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Open Access Research article

Translational signatures and mRNA levels are highly correlated in human stably expressed genes

Sergio R P Line12*, Xiaoming Liu3, Ana Paula de Souza1 and Fuli Yu2

Author Affiliations

1 Piracicaba Dental School, University of Campinas, PO Box 52, Piracicaba, SP, 13414-903, Brazil

2 The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA

3 Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA

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BMC Genomics 2013, 14:268  doi:10.1186/1471-2164-14-268

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2164/14/268


Received:19 September 2012
Accepted:16 April 2013
Published:19 April 2013

© 2013 Line et al.; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Gene expression is one of the most relevant biological processes of living cells. Due to the relative small population sizes, it is predicted that human gene sequences are not strongly influenced by selection towards expression efficiency. One of the major problems in estimating to what extent gene characteristics can be selected to maximize expression efficiency is the wide variation that exists in RNA and protein levels among physiological states and different tissues. Analyses of datasets of stably expressed genes (i.e. with consistent expression between physiological states and tissues) would provide more accurate and reliable measurements of associations between variations of a specific gene characteristic and expression, and how distinct gene features work to optimize gene expression.

Results

Using a dataset of human genes with consistent expression between physiological states we selected gene sequence signatures related to translation that can predict about 42% of mRNA variation. The prediction can be increased to 51% when selecting genes that are stably expressed in more than 1 tissue. These genes are enriched for translation and ribosome biosynthesis processes and have higher translation efficiency scores, smaller coding sequences and 3 UTR sizes and lower folding energies when compared to other datasets. Additionally, the amino acid frequencies weighted by expression showed higher correlations with isoacceptor tRNA gene copy number, and smaller absolute correlation values with biosynthetic costs.

Conclusion

Our results indicate that human gene sequence characteristics related to transcription and translation processes can co-evolve in an integrated manner in order to optimize gene expression.

Background

The control of gene expression is one of the most important biological processes, which can be regulated at diverse steps. Gene expression is the most energetically expensive process within a cell, and an efficient usage of gene expression machinery is of key importance for proper cell functioning. The rates of gene expression are mainly determined by the DNA sequences that modulate transcription and translation processes [1], and the selection for efficient ribosome usage seems to be a major force that shapes the evolution of gene sequences towards optimum gene expression [2]. Optimum gene expression can be understood as the maximum possible ratio between the benefit due to expression of the gene at a determined level and the costs of its production [3]. Optimum gene expression requires a balanced contribution of the diverse processes that control this process, as a gene that is transcribed at high rates should also be efficiently translated [4]. Therefore, it is expected that gene sequence characteristics that participate in the different aspects of gene expression machinery will work in an integrated manner and co-evolve in order to cope with demands for optimum ribosome usage.

Analysis of diverse prokaryotic and invertebrate species showed significant correlations between gene characteristics related to expression, such as codon usage [5-7], gene size [8,9], and folding energy at 5′ of mRNA [10]. Due to the relative small population sizes, the gene sequences of human, and other mammalian species, are not expected to be strongly influenced by selection towards expression efficiency [11,12]. In fact, except for gene and CDS sizes, there seems to exist a weak correlation between gene expression and specific gene signatures in humans, and higher correlation values are only obtained when analyzing pooled groups containing several hundred genes in each group [13-15].

One of the major problems in estimating to what extent a specific gene feature has undergone selection to maximize expression efficiency in humans is the wide variation that exists in RNA and protein levels among distinct tissues and even within the same tissue in different physiological and pathological conditions. In this sense, studies on this subject matter would benefit from the use of datasets of stably expressed genes (i.e. with consistent expression between physiological states and tissues), as those would provide more accurate and reliable measurements of associations between variations of a specific gene characteristic and expression, and how a distinct gene signature contribute to optimize gene expression. In this study we analyzed the association of gene characteristics related to translation efficiency or speed and mRNA expression using a dataset that was curated specifically for stably expressed genes [16].

Methods

Datasets and sequences

The genes used in this work were subdivided in two groups as follow:

Group 1: Formed by a dataset of stably expressed genes [16]. The genes were strictly selected using uniform data preprocessing and data quality control of 4,804 Affymetrix HU-133A arrays performed in clinical samples. Details of gene selection and analysis can be obtained in the Material and Methods section of the above referred paper. The complete list with Affimetrix GeneChip expression intensity and variation was download from supplementary Data S1 file of reference 16 (n = 575).

Group 2: formed by genes of Group 1, which were expressed in at least 2 tissues and had a/standard deviation/mean mRNA expression values < 0.4 (n = 196).

Group 3: formed by genes of Group 1, which were expressed in at least 3 tissues and had a standard deviation/mean ratio of mRNA expression values < 0.4 (n = 99).

Group 4: A list of genes with determined mRNA and Protein concentrations, obtained from Vogel et al [17]. The mRNA and protein were extracted from the human medulloblastoma Daoy cell line. mRNA expression values were generated using NimbleScan expression Robust Multi-array Analysis. Details of gene selection and analysis can be obtained in the Material and Methods and Supplementary Information of the referred paper. The complete list with Protein and mRNA expression intensity and variation was downloaded from Supplementary Data File. This and the previously described dataset were made non-redundant as genes included in Group 1 dataset were removed from this dataset (Group 4 = Vogel et al dataset - Group 1, n = 503).

cDNA, coding, 5 UTR, 3 UTR sequences and gene sizes were obtained from Biomart [18], http://www.biomart.org webcite. Sequences with “N” representing unknown bases were removed from analysis. Coding sequences that do not start with “ATG” or do not finish with a stop codon or whose sequence sizes are not divisible by 3 were also removed. The databases of sequences were made non-redundant with respect to alternative splice variants, as only the longest sequence was used to represent each gene sequence. Since we used measures of codon bias, genes that do not code for proteins were removed from the analysis.

Gene information and characteristics

Gene ontology was used to analyze the gene functions of the datasets used in the present work [19], http://www.geneontology.org webcite. A set of 21 Biological Process Terms were selected from the Refine Selection menu of QuickGO [20], http://www.ebi.ac.uk/QuickGO webcite. The genes were downloaded from Biomart [18], http://www.biomart.org webcite.

tRNA Adaptation index (tAi) was measured according to dos Reis et al. [7]. The relative adaptiveness values for all human codons were obtained from Waldman et al. [15]. Codon Adaptation index (CAI), GC and GC3 contents were obtained using the CAI server [21], http://genomes.urv.cat/CAIcal/E-CAI webcite. Isoaccepting tRNA gene copy numbers were obtained from Lavner and Kotlar [22].

Since previous reports have shown correlations between amino acid and gene expression [22], we also performed correlation analysis of the frequencies of amino acids and mRNA levels. The frequency of amino acids weighted by expression was calculated according to Lavner and Kotlar [22] and the amino acids size/complexity scores were obtained from Dufton [23].

RNA folding energies of 50 bp fragments of specific gene regions were obtained with the RNAFold program [24] using default settings. Only the minimum-free-energy (dG) structure was taken into account. The regions analyzed were:

a) The first 50 bases of cDNA.

b) The bases -52 to -2 of 5 UTR.

c) The first 50 bases of coding sequence.

d) The last 50 bases of coding sequence.

e) The last 50 bases of cDNA.

f) 50 random bases from cDNA.

Statistical analysis

All comparisons and statistical analysis were performed using R Statistical Package version 2.12.1 (http://www.r-project.org webcite).

Results

Correlations between gene structural parameters and gene expression are higher in stably expressed genes

