Canine papillomavirus: status of diagnostic methods and vaccine innovations
Canine Papillomavirus (CPV) is a prevalent viral infection in dogs, characterized by the formation of benign warts or papillomas on the skin and mucous membranes. While most CPV types result in non-malignant growths, certain strains, particularly in immunocompromised dogs (e.g., sick or elderly animals), can lead to malignant transformations.
This highlights the need for early, accurate diagnosis, alongside preventive vaccination, to manage the disease effectively. Diagnostic methods leverage CPV’s unique characteristics, including histopathology with hematoxylin and eosin (H&E) staining for assessing neoplastic tissue growth and cytopathy, molecular techniques like polymerase chain reaction (PCR), rolling circle amplification (RCA), DNA in situ hybridization (ISH), and next-generation sequencing (NGS) for detecting CPV genomic DNA, immunohistochemistry (IHC) and enzyme-linked immunosorbent assay (ELISA) for identifying viral antigen such as the L1 protein, as well as transmission electron microscopy (TEM) for visualizing viral particles in lesions. These approaches display appreciable sensitivity and specificity and are often utilized in CPV research, though they also have certain intrinsic limitations, such as accessibility, technical complexity.
Advancements in CPV vaccine development, including inactivated, live-attenuated, DNA-based, and recombinant protein-based formulations, show promise in achieving effective protection. However, a commercially available vaccine has yet to be developed. Furthermore, challenges persist in developing convenient, cost-effective diagnostics suitable for diverse clinical applications and in formulating affordable, cross-protective vaccines.
This review emphasizes the importance of continued innovation in CPV diagnostics and vaccine development to mitigate both benign and malignant papillomatosis, enhance disease prevention, and safeguard canine health.
Author: Yuan Feng, Kaixin Wang, Dan Zhou, Youqing Yuan, Yingyi Chen, Jixian Wang, Haojie Sun, Xiaojiu Huang, Xiaoye Peng, Yi Yang, Deyong Duan, Aibing Wang
Source: https://link.springer.com/
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