Lactic acid bacteria derived-bioactive molecules condition an <i>in vitro</i> cell model into a tolerogenic dendritic-like cell phenotype
Bibliographic record
Abstract
Abstract Retinoic acid (RA) is a vitamin A metabolite; CD103+ dendritic cells (DCs) acquire a tolerogenic phenotype through an RA-dependent mechanism, driving the differentiation of gut-homing regulatory T cells in the gut mucosa. Growing evidence shows bioactive molecules derived from lactic acid bacteria exhibit immunomodulatory activity by interacting with antigen presenting cells (APCs), stimulating their differentiation and altering their phenotype to further shape adaptive immunity. We utilized the monocytic THP-1 cell line as an in vitro model to examine the impact of the Lacticaseibacillus rhamnosus secretome (LrS) and interactions with RA on APC differentiation into a tolerogenic dendritic-like cell (DLC) phenotype. THP-1 monocytes were treated with Phorbol 12-myristate 13-acetate to drive differentiation into macrophages, then conditioned with the LrS and/or RA for 24, 48 or 72 hours. Expression of key DC markers CD11b, CD11c, DC-SIGN, CD83, CD80 and CD103 increased, while expression of proinflammatory marker CD64, co-stimulatory marker CD86 and MHC class II molecule HLA-DR was reduced. Cytokine profiling revealed LrS + RA co-treatment increased the production of immunoregulatory cytokines IL-10 and IL-1RA. Expression of NFKB1 and NFKB2, key genes involved in the activation of the NF-κB canonical and non-canonical pathways was induced by 48 hours of LrS conditioning, accompanied by increased expression of CD40 and IDO1, indicating an ability to drive DLC maturation and induce co-stimulatory capacity. Deciphering the mechanisms behind these types of microbiota-host interactions gives insight into how microbial metabolites can influence immune maturation and maintain immune homeostasis. Supported by Natural Sciences and Engineering Research Council of Canada Discovery Grant RGPIN-2017-05237
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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 | 0.001 |
| 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".