Citation: Zhong-yi Lei, Qian-tong Jin, Xiao-min Zhang, Xiao-chen Bo, Zi-lin Ren, Yi-gang Tong, Ming Ni. Genomic-epidemiological analysis of 15 million SARS-CoV-2 genomes reveals accelerated fitness gain of JN.1 lineage .VIROLOGICA SINICA, 2026, 41(2) : 382-391.  http://dx.doi.org/10.1016/j.virs.2026.03.015

Genomic-epidemiological analysis of 15 million SARS-CoV-2 genomes reveals accelerated fitness gain of JN.1 lineage

  • The evolution of SARS-CoV-2 has been driven by successive globally circulating waves, including the Alpha and Delta lineages, early Omicron (BA.1-BA.5), XBB, and the recently dominant JN.1 lineages. Although the marked advantage in fitness of early Omicron over Delta lineages has been recognized, there is a lack of systematic evaluation of SARS-CoV-2 fitness across 2020 to 2025. Here, we analyzed 15.23 million SARS-CoV-2 genomes available through May 2025. The accumulation of mutations in the spike protein of the virus has continued to accelerate over time, whereas the trend slowed in the other viral proteins. Using a Bayesian genomic-epidemiological framework, we estimated that lineage fitness increased approximately linearly from 2021 to 2025. Notably, JN.1 lineages exhibited a significantly higher rate of fitness gain than their predecessor XBB and earlier Omicron lineages. We further analyzed characteristic mutations of JN.1 and found that those in the receptor-binding domain were associated with larger alterations in residue hydropathy, charge, and structural surface exposure relative to other lineages. These findings suggest JN.1 as a distinct evolutionary stage and underscore the importance of sustained genomic surveillance.

  • 加载中
  • 10.1016j.virs.2026.03.015-ESM.docx
    1. Cao, Y., Jian, F., Wang, J., Yu, Y., Song, W., Yisimayi, A., Wang, J., An, R., Chen, X., Zhang, N., Wang, Yao, Wang, P., Zhao, L., Sun, H., Yu, L., Yang, S., Niu, X., Xiao, T., Gu, Q., Shao, F., Hao, X., Xu, Y., Jin, R., Shen, Z., Wang, Youchun, Xie, X.S., 2023. Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution. Nature 614, 521-529.

    2. Chaguza, C., Coppi, A., Earnest, R., Ferguson, D., Kerantzas, N., Warner, F., Young, H.P., Breban, M.I., Billig, K., Koch, R.T., Pham, K., Kalinich, C.C., Ott, I.M., Fauver, J.R., Hahn, A.M., Tikhonova, I.R., Castaldi, C., De Kumar, B., Pettker, C.M., Warren, J.L., Weinberger, D.M., Landry, M.L., Peaper, D.R., Schulz, W., Vogels, C.B.F., Grubaugh, N.D., 2022. Rapid emergence of SARS-CoV-2 Omicron variant is associated with an infection advantage over Delta in vaccinated persons. Med 3, 325-334.e4.

    3. Crow, J.F., 2017. An Introduction to Population Genetics Theory. Scientific Publishers.

    4. Guo, C., Yu, Y., Liu, J., Jian, F., Yang, S., Song, W., Yu, L., Shao, F., Cao, Y., 2025. Antigenic and virological characteristics of SARS-CoV-2 variants BA.3.2, XFG, and NB.1.8.1. The Lancet Infectious Diseases 25, e374-e377.

    5. Harvey, W.T., Carabelli, A.M., Jackson, B., Gupta, R.K., Thomson, E.C., Harrison, E.M., Ludden, C., Reeve, R., Rambaut, A., Peacock, S.J., Robertson, D.L., 2021. SARS-CoV-2 variants, spike mutations and immune escape. Nat Rev Microbiol 19, 409-424.

    6. Ioannou, G.N., Berry, K., Yan, L., Huang, Y., Lin, H.-M., Bui, D., Hynes, D.M., Boyko, E.J., Ferguson, J.M., Aslan, M., Bajema, K.L., 2025. Effectiveness of the 2024-2025 KP.2 COVID-19 vaccines in the United States during long-term follow-up. Nat Commun 17, 1043.

    7. Ito, J., Strange, A., Liu, W., Joas, G., Lytras, S., Sato, K., 2025. A protein language model for exploring viral fitness landscapes. Nat Commun 16, 4236.

    8. Jian, F., Wang, J., Yisimayi, A., Song, W., Xu, Y., Chen, X., Niu, X., Yang, S., Yu, Y., Wang, P., Sun, H., Yu, L., Wang, J., Wang, Yao, An, R., Wang, W., Ma, M., Xiao, T., Gu, Q., Shao, F., Wang, Youchun, Shen, Z., Jin, R., Cao, Y., 2025. Evolving antibody response to SARS-CoV-2 antigenic shift from XBB to JN.1. Nature 637, 921-929.

