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Causes and Implications of Codon Usage Bias in RNA Viruses

  • Ilya S. Belalov,

    Affiliation Chumakov Institute of Poliomyelitis and Viral Encephalitides, Russian Academy of Medical Sciences, Moscow, Russia

  • Alexander N. Lukashev

    alexander_lukashev@hotmail.com

    Affiliations Chumakov Institute of Poliomyelitis and Viral Encephalitides, Russian Academy of Medical Sciences, Moscow, Russia, Institute for Virology, University of Bonn Medical Center, Bonn, Germany

Causes and Implications of Codon Usage Bias in RNA Viruses

  • Ilya S. Belalov, 
  • Alexander N. Lukashev
PLOS
x

Abstract

Choice of synonymous codons depends on nucleotide/dinucleotide composition of the genome (termed mutational pressure) and relative abundance of tRNAs in a cell (translational pressure). Mutational pressure is commonly simplified to genomic GC content; however mononucleotide and dinucleotide frequencies in different genomes or mRNAs may vary significantly, especially in RNA viruses. A series of in silico shuffling algorithms were developed to account for these features and analyze the relative impact of mutational pressure components on codon usage bias in RNA viruses. Total GC content was a poor descriptor of viral genome composition and causes of codon usage bias. Genomic nucleotide content was the single most important factor of synonymous codon usage. Moreover, the choice between compatible amino acids (e.g., leucine and isoleucine) was strongly affected by genomic nucleotide composition. Dinucleotide composition at codon positions 2-3 had additional effect on codon usage. Together with mononucleotide composition bias, it could explain almost the entire codon usage bias in RNA viruses. On the other hand, strong dinucleotide content bias at codon position 3-1 found in some viruses had very little effect on codon usage. A hypothetical innate immunity sensor for CpG in RNA could partially explain the codon usage bias, but due to dependence of virus translation upon biased host translation machinery, experimental studies are required to further explore the source of dinucleotide bias in RNA viruses.

Introduction

Amino acid sequence of proteins is encoded by nucleotide triplets. The majority of organisms use the standard genetic code, with 61 sense codons translated into 20 amino acids. Therefore, most amino acids are encoded by several synonymous codons, which are not used evenly. This codon usage bias (CUB) can have distinct causes and consequences in different organisms. Two major factors are implicated to explain CUB, termed (somewhat ambiguously) translational (selection) and mutational pressure [1]. Translational pressure means preference towards codons that are most suitable for translation in a given context. Evidence for translational pressure towards common codons has been found in certain highly expressed bacterial genes [2][4].Use of rare codons was hypothesized to slow down the translation rate to ensure optimal levels of protein expression and proper folding [5], however regulation of translation rate in bacteria might rather rely on specific sequence fragments than simply on rare codons [6]. Moreover, it was shown that a synonymous substitution can result in a protein with different folding and different function, probably by causing ribosome stalling [7]. There are also reports of translational pressure in eukaryotes [8], [9]. While the implications of translational pressure are ubiquitous (reviewed in [10], [11]), a growing body of evidence suggests that it is not the main driver of synonymous codon preference.

Mutational pressure is produced by distinct probability of different substitution types. A predominant factor driving codon usage is believed to be GC content, i.e. the summed relative abundance of G and C nucleotides [12], [13]. Additional factors that might contribute to mutational pressure are deoxycytidine methylation in CpG (C-phosphate-G) dinucleotide context and subsequent deamination that results in C-T substitution [14]. Nucleotide content bias in viral RNA genomes could also be produced by RNA editing by cellular transaminases (e.g. ADAR and APOBEC); however, the extent of such editing of cellular mRNA is still a matter of controversy [15], [16]. Although RNA editing has been speculated to complement to variability of viral genomes [17], no mechanistic proof has been presented, and its role in the evolution of viruses remains to be proven.

