Destabilizing mutations encode nongenetic variation that drives evolutionary innovation

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Science  30 Mar 2018:
Vol. 359, Issue 6383, pp. 1542-1545
DOI: 10.1126/science.aar1954

Nongenetic variation drives viral evolution

Bacteriophage λ is a virus that infects bacteria by exploiting various membrane proteins in a well-characterized manner. Petrie et al. show how the evolution of variable folding conformations of isogenic proteins, which do not differ in their genetic sequences, contributed to λ's ability to exploit an additional host receptor for infection. Because the protein can take on two shapes, this genotype can have two phenotypes. Natural selection may thus be able to act on this nongenetic heterogeneity to connect phenotypic heterogeneity, evolvability, and protein stability.

Science, this issue p. 1542


Evolutionary innovations are often achieved by repurposing existing genes to perform new functions; however, the mechanisms enabling the transition from old to new remain controversial. We identified mutations in bacteriophage λ’s host-recognition gene J that confer enhanced adsorption to λ’s native receptor, LamB, and the ability to access a new receptor, OmpF. The mutations destabilize λ particles and cause conformational bistability of J, which yields progeny of multiple phenotypic forms, each proficient at different receptors. This work provides an example of how nongenetic protein variation can catalyze an evolutionary innovation. We propose that cases where a single genotype can manifest as multiple phenotypes may be more common than previously expected and offer a general mechanism for evolutionary innovation.

The tree of life is punctuated with numerous biological innovations, but explaining the origin of novelty has been problematic. Natural selection, the predominant theory for adaptation, is unable to explain how mutations encode new function (1, 2). This dilemma is epitomized by the progress made in understanding the evolution of the model virus bacteriophage λ. Under typical laboratory conditions, λ evolves to exploit a new receptor (OmpF) (3). This innovation is achieved through four mutations in its host-recognition gene, J (3). The mutations evolve by natural selection because they accelerate adsorption to the original receptor (LamB) (4) and later are coopted for OmpF function (3). Remaining to be explained is how J mutations improve LamB adsorption and open access to OmpF. Here we report studies of the molecular mechanisms of J mutations and offer an answer to the question of how novelty arises.

Protein functional evolution is typically thought to occur through gene duplication and divergence (5). This model cannot explain λ’s evolution because the J gene did not duplicate (3). Instead, λ evolved a promiscuous J protein capable of using LamB and OmpF receptors, which then diverged into two receptor specialists (6). The leading explanation for the evolution of promiscuity is through protein-destabilizing mutations that cause structural disorder, allowing proteins to occupy multiple conformations (710). Once a promiscuous protein is evolved, additional evolutionary refinement of specific function leads to specialization and restabilization (8, 9, 11) (Fig. 1A).

Fig. 1 Evolutionary innovation through functional bistability.

(A) Schematic overview of λ evolution. Mutations to improve ancestral receptor binding reduce stability until a critical mutation produces a multifunctional genotype. Further evolution can restabilize the protein. (B) Temperature sensitivity of genotypes with indicated numbers of mutations relative to the ancestor. Some have more than four mutations because λ often evolves additional mutations before achieving OmpF use. The y axis shows the fraction of infectious particles remaining after incubation. Curves are logistic models fit to data from multiple trials; fig. S1 shows all data (12). OmpF+ phages are shown in teal, and LamB-reliant genotypes are shown in black. (C) Temperature sensitivity of the ancestor, an OmpF+ generalist, and the generalist’s specialist descendants. Bars indicate the data range; fig. S2 shows all data.

We tested this hypothesis by measuring the thermostability of five λ genotypes that only varied in the number of J mutations. Each strain was genetically engineered with mutations that have been observed to evolve on the path to OmpF+ (3). Thermostability was assessed by measuring the temperature dependence of phage survival after 1 hour of exposure to multiple temperatures, ranging from λ’s natural 37°C to a destabilizing 55°C. The number of mutations and the degree of sensitivity were correlated [Spearman’s r = 0.90, P = 0.0167; Fig. 1B and fig. S1 (12)]. One caveat is that nonsynonymous mutations tend to destabilize proteins (11), so this relationship is also expected under a null model of protein mutagenesis.

We tested whether additional J mutations that cause specialization restabilize the protein. We measured the thermostability of the ancestral λ and an OmpF+ generalist variant, as well as 10 of the generalist’s descendants that evolved to specialize on either LamB or OmpF (table S1) (6). The null expectation is that all specialists should lose stability because each evolved additional J mutations; however, none did. In fact, five out of five LamB specialists and three out of five OmpF specialists regained ancestral stability (fig. S2). On average, the specialists were significantly more stable than the OmpF+ generalist [Fisher’s method, Χ2(4) = 22.6, P = 0.0001] and indistinguishable from the ancestor [Fisher’s method, Χ2(4) = 5.56, P = 0.234] [Fig. 1C; comparisons made at 51.4°C (12)].

An additional investigation of λ particle decay yielded a surprising discovery. We studied the temporal dynamics of the most and least stable strains observed (ancestor and seven mutations; Fig. 1B) decaying at 37°C for 18 days (Fig. 2A). The seven-mutation OmpF+ generalist phage decayed at a faster rate, but, unexpectedly, both genotypes decayed at decreased rates over time. If phage stocks contained just a single phage phenotype, then all particles should share a common half-life. Instead, a model of fast-decaying and slow-decaying subpopulations fits the data better than a model of single exponential decay [fig. S3 and table S3 (12)]. Thus, despite originating from an isogenic stock, λ particles exhibit phenotypic heterogeneity: Some are stable and some are unstable. In line with this observation was the previous discovery that genetically identical λ particles can have different rates of LamB adsorption as well (13).

Fig. 2 Decay rate heterogeneity.

Decay of ancestor (black) or OmpF+ generalist (teal) phage over (A) 18 days or (B) 2 days. Four replicates (pale) and the average (bold) are shown in (A). Individual replicates are shown in (B). (C) Number of phage particles able to form plaques on LamB-only (solid) or OmpF-only (dashed) bacterial lawns after 2 days. Individual replicates are shown. pfu, plaque forming units.

Destabilized promiscuous intermediates may facilitate innovation by permitting proteins to flexibly accommodate different ligands (11, 14), or they may produce polypeptide chains that are sensitive to contingent folding processes that result in multiple isoforms with different capacities (9, 15). Sometimes, the energy barrier between these isoforms may be high enough to prevent their refolding, making the protein appear conformationally bistable (9, 16). The repeated observations of nongenetic phenotypic heterogeneity suggest that this mechanism may be acting during λ evolution.

To test whether the heterogeneity in decay was related to the mutations spurring the innovation, we reexamined decay dynamics (12). First, we studied whether the accelerated OmpF+ decay is due to the emergence of a new, fast-decaying phenotypic class. We found that the decay rate of this strain was significantly faster during the first day of incubation than the second (t test, t8 = –14.2, P < 0.000001); there was no significant change in the ancestral λ decay rate over 2 days (t8 = –1.35, P = 0.214) (Fig. 2B). This pattern is consistent with a new fast-decaying subgroup that is mostly lost within the first day. Next, we predicted that the newly evolved unstable subgroup was composed of OmpF+ particles. We repeated the decay experiment with the OmpF+ strain, but we quantified the number of OmpF-binding and LamB-binding particles by plating subsamples on hosts that express only OmpF (lamB) or only LamB (ompF) (12). The number of particles that produced infections on OmpF lawns declined significantly faster than the number on LamB lawns (t test, t3 = –7.31, P = 0.00528; Fig. 2C), indicating that among genetically identical λ particles, we observed unstable OmpF+ and stable LamB-reliant particles. We confirmed that the increased frequency of LamB-reliant particles was due to nongenetic heterogeneity and not genetic changes by regrowing LamB-reliant phages and showing that nearly all cultures regained function on OmpF [fig. S4 (12)]. We also confirmed that the instability was not due to proteinase sensitivity [fig. S5 (12)].

Further confirmation that only a fraction of particles can exploit OmpF was gained through an OmpF+ pull-down experiment in which λs that bind OmpF were selectively removed (12). If all λ particles are equivalent, then this assay would not affect the frequency of remaining particles that exploit LamB or OmpF. However, if there is heterogeneity, then we would expect a decline in the ratio of OmpF+ to LamB+ particles. OmpF pull-downs preferentially depleted λs that could form plaques on the OmpF lawns (t test, t2 = –5.15, P = 0.0357; Fig. 3A). As a control, we confirmed that this depletion by binding was greater than the decay observed when the incubation was done with no complementary receptors (t test, t3 = –4.14, P = 0.0255; Fig. 3B).

Fig. 3 Isolation and properties of λ subpopulations.

