BOT 6516 Plant Metabolism

Lecture 1

Plant Metabolism, Metabolomics and Metabolite Profiling

Why Study Plant Metabolism?1.

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Boehringer Metabolic Pathways

http://www.expasy.ch/cgi-bin/search-biochem-index

Fatty Acid Synthesis

Table 1 Some useful working definitions

Term DefinitionMetabolome The complete complement of small molecules present in an organism.Metabolomics The technology geared towards providing an essentially unbiased,

comprehensive qualitative and quantitative overview of the metabolites present in an organism

Metabonomics A nonplant term generally used to define the technology used to measure quantitatively the metabolic composition of body fluids following a response to pathophysiological stimuli or genetic modification

Metabolic fingerprinting

High throughput qualitative screening of the metabolic composition of an organism or tissue with the primary aim of sample comparison anddiscrimination analysis. Generally no attempt is initially made to identify the metabolites present. All steps from sample preparation, separation and detection should be rapid and as simple as is feasible. Often used as a forerunner to metabolic profiling

Metabolic or Metabolite profiling

Identification and quantification of the metabolites present in an organism. For practical reasons this is generally only feasible for a limited number of components, which are generally chosen on the basis of discriminant analysis or on molecular relationships based upon molecular pathways/networks

Targeted analysis

Following broad-scale metabolomics analysis, or based upon previous knowledge, biochemical profiling can be performed in greater detail on selected groups of metabolites by using optimized extraction and dedicated separation/detection techniques

From: Hall, Robert D. (2006) Plant metabolomics: From holistic hope, to hype, to hot topic.New Phytologist 169, 453-468.

Table 2 Metabolomics technology abbreviations

Term DefinitionAPCI Atmospheric pressure chemical ionizationCE Capillary electrophoresisESI Electrospray ionizationFIA/DIA Flow injection analysis/direct infusion analysisFT-ICR-MS Fourier transform–ion cyclotron resonance–mass spectrometry (or

FTMS)HPLC High performance (pressure) liquid chromatographyLC/GC Liquid/gas chromatographyMS Mass spectrometryNMR Nuclear magnetic resonancePCA Principal components analysisPDA (DAD) Photodiode array detectionPI PhotoionizationSPME Solid phase micro-extractionTOF Time of flight

From: Hall, Robert D. (2006) Plant metabolomics: From holistic hope, to hype, to hot topic.New Phytologist 169, 453-468.

Mass spectrometry (MS) is the primary detection method of choice for plant metabolomics due to its sensitivity, speed and broad application. Depending upon the type of extract made, GC (gas chromatography) or LC (Liquid Chromatography) are most routinely used for metabolite separation before the samples pass into the mass spectrometer.

•Gas chromatography–mass spectrometry (GC-MS)•Liquid chromatography-MS•Capillary electrophoresis-MS•Fourier transform-ICR-MS (often shortened to FTMS)•Flow- or direct-injection/infusion (FI/DI)-MS•Direct Laser Desorption-MS (perhaps someday in the future)

•Nuclear magnetic resonance seems currently to be the method of choice for medical metabolomics (metabonomics).

Major Steps in handling machine output1. Raw data preprocessing2. Data mining and visualization3. Data storage and database building

Metabolite Profiling Platforms

From: Hall, Robert D. (2006) Plant metabolomics: From holistic hope, to hype, to hot topic.New Phytologist 169, 453-468.

What Is Mass Spectrometry?

Mass spectrometry is a powerful analytical technique used to identify unknown compounds, to quantify known compounds, and elucidate the structure and chemical properties of molecules.

Detection of compounds can be accomplished with very minute quantities (as little as 10-12g, 10-15 moles for a compound of mass 1000 Daltons). This means that compounds can be identified at very low concentrations (one part in 1012) in chemically complex mixtures.

