Codon usage bias and A+T content variation in human papillomavirus genomes

We analyzed the codon usage bias of eight open reading frames (ORFs) across up to 79 human papillomavirus (HPV) genotypes from three distinct phylogenetic groups. All eight ORFs across HPV genotypes show a strong codon usage bias, amongst degenerately encoded amino acids, toward 18 codons mainly with T at the 3rd position. For all 18 degenerately encoded amino acids, codon preferences amongst human and animal PV ORFs are significantly different from those averaged across mammalian genes. Across the HPV types, the L2 ORFs show the highest codon usage bias (73.2 +/- 1.6% and the E4 ORFs the lowest (51.1 +/- 0.5%), reflecting as similar bias in codon 3rd position A + T content (L2: 76.1 +/- 4.2%; E4: 58.6 +/- 4.5%). The E4 ORF, uniquely amongst the HPV ORFs, is G + C rich, while the other ORFs are A + T rich. Codon usage bias correlates positively with A + T content at the codon 3rd position in the E2, E6, L1 and L2 ORFs, but negatively in the E4 ORFs. A general conservation of preferred codon usage across human and non-human PV genotypes whether they originate from a same supergroup or not, together with observed difference between the preferred codon usage for HPV ORFs and for genes of the cells they infect, suggests that specific codon usage bias and A + T content variation may somehow increase the replicational fitness of HPVs in mammalian epithelial cells, and have practical implications for gene therapy of HPV infection. (C) 2003 Elsevier B.V. All rights reserved.

Fine-Tuning Translation Kinetics Selection as the Driving Force of Codon Usage Bias in the Hepatitis A Virus Capsid

Hepatitis A virus (HAV), the prototype of genus Hepatovirus, has several unique biological characteristics that distinguish it from other members of the Picornaviridae family. Among these, the need for an intact eIF4G factor for the initiation of translation results in an inability to shut down host protein synthesis by a mechanism similar to that of other picornaviruses. Consequently, HAV must inefficiently compete for the cellular translational machinery and this may explain its poor growth in cell culture. In this context of virus/cell competition, HAV has strategically adopted a naturally highly deoptimized codon usage with respect to that of its cellular host. With the aim to optimize its codon usage the virus was adapted to propagate in cells with impaired protein synthesis, in order to make tRNA pools more available for the virus. A significant loss of fitness was the immediate response to the adaptation process that was, however, later on recovered and more associated to a re-deoptimization rather than to an optimization of the codon usage specifically in the capsid coding region. These results exclude translation selection and instead suggest fine-tuning translation kinetics selection as the underlying mechanism of the codon usage bias in this specific genome region. Additionally, the results provide clear evidence of the Red Queen dynamics of evolution since the virus has very much evolved to re-adapt its codon usage to the environmental cellular changing conditions in order to recover the original fitness.

Fine-Tuning Translation Kinetics Selection as the Driving Force of Codon Usage Bias in the Hepatitis A Virus Capsid

Hepatitis A virus (HAV), the prototype of genus Hepatovirus, has several unique biological characteristics that distinguish it from other members of the Picornaviridae family. Among these, the need for an intact eIF4G factor for the initiation of translation results in an inability to shut down host protein synthesis by a mechanism similar to that of other picornaviruses. Consequently, HAV must inefficiently compete for the cellular translational machinery and this may explain its poor growth in cell culture. In this context of virus/cell competition, HAV has strategically adopted a naturally highly deoptimized codon usage with respect to that of its cellular host. With the aim to optimize its codon usage the virus was adapted to propagate in cells with impaired protein synthesis, in order to make tRNA pools more available for the virus. A significant loss of fitness was the immediate response to the adaptation process that was, however, later on recovered and more associated to a re-deoptimization rather than to an optimization of the codon usage specifically in the capsid coding region. These results exclude translation selection and instead suggest fine-tuning translation kinetics selection as the underlying mechanism of the codon usage bias in this specific genome region. Additionally, the results provide clear evidence of the Red Queen dynamics of evolution since the virus has very much evolved to re-adapt its codon usage to the environmental cellular changing conditions in order to recover the original fitness.

