Analysis of codon usage pattern in the radioresistant bacterium Deinococcus radiodurans

The main factors shaping codon usage bias in the Deinococcus radiodurans genome were reported. Correspondence analysis (COA) was carried out to analyze synonymous codon usage bias. The results showed that the main trend was strongly correlated with gene expression level assessed by the "Codon Adaptation Index" (CAI) values, a result that was confirmed by the distribution of genes along the first axis. The results of correlation analysis, variance analysis and neutrality plot indicated that gene nucleotide composition was clearly contributed to codon bias. CDS length was also key factor in dictating codon usage variation. A general tendency of more biased codon usage of genes with longer CDS length to higher expression level was found. Further, the hydrophobicity of each protein also played a role in shaping codon usage in this organism, which could be confirmed by the significant correlation between the positions of genes placed on the first axis and the hydrophobicity values (r=-0.100, P<0.01). In summary, gene expression level played a crucial role, nucleotide mutational bias, CDS length and the hydrophobicity of each protein just in a minor way in shaping the codon usage pattern of D. radiodurans. Notably, 19 codons firstly defined as "optimal codons" may provide useful clues for molecular genetic engineering and evolutionary studying.     

Codon Bias is a Major Factor Explaining Phage Evolution in Translationally Biased Hosts

The size and diversity of bacteriophage populations require methodologies to quantitatively study the landscape of phage differences. Statistical approaches are confronted with small genome sizes forbidding significant single-phage analysis, and comparative methods analyzing full phage genomes represent an alternative but they are of difficult interpretation due to lateral gene transfer, which creates a mosaic spectrum of related phage species. Based on a large-scale codon bias analysis of 116 DNA phages hosted by 11 translationally biased bacteria belonging to different phylogenetic families, we observe that phage genomes are almost always under codon selective pressure imposed by translationally biased hosts, and we propose a classification of phages with translationally biased hosts which is based on adaptation patterns. We introduce a computational method for comparing phages sharing homologous proteins, possibly accepted by different hosts. We observe that throughout phages, independently from the host, capsid genes appear to be the most affected by host translational bias. For coliphages, genes involved in virion morphogenesis, host interaction and ssDNA binding are also affected by adaptive pressure. Adaptation affects long and small phages in a significant way. We analyze in more detail the Microviridae phage space to illustrate the potentiality of the approach. The small number of directions in adaptation observed in phages grouped around ϕX174 is discussed in the light of functional bias. The adaptation analysis of the set of Microviridae phages defined around ϕMH2K shows that phage classification based on adaptation does not reflect bacterial phylogeny. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Computational Prediction of Genomic Functional Cores Specific to Different Microbes

Computational and experimental attempts tried to characterize a universial core of genes representing the minimal set of functional needs for an organism. Based on the increasing number of available complete genomes, comparative genomics has concluded that the universal core contains < 50 genes. In contrast, experiments suggest a much larger set of essential genes (certainly more than several hundreds, even under the most restrictive hypotheses) that is dependent on the biological complexity and environmental specificity of the organism. Highly biased genes, which are generally also the most expressed in translationally biased organisms, tend to be over represented in the class of genes deemed to be essential for any given bacterial species. This association is far from perfect; nevertheless, it allows us to propose a new computational method to detect, to a certain extent, ubiquitous genes, nonorthologous genes, environment-specific genes, genes involved in the stress response, and genes with no identified function but highly likely to be essential for the cell. Most of these groups of genes cannot be identified with previously attempted computational and experimental approaches. The large variety of life-styles and the unusually detectable functional signals characterizing translationally biased organisms suggest using them as reference organisms to infer essentiality in other microbial species. The case of small parasitic genomes is discussed. Data issued by the analysis are compared with previous computational and experimental studies. Results are discussed both on methodological and biological grounds. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users. [Reviewing Editor: Martin Kreitman]     

Are codon usage patterns in unicellular organisms determined by selection-mutation balance?

