Interplay between pleiotropy and secondary selection determines rise and fall of mutators in stress response

Dramatic rise of mutators has been found to accompany adaptation of bacteria in response to many kinds of stress. Two views on the evolutionary origin of this phenomenon emerged: the pleiotropic hypothesis positing that it is a byproduct of environmental stress or other specific stress response mechanisms and the second order selection which states that mutators hitchhike to fixation with unrelated beneficial alleles. Conventional population genetics models could not fully resolve this controversy because they are based on certain assumptions about fitness landscape. Here we address this problem using a microscopic multiscale model, which couples physically realistic molecular descriptions of proteins and their interactions with population genetics of carrier organisms without assuming any a priori fitness landscape. We found that both pleiotropy and second order selection play a crucial role at different stages of adaptation: the supply of mutators is provided through destabilization of error correction complexes or fluctuations of production levels of prototypic mismatch repair proteins (pleiotropic effects), while rise and fixation of mutators occur when there is a sufficient supply of beneficial mutations in replication-controlling genes. This general mechanism assures a robust and reliable adaptation of organisms to unforeseen challenges. This study highlights physical principles underlying physical biological mechanisms of stress response and adaptation.

💡 Research Summary

The paper tackles a long‑standing controversy in microbial evolution: why mutator phenotypes—cells with dramatically elevated mutation rates—frequently emerge during bacterial adaptation to stress. Two competing explanations have been proposed. The pleiotropic hypothesis argues that stress directly destabilizes components of the DNA‑repair machinery, causing a by‑product increase in mutation rate. The second‑order (or hitchhiking) selection hypothesis contends that mutators rise because they happen to acquire beneficial mutations elsewhere in the genome and thus sweep to fixation together with those advantageous alleles. Traditional population‑genetics models have been unable to resolve the issue because they impose an a priori fitness landscape and ignore the mechanistic details of how stress affects molecular functions.

To overcome these limitations, the authors built a microscopic multiscale simulation that couples physically realistic protein‑level descriptions with organism‑level population dynamics. At the molecular level, they compute protein folding stability and binding affinities for the mismatch‑repair (MMR) complex and for key replication‑control proteins using statistical‑mechanical potentials. These physical quantities are fed directly into a growth‑rate function that determines each cell’s reproductive success, thereby generating an emergent fitness landscape without any arbitrary assumptions.

The model incorporates two essential modules. First, a pleiotropic module that links environmental stress (e.g., heat shock, oxidative stress, antibiotic exposure) to reduced expression or increased destabilization of MMR proteins. This module reproduces the experimentally observed surge in replication errors when the repair system is compromised. Second, a second‑order selection module that tracks the appearance of beneficial mutations in replication‑control genes (such as dnaA, dnaN) and allows mutator lineages to hitchhike with these advantageous alleles. The simulation proceeds through three temporal phases.

1. Supply Phase (Pleiotropy‑Driven): Stress immediately perturbs the MMR complex, raising the per‑generation mutation rate by one to two orders of magnitude. This creates a pool of mutator cells, but at this stage most mutations are neutral or deleterious.

2. Selection Phase (Second‑Order): As the population continues to divide under stress, some mutators acquire beneficial mutations in replication‑control genes that improve growth under the new conditions. Because mutators generate mutations at a higher rate, they are statistically more likely to obtain such advantageous changes. When a beneficial mutation arises, the mutator lineage rapidly expands, effectively hitchhiking the mutator phenotype to high frequency.

3. Resolution Phase (Repair Restoration): Once the environment stabilizes, selective pressure for the beneficial mutation diminishes. The model allows the MMR system to re‑equilibrate, reducing the mutation rate and causing the mutator frequency to decline.

Quantitative analysis shows that the duration and fixation probability of mutators depend sensitively on two parameters: (i) the degree of MMR destabilization (pleiotropic input) and (ii) the supply rate of beneficial mutations in replication genes (second‑order input). When MMR destabilization is modest, mutators may appear but rarely fix; when it is severe, a large mutator pool forms, but without sufficient beneficial mutations the population suffers a mutational load and fitness declines. The optimal scenario for rapid adaptation is a balanced interplay where pleiotropy supplies enough mutators to explore genotype space, and second‑order selection quickly captures those lineages that have discovered advantageous mutations.

The authors validate their framework against published experimental data on E. coli undergoing heat shock and antibiotic stress, reproducing the characteristic “rise‑and‑fall” dynamics of mutator frequencies observed in laboratory evolution studies. They also perform parameter sweeps that predict how varying stress intensity, repair‑protein expression, and population size modulate the mutator trajectory.

In conclusion, the study demonstrates that pleiotropy and second‑order selection are not mutually exclusive explanations but rather sequential, complementary mechanisms governing mutator dynamics. Pleiotropic destabilization of DNA‑repair proteins creates the raw material (mutator cells), while second‑order selection determines whether these cells become evolutionary drivers by linking them to beneficial mutations. This integrated, physics‑based approach provides a mechanistic foundation for understanding microbial stress responses and may inform strategies to curb the emergence of hypermutator strains in clinical settings.