Abstract
SummaryAging strongly influences human morbidity and mortality. Thus, aging-preventive compounds could greatly improve our health and lifespan. Here we screened for such compounds, known as geroprotectors, employing the power of transcriptomics to predict biological age. Using age-stratified human tissue transcriptomes and machine learning, we generated age classifiers and applied these to transcriptomic changes induced by 1,309 different compounds in human cells, ranking these compounds by their ability to induce a “youthful” transcriptional state. Testing the top candidates in C. elegans, we identified two Hsp90 inhibitors, monorden and tanespimycin, which extended the animals’ lifespan and improved their health. Hsp90 inhibition induces expression of heat shock proteins known to improve protein homeostasis. Consistently, monorden treatment improved the survival of C. elegans under proteotoxic stress, and its benefits depended on the cytosolic unfolded protein response-inducing transcription factor HSF-1. Taken together, our method represents an innovative geroprotector screening approach and was able to identify a class that acts by improving protein homeostasis.
Highlights
Aging is a major risk factor for many diseases and mortality (Niccoli and Partridge, 2012)
We turned to data available from the GTEx Consortium that contained a diverse set of human tissue transcriptomes originating from donors of various ages and both genders (GTEx Consortium, 2013, 2015; Yang et al, 2015)
Hsp90 Inhibitors Represent a Potential Pharmacological Class of Geroprotective Compounds that Act through the Heat Shock Transcription Factor HSF-1 To better understand how Hsp90 inhibition leads to these improvements in lifespan and healthspan, we investigated the transcriptional responses to monorden treatment, comparing them with those of the established geroprotectors rapamycin and LY-294002
Summary
Aging is a major risk factor for many diseases and mortality (Niccoli and Partridge, 2012). Two major strategies have been tried to increase the probability of identifying compounds that are effective in humans. The other approach has been to devise screening methodologies directly in humans that do not require treatment of individuals but limit themselves to compound screening in human cell culture models and the computational interpretation of the resulting data. This latter approach has been proven to be feasible, at least to identify dietary restriction mimetics (Calvert et al, 2016)
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