A metabolomics-based strategy for identification of gene targets for phenotype improvement and its application to 1-butanol tolerance in Saccharomyces cerevisiae

Biotechnology for Biofuels

Eiichiro Fukusaki¹  Takeshi Bamba¹  Yukio Mukai²  Sastia Putri¹  Shao Thing Teoh¹ 

[1]Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan

[2]Department of Bioscience, Nagahama Institute of Bio-Science and Technology, 1266 Tamura, Nagahama 526-0829, Shiga, Japan

关键词: Orthogonal projections to latent structures;    Regression model;    Saccharomyces cerevisiae;    1-Butanol tolerance;    Phenotype improvement;    Semi-rational strain engineering;    Metabolomics;   

Others  :  1228136
DOI  :  10.1186/s13068-015-0330-z

 received in 2015-04-16, accepted in 2015-08-28,  发布年份 2015

【 摘 要 】

Background

Traditional approaches to phenotype improvement include rational selection of genes for modification, and probability-driven processes such as laboratory evolution or random mutagenesis. A promising middle-ground approach is semi-rational engineering, where genetic modification targets are inferred from system-wide comparison of strains. Here, we have applied a metabolomics-based, semi-rational strategy of phenotype improvement to 1-butanol tolerance in Saccharomyces cerevisiae.

Results

Nineteen yeast single-deletion mutant strains with varying growth rates under 1-butanol stress were subjected to non-targeted metabolome analysis by GC/MS, and a regression model was constructed using metabolite peak intensities as predictors and stress growth rates as the response. From this model, metabolites positively and negatively correlated with growth rate were identified including threonine and citric acid. Based on the assumption that these metabolites were linked to 1-butanol tolerance, new deletion strains accumulating higher threonine or lower citric acid were selected and subjected to tolerance measurement and metabolome analysis. The new strains exhibiting the predicted changes in metabolite levels also displayed significantly higher growth rate under stress over the control strain, thus validating the link between these metabolites and 1-butanol tolerance.

Conclusions

A strategy for semi-rational phenotype improvement using metabolomics was proposed and applied to the 1-butanol tolerance of S. cerevisiae. Metabolites correlated with growth rate under 1-butanol stress were identified, and new mutant strains showing higher growth rate under stress could be selected based on these metabolites. The results demonstrate the potential of metabolomics in semi-rational strain engineering.

