Macrophage interleukin‐6 and tumour necrosis factor‐α are induced by coronavirus fixation to Toll‐like receptor 2/heparan sulphate receptors but not carcinoembryonic cell adhesion antigen 1a
Bibliographic record
Abstract
A rapid antiviral immune response may be related to viral interaction with the host cell leading to activation of macrophages via pattern recognition receptors (PPRs) or specific viral receptors. Carcinoembryonic cell adhesion antigen 1a (CEACAM1a) is the specific receptor for the mouse hepatitis virus (MHV), a coronavirus known to induce acute viral hepatitis in mice. The objective of this study was to understand the mechanisms responsible for the secretion of high-pathogenic MHV3-induced inflammatory cytokines. We report that the induction of the pro-inflammatory cytokines interleukin (IL)-6 and tumour necrosis factor (TNF)-alpha in peritoneal macrophages does not depend on CEACAM1a, as demonstrated in cells isolated from Ceacam1a(-/-) mice. The induction of IL-6 and TNF-alpha production was related rather to the fixation of the spike (S) protein of MHV3 on Toll-like receptor 2 (TLR2) in regions enriched in heparan sulphate and did not rely on viral replication, as demonstrated with denatured S protein and UV-inactivated virus. High levels of IL-6 and TNF-alpha were produced in livers from infected C57BL/6 mice but not in livers from Tlr2(-/-) mice. The histopathological observations were correlated with the levels of those inflammatory cytokines. Depending on mouse strain, the viral fixation to heparan sulfate/TLR2 stimulated differently the p38 mitogen-activated protein kinase (MAPK) and nuclear factor (NF)-kappaB in the induction of IL-6 and TNF-alpha. These results suggest that TLR2 and heparan sulphate receptors can act as new viral PPRs involved in inflammatory responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| 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) | 0.001 | 0.000 |
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".