The feline panleukopenia virus (FPV) is a highly contagious virus that affects cats worldwide, characterized by leukopenia, high temperature and diarrhea. Recently, the continuous prevalence and variation of FPV have attracted widespread concern. The aim of this study was to investigate the isolation, genetic evolution, molecular characterization and epidemiological analysis of FPV strains among cats and dogs in China from 2019 to 2024. The 41 FPV strains, including 38 feline strains and 3 canine strains, were isolated from rectal swab samples by inoculating monolayer FK81 cells and performing a plaque purification assay. The viral and hemagglutination titers of these 41 FPV strains were 10^4.33~10^6.33 TCID₅₀/0.1 mL and 7.0 log₂~9.7 log₂, respectively. Based on the complete VP2 gene, the nucleotide homology of these FPV strains was 98.91~100%, and the homology with 24 reference FPV strains from different countries and hosts was 98.85~100%. The phylogenetic analysis revealed that 41 FPV strains were more closely related to the FPV strains of Asian origin (Asian FPV strain group) than those of European and American origin (European and American FPV strain group). Furthermore, 12 mutation sites of the VP2 protein were found in these FPV strains, of which 91 and 232 amino acid sites were previously reported. Moreover, the 91 amino acid site was found to be a positive selection site with the highest dN/dS value in the selection pressure analysis. Importantly, 35 FPV strains with 91S substitution in the VP2 protein (FPV-VP2-91S strains) had formed obvious evolutionary branches in the Asian FPV strain group. The analysis of all available VP2 protein sequences of Chinese FPV strains in the GenBank database showed that the occurrence rate of FPV-VP2-91S strains had been increasing from 15.63% to 100% during 2017~2024, indicating that the FPV-VP2-91S substitution in the VP2 protein was a noteworthy molecular characteristic of the dominant FPV strains in China. These results contribute to a better understanding of their genetic evolution and renew the knowledge of FPV molecular epidemiology.

Multi-omics integration has emerged as a powerful approach for gaining insights into complex biological system. By combining data from genomics, transcriptomics, proteomics, metabolomics, or other omics layers. This strategy enables a richer understanding of hostpathogen interactions, disease mechanisms, and therapeutic targets. Current advancements include three key areas: applications of multi-omics integration, innovations in algorithmic approaches, and emerging fields where these techniques are being applied.Multi-omics integration enhances the ability to characterize and manage pathogens by providing comprehensive data-driven insights into disease pathogenesis, biomarker discovery, and therapeutic interventions. In terms of disease diagnosis, multi-omics dataspanning genomic, transcriptomic, proteomic, and metabolomic layers-has significantly improved early detection and cancer subtype classification (Milner and Lennerz, 2024) (Zhao et al., 2024). Pathogen genomics plays a crucial role in monitoring and forecasting outbreaks, integrating methods allows experts to track the spread of pathogens and identify emerging threats, thereby supporting evolutionary and epidemiological studies (Knijn et al., 2023;Wu et al., 2024). Multi-omics data also reveals potential mechanisms underlying disease pathogenesis. For example, joint analysis of multi-omics data in chronic liver disease (CLD) or psychiatric conditions linked to infectious pathogens highlights novel aspects for drug development (Lin et al., 2022;Okobi et al., 2024). Zheng et al. integrated virome data of bronchoalveolar lavage fluid (BALF) and peripheral blood samples to provide novel insights into pulmonary microbial ecology(Ning Zheng, 2025). Shaji et al. have combined the data of kinase-substrate phosphomotif pattern analysis on the SARS-CoV-2 proteome with literature review and in-silico protein-protein docking approach, presents a framework for predicting SARS-CoV-2 phosphosites, suggesting MAP2K1 to be a key host kinase (Shaji V, 2025). Therefore, multi-omics integration provides a multidimensional view of diseases, significantly improving diagnostics, surveillance, and treatment strategies in pathogen-related contexts.Algorithmic innovations are critical for overcoming challenges in multi-omics data analysis, such as handling high-dimensional datasets, integrating diverse omics layers, and improving predictive accuracy. Recent advances leverage deep learning, machine learning, and novel computational frameworks to enhance integration. Deep learning has revolutionized multiomics integration by enabling feature learning and flexible data fusion strategies. Methods like MoAGL-SA introduced adaptive integration techniques for cancer subtype classification, addressing key issues such as embedding sample structure information into the feature space (Cheng et al., 2024). Supervised deep generalized canonical correlation analysis (SDGCCA) is another innovation that models nonlinear correlations between omics manifolds, improving phenotype classification and revealing relevant biological mechanisms (Moon et al., 2022). These approaches categorize architectures-such as neural networks-that handle complex, non-linear relationships in multi-omics data, facilitating more robust predictions for disease subtypes and biomarker identification (Ballard et al., 2024;ElKarami et al., 2022). Innovations in single-cell multi-omics integration allow for joint analysis at the resolution of individual cells, providing insights into cellular heterogeneity and complex traits like therapeutic resistance and immune evasion. Techniques focus on integrating omics data at the singlecell level, making them accessible for non-experts and enhancing understanding of cell states and gene functions (Hu et al., 2024;Xiao et al., 2024). Additionally, machine learning-based natural language processing (NLP) algorithms are employed to automate the extraction of pathogen-specific information from literature, such as detecting papers on experimental pathogen studies. Tools such as READBiomed-Pathogens train models to identify and characterize mentions of pathogens in scientific texts, filling gaps in manually annotated corpora and accelerating knowledge discovery (Jimeno Yepes and Verspoor, 2023).These innovations address challenges like data dimensionality reduction, integration of sparse datasets, and the incorporation of machine learning for refinement, thereby enabling more intuitive and informative interpretations in pathogen research (Ngai et al., 2025).Driven by technological advancements, omics are expanding into novel aspects. For instance, the integration of multi-omics with electronic health records (EHR) and clinical imaging represents a frontier in precision health, allowing for disentangling host-microbe and hostenvironmental interactions (Babu and Snyder, 2023;Lin et al., 2022) . Wang et al. integrated proteomics and phosphoproteomics of freshwater mollusks to provide novel insights as potential food source(Tielin Wang, 2025). Additionally, Li et al. innovatively utilized humanized major histocompatibility complex (hMHC) murine models to reflect immune responses in SARS-CoV-2 infection, showing elevated immune response, especially IFN-γ signaling pathways and neutrophil mobilization dynamics that closely parallel human postvaccination responses, confirmed that the hMHC murine model can serve as an effective platform for vaccine research (Siyue Li, 2025). Chen et al. applied transcriptomic analysis to aquatilia, explained Chinese mitten crab immunity against bacterial infection, showing the differences between disease-resistant and susceptible species(Duanduan Chen, 2024). Research on novel or understudied pathogens emphasizes the need for multidisciplinary approaches, such as collaborations across animal, plant, and human disease contexts to understand cross-sectional involvement.Multi-omics integration has become a cornerstone of modern...