TLR3 or TLR4 Activation Enhances MSC-Mediated Treg Generation Via Notch Signaling
Bibliographic record
Abstract
Abstract Mesenchymal stromal cells (MSCs) are used as cell therapy for a variety of disorders, largely because of their immunosuppressive and regenerative functions by exerting immune effects via direct and indirect interactions with many types of immune cells. MSCs recruit and promote the generation of regulatory T cells (Tregs) both in vitro and in vivo. Toll-like receptors (TLRs), known for roles in innate and adaptive immunity, are involved in numerous pathological conditions, including graft-versus-host disease (GVHD). Several TLRs, especially TLR3 and TLR4, are highly expressed on MSCs and affect immunomodulatory functions and possibly, therapeutic potency. Indeed, two distinct anti- and pro-inflammatory MSC phenotypes have been reported after activation of TLR3 and TLR4, respectively. The role of TLRs on MSC-mediated Treg generation, however, is not known. In this study, we investigated the role of TLR3 and TLR4 in the MSC-mediated generation of Tregs in an allogeneic co-culture model. Data for each experiment were collected from 1 PBMC donor and 3 MSC donors. We found that pre-activation of TLR3 and TLR4 by their ligands (poly I:C for TLR3, LPS for TLR4) enhanced the generation of Tregs by MSCs: 1.2 ± 0.2% in CD4+ cells cultured alone, 3.9 ± 0.3% in co-culture with control MSCs, 6.04 ± 0.1% in co-culture with TLR3-activated MSCs and 6.6 ± 0.4% in co-culture with TLR4-activated MSCs. siRNA-mediated silencing of TLR3 and TLR4 reduced Tregs by 51.7% and 61.8% in co-culture with poly I:C- and LPS-primed MSCs, respectively. Treg levels for the poly I:C-activated group were 6.3 ± 0.2% for co-cultures with control MSCs, 5.2 ± 0.3% for MSCs treated with scrambled RNA and 3 ± 0.3% for MSCs treated with TLR3-siRNA. For the LPS-activated group, Treg levels were 6.7 ± 0.3% with control MSCs, 5.7 ± 0.5% with MSCs treated with scrambled RNA and 2.5 ± 0.3% for MSCs treated with TLR4-siRNA. MSC-mediated Treg induction required cell-cell contact as conditioned media (CM) from TLR-activated or control MSCs failed to induce Tregs among CD4+ enriched cells: 4.75 ± 0.1% in direct co-culture vs 2.72 ± 0.3%, P= 0.004 in CM from control MSCs, 6.35 ± 0.2% in direct co-culture vs 2.97 ± 0.2%, P=0.0008 in CM from TLR3-activated MSCs, 6.7 ± 0.3% in direct co-culture vs 3.2 ± 0.3, P=0.001 in CM from the TLR4-activated group. We showed that the notch pathway is activated in CD4+ cells co-cultured with TLR-activated, but not control MSCs, and inhibition of notch signaling reduced MSC-mediated Tregs in co-cultures with TLR3- and TLR4-activated, but not control MSCs: 4.75 ± 0.1% vs 3.76 ± 0.4%, P=0.09 in control MSCs, 6.35 ± 0.2% vs 4.43 ± 0.3%, P=0.012 in TLR3-activated MSCs, 6.7 ± 0.3% vs 3.97 ± 0.1%, P=0.001 in TLR4-activated MSCs. Our data show a new role for TLR3 and TLR4 in the immunoregulatory function of human MSCs, and indicate the involvement of notch signaling as a mechanism for the further induction of Tregs in TLR3- and TLR4-activated MSCs. These studies have implications for the use of TLR-activated MSCs in the enhanced generation of Tregs such as for the treatment of acute GVHD. Disclosures No relevant conflicts of interest to declare.
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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".