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Metabolic network remodelling enhances yeast’s fitness on xylose using aerobic glycolysis

Abstract

The reprogramming of metabolism in response to switching the carbon source from glucose to non-preferred carbon sources is well-studied for yeast. However, understanding how metabolic networks respond to utilize a non-natural carbon source such as xylose is limited due to the incomplete knowledge of cellular response mechanisms. Here we applied a combination of metabolic engineering, systems biology and adaptive laboratory evolution to gain insights into how yeast can perform a global rewiring of cellular processes to efficiently accompany metabolic transitions. Through metabolic engineering, we substantially enhanced the cell growth on xylose after the growth on glucose. Transcriptome analysis of the engineered strains demonstrated that multiple pathways were involved in the cellular reprogramming. Through genome resequencing of the evolved strains and reverse engineering, we further identified that SWI/SNF chromatin remodelling, osmotic response and aldehyde reductase were responsible for the improved growth. Combined, our analysis showed that glycerol-3-phosphate and xylitol serve as two key metabolites that affect cellular adaptation to growth on xylose.

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Fig. 1: XI-driven xylose assimilation in engineered yeast.
Fig. 2: Enhancing cell fitness on xylose through metabolic engineering.
Fig. 3: Global transcriptional analysis.
Fig. 4: The yeast native xylose responsive network can be globally rewired.
Fig. 5: TF analysis.
Fig. 6: ALE of two rationally designed engineered yeast strains.
Fig. 7: Mutations in the evolved strains highlights glycerol-3-P and xylitol as two critical metabolites.

Data availability

The RNA-seq raw data are available at the Genome Expression Omnibus website (https://www.ncbi.nlm.nih.gov/geo/) using series number GSE151478. The genome sequence data of the evolved strains used in this article are available at the Sequence Read Archive website (https://www.ncbi.nlm.nih.gov/sra) with the accession number PRJNA636080. All other data that support the findings in this study are available upon reasonable request. All plasmids and strains used in this study can be obtained from the corresponding author under a material transfer agreement. Source data are provided with this paper.

References

  1. 1.

    Botstein, D., Chervitz, S. A. & Cherry, M. Yeast as a model organism. Science 277, 1259–1260 (1997).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Nielsen, J. & Keasling, J. D. Engineering cellular metabolism. Cell 164, 1185–1197 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  3. 3.

    Kavšček, M., Stražar, M., Curk, T., Natter, K. & Petrovič, U. Yeast as a cell factory: current state and perspectives. Microb. Cell Fact. 14, 1–10 (2015).

    Article  CAS  Google Scholar 

  4. 4.

    Nielsen, J. Yeast systems biology: model organism and cell factory. Biotechnol. J. 14, 1800421 (2019).

    Article  CAS  Google Scholar 

  5. 5.

    Frendt, S. M. et al. Unraveling condition‐dependent networks of transcription factors that control metabolic pathway activity in yeast. Mol. Syst. Biol. 6, 432 (2010).

    Article  CAS  Google Scholar 

  6. 6.

    Kayikci, Ö. & Nielsen, J. Glucose repression in Saccharomyces cerevisiae. FEMS Yeast Res. 15, fov068 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Ramsey, S. A. et al. Dual feedback loops in the GAL regulon suppress cellular heterogeneity in yeast. Nat. Genet. 38, 1082–1087 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8.

    Zampar, G. G. et al. Temporal system‐level organization of the switch from glycolytic to gluconeogenic operation in yeast. Mol. Syst. Biol. 9, 651 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Li, J. et al. Molecular mechanism of environmental d-xylose perception by a XylFII–LytS complex in bacteria. Proc. Natl Acad. Sci. USA 114, 8235–8240 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Kwak, S. & Jin, Y.-S. Production of fuels and chemicals from xylose by engineered Saccharomyces cerevisiae: a review and perspective. Microb. Cell Fact. 16, 82 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  11. 11.

    Li, X., Chen, Y. & Nielsen, J. Harnessing xylose pathways for biofuels production. Curr. Opin. Biotechnol. 57, 56–65 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12.

    Jeffries, T. W. Engineering yeasts for xylose metabolism. Curr. Opin. Biotechnol. 17, 320–326 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  13. 13.

    Harhangi, H. R. et al. Xylose metabolism in the anaerobic fungus Piromyces sp. strain E2 follows the bacterial pathway. Arch. Microbiol. 180, 134–141 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Moon, J., Liu, Z. L., Ma, M. & Slininger, P. J. New genotypes of industrial yeast Saccharomyces cerevisiae engineered with YXI and heterologous xylose transporters improve xylose utilization and ethanol production. Biocatal. Agric. Biotechnol. 2, 247–254 (2013).

    Article  Google Scholar 

  15. 15.

    Lee, S.-M., Jellison, T. & Alper, H. S. Directed evolution of xylose isomerase for improved xylose catabolism and fermentation in the yeast Saccharomyces cerevisiae. Appl. Environ. Microbiol. 78, 5708–5716 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Brat, D., Boles, E. & Wiedemann, B. Functional expression of a bacterial xylose isomerase in Saccharomyces cerevisiae. Appl. Environ. Microbiol. 75, 2304–2311 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Ha, S.-J., Kim, S. R., Choi, J.-H., Park, M. S. & Jin, Y.-S. Xylitol does not inhibit xylose fermentation by engineered Saccharomyces cerevisiae expressing xylA as severely as it inhibits xylose isomerase reaction in vitro. Appl. Microbiol. Biotechnol. 92, 77–84 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  18. 18.

    Dos Santos, L. V. et al. Unraveling the genetic basis of xylose consumption in engineered Saccharomyces cerevisiae strains. Sci. Rep. 6, 38676 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. 19.

    de Figueiredo Vilela, L. et al. Functional expression of Burkholderia cenocepacia xylose isomerase in yeast increases ethanol production from a glucose–xylose blend. Bioresour. Technol. 128, 792–796 (2013).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  20. 20.

    Teunissen, A. W. R. H. & De Bont, J. A. M. Xylose isomerase genes and their use in fermentation of pentose sugars. US patent 9,334,488 (2016).

  21. 21.

    Träff, K., Cordero, R. O., Van Zyl, W. & Hahn-Hägerdal, B. Deletion of the GRE3 aldose reductase gene and its influence on xylose metabolism in recombinant strains of Saccharomyces cerevisiae expressing the xylA and XKS1 genes. Appl. Environ. Microbiol. 67, 5668–5674 (2001).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. 22.

    Hou, J., Jiao, C., Peng, B., Shen, Y. & Bao, X. Mutation of a regulator Ask10p improves xylose isomerase activity through up-regulation of molecular chaperones in Saccharomyces cerevisiae. Metab. Eng. 38, 241–250 (2016).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Xu, H. et al. PHO13 deletion-induced transcriptional activation prevents sedoheptulose accumulation during xylose metabolism in engineered Saccharomyces cerevisiae. Metab. Eng. 34, 88–96 (2016).

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Sato, T. K. et al. Directed evolution reveals unexpected epistatic interactions that alter metabolic regulation and enable anaerobic xylose use by Saccharomyces cerevisiae. PLoS Genet. 12, e1006372 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Wenger, J. W., Schwartz, K. & Sherlock, G. Bulk segregant analysis by high-throughput sequencing reveals a novel xylose utilization gene from Saccharomyces cerevisiae. PLoS Genet. 6, e1000942 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. 26.

    Young, E., Poucher, A., Comer, A., Bailey, A. & Alper, H. Functional survey for heterologous sugar transport proteins, using Saccharomyces cerevisiae as a host. Appl. Environ. Microbiol. 77, 3311–3319 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Wang, C. et al. Cloning and characterization of heterologous transporters in Saccharomyces cerevisiae and identification of important amino acids for xylose utilization. Metab. Eng. 30, 79–88 (2015).

    PubMed  Article  CAS  Google Scholar 

  28. 28.

    Apel, A. R., Ouellet, M., Szmidt-Middleton, H., Keasling, J. D. & Mukhopadhyay, A. Evolved hexose transporter enhances xylose uptake and glucose/xylose co-utilization in Saccharomyces cerevisiae. Sci. Rep. 6, 19512 (2016).

    Article  CAS  Google Scholar 

  29. 29.

    Madhani, H. D. & Fink, G. R. Combinatorial control required for the specificity of yeast MAPK signaling. Science 275, 1314–1317 (1997).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Domitrovic, T. et al. Structural and functional study of YER067W, a new protein involved in yeast metabolism control and drug resistance. PLoS ONE 5, e11163 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. 31.

    Guaragnella, N. & Butow, R. A. ATO3 encoding a putative outward ammonium transporter is an RTG-independent retrograde responsive gene regulated by GCN4 and the Ssy1-Ptr3-Ssy5 amino acid sensor system. J. Biol. Chem. 278, 45882–45887 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32.

    Edmunds, J. W. & Mahadevan, L. C. MAP kinases as structural adaptors and enzymatic activators in transcription complexes. J. Cell Sci. 117, 3715–3723 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Hinnebusch, A. G. Translational regulation of GCN4 and the general amino acid control of yeast. Annu. Rev. Microbiol. 59, 407–450 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  34. 34.

    Blom, J., De Mattos, M. J. T. & Grivell, L. A. Redirection of the respiro-fermentative flux distribution in Saccharomyces cerevisiae by overexpression of the transcription factor Hap4p. Appl. Environ. Microbiol. 66, 1970–1973 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Dabirian, Y. et al. Expanding the dynamic range of a transcription factor-based biosensor in Saccharomyces cerevisiae. ACS Synth. Biol. 8, 1968–1975 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  36. 36.

    Kim, J.-H., Polish, J. & Johnston, M. Specificity and regulation of DNA binding by the yeast glucose transporter gene repressor Rgt1. Mol. Cell. Biol. 23, 5208–5216 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Michael, D. G. et al. Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast. Proc. Natl Acad. Sci. USA 113, E7428–E7437 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Demeke, M. M., Foulquie-Moreno, M. R., Dumortier, F. & Thevelein, J. M. Rapid evolution of recombinant Saccharomyces cerevisiae for xylose fermentation through formation of extra-chromosomal circular DNA. PLoS Genet. 11, e1005010 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39.

    Westfall, P. J., Ballon, D. R. & Thorner, J. When the stress of your environment makes you go HOG wild. Science 306, 1511–1512 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. 40.

    Dihazi, H., Kessler, R. & Eschrich, K. High osmolarity glycerol (HOG) pathway-induced phosphorylation and activation of 6-phosphofructo-2-kinase are essential for glycerol accumulation and yeast cell proliferation under hyperosmotic stress. J. Biol. Chem. 279, 23961–23968 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41.

    Larroy, C., Pares, X. & Biosca, J. A. Characterization of a Saccharomyces cerevisiae NADP(H)‐dependent alcohol dehydrogenase (ADHVII), a member of the cinnamyl alcohol dehydrogenase family. Eur. J. Biochem. 269, 5738–5745 (2002).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. 42.

    Intlekofer, A. M. & Finley, L. W. Metabolic signatures of cancer cells and stem cells. Nat. Metab. 1, 177–188 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Liberti, M. V. & Locasale, J. W. The Warburg effect: how does it benefit cancer cells? Trends Biochem. Sci. 41, 211–218 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Boehlke, K. & Friesen, J. Cellular content of ribonucleic acid and protein in Saccharomyces cerevisiae as a function of exponential growth rate: calculation of the apparent peptide chain elongation rate. J. Bacteriol. 121, 429–433 (1975).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Waldron, C. & Lacroute, F. Effect of growth rate on the amounts of ribosomal and transfer ribonucleic acids in yeast. J. Bacteriol. 122, 855–865 (1975).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Dutta, A. et al. Composition and function of mutant Swi/Snf complexes. Cell Rep. 18, 2124–2134 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Vuoristo, K. S., Mars, A. E., Sanders, J. P., Eggink, G. & Weusthuis, R. A. Metabolic engineering of TCA cycle for production of chemicals. Trends Biotechnol. 34, 191–197 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. 48.

    Zhou, Y. J. et al. Production of fatty acid-derived oleochemicals and biofuels by synthetic yeast cell factories. Nat. Commun. 7, 11709 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Verduyn, C., Postma, E., Scheffers, W. A. & Van Dijken, J. P. Effect of benzoic acid on metabolic fluxes in yeasts: a continuous‐culture study on the regulation of respiration and alcoholic fermentation. Yeast 8, 501–517 (1992).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. 50.

    Mans, R. et al. CRISPR/Cas9: a molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae. FEMS Yeast Res. 15, fov004 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  51. 51.

    Liu, Q. et al. Rewiring carbon metabolism in yeast for high level production of aromatic chemicals. Nat. Commun. 10, 4976 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Yu, T. et al. Reprogramming yeast metabolism from alcoholic fermentation to lipogenesis. Cell 174, 1549–1558 (2018).

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Mikkelsen, M. D. et al. Microbial production of indolylglucosinolate through engineering of a multi-gene pathway in a versatile yeast expression platform. Metab. Eng. 14, 104–111 (2012).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Zhou, Y. J. et al. Modular pathway engineering of diterpenoid synthases and the mevalonic acid pathway for miltiradiene production. J. Am. Chem. Soc. 134, 3234–3241 (2012).

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56.

    Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  Article  Google Scholar 

  58. 58.

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Nat. Preced. https://doi.org/10.1038/npre.2010.4282.2 (2010).

  59. 59.

    Väremo, L., Nielsen, J. & Nookaew, I. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 41, 4378–4391 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. 60.

    Patil, K. R. & Nielsen, J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc. Natl Acad. Sci. USA 102, 2685–2689 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Oliveira, A. P., Patil, K. R. & Nielsen, J. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks. BMC Syst. Biol. 2, 17 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  62. 62.

    Deatherage, D. E. et al. in Engineering and Analyzing Multicellular Systems: Methods and Protocols (eds Sun, L. & Shou, W.) 165–188 (Springer, 2014).

  63. 63.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Jenjaroenpun, P. et al. Complete genomic and transcriptional landscape analysis using third-generation sequencing: a case study of Saccharomyces cerevisiae CEN.PK113-7D. Nucleic Acids Res. 46, e38 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

We thank C. Zhan, Z. Dai, H. Lu, T. Doughty, K. Campbell, R. Yu and L. F.-Y. Chao for helpful discussions. We thank J. Hellgren for the help with the RNA-seq data processing and analysis. We thank X. Chen, Z. Zhu and B. Ji for giving valuable advice on writing the manuscript. We thank X. Chen and L. F.-Y. Chao for help with the final polishing of the manuscript. This research was supported by The Novo Nordisk Foundation (NNF10CC1016517, J.N.), the Knut and Alice Wallenberg Foundation (J.N.), FORMAS (2015-01546, Y.C.), the Swedish Energy Agency (43548-1, J.N.), Carl Tryggers Stiftelse (Y.C.) and Ångpanneföreningens Forskningsstiftelse (Y.C.).

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J.N., Y.C. and X.L. conceived the study. X.L. performed most of the experiments. X.L., Y.C. and J.N. analysed all the experimental data. G.L. analysed the partial RNA-seq data. Y.W. and Q.L. assisted with the experimental performance. R.P. and X.L. analysed the genome sequence data. X.L., Y.C. and J.N. wrote the manuscript.

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Correspondence to Jens Nielsen.

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Li, X., Wang, Y., Li, G. et al. Metabolic network remodelling enhances yeast’s fitness on xylose using aerobic glycolysis. Nat Catal 4, 783–796 (2021). https://doi.org/10.1038/s41929-021-00670-6

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