- Open Access
Codon usage in vertebrates is associated with a low risk of acquiring nonsense mutations
© Schmid and Flegel; licensee BioMed Central Ltd. 2011
Received: 31 May 2011
Accepted: 8 June 2011
Published: 8 June 2011
Codon usage in genomes is biased towards specific subsets of codons. Codon usage bias affects translational speed and accuracy, and it is associated with the tRNA levels and the GC content of the genome. Spontaneous mutations drive genomes to a low GC content. Active cellular processes are needed to maintain a high GC content, which influences the codon usage of a species. Loss-of-function mutations, such as nonsense mutations, are the molecular basis of many recessive alleles, which can greatly affect the genome of an organism and are the cause of many genetic diseases in humans.
We developed an event based model to calculate the risk of acquiring nonsense mutations in coding sequences. Complete coding sequences and genomes of 40 eukaryotes were analyzed for GC and CpG content, codon usage, and the associated risk of acquiring nonsense mutations. We included one species per genus for all eukaryotes with available reference sequence.
We discovered that the codon usage bias detected in genomes of high GC content decreases the risk of acquiring nonsense mutations (Pearson's r = -0.95; P < 0.0001). In the genomes of all examined vertebrates, including humans, this risk was lower than expected (0.93 ± 0.02; mean ± SD) and lower than the risk in genomes of non-vertebrates (1.02 ± 0.13; P = 0.019).
While the maintenance of a high GC content is energetically costly, it is associated with a codon usage bias harboring a low risk of acquiring nonsense mutations. The reduced exposure to this risk may contribute to the fitness of vertebrates.
Codon usage bias in genomes is relevant for organisms. It influences the translation speed and thus gene expression . Artificially deoptimized codon usage can decrease gene expression and create an attenuated viral virulence that may be used for vaccine production . HIV-1 modifies the tRNA pool of the infected cells to increase translation efficiency of its own genes . Initial studies on codon usage bias were based on few genes in single species: lists of the codon usage , determination of the number of codons used in genes , and models, such as the codon adaptation index (CAI). The CAI compared the codon usage of each gene with an "optimal" codon usage, which is inferred from high-expression gene sets . Whole genome sequencing data and newer algorithms have allowed researchers to overcome previous limitations, study more genes, and classify genes in more detailed categories . Codon usage bias is associated with tRNA concentration  and also the GC content of genomes [9–12].
Loss-of-function mutations, such as nonsense mutations, are the molecular basis of many recessive disorders, conditions that stem from non-functional gene products or, in case of null alleles, a lack of gene products. Nonsense mutations cause the premature stop of translation with shortened and often non-functional proteins. As part of the RNA surveillance, nonsense-mediated decay efficiently eliminates any mRNA that harbors nonsense mutations . For example, loss of tumor suppressor genes have been recognized as a key mechanism in many cancers . Retaining one functional allele of critical genes is essential for survival. Still, null alleles are common: the blood group O is a widely recognized and clinically relevant example . Rare null phenotypes of blood groups have been used to identify null alleles in large populations using routine clinical methods [16, 17].
We wondered if the codon usage bias in organisms is associated with a propensity of acquiring nonsense mutations. The consequence of a single nucleotide substitution, like a synonymous, missense or nonsense mutation, is intrinsic in the genetic code. Based on this association, we developed a method to calculate the risk of acquiring nonsense mutations in coding sequences (CDS) relative to an unbiased random codon usage. We applied this method to investigate the codon usage in the whole genome sequences of 40 eukaryotic species.
Risk of acquiring nonsense mutations
This F defines the risk of acquiring nonsense mutations for each species relative to the risk with an unbiased codon usage. With the intention to compare the risk of acquiring nonsense mutations among various species, we concluded that a random codon usage was the most neutral denominator. These calculations allowed a novel approach to study codon usage bias in whole genomes.
