Immune Activation of Type I IFNs by <i>Listeria monocytogenes</i> Occurs Independently of TLR4, TLR2, and Receptor Interacting Protein 2 but Involves TANK-Binding Kinase 1
Bibliographic record
Abstract
Type I IFNs are well established antiviral cytokines that have also been shown to be induced by bacteria. However, the signaling mechanisms regulating the activation of these cytokines during bacterial infections remain poorly defined. We show that although Gram-negative bacteria can activate the type I IFN pathway through TLR4, the intracellular Gram-positive bacterium Listeria monocytogenes (LM) can do so independently of TLR4 and TLR2. Furthermore, experiments using genetic mutants and chemical inhibitors suggest that LM-induced type I IFN activation occurs by an intracellular pathway involving the serine-threonine kinase TNFR-associated NF-kappaB kinase (TANK)-binding kinase 1 (TBK1). Interestingly, receptor-interacting protein 2, a component of the recently discovered nucleotide-binding oligomerization domain-dependent intracellular detection pathway, was not involved. Taken together, our data describe a novel signal transduction pathway involving TBK1 that is used by LM to activate type I IFNs. Additionally, we provide evidence that both the LM- and TLR-dependent pathways converge at TBK1 to activate type I IFNs, highlighting the central role of this molecule in modulating type I IFNs in host defense and disease.
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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) | 0.002 | 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".