Towards a comprehensive landscape and mechanism understanding of virus-disease associations

Article Open access Published: 16 October 2026 Yifan Wu 1 na1 , Ruina You 1 na1 , Yang Feng 1 na1 , Shangwen Lu 1 na1 , Siang Chen 1 na1 , Ping Fu 1 , Zhaozhong Zhu 1 , 2 , Congyu Lu 1 , 3 , Zhiyuan Zhang 1 , Lihua Xu 1 , Huiru Li 1 , Xinglian Wang 1 , Xinyi Wang 1 , Qian Huang 1 , Xun Liu 1 , Zhuoya Xu 1 , Guolin Dong 1 , Shiqing Li 1 , Xiaowen Hu 1 , Zefeng Xiang 1 , Yixuan Hu 1 , Longfei Mao 1 , Zhichao Miao 3 , Xiangxian Meng 1 & ...

Yousong Peng ORCID: orcid.org/0000-0002-5482-9506 1 Communications Biology ( 2026 ) Cite this article We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record.

All legal disclaimers apply. Abstract Viruses are linked to human diseases through complex molecular mechanisms, yet a comprehensive investigation of virus-disease associations and their molecular basis remains lacking. We curated 3,555 high-confidence virus-disease associations involving 267 viruses and 954 diseases through text-mining over 28 million PubMed abstracts followed by rigorous manual verification, establishing the most comprehensive resource in this field to date.

We then developed AVD, a user-friendly online database to store, visualize, and analyze these associations and their molecular mechanisms. Our analysis revealed that virus-disease association patterns are significantly influenced by viral taxonomy, phenotype, and key biological traits such as transmissibility, transmission routes, and tissue tropism.

Molecular analysis identified a core set of bridging genes involved in the majority of virus-disease associations. Notably, these bridging genes were extensively involved in viral infections and diseases, and exhibit distinct characteristics, including a high proportion of secreted proteins, elevated expression across human tissues, and high centrality in the human protein-protein interaction network.

Furthermore, we developed a machine-learning model capable of accurately predicting virus-disease associations. Collectively, this study provides a valuable data resource and analytical framework for studying virus-disease relationships and offers novel insights into their molecular mechanisms.

Acknowledgements We thank members of PengLab for helpful discussions on the manuscript. Funding This work was supported by the Hunan Provincial Natural Science Foundation of China (2024JJ2015), National Natural Science Foundation of China (32370700 & 32170651), R&D Program of Guangzhou National Laboratory (GZNL2024A01002), and Hunan Science and Technology Innovation Plan (2025ZYJ003) .

Author information Author notes These authors contributed equally: Yifan Wu, Ruina You, Yang Feng, Shangwen Lu, Siang Chen. Authors and Affiliations College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan Research Center of the Basic Discipline for Cell Signaling, Hunan University, Changsha, China Yifan Wu, Ruina You, Yang Feng, Shangwen Lu, Siang Chen, Ping Fu, Zhaozhong Zhu, Congyu Lu, Zhiyuan Zhang, Lihua Xu, Huiru Li, Xinglian Wang, Xinyi Wang, Qian Huang, Xun Liu, Zhuoya Xu, Guolin Dong, Shiqing Li, Xiaowen Hu, Zefeng Xiang, Yixuan Hu, Longfei Mao, Xiangxian Meng & Yousong Peng School of Public Health, Hengyang Medical School, University of South China, Hengyang, China Zhaozhong Zhu GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou International Bio Island, Guangzhou, China Congyu Lu & Zhichao Miao Authors Yifan Wu Ruina You Yang Feng Shangwen Lu Siang Chen Ping Fu Zhaozhong Zhu Congyu Lu Zhiyuan Zhang Lihua Xu Huiru Li Xinglian Wang Xinyi Wang Qian Huang Xun Liu Zhuoya Xu Guolin Dong Shiqing Li Xiaowen Hu Zefeng Xiang Yixuan Hu Longfei Mao Zhichao Miao Xiangxian Meng Yousong Peng Corresponding authors Correspondence to Xiangxian Meng or Yousong Peng .

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Reprints and permissions About this article Cite this article Wu, Y., You, R., Feng, Y. et al. Towards a comprehensive landscape and mechanism understanding of virus-disease associations. Commun Biol (2026). https://doi.org/10.1038/s42003-026-11130-x Download citation Received : 18 November 2025 Accepted : 27 September 2026 Published : 16 October 2026 DOI : https://doi.org/10.1038/s42003-026-11130-x