Analysis of synonymous codon usage and evolution of begomoviruses

Analysis of synonymous codon usage and evolution of begomoviruses 1 first-author 2 1 1 1 corresp ‡Corresponding Author ‡Corresponding Author †E-mail:zzhou@zju.edu.cn †E-mail:zzhou@zju.edu.cn zzhou@zju.edu.cn College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310029, China College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310029, China School of Agriculture and Food Science, Zhejiang Forestry University, Lin’an 311300, China School of Agriculture and Food Science, Zhejiang Forestry University, Lin’an 311300, China Begomoviruses are single-stranded DNA viruses and cause severe diseases in major crop plants worldwide. Based on current genome sequence analyses, we found that synonymous codon usage variations in the protein-coding genes of begomoviruses are mainly influenced by mutation bias. Base composition analysis suggested that the codon usage bias of AV1 and BV1 genes is significant and their expressions are high. Fourteen codons were determined as translational optimal ones according to the comparison of codon usage patterns between highly and lowly expressed genes. Interestingly the codon usages between begomoviruses from the Old and the New Worlds are apparently different, which supports the idea that the bipartite begomoviruses of the New World might originate from bipartite ones of the Old World, whereas the latter evolve from the Old World monopartite begomoviruses. Begomovirus Synonymous codon usage Evolution Bioinformatics INTRODUCTION 1 Synonymous codon usage bias has been investigated in many organisms, as the genetic code is degenerate. The synonymous codons are also non-randomly used in viruses infecting living organisms. Several factors such as mutational bias (Jenkins and Holmes, 2003 2003 ; Gu et al., 2004 2004 ; Zhou et al., 2005 2005 ), translational selection (Sau et al., 2005a 2005a ; 2005b 2005b ; 2005c 2005c ), gene function (Wang et al., 2002 2002 ; Gu et al., 2004 2004 ; Zhou et al., 2005 2005 ), gene length (Sau et al., 2005a 2005a ), and CpG island (Shackelton et al., 2006 2006 ) were found to influence codon usage in animal viruses and phages, and mutational bias was found as the major determinant factor. Adams and Antoniw ( 2004 2004 ) also suggested that mutational bias rather than translational selection was the major determinant of codon usage variation amongst plant viruses. Geminiviruses (family Geminiviridae ) are single-stranded DNA (ssDNA) viruses that cause severe disease in major crop plants worldwide. Most geminiviruses belong to the genus Begomovirus , which are transmitted exclusively by the whitefly Bemisia tabaci (Harrison and Robinson, 1999 1999 ). Many begomoviruses have bipartite genomes known as DNA A and DNA B. DNA A contains the AV1 (coat protein) and AV2 ORFs (open reading frames) in the virus strand and, on the complementary strand, four ORFs: AC1 (replication initiation protein), AC2 (transcriptional activator protein), AC3 (replication enhancer protein) and AC4. The virus and complementary strands of DNA B contain two ORFs: BV1 (nuclear shuttle protein) and BC1 (movement protein). Some begomoviruses have a monopartite genome and lack DNA B. Phylogenetic analysis shows that begomoviruses can be generally divided into two groups, the Old World begomoviruses (eastern hemisphere, Asia, Africa, Europe, the Mediterranean areas) and the New World begomoviruses (western hemisphere, the Americas). All the New World begomoviruses are evolved to bipartite with lack of AV2 ORF in DNA A, whereas both bipartite and monopartite begomoviruses in the Old World encode AV2 ORF (Harrison and Robinson, 1999 1999 ). Because of their destructive effect on cash crops (Moffat, 1999 1999 ; Moriones and Navas-Castillo, 2000 2000 ; Mansoor et al., 2006 2006 ), numerous studies on begomoviruses have been conducted to understand their symptoms, host range, distribution, genome structure, gene function, and so on (Fauquet et al., 2003 2003 ; Zhou et al., 2003 2003 ). In this paper, we report the analysis of codon usage bias in begomoviruses and also perform an evolutionary analysis based on their codon usage pattern. MATERIALS AND METHODS 1 Dataset 2 The complete genomic sequences of 147 begomovirus species were downloaded from the GenBank database, from which a total of 932 variants of the 8 known protein-coding genes were extracted. To minimize sampling errors, 915 variants were selected for further analysis by the following sifting standard: (1) the selected genes should be complete coding DNA sequences (CDS) with correct initial and terminal codons; (2) only those CDS including at least 80 codons were