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Clustering of low usage codons in the translation initiation region of hepatitis C virus
Infection, Genetics and Evolution 18 (2013) 8–12
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Clustering of low usage codons in the translation initiation region of hepatitis C virus Jian-hua Zhou a, Jun-hong Su a, Hao-tai Chen a, Jie Zhang a, Li-na Ma a, Yao-zhong Ding a, Laszlo Stipkovits b, Susan Szathmary b, Zygmunt Pejsak c, Yong-sheng Liu a,⇑ a State Key Laboratory of Veterinary Etiological Biology, National Foot-and-Mouth Disease Reference Laboratory, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730046, Gansu, PR China b RT-Europe Center, Budapest, Hungary c Department of Swine Diseases, National Veterinary Research Institute, 57 Partyzantow, 24-100 Puławy, Poland
a r t i c l e
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Article history: Received 17 November 2012 Received in revised form 26 February 2013 Accepted 24 March 2013 Available online 22 April 2013 Keywords: Hepatitis C virus Synonymous codon usage value Overall codon usage pattern Translation initiation region
a b s t r a c t The adaptation of the overall codon usage pattern of hepatitis C virus (HCV) to that of human is estimated by the synonymous codon usage value (RSCU). The synonymous codon usage biases for the translation initiation region (TIR) of this virus are also analyzed by calculation of usage fluctuation of each synonymous codon along the TIR (the first 30 codon sites of the whole coding sequence of HCV). As for the overall codon usage pattern of HCV, this virus has a significant tendency to delete the codons with CpG or TpA dinucleotides. Turning to the adaptation of the overall codon usage of HCV to that of human, over half part of codons has a similar usage pattern between this virus and human, suggesting that the host cellular environment of the overall codon usage pattern influences the formation of codon usage for HCV. In addition, there is no obvious phenomenon that the codons with relatively low energy tend to be highly selected in the TIR of HCV, suggesting that the synonymous codon usage patterns for the TIR of HCV might be not affected by the secondary structure of nucleotide sequence, however, the formation of synonymous codons usage in the TIR of HCV is influenced by the overall codon usage patterns of human to some degree. Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction Hepatitis C virus (HCV) infection is one of the important public health problems in the world. This virus contains a single-stranded RNA genome encoding a polyprotein. By phylogenetic analysis, HCV has been divided into six major genotypes and many subtypes (Bukh et al., 1993, 1994; Khaliq et al., 2011). After post translational cleavages for the polyprotein of HCV, about ten proteins can take part in replication (Tariq et al., 2012). During the course of translation of ployprotein of HCV, the internal ribosomal entry site (IRES) controls binding of 40S ribosomal subunit to initiate this translation (Bartenschlager and Lohmann, 2001). The HCV IRES is a highly structured RNA element which regulates the cap-independent translation initiation in host cells (Lukavsky, 2009). In the process of the translation initiation mediated by the HCV IRES element, 48S complex formation does not require a 5’-cap structure or scanning, and a small subset of the canonical eukaryotic initiation factors (eIFs) is indispensable (Hellen and Sarnow, 2001).
⇑ Corresponding author. Tel.: +86 931 8342771; fax: +86 931 8340977. E-mail address: [email protected] (Y.-s. Liu). 1567-1348/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.meegid.2013.03.043
Compared with other IRES-containing viruses which impair the cap-dependent translation in eukaryotic cells, the translation of HCV does not obviously tether that of host cells (Mohr et al., 2007), and furthermore the HCV infection often leads to chronic infection with little cytotoxicity (Afdhal, 2004). Recently, Huang et al. (2012) reported that the intracellular level of 40S ribosomal subunit plays an important role in translation initiation of HCV (Huang et al., 2012). But how HCV IRES-mediated translation is controlled in the infected cells remains unclear at the aspect of the coding sequence. Although it was reported that the codon usage patterns can control ribosome scanning speed along the coding sequence and the synonymous codon usage bias plays a role in influencing the translation initiation efficiency at the 5’-termination of the target coding sequence in eukaryotic cells (Cannarozzi et al., 2010; Fredrick and Ibba, 2010; Plotkin and Kudla, 2011; Tuller et al., 2010), there is little information about RNA viruses. Here, we analyzed the relationship of overall codon usage pattern between HCV and human, and also analyzed the synonymous codon usage bias in the translation initiation region (TIR), coding for the N-termination of the whole coding sequence of HCV to evaluate the effect of usage of synonymous codons on the translation initiation of the polyprotein of this virus.
J.-h. Zhou et al. / Infection, Genetics and Evolution 18 (2013) 8–12
2. Materials and methods 2.1. Sequences and database The 239 open reading frames (ORFs) of HCV were downloaded from the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/Genbank/) and the relevant information about this virus was listed in Table S1. And the first 30 codons of HCV ORF, termed as the translation initiation region (TIR), were targeted to investigate the relationship of synonymous codon usage pattern between the TIR and the corresponding ORF, respectively. Additionally, in order to evaluate the adaptation of the overall codon usage pattern of HCV to its natural host, codon usage frequencies of human were obtained from the codon usage database (Nakamura et al., 2000). 2.2. Calculating dinucleotide odds ratios for TpA and CpG in the HCV ORF A common assessment of dinucleotide relative abundance is through the odds ratio, termed as the quotient of the probability of the specific dinucleotide in HCV ORF divided by the product of the probabilities of each nucleotide which consists of the corresponding dinucleotide in the same ORF.