We selected a number of parameters representing the characteristics of the gene sequences that could be influential to the expression variation: Coding sequence size, untranslated regions size, mRNA stability, translation efficiency measured by tAi and CAI indexes and amino acid frequencies. Our rationale is that if gene sequences are selected to maximize expression efficiency we would expect a significant correlation between gene transcription, represented by mRNA levels, and parameters related to translational. The results of Spearman correlation analysis between gene characteristics and mRNA levels are shown on Table 1. There was a significant negative correlation between mRNA levels, CDS size and 3 UTR size in Group 1 and Group 4. tRNA adaptation index (tAi) showed a positive correlation with mRNA levels in Group 1 and Group 4. There was a significant positive correlation between mRNA levels and folding energy of the 5 UTR mRNA sequences from -2 to -52 in Group 1. There were no significant correlations between 5 UTR sizes of and mRNA levels. Group 1 showed higher correlation values with CDS length, and folding energy in the end of 5 UTR of mRNA than Group 4. The correlation values among gene characteristics and mRNA levels of Group 1, and to a lower extent of Group 4, are higher than the values reported in the literature. The correlation values presented with stably expressed dataset (Group 1) are the highest reported up to the present date, and they indicate that selection for gene sequence characteristics towards expression efficiency in human genes may be more relevant than previously believed. Significant Spearman correlations between mRNA levels and frequency of amino acids in Group 1 (p < 0.05) were found for amino acids Cys, Asp, Gly, His, Ile, Lys, Leu, Met, Pro, Gln, Ser, Val, Trp (Additional file 1: Table S1). Except for Gly and Trp, the amino acids showing significant correlations in Group 1 also exhibited significant correlation in Group 4. The signals of the correlations for Groups 1 and 4 (positive or negative) were coincident for all 20 amino acids. The higher correlations in Group 1 were found for amino acids Ser (cor = -0.4, p < 2.2e-16), Lys (cor = 0.35 p < 2.2e-16) and Gln (cor -0.25, p = 6.7e-10). These values were higher than previously published correlations in mice [25] and human [17] data. In order to further analyze the correlations between amino acids usage and gene expression, and to compare our data with previously published results of human genes [22], we correlated (spearman rank) the frequency of amino acids weighted by expression with the isoaccepting tRNA gene copy numbers and amino acids size/complexity score, which gives an estimate of the biosynthetic cost of amino acids [22,23]. Notably, Groups 1, 2 and 3 showed a higher positive correlation with isoaccepting tRNA gene copy number for each amino acid than Group 4 and from the groups reported by Lavner and Kotlar [22]. Groups 1, 2, and 3 also showed smaller negative correlations scores between the frequency of amino acids weighted by expression and amino acids size/complexity (Table 2). There is a significant negative linear correlation (r = -0.55, p = 0.01) between amino acids size/complexity and isoaccepting tRNA gene copy number [22], where amino acids with more tRNA genes have also smaller biosynthetic costs.

Additional file 1: Table S1. Correlation analysis between aminoacids frequency and mRNA expression. S2. Gene ontology analysis in Groups 1, 2, 3 and 4.

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Table 1. Correlation analysis between gene parameters and mRNA expression

Table 2. Correlations between amino acids frequencies weighted by expression and isoaccepting tRNA gene copy number, and amino acids size/complexity score

The parameters with significant correlation values (p < 0.05) were used as variables in multiple linear regressions and tested for their combinatorial effects in gene expression variation. CAI was not included in this analysis since tAi, which is also an index of codon bias, produced higher correlation coefficients and smaller p values. Using Akaike information criterion (AIC) we determined the best fitted regression models for Groups 1 (stable expression) and 4. This analysis was performed with the step command from library MASS, and the penalty per parameter used was log(# parameters).

The best regression model for Group 1 (model1, Table 3), included as independent variables the coding sequence length (Lcds), tRNA Adaptation index (tAi), length of 3 UTR (L3utr), folding energy of the 50 bases from -2 to -52 of 5 UTR (dG), and the frequencies of amino acids Cys, Glu, Leu, Gln, Ser, Asp. This model could predict 42% of mRNA variation in Group 1 (R-squared = 0.418, adjusted R-squared = 0.414, p < 2.2e-16, Figure 1A). Interestingly when tAi and log(Lcds) were replaced by the ratio tAi/log(Lcds) in model 1 there was a slight increase in the model performance (R-squared = 0.420, adjusted R-squared = 0.418, p < 2.2e-16).

Table 3. Summary of regression analysis (Group 1 & regression model1)

thumbnailFigure 1. Scatter plots of mRNA levels (x-axis) vs fitted model values (y-axis). A. Regression of model1 in Group 1 (adjusted R-squared = 0.41, p < 2.2e-16, n = 575). B. Regression using variables from model1 in Group 4 (adjusted R-squared = 0.31, p < 2.2e-16, n = 503). C. Regression using variables from model2 in Group 1 (adjusted R-squared = 0.35, p < 2.2e-16, n = 575). D. Regression of model2 in Group 4 (adjusted R-squared = 0.33, p < 2.2e-16, n = 503).