【 授权许可】

   
2015 Teoh et al.

【 图 表 】

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【 参考文献 】

• [1]Fischer CR, Klein-Marcuschamer D, Stephanopoulos G: Selection and optimization of microbial hosts for biofuels production. Metab Eng. 2008, 10:295-304.
• [2]Skretas G, Kolisis FN: Combinatorial approaches for inverse metabolic engineering applications. Comput Struct Biotechnol J. 2012, 3:e201210021.
• [3]Alper H, Moxley J, Nevoigt E, Fink GR, Stephanopoulos G: Engineering yeast transcription machinery for improved ethanol tolerance and production. Science. 2006, 314:1565-1568.
• [4]Santos CNS, Stephanopoulos G: Combinatorial engineering of microbes for optimizing cellular phenotype. Curr Opin Chem Biol. 2008, 12:168-176.
• [5]Sauer U: Evolutionary engineering of industrially important microbial phenotypes. Adv Biochem Eng Biotechnol. 2001, 73:129-169.
• [6]Adrio JL, Demain AL: Genetic improvement of processes yielding microbial products. FEMS Microbiol Rev. 2006, 30:187-214.
• [7]Snoek T, et al.: Large-scale robot-assisted genome shuffling yields industrial Saccharomyces cerevisiae yeasts with increased ethanol tolerance. Biotechnol Biofuels. 2015, 8:32. BioMed Central Full Text
• [8]Pang Z, Liang J, Qin X, Wang J, Feng J, Huang R: Multiple induced mutagenesis for improvement of ethanol production by Kluyveromyces marxianus. Biotechnol Lett. 2010, 32:1847-1851.
• [9]Lee SY, Lee D-Y, Kim TY: Systems biotechnology for strain improvement. Trends Biotechnol. 2005, 23:349-358.
• [10]Park SJ, Lee SY, Cho J, Kim TY, Lee JW, Park JH, Han M-J: Global physiological understanding and metabolic engineering of microorganisms based on omics studies. Appl Microbiol Biotechnol. 2005, 68:567-579.
• [11]Yoshikawa K, Furusawa C, Hirasawa T, Shimizu H: Design of Superior Cell Factories Based on Systems Wide Omics Analysis. In Systems Metabolic Engineering. Edited by Wittmann C, Lee SY. Springer, The Netherlands; 2012:57-81.
• [12]Hong M-E, Lee K-S, Yu BJ, Sung Y-J, Park SM, Koo HM, Kweon D-H, Park JC, Jin Y-S: Identification of gene targets eliciting improved alcohol tolerance in Saccharomyces cerevisiae through inverse metabolic engineering. J Biotechnol. 2010, 149:52-59.
• [13]Hirasawa T, Yoshikawa K, Nakakura Y, Nagahisa K, Furusawa C, Katakura Y, Shimizu H, Shioya S: Identification of target genes conferring ethanol stress tolerance to Saccharomyces cerevisiae based on DNA microarray data analysis. J Biotechnol. 2007, 131:34-44.
• [14]Yoshikawa K, Tanaka T, Furusawa C, Nagahisa K, Hirasawa T, Shimizu H: Comprehensive phenotypic analysis for identification of genes affecting growth under ethanol stress in Saccharomyces cerevisiae. FEMS Yeast Res 2009, 9:32-44.
• [15]Atsumi S, Wu T-Y, Machado IMP, Huang W-C, Chen P-Y, Pellegrini M, Liao JC: Evolution, genomic analysis, and reconstruction of isobutanol tolerance in Escherichia coli. Mol Syst Biol 2010, 6:449.
• [16]Birrell GW, Brown JA, Wu HI, Giaever G, Chu AM, Davis RW, Brown JM: Transcriptional response of Saccharomyces cerevisiae to DNA-damaging agents does not identify the genes that protect against these agents. Proc Natl Acad Sci USA. 2002, 99:8778-8783.
• [17]Rochfort S: Metabolomics reviewed: a new “omics” platform technology for systems biology and implications for natural products research. J Nat Prod. 2005, 68:1813-1820.
• [18]Putri SP, Nakayama Y, Matsuda F, Uchikata T, Kobayashi S, Matsubara A, Fukusaki E: Current metabolomics: Practical applications. J Biosci Bioeng 2013, 115:579-589.
• [19]Fiehn O: Metabolomics—the link between genotypes and phenotypes. In Functional Genomics. Edited by Town C. Springer, The Netherlands; 2002:155-171.
• [20]Fukusaki E, Kobayashi A: Plant metabolomics: potential for practical operation. J Biosci Bioeng. 2005, 100:347-354.
