Inactivation of <i>S6</i> ribosomal protein gene in T lymphocytes activates a p53-dependent checkpoint response
Bibliographic record
Abstract
Ribosome biogenesis has been associated with regulation of cell growth and cell division, but the molecular mechanisms that integrate the effect of ribosome biogenesis on these processes in mammalian cells remain unknown. To study the effect of impaired ribosome functions in vivo, we conditionally deleted one or two alleles of the 40S ribosomal protein S6 gene in T cells in the mouse. While complete deletion of S6 abrogated T-cell development, hemizygous expression did not have any effect on T-cell maturation in the thymus, but inhibited the accumulation of T cells in the spleen and lymph nodes, as a result of their decreased survival in the peripheral lymphoid organs. Additionally, TCR-mediated stimulation of S6-heterozygous T cells induced a normal increase in their size, but cell cycle progression was impaired. Genetic inactivation of p53 tumor suppressor rescued development of S6-homozygous null thymocytes and proliferative defect of S6-heterozygous T cells. These results demonstrate the existence of a p53-dependent checkpoint mechanism that senses changes in the fidelity of the translational machinery to prevent aberrant cell division or eliminate defective T cells in vivo. Failure to activate this checkpoint response could potentially lead to a development of pathological processes such as tumors and autoimmune diseases.
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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".