This dataset of genes associated with longevity or aging in model organisms is essentially a list of genes with one or a few key references, a brief description of the phenotype or effects of genetic manipulations of the gene, human homologues of the gene, and a few external links. For a gene to be featured, its association with aging and/or longevity must be unambiguous, and hence most genes were selected based on genetic manipulations and not mere correlations, such as a gene’s upregulation with age, in which causality is impossible to determine.
Gene expression changes likely play important roles in aging and could serve as biomarkers of physiological decline and disease, yet age-related gene expression profiles tend to be noisy, making it difficult to identify key genes and processes. In this work, a meta-analysis of 27 age-related gene expression profiles from mice, rats, and humans was done. By analyzing the combined profiles from these studies, common molecular signatures of aging were identified: genes consistently over – or underexpressed with age in multiple mammalian tissues and species. In this study, it was found that age-related gene expression changes most notably involve an overexpression of genes related to inflammation and the lysosome as well as an underexpression of genes related to mitochondrial energy metabolism.
Genes associated with longevity or aging from model organisms can be useful as a reference and educational resource for researchers. This dataset also serves as the primary resource for deriving the list of genes that may be associated with human aging. Typically, the best genes from model organisms serve as the basis for deriving the dataset of putative human aging-related genes.
The human dataset in GenAge is a curated database of genes that may regulate human aging or that at least might be considerably associated with the human aging phenotype. It is a functional genomics database designed to provide up-to-date information in the context of aging and molecular genetics.
Because the focus is on the fundamental aging process, what some authors call senescence, and not just age-related pathologies, the human dataset features primarily genes related to biological aging rather than genes that only affect longevity by having an impact on overall health. This is an important point because longevity can be influenced by factors unrelated to aging, and the distinction is crucial, albeit often difficult. (For those interested in genes associated with human longevity, please refer to the LongevityMap). Likewise, a gene is differentially expressed during aging is not by itself proof that this gene is causally involved in the aging process. Nonetheless, for researchers studying transcriptional changes with age, also available are genes commonly differentially expressed during mammalian aging which were identified by performing a meta-analysis of aging microarray data.
Each gene in the human dataset was selected after an extensive review of the literature. They were identified genes associated with aging in model organisms as well as those that may directly modulate aging in mammals, including humans. Each gene was selected or excluded based on its association with aging in the different model systems, with priority being given to organisms biologically and evolutionary more closely related to humans. Because the focus is on the genetic basis of human aging, there was no in-depth description of aging in model systems but was rather incorporated in the information gathered from multiple models to gather clues about the genetics of human aging.
In each human gene entry, the main reason for inclusion in the database is given. The following criteria are used:
1. Evidence directly linking the gene product to aging in humans (human)
2. Evidence directly linking the gene product to aging in a mammalian model organism (mammal)
3. Evidence directly linking the gene product to aging in a non-mammalian model organism (model)
4. Evidence directly linking the gene product to aging in a cellular model system (cell)
5. Evidence linking the gene or its product to human longevity and/or multiple age-related phenotypes (human link)
6. Evidence directly linking the gene product to the regulation or control of genes previously linked to aging (upstream)
7. Evidence linking the gene product to a pathway or mechanism linked to aging (functional)
8. Evidence showing the gene product to act downstream of a pathway, mechanism, or other gene product linked to aging (downstream)
9. Indirect or inconclusive evidence linking the gene product to aging (putative)
GenAge has its limits but the aim is to include the most relevant information, but not all the data are available. The human dataset in GenAge can be helpful in more classical genetic studies of aging and longevity. For example, if a given chromosomal region is identified, it is possible to look up which genes are present in that region. Although GenAge is not a bibliographic database, the bibliographic references in the human dataset can be a useful resource.
Values of individual experiments represent the log2 ratio of the expression signal old / young normalized to common ages, as described in the Methods.
n Genes = number of experiments with gene expression measurements.
n Overexpressed = number of experiments with a significant overexpression using p < 0.05 as statistical cutoff (individual experiments are marked with *).
n Underexpressed = number of experiments with a significant underexpression using p < 0.05 as statistical cutoff (individual experiments are marked with *).
p value and q value were calculated for the combined profiles of all experiments, as described in the Methods.