The Proapoptotic Gene SIVA Is a Direct Transcriptional Target for the Tumor Suppressors p53 and E2F1
Bibliographic record
Abstract
The p53 tumor suppressor gene is believed to play an important role in neuronal cell death in acute neurological disease and in neurodegeneration. The p53 signaling cascade is complex, and the mechanism by which p53 induces apoptosis is cell type-dependent. Using DNA microarray analysis, we have found a striking induction of the proapoptotic gene, SIVA. SIVA is a proapoptotic protein containing a death domain and interacts with members of the tumor necrosis factor receptor family as well as anti-apoptotic Bcl-2 family proteins. SIVA is induced following direct p53 gene delivery, treatment with a DNA-damaging agent camptothecin, and stroke injury in vivo. SIVA up-regulation is sufficient to initiate the apoptotic cascade in neurons. Through isolation and analysis of the SIVA promoter, we have identified response elements for both p53 and E2F1. Like p53, E2F1 is another tumor suppressor gene involved in the regulation of apoptosis, including neuronal injury models. We have identified E2F consensus sites in the promoter region, whereas p53 recognition sequences were found in intron1. Sequence analysis has shown that these consensus sites are also conserved between mouse and human SIVA genes. Electrophoretic mobility shift assays reveal that both transcription factors are capable of binding to putative consensus sites, and luciferase reporter assays reveal that E2F1 and p53 can activate transcription from the SIVA promoter. Here, we report that the proapoptotic gene, SIVA, which functions in a broad spectrum of cell types, is a direct transcriptional target for both tumor suppressors, p53 and E2F1.
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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) | 0.001 | 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".