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中国生物工程杂志

CHINA BIOTECHNOLOGY
中国生物工程杂志  2014, Vol. 34 Issue (2): 118-128    DOI: 10.13523/j.cb.20140219
综述     
“组学”技术在燃料乙醇生产用酿酒酵母菌株构建中的应用
李云成, 汤岳琴, 木田建次
1. 四川大学轻纺与食品学院 成都 610065;
2. 四川大学建筑与环境学院 成都 610207
Application of OMICS Technology in Construction of Saccharomyces cerevisiae Strains for Ethanol Production
LI Yun-cheng, TANG Yue-qin, KIDA Kenji
1. College of Light Industry, Textile and Food Engineering, Sichuan University, Chengdu 610065, China;
2. College of Architecture and Environment, Sichuan University, Chengdu 610207, China
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摘要: 酿酒酵母(Saccharomyces cerevisiae)是工业乙醇生产广泛使用的微生物,构建能同时发酵戊糖以及具有优良抑制物耐受能力的高产菌株,在燃料乙醇生产技术开发中具有十分重要的地位。基因组学、蛋白组学、转录组学、代谢组学和流量组学等“组学”技术可从基因型到表现型不同层次揭示生命活动规律,在燃料乙醇生产用酵母菌株的构建中具有广阔的应用前景,特别是目标基因的识别、代谢途径的优化、代谢机理的揭示等。运用“组学”技术可使菌株构建工作更具靶向性和明确性,更加易于获得目标性状,并大大缩短育种周期。综述了“组学”技术在燃料乙醇生产用酿酒酵母菌株构建中的应用及相关研究进展。
关键词: 燃料乙醇酿酒酵母菌株构建组学技术    
Abstract: Bioethanol is considered as the most important alternative of fossil fuel. Saccharomyces cerevisiae is the most effective ethanol-producing microorganism widely used in the current bioethanol industry. It is very important and urgent to construct strains with excellent inhibitor tolerance and pentose fermenting ability for the production of bioethanol from lignocellulosic biomass. Since the OMICS technologies, including genomics, proteomics, transcriptomics, metabolomics and fluxomics, can reveal the laws of life activities of strains in different level from genotype to phenotype, it has been broadly applied in construction of engineered strains, especially in the identification of target genes, the optimization of metabolic pathway and the reveal of metabolic mechanism. The application of OMICS makes strain construction works more targeted and clarity, and the breeding cycle is greatly shortened.The application and recent progress of OMICS in construction of Saccharomyces cerevisia strains for bioethanol production are discussed.
Key words: Fuel ethanol    Saccharomyces cerevisiae    Strain construction    OMICS technology
收稿日期: 2013-10-17 出版日期: 2014-02-25
ZTFLH:  Q815  
基金资助: 国家自然科学基金(31170093)资助项目
通讯作者: 汤岳琴     E-mail: tangyq@scu.edu.cn
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引用本文:

李云成, 汤岳琴, 木田建次. “组学”技术在燃料乙醇生产用酿酒酵母菌株构建中的应用[J]. 中国生物工程杂志, 2014, 34(2): 118-128.

LI Yun-cheng, TANG Yue-qin, KIDA Kenji. Application of OMICS Technology in Construction of Saccharomyces cerevisiae Strains for Ethanol Production. China Biotechnology, 2014, 34(2): 118-128.

链接本文:

https://manu60.magtech.com.cn/biotech/CN/10.13523/j.cb.20140219        https://manu60.magtech.com.cn/biotech/CN/Y2014/V34/I2/118

[1] Matsushika A, Inoue H, Kodaki T, et al. Ethanol production from xylose in engineered Saccharomyces cerevisiae strains: current state and perspectives. Appl Microbiol Biotechnol, 2009, 84 (1): 37-53.
[2] Castrillo J I, Oliver S G. Yeast Systems Biology: Methods and Protocols [M]. New York: Humana Press, c/o Springer Science+ Business Media, 2011.
