Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

SARS-CoV-2-related bat viruses evade human intrinsic immunity but lack efficient transmission capacity

Nature Microbiology volume 9, pages 2038–2050 (2024)Cite this article

Subjects

Abstract

Circulating bat coronaviruses represent a pandemic threat. However, our understanding of bat coronavirus pathogenesis and transmission potential is limited by the lack of phenotypically characterized strains. We created molecular clones for the two closest known relatives of SARS-CoV-2, BANAL-52 and BANAL-236. We demonstrated that BANAL-CoVs and SARS-CoV-2 have similar replication kinetics in human bronchial epithelial cells. However, BANAL-CoVs have impaired replication in human nasal epithelial cells and in the upper airway of mice. We also observed reduced pathogenesis in mice and diminished transmission in hamsters. Further, we observed that diverse bat coronaviruses evade interferon and downregulate major histocompatibility complex class I. Collectively, our study demonstrates that despite high genetic similarity across bat coronaviruses, prediction of pandemic potential of a virus necessitates functional characterization. Finally, the restriction of bat coronavirus replication in the upper airway highlights that transmission potential and innate immune restriction can be uncoupled in this high-risk family of emerging viruses.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Molecular clones of BANAL-CoVs exhibit similar kinetics and drug susceptibility to SARS-CoV-2.
Fig. 2: Bat SARS-related viruses evade human innate immunity and downregulate MHC-I.
Fig. 3: BANAL-52, BANAL-236 and SARS-CoV-2 display a gradient of pathogenesis in K18-hACE2 mice.
Fig. 4: BANAL-CoVs have impaired transmission capacity in a hamster model.
Fig. 5: The S1/S2 FCS promotes replication in HNECs and hamsters.

Similar content being viewed by others

Data availability

All data related to this manuscript were included in the source data files. GenBank accession codes for the molecular clones of BANAL-52 and BANAL-236 are PP856573 and PP856574, respectively. Viral stocks and plasmids are available with a material transfer agreement (MTA) and confirmation of appropriate biosafety facilities and procedures. Since potential experiments of concern could be performed with these viral strains, we will require a list of planned experimental procedures, to be reviewed before the MTA is executed. Yale University will review and determine the MTA timeline. Correspondence should be sent to A.I. ([email protected]) and C.B.W. ([email protected]).

Code availability

The NGS sequencing raw data and the code to reproduce the analysis of the SARS-CoV-2 FCS from the Extended Data can be found on Zenodo at https://zenodo.org/records/11519958 (ref. 56).

References

  1. Sánchez, C. A. et al. A strategy to assess spillover risk of bat SARS-related coronaviruses in Southeast Asia. Nat. Commun. 13, 4380 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Temmam, S. et al. Bat coronaviruses related to SARS-CoV-2 and infectious for human cells. Nature 604, 330–336 (2022).

    Article  CAS  PubMed  Google Scholar 

  3. Zhou, H. et al. Identification of novel bat coronaviruses sheds light on the evolutionary origins of SARS-CoV-2 and related viruses. Cell 184, 4380–4391.e14 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wang, J. et al. Individual bat virome analysis reveals co-infection and spillover among bats and virus zoonotic potential. Nat. Commun. 14, 4079 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Seifert, S. N. et al. An ACE2-dependent Sarbecovirus in Russian bats is resistant to SARS-CoV-2 vaccines. PLoS Pathog. 18, e1010828 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Murakami, S. et al. Detection and characterization of bat Sarbecovirus phylogenetically related to SARS-CoV-2, Japan. Emerg. Infect. Dis. 26, 3025–3029 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wacharapluesadee, S. et al. Evidence for SARS-CoV-2 related coronaviruses circulating in bats and pangolins in Southeast Asia. Nat. Commun. 12, 972 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Guo, H. et al. Identification of a novel lineage bat SARS-related coronaviruses that use bat ACE2 receptor. Emerg. Microbes Infect. 10, 1507–1514 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Delaune, D. et al. A novel SARS-CoV-2 related coronavirus in bats from Cambodia. Nat. Commun. 12, 6563 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hassanin, A. et al. Phylogeographic evolution of horseshoe bat sarbecoviruses in Vietnam and implications for the origins of SARS-CoV and SARS-CoV-2. Res. Square https://doi.org/10.21203/rs.3.rs-3227228/v1 (2023).

