The genome era and the advent of high-throughput technologies have brought about a huge increase in the amount of data available to biologists: each genome contains tens of thousands of genes, whose products can potentially interact with each other in an astronomical number of ways. This quantitative change has created a need for a qualitative change in the way we perform analyses: the human brain is not very good at understanding thousands of things at once, let alone millions or billions, so we must find new ways to extract comprehensible patterns from torrents of data.

Many of the techniques being developed to analyze large biological networks fall under the umbrella of systems biology. Some of the newest tools have been used guide genetic perturbation studies in yeast, resulting in the discovery of novel lifespan control genes. What can such network analysis tell us about human aging?

To address this question, Bell et al. compiled a list of gerontogenes (i.e., genes whose wildtype function is associated with accelerated aging, and whose loss-of-function mutants are associated with longer life) from model systems, and studied the connectivity of these genes within the context of interaction data obtained from a large-scale (though not comprehensive) two-hybrid screen of human proteins.

A Human Protein Interaction Network Shows Conservation of Aging Processes between Human and Invertebrate Species
We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

The authors’ focus on genes studied in model organisms is well motivated; genes that control aging in one species are more likely than one would expect from chance to affect aging in another species, even if those species are as diverged as yeast and worms.

The findings: compared to the genome as a whole, longevity genes tend to be more highly connected network, often acting as “hubs” within the network; furthermore, these genes are more connected to one another than the average gene, forming a “longevity network” that stands out against the web of all interactions.

In conjunction with expression data, this network has predictive power: genes that interact with components of the longevity network and exhibit increased expression in aging muscle are very likely to function as gerontogenes in C. elegans. This finding demonstrates once again the significant conservation of lifespan control systems across large evolutionary distances. Perhaps more importantly, it also shows that applying network analyses to large data sets can do more than merely catalog information. With the right combination of high-throughput data, a good network model and the right kinds of statistics, the tools of systems biology can reveal new biology that otherwise would have taken us a very long time to discover.