Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The metabolic background is a global player in Saccharomyces gene expression epistasis


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Gene expression response to 16 combinatorial differences in the metabolic–genetic background.
Figure 2: Transcriptional response to a metabolic gene deletion is sensitive to the metabolic–genetic background.
Figure 3: Metabolic perturbations interact epistatically and dominate quantitative expression profiles.
Figure 4: Metabolism-induced epistasis propagates to the proteome and increases at the metabolome.


  1. 1

    Albert, R. Scale-free networks in cell biology. J. Cell Sci. 118, 4947–4957 (2005).

    Article  Google Scholar 

  2. 2

    Barabási, A.-L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nature Rev. Genet. 5, 101–113 (2004).

    Article  Google Scholar 

  3. 3

    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).

    Article  Google Scholar 

  4. 4

    Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabási, A.-L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).

    Article  Google Scholar 

  5. 5

    Newman, M. E. J. Modularity and community structure in networks. Proc. Natl Acad. Sci. 103, 8577–8582 (2006).

    Article  Google Scholar 

  6. 6

    Romero, P. et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 6, R2 (2005).

    Article  Google Scholar 

  7. 7

    Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nature Biotechnol. 31, 419–425 (2013).

    Article  Google Scholar 

  8. 8

    Clark, A. G. & Fucito, C. D. Stress tolerance and metabolic response to stress in Drosophila melanogaster. Heredity 81, 514–527 (1998).

    Article  Google Scholar 

  9. 9

    Ihmels, J., Levy, R. & Barkai, N. Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae. Nature Biotechnol. 22, 86–92 (2004).

    Article  Google Scholar 

  10. 10

    Liu, L., Li, Y. & Tollefsbol, T. O. Gene–environment interactions and epigenetic basis of human diseases. Curr. Issues Mol. Biol. 10, 25–36 (2008).

    Google Scholar 

  11. 11

    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).

    Article  Google Scholar 

  12. 12

    Campbell, K. et al. Self-establishing communities enable cooperative metabolite exchange in a eukaryote. eLife (2015).

  13. 13

    Fink, G. R. Gene–enzyme relations in Histidine biosynthesis in yeast. Science 146, 525–527 (1964).

    Article  Google Scholar 

  14. 14

    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).

    Google Scholar 

  15. 15

    Lacroute, F. Regulation of pyrimidine biosynthesis in Saccharomyces cerevisiae. J. Bacteriol. 95, 824–832 (1968).

    Google Scholar 

  16. 16

    Masselot, M. & De Robichon-Szulmajster, H. Methionine biosynthesis in Saccharomyces cerevisiae. I. Genetical analysis of auxotrophic mutants. Mol. Gen. Genet. 139, 121–132 (1975).

    Article  Google Scholar 

  17. 17

    Mülleder, M. et al. A prototrophic deletion mutant collection for yeast metabolomics and systems biology. Nature Biotechnol. 30, 1176–1178 (2012).

    Article  Google Scholar 

  18. 18

    Brazma, A. et al. ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68–71 (2003).

    Article  Google Scholar 

  19. 19

    Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264–276 (2003).

    Article  Google Scholar 

  20. 20

    Schellenberger, J. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protoc. 6, 1290–1307 (2011).

    Article  Google Scholar 

  21. 21

    Fisher, R. A. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edin. 52, 399–433 (1918).

    Article  Google Scholar 

  22. 22

    Park, S. & Lehner, B. Epigenetic epistatic interactions constrain the evolution of gene expression. Mol. Syst. Biol. 9, 645 (2013).

    Article  Google Scholar 

  23. 23

    Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    Article  Google Scholar 

  24. 24

    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).

    Article  Google Scholar 

  25. 25

    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).

    Article  Google Scholar 

  26. 26

    Kemmeren, P. et al. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell 157, 740–752 (2014).

    Article  Google Scholar 

  27. 27

    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).

    Article  Google Scholar 

  28. 28

    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).

    Article  Google Scholar 

  29. 29

    Silva, J. C. et al. Quantitative proteomic analysis by accurate mass retention time pairs. Anal. Chem. 77, 2187–2200 (2005).

    Article  Google Scholar 

  30. 30

    Grüning, N.-M., Lehrach, H. & Ralser, M. Regulatory crosstalk of the metabolic network. Trends Biochem. Sci. 35, 220–227 (2010).

    Article  Google Scholar 

  31. 31

    Patel, A. et al. A liquid-to-solid phase transition of the ALS protein FUS accelerated by disease mutation. Cell 162, 1066–1077 (2015).

    Article  Google Scholar 

  32. 32

    Jaenisch, R. & Bird, A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nature Genet. 33, 245–254 (2003).

    Article  Google Scholar 

  33. 33

    Hashimoto, S. et al. Isolation of auxotrophic mutants of diploid industrial yeast strains after UV mutagenesis. Appl. Environ. Microbiol. 71, 312–319 (2005).

    Article  Google Scholar 

  34. 34

    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).

    Article  Google Scholar 

  35. 35

    Low, B. Rapid mapping of conditional and auxotrophic mutations in Escherichia coli K-12. J. Bacteriol. 113, 798–812 (1973).

    Google Scholar 

  36. 36

    Pronk, J. T. Auxotrophic yeast strains in fundamental and applied research. Appl. Environ. Microbiol. 68, 2095–2100 (2002).

    Article  Google Scholar 

  37. 37

    Hack, C. J. Integrated transcriptome and proteome data: the challenges ahead. Brief. Funct. Genom. Proteom. 3, 212–219 (2004).

    Article  Google Scholar 

  38. 38

    Payne, S. H. The utility of protein and mRNA correlation. Trends Biochem. Sci. 40, 1–3 (2015).

    Article  Google Scholar 

  39. 39

    Ryan, O. et al. Global gene deletion analysis exploring yeast filamentous growth. Science 337, 1353–1356 (2012).

    Article  Google Scholar 

  40. 40

    Dowell, R. D. et al. Genotype to phenotype: a complex problem. Science 328, 469 (2010).

    Article  Google Scholar 

  41. 41

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  Google Scholar 

  42. 42

    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).

    Google Scholar 

  43. 43

    Cherry, J. M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–D705 (2012).

    Article  Google Scholar 

  44. 44

    von der Haar, T. Optimized protein extraction for quantitative proteomics of yeasts. PLoS ONE 2, e1078 (2007).

    Article  Google Scholar 

  45. 45

    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).

    Article  Google Scholar 

  46. 46

    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).

    Article  Google Scholar 

  47. 47

    Kelly, R. T. et al. Chemically etched open tubular and monolithic emitters for nanoelectrospray ionization mass spectrometry. Anal. Chem. 78, 7796–7801 (2006).

    Article  Google Scholar 

  48. 48

    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).

    Article  Google Scholar 

  49. 49

    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).

    Article  Google Scholar 

  50. 50

    Gentleman, R., Carey, V. J., Huber, W., Irizarry, R. A. & Dudoit, S. (eds) Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Springer, 2005).

    Book  Google Scholar 

  51. 51

    Andrews, D. Robust Estimates of Location (Princeton Univ. Press, 1972).

    Google Scholar 

  52. 52

    Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  Google Scholar 

  53. 53

    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).

    Article  Google Scholar 

  54. 54

    Buescher, J. M. et al. Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism. Science 335, 1099–1103 (2012).

    Article  Google Scholar 

  55. 55

    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).

    Article  Google Scholar 

  56. 56

    Dixon, S. J., Costanzo, M., Baryshnikova, A., Andrews, B. & Boone, C. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).

    Article  Google Scholar 

  57. 57

    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).

    Article  Google Scholar 

  58. 58

    Segrè, D., Deluna, A., Church, G. M. & Kishony, R. Modular epistasis in yeast metabolism. Nature Genet. 37, 77–83 (2005).

    Article  Google Scholar 

  59. 59

    Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).

    Article  Google Scholar 

  60. 60

    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).

    Article  Google Scholar 

  61. 61

    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).

    Article  Google Scholar 

  62. 62

    Dymond, J. S. et al. Synthetic chromosome arms function in yeast and generate phenotypic diversity by design. Nature 477, 471–476 (2011).

    Article  Google Scholar 

  63. 63

    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).

    Article  Google Scholar 

  64. 64

    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).

    Article  Google Scholar 

  65. 65

    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).

    Article  Google Scholar 

  66. 66

    Miller, C. et al. Mediator phosphorylation prevents stress response transcription during non-stress conditions. J. Biol. Chem. 287, 44017–44026 (2012).

    Article  Google Scholar 

  67. 67

    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).

    Article  Google Scholar 

  68. 68

    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).

    Article  Google Scholar 

  69. 69

    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).

    Article  Google Scholar 

  70. 70

    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).

    Article  Google Scholar 

  71. 71

    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).

    Article  Google Scholar 

  72. 72

    Tauber, E. et al. Functional gene expression profiling in yeast implicates translational dysfunction in mutant huntingtin toxicity. J. Biol. Chem. 286, 410–419 (2011).

    Article  Google Scholar 

  73. 73

    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).

    Article  Google Scholar 

  74. 74

    Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nature Genet. 43, 656–662 (2011).

    Article  Google Scholar 

  75. 75

    Vizcaíno, J. A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–D1069 (2013).

    Article  Google Scholar 

  76. 76

    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).

    Article  Google Scholar 

Download references


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.

Author information




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.

Corresponding author

Correspondence to Markus Ralser.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

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)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing