Aging is what geneticists like to call a “complex trait” — simply put, a trait that is controlled by a large number of genes and the interactions between them. Complex traits differ from simple traits in the following way: When one is studying a simple trait, one simply identifies a mutant in the relevant trait and, after an ingenious combination of clever crosses and muscular cloning steps, finds the defective gene — thus gaining a great deal of explanatory power about the trait in question.
When one is studying a complex trait, however, approaches like a mutant screen fall short. They don’t fall totally flat — one of the great innovations of the last decade or so is the realization that we can learn quite a bit by studying aging at the single-gene level — but they can’t get us all the way home. Suppose you do a screen and find fifty mutants that all lengthen lifespan by forty percent (not far from the situation in worm) — or, speaking more generally about complex traits, you find fifty loci in the human genome that are associated with a higher risk of schizophrenia. What then? What have you really learned about how the system works?
In order to really get a handle on complex traits like aging, we need new tools — not only to discover genes involved in our favorite traits, but also the interactions between the gene’s products and the environment. Indeed, we need a whole new toolbox, something that would be barely recognizable to the geneticists of fifty years ago.
According to the principle that one should simultaneously wage as few battles at possible, the development of the new toolbox for analysis of complex traits won’t happen in the most complex organisms. Instead, we will look to the simplest and most malleable models in which to test our new techniques.
That’s just what Lorenz et al. have accomplished in a recent study. Their simple model was yeast, a reliable workhorse in aging research since the dawn of modern biogerontology. Their toolbox is called “network inference” — perturbing the expression of single genes within a network, measuring the resulting changes throughout the transcriptome, and using this data to learn about the connectivity of the network.
A network biology approach to aging in yeast
In this study, a reverse-engineering strategy was used to infer and analyze the structure and function of an aging and glucose repressed gene regulatory network in the budding yeast Saccharomyces cerevisiae. The method uses transcriptional perturbations to model the functional interactions between genes as a system of first-order ordinary differential equations. The resulting network model correctly identified the known interactions of key regulators in a 10-gene network from the Snf1 signaling pathway, which is required for expression of glucose-repressed genes upon calorie restriction. The majority of interactions predicted by the network model were confirmed using promoter-reporter gene fusions in gene-deletion mutants and chromatin immunoprecipitation experiments, revealing a more complex network architecture than previously appreciated. The reverse-engineered network model also predicted an unexpected role for transcriptional regulation of the SNF1 gene by hexose kinase enzyme/transcriptional repressor Hxk2, Mediator subunit Med8, and transcriptional repressor Mig1. These interactions were validated experimentally and used to design new experiments demonstrating Snf1 and its transcriptional regulators Hxk2 and Mig1 as modulators of chronological lifespan. This work demonstrates the value of using network inference methods to identify and characterize the regulators of complex phenotypes, such as aging.
It’s sort of like pulling on one strand of spider silk and watching how other strands move, in order to build a model about the connectedness of the entire web. Like much modern systems biology, the idea is to study the parts and learn about the whole.
In their first foray, they re-discovered the known interactions in a calorie restriction-regulated gene network in yeast. Before you yawn: rediscovery of prior knowledge is an important validation for a new technique; what’s additionally impressive is that this system, in just a few weeks of experimentation and analysis, was able to recapitulate the results of (literally) years of prior work. Beyond that, the authors were able to detect evidence of novel (i.e., heretofore unknown) interactions between network components.
The critic might argue that this is even duller than the validation attained by re-discovering prior knowledge — but the critic would be wrong. Whether we’re talking about schizophrenia, heart disease or aging, we ultimately want to understand complex traits well enough to intervene in them without doing more harm than good. Approaches like network inference, which reveal the fine detail of biological systems, make it possible to observe the relationships between genes we might target with drugs — as well as predict second-order effects and undesired consequences of specific types of intervention — bringing us that much closer to our goal.