Strongly biased codon usage is common in unicellular organisms, particularly in highly expressed genes. The bias is most simply explained as a balance between selection and mutation, with selection favouring those codons which are more efficiently translated. In a review Ikemura (1985) has proposed four rules for predicting which codons will be preferred, based on the properties of the transfer RNAs responsible for translating messenger RNA into protein. In this paper codon usage in E. coli and yeast is re-examined using the recent compilation of Maruyama et al. (1986). The codon adaptation index of Sharp and Li (1986a) is used as a measure of gene expression to investigate the importance of this factor. It is found that Ikemura's rules successfully predict preferred codons for yeast, but that two of them work less well for E. coli, and it is suggested that some of the apparent bias in weakly expressed genes of E. coli may be due to contextual effects on mutation rates.     

A dissection of volatility in yeast

It has been suggested that volatility, the proportion of mutations which change an amino acid, can be used to infer the level of natural selection acting upon a gene. This conjecture is supported by a correlation between volatility and the rate of nonsynonymous substitution (dN), or the ratio of nonsynonymous and synonymous substitution rates, in a variety of organisms. These organisms include yeast, in which the correlations are quite strong. Here we show that these correlations are a by-product of a correlation between synonymous codon bias toward translationally optimal codons and dN. Although this analysis suggests that volatility is not a good measure of the selection, we suggest that it might be possible to infer something about the level of natural selection, from a single genome sequence, using translational codon bias.     

Studies on codon usage in Entamoeba histolytica

Codon usage bias of Entamoeba histolytica, a protozoan parasite, was investigated using the available DNA sequence data. Entamoeba histolytica having AT rich genome, is expected to have A and/or T at the third position of codons. Overall codon usage data analysis indicates that A and/or T ending codons are strongly biased in the coding region of this organism. However, multivariate statistical analysis suggests that there is a single major trend in codon usage variation among the genes. The genes which are supposed to be highly expressed are clustered at one end, while the majority of the putatively lowly expressed genes are clustered at the other end. The codon usage pattern is distinctly different in these two sets of genes. C ending codons are significantly higher in the putatively highly expressed genes suggesting that C ending codons are translationally optimal in this organism. In the putatively lowly expressed genes A and/or T ending codons are predominant, which suggests that compositional constraints are playing the major role in shaping codon usage variation among the lowly expressed genes. These results suggest that both mutational bias and translational selection are operational in the codon usage variation in this organism.     

Schematic for efficient computation of GC, GC3, and AT3 bias spectra of genome

Selection of synonymous codons for an amino acid is biased in protein translation process. This biased selection causes repetition of synonymous codons in structural parts of genome that stands for high N/3 peaks in DNA spectrum. Period-3 spectral property is utilized here to produce a 3-phase network model based on polyphase filterbank concepts for derivation of codon bias spectra (CBS). Modification of parameters in this model can produce GC, GC3, and AT3 bias spectra. Complete schematic in LabVIEW platform is presented here for efficient and parallel computation of GC, GC3, and AT3 bias spectra of genomes alongwith results of CBS patterns. We have performed the correlation coefficient analysis of GC, GC3, and AT3 bias spectra with codon bias patterns of CBS for biological and statistical significance of this model.     

Absence of Classical Heat Shock Response in the Citrus Pathogen <Emphasis Type="Italic">Xylella fastidiosa</Emphasis>

The fastidious bacterium Xylella fastidiosa is associated with important crop diseases worldwide. We have recently shown that X. fastidiosa is a peculiar organism having unusually low values of gene codon bias throughout its genome and, unexpectedly, in the group of the most abundant proteins. Here, we hypothesized that the lack of codon usage optimization in X. fastidiosa would incapacitate this organism to undergo quick and massive changes in protein expression as occurs in a classical stress response. Proteomic analysis of the response to heat stress in X. fastidiosa revealed that no changes in protein expression can be detected. Moreover, stress-inducible proteins identified in the closely related citrus pathogen Xanthomonas axonopodis pv citri were found to be constitutively expressed in X. fastidiosa. These proteins have extremely high codon bias values in the X. citri and other well-studied organisms, but low values in X. fastidiosa. Because biased codon usage is well known to correlate to the rate of protein synthesis, we speculate that the peculiar codon bias distribution in X. fastidiosa is related to the absence of a classical stress response, and, probably, alternative strategies for survival of X. fastidiosa under stressfull conditions.     