selected in the dataset; (3) those CDS with uncertain annotation or annotated as hypothetical protein-coding genes were excluded from this study. Measures of synonymous codon usage bias 2 GC content is the frequency of G+C in a coding gene. GC1, GC2 and GC3 contents are the frequencies of G+C at the first, second and third positions of codons, respectively. A3, T3, G3 and C3 contents are the frequencies of A, T, G and C at the synonymous third position of codons, respectively. Effective number of codons ( N c ), ranging from 20 to 61, is generally used to measure the bias of synonymous codons. When N c value approaches 20, only one codon is used with extreme bias for one amino acid and, if the value is up to 61, the anonymous codons are used equally with no bias (Wright, 1990 1990 ). Relative synonymous codon usage (RSCU) is defined as the ratio of the observed frequency of codons to the expected frequency given that all the synonymous codons for the same amino acids are used equally. RSCU values have no relation to the amino acids usage and the abundance ratio of synonymous codons, which can directly reflect the bias of synonymous codon usage (Sharp and Li, 1986 1986 ). The codon adaptation index (CAI) was used to estimate the extent of bias towards codons that were known to be preferred in highly expressed genes (Sharp and Li, 1987 1987 ). It is now proved that CAI values mostly approach the theoretical values to reflect the expression level of a gene. Thus it has been widely utilized to measure the gene expression level (Naya et al., 2001 2001 ; Gupta et al., 2004 2004 ). A CAI value ranges from 0 to 1.0, and a higher value indicates a stronger codon usage bias and a higher expression level. Statistical analysis 2 CodonW version 1.4.2 (John Peden, available at http://sourceforge.net/projects/codonw/ http://sourceforge.net/projects/codonw/ ), an integrated program, was utilized for calculating GC, GC3 contents and N c values and then carrying out correspondence analysis (CA), while GC, GC1, GC2 and GC3 contents were calculated by practical extraction and report language (PERL) scripts which were written by us. A3, T3, G3 and C3 contents, as well as RSCU and CAI values, were also calculated by using PERL scripts. Correspondence analysis (CA) on synonymous codon usage 2 CA is the most commonly used multivariate statistical analysis at present (Greenacre, 1984 1984 ). This method can successfully present variation trends among genes, and then distribute them along the continuous axis by using RSCU value as variable data. In CA, all genes were plotted in a 59-dimensional hyperspace, according to the usage of the 59 sense codons. Major variation trends can be determined using these RSCU values and genes ordered according to their positions along the major axis, which can also be used to distinguish the major factors influencing the codon usage of a gene. Determination of optimal codons 2 The set of reference sequences used to calculate CAI values in this study were the genes coding for coat proteins. According to the calculated CAI values, 5% of the total genes with extremely high and low CAI values were regarded as the high and low dataset, respectively. Then we calculated the average RSCU values of the two gene samples and subtracted them subsequently in each dataset group (Δ RSCU ). If the Δ RSCU values are larger than 0.08, then this codon will be defined as the optimal codon (Duret and Mouchiroud, 1999 1999 ). RESULTS 1 Base composition analysis 2 In order to examine the base composition variation among different genes, the base composition of different protein-coding genes was calculated. Table 1 1 shows that with the exception of the AC3 gene that has a lower average GC content (0.391), no obvious difference in GC content was found among other tested genes. However, differences in GC content at the different synonymous positions of codons were apparent. For example, GC content at the synonymous first position of codons for the AV2 gene is 0.563, while that for the AC4 gene is only 0.412. GC content at the synonymous third position of codons for the AC4 gene is 0.534, while that for the BV1 gene is only 0.399. Values in parenthesis indicate standard deviation ( SD ) Genes No. GC GC1 GC2 GC3 A3 T3 G3 C3 AV1 ( V1 ) 147 0.452 (0.021) * * 0.465 (0.015) 0.408 (0.013) 0.485 (0.059) 0.159 (0.030) 0.357 (0.044) 0.266 (0.028) 0.219 (0.041) AV2 ( V2 ) 92 0.484 (0.018) 0.563 (0.040) 0.404 (0.020) 0.487 (0.042) 0.220 (0.030) 0.294 (0.033) 0.245 (0.030) 0.241 (0.028) AC1 ( C1 ) 147 0.448 (0.018) 0.497 (0.018) 0.395 (0.012) 0.452 (0.040) 0.275 (0.027) 0.273 (0.028) 0.190 (0.027) 0.262 (0.023) AC2 ( C2 ) 143 0.471 (0.022) 