Thermotolerance improvement of engineered Saccharomyces cerevisiae ERG5 Delta ERG4 Delta ERG3 Delta, molecular mechanism, and its application in corn ethanol production

Peizhou Yang1, Wenjing Wu1, Jianchao Chen1, Suwei Jiang2, Zhi Zheng1, Yanhong Deng3, Jiuling Lu3, Hu Wang3, Yong Zhou3, Yuyou Geng3, Kanglin Wang4
1School of Food and Biological Engineering, Anhui Key Laboratory of Intensive Processing of Agricultural Products, Hefei University of Technology, 420 Feicui Road, Shushan District, Hefei, 230601, Anhui, China
2Department of Biological, Food and Environment Engineering, Hefei University, 158 Jinxiu Avenue, Hefei, 230601, China
3Suzhou Cofco Biochemical Co., Ltd., Suzhou, 234001, China
4Hefei Knature Bio-Pharm Co., Ltd., Hefei, 231131, China

Tóm tắt

Abstract Background

The thermotolerant yeast is beneficial in terms of efficiency improvement of processes and reduction of costs, while Saccharomyces cerevisiae does not efficiently grow and ferment at high-temperature conditions. The sterol composition alteration from ergosterol to fecosterol in the cell membrane of S. cerevisiae affects the thermotolerant capability.

Results

In this study, S. cerevisiae ERG5, ERG4, and ERG3 were knocked out using the CRISPR–Cas9 approach to impact the gene expression involved in ergosterol synthesis. The highest thermotolerant strain was S. cerevisiae ERG5ΔERG4ΔERG3Δ, which produced 22.1 g/L ethanol at 37 °C using the initial glucose concentration of 50 g/L with an increase by 9.4% compared with the wild type (20.2 g/L). The ethanol concentration of 9.4 g/L was produced at 42 ℃, which was 2.85-fold of the wild-type strain (3.3 g/L). The molecular mechanism of engineered S. cerevisiae at the RNA level was analyzed using the transcriptomics method. The simultaneous deletion of S. cerevisiae ERG5, ERG4, and ERG3 caused 278 up-regulated genes and 1892 down-regulated genes in comparison with the wild-type strain. KEGG pathway analysis indicated that the up-regulated genes relevant to ergosterol metabolism were ERG1, ERG11, and ERG5, while the down-regulated genes were ERG9 and ERG26. S. cerevisiae ERG5ΔERG4ΔERG3Δ produced 41.6 g/L of ethanol at 37 °C with 107.7 g/L of corn liquefied glucose as carbon source.

Conclusion

Simultaneous deletion of ERG5, ERG4, and ERG3 resulted in the thermotolerance improvement of S. cerevisiae ERG5ΔERG4ΔERG3Δ with cell viability improvement by 1.19-fold at 42 °C via modification of steroid metabolic pathway. S. cerevisiae ERG5ΔERG4ΔERG3Δ could effectively produce ethanol at 37 °C using corn liquefied glucose as carbon source. Therefore, S. cerevisiae ERG5ΔERG4ΔERG3Δ had potential in ethanol production at a large scale under supra-optimal temperature.

Từ khóa


Tài liệu tham khảo

Parapouli M, Vasileiadis A, Afendra A-S, Hatziloukas E. Saccharomyces cerevisiae and its industrial applications. AIMS Microbiology. 2020;6(1):1–31.

Wang PM, Zheng DQ, Chi XQ, Li O, Qian CD, Liu TZ, Zhang XY, Du FG, Sun PY, Qu AM, et al. Relationship of trehalose accumulation with ethanol fermentation in industrial Saccharomyces cerevisiae yeast strains. Biores Technol. 2014;152:371–6.

Cunha JT, Soares PO, Baptista SL, Costa CE, Domingues L. Engineered Saccharomyces cerevisiae for lignocellulosic valorization: a review and perspectives on bioethanol production. Bioengineered. 2020;11(1):883–903.

Shahsavarani H, Sugiyama M, Kaneko Y, Chuenchit B, Harashima S. Superior thermotolerance of Saccharomyces cerevisiae for efficient bioethanol fermentation can be achieved by overexpression of RSP5 ubiquitin ligase. Biotechnol Adv. 2012;30(6):1289–300.

Pinheiro T, Lip KYF, García-Ríos E, Querol A, Teixeira J, van Gulik W, Guillamón JM, Domingues L. Differential proteomic analysis by SWATH-MS unravels the most dominant mechanisms underlying yeast adaptation to non-optimal temperatures under anaerobic conditions. Sci Rep. 2020;10(1):22329.

Lip KYF, García-Ríos E, Costa CE, Guillamón JM, Domingues L, Teixeira J, van Gulik WM. Selection and subsequent physiological characterization of industrial Saccharomyces cerevisiae strains during continuous growth at sub- and- supra optimal temperatures. Biotechnology Reports. 2020;26: e00462.

Mojovic L, Pejin D, Grujic O, Markov S, Pejin J, Rakin M, Vukasinovic M, Nikolic S, Savic D. Progress in the production of bioethanol on starch-based feedstocks. Chem Ind Chem Eng Q. 2009;15(4):211–26.

Rehman O, Shahid A, Liu CG, Xu JR, Javed MR, Eid NH, Gull M, Nawaz M, Mehmood MA. Optimization of low-temperature energy-efficient pretreatment for enhanced saccharification and fermentation of Conocarpus erectus leaves to produce ethanol using Saccharomyces cerevisiae. Biomass Conv Biorefinery. 2020;10(4):1269–78.

Favaro L, Cagnin L, Basaglia M, Pizzocchero V, van Zyl WH, Casella S. Production of bioethanol from multiple waste streams of rice milling. Biores Technol. 2017;244:151–9.

Caspeta L, Caro-Bermudez MA, Ponce-Noyola T, Martinez A. Enzymatic hydrolysis at high-solids loadings for the conversion of agave bagasse to fuel ethanol. Appl Energy. 2014;113:277–86.

Abdel-Banat BMA, Hoshida H, Ano A, Nonklang S, Akada R. High-temperature fermentation: how can processes for ethanol production at high temperatures become superior to the traditional process using mesophilic yeast? Appl Microbiol Biotechnol. 2010;85(4):861–7.

Boonchuay P, Techapun C, Leksawasdi N, Seesuriyachan P, Hanmoungjai P, Watanabe M, Srisupa S, Chaiyaso T. Bioethanol production from cellulose-rich corncob residue by the thermotolerant Saccharomyces cerevisiae TC-5. J Fungi. 2021;7(7):1–8.

Favaro L, Basaglia M, Trento A, Van Rensburg E, Garcia-Aparicio M, Van Zyl WH, Casella S. Exploring grape marc as trove for new thermotolerant and inhibitor-tolerant Saccharomyces cerevisiae strains for second-generation bioethanol production. Heat shock protein 104 (Hsp104). Biotechnol Biofuels. 2013;6:1–8.

Johnston EJ, Moses T, Rosser SJ. The wide-ranging phenotypes of ergosterol biosynthesis mutants, and implications for microbial cell factories. Yeast. 2020;37(1):27–44.

Zhao XH, Rodriguez R, Silberman RE, Ahearn JM, Saidha S, Cummins KC, Eisenberg E. Greene LE:)-mediated curing of PSI+ yeast prions depends on both PSI+ conformation and the properties of the Hsp104 homologs. J Biol Chem. 2017;292(21):8630–41.

Tereshina VM. Thermotolerance in fungi: The role of heat shock proteins and trehalose. Microbiology. 2005;74(3):247–57.

Semkiv M, Kata I, Ternavska O, Sibirny W, Dmytruk K, Sibirny A. Overexpression of the genes of glycerol catabolism and glycerol facilitator improves glycerol conversion to ethanol in the methylotrophic thermotolerant yeast Ogataea polymorpha. Yeast. 2019;36(5):329–39.

Volkman JK. Sterols in microorganisms. Appl Microbiol Biotechnol. 2003;60(5):495–506.

Nahlik J, Hrncirik P, Mares J, Rychtera M, Kent CA. Towards the design of an optimal strategy for the production of ergosterol from Saccharomyces cerevisiae yeasts. Biotechnol Prog. 2017;33(3):838–48.

Jorda T, Puig S. Regulation of ergosterol biosynthesis in Saccharomyces cerevisiae. Genes. 2020;11(7):1–9.

Caspeta L, Chen Y, Ghiaci P, Feizi A, Buskov S, Hallstrom BM, Petranovic D, Nielsen J. Altered sterol composition renders yeast thermotolerant. Science. 2014;346(6205):75–8.

Mo CQ, Valachovic M, Bard M. The ERG28-encoded protein, Erg28p, interacts with both the sterol C-4 demethylation enzyme complex as well as the late biosynthetic protein, the C-24 sterol methyltransferase (Erg6p). BBA-Mol Cell Biol L. 2004;1686(1–2):30–6.

Liu GD, Chen Y, Faergeman NJ, Nielsen J. Elimination of the last reactions in ergosterol biosynthesis alters the resistance of Saccharomyces cerevisiae to multiple stresses. FEMS Yeast Res. 2017;17(6):1–7.

Zweytick D, Hrastnik C, Kohlwein SD, Daum G. Biochemical characterization and subcellular localization of the sterol C-24(28) reductase, erg4p, from the yeast Saccharomyces cerevisiae. FEBS Lett. 2000;470(1):83–7.

Radecka D, Mukherjee V, Mateo RQ, Stojiljkovic M, Foulquie-Moreno MR, Thevelein JM. Looking beyond Saccharomyces: the potential of non-conventional yeast species for desirable traits in bioethanol fermentation. FEMS Yeast Res. 2015;15(6):1–9.

Suutari M, Liukkonen K, Laakso S. Temperature adaptation in yeasts: the role of fatty acids. J Gen Microbiol. 1990;136(8):1469–74.

Araque E, Parra C, Freer J, Contreras D, Rodriguez J, Mendonca R, Baeza J. Evaluation of organosolv pretreatment for the conversion of Pinus radiata D. Don to ethanol Enzyme Microb Technol. 2008;43(2):214–9.

Kontoyiannis DP. Modulation of fluconazole sensitivity by the interaction of mitochondria and erg3p in Saccharomyces cerevisiae. J Antimicrob Chemother. 2000;46(2):191–7.

Cunha JT, Aguiar TQ, Romaní A, Oliveira C. Contribution of PRS3, RPB4 and ZWF1 to the resistance of industrial Saccharomyces cerevisiae CCUG53310 and PE-2 strains to lignocellulosic hydrolysate-derived inhibitors. Biores Technol. 2015;191:7–16.

Gietz RD, Schiestl RH. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat Protoc. 2007;2(1):31–4.

Hammer KA, Carson CF, Riley TV. Antifungal effects of Melaleuca alternifolia (tea tree) oil and its components on Candida albicans, Candida glabrata and Saccharomyces cerevisiae. J Antimicrob Chemother. 2004;53(6):1081–5.

Chen YH, Zhang X, Zhang M, Zhu JY, Wu ZF, Zheng XJ. A transcriptome analysis of the ameliorate effect of Cyclocarya paliurus triterpenoids on ethanol stress in Saccharomyces cerevisiae. World J Microbiol Biotechnol. 2018;34(12):1–10.

Holm JB, Humphrys MS, Robinson CK, Settles ML, Ott S, Fu L, Yang HQ, Gajer P, He X, McComb E, et al. Ultrahigh-throughput multiplexing and sequencing of >500-base-pair amplicon regions on the Illumina HiSeq 2500 platform. Msystems. 2019;4(1):1–8.

Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.

Zhao Q, Pan LQ, Ren Q, Hu DX. Digital gene expression analysis in hemocytes of the white shrimp Litopenaeus vannamei in response to low salinity stress. Fish Shellfish Immunol. 2015;42(2):400–7.

Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37(8):907–15.

Wang LG, Wang SQ, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28(16):2184–5.

Wang LK, Feng ZX, Wang X, Wang XW, Zhang XG. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26(1):136–8.

Du JL, Li ML, Yuan ZF, Guo MC, Song JZ, Xie XZ, Chen YL. A decision analysis model for KEGG pathway analysis. BMC Bioinformatics. 2016;17:1–8.