Speaker
Description
Fitness landscapes map genotypes to fitness, visualizing possible evolutionary paths. These landscapes are studied both at the conceptual level and made explicit by measuring the fitness of nearby genotypes to create empirical fitness landscapes. Since the mapping of the genotype to fitness depends on the environment, several approaches have been taken to include the environment. One of them is using seascapes, landscapes that change over time, resembling a changing environment. Another aspect is that genotypes themselves might change the environment, leading to the deformability of a fitness landscape. All these methods assume a new genotype removes the old phenotype from the population. However, certain changes of the environment, such as the production of cross-feeding components or the removal of toxins from the environment, allow strains to coexist. Recent results in the sequencing of long-term cultures have shown that coexistence is common and can last at evolutionary timescales. To study the effect of coexistence on evolutionary paths, we introduce 'eco-fitness landscapes', which include ecological mechanisms of coexistence. Coexistence states greatly increase the connectivity of a fitness landscape, and could lead to accessibility of fitness peaks that are otherwise unreachable. Coexistence states can also be incorporated in empirical fitness landscapes. As an example we use a previously characterized empirical fitness landscape of increased cefotaxime resistance in E. coli with a TEM1 gene coding for a beta-lactamase, which can break down the antibiotic cefotaxime. The breakdown results in lower cefotaxime concentrations, which can allow for growth of more susceptible strains, a mechanism called cross-protection. Cross-protection is more pronounced at higher cell densities, because the extracellular concentrations are stronger affected when there are more cells. We measured the properties affecting these dynamics, such as growth rates and antibiotic clearance rates, of eight different mutants, with mutations in the TEM1 gene, and show experimentally that at some densities coexistence is possible. Using this experimental data in a mathematical model we can create an empirical 'eco-fitness landscape'. Then we can predict how evolutionary trajectories are affected by coexistence due to cross-protection, depending on the density of the culture. Including ecology in fitness landscapes may lead to more realistic prediction of evolutionary trajectories and outcomes when processes that promote coexistence are involved. Moreover, including coexistence states in conceptual studies of fitness landscapes can lead to a different view on the reachability of fitness optima.
