Evolutionary Stalling and a Limit on the Power of Natural Selection to Improve a Cellular Module

Abstract

10Biological organisms are modular. Theory predicts that natural selection would steadily improve 11 modules towards their performance optima up to the margin of effective neutrality. This classical 12 theory may break down for populations evolving in the clonal interference regime because 13 natural selection may focus on some modules while adaptation of others stalls. Such evolutionary 14 stalling has not been observed and it is unclear whether it limits the power of natural selection to 15 optimize module performance. To empirically characterize evolutionary stalling, we evolved 16 populations of Escherichia coli with genetically perturbed translation machineries (TMs). We 17show that populations with different suboptimal TMs embark on statistically distinct trajectories 18 of TM optimization. Yet, before TMs approach the margin of effective neutrality, the focus of 19 natural selection shifts to other cellular modules, and TM optimization stalls. Our results suggest 20 that module optimization within an organism may take much longer than suggested by classical 21 theory.

Asymmetric Evolvability Leads to Specialization without Trade-Offs

“…These results leverage the findings of Schiffels et al (2011), and later work by Gomez et al (2020) and Venkataram et al (2019), to illustrate that asexual adaptation is not just slower than adaptation with recombination, but differs in other significant aspects. Other models have predicted differences in the nature of adaptation in asexual versus sexual organisms, such as difference in epistasis among fixed mutations (Livnat et al 2008) and, as discussed above, asexuality is linked with extreme generalism in some insects (Gibson 2019).…”

“…Recent theory focusing on asexual evolution has predicted that adaptation among sites with small selection coefficients can be effectively stalled by interference from rapidly evolving sites with larger effects (Schiffels et al 2011). Extending this model to multiple traits, Gomez et al (2019) showed that clonal interference can produce a false signature of trade-offs where none exist, and Gomez et al (2020) showed that a trait with more frequent or larger beneficial mutations can effectively stall adaptation in a trait with lower evolvability, an effect that Venkataram et al (2019) recently demonstrated experimentally. Here, specialization is seen to evolve when a rare environment becomes unprofitable in comparison to the more common environment, solely because of slow relative improvement in how organisms can exploit the rare environment.…”

Discovery of positive and purifying selection in metagenomic time series of hypermutator microbial populations

“…For instance, it is possible that early beneficial mutations become deleterious due to further mutations (Zee, et al 2014;Quandt, et al 2015). Furthermore, natural selection may greedily favor mutationally accessible but suboptimal trajectories (Rodrigues and Shakhnovich 2019; Venkataram, et al 2019) that then open new, idiosyncratic paths for further refinement.…”

Evolutionary stalling and a limit on the power of natural selection to improve a cellular module

“…All analyses and plots reported in this manuscript have been performed using the R computing environment. The script, modified reference genomes, and raw data (except for raw sequencing data) used for analysis can be found on GitHub at https://github.com/sandeepvenkataram/EvoStalling (83). Raw sequencing data for this project have been deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under project PRJNA560969 (84).…”