Viruses are excellent biological models for understanding how populations evolve because they have characteristics (like high mutation rates and large population sizes) that make it easier to observe evolution in real-time. However, as argued in a new review paper in the journal Heredity, the unusual biology of viruses means that some common, simplifying assumptions of population genetics are not met by viral populations.

In particular, viruses tend to have highly skewed offspring distributions, with some virions producing either many more or many fewer offspring than assumed, and viral populations often experience drastic changes in size (i.e. bottlenecks) as a result of transmission or infection. These features of virus biology mean that the Kingman coalescent and Wright-Fisher model (part a in the Figure below) that are traditionally used in population genetics can lead to an erroneous inference of how the virus populations are evolving. The authors argue that the multiple merger coalescent class of models (part b of the Figure) can account for these limitations of traditional models by allowing more than two lineages to coalesce at a time.

New computational approaches, such as the use of multiple merger coalescent models or forward simulations, will elucidate how the unusual biology of viruses influences their genomic diversity and evolution.

Figure: Each row of dots shows the alleles in a single generation, with the lines connecting dots showing reproduction events. For each type of coalescent (a and b), the left panel shows the evolutionary process of the whole population, whereas the right panel shows a possible sampling and its genealogy. Unlike the Kingman coalescent (a), the multiple merger coalescent (b) allows parents to give rise to more than two offspring in the next generation. (Figure taken from figure 1 of the paper.)

–This summary was written by Telmo Cunha & Hermina Ghenu

One interest of our lab is predicting where evolution will go, in the genotype space, and which paths evolution can take to improve fitness. To this end, fitness landscapes are a useful concept for understanding the predictability of evolution since they relate the genotype to the reproductive success of an individual. Empirical fitness landscapes are especially important because they allow us to compare real data with the theoretical work that has accumulated over more than 80 years of research.

The latest collaboration between the Bank and Jensen labs examines an unprecedentedly large empirical fitness landscape composed of amino-acid changing mutations in the heat shock protein Hsp90 in yeast. Our collaborator Ryan Hietpas generated and screened mutants that are up to six amino-acids away from the parental genotype, allowing the authors to look at the interactions between mutations (epistasis) and compare subsets of the data (local landscapes) to the entire, global landscape. Their main findings are:

1. The landscape is dominated by epistasis. Most of the landscape experiences antagonistic epistasis, where mutations that are neutral or beneficial when alone have a detrimental effect when they are found together. In contrast, the global fitness peak of the landscape is the result of synergistic epistasis, where mutations that are neutral or slightly beneficial when alone yield high reproductive success in combination.
2. Studying potential adaptive walks on the landscape shows that although the global peak can theoretically be reached most of the time, adaptation may stall at an intermediate fitness peak, which is reached with high probability from the parental genotype.
3. Analyses of the entire set of mutations by comparing estimated landscape statistics to expectations from theoretical landscape models suggest that the landscape is heterogeneous and its topography is globally hard to predict.

This work highlights the dual nature of epistasis: a landscape with no interactions between mutations is just a single global peak (globally predictable), and evolution will always end up at the top but it is impossible to know what paths it will take (locally unpredictable). Conversely, a landscape dominated by interactions has few viable paths (locally predictable) but each one leads to a different peak (globally unpredictable).

The empirical fitness landscape studied here suggests that our current theoretical understanding of fitness landscapes is not sufficient to describe empirical landscapes. More generally, this work draws attention to the importance of comparing and informing theoretical models with empirical data, and proposes approaches to improve this process in the future. The paper is available online in PNAS or as a preprint.

Despite their deleterious effects on hybrids, epistatic genetic interactions in the form of Dobzhansky-Muller incompatibilities (DMIs) seem to persist, and potentially even accumulate, in populations in the presence of gene flow.

In this paper, we provide a rigorous theoretical framework to study this phenomenon. We determine the maximum amount of gene flow that allows for the accumulation or maintenance of DMIs, and interpret our results with respect to the relative importance of ecological and mutation-order speciation; two concepts that are controversially discussed in the field of speciation.

Link to our paper in Genetics: The limits to parapatric speciation: Dobzhansky-Muller incompatibilities in a continent-island model

In this publication, we present a statistical approach for estimating the fitness of engineered mutations in high-throughput bulk competitions, which we apply to a data set of the same 560 mutations in yeast Hsp90 under six environmental conditions.

Analyzing the distribution of fitness effects across environments and between one-, two- and three-nucleotide step mutations, we find that larger step sizes harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment; an observation consistent with predictions from Fisher’s geometric model (which was proposed by Fisher in 1930).

Furthermore, the shape of the beneficial tail of the fitness distribution becomes heavier under the most severe environmental challenge (here represented by a cold environment with high salinity), indicating a higher potential for rapid adaptation through mutations of unpredictably large size

Link to our paper in Genetics: A Bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: uncovering the potential for adaptive walks in challenging environments