Abstract
Centella asiatica is an important medicinal plant with a wide range of bioactivities associated with its secondary metabolites. Using two extraction procedures, metabolomic approaches were used to investigate changes in the metabolome of C. asiatica cells treated with exogenous MeJA. GC–MS and LC–MS platforms were employed for semi-targeted and untargeted analyses, respectively. Multivariate data analyses indicated concentration-dependent changes in the metabolite profiles, indicative of the cellular response to MeJA. Annotation of biomarkers correlated with the treatment indicate differential responses in flavonoid-, phenylpropanoid (cinnamates)- and terpenoid pathways and changes in fatty acid profiles. MeJA treatment triggered the accumulation of bicyclic sesquiterpenoids (aristolochene, deoxy-capsidiol, 15-hydroxysolavetivone, solavetivone, 3-hydroxylubimin) and a tricyclic sesquiterpenoid (phytuberin), indicating the stimulatory effect of MeJA on this branch of the terpenoid pathways. In contrast, flavonoids were mostly negatively correlated with the treatment. The presence of the sesquiterpenoids in MeJA-elicited cells and other tentatively identified metabolites (abscisic acid, fatty acids, phytosterols and metabolites of shikimate–phenylpropanoid pathways) indicates that the changes in the metabolome are associated with a defensive function in response to elicitation by MeJA, rather than just the amplification of existing terpene pathways. These results provide a detailed and comprehensive picture of metabolic changes occurring in C. asiatica cells in response to MeJA elicitation and contribute to the understanding of flexible and controllable aspects of metabolic manipulation.
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References
Allwood JW, Ellis DI, Goodacre R (2008) Metabolomic technologies and their application to the study of plants and plant–host interactions. Physiol Plant 132:117–135. doi:10.1111/j.1399-3054.2007.01001.x
Asakawa Y, Matsuda R, Takemoto T (1982) Mono- and sesquiterpenoids from Hydrocotyle and Centella species. Phytochemistry 21:2590–2592. doi:10.1016/0031-9422(82)85264-3
Benton HP, Wong DM, Trauger SA, Siuzdak G (2008) XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. Anal Chem 80:6382–6389. doi:10.1021/ac800795f
Bhalla R, Narasimhan K, Swarup S (2005) Metabolomics and its role in understanding cellular responses in plants. Plant Cell Rep 24:562–571. doi:10.1007/s00299-005-0054-9
Bino RJ, Hall RD, Fiehn O et al (2004) Potential of metabolomics as a functional genomics tool. Trends Plant Sci 9:418–425. doi:10.1016/j.tplants.2004.07.004
Brown M, Wedge DC, Goodacre R et al (2011) Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics 27:1108–1112. doi:10.1093/bioinformatics/btr079
Camacho D, De Fuente A, Mendes P (2005) The origin of correlations in metabolomics data. Metabolomics 1:53–63. doi:10.1007/s11306-005-1107-3
Cheong J-J, Choi Y Do (2003) Methyl jasmonate as a vital substance in plants. Trends Genet 19:409–413. doi:10.1016/S0168-9525(03)00138-0
Chong I-G, Jun C-H (2005) Performance of some variable selection methods when multicollinearity is present. Chemom Intell Lab Syst 78:103–112. doi:10.1016/j.chemolab.2004.12.011
Dai Y, Li Z, Xue L et al (2010) Metabolomics study on the anti-depression effect of xiaoyaosan on rat model of chronic unpredictable mild stress. J Ethnopharmacol 128:482–489. doi:10.1016/j.jep.2010.01.016
De Vos RC, Moco S, Lommen A et al (2007) Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat Protoc 2:778–791. doi:10.1038/nprot.2007.95
Degenhardt J, Köllner TG, Gershenzon J (2009) Monoterpene and sesquiterpene synthases and the origin of terpene skeletal diversity in plants. Phytochemistry 70:1621–1637. doi:10.1016/j.phytochem.2009.07.030
Dunn WB, Ellis DI (2005) Metabolomics: current analytical platforms and methodologies. Trends Anal Chem 24:285–294. doi:10.1016/j.trac.2004.11.021
