TLR3 or TLR4 Activation Enhances Mesenchymal Stromal Cell-Mediated Treg Induction via Notch Signaling
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) are the subject of numerous clinical trials, largely due to their immunomodulatory and tissue regenerative properties. Toll-like receptors (TLRs), especially TLR3 and TLR4, are highly expressed on MSCs and their activation can significantly modulate the immunosuppressive and anti-inflammatory functions of MSCs. While MSCs can recruit and promote the generation of regulatory T cells (Tregs), the effect of TLR activation on MSC-mediated Treg induction is unknown. In this study, we investigated the effect of ligand-mediated activation of TLR3 and TLR4 on Treg induction by human MSCs. We found that generation of Tregs in human CD4(+) lymphocyte and MSC cocultures was enhanced by either TLR3 or TLR4 activation of MSCs and that the increase was abolished by TLR3 and TLR4 gene-silencing. Augmented Treg induction by TLR-activated MSCs was cell contact-dependent and associated with increased gene expression of the Notch ligand, Delta-like 1. Moreover, inhibition of Notch signaling abrogated the augmented Treg levels in the MSC cocultures. Our data show that TLR3 or TLR4 activation of MSCs increases Treg induction via the Notch pathway and suggest new means to enhance the potency of MSCs for treating disorders with an underlying immune dysfunction, including steroid resistant acute graft-versus-host disease. Stem Cells 2017;35:265-275.
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) | 0.002 | 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".