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codons, the first 25 codons and the first 30 codons) and the whole coding sequence of HCV, we depended on a simple methods based on the previous reports (Ohno et al., 2001; Zhou et al., 2011).
R ¼ ln
fn =F n f =F
where fn is the sum of a certain synonymous codon in the size which ranges from the initiation codon (AUG) to the nth codon, Fn is the sum of the corresponding amino acids in the given region, f is the sum of this synonymous codon in the whole coding sequence, F is the sum of the corresponding amino acid in the whole coding sequence. In addition, we can employ this formula to calculate the R value which can reflect the usage bias of the specific codon which is selected by the TIR and the coding sequence outside the TIR.
R ¼ ln
fTIR =F TIR fORF-TIR =F ORF-TIR
where fTIR is the sum of a certain synonymous codon in the size which ranges from the initiation codon (AUG) to the 30th codon, FTIR is the sum of the corresponding amino acids in the same region, fORF-TIR is the sum of this synonymous codon in the whole coding sequence excluding the first 30 codons, FORF-TIR is the sum of the corresponding amino acid in the given sequence.
Pxy ¼ fxy =fx fy 3. Results where fx and fy denote the frequency of the nucleotide x and y in the interesting sequence; fxy means the frequency of the dinucleotide xy in the same region. Pxy value 6 0.78 means that the interesting dinucleotide has a low relative abundance in the target sequence; Pxy value P 1.25 means that the interesting dinucleotide has a high relative abundance in the target sequence (Karlin and Burge, 1995; Karlin et al., 1994). 2.3. Comparison of the overall codon usage between HCV and human To investigate the codon usage pattern without the confounding influence of amino acid composition among different coding sequences, the relative synonymous codon usage (RSCU) values for synonymous codons in this study was calculated according to the published equation (Sharp et al., 1986). The five codons, including three stop codons (UAA, UAG and UGA), AUG for Met and UGG for Trp, were not introduced into the RSCU calculation. For codon usage frequencies of human genome, the RSCU values were calculated for the 59 synonymous codons by the approach mentioned above. In order to identify the usage bias of each synonymous codon, based on the standard of codon usage bias defined by the previous report (Wong et al., 2010), a synonymous codon with RSCU value <0.6 or >1.6 can be regarded as a bias one in this study. In comparison with the synonymous codon usage pattern between HCV and human, if both RSCU values for a codon of HCV and that of the same codon for human are more than 1.6 or less than 0.6, the codon usage can be thought to be a similar pattern (Zhou et al., 2013). Here, a group of codons with RSCU values ranging from 0.6 to 1.6 need to be divided deeply, namely, when both RSCU values of HCV and that of human for the same codon range from 0.6 to 1.0 or from 1.0 to 1.6, the usage pattern of the specific codon between the virus and the host is thought to be similar.
3.1. The dinucleotide bias of TpA and CpG for HCV ORF Fig. 1 shows the general trends of relative abundance for TpA and CpG of HCV ORF. The average PTpA value is 0.77 and the average PCpG value is 0.71, suggesting that TpA and CpG have a low relative abundance in HCV ORF. Like some organisms which strongly tend to weaken the usage degree of codons with CpG or TpA dinucleotides (Cooper and Youssoufian, 1988; De Amicis and Marchetti, 2000; Karlin and Burge, 1995; Wong et al., 2010; Zhou et al., 2012), HCV has a significant tendency to delete the codons with CpG or TpA dinucleotides. 3.2. The similarity of the overall codon usage between HCV and human In terms of the overall codon usage pattern of 239 strains of HCV, there are nine codons (UUA, CUA for Leu, AUU for Ile, GUA for Val, AGU for Ser, GAU for Asp, GAA for Glu, CGU, CGA for Arg) with under-representation and four ones (CUC for Leu, AUC for
2.4. Calculation of the synonymous codon usage in the translation initiation region of HCV To analyze the discrepancy in the synonymous codon usage preference between the specific TIR with six specific lengths (the first 5 codons, the first 10 codons, the first 15 codons, the first 20
Fig. 1. Differential dinucleotide usage in HCV ORF. Dinucleotide (TpA and CpG) odds ratio values were calculated for each ORF of HCV, and the mean values (±SD).