[3] Cai Z, Zhang B, Li Y. Engineering Saccharomyces cerevisiae for efficient anaerobic xylose fermentation: Reflections and perspectives. Biotechnol J, 2012, 7 (1): 34-46.
[4] 赵心清, 白凤武, 李寅. 系统生物学和合成生物学研究在生物燃料生产菌株改造中的应用. 生物工程学报, 2010, 26 (7): 880-887. Zhao X Q, Bai F W, Li Y. Application of systems biology and synthetic biology in strain improvement for biofuel production. Chinese Journal of Biotechnology,2010,26(7):880-887.
[5] 张晓阳, 李余动, 吴雪昌. 酿酒酵母的"组学"技术研究进展及其在工程菌株构建中的应用. 中国生物工程杂志, 2011, 31 (8): 139-144. Zhang X Y, Li Y D, Wu X CH. Research progress of "Omics" technologies and its application in construction of engineering strain of Saccharomyces cerevisiae . China Biotechnology,2011,31(8):139-144.
[6] 陈洵, 周世奇, 陈涛, 等. 功能基因组学与代谢工程: 微生物菌种改进与生物过程优化. 化工学报, 2006, 57 (8): 1792-1801. Chen X, Zhou S Q, Chen T, et al. Functional genomics and metabolic engineering: microbial strain improvement and bioprocess optimization. Journal of Chemical Industry and Engineering(China),2006,57(8):1792-1801.
[7] Mukhopadhyay A, Redding A M, Rutherford B J, et al. Importance of systems biology in engineering microbes for biofuel production. Curr Opin Biotechnol, 2008, 19 (3): 228-234.
[8] Park J H, Lee S Y, Kim T Y, et al. Application of systems biology for bioprocess development. Trends Biotechnol, 2008, 26 (8):404-412.
[9] Winter G, Krmer J O. Fluxomics-connecting 'omics analysis and phenotypes. Environ Microbiol, 2013, 15 (7): 1901-1916.
[10] Snchez B, Ruiz L, Gueimonde M, et al. Omics for the study of probiotic microorganisms. Food Res Int, 2013,54(1):1061-1071.
[11] Linial M, Yona G. Methodologies for target selection in structural genomics. Prog Biophys Mol Biol, 2000, 73 (5):297-320.
[12] 庄金秋, 杨丽梅, 贾杏林. 功能基因组学研究概述. 中国生物工程杂志, 2005, (S1): 204-209.
[13] Goffeau A, Barrell B G, Bussey H, et al. Life with 6000 genes. Science, 1996, 274 (5287): 546, 563-567.
[14] Dujon B. The yeast genome project: what did we learn?. Trends Genet, 1996, 12 (7): 263-270.
[15] Kvitek D J, Sherlock G. Reciprocal sign epistasis between frequently experimentally evolved adaptive mutations causes a rugged fitness landscape. PLoS Genet, 2011, 7 (4): 1-11.
[16] Brenner S, Johnson M, Bridgham J, et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat Biotechnol, 2000, 18 (6): 630-634.
[17] Argueso J L, Carazzolle M F, Mieczkowski PA, et al. Genome structure of a Saccharomyces cerevisiae strain widely used in bioethanol production. Genome Res, 2009, 19 (12): 2258-2270.
[18] Gharizadeh B, Herman Z S, Eason R G, et al. Large-scale pyrosequencing of synthetic DNA: a comparison with results from Sanger dideoxy sequencing. Electrophoresis, 2006, 27 (15): 3042-3047.
[19] Zheng D Q, Wang PM, Chen J, et al. Genome sequencing and genetic breeding of a bioethanol Saccharomyces cerevisiae strain YJS329. BMC Genomics, 2012, 13: 479.
[20] Li H, Ma M L, Luo S, et al. Metabolic responses to ethanol in Saccharomyces cerevisiae using a gas chromatography tandem mass spectrometry-based metabolomics approach. Int J Biochem Cell Biol, 2012, 44 (7): 1087-1096.
