Live <i>Lactobacillus reuteri</i> Is Essential for the Inhibitory Effect on Tumor Necrosis Factor Alpha-Induced Interleukin-8 Expression
Bibliographic record
Abstract
The mechanism of the apparent anti-inflammatory action of probiotic organisms is unclear. Lactobacillus reuteri is effective in inhibiting colitis in interleukin-10 (IL-10)-deficient mice. Nerve growth factor (NGF), in addition to its activity on neuronal cell growth, has significant anti-inflammatory effects in several experimental systems in vitro and in vivo, including a model of colitis. Our experiments were designed to explore the mechanism of effect of L. reuteri in the human epithelial cell lines T84 and HT29 on cytokine and NGF synthesis and IL-8 response to tumor necrosis factor alpha (TNF-alpha). Epithelial cells were cultured for various times with live and killed L. reuteri and examined by reverse transcription-PCR for NGF, IL-10, and TNF-alpha-induced IL-8 expression. An enzyme-linked immunosorbent assay was used to quantitate intracellular IL-8 and secreted product. Western blotting and confocal microscopy were used to determine the effects on IkappaB and NF-kappaB, respectively. Live but not heat-killed or gamma-irradiated L. reuteri upregulated NGF and dose dependently inhibited constitutive synthesis by T84 and HT29 cells of IL-8 and that induced by TNF-alpha in terms of mRNA and intracellular and secreted protein. Similarly, L. reuteri inhibited IL-8 synthesis induced by Salmonella enterica serovar Typhimurium. L. reuteri required preincubation and adherence for effect, inhibited translocation of NF-kappaB to the nuclei of HeLa cells, and prevented degradation of IkappaB. Neither cellular lysates nor media supernatants had any effect on TNF-alpha-induced IL-8. The conclusion is that L. reuteri has potent direct anti-inflammatory activity on human epithelial cells, which is likely to be related to the activity of ingested probiotics. L. reuteri also upregulates an unusual anti-inflammatory molecule, NGF, and inhibits NF-kappaB translocation to the nucleus.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".