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J.-h. Zhou et al. / Infection, Genetics and Evolution 18 (2013) 8–12
Ile, UCC for Ser, AGG for Arg) with over-representation (Table 1). Turning to the usage pattern of the same codon between HCV and human, 34 out of 59 codons are similarly used. Among the codons with the different usage patterns between this virus and human, there is no codon with RSCU value < 0.6 for virus, and that >1.6 for human or RSCU value > 1.6 for virus, and that <0.6 for human at the same time (Table 1). These results might suggest that during the process of interaction between HCV cycling and anti-viral responses of the host, this virus, in spite of maintaining the specific genetic features, has a strong tendency to adapt the environment of host cell to some degree. Table 1 The data of HCV and human in terms of synonymous codon usage values. Codon
HCV
Human
UUU(F) UUC(F) UUA(L) UUG(L) CUU(L) CUC(L) CUA(L) CUG(L) AUU(I) AUC(I) AUA(I) GUU(V) GUC(V) GUA(V) GUG(V) UCU(S) UCC(S) UCA(S) UCG(S) AGU(S) AGC(S) CCU(P) CCC(P) CCA(P) CCG(P) ACU(T) ACC(T) ACA(T) ACG(T) GCU(A) GCC(A) GCA(A) GCG(A) UAU(Y) UAC(Y) CAU(H) CAC(H) CAA(Q) CAG(Q) AAU(N) AAC(N) AAA(K) AAG(K) GAU(D) GAC(D) GAA(E) GAG(E) UGU(C) UGC(C) CGU(R) CGC(R) CGA(R) CGG(R) AGA(R) AGG(R) GGU(G) GGC(G) GGA(G) GGG(G)
0.66 1.34 0.26 0.96 0.89 1.72 0.59 1.59 0.58 1.71 0.71 0.61 1.35 0.48 1.56 0.93 1.84 0.85 0.74 0.41 1.22 0.96 1.51 0.83 0.7 0.85 1.57 0.8 0.78 0.96 1.45 0.71 0.89 0.7 1.3 0.79 1.21 0.77 1.23 0.66 1.34 0.66 1.34 0.57 1.43 0.51 1.49 0.63 1.37 0.57 1.16 0.52 1.22 0.82 1.71 0.65 1.5 0.64 1.21
0.87 1.13 0.39 0.73 0.73 1.21 0.40 2.53 1.03 1.52 0.44 0.69 1.00 0.42 1.90 1.11 1.39 0.84 0.33 0.84 1.50 1.12 1.35 1.07 0.46 0.94 1.52 1.07 0.46 1.09 1.64 0.85 0.42 0.84 1.16 0.81 1.19 0.51 1.49 0.89 1.11 0.82 1.18 0.89 1.11 0.81 1.19 0.86 1.14 0.51 1.20 0.63 1.20 1.20 1.26 0.64 1.40 0.98 0.98
3.3. The bias of synonymous codon usage in the TIR For these HCV strains in this study, the synonymous codons fail to be selected in equal frequency in the TIR. In details, for the three amino acids Phe, Tyr, and His, UUC for Phe, UAC for Tyr, and CAU for His are generally selected in the TIR (Fig. S1). As for the usage preferences of all synonymous codons for Gln and Asn, the usage tendencies of CAA for Gln and AAU for Asn gradually go down to the relatively low level along the interesting region (Fig. S1). Turning to the usage degree of synonymous codons for Lys, Asp, Glu and Cys, there is no significant difference of the usage tendencies among the synonymous codons for Cys. The usage tendencies of Asp show a weak selection. The usage degree of AAA for Lys and GAA for Glu is generally higher than that of synonymous members (Fig. S2). Among the synonymous codons for Ile, the usage tendency of AUU for Ile represents a regular falling in comparison with those of AUC and AUA (Fig. S3). As for Val, the overall usage tendency of GUC holds a high preference, the others fail to be selected in the front half part of the first 30 codons and GUG tends to avoid existing the first 30 codons (Fig. S4). Turning to Pro, the usage degree of CCU is generally higher than those of synonymous members, and CCC has a strong tendency to avoid existing in the target region (Fig. S4). As for Thr, the usage degrees of ACG and ACA are generally higher than those of ACU and ACC in the first 10 codons of the TIR, and the usage degree of ACU generally sustains a considerably low level (Fig. S5). As for Ala, compared with the usage degrees of GCC and GCU, the GCA and GCG are not selected in the TIR (Fig. S5). As for Gly, the usage degree of GGC is generally highest in the first 25 codons, and GGA and GGG are not used in the TIR (Fig. S6). As for Leu, the usage degree of CUU is highest in the synonymous family and those of UUG, CUC and CUG are not used in the TIR (Fig. S7). As for the usage degrees of the synonymous codons for Ser, AGC is highest, and UCU, UCA and UCC are generally low, and AGU and UCG are not used in the TIR (Fig. S8). Turning to the usage tendencies of synonymous codons for Arg, AGA and CGU are generally higher than other four members and AGG, CGG and CGA are low in the TIR (Fig. S9). In general, the usage pattern of codon with relatively low energy has no tendency to exist in the TIR of HCV, suggesting that the synonymous codon usage patterns for the TIR of HCV might be not affected by the secondary structure of nucleotide sequence. 3.4. The adaptation of codon usage of the TIR of HCV to that of human Fig. 2 shows the relationship between the codon usage bias for the TIR of HCV and the RSCU value for the corresponding codon of human. Generally, several codons which are more selected by the TIR of HCV than the coding sequence excluding TIR are under-represented codons in human. It is interesting that although AAU for Asn, CAA for Gln, GAA for Glu, AAA for Lys, AUU for Ile, GCA for Ala, ACA for Thr, CGU for Arg are selected in relatively low frequencies by HCV ORF, these codons are highly selected by the TIR of HCV; in addition, GUG for Val, CCC for Pro, CUC for Leu, UCC for Ser and AGG for Arg, which are highly selected by this virus, are used by the TIR in considerably low frequencies (Figs. S1–S9 and Table 1). In terms of the adaptation of these codons mentioned above to human, usage degrees of some codons in the HCV TIR (CAU for His, UUC for Phe, UAC for Tyr, CCU for Pro, GCA for Ala, GGC for Gly, CUU for Leu, AGC for Ser, CGU for Arg, AUA for Tyr, AAG for Lys, GUA and GUG for Val, CCC for Pro, ACU for Thr, GGG for Gly, UUG for Leu, UCU and UCA for Ser, CGG and CGA for Arg) are similar with the overall usage degrees of the corresponding codons of human to some degree. These results might suggest that the formation of synonymous codons usage in the TIR of HCV is influenced by the overall codon usage patterns of human to some degree.