  11. Kenney, S. P., Wang, Q., Vlasova, A., Jung, K. & Saif, L. Naturally occurring animal coronaviruses as models for studying highly pathogenic human coronaviral disease. Vet. Pathol. 58, 438–452 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. V’kovski, P., Kratzel, A., Steiner, S., Stalder, H. & Thiel, V. Coronavirus biology and replication: implications for SARS-CoV-2. Nat. Rev. Microbiol. 19, 155–170 (2021).

    Article  PubMed  Google Scholar 

  13. Wong, A. C. P., Lau, S. K. P. & Woo, P. C. Y. Interspecies jumping of bat coronaviruses. Viruses https://doi.org/10.3390/v13112188 (2021).

  14. McGrath, M. E. et al. SARS-CoV-2 variant spike and accessory gene mutations alter pathogenesis. Proc. Natl Acad. Sci. USA 119, e2204717119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chen, D.-Y. et al. Spike and nsp6 are key determinants of SARS-CoV-2 Omicron BA.1 attenuation. Nature 615, 143–150 (2023).

    Article  PubMed  Google Scholar 

  16. Banerjee, A., Kulcsar, K., Misra, V., Frieman, M. & Mossman, K. Bats and coronaviruses. Viruses 11, 41 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ge, X.-Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Menachery, V. D. et al. SARS-like WIV1-CoV poised for human emergence. Proc. Natl Acad. Sci. USA 113, 3048–3053 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Menachery, V. D. et al. A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence. Nat. Med. 21, 1508–1513 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hu, B. et al. Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog. 13, e1006698 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yang, X.-L. et al. Isolation and characterization of a novel bat coronavirus closely related to the direct progenitor of severe acute respiratory syndrome coronavirus. J. Virol. 90, 3253–3256 (2016).

    Article  CAS  PubMed Central  Google Scholar 

  22. Lau, S. K. P. et al. Isolation of MERS-related coronavirus from lesser bamboo bats that uses DPP4 and infects human-DPP4-transgenic mice. Nat. Commun. 12, 216 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Guo, H. et al. Isolation of ACE2-dependent and -independent sarbecoviruses from Chinese horseshoe bats. J. Virol. 97, e00395-23 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Tse, L. V. et al. A MERS-CoV antibody neutralizes a pre-emerging group 2c bat coronavirus. Sci. Transl. Med. 15, eadg5567 (2023).

  25. Adams, L. E. et al. Fc-mediated pan-sarbecovirus protection after alphavirus vector vaccination. Cell Rep. 42, 112326 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Keusch, G. T. et al. Pandemic origins and a One Health approach to preparedness and prevention: solutions based on SARS-CoV-2 and other RNA viruses. Proc. Natl Acad. Sci. USA 119, e2202871119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Yount, B., Denison, M. R., Weiss, S. R. & Baric, R. S. Systematic assembly of a full-length infectious cDNA of mouse hepatitis virus strain A59. J. Virol. 76, 11065–11078 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yount, B. et al. Reverse genetics with a full-length infectious cDNA of severe acute respiratory syndrome coronavirus. Proc. Natl Acad. Sci. USA 100, 12995–13000 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Xie, X. et al. An infectious cDNA clone of SARS-CoV-2. Cell Host Microbe 27, 841–848.e3 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Rihn, S. J. et al. A plasmid DNA-launched SARS-CoV-2 reverse genetics system and coronavirus toolkit for COVID-19 research. PLoS Biol. 19, e3001091 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhao, H. et al. SARS-CoV-2 Omicron variant shows less efficient replication and fusion activity when compared with Delta variant in TMPRSS2-expressed cells. Emerg. Microbes Infect. 11, 277–283 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhang, Y. et al. The ORF8 protein of SARS-CoV-2 mediates immune evasion through down-regulating MHC-Ι. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2024202118 (2021).

