Skip to main content
Download PDF

Codon usage of host-specific P genotypes (VP4) in group A rotavirus

BMC Genomics volume 23, Article number: 518 (2022) Cite this article

Abstract

Background

Group A rotavirus (RVA) is a common causative agent of acute gastroenteritis in infants and young children worldwide. RVA P genotypes, determined by VP4 sequences, have been confirmed to infect humans and animals. However, their codon usage patterns that are essential to obtain insights into the viral evolution, host adaptability, and genetic characterization remained unclear, especially across animal hosts.

Results

We performed a comprehensive codon usage analysis of eight host-specific RVA P genotypes, including human RVA (P[4] and P[8]), porcine RVA (P[13] and P[23]), and zoonotic RVA (P[1], P[6], P[7] and P[19]), based on 233 VP4 complete coding sequences. Nucleotide composition, relative synonymous codon usage (RSCU), and effective number of codons (ENC) were calculated. Principal component analysis (PCA) based on RSCU values was used to explore the codon usage patterns of different RVA P genotypes. In addition, mutation pressure and natural selection were identified by using ENC-plot, parity rule 2 plot, and neutrality plot analyses. All VP4 sequences preferred using A/U nucleotides (A: 0.354-0.377, U: 0.267-0.314) than G/C nucleotides across genotypes. Similarly, majority of commonly used synonymous codons were likely to end with A/U nucleotides (A: 9/18-12/18, U: 6/18-9/18). In PCA, human, porcine, and zoonotic genotypes clustered separately in terms of RSCU values, indicating the host-specific codon usage patterns; however, porcine and zoonotic genotypes were partly overlapped. Human genotypes, P[4] and P[8], had stronger codon usage bias, as indicated by more over-represented codons and lower ENC, compared to porcine and zoonotic genotypes. Moreover, natural selection was determined to be a predominant driver in shaping the codon usage bias across the eight P genotypes. In addition, mutation pressure contributed to the codon usage bias of human genotypes.

Conclusions

Our study identified a strong codon usage bias of human RVA P genotypes attributable to both natural selection and mutation pressure, whereas similar codon usage bias between porcine and zoonotic genotypes predominantly attributable to natural selection. It further suggests possible cross-species transmission. Therefore, it warrants further surveillance of RVA P genotypes for early identification of zoonotic infection.

Peer Review reports

Introduction

Rotavirus is the most common cause of severe diarrheal disease in infants and young children globally. According to World Health Organization, rotavirus is responsible for approximately 453,000 deaths in children under five years of age worldwide yearly [1]. Rotavirus is a spherical, non-enveloped and double-stranded RNA virus belonging to the Reoviridae family, Sedoreovirinae subfamily and Rotavirus genus [2]. The rotavirus genome is approximately 18.5 Kb in size and consists of 11 double-stranded RNA segments, encoding six structural proteins (VP1-4, VP6 and VP7) and six non-structural proteins (NSP1-NSP6). Each segment possesses a single open reading frame except segment 11, which contains two genes [3]. Based on the antigenicity of the VP6 protein, rotavirus genus has been classified into 10 species (group A-J) [4]. Among them, group A rotavirus (RVA) is the main cause of acute dehydrating diarrhea in humans and numerous animal species [5]. Furthermore, VP4 and VP7, the two capsid proteins of rotavirus, are involved in a dual classification system defining P and G genotypes, respectively [2]. Currently, more than 40 G and 50 P genotypes have been reported worldwide [6].

Generally, genetic codons are degenerated, with an amino acid being encoded by more than one codon. During protein synthesis, a species or a gene usually tends to use one or more specific synonymous codons, i.e. codon usage bias [7]. Many studies have considered that different species of viruses selected specific codon usage, possibly as a means of exercising control over the translation of viral proteins [8,9,10,11,12,13]. Deciphering the extent and causes of viral codon usage bias is essential for viral evolution [14]. Codon usage patterns and corresponding driving forces in some RNA viruses have been reported. For example, Chinese porcine circovirus (PCV), rabies virus (RABV), chikungunya virus (CHIKV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exhibited a low codon usage bias; however, the main driving factors were natural selection for PCV and RABV and mutation pressure for CHIKV and SARS-CoV-2 [15,16,17,18]. In addition, dinucleotide abundance, tRNA abundance, gene function and length can affect codon usage patterns [16]. Thus, a comprehensive codon usage analysis is of significance to understand the viral evolution, host adaptability, and genetic characterization.