    9. Jiang, S.-Y., Zhao, S.-S., Wei, J.-Q., Zhang, S., Zhao, Z., Tong, Y., Liu, W., Wang, J., Jiang, T., Li, J., 2025. General Intelligence Framework to Predict Virus Adaptation Based on a Genome Language Model. Research 8, 0871.

    10. Lei, Z., Zhang, X., Han, J., Xue, J., Xu, J., Ren, Z., Tong, Y., Bo, X., Ni, M., 2025. Integrating genomic epidemiology and deep mutational scanning data for prevalence forecasting of SARS-CoV-2 Omicron lineages. PLOS ONE 20, e0335520.

    11. Li, J., Lai, S., Gao, G.F., Shi, W., 2021. The emergence, genomic diversity and global spread of SARS-CoV-2. Nature 600, 408-418.

    12. Li, X., Yan, H., Wong, G., Ouyang, W., Cui, J., 2023. Identifying featured indels associated with SARS-CoV-2 fitness. Microbiology Spectrum 11, e02269-23.

    13. Li, J., Yang, J., Ding, X., Zhou, H., Han, N., Wu, A., 2024. The spatiotemporal analysis of SARS-CoV-2 transmission in China since the termination of the dynamic zero-COVID policy. Virologica Sinica 39, 737-746.

    14. Liu, J., Yu, Y., Yang, S., Jian, F., Song, W., Yu, L., Shao, F., Cao, Y., 2025. Virological and antigenic characteristics of SARS-CoV-2 variants LF.7.2.1, NP.1, and LP.8.1. The Lancet Infectious Diseases 25, e128-e130.

    15. Ma, W., Fu, H., Jian, F., Cao, Y., Li, M., 2023. Immune evasion and ACE2 binding affinity contribute to SARS-CoV-2 evolution. Nat Ecol Evol 7, 1457-1466.

    16. Markov, P.V., Ghafari, M., Beer, M., Lythgoe, K., Simmonds, P., Stilianakis, N.I., Katzourakis, A., 2023. The evolution of SARS-CoV-2. Nat. Rev. Microbiol. 21, 361-379.

    17. Moulana, A., Dupic, T., Phillips, A.M., Chang, J., Nieves, S., Roffler, A.A., Greaney, A.J., Starr, T.N., Bloom, J.D., Desai, M.M., 2022. Compensatory epistasis maintains ACE2 affinity in SARS-CoV-2 Omicron BA.1. Nat Commun 13, 7011.

    18. Obermeyer, F., Jankowiak, M., Barkas, N., Schaffner, S.F., Pyle, J.D., Yurkovetskiy, L., Bosso, M., Park, D.J., Babadi, M., MacInnis, B.L., Luban, J., Sabeti, P.C., Lemieux, J.E., 2022. Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness. Science 376, 1327-1332.

    19. Paton, R.S., Overton, C.E., Ward, T., 2022. The rapid replacement of the SARS-CoV-2 Delta variant by Omicron (B.1.1.529) in England. Science Translational Medicine 14, eabo5395.

    20. Rambaut, A., Holmes, E.C., O’Toole, A., Hill, V., McCrone, J.T., Ruis, C., du Plessis, L., Pybus, O.G., 2020. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat Microbiol 5, 1403-1407.

    21. Viana, R., Moyo, S., Amoako, D.G., Tegally, H., Scheepers, C., Althaus, C.L., Anyaneji, U.J., Bester, P.A., Boni, M.F., Chand, M., Choga, W.T., Colquhoun, R., Davids, M., Deforche, K., Doolabh, D., du Plessis, L., Engelbrecht, S., Everatt, J., Giandhari, J., Giovanetti, M., Hardie, D., Hill, V., Hsiao, N.-Y., Iranzadeh, A., Ismail, A., Joseph, C., Joseph, R., Koopile, L., Kosakovsky Pond, S.L., Kraemer, M.U.G., Kuate-Lere, L., Laguda-Akingba, O., Lesetedi-Mafoko, O., Lessells, R.J., Lockman, S., Lucaci, A.G., Maharaj, A., Mahlangu, B., Maponga, T., Mahlakwane, K., Makatini, Z., Marais, G., Maruapula, D., Masupu, K., Matshaba, M., Mayaphi, S., Mbhele, N., Mbulawa, M.B., Mendes, A., Mlisana, K., Mnguni, A., Mohale, T., Moir, M., Moruisi, K., Mosepele, M., Motsatsi, G., Motswaledi, M.S., Mphoyakgosi, T., Msomi, N., Mwangi, P.N., Naidoo, Y., Ntuli, N., Nyaga, M., Olubayo, L., Pillay, S., Radibe, B., Ramphal, Y., Ramphal, U., San, J.E., Scott, L., Shapiro, R., Singh, L., Smith-Lawrence, P., Stevens, W., Strydom, A., Subramoney, K., Tebeila, N., Tshiabuila, D., Tsui, J., van Wyk, S., Weaver, S., Wibmer, C.K., Wilkinson, E., Wolter, N., Zarebski, A.E., Zuze, B., Goedhals, D., Preiser, W., Treurnicht, F., Venter, M., Williamson, C., Pybus, O.G., Bhiman, J., Glass, A., Martin, D.P., Rambaut, A., Gaseitsiwe, S., von Gottberg, A., de Oliveira, T., 2022. Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa. Nature 603, 679-686.

