Evolutionary trajectories in rugged fitness landscapes

The mutation rate is an important determinant of evolutionary dynamics. Because the mutation rate determines the rate of appearance of beneficial and deleterious mutations, it is subject to second-order selection. The mutation rate varies between and within species and populations, increases under stress, and is genetically controlled by mutator alleles. The mutation rate may also vary among genetically identical individuals: empirical evidence from bacteria suggests that the mutation rate may be affected by translation errors and expression noise in various proteins (1). Importantly, this non-genetic variation may be heritable via transgenerational epigenetic inheritance. Here we investigate how the inheritance mode of the mutation rate affects the rate of adaptive evolution on rugged fitness landscapes. We model an asexual population with two mutation rate phenotypes, non-mutator and mutator. An offspring may switch from its parental phenotype to the other phenotype. The rate of switching between the mutation rate phenotypes is allowed to span a range of values. Thus, the mutation rate can be interpreted as a genetically inherited trait when the switching rate is low, as an epigenetically inherited trait when the switching rate is intermediate, or as a randomly determined trait when the switching rate is high. We find that epigenetically inherited mutation rates result in the highest rates of adaptation on rugged fitness landscapes for most realistic parameter sets. This is because an intermediate switching rate can maintain the association between a mutator phenotype and pre-existing mutations, which facilitates the crossing of fitness valleys. Our results provide a rationale for the evolution of epigenetic inheritance of the mutation rate, suggesting that it could have been selected because it facilitates adaptive evolution.

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DISSEQT—DIStribution-based modeling of SEQuence space Time dynamics†

Virus Evolution ◽

10.1093/ve/vez028 ◽

2019 ◽

Vol 5 (2) ◽

Cited By ~ 1

Author(s):

R Henningsson ◽

G Moratorio ◽

A V Bordería ◽

M Vignuzzi ◽

M Fontes

Keyword(s):

Sequence Space ◽

Rna Virus ◽

Population Based ◽

Space Time ◽

Fitness Landscapes ◽

Microbial Populations ◽

Dynamic Nature ◽

Time Dynamics ◽

Low Dimension ◽

Evolutionary Trajectories

Abstract Rapidly evolving microbes are a challenge to model because of the volatile, complex, and dynamic nature of their populations. We developed the DISSEQT pipeline (DIStribution-based SEQuence space Time dynamics) for analyzing, visualizing, and predicting the evolution of heterogeneous biological populations in multidimensional genetic space, suited for population-based modeling of deep sequencing and high-throughput data. The pipeline is openly available on GitHub (https://github.com/rasmushenningsson/DISSEQT.jl, accessed 23 June 2019) and Synapse (https://www.synapse.org/#!Synapse: syn11425758, accessed 23 June 2019), covering the entire workflow from read alignment to visualization of results. Our pipeline is centered around robust dimension and model reduction algorithms for analysis of genotypic data with additional capabilities for including phenotypic features to explore dynamic genotype–phenotype maps. We illustrate its utility and capacity with examples from evolving RNA virus populations, which present one of the highest degrees of genetic heterogeneity within a given population found in nature. Using our pipeline, we empirically reconstruct the evolutionary trajectories of evolving populations in sequence space and genotype–phenotype fitness landscapes. We show that while sequence space is vastly multidimensional, the relevant genetic space of evolving microbial populations is of intrinsically low dimension. In addition, evolutionary trajectories of these populations can be faithfully monitored to identify the key minority genotypes contributing most to evolution. Finally, we show that empirical fitness landscapes, when reconstructed to include minority variants, can predict phenotype from genotype with high accuracy.

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