Establishment and application of a quadruple RT-qPCR method for simultaneous detection of porcine enteric coronaviruses

Introduction: Porcine enteric coronaviruses (PECs) are a group of viruses that cause severe diarrhea in piglets, significantly impacting the pig industry and resulting in huge economic losses. Important PECs include porcine epidemic diarrhea virus (PEDV), porcine enteric alphacoronavirus (PEAV), porcine deltacoronavirus (PDCoV), and transmissible gastroenteritis virus (TGEV). These pathogens cause highly similar clinical symptoms and pathological changes in piglets. Therefore, it is necessary to develop a simultaneous detection method for the precise prevention and control of porcine diarrheal diseases.

Methods: To establish a rapid, simple, and accurate detection method for differential diagnosis on these four pathogens, this study designed specific primers and probes based on the conserved regions of the M gene of PEDV, PEAV, and PDCoV, and the N gene of TGEV. By optimizing the reaction conditions, a quadruple fluorescence real-time quantitative RT-PCR (RT-qPCR) method for simultaneous detection of the four porcine diarrhea viruses was developed.

Results: This method demonstrated high specificity, with no cross-reactivity with other common porcine pathogens such as porcine reproductive and respiratory syndrome virus, porcine circovirus, classical swine fever virus, porcine rotavirus, and pseudorabies virus. The sensitivity was excellent, with the limit of detection of 100 copies/μL in multiplex real-time RT-qPCR assays and all correlation coefficients (R2) exceeding 0.99. Repeatability was also strong, with the coefficient of variation of the intra- and inter-assay repeatability tests ranging from 0.3% to 1.0%.When applied to 231 clinical samples from Fujian province, the multiplex RT-qPCR method identified PEDV as the predominant pathogen, and often in co-infections. These results were 100% consistent with those from the commercial RT-qPCR kits, demonstrating the high accuracy of the developed method.

Discussion: In summary, this study established a specific, sensitive, and accurate multiplex RT-qPCR assay for the simultaneous detection of PEDV, PEAV, TGEV, and PDCoV, providing a valuable tool for the monitoring and differential diagnosis of these four PECs.

1 Introduction

In recent years, porcine diarrheal diseases have become common on pig farms in China, causing significant economic losses to the pig industry (1). Porcine enteric coronaviruses (PECs), including porcine epidemic diarrhea virus (PEDV), porcine enteric alphacoronavirus (PEAV), porcine deltacoronavirus (PDCoV), and transmissible gastroenteritis virus (TGEV), are the primary pathogens responsible for these outbreaks. They cause intestinal diseases, watery diarrhea, and high mortality in infected piglets (1). Consequently, the prevalence of PECs poses a major threat to the pig industry in China and worldwide, a concern heightened by the risk of cross-species transmission (1).

PECs are large, single-stranded, positive-sense RNA viruses belonging to the Coronaviridae family and order Nidovirales (2). PEDV and TGEV cause highly similar clinical symptoms, including diarrhea, vomiting, anorexia, dehydration, and weight loss in piglets (3). PEDV was first isolated and identified in the UK in 1978 (4) and has since spread globally, causing ongoing economic losses (5). TGEV was first reported in the United States in 1946, and then it was detected in Americas, Asia, and Europe, showing its ability to spread across countries (5, 6). It is most severe in piglets under 2 weeks of age, with a mortality rate approaching 100% (5). PDCoV, a member of the genus Deltacoronavirus, is an enveloped virus with a single-stranded, positive-sense RNA genome and characteristic spike proteins on its surface (2). The first major PDCoV outbreak occurred in the US (7), and the virus has now been detected in many countries, including China, South Korea, Canada, Japan, Mexico, and Thailand, posing a serious threat to the global pig industry (2). Moreover, many other species have been confirmed to be susceptible to PDCoV infection, including calves, chickens, turkey poults, and mice (2). A report of human infection in Haitian children (8) underscores its potential for cross-species transmission and the associated threat to public health. PEAV, which belongs to the genus Alphacoronaviruses, was first reported in China in 2017 (9). The clinical symptoms it causes are extremely similar to those of other PECs (5), which often leads to its being overlooked or misdiagnosed, thereby increasing the threat to herd health.