The codon usage related to RVA has also been reported in several studies. One study focused on human G2P[4] found a high codon usage bias of VP4 and VP7 sequences [19]. Another study confirmed that dominance of mutational pressure rather than natural selection accounted for the codon usage bias of avian VP6 sequences [20]. Besides, a study including 789 complete mammalian RVA genomes showed that natural selection and mutation pressure played 81.3% and 18.7% roles in shaping the codon usage bias of VP4, while VP7 was under more selective pressure [21]. Notably, RVA VP4 has distinct hosts according to a previous study [22], compared to other segments. P[4] and P[8] genotypes infect only humans, while P[6] genotype infects both humans and swine. The above three P genotypes are dominant in human rotavirus infections. In addition, swine is a significant animal reservoir of rotavirus P genotypes. Multiple genotypes, such as P[13], P[23], P[26], P[27], P[32] and P[34], infect only swine. In contrast, P[1], P[3], P[7], P[9], P[14], and P[19] infect humans and other animals including swine [22]. However, codon usage patterns of RVA P genotypes remains unclear, especially across animal hosts.

Therefore, this study aimed to comprehensively determine and compare the host-specific codon usage bias among human, animal, and zoonotic (infects both humans and animals) RVA. Considering that RVA P genotypes should cover identical animals between animal and zoonotic RVA, and the number of complete VP4 coding sequences available in the GanBank, we finally selected eight P genotypes for codon usage analysis. These P genotypes were divided into three groups: human group (P[4] and P[8], infect only humans), porcine group (P[13] and P[23], infect only swine) and zoonotic group (P[1], P[6], P[7] and P[19], infect both humans and swine) [22]. Our findings would facilitate new insights into molecular evolution, host selection and regulation of viral gene expression of RVA.

Results

Nucleotide compositions and properties

Nucleotides A and U of VP4 coding sequences of RVA were more abundant than those of G and C (A: 0.354-0.377, U: 0.267-0.314, P < 0.01), regardless of P genotypes (Table 1). Similarly, the two more abundant nucleotides at the third position of synonymous codons (A3, U3, G3, C3) were A3 (0.515-0.575) and U3 (0.362-0.497) across the eight P genotypes (P < 0.01). In addition, GC1 contents were the highest (0.396-0.441), followed by GC2 and GC3 in all genotypes (P < 0.01). Accordingly, human RVA (P[4], P[8]), porcine RVA (P[13], P[23]), and zoonotic RVA (P[1], P[6], P[7], P[19]) shared similar nucleotide compositions and properties of codons. Detailed nucleotide compositions and properties of each P genotype were showed in Additional file 1.

Table 1 Nucleotide compositions and properties of VP4 coding sequences for RVA P genotypes
Full size table

Host-specific codon usage patterns

Relative synonymous codon usage (RSCU) values were calculated to determine the codon usage pattern of each P genotype. Among the 18 most commonly used synonymous codons, all the eight genotypes, except P[7], ended with A or U nucleotide (Fig. 1). Moreover, 15 and 16 of the 18 codons were over-represented (RSCU>1.6) in P[4] and P[8] genotypes (human genotypes), respectively, which were remarkably high. In regards to the other genotypes, P[13] and P[23] (porcine genotypes), had 8 and 9 most used codons with RSCU values > 1.6, respectively; P[1], P[6], P[7] and P[19] (zoonotic genotypes) had 11, 11, 8 and 12 most used codons with RSCU values > 1.6, respectively (Table 2). Majority of the most used codons of human genotypes had RSCU values > 1.6, suggesting a strong codon usage bias. Detailed RSCU values of each P genotype were displayed in Additional file 2.

Fig. 1
figure 1

Frequency of ending nucleotides across the 18 preferred synonymous codons for eight RVA P genotypes

Full size image
Table 2 Number of most commonly used codons with RSCU values > 1.6 for RVA P genotypes
Full size table

In the principal component analysis (PCA) analysis, principal component 1 and principal component 2 explained 33.3% and 15.6% of the total RSCU variance, respectively (Additional file 3). The sequences of human, porcine and zoonotic genotypes formed three distinct clusters (Fig. 2), implying the host-specific codon usage patterns across the RVA P genotypes. However, the predicting ellipses of the sequences of porcine and zoonotic genotypes were overlapped. This indicated some similarities in the codon usage pattern, which may be a clue to the cross-species transmission.

Fig. 2
figure 2

Principal component analysis (PCA) of VP4 coding sequences for eight RVA P genotypes. The eight P genotypes were classified into three groups by hosts: human, porcine and zoonotic groups. X and Y axis represented principal component 1 and principal component 2, respectively. The dots represented VP4 sequences. The ellipses in the figure predicted new observations with a probability of 0.95. New observations from the same group were expected to fall inside the ellipses

Full size image

Codon usage bias

By calculating the effective number of codons (ENC) values of RVA VP4 sequences, we estimated the codon usage bias. ENC values of human genotypes (38.18±0.54 and 37.86±1.08 for P[4] and P[8], respectively) were lower than those of porcine (42.93±0.73 and 42.23±0.80 for P[13] and P[23], respectively) and zoonotic genotypes (41.26±0.40, 42.24±1.01, 43.02±0.37 and 41.75±0.91 for P[1], P[6], P[7] and P[19], respectively), indicating a stronger codon usage bias in the human genotypes (P < 0.05), which was consistent with the RSCU analysis (Table 3).