    22. Vita, R., Blazeska, N., Marrama, D., IEDB Curation Team Members, Duesing, S., Bennett, J., Greenbaum, J., De Almeida Mendes, M., Mahita, J., Wheeler, D.K., Cantrell, J.R., Overton, J.A., Natale, D.A., Sette, A., Peters, B., 2025. The Immune Epitope Database (IEDB): 2024 update. Nucleic Acids Res 53, D436-D443.

    23. Wang, Q., Mellis, I.A., Ho, J., Bowen, A., Kowalski-Dobson, T., Valdez, R., Katsamba, P.S., Wu, M., Lee, C., Shapiro, L., Gordon, A., Guo, Y., Ho, D.D., Liu, L., 2024. Recurrent SARS-CoV-2 spike mutations confer growth advantages to select JN.1 sublineages. Emerging Microbes & Infections 13, 2402880.

    24. World Health Organization, 2024. Updated Risk Evaluation of JN.1.

    25. World Health Organization, 2025. WHO TAG-VE Risk Evaluation for SARS-CoV-2 Variant Under Monitoring: XFG.

    26. Xu, K., An, Y., Liu, X., Xie, H., Li, D., Yang, T., Duan, M., Wang, Y., Zhao, X., Dai, L., Gao, G.F., 2024. Neutralization of SARS-CoV-2 KP.1, KP.1.1, KP.2 and KP.3 by human and murine sera. npj Vaccines 9, 215.

    27. Xu, K., An, Y., Liu, X., Xie, H., Li, D., Yang, T., Duan, M., Wang, Y., Zhao, X., Dai, L., Gao, G.F., 2024. Neutralization of SARS-CoV-2 KP.1, KP.1.1, KP.2 and KP.3 by human and murine sera. npj Vaccines 9, 215.

    28. Yang, S., Yu, Y., Xu, Y., Jian, F., Song, W., Yisimayi, A., Wang, P., Wang, J., Liu, J., Yu, L., Niu, X., Wang, J., Wang, Yao, Shao, F., Jin, R., Wang, Youchun, Cao, Y., 2024. Fast evolution of SARS-CoV-2 BA.2.86 to JN.1 under heavy immune pressure. The Lancet Infectious Diseases 24, e70-e72.

    29. Zhang, X., Lei, Z., Zhang, J., Yang, T., Liu, X., Xue, J., Ni, M., 2025. AnnCovDB: a manually curated annotation database for mutations in SARS-CoV-2 spike protein. Database 2025, baaf002.

  • 加载中

Figures(1)

Article Metrics

Article views(2083) PDF downloads(11) Cited by()

Related
Proportional views

    Genomic-epidemiological analysis of 15 million SARS-CoV-2 genomes reveals accelerated fitness gain of JN.1 lineage

      Corresponding author: Zi-lin Ren, zilin.ren@outlook.com
      Corresponding author: Yi-gang Tong, tongyigang@mail.buct.edu.cn
      Corresponding author: Ming Ni, niming@bmi.ac.cn
    • a. College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China;
    • b. Advanced & Interdisciplinary Biotechnology, Academy of Military Medical Sciences, Beijing, 100850, China;
    • c. Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, State Key Laboratory of Pathogen and Biosecurity, Key Laboratory of Jilin Province for Zoonosis Prevention and Control, Changchun, 130122, China;
    • d. School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China

    Abstract: The evolution of SARS-CoV-2 has been driven by successive globally circulating waves, including the Alpha and Delta lineages, early Omicron (BA.1-BA.5), XBB, and the recently dominant JN.1 lineages. Although the marked advantage in fitness of early Omicron over Delta lineages has been recognized, there is a lack of systematic evaluation of SARS-CoV-2 fitness across 2020 to 2025. Here, we analyzed 15.23 million SARS-CoV-2 genomes available through May 2025. The accumulation of mutations in the spike protein of the virus has continued to accelerate over time, whereas the trend slowed in the other viral proteins. Using a Bayesian genomic-epidemiological framework, we estimated that lineage fitness increased approximately linearly from 2021 to 2025. Notably, JN.1 lineages exhibited a significantly higher rate of fitness gain than their predecessor XBB and earlier Omicron lineages. We further analyzed characteristic mutations of JN.1 and found that those in the receptor-binding domain were associated with larger alterations in residue hydropathy, charge, and structural surface exposure relative to other lineages. These findings suggest JN.1 as a distinct evolutionary stage and underscore the importance of sustained genomic surveillance.

    Figure (1)  Reference (29) Relative (20)

    目录

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return