Our Research . . .


  Evolutionary Genetics and Genomics

  Experimental Evolution and Evolutionary Dynamics

  Mutation Rate

 



Evolutionary Genetics and Genomics

Yeast is an ideal system for the study of genome evolution.  Phylogenically, Saccharomyces cerevisiae is well positioned with a number of closely related species with nearly sytenic genomes.  The small compact nature of yeast genomes makes them readily amenable to genomics studies, and the existing tools of yeast genetics allow for reconstruction experiments. 



   
    Evolutionary forces acting on genome structure.

    Yeast lack true operons like bacteria, but do possess clusters of functionally-related genes. A prime example is the GAL1-GAL10-GAL7 gene cluster. This gene cluster evolved independently through gene relocation in two fungal phyla (Ascomycota and Basidiomycota) and has been horizontally transferred within Ascomycota.

     It is not clear what evolutionary forces favored the formation and maintenance of gene clusters. Two hypotheses have been advanced to explain the origin and maintenance of metabolic gene clusters: coordinated gene expression and genetic linkage. We specifically tested the coordinated expression hypothesis by breaking up the GAL1-GAL10-GAL7 gene cluster and measuring the effect on gene expression and fitness.

     We find that, although clustering coordinates the expression of GAL1 and GAL10, disrupting the GAL cluster does not impair fitness, suggesting that other mechanisms, such as genetic linkage, drive the origin and maintenance metabolic gene clusters


The GAL1-GAL10-GAL7 gene cluster. The GAL cluster consists of three genes (GAL1, GAL10, and GAL7), encoding enzymes that catalyze four sequential steps in galactose assimilation, that are clustered in a 7 kb region of Chromosome II .


   
   

Experimental Evolution and Evolutionary Dynamics

The yeast Saccharomyces cerevisiae is increasingly becoming a model system for studying evolution in the laboratory.  Yeast is a well characterized eukaryotic organism with a short doubling time.  Population sizes and mutation rates can be maintained over several orders of magnitude, and population samples can be stored in suspended animation creating a frozen "fossil record" of evolutionary changes.  The yeast life-cycle provides several additional advantages: cells can be propagated as either haploids or diploids, either sexually or asexually, and evolved strains can be backcrossed to ancestral lineages and evolved mutations can be identified. 



   
    The dynamics of genome sequence evolution

    The traditional view is that adaptation is dominated by rare beneficial mutations that occasionally occur and sweep through a population. This view is, at best, an oversimplification.

     By performing whole-genome whole-population sequencing on 40 of our evolved populations we have produced a detailed picture of the dynamics of genome sequence evolution. The most striking feature of our results is that selective sweeps are rarely single mutation/single phase events. Instead, mutations often move through the population in groups, which we have called "cohorts."


The dynamics of genome sequence evolution in a single population. In the top panel, the trajectories of the 15 mutations that attain a frequency of at least 30%, hierarchically clustered into several distinct mutation cohorts, each of which is represented by a different color. The bottom panel is a Muller diagram showing the dynamics of the six main cohorts in the population. The number of times a mutation was observed in a given gene across all 40 populations is indicated in parentheses. Mutations in genes observed in more than three replicate populations are indicated in bold.


   
    The emergence and fate of beneficial mutations in asexual populations

    The spread of new beneficial mutations within a population depends on parameters such as population size, mutation rates, and selective advantages. How these parameters determine the fate of individual beneficial mutations is not well understood.

    Taking advantage of the observation (below) that sterile mutations confer a fitness advantage, we measured the distribution of possible fates of new beneficial mutations in experimental budding yeast populations by following the trajectories of individual spontaneously arising sterile mutations in about 600 populations over 1000 generations.

    We find that the fitness advantage of each mutant plays a surprisingly small role in determining its ultimate fate. Rather, underlying genetic variation is quickly generated and plays a dominant role in determining the fate of new beneficial mutations.


The fates of beneficial mutations. Sterile mutations experienced four general fates over 1000 generations of experimental evolution. The simplest case is a selective sweep: a spontaneous sterile mutation arises and increases in frequency until it fixes (upper left). More commonly, sterile alleles rose to some frequency, but were outcompeted by a more-fit lineage before they were able to fix (clonal interference, upper right). More complicated trajectories were also observed, wherein sterile strains rise, are subjected to clonal interference, and then increase in frequency again (lower left). This could reflect a second beneficial mutation occurring in a declining sterile population, or a second sterile mutation occurring in the background of the competing mutation (as shown). We were surprised to also observe a fourth type of trajectory where sterile strains rise to some frequency and remain there for hundreds of generations, suggesting the action of frequency-dependent selection (lower right).


   
    Basal signaling through the mating pathway entails a fitness cost

    During long-term evolution experiments, we have observed that many haploid strains become sterile. There are two possible explanations for this: (1) There are a large target size for sterile mutations and cultures fix these mutations through neutral evolution, or (2) Sterile strains have a fitness benefit and are selected in the population.

    To distinguish between these two possibilities, I measured the fitness of ~100 alpha-factor resistant clones and found that on average, sterile strains have a higher fitness (up to 3% greater than wild-type).

    To determine the basis for this advantage, I measured the fitness of select gene deletions. Deletion of Ste4, Ste7, and Ste12 results in a fitness advantage; however deletion of the receptor (Ste2) or the protein responsible for cell cycle arrest (Far1) does not confer an increased fitness. This suggests that the fitness advantage comes from loss of basal signaling through the mating pathway.


