Estrogen-related receptor-α is a metabolic regulator of effector T-cell activation and differentiation
Bibliographic record
Abstract
Stimulation of resting CD4(+) T lymphocytes leads to rapid proliferation and differentiation into effector (Teff) or inducible regulatory (Treg) subsets with specific functions to promote or suppress immunity. Importantly, Teff and Treg use distinct metabolic programs to support subset specification, survival, and function. Here, we describe that the orphan nuclear receptor estrogen-related receptor-α (ERRα) regulates metabolic pathways critical for Teff. Resting CD4(+) T cells expressed low levels of ERRα protein that increased on activation. ERRα deficiency reduced activated T-cell numbers in vivo and cytokine production in vitro but did not seem to modulate immunity through inhibition of activating signals or viability. Rather, ERRα broadly affected metabolic gene expression and glucose metabolism essential for Teff. In particular, up-regulation of Glut1 protein, glucose uptake, and mitochondrial processes were suppressed in activated ERRα(-/-) T cells and T cells treated with two chemically independent ERRα inhibitors or by shRNAi. Acute ERRα inhibition also blocked T-cell growth and proliferation. This defect appeared as a result of inadequate glucose metabolism, because provision of lipids, but not increased glucose uptake or pyruvate, rescued ATP levels and cell division. Additionally, we have shown that Treg requires lipid oxidation, whereas Teff uses glucose metabolism, and lipid addition selectively restored Treg--but not Teff--generation after acute ERRα inhibition. Furthermore, in vivo inhibition of ERRα reduced T-cell proliferation and Teff generation in both immunization and experimental autoimmune encephalomyelitis models. Thus, ERRα is a selective transcriptional regulator of Teff metabolism that may provide a metabolic means to modulate immunity.
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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) | 0.002 | 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".