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The Importance of Experimental Design, Quality Assurance, and Control in Plant Metabolomics Experiments

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Book cover Plant Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1778))

Abstract

The output of metabolomics relies to a great extent upon the methods and instrumentation to identify, quantify, and access spatial information on as many metabolites as possible. However, the most modern machines and sophisticated tools for data analysis cannot compensate for inappropriate harvesting and/or sample preparation procedures that modify metabolic composition and can lead to erroneous interpretation of results. In addition, plant metabolism has a remarkable degree of complexity, and the number of identified compounds easily surpasses the number of samples in metabolomics analyses, increasing false discovery risk. These aspects pose a large challenge when carrying out plant metabolomics experiments. In this chapter, we address the importance of a proper experimental design taking into consideration preventable complications and unavoidable factors to achieve success in metabolomics analysis. We also focus on quality control and standardized procedures during the metabolomics workflow.

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References

  1. Dixon RA, Strack D (2003) Phytochemistry meets genome analysis, and beyond. Phytochemistry 62:815–816

    Article  CAS  PubMed  Google Scholar 

  2. Hall R, Beale M, Fiehn O et al (2002) Plant metabolomics: the missing link in functional genomics strategies. Plant Cell 14:1437–1440

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Li D, Heiling S, Baldwin IT, Gaquerel E (2016) Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory. Proc Natl Acad Sci U S A 113:E7610–E7618

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Heiling S, Khanal S, Barsch A et al (2016) Using the knowns to discover the unknowns: MS-based dereplication uncovers structural diversity in 17-hydroxygeranyllinalool diterpene glycoside production in the Solanaceae. Plant J 85:561–577

    Article  CAS  PubMed  Google Scholar 

  5. Wen W, Li D, Li X et al (2014) Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat Commun 5:3438

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fernie AR, Aharoni A, Willmitzer L et al (2011) Recommendations for reporting metabolite data. Plant Cell 23:2477–2482

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Giavalisco P, Kohl K, Hummel J et al (2009) 13C isotope-labeled metabolomes allowing for improved compound annotation and relative quantification in liquid chromatography-mass spectrometry-based metabolomic research. Anal Chem 81:6546–6551

    Article  CAS  PubMed  Google Scholar 

  8. Aharoni A, Ric de Vos CH, Verhoeven HA et al (2002) Nontargeted metabolome analysis by use of fourier transform ion cyclotron mass spectrometry. OMICS 6:217–234

    Article  CAS  PubMed  Google Scholar 

  9. Iijima Y, Nakamura Y, Ogata Y et al (2008) Metabolite annotations based on the integration of mass spectral information. Plant J 54:949–962

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hong J, Yang L, Zhang D, Shi J (2016) Plant metabolomics: an indispensable system biology tool for plant science. Int J Mol Sci 17(6). https://doi.org/10.3390/ijms17060767

    Article  CAS  PubMed Central  Google Scholar 

  11. Zampieri M, Sekar K, Zamboni N, Sauer U (2017) Frontiers of high-throughput metabolomics. Curr Opin Chem Biol 36:15–23

    Article  CAS  PubMed  Google Scholar 

  12. Sampaio BL, Edrada-Ebel R, Da Costa FB (2016) Effect of the environment on the secondary metabolic profile of Tithonia diversifolia: a model for environmental metabolomics of plants. Sci Rep 6:29265

    Article  PubMed  PubMed Central  Google Scholar 

  13. Glaubitz U, Erban A, Kopka J et al (2015) Metabolite profiling reveals sensitivity-dependent metabolic shifts in rice (Oryza Sativa L.) cultivars under high night temperature stress. Procedia Environ Sci 29:72

    Article  CAS  Google Scholar 

  14. Liu X, Vrieling K, Klinkhamer PGL (2017) Interactions between plant metabolites affect herbivores: a study with pyrrolizidine alkaloids and chlorogenic acid. Front Plant Sci 8:903

