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Type III interferons disrupt the lung epithelial barrier upon viral recognition
Open accessAbstract
Interferons interfere with lung repair: Interferons (IFNs) are central to antiviral immunity. Viral recognition elicits IFN production, which in turn triggers the transcription of IFN-stimulated genes (ISGs), which engage in various antiviral functions. Type I IFNs (IFN-α and IFN-β) are widely expressed and can result in immunopathology during viral infections. By contrast, type III IFN (IFN-λ) responses are primarily restricted to mucosal surfaces and are thought to confer antiviral protection without driving damaging proinflammatory responses. Accordingly, IFN-λ has been proposed as a therapeutic in coronavirus disease 2019 (COVID-19) and other such viral respiratory diseases (see the Perspective by Grajales-Reyes and Colonna). Broggi et al. report that COVID-19 patient morbidity correlates with the high expression of type I and III IFNs in the lung. Furthermore, IFN-λ secreted by dendritic cells in the lungs of mice exposed to synthetic viral RNA causes damage to the lung epithelium, which increases susceptibility to lethal bacterial superinfections. Similarly, using a mouse model of influenza infection, Major et al. found that IFN signaling (especially IFN-λ) hampers lung repair by inducing p53 and inhibiting epithelial proliferation and differentiation. Complicating this picture, Hadjadj et al. observed that peripheral blood immune cells from severe and critical COVID-19 patients have diminished type I IFN and enhanced proinflammatory interleukin-6– and tumor necrosis factor-α–fueled responses. This suggests that in contrast to local production, systemic production of IFNs may be beneficial. The results of this trio of studies suggest that the location, timing, and duration of IFN exposure are critical parameters underlying the success or failure of therapeutics for viral respiratory infections. Science , this issue p. 706 , p. 712 , p. 718 ; see also p. 626
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Virological assessment of hospitalized patients with COVID-2019
Wölfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Müller, M. A., Niemeyer, D., Jones, T. C., Vollmar, P., Rothe, C., et al. (2020) · Nature · doi:10.1038/s41586-020-2196-x · Cited by 6,661
Differential roles of MDA5 and RIG-I helicases in the recognition of RNA viruses
Kato, H., Takeuchi, O., Sato, S., Yoneyama, M., Yamamoto, M., Matsui, K., Uematsu, S., Jung, A., Kawai, T., Ishii, K. J., et al. (2006) · Nature · doi:10.1038/nature04734 · Cited by 3,888
SARS and MERS: recent insights into emerging coronaviruses
de Wit, E., van Doremalen, N., Falzarano, D., & Munster, V. J. (2016) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2016.81 · Cited by 3,510
Innate antiviral responses by means of TLR7-mediated recognition of single-stranded RNA
Diebold, S. S., Kaisho, T., Hemmi, H., Akira, S., & Reis e Sousa, C. (2004) · Science · doi:10.1126/science.1093616 · Cited by 3,444
Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages
Merad, M., & Martin, J. C. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0331-4 · Cited by 2,472
Viral dynamics in mild and severe cases of COVID-19
Liu, Y., Yan, L.-M., Wan, L., Xiang, T.-X., Le, A., Liu, J.-M., Peiris, M., Poon, L. L. M., & Zhang, W. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30232-2 · Cited by 1,727
Insights into the Interaction between Influenza Virus and Pneumococcus
McCullers, J. A. (2006) · Clinical Microbiology Reviews · doi:10.1128/cmr.00058-05 · Cited by 846
The co-pathogenesis of influenza viruses with bacteria in the lung.
