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. 2024 Feb 15;103(4):103566. doi: 10.1016/j.psj.2024.103566

Simultaneous detection and differentiation of DuCV-1 and DuCV-2 by high-resolution melting analysis

Huanru Fu *,†, Min Zhao *,†, Shuyu Chen *,†, Yu Huang *, Chunhe Wan *,1
PMCID: PMC10907865  PMID: 38417341

Abstract

Birds infected with duck circovirus (DuCV) can potentially cause immunosuppression by damaging lymphoid tissues, causing great losses in the duck breeding industry. Duck circovirus can be divided into two genotypes (DuCV-1 and DuCV-2), but simultaneous detection and differentiation of DuCV-1 and DuCV-2 by high-resolution melting (HRM) analysis is still lacking. Here, we designed specific primers according to the sequence characteristics of the newly identified ORF3 gene and then established a PCR-HRM method for the simultaneous detection and differentiation of DuCV-1 and DuCV-2 via high-resolution melting analysis. Our data showed that the established PCR-HRM assay had the advantages of specificity, with the lowest detection limits of 61.9 copies/μL (for DuCV-1) and 60.6 copies/μL (for DuCV-2). The melting curve of the PCR-HRM results indicated that the amplification product was specific, with no cross-reaction with common waterfowl origin pathogens and a low coefficient of variation less than 1.50% in both intra-batch and inter-batch repetitions, indicating the advantages of repeatability. We found that the percentage of DuCV-2-positive ducks was higher than that of DuCV-1-positive ducks, with 8.62% rate of DuCV-1 and DuCV-2 coinfection. In addition, we found DuCV-2-positive in geese firstly. In conclusion, this study provides a candidate PCR-HRM assay for the detection and accurate differentiation of DuCV-1 and DuCV-2 infection, which will help us for further epidemiological surveillance of DuCVs.

Key words: duck circovirus, DuCV-1, DuCV-2, differentiation, high-resolution melting

INTRODUCTION

The high-resolution melting analysis (HRM) platform is a quick, cost-effective, and efficient technology that can be used to accurately distinguish single nucleotide polymorphisms (Hou et al., 2018). High-resolution melting is based on a real-time fluorescence quantitative PCR assay in the presence of fluorescent intercalating fluorescence quantitative PCR DNA dye with subsequent melting analysis. The HRM platform can be used to continuously monitor the change in fluorescence caused by the release of dye from a DNA duplex as it is denatured by increasing temperature. The melting profile of a DNA duplex depends on its GC content, length, sequence, and heterozygosity. Additionally, subtraction of the melting curves from a reference melting curve (baseline) gives rise to a difference plot (Garganese et al., 2018; England et al., 2021).

The Circoviridae family is composed of two recognized genera, Circovirus (containing 60 species) and Cyclovirus (containing 89 species), according to the International Committee on Taxonomy of Viruses (ICTV) database (https://ictv.global/). The presence of circoviruses, such as beak and feather disease virus (BFDV), pigeon circovirus (PiCV), goose circovirus (GoCV), and duck circovirus (DuCV), has also been reported in birds (Zhang et al., 2021a). Infection of birds with any of these viruses can potentially cause diseases characterized by immunosuppression, caused by damage to lymphoid tissues (Todd, 2010). Epidemiological studies have indicated that the clinical symptoms induced by DuCVs include feathering disorders, growth retardation, and low body weight (Hattermann et al., 2003; Soike et al., 2004). Moreover, DuCVs can increase the incidence of infections caused by other pathogens, causing huge losses to duck breeding industry due to associated health problems (Banda et al., 2007; Zhang et al., 2009; Wan et al., 2011a; Hong et al., 2018; Li et al., 2018). The widespread prevalence of DuCVs is a global concern, especially in the farmed duck industry.

Like other members of the Circoviridae family, DuCVs are small nonenveloped, single-stranded circular DNAs. The length of the viruses is approximately 1,900 nt, and the viruses have two major open reading frames (ORF): ORF1 and ORF2. The left side (ORF1) shows the viral replication protein, which encodes the Rep proteins. The right side (ORF2) contains the viral structural protein, which encodes a viral replication protein (Zhang et al., 2009; Wan et al., 2011a; Cha et al., 2014).

