Yilin Du_No.4

Q: How did the anthor address the question?

■Step1 1) Construction of a universal transcriptomic ageing model The authors integrated more than 11,000 transcriptomic datasets from multiple mammalian species, including mice, rats, macaques and humans, across numerous tissues and ageing interventions. Using elastic-net regression, they developed transcriptomic ageing clocks that predict chronological age, normalized biological age and mortality risk. These models showed high predictive accuracy across species, tissues and experimental platforms, demonstrating the existence of a conserved transcriptomic ageing program. ■Step2 2) Identification of conserved molecular hallmarks of ageing By analysing transcriptomic changes across species, the authors identified evolutionarily conserved ageing-associated gene modules. Ageing was consistently characterized by activation of inflammatory and stress-response pathways together with suppression of mitochondrial metabolism, oxidative phosphorylation and protein synthesis. Module-specific clocks further demonstrated that different biological processes age at distinct rates, providing a modular view of ageing. ■Step3 3) Validation using longevity interventions and mortality prediction The transcriptomic clocks were validated using multiple lifespan-extending and lifespan-shortening interventions. Anti-ageing treatments, including caloric restriction and rapamycin, reduced transcriptomic age, whereas disease models accelerated it. Importantly, mortality clocks outperformed chronological age in predicting lifespan-related outcomes, suggesting that transcriptomic ageing better reflects biological ageing than chronological ageing alone.

kawaokashinpei3

9月9日

読了時間: 2分

Selected journal : Nature

Universal transcriptomic hallmarks of mammalian ageing and mortality

https://www.nature.com/articles/s41586-026-10542-3

What is the main question of the paper?

Can universal transcriptomic signatures across mammalian species be used to quantify biological ageing, predict mortality risk, and reveal conserved mechanisms underlying ageing?

How did the anthor address the question?

■Step1

1) Construction of a universal transcriptomic ageing model

The authors integrated more than 11,000 transcriptomic datasets from multiple mammalian species, including mice, rats, macaques and humans, across numerous tissues and ageing interventions. Using elastic-net regression, they developed transcriptomic ageing clocks that predict chronological age, normalized biological age and mortality risk. These models showed high predictive accuracy across species, tissues and experimental platforms, demonstrating the existence of a conserved transcriptomic ageing program.

■Step2

2) Identification of conserved molecular hallmarks of ageing

By analysing transcriptomic changes across species, the authors identified evolutionarily conserved ageing-associated gene modules. Ageing was consistently characterized by activation of inflammatory and stress-response pathways together with suppression of mitochondrial metabolism, oxidative phosphorylation and protein synthesis. Module-specific clocks further demonstrated that different biological processes age at distinct rates, providing a modular view of ageing.

■Step3

3) Validation using longevity interventions and mortality prediction

The transcriptomic clocks were validated using multiple lifespan-extending and lifespan-shortening interventions. Anti-ageing treatments, including caloric restriction and rapamycin, reduced transcriptomic age, whereas disease models accelerated it. Importantly, mortality clocks outperformed chronological age in predicting lifespan-related outcomes, suggesting that transcriptomic ageing better reflects biological ageing than chronological ageing alone.

What is the strength of the paper?

A major strength of this study is its unprecedented integration of large-scale multi-species transcriptomic datasets, allowing the identification of a conserved molecular program of ageing. Rather than focusing on individual genes, the study establishes biologically interpretable ageing clocks and modular ageing signatures that link transcriptomic alterations to mortality risk and responses to longevity interventions. This framework provides both mechanistic insights and practical tools for evaluating anti-ageing therapies.

Comment

I think the most unique aspect of this paper is that the authors reframed the problem from predicting chronological age to predicting mortality risk. This allowed them to identify transcriptomic signatures that reflect health status and damage accumulation rather than the mere passage of time. By further decomposing these signatures into pathway-specific modules, they could reveal which biological systems were specifically undergoing age-associated changes.

Comment by Tomoaki Shirakawa