Open Access

Bioinformatics analysis of rabbit haemorrhagic disease virus genome

  • Xiao-ting Tian1,
  • Bao-yu Li1,
  • Liang Zhang1,
  • Wen-qiang Jiao1 and
  • Ji-xing Liu1Email author
Contributed equally
Virology Journal20118:494

https://doi.org/10.1186/1743-422X-8-494

Received: 14 September 2011

Accepted: 1 November 2011

Published: 1 November 2011

Abstract

Background

Rabbit haemorrhagic disease virus (RHDV), as the pathogeny of Rabbit haemorrhagic disease, can cause a highly infectious and often fatal disease only affecting wild and domestic rabbits. Recent researches revealed that it, as one number of the Caliciviridae, has some specialties in its genome, its reproduction and so on.

Results

In this report, we firstly analyzed its genome and two open reading frameworks (ORFs) from this aspect of codon usage bias. Our researches indicated that mutation pressure rather than natural is the most important determinant in RHDV with high codon bias, and the codon usage bias is nearly contrary between ORF1 and ORF2, which is maybe one of factors regulating the expression of VP60 (encoding by ORF1) and VP10 (encoding by ORF2). Furthermore, negative selective constraints on the RHDV whole genome implied that VP10 played an important role in RHDV lifecycle.

Conclusions

We conjectured that VP10 might be beneficial for the replication, release or both of virus by inducing infected cell apoptosis initiate by RHDV. According to the results of the principal component analysis for ORF2 of RSCU, we firstly separated 30 RHDV into two genotypes, and the ENC values indicated ORF1 and ORF2 were independent among the evolution of RHDV.

Keywords

Rabbit haemorrhagic disease virus (RHDV) Codon usage Evolution Expression

1. Background

Synonymous codons are not used randomly [1]. The variation of codon usage among ORFs in different organisms is accounted by mutational pressure and translational selection as two main factors [2, 3]. Levels and causes of codon usage bias are available to understand viral evolution and the interplay between viruses and the immune response [4]. Thus, many organisms such as bacteria, yeast, Drosophila, and mammals, have been studied in great detail up on codon usage bias and nucleotide composition [5]. However, same researches in viruses, especially in animal viruses, have been less studied. It has been observed that codon usage bias in human RNA viruses is related to mutational pressure, G+C content, the segmented nature of the genome and the route of transmission of the virus [6]. For some vertebrate DNA viruses, genome-wide mutational pressure is regarded as the main determinant of codon usage rather than natural selection for specific coding triplets [4]. Analysis of the bovine papillomavirus type 1 (BPV1) late genes has revealed a relationship between codon usage and tRNA availability [7]. In the mammalian papillomaviruses, it has been proposed that differences from the average codon usage frequencies in the host genome strongly influence both viral replication and gene expression [8]. Codon usage may play a key role in regulating latent versus productive infection in Epstein-Barr virus [9]. Recently, it was reported that codon usage is an important driving force in the evolution of astroviruses and small DNA viruses [10, 11]. Clearly, studies of synonymous codon usage in viruses can reveal much about the molecular evolution of viruses or individual genes. Such information would be relevant in understanding the regulation of viral gene expression.

Up to now, little codon usage analysis has been performed on Rabbit haemorrhagic disease virus (RHDV), which is the pathogen causing Rabbit haemorrhagic disease (RHD), also known as rabbit calicivirus disease (RCD) or viral haemorrhagic disease (VHD), a highly infectious and often fatal disease that affects wild and domestic rabbits. Although the virus infects only rabbits, RHD continues to cause serious problems in different parts of the world. RHDV is a single positive stranded RNA virus without envelope, which contains two open reading frames (ORFs) separately encoding a predicted polyprotein and a minor structural protein named VP10 [12]. After the hydrolysis of self-coding 3C-like cysteinase, the polyprotein was finally hydrolyzed into 8 cleavage products including 7 nonstructural proteins and 1 structural protein named as VP60 [13, 14]. Studies on the phylogenetic relationship of RHDVs showed only one serotype had been isolated, and no genotyping for RHDV was reported. It reported that the VP10 was translated with an efficiency of 20% of the preceding ORF1 [15]. In order to better understand the characteristics of the RHDV genome and to reveal more information about the viral genome, we have analyzed the codon usage and dinucleotide composition. In this report, we sought to address the following issues concerning codon usage in RHDV: (i) the extent and causes of codon bias in RHDV; (ii) A possible genotyping of RHDV; (iii) Codon usage bias as a factor reducing the expression of VP10 and (iiii) the evolution of the ORFs.

