Abstract
Codon usage bias in prokaryotic genomes is largely a consequence of background substitution patterns in DNA, but highly expressed genes may show a preference towards codons that enable more efficient and/or accurate translation. We introduce a novel approach based on supervised machine learning that detects effects of translational selection on genes, while controlling for local variation in nucleotide substitution patterns represented as sequence composition of intergenic DNA. A cornerstone of our method is a Random Forest classifier that outperformed previous distance measure-based approaches, such as the codon adaptation index, in the task of discerning the (highly expressed) ribosomal protein genes by their codon frequencies. Unlike previous reports, we show evidence that translational selection in prokaryotes is practically universal: in 460 of 461 examined microbial genomes, we find that a subset of genes shows a higher codon usage similarity to the ribosomal proteins than would be expected from the local sequence composition. These genes constitute a substantial part of the genome—between 5% and 33%, depending on genome size—while also exhibiting higher experimentally measured mRNA abundances and tending toward codons that match tRNA anticodons by canonical base pairing. Certain gene functional categories are generally enriched with, or depleted of codon-optimized genes, the trends of enrichment/depletion being conserved between Archaea and Bacteria. Prominent exceptions from these trends might indicate genes with alternative physiological roles; we speculate on specific examples related to detoxication of oxygen radicals and ammonia and to possible misannotations of asparaginyl–tRNA synthetases. Since the presence of codon optimizations on genes is a valid proxy for expression levels in fully sequenced genomes, we provide an example of an “adaptome” by highlighting gene functions with expression levels elevated specifically in thermophilic Bacteria and Archaea.
Highlights
Due to non-random use of synonymous codons, protein coding sequences contain a layer of information on the DNA level that is not reflected at the protein sequence level
We show that the gene functional category has a great bearing on whether that gene is subject to translational selection
We introduce a supervised machine learning-based computational framework that couples a classifier to standard statistical tests, an approach that exhibits an increased accuracy over commonly used unsupervised techniques, and the ability to control for a strong confounding factor – the nucleotide substitution patterns – that shape codon usage, but in a manner not related to protein translation
Summary
Due to non-random use of synonymous codons, protein coding sequences contain a layer of information on the DNA level that is not reflected at the protein sequence level. There is significant variation in direction and strength of these nucleotide substitution biases along the prokaryotic chromosome [3] with a general tendency toward A+T-enrichment near the replication terminus Another common intra-genomic trend in nucleotide composition concerns the distinction between the two DNA strands where the leading strand is ‘GC-skewed’, i.e. enriched in G over C and T over A [4] mostly due to deamination of cytosine in single-stranded DNA exposed during replication. Such biases in mutational processes may result from the nature of chemical changes to the nucleotides, and from biases in errors of DNA replication and repair, and appear to be an important contribution to the background substitution patterns. We refer the reader to a review of the organizational features of prokaryotic genomes with respect to local sequence composition and gene distribution [7]
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