Leveraging mRNA-based transcriptional signatures and machine learning for healthy aging drug discovery

doi:

Abstract

Aging is a complex phenomenon involving a gradual decline in physical, mental, and reproductive functions, and a higher risk of disease. While chronological age is a simple index of functional health, biological age can better account for individual differences influenced by genetics, lifestyle, and environment. This review focuses on messenger RNA (mRNA) transcriptome-based approaches to estimating biological age. We describe advances, challenges, and opportunities in transcriptome-based biological aging clocks trained on human and worm datasets using machine learning approaches. Stemming from this, we furthermore describe how mRNA signatures have been used for the development of healthy aging drugs. This includes compounds that transcriptionally reverse the mRNA signatures of aging, or mimic mRNA signatures of longevity. Compounds identified through these efforts include HSP90 inhibitors monorden and tanespemycin, calorie restriction mimetic allantoin, HDAC inhibitor cmpd60, as well as atracurium, zidovudine, and low dose naltrexone, amongst others. In conclusion, mRNA signatures effectively serve biological age clocks as well as longevity medicine development

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