2. Materials and methods

2.1 Sequences

The 30 available complete RNA sequences of RHDV were obtained from GenBank randomly in January 2011. The serial number (SN), collection dates, isolated areas and GenBank accession numbers are listed in Table 1.
Table 1

Information of RHDV genomes

SN

Strain

Isolation

Date

Accession No.

1

UT-01

USA:Utah

2001

EU003582.1

2

NY-01

USA: New York

2001

EU003581.1

3

Italy-90

Italy

1990

EU003579.1

4

IN-05

USA: Indiana

2005

EU003578.1

5

NJ-2009

China: Nanjing

2009

HM623309.1

6

Iowa2000

USA: Iowa

2000

AF258618.2

7

pJG-RHDV-DD06

Ramsay Island

2007

EF363035.1

8

Bahrain

Bahrain

2006

DQ189077.1

9

CD/China

Changchun, China

2004

AY523410.1

10

RHDV-V351

Czech

1996

U54983.1

11

RHDV-Hokkaido

Japan

2002

AB300693.2

12

RHDV-FRG

Germany

1991

NC_001543.1

13

Meiningen

Germany

2007

EF558577.1

14

Jena

Germany

2007

EF558576.1

15

Hartmannsdorf

Germany

2007

EF558586.1

16

Rossi

Germany

2007

EF558584.1

17

Triptis

Germany

2007

EF558583.1

18

Dachswald

Germany

2007

EF558582.1

19

Erfurt

Germany

2007

EF558581.1

20

NZ61

New Zealand

2007

EF558580.1

21

NZ54

New Zealand

2007

EF558579.1

22

Eisenhuttenstadt

Germany

2007

EF558578.1

23

Ascot

United Kingdom

2007

EF558575.1

24

Wika

Germany

2007

EF558574.1

25

Frankfurt5

Germany

2007

EF558573.1

26

Frankfurt12

Germany

2007

EF558572.1

27

WHNRH

China

2005

DQ280493.1

28

BS89

Italy

1995

X87607.1

29

RHDV-SD

France

1993

Z29514.1

30

M67473.1

Germany

1991

M67473.1

2.2 The relative synonymous codon usage (RSCU) in RHDV

To investigate the characteristics of synonymous codon usage without the influence of amino acid composition, RSCU values of each codon in a ORF of RHDV were calculated according to previous reports (2 Sharp, Tuohy et al. 1986) as the followed formula:
RSCU = g i j j n i g i j n i

Where gij is the observed number of the i th codon for j th amino acid which has ni type of synonymous codons. The codons with RSCU value higher than 1.0 have positive codon usage bias, while codons with value lower than 1.0 has relative negative codon usage bias. As RSCU values of some codons are nearly equal to 1.0, it means that these codons are chosen equally and randomly.

2.3 The content of each nucleotides and G+C at the synonymous third codon position (GC3s)

The index GC3s means the fraction of the nucleotides G+C at the synonymous third codon position, excluding Met, Trp, and the termination codons.

2.4 The effective number of codons (ENC)

The ENC, as the best estimator of absolute synonymous codon usage bias [16], was calculated for the quantification of the codon usage bias of each ORF [17]. The predicted values of ENC were calculated as
ENC = 2 + s + 2 9 s 2 + ( 1 - s 2 )

where s represents the given (G+C)3% value. The values of ENC can also be obtained by EMBOSS CHIPS program [18].

2.5 Dn and ds of two ORFs

Analyses were conducted with the Nei-Gojobori model [19], involving 30 nucleotide sequences. All positions containing gaps and missing data were eliminated. The values of dn, ds and ω (dn/ds) were calculated in MEGA4.0 [20].

2.6 Correspondence analysis (COA)

Multivariate statistical analysis can be used to explore the relationships between variables and samples. In this study, correspondence analysis was used to investigate the major trend in codon usage variation among ORFs. In this study, the complete coding region of each ORF was represented as a 59 dimensional vector, and each dimension corresponds to the RSCU value of one sense codon (excluding Met, Trp, and the termination codons) [21].

