The functional basis of adaptive evolution in chemostats

Abstract

Two of the central problems in biology are determining the molecular basis of adaptive evolution and understanding how cells regulate their growth. The chemostat is a device for culturing cells that provides great utility in tackling both of these problems: it enables precise control of the selective pressure under which organisms evolve and it facilitates experimental control of cell growth rate. The aim of this review is to synthesize results from studies of the functional basis of adaptive evolution in long-term chemostat selections using Escherichia coli and Saccharomyces cerevisiae. We describe the principle of the chemostat, provide a summary of studies of experimental evolution in chemostats, and use these studies to assess our current understanding of selection in the chemostat. Functional studies of adaptive evolution in chemostats provide a unique means of interrogating the genetic networks that control cell growth, which complements functional genomic approaches and quantitative trait loci (QTL) mapping in natural populations. An integrated approach to the study of adaptive evolution that accounts for both molecular function and evolutionary processes is critical to advancing our understanding of evolution. By renewing efforts to integrate these two research programs, experimental evolution in chemostats is ideally suited to extending the functional synthesis to the study of genetic networks.

Keywords: adaptive evolution; cell growth; chemostats; copy number variation; nutrient limitation; selection.

Understanding how microorganisms evolve under conditions of continuous culturing using chemostats provides insight into the genetic networks that regulate cell growth and how those networks are rewired during evolution.

Figure 1.

Design of a chemostat. Typically, a chemostat comprises a culture vessel in which the population grows under continuous agitation and aeration. New media flows into the vessel at a defined rate. At the same rate, culture containing cells and medium is removed from the chemostat. The flow of media and culture is maintained using a pumping apparatus and holding the chemostat vessel under positive pressure by means of a constant air flow.

Figure 2.

Establishment of a steady state in the chemostat. Following inoculation and initiation of culture dilution, the chemostat is characterized by a period during which the population increases and nutrient abundance declines. Eventually, a steady state is established in which the cell population remains high and the concentration of the limiting nutrient remains low. The steady state is predicted by the fundamental equations of the chemostat and depends on the parameter values used in the simulation. In this simulation, μ_max = 0.4 h⁻¹, K_s = 0.05 mM, Y = 4.6 × 10⁷ cells mmol⁻¹, R = 0.8 mM, and D = 0.12 h⁻¹. The simulation was initialized with x = 1 × 10⁷ cells mL⁻¹ and s = 0.8 mM.

Figure 3.

Expected waiting time for beneficial mutations. The time until a beneficial mutations rises to a 50%, allele frequency in the population depends on the fitness effect of the mutation. Increased initial frequencies of a mutant genotype due to the occurrence of mutations during the establishment phase of the chemostat further reduce the waiting time.

Figure 4.

Systematic variation of selection intensity and population size in a chemostat. (a) Steady-state nutrient concentration increases with increasing dilution rates. (b) Population size (red) in the chemostat can be controlled by varying the concentration of the limiting nutrient in the feed media (R) and maintaining a constant dilution rate. In this case, the steady-state concentration of the limiting nutrient in the chemostat (blue) remains constant.

Figure 5.

The fitness landscape in a chemostat is determined by changes in K_s and μ_max due to adaptive mutations. A mutation that (A) increases μ_max or (B) decreases K_s will result in increased fitness. Pleiotropic mutations that (C) synergistically increase μ_max and decrease K_s will be adaptive. Pleiotropic mutations that act (D) antagonistically by decreasing μ_max and decreasing K_s can still be beneficial depending on the relative effects on the two parameters. Data were simulated using s = 10 mM, ancestral K_s = 10 μM, and ancestral μ_max = 0.4 h⁻¹.

Figure 6.

Modularity of cell growth regulating processes. A hierarchy of cellular processes control cell growth and are potential targets of selection in the chemostat.

Figure 7.

Convergence of cellular processes that are targets of adaptive evolution in chemostats. We performed GO term enrichment of networks generated using genemania (Warde-Farley et al., 2010) seeded with loci containing sequence variants identified in lineages and whole populations evolved in glucose-limited and nitrogen-limited chemostats from Gresham et al. (2008), Kvitek and Sherlock (2011) and Hong and Gresham (2014).