    Article  PubMed  PubMed Central  Google Scholar 

  15. Sade D, Shriki O, Cuadros-Inostroza A et al (2015) Comparative metabolomics and transcriptomics of plant response to Tomato yellow leaf curl virus infection in resistant and susceptible tomato cultivars. Metabolomics 11:81–97

    Article  CAS  Google Scholar 

  16. Kogovsek P, Pompe-Novak M, Petek M et al (2016) Primary metabolism, phenylpropanoids and antioxidant pathways are regulated in potato as a response to potato virus Y infection. PLoS One 11:e0146135

    Article  PubMed  PubMed Central  Google Scholar 

  17. Obata T, Witt S, Lisec J et al (2015) Metabolite profiles of maize leaves in drought, heat, and combined stress field trials reveal the relationship between metabolism and grain yield. Plant Physiol 169:2665–2683

    PubMed  PubMed Central  CAS  Google Scholar 

  18. Chen W, Wang W, Peng M et al (2016) Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. Nat Commun 7:12767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wen W, Liu H, Zhou Y et al (2016) Combining quantitative genetics approaches with regulatory network analysis to dissect the complex metabolism of the maize kernel. Plant Physiol 170:136–146

    Article  CAS  PubMed  Google Scholar 

  20. Meyer RC, Steinfath M, Lisec J et al (2007) The metabolic signature related to high plant growth rate in Arabidopsis thaliana. Proc Natl Acad Sci U S A 104:4759–4764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hill CB, Roessner U (2015) Advances in high-throughput untargeted LC-MS analysis for plant metabolomics. Adv LC-MS Appl Metabolomics 38:58–71

    Article  Google Scholar 

  22. Allwood JW, Heald J, Lloyd AJ et al (2012) Separating the inseparable: the metabolomic analysis of plant-pathogen interactions. Methods Mol Biol 860:31–49

    Article  CAS  PubMed  Google Scholar 

  23. Stitt M, Sulpice R, Keurentjes J (2010) Metabolic networks: how to identify key components in the regulation of metabolism and growth. Plant Physiol 152:428–444

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ap Rees T, Hill SA (1994) Metabolic control analysis of plant metabolism. Plant Cell Environ 17:587–599

    Article  Google Scholar 

  25. Lunn JE (2007) Compartmentation in plant metabolism. J Exp Bot 58:35–47

    Article  CAS  PubMed  Google Scholar 

  26. Vigani G, Bashir K, Ishimaru Y et al (2016) Knocking down mitochondrial iron transporter (MIT) reprograms primary and secondary metabolism in rice plants. J Exp Bot 67:1357–1368

    Article  CAS  PubMed  Google Scholar 

  27. Fukushima A, Kusano M, Mejia RF et al (2014) Metabolomic characterization of knockout mutants in Arabidopsis: development of a metabolite profiling database for knockout mutants in Arabidopsis. Plant Physiol 165:948–961

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Pham PA, Wahl V, Tohge T et al (2015) Analysis of knockout mutants reveals non-redundant functions of poly(ADP-ribose)polymerase isoforms in Arabidopsis. Plant Mol Biol 89:319–338

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Schwahn K, de Souza LP, Fernie AR, Tohge T (2014) Metabolomics-assisted refinement of the pathways of steroidal glycoalkaloid biosynthesis in the tomato clade. J Integr Plant Biol 56:864–875

    Article  CAS  PubMed  Google Scholar 

  30. Strauch RC, Svedin E, Dilkes B et al (2015) Discovery of a novel amino acid racemase through exploration of natural variation in Arabidopsis thaliana. Proc Natl Acad Sci U S A 112:11726–11731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ghaffari MR, Shahinnia F, Usadel B et al (2016) The metabolic signature of biomass formation in barley. Plant Cell Physiol 57:1943–1960

    Article  CAS  PubMed  Google Scholar 

  32. Riedelsheimer C, Czedik-Eysenberg A, Grieder C et al (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44:217–220

    Article  CAS  PubMed  Google Scholar 

  33. Tzin V, Fernandez-Pozo N, Richter A et al (2015) Dynamic maize responses to aphid feeding are revealed by a time series of transcriptomic and metabolomic assays. Plant Physiol 169:1727–1743

