Secretome-Mediated Interactions with Intestinal Epithelial Cells: A Role for Secretome Components from <i>Lactobacillus rhamnosus</i> R0011 in the Attenuation of <i>Salmonella enterica</i> Serovar Typhimurium Secretome and TNF-α–Induced Proinflammatory Responses
Bibliographic record
Abstract
Abstract Recent evidence suggests that lactic acid bacteria communicate with host cells via secretome components to influence immune responses but less is known about gut-pathogen secretomes, impact of lactic acid bacteria secretomes on host–pathogen interactions, and the mechanisms underlying these interactions. Genome-wide microarrays and cytokine profiling were used to interrogate the impact of the Lactobacillus rhamnosus R0011 secretome (LrS) on TNF-α and Salmonella enterica subsp. enterica serovar Typhimurium secretome (STS)–induced outcomes in human intestinal epithelial cells. The LrS attenuated both TNF-α– and STS-induced gene expression involved in NF-κB and MAPK activation, as well as expression of genes involved in other immune-related signaling pathways. Specifically, the LrS induced the expression of dual specificity phosphatase 1 (DUSP1), activating transcription factor 3 (ATF3), and tribbles pseudokinase 3 (TRIB3), negative regulators of innate immune signaling, in HT-29 intestinal epithelial cells challenged with TNF-α or STS. TNF-α– and STS-induced acetylation of H3 and H4 histones was attenuated by the LrS, as was the production of TNF-α– and STS-induced proinflammatory cytokines and chemokines. Interestingly, the LrS induced production of macrophage migration inhibitory factor (MIF), a cytokine involved in host–microbe interactions at the gut interface. We propose that the LrS attenuates proinflammatory mediator expression through increased transcription of negative regulators of innate immune activity and changes in global H3 and H4 histone acetylation. To our knowledge, these findings provide novel insights into the complex multifaceted mechanisms of action behind secretome-mediated interdomain communication at the gut-mucosal interface.
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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".