Age-linked lung pathology is reduced by immunotherapeutic targeting of isoDGR protein damage
Bibliographic record
Abstract
Abstract Advancing age is the primary risk factor for pulmonary diseases. Our investigation revealed an 8-fold increase in aging induced isoDGR-damaged proteins in lung tissue from human pulmonary fibrosis patients compared to healthy tissues, accompanied by elevated frequencies of CD68+/CD11b+ macrophages, indicating lung tissue is susceptible to time-dependent accumulation of isoDGR-proteins. To elucidate the mechanisms through which isoDGR-proteins may exacerbate aging lung disorders for potential therapeutic targeting, we assessed the functional role of this isoDGR-motif in naturally-aged mice and mice lacking the corresponding isoDGR repair enzyme (Pcmt1-/-). IsoDGR-protein accumulation in mouse lung tissue and blood vessels correlated with chronic low-grade inflammation, pulmonary edema, and hypoxemia. IsoDGR accretion induced mitochondrial and ribosomal dysfunctions, cellular senescence, and apoptosis, contributing to progressive lung damage over time. Treatment with anti-isoDGR antibodies suppressed TLR pathway activity, mitigated cytokine-driven inflammation, restored mtDNA expression, and significantly reduced lung pathology in-vivo. Similarly, exposure of lung endothelial cells to isoDGR-modified fibronectin impaired oxygen consumption, increased reactive oxygen species levels, and disrupted acidification, but these effects were efficiently reversed by target-specific antibody therapy. Collectively, our findings underscore the significant contribution of isoDGR-damaged proteins to age-linked lung pathology. IsoDGR-specific therapy emerges as a promising treatment approach for pulmonary disorders in older patients.
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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) | 0.001 | 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".