A GMCSF and IL-15 fusokine leads to paradoxical immunosuppression in vivo via asymmetrical JAK/STAT signaling through the IL-15 receptor complex
Bibliographic record
Abstract
We hypothesized that a granulocyte macrophage colony-stimulating factor (GMCSF) and interleukin 15 (IL-15) fusokine (GIFT15) would possess greater immune-stimulatory properties than their combined use. Unexpectedly, tumor cells engineered to secrete GIFT15 protein led to suppression of natural killer (NK) and NKT-cell recruitment in vivo, suggesting an unanticipated immune-suppressive effect. We found GIFT15 to have pleiotropic effects on an array of immune-competent cells. Among these, macrophages treated with GIFT15 secrete de novo the tissue inhibitor of metalloproteinase-2 (TIMP-2); activated matrix metalloproteinase-2 (MMP-2); transforming growth factor-beta (TGF-beta); as well as vascular endothelial growth factor (VEGF). We show that the GIFT15 fusokine has increased affinity for the alpha chain component of the IL-15R, leading to aberrant signaling through the beta chain manifested by the hyperphosphorylation of STAT3 both in macrophages and splenocytes. Suppression of common gamma chain-mediated STAT5 phosphorylation and blockade of the IL-15-dependent IFN-gamma response in mouse splenocytes were also observed. We tested GIFT15 as an immunosuppressor and demonstrated that it allowed engraftment of allogeneic B16F0 and human xenograft U87GM glioma cells in immunocompetent mice. Thus, GIFT15 defines a new class of fusokine that mediates proangiogenic and immunosuppressive effects via aberrant signaling by the IL-15R in lymphomyeloid cells.
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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".