Several additional factors of DNA/RNA variation can be classified as selection, but not translational pressure. Firstly, there may be a selection against a sequence pattern that triggers innate immunity, e.g. toll-like receptor 9 (TLR9) by CpG rich bacterial DNA [18], or against a target of immunity effectors. The latter case is exemplified by UpA dinucleotide, targeted by RNAse L [19], which presumably resulted in reduced UpA content in, e.g. hepatitis C virus [20]. Another factor that might constrain synonymous codon usage, especially in RNA viruses, is the secondary RNA structure, either nonspecific RNA folding, presumably to protect it from intracellular defense mechanisms [21], [22], or regulatory structural RNA elements commonly found in viruses within the coding sequence [23], [24]. These forces are hard to distinguish from the mutational pressure because they act above the translation level.

Investigation of CUB source is most commonly performed in silico and relies on several approaches, which can be generally classified as host-dependent and host-independent. Host-dependent methods rely upon either total codon usage statistics for an organism and compare it to codon usage in specific genes, or upon tRNA abundance, which can be measured experimentally or extrapolated from the number of different tRNA genes in the genome [25][27]. These approaches have limited value in virology because tRNA statistics are available only for a few model organisms. Also, the native host of a virus is often not known, and virus replication commonly takes place only in a specific type of cell, which may have relative tRNA concentrations and codon usage distinct from other tissues [28]. In addition, viral infection can result in inhibition or activation of RNA polymerase III and thus affect transcription of multiple genes, including tRNAs, in an infected cell [29], [30]. The host-independent approach accounts only for uneven frequency of synonymous codons and is commonly expressed as effective number of codons, ENC [31]. This single number theoretically ranges from 61 (all codons used equally) to 20 (only one codon used per amino acid). ENC below 40 is commonly treated as evidence of a strong CUB. It is a robust and organism-independent method that is universally used to evaluate CUB. A statistical correction termed Nc' was subsequently developed to account for genomic GC content [32]. While a notable improvement over the original method, Nc' has its faults and limitations, especially in the case of short analyzed sequences [33]. In addition, Nc' does not explicitly account for mononucleotide and dinucleotide composition bias that may be especially high in viral genomes.

Most RNA viruses display low to moderate CUB, which was attributed mainly to GC and dinucleotide content [34]. Combined GC content was the first measure of genome composition, implemented even before the genetic code was deciphered [35]. While this simple criterion covers the majority of genome composition bias, it does not account for different mononucleotide frequencies. This work investigates the impact of different types of mutation pressure on CUB in RNA viruses and provides an analysis of CUB in common RNA viruses.

Materials and Methods

Coding sequences of 29 animal RNA viruses representing 12 families (Table 1) were analyzed for codon usage and dinucleotide bias. Reference GenBank records and sequences of prototype strains were preferred. Concatemers of open reading frames (ORFs) were used for viruses with segmented genomes. For viruses with a complex coding strategy concatemers of ORFs, excluding regions with overlapping frames (as described in the corresponding Genbank records), were used. The exact genes used in the study and virus name acronyms are provided in Table 1. Additionally, five representative datasets of common RNA viruses were created. The sequences were chosen from all available complete coding sequences by automatically omitting identical or very similar sequences. The cutoff for dropping similar sequences was selected individually for each virus to produce datasets of a manageable size. The datasets were 69 out of 1525 available complete coding sequences of hepatitis C virus (HCV), all of 39 of hepatitis A virus (HAV) sequences, 74 out of 1283 Dengue 1 sequences, 68 out of 154 human enterovirus C sequences, and 79 out of 12327 segment 2 coding sequences of influenza A virus (PB1 polymerase fragment, not excluding PB1-F2 ORF). Simulated random sequences were created by first generating random values of third-position nucleotide frequencies. The randomization algorithm was designed to generate extreme values to cover all theoretically possible range of values in a reasonably sized dataset. Then 1000 random amino acid sequences were generated using these third-position nucleotide frequencies for synonymous codon choice.