McCullers, J. A. (2014) · Nature Reviews Microbiology · doi:10.1038/nrmicro3231 · Cited by 722 · cited 1×
“Superinfections represent the first cause of lethality upon 66 influenza virus infection (21) and correlate with severity in coronavirus disease (COVID)-19 67”
BackgroundA mouse-adapted model of SARS-CoV-2 to test COVID-19 countermeasures
Dinnon, K. H., Leist, S. R., Schäfer, A., Edwards, C. E., Martinez, D. R., Montgomery, S. A., West, A., Yount, B. L., Hou, Y. J., Adams, L. E., et al. (2020) · Nature · doi:10.1038/s41586-020-2708-8 · Cited by 675
Sun, K., & Metzger, D. W. (2008) · Nature Medicine · doi:10.1038/nm1765 · Cited by 587
Middle East Respiratory Syndrome
Arabi, Y. M., Balkhy, H. H., Hayden, F. G., Bouchama, A., Luke, T., Baillie, J. K., Al-Omari, A., Hajeer, A. H., Senga, M., Denison, M. R., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmsr1408795 · Cited by 518
Depletion of alveolar macrophages during influenza infection facilitates bacterial superinfections
Ghoneim, H. E., Thomas, P. G., & McCullers, J. A. (2013) · The Journal of Immunology · doi:10.4049/jimmunol.1300014 · Cited by 394
Cameron, M. J., Ran, L., Xu, L., Danesh, A., Bermejo-Martin, J. F., Cameron, C. M., Muller, M. P., Gold, W. L., Richardson, S. E., Poutanen, S. M., et al. (2007) · Journal of Virology · doi:10.1128/jvi.00527-07 · Cited by 386
Interferon-λ restricts West Nile virus neuroinvasion by tightening the blood-brain barrier
Lazear, H. M., Daniels, B. P., Pinto, A. K., Huang, A. C., Vick, S. C., Doyle, S. E., Gale, M., Klein, R. S., & Diamond, M. S. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa4304 · Cited by 234
Innate immune responses to influenza A H5N1: friend or foe?
Peiris, J. S. M., Cheung, C. Y., Leung, C. Y. H., & Nicholls, J. M. (2009) · Trends in Immunology · doi:10.1016/j.it.2009.09.004 · Cited by 232
Frieman, M. B., Chen, J., Morrison, T. E., Whitmore, A., Funkhouser, W., Ward, J. M., Lamirande, E. W., Roberts, A., Heise, M., Subbarao, K., et al. (2010) · PLoS Pathogens · doi:10.1371/journal.ppat.1000849 · Cited by 158
Influenza A Exacerbates Staphylococcus aureus Pneumonia by Attenuating IL-1β Production in Mice
Robinson, K. M., Choi, S. M., McHugh, K. J., Mandalapu, S., Enelow, R. I., Kolls, J. K., & Alcorn, J. F. (2013) · The Journal of Immunology · doi:10.4049/jimmunol.1301237 · Cited by 126
Planet, P. J., Parker, D., Cohen, T. S., Smith, H., Leon, J. D., Ryan, C., Hammer, T. J., Fierer, N., Chen, E. I., & Prince, A. S. (2016) · mBio · doi:10.1128/mbio.01939-15 · Cited by 113
Cited by
Phetsouphanh, C., Darley, D. R., Wilson, D. B., Howe, A., Munier, C. M. L., Patel, S. K., Juno, J. A., Burrell, L. M., Kent, S. J., Dore, G. J., et al. (2022) · Nature Immunology · doi:10.1038/s41590-021-01113-x · Cited by 1,015
Galani, I.-E., Rovina, N., Lampropoulou, V., Triantafyllia, V., Manioudaki, M., Pavlos, E., Koukaki, E., Fragkou, P. C., Panou, V., Rapti, V., et al. (2021) · Nature Immunology · doi:10.1038/s41590-020-00840-x · Cited by 503
Type I and III interferons disrupt lung epithelial repair during recovery from viral infection
Major, J., Crotta, S., Llorian, M., McCabe, T. M., Gad, H. H., Priestnall, S. L., Hartmann, R., & Wack, A. (2020) · Science · doi:10.1126/science.abc2061 · Cited by 503
COVID-19-associated fungal infections
Hoenigl, M., Seidel, D., Sprute, R., Cunha, C., Oliverio, M., Goldman, G. H., Ibrahim, A. S., & Carvalho, A. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01172-2 · Cited by 482
Syncytia formation by SARS‐CoV‐2‐infected cells
Buchrieser, J., Dufloo, J., Hubert, M., Monel, B., Planas, D., Rajah, M. M., Planchais, C., Porrot, F., Guivel‐Benhassine, F., Van der Werf, S., et al. (2020) · The EMBO Journal · doi:10.15252/embj.2020106267 · Cited by 462
Metadata
- Authors
- Broggi, A., Ghosh, S., Sposito, B., Spreafico, R., Balzarini, F., Lo Cascio, A., Clementi, N., De Santis, M., Mancini, N., Granucci, F., & Zanoni, I.
- Venue
- Science
- Published
- 2020-08-07
- Volume
- 369
- Issue
- 6504
- Pages
- 706-712
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3034873991
- Cited by
- 402
- References
- 18
- FWCI
- 21.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
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