Cytokine Modulation of TLR Expression and Activation in Mesenchymal Stromal Cells Leads to a Proinflammatory Phenotype
Bibliographic record
Abstract
Bone marrow-derived mesenchymal stromal cells (MSC) possess an immune plasticity manifested by either an immunosuppressive or, when activated with IFN-gamma, an APC phenotype. Herein, TLR expression by MSC and their immune regulatory role were investigated. We observed that human MSC and macrophages expressed TLR3 and TLR4 at comparable levels and TLR-mediated activation of MSC resulted in the production of inflammatory mediators such as IL-1beta, IL-6, IL-8/CXCL8, and CCL5. IFN-alpha or IFN-gamma priming up-regulated production of these inflammatory mediators and expression of IFNB, inducible NO synthase (iNOS), and TRAIL upon TLR activation in MSC and macrophages, but failed to induce IL-12 and TNF-alpha production in MSC. Nonetheless, TLR activation in MSC resulted in the formation of an inflammatory site attracting innate immune cells, as evaluated by human neutrophil chemotaxis assays and by the analysis of immune effectors retrieved from Matrigel-embedded MSC injected into mice after in vitro preactivation with cytokines and/or TLR ligands. Hence, TLR-activated MSC are capable of recruiting immune inflammatory cells. In addition, IFN priming combined with TLR activation may increase immune responses induced by Ag-presenting MSC through presentation of Ag in an inflammatory context, a mechanism that could be applied in a cell-based vaccine.
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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".