Eriksson L, Trygg J, Wold S (2008) CV-ANOVA for significance testing of PLS and OPLS® models. J Chemom 22:594–600. doi:10.1002/cem.1187
Fiehn O (2002) Metabolomics-the link between genotypes and phenotypes. Plant Mol Biol 48:155–171
Fiehn O, Kopka J, Dörmann P et al (2000) Metabolite profiling for plant functional genomics. Nat Biotechnol 18:1157–1161. doi:10.1038/81137
Flores-Sanchez IJ, Pec J, Fei J et al (2009) Elicitation studies in cell suspension cultures of Cannabis sativa L. J Biotechnol 143:157–168. doi:10.1016/j.jbiotec.2009.05.006
Fukusaki E, Kobayashi A (2005) Plant metabolomics: potential for practical operation. J Biosci Bioeng 100:347–354. doi:10.1263/jbb.100.347
Goodacre R, York EV, Heald JK, Scott IM (2003) Chemometric discrimination of unfractionated plant extracts analyzed by electrospray mass spectrometry. Phytochemistry 62:859–863. doi:10.1016/S0031-9422(02)00718-5
Gowda H, Ivanisevic J, Johnson CH et al (2014) Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal Chem 86:6931–6939. doi:10.1021/ac500734c
Gray NE, Morré J, Kelley J et al (2014) Caffeoylquinic acids in Centella asiatica protect against amyloid-β toxicity. J Alzheimers Dis 40:359–373. doi:10.3233/JAD-131913
Gromski PS, Xu Y, Hollywood KA et al (2014) The influence of scaling metabolomics data on model classification accuracy. Metabolomics. doi:10.1007/s11306-014-0738-7
Hall RD (2006) Plant metabolomics: from holistic hope, to hype, to hot topic. New Phytol 169:453–468. doi:10.1111/j.1469-8137.2005.01632.x
Haug K, Salek RM, Conesa P et al (2013) MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res 41:D781–D786. doi:10.1093/nar/gks1004
James JT, Dubery IA (2009) Pentacyclic triterpenoids from the medicinal herb, Centella asiatica (L.) Urban. Molecules 14:3922–3941. doi:10.3390/molecules14103922
James JT, Meyer R, Dubery IA (2008) Characterisation of two phenotypes of Centella asiatica in Southern Africa through the composition of four triterpenoids in callus, cell suspensions and leaves. Plant Cell, Tissue Organ Cult 94:91–99. doi:10.1007/s11240-008-9391-z
James JT, Tugizimana F, Steenkamp PA, Dubery IA (2013) Metabolomic analysis of methyl jasmonate-induced triterpenoid production in the medicinal herb Centella asiatica (L.) urban. Molecules 18:4267–4281. doi:10.3390/molecules18044267
Kaal E, Janssen H-G (2008) Extending the molecular application range of gas chromatography. J Chromatogr A 1184:43–60. doi:10.1016/j.chroma.2007.11.114
Kanani HH, Klapa MI (2007) Data correction strategy for metabolomics analysis using gas chromatography-mass spectrometry. Metab Eng 9:39–51. doi:10.1016/j.ymben.2006.08.001
Kanani H, Chrysanthopoulos PK, Klapa MI (2008) Standardizing GC–MS metabolomics. J Chromatogr B 871:191–201. doi:10.1016/j.jchromb.2008.04.049
Kieran PM, MacLoughlin PF, Malone DM (1997) Plant cell suspension cultures: some engineering considerations. J Biotechnol 59:39–52
Kim OT, Kim SH, Ohyama K, Muranaka T, Choi YE, Lee HY, Kim MY, Hwang B (2010) Upregulation of phytosterol and triterpene biosynthesis in Centella asiatica hairy roots overexpressed ginseng farnesyl diphosphate synthase. Plant Cell Rep 29:403–411
Kim HK, Choi YH, Verpoorte R (2011) NMR-based plant metabolomics: where do we stand, where do we go? Trends Biotechnol 29:267–275. doi:10.1016/j.tibtech.2011.02.001
Kim OT, Um Y, Jin ML, Kim YC, Bang KH, Hyun DY, Lee HS, Lee Y (2014) Analysis of expressed sequence tags from Centella asiatica (L.) Urban hairy roots elicited by methyl jasmonate to discover genes related to cytochrome P450 s and glucosyltransferases. Plant Biotechnol Rep 8:211–220
Lange BM, Ahkami A (2013) Metabolic engineering of plant monoterpenes, sesquiterpenes and diterpenes–current status and future opportunities. Plant Biotechnol J 11:169–196. doi:10.1111/pbi.12022
