Aging 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.
Keywords: Caenorhabditis elegans; Hsp90; aging; drug discovery; geroprotectors; healthspan; lifespan; machine learning; monorden; tanespimycin.
Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.