SynergyAge, a curated database for synergistic and antagonistic interactions of longevity-associated genes

Gabriela Bunu,Catalin-Florentin Ion,João Pedro De Magalhães,Diogo Barardo,Dmitri Toren,Vadim E Fraifeld,Robi Tacutu,Laurentiu Gabriel Grigore,Larisa Sârghie

doi:10.1038/s41597-020-00710-z

Abstract

Interventional studies on genetic modulators of longevity have significantly changed gerontology. While available lifespan data are continually accumulating, further understanding of the aging process is still limited by the poor understanding of epistasis and of the non-linear interactions between multiple longevity-associated genes. Unfortunately, based on observations so far, there is no simple method to predict the cumulative impact of genes on lifespan. As a step towards applying predictive methods, but also to provide information for a guided design of epistasis lifespan experiments, we developed SynergyAge - a database containing genetic and lifespan data for animal models obtained through multiple longevity-modulating interventions. The studies included in SynergyAge focus on the lifespan of animal strains which are modified by at least two genetic interventions, with single gene mutants included as reference. SynergyAge, which is publicly available at www.synergyage.info, provides an easy to use web-platform for browsing, searching and filtering through the data, as well as a network-based interactive module for visualization and analysis.

Highlights

Background & SummaryThe aging process can be genetically modulated
Many previous studies have shown that average lifespan, and in some cases, even maximum lifespan can be modified by genetic interventions
Genetic mutants have been observed with lifespan, up to ten times longer compared to wild type in C. elegans[1], and up to 150% and 46% in D. melanogaster and M. musculus, respectively[2,3]

Summary

Introduction

Background & SummaryThe aging process can be genetically modulated. Many previous studies have shown that average lifespan, and in some cases, even maximum lifespan can be modified by genetic interventions. A comprehensive list of these longevity-associated genes (LAGs), including more detailed information about lifespan experiments, can be found in the GenAge database[4]. This type and amount of data have made it possible to perform higher-level analyses[5,6,7,8], and the collection of LAGs in public repositories has significantly pushed biogerontology towards more integrative approaches to study longevity. In most cases when combining two or more genetic interventions, the effect is rarely additive, as genes are generally epistatic and interact in nonlinear ways[12,13]. The much more common case is that of studies where partially dependent gene interactions are revealed, making it even more important to understand and predict genetic dependencies

Objectives

Findings

Methods