GC and CpG contents
GC content was calculated as C+G per total nucleotide count, and CpG content as number of CpG dinucleotides per total nucleotide count. The CpG content of genomes was comparable to the results of a recent in silico study  for Pan troglodytes, Mus musculus, Rattus norvegicus, Bos taurus, Canis lupus familiaris, and Danio rerio. Our calculated figures for CpG content match the data obtained by the original in vitro method [19, 20].
Database and species selection
The NCBI table Eukaryotic Genome Sequencing Projects (March 30, 2010)  was used to include all species with a genome status "complete" or "assembly" and an available RefSeq. We restricted analysis to one species per genus (Additional file 1, Figure S1 and Table S1). Sequence data represent NCBI RefSeq database release 40 (March 2010) for 39 species plus GRCh37.p2 (August 2010) for the human genome .
We developed a script driven software package, which parsed the genomic data (FASTA for nucleotide sequences and GenBank flatfile for meta-data including CDS definitions) and calculated the parameters defined in this study, in particular the stop risk factor F. In total, 145 GB of data were analyzed.
(i) Data selection. The whole genomes of the species were scanned by the software. Non-standard code sequences, in particular mitochondrial sequences, were excluded from analysis. (ii) Analysis of the whole genomes. Nucleotide count, GC content and CpG content were calculated for the genomic sequences of the analyzed species. Non-ACGT nucleotides (3.8%) were excluded. (iii) Analysis of CDS. CDS were used as defined in the RefSeq . CDS were excluded that were incomplete at their 5' or 3' end (4.2%) or contained errors (non-triplets 1.3%, no stop codon 0.5%, non-ACGT nucleotides 0.4%). If CDS were associated with an identical geneID, like in splice variants, the longest CDS was used and the alternate sequences (multiples, 13.0%) excluded (Additional file 1, Table S2). F, GC content, CpG content and relative codon collection usage were calculated for the CDS.
Results are shown as mean and standard deviation (mean ± SD) or 95% confidence interval (CI) based on the normal distribution, which was tested by D'Agostino-Pearson. We evaluated correlations by Pearson's correlation coefficient r and compared the GC content of CDS and genomes among species groups by two-sided Mann-Whitney U test. P < 0.05 was considered statistically significant. Statistical analysis was done with MedCalc (MedCalc Software, Mariakerke, Belgium).
Results and Discussion
We analyzed the whole genomes and CDS of 40 eukaryotes (Additional file 1, Tables S1 to S4) to determine the stop risk factor F using the propensity of each codon to acquire a nonsense mutation (Figure 1).
Risk F of acquiring nonsense mutations
F and GC content
The GC content of codons correlates with the overall GC content of the genomes in many species [9, 12, 24]. This was confirmed by our data (Additional file 1, Tables S3 and S4). Genes and gene families occur more frequently in genome regions with a high GC content [25, 26]. Both observations have been attributed to mechanisms that enrich the GC content, e.g. the increased recombination rates in GC rich regions . High GC content is also associated with increased gene density [28, 29], shorter introns [26, 28], and longer exons .
However, CpG hypermutability, a tenfold increased mutation risk at the position of CpG dinucleotides, causes genomes to drift from a high GC content to a high AT content [31, 32]. Active cellular processes are therefore needed to maintain a high GC content . Silencing of specific repair enzymes in S. typhimurium strains increases the mutation rate 6-fold to 100-fold with 98% of the mutations converting GC to AT; organisms with AT rich genomes have been explained by the lack of these repair enzymes . Despite knowing several mechanisms to increase and maintain a high GC content in a genome, the utility of a high GC content for an organism is not obvious. The maintenance of a high GC content costs energy and inflicts CpG hypermutability, but is associated with a low risk of acquiring nonsense mutations.