Sequence analysis demonstrated that another major conserved ORF (designated ORF3) is located in the complementary strand of ORF1 of DuCV, and has apoptotic activity through the mitochondrial pathway in duck embryo fibroblasts (DEFs) (Wu et al., 2018; Xiang et al., 2012; Zhang et al., 2021b). Based on the results of ORF3 genetic characterization, DuCVs can be divided into two genotypes, DuCV-1 and DuCV-2, and the nucleotide homology between different genotypes is only 87.8 to 91.6% (Wang et al., 2011; Zhang et al., 2013). Like those of PCV1 and PCV2, the ORF3 protein of DuCV-1 is 20 aa shorter at the C-terminus than is that of DuCV-2 because of a T/A difference at nucleotide 236 in DuCV1, and the ORF3 protein (78 aa) is truncated by a premature stop codon (Wu et al., 2018). Despite the widespread prevalence of DuCV, information on the pathogenesis of different DuCV genotypes (DuCV-1 and DuCV-2) is still lacking. Thus, we need to establish different DuCV genotypes for differential diagnosis, which can be further used for large-scale monitor and pathogenesis studies. Due to these characteristics, HRM can serve as a candidate assay for the detection and accurate differentiation of DuCV-1 and DuCV-2 (via PCR-HRM), which will help us epidemiological investigations and study the pathogenesis of DuCV genotypes.

MATERIALS AND METHODS

Viruses and Clinical Samples

Muscovy duck parvovirus (MDPV), goose parvovirus (GPV), novel goose parvovirus (N-GPV), duck hepatitis A virus types 1 and 3 (DHAV-1 and DHAV-3), avian influenza virus (AIV), avian Tambusu virus (ATmV), avian paramyxovirus type 1 (APMV-1), Muscovy duck reovirus (MDRV), novel variant duck reovirus (N-DRV), DuCV-1 and DuCV-2 (Wan, et al., 2011a; 2011b; 2016) were preserved by the Institute of Animal Science and Veterinary Medicine, Fujian Academy of Agricultural Sciences.

A total of 85 waterfowl samples (including 58 duck-origin and 27 goose-origin samples) were collected from clinical samples in Fujian Province. All samples used in this study was obtained by Animal Disease Detection Center, of Fujian Academy of Agricultural Sciences.

Primer design

The ORF3 sequences of DuCV-1 and DuCV-2 were retrieved from GenBank. Based on the genetic comparison of DuCV-1 and DuCV-2, a pair of PCR-HRM primers (HRMF1 and HRMR1; Table 1) was designed with 126-nt target amplification fragments. The ORF3 sequence variations in DuCV-1 and DuCV-2 are listed in Table 1. The primers used in this study were synthesized by Sangon Bioengineering Co., Ltd. (Sangon Biotech, Shanghai, China).

Table 1.

ORF3 sequence variation in DuCV-1 and DuCV-2.

Position 77-97A1 98 146 163 167 170 176 179 184-202A2
DuCV-1B GCAATATTCTTCATTATCTTC A C G G A A G GGAATCCCTGAAGGTGAGG
DuCV-2B GCAATATTCTTCATTATCTTC G T T A G G A GGAATCCCTGAAGGTGAGG
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A1

The forward primer pair HRMF1 used in this study, shared100% matched with DuCV-1 and DuCV-2.

A2

The forward primer pair HRMR1 (reverse sequence) used in this study, shared 100% identical to DuCV-1 and DuCV-2.

B

The GenBank accession numbers of the DuCV-1 strains used in this study are as follows: EF451117, EU344804, GQ334370, GQ334371, GQ334377, GU131340, and GU168779. The GenBank accession numbers of the DuCV-2 strains used in this study are as follows: EU499310, GQ334371, GQ334373, GQ334375, GQ334376, JX499186, and MN928804. Only the ORF3 sequence variations in DuCV-1 (marked with red) and DuCV-2 (marked with blue) are listed in the table.

