Convergence of NF-κB-mediated inflammatory and HNF4α-mediated metabolic signaling networks in age-related thymic involution (HEM4P.229)
Bibliographic record
Abstract
Abstract Thymic epithelial cell loss results in age-related thymic involution and immune deficiency. We investigated mechanisms of TEC loss during chronological aging (6 weeks, 7 months, 15 months) of normal C57BL6/J mice. Aged TEC showed replicative senescence with increased apoptosis, decreased proliferation, decreased telomerase activity and shortened telomere length. A subpopulation of aged TEC expressed Fas in response to pro-inflammatory cytokines (TNFα,IL-1β) made by aged thymocytes. Fas signaling and caspase-8 activation were induced by FasL selectively expressed by aged intrathymic memory T cells. Combined pharmacological inhibition of Caspase-8 and TEC growth stimulation by KGF restored thymopoiesis to neonatal levels. Aged TEC differentially expressed a 175-gene set predicted to be dually regulated by two transcription factors, NF-κB and HNF4α, which are activated by inflammatory cytokines and fatty acids, respectively. NF-κB and HNF4α signaling in aged TEC was confirmed by ChIP assays demonstrating binding to promoters of age-related genes, gene reporter assays, and synergistic induction of TEC apoptosis by inflammatory cytokines and fatty acids. TEC aging is mediated by the convergence of different TFs responsive to inflammatory and metabolic signals on shared promoters. Thymic regeneration in aging will require mitigation of the effects of non-autonomous inflammatory and metabolic signals, e.g., from infection and obesity, which drive TEC death and thymic involution.
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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) | 0.001 | 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".