  33. Arshad, N. et al. SARS-CoV-2 accessory proteins ORF7a and ORF3a use distinct mechanisms to down-regulate MHC-I surface expression. Proc. Natl Acad. Sci. USA 120, e2208525120 (2023).

    Article  CAS  PubMed  Google Scholar 

  34. Moriyama, M. et al. Enhanced inhibition of MHC-I expression by SARS-CoV-2 Omicron subvariants. Proc. Natl Acad. Sci. USA 120, e2221652120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. McCray, P. B. Jr. et al. Lethal infection of K18-hACE2 mice infected with severe acute respiratory syndrome coronavirus. J. Virol. 81, 813–821 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Sia, S. F. et al. Pathogenesis and transmission of SARS-CoV-2 in golden hamsters. Nature 583, 834–838 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zhou, P. et al. Contraction of the type I IFN locus and unusual constitutive expression of IFN-α in bats. Proc. Natl Acad. Sci. USA 113, 2696–2701 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Temmam, S. et al. SARS-CoV-2-related bat virus behavior in human-relevant models sheds light on the origin of COVID-19. EMBO Rep. 24, e56055 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Johnson, B. A. et al. Nucleocapsid mutations in SARS-CoV-2 augment replication and pathogenesis. PLoS Pathog. 18, e1010627 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Johnson, B. A. et al. Loss of furin cleavage site attenuates SARS-CoV-2 pathogenesis. Nature 591, 293–299 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Peacock, T. P. et al. The furin cleavage site in the SARS-CoV-2 spike protein is required for transmission in ferrets. Nat. Microbiol. 6, 899–909 (2021).

    Article  CAS  PubMed  Google Scholar 

  42. Zhang, Y. et al. A second functional furin site in the SARS-CoV-2 spike protein. Emerg. Microbes Infect. 11, 182–194 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Peiris, J. S. M., Yuen, K. Y., Osterhaus, A. D. M. E. & Stöhr, K. The severe acute respiratory syndrome. N. Engl. J. Med. 349, 2431–2441 (2003).

  44. Fan, Y., Zhao, K., Shi, Z. L. & Zhou, P. Bat coronaviruses in China. Viruses https://doi.org/10.3390/v11030210 (2019).

  45. Onorati, M. et al. Zika virus disrupts phospho-TBK1 localization and mitosis in human neuroepithelial stem cells and radial glia. Cell Rep. 16, 2576–2592 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wei, J. et al. Pharmacological disruption of mSWI/SNF complex activity restricts SARS-CoV-2 infection. Nat. Genet. 55, 471–483 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Thi Nhu Thao, T. et al. Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform. Nature 582, 561–565 (2020).

    Article  Google Scholar 

  48. Wei, J. et al. Genome-wide CRISPR screens reveal host factors critical for SARS-CoV-2 infection. Cell 184, 76–91.e13 (2021).

    Article  CAS  PubMed  Google Scholar 

  49. Grubaugh, N. D. et al. An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar. Genome Biol. 20, 8 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Fauver, J. R. et al. Coast-to-coast spread of SARS-CoV-2 during the early epidemic in the United States. Cell 181, 990–996.e5 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Ravindra, N. G. et al. Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes. PLoS Biol. 19, e3001143 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Mao, T. et al. Unadjuvanted intranasal spike vaccine elicits protective mucosal immunity against sarbecoviruses. Science 378, eabo2523 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Crawford, K. H. D. et al. Protocol and reagents for pseudotyping lentiviral particles with SARS-CoV-2 spike protein for neutralization assays. Viruses 12, 513 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Emkeeler. Analysis data and code for banal_fcs_query. Zenodo https://doi.org/10.5281/zenodo.11519957 (2024).