0.521 (0.031) 0.466 (0.026) 0.427 (0.037) 0.293 (0.029) 0.280 (0.028) 0.185 (0.029) 0.241 (0.032) AC3 ( C3 ) 145 0.391 (0.021) 0.421 (0.028) 0.308 (0.027) 0.442 (0.040) 0.283 (0.034) 0.275 (0.026) 0.200 (0.030) 0.242 (0.028) AC4 ( C4 ) 117 0.483 (0.023) 0.412 (0.026) 0.502 (0.055) 0.534 (0.030) 0.265 (0.031) 0.201 (0.039) 0.331 (0.038) 0.203 (0.025) BV1 53 0.423 (0.028) 0.459 (0.026) 0.411 (0.025) 0.399 (0.059) 0.218 (0.030) 0.383 (0.050) 0.218 (0.036) 0.181 (0.043) BC1 53 0.447 (0.028) 0.457 (0.017) 0.412 (0.017) 0.473 (0.068) 0.300 (0.046) 0.227 (0.041) 0.230 (0.047) 0.243 (0.053) It was also observed that the average percentage of GC content was generally higher at the first than at the second codon position (Table 1 1 ), except for the AC4 gene whose GC2 content was larger than GC1 content. This result suggested that the AC4 gene might have a special codon usage pattern. In addition we found that AV1 and BV1 genes had a tendency to usage bias at the synonymous third codon position. The AV1 gene does not tend to use A(T)-ending or G(C)-ending codons but tends to use T-ending codons relative to A-ending codons. However, BV1 gene apparently uses T-ending codons and seldom used C-ending codons. Therefore, AV1 and BV1 genes should show a stronger codon usage bias among the begomovirus genes. Analysis of factors shaping codon usage 2 An N c plot (a plot of N c vs GC3 content) was widely used to investigate the determinants of the codon usage variations among genes in different organisms. It was suggested that if GC3 content was the only determinant of the codon usage variation among the genes, then the N c value would fall on the continuous curve between N c value and GC3 content (Wright, 1990 1990 ). In general, if genes are distributed in N c plots approaching the expected continuous curve with no selection, then codon usage bias of genes is mainly influenced by compositional constraints. Otherwise the codon usage bias of genes is more affected by other factors such as translational selection, etc. The N c plot for genes of the 147 begomoviruses showed that although a small number of genes were located on the expected curve, most points lay far below the expected curve (Fig. 1 1 ), suggesting that apart from mutation bias, other factors might also play a role in shaping the codon usage bias of begomoviruses (Guo et al., 2007 2007 ). Base composition analysis suggested that the codon usage of the AC4 gene, which is always embedded in the AC1 gene, might differentiate from other genes. In addition, strong codon bias was observed in AV1 gene. Because of the special characteristics of the abovementioned genes, we selected them for examining the influence factors in shaping the codon usage (Fig. 2a 2a ). The N c plots of AV1 , AC1 and AC4 genes suggested that apart from compositional constraints, other factors might play important roles in shaping their codon usage, although high translational selection seems to lay stress on the AC4 gene because of the wide range of N c values for the same GC3 content. To examine the reason for N c variation under the same GC3 content, the relationship between the GC1, GC2 and GC3 contents for AV1 , AC1 and AC4 genes was further examined (Fig. 2b 2b ). It was observed that the GC1 content was always higher than GC2 content for the AV1 and AC1 genes, whereas the GC1 content of the AC4 gene was generally lower than the GC2 content. This result was coincident with the base composition analysis (Table 1 1 ). Thus the variations of the synonymous first and second codon positions for the AC4 gene might be caused by the translational selection utilizing G or C at the synonymous second position. As the AC4 gene is always embedded in the AC1 gene, the codon usage pattern of the AC4 gene might be influenced by the AC1 gene. The sequences of AC1 and AC4 genes were aligned. It was found that the synonymous third codon position of the AC1 gene corresponded to the second codon position of the AC4 gene for all the begomoviruses. Thus the base compositional environment of the AC1 gene might also influence the codon usage pattern of the AC4 gene. It could be postulated that the second codon position of the AC4 gene that tended to use G or C should be influenced by compositional constraints other than translational selection. Determination of optimal codons 2 According to the variation analysis of base composition for the genes of begomoviruses, the AV1 gene was selected as a reference dataset to calculate the CAI values of all genes. The correlation analysis between CAI value and the positions of the genes along the first two major axes (generated by correspondence analysis), as well as other