The Selective Advantage of Synonymous Codon Usage Bias in Salmonella

The genetic code in mRNA is redundant, with 61 sense codons translated into 20 different amino acids. Individual amino acids are encoded by up to six different codons but within codon families some are used more frequently than others. This phenomenon is referred to as synonymous codon usage bias. The genomes of free-living unicellular organisms such as bacteria have an extreme codon usage bias and the degree of bias differs between genes within the same genome. The strong positive correlation between codon usage bias and gene expression levels in many microorganisms is attributed to selection for translational efficiency. However, this putative selective advantage has never been measured in bacteria and theoretical estimates vary widely. By systematically exchanging optimal codons for synonymous codons in the tuf genes we quantified the selective advantage of biased codon usage in highly expressed genes to be in the range 0.2–4.2 x 10⁻⁴ per codon per generation. These data quantify for the first time the potential for selection on synonymous codon choice to drive genome-wide sequence evolution in bacteria, and in particular to optimize the sequences of highly expressed genes. This quantification may have predictive applications in the design of synthetic genes and for heterologous gene expression in biotechnology.     

Correlation between gene expression profiles and protein-protein interactions within and across genomes

MOTIVATION: Function annotation of an unclassified protein on the basis of its interaction partners is well documented in the literature. Reliable predictions of interactions from other data sources such as gene expression measurements would provide a useful route to function annotation. We investigate the global relationship of protein-protein interactions with gene expression. This relationship is studied in four evolutionarily diverse species, for which substantial information regarding their interactions and expression is available: human, mouse, yeast and Escherichia coli. RESULTS: In E.coli the expression of interacting pairs is highly correlated in comparison to random pairs, while in the other three species, the correlation of expression of interacting pairs is only slightly stronger than that of random pairs. To strengthen the correlation, we developed a protocol to integrate ortholog information into the interaction and expression datasets. In all four genomes, the likelihood of predicting protein interactions from highly correlated expression data is increased using our protocol. In yeast, for example, the likelihood of predicting a true interaction, when the correlation is > 0.9, increases from 1.4 to 9.4. The improvement demonstrates that protein interactions are reflected in gene expression and the correlation between the two is strengthened by evolution information. The results establish that co-expression of interacting protein pairs is more conserved than that of random ones.     

Protein Evolutionary Rates Correlate with Expression Independently of Synonymous Substitutions in Helicobacter pylori

In free-living microorganisms, such as Escherichia coli and Saccharomyces cerevisiae, both synonymous and nonsynonymous substitution frequencies correlate with expression levels. Here, we have tested the hypothesis that the correlation between amino acid substitution rates and expression is a by-product of selection for codon bias and translational efficiency in highly expressed genes. To this end, we have examined the correlation between protein evolutionary rates and expression in the human gastric pathogen Helicobacter pylori, where the absence of selection on synonymous sites enables the two types of substitutions to be uncoupled. The results revealed a statistically significant negative correlation between expression levels and nonsynonymous substitutions in both H. pylori and E. coli. We also found that neighboring genes located on the same, but not on opposite strands, evolve at significantly more similar rates than random gene pairs, as expected by co-expression of genes located in the same operon. However, the two species differ in that synonymous substitutions show a strand-specific pattern in E. coli, whereas the weak similarity in synonymous substitutions for neighbors in H. pylori is independent of gene orientation. These results suggest a direct influence of expression levels on nonsynonymous substitution frequencies independent of codon bias and selective constraints on synonymous sites. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users. [Reviewing Editor: Dr. Nicolas Galtier]     

Unusual codon usage bias in low expression genes of Vibrio cholerae

Positive correlation between gene expression and synonymous codon usage bias is well documented in the literature. However, in the present study of Vibrio cholerae genome, we have identified a group of genes having unusually high codon usage bias despite being low potential expressivity. Our results suggest that codon usage in lowly expressed genes might also be selected on to preferably use non-optimal codons to maintain a low cellular concentration of the proteins that they encode. This would predict that lowly expressed genes are also biased in codon usage, but in a way that is opposite to the bias of highly expressed genes.     

Predicting gene expression level from codon usage bias

The "expression measure" of a gene, E(g), is a statistic devised to predict the level of gene expression from codon usage bias. E(g) has been used extensively to analyze prokaryotic genome sequences. We discuss 2 problems with this approach. First, the formulation of E(g) is such that genes with the strongest selected codon usage bias are not likely to have the highest predicted expression levels; indeed the correlation between E(g) and expression level is weak among moderate to highly expressed genes. Second, in some species, highly expressed genes do not have unusual codon usage, and so codon usage cannot be used to predict expression levels. We outline a simple approach, first to check whether a genome shows evidence of selected codon usage bias and then to assess the strength of bias in genes as a guide to their likely expression level; we illustrate this with an analysis of Shewanella oneidensis.     

Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models

Highly expressed genes in many bacteria and small eukaryotes often have a strong compositional bias, in terms of codon usage. Two widely used numerical indices, the codon adaptation index (CAI) and the codon usage, use this bias to predict the expression level of genes. When these indices were first introduced, they were based on fairly simple assumptions about which genes are most highly expressed: the CAI was originally based on the codon composition of a set of only 24 highly expressed genes, and the codon usage on assumptions about which functional classes of genes are highly expressed in fast-growing bacteria. Given the recent advent of genome-wide expression data, we should be able to improve on these assumptions. Here, we measure, in yeast, the degree to which consideration of the current genome-wide expression data sets improves the performance of both numerical indices. Indeed, we find that by changing the parameterization of each model its correlation with actual expression levels can be somewhat improved, although both indices are fairly insensitive to the exact way they are parameterized. This insensitivity indicates a consistent codon bias amongst highly expressed genes. We also attempt direct linear regression of codon composition against genome-wide expression levels (and protein abundance data). This has some similarity with the CAI formalism and yields an alternative model for the prediction of expression levels based on the coding sequences of genes. More information is available at http://bioinfo.mbb.yale.edu/expression/codons.     

Evaluation of Reference Genes for RT-qPCR Expression Studies in Hop (Humulus lupulus L.) during Infection with Vascular Pathogen Verticillium albo-atrum

Hop plant (Humulus lupulus L.), cultivated primarily for its use in the brewing industry, is faced with a variety of diseases, including severe vascular diseases, such as Verticillium wilt, against which no effective protection is available. The understanding of disease resistance with tools such as differentially expressed gene studies is an important objective of plant defense mechanisms. In this study, we evaluated twenty-three reference genes for RT-qPCR expression studies on hop under biotic stress conditions. The candidate genes were validated on susceptible and resistant hop cultivars sampled at three different time points after infection with Verticillium albo-atrum. The stability of expression and the number of genes required for accurate normalization were assessed by three different Excel-based approaches (geNorm v.3.5 software, NormFinder, and RefFinder). High consistency was found among them, identifying the same six best reference genes (YLS8, DRH1, TIP41, CAC, POAC and SAND) and five least stably expressed genes (CYCL, UBQ11, POACT, GAPDH and NADH). The candidate genes in different experimental subsets/conditions resulted in different rankings. A combination of the two best reference genes, YLS8 and DRH1, was used for normalization of RT-qPCR data of the gene of interest (PR-1) implicated in biotic stress of hop. We outlined the differences between normalized and non-normalized values and the importance of RT-qPCR data normalization. The high correlation obtained among data standardized with different sets of reference genes confirms the suitability of the reference genes selected for normalization. Lower correlations between normalized and non-normalized data may reflect different quantity and/or quality of RNA samples used in RT-qPCR analyses.     

Codon usage and amino acid usage influence genes expression level

Highly expressed genes in any species differ in the usage frequency of synonymous codons. The relative recurrence of an event of the favored codon pair (amino acid pairs) varies between gene and genomes due to varying gene expression and different base composition. Here we propose a new measure for predicting the gene expression level, i.e., codon plus amino bias index (CABI). Our approach is based on the relative bias of the favored codon pair inclination among the genes, illustrated by analyzing the CABI score of the Medicago truncatula genes. CABI showed strong correlation with all other widely used measures (CAI, RCBS, SCUO) for gene expression analysis. Surprisingly, CABI outperforms all other measures by showing better correlation with the wet-lab data. This emphasizes the importance of the neighboring codons of the favored codon in a synonymous group while estimating the expression level of a gene.     

