Scientists uncover shared gene signatures that reveal how mammals age

By Vijay Kumar MalesuReviewed by Susha Cheriyedath, M.Sc.May 29 2026

A sweeping study across four mammalian species shows that aging leaves a shared molecular fingerprint, opening new ways to measure biological decline, compare interventions and uncover pathways that may shape healthier lifespans.

Study: Universal transcriptomic hallmarks of mammalian ageing and mortality. Image Credit: Igor Kyrlytsya / Shutterstock

In a recent study published in the journal Nature, an international team of researchers identified universal transcriptomic signatures of aging and mortality across mammalian species and developed molecular clocks capable of predicting lifespan, associations with chronic diseases and human outcomes, and transcriptomic markers of biological aging.

Molecular Aging Across Species

With populations aging, age-associated disorders such as dementia, cardiovascular pathology, and metabolic disorders have been identified as significant health burdens. Researchers have discovered that genes change their activity as organisms age, but many existing biological aging markers focus only on specific tissues or species.

Previous molecular clocks established using deoxyribonucleic acid (DNA) methylation were also poorly interpretable from a biological standpoint. By exploring how gene activity associated with aging differs by organ and across species, scientists might develop therapies that delay disease, enhance lifespan, and advance healthy aging. 

Transcriptomic Clock 

The researchers analyzed more than 11,000 transcriptomes from humans, mice, rats, and crab-eating macaques to identify common molecular patterns associated with aging and mortality.

The study combined publicly available datasets with newly generated ribonucleic acid sequencing (RNA-seq) data from genetically diverse UM-HET3 mice exposed to 20 pharmacological Interventions Testing Program treatments, including rapamycin, canagliflozin, captopril, 17α-oestradiol, and rapamycin plus acarbose.

Other lifespan-altering models, including caloric restriction and high-fat diets, were incorporated through the broader aggregated datasets.

Researchers used Gompertz survival models to estimate expected mortality and lifespan from survival data, accounting for cohort, sex, site, strain, and intervention, rather than using tissue type itself as the sole survival predictor. They also used machine learning methods (including elastic net and Bayesian ridge regression) to generate transcriptomic clocks that estimate chronological age, normalized age, transcriptomic age, and mortality risk.

Finally, they conducted leave-one-tissue-out and leave-one-dataset-out validation tests to ensure that their models were accurate across different organ types and datasets.

Single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) datasets were analyzed to determine whether aging signals were consistently present in specific cell types. To better understand how other biological factors affect aging-related molecular changes, researchers examined endogenous and exogenous stimuli, including inflammatory stress, caloric restriction, and the Klotho knockout mouse model.

Aging Markers and Mortality Clocks

By identifying highly conserved transcriptomic markers of aging across mammals, the study found similar patterns of gene expression associated with aging in mice, rats, macaques, and humans.

Genes involved in inflammatory, immune-activation, and cellular stress pathways showed increased expression with age, while genes involved in mitochondrial energy production, wound-healing, and extracellular matrix pathways tended to decrease.

Researchers created transcriptomic aging and mortality clocks that accurately estimated chronological age, transcriptomic age, and expected mortality risk across tissues and species. The transcriptomic aging clocks accurately predicted chronological age, were validated by multiple methods, and could evaluate both positive and negative effects on aging processes.

Lifespan-extending treatments such as caloric restriction and rapamycin reduced transcriptomic age, whereas progeroid conditions, high-fat diets, and inflammatory stress accelerated molecular aging.

 The mortality clocks outperformed traditional chronological age measurements because they captured molecular deterioration linked to mortality risk rather than simply the passage of time.

The researchers also demonstrated that aging impacts cellular function in many tissues. Immune cells, endothelial cells, hepatocytes (liver cells), stem cells, and muscle-related cells showed age-associated changes in molecular pathways using single-cell analysis.

Inflammation and Mitochondrial Aging Pathways

In terms of the drivers of age-associated molecular changes, inflammation was found to be an important factor. The researchers discovered that key signaling pathways, such as interferon, tumor necrosis factor, interleukin, and p53 signaling, became increasingly active with age and were associated with increased mortality risk.

In addition, several cellular processes associated with oxidative phosphorylation, mitochondrial protein production, lipid metabolism, and cellular respiration showed reduced activity with age.

Investigators also identified modular networks comprising genes that contribute to the aging process. Some of these modules were dominated by immune response and inflammatory genes; the rest primarily reflected genes that regulate chromatin, mitochondrial activity, extracellular matrix organization, or metabolism.

The Klotho-knockout mice experiment also showed a connection between metabolism and aging. Mice showed accelerated molecular aging, especially in the kidney and muscle tissues.

The expression of genes involved in mitochondrial respiration and energy metabolism was suppressed, while senescence-associated genes, such as cyclin-dependent kinase inhibitor 1A, were significantly upregulated. 

Interestingly, inflammation-based pathways were not the primary drivers in this model, suggesting that different biological systems can dominate molecular aging in different contexts.

Reversible Aging Signals and Human Outcomes

The study also demonstrated that molecular aging signatures could be partially reversed. Rejuvenation-related interventions, including cellular reprogramming, heterochronic parabiosis, and early embryonic development, reduced aging-associated transcriptomic patterns.

The authors further linked several conserved biomarkers to human outcomes. Protein levels of genes such as CDKN1A, LGALS3, and Glycoprotein Nonmetastatic Melanoma Protein B">GPNMB were associated with mortality and multimorbidity in the UK Biobank, supporting the relevance of these transcriptomic signatures beyond animal models.

Transcriptomic Clocks for Healthy Aging

The findings show that aging and mortality share universal transcriptomic signatures across mammalian species, tissues, and cell types. Biological aging is closely linked to inflammation, mitochondrial dysfunction, impaired metabolism, and reduced wound-healing and extracellular matrix activity.

The newly developed transcriptomic clocks accurately measured transcriptomic age and expected mortality-related molecular change and captured the effects of interventions that either accelerated or slowed aging. 

In the future, these tools could support research into early molecular markers of age-related decline, before symptoms of disease appear. The research also shows that biological aging is determined by multiple interconnected biological pathways.

By understanding these pathways, researchers may be able to develop therapies to extend a healthy life span and reduce the global burden of chronic diseases associated with aging. Further research is needed to determine how these molecular pathways can be safely targeted in humans.

Download your PDF copy by clicking here.