indices, was then calculated. It was observed that the CAI values were negatively correlated with the GC3 and GC contents ( r =−0.234 and r =−0.535 respectively, P < 0.01), while significantly positively correlated with axis 1 ( r =0.587, P < 0.01). Moreover the CAI value was also significantly negatively correlated with the N c value, indicating a tendency to a higher CAI value or a lower N c value and a higher expression level for begomovirus genes. Thus it is feasible to use the AV1 gene as a reference dataset for our estimation of the CAI value of begomovirus genes. Based on the calculated CAI values, 5% of the total genes with extremely high and low CAI values were regarded as the high and the low datasets, respectively. Then we compared the codon usage of the high dataset to the low dataset. Table 2 2 shows that 14 codons that code 13 amino acids were apparently used at a high level, and can be determined as translational optimal codons. Out of the 14 optimal codons, 5 were ended with G, 1 with C and 8 with T. Major codons, defined by a higher RSCU value in highly expressed genes, are indicated as (Δ RSCU ≥0.30) Major codons, defined by a higher RSCU value in highly expressed genes, are indicated as (Δ RSCU ≥0.20) Major codons, defined by a higher RSCU value in highly expressed genes, are indicated as (Δ RSCU ≥0.08) Amino acid Codon High Low Δ RSCU Ala GCA 1.107 0.885 0.222 ** ** GCC 0.734 1.401 −0.667 GCG 0.438 0.977 −0.539 GCT 1.722 0.737 0.985 *** *** Cys TGC 0.060 1.556 −1.495 TGT 1.940 0.444 1.495 *** *** Asp GAC 0.387 1.043 −0.657 GAT 1.613 0.957 0.657 *** *** Glu GAA 0.859 1.087 −0.228 GAG 1.141 0.913 0.228 ** ** Phe TTC 0.748 1.106 −0.358 TTT 1.252 0.894 0.358 *** *** Gly GGA 0.647 1.485 −0.838 GGC 0.849 0.782 0.067 GGG 0.696 1.042 −0.347 GGT 1.809 0.691 1.118 *** *** His CAC 0.376 1.075 −0.698 CAT 1.624 0.925 0.698 *** *** Ile ATA 0.907 0.692 0.215 ** ** ATC 0.804 1.286 −0.481 ATT 1.288 1.022 0.266 Lys AAA 0.451 1.034 −0.583 AAG 1.549 0.966 0.583 *** *** Leu CTA 0.186 1.330 −1.145 CTC 0.594 0.923 −0.329 CTG 0.755 1.045 −0.291 CTT 0.829 1.412 −0.583 TTA 1.113 0.937 0.177 * * TTG 2.524 0.353 2.171 *** *** Asn AAC 0.866 0.835 0.031 AAT 1.134 1.165 −0.031 Pro CCA 1.096 1.383 −0.287 CCC 1.773 0.697 1.077 *** *** CCG 0.162 0.886 −0.723 CCT 0.968 1.035 −0.067 Gln CAA 0.780 1.277 −0.497 CAG 1.220 0.723 0.497 *** *** Arg AGA 1.199 1.192 0.007 AGG 1.438 1.611 −0.174 CGA 0.570 1.336 −0.767 CGC 0.526 0.537 −0.011 CGG 0.461 0.511 −0.050 CGT 1.807 0.812 0.994 *** *** Ser AGC 1.142 0.748 0.394 *** *** AGT 1.042 0.830 0.212 ** ** TCA 0.592 1.477 −0.886 TCC 0.917 0.985 −0.068 TCG 0.867 1.094 −0.228 TCT 1.442 0.866 0.575 *** *** Thr ACA 0.661 1.237 −0.576 ACC 0.602 0.728 −0.127 ACG 0.558 1.546 −0.988 ACT 2.179 0.489 1.690 *** *** Val GTA 0.448 1.208 −0.760 GTC 0.998 0.849 0.149 * * GTG 1.034 0.784 0.250 ** ** GTT 1.520 1.159 0.361 Tyr TAC 0.540 0.938 −0.399 TAT 1.460 1.062 0.399 Codon usage reflecting the evolution process 2 CA was performed on the RSCU value based on the concatenated genes for each begomovirus genome. Fig. 3 3 shows the positions of all tested begomoviruses along the first two major axes. The first major axis accounted for 15.2% of the total variations, while the second major axis accounted for 9.9% of the total variations. In order to detect the codon usage variation of different genomes, the begomoviruses were divided into three groups including the Old World begomoviruses with monopartite genomes (OM), the Old World begomoviruses with bipartite genomes (OB), and the New World begomoviruses with bipartite genomes (NB). The distribution of the three groups along the first two major axes showed that the Old World monopartite begomoviruses and the New World bipartite ones were located in two independent fields, indicating that the two groups of begomoviruses exhibit a different codon usage pattern. Because the species with a close genetic relationship always present a similar codon usage pattern (Sharp et al., 1988 1988 ), the genetic relationship of the two groups of begomoviruses should be far removed from each other. As to the Old World begomoviruses with bipartite genomes, we found that the majority of them exhibited a similar codon usage pattern with the Old World monopartite begomoviruses, and a few of them showed a similar codon usage to the New World bipartite begomoviruses. An explication of this result might be that a number of Old World bipartite begomoviruses evolved to adopt the codon usage pattern of some Old World monopartite begomoviruses. Moreover, the New World bipartite begomoviruses were closely related to a small number of the Old World bipartite ones and far removed from the Old