Regulatory effects of SKAR in interferon α signaling and its role in the generation of type I IFN responses
Bibliographic record
Abstract
We provide evidence that S6 kinase 1 (S6K1) Aly/REF-like target (SKAR) is engaged in IFN-α signaling and plays a key role in the generation of IFN responses. Our data demonstrate that IFN-α induces phosphorylation of SKAR, which is mediated by either the p90 ribosomal protein S6 kinase (RSK) or p70 S6 kinase (S6K1), in a cell type-specific manner. This type I IFN-inducible phosphorylation of SKAR results in enhanced interaction with the eukaryotic initiation factor (eIF)4G and recruitment of activated RSK1 to 5' cap mRNA. Our studies also establish that SKAR is present in cap-binding CBP80 immune complexes and that this interaction is mediated by eIF4G. We demonstrate that inducible protein expression of key IFN-α-regulated protein products such as ISG15 and p21(WAF1/CIP1) requires SKAR activity. Importantly, our studies define a requirement for SKAR in the generation of IFN-α-dependent inhibitory effects on malignant hematopoietic progenitors from patients with chronic myeloid leukemia or myeloproliferative neoplasms. Taken altogether, these findings establish critical and essential roles for SKAR in the regulation of mRNA translation of IFN-sensitive genes and induction of IFN-α biological responses.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".