The best regression model for Group 4 included tAi, L3utr, dG and the frequencies of amino acids Cys, Glu, Leu, Gln, His, Arg, Tyr as independent variables (model2, Table 4). This model could predict 33% of mRNA variation in Group 4 (R-squared = 0.34, adjusted R-squared = 0.33, p < 2.2e-16, Figure 1D).

Table 4. Summary of regression analysis (Group 4 & regression model2)

When variables from model1 were used in regression analysis in Group 4 dataset it was possible to predict about 35% of mRNA variation (R-squared = 0.36, adjusted R-squared = 0.35, p < 2.2e-16, Figure 1C), while variables of model2 in Group 1 could predict about 31% of mRNA variations (R-squared = 0.32, adjusted R-squared = 0.31, p < 2.2e-16, n = 503, Figure 1C). These results show that both regression models performed satisfactorily in both datasets and model1 produced higher adjusted R-squared values.

The independent variables selected in model1 were used for regression analysis in two subsets of Group 1, comprised by genes stably expressed in at least 2 tissues and a standard deviation/mean ratio of mRNA expression values < 0.4 (Group 2, n = 196, Figure 2A), and with genes stably expressed in at least 3 tissues with a standard deviation/mean ratio of mRNA expression values < 0.4 (Group 3, n = 99, Figure 2B). It was possible to predict about 51% of mRNA levels in Group 2 (R-squared = 0.532, adjusted R-squared = 0.51, p < 2.2e-16), and about 50% of mRNA levels (R-squared = 0.553, adjusted R-squared = 0.50, p < 2.2e-16) in Group 3. tAi did not significantly contribute to the prediction of mRNA levels in Group 3, possibly due to little variation of tAi values within this group, which was formed by genes with the highest mRNA levels (see below and Table 5).

thumbnailFigure 2. Scatter plots of mRNA levels (x-axis) vs fitted model values (y-axis). A. Regression using variables from model1 in Group 2, formed by genes that were expressed in at least 2 tissues and had a standard deviation/mean ratio of mRNA expression values < 0.4 (adjusted R-squared = 0.51, p < 2.2e-16, n = 196). B. Regression using variables from model1 in Group 3, formed by genes that were expressed in at least 3 tissues and had a standard deviation/mean ratio of mRNA expression values < 0.4 (adjusted R-squared = 0.50, p < 2.2e-16, n = 99).

Table 5. Gene characteristics in the groups analyzed

These results clearly show a high association between mRNA levels and gene sequence characteristics related to translation in stably expressed datasets. These correlations indicate that sequence characteristics that modulate transcription and translation processes co-evolve in order to optimize ribosomal usage. This phenomenon, which has been underestimated due to the low correlations between gene expression and gene sequence characteristics reported in other studies, may have played a relevant role on the evolution of the human gene sequences.

Stably expressed genes tend to have distinct structural characteristics and biological functions

Gene characteristics related to translation were compared using Kruskal. Wallis-test followed by post-hoc analysis using Mann–Whitney tests with Bonferroni correction. The results show that stably expressed genes (Groups 1, 2 and 3) tend to have smaller CDS lengths than “non-stably expressed” Group 4 dataset (p < 0.001). Group 1 and Group 4 had similar tAi values, while comparison among stably expressed Groups 1, 2 and 3 shows that the more tissues the gene is stably expressed the higher the tAi (p < 0.05). 3 UTR sizes in Groups 2 and 3 were significantly smaller than in Groups 1 and 4. There was a small progressive decrease in folding energy when comparing Groups 4, 1,2, 3. Significant differences (p < 0.01), however, were only found between Group 4 and the other 3 groups. The highest differences were found when comparing the expression levels among Groups 1, 2 and 3, that showed a progressive ~4 fold increase in mRNA levels (p < 0.001) (Table 5).

