Predicting synthetic rescues in metabolic networks
Version of Record online: 12 FEB 2008
Copyright © 2008 EMBO and Nature Publishing Group
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Molecular Systems Biology
Volume 4, Issue 1, 2008
How to Cite
Motter, A. E., Gulbahce, N., Almaas, E. and Barabási, A.-L. (2008), Predicting synthetic rescues in metabolic networks. Molecular Systems Biology, 4: n/a. doi: 10.1038/msb.2008.1
- Issue online: 12 FEB 2008
- Version of Record online: 12 FEB 2008
- Manuscript Accepted: 16 DEC 2007
- Manuscript Received: 18 SEP 2007
- complex networks;
- genetic interactions;
- genetic recovery;
- systems biology
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 non-viable 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.
Advances in studies of genome-level cellular networks have highlighted striking properties in their large-scale structure, such as a heavy-tailed connectivity distribution and hierarchical or modular organization (Barabási and Oltvai, 2004). However, to capture functional aspects of biological systems, it is necessary to take into account dynamical processes, such as enzymatic activity in the case of metabolism.
Here, we use metabolic reaction fluxes as a representation of cellular phenotypes to develop network-based strategies to recover metabolic function that may have been lost due to defective genes. A recent study of cascading failures in generic complex networks (Motter, 2004) suggested that these cascades can be mitigated through the intentional removal of selected links. In the context of biological networks, this result raises the rather counterintuitive possibility that damage to cellular phenotypes, such as growth, can be alleviated by the targeted removal or downregulation of selected genes. In this study, we have developed a systematic approach to identify such rescue gene knockouts in genome-wide metabolic networks.
Focusing on the metabolism of single-cell organisms, we demonstrate our approach by computationally analyzing reconstructed metabolic networks of Escherichia coli (Edwards and Palsson, 2000) and Saccharomyces cerevisiae (Duarte et al, 2004). We identify genes whose removal can improve the growth of knockout mutants with reduced growth performance relative to the wild type. This is achieved by forcing the cell to either bypass the functions affected by the initial gene loss or to compensate for the lost function. In the extreme case of mutants with zero growth, our analysis predicts that it is sometimes possible to identify single genes whose removal will make it possible for the organism to regain the ability to grow. Consequently, our results suggest the possibility of synthetic-rescue genes, and thus, promise to represent a paradigm shift in the study of gene essentiality.
Figure 1 is a schematic description of our framework to identify possible cases of metabolic rescue. It is based on a combination of the constraint-based approach of flux balance analysis (FBA) (Edwards and Palsson, 2000) and the related method of minimization of metabolic adjustment (MOMA) (Segrè et al, 2002). Simply stated, FBA is a computational technique that identifies possible steady-state reaction fluxes in genome-scale metabolic networks, and MOMA is a variant that predicts postmutational flux states.
We use MOMA to predict the growth of mutants, since the decrease in an organism's growth rate that often follows a gene deletion frequently could be just a transient effect: experiments in fixed media show that after many generations both wild-type and mutant strains typically increase their growth rate (Edwards and Palsson, 2000; Fong and Palsson, 2004) through the accumulation of appropriate regulatory changes and mutations that bring the metabolic system to an optimal steady state. Consequently, FBA is appropriate for predicting the growth phenotype of adapted wild-type strains, as well as the maximum potential for growth recovery in mutant strains.
Our analysis has identified multiple examples of the rescue effect, for both growing and non-growing mutants (Figure 4). We refer to the former case as a suboptimal recovery and the latter as the Lazarus effect. In particular, for E. coli cells in a minimal medium with glucose as the single carbon source, we predict that mutants with the lethal deletion of gene fbaA, pfk, or tpiA can be rescued through the concurrent deletion of other genes (Figure 4A). Sometimes, such as in the case of fbaA mutants in arabinose medium, the Lazarus effect comes along with the deletion of a single additional gene (Figure 4B). Counterintuitively, however, we find that the strength of the recovery generally increases with the number of genes that can be deleted.
The mechanism underlying the rescue effect is general and does not depend on the specific details of MOMA or FBA. In particular, any computational or experimental methodology that can help estimate metabolic fluxes can be used to identify candidates for rescue deletions. This study thus suggests a promising approach to restore metabolic function and identify genetic compensatory interactions, with potentially important implications for disease treatment research.