Identification of Most Stable Endogenous Control Genes for MicroRNA Quantification in the Developing Mouse Lung

MicroRNAs (miRNAs) are endogenous small non coding RNAs acting as negative regulators. miRNA are involved in lung development and pulmonary diseases. Measurement of their levels by qPCR is directly influenced by the stability of normalization gene(s), which can be affected by the experimental conditions. The developing lung is a changing tissue and one normalization gene showing stability on one developmental day may be modulated over time. Moreover, some developmental events are affected by sex, which also has to be considered. In this study, we compared stability of five putative control genes in the lung between sexes from the pseudoglandular to the alveolar stages and in adult lungs. Expression of sno135, sno142, sno202, sno234, and sno251 was studied by qPCR in male and female lung samples collected at seven time points from GD 15.5 to PN 30. Cq values of sno251 showed the highest variation across the different developmental stages, while sno234 was the most stable gene. Gene expression stability was studied by geNorm, NormFinder and BestKeeper. Our data showed that ranking of genes based on expression stability changed according to developmental time and sex. sno135/sno234 and sno142/sno234 were proposed as best combinations of normalization genes when both sexes and all the studied developmental stages are considered. Normalization of let7-a RNA levels with different pairs of control genes proposed by geNorm and NormFinder gave similar data, while the use of less stable genes introduced a statistically significant difference on PN 0. In conclusion, variations in stability of normalization gene expression are observed over time and according to sex during lung development. Best pairs of normalization genes are presented for specific developmental stages, and for the period extending from the pseudoglandular to the alveolar stages. The use of normalization genes selected for their expression stability is essential in lung development studies.     

Transfer RNA Gene Numbers may not be Completely Responsible for the Codon Usage Bias in Asparagine, Isoleucine, Phenylalanine, and Tyrosine in the High Expression Genes in Bacteria

It is generally believed that the effect of translational selection on codon usage bias is related to the number of transfer RNA genes in bacteria, which is more with respect to the high expression genes than the whole genome. Keeping this in the background, we analyzed codon usage bias with respect to asparagine, isoleucine, phenylalanine, and tyrosine amino acids. Analysis was done in seventeen bacteria with the available gene expression data and information about the tRNA gene number. In most of the bacteria, it was observed that codon usage bias and tRNA gene number were not in agreement, which was unexpected. We extended the study further to 199 bacteria, limiting to the codon usage bias in the two highly expressed genes rpoB and rpoC which encode the RNA polymerase subunits β and β′, respectively. In concordance with the result in the high expression genes, codon usage bias in rpoB and rpoC genes was also found to not be in agreement with tRNA gene number in many of these bacteria. Our study indicates that tRNA gene numbers may not be the sole determining factor for translational selection of codon usage bias in bacterial genomes.     

Expressed Repeat Elements Improve RT-qPCR Normalization across a Wide Range of Zebrafish Gene Expression Studies

The selection and validation of stably expressed reference genes is a critical issue for proper RT-qPCR data normalization. In zebrafish expression studies, many commonly used reference genes are not generally applicable given their variability in expression levels under a variety of experimental conditions. Inappropriate use of these reference genes may lead to false interpretation of expression data and unreliable conclusions. In this study, we evaluated a novel normalization method in zebrafish using expressed repetitive elements (ERE) as reference targets, instead of specific protein coding mRNA targets. We assessed and compared the expression stability of a number of EREs to that of commonly used zebrafish reference genes in a diverse set of experimental conditions including a developmental time series, a set of different organs from adult fish and different treatments of zebrafish embryos including morpholino injections and administration of chemicals. Using geNorm and rank aggregation analysis we demonstrated that EREs have a higher overall expression stability compared to the commonly used reference genes. Moreover, we propose a limited set of ERE reference targets (hatn10, dna15ta1 and loopern4), that show stable expression throughout the wide range of experiments in this study, as strong candidates for inclusion as reference targets for qPCR normalization in future zebrafish expression studies. Our applied strategy to find and evaluate candidate expressed repeat elements for RT-qPCR data normalization has high potential to be used also for other species.     

Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome

Escherichia coli has long been regarded as a model organism in the study of codon usage bias (CUB). However, most studies in this organism regarding this topic have been computational or, when experimental, restricted to small datasets; particularly poor attention has been given to genes with low CUB. In this work, correspondence analysis on codon usage is used to classify E.coli genes into three groups, and the relationship between them and expression levels from microarray experiments is studied. These groups are: group 1, highly biased genes; group 2, moderately biased genes; and group 3, AT-rich genes with low CUB. It is shown that, surprisingly, there is a negative correlation between codon bias and expression levels for group 3 genes, i.e. genes with extremely low codon adaptation index (CAI) values are highly expressed, while group 2 show the lowest average expression levels and group 1 show the usual expected positive correlation between CAI and expression. This trend is maintained over all functional gene groups, seeming to contradict the E.coli-yeast paradigm on CUB. It is argued that these findings are still compatible with the mutation-selection balance hypothesis of codon usage and that E.coli genes form a dynamic system shaped by these factors.