J.-h. Zhou et al. / Infection, Genetics and Evolution 18 (2013) 8–12
Fig. 2. The relationship between the codon usage bias for the HCV TIR and the overall codon usage pattern of human. The R value which can reflect the usage degree of codon for the HCV TIR in comparison with the whole coding sequence excluding the TIR. RSCU value for human can reflect the overall codon usage pattern.
4. Discussion RNA viruses are generally ubiquitous cellular parasites and have a strong ability to replicate and evolve rapidly under the selection pressure deriving from their natural hosts. The overall usage degrees of over half of 59 synonymous codons are similar with those of human. As for the codon usage pattern of HCV ORF, the high-usage codons (RSCU > 1.0) are ended with C or G. This codon usage pattern is similar with that of human, suggesting that the co-evolution between HCV and human likely exists. This genetic characteristic may assist viruses to cater for the cellular environments and replicate in host (Bahir et al., 2009; Wong et al., 2010; Zhou et al., 2012). Furthermore, the adaptation refers to the usage of the highly abundant tRNAs within host cell by the virus, which would be optimal when the codon usage of HCV matches with that of the host. Depending on maximizing the translation speed of viral protein synthesis, the perfect viral production impairs the immune response inside virus-infected cells, otherwise a large of non-preferred codons highly selected in viral genes could lead to low yield of viral proteins (Dupas et al., 2003; Sanchez et al., 2003). The inefficiency of the immune response of human is not deeply understood, and the future studies should dissect mechanisms that result to quantitatively or qualitatively inadequate immune responses, the role of the high variability of HCV, the relevance of hosts genetic features and mechanisms of immunosupppression induced by HCV (Gremion and Cerny, 2005). It is noted that CpG dinucleotides is involved in effects on both innate and adaptive immune responses (Ballas, 2007; Krieg et al., 1995; Kumagai et al., 2008). Based on the CpG dinucleotides of vertebrates (Lobo et al., 2009), HCV should have a similar pattern of CpG dinucleotides with human, suggesting that HCV-human co-evolution might occur. As for the relationship of the pattern of CpG dinucleotides between RNA viruses and the hosts, the common feature about the similar low CpG relative abundance between RNA viruses and the hosts suggests that the strong selection pressures acting on these RNA viruses are most likely related to the innate immune response and to nucleotide motifs in the host DNA and RNA (Greenbaum et al., 2008; Greenbaum et al., 2009). For some viruses, codons with CpG dinucleotides tend to be selected in the viral genome at the considerably low level in order to reduce the thermodynamic stacking energy of the coding sequence (Delcourt and Blake, 1991; Greenbaum et al., 2008; Shackelton et al.,
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2006). It has been accepted that secondary structure of nucleotide sequence with high stacking energy caused by CpG dinucleotides can affect viral gene replication and transcription in high efficacy. But it is interesting that HCV has no obvious tendencies to eliminate codons with CpG dinucleotides from its whole coding sequence. Although this genetic feature may impair viral genome replication and transcription of HCV to some degree, it probably serves as a mechanism of limiting the error prone replication to some degree. It has been accepted that between the six major genotypes of HCV, there is over two thirds sequence identity at the nucleotide level along the whole genomes, about 78% among subtypes of the same genotype and about 95% within the same subtypes (Bukh et al., 1995; Simmonds, 1995). As for the life cycle of viruses, production of viral proteins is often influenced by their host cell environments and strategies of translation initiation (Davy and Doorbar, 2007; Firth and Brierley, 2012; Jackson et al., 2010). For the feature of synonymous codon usage in the TIR of HCV, the usage degrees of 59 synonymous codons in this region have been analyzed, and it is found that the usage patterns of some synonymous codons are not similar with the overall usage degrees of the same codons of HCV ORF, but are similar with the overall usage degrees of the same codons of human. The bias of synonymous codon usage for the translation initiation region of gene is important for regulating the translation initiation efficiency (Chen and Inouye, 1994; Eyre-Walker and Bulmer, 1993; Stenstrom et al., 2001a,b). Based on R values, some synonymous codons are highly selected in the TIR, while some synonymous codons are lower used in the TIR. These codons with obvious usage bias in HCV TIR might influence the translation initiation efficiency of HCV. There was a report that optimization of the first 5–17 codons of the human chorionic gonadotropin gene contributes to 4- to 5-fold expression levels (Vervoort et al., 2000), in contrast, the clustering of low-usage codons in the TIR can impair the movement of ribosomal scanning to regulate the expression of genes (Zhang et al., 1994), suggesting that the synonymous codon usage bias for the TIR is important for the regulation of genes. Turing to the usage degree of some synonymous codons for the TIR of HCV being similar with the overall usage degree of the same codons of its host, these codons likely reduce the probability of abortive translation initiation of HCV genome by means of adaptation of codon usage pattern of this region to the environment of host. The adaptation of codon usage patterns of exogenous genes to the overall codon usage pattern of the hosts is an effective approach to produce functional proteins (Burns et al., 2006; Gustafsson et al., 2004; Hershberg and Petrov, 2008; Sharp et al., 2010). Acknowledgements This work was supported in parts by grants International Science & Technology Cooperation Program of China (No. 2010DFA32640 and No. 2012DFG31890) and Gansu Provincial Funds for Distinguished Young Scientists (1111RJDA005). This study was also supported by National Natural Science foundation of China (No. 31172335 and No. 31072143). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.meegid.2013.03. 043. Reference Afdhal, N.H., 2004. The natural history of hepatitis C. Semin. Liver Dis. 24 (Suppl. 2), 3–8.