F and CpG content
F and codon usage
We show that the codon usage bias in genomes of high GC content is associated with a low risk of acquiring nonsense mutations. Despite their high GC content, the 10 vertebrate genomes had a low CpG content of < 0.04 (Figure 4). The low risk of acquiring nonsense mutations combined with a low exposure to CpG hypermutability  is unique in vertebrates. It was not a common feature in the 30 examined non-vertebrates. A low risk of acquiring nonsense mutations may have advantages for organisms with relatively long lifespans and small numbers of offspring.
Calculating F is a novel tool for addressing codon usage bias in genes and genomes. Here we applied this approach for comparing the whole genomes among species. F can be applied to study GC content shift within the genome of one species . F should also provide novel insights in the analysis of individual genes, like oncogenes and evolutionary conserved genes. Based on the fact that a very low F indicates a gene with a low risk of acquiring nonsense mutations, F may be used as a screening tool among the genes with presently unknown function. First, genes with a very low F may more likely belong to the set of crucial genes, whose loss is deleterious for an organism. Second, genes with a very high F may have a large number of null alleles in the population, which allows a wider variety of recessive alleles to become phenotypically expressed. Third, the fitness of a species is not just influenced by mutations in its germ line but also in the organism's somatic cells, which could be evaluated using our novel method.
We restricted our current approach to nonsense mutations. It is feasible to broaden our technique and to encompass missense mutations. While nonsense mutations are a more stringent criterion than missense mutations, more codon usage bias could be explained by including unfavorable non-conservative missense mutations in the analysis.
Conflict of interest disclosure
The authors declare that they have no competing interests.
Acknowledgements and Funding
We acknowledge the discussions with Franz F. Wagner in 1996 while working on Bombay blood group alleles  when the idea for this study was conceived. We thank Elizabeth Furlong and Michael J. Huvard for English editing. This research was supported by the Intramural Research Program of the NIH Clinical Center. PS was initially supported by a Swiss National Science Foundation fellowship (SNSF no. PBBEA-121056).
The views expressed do not necessarily represent the view of the National Institutes of Health, the Department of Health and Human Services, or the U.S. Federal Government.
- Fredrick K, Ibba M: How the sequence of a gene can tune its translation. Cell. 2010, 141: 227-229. 10.1016/j.cell.2010.03.033.PubMed CentralView ArticlePubMedGoogle Scholar
- Mueller S, Coleman JR, Papamichail D, Ward CB, Nimnual A, Futcher B, Skiena S, Wimmer E: Live attenuated influenza virus vaccines by computer-aided rational design. Nat Biotechnol. 2010, 28: 723-726. 10.1038/nbt.1636.PubMed CentralView ArticlePubMedGoogle Scholar
- van Weringh A, Ragonnet-Cronin M, Pranckeviciene E, Pavon-Eternod M, Kleiman L, Xia X: HIV-1 modulates the tRNA pool to improve translation efficiency. Mol Biol Evol. 2011Google Scholar
- Grantham R, Gautier C, Gouy M, Pavé A: Codon catalog usage and the genome hypothesis. Nucleic Acids Res. 1980, 8: r49-r62.PubMed CentralPubMedGoogle Scholar
