Evolution in Action

Wednesday, September 30, 2015

It is surprising how little we know about evolution. In 2015, evolution is considered an older discovery, and perhaps seems dusty to a generation piqued by CRISPR gene editing, pluripotent stem cells, and the still opaque mysteries of the microbiome. One might think that the learning curve would have flattened out over time, that advances in understanding would be now incremental in a field that has been attracting research, and public controversy, for so long.

Since the mid 19th century, scientists have unearthed an impressive amount of information about the history of life on earth, initially through the fossil record and more recently through whole genome sequencing. Despite all this, we still struggle to explain evolution in the context of a cell -- an elaborate network of proteins, RNA, and DNA, interacting in real time in three-dimensional space, and always in communication with other cells.

Within this intricate system it is difficult, perhaps impossible, to fathom exactly how DNA mutations occurring randomly might create new proteins and cellular functions that can change the fitness of an organism.

Fortunately, one lab at UCSF, that of Dr. Alexander “Sandy” Johnson, is making important strides in the field of molecular evolution. Trevor Sorrells is a graduate student in the Johnson Lab and lead author of a recent study titled “Intersecting transcription networks constrain gene regulatory evolution,” published in the July 16 edition of Nature.

“To understand how evolution works, we need to integrate our understanding of an organism, the molecular biology of an organism, and the [DNA] sequence,” explains Sorrells.

At the interface of these demands is the Johnson lab’s model organism, yeast, which boasts a simple molecular biology where making genetic changes is easy. For studying molecular evolution, yeast also provides an additional weapon, which is the diversity of yeast species available to researchers.

Most yeast research is done in brewer’s yeast, S. cerevisiae, but the Johnson Lab utilizes a number of non-canonical species, including K. lactis, which diverged from S. cerevisiae about 100 million years ago, around the same time as the last common ancestor of mice and humans. By sequencing and comparing the genomes of these different species, the lab can tell how evolutionarily close these species are, and can infer the genomes of ancestral species.

In some cases, a certain living yeast species will have a sequence similar to an inferred ancestral species, and can be used as a proxy for the ancestor. Because this proxy is available for making targeted genetic mutations, the Johnson Lab can construct real live versions of possible evolutionary intermediates and probe their molecular biology to examine the effects of a given mutation on the organism’s function.

By examining the genome and function of distinct species, the Johnson Lab has seen that evolution can lead to two separate species solving the same problem with different “molecular solutions.”

An example of a problem that has different molecular solutions across species is that of mating type. Yeast has two mating types, referred to as ‘a’ cells and ‘α’ cells. Each cell secretes a distinct pheromone recognized only by cells of the opposite type and this allows yeast to mate.

Creating recognizable differences between ‘a’ and ‘α‘ cells requires turning on a different suite of proteins in each mating type. For mating to work, ‘a’ cells need to turn on the a-specific genes (asgs) and turn off the α-specific genes, and vice versa.

Interestingly, there are different ways of accomplishing this outcome, which vary across yeast species. In S. cerevisiae, the protein Ste12 binds the DNA adjacent to the asgs and turns on asg expression. In a common yeast ancestor, approximated by the Johnson Lab with K. lactis, Ste12 does not bind DNA directly, but rather activates asgs indirectly through another protein. [node:field_syn_pull_quote]

Loosely, it’s like turning on a software program with a mouse versus a touch screen. In the ancestral K. lactis, an adaptor (the mouse) was required for Ste12 to turn on asg expression, but in S. cerevisiae, Ste12 can access the DNA directly by simply binding to it.

What has changed over time is not the Ste12 protein itself, but the sequence of DNA adjacent to the asgs, which has changed to allow the direct binding of Ste12. The screen itself changed to allow new connections, rendering the mouse unnecessary. Sorrells and company discovered that the switch from indirect to direct binding was not so clear-cut. They tried making the mutation in K. lactis, the “ancestral” species, and found that introducing direct Ste12 binding at the asgs led to a major problem: the asgs were expressed not only in ‘a’ cells but in ‘α’ cells as well, ruining the important function of mating type specification that is essential for yeast reproduction and survival.

This mis-regulation does not happen in S. cerevisiae, because that species contains another important difference from the ancestor: the presence of a protein, α2, which represses expression of the asgs in ‘α’ cells. Ultimately, the researchers were able to put these two evolutionary changes in order. The evolution of direct Ste12 binding sites at the asgs could not have occurred before the evolution of the α2 repressor.

This demonstrates that the order in which mutations occur matters, implying that there are only certain paths open to evolution. This is a phenomenon known as “historical contingency”. While previous research had demonstrated this phenomenon in the context of single proteins, this study was the first to show how it applies to a whole network of gene transcription.

This is an important advance since proteins do not function in isolation but are part of a cellular environment whose full complexity we are only beginning to understand. For Sorrells, the challenge is to “think about evolution at this higher level where everything is connected to everything else.”

While this study breaks new ground in showing the evolutionary pathways available to gene networks, it also shows how much more we have to learn about evolutionary progression.

“I was only able to test a few different trajectories out of the possibilities…there is a vast number of different possible orders of rewiring and mutation,” says Sorrells. “The next direction is to include thousands or millions of trajectories in understanding how evolution of gene networks takes place…it’s a much larger problem than we were able to address.”