Standard Plasmid Preparation

The DuCV-1-positive and DuCV-2-positive recombinant plasmids G1-MH25 (GenBank accession number EF451117) and G2-FJPT09 (GenBank accession number GQ423741) were prepared as described in our previous work (Wan et al., 2011a; 2016). Then, the samples were quantified using an ND-2000c spectrophotometer (NanoDrop2000, Wilmington, NC). The copy number was calculated according to the methods of a previous study (Yun et al., 2006). Ten-fold dilutions of G1-MH25 (ranging from 6.19 × 109 to 6.19 × 10° copies/μL) and G2-FJPT09 (ranging from 6.06 × 109 to 6.06 × 10° copies/μL) were prepared using TE buffer (10 mmol/L Tris–HCl, 1 mmol/L EDTA) and then stored at −80°C until use.

PCR-HRM Assay

A 20 μL reaction mixture was prepared according to the instructions of the SsoFast EvaGreen Supermix Kit (BIO-Rad, Shanghai, China): 1 μL each of 10 μmol/L PCR-HRM primers (HRMF1 and HRMR1), 1 μL of template, 10 μL of 2 × SsoFast EvaGreen Supermix, and ddH2O were added to a final volume of 20 μL. The amplification reaction conditions were set as follows: predenaturation at 95°C for 2 min; 40 cycles were performed at 95 °C for 10 s followed by 60°C for 30 s. Gradient PCR was used to optimize the annealing temperature (54, 56, 58, 60, 62 and 64°C), and a matrix assay was used to optimize the primer concentration (0.2, 0.4, 0.8, 1.0, 1.5, 1.75, and 2.0 pmol/L). The melting curve parameters were 95°C for 1 min, 40°C for 1 min, and 65°C for 1 s, after which the temperature was increased to 97°C. The optimized melting rates of 0.05, 0.10, and 0.20°C/s were selected to screen for the most efficient melting rate. The PCR-HRM products were amplified using a LightCycler 96 real-time fluorescent quantitative PCR machine (Roche, CA). Normalized melting curves and difference plots were obtained by analyzing the active regions. HRM software (Roche) was also used to further analyze the melting curves of the amplicons to enhance and clarify the data analysis.

Specificity and Sensitivity Evaluation

Two plasmid standards (G1-MH25-ORF3 and G2-FJPT09-ORF3) were used as positive controls; common pathogens (MDPV, GPV, N-GPV, DHAV-1, DHAV-3, AIV, ATmV, APMV-1, MDRV and N-DRV) were selected as templates; and a blank control (ddH2O) was used. Serial dilutions of G1-MH25 (ranging from 6.19 × 103 to 6.19 × 10° copies/μL) and G2-FJPT09 (ranging from 6.06 × 103 to 6.06 × 10° copies/μL) were used for sensitivity evaluation, and the limit of detection (LOD) of the PCR-HRM method was assessed using these plasmid standard templates. Three replicates of each different plasmid concentration were determined.

Repeatability Evaluation

Serial dilutions of G1-MH25 (ranging from 6.19 × 106 to 6.19 × 103 copies/μL) and G2-FJPT09 (ranging from 6.06 × 106 to 6.06 × 103 copies/μL) were used for repeatability evaluation, and three replicates were performed for each concentration of plasmid to determine the coefficient of variation (CV). The intra-group and inter-group coefficients of variation (CVs) for cycle quantification (Cq) values were calculated.

Epidemiological Investigation

All 85 samples were homogenized with sterile PBS in a mortar. Then, the suspensions were collected after centrifugation at 4,000 rpm for 15 min. Nucleic acid was extracted from the supernatant using the Magnetic Animal Tissue Genomic DNA Kit (Tiangen Biotech, Beijing, China) according to the manufacturer's instructions. These samples were tested using PCR-HRM in this study, and compared with the PCR-RFLP method described by us previously (Wan et al., 2016).

