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Biological age & clocks
Research brief

Cross-species transcriptomic signatures of mammalian ageing and mortality

An integrated analysis of over 11,000 transcriptomes from more than 25 tissues in mouse, rat, macaque and human reports conserved ageing- and mortality-associated signatures and a modular architecture, with module-specific clocks. [1]

Automatic summary · not editorially reviewed

The key findings

  1. The analysis integrated more than 11,000 transcriptomes from more than 25 tissues across mouse, rat, macaque and human. [1]

  2. Ageing-related changes were conserved across species and cell types, including CDKN1A and LGALS3, whose protein levels were also associated with mortality and multimorbidity in UK Biobank. [1]

  3. Module-specific clocks showed pathway-specific effects: chronic diseases mainly accelerated inflammatory-module ageing, whereas caloric restriction and Klotho deficiency targeted mitochondrial and metabolic modules. [1]

The source at a glance

Study type / format
Abstract of an integrative multi-species transcriptomic analysis with biomarker development
Population / context
Transcriptomes from more than 25 tissues of mouse, rat, macaque and human, plus UK Biobank protein data.
Summary based on
Study abstract
Original publication
May 27, 2026

Integrated transcriptomes across four mammals

The authors combined more than 11,000 transcriptomes from more than 25 tissues across four mammals: mouse, rat, macaque and human. From these data they developed rodent and multi-species biomarkers intended to be accurate and interpretable, for chronological age and for expected mortality. The biomarkers were reported to predict lifespan-modulating interventions, time to death, chronic diseases and rejuvenation.

Supporting source [1]

Conserved signatures and modular hallmarks

Ageing-related changes were conserved across species and cell types, revealing what the authors call universal transcriptomic signatures of mammalian ageing and mortality, including CDKN1A and LGALS3. Protein levels of these two were also associated with mortality and multimorbidity in UK Biobank. Network analysis uncovered a modular architecture of ageing- and mortality-associated hallmarks covering inflammation, interferon signalling, mitochondrial function, chromatin modification and extracellular matrix organization.

Supporting source [1]

Module clocks and intervention-specific effects

Mortality-associated features were reproduced in in vivo and in vitro damage-accumulation models, including inflammation, replicative senescence, metabolic inhibition and γ-irradiation, and were attenuated or reversed by cell immortalization, reprogramming, heterochronic parabiosis and early embryogenesis. Module-specific clocks showed pathway-specific effects: chronic diseases mainly accelerated ageing of the inflammatory module, whereas caloric restriction and Klotho deficiency targeted mitochondrial and metabolic modules. Transcriptomic and DNA methylation clocks showed correlated age acceleration in human blood, strongest for the chromatin-associated module clock.

Supporting source [1]

How to interpret this

This explainer is based only on the abstract; the full text was not reviewed. The abstract reports an integrative analysis of existing transcriptomes and biomarker development, not a prospective trial. Associations with mortality and multimorbidity, including the UK Biobank protein findings, are associations, not evidence of causation. Findings in damage-accumulation models, cell immortalization, reprogramming, parabiosis and embryogenesis are experimental observations and do not establish human lifespan benefits. The proposed framework is not described as validated for clinical use.

Based on the study abstract; the full paper, tables and supplementary data were not reviewed.

Source & supporting passages

[1]
Universal transcriptomic hallmarks of mammalian ageing and mortality.

Tyshkovskiy A, Kholdina D, Davitadze M, Molière A, Moldakozhayev A, Tongu Y, Kasahara T, Glubokov D, Eames A, Kats LM, Vladimirova A, Ying K, Liu H, Zhang B, Khasanova U, Moqri M, Van Raamsdonk JM, Harrison DE, Strong R, Abe T, Dmitriev SE, Gladyshev VN. · europepmc.org · May 27, 2026

Read the quoted passagesIn the language of the original source
  1. Finding 1 ↑

    more than 11,000 transcriptomes from more than
  2. Finding 2 ↑

    conserved across species and cell types
  3. Finding 3 ↑

    chronic diseases primarily accelerated inflammatory-module ageing

AI-assisted summary with linked source passages. No independent editorial or clinical review.

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