. doi: 10.1016/j.virs.2024.01.010
Citation: Zigui Chen, Rita Way Yin Ng, Grace Lui, Lowell Ling, Agnes S. Y. Leung, Chit Chow, Siaw Shi Boon, Wendy C. S. Ho, Maggie Haitian Wang, Renee Wan Yi Chan, Albert Martin Li, David Shu Cheong Hui, Paul Kay Sheung Chan. Quantitative and qualitative subgenomic RNA profiles of SARS-CoV-2 in respiratory samples: A comparison between Omicron BA.2 and non-VOC-D614G .VIROLOGICA SINICA, 2024, 39(2) : 218-227.  http://dx.doi.org/10.1016/j.virs.2024.01.010

严重急性呼吸系统综合症冠状病毒2型(SARS-CoV-2)Omicron BA.2变异株和非关注D614G变异株(non-VOC-D614G)亚基因组RNA的定量和定性研究

  • 严重急性呼吸系统综合症冠状病毒2型(SARS-CoV-2)欧密克戎(Omicron)变异株因其高传播性而臭名昭著,但对其亚基因组RNA(sgRNA)的表达知之甚少。本研究应用RNA高通量测序技术描绘了118例欧密克戎BA.2变异株(Omicron BA.2)和338例非关注D614G变异株(non-VOC-D614G)的典型sgRNA的定量和定性特征。结果显示,无论患者的性别、年龄以及是否患有肺炎,Omicron BA.2和non-VOC-D614G均表现出由9个典型sgRNA相对丰度组成的独特特征定量表达谱。这种表达谱在病毒载量低时会消失,表明sgRNA模式有可能用于指示特定时间点患者的病毒活性。对Omicron BA.2和non-VOC-D614G典型sgRNA的特征定性表达谱的描述发现,所有9个典型sgRNA均表达的模式与病毒载量具一致相关性(AUC = 0.91,95% CI 0.88 - 0.94),其中的sgRNA ORF7b可被视为最佳替代标志物,用于检测患者个体化感染状况。sgRNA在疫苗和抗病毒药物开发中的应用潜力值得进一步挖掘。

Quantitative and qualitative subgenomic RNA profiles of SARS-CoV-2 in respiratory samples: A comparison between Omicron BA.2 and non-VOC-D614G

  • Corresponding author: Paul Kay Sheung Chan, paulkschan@cuhk.edu.hk
  • Received Date: 18 October 2023
    Accepted Date: 31 January 2024
  • The SARS-CoV-2 Omicron variants are notorious for their transmissibility, but little is known about their subgenomic RNA (sgRNA) expression. This study applied RNA-seq to delineate the quantitative and qualitative profiles of canonical sgRNA of 118 respiratory samples collected from patients infected with Omicron BA.2 and compared with 338 patients infected with non-variant of concern (non-VOC)-D614G. A unique characteristic profile depicted by the relative abundance of 9 canonical sgRNAs was reproduced by both BA.2 and non-VOC-D614G regardless of host gender, age and presence of pneumonia. Remarkably, such profile was lost in samples with low viral load, suggesting a potential application of sgRNA pattern to indicate viral activity of individual patient at a specific time point. A characteristic qualitative profile of canonical sgRNAs was also reproduced by both BA.2 and non-VOC-D614G. The presence of a full set of canonical sgRNAs carried a coherent correlation with crude viral load (AUC = 0.91, 95% CI 0.88–0.94), and sgRNA ORF7b was identified to be the best surrogate marker allowing feasible routine application in characterizing the infection status of individual patient. Further potentials in using sgRNA as a target for vaccine and antiviral development are worth pursuing.

