Gene expression‐based drug repurposing to target aging

Abstract

Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target‐centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug‐perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.

Using the drug-protein interactome to identify anti-ageing compounds for humans

“…Three of the top 10 compounds from the combined ranked list have been previously proposed as anti-ageing candidates for humans using bioinformatic analysis. Specifically, tanespimycin, geldanamycin and trichostatin were among the 24 drugs predicted by Dönertas et al (2018)[18] and Calvert et al (2016)[10]. In contrast, we did not observe any overlap with the top results from Fernandes et al (2016)[15] possibly due to the use of a different drug-protein interaction database (DGIdb [46]) or source of ageing data.…”

“…Using annotated databases, our method evaluated the enrichment for pro-longevity of all compounds analysed, rather than only those with significant scores, and we observe that in all cases pro-longevity compounds are ranked higher than expected by chance. Although tanespimycin acts as a senolytic [31], and has been predicted to be geroprotective by two previous studies [10,18], we have demonstrated its effect on longevity experimentally.…”

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

“…Examining the top-ranked GO terms that these two methods agreed on (Tables Table 4 and Table 5) gives an overview of the ageing transcriptome, but also reveals some interesting differences and similarities between the studied tissues. The picture given by the global analysis, comprising all 127 datasets, is typical of previous large-scale expression studies and meta-analyses (de Magalhães, Curado and Church, 2009;Yang et al, 2015;Donertas et al, 2018), showing an overexpression of immune genes, stress responses and proteolysis (Table 4A), as well as an underexpression of metabolic and energy metabolism. The preponderance of inflammatory and stress response genes in particular is characteristic of the inflammageing hypothesis (Franceschi et al, 2000), which argues that ageing is caused by steadily failing responses to stress, in particular responses to the increased antigenic load that comes with age.…”

Transcriptome-Guided Drug Repositioning

“…Drug repositioning opens new possibilities for the fast delivery of new treatment options by reducing the time and resources spent on drug development and testing [1,2]. Systematic analysis of concerted transcriptome changes in response to disease and drug action provides a useful concept in this field [63][64][65]. However, it is essential to consider gene expression changes on the whole transcriptome level and not only identify drug-target processes or genes to improve the predictive power of the method but also to understand how off-target genes may interfere with disease development, drug action or be related to the risk of adverse effects.…”