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Modular epistasis in yeast metabolism

Nature Genetics volume 37, pages 77–83 (2005)Cite this article

Abstract

Epistatic interactions, manifested in the effects of mutations on the phenotypes caused by other mutations, may help uncover the functional organization of complex biological networks1,2,3. Here, we studied system-level epistatic interactions by computing growth phenotypes of all single and double knockouts of 890 metabolic genes in Saccharomyces cerevisiae, using the framework of flux balance analysis4. A new scale for epistasis identified a distinctive trimodal distribution of these epistatic effects, allowing gene pairs to be classified as buffering, aggravating or noninteracting2,5. We found that the ensuing epistatic interaction network6 could be organized hierarchically into function-enriched modules that interact with each other 'monochromatically' (i.e., with purely aggravating or purely buffering epistatic links). This property extends the concept of epistasis from single genes to functional units and provides a new definition of biological modularity, which emphasizes interactions between, rather than within, functional modules. Our approach can be used to infer functional gene modules from purely phenotypic epistasis measurements.

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Figure 1: Epistatic interactions between mutations can be classified into three distinct classes.
Figure 2: Epistatic interactions between genes classified by functional annotation groups tend to be of a single sign (i.e., monochromatic).
Figure 3: Schematic description of the Prism algorithm.
Figure 4: Unsupervised organization of the gene interaction network using the Prism algorithm.

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Acknowledgements

We thank L. Garwin, A. Murray, D. Fisher and D. Hartl for feedback and advice; A. Murray for the idea of complete buffering as indicative of biological modularity; Z. Kang for computational help; and J. Aach, U. Alon, N. Berger, A. Dudley, M. Elowitz, D. Fraenkel, G. Getz, M. Hegreness, P. Ma, B. Palsson D. Peer, O. Rando, A. Regev, U. Sauer, N. Shoresh, M. Turelli, D. Weinreich and M. Wright for comments. D.S. and G.C. acknowledge support from the Defense Advanced Research Projects Agency, the Genomes to Life program of the US Department of Energy and the Pharmaceutical Research and Manufacturers of America Foundation. R.K. acknowledges support from the Bauer Center for Genomics Research.

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Authors and Affiliations

  1. Lipper Center for Computational Genetics and Department of Genetics, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Daniel Segrè & George M Church

  2. Bauer Center for Genomics Research, Harvard University, 7 Divinity Avenue, Cambridge, 02138, Massachusetts, USA

    Alexander DeLuna & Roy Kishony

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Correspondence to Roy Kishony.

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Supplementary information

Supplementary Fig. 1

The epistasis scale captures the strength of the epistasis effect relative to natural extreme values. (PDF 4 kb)

Supplementary Fig. 2

Analysis of the effects of double deletions of metabolic enzyme genes in simple metabolic networks demonstrating examples of multiplicative, aggravating and buffering gene deletion interactions. (PDF 27 kb)

Supplementary Fig. 3

Sensitivity analysis with respect to free parameters and physiological conditions. (PDF 3 kb)

Supplementary Fig. 4

Sub-classification of buffering interactions into directional and non-directional links is related to the underlying biochemical network. (PDF 6 kb)

Supplementary Fig. 5

Changes in monochromatically interacting epistasis modules following variation of oxygen uptake rate. (PDF 7 kb)

Supplementary Fig. 6

Examples of monochromatic clustering of three toy networks using the Prism algorithm. (PDF 11 kb)

Supplementary Fig. 7

Randomization algorithms and statistical tests for monochromaticity in the epistasis network. (PDF 7 kb)

Supplementary Table 1

List of free parameters. (PDF 179 kb)

Supplementary Methods

The yeast flux balance model. (PDF 32 kb)

Supplementary Note

Discussion of Prism modules and predicted interactions. (PDF 46 kb)

Supplementary Video 1

Schematic demonstration of monochromatic classification. A network of two types of connections, such as buffering (green) and aggravating (red) epistasis, is sorted into module of genes that interact with one another in a purely monochromatic way. (AVI 27 kb)

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Segrè, D., DeLuna, A., Church, G. et al. Modular epistasis in yeast metabolism. Nat Genet 37, 77–83 (2005). https://doi.org/10.1038/ng1489

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