Author response: Interleukin-1 prevents SARS-CoV-2-induced membrane fusion to restrict viral transmission via induction of actin bundles
Bibliographic record
Abstract
SARS-CoV-2, the agent responsible for COVID-19, has claimed millions of lives across the globe. To better manage this disease and develop new treatments, it is fundamental to understand how the immune system responds to this virus – and, in particular, how it can be thwarted. Like all viruses, SARS-CoV-2 replicates within host cells and bursts out when it has made more of itself and is ready to infect more tissues. It can also cause neighbouring cells to merge, allowing the virus to replicate and spread without stepping outside. This strategy makes it harder for the immune system to access and deactivate the threat. A group of molecules called proinflammatory cytokines (such as IL-1β and IL-1α) are released upon SARS-CoV-2 infection. People receiving immunosuppressive therapies, which can reduce proinflammatory cytokine levels to harness inflammatory damage, find it harder to tackle the virus. However, the full role of these molecules in clearing SARS-CoV-2 remains unknown. To investigate this question, Zheng, Yu, Zhou and Yu et al. developed different experimental models that could examine how proinflammatory cytokines might protect cells from SARS-CoV-2 challenge. The results showed that IL-1β and IL-1α stop the virus from being able to fuse cells together. Further cell studies revealed the underlying mechanism: IL-1β triggers cells to increase the levels of essential components, known as actin bundles, which form the structures that prevent cells from fusing with each other. Experiments in live mice showed that IL-1β treatment significantly prevented SARS-CoV-2 from spreading within the lining of the lungs. Taken together, these findings reveal new insights into how the immune system protects hosts against SARS-CoV-2 infection; further investigation may help identify new treatments for 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.019 |
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".