<scp>LKB</scp>1 and <scp>AMPK</scp>: central regulators of lymphocyte metabolism and function
Bibliographic record
Abstract
When T cells encounter foreign antigen and appropriate costimulatory signals from professional antigen-presenting cells (APCs), they initiate a coordinated program of rapid proliferation and differentiation, leading to the development of activated T cells with specific effector functions tailored toward pathogen clearance or control. One of the fundamental programs that underpin T-cell proliferation and function is the regulation of cellular metabolism. Recent efforts to identify the signal transduction pathways that regulate T-cell metabolism have led to the identification of liver kinase B1 (LKB1) and AMP-activated protein kinase (AMPK) as key regulators of T-cell metabolism. LKB1 and AMPK are part of an evolutionarily conserved signal transduction pathway that monitors cellular energy status. AMPK senses bioenergetic fluctuations in cells and works in concert with LKB1 to maintain cellular energy homeostasis by promoting catabolic pathways of ATP production and limiting processes that consume ATP. Recent data indicate that LKB1 and AMPK can influence diverse aspects of T-cell biology beyond metabolism, including T-cell development, peripheral T-cell homeostasis, and T-cell effector function. In this review, we focus on the regulation of lymphocyte metabolism by this energy-sensing pathway and discuss its influence on T-cell function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".