Transcriptomic Approaches to Guiding the Discovery of Geroprotective Compounds
A great deal of drug discovery starts by screening compounds from the vast libraries of molecules that exist. The libraries are too large to screen everything (and even then the scope of the libraries is only a sampling of the space of the possible). Thus even unbiased screening starts with some hypothesis to cut down the number of molecules being screened to a tractable number for the time and budget at hand. Here, researchers discuss the use of existing transcriptomic data to guide drug screening approaches intended to uncover geroprotective compounds that adjust metabolism to modestly slow aging. Based on results to date, this side of aging research seems unlikely to produce exciting outcomes, as near all of what has been discovered in this way triggers the same array of known beneficial stress responses, and thus doesn't improve on the present state of the art.
To date, invertebrate-based screens have identified hundreds of life span-extending compounds. However, relatively few have been validated across multiple species, and most of the geroprotectors belong to extensively studied classes like mechanistic target of rapamycin (mTOR) inhibitors, AMPK activators, or histone deacetylase (HDAC) inhibitors. Because these pathways regulate broad cellular processes, direct pharmacological modulation of their core components may produce pleiotropic effects. This reliance on a limited set of well-studied targets and drug classes underscores the need for unbiased tools that can prioritize diverse candidate geroprotectors.
Currently, several strategies have been developed to identify small-molecule geroprotectors, ranging from unbiased screening to transcriptomic-based drug prediction. The latter, which infers the geroprotective effects of drugs based on drug-induced transcriptomic changes, is an emerging method and has gained traction thanks to availability of large-scale transcriptomic resources. Several methodologies have been developed within this framework. One widely used strategy is the "reversal approach," which identifies drugs that counteract aging-associated gene expression changes. This approach has led to discoveries such as oridonin, an AKT inhibitor extending life span in yeast and mice. However, the reversal approach assumes that all age-related changes are detrimental despite evidence that some may be adaptive.
Another strategy, the "mimic approach," matches drug-induced transcriptomic profiles to those of known antiaging interventions. This method has been applied to identify compounds like allantoin and rilmenidine, which extend life span in Caenorhabditis elegans. However, this method tends to rediscover drugs similar to known interventions, limiting its capacity to reveal novel mechanisms.
Here, we used C. elegans and mice to explore transcriptomic strategies for geroprotector discovery. Applying transcriptomic clocks and coexpression network analysis, we found that many aging-associated changes in C. elegans are adaptive - some transcriptomic changes that resemble aging may contribute to longevity. We identified conserved coexpression modules associated with life span: "aging modules" enriched in translation and mitochondrial genes and "longevity modules" enriched in stress response genes, including daf-16 targets. Several of these longevity-associated modules are preserved in specific mouse tissues and are associated with mouse health span. Last, we conducted a small-scale screen and identify seven novel geroprotectors using the module gene expressions as our transcriptomic signatures. Our findings demonstrate that expression profiles of conserved aging-related processes can serve as predictive signatures for finding effective geroprotective compounds.