Table 3 ENC values of VP4 complete coding sequences for RVA P genotypes
Full size table

Forces driving codon usage bias

In ENC-plot analysis, ENC values were plotted against GC3 values to explore the forces driving codon usage bias (Fig. 3). We found that all the VP4 sequences fell below the expected ENC curve and clustered together. This revealed that natural selection also played roles in codon usage bias, in addition to mutation pressure, regardless of P genotypes. To further estimate the effects of mutation pressure and natural selection, parity rule 2 (PR2) analysis was performed (Fig. 4). All the sequences were away from the origin (0.5, 0.5). Accordingly, mutation pressure and natural selection both contributed to the codon usage bias for all genotypes; however, these two factors had different impacts.

Fig. 3
figure 3

ENC-plot analysis of VP4 coding sequences for the eight RVA P genotypes. The ENC values were plotted against the GC contents at the third codon position (GC3). The expected curve represented the expected ENC values according to corresponding GC3 contents. The dots represented VP4 sequences

Full size image
Fig. 4
figure 4

Parity rule 2 (PR2) analysis of VP4 coding sequences for eight RVA P genotypes. A Human genotypes. B Porcine genotypes. C Zoonotic genotypes. A3/(A3 + U3) and G3/(G3 + C3) of fourfold degenerate codon families represented the ordinate and abscissa, respectively. In the centre of the graph, both coordinates had a value of 0.5, i.e. A = U and G = C. The dots represented VP4 sequences

Full size image

Moreover, neutrality plot analysis was performed to compare the contributions to codon usage bias between natural selection and mutation pressure. P[1], P[13] and P[19] genotypes were excluded from the neutrality plot analysis due to the limited number of VP4 sequences (n = 2, n = 3 and n = 3, respectively). The correlation between GC12 and GC3 was calculated (Fig. 5). In human group, the slopes of the regression line were -0.1471 (P = 0.0051) and 0.0464 (P = 0.0019) for P[4] and P[8] genotypes, respectively, suggesting the constraint of 14.71% and 4.64% by mutation pressure, and 85.29% and 95.36% by natural selection, respectively, in shaping the codon usage bias. In contrast, for porcine and zoonotic groups, there was no significant correlation between GC12 and GC3 (P[23]: P = 0.0804, P[6]: P = 0.5090, P[7]: P = 0.7817), indicating natural selection totally driving the codon usage bias [23]. Accordingly, natural selection played a major role in shaping the codon usage bias, regardless of P genotypes.

Fig. 5
figure 5

Neutrality plot analysis of VP4 coding sequences for eight RVA P genotypes. The GC12 values were plotted against the GC3 values. The dots represented VP4 sequences. * Represented correlation significant at P < 0.05

Full size image

Discussion

Currently, phylogenetic analysis is well studied to explore the evolution of RVA [24,25,26]. Here, we carried out a systematic and comprehensive codon usage analysis of RVA VP4 coding sequences across the eight P genotypes, covering human, porcine and zoonotic genotypes, to demonstrate the viral evolution from a new perspective. So far, more than 50 P genotypes have been identified [6]; however, host species have been documented in only 35 P genotypes [22]. In our study, we aimed to determine the host-specific codon usage bias across humans and animals, so we included P genotypes that isolated only in humans, only in certain animals, and in both humans and identical animals. Considering the VP4 sequences available for analysis, we finally included eight RVA P genotypes in the study, including P[4] and P[8] (human genotypes), P[13] and P[23] (porcine genotypes), and P[1], P[6], P[7, 19] genotypes (zoonotic genotypes that were isolated in both humans and swine). We found that all RVA VP4 coding sequences, regardless of P genotypes, preferred using A and U nucleotides. Similarly, most commonly used synonymous codons were likely to end with A/U in RSCU analysis. Furthermore, three distinct clusters were found in PCA across the eight P genotypes, indicating different evolutionary groups. However, there were some overlaps in the predicted area of the sequences of porcine and zoonotic genotypes, suggesting possible cross-species transmission. Based on ENC analysis, human genotypes had higher codon usage bias compared to porcine and zoonotic genotypes. Morever, natural selection was a predominant driver in shaping the codon usage bias across the eight P genotypes through ENC-plot, PR2, and neutrality plot analyses.

Nucleotide composition bias is possibly caused by mutational bias or selection for function [27, 28] or evasion of innate immune system [29, 30]. Similar to RVA VP4 coding sequences, sequences of many other RNA viruses, such as transmissible gastroenteritis virus (TGEV), porcine deltacoronavirus (PDCoV) and SARS-CoV-2 [23, 31, 32], were also AU-rich. However, sequences of rubella virus were GC-rich [8]. A previous study found that the biased nucleotide composition (A-rich) of human immunodeficiency virus-1 may be related to its pathogenicity [30]. Thus, it warrants further study to explore the association with RVA pathogenicity.

