Adaptive Landscapes in the Age of Synthetic Biology

Abstract

For nearly a century adaptive landscapes have provided overviews of the evolutionary process and yet they remain metaphors. We redefine adaptive landscapes in terms of biological processes rather than descriptive phenomenology. We focus on the underlying mechanisms that generate emergent properties such as epistasis, dominance, trade-offs and adaptive peaks. We illustrate the utility of landscapes in predicting the course of adaptation and the distribution of fitness effects. We abandon aged arguments concerning landscape ruggedness in favor of empirically determining landscape architecture. In so doing, we transform the landscape metaphor into a scientific framework within which causal hypotheses can be tested.

Keywords: adaptive landscape; distribution of fitness effects; epistasis; genotype by environment interaction; genotype–phenotype gap; pleiotropy.

Fig. 1.

Three elements of an adaptive landscape: G, genotypes; P, phenotypes; and W, fitnesses. A fourth element, the environment (E, not shown), enters implicitly in transforming genotypes into phenotypes (the GP map) and phenotypes into fitnesses (the PW map).

Fig. 2.

(A). The genotype network of TEM-1 β-lactamase showing the 18 possible routes of ever increasing fitness (log₁₀ MIC) from wildtype to the cefotaxime resistant TEM*. The 16 genotypes accessible only by mutations that lower fitness are not shown. (B) An example of sign epistasis. Replacement M182T enhances resistance only in the presence of replacement G238S. Evolution from M182, G238 to T182, S238 must proceed counter clockwise through M182, S238.

Fig. 3.

Spectral tuning in visual pigments is achieved by modulating the relative energies of the ground and excited states. (A) Upon photoexcitation, the retinal chromophore isomerizes from the 11-cis ground state to the all-trans conformation. (B) QM/MM (Altun et al. 2011) and PBQC/MM (Collette et al. 2018) calculations accurately predict experimentally observed shifts in λ_max: from UV to violet in mutants of ancestral and modern fish SWS1 opsins (dots) and from red to green in mutants of bovine M/LWS opsin (squares). (C and D) QM/MM calculations showed, and mutagenesis experiments confirmed (Tada et al. 2009), that the shift in λ_max from UV to violet in fish SWS1 opsins was achieved by the deleting Phe-86. This rearranged the hydrogen bond network surrounding the retinal and converted the unprotonated Schiff base–linked 11-cis-retinal (C) to the protonated form (D). (C) and (D) from Tada et al. (2009).

Fig. 4.

(A) The lactose pathway of Escherichia coli consists of three steps: 1) passive diffusion of lactose through porin pores (green) into the periplasm, 2) active transport of lactose by the lacY encoded permease (blue) into the cytoplasm, and 3) irreversible hydrolysis by the lacZ-encoded β-galactosidase (red). (B) Starvation in chemostats ensures that the growth rate, μ, is proportional to the flux of lactose, J, into central metabolism. Flux is analogous to current in Ohm’s law of resistance, I = V/R: I ≡ J is current, V ≡ [Environmental Lactose] is the potential and R = Σ1/C_i ≡ Σ1/E_i is resistance. The conductance of each component (C_i) is analogous to enzyme activity (E_i ∝ [A_i] k_cat.i/K_m.i, where A_i is the concentration of active enzyme and k_cat.i and K_m.i are the Michaelis–Menten parameters). Hence, relative growth rate (relative fitness) equals relative flux (μ_operon/μ_K12 = J_operon/J_K12). (C) This mechanistic biochemical model (the straight line) accurately predicts relative fitness: dark blue is the E. coli K12 operon with β-galactosidase mutants (red), permease mutants (blue), operons from natural isolates (green), and lac⁻ mutant at the origin (yellow). (D) Strong directional selection drives β-galactosidase activity onto a fitness plateau, a limit of adaptation where evolution is governed by neutral processes. (E) Not all steps in a pathway can lie in a limit of adaptation (Kacser and Burns 1981; Hartl et al. 1985); mutants with half wildtype activity might be selectively neutral (or nearly so) at β-galactosidase (red line), yet mildly deleterious at the permease (blue line) and strongly selected against at the porin step (green line). (F) A 100-fold increase in activity brings the porins close to their limit of adaptation. Necessarily, the β-galactosidase and permease become more rate limiting and so selection against their mutants intensifies, providing an example of intergenic epistasis.