[21] Teixeira M C, Raposo L R, Mira N P, et al. Genome-wide identification of Saccharomyces cerevisiae genes required for maximal tolerance to ethanol. Appl Environ Microbiol, 2009, 75 (18): 5761-5772.
[22] You K M, Rosenfield C L, Knipple D C. Ethanol tolerance in the yeast Saccharomyces cerevisiae is dependent on cellular oleic acid content. Appl Environ Microbiol, 2003, 69 (3): 1499-503.
[23] Aguilera F, Peinado R A, Millán C, et al. Relationship between ethanol tolerance, H+-ATPase activity and the lipid composition of the plasma membrane in different wine yeast strains. Int J Food Microbiol, 2006, 110 (1): 34-42.
[24] Lin F M, Qiao B, Yuan Y J. Comparative proteomic analysis of tolerance and adaptation of ethanologenic Saccharomyces cerevisiae to furfural, a lignocellulosic inhibitory compound. Appl Environ Microbiol, 2009, 75 (11): 3765-3776.
[25] Mira N P, Palma M, Guerreiro J F, et al. Genome-wide identification of Saccharomyces cerevisiae genes required for tolerance to acetic acid. Microb Cell Fact, 2010, 9: 79.
[26] Velculescu V E, Zhang L, Zhou W, et al. Characterization of the yeast transcriptome. Cell, 1997, 88 (2): 243-251.
[27] Oliver S G, Winson M K, Kell D B, et al. Systematic functional analysis of the yeast genome. Trends Biotechnol, 1998, 16 (9): 373-378.
[28] Brown N A, de Castro PA, de Castro Pimentel Figueiredo, et al. Transcriptional profiling of Brazilian Saccharomyces cerevisiae strains selected for semi-continuous fermentation of sugarcane must. FEMS Yeast Res, 2013, 13 (3): 277-290.
[29] Jiménez-Martí E, Zuzuarregui A, Gomar-Alba M, et al. Molecular response of Saccharomyces cerevisiae wine and laboratory strains to high sugar stress conditions. Int J Food Microbiol, 2011, 145 (1): 211-220.
[30] Pérez-Torrado R, Gómez-Pastor R, Larsson C, et al. Fermentative capacity of dry active wine yeast requires a specific oxidative stress response during industrial biomass growth. Appl Microbiol Biotechnol, 2009, 81 (5): 951-960.
[31] Li B Z, Cheng J S, Ding M Z, et al. Transcriptome analysis of differential responses of diploid and haploid yeast to ethanol stress. J Biotechnol, 2010, 148 (4): 194-203.
[32] Ismail K S, Sakamoto T, Hatanaka H, et al. Gene expression cross-profiling in genetically modified industrial Saccharomyces cerevisiae strains during high-temperature ethanol production from xylose. J Biotechnol, 2013, 163 (1): 50-60.
[33] Ismail K S, Sakamoto T, Hatanaka H, et al. Time-based comparative transcriptomics in engineered xylose-utilizing Saccharomyces cerevisiae identifies temperature-responsive genes during ethanol production. J Ind Microbiol Biotechnol, 2013, 40 (9): 1039-1050.
[34] Shahsavarani H, Sugiyama M, Kaneko Y, et al. Superior thermotolerance of Saccharomyces cerevisiae for efficient bioethanol fermentation can be achieved by overexpression of RSP5 ubiquitin ligase. Biotechnol Adv, 2012, 30 (6): 1289-1300.
[35] Benjaphokee S, Koedrith P, Auesukaree C, et al. CDC19 encoding pyruvate kinase is important for high-temperature tolerance in Saccharomyces cerevisiae. N Biotechnol, 2012, 29 (2): 166-176.
[36] Auesukaree C, Koedrith P, Saenpayavai P, et al. Characterization and gene expression profiles of thermotolerant Saccharomyces cerevisiae isolates from Thai fruits. J Biosci Bioeng, 2012, 114 (2): 144-149.
[37] Li B Z, Yuan Y J. Transcriptome shifts in response to furfural and acetic acid in Saccharomyces cerevisiae. Appl Microbiol Biotechnol, 2010, 86 (6): 1915-1924.