Download references

Acknowledgements

We thank M. Linehan, J. Klein, J. Frank, I. Ott and J. Wei (Yale) for technical assistance and helpful discussions; R. Baric from UNC-Chapel Hill for kindly providing WIV1 and SHC014 stocks; B. Graham from VRC-NIAID for providing Vero-ACE2/TMPRSS2 cells; D. Brackney from Connecticut Agricultural Experiment Section for providing BHK-21 clone 15 cells; B. Fontes and Yale EH&S for assistance in performing enhanced biosafety level 3 research. D.R.M. was funded by a Hanna H. Gray Fellowship from the Howard Hughes Medical Institute. B.L.M. was supported by NIH T32 HL007974. This study was partly funded by Howard Hughes Medical Institute (to A.I.). C.B.W. was supported by Burroughs Wellcome Fund, NIH R21 AI173821 and the Smith Family Foundation. This study was partially funded by ASAP and MAVDA Development Research programme grants (B.D.L, C.B.W.). The funders had no role in study design, data collection and analysis, the decision to publish or the preparation of the manuscript. This manuscript is dedicated in loving memory of Brett D. Lindenbach, who will be deeply missed as a mentor, friend and colleague in the scientific and virology community.

Author information

Authors and Affiliations

  1. Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA

    Mario A. Peña-Hernández, Miyu Moriyama, Yong Kong, Tianyang Mao, Madeleine C. Mankowski, David R. Martinez, Benjamin Israelow, Akiko Iwasaki & Craig B. Wilen

  2. Department of Microbial Pathogenesis, Yale University School of Medicine, New Haven, CT, USA

    Mario A. Peña-Hernández, Emma L. Keeler, Shuo Zhang & Brett D. Lindenbach

  3. Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA

    Mario A. Peña-Hernández, Mia Madel Alfajaro, Renata B. Filler, Emma L. Keeler, Zara E. Ranglin, Bridget L. Menasche, Madeleine C. Mankowski, Zhe Zhao, Shuo Zhang & Craig B. Wilen

  4. Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA

    Chantal B. F. Vogels, Anne M. Hahn, Chaney C. Kalinich, Nathan D. Grubaugh & Akiko Iwasaki

  5. Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA

    Nicholas Huston, Han Wan, Rafael Araujo-Tavares, Anna Marie Pyle & Akiko Iwasaki

  6. Department of Pathology, Yale School of Medicine, New Haven, CT, USA

    Robert Homer

  7. Department of Chemistry, Yale University, New Haven, CT, USA

    Anna Marie Pyle

  8. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Anna Marie Pyle & Akiko Iwasaki

  9. Department of Internal Medicine, Section of Infectious Diseases, Yale School of Medicine, New Haven, CT, USA

    Benjamin Israelow

Authors
  1. Mario A. Peña-Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mia Madel Alfajaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Renata B. Filler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miyu Moriyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emma L. Keeler
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zara E. Ranglin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yong Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tianyang Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bridget L. Menasche
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Madeleine C. Mankowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Zhe Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Chantal B. F. Vogels
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Anne M. Hahn
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Chaney C. Kalinich
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Shuo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Nicholas Huston
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Han Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Rafael Araujo-Tavares
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Brett D. Lindenbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Robert Homer
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Anna Marie Pyle
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. David R. Martinez
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Nathan D. Grubaugh
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Benjamin Israelow
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Akiko Iwasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Craig B. Wilen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.A.P-H., A.I. and C.B.W. conceptualized the project. M.A.P.-H., M.M.A., R.B.F., B.L.M., M.M., E.L.K., Z.E.R., Y.K., C.B.F.V., A.M.H., C.C.K., B.D.L, R.H., A.M.P., D.R.M., N.D.G., B.I., A.I. and C.B.W. developed the methodology. M.A.P.-H., M.M.A., R.B.F., B.L.M., M.M., Z.E.R., Y.K., T.M., M.C.M., Z.Z., C.B.F.V., S.Z., N.H., H.W., R.A.-T., R.H., N.D.G. and B.I. conducted investigations. M.A.P.-H., M.M., Z.E.R., Y.K., C.B.F.V., R.H., A.I. and C.B.W. performed visualization. A.I. and C.B.W. acquired funding. M.A.P.-H., A.I. and C.B.W. wrote the original draft. All authors reviewed and edited the manuscript.