The pathway tools pathway prediction algorithm

The PathoLogic component of the Pathway Tools software performs prediction of metabolic pathways in sequenced and annotated genomes. This article provides a detailed presentation of the PathoLogic algorithm. The algorithm consists of two phases. The reactome inference phase infers the reactions catalyzed by the organism from the set of enzymes present in the annotated genome. The pathway inference phase infers the metabolic pathways present in the organism from the reactions catalyzed by the organism. Both phases draw on the MetaCyc database of metabolic reactions and pathways. MetaCyc contains two data fields to support pathway inference: the expected taxonomic range of each pathway, and a list of key reactions for pathways. These fields have significantly increased the predictive accuracy of PathoLogic.     

An evolutionary perspective on synonymous codon usage in unicellular organisms

Summary Observed patterns of synonymous codon usage are explained in terms of the joint effects of mutation, selection, and random drift. Examination of the codon usage in 165Escherichia coli genes reveals a consistent trend of increasing bias with increasing gene expression level. Selection on codon usage appears to be unidirectional, so that the pattern seen in lowly expressed genes is best explained in terms of an absence of strong selection. A measure of directional synonymous-codon usage bias, the Codon Adaptation Index, has been developed. In enterobacteria, rates of synonymous substitution are seen to vary greatly among genes, and genes with a high codon bias evolve more slowly. A theoretical study shows that the patterns of extreme codon bias observed for someE. coli (and yeast) genes can be generated by rather small selective differences. The relative plausibilities of various theoretical models for explaining nonrandom codon usage are discussed.Presented at the FEBS Symposium on Genome Organization and Evolution, held in Crete, Greece, September 1–5, 1986     

The Pathway Tools cellular overview diagram and Omics Viewer

The Pathway Tools cellular overview diagram is a visual representation of the biochemical network of an organism. The overview is automatically created from a Pathway/Genome Database describing that organism. The cellular overview includes metabolic, transport and signaling pathways, and other membrane and periplasmic proteins. Pathway Tools supports interrogation and exploration of cellular biochemical networks through the overview diagram. Furthermore, a software component called the Omics Viewer provides visual analysis of whole-organism datasets using the overview diagram as an organizing framework. For example, gene expression and metabolomics measurements, alone or in combination, can be painted onto the overview, as can computed whole-organism datasets, such as predicted reaction-flux values. The cellular overview and Omics Viewer provide a mechanism whereby biologists can apply the pattern-recognition capabilities of the human visual system to analyze large-scale datasets in a biologically meaningful context. SRI's BioCyc.org website provides overview diagrams for more than 200 organisms. This article describes enhancements to the overview made since a 1999 publication, including the automatic layout capability, expansion of the cellular machinery that it includes, new semantic zooming and poster-generating capabilities, and extension of the Omics Viewer to support painting of metabolites, animations and zooming to individual pathway diagrams.     

Intragenomic variation in non-adaptive nucleotide biases causes underestimation of selection on synonymous codon usage

Patterns of non-uniform usage of synonymous codons vary across genes in an organism and between species across all domains of life. This codon usage bias (CUB) is due to a combination of non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most models quantify the effects of mutation bias and selection on CUB assuming uniform mutational and other non-adaptive forces across the genome. However, non-adaptive nucleotide biases can vary within a genome due to processes such as biased gene conversion (BGC), potentially obfuscating signals of selection on codon usage. Moreover, genome-wide estimates of non-adaptive nucleotide biases are lacking for non-model organisms. We combine an unsupervised learning method with a population genetics model of synonymous coding sequence evolution to assess the impact of intragenomic variation in non-adaptive nucleotide bias on quantification of natural selection on synonymous codon usage across 49 Saccharomycotina yeasts. We find that in the absence of a priori information, unsupervised learning can be used to identify genes evolving under different non-adaptive nucleotide biases. We find that the impact of intragenomic variation in non-adaptive nucleotide bias varies widely, even among closely-related species. We show that the overall strength and direction of translational selection can be underestimated by failing to account for intragenomic variation in non-adaptive nucleotide biases. Interestingly, genes falling into clusters identified by machine learning are also physically clustered across chromosomes. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable non-adaptive nucleotide biases on codon frequencies.     

