γc receptor antagonist, EQ102, prevents the NK and T cell-mediated responses driven by IL-15 and IL-21
Bibliographic record
Abstract
Abstract Synergistic signaling of IL-15 and IL-21 is important in driving pathogenic T and NK cell responses in multiple inflammatory diseases. IL-15 and IL-21 are part of the common gamma chain (γc) cytokine family, which use the shared γc receptor subunit in their signaling complexes. IL-15 and IL-21 each promote cytolytic activity and IFNγ production but can also synergistically enhance the function of other cytotoxic cytokines. Consequently, blockade of IL-15 or IL-21 alone is likely insufficient to improve disease outcome in pathologic environments. EQ102 is a multi-cytokine inhibitor that selectively blocks IL-15 and IL-21 signaling while preserving signaling of other γc family members. Here, we sought to investigate the effect of synergistic signaling from both IL-15 and IL-21 on NK and T cell activities and the ability of EQ102 to inhibit these signals and resultant cellular response. PBMCs from healthy donors or NK-92 cells were incubated with EQ102 for 1 hour and then stimulated with IL-15, IL-21, or in combination. Following stimulation, cellular expression of transcription factors and activation markers were analyzed by flow cytometry. Cell supernatant was assessed for T and NK cell cytokines. IL-15 stimulation increases proliferation of T and NK cells, where IL-21 had a modest effect. However, co-stimulation of IL-15 and IL-21 enhanced proliferation, activation, and upregulation of IFNγ production over single cytokine IL-15 conditions. EQ102 treatment effectively inhibits the IL-15/IL-21 co-stimulatory cytolytic responses of these cell populations. These results suggest that selective blockade of IL-15 and IL-21 by EQ102 inhibits the synergistic signaling that mediates NK and T cell responses in multiple immune disorders. None
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".