Mass spectrometry provides valuable information to a wide range of professionals: chemists, food safety, Homeland Security, physicians, astronomers, and biologists, Olympics, and Major League Baseball, just to name a few.

From: ASMS - American Society for Mass Spectrometry

What Does a Mass Spectrometer Measure?

A mass spectrometer is an instrument that measures the masses of individual molecules that have been converted into ions, i.e., molecules that have been electrically charged.

Since molecules are so small, it is not convenient to measure their masses is kilograms, or grams, or pounds. In fact, the mass of a single hydrogen atom is approximately 1.66 X 10-24 grams. We therefore need a more convenient unit for the mass of individual molecules. This unit of mass is often referred to by chemists and biochemists as the dalton (Da for short), and is defined as follows: 1 Da=(1/12) of the mass of a single atom of the isotope of carbon-12(12C). This follows the accepted convention of defining the 12C isotope as having exactly 12 mass units.

A mass spectrometer measures the mass-to-charge ratio of the ions formed from the molecules. The fundamental unit of charge is the magnitude of the charge on an electron. The charge on an ion is denoted by the integer number z of the fundamental unit of charge, and the mass-to-charge ratio m/z therefore represents daltons per fundamental unit of charge. In many cases, the ions encountered in mass spectrometry have just one charge (z=1) so the m/z value is numerically equal to the molecular (ionic) mass in Da. Mass spectrometrists often speak of the "mass of an ion" but really mean the m/z ratio.

From: ASMS - American Society for Mass Spectrometry

From: ASMS - American Society for Mass Spectrometry http://www.asms.org/whatisms/index.html

How a Mass Spectrometer Works

Formation of gas phase sample ions is an essential prerequisite to the mass sorting and detection process. Early mass spectrometers required a sample to be a gas. However, the applicability of mass spectrometry has been extended to include samples in liquid solutions or embedded in a solid matrix. The sample, which may be a solid, liquid, or vapor, enters the vacuum chamber through an inlet. Depending on the type of inlet and ionization techniques used, the sample may already exist as ions in solution, or it may be ionized in conjunction with its volatilization or by other methods in the ion source.

The gas phase ions are sorted in the mass analyzer according to their mass-to-charge (m/z) ratios and then collected by a detector. In the detector the ion flux is converted to a proportional electrical current. The data system records the magnitude of these electrical signals as a function of m/z and converts this information into a mass spectrum.

From: ASMS - American Society for Mass Spectrometry

Table 1. Mass Analyzers and their PerformancesGlinski, M. and Weckwerth, W. 2006. The role of mass spectrometry in plant systems biology. Mass Spectrom Rev. 1: 173-214.

Fig. 1. Scheme of analytical method validation.Mirko Glinski, M. and Weckwerth, W. 2006. The role of mass spectrometry in plant systems biology. Mass Spectrom Rev. 1: 173-214.

1. Genotyping and phenotyping (metabotyping)

2. Population screening

3. Understanding physiological processes

4. Biomarkers and bioactivity

5. Quality and breeding

From: Hall, Robert D. (2006) Plant metabolomics: From holistic hope, to hype, to hot topic.New Phytologist 169, 453-468.

Metabolite Profiling / Metabolomic Applications

Please read this recent paper: Keurentjes JJ, Fu J, de Vos CH, Lommen A, Hall RD, Bino RJ, van der Plas LH, Jansen RC, Vreugdenhil D, Koornneef M. The genetics of plant metabolism. Nat Genet. 2006 38: 842-9.

Integrated Network

Fig. 7. Overall process scheme for the identification of regulatory hubs from omics data. A network is shown exemplifying the interaction between transcripts (t), proteins (p), metabolites (m), and environment (e). The nodes (t, p, m, e) are the components and the edges reveal their distance (for further details see text) (modified from Weckwerth, Wenzel, & Fiehn, [2004]).

Glinski, M. and Weckwerth, W. 2006. The role of mass spectrometry in plant systems biology. Mass Spectrom Rev. 1: 173-214.

