International Journal of Molecular Veterinary Research, 2025, Vol.15, No.1, 22-31 http://animalscipublisher.com/index.php/ijmvr 29 data with ecological and behavioral studies allows us to upgrade modeling of transmission networks and identify pivotal interfaces-human-dog-wildlife interface sites-where spillover risk is heightened. Comparative genomics thus integrates molecular biology with true epidemiology to yield better prevention measures. Upcoming research will increasingly make use of artificial intelligence (AI) and multi-omics fusion to enable pathogen surveillance and predictive capability. Genomics can be integrated with transcriptomics, proteomics, metabolomics, and microbiome information to reveal dynamic interaction of pathogen adaptation with host physiology. AI-based modeling such as machine learning and network inference can interrogate large multi-dimensional data sets to forecast future host jumps, emergence hotspots, and resistance evolution. For example, deep learning programs can pick up faint genomic signs of virulence or transmissibility that would go undetected under regular analysis. Working together, these technologies can render comparative pathogen genomics a predictive and preventive science rather than a descriptive science. The inferences from comparative genomics of dog-related zoonotic pathogens have important implications for One Health—an integrated framework linking human, animal, and environmental health. The identification of shared evolutionary processes and transmission pathways between domestic dogs, wildlife canids, and humans underscores the need for integrated surveillance systems across discipline and geographical boundaries. Genomic data can inform cross-species vaccine development, increase diagnostic specificity, and enable early detection of zoonoses at the animal-human interface. Finally, a One Health use of comparative genomics will inform evidence-based policy, interventions, and sustainable control measures, building resilience to future zoonotic risks. Acknowledgments The authors sincerely thank the research team for their patient cooperation and strong support during the conduct of the study and the organization of data and materials. The authors also express heartfelt gratitude to the two anonymous peer reviewers for their valuable comments and constructive suggestions during the review process. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Arnold B., Huang I., and Hanage W., 2021, Horizontal gene transfer and adaptive evolution in bacteria, Nature Reviews Microbiology, 20: 206-218. https://doi.org/10.1038/s41579-021-00650-4 Beltran P., Federspiel J., Sheng X., and Cristea I., 2017, Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases, Molecular Systems Biology, 13: 941. https://doi.org/10.15252/msb.20167062 Bonnaud E., Troupin C., Dacheux L., Holmes E., Monchatre-Leroy E., Tanguy M., Bouchier C., Cliquet F., Barrat J., and Bourhy H., 2019, Comparison of intra- and inter-host genetic diversity in rabies virus during experimental cross-species transmission, PLoS Pathogens, 15(7): e1007799. https://doi.org/10.1371/journal.ppat.1007799 Catalano S., Battelli F., Traore Z., Raghwani J., Faust C., and Standley C., 2024, Pathogen genomics and One Health: a scoping review of current practices in zoonotic disease research, IJID One Health, 7: 100248. https://doi.org/10.1101/2024.02.05.24302264 Chen S., Shang K., Chen J., Yu Z., Wei Y., He L., and Ding K., 2024, Global distribution, cross-species transmission, and receptor binding of canine parvovirus-2: Risks and implications for humans, The Science of the Total Environment, 905: 172307. https://doi.org/10.1016/j.scitotenv.2024.172307 Cilia G., Fratini F., Turchi B., Ebani V., Turini L., Bilei S., Bossù T., De Marchis M., Cerri D., and Bertelloni F., 2021, Presence and characterization of zoonotic bacterial pathogens in wild boar hunting dogs (Canis lupus familiaris) in Tuscany (Italy), Animals, 11(4): 1139. https://doi.org/10.3390/ani11041139 Elrashedy A., Mousa W., Nayel M., Salama A., Zaghawa A., Elsify A., and Hasan M., 2025, Advances in bioinformatics and multi-omics integration: transforming viral infectious disease research in veterinary medicine, Virology Journal, 22: 40. https://doi.org/10.1186/s12985-025-02640-x Epping L., Walther B., Piro R., Knüver M., Huber C., Thürmer A., Flieger A., Fruth A., Janecko N., Wieler L., Stingl K., and Semmler T., 2021, Genome-wide insights into population structure and host specificity of Campylobacter jejuni, Scientific Reports, 11: 17357. https://doi.org/10.1038/s41598-021-89683-6
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