Contribution of Interferon-β to the Murine Macrophage Response to the Toll-like Receptor 4 Agonist, Lipopolysaccharide
Bibliographic record
Abstract
Interferon-beta (IFN-beta) has been identified as the signature cytokine induced via the Toll-like receptor (TLR) 4, "MyD88-independent" signaling pathway in macrophages stimulated by Gram-negative bacterial lipopolysaccharide (LPS). In this study, we analyzed the responses of macrophages derived from wild-type (IFN-beta(+/+)) mice or mice with a targeted mutation in IFN-beta (IFN-beta(-/-)) to the prototype TLR4 agonist, Escherichia coli LPS. A comparison of basal and LPS-induced gene expression (by reverse transcription-PCR, real-time PCR, and Affymetrix microarray analyses) resulted in the identification of four distinct patterns of gene expression affected by IFN-beta deficiency. Analysis of a subset of each group of differentially regulated genes by computer-assisted promoter analysis revealed putative IFN-responsive elements in all genes examined. LPS-induced activation of intracellular signaling molecules, STAT1 Tyr-701, STAT1 Ser-727, and Akt, but not p38, JNK, and ERK MAPK proteins, was significantly diminished in IFN-beta(-/-) versus IFN-beta(+/+) macrophages. "Priming" of IFN-beta(-/-) macrophages with exogenous recombinant IFN-beta significantly increased levels of LPS-induced gene expression for induction of monocyte chemotactic protein 5, inducible nitric-oxide synthase, IP-10, and IL-12 p40 mRNA, whereas no increase or relatively small increases were observed for IL-1beta, IL-6, monocyte chemotactic protein 1, and MyD88 mRNA. Finally, IFN-beta(-/-) mice challenged in vivo with LPS exhibited increased survival when compared with wild-type IFN-beta(+/+) controls, indicating that IFN-beta contributes to LPS-induced lethality; however, not to the extent that one observes in mice with more complete pathway deficiencies (e.g. TLR4(-/-) or TRIF(-/-) mice). Collectively, these findings reveal unanticipated regulatory roles for IFN-beta in response to LPS in vitro and in vivo.
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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 | 0.000 | 0.000 |
| 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".