Predicting synthetic rescues in metabolic networks

TL;DR

This paper presents a computational network-based method to identify gene deletions that can restore growth in mutants lacking essential enzymes, offering a novel approach for genetic interventions in metabolic networks.

Contribution

It introduces a systematic flux balance analysis approach to predict synthetic rescue gene pairs that can compensate for lethal mutations in metabolic networks.

Findings

Identified gene pairs where additional deletions rescue organism viability.

Demonstrated that targeted network damage can restore growth in mutants.

Proposed a new strategy for genetic intervention based on synthetic rescues.

Abstract

An important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory function. Here, we propose an alternative, network-based strategy that aims to restore biological function by forcing the cell to either bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of single-cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network. Using flux balance-based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one enzyme-encoding gene results in a nonviable phenotype, while the deletion of a second enzyme-encoding gene rescues the organism. The systematic network-based identification of compensatory rescue effects may open new avenues for genetic interventions.