Protein Kinase B Regulates T Lymphocyte Survival, Nuclear Factor κb Activation, and Bcl-XL Levels in Vivo
Bibliographic record
Abstract
The serine/threonine kinase protein kinase B (PKB)/Akt mediates cell survival in a variety of systems. We have generated transgenic mice expressing a constitutively active form of PKB (gag-PKB) to examine the effects of PKB activity on T lymphocyte survival. Thymocytes and mature T cells overexpressing gag-PKB displayed increased active PKB, enhanced viability in culture, and resistance to a variety of apoptotic stimuli. PKB activity prolonged the survival of CD4(+)CD8(+) double positive (DP) thymocytes in fetal thymic organ culture, but was unable to prevent antigen-induced clonal deletion of thymocytes expressing the major histocompatibility complex class I-restricted P14 T cell receptor (TCR). In mature T lymphocytes, PKB can be activated in response to TCR stimulation, and peptide-antigen-specific proliferation is enhanced in T cells expressing the gag-PKB transgene. Both thymocytes and T cells overexpressing gag-PKB displayed elevated levels of the antiapoptotic molecule Bcl-X(L). In addition, the activation of peripheral T cells led to enhanced nuclear factor (NF)-kappaB activation via accelerated degradation of the NF-kappaB inhibitory protein IkappaBalpha. Our data highlight a physiological role for PKB in promoting survival of DP thymocytes and mature T cells, and provide evidence for the direct association of three major survival molecules (PKB, Bcl-X(L), and NF-kappaB) in vivo in T lymphocytes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".