Sensing of SARS-CoV-2 by pDCs and their subsequent production of IFN-I contribute to macrophage-induced cytokine storm during COVID-19
Bibliographic record
Abstract
Abstract Lung-infiltrating macrophages create a marked inflammatory milieu in a subset of patients with COVID-19 by producing a cytokine storm, which correlates with increased lethality. However, these macrophages are largely not infected by SARS-CoV-2, so the mechanism underlying their activation in the lung is unclear. Type I interferons (IFN-I) contribute to protecting the host against SARS-CoV-2 but may also have some deleterious effect, and the source of IFN-I in the lungs of infected patients is not well defined. Plasmacytoid dendritic cells (pDCs), a key cell type involved in antiviral responses, can produce IFN-I in response to SARS-CoV-2. We observed the infiltration of pDCs in the lungs of SARS-CoV-2–infected patients, which correlated with strong IFN-I signaling in lung macrophages. In patients with severe COVID-19, lung macrophages expressed a robust inflammatory signature, which correlated with persistent IFN-I signaling at the single-cell level. Hence, we observed the uncoupling in the kinetics of the infiltration of pDCs in the lungs and the associated IFN-I signature, with the cytokine storm in macrophages. We observed that pDCs were the dominant IFN-α–producing cells in response to the virus in the blood, whereas macrophages produced IFN-α only when in physical contact with infected epithelial cells. We also showed that IFN-α produced by pDCs, after the sensing of SARS-CoV-2 by TLR7, mediated changes in macrophages at both transcriptional and epigenetic levels, which favored their hyperactivation by environmental stimuli. Together, these data indicate that the priming of macrophages can result from the response by pDCs to SARS-CoV-2, leading to macrophage activation in patients with severe COVID-19. Supported by NCI grant T32CA203702. Supported by NIH grant 1R01AI132447, the Scleroderma Research Foundation, the Scleroderma Foundation, and a grant from the HSS Research Institute to study the role of pDCs in SARS-CoV-2 pathogenesis. Supported by NIH grants AI046712 and AR050401. Supported by NIH grants UL1TR002384, R01CA194547, and P01CA214274; LLS SCOR grants 180078-01 and 7021-20; and Starr Cancer Consortium grant I11-0027. Supported by NIDDK R01DK121072, NIDDK R01DK119667, NCI R01CA234614, and NIAID R01AI107301. Supported as an Irma Hirschl Trust Research Award Scholar.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".