Fig. 5.

The adaptive landscape controlling coenzyme use by IMDH (Lunzer et al. 2005). (A) The NAD⁺-dependent wildtype (blue ball) lies on a high fitness plateau (right), whereas the NADP⁺-dependent RKYVYR mutant (red ball) lies on a lower-fitness plateau (left). A trade-off in activity leaves the interior largely devoid of mutants. (B) Structural biology (Gonçalves et al. 2012; Palló et al. 2014) shows the nicotinamide ring of the coenzyme above the γ-isopropyl moiety of the bound substrate/product. NADH and NADPH are potent inhibitors of IMDH because the reduced nicotinamide ring binds the γ-isopropyl moiety tightly (Dean and Dvorak 1995; Miller et al. 2006). (C) NADH and NADPH are weak inhibitors of the related IDH because the reduced nicotinamide rings have no affinity for the negatively charged γ-carboxylate of the isocitrate substrate (Dean and Koshland 1993). (D) A maximum likelihood phylogeny of the IDH-IMDH family of enzymes reveals that all IMDHs use NAD, whereas the related IDHs have evolved NADP use several times.

Fig. 6.

The adaptive landscape for methanol catabolism by the Paracoccus denitrificans glutathione-dependent pathway placed in Methylobacterium extorquens reveals a single adaptive peak that could not have been predicted a priori. The surface represents the fit to a model in which fitness is proportional to the methanol flux minus the costs associated with protein expression and with the buildup of formaldehyde, a toxic metabolite. The peak is reached either by a single mutation or by a combination of mutations. Both diminishing returns epistasis and sign epistasis are present. Ancestor (asterisk), single mutants (gray circles), mutational combinations (white squares), and inducible expression from plasmids (black circles). From Chou et al. (2014).

Fig. 7.

Evolution of information processing in a synthetic operon. (A) The wildtype regulatory system of the Escherichia coli lac operon was used to control expression of sacB, which confers sensitivity to sucrose (Suc), and cmR, which confers resistance to chloramphenicol (Clm). (B) Induction of the operon by IPTG modulates sensitivity to sucrose (orange) and resistance to chloramphenicol (blue). (C) The phenotype–fitness map of the operon when alternating between two media, one with sucrose and the other with chloramphenicol. Operons expressed only in the presence of chloramphenicol occupy the adaptive peak (green dot). Operons whose expression is insensitive to the environmental cue lie on the blue line according their level of constitutive expression. Operons expressed only in the presence of sucrose occupy the maladaptive valley (red dot). The light ellipse depicts the region from which deregulated mutants were first isolated.

Fig. 8.

Evolution of phenotypic plasticity to escape from an adaptive peak. (A) The selection regime where cells must chemotax into a capillary of fresh medium to be transferred to fresh medium. (B) Fitness is a function of growth rate and the ability to chemotax. Logistic growth produces curved contours in the phenotype–fitness map (exponential growth would produce linear contours). The dashed line represents the empirically determined position of the trade-off between chemotaxis and growth rate. The peak (red dot) is broad because the trade-off lies parallel to the contours. Each point is the mean of six weekly isolates from an evolving population. Ancestor (Anc), optimum (Opt), and week sampled (numbers). The large standard standard errors at week 8 are a consequence of transient polymorphisms. (C) The ancestor (Anc) swims fastest during exponential growth when resources are abundant. Week 7 isolates increase the overall swimming speed. Week 9 isolates reduce swimming speed during midlog phase while maintain increased swimming speed during transfer. (D) Amino acid replacement Arg220Trp removes a positive charge and hydrogen bond between transcription factor FliA and the promotor DNA. From Yi and Dean (2016).

Fig. 9.

(A) DFE for amino acid replacements in Escherichia coli β-galactosidase with a limit of resolution of ±0.5%/generation (Dean et al. 1988). (B) A similar U-shaped DFE is seen for TEM-1 β-lactamase (Stiffler et al. 2015) in the presence of 156-μM ampicillin. Near wildtype, the distribution barely differs from the bell curve (blue) in the absence of ampicillin. (C) Increasing the ampicillin concentration effectively pushes the wildtype TEM-1 β-lactamase off its fitness plateau. Now most replacements are strongly deleterious.