These four PECs are common pathogens that cause fatal diarrheal syndrome in newborn piglets. Co-infections are frequent and present a more complex clinical situation than single infections, making accurate diagnosis, targeted prevention, and effective treatment challenging (10). Therefore, developing a method for the simultaneous detection of these viruses is necessary for the precise prevention and control of porcine diarrheal diseases. Multiplex fluorescence real-time quantitative PCR (RT-qPCR) is a high-throughput technique that allows for the simultaneous detection of multiple pathogens in a single reaction, featuring high sensitivity, high efficiency, and high specificity (11). Compared to traditional diagnostic methods for detecting these four PECs, such as conventional RT-PCR and singleplex RT-qPCR, multiplex RT-qPCR can significantly shorten the diagnostic time and enable precise and early diagnosis of co-infections (12). Previous studies have reported three assays for the detection and differentiation of PEDV, PEAV, PDCoV, and TGEV (13–15). The first assay used PEDV M (membrane), PEAV N (nucleocapsid), PDCoV M, and TGEV N genes as target templates. The second used PEDV N, PEAV N, PDCoV N, and TGEV S genes, and the third used PEDV N, PEAV N, PDCoV M, and TGEV M genes (Table 1). In this study, we developed a multiplex real-time RT-qPCR assay to simultaneously detect all four viruses. We designed specific primers and probes for conserved regions of the M gene of PEDV, PEAV, and PDCoV, and the N gene of TGEV. After optimizing the reaction conditions, we rigorously evaluated the assay's sensitivity, specificity, and repeatability. When applied to a wide range of clinical samples, including feces, intestinal tissue, and anal swabs, this method proved effective for the rapid and precise detection of these four viruses.

Table 1

Table 1. Comparison with previous reports on quadruple RT-qPCR for the detection of PEDV, PEAV, PDCoV, and TGEV.

2 Materials and methods

2.1 Viruses and clinical samples

PEDV, PEAV, PDCoV, TGEV, porcine reproductive and respiratory syndrome (PRRSV), porcine circovirus (PCV), classical swine fever virus (CSFV), porcine rotavirus (PoRV), and pseudorabies virus (PRV) were provided and stored in the Fujian Provincial Key Laboratory for Prevention and Control of Livestock Infectious Diseases and Biotechnology. A total of 231 clinical diarrheal samples including 74 intestinal samples, 92 fecal samples, and 65 anal swabs were collected from large-scale pig farms in different regions in Fujian between 2016 and 2022.

2.2 Primers and probes design

Based on the sequence of the conserved regions of M gene of PEDV, PEAV, and PDCoV strains and N gene of a TGEV strain recorded in GenBank (accession number: JX188454.1, MF167434.1, KP757890.1, and KX900409.1, respectively), four pairs of specific primers and TaqMan probes were designed using Oligo 7.37 (Table 2). Both primers and probes were synthesized by ShangHai Sangon Biotech (Shanghai, China).

Table 2

Table 2. Primer and probe information and the components of reaction system for multiple RT-qPCR.

2.3 Construction and identification of the standard plasmids

Sample RNA of PEDV, PEAV, PDCoV, and TGEV was extracted using the FastPure Viral DNA/RNA Mini Kit V2 (Vazyme Biotech, China), and then was reverse-transcribed to cDNA using HiScript III 1st Strand cDNA Synthesis Kit (Vazyme Biotech, China) and used as the templates. M gene and N gene were amplified using specific primers in Table 1, and the amplification products were ligated into the pMD19-T vector (Takara Biotechnology, China). The recombinant plasmids were then transformed into DH5α competent cells (Shanghai Weidi Biotech, China). Single-positive colonies were selected, cultured, and sent to Shanghai Sangon Biotechnology for sequencing. The correctly sequenced plasmids were designated as pPEDV-M, pPEAV-M, pPDCoV-M, and pTGEV-N, respectively. Plasmids were extracted using FastPure Plasmid Mini Kit (Vazyme Biotech, China). The concentration of the recombination plasmids was determined using a NanoDrop One ultramicrospectrophotometer (ThermoFisherScientific, Shanghai, China), and the copy number was calculated according to the formula: plasmid (copies/μL) = (6.02 × 10²³) × (plasmid concentration) (ng/μL) × 10⁻⁹/(plasmid length × 660) (16).

2.4 Optimization of reaction conditions

Four standard plasmids of pPEDV-M, pPEAV-M, pPDCoV-M, and pTGEV-N were mixed and used as templates. The reaction system was prepared according to instructions of 2 × Animal Detection U Probe Master Mix (Vazyme Biotech, China). The optimal reaction conditions for multiple RT-qPCR were obtained by optimizing the primer concentration (final concentration of 0.1–0.8 pmol/μL), probe concentration (final concentration of 0.1–0.8 pmol/μL), and annealing temperature (58 °C−64 °C) using the control variate method.

