Open Longevity Foundation has announced Open Genes, a database of longevity-associated genes and a tool for future anti-aging therapy development. The full press release is included here.
Open Longevity Foundation released a novel database of genes involved in human aging and longevity — Open Genes. It includes information of more than 2,400 genes, which is the most extensive mapping of human genetics of aging to date. Open Genes provides comprehensive analysis of all human genes involved in major aging processes, contribution into life expectancy and genetic interventions, which affect lifespan of model animals. Open Genes is designed for a wide range of people interested in aging biology, and provides a potent tool for scientists working on the problems of aging and life extension.
The Open Genes database is created to provide the most comprehensive information on genes involved in aging processes, as well as to enhance and simplify the search for potential aging therapy targets. The database includes a detailed description for genes: lifespan-extending interventions, aging-related changes, longevity associations, connections to diseases and hallmarks of aging, gene evolution and functions of gene products. It aims to combine all available data on the genetics of aging and provide convenient tools for searching, assorting, and comparing genes. It describes 2,402 age-related genes, from 1,700 unique research articles; more than 2,000 genetic interventions, affecting lifespan of model animals, 4,648 records of age-related changes in gene activity, 1,458 records on longevity associations with gene variants. Data on each gene associated with aging is much more diverse and detailed than in existing databases (GeneAge, Digital Ageing Atlas, LongevityMap).
Open Genes is created by aging biologists and anti-aging enthusiasts from Open Longevity Foundation (CA, USA), under the direction of Ekaterina Rafikova, Constantine Rafikov and Mikhail Batin. Open Longevity Foundation promotes aging research and life extension ideas, as well as organizes and conducts its own experiments in the field of aging and longevity. The Foundation is based on the principles of open science, where the results of the research should be transparent and available to everyone. The development of Open Genes is a step towards achieving this goal, being an open-source database, open and free for the users.
Open Genes establishes the relationship between the genes in the database and biological processes, which dysregulations characterize human aging. The genes are attributed to the main aging mechanisms revealed to date (so called “hallmarks of aging”), such as genomic instability (e.g., accumulation of damages), cellular senescence, attrition of telomeres, stem cell exhaustion, and altered intercellular communication. Open Genes also summarizes the existing knowledge on genetic manipulations, extending the life of model animals: >9 times in nematodes, >3 times in flies, and >1.5 times in mice. Open Genes provides a structured and detailed description for each experiment (up to 40 parameters), which allows one to more accurately interpret the results of the study. In order to decide which genes need to be added to the database, 6 types of studies and 12 criteria were used. Genes were classified according to the confidence level of the link between the gene and aging. All genes were divided into five confidence levels: highest, high, moderate, low and lowest.
Open Genes database aims to assist in the selection of the most confirmed targets for anti-aging therapy and the search for new ones. The Open Genes team has already identified 25 genes that extended life in mammals and at the same time showed an association with longevity in humans were given the highest level of confidence. The ultimate goal of the project is to create a system of constantly updated databases on aging biology and aging therapy methods with the most qualitative and organized data. These data will be convenient to use in meta-analyses, in search of new targets for aging therapy, in search of potentially successful combinations of genetic interventions, and for experimental design.