A phage pull-down assay shows the number of phage particles capable of infecting LamB-only (solid) or OmpF-only (dashed) hosts before and after incubation (A) in the presence of OmpF-only cells or (B) alone (in the absence of cells). (C) Adsorption rate to LamB-only cells before or after OmpF innovation; genotypes differ by one mutation. (D) LamB adsorption rate of the entire OmpF+ culture compared with that of the LamB-faithful subpopulation. P values are from paired t tests [(A) and (B)] or standard t tests [(C) and (D)]. h, hours.

Taken together, our results indicate that J mutations that move λ toward innovation destabilize the phage and allow it to access multiple phenotypes. We reason that this observed heterogeneity originates from the stochastic folding of individual J proteins, because J mutations alone are sufficient to alter the heterogeneity. However, because we studied the whole phage, we are unable to confirm whether the heterogeneity is entirely explained by J protein variation or whether it stems from an interaction between J and the rest of the particle.

Next, we studied how natural selection promotes the evolution of nongenetic phenotypic heterogeneity. We hypothesized that the OmpF+ subpopulation is a by-product of selection for improved LamB adsorption and a result of protein misfolding. To test this, we compared the adsorption rates of an OmpF+ genotype and its OmpF progenitor, which is one mutation removed from OmpF function (3). As expected, even the last mutation required for the innovation improved binding to the original receptor (t test, t4 = 11.7, P = 0.0003; Fig. 3C). Next, we predicted that the LamB-reliant fraction of OmpF+ λ particles should have a faster adsorption rate on LamB than the misfolded OmpF+ particles. We measured the adsorption to LamB of the entire population versus that of the LamB-reliant subpopulation by removing OmpF+ particles with the OmpF pull-down technique. The pull-down technique enriched the unbound fraction for particles that were 13.4% faster at adsorbing to LamB (t test, t8 = 2.77, P = 0.0244; Fig. 3D), indicating that there is a cost for producing the subpopulation of OmpF+ particles. This demonstrates that a single gene can evolve to improve two functions simultaneously by expressing multiple forms, even if the functions are antagonistic.

Nongenetic phenotypic heterogeneity may arise by chance, or it could evolve by natural selection if a distribution of phenotypes confers higher fitness than a single type. We tested this by mathematical evaluations of the fitness of generalist λs that only vary in the width of their adsorption rate distributions. We assumed that the λ particles’ adsorption rates to LamB and OmpF are normally distributed but constrained by a trade-off curve (6) and adsorption rates ≥0. We found that small variances resulted in substantial loss of mean population fitness; however, at large variances, this cost was recouped, and fitness exceeded that at zero variance [Fig. 4 (12)].

Fig. 4 The fitness of ensembles of phenotypes.

(A) Population fitness as a function of phenotypic variance. (B) Example of bivariate normal distribution of phenotypes constrained by a convex Pareto front (19). The variance of the colored points in (A) is the parameter used to calculate corresponding colored outlines in (B). The outlines represent the largest variance at which 50% of the population is contained. Error bars in (A) represent numerical error in estimating the integrals.

We show that the OmpF innovation evolved by violating the “one sequence, one structure” dogma (17) with a DNA sequence that encodes multiple protein forms. This allowed the virus to simultaneously optimize binding to its original receptor and experiment with new functions. Nongenetic heterogeneity in gene expression levels is predicted to facilitate evolutionary novelty (18); here, we extend this hypothesis to the expressed molecules themselves and demonstrate the role of nongenetic heterogeneity in action. Our mathematical model establishes circumstances where nongenetic heterogeneity is adaptive, and thus this mechanism is more likely than if heterogeneity evolved by chance alone.

Supplementary Materials

Materials and Methods

Supplementary Text

Figs. S1 to S6

Tables S1 to S6

References (2037)

References and Notes

  1. Supplementary materials.
Acknowledgments: We thank A. Gupta and A. Tripathi for help in the laboratory. Funding: K.L.P. is supported by the ELSI Origins Network, funded by the John Templeton Foundation. A.R.B. was supported by the U.S. NSF (award DGE-1424871). The ideas expressed herein are not necessarily those of the funders. Author contributions: K.L.P. and J.R.M. conceived the project, N.D.P. developed mathematical models, all authors ran experiments, and J.R.M. and K.L.P. wrote the manuscript. Competing interests: The authors have none. Data and materials availability: Data are deposited at Biological material is available from J.R.M. under a material transfer agreement with UC San Diego (

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