Proposed RNA-damage framework and tDamAge clock in mice and human blood
The abstract presents a proposed computational framework that scores four kinds of aberrant RNA transcripts from standard RNA-sequencing data, and a machine-learning tDamAge clock trained on mouse tissues or human peripheral blood that predicted age and shifted under pro-aging and anti-aging conditions. [1]
Automatic summary · not editorially reviewed
The key findings
The proposed framework quantifies damage from standard RNA-sequencing data across four classes of aberrant transcript structures. [1]
The tDamAge clock used machine-learning models trained across mouse tissues or human peripheral blood and could predict age. [1]
The authors report elevated damage age acceleration in Alzheimer's disease and rejuvenation-like reductions during embryonic development. [1]
The source at a glance
- Study type / format
- Abstract of a computational methods and biomarker study
- Population / context
- Mouse tissues and human peripheral blood samples analyzed with RNA-sequencing data, including progeroid models, intervention conditions, Alzheimer's disease and embryonic development.
- Summary based on
- Study abstract
- Publication status
- Preprint · peer-review status not confirmed
- Original publication
- June 29, 2026
Proposed RNA-damage score from sequencing data
The authors describe a computational framework that quantifies damage from standard RNA-sequencing data. It captures four classes of aberrant transcript structures: premature termination upon intron retention, domain-disrupting splice variants, repeat elements and gene fusion events. Each class is said to reflect a distinct form of RNA integrity loss. This is a proposed method, not a validated product.
Supporting source [1]
tDamAge clock trained on mouse tissues or human blood
To combine the measurements into one biomarker, the authors built a transcriptomic damage-based aging clock, tDamAge, using machine-learning models trained across mouse tissues or human peripheral blood. It could predict age and detect transcriptomic shifts under both pro-aging and anti-aging conditions. Progeroid models showed accelerated tDamAge, while caloric restriction, rapamycin and methionine restriction lowered tDamAge.
Supporting source [1]
Cross-dataset patterns, Alzheimer findings and embryonic reversal
Cross-dataset analysis indicated that diverse anti-aging interventions converge on shared transcriptomic signatures, particularly RNA processing and chromatin organization pathways, and that these age-associated patterns could be reversed by interventions. The authors also report elevated damage age acceleration in Alzheimer's disease and rejuvenation-like reductions during embryonic development. They conclude that tDamAge could detect age progression, acceleration, deceleration and reversal.
Supporting source [1]
How to interpret this
This is an abstract; the full text was not reviewed. The framework and tDamAge clock are proposed methods, and the reported associations, including intervention effects and disease patterns, are not established causes or human lifespan benefits.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Zhang S, Iqbal S, Tyshkovskiy A, Gladyshev VN. · europepmc.org · June 29, 2026