TGF-β and IL-15 Synergize through MAPK Pathways to Drive the Conversion of Human NK Cells to an Innate Lymphoid Cell 1–like Phenotype
Bibliographic record
Abstract
Circulating NK cells are known to convert to a type 1 innate lymphoid cell (ILC1)-like phenotype in response to TGF-β exposure. However, the precise cellular changes defining this process as well as the downstream signaling pathways guiding it remain poorly defined, particularly in humans. We used mass cytometry by time-of-flight (CyTOF) to model this phenotypic shift in vitro and identify a synergistic activity of TGF-β and IL-15 in this cellular conversion. CyTOF profiling identified substantial heterogeneity in the propensity of NK cells to adopt an ILC1-like phenotype in culture, characterized by the step-wise acquisition of various markers, including CD69, CD9, CD103, and CD49a. Activating and inhibitory receptors, including NKG2A, NKG2D, KIR2DL1, KIR3DL1, NKp30, NKp44, and NKp46, were all found to be upregulated exclusively on the cellular subsets that converted most readily in response to TGF-β. An assessment of downstream TGF-β signaling identified TAK1-mediated activation of p38 MAPK as the critical pathway driving conversion. IL-15 enhanced TGF-β-mediated conversion through Ras:RAC1 signaling as well as via the activation of MEK/ERK. Interestingly, the adoption of an ILC1-like phenotype was independent of the effect of IL-15 or TGF-β on mTOR, as the culture of NK cells in the presence of mTOR inhibitors, such as rapamycin or torin1, had minimal impact on the degree of conversion. In conclusion, we have used in vitro human culture systems and CyTOF to define the conversion of circulating NK cells to an ILC1-like phenotype and have clarified the pathways responsible for this process.
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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".