Genotype to phenotype mapping and the fitness landscape of the E. coli lac promoter

TL;DR

This study constructs a detailed genotype-to-phenotype map for the E. coli lac promoter, revealing the roles of additive effects and pairwise epistasis in shaping the fitness landscape, with implications for understanding gene regulation.

Contribution

The paper introduces a high-throughput approach to map the fitness landscape of the lac promoter, quantifying epistatic interactions and identifying regulatory mechanisms without complex optimization.

Findings

Additive effects explain about two-thirds of phenotype variance.

Pairwise epistasis accounts for 7-15% of variance.

The fitness landscape is essentially single peaked with minimal higher-order epistasis.

Abstract

Genotype-to-phenotype maps and the related fitness landscapes that include epistatic interactions are difficult to measure because of their high dimensional structure. Here we construct such a map using the recently collected corpora of high-throughput sequence data from the 75 base pairs long mutagenized E. coli lac promoter region, where each sequence is associated with its phenotype, the induced transcriptional activity measured by a fluorescent reporter. We find that the additive (non-epistatic) contributions of individual mutations account for about two-thirds of the explainable phenotype variance, while pairwise epistasis explains about 7% of the variance for the full mutagenized sequence and about 15% for the subsequence associated with protein binding sites. Surprisingly, there is no evidence for third order epistatic contributions, and our inferred fitness landscape is essentially single peaked, with a small amount of antagonistic epistasis. There is a significant selective pressure on the wild type, which we deduce to be multi-objective optimal for gene expression in environments with different nutrient sources. We identify transcription factor (CRP) and RNA polymerase binding sites in the promotor region and their interactions without difficult optimization steps. In particular, we observe evidence for previously unexplored genetic regulatory mechanisms, possibly kinetic in nature. We conclude with a cautionary note that inferred properties of fitness landscapes may be severely influenced by biases in the sequence data.