• [21]Hayashi S, Akiyama S, Tamaru Y, Takeda Y, Fujiwara T, Inoue K, Kobayashi A, Maegawa S, Fukusaki E: A novel application of metabolomics in vertebrate development. Biochem Biophys Res Commun. 2009, 386:268-272.
• [22]Yoshida R, Tamura T, Takaoka C, Harada K, Kobayashi A, Mukai Y, Fukusaki E: Metabolomics-based systematic prediction of yeast lifespan and its application for semi-rational screening of ageing-related mutants. Aging Cell. 2010, 9:616-625.
• [23]Nicolaou SA, Gaida SM, Papoutsakis ET: A comparative view of metabolite and substrate stress and tolerance in microbial bioprocessing: from biofuels and chemicals, to biocatalysis and bioremediation. Metab Eng. 2010, 12:307-331.
• [24]Dunlop M: Engineering microbes for tolerance to next-generation biofuels. Biotechnol Biofuels. 2011, 4:32. BioMed Central Full Text
• [25]Steen EJ, Chan R, Prasad N, Myers S, Petzold CJ, Redding A, Ouellet M, Keasling JD: Metabolic engineering of Saccharomyces cerevisiae for the production of n-butanol. Microb Cell Fact. 2008, 7:36. BioMed Central Full Text
• [26]Dwight SS, Harris MA, Dolinski K, Ball CA, Binkley G, Christie KR, Fisk DG, Issel-Tarver L, Schroeder M, Sherlock G, Sethuraman A, Weng S, Botstein D, Cherry JM: Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO). Nucl Acids Res. 2002, 30:69-72.
• [27]Goodarzi H, Bennett BD, Amini S, Reaves ML, Hottes AK, Rabinowitz JD, Tavazoie S: Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli. Mol Syst Biol 2010, 6:378.
• [28]Schaub J, Reuss M: In vivo dynamics of glycolysis in Escherichia coli shows need for growth-rate dependent metabolome analysis. Biotechnol Progress. 2008, 24:1402-1407.
• [29]Raamsdonk LM, Teusink B, Broadhurst D, Zhang N, Hayes A, Walsh MC, Berden JA, Brindle KM, Kell DB, Rowland JJ, Westerhoff HV, van Dam K, Oliver SG: A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol. 2001, 19:45-50.
• [30]Pongsuwan W, Fukusaki E, Bamba T, Yonetani T, Yamahara T, Kobayashi A: Prediction of Japanese Green Tea Ranking by gas chromatography/mass spectrometry-based hydrophilic metabolite fingerprinting. J Agric Food Chem. 2007, 55:231-236.
• [31]Tsugawa H, Tsujimoto Y, Arita M, Bamba T, Fukusaki E: GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA). BMC Bioinform. 2011, 12:131. BioMed Central Full Text
• [32]Trygg J, Wold S: Orthogonal projections to latent structures (O-PLS). J Chemom. 2002, 16:119-128.
• [33]Umetrics. SIMCA-P and Multivariate Analysis FAQ. Version 1.01. http://www.umetrics.com/sites/default/files/kb/multivariate_faq.pdf. Accessed 2 Mar 2015.
• [34]Yeast Biochemical Pathways Database. SGD Project, Stanford University. 2002. http://pathway.yeastgenome.org. Accessed 2 Mar 2015.
• [35]Almaas E, Kovács B, Vicsek T, Oltvai ZN, Barabási A-L: Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature. 2004, 427:839-843.
• [36]Hartman J, Tippery N: Systematic quantification of gene interactions by phenotypic array analysis. Genome Biol. 2004, 5:R49. BioMed Central Full Text
• [37]Hartman JL: Buffering of deoxyribonucleotide pool homeostasis by threonine metabolism. Proc Natl Acad Sci USA. 2007, 104:11700-11705.
• [38]Berg JM, Tymoczko JL, Stryer L. An overview of the Citric Acid Cycle. In: Biochemistry, 5th edn. New York: W H Freeman; 2002. p. 698–701.
• [39]Giaever G, Chu AM, Ni L, Connelly C, Riles L, et al.: Functional profiling of the Saccharomyces cerevisiae genome. Nature. 2002, 418:387-391.
• [40]Hashim Z, Teoh ST, Bamba T, Fukusaki E: Construction of a metabolome library for transcription factor-related single gene mutants of Saccharomyces cerevisiae. J Chromatogr B. 2014, 966:83-92.
• [41]Lommen A: MetAlign: Interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal Chem. 2009, 81:3079-3086.
• [42]Tikunov YM, Laptenok S, Hall RD, Bovy A, de Vos RCH: MSClust: a tool for unsupervised mass spectra extraction of chromatography–mass spectrometry ion-wise aligned data. Metabolomics. 2012, 8:714-718.