Signalling Induced by Pathogens
Bibliographic record
Abstract
Epigenetic histone modifications contribute to the regulation of eukaryotic gene transcription.The role of epigenetic regulation in immunity to intracellular pathogens is poorly understood.We tested the hypothesis that epigenetic histone modifications influence cytokine expression by intracellular bacteria.Intracellular L. monocytogenes, but not non-invasive L. innocua, induced release of distinct CC and CXC, as well as Th1 and Th2 cytokines and growth factors by endothelial cells.Cytokine-expression was in part dependent on p38 MAP kinase and MEK1.We analyzed global histone modification and modifications in detail at the gene-promoter of IL-8, which depended on both kinase pathways, and of IFNg, that was not blocked by kinase inhibition.Intracellular Listeria induced time-dependent acetylation (Lys-8) of histone H4 and phosphorylation/acetylation (Ser-10/Lys-14) of histone H3 globally and at the il8 promoter in HUVEC, as well as recruitment of histone acetylase CBP.Inhibitors of p38 MAP kinase and MEK1 reduced Lys-8-acetylation of histone H4 and Ser-10/Lys-14-phosphorylation/acetylation of histone H3 in Listeria-infected endothelial cells and disappearance of histone deactelyase HDAC1 at the il8 promoter in HUVEC.In contrast, IFNg gene transcription was activated by L. monocytogenes independently of p38 MAP kinase and MEK1, and histone phosphorylation/acetylation remained unchanged in infected cells at the IFNg promoter.Specific inhibition of histone deacetylases by trichostatin A increased Listeria-induced expression of IL-8, but not of IFNg, underlining the specific physiological impact of histone acetylation.In conclusion, MAP kinase-dependent epigenetic modifications differentially contributed to L. moncytogenes-induced cytokine expression by human endothelial cells.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".