The effective number of codons, ENC [31], was calculated with the “chips” module in the EMBOSS package (http://emboss.sourceforge.net/). ENC of 20 corresponds to extreme bias (only one synonymous codon per amino acid), ENC of 61 indicates absence of bias. Python 2.6 scripts were developed for dinucleotide content analysis (dint-stat), sequence randomization and shuffling (cub-stat) (available at http://www.poliomielit.ru/images/doc/enc-stat.rar). The difference between these two approaches is that shuffling moves sequence fragments (e.g. dinucleotides) from one place in a sequence to another, while randomization first counts sequence statistics, and then generates a random sequence aiming to reach these values. As generation of sequences is random, they never have precisely the same composition as the original sequence. A total of 1000 scrambled replicates were analyzed for each virus and each algorithm. The algorithms of these scripts are summarized below.

N3 correction

N3 correction shuffled third positions of synonymous codons, thus precisely preserving genomic mononucleotide frequencies at the third codon position (Fig. 1). Technically, all synonymous third position nucleotides were first extracted from the genome to a pool, and then randomly distributed back among synonymous positions whilst preserving the amino acid sequence.

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Figure 1. Effective number of codons in the original and shuffled sequences.

(A) Explanation of shuffling algorithms. (B) Effective number of codons in the original virus sequence and the mean ENC of 1000 sequences randomized or scrambled using different algorithms. Virus name acronyms are provided in Table 1.

https://doi.org/10.1371/journal.pone.0056642.g001

(GC)3 correction

(GC)3 correction shuffled third positions of synonymous codons like N3 correction, but prior to distribution of the third-position nucleotides from the pool back to sequence G content was set equal to C, and A equal to T, therefore preserving combined GC content, but not individual nucleotide content.

dN23 and dN31 corrections

dN23 and dN31 corrections similarly shuffled dinucleotides between positions 2-3 (or 3-1, respectively) of synonymous codons whilst preserving amino acid sequence; 6-fold degenerate codons were treated as independent 2- and 4-fold degenerate. As a result, first or second codon position was intact and only the third position was changed. Use of dN23 dinucleotide shuffling ensured precise preservation of position 2-3, but not position 3-1 dinucleotide content, while the dN31 had an opposite effect.

dN231 correction

dN231 correction swapped compatible trinucleotides between codon positions 2-3-1 whilst preserving amino acid sequence (Fig. 1); 6-fold degenerate codons were treated as independent 2- and 4-fold degenerate. As a result, codon position 2 and position 1 of the following codon were identical before and after shuffling, and distinct genomic dinucleotide content at both codon positions 2-3 and 3-1 was the same as in the original sequence (Fig. 1).

Relative dinucleotide bias

Relative dinucleotide bias (RDB) was calculated as the ratio of observed to expected dinucleotide frequency (the product of genomic nucleotide frequencies at the corresponding codon positions, e.g. £ [C2]×£ [G3] for CpG23 dinucleotide) to exclude the effect of mononucleotide composition on apparent dinucleotides ratio.

Statistical analysis was carried out with Student's t-test as implemented in Microsoft Excel. Spearman correlation was calculated with GraphPad Prism 4.0 (GraphPad Software Inc.). Variance of nucleotide frequencies was calculated as , where n is the number of states (2 for GC content, 4 for mononucleotides, 16 for dinucleotides) and x is the frequency of a (di)nucleotide.

Results

A well-known feature of genomes in general, and RNA viruses in particular, is dinucleotide bias [36], which also complements to CUB [34]. There are three dinucleotide positions in a codon. Variation of dinucleotide 1–2 is strongly restricted by amino acid sequence. Dinucleotides 2-3 and 3-1 may seem equally evolutionarily flexible because they both include the variable third codon position. Usually, a correction for either position 2-3 or 3-1 dinucleotide content is used to explore origins of CUB [37]. However, dinucleotide content varies between codon positions 2-3 and 3-1. To illustrate this, the frequency of the CpG dinucleotide at these codon positions was calculated in RNA viruses. As reported previously [36], CpG dinucleotide was suppressed in all RNA viruses, albeit to a different extent. Relative frequency of CpG dinucleotide was lower at codon positions 2-3 compared to 3-1 in 26 out of 29 RNA viruses (Fig. 1A). To evaluate the significance of this observation, extended datasets were tested for five viruses (Fig. 1B). In four viruses lower CpG frequency at codon position 2-3 was statistically significant with p-values below 10−7 (Student's t-test). In influenza A virus there was an insignificant excess of CpG at codon positions 2-3 (p>0.05), which could be explained, for example, by different selection pressures in the mammalian and bird hosts [38]. Statistically significant difference of RDB between codon positions could also be observed for other dinucleotides in many RNA viruses (data not shown), but without an obvious common pattern among the codon positions, as seen for the CpG dinucleotide.

