- Open Access
Close evolutionary relationship between rice black-streaked dwarf virus and southern rice black-streaked dwarf virus based on analysis of their bicistronic RNAs
- Zenghui Wang†1, 2,
- Chengming Yu†2,
- Yuanhao Peng†1,
- Chengshi Ding1,
- Qingliang Li1,
- Deya Wang1Email authorView ORCID ID profile and
- Xuefeng Yuan2Email author
© The Author(s). 2019
- Received: 24 January 2019
- Accepted: 15 April 2019
- Published: 27 April 2019
Rice black-streaked dwarf virus (RBSDV) and Southern rice black-streaked dwarf virus (SRBSDV) seriously interfered in the production of rice and maize in China. These two viruses are members of the genus Fijivirus in the family Reoviridae and can cause similar dwarf symptoms in rice. Although some studies have reported the phylogenetic analysis on RBSDV or SRBSDV, the evolutionary relationship between these viruses is scarce.
In this study, we analyzed the evolutionary relationships between RBSDV and SRBSDV based on the data from the analysis of codon usage, RNA recombination and phylogenetic relationship, selection pressure and genetic characteristics of the bicistronic RNAs (S5, S7 and S9).
RBSDV and SRBSDV showed similar patterns of codon preference: open reading frames (ORFs) in S7 and S5 had with higher and lower codon usage bias, respectively. Some isolates from RBSDV and SRBSDV formed a clade in the phylogenetic tree of S7 and S9. In addition, some recombination events in S9 occurred between RBSDV and SRBSDV.
Our results suggest close evolutionary relationships between RBSDV and SRBSDV. Selection pressure, gene flow, and neutrality tests also supported the evolutionary relationships.
- Phylogenetic analysis
- Selection pressure
In major rice-growing regions, virus diseases occurred frequently and caused severe damages to rice, among which Rice black-streaked dwarf virus (RBSDV) and Southern rice black-streaked dwarf virus (SRBSDV) are major viral pathogens in China. RBSDV is a member of the genus Fijivirus in the family Reoviridae, which infects rice, maize and wheat [1–3]. RBSDV-infected rice plants have typical dwarf symptoms, such as dark leaves and small white waxy galls on the underside of the leaves [2, 3]. Another disease with analogous symptoms was first reported in Guangdong Province, China in 2001 , and later was identified as a disease caused by Southern rice black-streaked dwarf virus (SRBSDV), a new member of the genus Fijivirus . SRBSDV was subsequently reported to be found throughout southern China and Vietnam . In recent years, both viruses caused a significant reduction in rice yield. However, the identification of these diseases simply based on the symptoms on rice leaves is problematic.
List of abbreviations
Rice black-streaked dwarf virus
Southern rice black-streaked dwarf virus
Open reading frames
Effective number of codons
Codon bias index
G + C content at third position
G + C content at coding positions
Small brown planthopper
Relative synonymous codon usage
Recombination Detection Program version
Analysis of codon usage bias based on ORFs of S5, S7 and S9 in RBSDV and SRBSDV
Codon usage for ORFs of S5, S7 and S9 in Rice black-streaked dwarf virus and Southern rice black-streaked dwarf virus
Recombination events of S7 and S9 in RBSDV and SRBSDV
1.456 × 10−9
2.390 × 10−10
2.390 × 10−10
1.081 × 10− 62
4.518 × 10−21
1.081 × 10−62
2.673 × 10−9
1.011 × 10−14
7.524 × 10−14
4.625 × 10−19
Phylogenetic analysis of S5, S7 and S9 in RBSDV and SRBSDV
Selection pressure on ORFs of S5, S7 and S9
Non-synonymous and synonymous substitution in ORFs of S5, S7 and S9
