Involvement of TBK1 and IKKϵ in lipopolysaccharide‐induced activation of the interferon response in primary human macrophages
Bibliographic record
Abstract
Interferon (IFN) is an important effector of the innate immune response, induced by different viral or bacterial components through Toll-like receptor-dependent and -independent mechanisms. In human macrophages and macrophage-activated killer cells, we demonstrate that (i) the type I IFN response to lipopolysaccharide (LPS) is weak compared to the host response to virus infection; (ii) there is a temporal difference in the induction of tank-binding kinase-1 (TBK1) and IkappaB kinase (IKK)-related kinase epsilon (IKKepsilon) kinase activities in response to LPS, with TBK1 activated early and IKKepsilon induced in the late phase of IFN induction; and (iii) interferon regulatory factor (IRF)-7 is induced following LPS treatment, but there is no evidence that IRF-7 becomes activated by phosphorylation in vivo. Specifically, TBK1 kinase activity is rapidly increased after LPS stimulation (15 min) whereas IKKepsilon activation occurs at 8 h. RNA interference-mediated inhibition of TBK1 and IKKepsilon expression in macrophages interfere with IFNB and IRF7 gene expression following LPS activation. Macrophage priming with rIFN-alpha increased IRF-7 expression, led to a sharp up-regulation of the IFNB gene and to a rapid induction of IFNA2 upon LPS stimulation. These data support a differential role of TBK1 and IKKepsilon in the downstream response mediated by IRF-3 and IRF-7 to LPS in primary human macrophages.
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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) | 0.001 | 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".