[38] Allen S A, Clark W, McCaffery J M, et al. Furfural induces reactive oxygen species accumulation and cellular damage in Saccharomyces cerevisiae. Biotechnol Biofuels, 2010, 3: 2.
[39] 朱玉贤. 现代分子生物学. 北京:高等教育出版社, 2007. Zhu Y X.Modern Molecular Biology.3rd.Beijing:Higher Education Press,2007.
[40] Yadav S P. The wholeness in suffix -omics, -omes, and the word om. J Biomol Tech, 2007, 18 (5): 277.
[41] Krogan N J, Cagney G, Yu H, et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature, 2006, 440 (7084): 637-643.
[42] 周晓. 反复间歇真空发酵过程中酿酒酵母适应性进化的系统分析. 天津:天津大学, 2011. Zhou X.Systematic Investigation on Adaptive Evolution of Saccharomyces cerevisiae to Repeated-batch Vacuum Fermentation.Tianjin:Tianjin University.2011.
[43] Ding M Z, Wang X, Liu W, et al. Proteomic research reveals the stress response and detoxification of yeast to combined inhibitors. PLoS One, 2012, 7 (8): e43474.
[44] Hao X C, Yang X S, Wan P, et al. Comparative proteomic analysis of a new adaptive Pichia stipitis strain to furfural, a lignocellulosic inhibitory compound. Biotechnol Biofuels, 2013, 6 (1): 34.
[45] Picotti P, Clément-Ziza M, Lam H, et al. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature, 2013, 494 (7436): 266-270.
[46] Zhu H, Snyder M. Protein chip technology. Curr Opin Chem Biol, 2003, 7 (1): 55-63.
[47] Yin Z, Wilson S, Hauser N C, et al. Glucose triggers different global responses in yeast, depending on the strength of the signal, and transiently stabilizes ribosomal protein mRNAs. Mol Microbiol, 2003, 48 (3), 713-724.
[48] Giardina B J, Stanley B A, Chiang H L. Comparative proteomic analysis of transition of Saccharomyces cerevisiae from glucose-deficient medium to glucose-rich medium. Proteome Sci, 2012, 10 (1):40.
[49] Kolkman A, Daran-Lapujade P, Fullaondo A, et al. Proteome analysis of yeast response to various nutrient limitations. Mol Syst Biol, 2006, 2:26.
[50] Pham T K, Chong P K, Gan C S, et al. Proteomic analysis of Saccharomyces cerevisiae under high gravity fermentation conditions. J Proteome Res, 2006, 5 (12): 3411-3419.
[51] Pham T K, Wright PC. The proteomic response of Saccharomyces cerevisiae in very high glucose conditions with amino acid supplementation. J Proteome Res, 2008, 7 (11): 4766-4774.
[52] Almeida B, Ohlmeier S, Almeida A J, et al. Yeast protein expression profile during acetic acid-induced apoptosis indicates causal involvement of the TOR pathway. Proteomics, 2009, 9 (3): 720-732.
[53] Lin F M, Tan Y, Yuan Y J. Temporal quantitative proteomics of Saccharomyces cerevisiae in response to a nonlethal concentration of furfural. Proteomics, 2009, 9 (24): 5471-5483.
[54] Westman J O, Taherzadeh M J, Franzén C J. Proteomic analysis of the increased stress tolerance of Saccharomyces cerevisiae encapsulated in liquid core alginate-chitosan capsules. PLoS One, 2012, 7 (11): e49335.
[55] Hesselberth J R, Miller J P, Golob A, et al. Comparative analysis of Saccharomyces cerevisiae WW domains and their interacting proteins. Genome Biol, 2006, 7: R30.
[56] Kim I K, Roldo A, Siewers V, et al. A systems-level approach for metabolic engineering of yeast cell factories. FEMS Yeast Res, 2012, 12 (2): 228-248.
[57] Nicholson J K, Lindon J C, Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 1999, 29 (11): 1181-1189.