Codon usage bias leads to different levels of translation efficiency, with highly expressed genes showing stronger bias for codons [33, 34]. It has been reported that RVA codon usage patterns appeared optimally for expression in humans and birds, compared to other hosts [21]. It may explain why the two human genotypes, P[4] and P[8], had stronger codon usage bias and had the most over-represented preferred codons, compared to porcine and zoonotic genotypes. However, porcine genotypes (ENC = 42.34±0.81) and zoonotic genotypes (ENC = 42.27±0.98) remained moderate codon usage bias, compared to other viruses including Ebola virus (ENC = 57.23±0.51), SARS-CoV-2 (ENC = 48.54±2.34), and Middle East respiratory syndrome coronavirus (ENC = 49.82±0.08) [11, 35, 36]. Low codon usage bias, which contribute to more efficient viral replication and overcoming host defense mechanisms, allows persistent infection in optimized host [8, 16]. These findings may illustrate why RVA have a diversity of P genoptyes and a broad range of hosts, which has raised a concern of zoonotic transmission.

Mutation pressure and natural selection, the two main factors accounting for codon usage bias, exist in genes of different organisms [37]. Generally, for RNA viruses, mutation pressure was found to be the predominant factor compared with natural selection, as RNA viruses have a higher mutation rate [37, 38]. However, our findings revealed that natural selection was determined to be a principal driver in shaping the codon usage bias across the eight RVA P genotypes, which was consistent with some other viruses, such as TGEV, PDCoV and Zika virus [23, 31, 39]. We suggested that the dominant selection pressure was responsible for the rapid rate of viral evolution, resulting in a diversity of genotypes. The mechanisms of the imbalance between natural selection and mutation pressure need further study.

Our study had some strengths. Firstly, we selected a variety of host-specific RVA P genotypes, covering human, porcine and zoonotic genotypes. The comparison among the P genotypes with different hosts may explore the possible cross-species transmission with a perspective of codon usage. Secondly, the sequences included in the study were complete VP4 coding sequences, containing more biological information. Thirdly, we used multiple methods, including nucleotide composition, RSCU, ENC-plot, PR2 analyses, and so on, to comprehensively demonstrate codon usage bias and driving forces. However, the study had also limitations. Some genotypes, such as P[1], P[13] and P[19], have few full-length sequences, which might result in unreliable findings in the codon usage analysis. In addition, we included only porcine genotypes and zoonotic genotypes that infects swine in the study among multiple RVA animal genotypes, due to the data availability in Genbank. Thus, the further epidemiological surveillance is essential to learn more about RVA.

Conclusions

The RVA VP4 coding sequences were AU-rich, regardless of P genotypes. However, human genotypes, P[4] and P[8], had stronger codon usage bias that was shaped by both natural selection and mutation pressure. In contrast, porcine and zoonotic genotypes (P[13], P[23], P[1], P[6], P[7] and P[19]) shared similar codon usage bias, in which natural selection was a predominant driver. Furthermore, it may be attributable to possible cross-species transmission. Therefore, it warrants further surveillance of RVA P genotypes, which may facilitate early identification of zoonotic infection.

Methods

Sequence collection

Currently, a total of 35 RVA P genotypes had specific hosts [22]. This study aimed to determine the host-specific codon usage bias across humans and animals, so we included P genotypes that isolated only in humans, only in certain animals, and in both humans and identical animals. We included P genotypes due to following criteria: 1) qualified sequences, which was defined as a VP4 full-length sequence, without undetermined codon (X, W, K, and R), and not a vaccine-related sequences; and 2) number of qualified sequences for each host within each P genotype was ≥ 2. Finally, we included eight RVA P genotypes in the study. Complete VP4 coding sequence of RVA P[4], P[8], P[13], P[23], P[1], P[6], P[7] and P[19] genotypes were retrieved from the GenBank of the National Center for Biotechnological Information available through 21 April 2022 [40]. The total number of P[4], P[8], P[13], P[23], P[1], P[6], P[7] and P[19] sequences were 128, 593, 5, 17, 2, 31, 10, and 3, respectively. In order to remove redundancy of P[4] and P[8] sequences, some sequences were randomly excluded due to same collection year with an identity ≥ 98% at the nucleotide level. Consequently, a total of 233 VP4 coding sequences were included in the analysis, of which three groups, human RVA, porcine RVA, and zoonotic RVA, were classified (Table 4). The detailed sequence information (accession number, strain name, P genotype, host, country, and collection year) were displayed in Additional file 4.