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    1. Alexandersen, S., Chamings, A., Bhatta, T. R., 2020. SARS-CoV-2 genomic and subgenomic RNAs in diagnostic samples are not an indicator of active replication. Nat. Commun. 11, 6059. https://doi.org/10.1038/s41467-020-19883-7

    2. Bolger, A. M., Lohse, M., Usadel, B., 2014. Trimmomatic:a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120. https://doi.org/10.1093/bioinformatics/btu170

    3. Cao, Y., Wang, J., Jian, F., Xiao, T., Song, W., Yisimayi, A., et al, 2022. Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies. Nature 602, 657-663. https://doi.org/10.1038/s41586-021-04385-3

    4. Carabelli, A. M., Peacock, T. P., Thorne, L. G., Harvey, W. T., Hughes, J., COVID-19 Genomics UK Consortium, et al, 2023. SARS-CoV-2 variant biology:immune escape, transmission and fitness. Nat. Rev. Microbiol. 21, 162-177. https://doi.org/10.1038/s41579-022-00841-7

    5. Chen, Z., Chong, K. C., Wong, M. C. S., Boon, S. S., Huang, J., Wang, M. H., et al, 2021. A global analysis of replacement of genetic variants of SARS-CoV-2 in association with containment capacity and changes in disease severity. Clin. Microbiol. Infect. 27, 750-757. Advance online publication. https://doi.org/10.1016/j.cmi.2021.01.018

    6. Chen, Z., Ng, R. W. Y., Lui, G., Ling, L., Chow, C., Yeung, A. C. M., et al, 2022. Profiling of SARS-CoV-2 Subgenomic RNAs in Clinical Specimens. Microbiol. Spectr. 10, e0018222. https://doi.org/10.1128/spectrum.00182-22

    7. Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., et al, 2013. STAR:ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21. https://doi.org/10.1093/bioinformatics/bts635

    8. Hu, J., Peng, P., Cao, X., Wu, K., Chen, J., Wang, K., et al, 2022. Increased immune escape of the new SARS-CoV-2 variant of concern Omicron. Cell. Mol. Immunol. 19, 293-295. https://doi.org/10.1038/s41423-021-00836-z

    9. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., et al, 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5.

    10. Katoh, K., Toh, H., 2010. Parallelization of the MAFFT multiple sequence alignment program. Bioinformatics 26, 1899-1900. https://doi.org/10.1093/bioinformatics/btq224

    11. Kim, D., Langmead, B., Salzberg, S. L., 2015. HISAT:a fast spliced aligner with low memory requirements. Nat. methods, 12, 357-360. https://doi.org/10.1038/nmeth.3317

    12. Kim, D., Lee, J. Y., Yang, J. S., Kim, J. W., Kim, V. N., Chang, H., 2020. The Architecture of SARS-CoV-2 Transcriptome. Cell 181, 914-921.e10. https://doi.org/10.1016/j.cell.2020.04.011

    13. Li H., 2011. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987-2993. https://doi.org/10.1093/bioinformatics/btr509

    14. Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., et al, 2020. Genomic characterisation and epidemiology of 2019 novel coronavirus:implications for virus origins and receptor binding. Lancet 395, 565-574. https://doi.org/10.1016/S0140-6736(20)30251-8.

    15. Lui, G., Ling, L., Lai, C. K., Tso, E. Y., Fung, K. S., Chan, V., et al, 2020. Viral dynamics of SARS-CoV-2 across a spectrum of disease severity in COVID-19. J. Infect. 81, 318-356. https://doi.org/10.1016/j.jinf.2020.04.014

    16. Mannar, D., Saville, J. W., Zhu, X., Srivastava, S. S., Berezuk, A. M., Tuttle, K. S., et al, 2022. SARS-CoV-2 Omicron variant:Antibody evasion and cryo-EM structure of spike protein-ACE2 complex. Science, 375, 760-764. https://doi.org/10.1126/science.abn7760

    17. Menni, C., Valdes, A. M., Polidori, L., Antonelli, M., Penamakuri, S., Nogal, A., et al, 2022. Symptom prevalence, duration, and risk of hospital admission in individuals infected with SARS-CoV-2 during periods of omicron and delta variant dominance:a prospective observational study from the ZOE COVID Study. Lancet 399, 1618-1624. https://doi.org/10.1016/S0140-6736(22)00327-0

    18. Parker, M. D., Stewart, H., Shehata, O. M., Lindsey, B. B., Shah, D. R., Hsu, S., et al, 2022. Altered subgenomic RNA abundance provides unique insight into SARS-CoV-2 B.1.1.7/Alpha variant infections. Commun. Biol. 5, 666. https://doi.org/10.1038/s42003-022-03565-9

