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An integrated approach to characterize genetic interaction networks in yeast metabolism

Abstract

Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between 185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.

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Figure 1: Distribution and monochromaticity of genetic interactions between functional groups.
Figure 2: Degree distribution of genetic interaction networks and gene dispensability.
Figure 3: Comparison of computationally predicted and empirically determined genetic interactions.
Figure 4: Automated model refinement procedure.
Figure 5: Automated model refinement suggests modifications in NAD biosynthesis.

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Acknowledgements

This work was supported by grants from The International Human Frontier Science Program Organization, the Hungarian Scientific Research Fund (OTKA PD 75261) and the 'Lendület Program' of the Hungarian Academy of Sciences (B.P.), European Research Council (202591), Wellcome Trust and Hungarian Scientific Research Fund (C.P.), FEBS Long-Term Fellowship (B. Szamecz), Biotechnology & Biological Sciences Research Council (Grant BB/C505140/1) and the UNICELLSYS Collaborative Project (No. 201142) of the European Commission (S.G.O.), the US National Institutes of Health (1R01HG005084-01A1) and a seed grant from the University of Minnesota Biomedical Informatics and Computational Biology program (C.L.M.), the Canadian Institutes of Health Research (MOP-102629) (C.B. and B.J.A.) and the US National Institutes of Health (1R01HG005853-01) (C.B., B.J.A. and C.L.M.).

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Authors

Contributions

M.C., C.L.M., B.J.A. and C.B. designed genetic interaction screens; A.B., M.C. and C.L.M. collected and analyzed raw data; B.P., C.P., M.J. and S.G.O. designed the computational study; B. Szappanos, K.K., F.H. and B.P. performed computational and statistical analyses; B. Szamecz performed auxotrophy experiments; G.G.-D. and M.J.L. developed software tools; and B.P., C.P., B. Szappanos, K.K., M.J. and S.G.O. wrote the paper.

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Correspondence to Balázs Papp.

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The authors declare no competing financial interests.

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Supplementary Figures 1–4, Supplementary Tables 1–3 and Supplementary Note. (PDF 1192 kb)

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Szappanos, B., Kovács, K., Szamecz, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat Genet 43, 656–662 (2011). https://doi.org/10.1038/ng.846

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