Corresponding authors

Correspondence to Akiko Iwasaki or Craig B. Wilen.

Ethics declarations

Competing interests

A.I. serves as a consultant for RIGImmune, Xanadu Bio, Paratus Bio and Invisishield, and serves on the Board of Directors for Roche Holdings Ltd. A.I. is an investigator of the Howard Hughes Medical Institute. C.B.W. is on the Scientific Advisory Board for ExcepGen. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks Zheng-Li Shi, Youchun Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 BANAL-236 spike NTD is divergent from SARS-2 and BANAL-52.

(A) Protein alignment of BANAL-CoV and SARS-2 spikes. The S1 NTD and S1/S2 FCS are highlighted with red boxes.

Extended Data Fig. 2 BANAL-CoVs mNGs are susceptible to small molecule inhibitors and anti-SARS-2 sera.

(A) Representative image of an agarose gel showing all the BANAL-CoVs fragments synthesized and amplified before cloning them to their respective vectors. (B) Representative image of an agarose gel showing full-length genome ligation of BANAL-52 and BANAL-236 cDNA used for in vitro transcription and to produce infectious viruses. (C) Bat CoV titres in Vero-ACE2/TMPRSS2 cells. (D) BANAL-CoV and BANAL-CoV mNG titres in Vero-ACE2/TMPRSS2 cells. (E) Representative images of Vero-ACE2/TMPRSS2 cells infected with mNeonGreen viruses. (F-H) Inhibition of the three viruses by two doses of Pfizer (F) of Moderna (G) vaccinated pooled sera assessed at 48 hpi (MOI 0.1) as determined by cell viability measured by Cell-Titer Glo. Inhibition of the three viruses by convalescent pooled sera (H) assessed at 48 hpi (MOI 0.1) as determined by cell viability measured by Cell-Titer Glo. Color-coded cures represent a non-linear regression least square fit of biological replicates (n = 3) and shading represents 95% confidence intervals of the fit. For panels F-H, data are represented by mean ± SD. All figure panels are representative of two independent experiments.

Source data

Extended Data Fig. 3 BANAL-CoVs utilize similar entry factors than SARS-2.

(A-C) Inhibition of the three sarbecoviruses by (A) E64d assessed at 24 hpi in Vero-E6 as determined by % mNG expressing cells, (B) Camostat mesylate and (C) E64d assessed at 48 hpi in Vero-ACE2/TMPRSS2 cells as determined by cell viability measured by Cell-Titer Glo. Color-coded curves represent a non-linear regression least square fit of 4 biological replicates and shading represents 95% confidence intervals of the fit. Panels A-C are representative of two independent experiments. (D-E). Inhibition of the three sarbecoviruses by (D) camostat mesylate and (E) E64d in HBECs culture at 48 hpi. Error bars illustrate standard deviation. Significance between groups was assessed by two-way ANOVA corrected to multiple comparisons. For panel D, p values of mock and camostat treated cells were all p < 0.0001 for SARS-2, BANAL-52 and BANAL-236. Panels D and E are pooled from two independent experiments with n = 3 biological replicates each (n = 6 total). Mock treated wells are same for panels D and E. For all panels, data are represented by mean ± SD.

Source data

Extended Data Fig. 4 ISG induction in primary human airway cells after BANAL-CoV infection.