RESULTS

PCR-HRM Assay

The optimal reaction mixture for the HRM assay after optimization (20 μL) was as follows: 1 μL of HRMF1 and HRMR1 (final concentration was 10 μmol/L), 1 μL of template, 10 μL of SsoFast EvaGreen supermix, and ddH2O supplement to a final volume of 20 μL. The optimal reaction conditions of the PCR-HRM assay were as follows: predenaturation at 95°C for 2 min; 40 cycles of 95 °C for 10 s and 60 °C for 30 s. The melting curve parameters were set at 95 °C for 1 min, 40°C for 1 min, and 65°C for 1 s, and then the temperature was heated to 97°C, and a melting rate of 0.1°C/s was selected.

Standard Curve

A standard curve was plotted with the template concentration on the X-axis and the Cq value on the Y-axis. There was a good linear relationship between the template concentration and the Cq value. The results (Figure 1) showed standard curves for DuCV-1 and DuCV-2, with correlation coefficient of R2=1.00 (both for DuCV-1 and DuCV-2) and linear equation of y = −3.213(x) + 37.70 and y = −3.339(x) + 38.13, respectively.

Figure 1.

Figure 1

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(A) Amplification curve of DuCV-1 determined via PCR-HRM (B) The standard curve of DuCV-1 determined by PCR-HRM (C) Amplification curve of DuCV-2 determined by PCR-HRM (D) The standard curve of DuCV-2 determined by PCR-HRM.

Coinfection Simulation

To evaluate the ability of this assay to distinguish coinfected samples, single plasmids and mixtures of DuCV-1 (G1-MH25) and DuCV-2 (G2-FJPT09) were tested via a PCR-HRM assay. The melting curves of the DuCV-1- and DuCV-2-positive plasmid DNA samples were generated. The Tm values of the DuCV-1 and DuCV-2 melting curves were (86.75 ± 0.04)°C and (85.72 ± 0.04)°C, respectively. No primer dimers or nonspecific peaks appeared. The different shapes generated by HRM analysis software can distinguish DuCV-1 from DuCV-2 according to the specific melting temperature and shape of the melting curve (Figure 2).

Figure 2.

Figure 2

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Discrimination between DuCV-1 and DuCV-2 determined by HRM analysis 1, DuCV-1; 2, DuCV-2; 3, DuCV-1 and DuCV-2 coinfection.

Specificity Analysis

Our results showed that the PCR-HRM assay was highly specific for DuCV-1 and DuCV-2, and there was no cross-reaction with common pathogens (such as MDPV, GPV, N-GPV, DHAV-1, DHAV-3, AIV, ATmV, APMV-1, MDRV, N-DRV or ddH2O (Figure 3).

Figure 3.

Figure 3

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Specificity analysis of the PCR-HRM assay 1, DuCV-1; 2, DuCV-2; 3, Controls: MDPV, GPV, N-GPV, DHAV-1, DHAV-3, AIV, ATmV, APMV-1, MDRV, N-DRV, and ddH2O. These controls were all found to have no positive signals. The results of these controls could not be effectively distinguished by the naked eye.

Sensitivity Analysis

To evaluate sensitivity, a series of gradient dilutions of recombinant plasmid DNA were tested via the established PCR-HRM assay. Our data showed that DuCV-1 and DuCV-2 were successfully distinguished by the PCR-HRM assay, and the detection limits of DuCV-1 and DuCV-2 were 61.9 copies/μL (Figure 4A) and 60.6 copies/μL (Figure 4B), respectively.

Figure 4.

Figure 4

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(A) Sensitivity analysis of DuCV-1 by PCR-HRM assay 1-4, the DuCV-1 plasmid concentrations were 6.19 × 103∼6.19 × 10° copies/μL; 5, negative control (ddH2O) (B) Sensitivity analysis of DuCV-2 by PCR-HRM assay 1-4, the DuCV-2 plasmid concentrations were 6.06 × 103∼6.06 × 10° copies/μL; 5, negative control (ddH2O).

Repeatability Analysis

As determined in triplicate, our PCR-HRM analysis revealed intra-assay and inter-assay coefficients of variation for DuCV-1 of 0.40% to 1.23% and 0.88% to 1.46%, respectively (Table 2A). Additionally, the intra-assay and inter-assay coefficients of variation of DuCV-2 determined by PCR-HRM were 0.63%-0.92% and 0.73%-1.22%, respectively (Table 2B). The intra-assay and inter-assay reproducibility tests indicated that the PCR-HRM assay was reproducible.