[58] Hasunuma T, Sanda T, Yamada R, et al. Metabolic pathway engineering based on metabolomics confers acetic and formic acid tolerance to a recombinant xylose-fermenting strain of Saccharomyces cerevisiae. Microb Cell Fact, 2011, 10 (1): 2.
[59] Ding M Z, Li B Z, Cheng J S,et al. Metabolome analysis of differential responses of diploid and haploid yeast to ethanol stress. OMICS, 2010, 14 (5): 553-561.
[60] Loureno A B, Roque F C, Teixeira M C, et al. Quantitative 1H-NMR-metabolomics reveals extensive metabolic reprogramming and the effect of the aquaglyceroporin FPS1 in ethanol-stressed yeast cells. PLoS One. 2013, 8 (2): e55439.
[61] Ding M Z, Wang X, Yang Y, et al. Metabolomic study of interactive effects of phenol, furfural, and acetic acid on Saccharomyces cerevisiae. OMICS, 2011, 15 (10): 647-653.
[62] Wang X, Li B Z, Ding M Z, et al. Metabolomic analysis reveals key metabolites related to the rapid adaptation of Saccharomyce cerevisiae to multiple inhibitors of furfural, acetic acid, and phenol. OMICS, 2013, 17 (3): 150-159.
[63] Klimacek M, Krahulec S, Sauer U, et al. Limitations in xylose-fermenting Saccharomyces cerevisiae, made evident through comprehensive metabolite profiling and thermodynamic analysis. Appl Environ Microbiol, 2010, 76 (22): 7566-7574.
[64] Bergdahl B, Heer D, Sauer U, et al. Dynamic metabolomics differentiates between carbon and energy starvation in recombinant Saccharomyces cerevisiae fermenting xylose. Biotechnol Biofuels, 2012, 5 (1): 34.
[65] Ma N L, Rahmat Z, Lam S S. A review of the "omics" approach to biomarkers of oxidative stress in Oryza sativa. Int J Mol Sci, 2013, 14 (4): 7515-7541.
[66] Feng X, Page L, Rubens J, et al. Bridging the gap between fluxomics and industrial biotechnology. J Biomed Biotechnol, 2011: 460717.
[67] Brochado A R, Matos C, Mller B L, et al. Improved vanillin production in baker's yeast through in silico design. Microb Cell Fact, 2010, 9: 84.
[68] Frick O, Wittmann C. Characterization of the metabolic shift between oxidative and fermentative growth in Saccharomyces cerevisiae by comparative 13C flux analysis. Microb Cell Fact, 2005, 4: 30.
[69] Pagliardini J, Hubmann G, Alfenore S, et al. The metabolic costs of improving ethanol yield by reducing glycerol formation capacity under anaerobic conditions in Saccharomyces cerevisiae. Microb Cell Fact, 2013, 12: 29.
[70] Vargas F A, Pizarro F, Pérez-Correa J R, et al. Expanding a dynamic flux balance model of yeast fermentation to genome-scale. BMC Syst Biol, 2011, 5: 75.
[71] Bro C, Regenberg B, Frster J, et al. In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab Eng, 2006, 8 (2): 102-111.
[72] Celton M, Goelzer A, Camarasa C, et al. A constraint-based model analysis of the metabolic consequences of increased NADPH oxidation in Saccharomyces cerevisiae. Metab Eng, 2012, 14 (4): 366-379.
[73] Celton M, Sanchez I, Goelzer A,et al. A comparative transcriptomic, fluxomic and metabolomic analysis of the response of Saccharomyces cerevisiae to increases in NADPH oxidation. BMC Genomics, 2012, 13: 317.
[74] Petranovic D, Vemuri G N. Impact of yeast systems biology on industrial biotechnology. J Biotechnol, 2009, 144 (3): 204-211.
[75] 蒋太交, 薛艳红, 徐涛. 系统生物学—生命科学新领域. 生物化学与生物物理进展, 2004, 31 (2): 957-964. Jiang T J,Xue Y H,Xu T.Systems biology:a new field of biological science.Progress in Biochemistry and Biophysics,2004,31(2):957-964.
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