Co-Expression Network Models Suggest that Stress Increases Tolerance to Mutations.

Sonja Lehtinen,Christine Orengo,Jürg Bähler

doi:10.1038/srep16726

Abstract

Network models are a well established tool for studying the robustness of complex systems, including modelling the effect of loss of function mutations in protein interaction networks. Past work has concentrated on average damage caused by random node removal, with little attention to the shape of the damage distribution. In this work, we use fission yeast co-expression networks before and after exposure to stress to model the effect of stress on mutational robustness. We find that exposure to stress decreases the average damage from node removal, suggesting stress induces greater tolerance to loss of function mutations. The shape of the damage distribution is also changed upon stress, with a greater incidence of extreme damage after exposure to stress. We demonstrate that the change in shape of the damage distribution can have considerable functional consequences, highlighting the need to consider the damage distribution in addition to average behaviour.

Highlights

Robustness, the ability to maintain biological function in the face of perturbation, is considered a fundamental property of evolvable complex systems[1]
In physical protein interaction networks, there is evidence to suggest that node removal captures the consequences of loss of function mutations: high degree nodes have been shown to (a) considerably disrupt measures of network connectivity when removed from the network, and (b) correspond to genes likely to be essential[12]
The centrality-lethality correlation has been found to be missing in high quality physical interaction networks mapped using techniques less likely to exhibit these types of biases[14]

Summary

Introduction

Robustness, the ability to maintain biological function in the face of perturbation, is considered a fundamental property of evolvable complex systems[1]. In physical protein interaction networks (where edges indicate protein binding), there is evidence to suggest that node removal captures the consequences of loss of function mutations: high degree nodes have been shown to (a) considerably disrupt measures of network connectivity when removed from the network, and (b) correspond to genes likely to be essential[12]. To some extent, this centrality-lethality relationship may be accentuated by biases in interactome mapping: high-throughput protein interaction detection techniques have been found to favour highly expressed and highly conserved proteins[13], both of which are likely features of essential proteins. The effect is unlikely to be entirely caused by bias because it has been documented in a wide range of networks[15], including co-expression networks (see below)[16], which are not susceptible to the same mapping biases as physical interaction networks

Methods

Results

Conclusion