Antibody targeting of aging damaged isoDGR-proteins doubles lifespan in a mouse model of chronic inflammation
Bibliographic record
Abstract
ABSTRACT Aging is the result of the accumulation of molecular damages that impair normal biochemical activities. We previously reported that aging-damaged amino acid sequence NGR (Asn-Gly-Arg) results in a ‘gain-of-function’ conformational switching to isoDGR (isoAsp-Gly-Arg) motif. This integrin-binding motif activates leukocytes to induce chronic inflammation, which are characteristic features of age-linked cardiovascular disorders. We now report that anti-isoDGR immunotherapy doubles lifespan in mouse model of chronic inflammation. We observed extensive accumulation of isoDGR and inflammatory cytokine expression in multiple tissues from Pcmt1-KO and old WT animals, which could also be induced via injection of isoDGR-modified plasma proteins or synthetic peptides into young WT animals. However, weekly injection of anti-isoDGR mAb (1mg/kg) was sufficient to significantly reduce isoDGR-modified proteins and pro-inflammatory cytokine expression, improve behaviour and coordination, and double the average lifespan of Pcmt1-KO mice. Mechanistically, isoDGR-mAb mediated the immune clearance of damaged isoDGR-proteins by antibody-dependent cellular phagocytosis. These results indicate that immunotherapy targeting aging-damaged proteins may represent effective interventions for a range of age-linked degenerative disorders. Graphical Abstract Anti-isoDGR immunotherapy induces immune clearance of aging damaged isoDGR-proteins to reduce chronic inflammation, improve behaviour and coordination, and double lifespan in PCMT -/- mice.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".