2.7 Correlation analysis

Correlation analysis was used to identify the relationship between nucleotide composition and synonymous codon usage pattern [22]. This analysis was implemented based on the Spearman's rank correlation analysis way.

All statistical processes were carried out by with statistical software SPSS 17.0 for windows.

3. Results

3.1 Measures of relative synonymous codon usage

The values of nucleotide contents in complete coding region of all 30 RHDV genomes were analyzed and listed in Table 2 and Table 3. Evidently, (C+G)% content of the ORF1 fluctuated from 50.889 to 51.557 with a mean value of 51.14557, and (C+G)% content of the ORF2 were ranged from 35.593 to 40.113 with a mean value of 37.6624, which were indicating that nucleotides A and U were the major elements of ORF2 against ORF1. Comparing the values of A3%, U3%, C3% and G3%, it is clear that C3% was distinctly high and A3% was the lowest of all in ORF1 of RHDV, while U3% was distinctly high and C3% was the lowest of all in ORF2 of RHDV. The (C3+G3) % in ORF1 fluctuated from 57.014 to 58.977 with a mean value of 57.68287 and (C3+G3)% were range from 31.356 to 39.831 with a mean value of 34.8337. And the ENC values of ORF1 fluctuated from 54.192 to 55.491 with a mean value of 54.95 and ENC values of ORF2 displayed a far-ranging distribution from 39.771 to 51.964 with a mean value of 44.46. The ENC values of ORF1 were a little high indicating that there is a particular extent of codon preference in ORF1, but the codon usage is relatively randomly selected in ORF2 on the base of ENC values. The details of the overall relative synonymous codon usage (RSCU) values of 59 codons for each ORF in 30 RHDV genomes were listed in Table 4. Most preferentially used codons in ORF1 were C-ended or G-ended codons except Ala, Pro and Ser, however, A-ended or G-ended codons were preferred as the content of ORF2.
Table 2

Identified nucleotide contents in complete coding region (length > 250 bps) in the ORF1 of RHDV (30 isolates) genome