Table 2-A.

Reproducibility of the DuCV-1 detection by PCR-HRM.

DuCV-1 Copies/μL Intra-assay CVs
 Inter-assay CVs
Ct Standard deviation CV (%) Ct Standard deviation CV (%)
1 6.19 × 106 15.98 0.20 1.23 16.07 0.23 1.46
2 6.19 × 105 19.14 0.09 0.47 19.18 0.17 0.88
3 6.19 × 104 22.03 0.21 0.97 22.21 0.24 1.08
4 6.19 × 103 25.28 0.10 0.40 25.46 0.24 0.92
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Table 2-B.

Reproducibility of the DuCV-2 detection by PCR-HRM.

DuCV-2 Copies/μL Intra-assay CVs
 Inter-assay CVs
Ct Standard deviation CV (%) Ct Standard deviation CV (%)
1 6.02 × 106 15.42 0.14 0.91 15.57 0.19 1.19
2 6.02 × 105 18.84 0.17 0.92 18.99 0.23 1.22
3 6.02 × 104 22.06 0.18 0.80 22.16 0.24 1.07
4 6.02 × 103 25.79 0.16 0.63 25.81 0.19 0.73
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Epidemiological Investigation Results

The established PCR-HRM assay and PCR-RFLP assay were used to analyze the 85 samples (Table 3). Among the 58 duck origin samples determined by PCR-HRM, 7 were DuCV-1 positive (positive rate of 12.07%), 15 were DuCV-2 positive (positive rate of 25.86%), and 5 were DuCV-1 and DuCV-2 coinfected (positive rate of 8.62%). For 27 goose origin samples determined by PCR-HRM, 3 DuCV-2-positive samples (positive rate of 11.11%), with no DuCV-1 or coinfection. As shown in Table 3 with PCR-RFLP, for duck-origin samples, 6 were DuCV-1 positive (positive rate of 10.34%), 13 were DuCV-2 positive (positive rate of 22.41%), and 4 were DuCV-1 and DuCV-2 coinfected (positive rate of 6.90%). For goose-origin samples, 2 DuCV-2-positive samples (positive rate of 7.41%), with no DuCV-1-positive or coinfection sample.

Table 3.

The testing results for clinical samples.

Number HRM
 PCR-RFLP
1 R* 2 R* 1+2 R* 1 R* 2 R* 1+2 R*
Duck 58 7 12.07 15 25.86 5 8.62 6 10.34 13 22.41 4 6.90
Goose 27 0 0 3 11.11 0 0 0 0 2 7.41 0 0
Total 85 7 8.24 18 21.18 5 5.88 6 7.06 15 17.65 4 4.71
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Abbreviations: 1 indicates DuCV-1-positive, 2 indicates DuCV-2-positive, 1+2 indicates DuCV-1 and DuCV-2 coinfection, and R* indicates a positive ratio (/%).

DISCUSSION

High-resolution melting (HRM) analysis is a simple, rapid, and inexpensive real-time polymerase chain reaction (PCR)-based method used to identify genetic variation among populations and to detect single nucleotide polymorphisms (SNPs) in nucleic acid sequences. In recent years, with the popularity of real-time PCR instruments with detection functions, HRM genotyping technology has received widespread attention in the field of molecular diagnosis. Animal gene and animal pathogen gene detection technology has also been developed (Hata et al., 2014). Compared with traditional PCR methods, which are mainly based on melt curve analysis, HRM technology adopts closed-tube PCR without expensive fluorescent probes. The ability to analyze a large number of samples in one amplification without the need for further manual separation steps or PCR postprocessing manual analysis helps to avoid errors in gelling and reduce analysis time (Garganese et al., 2018; England et al., 2021).

