Periodic Reporting for period 3 - FIT2GO (A toolbox for fitness landscapes in evolution)
Reporting period: 2022-03-01 to 2023-08-31
Quantifying how populations adapt to new environments is at the heart of evolutionary research. The possibility to adapt and the speed at which this is possible has direct implications for conservation and the response of populations to the climate crisis.
A factor that is often ignored when looking at genetic adaptation is the potential interaction of genes (epistasis), which may result in a different effect of two mutations that appear together from the effect of each individual mutation. How frequent such interactions are is contentious. If they are frequent, this crucially affects the predictability of evolution. In addition, epistasis can constrain the adaptive paths that populations can take to improve their fitness in a new environment.
The concept of fitness landscapes, which are mappings of genotype or phenotypes to fitness, has frequently been used to describe the consequences of epistasis. There exists a rich body of theory, and recently, technological advances have allowed for increasingly large experimental assessments of fitness landscapes.
In this project, we build on the theory of fitness landscapes to understand the prevalence of epistasis across levels of biological organization (e.g. protein, pathway, population levels) and across environments (e.g. hot, cold, in the presence of an antimicrobial treatment). Combining models and experiments, we will test whether the change of epistasis across environments is predictable and how much it affects adaptation upon environmental change.
A factor that is often ignored when looking at genetic adaptation is the potential interaction of genes (epistasis), which may result in a different effect of two mutations that appear together from the effect of each individual mutation. How frequent such interactions are is contentious. If they are frequent, this crucially affects the predictability of evolution. In addition, epistasis can constrain the adaptive paths that populations can take to improve their fitness in a new environment.
The concept of fitness landscapes, which are mappings of genotype or phenotypes to fitness, has frequently been used to describe the consequences of epistasis. There exists a rich body of theory, and recently, technological advances have allowed for increasingly large experimental assessments of fitness landscapes.
In this project, we build on the theory of fitness landscapes to understand the prevalence of epistasis across levels of biological organization (e.g. protein, pathway, population levels) and across environments (e.g. hot, cold, in the presence of an antimicrobial treatment). Combining models and experiments, we will test whether the change of epistasis across environments is predictable and how much it affects adaptation upon environmental change.
We have characterized the distribution of fitness effects of new mutations in the heat-shock protein Hsp90 (Cote-Hammarlof, Fragata et al., MBE, 2020, Flynn et al., eLife, 2020). With large data sets comprising more than 100 amino-acids (compared to 9 in previous studies) and across various environments, we were able to map the fitness variation of mutations to the protein sequence.
We studied evolutionary dynamics in the mouse gut and in the presence of inversions. We quantified the mutational load (i.e. how many deleterious mutations accumulate over time in a genome) in the mouse gut (Ramiro et al., Plos Biology, 2020) and found that the mutational load in the mouse gut is very low even in the presence of mutator genotypes (i.e. genotypes that mutate more than average). In a simulation project, we found that inversions (i.e. "flipped" parts of the genome) can lead to rapid accumulation of deleterious mutations, even resulting in recessive lethal systems, where only heterozygous individuals survive (Berdan, Blanckaert et al., Plos Genetics, 2021).
In the research direction of speciation, we developed a model demonstrating that with fitness landscapes involving three loci and cryptic epistasis (i.e. a non-trivial fitness landscape), speciation in the presence of gene flow is possible (Blanckaert et al., Phil Trans B, 2020). We also developed a new statistical method to detect hybrid incompatibilities (Li et al., biorxiv, 2021), which can infer interacting genetic loci that are located on the same or different chromosomes. This is an important step to quantifying the role of hybrid incompatibilities in the maintenance of species barriers. Moreover, we were involved in the writing of a perspective on leveraging haplodiploid organisms for the better understanding of hybridization and the detection of hybrid incompatibilities (Nouhaud et al., TrEE, 2020).
We studied evolutionary dynamics in the mouse gut and in the presence of inversions. We quantified the mutational load (i.e. how many deleterious mutations accumulate over time in a genome) in the mouse gut (Ramiro et al., Plos Biology, 2020) and found that the mutational load in the mouse gut is very low even in the presence of mutator genotypes (i.e. genotypes that mutate more than average). In a simulation project, we found that inversions (i.e. "flipped" parts of the genome) can lead to rapid accumulation of deleterious mutations, even resulting in recessive lethal systems, where only heterozygous individuals survive (Berdan, Blanckaert et al., Plos Genetics, 2021).
In the research direction of speciation, we developed a model demonstrating that with fitness landscapes involving three loci and cryptic epistasis (i.e. a non-trivial fitness landscape), speciation in the presence of gene flow is possible (Blanckaert et al., Phil Trans B, 2020). We also developed a new statistical method to detect hybrid incompatibilities (Li et al., biorxiv, 2021), which can infer interacting genetic loci that are located on the same or different chromosomes. This is an important step to quantifying the role of hybrid incompatibilities in the maintenance of species barriers. Moreover, we were involved in the writing of a perspective on leveraging haplodiploid organisms for the better understanding of hybridization and the detection of hybrid incompatibilities (Nouhaud et al., TrEE, 2020).
Our work is expanding current knowledge in the field of fitness landscape by considering the change of fitness and epistasis across environments, and by inferring epistasis from genomic data. We are currently developing a new set of dynamic fitness landscape models that will be an important step towards quantifying eco-evolutionary dynamics in the presence of epistasis, and we are addressing how prevalent epistasis is in the experimental data we obtained from E. coli and influenza experiments.