The analysis of gene ontology showed that most genes in our datasets belong to at least one of the 21 Biological Process Terms used (Additional file 1: Table S2). Genes related to cell transport were the most frequent in Groups 4 and 1, while genes related to translation were the most frequent in Groups 2 and 3. The highest variations were seen in genes related to translation process and ribosome biosynthesis, whose frequencies increased progressively in groups 4, 1, 2, 3. Since translation and ribosome biogenesis categories are related, their genes were pooled and compared with the pool of genes present in the remaining 19 categories. Since a gene can be present in more than one category the genes present in the two pools were rendered unique (i.e. repeated genes were counted only once), and the genes present in the translation and ribosome biogenesis categories were removed from the second pool formed by the genes present in the remaining 19 categories. After this procedure the frequencies of translation and ribosome biosynthesis genes pool in groups 4, 1, 2 and 3 were 18.02% (62/344), 27.82% (111/399), 50.62% (81/170), 68.97% (60/87) (Table 5). The frequencies between each pair of groups are significantly different (Pearson chi-squared test with Yates’ correction, largest p < 0.009). These results show that stably expressed genes tend to be smaller than genes in the other two databases and are enriched for genes directly related to protein synthesis. These two characteristics are likely to be connected to gene expression levels, since genes related to translational machinery are frequently highly expressed, and highly expressed genes tend to have small size.

Discussion

In the present paper we show that by using a dataset of stably expressed genes it is possible to predict about 42% of mRNA variation using 8 independent variables composed by gene characteristics related only to translation process. The predicted variation can be increased to about 51% when selecting genes that are stably expressed in at least 3 tissues (Group ST3). Although our results are based on the analysis of a dataset of 1078 genes, which comprises about 3-4% of the human coding sequences, our results clearly demonstrate that gene sequence characteristics may contribute significantly to the optimization of gene expression in human cells. It is worth mentioning that the p values obtained for CDS size and tAi in the correlation analysis were comparable to other studies that used much larger datasets [13-15,26]. The comparative analysis of mRNA of groups 1, 2 and 3 showed that the higher the mRNA expression the higher the frequency of genes related to translation and ribosome biosynthesis. These gene classes comprise the majority of RNA within the cell. Gene ontology analysis of Group 4 showed that genes in this group were somewhat enriched for genes related to translation and ribosome synthesis (18.02%). This may explain the fact that the correlation values for this group were in general larger than those reported in other published datasets [13-15]. Therefore, although, our sample represents only a fraction of genes within the cell, it may represent a significant fraction of expressed mRNA. Interestingly, groups 2 and 3, which are highly enriched for highly expressed translation and ribosome biosynthesis genes have the highest correlations between amino acids frequencies weighted by expression and amino acids isoaccepting tRNA gene copy number, and the smallest absolute correlation values with biosynthetic costs. One possible explanation for these results is that high demands for translation efficiency may occur at the expense of other aspects such as higher biosynthetic costs.

Two measures of codon bias were used in our analysis, tAi and CAI. In both datasets tAi produced higher correlation scores and lower p values than CAI. While CAI and some other indexes that estimate the role of codon bias in translational efficiency are based on the frequency of synonymous optimal codons found in highly expressed genes [27], tAi considers the tRNA pool within the cell, in which each codon is assigned a value that corresponds to the disponibility of the corresponding tRNAs [7]. tAi is, therefore, a more direct measure of biological function than CAI and other indexes based on codon frequency. In mammals, the few reports showing significant effect of synonymous mutations in gene expression and/or function, have mainly attributed its effects to changes in mRNA conformation [28] and disruption of mRNA splicing [12,29]. The significant correlation between tAi and mRNA levels observed here suggests that synonymous mutations may have a more active and broad role on gene expression than previously believed. Although synonymous mutations have been classically considered as “silent”, since they not alter the amino acid sequences of proteins, recent evidences indicate that human synonymous SNPs are targeted by positive and negative selection and can modulate the phenotype in humans [30-32]. In fact, besides modulating the levels of protein products changes in translation efficiency can further affect protein function by altering protein folding [33].