SN

A%

A3%

U%

U3%

C%

C3%

G%

G3%

(C+G)%

(C3+G3)%

ENC

1

25.302

18.252

23.340

23.497

25.544

33.348

25.814

24.904

51.358

58.252

54.786

2

25.387

18.294

23.738

24.691

25.146

32.281

25.729

24.733

51.386

57.014

55.201

3

25.515

18.678

23.298

23.795

25.657

33.220

25.529

24.307

51.186

57.527

55.05

4

25.899

19.488

22.758

21.876

26.141

35.053

25.203

23.582

51.344

58.635

54.68

5

25.515

18.593

23.554

24.136

25.373

32.878

25.558

24.392

50.931

57.270

55.491

6

25.458

18.294

23.554

24.222

25.444

32.921

25.544

24.563

50.988

57.484

55.268

7

25.359

18.806

23.454

23.667

25.487

33.262

25.700

24.264

51.187

57.526

54.723

8

25.402

18.721

23.412

23.625

25.544

33.305

25.643

24.350

51.187

57.655

55.031

9

25.615

19.062

23.383

23.625

25.544

33.433

25.458

23.881

51.002

57.314

54.906

10

25.430

18.593

23.383

23.966

25.629

33.006

25.558

24.435

51.187

57.441

55.439

11

25.288

17.910

23.596

24.435

25.402

32.751

25.714

24.904

51.116

57.665

54.984

12

25.529

18.635

23.412

23.838

25.515

33.092

25.544

24.435

51.059

57.527

55.203

13

25.387

18.380

23.611

23.966

25.316

33.006

25.686

24.648

51.002

57.654

54.681

14

25.274

18.124

23.426

23.582

25.544

33.433

25.757

24.861

51.301

58.294

54.548

15

25.203

18.166

23.724

24.691

25.188

32.239

25.885

24.904

51.073

57.143

55.429

16

25.487

18.721

23.326

23.326

25.601

33.603

25.586

24.350

51.187

57.953

55.148

17

25.444

18.507

23.369

23.582

25.572

33.433

25.615

24.478

51.187

57.911

55.27

18

25.572

18.806

23.539

24.179

25.416

32.836

25.473

24.179

50.889

57.015

55.417

19

25.487

18.507

23.582

24.136

25.359

32.964

25.572

24.392

50.931

57.356

55.384

20

25.558

18.806

23.426

23.966

25.473

32.878

25.544

24.350

51.017

57.228

55.165

21

25.544

18.721

23.426

24.009

25.529

33.006

25.501

24.264

51.030

57.270

55.156

22

25.160

17.783

23.312

23.326

25.729

33.689

25.800

25.203

51.529

58.892

54.682

23

25.487

18.806

23.511

23.710

25.529

33.433

25.473

24.051

51.002

57.487

54.192

24

25.387

18.593

23.497

23.667

25.572

33.348

25.544

24.392

51.116

57.740

54.213

25

25.330

18.635

23.483

23.582

25.615

33.433

25.572

24.350

51.187

57.783

54.238

26

25.387

18.593

23.511

23.710

25.572

33.390

25.529

24.307

51.101

57.697

54.285

27

25.330

18.209

23.511

24.264

25.487

32.964

25.672

24.563

51.159

57.527

55.267

28

25.448

18.643

23.443

23.635

25.576

33.362

25.533

24.360

51.109

57.722

54.614

29

25.174

17.868

23.269

23.156

25.686

33.817

25.871

25.160

51.557

58.977

54.842

30

25.529

18.635

23.412

23.838

25.515

33.092

25.544

24.435

51.059

57.527

55.203

Table 3

Identified nucleotide contents in complete coding region (length > 250 bps) in the ORF2 of RHDV (30 isolates) genome