Fig. 9. Integration of systematic metabolite and protein profiling, pattern recognition & biomarker identification and the measurement of network in vivo dynamics (modified from Weckwerth, Wenzel, & Fiehn, [2004]; and Morgenthal et al., 2005). A and B: Metabolite correlation network topologies discriminating between WT and corresponding mutant plants (a PGM deficient mutant that accumulates sugars during the day). C: Independent component analysis separating the WT from the mutant and the day from the night samples. This dataset enables the biomarker selection out of correlative metabolite-protein networks. D: Visualization of the diurnal rhythm of a plant using an integrative metabolite-protein data matrix.

Glinski, M. and Weckwerth, W. 2006. The role of mass spectrometry in plant systems biology. Mass Spectrom Rev. 1: 173-214.

Fig. 1. Correlation maps for selected metabolites from source leaf material of A. thaliana (col-0), N. tabacum cv. samsun nn (SNN) WT and S. tuberosum cv. desiree as well as S. tuberosum cv. desiree tuber tissue. In each case, the pair-wise metabolite correlations were quantified using the Pearson correlation. As can be observed there are pronounced differences in the actively expressed network structures. For a more detailed discussion of the differences within the correlation patterns, see Section 3.

Morgenthal K, Weckwerth W, Steuer R. 2006. Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation. Biosystems 83, 108-117.

Fig. 4. Examples of pair-wise metabolite correlations for S. tuberosum tuber (43 samples), S. tuberosum leaf (34 samples), A. thaliana leaf (24 samples) and N. tabacum leaf (29 samples).Correlations matching a significance level of p < 0.001 are depicted in green (see Appendix A and Appendix B for statistical methods). The third and fourth columns correspond to cases of preserved correlations within all four datasets. In the first and second columns, examples of differential correlations are shown. All values are given in arbitrary units and the data range is scaled to unity in all plots.

Morgenthal K, Weckwerth W, Steuer R. 2006. Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation. Biosystems 83, 108-117.

Fig. 2. A schematic view of the simplified Calvin cycle with subsequent sucrose phosphate synthase in the cytoplasm.The subscript ‘m’ indicates cytosolic metabolites, whereas ‘ch’refers to the chloroplast. For details on the rate equations andparameters, see Appendix A and Appendix B. Abbreviations: TP, triosephosphates; FBP, fructose 1,6-bisphosphate; F6P, fructose 6-phosphate; G6P, glucose 6-phosphate; Ru5P, ribulose 5-phosphate; RuBP, ribulose 1,5-bisphosphate; SP, sucrose phosphate; Suc, sucrose; Pm, free cytosolic inorganic phosphate; Pch, inorganic phosphate, chloroplast.

Fig. 3. Examples of pair-wise metabolite correlations obtained numerically from the model depicted in Fig. 2. All concentrations are given in arbitrary units. Light intensity wassimulated as a time-dependent random variable, using stochastic differential equations. ‘Measurements’ were taken from successive simulations using independent realizations of the fluctuations. Note that, in analogy to the experimental setup, the concentration of metabolites that appear in the cytoplasm as well as in the chloroplast are the weighted average over both compartments. For details on the model and parameters, see Appendix A and Appendix B.

Morgenthal K, Weckwerth W, Steuer R. 2006. Metabolomicnetworks in plants: Transitions from pattern recognition to biological interpretation. Biosystems 83, 108-117.

Fig. 8. GC-TOF-MS analysis of a complete methanol-chloroform-water plant extract. The injection of a plant metabolite extract without separation of polar and hydrophobic phase is shown. Most of the metabolite compound classes are found in such a chromatogram (modified from Weckwerth, Wenzel, & Fiehn, [2004]).

Glinski, M. and Weckwerth, W. 2006. The role of mass spectrometry in plant systems biology. Mass Spectrom Rev. 1: 173-214.