Abstract
The regulation of gene expression in response to nutrient availability is fundamental to the genotype–phenotype relationship. The metabolic–genetic make-up of the cell, as reflected in auxotrophy, is hence likely to be a determinant of gene expression. Here, we address the importance of the metabolic–genetic background by monitoring transcriptome, proteome and metabolome in a repertoire of 16 Saccharomyces cerevisiae laboratory backgrounds, combinatorially perturbed in histidine, leucine, methionine and uracil biosynthesis. The metabolic background affected up to 85% of the coding genome. Suggesting widespread confounding, these transcriptional changes show, on average, 83% overlap between unrelated auxotrophs and 35% with previously published transcriptomes generated for non-metabolic gene knockouts. Background-dependent gene expression correlated with metabolic flux and acted, predominantly through masking or suppression, on 88% of transcriptional interactions epistatically. As a consequence, the deletion of the same metabolic gene in a different background could provoke an entirely different transcriptional response. Propagating to the proteome and scaling up at the metabolome, metabolic background dependencies reveal the prevalence of metabolism-dependent epistasis at all regulatory levels. Urging a fundamental change of the prevailing laboratory practice of using auxotrophs and nutrient supplemented media, these results reveal epistatic intertwining of metabolism with gene expression on the genomic scale.
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Albert, R. Scale-free networks in cell biology. J. Cell Sci. 118, 4947–4957 (2005).
Barabási, A.-L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nature Rev. Genet. 5, 101–113 (2004).
Herrgård, M. J. et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnol. 26, 1155–1160 (2008).
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabási, A.-L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).
Newman, M. E. J. Modularity and community structure in networks. Proc. Natl Acad. Sci. 103, 8577–8582 (2006).
Romero, P. et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 6, R2 (2005).
Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nature Biotechnol. 31, 419–425 (2013).
Clark, A. G. & Fucito, C. D. Stress tolerance and metabolic response to stress in Drosophila melanogaster. Heredity 81, 514–527 (1998).
Ihmels, J., Levy, R. & Barkai, N. Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae. Nature Biotechnol. 22, 86–92 (2004).
Liu, L., Li, Y. & Tollefsbol, T. O. Gene–environment interactions and epigenetic basis of human diseases. Curr. Issues Mol. Biol. 10, 25–36 (2008).
Tu, B. P., Kudlicki, A., Rowicka, M. & McKnight, S. L. Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes. Science 310, 1152–1158 (2005).
Campbell, K. et al. Self-establishing communities enable cooperative metabolite exchange in a eukaryote. eLife http://dx.doi.org/10.7554/eLife.09943 (2015).
Fink, G. R. Gene–enzyme relations in Histidine biosynthesis in yeast. Science 146, 525–527 (1964).
Satyanarayana, T., Umbarger, H. E. & Lindegren, G. Biosynthesis of branched-chain amino acids in yeast: regulation of leucine biosynthesis in prototrophic and leucine auxotrophic strains. J. Bacteriol. 96, 2018–2024 (1968).
Lacroute, F. Regulation of pyrimidine biosynthesis in Saccharomyces cerevisiae. J. Bacteriol. 95, 824–832 (1968).
Masselot, M. & De Robichon-Szulmajster, H. Methionine biosynthesis in Saccharomyces cerevisiae. I. Genetical analysis of auxotrophic mutants. Mol. Gen. Genet. 139, 121–132 (1975).
Mülleder, M. et al. A prototrophic deletion mutant collection for yeast metabolomics and systems biology. Nature Biotechnol. 30, 1176–1178 (2012).
Brazma, A. et al. ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68–71 (2003).
Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264–276 (2003).
Schellenberger, J. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protoc. 6, 1290–1307 (2011).
Fisher, R. A. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edin. 52, 399–433 (1918).
Park, S. & Lehner, B. Epigenetic epistatic interactions constrain the evolution of gene expression. Mol. Syst. Biol. 9, 645 (2013).
Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).
Kim, H. et al. YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae. Nucleic Acids Res. 42, D731–D736 (2014).
Breen, M. S., Kemena, C., Vlasov, P. K., Notredame, C. & Kondrashov, F. A. Epistasis as the primary factor in molecular evolution. Nature 490, 535–538 (2012).
Kemmeren, P. et al. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell 157, 740–752 (2014).
Alam, M. T., Medema, M. H., Takano, E. & Breitling, R. Comparative genome-scale metabolic modeling of actinomycetes: the topology of essential core metabolism. FEBS Lett. 585, 2389–2394 (2011).
Shliaha, P. V., Bond, N. J., Gatto, L. & Lilley, K. S. Effects of traveling wave ion mobility separation on data independent acquisition in proteomics studies. J. Proteome Res. 12, 2323–2339 (2013).
Silva, J. C. et al. Quantitative proteomic analysis by accurate mass retention time pairs. Anal. Chem. 77, 2187–2200 (2005).
Grüning, N.-M., Lehrach, H. & Ralser, M. Regulatory crosstalk of the metabolic network. Trends Biochem. Sci. 35, 220–227 (2010).
Patel, A. et al. A liquid-to-solid phase transition of the ALS protein FUS accelerated by disease mutation. Cell 162, 1066–1077 (2015).
Jaenisch, R. & Bird, A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nature Genet. 33, 245–254 (2003).
Hashimoto, S. et al. Isolation of auxotrophic mutants of diploid industrial yeast strains after UV mutagenesis. Appl. Environ. Microbiol. 71, 312–319 (2005).
Kokina, A., Kibilds, J. & Liepins, J. Adenine auxotrophy—be aware: some effects of adenine auxotrophy in Saccharomyces cerevisiae strain W303-1A. FEMS Yeast Res. 14, 697–707 (2014).
Low, B. Rapid mapping of conditional and auxotrophic mutations in Escherichia coli K-12. J. Bacteriol. 113, 798–812 (1973).
Pronk, J. T. Auxotrophic yeast strains in fundamental and applied research. Appl. Environ. Microbiol. 68, 2095–2100 (2002).
Hack, C. J. Integrated transcriptome and proteome data: the challenges ahead. Brief. Funct. Genom. Proteom. 3, 212–219 (2004).
Payne, S. H. The utility of protein and mRNA correlation. Trends Biochem. Sci. 40, 1–3 (2015).
Ryan, O. et al. Global gene deletion analysis exploring yeast filamentous growth. Science 337, 1353–1356 (2012).
Dowell, R. D. et al. Genotype to phenotype: a complex problem. Science 328, 469 (2010).
Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).
Cherry, J. M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–D705 (2012).
von der Haar, T. Optimized protein extraction for quantitative proteomics of yeasts. PLoS ONE 2, e1078 (2007).
Fic, E., Kedracka-Krok, S., Jankowska, U., Pirog, A. & Dziedzicka-Wasylewska, M. Comparison of protein precipitation methods for various rat brain structures prior to proteomic analysis. Electrophoresis 31, 3573–3579 (2010).
Vowinckel, J. et al. The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics. F1000Research 2, 272 (2014).
Kelly, R. T. et al. Chemically etched open tubular and monolithic emitters for nanoelectrospray ionization mass spectrometry. Anal. Chem. 78, 7796–7801 (2006).
Li, G.-Z. et al. Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics 9, 1696–1719 (2009).
Bond, N. J., Shliaha, P. V., Lilley, K. S. & Gatto, L. Improving qualitative and quantitative performance for MSE-based label-free proteomics. J. Proteome Res. 12, 2340–2353 (2013).
Gentleman, R., Carey, V. J., Huber, W., Irizarry, R. A. & Dudoit, S. (eds) Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Springer, 2005).
Andrews, D. Robust Estimates of Location (Princeton Univ. Press, 1972).
Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Ewald, J. C., Heux, S. & Zamboni, N. High-throughput quantitative metabolomics: workflow for cultivation, quenching, and analysis of yeast in a multiwell format. Anal. Chem. 81, 3623–3629 (2009).
Buescher, J. M. et al. Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism. Science 335, 1099–1103 (2012).
Boyle, E. I. et al. GO::TermFinder—open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 20, 3710–3715 (2004).
Dixon, S. J., Costanzo, M., Baryshnikova, A., Andrews, B. & Boone, C. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).
Mani, R., St. Onge, R. P., Hartman, J. L., Giaever, G. & Roth, F. P. Defining genetic interaction. Proc. Natl Acad. Sci. USA 105, 3461–3466 (2008).
Segrè, D., Deluna, A., Church, G. M. & Kishony, R. Modular epistasis in yeast metabolism. Nature Genet. 37, 77–83 (2005).
Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
Ansari, S. A. et al. Distinct role of Mediator tail module in regulation of SAGAdependent, TATA-containing genes in yeast. EMBO J. 31, 44–57 (2012).
Dymond, J. S. et al. Synthetic chromosome arms function in yeast and generate phenotypic diversity by design. Nature 477, 471–476 (2011).
Fournier, M. L. et al. Delayed correlation of mRNA and protein expression in rapamycin-treated cells and a role for Ggc1 in cellular sensitivity to rapamycin. Mol. Cell. Proteom. 9, 271–284 (2010).
Jimeno, S. et al. New suppressors of THO mutations identify Thp3 (Ypr045c)-Csn12 as a protein complex involved in transcription elongation. Mol. Cell. Biol. 31, 674–685 (2011).
Lu, L., Roberts, G. G., Oszust, C. & Hudson, A. P. The YJR127C/ZMS1 gene product is involved in glycerol-based respiratory growth of the yeast Saccharomyces cerevisiae. Curr. Genet. 48, 235–246 (2005).
Miller, C. et al. Mediator phosphorylation prevents stress response transcription during non-stress conditions. J. Biol. Chem. 287, 44017–44026 (2012).
Morillo-Huesca, M., Clemente-Ruiz, M., Andújar, E. & Prado, F. The SWR1 histone replacement complex causes genetic instability and genome-wide transcription misregulation in the absence of H2A.Z. PloS ONE 5, e12143 (2010).
Santos-Pereira, J. M., García-Rubio, M. L., González-Aguilera, C., Luna, R. & Aguilera, A. A genome-wide function of THSC/TREX-2 at active genes prevents transcription–replication collisions. Nucleic Acids Res. 42, 12000–12014 (2014).
Sanz, A. B. et al. Chromatin remodeling by the SWI/SNF complex is essential for transcription mediated by the yeast cell wall integrity MAPK pathway. Mol. Biol. Cell 23, 2805–2817 (2012).
Schulz, D., Pirkl, N., Lehmann, E. & Cramer, P. Rpb4 functions mainly in mRNA synthesis by RNA polymerase II. J. Biol. Chem. 289, 17446–17752 (2014).
Seizl, M., Larivière, L., Pfaffeneder, T., Wenzeck, L. & Cramer, P. Mediator head subcomplex Med11/22 contains a common helix bundle building block with a specific function in transcription initiation complex stabilization. Nucleic Acids Res. 39, 6291–6304 (2011).
Tauber, E. et al. Functional gene expression profiling in yeast implicates translational dysfunction in mutant huntingtin toxicity. J. Biol. Chem. 286, 410–419 (2011).
Mo, M. L., Palsson, B. O. & Herrgård, M. J. Connecting extracellular metabolomics measurements to intracellular flux states in yeast. BMC Syst. Biol. 3, 37 (2009).
Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nature Genet. 43, 656–662 (2011).
Vizcaíno, J. A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–D1069 (2013).
Haug, K. et al. MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41, D781–D786 (2013).
Acknowledgements
The authors thank U. Sauer (ETH Zurich) for support with metabolite measurements and scientific discussions and M. Werber and S. Klages (Max Planck Institute for Molecular Genetics) for support with RNA sequencing analysis. The authors acknowledge the Wellcome Trust (RG 093735/Z/10/Z), the ERC (starting grant 260809), the Isaac Newton Trust (RG 68998) and the Darwin Trust of Edinburgh for a studentship for P.V.S. A.Z. is an EMBO fellow. M.R. is a Wellcome Trust Research Career Development and Wellcome-Beit Prize fellow.
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M.T.A., A.Z., R.S., E.R. and S.B. performed data analysis. M.M., P.S. and S.C. carried out raw data processing. M.M., P.S., F.C., J.V., A.K., E.C., S.M. and S.C. conducted the experiments. K.R.P., B.T., K.S.L. and M.R. conceived the study. M.R. wrote the first draft. M.T.A., A.Z. and M.R. wrote the paper. All authors contributed to preparing the final version.
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Supplementary Notes 1,2 and Figures 1–11 (PDF 10202 kb)
Supplementary Data 1
List of knock-out transcriptomes. (XLSX 89 kb)
Supplementary Data 2
Processed transcriptome, proteome and metabolome data, and epistasis values. (XLSX 5787 kb)
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Alam, M., Zelezniak, A., Mülleder, M. et al. The metabolic background is a global player in Saccharomyces gene expression epistasis. Nat Microbiol 1, 15030 (2016). https://doi.org/10.1038/nmicrobiol.2015.30
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DOI: https://doi.org/10.1038/nmicrobiol.2015.30
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