López-Gresa MP, Maltese F, Bellés JM et al (2010) Metabolic response of tomato leaves upon different plant-pathogen interactions. Phytochem Anal 21:89–94. doi:10.1002/pca.1179
Madala NE, Piater LA, Steenkamp PA, Dubery IA (2014) Multivariate statistical models of metabolomic data reveals different metabolite distribution patterns in isonitrosoacetophenone-elicited Nicotiana tabacum and Sorghum bicolor cells. Springerplus 3:1–10. doi:10.1186/2193-1801-3-254
Mangas S, Bonfill M, Osuna L et al (2006) The effect of methyl jasmonate on triterpene and sterol metabolisms of Centella asiatica, Ruscus aculeatus and Galphimia glauca cultured plants. Phytochemistry 67:2041–2049. doi:10.1016/j.phytochem.2006.06.025
Mehmood T, Liland KH, Snipen L, Sæbø S (2012) A review of variable selection methods in Partial Least Squares Regression. Chemom Intell Lab Syst 118:62–69. doi:10.1016/j.chemolab.2012.07.010
Oyedeji OA, Afolayan AJ (2005) Chemical composition and antibacterial activity of the essential oil of Centella asiatica growing in South Africa. Pharm Biol 43:249–252. doi:10.1080/13880200590928843
Patti GJ, Tautenhahn R, Siuzdak G (2012) Meta-analysis of untargeted metabolomic data from multiple profiling experiments. Nat Protoc 7:508–516. doi:10.1038/nprot.2011.454
Patti GJ, Tautenhahn R, Rinehart D et al (2013) A view from above: cloud plots to visualize global metabolomic data. Anal Chem 85:798–804. doi:10.1021/ac3029745
Rajkumar S, Jebanesan A (2007) Repellent activity of selected plant essential oils against the malarial fever mosquito Anopheles stephensi. Trop Biomed 24:71–75
Saccenti E, Hoefsloot HCJ, Smilde AK et al (2013) Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics 10:361–374. doi:10.1007/s11306-013-0598-6
Sadeghi-bazargani H, Bangdiwala SI, Mohammad K (2011) Compared application of the new OPLS-DA statistical model versus partial least squares regression to manage large numbers of variables in an injury case-control study. Sci Res Eassays 6:4369–4377. doi:10.5897/SRE10.1147
Salek RM, Haug K, Conesa P et al (2013) The MetaboLights repository: curation challenges in metabolomics. Database Oxford 2013:1–8. doi:10.109/database/bat029
Schauer N, Fernie AR (2006) Plant metabolomics: towards biological function and mechanism. Trends Plant Sci 11:508–516. doi:10.1016/j.tplants.2006.08.007
Shulaev V, Cortes D, Miller G, Mittler R (2008) Metabolomics for plant stress response. Physiol Plant 132:199–208. doi:10.1111/j.1399-3054.2007.01025.x
Smith CA, Want EJ, O’Maille G et al (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779–787. doi:10.1021/ac051437y
Sumner LW, Mendes P, Dixon RA (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62:817–836. doi:10.1016/S0031-9422(02)00708-2
Sumner LW, Amberg A, Barrett D et al (2007) Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3:211–221. doi:10.1007/s11306-007-0082-2
Tautenhahn R, Cho K, Uritboonthai W et al (2012a) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30:826–828. doi:10.1038/nbt.2348
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012b) XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem 84:5035–5039. doi:10.1021/ac300698c
Thaler JS, Humphrey PT, Whiteman NK (2012) Evolution of jasmonate and salicylate signal crosstalk. Trends Plant Sci 17:260–270. doi:10.1016/j.tplants.2012.02.010
Trivedi KD, Iles KR (2012) The application of SIMCA P + in Shotgun metabolomics analysis of ZIC® HILIC-MS spectra of human urine - experience with the Shimadzu IT-TOF and profiling solutions data extraction software. J Chromatogr Sep Tech 03:1–5. doi:10.4172/2157-7064.1000145
Trygg J, Holmes E, Lundstedt T (2007) Chemometrics in metabonomics. J Proteome Res 6:469–479. doi:10.1021/pr060594q
Tugizimana F, Steenkamp PA, Piater LA, Dubery IA (2012) Ergosterol-induced sesquiterpenoid synthesis in tobacco cells. Molecules 17:1698–1715. doi:10.3390/molecules17021698
