Regulation of gene expression in mouse macrophages stimulated with bacterial CpG-DNA and lipopolysaccharide
Bibliographic record
Abstract
CpG-DNA is known as a potent immunostimulating agent and may contribute in therapeutic treatment of many immune disorders. CpG-DNA triggers innate and acquired immune responses through activated expression of various genes in immune cells, including macrophages. To define the molecular mechanism(s) by which CpG-DNA activates immune cells, we studied macrophage gene expression following CpG-DNA exposure using high-density oligonucleotide microarrays. As CpG-DNA receptor Toll-like receptor 9 (TLR9) shares homology with the lipopolysaccharide (LPS)-TLR4 receptor, we compared gene expression profiles in macrophages stimulated by LPS versus CpG-DNA. CpG-DNA and LPS modulate expression of many genes encoding cytokines, cell surface receptors, transcription factors, and proteins related to cell proliferation/differentiation. However, LPS modulated expression of significantly more genes than did CpG-DNA, and all genes induced or repressed by CpG-DNA were induced or repressed by LPS. We conclude that CpG-DNA signaling through TLR9 activates a subset of genes induced by LPS-TLR4 signaling.
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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 | 0.001 | 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) | 0.002 | 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".