    PubMed  PubMed Central  CAS  Google Scholar 

  34. Rudd JJ, Kanyuka K, Hassani-Pak K et al (2015) Transcriptome and metabolite profiling of the infection bycle of Zymoseptoria tritici on wheat reveals a biphasic interaction with plant immunity involving differential pathogen chromosomal contributions and a variation on the hemibiotrophic lifestyle def. Plant Physiol 167:1158–1185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Perez-Bueno ML, Pineda M, Diaz-Casado E, Baron M (2015) Spatial and temporal dynamics of primary and secondary metabolism in Phaseolus vulgaris challenged by Pseudomonas syringae. Physiol Plant 153:161–174

    Article  CAS  PubMed  Google Scholar 

  36. Bénard C, Bernillon S, Biais B et al (2015) Metabolomic profiling in tomato reveals diel compositional changes in fruit affected by source–sink relationships. J Exp Bot 66:3391–3404

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Figueroa CM, Feil R, Ishihara H et al (2016) Trehalose 6-phosphate coordinates organic and amino acid metabolism with carbon availability. Plant J 85:410–423

    Article  CAS  PubMed  Google Scholar 

  38. Espinoza C, Degenkolbe T, Caldana C et al (2010) Interaction with diurnal and circadian regulation results in dynamic metabolic and transcriptional changes during cold acclimation in arabidopsis. PLoS One 5:1–19

    Article  CAS  Google Scholar 

  39. Windsor AJ, Reichelt M, Figuth A et al (2005) Geographic and evolutionary diversification of glucosinolates among near relatives of Arabidopsis thaliana (Brassicaceae). Phytochemistry 66:1321–1333

    Article  CAS  PubMed  Google Scholar 

  40. Keurentjes JJB, Fu J, de Vos CHR et al (2006) The genetics of plant metabolism. Nat Genet 38:842–849

    Article  CAS  PubMed  Google Scholar 

  41. Blainey P, Krzywinski M, Altman N (2014) Points of significance: replication. Nat Meth 11:879–880

    Article  CAS  Google Scholar 

  42. Lisec J, Schauer N, Kopka J et al (2006) Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat Protoc 1:387–396

    Article  CAS  PubMed  Google Scholar 

  43. Riedelsheimer C, Lisec J, Czedik-Eysenberg A et al (2012) Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proc Natl Acad Sci U S A 109:8872–8877

    Article  PubMed  PubMed Central  Google Scholar 

  44. Arrivault S, Obata T, Szecowka M et al (2017) Metabolite pools and carbon flow during C4 photosynthesis in maize: 13CO2 labeling kinetics and cell type fractionation. J Exp Bot 68:283–298

    Article  CAS  PubMed  Google Scholar 

  45. Heise R, Arrivault S, Szecowka M et al (2014) Flux profiling of photosynthetic carbon metabolism in intact plants. Nat Protoc 9:1803–1824

    Article  CAS  PubMed  Google Scholar 

  46. Gibon Y, Usadel B, Blaesing OE et al (2006) Integration of metabolite with transcript and enzyme activity profiling during diurnal cycles in Arabidopsis rosettes. Genome Biol 7:R76

    Article  PubMed  PubMed Central  Google Scholar 

  47. Huseby S, Koprivova A, Lee B-R et al (2013) Diurnal and light regulation of sulphur assimilation and glucosinolate biosynthesis in Arabidopsis. J Exp Bot 64:1039–1048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ap Rees T, Fuller WA, Wright BW (1977) Measurements of glycolytic intermediates during the onset of thermogenesis in the spadix of Arum maculatum. Biochim Biophys Acta Bioenerg 461:274–282

    Article  CAS  Google Scholar 

  49. Glauser G, Boccard J, Wolfender JL, Rudaz S (2013) Metabolomics: application in plant sciences. In: Lämmerhofer M, Weckwerth W (eds) Metabolomics in practice: successful strategies to generate and analyze metabolic data. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 313–343