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Short communication
Clustering of low usage codons in the translation initiation region of hepatitis C virus Jian-hua Zhou a, Jun-hong Su a, Hao-tai Chen a, Jie Zhang a, Li-na Ma a, Yao-zhong Ding a, Laszlo Stipkovits b, Susan Szathmary b, Zygmunt Pejsak c, Yong-sheng Liu a,⇑ a State Key Laboratory of Veterinary Etiological Biology, National Foot-and-Mouth Disease Reference Laboratory, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730046, Gansu, PR China b RT-Europe Center, Budapest, Hungary c Department of Swine Diseases, National Veterinary Research Institute, 57 Partyzantow, 24-100 Puławy, Poland
a r t i c l e
i n f o
Article history: Received 17 November 2012 Received in revised form 26 February 2013 Accepted 24 March 2013 Available online 22 April 2013 Keywords: Hepatitis C virus Synonymous codon usage value Overall codon usage pattern Translation initiation region
a b s t r a c t The adaptation of the overall codon usage pattern of hepatitis C virus (HCV) to that of human is estimated by the synonymous codon usage value (RSCU). The synonymous codon usage biases for the translation initiation region (TIR) of this virus are also analyzed by calculation of usage fluctuation of each synonymous codon along the TIR (the first 30 codon sites of the whole coding sequence of HCV). As for the overall codon usage pattern of HCV, this virus has a significant tendency to delete the codons with CpG or TpA dinucleotides. Turning to the adaptation of the overall codon usage of HCV to that of human, over half part of codons has a similar usage pattern between this virus and human, suggesting that the host cellular environment of the overall codon usage pattern influences the formation of codon usage for HCV. In addition, there is no obvious phenomenon that the codons with relatively low energy tend to be highly selected in the TIR of HCV, suggesting that the synonymous codon usage patterns for the TIR of HCV might be not affected by the secondary structure of nucleotide sequence, however, the formation of synonymous codons usage in the TIR of HCV is influenced by the overall codon usage patterns of human to some degree. Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction Hepatitis C virus (HCV) infection is one of the important public health problems in the world. This virus contains a single-stranded RNA genome encoding a polyprotein. By phylogenetic analysis, HCV has been divided into six major genotypes and many subtypes (Bukh et al., 1993, 1994; Khaliq et al., 2011). After post translational cleavages for the polyprotein of HCV, about ten proteins can take part in replication (Tariq et al., 2012). During the course of translation of ployprotein of HCV, the internal ribosomal entry site (IRES) controls binding of 40S ribosomal subunit to initiate this translation (Bartenschlager and Lohmann, 2001). The HCV IRES is a highly structured RNA element which regulates the cap-independent translation initiation in host cells (Lukavsky, 2009). In the process of the translation initiation mediated by the HCV IRES element, 48S complex formation does not require a 5’-cap structure or scanning, and a small subset of the canonical eukaryotic initiation factors (eIFs) is indispensable (Hellen and Sarnow, 2001).
⇑ Corresponding author. Tel.: +86 931 8342771; fax: +86 931 8340977. E-mail address: [email protected] (Y.-s. Liu). 1567-1348/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.meegid.2013.03.043
Compared with other IRES-containing viruses which impair the cap-dependent translation in eukaryotic cells, the translation of HCV does not obviously tether that of host cells (Mohr et al., 2007), and furthermore the HCV infection often leads to chronic infection with little cytotoxicity (Afdhal, 2004). Recently, Huang et al. (2012) reported that the intracellular level of 40S ribosomal subunit plays an important role in translation initiation of HCV (Huang et al., 2012). But how HCV IRES-mediated translation is controlled in the infected cells remains unclear at the aspect of the coding sequence. Although it was reported that the codon usage patterns can control ribosome scanning speed along the coding sequence and the synonymous codon usage bias plays a role in influencing the translation initiation efficiency at the 5’-termination of the target coding sequence in eukaryotic cells (Cannarozzi et al., 2010; Fredrick and Ibba, 2010; Plotkin and Kudla, 2011; Tuller et al., 2010), there is little information about RNA viruses. Here, we analyzed the relationship of overall codon usage pattern between HCV and human, and also analyzed the synonymous codon usage bias in the translation initiation region (TIR), coding for the N-termination of the whole coding sequence of HCV to evaluate the effect of usage of synonymous codons on the translation initiation of the polyprotein of this virus.
J.-h. Zhou et al. / Infection, Genetics and Evolution 18 (2013) 8–12
2. Materials and methods 2.1. Sequences and database The 239 open reading frames (ORFs) of HCV were downloaded from the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/Genbank/) and the relevant information about this virus was listed in Table S1. And the first 30 codons of HCV ORF, termed as the translation initiation region (TIR), were targeted to investigate the relationship of synonymous codon usage pattern between the TIR and the corresponding ORF, respectively. Additionally, in order to evaluate the adaptation of the overall codon usage pattern of HCV to its natural host, codon usage frequencies of human were obtained from the codon usage database (Nakamura et al., 2000). 2.2. Calculating dinucleotide odds ratios for TpA and CpG in the HCV ORF A common assessment of dinucleotide relative abundance is through the odds ratio, termed as the quotient of the probability of the specific dinucleotide in HCV ORF divided by the product of the probabilities of each nucleotide which consists of the corresponding dinucleotide in the same ORF.