SN

A%

A3%

U%

U3%

C%

C3%

G%

G3%

(C+G)%

(C3+G3)%

ENC

1

29.944

17.797

30.791

44.068

13.842

16.102

25.424

22.034

39.266

38.136

49.377

2

29.944

18.644

30.226

43.220

14.407

16.949

25.424

21.186

39.831

38.135

48.182

3

31.356

20.339

31.638

46.610

12.994

13.559

24.011

19.492

37.005

33.051

44.567

4

30.508

18.644

30.791

44.915

13.842

15.254

24.859

21.186

38.701

36.440

46.686

5

29.944

17.797

31.921

46.610

12.712

13.559

25.424

22.034

38.136

35.593

41.215

6

30.226

16.949

30.226

43.220

14.407

16.949

25.141

22.881

39.548

39.830

51.964

7

31.356

19.492

30.791

45.763

14.124

15.254

23.729

19.492

37.853

34.764

45.757

8

30.226

16.949

29.661

43.220

15.254

17.797

24.859

22.034

40.113

39.831

47.242

9

30.508

18.644

31.356

45.763

13.277

14.407

24.859

21.186

38.136

35.593

43.017

10

31.356

20.339

31.638

46.610

12.994

13.559

24.011

19.492

37.005

33.051

44.576

11

29.782

17.518

33.898

48.175

12.107

13.139

24.213

21.168

36.320

34.307

43.088

12

31.638

21.186

31.073

45.763

12.994

13.559

24.294

19.492

37.288

33.051

44.997

13

31.073

18.644

31.638

46.610

13.277

14.407

24.011

20.339

37.288

34.746

43.213

14

31.638

19.492

31.921

47.458

12.994

13.559

23.446

19.492

36.440

33.051

47.214

15

31.921

20.339

31.921

46.610

12.712

13.559

23.446

19.492

36.158

33.051

41.964

16

30.226

18.644

30.508

43.220

14.124

16.949

25.141

21.186

39.265

38.135

47.603

17

30.508

19.492

30.508

43.220

13.559

15.254

25.424

22.034

38.983

37.288

47.615

18

29.096

16.102

31.356

45.763

13.277

14.407

26.271

23.729

39.548

38.136

44.343

19

30.226

19.492

31.073

44.915

13.559

15.254

25.141

20.339

38.700

35.593

46.768

20

31.638

19.492

32.768

49.153

11.864

11.017

23.729

20.339

35.593

31.356

39.771

21

31.638

19.492

32.768

49.153

11.864

11.017

23.729

20.339

35.593

31.356

39.771

22

31.073

19.492

31.356

45.763

12.994

13.559

24.576

21.186

37.570

34.745

43.282

23

31.356

19.492

31.921

47.458

12.994

13.559

23.729

19.492

36.723

33.051

42.633

24

31.638

20.339

31.921

47.458

12.994

13.559

23.446

18.644

36.440

32.203

42.157

25

31.638

20.339

32.203

48.305

12.712

12.712

23.446

18.644

36.185

31.356

40.006

26

31.638

20.339

32.203

48.305

12.712

12.712

23.446

18.644

36.185

31.356

40.006

27

30.226

17.797

31.073

44.915

13.559

15.254

25.141

22.034

38.700

37.288

42.799

28

31.356

18.644

31.356

45.763

13.559

15.254

23.729

20.339

37.288

35.593

45.413

29

31.638

21.186

31.638

46.610

12.712

12.712

24.011

19.492

36.723

32.204

43.618

30

31.638

21.186

31.073

45.763

12.994

13.559

24.294

19.492

37.288

32.721

44.997

Table 4

Synonymous codon usage of the whole coding sequence in RHDV

AAa

Codon

RSCU in ORF1

RSCU in ORF2

AAa

Codon

RSCU in ORF1

RSCU in ORF2

Ala

GCA

1.238761

0.877698

Leu

CUA

0.582651

0.410596

 

GCC

1.224431

1.165468

 

CUC

1.349825

0.397351

 

GCG

0.567437

0.014388

 

CUG

1.188367

0.900662

 

GCU

0.969371

1.942446

 

CUU

1.107137

0.821192

Arg

AGA

1.266604

1.481013

 

UUA

0.498412

1.350993

 

AGG

2.026193

3.341772

 

UUG

1.273609

2.119205

 

CGA

0.303087

0

Lys

AAA

0.699282

0.837209

 

CGC

0.991581

1.177215

 

AAG

1.300718

1.162791

 

CGG

0.445276

0

Phe

UUC

0.909962

0.360902

 

CGU

0.967259

0

 

UUU

1.090038

1.639098

Asn

AAC

1.562517

0.140845

Pro

CCA

1.370342

2

 

AAU

0.437483

1.859155

 

CCC

1.204832

0.451613

Asp

GAC

1.576108

0.909091

 

CCG

0.45541

0

 

GAU

0.423892

1.090909

 

CCU

0.969417

1.548387

Cys

UGC

1.034803

0

Ser

AGC

0.969041

1.567416

 

UGU

0.965197

0

 

AGU

1.104135

3.370787

Gln

CAA

0.798416

1.651613

 

UCA

1.437974

0

 

CAG

1.201584

0.348387

 

UCC

1.226239

0.522472

Glu

GAA

0.843523

0.8

 

UCG

0.558562

0

 

GAG

1.156477

1.2

 

UCU

0.704048

0.539326

Gly

GGA

0.669081

0.797508

Ile

AUA

0.574538

0

 

GGC

1.262976

0.984424

 

AUC

1.247451

0.525

 

GGG

0.944991

0.398754

 

AUU

1.17801

2.475

 

GGU

1.122952

1.819315

Tyr

UAC

1.285714

0.086022

His

CAC

1.412429

0

 

UAU

0.714286

1.913978

 

CAU

0.587571

2

Val

GUA

0.316211

0.763077

Thr

ACA

1.212516

0.129032

 

GUC

1.050408

0.258462

 

ACC

1.379635

2

 

GUG

1.163066

0.615385

 

ACG

0.496292

0

 

GUU

1.470315

2.363077

 

ACU

0.911557

1.870968

    

In addition, the dn, ds and ω(dN/dS) values of ORF1 were separately 0.014, 0.338 and 0.041, and the values of ORF2 were 0.034, 0.103 and 0.034, respectively. The ω values of two ORFs in RHDV genome are generally low, indicating that the RHDV whole genome is subject to relatively strong selective constraints.

3.2 Correspondence analysis

COA was used to investigate the major trend in codon usage variation between two ORFs of all 30 RHDV selected for this study. After COA for RHDV Genome, one major trend in the first axis (f'1) which accounted for 42.967% of the total variation, and another major trend in the second axis (f'2) which accounted for 3.632% of the total variation. The coordinate of the complete coding region of each ORF was plotted in Figure 1 defining by the first and second principal axes. It is clear that coordinate of each ORF is relatively isolated. Interestingly, we found that relatively isolated spots from ORF2 tend to cluster into two groups: the ordinate value of one group (marked as Group 1) is positive value and the other one (marked as Group 2) is negative value. Interestingly, all of those strains isolated before 2000 belonged to Group 2.
Figure 1

A plot of value of the first and second axis of RHDV genome in COA. The first axis (f'1) accounts for 42.967% of the total variation, and the second axis (f'2) accounts for 3.632% of the total variation.