    Chapter  Google Scholar 

  50. T’Kindt R, Morreel K, Deforce D et al (2009) Joint GC-MS and LC-MS platforms for comprehensive plant metabolomics: Repeatability and sample pre-treatment. J Chromatogr B Anal Technol Biomed Life Sci 877:3572–3580

    Article  CAS  Google Scholar 

  51. Tohge T, Mettler T, Arrivault S et al (2011) From models to crop species : caveats and solutions for translational metabolomics. Front Plant Sci 2:1–15

    Article  CAS  Google Scholar 

  52. Yang D, Song D, Kind T et al (2015) Lipidomic analysis of chlamydomonas reinhardtii under nitrogen and sulfur deprivation. PLoS One 10:e0137948

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Quinn RA, Vermeij MJA, Hartmann AC et al (2016) Metabolomics of reef benthic interactions reveals a bioactive lipid involved in coral defence. Proc R Soc B 283:20160469

    Article  CAS  PubMed  Google Scholar 

  54. Veyel D, Erban A, Fehrle I et al (2014) Rationales and approaches for studying metabolism in eukaryotic microalgae. Meta 4:184–217

    Google Scholar 

  55. Kim HK, Choi YH, Verpoorte R (2010) NMR-based metabolomic analysis of plants. Nat Protoc 5:536–549

    Article  CAS  PubMed  Google Scholar 

  56. Huege J, Krall L, Steinhauser M-C et al (2011) Sample amount alternatives for data adjustment in comparative cyanobacterial metabolomics. Anal Bioanal Chem 399:3503–3517

    Article  CAS  PubMed  Google Scholar 

  57. Cajka T, Fiehn O (2016) Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal Chem 88:524–545

    Article  CAS  PubMed  Google Scholar 

  58. Beltran A, Suarez M, Rodriguez MA et al (2012) Assessment of compatibility between extraction methods for NMR- and LC/MS-based metabolomics. Anal Chem 84:5838–5844

    Article  CAS  PubMed  Google Scholar 

  59. Salem MA, Juppner J, Bajdzienko K, Giavalisco P (2016) Protocol: a fast, comprehensive and reproducible one-step extraction method for the rapid preparation of polar and semi-polar metabolites, lipids, proteins, starch and cell wall polymers from a single sample. Plant Methods 12:45

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Mushtaq MY, Choi YH, Verpoorte R, Wilson EG (2014) Extraction for metabolomics: access to the metabolome. Phytochem Anal 25:291–306

    Article  CAS  PubMed  Google Scholar 

  61. Kim HK, Verpoorte R (2010) Sample preparation for plant metabolomics. Phytochem Anal 21:4–13

    Article  CAS  PubMed  Google Scholar 

  62. Maltese F, van der Kooy F, Verpoorte R (2009) Solvent derived artifacts in natural products chemistry. Nat Prod Commun 4:447–454

    PubMed  CAS  Google Scholar 

  63. Bais P, Moon SM, He K et al (2010) PlantMetabolomics.org: a web portal for plant metabolomics experiments. Plant Physiol 152:1807–1816

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Roberts LD, Souza AL, Gerszten RE, Clish CB (2012) Targeted metabolomics. Curr Protoc Mol Biol. 98:30.2:30.2.1–30.2.24

    Article  Google Scholar 

  65. Jorge TF, Rodrigues JA, Caldana C et al (2016) Mass spectrometry-based plant metabolomics: metabolite responses to abiotic stress. Mass Spectrom Rev 35:620–649

    Article  CAS  PubMed  Google Scholar 

  66. Kopka J, Schauer N, Krueger S et al (2005) GMD@CSB.DB: the Golm metabolome database. Bioinformatics 21:1635–1638

    Article  CAS  PubMed  Google Scholar 

  67. Kind T, Wohlgemuth G, Lee DY et al (2009) FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal Chem 81:10038–10048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Simmler C, Napolitano JG, McAlpine JB et al (2014) Universal quantitative NMR analysis of complex natural samples. Curr Opin Biotechnol 25:51–59

