A Reassessment of Positive Growth Effects of Expressed Random Sequence Clones in E. coli Suggests Direct Adaptive Functions
Year: 2026
Type: article
Abstract: De novo gene emergence from noncoding sequences is an important evolutionary mechanism, yet the functional potential of random sequences remains debated. Previous experiments suggested that expression of random sequence clones in Escherichia coli can enhance the growth of cells, ie they can provide a direct fitness advantage. However, these findings have been questioned, regarding potential confounding effects of the clone mixtures and a possibly negatively acting peptide expressed from the cloning vector. Here, we performed controlled competitive growth assays using a defined subset of random sequence clones representing a spectrum of fitness effects. Experiments across multiple conditions, including two different growth cycle durations, induction states, and replicate sets, showed high technical reproducibility and consistent clone-specific growth trajectories for the majority of the clones, but for some, also influences of genomic background and experimental conditions. While previous results for vector-derived control constructs could be confirmed, several random sequence clones exhibited higher positive selection coefficients than these vector constructs. These relative effects persisted even when negative clones were excluded, indicating that they are not driven by competition dynamics with negative clones. Our results demonstrate that positive growth effects of random sequence clones cannot be explained by clone mixture and vector artifacts alone. Instead, a subset of random sequences confers genuine fitness advantages comparable to beneficial mutations observed in experimental evolution studies. These findings provide experimental support for the capacity of random sequences to directly generate adaptive effects without the necessity to acquire additional adaptive mutations.
Source: Genome Biology and Evolution
DOI: 10.1093/gbe/evag159
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,850
OpenAlex ID: W7166156808