Long- and short-lived mutants: synergistic/antagonistic genetic interventions
The main goal of the SynergyAge database is to host high-quality, manually curated information about the synergistic and antagonistic lifespan effects of genetic interventions in model organisms. Although our group aims to better understand human aging, data on the effect of multiple genetic manipulations in humans is inexistent (for obvious reasons). As such, SynergyAge relies on reporting combinations of genetic manipulations from model organisms only. Currently three organisms are included, worms, flies and mice, with data curated so far coming mostly from worms. This bias is mainly due to a easier methodology of modulating gene expression in worms (e.g. through RNAi) but also due to lifespan screening in worms being much faster (worms live much less and are a friendly model for this type of studies). All entries in SynergyAge are based on experimentally validated results from peer-reviewed scientific literature and are manually extracted by our database curators. We also aim to make the searching pattern for new combinations as unbias as possible, however this will always have a subjectivity component from human selection (if you feel we missed a paper that should be here, please do not hesitate to contact us).
For more details, about the way we select papers and mutants for inclusion, please see our Methods section. Briefly, the genes included in this database are generally model organism longevity-associated genes, meaning that it was shown that they significantly affect lifespan through genetic manipulations in healthy animals (for more details, see the definition of longevity-associated genes in the GenAge database or in the HAGR paper). For each combination of genes, a description about the lifespan effect and experimental conditions is given, reference(s) to the scientific study describing the effect, as well as informations about genes and lifespans of single mutants. This information is presented both in a textual form and a graphical form, allowing users to look at a bigger picture on the additivity effect (or lack of) for multiple genetic interventions.
Currently, the database contains a few hundreds mutants, however our curation process is just beginning and we expect to collect more and more data. This data will be made public in periodic data versions (for user to easily keep track of the dataset that they use).
If you would like to cite this database please use:
Bunu, G., Toren, D., Ion, C. et al. SynergyAge, a curated database for synergistic and antagonistic interactions of longevity-associated genes. Sci Data 7, 366 (2020). https://doi.org/10.1038/s41597-020-00710-z
The Systems Biology of Aging Group is a research group based in the Institute of Biochemistry of the Romanian Academy. With a highly multi-disciplinary team, our areas of interest span biogerontology, systems biology and bioinformatics, and our projects include both computational aspects (data aggregation and processing, multidimensional data analysis, network-based methods, systems theory approaches, deep learning, etc.) as well as wet-lab experiments (in particular in-vivo testing of the computationally predicted interventions). Broadly speaking, our mission is to use and develop computational tools, with the goal of enhancing our knowledge and understanding of the aging process.
This project is currently part of a larger EU/RO grant, Gerontomics, to develop a Multi-omics Prediction System for Prioritization of Gerontological Interventions.
If you would like to read more about our group or about the Gerontomics project, please visit our page at www.aging.biochim.ro.
This work is licensed under a Creative Commons Attribution 4.0 Unported License.
The SynergyAge database is free for all purposes, including commercial, educational, and research purposes provided however that you cite the usage of SynergyAge in subsequent presentations, publications, etc. Although referring to the URL might be enough in some cases (e.g., teaching and websites), for scientific publications we ask that you cite the paper describing SynergyAge. You may link freely to our databases.
The SynergyAge database does not give, share, sell, or transfer any personal information about our visitors unless required by law enforcement or statute.
Currently, a scientific paper describing this database is under preparation. If you would like to cite SynergyAge before the publication we ask you to please contact us.
This website is in compliance with the EU General Data Protection Regulation (GDPR). Please note that our website collects e-mail addresses from submitters of article suggestions. We need this information to potentially contact the submitter in case the reason for suggesting a certain scientific article for inclusion is not straightforward. The data is kept until the article suggestion is fully processed. If however at any time you would like your information to be completely erased from our database, please send an e-mail to the head of our group , and we will immediately proceed to do this. No other personal information (e.g. cookies, location, etc) is collected by our website.