Expression pattern and, surprisingly, gene length shape codon usage inCaenorhabditis,Drosophila, andArabidopsis

Abstract

We measured the expression pattern and analyzed codon usage in 8,133, 1,550, and 2,917 genes, respectively, from Caenorhabditis elegans, Drosophila melanogaster, and Arabidopsis thaliana. In those three species, we observed a clear correlation between codon usage and gene expression levels and showed that this correlation is not due to a mutational bias. This provides direct evidence for selection on silent sites in those three distantly related multicellular eukaryotes. Surprisingly, there is a strong negative correlation between codon usage and protein length. This effect is not due to a smaller size of highly expressed proteins. Thus, for a same-expression pattern, the selective pressure on codon usage appears to be lower in genes encoding long rather than short proteins. This puzzling observation is not predicted by any of the current models of selection on codon usage and thus raises the question of how translation efficiency affects fitness in multicellular organisms.

Codon Bias and Noncoding GC Content Correlate Negatively with Recombination Rate on the Drosophila X Chromosome

“…Our data revealed several shared correlations between the X chromosome and the autosomes, with respect to both the magnitude of the correlation and the direction ( Table 2). As previously reported (Bulmer 1988;Duret and Mouchiroud 1999;Hey and Kliman 2002;Sharp and Li 1986), codon bias is significantly positively correlated with expression level (Kendall partial correlation s = 0.234 and 0.221 for autosomes and X, respectively, Bonferroni-corrected p << 0.0001, both comparisons), which likely reflects increased selective benefits of translational efficiency for highly expressed genes. In addition, we confirm the negative correlation between protein length and codon bias (Kendall partial correlation s = )0.315 and )0.321 for autosomes and X, respectively.…”

“…For some genes, there were several transcripts listed; for these genes, we only included the first transcript listed in our data set, and both the protein length and codon bias estimates are based solely on this first listed transcript. For each gene, we calculated the frequency of optimal codons (FOP) based on optimal codons as defined by Duret and colleagues (Duret and Mouchiroud 1999).…”

“…Bonferroni-corrected p << 0.0001, both comparisons). This may be because increased codon bias has stronger effects on translational efficiency in short genes as opposed to long genes (Akashi 1996;Duret and Mouchiroud 1999;EyreWalker 1996; but could alternatively result from Hill-Robertson interference (Comeron et al 1999).…”

The complete mitochondrial genome of Cephalothrix simula (Iwata) (Nemertea: Palaeonemertea)

“…Synonymous codons end in T more frequently than C within the AT-rich group: Phe (111, 12.74%; TTC, 0.92%), He (ATT, 6.65%; ATC, 0.38%), Tyr (TAT, 4.15%; TAC, 0.46%), and Asn (AAT, 2.96%; AAC, 0.30%). As preferred usage of synonymous codons is proposed to be highest in gene regions of functional significance, codon bias is believed to be related mainly to selection at silent sites and proposed to enhance translation efficiency (Sharp and Matassi, 1994;Durent and Mouchiroud, 1999). However, the extent to which synonymous codon usage is determined by selection, or even whether this plays a role in animal mitochondrial systems is unclear (Helfenbein et al, 2001).…”

Gene algebra from a genetic code algebraic structure

“…Let us suppose that, with successive mutations of this, it is turned out into the sequence β=UGUAUAAGUCAG. If the codons values are taken from the primal algebra (Table 2) these sequences correspond in the Z 64 -algebra of (C g ) 4 to the vectors α= (15,50,35,9) and β=(47, 48, 35, 6). As can be seen in the Table 4 for all vector components the inequalities Automorphism representing matrices give us additional information about the mutational pathway in the N-dimensional space.…”