Several detection methods for two different genotypes of DuCV have been developed. According to the conserved sequences of the Cap genes of DuCV-1 and DuCV-2, Li et al. (2014) established a dual PCR detection method that can simultaneously detect these two genotypes. We also established a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method to distinguish DuCV-1 from DuCV-2 (Wan et al., 2016). Zhang et al. (2021a) developed a double-label, hydrolytic probe-based real-time PCR assay to detect the two genotypes DuCV-1 and DuCV-2.

Based on the advantages of PCR-HRM in virus detection, a PCR-HRM assay was developed for the simultaneous detection and differentiation of DuCV-1 and DuCV-2 according to ORF3 characterization. It can simultaneously amplify DuCV-1 and DuCV-2 infection, which also has the advantage of sensitivity. The melting curve of the PCR-HRM results indicated that the amplification product was single, with no cross-reaction with common waterfowl-origin pathogens (such as MDPV, GPV, N-GPV, DHAV-1, DHAV-3, AIV, ATmV, APMV-1, MDRV or N-DRV), indicating the advantages of specificity. A low coefficient of variation and values less than 1.50% in both intra-batch and inter-batch repeated experiments indicated the advantages of repeatability. The PCR-HRM method established in this study was used for the detection of 85 clinical waterfowl samples. As shown in our data, we found that the DuCV-2-positive rate was greater than the DuCV-1-positive rate in duck-origin samples, with an 8.62% positive rate of coinfection. Like previous results, the real-time fluorescence quantitative PCR assay has advantages in terms of sensitivity compared with conventional PCR technology (Wan et al., 2011a; Zhang et al., 2021a; Zhang et al. 2023). Compared with the PCR-RFLP assay, the positive samples tested by the PCR-RFLP assay also showed positive signals using PCR-HRM assay, with a coincidence rate of 100%. Our results indicated that the PCR-HRM assay is more sensitive than the PCR-RFLP assay.

China is the largest country in which waterfowl (including ducks and geese) are bred and consumed. As shown in Table 3, we found DuCV-2 in goose-origin samples. These data expand the knowledge about the genetic evolution of DuCV, especially for DuCV-2. Different DuCV genotypes (DuCV-1 and DuCV-2) have been found worldwide, but the differences in pathogenicity between DuCV-1 and DuCV-2 are still unknown (Liu et al., 2010; Liu et al., 2020; Zhang et al., 2013; Zhang et al., 2021a). Moreover, unfortunately, there is currently no commercial vaccine capable of controlling DuCV or GoCV infection. Considering the large number of geese breeding in China, the cross-species infection potential of different DuCV genotypes still needs further research. Moreover, more attention should be given to the commercial vaccines for DuCVs in both ducks and geese.

In conclusion, a PCR-HRM platform that can be used for the differentiation of DuCV-1 and DuCV-2 was developed, with the advantages of eliminating the risk of contamination and reducing expenses. With good specificity, sensitivity and reproducibility, the current study provides the basis for this novel platform for the simultaneous detection and differentiation of DuCV-1 and DuCV-2 infections. Our work could use for further epidemiological surveillance of DuCVs.

ACKNOWLEDGMENTS

This study was funded by grants from the Fujian Science and Technology Program (grant no. 2020J06029, 2021R1026006), the National Natural Science Foundation of China (grant no. 32372995), the China Agriculture Research System (grant no. CARS-42), and the Research and Technology Program of Fujian Academy of Agricultural Sciences (grant no. YCZX202412, DWHZ2024-13, and CXTD2021005). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors' Contributions: Huanru Fu and Min Zhao performed most of the experiments. Shuyu Chen, and Yu Huang provided help with the experiment. Huanru Fu wrote the manuscript. Chunhe Wan designed the study and edited the article. All the authors read, commented on and approved the final version of the manuscript.

Consent for Publication: Not applicable.

Availability of Data and Material: The datasets supporting the conclusions of this article are included within the article. All the datasets are available from the corresponding author upon reasonable request.

DISCLOSURES

The authors declare that they have no conflicts of interest.