0.013 ± 0.002
0.105 ± 0.008
0.006 ± 0.001
0.021 ± 0.003
0.015 ± 0.004
0.053 ± 0.012
0.005 ± 0.002
0.012 ± 0.006
0.002 ± 0.001
0.071 ± 0.009
0.001 ± 0.001
0.009 ± 0.004
0.010 ± 0.002
0.089 ± 0.013
0.001 ± 0.001
0.012 ± 0.006
0.033 ± 0.004
0.259 ± 0.028
0.008 ± 0.002
0.014 ± 0.003
0.032 ± 0.012
0.348 ± 0.101
0.090 ± 0.011
0.193 ± 0.049
0.002 ± 0.001
0.025 ± 0.006
0.605 ± 0.067
1.039 ± 0.135
0.004 ± 0.003
0.028 ± 0.015
1.289 ± 0.391
1.363 ± 0.362
0.001 ± 0.001
0.023 ± 0.007
0.085 ± 0.012
0.772 ± 0.140
0.003 ± 0.002
0.003 ± 0.011
0.168 ± 0.055
0.994 ± 0.316
0.101 ± 0.010
0.763 ± 0.113
0.091 ± 0.010
0.683 ± 0093
0.102 ± 0.046
1.081 ± 0.394
0.122 ± 0.030
0.980 ± 0.280
Based on dN and dS value, non-synonymous and synonymous substitution rates in ORFs were also identified. ORF5–1 and ORF5–2 in viruses infecting rice have relatively high dN value (0.605 and 1.289, respectively), indicating the high rates of non-synonymous substitutions in these ORFs (Table 3). dS values of ORF9–1 (0.259) and ORF9–2 (0.348) in RBSDV and ORF9–2 (0.193) in SRBSDV were relatively high, indicating frequent synonymous nucleotide substitutions in these ORFs (Table 4). For maize infecting viruses, ORF9–1 and ORF9–2 had relatively high dS value (0.763 and 1.081, respectively). However, for rice infecting viruses, all ORFs, especially ORF5–1 (dS: 1.039) and ORF5–2 (dS: 1.363), have relatively high dS values with ORF5–1 (dS: 1.039) and ORF5–2 (dS: 1.363) had the highest values. It suggests that RBSDV and SRBSDV have a higher synonymous substitution rate when infecting rice, rather than maize.
Genetic differentiation and gene flow in S5, S7 and S9
Genetic differentiation and gene flow between subpopulations
Different subpopulations showed different gene flow. For S5, gene flow within RBSDV or SRBSDV was frequent with the absolute value of Fst less than 0.33 (Table 5). This pattern also applied to S7 and S9 within RBSDV and SRBSDV. For S5, S7 and S9 between RBSDV and SRBSDV, there was an infrequent gene flow with Fst more than 0.33. We also observed a substantial local differentiation with Nm less than 1. For S5 and S7 between maize and rice, there was an infrequent gene flow with Fst more than 0.33. However, we found a low frequency of genetic differentiation with Nm more than 1. For S9 between maize and rice, there is a frequent gene flow with Fst less than 0.33 and low frequency of genetic differentiation with Nm more than 1. Different regions had different gene flow patterns. For S5 and S7 between Jiangsu and Zhejiang, or between Zhejiang and Anhui, there is an infrequent gene flow with substantial local differences. For S5, S7 and S9 between Jiangsu and Anhui, there was a low frequency of genetic differentiation with Nm more than 1.
Neutrality tests on S5, S7 and S9 in RBSDV and SRBSDV
Neutrality test of S5, S7 and S9 in RBSDV and SRBSDV
Fu & Li’s D
Fu & Li’s F
Fu & Li’s D
Fu &Li’s F
In terms of Fu & Li’s D and F statistical tests, we found different patterns exist in RBSDV and SRBSDV. For SRBSDV, all values of Fu & Li’s D and F are negative, indicating that S5, S7 and S9 of SRBSDV have a tendency to expand with a low frequency of polymorphism. RBSDV, ORF7–1, ORF9–1 and ORF9–2 had a low frequency of polymorphism with a negative value of Fu & Li’s D and F. In contrast ORF5–1, ORF5–2 and ORF7–2 may have a high frequency of polymorphism with a positive value of Fu & Li’s D and F.For SRBSDV, all values of Fu and Li’s D and F were negative, indicating that S5, S7 and S9 of SRBSDV may have a tendency to expand with a low frequency of polymorphism.