Table 4 Basic information of VP4 coding sequences for RVA P genotypes
Full size table

Nucleotide composition analysis

Nucleotide composition (A%, U%, C%, G% , AU% and GC%) of whole codons and at the third position (A3%, U3%, C3%, G3%) were calculated by MEGA 11 [41] and Codon W 1.4.2 (http://codonw.sourceforge.net/), respectively. The GC contents in synonymous codons at each position (GC1%, GC2% and GC3%) were calculated using Emboss: cusp [42]. The five codons (AUG, UAG: only encode for Met and Trp amino acids, respectively; UAA, UAG, UGA: termination codons) that do not lead to usage bias were removed from the codon usage analysis. One-way analysis of variance (ANOVA) was utilized for the comparsion of nucleotide compositions and other nucleotide properties. A P value < 0.05 was considered statistically significant.

Relative synonymous codon usage (RSCU)

RSCU is the observed frequencies divided by that expected if usage of synonymous codons is unbiased. The RSCU is calculated as:

$${\mathrm{RSCU}}_{\mathrm{i}\mathrm{j}}=\frac{{\mathrm{X}}_{\mathrm{i}\mathrm{j}}}{\sum_{\mathrm{j}=1}^{{\mathrm{n}}_{\mathrm{i}}}\ {\mathrm{X}}_{\mathrm{i}\mathrm{j}}}\ {\mathrm{n}}_{\mathrm{i}}$$
(1)

where xij is the number of occurrence of the jth codon for the ith amino acid encoding by ni synonymous codons [43]. RSCU = 1 , > 1 and < 1 indicate absent, positive, and negative codon bias, respectively. In addition, RSCU > 1.6 or < 0.6 indicates high or low expression of the synonymous codon [44]. MEGA 11 was used to calculated RSCU values [41].

Principal component analysis (PCA)

PCA is a widely used data dimension reduction method to simplify the simultaneous interpretation of a number of related variables [45]. In this study, the RSCU values of the 59 codons of each sequence (with AUG, UGG and three stop codons removed) formed 59-dimensional vector, which was converted into two composite variables (i.e. principal component 1 and principal component 2). This removed redundant information and make the results easier to understand. PCA was performed using “psych” package [46] of R 4.1.1 (https://www.r-project.org/).

Effective number of codons (ENC)

ENC values reflect the extent of codon preference in a gene, which range from 20 (only one codon used per amino acid) through 61 (all codons used equally), with smaller ENC values indicating stronger codon usage bias [47]. In general, an ENC value of less than or equal to 40 indicates a strong codon usage bias [48]. The ENC values are calculated as:

$$\mathrm{ENC}=2+\frac9{{\overline{\mathrm F}}_2}+\frac1{{\overline{\mathrm F}}_3}+\frac5{{\overline{\mathrm F}}_4}+\frac3{{\overline{\mathrm F}}_6}$$
(2)

where \({\overline{\mathrm{F}}}_{\mathrm{i}}\) (i = 2, 3, 4, 6) represents the average value of \({\overline{\mathrm{F}}}_{\mathrm{i}}\) for i-fold degenerate codon families. Using the follwing formula to calculate \({\overline{\mathrm{F}}}_{\mathrm{i}}\) value:

$${\overline{\mathrm{F}}}_{\mathrm{i}}=\frac{\mathrm{n}{\sum}_{\mathrm{j}=1}^{\mathrm{i}}{\left(\frac{n_j}{n}\right)}^2-1}{\mathrm{n}\hbox{-} 1}$$
(3)

where n represents the whole number of occurrence of the codons for that amino acid and nj is the number of occurrence of the jth codon for that amino acid. Furthermore, one-way ANOVA was utilized to test ENC difference among genotypes. A P value < 0.05 was considered statistically significant.

ENC-plot analysis

The ENC values are plotted against the GC3 values of each sequence in ENC-plot analysis. The expected ENC values are calculated using the following formula:

$${\mathrm{ENC}}_{\exp \mathrm{ected}}=2+\mathrm{s}+\frac{29}{s^2+{\left(1-\mathrm{s}\right)}^2}$$
(4)

where s denotes the GC3. When the codon usage bias is influenced only by mutation pressure, the ENC values will fall on the expected curve. If the actual ENC values fall below the expected curve, then the codon usage bias is drived by other factors such as natural selection in addition to mutation pressure [16].

Parity rule 2 (PR2) analysis

PR2 is performed to explore the effects of mutation pressure and natural selection on codon usage bias. The AU bias [A3/(A3 + U3)] and GC bias [G3/(G3 + C3)] of fourfold degenerate codon families (alanine, arginine, glycine, leucine, proline, serine, threonine and valine) represent the ordinate and abscissa, respectively. In the centre of the graph, both coordinates have a value of 0.5, i.e. A = U and G = C, indicating that mutation pressure and natural selection are equal [49]. A value of bias greater than 0.5 indicates the preference of purine over pyrimidine, and vice versa, which means deviation between the mutation pressure and natural selection [31, 50].

