The SynergyAge database is intended to collect information on interactions between longevity-related mutations. Many such genetic alterations have been studied in laboratory species – yeast, flies, worms, mice, and so forth – but interactions between mutations are only sparsely investigated in comparison. This is true for all interventions in aging, as a rule. The scientific and development communities operate under incentives that tend to steer them away from combining effects in search of a larger outcome when it comes to slowed or reversed aging. This is already a problem now, in the early stages of the era of treating aging as a medical condition, and will become more pressing in the years ahead as effective means of addressing the molecular damage of aging continue to emerge.
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Genetic mutants have been observed with lifespan, up to ten times longer compared to wild type in C. elegans, and up to 150% and 46% in D. melanogaster and M. musculus, respectively. In model organisms, at least 2,205 genes have been identified to produce a long-lived or short-lived phenotype when mutated, knocked down, or overexpressed. A comprehensive list of these longevity-associated genes (LAGs), including more detailed information about lifespan experiments, can be found in the GenAge database.
This type and amount of data have made it possible to perform higher-level analyses, and the collection of LAGs in public repositories has significantly pushed biogerontology towards more integrative approaches to study longevity. One important aspect observed is that many LAGs seem to act in a cooperative manner and are not independent regulators of lifespan. In fact, 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. While in most cases combined interventions seem to have lower than expected results in how much they extend lifespan, there are also a minority of cases where genes act synergistically. Even so, 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.
Unfortunately, data on epistasis is much harder to obtain through wide-screen experimental studies, which has been for example the case for the discovery of most LAG interventions in worms. The main impediment comes from the combinatorial explosion of multiple gene groups for which lifespan assays would need to be measured in a “blind” search, through wet-lab experiments. A more efficient approach is to use existing epistasis data to explore predicted synergies in guided lifespan experiments. Fortunately, an accumulating number of papers has been published in the last two decades with reported lifespans for double, triple, and even quadruple mutants. As such, it has been now possible for us: (i) to collect the data from existing studies containing lifespan records for strains that have multiple genes modulated, and (ii) to create an intuitive, network-based tool, which allows users to explore in a fast, visual and interactive way the lifespan relationships between these strains.
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, 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.
Link: https://doi.org/10.1038/s41597-020-00710-z
Source: Fight Aging!