Evolution of codon usage bias in Drosophila

We first review what is known about patterns of codon usage bias in Drosophila and make the following points: (i) Drosophila genes are as biased or more biased than those in microorganisms. (ii) The level of bias of genes and even the particular pattern of codon bias can remain phylogenetically invariant for very long periods of evolution. (iii) However, some genes, even very tightly linked genes, can change very greatly in codon bias across species. (iv) Generally G and especially C are favored at synonymous sites in biased genes. (v) With the exception of aspartic acid, all amino acids contribute significantly and about equally to the codon usage bias of a gene. (vi) While most individual amino acids that can use G or C at synonymous sites display a preference for C, there are exceptions: valine and leucine, which prefer G. (vii) Finally, smaller genes tend to be more biased than longer genes. We then examine possible causes of these patterns and discount mutation bias on three bases: there is little evidence of regional mutation bias in Drosophila, mutation bias is likely toward A+T (the opposite of codon usage bias), and not all amino acids display the preference for the same nucleotide in the wobble position. Two lines of evidence support a selection hypothesis based on tRNA pools: highly biased genes tend to be highly and/or rapidly expressed, and the preferred codons in highly biased genes optimally bind the most abundant isoaccepting tRNAs. Finally, we examine the effect of bias on DNA evolution and confirm that genes with high codon usage bias have lower rates of synonymous substitution between species than do genes with low codon usage bias. Surprisingly, we find that genes with higher codon usage bias display higher levels of intraspecific synonymous polymorphism. This may be due to opposing effects of recombination.

Study of tRNA modifying enzymes and codon usage bias in cancer

Recent evidences indicate that tRNA modifications and tRNA modifying enzymes may play important roles in complex human diseases such as cancer, neurological disorders and mitochondrial-linked diseases. We postulate that expression deregulation of tRNA modifying enzymes affects the level of tRNA modifications and, consequently, their function and the translation efficiency of their tRNA corresponding codons. Due to the degeneracy of the genetic code, most amino acids are encoded by two to six synonymous codons. This degeneracy and the biased usage of synonymous codons cause alterations that can span from protein folding to enhanced translation efficiency of a select gene group. In this work, we focused on cancer and performed a meta-analysis study to compare microarray gene expression profiles, reported by previous studies and evaluate the codon usage of different types of cancer where tRNA modifying enzymes were found de-regulated. A total of 36 different tRNA modifying enzymes were found de-regulated in most cancer datasets analyzed. The codon usage analysis revealed a preference for codons ending in AU for the up-regulated genes, while the down-regulated genes show a preference for GC ending codons. Furthermore, a PCA biplot analysis showed this same tendency. We also analyzed the codon usage of the datasets where the CTU2 tRNA modifying enzyme was found deregulated as this enzyme affects the wobble position (position 34) of specific tRNAs. Our data points to a distinct codon usage pattern between up and downregulated genes in cancer, which might be caused by the deregulation of specific tRNA modifying enzymes. This codon usage bias may augment the transcription and translation efficiency of some genes that otherwise, in a normal situation, would be translated less efficiently.

Avoidance of Long Mononucleotide Repeats in Codon Pair Usage

Protein is an essential component for life, and its synthesis is mediated by codons in any organisms on earth. While some codons encode the same amino acid, their usage is often highly biased. There are many factors that can cause the bias, but a potential effect of mononucleotide repeats, which are known to be highly mutable, on codon usage and codon pair preference is largely unknown. In this study we performed a genomic survey on the relationship between mononucleotide repeats and codon pair bias in 53 bacteria, 68 archaea, and 13 eukaryotes. By distinguishing the codon pair bias from the codon usage bias, four general patterns were revealed: strong avoidance of five or six mononucleotide repeats in codon pairs; lower observed/expected (o/e) ratio for codon pairs with C or G repeats (C/G pairs) than that with A or T repeats (A/T pairs); a negative correlation between genomic GC contents and the o/e ratios, particularly for C/G pairs; and avoidance of C/G pairs in highly conserved genes. These results support natural selection against long mononucleotide repeats, which could induce frameshift mutations in coding sequences. The fact that these patterns are found in all kingdoms of life suggests that this is a general phenomenon in living organisms. Thus, long mononucleotide repeats may play an important role in base composition and genetic stability of a gene and gene functions.

