Conflict between heterozygote advantage and hybrid incompatibility in haplodiploids (and sex chromosomes)

How is speciation different between diploid and haplodiploid organisms?

Inspired by our collaborators’ findings in natural populations of wood ants in Finland, where the coexistence of hybrid incompatibility and heterozygote advantage create a rugged fitness landscape, we developed mathematical models to compare the evolutionary dynamics of hybrid populations of diploid and haplodiploid organisms. We showed that the evolutionary outcomes between genetic systems are dramatically different. Our results imply a specific signature of hybrid incompatibilities in haplodiploids. This, in turn, provides an alternative hypothesis why X chromosomes in diploids may appear as hotspots of speciation genes and sexual conflict.

Link to our paper in Molecular Ecology: Conflict between heterozygote advantage and hybrid incompatibility in haplodiploids (and sex chromosomes)


On the importance of skewed offspring distributions and background selection in viral population genetics

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

On the (un)predictability of a large intragenic fitness landscape

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.

An experimental evaluation of drug-induced mutational meltdown as an antiviral treatment strategy

In this new paper in Evolution the authors study the evolutionary dynamics of influenza A virus under different concentrations of Favipiravir, which is a drug that leads to an increase in mutation rate across the genome. By tracking down real-time evolution of several populations they are able to evaluate the extinction dynamics and the potential adaptive response of the virus to different drug treatments.

With this setup the authors were able to show that:

1) Extinction occurs under high mutation rate – The virus populations under an increasing drug concentration show an increased mutation rate and number of mutations accumulated, resulting in rapid extinction and providing support for mutational meltdown as driving mechanism;

2) The virus populations may be able to adapt to intermediate drug conditions – Populations subjected to constant intermediate concentrations of Favipiravir showed indications of an adaptive viral response, suggesting that resistance may emerge under specific drug treatments;

3) Evolution of drug resistance can be explored by a combination of population genetic models and experimental evolution – The combination of population genetic models and experimental evolution is an excellent means to understand the evolutionary dynamics and genetics of virus resistance to drug treatments and to test the efficacy of new possible treatments.



A statistical guide to the design of deep mutational scanning experiments

This new publication in Genetics comes from a collaboration between the Bank and Jensen labs; the statistical analyses were performed by Sebastian Matuszewski and Marcel Hildebrandt. The authors present a statistical guide (and web tool, implemented by Hermina) that can be used to design and improve your future experimental setup of a deep mutational scanning study. Deep mutational scanning is an approach in which hundreds or thousands of pre-engineered mutants are grown together and their relative abundance, directly related to their fitness, is assessed through deep sequencing at several time points.

The main requirements for the statistical model of the paper to be applicable are: 1) Large copy number of each mutant in the initial library; 2) Mutants need to grow exponentially during the experiment; 3) Both population and sample sizes need to be large; 4) You should to collect your data at two or more time points.

With the interactive tool that comes with the publication, it is possible to introduce and manipulate several experimental parameters, and it allows you to quantify and maximize not only your experimental resolution, but also to get estimates of the time and expenses for a particular experiment.

Btw, there is also an IGC paper video about the project, also featuring a Portuguese version!

The limits to parapatric speciation: Dobzhansky-Muller incompatibilities in a continent-island model

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

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

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