The intricate, reactive, self-regulating machinery of our cells is built from proteins. Those proteins are specified by the blueprints known as genes, coiled up in each cell nucleus. The operation of our metabolism proceeds as a dance of networks of related proteins, feedback loops and signaling cascades in which the amount of a given protein produced at any given time can rise and fall in response to the rise and fall of other levels of production. The full scope of how variations in metabolism and its response to environment and lifestyle can affect the pace of aging is a staggeringly complex system, and as yet poorly understood.
Still, researchers seek to fully understand metabolism and aging; this goal has broader support than any other in aging research. There are more researchers chasing that understanding at any given time than there are working on ways to intervene in the aging process. A post from last week took a look at one way in which that research can progress: given an established mutation or other single-gene or single-protein change that extends life, scientists then follow the effects of that change through the network of interactions that it impacts, in search of a greater understanding of the system as a whole.
This is a time-worn and well proven methodology in all sciences: if you want to understand how something works then change one small part of it and carefully watch what happens next. Repeat as necessary.
There are other ways in which a knowledge of protein networks and existing longevity genes can be used to further research. For example, the catalog of what is known today can be mined in order to guide the process of uncovering more of the relationship between aging and the operation of metabolism. In any given network of genes where one can be altered to increase longevity, it is to be expected that there may be others. The proteins produced by these genes existing in an interconnected system, and it is probably the case that you can change the behavior of such a system by intervening at more than one point.
Here is an example of this approach:
Intricate and interconnected pathways modulate longevity ... Because biological processes are often executed by protein complexes and fine-tuned by regulatory factors, the first-order protein-protein interactors of known longevity genes are likely to participate in the regulation of longevity. Data-rich maps of protein interactions have been established for many cardinal organisms such as yeast, worms, and humans.
We propose that these interaction maps could be mined for the identification of new putative regulators of longevity. For this purpose, we have constructed longevity networks in both humans and worms. We reasoned that the essential first-order interactors of known longevity-associated genes in these networks are more likely to have longevity phenotypes than randomly chosen genes.
We have used C. elegans to determine whether post-developmental inactivation of these essential genes modulates lifespan. Our results suggest that the worm and human longevity networks are functionally relevant and possess a high predictive power for identifying new longevity regulators. ... By combining a network-based approach with the selection of genes required for development, we identified new lifespan regulatory genes at a frequency far exceeding that achieved in genome-wide screens. Though the effect of the new [genes] on lifespan is relatively modest, one can speculate that they might function in pathways or complexes that modulate core longevity functions.
As a footnote, it perhaps something of a sign of progress that bringing to light a dozen or so longevity-associated genes comes and goes almost without mention these days. It is prosaic now, not worthy of comment in the broader scientific press: bring on the next trick.