Neutrality plot analysis

Neutrality plot analysis is used to demonstrate the effects of mutation pressure and natural selection on codon usage bias [51]. The GC12 contents are plotted against the GC3 contents. The contributions of mutation pressure and natural selection are tested by the regression slope between GC12 and GC3. Mutaton pressure plays a major role on codon usage bias if the regression slope is statistically significant and close to 1. Codon usage bias is completely drived by natural selection if the regression slope = 0 or is not statistically significant [23, 51]. Correlation between GC12 and GC3 was calculated by simple linear regression. A P value < 0.05 was considered statistically significant.

Availability of data and materials

The total 233 complete RVA VP4 coding sequences used in this study are available in the GenBank of the National Center for Biotechnological Information (https://www.ncbi.nlm.nih.gov/genbank/). The accession numbers of all sequences are showed in Additional file 4.

Abbreviations

A, U, C, G:

adenine, uracil, cytosine, guanine

ENC:

effective number of codons

PCA:

principal component analysis

PR2:

parity rule 2

RSCU:

relative synonymous codon usage

RVA:

Group A rotavirus

References

  1. World Health Organization. Rotavirus infections. https://www.who.int/westernpacific/health-topics/rotavirus-infections. Accessed 21 Jan 2022.

  2. Sadiq A, Bostan N, Yinda KC, Naseem S, Sattar S. Rotavirus: Genetics, pathogenesis and vaccine advances. Rev Med Virol. 2018;28(6):e2003.

    Article  PubMed  Google Scholar 

  3. Omatola CA, Olaniran AO. Rotaviruses: From Pathogenesis to Disease Control-A Critical Review. Viruses. 2022;14(5).

  4. International Committee on Taxonomy of Viruses. Virus Taxonomy: 2021 Release. 2021. https://talk.ictvonline.org/taxonomy/. Accessed 12 Feb 2022.

  5. Dóró R, Farkas SL, Martella V, Bányai K. Zoonotic transmission of rotavirus: surveillance and control. Expert Rev Anti Infect Ther. 2015;13(11):1337–50.

    Article  PubMed  CAS  Google Scholar 

  6. Rotavirus Classification Working Group. Newly assigned genotypes. 2021. https://rega.kuleuven.be/cev/viralmetagenomics/virus-classification/rcwg.  Accessed 12 Feb 2022.

  7. Plotkin JB, Kudla G. Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet. 2011;12(1):32–42.

    Article  CAS  PubMed  Google Scholar 

  8. van Hemert F, van der Kuyl AC, Berkhout B. Impact of the biased nucleotide composition of viral RNA genomes on RNA structure and codon usage. J Gen Virol. 2016;97(10):2608–19.

    Article  PubMed  CAS  Google Scholar 

  9. Gu WJ, Zhou T, Ma JM, Sun X, Lu ZH. Analysis of synonymous codon usage in SARS Coronavirus and other viruses in the Nidovirales. Virus Res. 2004;101(2):155–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Chen Y, Chen YF. Analysis of synonymous codon usage patterns in duck hepatitis A virus: a comparison on the roles of mutual pressure and natural selection. Virusdisease. 2014;25(3):285–93.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Cristina J, Moreno P, Moratorio G, Musto H. Genome-wide analysis of codon usage bias in Ebolavirus. Virus Res. 2015;196:87–93.

    Article  CAS  PubMed  Google Scholar 

  12. Sun JM, Zhao W, Wang RY, Zhang WY, Li GR, Lu M, et al. Analysis of the Codon Usage Pattern of HA and NA Genes of H7N9 Influenza A Virus. Int J Mol Sci. 2020;21(19):7129.

    Article  CAS  PubMed Central  Google Scholar 

  13. Si FS, Jiang L, Yu RS, Wei WQ, Li Z. Study on the Characteristic Codon Usage Pattern in Porcine Epidemic Diarrhea Virus Genomes and Its Host Adaptation Phenotype. Front Microbiol. 2021;12:738082.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Shackelton LA, Parrish CR, Holmes EC. Evolutionary Basis of Codon Usage and Nucleotide Composition Bias in Vertebrate DNA Viruses. J Mol Evol. 2006;62(5):551–63.

    Article  CAS  PubMed  Google Scholar 

  15. Feng H, Segalés J, Wang F, Jin Q, Wang A, Zhang G, et al. Comprehensive Analysis of Codon Usage Patterns in Chinese Porcine Circoviruses Based on Their Major Protein-Coding Sequences. Viruses. 2022;14(1):81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhang X, Cai Y, Zhai X, Liu J, Zhao W, Ji S, et al. Comprehensive Analysis of Codon Usage on Rabies Virus and Other Lyssaviruses. Int J Mol Sci. 2018;19(8):2397.