We then evaluated how accounting for the complex dinucleotide composition patterns could improve analysis of codon usage bias. A general rationale for analyzing impact of (di)nucleotide bias on ENC is randomizing or shuffling synonymous codons in the original genome sequence whilst preserving the factor under study [37]. When shuffled sequences have an ENC close to the original sequence, the constraint (e.g. the nucleotide content) fully explains the CUB (or, in simple words, at that (di)nucleotide composition ENC cannot possibly be higher than it is). On the other hand, a difference between ENC in the original and simulated sequences shows the extent of CUB that could not be explained by the kind of pressure (nucleotide or dinucleotide) that was used as a constraint for the shuffling algorithm. Shuffling was preferred to randomization because it precisely preserved the positional dinucleotide content, produced sequences with more uniform ENC values, evident as lower standard deviation (SD) in sequences that were shuffled vs. randomized under the same constraint, and slightly lower ENC values than upon randomization (data not shown). Unfortunately, common statistical tests were poorly applicable to this approach, because all factors that affect codon preference (e.g. structural RNA elements) could not be accounted for, and therefore even a negligible difference in ENC between the original and generated sequences (e.g. 0.2 SD) passed a formal significance test upon increasing the number of replicates. As the standard deviation of ENC in scrambled sequences of a virus was on average 0.29 upon (GC)3 correction, 0.27 upon N3 correction,0.22 upon dN23 correction and 0.21 upon dN231 correction), and maximum SD in any virus/algorithm combination was 0.62, ENC difference above 1 was considered as significant.

Consistent with previous studies [34], ENC in RNA viruses ranged from 38.2 to 58.3. In sequences shuffled with preserved third codon position GC content, ENC was below 61 in all viruses (Fig. 1), indicating the effect of GC content on CUB. This finding was compatible with ENC values in RNA viruses that were observed upon Nc' correction, which is also based on GC content [34]. However, the ENC in (GC)3 shuffled sequences was higher than the original ENC on average by 3.5, but by up to 8.9, indicating that GC content imparity cannot explain all CUB observed in RNA viruses. Viruses often have uneven mononucleotide content, thus compromising GC correction approaches. For example, Aichi picornavirus has 54% C and only 16% G content at the third codon position [39].

Shuffling of the third-position nucleotide (N3 correction) produced ENC values lower than (GC)3 correction in all viruses. In some instances the difference between (GC)3 and N3 randomization was marginal, but in 8 out of 29 viruses it was above 1 and up to 4 in, e.g. HTLV1 and HEV. Overall, N3 shuffling, although an improvement over GC randomization, produced ENC values that were higher than the original ENC by more than one in all viruses except for Rubella, indicating residual unexplained bias.

The impact of dinucleotide content on CUB was next tested. A notable disparity of dinucleotide frequencies at codon positions 2-3 and 3-1 (Fig. 2) justified treating them independently in shuffling algorithms. In 22 out of 29 viruses, shuffling of codon position 2-3 dinucleotide (dN23) produced ENC values that were lower by more than 1 than ENC upon N3 correction (Fig. 1). In 15 viruses, dinucleotide content-corrected ENC values were lower than N3-corrected values by more than 2, indicating a strong impact of position 2-3 dinucleotide content on CUB. Shuffling of dinucleotide position 3-1, on the contrary, did not result in a decrease of ENC, which differed from N3 shuffled sequences by less than 0.3 SD in all viruses. To evaluate the impact of distinct dinucleotide content in codon positions 2-3 and 3-1 simultaneously, the codon position 2-3-1 triplet was shuffled between compatible codon pairs (dN231 correction, Fig. 1). Consistent with results of dN23 and dN31 shuffling, ENC upon dN231 shuffling did not differ significantly from dN23 shuffling, supporting negligible impact of dinucleotide content at codon positions 3-1 on synonymous codon preference.

