Differential Immunomodulatory Effects of Human Bone Marrow-Derived Mesenchymal Stromal Cells on Natural Killer Cells
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) modulate immune responses through cell contact-dependent or paracrine mechanisms and are themselves known to have low immunogenicity. Given the increasing use of both natural killer (NK) cells and MSCs in cell-based therapies, we investigated the interaction between the two cell types using the NK cell lines, KHYG-1 and NK-92, and human bone marrow-derived MSCs. NK lines were cocultured with MSCs, either directly or in a transwell system, and the effects on proliferation, interferon-gamma (IFN-γ) production, and cytolytic activity of NK cells were analyzed. Cytotoxicity was measured in a 4 h chromium release assay. MSCs did not affect the proliferation of NK cell lines but reduced IFN-γ production by KHYG-1, but not NK-92, when cocultured directly at 10:1 NK:MSC ratio. MSCs suppressed K562 lysis by both KHYG-1 and NK-92 cells in contact-free transwell cocultures but only reduced cytotoxicity of KHYG-1 and not NK-92 cells when cells were in direct contact in coculture. Immunosuppressive effects of MSCs were mediated by indoleamine-2,3-dioxygenase (IDO) and prostaglandin E2 (PGE2) secreted by MSCs and were abrogated in the presence of IDO and PGE2 inhibitors. In the presence of MSCs, granule polarization was suppressed and induced respectively, in KHYG-1 and NK-92. Consistent with this, MSCs were susceptible to lysis by NK-92 but not KHYG-1. These studies indicate the differential crosstalk between MSCs and two highly cytotoxic NK lines and may be important when designing future cell therapy protocols with these two cell types.
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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".