Regulatory Effects of Programmed Cell Death 4 (PDCD4) Protein in Interferon (IFN)-Stimulated Gene Expression and Generation of Type I IFN Responses
Bibliographic record
Abstract
The precise mechanisms by which the activation of interferon (IFN) receptors (IFNRs) ultimately controls mRNA translation of specific target genes to induce IFN-dependent biological responses remain ill defined. We provide evidence that IFN-α induces phosphorylation of programmed cell death 4 (PDCD4) protein on Ser67. This IFN-α-dependent phosphorylation is mediated by either the p70 S6 kinase (S6K) or the p90 ribosomal protein S6K (RSK) in a cell-type-specific manner. IFN-dependent phosphorylation of PDCD4 results in downregulation of PDCD4 protein levels as the phosphorylated form of PDCD4 interacts with the ubiquitin ligase β-TRCP (β-transducin repeat-containing protein) and undergoes degradation. This process facilitates IFN-induced eukaryotic translation initiation factor 4A (eIF4A) activity and binding to translation initiation factor eIF4G to promote mRNA translation. Our data establish that PDCD4 degradation ultimately facilitates expression of several ISG protein products that play important roles in the generation of IFN responses, including IFN-stimulated gene 15 (ISG15), p21(WAF1/CIP1), and Schlafen 5 (SLFN5). Moreover, engagement of the RSK/PDCD4 pathway by the type I IFNR is required for the suppressive effects of IFN-α on normal CD34(+) hematopoietic precursors and for antileukemic effects in vitro. Altogether, these findings provide evidence for a unique function of PDCD4 in the type I IFN system and indicate a key regulatory role for this protein in mRNA translation of ISGs and control of IFN responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".