Peroxisome Proliferator-Activated Receptor–δ Supports the Metabolic Requirements of Cell Growth in TCRβ-Selected Thymocytes and Peripheral CD4+ T Cells
Bibliographic record
Abstract
Abstract During T cell development, progenitor thymocytes undergo a large proliferative burst immediately following successful TCRβ rearrangement, and defects in genes that regulate this proliferation have a profound effect on thymus cellularity and output. Although the signaling pathways that initiate cell cycling and nutrient uptake after TCRβ selection are understood, less is known about the transcriptional programs that regulate the metabolic machinery to promote biomass accumulation during this process. In this article, we report that mice with whole body deficiency in the nuclear receptor peroxisome proliferator-activated receptor–δ (PPARδmut) exhibit a reduction in spleen and thymus cellularity, with a decrease in thymocyte cell number starting at the double-negative 4 stage of thymocyte development. Although in vivo DNA synthesis was normal in PPARδmut thymocytes, studies in the OP9–delta-like 4 in vitro system of differentiation revealed that PPARδmut double-negative 3 cells underwent fewer cell divisions. Naive CD4+ T cells from PPARδmut mice also exhibited reduced proliferation upon TCR and CD28 stimulation in vitro. Growth defects in PPAR-δ–deficient thymocytes and peripheral CD4+ T cells correlated with decreases in extracellular acidification rate, mitochondrial reserve, and expression of a host of genes involved in glycolysis, oxidative phosphorylation, and lipogenesis. By contrast, mice with T cell–restricted deficiency of Ppard starting at the double-positive stage of thymocyte development, although exhibiting defective CD4+ T cell growth, possessed a normal T cell compartment, pointing to developmental defects as a cause of peripheral T cell lymphopenia in PPARδmut mice. These findings implicate PPAR-δ as a regulator of the metabolic program during thymocyte and T cell growth.
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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 | 0.000 |
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".