- Wright F: The 'effective number of codons' used in a gene. Gene. 1990, 87: 23-29. 10.1016/0378-1119(90)90491-9.View ArticlePubMedGoogle Scholar
- Sharp PM, Li WH: The codon adaptation index - a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 1987, 15: 1281-1295. 10.1093/nar/15.3.1281.PubMed CentralView ArticlePubMedGoogle Scholar
- Davis JJ, Olsen GJ: Modal codon usage: assessing the typical codon usage of a genome. Mol Biol Evol. 2010, 27: 800-810. 10.1093/molbev/msp281.PubMed CentralView ArticlePubMedGoogle Scholar
- Ikemura T: Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system. J Mol Biol. 1981, 151: 389-409. 10.1016/0022-2836(81)90003-6.View ArticlePubMedGoogle Scholar
- Sharp PM, Li WH: An evolutionary perspective on synonymous codon usage in unicellular organisms. J Mol Evol. 1986, 24: 28-38. 10.1007/BF02099948.View ArticlePubMedGoogle Scholar
- Jørgensen FG, Schierup MH, Clark AG: Heterogeneity in regional GC content and differential usage of codons and amino acids in GC-poor and GC-rich regions of the genome of Apis mellifera. Mol Biol Evol. 2007, 24: 611-619.View ArticlePubMedGoogle Scholar
- Chen SL, Lee W, Hottes AK, Shapiro L, McAdams HH: Codon usage between genomes is constrained by genome-wide mutational processes. Proc Natl Acad Sci USA. 2004, 101: 3480-3485. 10.1073/pnas.0307827100.PubMed CentralView ArticlePubMedGoogle Scholar
- Lü H, Zhao WM, Zheng Y, Wang H, Qi M, Yu XP: Analysis of synonymous codon usage bias in Chlamydia. Acta Biochim Biophys Sin (Shanghai). 2005, 37: 1-10. 10.1093/abbs/37.1.1.View ArticleGoogle Scholar
- Hentze MW, Kulozik AE: A perfect message: RNA surveillance and nonsense-mediated decay. Cell. 1999, 96: 307-310. 10.1016/S0092-8674(00)80542-5.View ArticlePubMedGoogle Scholar
- Hahn WC, Dunn IF, Kim SY, Schinzel AC, Firestein R, Guney I, Boehm JS: Integrative genomic approaches to understanding cancer. Biochim Biophys Acta. 2009, 1790: 478-484.PubMed CentralView ArticlePubMedGoogle Scholar
- Yamamoto F, Clausen H, White T, Marken J, Hakamori S: Molecular genetic basis of the histo-blood group ABO system. Nature. 1990, 345: 229-233. 10.1038/345229a0.View ArticlePubMedGoogle Scholar
- Wagner FF, Flegel WA: Polymorphism of the h allele and the population frequency of sporadic nonfunctional alleles. Transfusion. 1997, 37: 284-290. 10.1046/j.1537-2995.1997.37397240210.x.View ArticlePubMedGoogle Scholar
- Wagner FF, Bittner R, Petershofen EK, Doescher A, Müller TH: Cost-efficient sequence-specific priming-polymerase chain reaction screening for blood donors with rare phenotypes. Transfusion. 2008, 48: 1169-1173. 10.1111/j.1537-2995.2008.01682.x.View ArticlePubMedGoogle Scholar
- Su J, Zhang Y, Lv J, Liu H, Tang X, Wang F, Qi Y, Feng Y, Li X: CpG_MI: a novel approach for identifying functional CpG islands in mammalian genomes. Nucleic Acids Res. 2010, 38: e6-10.1093/nar/gkp882.PubMed CentralView ArticlePubMedGoogle Scholar
- Josse J, Kaiser AD, Kornberg A: Enzymatic synthesis of deoxyribonucleic acid. VIII. Frequencies of nearest neighbor base sequences in deoxyribonucleic acid. J Biol Chem. 1961, 236: 864-875.PubMedGoogle Scholar
- Swartz MN, Trautner TA, Kornberg A: Enzymatic synthesis of deoxyribonucleic acid. XI. Further studies on nearest neighbor base sequences in deoxyribonucleic acids. J Biol Chem. 1962, 237: 1961-1967.PubMedGoogle Scholar
- Bird AP: DNA methylation and the frequency of CpG in animal DNA. Nucleic Acids Res. 1980, 8: 1499-1504. 10.1093/nar/8.7.1499.PubMed CentralView ArticlePubMedGoogle Scholar