World monopartite ones. DISCUSSION 1 The results of the N c -plot and base composition analysis indicated that the codon usage pattern of begomoviruses was influenced by mutation bias as well as other factors such as translational selection. Comparative analysis of AC1 and AC4 genes showed that the compositional environment of the former genes might play a role in dictating the codon usage of the latter gene. Thus, although it seems that strong translational selection might have an influence on shaping the codon usage of AC4 genes, the compositional constraints derived from AC1 genes might be the major determinant in determining codon usage. Consequently it can be speculated that mutation bias might play a major role in shaping the codon usage pattern of begomoviruses. As to the gene itself, the selection pressures from the external environment always act as effective factors in promoting the gene to adapt to the change in the external environment. On the other hand, direct changes to a gene will interfere with or be harmful to the gene. Therefore, the base mutation at the synonymous third codon position may not affect the protein expression for AC1 gene because of the degeneration of genetic codons. But for the embedded AC4 gene, the corresponding base mutation occurs at the second codon position, which probably results in the loss of function of the translated protein. Thus we suggested that AC4 gene might degenerate step by step during the long period of evolution, which might be an important reason for explaining the loss of function for the AC4 gene in the bipartite begomoviruses with DNA B. The variation analysis of the base composition for begomovirus genes showed that AV1 and BV1 genes exhibit a stronger codon usage bias and a higher gene expression level. Thus CAI values of different gene samples were calculated using the AV1 gene as a reference set. The results of correlation analysis indicated the reliability of choosing AV1 gene as a high expression gene sample for begomoviruses. Then 14 codons were determined as the major optimal codons for begomoviruses. That will be very important during the design of degenerate primers, the introduction of point mutation, the modification of the virus genes and the investigation of the evolution mechanism of species at the molecule level. It was speculated that monopartite begomoviruses emerged approximately 130 million years ago (Rybicki, 1994 1994 ; Mansoor et al., 2006 2006 ), suggesting that the begomoviruses should have evolved from the original monopartite viruses. Rybicki ( 1994 1994 ) suggested that the significant expansion of the New World begomoviruses might have occurred after the transmission of the Old World begomoviruses to those of the New World by whiteflies. Based on codon usage pattern analysis, it could be inferred that there was no direct relationship between the Old World monopartite begomoviruses and the New World bipartite ones. Interestingly, a small number of the Old World bipartite begomoviruses exhibited a similar codon usage pattern to the New World bipartite ones, which suggested that the ancestor of begomoviruses could have evolved from monopartite to bipartite ones before they were transferred to the New World areas. Subsequently it was not the Old World monopartite begomoviruses but the Old World bipartite ones that transmitted to the New World in a certain way and finally evolved into the New World bipartite begomoviruses. In other words, the New World bipartite begomoviruses probably resulted directly from the Old World bipartite ones, while the latter evolved from the Old World monopartite begomoviruses. It still remains unclear whether the New World begomoviruses directly evolved from the Old World bipartite begomoviruses because of the sharp environmental change after their transfer to the New World, or whether the Old World bipartite begomoviruses had evolved into the New World bipartite ones before their transmission to the New World. Rybicki ( 1994 1994 ) suggested that the absence of the AV2 gene in all the New World begomoviruses could be attributed to its earlier loss after the New World begomoviruses arriving in the New World. These results suggest that the current New World begomoviruses evolved from ancient viruses after transferring to the New World. Ha et al.( 2006 2006 ) speculated that the new identified begomovirus termed Corchorus yellow vein virus (CoYVV) might belong to a New World begomovirus group that previously existed in the Old World, which suggested that the common ancestor of the New World begomovirus might originate from the Old World begomovirus. 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