(A-B) mRNA fold change of ISG induction relative to uninfected cells measured by RT-qPCR from infected HBECs (A) or HNECs (B). Fold change was assessed by 2–ΔΔCt method using gapdh as a control. Significance of expression between viruses was assessed by one-way ANOVA. Panels A-B are pooled from two-independent experiments with a total of n = 6 biological replicates. For panel A, exact p values for ISG15 SARS-2 vs BANAL-52 p = .0105 and SARS-2 vs BANAL.236 p = .0162. For IFIT3 SARS-2 vs BANAL-52 p = .0129 and SARS-2 vs BANAL.236 p = .0207. For panel B, exact p values for IFIT3 SARS-2 vs BANAL-52 p = .0125 and SARS-2 vs BANAL-236 p = .0386. For RSAD2 SARS-2 vs BANAL-52 p = .0449. (C-E) Reduction of infectious viral titres of IFN-pretreated HBECs normalized to the median of the mock group of SARS-CoV-2, BANAL-52 and BANAL-236 infection using (C) IFN-α, (D) IFN-γ, and (E) IFN-λ. Graphs represent pooled data from two independent experiments (n = 5 biological replicates for IFN pre-treated cells, n = 6 biological replicates for IFN untreated infected cells). Significance between viral titres was assessed by one-way ANOVA corrected for multiple comparisons. For panel C SARS-2 vs BANAL-52 and SARS-2 vs BANAL-236 p < 0.0001. For panel D, SARS-2 vs BANAL-52 p = .0001 and SARS-2 vs BANAL-236 p < 0.0001. For panel E, SARS-2 vs BANAL-52 p = .003 and SARS-2 vs BANAL-236 p = 0.007. (F) Infectious viral titres and (G) normalization values of SARS-CoV-2 and BANAL-CoVs in HNECs pre-treated with IFN-α. Graphs F-G represent pooled data from two independent experiments (n = 6). Significance between viruses in IFN normalized to mock values (second graph) was assessed by one-way ANOVA corrected for multiple comparisons. For panel F, Mock treated cells SARS-2 vs BANAL-52 p < 0.0001 and SARS-2 vs BANAL-236 p = .0006. For IFN treated cells, p = .0350. For panel G, SARS-2 vs BANAL-52 p = .0006 and SARS-2 vs BANAL-236 p = .0045. (H) Normalization of pseudovirus infectivity following IFN-α pre-treatment in Vero-ACE2/TMPRSS2 cells for SARS-CoV-2 and BANAL-CoVs. One out of three independent experiments are shown. N = 6 biological replicates for SARS-2 and BANAL-52, and n = 5 for BANAL-236. Data from panels C-H are represented by mean ± SD. (I) Gating strategy for flow cytometry analysis. (J) Representative graphs of detection efficiency of A549-ACE2/TMPRSS2 SARS-2- spike positive cells by flow cytometry. N = 4 biological replicates out of 2 independent experiments. Significance between viruses was assessed by one-way ANOVA corrected for multiple comparisons. P values for panel J of SARS-2 vs mock infected, SARS-2 vs BANAL-52, and SARS-2 vs BANAL-236 were all p < 0.0001. For all panels, error bars illustrate standard deviation, and ** indicates p < 0.01, *** indicates p < 0.001, **** indicates p < 0.0001.

Source data

Extended Data Fig. 5 BANAL-CoVs do not induce robust innate immune response in mouse nasal turbinates.

