Differential expression of IL-1 and TNF receptors in murine macrophages infected with virulent vs. avirulent<i>Legionella pneumophila</i>
Bibliographic record
Abstract
Infection of macrophages from genetically susceptible A/J mice with Legionella pneumophila induces high levels of various cytokines in serum as well as in cultures of spleen or peritoneal cells from the mice. However, modulation of receptor expression for these cytokines during infection has not been studied in detail, even though these receptors on macrophages have a critical role in inflammatory responses during the infection. In the present study, the differential expression of mRNA for TNF and IL-1 receptors as well as receptor antigens during infection of macrophages with virulent vs. avirulent L. pneumophila was investigated. Mouse thioglycollate-elicited peritoneal macrophages showed by RT-PCR constitutive steady-state levels of mRNA for TNF-type I and -type II receptors as well as IL-1 type I receptor. However, IL-1 type II receptor mRNA was not expressed in thioglycollate-elicited macrophages. Infection of macrophages with virulent bacteria caused an upregulation of IL-1 type I and TNF type I receptor mRNA, but had no effect on TNF type II receptor message. Avirulent L. pneumophila infection caused much less induction of these receptor mRNAs. The amount of receptor antigen of IL-1 type I on the surface of macrophages was also increased by infection with virulent L. pneumophila determined by flow cytometric analysis. These results indicate that L. pneumophila infection not only causes induction of various cytokines, but also modulation of certain cytokine receptors, which may regulate the susceptibility to infection.Key words: Legionella pneumophila, cytokine receptors, 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 | 0.000 |
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".