Scientists Map Universal Gene Expression Signatures of Aging and Mortality Across Mammals

Understanding why organisms age and die at different rates requires identifying the molecular changes that accumulate over time and ultimately drive mortality. Despite decades of research, a unified framework linking aging signatures, lifespan-shortening models, and longevity interventions across species and tissues has been lacking. This landmark study addresses that gap with unprecedented scale and rigor.

The researchers generated new RNA-sequencing data from livers of 170 genetically heterogeneous UM-HET3 mice subjected to 20 pharmacological interventions from the Interventions Testing Program (ITP), including rapamycin, canagliflozin, acarbose, 17-α-estradiol, and captopril, alongside young controls. These data were integrated with published transcriptomes and survival records, yielding 4,539 rodent samples across 26 tissues, and further combined with 6,626 primate samples (macaque and human) to build multi-species clocks. Both chronological age clocks and expected mortality clocks were constructed, with mortality clocks shown to better capture cumulative biological damage.

A key discovery was the identification of universal transcriptomic hallmarks of mammalian aging conserved across species, tissues, and cell types. Notable genes included CDKN1A (p21) and LGALS3 (galectin-3), and importantly, their protein-level counterparts were also associated with mortality and multimorbidity in UK Biobank participants, directly bridging animal models to human health outcomes. Mortality-associated gene expression patterns were recapitulated in multiple in vivo and in vitro damage models—including replicative senescence, metabolic inhibition, inflammation, and gamma-irradiation—and were attenuated or reversed by cell immortalization, partial reprogramming, heterochronic parabiosis, and early embryogenesis.

Using network analysis, the team identified a modular architecture of aging and mortality hallmarks organized into five key modules: inflammation, interferon signaling, mitochondrial function, chromatin modification, and extracellular matrix organization. Module-specific transcriptomic clocks revealed pathway-level effects of interventions with striking specificity: chronic diseases primarily accelerated inflammatory-module aging, while caloric restriction and Klotho deficiency predominantly affected mitochondrial and metabolic modules. The chromatin-associated module clock showed the strongest correlation with DNA methylation clock age acceleration in human blood, highlighting mechanistic links between epigenetic and transcriptomic aging modalities.

The study provides a freely accessible Transcriptomic Age Calculator Online (TACO) web application and R package tAge, enabling researchers to apply these biomarkers to new datasets. Together, these tools and findings establish a comprehensive framework for quantifying and targeting aging of specific cellular subsystems across species, tissues, and interventions—a major step toward mechanistically informed anti-aging strategies.