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codons, the first 25 codons and the first 30 codons) and the whole coding sequence of HCV, we depended on a simple methods based on the previous reports (Ohno et al., 2001; Zhou et al., 2011).
R ¼ ln
fn =F n f =F
where fn is the sum of a certain synonymous codon in the size which ranges from the initiation codon (AUG) to the nth codon, Fn is the sum of the corresponding amino acids in the given region, f is the sum of this synonymous codon in the whole coding sequence, F is the sum of the corresponding amino acid in the whole coding sequence. In addition, we can employ this formula to calculate the R value which can reflect the usage bias of the specific codon which is selected by the TIR and the coding sequence outside the TIR.
R ¼ ln
fTIR =F TIR fORF-TIR =F ORF-TIR
where fTIR is the sum of a certain synonymous codon in the size which ranges from the initiation codon (AUG) to the 30th codon, FTIR is the sum of the corresponding amino acids in the same region, fORF-TIR is the sum of this synonymous codon in the whole coding sequence excluding the first 30 codons, FORF-TIR is the sum of the corresponding amino acid in the given sequence.
Pxy ¼ fxy =fx fy 3. Results where fx and fy denote the frequency of the nucleotide x and y in the interesting sequence; fxy means the frequency of the dinucleotide xy in the same region. Pxy value 6 0.78 means that the interesting dinucleotide has a low relative abundance in the target sequence; Pxy value P 1.25 means that the interesting dinucleotide has a high relative abundance in the target sequence (Karlin and Burge, 1995; Karlin et al., 1994). 2.3. Comparison of the overall codon usage between HCV and human To investigate the codon usage pattern without the confounding influence of amino acid composition among different coding sequences, the relative synonymous codon usage (RSCU) values for synonymous codons in this study was calculated according to the published equation (Sharp et al., 1986). The five codons, including three stop codons (UAA, UAG and UGA), AUG for Met and UGG for Trp, were not introduced into the RSCU calculation. For codon usage frequencies of human genome, the RSCU values were calculated for the 59 synonymous codons by the approach mentioned above. In order to identify the usage bias of each synonymous codon, based on the standard of codon usage bias defined by the previous report (Wong et al., 2010), a synonymous codon with RSCU value <0.6 or >1.6 can be regarded as a bias one in this study. In comparison with the synonymous codon usage pattern between HCV and human, if both RSCU values for a codon of HCV and that of the same codon for human are more than 1.6 or less than 0.6, the codon usage can be thought to be a similar pattern (Zhou et al., 2013). Here, a group of codons with RSCU values ranging from 0.6 to 1.6 need to be divided deeply, namely, when both RSCU values of HCV and that of human for the same codon range from 0.6 to 1.0 or from 1.0 to 1.6, the usage pattern of the specific codon between the virus and the host is thought to be similar.
3.1. The dinucleotide bias of TpA and CpG for HCV ORF Fig. 1 shows the general trends of relative abundance for TpA and CpG of HCV ORF. The average PTpA value is 0.77 and the average PCpG value is 0.71, suggesting that TpA and CpG have a low relative abundance in HCV ORF. Like some organisms which strongly tend to weaken the usage degree of codons with CpG or TpA dinucleotides (Cooper and Youssoufian, 1988; De Amicis and Marchetti, 2000; Karlin and Burge, 1995; Wong et al., 2010; Zhou et al., 2012), HCV has a significant tendency to delete the codons with CpG or TpA dinucleotides. 3.2. The similarity of the overall codon usage between HCV and human In terms of the overall codon usage pattern of 239 strains of HCV, there are nine codons (UUA, CUA for Leu, AUU for Ile, GUA for Val, AGU for Ser, GAU for Asp, GAA for Glu, CGU, CGA for Arg) with under-representation and four ones (CUC for Leu, AUC for
2.4. Calculation of the synonymous codon usage in the translation initiation region of HCV To analyze the discrepancy in the synonymous codon usage preference between the specific TIR with six specific lengths (the first 5 codons, the first 10 codons, the first 15 codons, the first 20
Fig. 1. Differential dinucleotide usage in HCV ORF. Dinucleotide (TpA and CpG) odds ratio values were calculated for each ORF of HCV, and the mean values (±SD).