REFERENCES

  1. Banda A., Galloway-Haskins R.I., Sandhu T.S., Schat K.A. Genetic analysis of a duck circovirus detected in commercial Pekin ducks in New York. Avian Dis. 2007;51:90–95. doi: 10.1637/0005-2086(2007)051[0090:GAOADC]2.0.CO;2. [DOI] [PubMed] [Google Scholar]
  2. Cha S.Y., Song E.T., Kang M., Wei B., Seo H.S., Roh J.H., Yoon R.H., Moon O.K., Jang H.K. Prevalence of duck circovirus infection of subclinical Pekin ducks in South Korea. J. Vet. Med. Sci. 2014;76:597–599. doi: 10.1292/jvms.13-0447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. England A.D., Kheravii S.K., Musigwa S., Kumar A., Daneshmand A., Sharma N.K., Gharib-Naseri K., Wu S.B. Sexing chickens (Gallus gallus domesticus) with high-resolution melting analysis using feather crude DNA. Poult. Sci. 2021;100 doi: 10.1016/j.psj.2020.12.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Garganese F., Ippolito A., di Rienzo V., Lotti C., Montemurro C., Sanzani S.M. A new high-resolution melting assay for genotyping Alternaria species causing citrus brown spot. J. Sci. Food Agric. 2018;98:4578–4583. doi: 10.1002/jsfa.8986. [DOI] [PubMed] [Google Scholar]
  5. Hata A., Kitajima M., Tajiri-Utagawa E., Katayama H. Development of a high-resolution melting analysis for detection and differentiation of human astroviruses. J. Virol. Methods. 2014;200:29–34. doi: 10.1016/j.jviromet.2014.01.023. [DOI] [PubMed] [Google Scholar]
  6. Hattermann K., Schmitt C., Soike D., Mankertz A. Cloning and sequencing of Duck circovirus (DuCV) Arch. Virol. 2003;148:2471–2480. doi: 10.1007/s00705-003-0181-y. [DOI] [PubMed] [Google Scholar]
  7. Hong Y.T., Kang M., Jang H.K. Pathogenesis of duck circovirus genotype 1 in experimentally infected Pekin ducks. Poult. Sci. 2018;97:3050–3057. doi: 10.3382/ps/pey177. [DOI] [PubMed] [Google Scholar]
  8. Li P., Li J., Zhang R., Chen J., Wang W., Lan J., Xie Z., Jiang S. Duck “beak atrophy and dwarfism syndrome” disease complex: interplay of novel goose parvovirus-related virus and duck circovirus? Transbound. Emerg. Dis. 2018;65:345–351. doi: 10.1111/tbed.12812. [DOI] [PubMed] [Google Scholar]
  9. Li Z., Wang X., Zhang R., Chen J., Xia L., Lin S., Xie Z., Jiang S. Evidence of possible vertical transmission of duck circovirus. Vet. Microbiol. 2014;174:229–232. doi: 10.1016/j.vetmic.2014.09.001. [DOI] [PubMed] [Google Scholar]
  10. Liu H., Li L.X., Sun W.C., Shi N., Sun X.T., Jin N.Y., Si X.K. Molecular survey of duck circovirus infection in poultry in southern and southwestern China during 2018 and 2019. BMC Vet. Res. 2020;16:130–138. doi: 10.1186/s12917-020-02301-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Liu S.N., Zhang X.X., Zou J.F., Xie Z.J., Zhu Y.L., Zhao Q., Zhou E.M., Jiang S.J. Development of an Indirect ELISA for the detection of duck circovirus infection in duck flocks. Vet. Microbiol. 2010;145:41–46. doi: 10.1016/j.vetmic.2010.03.010. [DOI] [PubMed] [Google Scholar]
  12. Soike D., Albrecht K., Hattermann K., Schmitt C., Mankertz A. Novel circovirus in mulard ducks with developmental and feathering disorders. Vet. Rec. 2004;154:792–793. doi: 10.1136/vr.154.25.792. [DOI] [PubMed] [Google Scholar]
  13. Todd D. Circoviruses: immunosuppressive threats to avian species: a review. Avian. Pathol. 2010;29:373–394. doi: 10.1080/030794500750047126. [DOI] [PubMed] [Google Scholar]