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Figure 2. CpG content at different codon positions in RNA viruses.

(A) Relative CpG dinucleotide content at codon positions 2-3 and 3-1 in RNA viruses (Table 1) was calculated as a ratio of the observed CpG content to the expected, calculated as the product of genomic nucleotide frequencies at the corresponding codon positions, e.g. £ [C2]×£ [G3] for CpG23 dinucleotide. (B) Mean relative CpG dinucleotide content in extended datasets of five RNA viruses. White, codon positions 2-3; gray, codon positions 3-1. Error bars indicate standard errors of the mean. Virus name acronyms are provided in Table 1.

https://doi.org/10.1371/journal.pone.0056642.g002

Shuffling synonymous codons is a practical way to approach origins of CUB. Another way to evaluate the effect of RNA composition on CUB is plotting ENC against GC content in studied sequences and comparing this to the theoretically expected ENC at various (GC)3 content [31]. In RNA viruses, actual ENC was always lower than the expected ENC at that GC content, implying that GC content does not fully explain CUB [34]. Unfortunately, the formula suggested by Wright to predict ENC values as a function of GC content [31] is not applicable to distinct frequencies of four nucleotides. To study limitations of classic GC content correction methods and develop a better measure of genome composition, a data set of one thousand 10002 nucleotide (3334 codon) long simulated coding sequences with highly variable third-position nucleotide content was generated. Firstly, relation of ENC and GC content in simulated sequences was compared to viral genomes and to the theoretical prediction of the Wright's curve [31]. While the theoretical estimation properly described maximum possible ENC as a function of GC content (Fig. 3A), it was not informative of a possible range of ENC at a given GC content in simulated sequences with extreme mononucleotide biases (Fig. 3A, grey dots). In fact, ENC values close to 30 could be achieved at any GC content. ENC in actual viruses was always below the Wright's curve (Fig. 3A, black dots); however the result for simulated sequences indicated that this is not necessarily an evidence of translational bias. Importantly, the limitations of GC content as the sole genome composition measure equally apply to eukaryotic genomes, as mRNAs may also have uneven mononucleotide composition [40]. Next, ENC in the simulated sequences was plotted against the variance of sequence content characteristics (Fig. 3B, 3C, 3D). Consistent with Figure 2A, GC content variance was a poor measure of sequence composition because it described only maximum ENC in simulated sequences, but poorly reflected possible ENC range at a given variance (Fig. 3B). Variance of mononucleotide and dinucleotide frequencies in simulated sequences provided a single measure of genome composition bias with a narrow corresponding ENC range (Fig. 3C, 3D, grey dots). Actual RNA virus sequences processed similarly (Fig. 3C, 3D, black dots) had notably lower ENC values than random sequences with the same variance of the third-position mononucleotide frequencies (Fig. 3C). This was consistent with results of mononucleotide content shuffling that only partially explained CUB (Fig. 1). When ENC in RNA viruses was plotted against the variance of dinucleotide frequencies at codon position 2-3 (Fig. 3D), the values overlaid with ENC values of simulated sequences, implying that dinucleotide bias can explain almost all apparent CUB in RNA viruses. Also, consistent with shuffling studies, in few viruses ENC was slightly lower than that of simulated sequences with the same variance of nucleotide content, indicating additional sources of CUB. Therefore, variance of dinucleotide frequencies is a good descriptor of mutational bias and can be used to predict a range of ENC for a given genome composition.

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Figure 3. Plots of ENC vs. sequence content in 29 RNA viruses indicated in Table 1 (black dots) and in 1000 simulated sequences with random third-position nucleotide content (gray circles).