3.3 Correlation analysis

To estimate whether the evolution of RHDV genome on codon usage was regulated by mutation pressure or natural selection, the A%, U%, C%, G% and (C+G)% were compared with A3%, U3%, C3%, G3% and (C3+G3)%, respectively (Table 5). There is a complex correlation among nucleotide compositions. In detail, A3%, U3%, C3% and G3% have a significant negative correlation with G%, C%, U% and A% and positive correlation with A%, U%, C% and G%, respectively. It suggests that nucleotide constraint may influence synonymous codon usage patterns. However, A3% has non-correlation with U% and C%, and U3% has non-correlation with A% and G%, respectively, which haven't indicated any peculiarity about synonymous codon usage. Furthermore, C3% and G3% have non-correlation with A%, G% and U%, C%, respectively, indicating these data don't reflect the true feature of synonymous codon usage as well. Therefore, linear regression analysis was implemented to analyze the correlation between synonymous codon usage bias and nucleotide compositions. Details of correlation analysis between the first two principle axes (f'1 and f'2) of each RHDV genome in COA and nucleotide contents were listed in Table 6. In surprise, only f2 values are closely related to base nucleotide A and G content on the third codon position only, suggesting that nucleotide A and G is a factor influencing the synonymous codon usage pattern of RHDV genome. However, f'1 value has non-correlation with base nucleotide contents on the third codon position; it is observably suggest that codon usage patterns in RHDV were probably influenced by other factors, such as the second structure of viral genome and limits of host. In spite of that, compositional constraint is a factor shaping the pattern of synonymous codon usage in RHDV genome.
Table 5

Summary of correlation analysis between the A, U, C, G contents and A3, U3, C3, G3 contents in all selected samples

 

A3%

U3%

C3%

G3%

(C3+G3)%

A%

r = 0.869**

r = -0.340NS

r = -0.358NS

r = -0.865**

r = -0.266**

U%

r = -0.436NS

r = 0.921**

r = -0.902**

r = -0.366NS

r = -0.652**

C%

r = 0.376NS

r = -0.919**

r = 0.932**

r = -0.352NS

r = 0.692**

G%

r = -0.860**

r = -0.377NS

r = -0.437NS

r = 0.910**

r = 0.220**

(C+G)%

r = -0.331 NS

r = -0.649**

r = 0.636**

r = 0.399*

r = 0.915**

ar value in this table is calculated in each correlation analysis.

NS means non-significant (p > 0.05).

* means 0.01 < p < 0.05

**means p < 0.01

Table 6

Summary of correlation analysis between the f1, f2 contents and A3, U3, C3, G3, C3+G3 contents in all selected samples

Base compositions

f1'(42.967%)

f2'(3.632%)

A3%

r = -0.051NS

r = -0.740**

U3%

r = 0.243NS

r = 0.314NS

C3%

r = -0.291NS

r = -0.298NS

G3%

r = 0.108NS

r = 0.723**

(C3+G3)%

r = -0.216NS

r = 0.205NS

ar value in this table is calculated in each correlation analysis.

NS means non-significant.

* means 0.01 < p < 0.05

**means p < 0.01

4. Discussion

There have been more and more features that are unique to RHDV within the family Caliciviridae, including its single host tropism, its genome and its VP10 as a structural protein with unknown function. After we analyzed synonymous codon usage in RHDV (Table 2), we obtained several conclusions and conjectures as followed.

4.1 Mutational bias as a main factor leading to synonymous codon usage variation

ENC-plot, as a general strategy, was utilized to investigate patterns of synonymous codon usage. The ENC-plots of ORFs constrained only by a C3+G3 composition will lie on or just below the curve of the predicted values [18]. ENC values of RHDV genomes were plotted against its corresponding (C3+G3) %. All of the spots lie below the curve of the predicted values, as shown in Figure 2, suggesting that the codon usage bias in all these 30 RHDV genomes is principally influenced by the mutational bias.
Figure 2

Effective number of codons used in each ORF plotted against the GC3s. The continuous curve plots the relationship between GC3s and ENC in the absence of selection. All of spots lie below the expected curve.

