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  • Bioinformatics analysis of rabbit haemorrhagic disease virus genome

    • 1,
    • 1,
    • 1,
    • 1 and
    • 1Email author
    Contributed equally
    Virology Journal20118:494

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

    • Received: 14 September 2011
    • Accepted: 1 November 2011
    • Published:

    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
    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
    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, Lanzhou, Gansu, 730046, China

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