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

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.

References in corpus (6)

• ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
• Adaptive Filtering Enhances Information Transmission in Visual Cortex
• Life is physics: evolution as a collective phenomenon far from equilibrium
• Quantitative Characterization of Combinatorial Transcriptional Control of the Lactose Operon of E. coli
• Evolutionary accessibility of mutational pathways
• Ising models for networks of real neurons

Cited by in corpus (6)

• The context-dependence of mutations: a linkage of formalisms
• Inferring fitness landscapes by regression produces biased estimates of epistasis
• On the sufficiency of pairwise interactions in maximum entropy models of biological networks
• Recent advances in accelerated discovery through machine learning and statistical inference
• Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds
• Designing over uncertain outcomes with stochastic sampling Bayesian optimization