The stability at the 5 end of mRNA is related to the translation efficiency in unicellular organisms [10,33]. Among all 50 bp fragments of diverse regions tested for mRNA stability, the sequences from -52 to -2 of 5 UTR gave the smallest p values on both, correlation and multiple regression analysis. Using E. coli and S. cerevisiae, The 3 UTR of genes is usually larger than 5 UTR, and usually contains elements involved in post-transcriptional regulation of gene expression. In most cases, the binding of proteins and miRNA to 3′ UTR seems to increase degradation of mRNA molecules. In fact, miRNA regulation of gene expression seems to be avoided by decreasing the length of 3 UTR [34]. Interestingly, there is a significant correlation between the size of 3 UTR and frequency of tandem repeat sequences (r = 0.73, p = 0.0001) [35]. This may be a strategy to adapt the size 3 UTR in order to cope with changes in selective pressure for variations in gene expression, as tandem repeats are prone to size variation due to slipped-strand mispairing. Our results support the biological importance of 5 and 3 UTR regions for efficient gene expression in humans, as gene characteristics of these regions (dG + log(3UTR)) can explain about 14% of mRNA variation in Group 1.

The correlation between mRNA levels and CDS length could be explained by the correlation of the former with gene length, which is a characteristic related to translation (Spearman rank of log CDS length vs log gene length = 0.48, p < 2.2e-16). The regression models with these two variables, however, showed that gene size and CDS size independently contribute to the prediction of mRNA levels (not shown), suggesting that these gene characteristics can evolve with certain independence to cope with optimal gene expression.

There was a progressive increase in mRNA levels of about 4 fold when comparing the values of groups 1, 2 and 3, whereas the changes in the individual parameters used as independent variables in regression analysis was of lower intensity (i.e. 1.3 fold for CDS length, and 1.03 fold for tAi). This is likely to be related with the fact that the ratio of the two major correlating factors (tAi/log(CDS length) used as a single independent variable can explain about 26.3% of mRNA variation, indicating that the variations in these two characteristics occur in a concerted manner. In this scenario small variations in the diverse characteristics can lead to substantial variation in gene expression. Large variability increases the repertoire of individual differences, which may be positively selected in order to cope with demands for efficient ribosome usage without affecting the function of the final protein product that is mainly determined by secondary and tertiary structures of protein polypeptide.

The high prediction power of translation parameters of model1 in Groups 1, 2, and 3, evidence the concomitant evolution of gene signatures related to translational efficiency and transcription activity measured by mRNA levels. These results suggest that the evolution of coding sequences can be influenced by changes in non-coding sequences and vice-versa. Recent evidences indicate that selection for efficient ribosome usage is the central force in shaping codon usage at the genomic scale [2]. This relationship has been discussed by Gingold and Pilpel [3], where these authors argue that genes with higher mRNA levels would be using up more ribosomes, and thus are under stronger selection for global translation efficiency, therefore, presumably creating the correlation between mRNA levels and gene characteristics related to translation efficiency such as tAi, CDS, 3 UTR sizes and stability of secondary structures formed by folding of mRNA sequences near translation initiation site. Selection for ribosomal usage efficiency would be stronger in highly expressed genes, due to higher energetic costs. It is well known that there is a strong negative correlation between the expression level of a protein and its rate of evolution [36,37]. This relationship is currently explained by protein misfolding [37,38] and misinteraction avoidances [39]. Our analyses indicate that ribosomal usage efficiency may also be a relevant factor that determines gene the evolution of coding sequences in human genes and also possibly in other vertebrates.

Conclusion

Our results indicate that human gene sequence characteristics related to transcription and translation processes can co-evolve in an integrated manner in order to optimize gene expression.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

FY and SRPL conceived and designed the research project. SRPL performed the computational and statistical analysis. APS participated in the design and helped to draft the manuscript. XL participated in the design and helped to draft the manuscript. FY coordinated the research project. All authors read and approved the final manuscript.

Acknowledgements

Line SRP was supported by Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) proc 2010/09220-5.

References

  1. Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M: Global quantification of mammalian gene expression control.