    19. Qu, P., Evans, J. P., Faraone, J. N., Zheng, Y. M., Carlin, C., Anghelina, M., et al, 2023. Enhanced neutralization resistance of SARS-CoV-2 Omicron subvariants BQ.1, BQ.1.1, BA.4.6, BF.7, and BA.2.75.2. Cell Host Microbe. 31, 9-17.e3. https://doi.org/10.1016/j.chom.2022.11.012

    20. Sola, I., Almazán, F., Zúñiga, S., Enjuanes, L., 2015. Continuous and Discontinuous RNA Synthesis in Coronaviruses. Annu. Rev. Virol. 2, 265-288. https://doi.org/10.1146/annurev-virology-100114-055218

    21. Stamatakis A., 2006. RAxML-VI-HPC:maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688-2690. https://doi.org/10.1093/bioinformatics/btl446

    22. Suzuki, R., Yamasoba, D., Kimura, I., Wang, L., Kishimoto, M., Ito, J., et al, 2022. Attenuated fusogenicity and pathogenicity of SARS-CoV-2 Omicron variant. Nature 603, 700-705. https://doi.org/10.1038/s41586-022-04462-1

    23. Telenti, A., Hodcroft, E. B., Robertson, D. L., 2022. The Evolution and Biology of SARS-CoV-2 Variants. Cold Spring Harb. Perspect. Med.12, a041390. https://doi.org/10.1101/cshperspect.a041390

    24. van Kampen, J. J. A., van de Vijver, D. A. M. C., Fraaij, P. L. A., Haagmans, B. L., Lamers, M. M., Okba, N., et al, 2021. Duration and key determinants of infectious virus shedding in hospitalized patients with coronavirus disease-2019 (COVID-19). Nat. Commun. 12, 267. https://doi.org/10.1038/s41467-020-20568-4

    25. Viana, R., Moyo, S., Amoako, D. G., Tegally, H., Scheepers, C., Althaus, C. L., et al, 2022. Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa. Nature 603, 679-686. https://doi.org/10.1038/s41586-022-04411-y.

    26. Wölfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Müller, M. A., et al, 2020. Virological assessment of hospitalized patients with COVID-2019. Nature 581, 465-469. https://doi.org/10.1038/s41586-020-2196-x

    27. Wong, C. H., Ngan, C. Y., Goldfeder, R. L., Idol, J., Kuhlberg, C., Maurya, R., et al, 2021.Reduced subgenomic RNA expression is a molecular indicator of asymptomatic SARS-CoV-2 infection. Commun. Med. 1, 33. https://doi.org/10.1038/s43856-021-00034-y

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    Quantitative and qualitative subgenomic RNA profiles of SARS-CoV-2 in respiratory samples: A comparison between Omicron BA.2 and non-VOC-D614G

      Corresponding author: Paul Kay Sheung Chan, paulkschan@cuhk.edu.hk
    • a. Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China;
    • b. Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China;
    • c. Department of Anaesthesia and Intensive Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China;
    • d. Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China;
    • e. Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China;
    • f. Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China;
    • g. Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China

    Abstract: The SARS-CoV-2 Omicron variants are notorious for their transmissibility, but little is known about their subgenomic RNA (sgRNA) expression. This study applied RNA-seq to delineate the quantitative and qualitative profiles of canonical sgRNA of 118 respiratory samples collected from patients infected with Omicron BA.2 and compared with 338 patients infected with non-variant of concern (non-VOC)-D614G. A unique characteristic profile depicted by the relative abundance of 9 canonical sgRNAs was reproduced by both BA.2 and non-VOC-D614G regardless of host gender, age and presence of pneumonia. Remarkably, such profile was lost in samples with low viral load, suggesting a potential application of sgRNA pattern to indicate viral activity of individual patient at a specific time point. A characteristic qualitative profile of canonical sgRNAs was also reproduced by both BA.2 and non-VOC-D614G. The presence of a full set of canonical sgRNAs carried a coherent correlation with crude viral load (AUC = 0.91, 95% CI 0.88–0.94), and sgRNA ORF7b was identified to be the best surrogate marker allowing feasible routine application in characterizing the infection status of individual patient. Further potentials in using sgRNA as a target for vaccine and antiviral development are worth pursuing.

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