Human Alveolar Macrophages Detect SARS‐CoV‐2 Envelope Protein Through TLR2 and TLR4 and Secrete Cytokines in Response
Bibliographic record
Abstract
Alveolar macrophages (AMs) are the most numerous immune cells of the lung and are the resident, sentinel lung immunocytes that summon trafficking immune cells to the compartment. Immune profiling of AMs from COVID-19 patients implicates AMs in the immune circuits that drive pulmonary inflammation in severe COVID-19 infection. However, little is known about human AM responses to SARS-CoV-2 proteins, such as the spike protein and envelope protein. We aimed to understand if human AMs recognize SARS-CoV-2 proteins and how they respond. We found that human AMs do not sense SARS-CoV-2 spike protein but do sense envelope protein via the pattern recognition receptors TLR2 and TLR4, secreting IL-1β, IFNγ, IL-12p70, IL-6, and TNFα in response. AMs from donors over the age of 70 years produced significantly more cytokines than those from younger patients following stimulation with SARS-CoV-2 envelope protein. AMs from current smokers had lower cytokine secretion. This is the first report of human AMs producing cytokines in response to SARS-CoV-2 proteins and the first to correlate those responses with clinical risk factors. These results may partly explain why older adults are at such high risk of severe lung inflammation in COVID-19.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".