- NCBI: Eukaryotic Genome Sequencing Projects. Internet. 2010, accessed on 03-30-2010, [http://www.ncbi.nlm.nih.gov/genomes/leuks.cgi]Google Scholar
- Pruitt KD, Katz KS, Sicotte H, Maglott DR: Introducing RefSeq and Entrez Gene: curated human genome resources at the NCBI. Trends Genet. 2000, 16: 44-47. 10.1016/S0168-9525(99)01882-X.View ArticlePubMedGoogle Scholar
- Hershberg R, Petrov DA: General rules for optimal codon choice. PLoS Genet. 2009, 5: e1000556-10.1371/journal.pgen.1000556.PubMed CentralView ArticlePubMedGoogle Scholar
- Zeeberg B: Shannon information theoretic computation of synonymous codon usage biases in coding regions of human and mouse genomes. Genome Res. 2002, 12: 944-955. 10.1101/gr.213402.PubMed CentralView ArticlePubMedGoogle Scholar
- Galtier N, Paganeau G, Mouchiroud D, Duret L: GC-content evolution in mammalian genomes: the biased gene conversion hypothesis. Genetics. 2001, 159: 907-911.PubMed CentralPubMedGoogle Scholar
- Fullerton SM, Carvalho AB, Clark AG: Local rates of recombination are positively correlated with GC content in the human genome. Mol Biol Evol. 2001, 18: 1139-1142.View ArticlePubMedGoogle Scholar
- Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C: Initial sequencing and analysis of the human genome. Nature. 2001, 409: 860-921. 10.1038/35057062.View ArticlePubMedGoogle Scholar
- Mouchiroud D, D'Onofrio G, Aïssani B, Macaya G, Gautier C, Bernardi G: The distribution of genes in the human genome. Gene. 1991, 100: 181-187.View ArticlePubMedGoogle Scholar
- Oliver JL, Marín A: A relationship between GC content and coding-sequence length. J Mol Evol. 1996, 43: 216-223. 10.1007/BF02338829.View ArticlePubMedGoogle Scholar
- Coulondre C, Miller JH, Farabaugh PJ, Gilbert W: Molecular basis of base substitution hotspots in Escherichia coli. Nature. 1978, 274: 775-780. 10.1038/274775a0.View ArticlePubMedGoogle Scholar
- Duncan BK, Miller JH: Mutagenic deamination of cytosine residues in DNA. Nature. 1980, 287: 560-561. 10.1038/287560a0.View ArticlePubMedGoogle Scholar
- Michaels ML, Cruz C, Grollman AP, Miller JH: Evidence that MutY and MutM combine to prevent mutations by an oxidatively damaged form of guanine in DNA. Proc Natl Acad Sci USA. 1992, 89: 7022-7025. 10.1073/pnas.89.15.7022.PubMed CentralView ArticlePubMedGoogle Scholar
- Lind PA, Andersson DI: Whole-genome mutational biases in bacteria. Proc Natl Acad Sci USA. 2008, 105: 17878-17883. 10.1073/pnas.0804445105.PubMed CentralView ArticlePubMedGoogle Scholar
- Gilchrist MA, Shah P, Zaretzki R: Measuring and detecting molecular adaptation in codon usage against nonsense errors during protein translation. Genetics. 2009, 183: 1493-1505. 10.1534/genetics.109.108209.PubMed CentralView ArticlePubMedGoogle Scholar
- Luettringhaus TA, Cho D, Ryang DW, Flegel WA: An easy RHD genotyping strategy for D- East Asian persons applied to Korean blood donors. Transfusion. 2006, 46: 2128-2137. 10.1111/j.1537-2995.2006.01042.x.View ArticlePubMedGoogle Scholar
- Chamary JV, Parmley JL, Hurst LD: Hearing silence: non-neutral evolution at synonymous sites in mammals. Nat Rev Genet. 2006, 7: 98-108. 10.1038/nrg1770.View ArticlePubMedGoogle Scholar
- Misawa K, Kikuno RF: Evaluation of the effect of CpG hypermutability on human codon substitution. Gene. 2009, 431: 18-22. 10.1016/j.gene.2008.11.006.View ArticlePubMedGoogle Scholar
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