Plot of ENC against GC content (A), variance of third-position GC content (B), variance of third-position nucleotide frequencies (D) and variance of dinucleotide frequencies at codon position 2-3 (D). Solid line in panel (a) indicates theoretical prediction of ENC as a function of GC content bias [31].

https://doi.org/10.1371/journal.pone.0056642.g003

This and previous studies identified mutational pressure, and the third-position nucleotide content in particular, as the main driver of synonymous codon preference. It has also been reported that GC content affects all codon positions in plant viruses [41] and can influence amino acid usage in HIV [42]. A plot of e.g. C content at codon positions 1+2 against position 3 in studied viruses (Fig. 4A) exemplifies that nucleotide content is generally consistent at different codon positions. A similar observation has been reported for vertebrate DNA viruses and plant viruses [41], [43]. A further analysis showed that genomic nucleotide content could significantly affect choice between compatible amino acids. The ratio of isoleucine (encoded by AUH triplets) to leucine (encoded by CUN and UUR triplets) in the viral protein sequence correlated with A content at the synonymous third codon position (Fig. 4B). Similar but weaker correlation could be observed between the third codon position G content and valine (GUN codons) to leucine (CUN and UUR codons) ratio (data not shown), but not for the ratio between the third-position G content and poorly compatible arginine (CGN and AGR codons) and glycine (GGN codons) content in the encoded protein (Fig. 4C). Previously, it has been shown that GC content in genomes of higher organisms can affect amino acid composition [44][46]. Our results indicate that this effect is maintained in RNA viruses. Therefore, genomic nucleotide content is not only the key driver of synonymous codon choice, but also a significant factor that defines choice between compatible amino acids.

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Figure 4. Correlation between sequence content at synonymous positions and sequence content at non synonymous positions/encoded protein content.

Correlation between C content at codon positions 1+2 vs. 3 (A); ratio of isoleucine to leucine to third-position A content (B); ratio of arginine to glycine content to third-position G content (C) in 29 animal RNA viruses (Table 1).

https://doi.org/10.1371/journal.pone.0056642.g004

Discussion

The concept of synonymous and non-synonymous genetic variation is central to evolutionary studies. Synonymous, however, does not equal identical, because many factors can affect choice of synonymous codons. In RNA viruses “71% and 88% of CUB” was attributed to mutational pressure on nucleotide and dinucleotide content, respectively [34], which indicated the key role of mutational bias, but left a notable unexplained bias. A method that considered the impact of each of four nucleotides was superior to classical GC content correction (Figs. 1, 3), yet it failed to completely explain CUB in all but one virus. Dinucleotide bias at codon positions 2-3, but not 3-1, complemented notably to CUB in most viruses in our dataset. Overall, in 25 of 29 of the RNA viruses, mononucleotide and codon position 2-3 dinucleotide bias explained almost the entire apparent CUB. There is, however, a fundamental problem that complicates attributing dinucleotide bias to translational or mutational pressure. On one hand, in mammalian genomes there is CpG and UpA dinucleotide bias, and, consequently, NCG and NUA codons are relatively less frequent [47]. Therefore, translational bias towards common mammalian codons could produce secondary dinucleotide bias at codon positions 2-3. On the other hand, in RNA viruses there is also dinucleotide bias at codon positions 3-1 (Fig. 2), which implies additional types of pressure against, at least, CpG dinucleotide. Dinucleotide shuffling at distinct codon positions presented here allows distinguishing individual factors in a complex interplay of translational and mutational pressures on codon preference. Negligible effect of position 3-1 dinucleotide content on ENC (Fig. 1) may be explained by the fact that only few dinucleotides are usually highly biased, and e.g. pressure against the CpG dinucleotide at codon position 2-3 would strongly affect the four NCG codons and produce a strong bias. At positions 3-1 CpG bias would affect sixteen NNC codons, and only when they precede GNN codons. Therefore, the apparent effect of dinucleotide bias at codon positions 3-1 on CUB may be many times weaker that at codon positions 2-3, and below the detection limit of our method. Results of dinucleotide shuffling at codon positions 2-3 could also reflect the effect of translational bias, which could have produced secondary dinucleotide bias and increased dinucleotide content bias at codon position 2-3 compared 3-1 in most RNA viruses (Fig. 2). However, these are not the only possible explanations for the shuffling results.