    Nature 2011, 473:337-342. PubMed Abstract | Publisher Full Text OpenURL

  2. Shaha P, Gilchrist MA: Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift.

    Proc Natl Acad Sci USA 2011, 108:10231-10236. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  3. Gingold H, Pilpel Y: Determinants of translation efficiency and accuracy.

    Molecular Systems Biol 2011, 7:481. OpenURL

  4. Coghlan A, Wolfe H: Relationship of codon bias to mRNA concentration and protein length in Saccharomyces cerevisiae.

    Yeast 2000, 16:1131-1145. PubMed Abstract | Publisher Full Text OpenURL

  5. Gouy M, Gautier C: Codon usage in bacteria: correlation with gene expressivity.

    Nucleic Acids Res 1982, 10:7055-7074. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  6. Dekel E, Alon U: Optimality and evolutionary tuning of the expression level of a protein.

    Nature 2005, 436:588-592. PubMed Abstract | Publisher Full Text OpenURL

  7. dos Reis M, Wernisch L, Savva R: Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome.

    Nucleic Acids Res 2003, 31:6976-6985. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  8. Eyre-Walker A: Synonymous codon bias is related to gene length in Escherichia coli: selection for translational accuracy?

    Mol Biol Evol 1996, 13:864-872. PubMed Abstract | Publisher Full Text OpenURL

  9. Duret L, Mouchiroud D: Expression pattern and, surprisingly, gene length shape codon usage in Caenorhabditis, Drosophila, and Arabidopsis.

    Proc Natl Acad Sci USA 1999, 96:4482-4487. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  10. Kudla G, Murray AW, Tollervey D, Plotkin JB: Coding-sequence determinants of gene expression in Escherichia coli.

    Science 2009, 324:255-258. PubMed Abstract | Publisher Full Text OpenURL

  11. dos Reis M, Savva R, Wernisch L: Solving the riddle of codon usage preferences: a test for translational selection.

    Nucleic Acids Res 2004, 32:5036-5044. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  12. Chammary J, Parmley JL, Hurst LD: Hearing silence: non-neutral evolution at synonymous sites in mammals.

    Nat Rev Genet 2006, 7:98-108. PubMed Abstract | Publisher Full Text OpenURL

  13. Comeron JM: Selective and mutational patterns associated with gene expression in humans: Influences on synonymous composition and intron presence.

    Genetics 2004, 167:1293-1304. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  14. Urrutia AO, Hurst LD: The signature of selection mediated by expression on human genes.

    Genome Res 2003, 13:2260-2264. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  15. Waldman YY, Tuller T, Shlomi T, Sharan R, Ruppin E: Translation efficiency in humans: tissue specificity, global optimization and differences between developmental stages.

    Nucleic Acids Res. 2010, 38:2964-2974. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  16. Cheng WC, Chang CW, Chen CR, Tsai ML, Shu WY, Li CY, Hsu IC: Identification of reference genes across physiological states for qRT-PCR through microarray meta-analysis.

    PLoS One 2011, 6(2):e17347. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  17. Vogel C, Abreu Rde S, Ko D, Le SY, Shapiro BA, Burns SC, Sandhu D, Boutz DR, Marcotte EM, Penalva LO: Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line.

    Mol Syst Biol 2010, 6:400. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  18. Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W: BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis.

    Bioinformatics 2005, 21:3439-3440. PubMed Abstract | Publisher Full Text OpenURL

  19. The Gene Ontology Consortium: Gene ontology: tool for the unification of biology.

    Nat Genet 2000, 25:25-29. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  20. Binns D, Dimmer E, Huntley R, Barrell D, O’Donovan C, Apweiler R: QuickGO: a web-based tool for gene ontology searching.

    Bioinformatics 2009, 25:3045-3046. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  21. Puigbo P, Bravo IG, Garcia-Vallve S: E-CAI: a novel server to estimate an expected value of codon adaptation index (eCAI).

    BMC Bioinformatics 2008, 9:65. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  22. Lavner Y, Kotlar D: Codon bias as a factor in regulating expression via translation rate in the human genome.