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Ile, UCC for Ser, AGG for Arg) with over-representation (Table 1). Turning to the usage pattern of the same codon between HCV and human, 34 out of 59 codons are similarly used. Among the codons with the different usage patterns between this virus and human, there is no codon with RSCU value < 0.6 for virus, and that >1.6 for human or RSCU value > 1.6 for virus, and that <0.6 for human at the same time (Table 1). These results might suggest that during the process of interaction between HCV cycling and anti-viral responses of the host, this virus, in spite of maintaining the specific genetic features, has a strong tendency to adapt the environment of host cell to some degree. Table 1 The data of HCV and human in terms of synonymous codon usage values. Codon
HCV
Human
UUU(F) UUC(F) UUA(L) UUG(L) CUU(L) CUC(L) CUA(L) CUG(L) AUU(I) AUC(I) AUA(I) GUU(V) GUC(V) GUA(V) GUG(V) UCU(S) UCC(S) UCA(S) UCG(S) AGU(S) AGC(S) CCU(P) CCC(P) CCA(P) CCG(P) ACU(T) ACC(T) ACA(T) ACG(T) GCU(A) GCC(A) GCA(A) GCG(A) UAU(Y) UAC(Y) CAU(H) CAC(H) CAA(Q) CAG(Q) AAU(N) AAC(N) AAA(K) AAG(K) GAU(D) GAC(D) GAA(E) GAG(E) UGU(C) UGC(C) CGU(R) CGC(R) CGA(R) CGG(R) AGA(R) AGG(R) GGU(G) GGC(G) GGA(G) GGG(G)
0.66 1.34 0.26 0.96 0.89 1.72 0.59 1.59 0.58 1.71 0.71 0.61 1.35 0.48 1.56 0.93 1.84 0.85 0.74 0.41 1.22 0.96 1.51 0.83 0.7 0.85 1.57 0.8 0.78 0.96 1.45 0.71 0.89 0.7 1.3 0.79 1.21 0.77 1.23 0.66 1.34 0.66 1.34 0.57 1.43 0.51 1.49 0.63 1.37 0.57 1.16 0.52 1.22 0.82 1.71 0.65 1.5 0.64 1.21
0.87 1.13 0.39 0.73 0.73 1.21 0.40 2.53 1.03 1.52 0.44 0.69 1.00 0.42 1.90 1.11 1.39 0.84 0.33 0.84 1.50 1.12 1.35 1.07 0.46 0.94 1.52 1.07 0.46 1.09 1.64 0.85 0.42 0.84 1.16 0.81 1.19 0.51 1.49 0.89 1.11 0.82 1.18 0.89 1.11 0.81 1.19 0.86 1.14 0.51 1.20 0.63 1.20 1.20 1.26 0.64 1.40 0.98 0.98
3.3. The bias of synonymous codon usage in the TIR For these HCV strains in this study, the synonymous codons fail to be selected in equal frequency in the TIR. In details, for the three amino acids Phe, Tyr, and His, UUC for Phe, UAC for Tyr, and CAU for His are generally selected in the TIR (Fig. S1). As for the usage preferences of all synonymous codons for Gln and Asn, the usage tendencies of CAA for Gln and AAU for Asn gradually go down to the relatively low level along the interesting region (Fig. S1). Turning to the usage degree of synonymous codons for Lys, Asp, Glu and Cys, there is no significant difference of the usage tendencies among the synonymous codons for Cys. The usage tendencies of Asp show a weak selection. The usage degree of AAA for Lys and GAA for Glu is generally higher than that of synonymous members (Fig. S2). Among the synonymous codons for Ile, the usage tendency of AUU for Ile represents a regular falling in comparison with those of AUC and AUA (Fig. S3). As for Val, the overall usage tendency of GUC holds a high preference, the others fail to be selected in the front half part of the first 30 codons and GUG tends to avoid existing the first 30 codons (Fig. S4). Turning to Pro, the usage degree of CCU is generally higher than those of synonymous members, and CCC has a strong tendency to avoid existing in the target region (Fig. S4). As for Thr, the usage degrees of ACG and ACA are generally higher than those of ACU and ACC in the first 10 codons of the TIR, and the usage degree of ACU generally sustains a considerably low level (Fig. S5). As for Ala, compared with the usage degrees of GCC and GCU, the GCA and GCG are not selected in the TIR (Fig. S5). As for Gly, the usage degree of GGC is generally highest in the first 25 codons, and GGA and GGG are not used in the TIR (Fig. S6). As for Leu, the usage degree of CUU is highest in the synonymous family and those of UUG, CUC and CUG are not used in the TIR (Fig. S7). As for the usage degrees of the synonymous codons for Ser, AGC is highest, and UCU, UCA and UCC are generally low, and AGU and UCG are not used in the TIR (Fig. S8). Turning to the usage tendencies of synonymous codons for Arg, AGA and CGU are generally higher than other four members and AGG, CGG and CGA are low in the TIR (Fig. S9). In general, the usage pattern of codon with relatively low energy has no tendency to exist in the TIR of HCV, suggesting that the synonymous codon usage patterns for the TIR of HCV might be not affected by the secondary structure of nucleotide sequence. 3.4. The adaptation of codon usage of the TIR of HCV to that of human Fig. 2 shows the relationship between the codon usage bias for the TIR of HCV and the RSCU value for the corresponding codon of human. Generally, several codons which are more selected by the TIR of HCV than the coding sequence excluding TIR are under-represented codons in human. It is interesting that although AAU for Asn, CAA for Gln, GAA for Glu, AAA for Lys, AUU for Ile, GCA for Ala, ACA for Thr, CGU for Arg are selected in relatively low frequencies by HCV ORF, these codons are highly selected by the TIR of HCV; in addition, GUG for Val, CCC for Pro, CUC for Leu, UCC for Ser and AGG for Arg, which are highly selected by this virus, are used by the TIR in considerably low frequencies (Figs. S1–S9 and Table 1). In terms of the adaptation of these codons mentioned above to human, usage degrees of some codons in the HCV TIR (CAU for His, UUC for Phe, UAC for Tyr, CCU for Pro, GCA for Ala, GGC for Gly, CUU for Leu, AGC for Ser, CGU for Arg, AUA for Tyr, AAG for Lys, GUA and GUG for Val, CCC for Pro, ACU for Thr, GGG for Gly, UUG for Leu, UCU and UCA for Ser, CGG and CGA for Arg) are similar with the overall usage degrees of the corresponding codons of human to some degree. These results might suggest that the formation of synonymous codons usage in the TIR of HCV is influenced by the overall codon usage patterns of human to some degree.
