Monthly Archives: July 2015

In Defense of a PhD in Yeast Genetics 2 – Why Genetic Interactions?

Okay – hopefully the last post goes some of the way towards answering the question of why we study yeast. Here I’ll try to explain why we study genetic interactions in yeast, the research area I worked on during my PhD . This post will be longer and more detailed than the last, but hopefully no more difficult to follow. If you get lost, please comment.

Genotype to phenotype – some context!

First off, some context. A central problem in genetics is to understand the connection between genotype and phenotype, i.e. how variations in DNA (genotype) result in changes to specific observable characteristics (phenotypes). From a medical genetics point of view we may wish to identify the genetic variants that predispose us to specific diseases, while those involved in agriculture may wish to identify variants that make crops more resistant to drought. When such variants are found by geneticists, they are typically reported in the news in the form “Scientists find the gene for X“.

In reality things are rarely that simple, because genes and their protein products do not act individually. Many genes work together to carry out specific tasks – for example there is no one gene responsible for ensuring that your DNA gets replicated accurately when your cells divide. Rather, a whole host of genes work in tandem to ensure this process goes smoothly. This is also true of many diseases – many distinct variants can increase your likelihood of getting cancer or diabetes. Perhaps even more surprising than this (that mutations in many different genes result in the same apparent outcome) is that some outcomes are only visible in the presence of combinations of mutations. Such phenomena, where combinations of mutations result in different outcomes than what one would expect based on the outcome of each mutation in isolation, are generally referred to as genetic interactions.

Why study genetic interactions?

Understanding genetic interactions is an important challenge for a number of reasons. One is that they are believed to be responsible for some of the ‘missing heritability’ of complex diseases. In such instances we can see that a disease appears to be heritable (i.e. it runs in families) but a single variant cannot adequately explain the patterns of inheritance. In these diseases it may be the case that specific combinations of mutations are more important than any individual mutations.

Another reason is to understand genetic robustness. It may surprise you that most of us carry a large number of ‘broken’ genes, i.e. genes that appear mutated in such a way that they cannot function properly. How do we survive with these broken genes? One reason is because of a property of biological systems called robustness. Genetic interactions are key to such robustness – if there is a problem with gene A, then maybe gene B can cover for it, and vice versa. We may then only observe a problem if both gene A and gene B are mutated simultaneously.

So understanding genetic interactions may be a key part of understanding susceptibility to complex disease and also understanding the robustness of biological systems.

Why study genetic interactions experimentally?

In principle, we could study genetic interactions systematically by combining genotype (DNA sequence) and phenotype (observed characteristics including disease states) data. We could then ask questions such as “How often do people with mutation A and mutation B suffer from diabetes?”. However, this approach is complicated by the fact that each of us differ by many mutations. So it is not easy to control for the fact that two people differ not just in the status of genes A and B, but also in genes E,D,F…

A more direct way to explore genetic interactions is to experimentally introduce mutations into a well defined genetic background. This can be done using isogenic populations of organisms – effectively populations of clones that have identical DNA. We can then see the impact of introducing mutation A on its own, mutation B on its own, and then mutation A and B together, while keeping all other genes the same.

This is more or less what is done in yeast genetic interaction screens – two genes are mutated in a single yeast strain and the impact that this has on a phenotype (typically the growth of a colony) is then measured. This can result in surprising findings – sometimes it is possible to independently mutate gene A or gene B with little consequence for growth, but mutating both simultaneously results in cell death! This suggests that genes A and B might perform the same essential function – mutating gene A leaves gene B to carry out the function, mutating gene B leaves gene A to carry out the function, but mutating gene A and B together means there’s no gene to carry out the function and consequently the cells do not survive.

Why study genetic interactions in yeast?

For the reasons I mentioned in my previous post! Yeast are easy to grow and very easy to manipulate genetically. About 15 years ago, researchers at the University of Toronto developed a technique to create large numbers of double-mutants (organisms with two genes mutated) in yeast. This approach allowed us to move from identifying individual interactions (genes A and B cause cell death when mutated together) to large networks of interactions involving hundreds of different genes (gene A causes cell death when mutated together with genes B, C, D…). This technique was improved and refined over the next decade by a number of different labs and now we have over 6 million gene pairs tested for interaction in yeast and will likely have all possible pairs (~18 million) tested in the not too distant future.

We are nowhere near this stage with any other organism. Only in the last few years have we started to see analogous approaches developed in mammalian cell systems. These approaches have been used to create ‘proof-of-concept’ interaction networks in human cells and mice cells. These networks are still significantly smaller than those created in yeast a decade ago (<5,000 gene pairs) and have benefitted enormously from the computational tools and experimental designs perfected in yeast in the intervening years. As I mentioned in the previous post – yeast are a proving ground for new technologies!

So what do we actually learn from genetic interaction networks in yeast?

In my last post I mentioned two (of many) distinct reasons that geneticists study yeast – gaining insight into specific genes and processes and understanding general principles of biological systems. Large scale genetic interaction experiments (usually called screens) in yeast have delivered in spades on both fronts.

A few different labs have focussed on creating comprehensive networks for specific biological processes – for example testing for genetic interactions between every pair of genes known or suspected to be involved in DNA replication. This approach allows us to build up a comprehensive picture of a particular biological process – identifying which genes work together and depend on each other, effectively creating a functional ‘wiring’ map of that process. In addition these experiments have also led to the characterisation of genes previously unknown to be involved in specific processes, i.e. we did not know what function a particular gene carried out but thanks to genetic interaction screens we do!

At a more abstract level we have learned about the general principles underlying genetic interaction networks. For example we have found that two genes involved in the same process are significantly more likely to interact than genes involved in different processes – e.g. two genes involved in DNA replication are more significantly likely to interact than one gene involved in DNA replication and one gene involved in building the cell wall. Moreover we have found that some genes tend to have significantly more genetic interactions then others, and that this is true of specific types of genes.

Both of these latter observations have the potential to inform the search for genetic interactions in cancer a topic I will discuss in my next and final post in this series.