Does recombination improve selection on codon usage? Lessons from nematode and fly complete genomes

Understanding the factors responsible for variations in mutation patterns and selection efficacy along chromosomes is a prerequisite for deciphering genome sequences. Population genetics models predict a positive correlation between the efficacy of selection at a given locus and the local rate of recombination because of Hill–Robertson effects. Codon usage is considered one of the most striking examples that support this prediction at the molecular level. In a wide range of species including Caenorhabditis elegans and Drosophila melanogaster, codon usage is essentially shaped by selection acting for translational efficiency. Codon usage bias correlates positively with recombination rate in Drosophila, apparently supporting the hypothesis that selection on codon usage is improved by recombination. Here we present an exhaustive analysis of codon usage in C. elegans and D. melanogaster complete genomes. We show that in both genomes there is a positive correlation between recombination rate and the frequency of optimal codons. However, we demonstrate that in both species, this effect is due to a mutational bias toward G and C bases in regions of high recombination rate, possibly as a direct consequence of the recombination process. The correlation between codon usage bias and recombination rate in these species appears to be essentially determined by recombination-dependent mutational patterns, rather than selective effects. This result highlights that it is necessary to take into account the mutagenic effect of recombination to understand the evolutionary role and impact of recombination.

Modeling sequencing errors by combining Hidden Markov models.

Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.

Avian coronavirus Spike Glycoprotein Ectodomain Shows a Low Codon Adaptation to Gallus gallus with Virus-Exclusive Codons in Strategic Amino Acids Positions

This is a study on the Avian coronavirus IBV and chicken host-relationship from the codon usage point of view based on fifty-nine non-redundant IBV S1 sequences (nt 1-507) from strains detected worldwide and chicken tissue-specific protein genes sequences from IBV-replicating sites. The effective number of codons (ENC) values ranged from 36 to 47.8, indicating a high-to-moderate codon usage bias. The highest IBV codon adaptation index (CAI) value was 0.7, indicating a distant virus versus host synonymous codons usage. The ENC x GC3 % curve indicates that both mutational pressure and natural selection are the driving forces on codon usage pattern in S1. The low CAI values agree with a low S protein expression and considering that S protein is a determinant for attachment and neutralization, this could be a further mechanism besides mRNA transcription attenuation for a low expression of this protein leading to an immune camouflage.

The atypical codon usage of the plant psbA gene may be the remnant of an ancestral bias

The psbA gene of the chloroplast genome has a codon usage that is unusual for plant chloroplast genes. In the present study the evolutionary status of this codon usage is tested by reconstructing putative ancestral psbA sequences to determine the pattern of change in codon bias during angiosperm divergence. It is shown that the codon biases of the ancestral genes are much stronger than all extant flowering plant psbA genes. This is related to previous work that demonstrated a significant increase in synonymous substitution in psbA relative to other chloroplast genes. It is suggested, based on the two lines of evidence, that the codon bias of this gene currently is not being maintained by selection. Rather, the atypical codon bias simply may be a remnant of an ancestral codon bias that now is being degraded by the mutation bias of the chloroplast genome, in other words, that the psbA gene is not at equilibrium. A model for the evolution of selective pressure on the codon usage of plant chloroplast genes is discussed.

Deregulated MHC class II transactivator expression leads to a strong Th2 bias in CD4+ T lymphocytes.

The MHC class II (MHC-II) transactivator (CIITA) is the master transcriptional regulator of genes involved in MHC-II-restricted Ag presentation. Fine tuning of CIITA gene expression determines the cell type-specific expression of MHC-II genes. This regulation is achieved by the selective usage of multiple CIITA promoters. It has recently been suggested that CIITA also contributes to Th cell differentiation by suppressing IL-4 expression in Th1 cells. In this study, we show that endogenous CIITA is expressed at low levels in activated mouse T cells. Importantly CIITA is not regulated differentially in murine and human Th1 and Th2 cells. Ectopic expression of a CIITA transgene in multiple mouse cell types including T cells, does not interfere with normal development of CD4(+) T cells. However, upon TCR activation the CIITA transgenic CD4(+) T cells preferentially differentiate into IL-4-secreting Th2-type cells. These results imply that CIITA is not a direct Th1-specific repressor of the IL-4 gene and that tight control over the expression of CIITA and MHC-II is required to maintain the normal balance between Th1 and Th2 responses.

