Modulation of Bacterial Growth by Tumor Necrosis Factor-α <i>In Vitro</i> and <i>In Vivo</i>
Bibliographic record
Abstract
Tumor necrosis factor-alpha (TNF-alpha) plays an important role in innate immunity. Recent in vitro studies have shown that TNF-alpha may also serve as a growth factor for some bacteria. We examined the physiologic relevance of this phenomenon both in vitro and in vivo. Recombinant mouse TNF-alpha increased in vitro proliferation of Escherichia coli but not Pseudomonas aeruginosa in a concentration-dependent manner, and this effect was attenuated by anti-TNF-alpha antibodies. However, in vivo, TNF-alpha gene-deficient (TNF-alpha-/-) mice showed higher mortality than wild-type (TNF-alpha+/+) mice after inoculation of intranasal bacteria. An impaired bacterial clearance in TNF-alpha-/- mice was associated with decreased systemic concentrations of chemokine macrophage inflammatory protein-2, reduced pulmonary neutrophil recruitment, and depressed expression of neutrophil CD11b and CD16/CD32, suggesting that the effect of TNF-alpha on E. coli growth was outweighed by the recruited neutrophils. We also demonstrated that neutropenic TNF-alpha+/+ mice had approximately 100-fold higher E. coli counts in their lungs than TNF-alpha-/- mice, although survival rates in both groups were similar. We conclude that TNF-alpha augments E. coli growth in vitro and in vivo. However, in vivo, this effect becomes only apparent in neutropenic animals. The relevance of these findings for immune compromised patients remains to be investigated.
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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 | 0.001 |
| 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".