    Gene 2005, 345:127-138. PubMed Abstract | Publisher Full Text OpenURL

  23. Dufton MJ: Genetic code synonym quotas and amino acid complexity: cutting the cost of proteins?

    J Theor Biol 1997, 187:165-173. PubMed Abstract | Publisher Full Text OpenURL

  24. Bruhat A, Cherasse Y, Chaveroux C, Maurin AC, Jousse C, Fafournoux P: Amino acids as regulators of gene expression in mammals: molecular mechanisms.

    Biofactors 2009, 35:249-257. PubMed Abstract | Publisher Full Text OpenURL

  25. Zuker M, Stiegler P: Optimal computer folding of large RNA sequences using thermodynamic and auxiliary information.

    Nucleic Acid Res 1981, 9:133-148. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  26. Misawa K, Reiko F, Kikuno RF: Relationship between amino acid composition and gene expression in the mouse genome.

    BMC Research Notes 2011, 4:20. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  27. Rao YS, Wang ZF, Chai XW, Wu GZ, Zhou M, Nie QH, Zhang XQ: Selection for the compactness of highly expressed genes in Gallus gallus.

    Biol Direct 2010, 5:35. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  28. Sharp PM, Li WH: The codon Adaptation Index – a measure of directional synonymous codon usage bias, and its potential applications.

    Nucleic Acids Res 1987, 15:1281-1295. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  29. Duan J, Wainwright MS, Comeron JM, Saitou N, Sanders AR, Gelernter J, Gejman PV: Synonymous mutations in the human dopamine receptor D2 (DRD2) affect mRNA stability and synthesis of the receptor.

    Hum Mol Genet 2003, 12:205-216. PubMed Abstract | Publisher Full Text OpenURL

  30. Eskesen ST, Eskesen FN, Ruvinsky A: Natural selection affects frequencies of AG and GT dinucleotides at the 5′ and 3′ ends of exons.

    Genetics 2004, 167:543-550. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  31. Tennessen JA, Bigham AW, O’Connor TD, Fu W, Kenny EE, Gravel S, McGee S, Do R, Liu X, Jun G, Kang HM, Jordan D, Leal SM, Gabriel S, Rieder MJ, Abecasis G, Altshuler D, Nickerson DA, Boerwinkle E, Sunyaev S, Bustamante CD, Bamshad MJ, Akey JM, Broad GO, Seattle GO: NHLBI Exome sequencing project evolution and functional impact of rare coding variation from deep sequencing of human exomes.

    Science 2012, 337:64-69. PubMed Abstract | Publisher Full Text OpenURL

  32. Waldman YY, Tuller T, Keinan A, Ruppin E: Selection for translation efficiency on synonymous polymorphisms in recent human evolution.

    Genome Biol Evol 2011, 3:749-761. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  33. Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, Calcagno AM, Ambudkar SV, Gottesman MM: A “silent” polymorphism in the MDR1 gene changes substrate specificity.

    Science 2007, 315:525-528. PubMed Abstract | Publisher Full Text OpenURL

  34. Stark A, Brennecke J, Bushati N, Russell RB, Cohen SM: Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3 UTR evolution.

    Cell 2005, 123:1133-1146. PubMed Abstract | Publisher Full Text OpenURL

  35. Liu H, Yin J, Xiao M, Gao C, Mason AS, Zhao Z, Liu Y, Li J, Fu D: Characterization and evolution of 5′ and 3′ untranslated regions in eukaryotes.

    Gene 2012, 507:106-111. PubMed Abstract | Publisher Full Text OpenURL

  36. Hurst LD, Smith NG: Do essential genes evolve slowly?

    Curr Biol 1999, 9:747-750. PubMed Abstract | Publisher Full Text OpenURL

  37. Pál C, Papp B, Hurst LD: Highly expressed genes in yeast evolve slowly.

    Genetics 2001, 158:927-931. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  38. Zhang J, He X: Significant impact of protein dispensability on the instantaneous rate of protein evolution.

    Mol Biol Evol 2005, 22:1147-1155. PubMed Abstract | Publisher Full Text OpenURL

  39. Yanga J-R, Liaob B-Y, Zhuanga S-M, Zhangb J: Protein misinteraction avoidance causes highly expressed proteins to evolve slowly.

    Proc Natl Acad Sci USA 2012, 109:E831-E840. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL