Mitochondrial metabolism controls threshold of T cell activation or ‘How to fix an old engine’? 3441
Bibliographic record
Abstract
Abstract Description T cell activation occurs by triggering of the T cell receptor (TCR) and induces spectrum of signaling, transcriptomic and metabolic changes. TCR triggering leads also to mitochondrial generation of low, non-toxic levels of reactive oxygen species (ROS) which serve as necessary “oxidative signal” regulating transcription, and therefore, T cell differentiation to specialized sub-types. In the elderly, T cells often cannot mount an immune response due to aberrant activation and exhausted phenotype. The mitochondrial theory of aging states that age-acquired mutations of mitochondrial DNA (mtDNA) lead to a vicious cycle of macromolecular damage due to elevated mitochondrial ROS. To investigate novel aspects of mitochondrial function in T cells we applied murine model of accelerated aging – mtDNA polymerase γ (PolG) ‘Mutator’ mouse. These mice gradually accumulate mtDNA mutations, which lead to pre-mature aging and reduced life span. We aimed to analyze the underlying causes of T cells disfunction in pre-maturely aged PolG Mutator mice. To this end, we combined bone-marrow (BM) transfer in vivo approaches (chimeras, LCMV infection) with metabolic flux analysis (MS-mediated monitoring of 13C-labelled isotopomers). We demonstrate unexpected consequences of mitochondrial impairment for T cell function. Importantly, we show how remodeling of mitochondrial metabolism controls threshold of T cell activation and how to genetically alleviate T cell dysfunction caused by accelerated aging. Topic Categories Lymphocyte Differentiation and Peripheral Maintenance (LYM)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".