(A) mRNA fold change of ISGs and inflammatory cytokines from lungs of infected mice. (B) mRNA fold change of ISGs and inflammatory cytokines from nasal turbinates of infected mice. Panels A-B were measured by RT-qPCR at 2 dpi. Fold change was assessed by 2–ΔΔCt method using b-actin as a control. Significance of expression between viruses was assessed by one-way ANOVA corrected for multiple comparisons. * indicates p < 0.05, ** indicates p < 0.01, **** indicates p < 0.0001. Exact p values for panel A are: ISG15 (BANAL-52 vs BANAL-236 p = .0374), Mx1 (SARS-2 vs BANAL-52 p = .0213, BANAL-52 vs BANAL-236 p = .0172), IFIT1 (SARS-2 vs BANAL-52 p = .0035, BANAL-52 vs BANAL-236 p = .0049). OAS1 (BANAL-52 vs BANAL-236 p = .0062), TNF (SARS-2 vs BANAL-52 p < 0.0001, BANAL-52 vs BANAL-236 p = .0423), IL-6 (SARS-2 vs BANAL-52 p = .0166, BANAL-52 vs BANAL-236 p = .0109), CXCL10 (SARS-2 vs BANAL-52 p = .0047, BANAL-52 vs BANAL-236 p = .0116). For panel B, exact p values are: IFIT1 (SARS-2 vs BANAL-52 p = .0240), CXCL10 (SARS-2 vs BANAL-52 p = .04), CXCL19 (SARS-2 vs BANAL-52 p = .0118 and SARS-2 vs BANAL-236 p = .0123). (C) Heatmap of ISGs and cytokines expression from nasal turbinates of K18-mice infected with the three sarbecoviruses measured by qPCRs. Values are from panel S5B. For panel Ain this figure the n of mice is: SARS-2 n = 11, BANAL-52 n = 12, BANAL-236 n = 13. For panels B and C the n of mice is: SARS-2 n = 8, BANAL-52 n = 8, BANAL-236 n = 8. Figure is pooled from three-independent experiments.

Source data

Extended Data Fig. 6 BANAL-CoVs do not induce lung inflammation in hamsters.

(A) Measurement of hamster ACE2 usage by the three sarbecovirus spikes using lentiviral-based pseudoviruses (n = 4 biological replicates). Significance was assessed by two-way ANOVA. Data is represented by mean ± SD. Graphs represent one out of two independent experiments. Dashed line represents limit of detection. (B) H&E staining from infected hamster lungs with each of the three sarbecoviruses or uninfected control. Images are representative from 5 hamsters after 2 dpi.

Source data

Extended Data Fig. 7 SARS-2 FCS is enriched upon infection in hamsters.

(A) Nucleotide and aminoacid alignment of 7 most abundant reads from SARS-CoV-2 ΔFCS stock (Reads 1-7) and reads that have mutations in the FCS region but were not between the top 20 most abundant reads (Reads 8-10). (B) Raw reads from the S1/S2 spike junction region highlighting total number reads and reads with FCS sequence. (C) Percent of reads that possess FCS from virus stock and experimental samples from hamster lungs after NGS sequencing. (D) Fold change of the number of reads containing the FCS sequence comparing SARS-2 ΔFCS stock vs hamster infected with SARS-2 ΔFCS at 2 dpi. For sequencing analyses, 4 different tubes of SARS-2 ΔFCS stock and 2 samples from hamsters infected with SARS-2 WT and SARS-2 ΔFCS were used. For panels B and C, data are represented by mean ± SD. For panel D, individual values are shown.

Source data

Supplementary information

Supplementary Information

Uncropped gels for Extended Data Fig. 2, and raw images for Extended Data Figs. 2e and 6b.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–3.

Supplementary Data 1

Molecular clone design of BANAL-CoVs.

Supplementary Data 2

Code for analysing SARS-2 ΔFCS sequences.

Supplementary Data 3

BioRender license agreement for Figs. 1b, 3a and 4a.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peña-Hernández, M.A., Alfajaro, M.M., Filler, R.B. et al. SARS-CoV-2-related bat viruses evade human intrinsic immunity but lack efficient transmission capacity. Nat Microbiol 9, 2038–2050 (2024). https://doi.org/10.1038/s41564-024-01765-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-024-01765-z

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing