Genome-Wide Patterns of Codon Bias Are Shaped by Natural Selection in the Purple Sea Urchin,Strongylocentrotus purpuratus

Abstract

Codon usage bias has been documented in a wide diversity of species, but the relative contributions of mutational bias and various forms of natural selection remain unclear. Here, we describe for the first time genome-wide patterns of codon bias at 4623 genes in the purple sea urchin, Strongylocentrotus purpuratus. Preferred codons were identified at 18 amino acids that exclusively used G or C at third positions, which contrasted with the strong AT bias of the genome (overall GC content is 36.9%). The GC content of third positions and coding regions exhibited significant correlations with the magnitude of codon bias. In contrast, the GC content of introns and flanking regions was indistinguishable from the genome-wide background, which suggested a limited contribution of mutational bias to synonymous codon usage. Five distinct clusters of genes were identified that had significantly different synonymous codon usage patterns. A significant correlation was observed between codon bias and mRNA expression supporting translational selection, but this relationship was driven by only one highly biased cluster that represented only 8.6% of all genes. In all five clusters preferred codons were evolutionarily conserved to a similar degree despite differences in their synonymous codon usage distributions and magnitude of codon bias. The third positions of preferred codons in two codon usage groups also paired significantly more often in stems than in loops of mRNA secondary structure predictions, which suggested that codon bias might also affect mRNA stability. Our results suggest that mutational bias has played a minor role in determining codon bias in S. purpuratus and that preferred codon usage may be heterogeneous across different genes and subject to different forms of natural selection.

Functional insights into the testis transcriptome of the edible sea urchin Loxechinus albus

“…For instance, the GC content of the testis transcriptome of L. albus (40.4%) was closer to the testis (40.9%) and ovary (42.1%) transcriptomes of the black sea urchin Arbacia lixula 32, and showed slightly higher values in relation to the transcriptomes of the Antarctic sea urchin Sterechinus neumayeri (38.6%)15, the purple sea urchin, S. purpuratus (36.9%)33, and the kina sea urchin Evechinus chloroticus (39%)34. Moreover, in terms of the number of assembled unigene sequences, our de novo assembled transcriptome was within the range reported in other 454 sequencing projects of sea urchins15, sea cucumbers35, and sea stars36.…”

Genome-wide signals of positive selection in strongylocentrotid sea urchins

“…Codon bias and non-random patterns of synonymous codon usage have been recently described in S. purpuratus [46] that, if present in other species, has the potential to bias d S and d N /d S ratios. However, measures of codon bias and GC content across the protein-coding gene regions were indistinguishable between the PSGs and those not exhibiting positive selection (Additional file 1: Table S7, and Additional file 2: Figure S3).…”

Effective population size does not predict codon usage bias in mammals

“…This phenomenon has been termed codon usage bias (CUB), and many studies support a role of natural selection in this phenomenon (Shields et al 1988; Moriyama and Hartl 1993; Akashi et al 1998; Comeron and Kreitman 1998; Chamary et al 2006; Plotkin and Kudla 2011; Waldman et al 2011; Behura et al 2013; Kober and Pogson 2013). Proposed mechanisms influencing CUB include translational efficiency (Grantham et al 1981; Ikemura 1985; Bulmer 1991; Carlini and Stephan 2003; Rocha 2004; Stoletzki and Eyre-Walker 2007; Parmley and Huynen 2009; Hense 2010; Ran and Higgs 2010, 2012; Sharp et al 2010; Behura and Severson 2011; Shah and Gilchrist 2011; Qian et al 2012; Agashe et al 2013; Lawrie et al 2013; Michely 2013), mRNA stability or folding (Moriyama and Powell 1998; dos Reis et al 2004; Chamary and Hurst 2005; Chamary et al 2006; Novoa and Ribas de Pouplana 2012; Kober and Pogson 2013; Shabalina et al 2013), transcription factor binding (Stergachis 2013), overlap with other functional elements in the genome (Lin 2011), and/or a trade-off between rapid versus accurate translation (Yang et al 2014). …”