IFNα signaling through PKC-θ is essential for antitumor NK cell function
Bibliographic record
Abstract
We have previously shown that the development of a major histocompatibility complex class I (MHC-I)-deficient tumor was favored in protein kinase C-θ knockout (PKC-θ−/−) mice compared to that occurring in wild-type mice. This phenomenon was associated with scarce recruitment of natural killer (NK) cells to the tumor site, as well as impaired NK cell activation and reduced cytotoxicity ex vivo. Poly-inosinic:cytidylic acid (poly I:C) treatment activated PKC-θ in NK cells depending on the presence of a soluble factor produced by a different splenocyte subset. In the present work, we sought to analyze whether interleukin-15 (IL-15) and/or interferon-α (IFNα) mediate PKC-θ-dependent antitumor NK cell function. We found that IL-15 improves NK cell viability, granzyme B expression, degranulation capacity and interferon-γ (IFNγ) secretion independently of PKC-θ. In contrast, we found that IFNα improves the degranulation capability of NK cells against target cancer cells in a PKC-θ-dependent fashion both ex vivo and in vivo. Furthermore, IFNα induces PKC-θ auto-phosphorylation in NK cells, in a signal transduction pathway involving both phosphatidylinositol-3-kinase (PI3K) and phospholipase-C (PLC) activation. PKC-θ dependence was further implicated in IFNα-induced transcriptional upregulation of chemokine (C-X-C motif) ligand 10 (CXCL10), a signal transducer and activator of transcription-1 (STAT-1)-dependent target of IFNα. The absence of PKC-θ did not affect IFNα-induced STAT-1 Tyr701 phosphorylation but affected the increase in STAT-1 phosphorylation on Ser727, attenuating CXCL10 secretion. This connection between IFNα and PKC-θ in NK cells may be exploited in NK cell-based tumor immunotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".