2.5 Establishment of the standard curves

The four standard plasmids mixed in equal volume were diluted in a 10-fold series serial, resulting in a range of concentrations from 10² to 10⁷ copies/μL. Real-time fluorescent quantitative RT-qPCR amplification of the diluted standard plasmids was performed in triplicate for each dilution using the optimized reaction system and protocol, and the standard curve was established. The logarithm of the initial copy number of the standard plasmid DNA is plotted on the X-axis, whereas the cycle threshold (Ct) values obtained from real-time fluorescent quantitative PCR are plotted on the Y-axis.

2.6 Specificity test

The specificity of our multiplex RT-qPCR system was evaluated using the optimized conditions and system with DNA/cDNA templates from PEDV, PEAV, PDCoV, TGEV, PRRSV, PCV, CSFV, PoRV, and PRV. The standard plasmids and sterile double-distilled water (ddH₂O) were used as the positive control and negative control, respectively. All templates were verified by PCR or viral isolation methods to confirm their identity. All reactions were conducted in triplicates.

2.7 Sensitivity test

Four standard plasmids mixed in equal volume were diluted in a 10-fold series, and the resulted templates of 10¹ to 10⁹ copies/μL were used as templates. The sensitivity of our system was determined by amplifying various dilutions of the templates with this optimized reaction system. ddH₂O was used as the negative control. All reactions were conducted in triplicates.

2.8 Repeatability test

Four standard plasmids mixed in equal volume were diluted in a 10-fold series, and the resulted dilutions of 10², 10⁴, and 10⁶ copies/μL were selected as templates. Intra repeatability tests were conducted by preparing three replicates for each dilution, and inter repeatability tests were conducted by preparing the reaction reagents three times at different times. The average coefficient of variation (CV) was calculated according to the formula: CV (%) = (standard deviation/mean) × 100%, and it was calculated in Excel. All reactions were conducted in triplicates.

2.9 Clinical sample testing

A total of 293 clinical samples were tested. One milliliter PBS was added to 10 mg of the sample. Next, the intestinal samples were grounded in a grinder and then centrifugated at 5,000 rpm for 5min; the fecal samples and anal swabs were repeatedly twice and then centrifugated at 5,000 rpm for 10 min. Two hundred microliter of the supernatants were taken to extract RNA, followed by reverse-transcription to obtain cDNA as the templates and tested using the multiplex RT-qPCR developed in this study. Meanwhile, the commercialized VetMAX PEDV/TGEV/SDCoV real time RT-PCR Kit (ThermoFisher Scientific, China) and the Porcine Acute Diarrhea Syndrome Coronavirus Fluorescence Quantitative RT-PCR Kit (15-84900y, Yaji Biological, China; research only) were used to test above samples. The detection results of the method established in this study were compared with those of the above method, and the coincidence rate was calculated.

3 Results

3.1 Construction and identification of the standard plasmids

The M gene of PEDV, PEAV and PDCoV, and the N gene of TGEV were amplified from their cDNA and ligated into pMD19-T vectors and sent for sequencing. The plasmids with correct sequence of insertions were designated as pPEDV-M, pPEAV-M, pPDCoV-M, and pTGEV-N, respectively, and used as the standard plasmids. The concentration of above plasmids was determined using a ultramicrospectrophotometer: 122.6 ng/μL for pPEDV-M, 88.3 ng/μL for pPEAV-M, 120.8 ng/μL for pPDCoV-M, and 102.7 ng/μL pTGEV-N. The copy number were calculated according to the formula (16):

$\begin{array}{r}\text{copy}\text{number}\text{(copies/}\mu \text{L)}=\\ \frac{\text{Concentration}\text{(ng/}\mu \text{L)}\times {10}^{-9}\times 6.022\times {10}^{23}}{\text{DNA}\text{length}\times 660}& (1)\end{array}$

The copy number of these standard plasmids were calculated to be: 3.63 × 10¹⁰ copies/μL for pPEDV-M, 2.67 × 10¹⁰ copies/μL for pPEAV-M, 3.63 × 10¹⁰ copies/μL for pPDCoV-M, and 3.08 × 10¹⁰ copies/μL for pTGEV-N. These plasmids were mixed in equal volume and diluted to 10¹⁰ copies/μL.

3.2 Reaction system procedure and optimization

The quadruple RT-qPCR procedure was optimized using control variate method using the standard plasmids as the templates. The results of the optimal reaction were determined as follows: 2 × Animal Detection U Probe Master Mix 12.5 μL, probes (10 pmol/μL) of PEDV, PEAV, PDCoV, and TGEV were 0.2, 0.2, 0.5, and 0.5, respectively, and primers (10 pmol/μL) were 0.5 μL for each virus; 2 μL of the cDNA template and ddH₂O were added to a final volume of 25 μL (Table 1). All reactions were performed on a LightCycler 480 II real-time fluorescence quantitative PCR instrument (Roche, USA). The optimal amplification program was optimized as follows: 45 cycles of 95 °C for 30 s, 95 °C for 10 s, and 60 °C for 10 s.

3.3 Establishment of the standard curves

Standard curves were established based on the amplification results of real time fluorescence RT-qPCR on standard plasmids, ranging from 10² to 10⁷ copies/μL. Results showed a good amplification efficiency, with good linear relationships between log starting quantity (X-axis) and Ct (Y-axis). Their linear regression equations are listed as follows: y = −3.3049x + 43.802, R² = 0.9971 (PEDV); y = −3.432x + 45.019, R² = 0.9971 (PEAV); y = −3.2557x + 43.541, R² = 0.9983 (PDCoV); y = −3.1767x + 11.513, R² = 0.9978 (TGEV); (Figure 1).

Figure 1

Figure 1. Standard curves of quadruple RT-qPCR based on the amplification curves. (A–D) The amplification curves of pPEDV-M, pPEAV-M, pPDCoV-M, and pTGEV-N, respectively; amplification curves: 10²-10⁷ copies/μL for the mixture of four plasmid standards; negative control: ddH₂O; (E) The standard curves of pPEDV-M, pPEAV-M, pPDCoV-M, and pTGEV-N, respectively.

3.4 Specificity analysis

The DNA/cDNA templates from PEDV, PEAV, PDCoV, TGEV, PRRSV, PCV, CSFV, PoRV, and PRV were amplified using the optimized quadruple RT-qPCR system. Results showed that only PEDV, PEAV, PDCoV, TGEV, and the standard plasmids exhibited fluorescence signals, and neither fluorescence signals nor amplification curves were observed in the wells containing other interfering DNA/cDNA or negative controls (Figure 2). This demonstrates the high specificity of our quadruple RT-qPCR method.

Figure 2

Figure 2. Specificity of the quadruple RT-qPCR. (A–D) Specificity detection for PEDV, PEAV, PDCOV, and TGEV.

3.5 Sensitivity analysis

Sensitivity tests were performed using the mixture of four standard plasmids ranging from 10¹ to 10⁹ copies/μL as templates. Results showed that the lowest detectable levels of PEDV, PEAV, PDCoV, and TGEV were determined to be approximately 100 copies/μL, respectively (Figure 3), demonstrating the high sensitivity of this quadruple RT-qPCR method.

Figure 3

Figure 3. Sensitivity analysis of the quadruple RT-qPCR. (A–D) Amplification curves of pPEDV-M, pPEAV-M, pPDCoV-M, and pTGEV-N, respectively; amplification curves: 10¹-10⁹ copies/μL for the mixture of four plasmid standards; negative control: ddH₂O.

3.6 Repeatability analysis

The repeatability of the quadruplex RT-qPCR was determined by evaluating CVs of the intra-assay and inter-assay. Repeatability tests of three intra- and inter-assays were performed using the mixture of four standard plasmids of 10², 10⁴, and 10⁶ copies/μL, respectively. Results showed that CVs of the intra- and inter-assays were between 0.3 and 1.0% (Table 3), demonstrating the high repeatability and stability of this method.

Table 3

Table 3. Intra- and inter-assay reproducibility of the quadruple RT-PCR assay.

3.7 Clinical sample testing

The applicability of our quadruple RT-qPCR method was evaluated by testing a total of 231 clinical samples including intestinal tissue, fecal samples, and anal swabs collected from different regions in Fujian. Results showed that the total positive rate of the four enteric coronaviruses was 64.93% (150/231), with the positive rates of PEDV, PEAV, PDCoV, and TGEV were 51.52% (109/231), 1.73% (1/231), 8.66% (20/231), and 4.76% (11/231), respectively (Table 4); the co-infection rates of PEDV and PEAV, PEDV and PDCoV, PEDV and TGEV, and TGEV and PDCoV were 0.43% (1/231), 0.43% (1/231), 1.3% (3/231), and 0.43% (1/231), respectively (Figure 4). Above samples were also tested using the commercialized RT-qPCR kits, and the results were consistent with the results tested using the method established in this study, with a 100% agreement rate, indicating the high reliability and accuracy of this method in clinical sample testing.

Table 4

Table 4. Positive rates detected from the clinical samples.

Figure 4

Figure 4. Co-infection analysis of detected enteric coronaviruses. The Venn diagram shows the number of clinical samples infected with a single or multiple enteric coronaviruses. The overlapping numbers (1 and 3) represent the number of co-infection (e.g., 3 represents PEDV + TGEV co-infection).

4 Discussion

Piglet enteric diseases caused by viral infection pose a severe threat to the pig industry worldwide, leading to a high mortality in piglets and significant economic losses (5). A major challenge in managing these diseases is the frequent co-infection with multiple diarrheal viruses, which present highly similar clinical symptoms, thereby significantly complicating disease prevention and control. Among the main pathogens responsible for piglet diarrhea are PECs, including PEDV, PEAV, PDCoV, and TGEV. Infection with any of these viruses can lead to severe diarrhea, vomiting, dehydration, and high mortality in piglets (17). Given the high similarity in the clinical symptoms and pathological lesions caused by these coronaviruses, it is critical to establish a rapid, stable, and sensitive method for their simultaneous detection and discrimination. Molecular diagnostic techniques for detecting PECs have been developed such as loop-mediated isothermal amplification (LAMP) and microfluidics (18). The advantages of LAMP include visualization, rapidity, high sensitivity, and isothermal amplification, making it highly suitable for on-site detection (18, 19). However, it is prone to frequent false positives due to aerosol contamination, a problem that is difficult to resolve (20). Microfluidic chips represent an innovative diagnostic platform that enables rapid sample analysis by utilizing microliter-scale reaction volumes (21). However, the complex manufacturing process and the poor batch consistency make high repeatability difficult to achieve. Moreover, neither technique can simultaneously identify four PECs.

The Real-time qPCR technique is a detection tool known for its rapidity, high specificity, high sensitivity, and excellent reproducibility in virus detection (22, 23). In recent years, the application of multiplex qPCR has made great progress for detecting porcine intestinal coronaviruses, and many research groups have improved the detection efficiency by optimizing target genes and reaction systems (24–26). Initially, dual fluorescence qPCR methods achieved precise detection of common diarrheal coronaviruses (22). However, a limitation of these methods is their inability to detect newly emerged viruses such as PDCoV and PEAV, a capability that is increasingly necessary given the prevalence of co-infections. With the emergence of novel coronaviruses such as PDCoV and PEAV, multiplex RT-qPCR technique has gradually become a research hotspot. Several multiplex qPCR methods established for the differential diagnoses of PECs have been published in recent years, with primers and probes designed for different conserved regions of these viruses (10, 13–15, 24–26).

In this study, we established a highly specific multiplex real-time RT-qPCR assay for the detection and differential diagnosis of swine enteric coronaviruses. Specific TaqMan probes were designed to target the highly conserved regions of PEDV, PEAV, PDCoV, and TGEV. Among the open reading frames (ORFs) and four structural protein genes [M, N, S (spike), and E (envelope)] of coronaviruses, the M and N genes are more conserved and less homologous among the four porcine enteric coronaviruses than the S and E genes (13, 27). Therefore, M genes of three viruses (PEDV, PEAV, and PDCoV) and N gene of TGEV were selected as targets. The results demonstrated that each primer and probe set was highly specific, detecting only its intended target without cross-reaction with other viruses. Although the sensitivity of our multiplex qPCR assay is lower than that of singleplex qPCR, probably due to competition for primers, probes, templates, and reagents (13, 28), our clinical sample testing results demonstrated that this achieved sensitivity is sufficient for field applications for samples with typical viral loads. The established multiplex RT-qPCR method successfully identified all four coronaviruses and exhibited high stability, with a coefficient of variation between 0.3 and 1.0%. Application of this method to clinical samples revealed a high infection rate and a high frequency of co-infections. This common phenomenon highlights the clinical necessity and significance of developing a multiplex RT-qPCR for rapid diagnosis. The results demonstrated that PEDV was the predominant pathogen responsible for swine diarrhea on pig farms in Fujian, which is consistent with other studies (29, 30). Indeed, PEDV is the primary cause of piglet diarrhea today, complicating disease prevention and thus inflicting severe economic losses on the Chinese swine industry (31). Furthermore, co-infections with swine enteric coronaviruses were common in some pig farms. Co-infection may cause recombination between viruses and promote the evolution of non-pathogenic enteric viruses into highly pathogenic viruses (24). In addition, to validate the results, all samples were tested by the reference method, and the results showed 100% agreement between the two methods. Future studies employing this method should include samples from other regions to strengthen its epidemiological relevance. Furthermore, due to the sensitivity limitation of this method, future validation should be conducted on samples with low quality or low viral loads.

In summary, we established a quadruple RT-qPCR method with high specificity, sensitivity and stability for the simultaneous detection of four porcine diarrheal coronaviruses: PEDV, PEAV, PDCoV, and TGEV. This method provides a reliable tool for the synchronized monitoring of multiple pathogens on pig farms and offers a robust system for the early warning of diarrheal diseases.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

CY: Writing – original draft. JX: Data curation, Investigation, Validation, Writing – original draft. SF: Data curation, Investigation, Writing – review & editing. FC: Investigation, Resources, Writing – review & editing. SQ: Resources, Software, Writing – review & editing. YLi: Methodology, Visualization, Writing – review & editing. YLia: Data curation, Resources, Writing – review & editing. WL: Data curation, Software, Writing – review & editing. XX: Data curation, Validation, Writing – review & editing. XL: Investigation, Software, Writing – review & editing. YX: Methodology, Writing – review & editing. YK: Methodology, Software, Writing – review & editing. YZ: Investigation, Writing – review & editing. LH: Methodology, Writing – review & editing. XW: Methodology, Writing – review & editing. AD: Writing – review & editing. NY: Writing – original draft, Writing – review & editing. KF: Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing. HP: Writing – review & editing. SY: Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the Fujian Province Fourth Batch “Innovation Star” Talent Project (Min Ke Zhuan Han [2023] No. 110), the Fujian Province University-Industry Cooperation Project (2020N5012), the Fujian Province Forestry Science and Technology Project (2024KKJ06), the Longyan City Science and Technology Innovation Joint Fund -Social Development Science and Technology Project (2023LYF17119), and the Xinluo District Industry-University-Research Joint Science and Technology Innovation Project (2022XLXYZ009).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that Gen AI was used in the creation of this manuscript. During the preparation of this work the author(s) used Deepseek in order to improve the language. After using this tool, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the publication.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Yang YL, Yu JQ, Huang YW. Swine enteric alphacoronavirus (swine acute diarrhea syndrome coronavirus): an update three years after its discovery. Virus Res. (2020) 285:198024. doi: 10.1016/j.virusres.2020.198024

PubMed Abstract | Crossref Full Text | Google Scholar

2. Duan C. An updated review of porcine deltacoronavirus in terms of prevalence, pathogenicity, pathogenesis and antiviral strategy. Front Vet Sci. (2021) 8:811187. doi: 10.3389/fvets.2021.811187

PubMed Abstract | Crossref Full Text | Google Scholar

3. Lin CM, Saif LJ, Marthaler D, Wang Q. Evolution, antigenicity and pathogenicity of global porcine epidemic diarrhea virus strains. Virus Res. (2016) 226:20–39. doi: 10.1016/j.virusres.2016.05.023

PubMed Abstract | Crossref Full Text | Google Scholar

4. Chasey D, Cartwright SF. Virus-like particles associated with porcine epidemic diarrhoea. Res Vet Sci. (1978) 25:255–6. doi: 10.1016/S0034-5288(18)32994-1

PubMed Abstract | Crossref Full Text | Google Scholar

5. Liu Q, Wang HY. Porcine enteric coronaviruses: an updated overview of the pathogenesis, prevalence, and diagnosis. Vet Res Commun. (2021) 45:75–86. doi: 10.1007/s11259-021-09808-0

PubMed Abstract | Crossref Full Text | Google Scholar

6. Doyle LP, Hutchings LM. A transmissible gastroenteritis in pigs. J Am Vet Med Assoc. (1946) 108:257–9.

Google Scholar

7. Wang L, Byrum B, Zhang Y. Detection and genetic characterization of deltacoronavirus in pigs, Ohio, USA, 2014. Emerg Infect Dis. (2014) 20:1227–30. doi: 10.3201/eid2007.140296