J.-h. Zhou et al. / Infection, Genetics and Evolution 18 (2013) 8–12
Fig. 2. The relationship between the codon usage bias for the HCV TIR and the overall codon usage pattern of human. The R value which can reflect the usage degree of codon for the HCV TIR in comparison with the whole coding sequence excluding the TIR. RSCU value for human can reflect the overall codon usage pattern.
4. Discussion RNA viruses are generally ubiquitous cellular parasites and have a strong ability to replicate and evolve rapidly under the selection pressure deriving from their natural hosts. The overall usage degrees of over half of 59 synonymous codons are similar with those of human. As for the codon usage pattern of HCV ORF, the high-usage codons (RSCU > 1.0) are ended with C or G. This codon usage pattern is similar with that of human, suggesting that the co-evolution between HCV and human likely exists. This genetic characteristic may assist viruses to cater for the cellular environments and replicate in host (Bahir et al., 2009; Wong et al., 2010; Zhou et al., 2012). Furthermore, the adaptation refers to the usage of the highly abundant tRNAs within host cell by the virus, which would be optimal when the codon usage of HCV matches with that of the host. Depending on maximizing the translation speed of viral protein synthesis, the perfect viral production impairs the immune response inside virus-infected cells, otherwise a large of non-preferred codons highly selected in viral genes could lead to low yield of viral proteins (Dupas et al., 2003; Sanchez et al., 2003). The inefficiency of the immune response of human is not deeply understood, and the future studies should dissect mechanisms that result to quantitatively or qualitatively inadequate immune responses, the role of the high variability of HCV, the relevance of hosts genetic features and mechanisms of immunosupppression induced by HCV (Gremion and Cerny, 2005). It is noted that CpG dinucleotides is involved in effects on both innate and adaptive immune responses (Ballas, 2007; Krieg et al., 1995; Kumagai et al., 2008). Based on the CpG dinucleotides of vertebrates (Lobo et al., 2009), HCV should have a similar pattern of CpG dinucleotides with human, suggesting that HCV-human co-evolution might occur. As for the relationship of the pattern of CpG dinucleotides between RNA viruses and the hosts, the common feature about the similar low CpG relative abundance between RNA viruses and the hosts suggests that the strong selection pressures acting on these RNA viruses are most likely related to the innate immune response and to nucleotide motifs in the host DNA and RNA (Greenbaum et al., 2008; Greenbaum et al., 2009). For some viruses, codons with CpG dinucleotides tend to be selected in the viral genome at the considerably low level in order to reduce the thermodynamic stacking energy of the coding sequence (Delcourt and Blake, 1991; Greenbaum et al., 2008; Shackelton et al.,
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2006). It has been accepted that secondary structure of nucleotide sequence with high stacking energy caused by CpG dinucleotides can affect viral gene replication and transcription in high efficacy. But it is interesting that HCV has no obvious tendencies to eliminate codons with CpG dinucleotides from its whole coding sequence. Although this genetic feature may impair viral genome replication and transcription of HCV to some degree, it probably serves as a mechanism of limiting the error prone replication to some degree. It has been accepted that between the six major genotypes of HCV, there is over two thirds sequence identity at the nucleotide level along the whole genomes, about 78% among subtypes of the same genotype and about 95% within the same subtypes (Bukh et al., 1995; Simmonds, 1995). As for the life cycle of viruses, production of viral proteins is often influenced by their host cell environments and strategies of translation initiation (Davy and Doorbar, 2007; Firth and Brierley, 2012; Jackson et al., 2010). For the feature of synonymous codon usage in the TIR of HCV, the usage degrees of 59 synonymous codons in this region have been analyzed, and it is found that the usage patterns of some synonymous codons are not similar with the overall usage degrees of the same codons of HCV ORF, but are similar with the overall usage degrees of the same codons of human. The bias of synonymous codon usage for the translation initiation region of gene is important for regulating the translation initiation efficiency (Chen and Inouye, 1994; Eyre-Walker and Bulmer, 1993; Stenstrom et al., 2001a,b). Based on R values, some synonymous codons are highly selected in the TIR, while some synonymous codons are lower used in the TIR. These codons with obvious usage bias in HCV TIR might influence the translation initiation efficiency of HCV. There was a report that optimization of the first 5–17 codons of the human chorionic gonadotropin gene contributes to 4- to 5-fold expression levels (Vervoort et al., 2000), in contrast, the clustering of low-usage codons in the TIR can impair the movement of ribosomal scanning to regulate the expression of genes (Zhang et al., 1994), suggesting that the synonymous codon usage bias for the TIR is important for the regulation of genes. Turing to the usage degree of some synonymous codons for the TIR of HCV being similar with the overall usage degree of the same codons of its host, these codons likely reduce the probability of abortive translation initiation of HCV genome by means of adaptation of codon usage pattern of this region to the environment of host. The adaptation of codon usage patterns of exogenous genes to the overall codon usage pattern of the hosts is an effective approach to produce functional proteins (Burns et al., 2006; Gustafsson et al., 2004; Hershberg and Petrov, 2008; Sharp et al., 2010). Acknowledgements This work was supported in parts by grants International Science & Technology Cooperation Program of China (No. 2010DFA32640 and No. 2012DFG31890) and Gansu Provincial Funds for Distinguished Young Scientists (1111RJDA005). This study was also supported by National Natural Science foundation of China (No. 31172335 and No. 31072143). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.meegid.2013.03. 043. Reference Afdhal, N.H., 2004. The natural history of hepatitis C. Semin. Liver Dis. 24 (Suppl. 2), 3–8.
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