<i>Lactobacillus rhamnosus</i> GG and <i>Lactobacillus casei</i> Suppress <i>Escherichia coli</i>-Induced Chemokine Expression in Intestinal Epithelial Cells
Bibliographic record
Abstract
BACKGROUND: Recently, some strains of lactic acid bacteria (LAB) have been reported to prevent the development of atopic dermatitis and to improve allergic symptoms, especially in young children. However, the mechanisms involved in these effects are not fully understood. Intestinal microbiota play critical roles in the development of host immune development and are recognized and regulated by the host through intestinal epithelial cells (IECs). We thus hypothesized that LAB influence the host immune system through the activation of IECs. To begin testing this hypothesis, chemokine expression in IECs exposed to intestinal bacteria was investigated. METHODS: Caco-2 cell monolayers were stimulated with different concentrations of various live or heat-killed intestinal bacteria or bacterial components for up to 3 h. Changes in the gene expressions of various chemokines were measured using quantitative real-time PCR. RESULTS: The expressions of CCL20, CXCL8, CXCL10 and CX3CL1 were strongly induced by nonpathogenic Escherichia coli in a dose-dependent manner and were partially induced by some commensal LAB. In contrast, Lactobacillus rhamnosus GG (LGG) and Lactobacillus casei did not induce these chemokine expressions. In addition, LGG significantly suppressed the expressions of CCL20 and CXCL10 induced by E. coli, peptidoglycan or flagellin when cultured simultaneously. CONCLUSIONS: LGG and L. casei markedly suppressed E. coli-induced chemokine expression, presumably through the suppression of the Toll-like receptor-mediated signal transduction pathway, at least in part. The clinical importance of this suppressive effect and the mechanisms involved require further investigation; however, such effects can be used as a marker to identify clinically useful LAB.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".