Multistrain Probiotic Modulation of Intestinal Epithelial Cells' Immune Response to a Double-Stranded RNA Ligand, Poly(I·C)
Bibliographic record
Abstract
A commercially available product containing three probiotic bacterial strains (Lactobacillus helveticus R0052, Bifidobacterium longum subsp. infantis R0033, and Bifidobacterium bifidum R0071) was previously shown in animal trials to modulate both TH1 and TH2 immune responses. Clinical studies on this combination of bacteria have also shown positive health effects against seasonal winter diseases and rotavirus infection. The goal of this study was to use a well-established in vitro intestinal epithelial (HT-29) cell model that has been shown to constitutively express double-stranded RNA (dsRNA) sensors (Toll-like receptor 3 [TLR3], retinoic acid-inducible gene I, melanoma differentiation-associated gene 5, and dsRNA-activated protein kinase). By using the HT-29 cell model, we wanted to evaluate whether or not this combination of three bacteria had the capacity to immune modulate the host cell response to a dsRNA ligand, poly(I·C). Using a custom-designed, two-color expression microarray targeting genes of the human immune system, we investigated the response of HT-29 cells challenged with poly(I·C) both in the presence and in the absence of the three probiotic bacteria. We observed that the combination of the three bacteria had a major impact on attenuating the expression of genes connected to proinflammatory TH1 and antiviral innate immune responses compared to that obtained by the poly(I·C)-only challenge. Major pathways through which the multistrain combination may be eliciting its immune-modulatory effect include the TLR3 domain-containing adapter-inducing beta interferon (TRIF), mitogen-activated protein kinase, and NF-κB signaling pathways. Such a model may be useful for selecting potential biomarkers for the design of future clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".