Consistent with increasing trends in global export of agriculture commodities, RBSDV and SRBSDV have been spreading rapidly worldwide. During a rapid spread phase, these viruses may have maintained different evolutionary trajectories based on specific host and the environment. Although some previous studies have reported the phylogenetic analyses on RBSDV or SRBSDV separately [12–14], but the studies on the evolutionary relationship between RBSDV and SRBSDV was rare. In this study, we examined a potential evolutionary relationship between these viruses by performing an evolutionary analysis of S5, S7 and S9 in RBSDV and SRBSDV.
Codon usage bias has been reported for viruses (CITATION), including those infect humans (CITATION). Such biases provide information about virus-host coevolution [15, 16]. A detailed comparative analysis was performed to evaluate the degrees of codon usage bias about ORFs of S5, S7 and S9. In general, RBSDV and SRBSDV showed similar patterns of codon usage bias for ORFs of S5, S7 and S9 (Table 1). Compared with other ORFs, ORFs in S7 of RBSDV and SRBSDV had relatively higher codon usage bias with lowest ENC value. Low ENC value was usually correlated to high expression . The correlation implies the evolutionary requirements for high expression of the ORFs, particularly ORF7–2, in S7. This potential high expression of ORF7–2 was supported by a study of that evaluated a intergenic region between ORF7–1 and ORF7–2 that contains a high activity IRES (Yuan et al., in preparation). In addition, 5’UTR in S3 or S10 of RBSDV enhanced the translation of FLuc reporter gene and possess IRES activity in the absence or presence of the 5’cap structure. To the best of our knowledge, this is the first report on the effect of untranslated regions of ds RNA viruses on translation .
Recombination is a major evolutionary mechanism that commonly found in many plant RNA viruses, including RBSDV and SRBSDV [12, 13, 19, 20]. Such recombination may prevent accumulation of mutations, help adapt to new hosts or environmental changes and overcome host resistance [21–23]. In this study, recombination events were detected between RBSDV and SRBSDV (e.g., recombination event 9–7) (Table 3). These results highlight the potential evolutionary relationship between RBSDV and SRBSDV. Moreover, phylogenetic analysis supported a evolutionary relationship. Clade III in the phylogenetic tree of S7 contained both RBSDV S7 and SRBSDV S7. Similar pattern was also found in clade I and II in the phylogenetic tree of S7. The close evolutionary relationship highlights the frequent genetic exchange between RBSDV and SRBSDV when they infected the same host. It also suggests that these viruses may evolved from a single ancestor during evolution. Although the high nucleotide identity of these RBSDV and SRBSDV isolates, they may take different evolutionary path, and showed different host range or pathogenicity in the field. Finally, the selection pressure, gene flow, and recombination together promoted the evolution of the RBSDV and SRBSDV. The evolutionary trends under natural conditions may have a directing significance to the prevention and control of viral diseases.
RBSDV and SRBSDV presented similar patterns of codon usage bias: ORFs in S7 with higher codon usage bias and ORFs in S5 with lower codon usage bias. Some isolates from RBSDV and SRBSDV formed a clade in the phylogenetic tree of S7 and S9. In addition, some recombination events in S9 occurred between RBSDV and SRBSDV. Our results implied a close evolutionary relationship between RBSDV and SRBSDV. Analysis of selection pressure, gene flow, and neutrality test also supports this potential relationship.
Sources of virus sequences
The sequences of bicistronic RNAs (S5, S7 and S9) in RBSDV and SRBSDV were referenced from http://www.ncbi.nlm.nih.gov/nucleotide/.
Codon usage bias analysis
Codon usage bias analysis was performed using the DnaSP 5.0 [24–26]. An effective number of codons (ENC) indicates a bias for synonymous codons rather than codon number or amino acid composition [26, 27]. The value of ENC ranges from 20 to 61 . ENC value 20 indicates that only one type of codon is used for each amino acid and the codon bias is maximum; When the value is 61, all synonymous codons of each amino acid are equally used and there was no codon bias . When the gene is highly expressed, it has high codon bias with low ENC value . The codon bias index (CBI) is a measure of the deviation from the equal use of synonymous codons. The value of CBI ranges from 0 to 1 . If the value of CBI is higher, it indicates that the codon bias is higher. G + C3s is the G + C content at the third position. G + Cc is G + C content at coding positions . Relative Synonymous Codon Usage (RSCU) values represent the number of times a particular codon is observed, relative to the number of times that the codon would be observed for a uniform synonymous codon usage. In the absence of any codon usage bias, the RSCU values would be 1.00. A value less than 1 (or more than 1) indicates that the codons are used less frequently (or more frequently) than expected .
