understood, especially for mitochondria (Taquist et al., 2007). This means that for mitochondrial tRNAs, tfam does not necessarily predict the cognate amino acid as having the greatest potential for loading the tRNA. Indeed, for the 22 mitochondrial tRNAs, the aminoacylation potential (Table 2 in Seligmann (2010a)) predicted for the 19 non-cognate amino acids is greater than that of the cognate in 144 among 418 (34%) combinations of tRNAs

Tfam and aminoacylation of mitochondrial tRNAs

The analyses presented here rely centrally on the assumption that tfam scores of alignment quality of focal tRNA sequences with standard tRNA sequences with experimentally determined cognates reflect the focal tRNA's potential for being aminoacylated with the cognate of the corresponding standard tRNAs. This is a direct expansion of the rationale behind tfam: tRNA synthetases recognize their specific tRNA according to (largely unknown) signals (and combinations of signals) in the tRNA's

General conclusions

The relatively simple analyses described here answer in the affirmative the question whether codon–anticodon mismatches and misacylation potentials tend to favour mismatch and misacylation assortments that do not result in amino acid misinsertions in proteins, termed here as error compensation. Analyses suggest that such patterns exist more frequently than expected, but more quantitative estimations are missing. This requires converting stabilities of codon–anticodon duplexes into

Acknowledgments

I am grateful for the comments of several anonymous reviewers, which greatly improved the ms. The term ‘error compensation’ was coined by one of them.

References (72)

  • N. Shimada et al.

    Dual mode recognition of two isoacceptor tRNAs by mammalian mitochondrial seryl-tRNA synthetase

    J. Biol. Chem.

    (2001)
  • T.R. Singh et al.

    Ambush hypothesis revisited: evidences for phylogenetic trends

    Comp. Biol. Chem.

    (2009)
  • K.E. Splan et al.

    Transfer RNA modulates the editing mechanism used by class II prolyl-tRNA synthetase

    J. Biol. Chem.

    (2008)
  • K.L. Tong et al.

    Anticodon and wobble evolution

    Gene

    (2004)
  • N. Torabi et al.

    The case for an error minimizing set of coding amino acids

    J. Theor. Biol.

    (2007)
  • P.F. Agris

    Bringing order to translation: the contributions of transfer RNA anticodon-domain modifications

    EMBO Rep.

    (2008)
  • P.V. Baranov et al.

    Codon size reduction as the origin of the triplet genetic code

    PLoS ONE

    (2009)
  • Y. Benjamini et al.

    Controlling the false discovery rate: a practical and powerful approach to multiple testing

    J. R. Stat. Soc. B

    (1995)
  • G.R. Björk et al.

    Transfer RNA modification

    Ann. Rev. Biochem.

    (1987)
  • T. Bollenbach et al.

    Evolution and multilevel optimization of the genetic code

    Genome Res.

    (2007)
  • C. Florentz et al.

    Human mitochondrial tRNAs in health and disease

    Cell. Mol. Life Sci.

    (2003)
  • S.J. Freeland et al.

    The genetic code is one in a million

    J. Mol. Evol.

    (1998)
  • S.J. Freeland et al.

    Early fixation of an optimal genetic code

    Mol. Biol. Evol.

    (2000)
  • C.E. Felder et al.

    A server database for dipole moments of proteins

    Nucleic Acids Res.

    (2007)
  • D. Gilis et al.

    Optimality of the genetic code with respect to protein stability and amino-acid frequencies

    Genome Biol.

    (2001)
  • R. Grantham

    Amino acid difference formula to help explain protein evolution

    Science

    (1974)
  • R. Hao et al.

    Human mitochondrial tRNA modification and inherited encephalomyopathies

    Prog. Biochem. Biophys.

    (2006)
  • M.K. Hecht

    Natural selection in the genus Aristelliger

    Evolution

    (1952)
  • M. Ingman et al.

    mtDB: Human Mitochondrial Genome Database, a resource for population genetics and medical sciences

    Nucleic Acids Res.

    (2006)
  • S. Itzkovitz et al.

    The genetic code is nearly optimal for allowing additional information within protein-coding sequences

    Genome Res.

    (2007)
  • J.R. Jungck

    Genetic code as a periodic table

    J. Mol. Evol.

    (1978)
  • A.A. Kantardjiev et al.

    PHEMTO: protein pH-dependent electric moment tools

    Nucleic Acids Res.

    (2009)
  • R.D. Knight et al.

    Rewiring the keyboard: evolvability of the genetic code

    Nat. Rev. Genet.

    (2001)
  • F.A. Kondrashov

    Prediction of pathogenic mutations in mitochondrially encoded human tRNAs

    Hum. Mol. Genet.

    (2005)
  • N.M. Krishnan et al.

    Detecting gradients of asymmetry in site-specific substitutions in mitochondrial genomes

    DNA Cell Biol.

    (2004)
  • N.M. Krishnan et al.

    Relationship between mRNA secondary structure and sequence variability in chloroplast genes: possible life history implications

    BMC Genomics

    (2008)
  • Cited by (35)

    • Chimeric Translation for Mitochondrial Peptides: Regular and Expanded Codons

      2019, Computational and Structural Biotechnology Journal
    View all citing articles on Scopus
    View full text