    Article  CAS  Google Scholar 

  69. Alonso-herranz JGV, Barbas C, Grace E (2015) Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample. Metabolomics:518–528

    Google Scholar 

  70. Dunn WB, Broadhurst DI, Edison A et al (2017) Quality assurance and quality control processes : summary of a metabolomics community questionnaire. Metabolomics 13:1–6

    Article  CAS  Google Scholar 

  71. Sangster T, Major H, Plumb R et al (2006) A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst 131:1075

    Article  CAS  PubMed  Google Scholar 

  72. Gibon Y, Rolin D (2012) Aspects of experimental design for plant metabolomics experiments and guidelines for growth of plant material. Methods Mol Biol 860:13–30

    Article  CAS  PubMed  Google Scholar 

  73. Fernie AR, Stitt M (2012) On the discordance of metabolomics with proteomics and transcriptomics: coping with increasing complexity in logic, chemistry, and network interactions. Plant Physiol 158:1139–1145

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Steinbeck C, Conesa P, Haug K et al (2012) MetaboLights: towards a new COSMOS of metabolomics data management. Metabolomics 8:757–760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Kale NS, Haug K, Conesa P et al (2016) MetaboLights: an open-access database repository for metabolomics data. Curr Protoc Bioinform 53:14.13.1–14.13.18

    Article  Google Scholar 

  76. 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

    Article  CAS  PubMed  Google Scholar 

  77. Hunter A, Dayalan S, De Souza D et al (2017) MASTR-MS: a web-based collaborative laboratory information management system (LIMS) for metabolomics. Metabolomics 13:14

    Article  CAS  PubMed  Google Scholar 

  78. Hoermiller II, Naegele T, Augustin H et al (2017) Subcellular reprogramming of metabolism during cold acclimation in Arabidopsis thaliana. Plant Cell Environ 40:602–610

    Article  CAS  PubMed  Google Scholar 

  79. Jia X, Sun C, Zuo Y et al (2016) Integrating transcriptomics and metabolomics to characterise the response of Astragalus membranaceus Bge. var. mongolicus (Bge.) to progressive drought stress. BMC Genomics 17:188

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Todaka D, Zhao Y, Yoshida T et al (2017) Temporal and spatial changes in gene expression, metabolite accumulation and phytohormone content in rice seedlings grown under drought stress conditions. Plant J 90:61–78

    Article  CAS  PubMed  Google Scholar 

  81. Cuadros-Inostroza A, Ruiz-Lara S, Gonzalez E et al (2016) GC-MS metabolic profiling of Cabernet Sauvignon and Merlot cultivars during grapevine berry development and network analysis reveals a stage- and cultivar-dependent connectivity of primary metabolites. Metabolomics 12:39

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Monti LL, Bustamante CA, Osorio S et al (2016) Metabolic profiling of a range of peach fruit varieties reveals high metabolic diversity and commonalities and differences during ripening. Food Chem 190:879–888

    Article  CAS  PubMed  Google Scholar 

  83. Wiggins NL, Forrister DL, Endara M-J et al (2016) Quantitative and qualitative shifts in defensive metabolites define chemical defense investment during leaf development in Inga, a genus of tropical trees. Ecol Evol 6:478–492

    Article  PubMed  PubMed Central  Google Scholar 

  84. Massonnet C, Vile D, Fabre J et al (2010) Probing the reproducibility of leaf growth and molecular phenotypes : a comparison of three Arabidopsis accessions cultivated in ten laboratories. Plant Physiol 152:2142–2157

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

C. Caldana was funded by the São Paulo Research Foundation (FAPESP) grant no. 2012/19561-0 and the Max Planck Society.

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Correspondence to Camila Caldana .

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Martins, M.C.M., Caldana, C., Wolf, L.D., de Abreu, L.G.F. (2018). The Importance of Experimental Design, Quality Assurance, and Control in Plant Metabolomics Experiments. In: António, C. (eds) Plant Metabolomics. Methods in Molecular Biology, vol 1778. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7819-9_1

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  • DOI: https://doi.org/10.1007/978-1-4939-7819-9_1

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