Viruses are a unique form of life, and their life cycle that is largely distinct from host genome replication makes them both a valuable tool for exploring sources of CUB, and a source of confusion. In the mammalian host genome the CpG dinucleotide is underrepresented [48], because in this context C is methylated and then deaminated, producing C-T transition [14]. This creates an obvious mutational pressure on codon usage and results in underrepresentation of NCG codons. Such a feature of an organism's own genome allows recognition of non-self bacterial DNA by TLR9, an innate immunity sensor for CpG-rich DNA [18]. In mitochondria, CpG is also underrepresented [49], probably to avoid triggering TLR9. A similar sensor for CpG in RNA was predicted [38], but is not yet known. This hypothetical sensor or effector could explain CpG31 bias (Fig. 2) and also the dramatic effect of introduction of CpG dinucleotide at codon positions 3-1 on poliovirus viability [50]. If such a sensor was linked to translation, as, for example, translation-coupled endonucleases involved in no-go RNA decay [51], it could produce codon position-specific pressure on CpG dinucleotide content. Unfortunately, due to the host CUB driven by host mutational pressure, it is fundamentally impossible to unambiguously distinguish translational and mutational pressure on CUB in mammalian RNA viruses using only a computational approach.

Overall, dN23 and dN231 shuffling produced ENC values that differed from the original sequence by less than one in 25 of 29 viruses, indicating that we explored almost all types of pressure that affect overall codon usage. In three viruses (HIV, bunyavirus, rotavirus, and thogotovirus) ENC values upon any correction were still higher than ENC of the original sequence by more than 1. Therefore, additional factors are required to explain this residual CUB. In HIV, editing of cDNA by APOBEC3 could introduce additional genome composition bias [52]. One additional unaccounted factor could be the influence of (di)nucleotide bias on 6-fold degenerate codons. Unfortunately, it is not possible to prudently evaluate such an effect. Randomization of 6-fold degenerate codons would inevitably rely on inadequate estimates in genome composition, either nucleotide content at codon positions 1 and 2, which is dependent on amino acid sequence, or the third-position statistics, which would produce changes in positional (di)nucleotide content. Shuffling dinucleotides between different synonymous codon positions, on the other hand, would ruin the positional (di)nucleotide frequencies in the genome. The latter approach to 6-fold degenerate codons was not superior over the algorithms used here, moreover it could introduce additional bias evident as ENC values higher than upon dN231 correction or lower than in the original sequence (data not shown).

The algorithms presented here provide a substantial improvement in dissecting origins of codon usage bias in RNA viruses. It is obvious that corrections based on GC content are inadequate not only in viruses, but in any application, as conventional dsDNA genomes may have different genome composition biases for different genome strands [40]. As for dinucleotide composition, different codon positions should always be analyzed alongside, because they are affected by different kinds of pressure. Considering only dinucleotide content at positions 2-3 or 3-1, as well as combining different codon positions for analysis, can lead to false conclusions. Altogether, different shuffling algorithms allowed almost complete exploration of the correlation of nucleotide content and ENC in all but a few RNA viruses, but led to a conclusion that it is not possible to definitively discover forces affecting dinucleotide bias and CUB by only computational means. A number of additional questions remain unanswered in the field of synonymous variation in the genomes. Firstly, the origin of nucleotide bias that is strong enough not only to drive synonymous codon preference, but also to affect amino acid usage, remains largely unknown. Second, the source of primary dinucleotide content bias in RNA viruses remains obscure. Third, a single virus genome might not contain enough information to discover fine factors affecting CUB. Scanning window approaches working with an alignment of multiple genomes, akin to bootscanning, and the corresponding significance criteria, are required to analyze local CUB, while algorithms that properly address the synonymous variation in 6-fold degenerate codons and account for RNA secondary structure might further clarify the role of translation pressure. Such statistical methods, coupled with experimental analysis, can also elucidate fundamental steps of virus–cell interaction.

Acknowledgments

Authors are grateful to Alexey Nesterenko and Petr Mamonov for support with Python code and to an anonymous reviewer for helpful suggestions.

Author Contributions

Conceived and designed the experiments: ANL. Performed the experiments: ISB. Analyzed the data: ANL ISB. Wrote the paper: ANL.

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