Systematic analysis of the gerontome reveals links between aging and age-related diseases

Abstract

In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases.

Figure 1.

Protein–protein interactions between worm aging-related genes. Pro-longevity genes are depicted in red and anti-longevity genes in green. For each of the two gene sets, the smaller inside ellipse indicates genes that form a continuously connected network. Left right straight and curved arrows are used to summarize undirected interactions between genes from different and the same gene set, respectively.

Figure 2.

The y-axis quantifies the number of age-related diseases which significantly overlap with aging-related genes; the x-axis describes the aging-related gene sets studied according to the source organism (i.e., human plus human homologs of aging-related genes from each model organism). The columns have seven different colours to represent each age-related disease classe analysed: Neoplasms (light blue), Nutritional and Metabolic diseases (orange), Nervous System diseases (light grey), Cardiovascular diseases (yellow), Musculoskeletal diseases (blue), Respiratory Tract diseases (green) and Immune System diseases (dark blue). The first column represents the number of age-related diseases with a significant overlap with candidate human aging-associated genes. Model organisms are ordered by evolutionary proximity to humans. The genome was considered as background. The secondary y-axis displays the number of genes from the respective gene sets. (A) shows the number of significant overlapping aging-related genes with age-related diseases. (B) shows the number of significant overlapping aging-related genes with age-related diseases with PBC (i.e., only genes with more than 10 publications were used).

Figure 3.

Overlapping aging-related genes for various organisms with age-related disease genes sets. Green means significant overlap between aging-related and age-related disease genes and red means there is no significant overlap. Model organisms are in descending order of their proximity to humans.

Figure 4.

The y-axis quantifies the number of age-related diseases which significantly overlap with aging-related genes; the x-axis describes the aging-related gene sets studied according to the source organism (i.e., human plus human homologs of aging-related genes from each model organism). The columns have seven different colours to represent each age-related disease classe analysed: Neoplasms (light blue), Nutritional and Metabolic diseases (orange), Nervous System diseases (light grey), Cardiovascular diseases (yellow), Musculoskeletal diseases (blue), Respiratory Tract diseases (green) and Immune System diseases (dark blue). The first column represents the number of age-related diseases with a significant overlap with candidate human aging-associated genes. Model organisms are ordered by evolutionary proximity to humans. This analysis was performed with PBC. The secondary y-axis displays the number of genes from the respective gene sets. (A) shows the number of significant overlapping aging-related genes with age-related diseases, including first order interaction partners. The interactome plus aging-related and age-related disease genes was considered as background. (B) shows the number of significant overlapping aging-related genes with age-related diseases, including co-expressed genes. The genome was considered as background.

Figure 5.

Overlapping aging-related genes and their co-expressed partners with age-related diseases for various classes and organisms. Green means there is at least one age-related disease from that class that significantly overlaps with aging-related genes and red means no association. Model organisms are in descending order of their proximity to humans. This analysis was performed without PBC.

Figure 6.

(A) CAD-genes distribution as associated with age-related disease classes. (B) shows the genes involved in half or more disease classes. TNF is associated with all the age-related disease classes analysed. This analysis was performed with PBC.

Figure 7.

(A) CAD-gene distribution as associated with individual age-related diseases. (B) shows the CAD-genes involved in ten or more individual diseases, with PON1, TNF, APOE the top 3 genes associated with the greatest number of age-related diseases. This analysis was performed with PBC.

Figure 8.

Number of genes by age-related disease class (Total column) and shared with each other disease classes. The white cells present the number of genes shared between disease classes and the darker grey cells show the number of genes not shared with any other disease class.