  14. Wan C., Cheng L., Fu G., Shi S., Chen H., Fu Q., Liu R., Huang Y. Establishment a PCR-RFLP method for rapid differential duck circovirus genotype 1 and genotype 2. Chin. Virol. Poult. 2016;38:53–55. [Google Scholar]
  15. Wang D., Xie X., Zhang D., Ma G., Wang X., Zhang D. Detection of duck circovirus in China: a proposal on genotype classification. Vet. Microbiol. 2011;147:410–415. doi: 10.1016/j.vetmic.2010.07.014. [DOI] [PubMed] [Google Scholar]
  16. Wan C., Fu G., Shi S., Cheng L., Chen H., Peng C., Lin S., Huang Y. Epidemiological investigation and genome analysis of duck circovirus in Southern China. Virol. Sin. 2011;26:289–296. doi: 10.1007/s12250-011-3192-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Wan C., Huang Y., Cheng L., Fu G., Shi S.H., Chen H., Peng C., Lin F., Lin J. The development of a rapid SYBR green I-based quantitative PCR for detection of duck circovirus. Virol. J. 2011;8:465. doi: 10.1186/1743-422X-8-465. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Wu Z.C., Zhang R.H., Li Y.M., Shao D.H., Chen H., Jiang S.J., Ma Z.Y., Wang X. C-terminal 20 residues of ORF3 protein of duck circovirus genotype 2 regulates the nuclear localization and inhibits apoptotic activity of ORF3 protein. Vet. Microbiol. 2018;214:21–27. doi: 10.1016/j.vetmic.2017.12.002. [DOI] [PubMed] [Google Scholar]
  19. Xiang Q.W., Wang X., Xie Z.J., Sun Y.N., Zhu Y.L., Wang S.J., Liu H.J., Jiang S.J. ORF3 of duck circovirus: a novel protein with apoptotic activity. Vet. Microbiol. 2012;159:251–256. doi: 10.1016/j.vetmic.2012.03.045. [DOI] [PubMed] [Google Scholar]
  20. Yun J.J., Heisler L.E., Hwang I.I., Wilkins O., Lau S.K., Hyrcza M., Jayabalasingham B., Jin J., McLaurin J., Tsao M.S., Der S.D. Genomic DNA functions as a universal external standard in quantitative real-time PCR. Nucleic Acids Res. 2006;34:e85. doi: 10.1093/nar/gkl400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Zhang L., Jiang W., Zhang F., Li Y., Li J., Liang S., Yu X., Peng C., Liu S., Wang J., Sun S., Liu H. Development of a dual-labelled, hydrolysis probe-based, real-time quantitative PCR assay for detection of both genotypes of duck circovirus-1 (DuCV-1) and DuCV-2. Virus Genes. 2021;57:453–458. doi: 10.1007/s11262-021-01862-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Zhang Y., Zhang X., Cheng A., Wang M., Yin Z., Huang J., Jia R. Apoptosis Triggered by ORF3 Proteins of the Circoviridae family. Front. Cell. Infect. Microbiol. 2021;10 doi: 10.3389/fcimb.2020.609071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Zhang J., Fu H., Chen C., Jiang J., Lin Y., Jiang B., Lin L., Hu Q., Wan C. Rapid detection of pigeon Megrivirus using TaqMan real-time PCR technology. Poult. Sci. 2023;102 doi: 10.1016/j.psj.2023.103027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Zhang X., Jiang S., Wu J., Zhao Q., Sun Y., Kong Y., Li X., Yao M., Chai T. An investigation of duck circovirus and coinfection in Cherry Valley ducks in Shandong Province. China. Vet. Microbiol. 2009;133:252–256. doi: 10.1016/j.vetmic.2008.07.005. [DOI] [PubMed] [Google Scholar]
  25. Zhang Z.L., Jia R.Y., Lu Y.Y., Wang M.S., Zhu D.K., Chen S., Yin Z.Q., Chen X.Y., Cheng A.C. Identification, genotyping, and molecular evolution analysis of duck circovirus. Gene. 2013;529:288–295. doi: 10.1016/j.gene.2013.07.028. [DOI] [PubMed] [Google Scholar]

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