Endogenous interferon-lambda signaling restricts virus replication and disease severity in a murine model of SARS-CoV-2 infection
Bibliographic record
Abstract
Abstract Interferon-lambda (IFN-λ) is currently being investigated in a phase III clinical trial as a therapeutic against COVID-19. Exogenous IFN-λ restricts SARS-CoV-2 in vitro and in Balb/c and C57Bl/6 (WT) murine models of infection. However, roles of endogenously produced IFN-λ in SARS-CoV-2 pathogenesis are not currently known, and the overall mechanisms by which IFN-λ modulates the induction of protective immune responses in SARS-CoV-2 infections remains to be elucidated. We find that IFN-λ receptor deficient mice (Ifnlr1−/−) infected with mouse-adapted SARS-CoV-2 lose significantly more weight and have increased SARS-CoV-2 viral replication compared to WT through day 5 post infection. Intriguingly, Ifit3 and Ifitm3 are increased in the lungs of Ifnlr1−/− mice compared to WT following infection, despite similar IFN-α/β and IFN-λ mRNA levels, suggesting compensatory increases in type I IFN signaling are not driving the increased weight loss or ISG induction observed in Ifnlr1−/− mice. Global transcriptomics revealed induction of a suppressive immunoregulatory signature with increased IL-10 as a hallmark in Ifnlr1−/− lungs, suggesting IFN-λ is critically involved in regulating appropriate immune activation to limit SARS-CoV-2 pathogenesis. Histological analysis revealed a significant increase in CD45+ cells (but not neutrophils) in the lungs of Ifnlr1−/− mice compared to WT on day 5 post infection, and we identified increases in pathways associated with myeloid cell function and T cell activation in Ifnlr1−/− by comparative transcriptomics. Overall, broadening the understanding of how IFN-λ regulates SARS-CoV-2 infection, pathogenesis, and immunity will inform the utilization of IFN-λ as an immunotherapy and adjuvant. Supported by NIH Award K22 AI146141 to EAH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".