Global Metabolic Reconstruction and Metabolic Gene Evolution in the Cattle Genome

The sequence of cattle genome provided a valuable opportunity to systematically link genetic and metabolic traits of cattle. The objectives of this study were 1) to reconstruct genome-scale cattle-specific metabolic pathways based on the most recent and updated cattle genome build and 2) to identify duplicated metabolic genes in the cattle genome for better understanding of metabolic adaptations in cattle. A bioinformatic pipeline of an organism for amalgamating genomic annotations from multiple sources was updated. Using this, an amalgamated cattle genome database based on UMD_3.1, was created. The amalgamated cattle genome database is composed of a total of 33,292 genes: 19,123 consensus genes between NCBI and Ensembl databases, 8,410 and 5,493 genes only found in NCBI or Ensembl, respectively, and 266 genes from NCBI scaffolds. A metabolic reconstruction of the cattle genome and cattle pathway genome database (PGDB) was also developed using Pathway Tools, followed by an intensive manual curation. The manual curation filled or revised 68 pathway holes, deleted 36 metabolic pathways, and added 23 metabolic pathways. Consequently, the curated cattle PGDB contains 304 metabolic pathways, 2,460 reactions including 2,371 enzymatic reactions, and 4,012 enzymes. Furthermore, this study identified eight duplicated genes in 12 metabolic pathways in the cattle genome compared to human and mouse. Some of these duplicated genes are related with specific hormone biosynthesis and detoxifications. The updated genome-scale metabolic reconstruction is a useful tool for understanding biology and metabolic characteristics in cattle. There has been significant improvements in the quality of cattle genome annotations and the MetaCyc database. The duplicated metabolic genes in the cattle genome compared to human and mouse implies evolutionary changes in the cattle genome and provides a useful information for further research on understanding metabolic adaptations of cattle.     

The 'effective number of codons' revisited

Frank Wright [Gene 87 (1990) 23] derived a formula for calculation of a quantity termed the 'effective number of codons' (Nc) based on codon homozygosities. This quantity is a number between 20 and 61 and tells to what degree the codon usage in a gene is biased, i.e., it approaches 20 codons for the extremely biased genes, and approaches 61 for the genes where all possible codons are used with no preference. Among the different measures of codon bias Nc is considered the most useful and has found widespread use in papers dealing with codon usage phenomena. In this paper, the mathematical behaviours of codon homozygosities and Nc are evaluated, using Escherichia coli as the model organism. The results indicate that the classical formula for calculation of Nc could appropriately be substituted under circumstances, where there is bias discrepancy, i.e., when one amino acid (or more) within a degeneracy group is associated with strong codon bias while at the same time others in the same degeneracy group have little bias. An alternative estimator, termed Nc, is proposed and tested against Nc, and performs better when there is such bias discrepancy.     

A cross talk between codon usage bias in human oncogenes

Background: Oncogenes are the genes that have the potential to induce cancer. The extent and origin of codon usage bias is an important indicator of the forces shaping genome evolution in living organisms. Results: We observed moderate correlations between gene expression as measured by CAI and GC content at any codon site. The findings of our results showed that there is a significant positive correlation (Spearman''s r= 0.45, P<0.01) between GC content at first and second codon position with that of third codon position. Further, striking negative correlation (r = -0.771, P < 0.01) between ENC with the GC3s values of each gene and positive correlation (r=0.644, P<0.01) in between CAI and ENC was also observed. Conclusions: The mutation pressure is the major determining factor in shaping the codon usage pattern of oncogenes rather than natural selection since its effects are present at all codon positions. The results revealed that codon usage bias determines the level of oncogene expression in human. Highly expressed oncogenes had rich GC contents with high degree of codon usage bias.