Human MSC Suppression Correlates With Cytokine Induction of Indoleamine 2,3-Dioxygenase and Bystander M2 Macrophage Differentiation
Bibliographic record
Abstract
Clinical trials testing the use of either autologous or allogeneic human bone marrow-derived mesenchymal stromal cells (MSC) as a cell-based pharmaceutical for suppression of autoimmune and alloimmune ailments are underway. Reported results from completed trials vary in effectiveness within and between studies without any clear mechanistic explanation. We propose that these discrepancies may arise from intrinsic variability in the immunosuppressive potential of each MSC donor source. Here, we demonstrate that tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ)-activated MSC derived from normal adult volunteers suppress T cell proliferation in vitro in a variegated manner, an observation linked to IFN-mediated indoleamine 2,3-dioxygenase (IDO) upregulation. We also demonstrate that MSC IDO activity is implicated in the differentiation of monocytes into interleukin (IL)-10-secreting M2 immunosuppressive macrophages (CD14(+)/CD206(+)). Those monocyte-derived M2 are in turn implicated in the suppression of T cell proliferation in an IL-10-independent manner, thus amplifying the immunosuppressive effect generated by MSC. In summary, the immune plasticity of IFN-γ and TNF-α licensed veto function of MSC vary among donors and defines a central role to inducible IDO activity and its bystander effect on lymphomyeloid immune effectors.
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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) | 0.002 | 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".