Dataset Information

The signatures of selection for translational accuracy in plant genes.

ABSTRACT: Little is known about the natural selection of synonymous codons within the coding sequences of plant genes. We analyzed the distribution of synonymous codons within plant coding sequences and found that preferred codons tend to encode the more conserved and functionally important residues of plant proteins. This was consistent among several synonymous codon families and applied to genes with different expression profiles and functions. Most of the randomly chosen alternative sets of codons scored weaker associations than the actual sets of preferred codons, suggesting that codon position within plant genes and codon usage bias have coevolved to maximize translational accuracy. All these findings are consistent with the mistranslation-induced protein misfolding theory, which predicts the natural selection of highly preferred codons more frequently at sites where translation errors could compromise protein folding or functionality. Our results will provide an important insight in future studies of protein folding, molecular evolution, and transgene design for optimal expression.

SUBMITTER: Porceddu A

PROVIDER: S-EPMC3698923 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The signatures of selection for translational accuracy in plant genes.

Porceddu Andrea A Zenoni Sara S Camiolo Salvatore S

Genome biology and evolution 20130101 6

Little is known about the natural selection of synonymous codons within the coding sequences of plant genes. We analyzed the distribution of synonymous codons within plant coding sequences and found that preferred codons tend to encode the more conserved and functionally important residues of plant proteins. This was consistent among several synonymous codon families and applied to genes with different expression profiles and functions. Most of the randomly chosen alternative sets of codons scor ...[more]

PMID: 23695187

Similar Datasets

Project description:Gene transfer agents (GTAs) are virus-like elements that are encoded by some bacterial and archaeal genomes. The production of GTAs can be induced by carbon depletion and results in host lysis and the release of virus-like particles that contain mostly random fragments of the host DNA. The remaining members of a GTA-producing population act as GTA recipients by producing proteins needed for GTA-mediated DNA acquisition. Here, we detected a codon usage bias toward codons with more readily available tRNAs in the RcGTA-like GTA genes of alphaproteobacterial genomes. Such bias likely improves the translational efficacy during GTA gene expression. While the strength of codon usage bias fluctuates substantially among individual GTA genes and across taxonomic groups, it is especially pronounced in Sphingomonadales, whose members are known to inhabit nutrient-depleted environments. By screening genomes for gene families with trends in codon usage biases similar to those in GTA genes, we found a gene that likely encodes head completion protein in some GTAs where it appeared missing, and 13 genes previously not implicated in the GTA life cycle. The latter genes are involved in various molecular processes, including the homologous recombination and transport of scarce organic matter. Our findings provide insights into the role of selection for translational efficiency in the evolution of GTA genes and outline genes that are potentially involved in the previously hypothesized integration of GTA-delivered DNA into the host genome. IMPORTANCE Horizontal gene transfer (HGT) is a fundamental process that drives evolution of microorganisms. HGT can result in a rapid dissemination of beneficial genes within and among microbial communities and can be achieved via multiple mechanisms. One peculiar HGT mechanism involves viruses "domesticated" by some bacteria and archaea (their hosts). These so-called gene transfer agents (GTAs) are encoded in hosts' genomes, produced under starvation conditions, and cannot propagate themselves as viruses. We show that GTA genes are under selection to improve the efficiency of their translation when the host activates GTA production. The selection is especially pronounced in bacteria that occupy nutrient-depleted environments. Intriguingly, several genes involved in incorporation of DNA into a genome are under similar selection pressure, suggesting that they may facilitate the integration of GTA-delivered DNA into the host genome. Our findings underscore the potential importance of GTAs as a mechanism of HGT under nutrient-limited conditions, which are widespread in microbial habitats.

Project description:BACKGROUND: Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability. RESULTS: We applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV) classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature. CONCLUSIONS: Evaluation of a gene's contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive feature space that includes possible interactions of many genes. Performance of the algorithm applied to 9 datasets suggests that it is possible to improve the accuracy of cancer classification by a big margin when joint effects of many genes are considered.

Dataset Information

The signatures of selection for translational accuracy in plant genes.

Publications

The signatures of selection for translational accuracy in plant genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets