According to viral metagenomics investigations, even phenotypically healthy pigs can harbor endemic strains (17, 32), indicating widespread multiviral coinfection in pigs. Many of these viruses identified belonged to the linear and circular DNA virus families, including PCV2, PPV, PBoVs, and TTSuv (10, 17, 33). This is consistent with the findings for pork products in this investigation (Table. 4).
Moreover, in cases of syndromic-triggering viruses, it's important to consider the possibility of respiratory symptoms arising from a complex viral disease, wherein multiple pathogens may be involved. For instance, consider PCV2, which is the primary causative agent of porcine circovirus-associated diseases (PCVAD) (7), when present as a sole infection, its pathogenicity is relatively weak. However, some studies have shown that PCV2 can inhibit type I interferon (IFN- I) induction to promote other DNA virus infections (34, 35). In cases of PCV2 infection, where antigen-presenting cells are infected, this can lead to a suppressed early antiviral response, potentially causing a significant impact on the host's ability to generate a specific immune response (36). Experiments involving piglets showed that co-infections with PCV2 and viruses like PPV or PRRSV induced PMWS, a condition distinct from the outcome of a sole PCV2 infection. Co-infections of PCV2 with PRRSV, PPV, and PBoVs amplified the clinical signs of PCVAD (5, 8, 13). In addition to this, coinfections involving PCV2 have been observed with viruses such as CSFV, TTSuV, and other enteroviruses (10, 12, 15). Similarly, PRRSV also inhibits IFNs signaling by blocking STAT1/STAT2 nuclear translocation (37). This situation leads to a higher prevalence of co-infections, further underscoring the complex dynamics of virus-host interactions.
In our study, analysis of tissues or blood samples indicated that 82.93% (34/41) of the samples exhibited multiple pathogen detection, underscoring the severity of mixed infections in diseased pigs. Among PCV2 co-infections, the detection rate was 73.53% (25/34), involving PCV1, PCV3, PBoVs, PPV, PRRSV, and TTSuV. The influence of PCV2 was slightly more pronounced in diseased material samples compared to pork, though not statistically significant. This may be because not all respiratory symptomatic pigs from diseased material sources had PMWS. Notably, besides PCV2, the pathogen with the highest incidence detected in this investigation was TTSuV. It was found in 52.38% (22/48) of the diseased material samples, 40.48% (17/48) of the feed samples, and 37.50% (9/24) of the pork samples. TTSuV exhibited a relatively elevated prevalence among healthy pig populations, a trend consistent with findings from various other studies (38, 39). However, as of now, a definitive causal relationship between TTSuV infection and specific diseases in pigs remains unestablished (40–42). Despite its prevalent presence, further research is required to elucidate any potential pathogenic effects associated with TTSuV infection in pigs. Moreover, feed samples are susceptible to exogenous pathogen contamination. The mixed infection rate in our study reached 82.93%. However, it's important to note that this rate reflects the presence of diverse pathogen contamination in the samples and doesn't necessarily indicate a high rate of mixed infection in the originating diseased pigs. Contaminated feed samples may carry pathogens from various sources, and the slaughter, processing, and sales processes could further propagate these pathogens (18). This underscores the significance of accurately detecting multiple pathogens and understanding their role in swine herds for effective disease prevention and control measures.
Currently, a diverse array of virus detection methods are employed for animal quarantine purposes. In addition to traditional methods based on immunology, molecular biology-based methods like polymerase chain reaction (PCR), quantitative PCR, digital PCR, and others are utilized. Advancements in technology have also introduced second-generation sequencing (NGS), liquid-phase microarray, MALDI-TOF MS, and similar technologies into the realm of virus detection. Traditional methods can exhibit subjectivity in result assessment and may struggle to meet the demand for simultaneous detection of multiple pathogens within a short timeframe (43). PCR-based detection techniques, such as fluorescence-based quantitative PCR (qPCR), and digital PCR (dPCR) (44) offer high sensitivity and ease of operation, making them widely used in pathogen detection. However, their application in scenarios necessitating the detection of multiple pathogens is often hindered by challenges such as primer cross-reactivity within the system, or limitations in the fluorescence channels of the used fluorescent dyes and probes. This limitation makes the simultaneous detection of six or more targets a complex endeavor. NGS technology permits high-throughput detection of unknown pathogens in samples, but library preparation and interpretation of results is complex. Third-generation sequencing (TGS) offers long reads but lacks deep sequencing depth and accuracy compared to NGS. Both sequencing methods are costly and may not be suitable for scenarios involving large sample sizes, such as disease surveillance, epidemiological monitoring, and entry-exit inspections. In contrast, MALDI-TOF NAMS presents specific advantages when it comes to the concurrent detection of multiple pathogens within substantial sample sets.
MALDI-TOF NAMS methods offer high accuracy and specificity, making them suitable for medium to high throughput applications. The HAND (homo-tag assisted non-dimer) strategy (45) is used to design multiple primers. This tag serves to distinguish the mass range and effectively minimizes the occurrence of primer dimers during the PCR amplification process. Additionally, this approach enhances the amplification efficiency through a two-step amplification process, which is then followed by detection using mass spectrometry. The MALDI-TOF NAMS method identifies specific nucleic acid fragments by leveraging differences in molecular weights. This enables the analysis of 30 to 50 or even more targets, with the detection results being constrained only by the LOD of system. This technology's strengths offer promising prospects for overcoming the limitations posed by other methods. While MALDI-TOF NAMS can detect various pathogen types, it's limited to known pathogens, and detection of unknown viruses requires non-specific methods like NGS.