Role of Schlafen 2 (SLFN2) in the Generation of Interferon α-induced Growth Inhibitory Responses
Bibliographic record
Abstract
The precise STAT-regulated gene targets that inhibit cell growth and generate the antitumor effects of Type I interferons (IFNs) remain unknown. We provide evidence that Type I IFNs regulate expression of Schlafens (SLFNs), a group of genes involved in the control of cell cycle progression and growth inhibitory responses. Using cells with targeted disruption of different STAT proteins and/or the p38 MAP kinase, we demonstrate that the IFN-dependent expression of distinct Schlafen genes is differentially regulated by STAT complexes and the p38 MAP kinase pathway. We also provide evidence for a key functional role of a member of the SLFN family, SLFN2, in the induction of the growth-suppressive effects of IFNs. This is shown in studies demonstrating that knockdown of SLFN2 enhances hematopoietic progenitor colony formation and reverses the growth-suppressive effects of IFNalpha on normal hematopoietic progenitors. Importantly, NIH3T3 or L929 cells with stable knockdown of SLFN2 form more colonies in soft agar, implicating this protein in the regulation of anchorage-independent growth. Altogether, our data implicate SLFN2 as a negative regulator of the metastatic and growth potential of malignant cells and strongly suggest a role for the SLFN family of proteins in the generation of the antiproliferative effects of Type I IFNs.
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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".