ARTICLE: Walking on evolutionary landscapes

Epistasis can lead to fragmented neutral spaces and contingency in evolution

• Mutations can cause disease and diversity by changing structures in biology: we use a computational model to understand what changes are possible and how evolutionary future is constrained by the current state of organisms

Evolution can, in an abstract sense, be pictured as a journey through a genetic "space". A set of co-ordinates (like latitude and longitude, but more detailed) in this space corresponds to an organism's "genotype" -- the ordered set of As, Cs, Gs, and Ts found in its DNA. Each genotype encodes information about physical features of the organism -- its "phenotype". Mutations and other genetic changes cause steps from one point to another in genome space, and some of these steps will change the phenotype of an organism. As an artificially simple example, imagine a case where the genotypes AA and AC may give an organism red feet, but AG and AT give it blue feet. The offspring of a red-footed organism with genotype AA may pick up an A->C mutation in the second position (AA->AC) and keep their parent's red feet; or they may pick up an A->T mutation (AA->AT) and have a new blue-footed phenotype.

The structure of this evolutionary space -- the pattern of phenotypes encoded by connected genotypes -- clearly affects how mutations can change the form of organisms, and is thus central to our understanding of evolution. Mutational changes cause genetic diseases, allow bacteria and viruses to adapt to our immune response, and generate the beautiful biodiversity in the world around us. We aim to learn more about these systems, how they change with time, and their evolutionary limitations, by studying evolutionary spaces in a computer.

A schematic genome space. Each point on the grid is a genotype, which encodes a phenotype (red squares, blue circles, green diamonds, etc). Evolution can step between adjacent points through mutations -- a mutation may move an organism to its left neighbour, for example, or upwards by one point. Some mutations -- for example, a rightwards step from the top left corner -- keep the phenotype (green diamond) intact. Some (a downwards step from the top left corner) change the phenotype (green to blue). Not all genotypes encoding the same phenotype are connected, and different clusters have different evolutionary potential. For example, a gold triangle encoded by the cluster in the top right can only stay gold or become blue; a gold triangle encoded by the cluster on the left can stay gold or become blue, grey, or red.

We chose to look at the evolutionary space of RNA -- a class of biological molecule, examples of which play many vital roles in our cells. RNA, like DNA, consists of an ordered series of chemical groups denoted by letters, and RNA molecules fold into particular structures governed by this sequence of letters. These structures are central to the function of some RNAs, and the structure can be predicted by a computer program from the sequence of letters. So we have a model system where the phenotype (structure) corresponding to a genotype (letters) can easily be computed.

We explored the full genetic space of RNA molecules that consist of 15 letters (meaning that our computer had to fold over a billion structures!). In our survey in Proceedings of the Royal Society B here (free here) we found a large skew in the numbers of genotypes that encode a phenotype -- some structures are encoded by many different sets of letters and occupy a vast amount of the genetic space, and some are encoded only by very few genotypes. As mutations are random, we may expect to see these more frequent structures more commonly in the natural world (if all structures are affected equally by selective pressures). We also found that sets of genomes encoding the same phenotype are often disconnected in genome space. To pursue our simple example above, imagine AA and AC give red feet, AG and AT blue feet, TT and TG red feet and GT and GG green feet. The AA/AC red genomes aren't connected by single mutations to the TT/TG red genomes -- they form separate "clusters" in genospace. Furthermore, while a redfoot with genotype TT or TG can mutate to become either blue-footed (T->A in the first position) or green-footed (T->G in the first position), a redfoot with genotype AA or AC can only mutate to become blue-footed and cannot access the greenfoot genotypes without changing to something else first. One can see that this disconnected nature of genospace makes evolution contingent on genotype: redfoots with different genotypes can change in different ways. Understanding how this contingency appears in different systems will help us describe and even predict the outcomes of evolutionary processes. Iain