Intracellular localization of TDAG51 modulates its effect on cell death
Bibliographic record
Abstract
Our previously findings indicate that T‐cell death associated gene 51 (TDAG51) contributes to cell death in atherosclerotic lesions. To determine if these cell death effects are mediated by TDAG51 intracellular localization, we performed sequence analysis of the TDAG51 cDNA to identify novel cellular targeting domains. This analysis revealed two putative motifs; a nuclear exiting signal (NES) (70‐vasleppvkl‐80) and nuclear localization signal (NLS) (16‐krsdgllqlwkkk‐28). We hypothesized that these TDAG51 motifs play a critical role in its intracellular transport, thereby modulating its pro‐apoptotic effects. To investigate this phenomenon, VKL was mutated to AKA in NES to impair exiting of the TDAG51 protein from the nucleus to the cytoplasm and WKKK was mutated to WKAA in NLS to impair entrance of TDAG51 into the nucleus. Following transient transfection of the fused GFP constructs into HeLa cells, the cellular localization of these mutant TDAG51 proteins was assessed using confocal microscopy on fixed cells or visualized in living cells over a 2 to 48 hour time period. Our findings show that both motifs are involved in the intracellular transport of TDAG51. By determining cell viability, we were able to determine the predominant effects of nuclear TDAG51 on cell death. These studies provide a better understanding of the cellular motifs in TDAG51 that contribute to cell death. Supported by the CIHR (MOP‐74477).
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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) | 0.002 | 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".