    Article  CAS  PubMed Central  Google Scholar 

  17. Butt AM, Nasrullah I, Tong Y. Genome-wide analysis of codon usage and influencing factors in chikungunya viruses. PLoS One. 2014;9(3):e90905.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Khattak S, Rauf MA, Zaman Q, Ali Y, Fatima S, Muhammad P, et al. Genome-Wide Analysis of Codon Usage Patterns of SARS-CoV-2 Virus Reveals Global Heterogeneity of COVID-19. Biomolecules. 2021;11(6):912.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gómez MM, Tort LF, Volotao Ede M, Recarey R, Moratorio G, Musto H, et al. Analysis of human P[4]G2 rotavirus strains isolated in Brazil reveals codon usage bias and strong compositional constraints. Infect Genet Evol. 2011;11(3):580–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Kattoor JJ, Malik YS, Sasidharan A, Rajan VM, Dhama K, Ghosh S, et al. Analysis of codon usage pattern evolution in avian rotaviruses and their preferred host. Infect Genet Evol. 2015;34:17–25.

    Article  CAS  PubMed  Google Scholar 

  21. Hoxie I, Dennehy JJ. Rotavirus A Genome Segments Show Distinct Segregation and Codon Usage Patterns. Viruses. 2021;13(8):1460.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liu Y, Huang P, Tan M, Liu Y, Biesiada J, Meller J, et al. Rotavirus VP8*: phylogeny, host range, and interaction with histo-blood group antigens. J Virol. 2012;86(18):9899–910.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Cheng S, Wu H, Chen Z. Evolution of Transmissible Gastroenteritis Virus (TGEV): A Codon Usage Perspective. Int J Mol Sci. 2020;21(21):7898.

    Article  CAS  PubMed Central  Google Scholar 

  24. Castells M, Caffarena RD, Casaux ML, Schild C, Miño S, Castells F, et al. Phylogenetic Analyses of Rotavirus A from Cattle in Uruguay Reveal the Circulation of Common and Uncommon Genotypes and Suggest Interspecies Transmission. Pathogens. 2020;9(7):570.

    Article  CAS  PubMed Central  Google Scholar 

  25. Jamnikar-Ciglenecki U, Kuhar U, Steyer A, Kirbis A. Whole genome sequence and a phylogenetic analysis of the G8P[14] group A rotavirus strain from roe deer. BMC Vet Res. 2017;13(1):353.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. João ED, Strydom A, O'Neill HG, Cuamba A, Cassocera M, Acácio S, et al. Rotavirus A strains obtained from children with acute gastroenteritis in Mozambique, 2012-2013: G and P genotypes and phylogenetic analysis of VP7 and partial VP4 genes. Arch Virol. 2018;163(1):153–65.

    Article  PubMed  CAS  Google Scholar 

  27. Kutluay SB, Zang T, Blanco-Melo D, Powell C, Jannain D, Errando M, et al. Global changes in the RNA binding specificity of HIV-1 gag regulate virion genesis. Cell. 2014;159(5):1096–109.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Moeller A, Kirchdoerfer RN, Potter CS, Carragher B, Wilson IA. Organization of the influenza virus replication machinery. Science. 2012;338(6114):1631–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Berg RK, Melchjorsen J, Rintahaka J, Diget E, Søby S, Horan KA, et al. Genomic HIV RNA induces innate immune responses through RIG-I-dependent sensing of secondary-structured RNA. PLoS One. 2012;7(1):e29291.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Vabret N, Bailly-Bechet M, Najburg V, Müller-Trutwin M, Verrier B, Tangy F. The biased nucleotide composition of HIV-1 triggers type I interferon response and correlates with subtype D increased pathogenicity. PLoS One. 2012;7(4):e33502.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. He W, Wang N, Tan J, Wang R, Yang Y, Li G, et al. Comprehensive codon usage analysis of porcine deltacoronavirus. Mol Phylogenet Evol. 2019;141:106618.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Dutta R, Buragohain L, Borah P. Analysis of codon usage of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) and its adaptability in dog. Virus Res. 2020;288:198113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kanaya S, Yamada Y, Kinouchi M, Kudo Y, Ikemura T. Codon Usage and tRNA Genes in Eukaryotes: Correlation of Codon Usage Diversity with Translation Efficiency and with CG-Dinucleotide Usage as Assessed by Multivariate Analysis. J Mol Evol. 2001;53(4):290–8.

    Article  CAS  PubMed  Google Scholar 

  34. Zhou J, Liu WJ, Peng SW, Sun XY, Frazer I. Papillomavirus capsid protein expression level depends on the match between codon usage and tRNA availability. J Virol. 1999;73(6):4972–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tort FL, Castells M, Cristina J. A comprehensive analysis of genome composition and codon usage patterns of emerging coronaviruses. Virus Res. 2020;283:197976.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chen Y, Xu Q, Yuan X, Li X, Zhu T, Ma Y, et al. Analysis of the codon usage pattern in Middle East Respiratory Syndrome Coronavirus. Oncotarget. 2017;8(66):110337–49.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Zhang Y, Liu Y, Liu W, Zhou J, Chen H, Wang Y, et al. Analysis of synonymous codon usage in hepatitis A virus. Virol J. 2011;8:174.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jenkins GM, Holmes EC. The extent of codon usage bias in human RNA viruses and its evolutionary origin. Virus Res. 2003;92(1):1–7.

