Interplay between SARS-CoV-2 and the type I interferon response
Open accessAbstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the current COVID-19 pandemic. An unbalanced immune response, characterized by a weak production of type I interferons (IFN-Is) and an exacerbated release of proinflammatory cytokines, contributes to the severe forms of the disease. SARS-CoV-2 is genetically related to SARS-CoV and Middle East respiratory syndrome-related coronavirus (MERS-CoV), which caused outbreaks in 2003 and 2013, respectively. Although IFN treatment gave some encouraging results against SARS-CoV and MERS-CoV in animal models, its potential as a therapeutic against COVID-19 awaits validation. Here, we describe our current knowledge of the complex interplay between SARS-CoV-2 infection and the IFN system, highlighting some of the gaps that need to be filled for a better understanding of the underlying molecular mechanisms. In addition to the conserved IFN evasion strategies that are likely shared with SARS-CoV and MERS-CoV, novel counteraction mechanisms are being discovered in SARS-CoV-2-infected cells. Since the last coronavirus epidemic, we have made considerable progress in understanding the IFN-I response, including its spatiotemporal regulation and the prominent role of plasmacytoid dendritic cells (pDCs), which are the main IFN-I-producing cells. While awaiting the results of the many clinical trials that are evaluating the efficacy of IFN-I alone or in combination with antiviral molecules, we discuss the potential benefits of a well-timed IFN-I treatment and propose strategies to boost pDC-mediated IFN responses during the early stages of viral infection.
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A main evasion strategy seems to be blocking type I interferon responses at several steps, such as viral RNA recognition, nuclear translocation of inflammatory transcription factors, and STAT1/STAT2 phosphorylation.
“A predominant strategy appears to be the inhibition of the type I interferon response58–60 at multiple points, including impaired recognition of viral RNA61,62, decreased nuclear translocation of pro-inflammatory transcription factors (such as IRF3, IRF7 and STAT1)61,63 and suppression of STAT1 and STAT2 phosphorylation64,65.”
From Immunological mechanisms of vaccine-induced protection against COVID-19 in humans (2021) · cites this paper as [60]
SARS-CoV-2 uses a multipronged strategy to antagonize the IFN system, with the clinical consequence of an insufficient type I IFN response in early COVID-19.
“As an immune evasion strategy, SARS-CoV-2 uses a multipronged strategy to antagonize the IFN system with the clinical consequence of an insufficient type I IFN response having been recognized in early COVID-19 studies (Acharya et al., 2020; Blanco-Melo et al., 2020; Sa Ribero et al., 2020).”
From COVID-19 and the human innate immune system (2021) · cites this paper as Sa Ribero et al., 2020
The pattern recognition receptors involved in recognizing SARS-CoV-2 are not yet directly determined; based on other coronaviruses, the most likely candidates are endosomal TLR3 and TLR7 or cytosolic RIG-I and MDA5.
“Similarly, the pattern recognition receptors (PRRs) involved in recognizing SARS-CoV-2 are not yet directly determined. Based on findings from other coronaviruses, the most likely candidates are Toll-like receptors 3 (TLR3) and TLR7 in the endosome (Mazaleuskaya et al., 2012) or the cytosolic sensors retinoic acid-inducible gene 1 (RIG-I) and melanoma differentiation-associated gene 5 (MDA5) (Sa R”
From COVID-19 and the human innate immune system (2021) · cites this paper as Sa Ribero et al., 2020
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COVID-19 and the human innate immune system
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- Authors
- Sa Ribero, M., Jouvenet, N., Dreux, M., & Nisole, S.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Lyon 1 Université (FR)
- Corresponding author
- Jouvenet, Nolwenn
- Dreux, Marlène
- Nisole, Sébastien
- Venue
- PLOS Pathogens
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-07-29
- Volume
- 16
- Issue
- 7
- Pages
- e1008737
- ISSN
- 1553-7366 (ISSN-L)
- 1553-7374
- PMC
- PMC7390284
- OpenAlex
- W3045552416
- Cited by
- 443
- References
- 149
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 27 · 2023: 62 · 2022: 118 · 2021: 175 · 2020: 30 · 2019: 1
- FWCI
- 9.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Pathogens (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Funders
- Agence Nationale de la Recherche (10-LABX-12-01, ANR-10-LABX-12-01, ANR-Flash-COVID19, ANR-19-CE15-0025, ANR-16-CE15-0025, ANR-20-COVI-0099, ANR-10, ANR-10-LABX, ANR-10-LABX-0012)
- Centre National de la Recherche Scientifique
- Région Occitanie Pyrénées-Méditerranée
- Laboratoire d'Excellence EpiGenMed (ANR-10-LABX-12-01)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › interferon and immune responses
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › Viral Infections and Outbreaks Research
- Keywords
- SARS-CoV-2, COVID-19 pandemic, type I interferon response, interferon evasion, plasmacytoid dendritic cells, inflammatory cytokines, SARS-CoV, MERS-CoV, interferon therapy, viral immune evasion
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, COVID-19 Drug Treatment, Antiviral Agents, Dendritic Cells, Humans, Immunity, Innate, Interferon Type I, Pneumonia, Viral, Prognosis, Coronavirus Infections, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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