Newcastle disease (ND) has been a consistent risk factor to the poultry industry worldwide. Its pathogen, Newcastle disease virus (NDV), is also a promising antitumor treatment candidate. The pathogenic mechanism has intrigued the great curiosity of researchers, and advances in the last two decades have been summarized in this paper. The NDV’s pathogenic ability is highly related to the basic protein structure of the virus, which is described in the Introduction of this review. The overall clinical signs and recent findings pertaining to NDV-related lymph tissue damage are then described. Given the involvement of cytokines in the overall virulence of NDV, cytokines, particularly IL6 and IFN expressed during infection, are reviewed. On the other hand, the host also has its way of antagonizing the virus, which starts with the detection of the pathogen. Thus, advances in NDV’s physiological cell mechanism and the subsequent IFN response, autophagy, and apoptosis are summarized to provide a whole picture of the NDV infection process.

The hair follicle dermal papilla is critical for hair generation and de novo regeneration. When cultured in vitro, dermal papilla cells from different species demonstrate two distinguishable growth patterns under the conventional culture condition: a self-aggregative three dimensional spheroidal (3D) cell pattern and a two dimensional (2D) monolayer cell pattern, correlating with different hair inducing properties. Whether the loss of self-aggregative behavior relates to species-specific differences or the improper culture condition remains unclear. Can the fixed 2D patterned dermal papilla cells recover the self-aggregative behavior and 3D pattern also remains undetected. Here, we successfully constructed the two growth patterns using sika deer (Cervus nippon) dermal papilla cells and proved it was the culture condition that determined the dermal papilla growth pattern. The two growth patterns could transit mutually as the culture condition was exchanged. The fixed 2D patterned sika deer dermal papilla cells could recover the self-aggregative behavior and transit back to 3D pattern, accompanied by the restoration of hair inducing capability when the culture condition was changed. In addition, the global gene expressions during the transition from 2D pattern to 3D pattern were compared to detect the potential regulating genes and pathways involved in the recovery of 3D pattern and hair inducing capability.

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...

Goose-derived Newcastle disease (ND) and gosling plague (GP) are serious threats to the goose industry. Conventional vaccines have made significant contributions to preventing GP and ND. Nevertheless, the renewal of conventional vaccines and the application of novel vaccines are urgently needed to align with eco-friendly and efficient breeding concepts and achieve the final goal of epidemic purification. Therefore, based on the Newcastle disease virus-like particles (ND VLPs) vector platform, we developed novel chimeric ND-GP bivalent cVLPs (ND-GP cVLPs) displaying the NDV HN protein and the GPV VP3 protein. In vivo, immunization experiments revealed that geese immunized with 30 µg, 50 µg, or 70 µg of the ND-GP cVLPs and commercial vaccines produced highly effective hemagglutination inhibitory antibodies against NDV and neutralizing antibodies against GPV, respectively. Furthermore, 70 µg of the ND-GP cVLPs effectively protected against virulent NDV and GPV, reducing tissue damage from viral infection and virus shedding in the oropharynx and cloaca. In conclusion, we provide eco-friendly and efficient novel ND-GP cVLPs for preventing goose-derived ND and GP. Our findings provide the basis for using ND VLPs as foreign protein carriers for the developing of multi-conjugate vaccines.