Tugizimana F, Piater LA, Dubery IA (2013) Plant metabolomics: a new frontier in phytochemical analysis. S Afr J Sci 109:18–20. doi:10.1590/sajs.2013/20120005
Tugizimana F, Steenkamp PA, Piater LA, Dubery IA (2014) Multi-platform metabolomic analyses of ergosterol-induced dynamic changes in Nicotiana tabacum cells. PLoS ONE 9:e87846. doi:10.1371/journal.pone.0087846
Tulipani S, Llorach R, Jáuregui O et al (2011) Metabolomics unveils urinary changes in subjects with metabolic syndrome following 12-week nut consumption. J Proteome Res 10:5047–5058. doi:10.1021/pr200514h
Van den Berg RA, Hoefsloot HCJ, Westerhuis JA et al (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genom 7:1–15. doi:10.1186/1471-2164-7-142
Van Gulik WM, ten Hoopen HJG, Heijnen JJ (2001) The application of continuous culture for plant cell suspensions. Enzyme Microb Technol 28:796–805
Verpoorte R, Choi YH, Kim HK (2007) NMR-based metabolomics at work in phytochemistry. Phytochemistry 6:3–14. doi:10.1007/s11101-006-9031-3
Weckwerth W, Loureiro ME, Wenzel K, Fiehn O (2004) Differential metabolic networks unravel the effects of silent plant phenotypes. Proc Natl Acad Sci 101:7809–7814. doi:10.1073/pnas.0303415101
Wiklund S, Johansson E, Sjöström L et al (2008) Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal Chem 80:115–122. doi:10.1021/ac0713510
Yamamoto H, Yamaji H, Abe Y et al (2009) Dimensionality reduction for metabolome data using PCA, PLS, OPLS, and RFDA with differential penalties to latent variables. Chemom Intell Lab Syst 98:136–142. doi:10.1016/j.chemolab.2009.05.006
Yin S, Mei L, Newman J, Back K, Chappell J (1997) Regulation of sesquiterpene cyclase gene expression: characterization of an elicitor- and pathogen-inducible promoter. Plant Physiol 115:437–451
Zhu Z-J, Schultz AW, Wang J et al (2013) Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat Protoc 8:451–460. doi:10.1038/nprot.2013.004
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The South African National Research Foundation (NRF) and the University of Johannesburg are thanked for the fellowship (FT and EN) and financial support (IAD).
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11816_2015_350_MOESM1_ESM.tif
Supplementary material 1 (TIFF 7 kb). Fig. S1 OPLS-DA S-plot of GC–MS data: control (0 µM) and 400 µM MeJA treatment. The x-axis is the modelled covariation (variable magnitude) and the y-axis is the modelled correlation (reliability/loading vector of the predictive component). The mass ions in the upper right quadrant of the S-plot are positively correlated to the MeJA treatment (such as m/z 69.05, 71.15, 98.01 and 133.09). The ions in the lower quadrant are negatively related to the treatment: m/z 83.07, 240.96 and 300.92
11816_2015_350_MOESM2_ESM.tif
Supplementary material 2 (TIFF 127 kb). Fig. S2 Variable importance in projection (VIP) plots for OPLS-DA models of 400 µM-treated samples. (A) VIP plot of the OPLS-DA model of the LC–MS data displaying ions such as m/z 341.08, 353.08 and 577.13 (annotated as caffeic acid-glucoside, caffeoylquinate and pelargonidin3-O-beta-D-p-coumaroylglucoside, respectively, Table 1). (B) VIP plot of the OPLS-DA model of the GC–MS data showing ions such as m/z 69.05, 71.15, 85.1 and 109.19 (annotated as tridecanoic acid, lauric acid, cis-sesquisabinene hydrate and methyl epi-jasmonate, respectively, Table 1). These ions from the VIP plots were accountable for the significant separation in the models as their VIP scores exceeded 1.0
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Tugizimana, F., Ncube, E.N., Steenkamp, P.A. et al. Metabolomics-derived insights into the manipulation of terpenoid synthesis in Centella asiatica cells by methyl jasmonate. Plant Biotechnol Rep 9, 125–136 (2015). https://doi.org/10.1007/s11816-015-0350-y
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DOI: https://doi.org/10.1007/s11816-015-0350-y