    Article  CAS  PubMed  Google Scholar 

  39. Tao J, Yao H. Comprehensive analysis of the codon usage patterns of polyprotein of Zika virus. Prog Biophys Mol Biol. 2020;150:43–9.

    Article  CAS  PubMed  Google Scholar 

  40. Sayers EW, Cavanaugh M, Clark K, Ostell J, Pruitt KD, Karsch-Mizrachi I. GenBank. Nucleic Acids Res. 2019;47(D1):D94–d99.

    Article  CAS  PubMed  Google Scholar 

  41. Tamura K, Stecher G, Kumar S. MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Mol Biol Evol. 2021;38(7):3022–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Rice P, Longden I, Bleasby A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 2000;16(6):276–7.

    Article  CAS  PubMed  Google Scholar 

  43. Ikemura T. Codon Usage And Transfer-Rna Content In Unicellular And Multicellular Organisms. Mol Biol Evol. 1985;2(1):13–34.

    CAS  PubMed  Google Scholar 

  44. Wong EHM, Smith DK, Rabadan R, Peiris M, Poon LLM. Codon usage bias and the evolution of influenza A viruses. Codon Usage Biases of Influenza Virus. BMC Evol Biol. 2010;10.

  45. Jackson JE. Principal components and factor-analysis. 1. Principal components. J Qual Technol. 1980;12(4):201–13.

  46. Revelle W. psych. Procedures for Personality and Psychological Research. 2017.

  47. Wright F. The 'effective number of codons' used in a gene. Gene. 1990;87(1):23–9.

    Article  CAS  PubMed  Google Scholar 

  48. Belalov IS, Lukashev AN. Causes and implications of codon usage bias in RNA viruses. PLoS One. 2013;8(2):e56642.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sueoka N. Intrastrand parity rules of DNA base composition and usage biases of synonymous codons. J Mol Evol. 1995;40(3):318–25.

    Article  CAS  PubMed  Google Scholar 

  50. Khandia R, Singhal S, Kumar U, Ansari A, Tiwari R, Dhama K, et al. Analysis of Nipah Virus Codon Usage and Adaptation to Hosts. Front Microbiol. 2019;10:886.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Sueoka N. Directional mutation pressure and neutral molecular evolution. Proc Natl Acad Sci U S A. 1988;85(8):2653–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This research was funded by the Shanghai Three-year Action Plan (2020-2022) for Public Health (grant number: GWV-10.1-XK03) and the Science Technology Department of Zhejiang Province (grant number: LGF22H030020).

Author information

Authors and Affiliations

  1. Department of Epidemiology, Ministry of Education Key Laboratory of Public Health Safety (Fudan University), School of Public Health, Fudan University, Shanghai, 200032, China

    Han Wu, Bingzhe Li, Linjie Hu, Lu Zhou & Yihan Lu

  2. Institute of Communicable Diseases Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, 310052, Zhejiang, China

    Ziping Miao

Authors
  1. Han Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bingzhe Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziping Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Linjie Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yihan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, YL; methodology, YL and MZ; software, LZ and LH; formal analysis, HW and BL; data curation, HW, BL, LZ and LH; writing—original draft preparation, HW; writing—review and editing, YL and MZ. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yihan Lu.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Institutional Review Board (IRB) of the Fudan University School of Public Health (IRB 00002408 and FWA 00002399) under IRB #2021-04-0892. The study involved the use of RVA sequences available in the GenBank Database. All sequences included in this study did not have identifiers of humans or animals. No additional data was collected independently for this study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Nucleotide compositions and properties of VP4 coding sequences for group A rotavirus P[4], P[8], P[13], P[23], P[1], P[6], P[7] and P[19].

Additional file 2:

 Table S2. Relative synonymous codon usage (RSCU) patterns of VP4 coding sequences for group A rotavirus P[4], P[8], P[13], P[23], P[1], P[6], P[7] and P[19].

Additional file 3:

 Figure S1. Scree plot of percentage of explained variances for each principal component of the relative synonymous codon usage (RSCU) values of group A rotavirus VP4 coding sequences. This plot showed the proportion of variance in the RSCU values for each principal component (dimension), in descending order of magnitude.

Additional file 4:

 Table S3. VP4 coding sequences for group A rotavirus P[4], P[8], P[13], P[23], P[1], P[6], P[7] and P[19].

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, H., Li, B., Miao, Z. et al. Codon usage of host-specific P genotypes (VP4) in group A rotavirus. BMC Genomics 23, 518 (2022). https://doi.org/10.1186/s12864-022-08730-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-022-08730-2

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us