Recombination and phylogenetic analysis
Recombination analysis was performed using RDP, GENECONV, BOOTSCAN, MAXCHI, CHIMAERA, 3SEQ and SISCAN programs in RDP 4 software package with a default setting . When the p-values was less than 10− 6 and the value of Z was more than 3 at the same time, the events supported by at least four programs were considered to be recombination [19, 29, 30]. Phylogenetic analysis was performed using MAGE5. Phylogenetic trees were constructed using the neighbor-joining (NJ) method as described previously . The number of bootstrap replicates was 1000. Branches with less than 50% bootstrap value were collapsed.
Detection of selection pressure
Selection pressure was performed using the software MAGE5.0 as described in previous studies [31–33] The ratios of dN/dS was used to describe the selection pressure. Here, dN is the average number of non-synonymous substitutions per site. dS is the average number of synonymous substitutions per site . When a value of dN/dS is more than 1, the gene is considered to be under positive or diversifying selection; when a value of dN/dS is less than 1, the selection is negative or purifying; Finally, when dN/dS is equal to 1, the selection is neutral [31, 32].
Analysis of genetic differentiation and gene flow
Genetic differentiation and gene flow analyses were performed using the software DnaSP 5.0 . Genetic differentiation was evaluated using sequence based statistics, Ks*, Z, and Snn (the nearest-neighbor statistic). where Ks* is a weighted average of differences between the sequences. Z is a rank statistic, and Snn represents how often the nearest neighbors’ in the sequences are from the same location [35, 36]. Gene flow was estimated by measuring Fst and Nm, where Fst represents the component of genetic variation between populations and Nm represents the female effective size of female population (N) and migration rate among populations (m) . The Fst values ranges from 0 to 1. When a value is more than 0.33, it implies that there is an infrequent gene flow. A value of < 0.33 implies a frequent gene flow [20, 34]. A Nm value of less than 1, it implies a genetic drift with a substantial local differentiation. A value of Nm is more than 1 implies a frequent gene flow with a low frequency of genetic differentiation .
Neutrality tests and population demography
Software DnaSP5.0 was used to detect the value of Tajima’s D, Fu & Li’s D and F statistics . The Tajima’s D test statistic was proposed for testing the hypothesis that states all the mutations are selectively neutral . The test compares the differences between the number of segregating sites and the average number of nucleotide differences. A Tajima’s D value away from 0(i.e. < or > 0) implies population under natural selection. The Fu & Li’s D test statistics measure the differences between the number of singletons and the total number of mutations . The F test statistics measures the differences between the number of singletons and the average number of nucleotide differences between pairs of sequences [38, 39]. A negative value for Fu & Li’s D and F implies a low population diversity but still tends to expand. A negative value further implies a population with a low frequency of polymorphism.
We are grateful to Prof. Hongkai Wang, University of Zaozhuang for valuable suggestions and critical modification on the manuscript, and acknowledge TopEdit LLC for the linguistic editing and proofreading during the preparation of this manuscript.
This work was supported by grants from The National Natural Science Foundation of China (31872638 and 31670147), The Shandong Province Natural Sciences Foundation of China (ZR2013CM015), Scientific Research Foundation for Ph.D.Programs of Zaozhuang University (2018BS040, 2018BS045), and Science and technology Program of Zaozhuang.
ZH contributed to the design of the study. CM contributed to the statistical analysis, and drafting the manuscript. YH contributed to the data analysis. CS and QL contributed to drafting the manuscript. XF contributed to data analysis and drafting the manuscript. DY contributed to the design of the study, data analysis and drafting the manuscript. All authors read and approved the final manuscript.
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