Involvement of TLR2 in Recognition of Acute Gammaherpesvirus-68 Infection
Bibliographic record
Abstract
BACKGROUND: Toll-like receptors (TLRs) play a crucial role in the activation of innate immunity in response to many viruses. We previously reported the implication of TLR2 in the recognition of Epstein-Barr virus (EBV) by human monocytes. Because murine gammaherpesvirus-68 (MHV-68) is a useful model to study human gammaherpesvirus pathogenesis in vivo, we evaluated the importance of mouse TLR2 in the recognition of MHV-68. METHODOLOGY/PRINCIPAL FINDINGS: In studies using transfected HEK293 cells, MHV-68 lead to the activation of NF-κB reporter through TLR2. In addition, production of interleukin-6 (IL-6) and interferon-α (IFN-α) upon MHV-68 stimulation was reduced in murine embryonic fibroblasts (MEFs) derived from TLR2-/- and MyD88-/- mice as compared to their wild type (WT) counterpart. In transgenic mice expressing a luciferase reporter gene under the control of the mTLR2 promoter, MHV-68 challenge activated TLR2 transcription. Increased expression levels of TLR2 on blood granulocytes (CD115(-)Gr1(+)) and inflammatory monocytes (CD115(+)Gr1(+)), which mobilized to the lungs upon infection with MHV-68, was also confirmed by flow cytometry. Finally, TLR2 or MyD88 deficiency was associated with decreased IL-6 and type 1 IFN production as well as increased viral burden during short-term challenges with MHV-68. CONCLUSIONS/SIGNIFICANCE: TLR2 contributes to the production of inflammatory cytokines and type 1 IFN as well as to the control of viral burden during infection with MHV-68. Taken together, our results suggest that the TLR2 pathway has a relevant role in the recognition of this virus and in the subsequent activation of the innate immune response.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".