Sterility provides a fitness advantage. Targeted gene disruptions show that loss the yeast mating pathway G-beta subunit (Ste4), the MAP kinase kinase (Ste7), or the transcription factor (Ste12) increases growth rate; however, loss of the pheromone receptor (Ste2) or Far1 does not. Ste4, Ste7, and Ste12 are required for basal signaling in the mating pathway, Ste2 and Far1 only play a role in pheromone-induced signaling.


   
   

Mutation Rate

Mutation is a fundamental process in biology.  Despite its importance, the degree to which mutation rate can vary and the mechanisms underlying this variation are not well understood.  We are interested in determining how mutation rate varies within Saccharomyces cerevisiae and how mutation rate influences evolution (and vice versa). 



   
  Measuring the genome-wide distribution of mutations by mutation accumulation.

     In a mutation accumulation assay, the population is propagated through recurrent single cell bottlenecks, mitigating the effect of selection and allowing all mutations (other than lethals) to accumulate as if they were neutral. Sequencing the endpoint of a lineage reveals the number, positions, and identities of accumulated mutations. We used the mutation accumulation assay to passage 17 mismatch repair defective yeast lineages over 170 generations and determined the mutation rates, spectra and genome-wide distributions of mutations by whole-genome sequencing. We find that mismatch repair deficient strains accumulate ~1 mutation per genome per generation (corresponding to a ~200-300-fold increase in mutation rate relative to wild type).

     Because the mutation accumulation assay queries many types of mutation events and contexts simultaneously, it not only produces a more accurate estimate of the per-genome per-generation mutation rate, but also allows one to determine how the mutation rate is influenced by sequence-specific features and genomic context. We find that mutations occurred randomly across the genome, with no chromosomal, gene, or replication timing biases; however, mismatch repair defective cells do display a distinctive mutational signature, with deletions at homopolymeric runs representing the primary mutational event. We find that microsatellite instability increases with repeat length and that microsatellites adjacent to other repeats are more mutable.


Genome-wide distribution of mutations across 17 mutation accumulation lines. In the absence of mismatch repair, cells accumulate ~1 mutation per generation, and most of those mutations are frameshift mutations at microsatellites and homopolymeric runs. Each red line corresponds to an observed mutation.

   
  Mutation rate variation across the yeast genome is correlated with replication timing

    I have employed the fluctuation assay to determine how mutation rate varies across the genome. I constructed 43 strains, each of which has the URA3 gene integrated at a different location tiled across Chromosome VI.

    By measuring the rate of loss of function of URA3 in each strain, I find that mutation rate varies across the chromosome and is correlated to replication timing: regions that are replicated early have low mutation rates and regions that are replicated late have high mutation rates.

    This makes sense given how cells deal with damaged bases during replication. When a replicative polymerase encounters a lesion that is unusable as a template, the polymerase will restart replication downstream of the lesion leaving a daughter-strand gap. There are two ways a cell can fill in this gap: an error-prone method using a translesion polymerase to copy the damaged template or an error-free method using the newly formed sister strand as a template (template switching). Template switching can occur as soon as the replication fork has passed and the homologous sequence is available.

    Recent work suggests that translesion synthesis is used only as a last-ditch effort to fill in these gaps and cannot occur until the end of S-phase. Therefore, regions of the genome that are replicated early in S-phase have longer to undergo template switching to replicate past lesions, whereas regions replicated late are more likely to require translesion synthesis.


Mutation rate is correlated with replication timing. This figure shows the correlation between the replication profile and mutation rate across yeast Chromosome VI.

   
    Estimating the per-base pair mutation rate in the yeast Saccharomyces cerevisiae

    Mutation rate is an important parameter in evolution. It limits the speed of adaptation in populations with beneficial mutations; in the absence of beneficial mutations it sets the equilibrium fitness of the population. Despite its importance, there are large uncertainties in estimates of the per-genome per-generation mutation rate.

    Estimating this parameter is typically a three-step process: determining the mutation rate to a particular phenotype, converting this phenotypic rate into a per-base-pair mutation rate in a particular gene and extrapolating this local rate to the entire genome.

    During my graduate work, I focused on the technical challenge of determining phenotypic mutation rates accurately using the Luria-Delbruck Fluctuation Assay.

    In addition we took up the task of determining the effective target size of a gene, the probability that a mutation somewhere in a defined segment of the genome produces a mutation with a specified phenotypic effect. This required large-scale sequencing ofura3 and can1 mutants.

    Combining our estimates of phenotypic mutation rates and effective target sizes we conclude that the per-base-pair mutation rate at URA3 and CAN1 is 3.80 x 10-10 and 6.44 x 10-10 per base pair per generation, respectively, suggesting that the mutation rate varies across the yeast genome.


Failed cover art. When we were asked to submit potential cover art for the Lang and Murray Genetics paper, this is the best I could come up with.

The caption that went along with the cover art submission read as follows:

The Luria-Delbruck fluctuation assay is a common method for measuring mutation rates. In the fluctuation assay, many parallel cultures are inoculated with a small number of cells, grown under non-selective conditions, and plated to select for mutants. Although the number of mutations that arise during the growth of a culture will follow the Poisson distribution, the number of mutant cells will vary greatly since early mutations will lead to "jackpots," cultures that contain a large number mutant cells. Seventy-two parallel yeast cultures were grown and spot-plated onto eight agar plates containing the drug canavanine. The random nature of the mutational process can be seen in the variation in the number of canavanine resistant mutants in each culture, including a "jackpot" on the plate in the lower left corner.


   
  

Department of Biological Sciences | Lehigh University
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Last Updated: July 2013