PubMed Abstract | Crossref Full Text | Google Scholar

8. Lednicky JA, Tagliamonte MS, White SK, Elbadry MA, Alam MM, Stephenson CJ, et al. Independent infections of porcine deltacoronavirus among Haitian children. Nature. (2021) 600:133–7. doi: 10.1038/s41586-021-04111-z

PubMed Abstract | Crossref Full Text | Google Scholar

9. Gong L, Li J, Zhou Q, Xu Z, Chen L, Zhang Y, et al. A new bat-HKU2-like coronavirus in swine, China, 2017. Emerg Infect Dis. (2017) 23:1607–9. doi: 10.3201/eid2309.170915

PubMed Abstract | Crossref Full Text | Google Scholar

10. Chen J, Liu R, Liu H, Chen J, Li X, Zhang J, et al. Development of a multiplex quantitative PCR for detecting porcine epidemic diarrhea virus, transmissible gastroenteritis virus, and porcine deltacoronavirus simultaneously in China. Vet Sci. (2023) 10:402. doi: 10.3390/vetsci10060402

PubMed Abstract | Crossref Full Text | Google Scholar

11. Li L, Jiang Q, Li S, Li X, Sun S, Wang X, et al. Establishment of a multiplex qPCR assay for the detection of pathogens associated with bovine respiratory disease complex. Front Vet Sci. (2025) 12:1594488. doi: 10.3389/fvets.2025.1594488

PubMed Abstract | Crossref Full Text | Google Scholar

12. Yalew K, Zhang S, Gebreyowhans S, Xie N, Wang Y, Lv J, et al. Development of multiplex qPCR method for accurate detection of enzyme-producing psychrotrophic bacteria. Foods. (2025) 14:1975. doi: 10.3390/foods14111975

PubMed Abstract | Crossref Full Text | Google Scholar

13. Huang X, Chen J, Yao G, Guo Q, Wang J, Liu G. A TaqMan-probe-based multiplex real-time RT-qPCR for simultaneous detection of porcine enteric coronaviruses. Appl Microbiol Biotechnol. (2019) 103:4943–52. doi: 10.1007/s00253-019-09835-7

PubMed Abstract | Crossref Full Text | Google Scholar

14. Zhu JH, Rawal G, Aljets E, Yim-Im W, Yang YL, Huang YW, et al. Development and clinical applications of a 5-plex real-time RT-PCR for swine enteric coronaviruses. Viruses. (2022) 14:1536. doi: 10.3390/v14071536

PubMed Abstract | Crossref Full Text | Google Scholar

15. Zhou H, Shi K, Long F, Zhao K, Feng S, Yin Y, et al. A quadruplex qRT-PCR for differential detection of four porcine enteric coronaviruses. Vet Sci. (2022) 9:634. doi: 10.3390/vetsci9110634

PubMed Abstract | Crossref Full Text | Google Scholar

16. Huang YL, Pang VF, Pan CH, Chen TH, Jong MH, Huang TS, et al. Development of a reverse transcription multiplex real-time PCR for the detection and genotyping of classical swine fever virus. J Virol Methods. (2009) 160:111–8. doi: 10.1016/j.jviromet.2009.04.029

PubMed Abstract | Crossref Full Text | Google Scholar

17. Luo X, Zhou GZ, Zhang Y, Peng LH, Zou LP, Yang YS. Coronaviruses and gastrointestinal diseases. Mil Med Res. (2020) 7:49. doi: 10.1186/s40779-020-00279-z

PubMed Abstract | Crossref Full Text | Google Scholar

18. Flores-Contreras EA, Carrasco-González JA, Linhares DCL, Corzo CA, Campos-Villalobos JI, Henao-Díaz A, et al. Emergent molecular techniques applied to the detection of porcine viruses. Vet Sci. (2023) 10:609. doi: 10.3390/vetsci10100609

PubMed Abstract | Crossref Full Text | Google Scholar

19. Li C, Liang J, Yang D, Zhang Q, Miao D, He X, et al. Visual and rapid detection of porcine epidemic diarrhea virus (PEDV) using reverse transcription loop-mediated isothermal amplification method. Animals. (2022) 12:2712. doi: 10.3390/ani12192712

PubMed Abstract | Crossref Full Text | Google Scholar

20. Bao Y, Jiang Y, Xiong E, Tian T, Zhang Z, Lv J, et al. CUT-LAMP: contamination-free loop-mediated isothermal amplification based on the CRISPR/Cas9 cleavage. ACS Sens. (2020) 5: 1082–91. doi: 10.1021/acssensors.0c00034

PubMed Abstract | Crossref Full Text | Google Scholar

21. Li R, Tian X, Cao W, Jiang J, Yuan J, Li L, et al. Development of a paper-based microfluidic chip for point-of-care detection of PEDV. Vet Sci. (2025) 12:427. doi: 10.3390/vetsci12050427

PubMed Abstract | Crossref Full Text | Google Scholar

22. Kim SH, Kim IJ, Pyo HM, Tark DS, Song JY, Hyun BH. Multiplex real-time RT-PCR for the simultaneous detection and quantification of transmissible gastroenteritis virus and porcine epidemic diarrhea virus. J Virol Methods. (2007) 146:172–7. doi: 10.1016/j.jviromet.2007.06.021

PubMed Abstract | Crossref Full Text | Google Scholar

23. Jia S, Feng B, Wang Z, Ma Y, Gao X, Jiang Y, et al. Dual priming oligonucleotide (DPO)-based real-time RT-PCR assay for accurate differentiation of four major viruses causing porcine viral diarrhea. Mol Cell Probes. (2019) 47:101435. doi: 10.1016/j.mcp.2019.101435

PubMed Abstract | Crossref Full Text | Google Scholar

24. Zhao Y, Liu F, Li Q, Wu M, Lei L, Pan Z. A multiplex RT-PCR assay for rapid and simultaneous detection of four RNA viruses in swine. J Virol Methods. (2019) 269:38–42. doi: 10.1016/j.jviromet.2019.04.001

PubMed Abstract | Crossref Full Text | Google Scholar

25. Song W, Feng Y, Zhang J, Kong D, Fan J, Zhao M, et al. Development of a multiplex reverse transcription-quantitative PCR (qPCR) method for detecting common causative agents of swine viral diarrhea in China. Porcine Health Manag. (2024) 10:12. doi: 10.1186/s40813-024-00364-y

PubMed Abstract | Crossref Full Text | Google Scholar

26. Baek JH, Lee YM, Vu ND, Kim MH, Zhao J, Le VP, et al. (2024) A multiplex real-time RT-qPCR assay for simultaneous detection of porcine epidemic diarrhea virus, porcine deltacoronavirus, and swine acute diarrhea syndrome coronavirus. Arch Virol. 169: 82. doi: 10.1007/s00705-024-06003-9

Crossref Full Text | Google Scholar

27. Yang W, Chen W, Huang J, Jin L, Zhou Y, Chen J, et al. Generation, identification, and functional analysis of monoclonal antibodies against porcine epidemic diarrhea virus nucleocapsid. Appl Microbiol Biotechnol. (2019) 103:3705–14. doi: 10.1007/s00253-019-09702-5

PubMed Abstract | Crossref Full Text | Google Scholar

28. Hu L, Han B, Tong Q, Xiao H, Cao D. Detection of eight respiratory bacterial pathogens based on multiplex real-time PCR with fluorescence melting curve analysis. Can J Infect Dis Med Microbiol. (2020) 2020:2697230. doi: 10.1155/2020/2697230

PubMed Abstract | Crossref Full Text | Google Scholar

29. Guo Y, Sui L, Kong D, Liu D, Gao Y, Jiang Y, et al. Porcine epidemic diarrhea virus strain CH/HLJ/18 isolated in China: characterization and phylogenetic analysis. Virol J. (2024) 21: 28. doi: 10.1186/s12985-023-02233-6

PubMed Abstract | Crossref Full Text | Google Scholar

30. Zhang F, Luo S, Gu J, Li Z, Li K, Yuan W, et al. Prevalence and phylogenetic analysis of porcine diarrhea associated viruses in southern China from 2012 to 2018. BMC Vet Res. (2019) 15:470. doi: 10.1186/s12917-019-2212-2

PubMed Abstract | Crossref Full Text | Google Scholar

31. Li YY, Chen KF, Fan CH, Li HX, Zhen HQ, Zhu YQ, et al. Clade-specific recombination and mutations define the emergence of porcine epidemic diarrhea virus S-INDEL lineages. Animals. (2025) 15:2312. doi: 10.3390/ani15152312

PubMed Abstract | Crossref Full Text | Google Scholar