Metabolic regulation of CD4+ T cells by Estrogen Related Receptor-α modulates autoimmune and allergic disease (123.48)
Bibliographic record
Abstract
Abstract Upon activation, naïve CD4+ T cells differentiate into effector (Teff) subsets that provide protective immunity or inducible regulatory (Treg) subsets which balance inappropriate inflammation and autoimmunity. Consistent with distinct immunological roles, Teff and Treg utilize specialized metabolic programs for specification and function with Treg increasing lipid oxidation and Teff dependent on glucose metabolism. We have identified the nuclear receptor estrogen related receptor-α (ERRα) as a critical regulator of metabolic gene expression required for Teff. Here, we demonstrate through the use of an experimental autoimmune encephalomyelitis model that ERRα contributes to the severity of autoimmune disease as limited glucose metabolism in the absence of ERRα selectively diminished Th17, but not Treg generation. ERRα contributed to inappropriate inflammation as Teff glucose metabolism, expansion, and function were reduced in ERRα-/- mice and in siRNA treated human CD4+ T cells. Additionally, elevated ERRα expression and glycolytic metabolism were also observed in a murine model of asthma and from lavage fluid of asthmatic patients. Importantly, pharmacological inhibition of ERRα following an aerosol response alleviated airway inflammation, demonstrating a metabolic requirement for ERRα in Teff maintenance. 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 | 0.001 |
| 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".