HIV-1 coreceptor usage and CXCR4-specific viral load predict clinical disease progression during combination antiretroviral therapy

Background: Although combination antiretroviral therapy (cART) dramatically reduces rates of AIDS and death, a minority of patients experience clinical disease progression during treatment. <p>Objective: To investigate whether detection of CXCR4(X4)-specific strains or quantification of X4-specific HIV-1 load predict clinical outcome. Methods: From the Swiss HIV Cohort Study, 96 participants who initiated cART yet subsequently progressed to AIDS or death were compared with 84 contemporaneous, treated nonprogressors. A sensitive heteroduplex tracking assay was developed to quantify plasma X4 and CCR5 variants and resolve HIV-1 load into coreceptor-specific components. Measurements were analyzed as cofactors of progression in multivariable Cox models adjusted for concurrent CD4 cell count and total viral load, applying inverse probability weights to adjust for sampling bias. Results: Patients with X4 variants at baseline displayed reduced CD4 cell responses compared with those without X4 strains (40 versus 82 cells/mu l; P= 0.012). The adjusted multivariable hazard ratio (HR) for clinical progression was 4.8 [95% confidence interval (Cl) 2.3-10.0] for those demonstrating X4 strains at baseline. The X4-specific HIV-1 load was a similarly independent predictor, with HR values of 3.7(95%Cl, 1.2-11.3) and 5.9 (95% Cl, 2.2-15.0) for baseline loads of 2.2-4.3 and > 4.3 log(10)copies/ml, respectively, compared with < 2.2 log(10)copies/ml. Conclusions: HIV-1 coreceptor usage and X4-specific viral loads strongly predicted disease progression during cART, independent of and in addition to CD4 cell count or total viral load. Detection and quantification of X4 strains promise to be clinically useful biomarkers to guide patient management and study HIV-1 pathogenesis.

Transferring control demands across incidental learning tasks - Stronger sequence usage in serial reaction task after shortcut option in letter string checking

After incidentally learning about a hidden regularity, participants can either continue to solve the task as instructed or, alternatively, apply a shortcut. Past research suggests that the amount of conflict implied by adopting a shortcut seems to bias the decision for vs. against continuing instruction-coherent task processing. We explored whether this decision might transfer from one incidental learning task to the next. Theories that conceptualize strategy change in incidental learning as a learning-plus-decision phenomenon suggest that high demands to adhere to instruction-coherent task processing in Task 1 will impede shortcut usage in Task 2, whereas low control demands will foster it. We sequentially applied two established incidental learning tasks differing in stimuli, responses and hidden regularity (the alphabet verification task followed by the serial reaction task, SRT). While some participants experienced a complete redundancy in the task material of the alphabet verification task (low demands to adhere to instructions), for others the redundancy was only partial. Thus, shortcut application would have led to errors (high demands to follow instructions). The low control demand condition showed the strongest usage of the fixed and repeating sequence of responses in the SRT. The transfer results are in line with the learning-plus-decision view of strategy change in incidental learning, rather than with resource theories of self-control.

Input usage, output mix and industry deregulation: an analysis of the Australian dairy manufacturing industry

In this paper we estimate a Translog output distance function for a balanced panel of state level data for the Australian dairy processing sector. We estimate a fixed effects specification employing Bayesian methods, with and without the imposition of monotonicity and curvature restrictions. Our results indicate that Tasmania and Victoria are the most technically efficient states with New South Wales being the least efficient. The imposition of theoretical restrictions marginally affects the results especially with respect to estimates of technical change and industry deregulation. Importantly, our bias estimates show changes in both input use and output mix that result from deregulation. Specifically, we find that deregulation has positively biased the production of butter, cheese and powders.

On Ribosome Load, Codon Bias and Protein Abundance

Different codons encoding the same amino acid are not used equally in protein-coding sequences. In bacteria, there is a bias towards codons with high translation rates. This bias is most pronounced in highly expressed proteins, but a recent study of synthetic GFP-coding sequences did not find a correlation between codon usage and GFP expression, suggesting that such correlation in natural sequences is not a simple property of translational mechanisms. Here, we investigate the effect of evolutionary forces on codon usage. The relation between codon bias and protein abundance is quantitatively analyzed based on the hypothesis that codon bias evolved to ensure the efficient usage of ribosomes, a precious commodity for fast growing cells. An explicit fitness landscape is formulated based on bacterial growth laws to relate protein abundance and ribosomal load. The model leads to a quantitative relation between codon bias and protein abundance, which accounts for a substantial part of the observed bias for E. coli. Moreover, by providing an evolutionary link, the ribosome load model resolves the apparent conflict between the observed relation of protein abundance and codon bias in natural sequences and the lack of such dependence in a synthetic gfp library. Finally, we show that the relation between codon usage and protein abundance can be used to predict protein abundance from genomic sequence data alone without adjustable parameters.