SkeletAge: Transcriptomics-based Aging Clock Identifies 26 New Targets in Skeletal Muscle Aging

Area of Science:

• Genomics
• Aging Biology
• Biomarker Discovery

Background:

• Understanding healthy aging independent of illness is crucial for aging research.
• Epigenetic clocks estimate biological age but lack mechanistic insights for drug discovery.
• Tissue-specific gene expression patterns may reveal novel aging drivers.

Purpose of the Study:

• To develop a method for building tissue-specific RNA-seq-based age estimators.
• To identify the 'ageprint' – genes driving healthy, baseline aging.
• To discover druggable targets for extending healthspan and assess therapeutic interventions.

Main Methods:

• Developed a novel method for constructing tissue-specific bulk RNA-seq-based age estimators.
• Applied the method to human skeletal muscle, creating an age estimator named SkeletAge.
• Identified a set of 128 genes constituting the skeletal muscle ageprint.

Main Results:

• The skeletal muscle ageprint comprises 128 genes, with 26 previously unstudied in aging.
• The identified ageprint correlates with skeletal muscle aging and development phenotypes.
• The ageprint genes appear to drive aging along a growth-development-aging axis, distinct from illness-induced aging.

Conclusions:

• The developed RNA-seq-based age estimation method effectively identifies tissue-specific ageprints.
• The skeletal muscle ageprint provides novel insights into healthy aging mechanisms.
• This approach facilitates the discovery of druggable targets and accelerates the development of longevity-enhancing drugs.