The <i>Lactobacillus rhamnosus</i> R0011 secretome attenuates TNFα-induced pro-inflammatory gene expression in human HT-29 intestinal epithelial cells
Bibliographic record
Abstract
Abstract Probiotic lactic acid bacteria have been associated with a wide array of host-immune modulatory effects including modulation of pro-inflammatory gene expression. However, many questions remain about the exact cellular mechanisms through which these bacteria modulate host cell responses following pro-inflammatory challenge. The aim of this study was to elucidate the effects of the Lactobacillus rhamnosus R0011 secretome on TNFα-induced pro-inflammatory gene expression. Previous analysis has shown that the <10kDa fraction of the Lr R0011 secretome attenuates TNFα-induced IL-8 production from HT-29 intestinal epithelial cells (IECs). Whole-genome wide microarray analysis was used to further interrogate the impact of the <10kDa fraction of the Lr R0011 secretome on TNFα-induced gene expression. Contact with the <10kDa secretome fraction alone induced minimal changes in global gene expression by HT-29 IECs, with no increases in pro-inflammatory gene expression. However, the <10kDa fraction of the Lr R0011 secretome attenuated TNFα-induced expression of the pro-inflammatory mediators CCL10, CXCL1, CXCL10, CXCL11, IL-1β, IL-8, IL-17C, IL-23A, IL-32, and PTGS2, when compared to HT-29 IEC treated with TNFα alone. Co-challenge of HT-29 IECs with the <10kDa fraction of the Lr R0011 secretome and TNFα also resulted in an increase in the expression of dual specificity phosphatase 1 (DUSP1), a key regulator of the MAPK pathway, and of activating transcription factor 3 (ATF3), a negative regulator of innate immunity, relative to HT-29 IECs treated with TNFα alone. This provides insight into mechanisms through which these bacteria may influence innate immune activity at the IEC level through soluble mediators.
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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".