An interferon gamma response signature links myocardial aging and immunosenescence
Bibliographic record
Abstract
AIMS: Aging entails profound immunological transformations that can impact myocardial homeostasis and predispose to heart failure. However, preclinical research in the immune-cardiology field is mostly conducted in young healthy animals, which potentially weakens its translational relevance. Herein, we sought to investigate how the aging T-cell compartment associates with changes in myocardial cell biology in aged mice. METHODS AND RESULTS: We phenotyped the antigen-experienced effector/memory T cells purified from heart-draining lymph nodes of 2-, 6-, 12-, and 18-month-old C57BL/6J mice using single-cell RNA/T cell receptor sequencing. Simultaneously, we profiled all non-cardiomyocyte cell subsets purified from 2- to 18-month-old hearts and integrated our data with publicly available cardiomyocyte single-cell sequencing datasets. Some of these findings were confirmed at the protein level by flow cytometry. With aging, the heart-draining lymph node and myocardial T cells underwent clonal expansion and exhibited an up-regulated pro-inflammatory transcription signature, marked by an increased interferon-γ (IFN-γ) production. In parallel, all major myocardial cell populations showed increased IFN-γ responsive signature with aging. In the aged cardiomyocytes, a stronger IFN-γ response signature was paralleled by the dampening of expression levels of transcripts related to most metabolic pathways, especially oxidative phosphorylation. Likewise, induced pluripotent stem cells-derived cardiomyocytes exposed to chronic, low grade IFN-γ treatment showed a similar inhibition of metabolic activity. CONCLUSIONS: By investigating the paired age-related alterations in the T cells found in the heart and its draining lymph nodes, we provide evidence for increased myocardial IFN-γ signaling with age, which is associated with inflammatory and metabolic shifts typically seen in heart failure.
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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".