4.2 A proof for codon usage bias as a factor reducing the expression of VP10

As we know, the efficiency of gene expression is influenced by regulator sequences or elements and codon usage bias. It reported that the RNA sequence of the 3-terminal 84 nucleotides of ORF1were found to be crucial for VP10 expression instead of the encoded peptide. VP10 coding by ORF2 has been reported as a low expressive structural protein against VP60 coding by ORF1 [5]. And its efficiency of translation is only 20% of VP60. According to results showed by Table 4, it revealed the differences in codon usage patterns of two ORFs, which is a possible factor reducing the expression of VP10.

4.3 Negative selective constraints on the RHDV whole genome

Although VP10 encoded by ORF2, as a minor structural protein with unknown functions, has been described by LIU as a nonessential protein for virus infectivity, the ω value of ORF2 suggests VP10 plays an important role in the certain stage of whole RHDV lifecycle. After combining with low expression and ω value of VP10, we conjectured that VP10 might be beneficial for the replication, release or both of virus by inducing infected cell apoptosis initiate by RHDV. This mechanism has been confirmed in various positive-chain RNA viruses, including coxsackievirus, dengue virus, equine arterivirus, foot-and-mouth disease virus, hepatitis C virus, poliovirus, rhinovirus, and severe acute respiratory syndrome [2329], although the details remain elusive.

4.4 Independent evolution of ORF1 and ORF2

As preceding description, ENC reflects the evolution of codon usage variation and nucleotide composition to some degree. After the correlation analysis of ENC values between ORF1 and ORF2 (Table 7), the related coefficient of ENC values of two ORFs is 0.230, and p value is 0.222 more than 0.05. These data revealed that no correlation existed in ENC values of two ORFs, indicating that codon usage patterns and evolution of two ORFs are separated each other. Further, this information maybe helps us well understand why RSCU and ENC between two ORFs are quite different.
Table 7

Summary of correlation analysis between ENC value of ORF1 and ENC value of ORF2

 

ENC value of ORF1

ENC value of ORF2

ENC value of ORF1

r = 1, p = 0

r = 0.230, p = 0.222 > 0.05

ENC value of ORF2

r = 0.230, p = 0.222 > 0.05

r = 1, p = 0

4.5 A possible genotyping basis

Interestingly, we found that relatively isolated spots from ORF2 tend to cluster into two groups: the ordinate value of one group (marked as Group 1) is positive value and the other one (marked as Group 2) is negative value. And all of those strains isolated before 2000 belonged to Group 2, including Italy-90, RHDV-V351, RHDV-FRG, BS89, RHDV-SD and M67473.1. Although RHDV has been reported as only one type, this may be a reference on dividing into two genotypes.

5. Conclusion

In this report, we firstly analyzed its genome and two open reading frameworks (ORFs) from this aspect of codon usage bias. Our researches indicated that mutation pressure rather than natural is the most important determinant in RHDV with high codon bias, and the codon usage bias is nearly contrary between ORF1 and ORF2, which is maybe one of factors regulating the expression of VP60 (encoding by ORF1) and VP10 (encoding by ORF2). Furthermore, negative selective constraints on the RHDV whole genome implied that VP10 played an important role in RHDV lifecycle. We conjectured that VP10 might be beneficial for the replication, release or both of virus by inducing infected cell apoptosis initiate by RHDV. According to the results of the principal component analysis for ORF2 of RSCU, we firstly separated 30 RHDV into two genotypes, and the ENC values indicated ORF1 and ORF2 were independent among the evolution of RHDV. All the results will guide the next researches on the RHDV as a reference.

Notes

Declarations

Acknowledgements

This work was supported by the fund of Special Social Commonweal Research Programs for Research Institutions (2005DIB4J041, China).

Authors’ Affiliations

(1)
State Key Laboratory of Veterinary Etiological Biology, Key Laboratory of Grazing Animal Diseases of Ministry of Agriculture, Key Laboratory of Animal Virology of Ministry of Agriculture, State Key Laboratory of Veterinary Etiological Biology, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Yanchang bu

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