The genetic landscape of a physical interaction.

Guillaume Diss,Ben Lehner

doi:10.7554/elife.32472

Abstract

A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here, by developing a new sequencing-based protein interaction assay - deepPCA - we quantified the effects of >120,000 pairs of point mutations on the formation of the AP-1 transcription factor complex between the products of the FOS and JUN proto-oncogenes. Genetic interactions are abundant both in cis (within one protein) and trans (between the two molecules) and consist of two classes - interactions driven by thermodynamics that can be predicted using a three-parameter global model, and structural interactions between proximally located residues. These results reveal how physical interactions generate quantitatively predictable genetic interactions.

Highlights

Mutations often have outcomes that change depending upon additional genetic variation carried by an individual, making their effects difficult to predict (Lehner, 2011)
Gene deletions are rare in nature – most genetic variation consists of point mutations not deletions or null alleles
To quantify how mutations of diverse individual effect combine to alter protein interactions, we developed deepPCA, a protein-protein interaction assay that uses PCA and deep sequencing to Figure 1 continued input and output populations to be measured and a Protein-protein interactions (PPIs) score that represents the number of generation of each variant relative to the wild-type interaction to be computed. (C) Scatter plot of PPI scores between biological replicates 1 and 2. (D) Confirmation of single mutants by individual PCA growth assays

Summary

Introduction

Mutations often have outcomes that change depending upon additional genetic variation carried by an individual, making their effects difficult to predict (Lehner, 2011). One approach that has been taken to better understand how mutations interact to alter phenotypes has been to systematically combine together gene deletions or representative hypomorphic alleles (Baryshnikova et al, 2013). In budding yeast, this has been undertaken on a genomic scale, with the resulting network of interactions referred to as the ‘genetic landscape’ of a cell (Costanzo et al, 2010, 2016; Tong et al, 2004). Point mutations can have very diverse and difficult to predict effects (Shendure and Akey, 2015). There has been no systematic effort to map how point mutations in two genes combine together to alter biological functions

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