Remodeling of T-cell mitochondrial metabolism to treat autoimmune diseases
Bibliographic record
Abstract
T cells are key drivers of the pathogenesis of autoimmune diseases by producing cytokines, stimulating the generation of autoantibodies, and mediating tissue and cell damage. Distinct mitochondrial metabolic pathways govern the direction of T-cell differentiation and function and rely on specific nutrients and metabolic enzymes. Metabolic substrate uptake and mitochondrial metabolism form the foundational elements for T-cell activation, proliferation, differentiation, and effector function, contributing to the dynamic interplay between immunological signals and mitochondrial metabolism in coordinating adaptive immunity. Perturbations in substrate availability and enzyme activity may impair T-cell immunosuppressive function, fostering autoreactive responses and disrupting immune homeostasis, ultimately contributing to autoimmune disease pathogenesis. A growing body of studies has explored how metabolic processes regulate the function of diverse T-cell subsets in autoimmune diseases such as systemic lupus erythematosus (SLE), multiple sclerosis (MS), autoimmune hepatitis (AIH), inflammatory bowel disease (IBD), and psoriasis. This review describes the coordination of T-cell biology by mitochondrial metabolism, including the electron transport chain (ETC), oxidative phosphorylation, amino acid metabolism, fatty acid metabolism, and one‑carbon metabolism. This study elucidated the intricate crosstalk between mitochondrial metabolic programs, signal transduction pathways, and transcription factors. This review summarizes potential therapeutic targets for T-cell mitochondrial metabolism and signaling in autoimmune diseases, providing insights for future studies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".