TNF-α is a critical negative regulator of type 1 immune activation during intracellular bacterial infection
Bibliographic record
Abstract
TNF-alpha has long been regarded as a proimmune cytokine involved in antimicrobial type 1 immunity. However, the precise role of TNF-alpha in antimicrobial type 1 immunity remains poorly understood. We found that TNF-alpha-deficient (TNF(-/-)) mice quickly succumbed to respiratory failure following lung infection with replication-competent mycobacteria, because of apoptosis and necrosis of granuloma and lung structure. Tissue destruction was a result of an uncontrolled type 1 immune syndrome characterized by expansion of activated CD4 and CD8 T cells, increased frequency of antigen-specific T cells, and overproduction of IFN-gamma and IL-12. Depletion of CD4 and CD8 T cells decreased IFN-gamma levels, prevented granuloma and tissue necrosis, and prolonged the survival of TNF(-/-) hosts. Early reconstitution of TNF-alpha by gene transfer reduced the frequency of antigen-specific T cells and improved survival. TNF-alpha controlled type 1 immune activation at least in part by suppressing T cell proliferation, and this suppression involved both TNF receptor p55 and TNF receptor p75. Heightened type 1 immune activation also occurred in TNF(-/-) mice treated with dead mycobacteria, live replication-deficient mycobacteria, or mycobacterial cell wall components. Our study thus identifies TNF-alpha as a type 1 immunoregulatory cytokine whose primary role, different from those of other type 1 cytokines, is to keep an otherwise detrimental type 1 immune response in check.
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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 | 0.001 |
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".