Investigation of the impact of Epigenetic Modifiers and Computational-Guided Metabolic Engineering on Increasing Terpene Precursor Production in Yeast

Document Type : Research Paper

Authors

1 Ph.D. Student, Institute of Biotechnology, School of Agriculture, Shiraz University, Shiraz, Iran.

2 Associate Professor, Department of Chemical Engineering, School of Chemical, Petroleum and Gas Engineering, Shiraz University, Shiraz, Iran.

3 Professor, Institute of Biotechnology, School of Agriculture, Shiraz University, Shiraz, Iran.

10.22103/jab.2025.24553.1640

Abstract

This study aimed to enhance isopentenyl pyrophosphate (IPP) production, a key precursor in terpenoid biosynthesis, in Saccharomyces cerevisiae. Two major approaches were explored: epigenetic modifications using 5-azacytidine and sodium butyrate, and targeted genetic engineering based on computational simulations. Treatment with 5-azacytidine induced the demethylation of gene promoter regions, while sodium butyrate inhibited histone deacetylase (HDAC) enzymes, thereby modulating gene expression in the mevalonate pathway. Computational simulations, using the iMM904 model and an OptForce-FSEOF-inspired algorithm, were employed to identify key reactions and optimize metabolic flux. In this study, treatments of 5-azacytidine and sodium butyrate at optimal concentrations were added to the culture medium containing yeast, and the concentrations of metabolites were measured using LC-MS/MS. Results showed that both 5-azacytidine and sodium butyrate significantly increased the concentrations of key metabolites, including phosphomevalonate, IPP, and dimethylallyl diphosphate, thereby enhancing the mevalonate pathway. azacytidine was more effective than sodium butyrate in increasing the concentration of phosphomevalonate, while both treatments had similar effects on the production of IPP. Additionally, downregulation of the ACACT1m reaction via genetic engineering led to the reallocation of acetyl-CoA metabolic resources to the mevalonate pathway, resulting in a 7.25 mmol/gDW/h increase in IPP production, while maintaining a satisfactory cell growth rate of 0.36 gDW/h. Based on these findings, the combination of both approaches—epigenetic modifications and targeted genetic engineering—can be considered an effective strategy for optimizing terpenoid production at an industrial scale. This approach leverages the strengths of both methods, enhancing the production capacity of S. cerevisiae for valuable bioproducts.

while maintaining a satisfactory cell growth rate of 0.36 gDW/h. Based on these findings, the combination of both approaches—epigenetic modifications and targeted genetic engineering—can be considered an effective strategy for optimizing terpenoid production at an industrial scale. This approach leverages the strengths of both methods, enhancing the production capacity of S. cerevisiae for valuable bioproducts.

Keywords

References

Alavi, M., Mozafari, M. R., Ghaemi, S., Ashengroph, M., Hasanzadeh Davarani, F., & Mohammadabadi, M. (2022). Interaction of epigallocatechin gallate and quercetin with spike glycoprotein (S-Glycoprotein) of SARS-CoV-2: In silico study. Biomedicines, 10(12), 3074. https://doi.org/10.3390/biomedicines10123074.
Alter, T. B., & Ebert, B. E. (2019). Determination of growth-coupling strategies and their underlying principles. BMC Bioinformatics, 20(1), 447. https://doi.org/10.1186/s12859-019-2946-7.
Amiri Roudbar, M., Mohammadabadi, M. R., Ayatollahi Mehrgardi, A., Abdollahi-Arpanahi, R., Momen, M., Morota, G., Brito Lopes, F., Gianola, D., & Rosa, G. J. M. (2020). Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls. Heredity, 124(5), 658–674. https://doi.org/10.1038/s41437-020-0301-4
Barazandeh, A., Mohammadabadi, M. R., Ghaderi-Zefrehei, M., & Nezamabadi-Pour, H. (2016). Genome-wide analysis of CpG islands in some livestock genomes and their relationship with genomic features. Czech Journal of Animal Science, 61(11), 487–495. https://doi.org/10.17221/78/2015-CJAS
Bhatt, D. P., Mills, C. A., Anderson, K. A., Henriques, B. J., Lucas, T. G., Francisco, S., Liu, J., Ilkayeva, O. R., Adams, A. E., Kulkarni, S. R., Backos, D. S., Major, M. B., Grimsrud, P. A., Gomes, C. M., & Hirschey, M. D. (2022). Deglutarylation of glutaryl-CoA dehydrogenase by deacylating enzyme SIRT5 promotes lysine oxidation in mice. The Journal of Biological Chemistry, 298(4), 101723. https://doi.org/10.1016/j.jbc.2022.101723
Bind, S., Bind, S., Sharma, A. K., & Chaturvedi, P. (2022). Epigenetic modification: A key tool for secondary metabolite production in microorganisms. Frontiers in Microbiology, 13, 784109. https://doi.org/10.3389/fmicb.2022.784109
Bordbar, F., Mohammadabadi, M., Jensen, J., Xu, L., Li, J., & Zhang, L. (2022). Identification of candidate genes regulating carcass depth and hind leg circumference in Simmental beef cattle using Illumina Bovine Beadchip and next-generation sequencing analyses. Animals (Basel), 12(9), 1103. https://doi.org/10.3390/ani12091103
Borodina, I., & Nielsen, J. (2014). Advances in metabolic engineering of yeast Saccharomyces cerevisiae for production of chemicals. Biotechnology Journal, 9(5), 609–620. https://doi.org/10.1002/biot.201300445
Choi, H. S., Lee, S. Y., Kim, T. Y., & Woo, H. M. (2010). In silico identification of gene amplification targets for improvement of lycopene production. Applied and Environmental Microbiology, 76(10), 3097–3105. https://doi.org/10.1128/AEM.00115-10
Curran, K. A., Crook, N. C., & Alper, H. S. (2012). Using flux balance analysis to guide microbial metabolic engineering. Methods in Molecular Biology, 834, 197–216. https://doi.org/10.1007/978-1-61779-483-4_13
Dolce, V., Cappello, A. R., & Capobianco, L. (2014). Mitochondrial tricarboxylate and dicarboxylate-tricarboxylate carriers: from animals to plants. IUBMB life(International Union of Biochemistry and Molecular Biology), 66(7), 462–471. https://doi.org/10.1002/iub.1290
Dunn, W. B., & Winder, C. L. (2011). Sample preparation related to the intracellular metabolome of yeast methods for quenching, extraction, and metabolite quantitation. Methods in Enzymology, 500, 277–297. https://doi.org/10.1016/B978-0-12-385118-5.00015-3
Feist, A. M., Zielinski, D. C., Orth, J. D., Schellenberger, J., Herrgard, M. J., & Palsson, B. Ø. (2010). Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metabolic Engineering, 12(3), 173–186. https://doi.org/10.1016/j.ymben.2009.10.003
Goudarzi A. (2019). The recent insights into the function of ACAT1: A possible anti-cancer therapeutic target. Life sciences, 232, 116592. https://doi.org/10.1016/j.lfs.2019.116592
Gudmundsson, S., & Thiele, I. (2010). Computationally efficient flux variability analysis. BMC Bioinformatics, 11, 489. https://doi.org/10.1186/1471-2105-11-489
Heerboth, S., Lapinska, K., Snyder, N., Leary, M., Rollinson, S., & Sarkar, S. (2014). Use of epigenetic drugs in disease: An overview. Genetics & Epigenetics, 6, 9–19. https://doi.org/10.4137/GEG.S12270
Heidarpour, F., Mohammadabadi, M. R., Zaidul, I. S. M., Maherani, B., Saari, N., Hamid, A. A., Abas, F., Manap, M. Y. A, Mozafari, M. R, (2011). Use of prebiotics in oral delivery of bioactive compounds: a nanotechnology perspective. Die Pharmazie 66(5), 319-324. http://dx.doi.org/10.1691/ph.2011.0279
Hiser, L., Basson, M. E., & Rine, J. (1994). ERG10 from Saccharomyces cerevisiae encodes acetoacetyl-CoA thiolase. The Journal of Biological Chemistry, 269(50), 31383–31389. https://doi.org/10.1016/S0021-9258(18)31705-8
Kang, J. G., Park, J. S., Ko, J. H., & Kim, Y. S. (2019). Regulation of gene expression by altered promoter methylation using a CRISPR/Cas9-mediated epigenetic editing system. Scientific Reports, 9(1), 11960. https://doi.org/10.1038/s41598-019-48130-3
Khabiri, A., Toroghi, R., Mohammadabadi, M., & Tabatabaeizadeh, S. E. (2023). Introduction of a Newcastle disease virus challenge strain (sub-genotype VII.1.1) isolated in Iran. Veterinary Research Forum, 14(4), e221. https://doi.org/10.30466/vrf.2022.548152.3373
Kim, B., Kim, W. J., Kim, D. I., & Lee, S. Y. (2015). Applications of genome-scale metabolic network model in metabolic engineering. Journal of Industrial Microbiology & Biotechnology, 42(3), 339–348. https://doi.org/10.1007/s10295-014-1554-9
Ku, J. T., Chen, A. Y., & Lan, E. I. (2020). Metabolic engineering design strategies for increasing acetyl-CoA flux. Metabolites, 10(4), 166. https://doi.org/10.3390/metabo10040166
Lechner, A., Brunk, E., & Keasling, J. D. (2016). The need for integrated approaches in metabolic engineering. Cold Spring Harbor Perspectives in Biology, 8(11), a023903. https://doi.org/10.1101/cshperspect.a023903
Leschelle, X., Delpal, S., Goubern, M., Blottière, H. M., & Blachier, F. (2000). Butyrate metabolism upstream and downstream acetyl-CoA synthesis and growth control of human colon carcinoma cells. European Journal of Biochemistry, 267(21), 6435–6442. https://doi.org/10.1046/j.1432-1327.2000.01731.x
Liao, P., Hemmerlin, A., Bach, T. J., & Chye, M. L. (2016). The potential of the mevalonate pathway for enhanced isoprenoid production. Biotechnology Advances, 34(5), 697–713. https://doi.org/10.1016/j.biotechadv.2016.03.005
Ma, Y., Zu, Y., Huang, S., & Stephanopoulos, G. (2023). Engineering a universal and efficient platform for terpenoid synthesis in yeast. Proceedings of the National Academy of Sciences of the United States of America, 120(1), e2207680120. https://doi.org/10.1073/pnas.2207680120
Mohammadabadi, M., Babenko, I. O., Borshch, O., Kalashnyk, O., Ievstafiieva, Y., & Buchkovska, V. (2024). Measuring the relative expression pattern of the UCP2 gene in different tissues of the Raini Cashmere goat. Agricultural Biotechnology Journal, 16(3), 317-332. https://doi.org/10.22103/jab.2024.24337.1627
Mukherjee, M., Blair, R. H., & Wang, Z. Q. (2022). Machine-learning guided elucidation of contribution of individual steps in the mevalonate pathway and construction of a yeast platform strain for terpenoid production. Metabolic Engineering, 74, 139–149. https://doi.org/10.1016/j.ymben.2022.10.004
Nielsen, J., Larsson, C., van Maris, A., & Pronk, J. (2013). Metabolic engineering of yeast for production of fuels and chemicals. Current Opinion in Biotechnology, 24(3), 398–404. https://doi.org/10.1016/j.copbio.2013.03.023
Olano, C., Lombó, F., Méndez, C., & Salas, J. A. (2008). Improving production of bioactive secondary metabolites in actinomycetes by metabolic engineering. Metabolic Engineering, 10(5), 281–292. https://doi.org/10.1016/j.ymben.2008.07.001
Orsó, E., & Burkhardt, R. (2020). ATP-citrate lyase: A driver of metabolism and histone acetylation. Current Opinion in Lipidology, 31(6), 362–363. https://doi.org/10.1097/MOL.0000000000000719
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010). What is flux balance analysis?. Nature Biotechnology, 28(3), 245–248. https://doi.org/10.1038/nbt.1614
Park, J. M., Kim, T. Y., & Lee, S. Y. (2009). Constraints-based genome-scale metabolic simulation for systems metabolic engineering. Biotechnology Advances, 27(6), 979–988. https://doi.org/10.1016/j.biotechadv.2009.05.019
Pereira, R., Nielsen, J., & Rocha, I. (2016). Improving the flux distributions simulated with genome-scale metabolic models of Saccharomyces cerevisiae. Metabolic Engineering Communications, 3, 153–163. https://doi.org/10.1016/j.meteno.2016.05.002
Pietrocola, F., Galluzzi, L., Bravo-San Pedro, J. M., Madeo, F., & Kroemer, G. (2015). Acetyl coenzyme A: A central metabolite and second messenger. Cell Metabolism, 21(6), 805–821. https://doi.org/10.1016/j.cmet.2015.05.014
Ranganathan, S., Suthers, P. F., & Maranas, C. D. (2010). OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Computational Biology, 6(4), e1000744. https://doi.org/10.1371/journal.pcbi.1000744
Rangel, A. T., Gomez Ramirez, J. M., Gonzalez Barrios, A. F.(2020) From industrial by‐products to value‐added compounds: the design of efficient microbial cell factories by coupling systems metabolic engineering and bioprocesses. Biofuels, Bioproducts and Biorefining, 14(6) 1228–1238. https://doi.org/10.1002/bbb.2127
Safaei, S. M. H., Dadpasand, M., Mohammadabadi, M., Atashi, H., Stavetska, R., Klopenko, N., & Kalashnyk, O. (2022). An Origanum majorana leaf diet influences Myogenin gene expression, performance, and carcass characteristics in lambs. Animals (Basel), 13(1), 14. https://doi.org/10.3390/ani13010014
da Silva, T. L., Gouveia, L., & Reis, A. (2014). Integrated microbial processes for biofuels and high value-added products: The way to improve the cost effectiveness of biofuel production. Applied Microbiology and Biotechnology, 98(3), 1043–1053. https://doi.org/10.1007/s00253-013-5389-5
Simeonidis, E., & Price, N. D. (2015). Genome-scale modeling for metabolic engineering. Journal of Industrial Microbiology & Biotechnology, 42(3), 327–338. https://doi.org/10.1007/s10295-014-1576-3
Tomm, H. A., Ucciferri, L., & Ross, A. C. (2019). Advances in microbial culturing conditions to activate silent biosynthetic gene clusters for novel metabolite production. Journal of Industrial Microbiology & Biotechnology, 46(9-10), 1381–1400. https://doi.org/10.1007/s10295-019-02198-y
Westover, J. B., Goodman, S. I., & Frerman, F. E. (2001). Binding, hydration, and decarboxylation of the reaction intermediate glutaconyl-coenzyme A by human glutaryl-CoA dehydrogenase. Biochemistry, 40(46), 14106–14114. https://doi.org/10.1021/bi015637p
Williams, R. B., Henrikson, J. C., Hoover, A. R., Lee, A. E., & Cichewicz, R. H. (2008). Epigenetic remodeling of the fungal secondary metabolome. Organic & Biomolecular Chemistry, 6(11), 1895–1897. https://doi.org/10.1039/b804701d
Xue, M., Hou, X., Fu, J., Zhang, J., Wang, J., Zhao, Z., Xu, D., Lai, D., & Zhou, L. (2023). Recent advances in search of bioactive secondary metabolites from fungi triggered by chemical epigenetic modifiers. Journal of Fungi (Basel, Switzerland), 9(2), 172. https://doi.org/10.3390/jof9020172
Yang, B. C., Lee, M. S., Lin, M. K., & Chang, W. T. (2022). 5-Azacytidine increases tanshinone production in Salvia miltiorrhiza hairy roots through epigenetic modulation. Scientific Reports, 12(1), 9349. https://doi.org/10.1038/s41598-022-12577-8
Zhang, L., Liu, C., Jiang, Q., & Yin, Y. (2021). Butyrate in energy metabolism: There is still more to learn. Trends in endocrinology and metabolism: TEM, 32(3), 159–169. https://doi.org/10.1016/j.tem.2020.12.003
Agricultural Biotechnology Journal
Volume 17, Issue 2
May 2025
Pages 55-78

Files

Share

How to cite

Statistics