1

NMR SPECTRAL ANALYSIS OF ASCORABTE-DEFICIENT

MUTANTS OF ARABIDOPSIS THALIANA

Submitted by Felicia Charles Johnson

to the University of Exeter

as a dissertation for the degree of

Master of Philosophy

in Biological Sciences,

January, 2011.

This dissertation is available for Library use on the understanding that it is copyright

material and that no quotation from the dissertation may be published without proper

acknowledgement.

I certify that all material in this dissertation which is not my own work has been

identified and that no material has previously been submitted and approved for the

award of a degree by this or any other University.

.........................

2

ABSTRACT

Ascorbate is an important antioxidant, involved in the scavenging of reactive oxygen species

(ROS) such as hydrogen peroxide hydrogen. It is also a cofactor for 2-oxoglutarate-

dependent dioxygenases, which are involved in the biosynthesis of a number of metabolites.

In the current study, Arabidopsis thaliana ascorbate-deficient mutants (vtc1, vtc2, vtc3, and

vtc4) were used to investigate the effect of ascorbate deficiency on the metabolic changes.

NMR was used as an analytical technique for the metabolite profiling of Arabidopsis

thaliana vtc mutants. A total of 45 NMR spectra representing wild type and four mutants of

two different growth stages (rosette and flowering) were collected and spectral processed for

exploratory analysis. Analysis of variance (ANOVA) and multivariate data analysis

(hierarchical clustering) were used to find the differences between different strains and

metabolite clustering patterns. Combinations of different parameters were used to find the

best method for the analysis of NMR-based metabolites data. The method was developed

which allowed making multiple comparison such as two growth stages and five strains. It

was found the vtc mutants grouped separately from WT which contained the low ascorbate

showing ascorbate deficiency might have some effect on the metabolic level.

Two interesting resonances 1.32 and 1.33ppm were separated which were negatively

correlated with the ascorbate levels in the vtc mutants. These resonances (1.32 and 1.33) are

putatively identified as threonine and lactate. Further investigations are needed to

investigate the nature of relationship between ascorbate and threonine and lactate.

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CONTENTS

PAGE NO.

Chapter 1

INTRODUCTION

ASCORBATE METABOLISM AND FUNCTIONS 7

ASCORBATE BIOSYNTHESIS (D-MAN/L-GAL PATHWAY) 9

ASCORBATE BIOSYNTHESIS OTHER MULTIPLE PATHWAYS 10

CONTROL OF ASCORBATE BIOSYNTHESIS 11

ASCORBATE DEGRADATION 11

ASCORBATE-DEFICIENT MUTANTS 11

ISOLATION OF VTC MUTANTS 11

ASCORBATE LEVELS IN VTC MUTANTS 12

CHARACTERIZATION OF VTC MUTANTS 12

PLANT METABOLOMICS 14

METABOLITE/METABOLIC PROFILING 15

METABOLOME TECHNOLOGIES FOR DATA ACQUISITION 16

BIOINFORMATICS 21

DATA ANALYSIS 21

NMR SPECTRAL PROCESSING 22

STATISTICAL ANALYSIS 23

APPLICATION OF PLANT METABOLOMICS 24

LIMITATIONS OF PLANT METABOLOMICS 26

PRESENT STUDY 27

4

Chapter 2

MATERIALS AND METHODS

GERMINATION OF ARABIDOPSIS THALIANA SEEDS ON AGAROSE 29

TRANSFERRING ARABIDOPSIS THALIANA SEEDLINGS TO SOIL 32

HARVESTING OF ARABIDOPSIS THALIANA PLANTS FOR ANALYSIS 32

PREPARING ARABIDOPSIS THALIANA PLANTS FOR NMR ANALYSIS 33

ANALYSING ARABIDOPSIS THALIANA PLANTS BY NMR 34

NMR SPECTRAL PROCESSING WITH MESTRE-C 36

DATA ANALYSIS 37

Chapter 3

RESULTS AND DISCUSSION

NMR SPECTRAL FEATURES OF ASCORBATE- DEFICIENT MUTANTS 39

NMR DATA PRE-PROCESSING 41

MULTIVARIATE ANALYSIS OF ASCORBATE- DEFICIENT MUTANTS 42

DIFFERENTIALLY EXPRESSED BINS BETWEEN WT AND VTC MUTANTS 50

Chapter 4

CONCLUSION 54

REFERENCES 55

5

LIST OF TABLES

TABLE NO.

PAGE

NO.

Table 1.1 Strategies for metabolomic analysis 16

Table 1.2 Common analytical techniques used in metabolomics 19

Table 2.1 Plant types used for the study 29

Table 2.2 Parameters for NMR Data Collection 35

Table 3.1 Clustering analyses of all plant types in rosette stage with 45

0.01 bin width and different processing combinations

Table 3.2 Clustering analyses of all plant types in rosette stage with 46

0.04 bin width and different processing combinations

Table 3.3 Clustering analyses of all plant types in flowering stage with 47

0.01 bin width and different processing combinations

Table 3.4 Clustering analyses of all plant types in flowering stage with 48

0.04 bin width and different processing combinations

Table 3.5 List of resonances showing discrimination in the spectral regions 51

Table 3.6 Resonances different between strains at spectral level with 51

two bin widths

6

LIST OF FIGURES

FIGURE NO.

PAGE

NO.

Figure 1.1 The proposed pathways for L-ascorbic acid biosynthesis 8

in plants

Figure 1.2 Wild type (Col-0) and four ascorbate - deficient mutants 13

Figure 2.1 Spectral processing with MestRe-C 37

Figure 2.2 Schema of the data analysis 38

Figure 3.1 1H-NMR spectra of the Arabidopsis thaliana (Wild type) 40

extracted within D2O:CD3OD

Figure.3.2a 1H-NMR spectra of Arabidopsis thaliana at rosette stage 40

Figure.3.2b 1H-NMR spectra of Arabidopsis thaliana strains at 41

flowering stage

Figure 3.3 HCA showing the difference in growth stages 44

(rosette and flowering) and WT and the vtc mutants

Figure 3.4 Heatmap for the 1H NMR based metabolic profiles of 50

Arabidopsis thaliana Wild type and four ascorbate-deficient

mutants in rosette

Figure 3.5 The intensity of the resonances 1.32 and 1.33ppm across the strains 52

Figure 3.6 Identification of the resonances 1.32 and 1.33 ppm in the 53

original NMR spectra of one of the vtc mutant

7

Chapter 1

INTRODUCTION

Ascorbate metabolism and functions

L-ascorbic acid (vitamin C) has a key role in prevention of scurvy in humans and also have

shown to fulfil many other essential functions in animals and plants. The biological role of

ascorbate (Asc) depends on its ability as a reducing agent, which acts as an effective anti-

oxidant and a free radical scavenger (Smirnoff,2011).It is the most abundant soluble

antioxidant found in plants in almost all plant tissues, with the exception of dry seeds (De

Gara et al.,1997) and is effective at directly scavenging reactive oxygen species (ROS) such

as super oxide, hydrogen peroxide, singlet oxygen and the hydroxyl radical (Noctor &

Foyer, 1998).It is a cofactor for various enzymes, including Fe2+

/2-oxoglutarate-dependent

dioxygenases and violaxanthin de-epoxidase (De Tullio, 2004).Ascorbate is required for the

regeneration of the lipophilic antioxidant a-tocopherol from the α -chromanoxyl radical

(Fryer, 1992) and is also responsible for the regeneration of the carotenoid pigments:

carotenes and xanthophylls (Eskling and Akerlund,1997).

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Significant progress has been made in the elucidation of the plant ascorbate biosynthetic

pathway (Wheeler et al.,1998) to understand how ascorbate metabolism is controlled.

These progresses provide a basis for engineering or manipulating its accumulation. The

proposed pathway for L-ascorbate biosynthesis in plants is shown in Figure1, D-

Mannose/L-Galactose (D-Man/L-Gal) pathway is the predominant pathway. This pathway

is different in animals where the last enzyme in the pathway (L-gulonolactone oxidase) is

not expressed due to the lack of the enzyme. There are also other alternative biosynthetic

pathways in plants along with the D-Man/L-Gal pathway and their existence in ascorbate

biosynthesis is still under investigation. The alternative pathways are found to involve

intermediates such as D-glucurono-1, 4-lactone, D-galacturonate, methyl-D-galacturonate

and L-gulono-1,4-lactone (Smirnoff et al.,2001).

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Figure 1.1 The proposed pathways for L-ascorbic acid biosynthesis in plants.The D-mannose/L-

galactose pathway is shown in bold: D-mannose-1-phosphate is synthesised from D-glucose-1-

phosphate via D-fructose-6-phosphate. It is converted into L-ascorbic acid via a series of steps

catalysed by: 1, GDP-mannose pyrophosphorylase (Arabidopsis thaliana VTC1); 2, GDP-D-

mannose-3, 5-epimerase; 3, GDP-L-galactose phosphorylase (Arabidopsis VTC2); 4, L-galactose-1-

phosphate phosphatase (Arabidopsis thaliana VTC4); 5, L-galactose dehydrogenase; 6, L-galactono-

1,4-lactone dehydrogenase.Alternative pathways and some of the enzymes catalysing the various

reactions are shown:7,GDP-D-mannose-4, 6-dehydratase; 8,GDP-4-keto-6-deoxy-D-mannose-3,5-

epimerase-4-reductase;9,D-galacturonatereductase;10,myo-inositoloxygenase; 11, D-glucuronate

reductase;12, aldonolactonase; 13, L-gulonolactone dehydrogenase (Wheeler et al.,1998).

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Ascorbate biosynthesis (D-Man/L-Gal pathway)

Ascorbate is present in mill molar (mM) concentration in plant tissues which varies

considerably between tissues. The concentration of ascorbate also depends on the

physiological status of the plant as well and on the environmental factors (Luwe et al.,1993).

Advances in understanding of biosynthetic pathway of Asc in plants have been made by

studying Pisum sativum and Arabidopsis plants (Wheeler et al.,1998).Plants synthesize

ascorbate involving D-glucose-6-P, D-fructose-6-P, D-manose-1-P, GDP-D-mannose, GDP-

L-gulose, GDP-L-galactose, L-galactose-1-P and L-galactose as intermediaries in the

production of L-galactono-1-4-lactone, a precursor, oxidised to ascorbate by the enzyme L-

galactose 1-4 lactone dehydrogenase. There are many enzymes involved which catalyses

various steps in Asc biosynthesis. Some of those key enzymes are briefly described

below.Phosphomannose isomerase (PMI), is involved in the conversion of fructose 6-P to

mannose 6-P and its activity is readily detected in Arabidopsis leaf extracts and also in other

species (Smirnoff and Wheeler, 2000). Phosphomannose mutase (PMM) catalyses the

interconversion of mannose 6-P and Mannose 1-P. Studies show increase in ascorbate when

PMM was over expressed in Nicotiana benthamiana,Arabidopsis and virus-induced gene

silencing caused a decrease in ascorbate (Smirnoff, 2011). GDP-mannose pyrophosphorylase

(GMP) catalyses the formation of GDP-mannose 1-P and GTP, GDP-mannose-3, 4-epimerase

(GME) catalyses the double epimerisation of GDP-mannose with the production of GDP-L-

galactose.

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GDP-L-galactose phosphorylase/guanylyl transferase was identified in pea seedling extracts

(Dowdle et al.,2007;Ishikawa et al.,2006a and b) and in Arabidopsis by (Dowdle et al.,2007;

Laing et al.,2007 and Linster et al.,2007, 2008) catalyse the phosphorylytic breakdown of

GDP-L-galactose to L-Galactose 1-P.L-galactose 1-P phosphatase, hydrolyses L-galactose 1-

P to L-Gal. L-galactose dehydrogenase (L-GalDH) catalyses NAD+-dependent oxidation of L-

Gal at C1 to produce L-galactono lactone (Gatzek et al.,2002) and L-galactono-1,4 lactone

dehydrogenase (L-GalLDH) catalyses the last step which is the oxidation of L-GalL to

ascorbate.

Ascorbate biosynthesis Other Multiple Pathways

It has been proposed that plants not only synthesize ascorbate from D-Man/L-Gal but also

from D-galacturonic acid (D-GalUA),L-gulose and from myo-inostiol/D-glucuronic acid.

External application of uronic acid derivatives, labelling studies, gene expression studies,

over expression studies have shown to increase in ascorbate providing evidence for the

existence of other pathways involved in ascorbate biosynthesis(Smirnoff 2011).

Control of Ascorbate biosynthesis

The rate of biosynthesis depends on the amount of each enzymes and kinetic properties in

relation to substrate concentrations and other factors. Ascorbate concentration is regulated

by cell type and environmental conditions. Ascorbate concentration is high in leaves, peel of

fruits (Davey et al.,2000; Li et al.,2008), meristem cells (Cordoba-Pedregosa et al.,2003)

germinating seeds (Pallanca and Smirnoff 2000) and also in high light (Gautier et al.,2009;

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Li et al.,2009;Dowdle et al.,2007),low temperature (Schoner and Krause,1990),jasmonates

Wolucka etal.,2005) mechanical wounding (Suza et al.,2010) and also when over expressing

biosynthesis genes (Smirnoff,2011) however,a very little is known about how the

biosynthesis or breakdown is controlled.

Ascorbate degradation

Asc is generally involved as an electron donor transferring electrons by oxidation as

monodehydroascorbate (MDHA) and dehydroascorbate (DHA).MDHA does not readily

react with other molecules and is relatively harmless.DHA undergoes spontaneous and

irreversible hydrolysis to 2,3-diketogulonic acid (Washko et al.,1992).Ascorbate and DHA

are catabolised in plants and give rise to a number of end products, including L-threonate,

oxalate and L-tartrate (Loewus,1999).

Ascorbate-deficient mutants

To get a better understanding of the biosynthetic pathways mutants have been a valuable

tool and are utilized to uncover a number of plant primary and secondary metabolic

pathways and to identify the pathways of ascorbate catabolism. Gene function can be

discovered through the use of mutants that are altered in gene of interest and the

physiological and biochemical changes are observed comparing to the Wild type. Conklin et

al.,(2000) identified a number of ascorbate-deficient mutants (Figure 2a,2b).

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Isolation of vtc mutants

The vtc (vitamin-c) mutants were produced through ethyl methane sulfonate mutagenesis of

the Col-0 wt seeds, which is the Wild type plants of Arabidopsis and the plants were

screened by nitro blue tetrazolium (NBT) based assay for an Asc-deficient phenotype and

the produced mutants were also used for the analyses of genetic segregation and allelism.

Asc deficiency in the mutants vtc1-2, vtc2-1, vtc2-2, vtc3-1 and vtc4-1 is conferred by single

monogenic recessive traits. vtc mutants represent five different loci; VTC1, VTC2, VTC3,

VTC4 AND VTC5.Two of the mutants (vtc1-1 and vtc2-1) were isolated through their

increased ozone sensitivity. In total there are two vtc1 mutant alleles, three vtc2 mutants as

well as vtc3-1 and vtc4-1.vtc5-1 and vtc5-2 were produced by T-DNA insertional

mutagenesis (Linster and Clarke,2008).

The VTC1 locus has been cloned and it encodes GDP-mannose pyrophosphorylase (GMP)

(Conklin et al.,1999)which is an enzyme that catalyses the conversion of D-mannose-1-

phosphate to GDP-mannose in the ascorbate biosynthesis pathway. The vtc1 mutant has a

decreased ability to convert glucose and mannose to ascorbate.

VTC2, VTC5 and VTC4 have been identified as genes of the D-Mannose/L-Galactose

ascorbate biosynthesis pathway, encoding respectively GDP-L-galactose phosphorylase;

GDP-β-L-Gal: orthophosphate guanyltransferase (Dowdle et al.,2007;Ishikawa et al.,2006a

and b) and L-galactose 1-P phosphatase (Conklin, et al.,2006).

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Ascorbate levels in vtc mutants

The vtc mutants contain 1/3 to 1/2 the total Asc present in the wild type Col-0(Conklin et al.,

2000).They range from 50-70 % of wild-type levels in vtc2-3, vtc3 and vtc4, to 25-30% in

vtc1-1, vtc1-2 and 10-20% in vtc2-1 and vtc2-2. The Asc levels increases upon the transition

from vegetative to reproductive state (Gander 1982).The reproductive tissues contain twice

the amount of Asc found in mature leaves of both wt and mutants.

Characterization of vtc mutants

There has been many studies that provides strong evidence that low ascorbate reduces

growth and increases susceptibility to a range of stresses. Characterization of vtc1-1 and

vtc2-1 has revealed that both the mutants have slow growth rate (Pavet et al.,2005;Conklin

et al.,2000).vtc1-1 and vtc2-1 have been widely studied in relation to the stress responses,

while there is little information on other mutants. The Wild type Arabidopsis is quite

tolerant to ozone (O3),probably because these plants mount an effective antioxidant response

(Sharma and Davis,1994;Conklin and Last, 1995).vtc1-1 mutant is extremely O3 sensitive

with visible injury including lesion formation, enhanced chlorosis, and/or tissue

collapse.vtc2 mutants have varied O3 sensitive phenotypes.vtc2-1 was similar to vtc1-1,

vtc2-3 appeared somewhat sensitive but vtc2-2 was not visibly injured (Conklin et al.,2000).

All the four mutants are more salt-sensitive (Huang et al., 2005;Smirnoff, 2000). Both vtc1

and vtc2 has reduced basal thermo tolerance (Larkindale et al.,2005). vtc2-1 has greater

thermo induced photo emission indicating increased lipid peroxidation at high temperature

(Havaux,2003). In vtc 1-1,171 genes were found differentially expressed and identified by

transcriptomic analysis of which 12.9% genes were involved in cell defence (Pastori,2003).

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vtc1-1 and vtc2-1 have increased resistance to infection by virulent pathogens (Barth et

al.,2004,Pavet et al.,2005).Ascorbate deficient mutants also show increased salicylic acid,

increased expression of pathogenesis-related proteins, peroxidise activity and accumulation

of the phytoalexin camalexin (Kleibenstein, 2004).

Figure 1.2 Wild type (Col-0) and four ascorbate-deficient mutants (vtc 1, vtc 2, vtc 3 and vtc 4)

(vtc= vitamin c mutants).

Plant metabolomics

Metabolic networks are very complex as there is an enormous chemical diversity of

compounds. In the plant kingdom there are over approximately 200,000 metabolites and

about 5000 individual low molecular weight compounds (Fiehn,2002).Metabolomics is the

"systematic study of the unique chemical fingerprints that specific cellular processes leave

behind" - specifically, the study of their small-molecule metabolite profiles (Bennett,2005).

Metabolomics has emerged as an important functional genomics tool to aid in the

comprehensive analysis of all the metabolites in the cellular system adding to the

understanding of the complex molecular interactions in the biological systems (Sumner et

al.,2003; van der Greef et al.,2003;Saito and Matsuda,2010).Metabolomics currently in

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plants are more complex due to large number of metabolites and the presence of

differentiated tissues, including specialist storage organs, with different metabolite

compliments(Ward et al.,2002).The field of plant metabolomics is a complicated

interdisciplinary research field that requires bioscience, analytical chemistry, organic

chemistry, chemometrics, and informatics knowledge. Genome and transcript or protein

profiling offers only limited information about the chosen system but profiling metabolome

actually provides the most “functional” information (Sumner et al.,2003).The ultimate aim

of metabolomics is an unbiased and comprehensive monitoring of all the metabolites in a

cellular system and to nearly complete the molecular picture of the state of a particular

biological system at a given time (Fiehn 2002).There appear to be differences of opinion as

how best to define the comprehensive profiling of the metabolome. Sumner (2003) proposed

that any technology whose output is processed with pattern recognition software and without

differentiation of individual metabolites should be termed metabolic finger printing not

metabolomics or metabonomics. Metabolic finger printing which is also the chemometric

approach where the compounds are not initially identified only their spectral patterns and

intensities are recorded, statistically compared and used to identify the relevant spectral

features that distinguish sample classes (Wishart, 2008;Xia et al.,2009)

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Metabolite/metabolic profiling

Metabolite/metabolic profiling is the measurement of hundreds or potentially thousands of

metabolites in other words a quantitative approach which aims to formally identify and

quantify all detectable metabolites from the spectra, that requires the compounds of interest

to be known from a spectral reference library obtained from authentic standards prior to

subsequent data analysis(Xia et al.,2009).There is yet another approach of profiling

metabolome called targeted analysis which aims to measure the concentration of a limited

number of known metabolites precisely after knowing the structure of the target metabolite

which also requires the compounds of interest to be known a priori in purified form

(Shulaev, 2006).The strategies for metabolomic analysis given by Dunn and Ellis (2005) is

presented in Table 1.1below.

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Table 1.1 Strategies for metabolomic analysis (Dunn and Ellis, 2005)

Metabolomics

Non-biased identification and quantification of all metabolites in a

biological system.

Sample preparation must not exclude metabolites, and selectivity

and sensitivity of the analytical technique must be high.

Metabolite profiling

Identification and quantification of a selected number of pre-

defined metabolites, generally related to a specific metabolic

pathway(s).

Sample preparation and instrumentation are employed so to isolate

those compounds of interest from possible matrix effects prior to

detection, normally with chromatographic separation prior to

detection with MS. Widely used in pharmaceutical industry

Metabolic fingerprinting

High-throughput, rapid, global analysis of samples to provide

sample classification.

Quantification and metabolic identification are generally not

employed. A screening tool to discriminate between samples of

different biological status or origin.

Sample preparation is simple and, as chromatographic separation is

absent, rapid analysis times are small (normally 1 min or less)

Metabolite target

analysis

Qualitative and quantitative analysis of one or a few metabolites

related to a specific metabolic reaction.

Extensive sample preparation and separation from other

metabolites is required and this approach is especially

employed when low limits of detection are required. Generally,

chromatographic separation is used followed by

sensitive MS or UV detection

Metabonomics

Evaluation of tissues and biological fluids for changes in

endogenous metabolite levels that result from

disease or therapeutic treatments

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Metabolome technologies for data acquisition

The central to metabolomic processes are a variety of chemical profiling technologies. The

gross chemical composition of various biological fluids has been investigated by a variety of

chromatographic (separation methods) and spectroscopic techniques(detection methods),

notably gas and liquid chromatography (Fiehn et al.,2000b; Sauter et al.,1991;Roessner et

al.,2001), Mass spectrometry (Matsumoto and Kuhara, 1996; Wolfender and Hostettmann,

1996; Aharoni et al.,2002),NMR (Ward et al.,2002,Kikuchi et al.,2004)and Infrared

spectrophotometry (Jackson and Mantsch, 1996).Various technologies used are discussed

and their advantages and disadvantages (Shulaev,2006) are shown in Table 2 below:

Mass spectrometry (MS): Due to its high sensitivity and wide range of covered

metabolites, MS has become the technique of choice in many metabolomics studies (Halket

et al.,2005). MS can be used to analyse biological samples either directly via direct-injection

MS or following chromatographic or electrophoretic separation. Direct-injection MS,

especially when using a high-resolution mass spectrometer, provides a very rapid technique

to analyse large number of metabolites, and therefore is extensively used for metabolic

fingerprinting and metabolite profiling. Mass spectrometry has a serious drawback, known

as ionization suppression (which results from the presence of less volatile compounds that

can change the efficiency of droplet formation or droplet evaporation, which in turn affects

the amount of charged ion in the gas phase that ultimately reaches the detector) which might

diminish quantitative reproducibility. To avoid this problem and to decrease the complexity

of the sample, MS is often used as a hyphenated technique, i.e. it is coupled with gas

chromatography (GC), liquid chromatography (LC) or capillary electrophoresis (CE) where

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the sample mixture is separated by chromatography or electrophoresis, and individual

compounds or less complex mixtures of compounds (which cannot be separated due to

similar properties) that are eluted from the column or capillary are analysed by MS.

Gas chromatography: is one of the most widely used and is a powerful method especially

when interfaced with electron impact (EI) quadruple or time-of flight (TOF) mass

spectrometry (GC-MS). GC-MS is viewed as the gold standard of metabolomic techniques

due to the high separation power of GC combined with better deconvolution. The advantage

of this coupled approach is that, it is possible to profile several hundred chemically diverse

compounds including organic acids, most amino acids, sugars, sugar alcohols, aromatic

amines and fatty acids (Roessner,2000).It offers very high chromatographic resolution, but

requires chemical derivatization (chemical conversion of sample compounds into their

derivatives which makes them suitable for separation and analysis) for many bio molecules

and hence some large and polar metabolites cannot be analysed by GC (Schauer et al.,

2005).

Liquid chromatography/mass spectrometry (LC-MS): LC-MS is being increasingly used

due to its high sensitivity and a range in analyte polarity and molecular mass wider than GC-

MS. Liquid chromatography, and specifically high-performance liquid chromatography

(HPLC), combines both high resolution and analytical flexibility. It can be tailored for the

analysis of a specific metabolite or class of compounds, or it can be used for the analysis of

a broad range of compound classes. LC-MS has one advantage over GC-MS, in that; there is

largely no need for chemical derivatization of metabolites (Shulaev,2006).

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Capillary electrophoresis-mass spectrometry (CE-MS): CE-MS provides several

important advantages over other separation techniques. It has a very high resolving power;

very small sample is required for analysis and has short analysis time. CE has been used for

both targeted and non-targeted analysis of metabolites. One of the significant advantages of

the CE-MS is the ability to separate cations, anions and uncharged molecules in a single

analytical run, and therefore can be used for simultaneous profiling of many different

metabolite classes(Soga et al.,2002).This feature makes it a very attractive and promising

analytical technique for high-throughput non-targeted metabolomics.

Fourier transform infrared spectroscopy (FTIR): is useful in the field of large-scale

exhaustive profiling as an easy and high-throughput method (Gidman et al.,2003;Johnson et

al.,2003).It is used for the quantification of compounds that have specific functional groups

although it is not optimal for complicated metabolite profiling due to the lack of a separation

procedure. Fourier transformation ion cyclotron resonance mass spectrometry (FT-ICR-

MS), is the latest mass spectrometry technology. Infusion FT-ICR-MS analysis without pre-

separation by chromatography has been achieved for exhaustive metabolic profiling

(Aharoni, et al.,2002).

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Table 1.2 Common analytical techniques used in metabolomics (Shulev 2006)

Analytical method Advantage Disadvantage

NMR Rapid analysis Low sensitivity

High resolution Convoluted spectra

No derivatization needed More than one peak per

component

Non-destructive Libraries of limited use due

to complex matrix

GC/MS Sensitive Slow

Robust Often requires

derivatization

Large linear range

Many analytes thermally-

unstable or too large for

analysis

Large commercial and public

libraries

LC/MS

No derivatization required

(usually) Slow

Many modes of separation

available

Limited commercial

libraries

Large sample capacity

CE/MS High separation power

Limited commercial

libraries

Small sample requirements

Poor retention time

reproducibility

Rapid analysis

Can analyse neutrals, anions

and cations in single run

No derivatization (usually)

FTIR Rapid analysis

Extremely convoluted

spectra

Complete fingerprint of sample

chemical composition

More than one peak per

component

Metabolite identification nearly

impossible No derivatization needed

Requires sample drying

23

Nuclear magnetic resonance (NMR): can uniquely identify and simultaneously quantify a

wide range of organic compounds in the micromolar range (Viant et al.,2003; LeGall et

al.,2003; Krishnan et al.,2005).In recent times, high-resolution NMR well-suited to

metabolomics studies has become a key technology for the elucidation of biosynthetic

pathways and metabolite flux via quantitative assessment of multiple isotopologues

(Eisenreich and Bacher,2007).NMR spectroscopy is the only detection technique which does

not rely on separation of the analytes, since the sample preparation for NMR is

straightforward and also is non-destructive the sample can thus be recovered for further

analyses(Bailey et al.,2003). All kinds of small molecule metabolites can be measured

simultaneously - in this sense, NMR is close to being a universal detector.

1HNMR is also a rapid technique that characterizes and quantifies a broad range of

metabolites in a non- targeted way, since in principle any chemical species that contains

protons gives rise to signals.NMR signals (i.e. resonance) are observed when a sample is

irradiated with pulses of radiofrequency electromagnetic radiation in a strong magnetic field.

Each nucleus within a molecule experiences a slightly different magnetic field because of its

distinct chemical environment and absorbs energy at a slightly different frequency. The

separation of these resonance frequencies from an arbitrarily chosen reference is called the

chemical shift (ppm). The result of an NMR analysis of a tissue is a spectrum, that is, a plot

of intensity (area) against chemical shift in which each signal occurs at a characteristic

energy. These signals are profiled as NMR spectrum comprises peaks which, for a given

solvent appear at characteristic frequencies. The major limitation of NMR for

comprehensive metabolite profiling is its relatively low sensitivity, making it inappropriate

for the analysis of large number of low-abundance metabolites (Sumner et al.,2003).Some of

the studies using NMR has shown to cover most of the ‘‘organic’’ compounds such as

24

carbohydrates, amino acids, organic and fatty acids, amines, esters, ethers and lipids, which

are present in plant tissues (Ward et al.,2002) and when coupled with statistical analysis

serves as an excellent screen to analyse metabolome in a non-destructive way. NMR has

also provided information on biosynthesis (Lutterbach and Stockigt,1995; Prabhu et

al.,1996;Fiehn and Weckwerth, 2002),on metabolism (Ratcliffe and Shachar-Hilt, 2001),

and on the effects of herbicides on metabolism (Lutterbach and Stockigt, 1994 and1995) and

mode-of-action (Hole et al.,2000, Hadfield et al.,2001), or used in investigations of whole

plants (Schneider,1997;Pope et al.,1993).Although NMR and MS are most often used for

large-scale analysis, metabolomics is not limited to these techniques. Other alternatives

include thin-layer chromatography(Tweeddale et al.,1998 and1999).HPLC with UV/visible

absorbance, photodiode array (PDA) (Fraser et al., 2000) or electrochemical detectors

(Gamache et al., 2004,Kristal et al.,2002) FT-IR (Gidman et al.,2003), phenotype

microarrays (Bochner et al.,2001) and variety of other enzymatic assays. Combined use of

multiple techniques or multiple detectors in online or parallel analysis can significantly

increase the metabolite coverage, increase quantification limits and improve identification of

metabolites from a single biological sample.

25

Bioinformatics

Data Analysis

The major challenge of metabolomics studies is data processing and data interpretation

(Shulaev,2006). Database management systems for metabolomics are required to collect

both metadata (metadata is the data about the experiment), raw and processed experimental

data. Storing metadata, covering experimental design, the nature of the samples and their

treatment prior to the analysis and information about the analytical technique and data-

processing details are important to be able to reproduce the experimental conditions and

compare results obtained in different laboratories(Bino etal.,2004).All the metabolomic

approaches heavily relies upon bioinformatics for the storage, retrieval, and analysis of

larger datasets and many efforts have been made on software development (Katajamaa and

Oresic, 2007).There are many tools used to align, visualize and differentiate components in

datasets and the individual components are then correlated and placed in metabolic networks

or pathways. Changes in metabolite levels may be drastic or subtle and those subtle changes

will require statistical processing to determine whether the observed changes are significant

or not (Miller and Miller, 2000).

For NMR-based metabolomics approach, after NMR experimental data collection, post

experimental data handling including NMR spectral processing, data pre-processing and

data analysis, is critical for obtaining good results. To assist these procedures, several

commercial software tools have been released, such as: AMIX (Bruker Biospin, Germany),

KnowItALL (BIO-RAD, USA),Chenomx NMR Suite (Chenomx, Canada) and Hi-Res

(Zhao,2006). NMRPipe is a widely used traditional NMR data processing software tool

(Delaglio et al.,1995).These software tools provide plenty of functions involving spectral

26

processing, comprehensive identification and quantification of metabolites. However, due to

the complexity of NMR data and different application purposes, further data analysis

procedures, such as filtering out unwanted variations (e.g. background noise, uncorrelated

variation in data model, etc.) in a dataset, generating and applying predictive classification

or regression models, are usually required. To complete these tasks, researchers usually

invoke other advanced chemometrics tools or statistical tools. Commercial software

packages such as MATLAB (Mathworks, USA), SIMCA-P (Umetrics, Sweden) and SPSS

(SPSS, USA) are frequently used by researchers.

NMR spectral processing

Spectral data often include some disturbance according to each specificity and

environmental perturbation. Such disturbance needs to be corrected for by appropriate data

pre-processing. Several tactics, such as spectral difference, noise reduction, baseline

correction, normalization, mean centre, and scaling are often performed in common pre-

processing as for the metabolomics studies, spectral processing may have significant impact

on data analysis (Defernez and Colquhoun, 2003).The present study uses a commercial

software MestRe-C (Magnetic Resonance Companion) Cobas and Sardina, 2003) designed

specifically for metabolomics, will be evaluated.

Statistical Analysis

Statistical analyses must be performed to ensure good analytical rigor, but unfortunately are

often a burden when working with large datasets. Tools are therefore used for high

throughput statistical analyses of all components in a dataset to provide sound evidence for

the relevance of changes in a metabolite level contained in the raw data. Computer based

applications are used that can differentiate whether or not samples are statistically similar or

different and what the exact differences/similarities are. A single metabolite profile can yield

27

hundreds of distinct components. This wealth of information that needs to be interpreted,

leads to significant challenges in processing the data. To simplify the task, many researchers

have used techniques to reduce the dimensionality of the data set and to visualize the data

(Sumner et al.,2003)

Principle Component Analysis (PCA) is the choice for metabolomics for many researchers

as the data is complex (Le Gall,2001;Ward et al.,2002;Defernez and Colquhoun, 2003).PCA

provides a way to summarize the information contained in large sets of spectra. It transforms

the initial variables (or ‘measurements’) into a much smaller set of variables or PC scores.

The main purpose of PCA is to eliminate the collinear problem and then reduce the

dimensionality of the original feature (variable) space. It is an unsupervised method used to

reveal the internal structure of datasets in an unbiased way. PLS is a supervised method for

regression.PCA and PLS reveal the variable contribution for the separation between

different groups by loadings or regression coefficients (Fukusaki and Kobayashi

2005).Machine learning methods such as self-organising maps (SOM) and artificial neural

networks (ANN) are also promising in research (Fukusaki and Kobayashi, 2005).A variety

of approaches have been applied for the statistical analysis of spectral data. One approach is

to gather information (e.g. integrate individual peak areas) for as many constituents as

possible and study the occurrence of each of these compounds separately, for example by

calculating coefficients of variation or carrying out an Analysis of variance (ANOVA) (Le

Gall, 2001;Smilde et al.,2005).ANOVA estimates the factor effects and tests significance by

splitting the variations in orthogonal and independent parts (Searle, 1971).Another data

analysis approach, Hierarchical cluster analysis (HCA), is a method of cluster analysis based

on the multivariate distance between every pair of data points. In HCA, the data are not

partitioned into a particular cluster in a single step. Instead, a series of partitions takes place,

28

which may run from a single cluster containing all objects to n clusters each, containing a

single object (Mochida et al.,2009).For the present study the statistical analysis was carried

out and visualized in an application tool called Multi Experiment Viewer (MeV) (Saeed et

al.,2006).

In plant metabolomics bioinformatics tools provide methods of determining differences or

similarities in datasets. The metabolomic data sets must then be integrated and correlated in

a global manner with genetic and enzymatic data, pathways assembled into systems, and

literature references incorporated as learning tools to annotate existing data to yield in silico

biological information (Palsson,2000).

Application of Plant metabolomics

Although relatively young, and still very much in development, plant metabolomics is now

being widely applied and is already considered as a technology, which is a ‘maturing

science’ and is ‘established and robust’(Dixon et al.,2006;Schauer and Fernie 2006).Some

of the general applications of plant metabolomics are studied in plant tissues using NMR

(Kim, et al.,2010;Kruger et al.,2008) GC-MS (Lisec et al.,2006), LC-MS (De Vos et

al.,2007). Hall et al.,(2008), has elaborated the use of plant metabolomics and its potential

application for human nutrition. Metabolite profiling has been performed on a diverse array

of plant species, including tomato (Schauer et al.,2005;Tunali et al.,2003), potato (Roessner

et al.,2000, Roessner et al.,2001),rice (Sato et al.,2004),wheat(Hamzehzarghani et

al.,2005),strawberry(Aharoni, et al.,2002), medicago(Chen et al.,2003),cucumber(Tagashira

et al.,2005),lettuce(Garratt et al.,2005)tobacco(Blount et al.,2002) poplar(Robinson et

al.,2005,Brosche et al.,2005) and eucalyptus (Merchant et al.,2006). Metabolite profiling

has also been carried out widely in Arabidopsis thaliana, in a tissue specific level (Schad et

29

al.,2005), in transgenic plants (Le Gall et al.,2005) and also for comparison of ecotypes of

Arabidopsis thaliana (Ward et al.,2002).Hirai et al., (2004) linked the genomic data and the

function of metabolite under the deficiency of sulphur and nitrogen in Arabidopsis thaliana.

Metabolomics has been useful to ascertain, for example, the metabolic response to herbicide

(Ott et al., 2003), the equivalence of genetically modified (GM) and conventional crops

(Catchpole et al.,2005;Defernez et al.,2004;Barros et al.,2010;Matsuda et al.,2010) and the

classification of plant genotypes (Roessner et al.,2000;Roessner et al.,2001, Fiehn et

al.,2000a,Tikunov, et al.,2005,Kim, et al.,2010),Quality control of medicinal plants

viz.,Gingko(Agnolet et al.,2010), St. John’s wort (Roos et al.,2004),Rasmussen et al.,2006),

Ginseng (Yang et al.,2007), and to find activity-related compounds in medicinal plants

(Cardoso-Taketa et al.,2008,Modarai et al.,2010) and to study the plant interaction with

other organisms(Jahangir et al.,2008, Abdel-Farid et al.,2009,Leiss et al.,2009).Also there

have been significant recent successes in gaining new insights into the pathways of lignin

biosynthesis (Anterola and Lewis,2002)and isoflavone synthesis (Jung et al.,2000;Liu et

al.,2002) and progression in metabolite-profiling methods for isoprenoids (Lange et

al.,2001), oxylipins (Weichert et al.,2002), taxane diterpenoids(Ketchum et al.,2003),

alkaloids(Yamazaki,2003), flavonoid glycosides (Le Gall et al.,2003) and volatile

compounds (Verdonk et al.,2003;Flamini et al.,2003;Brown,2002).

More recently, metabolomics has been much used in descriptions of the response of plants to

a wide range of biotic or abiotic stresses, to decipher gene function, to investigate metabolic

regulation and (in combination with analysis of other molecular entities of the cell) as part of

integrative analyses of the systemic response to environmental or genetic perturbations

(Schauer and Fernie,2006).

30

Limitations of plant metabolomics

There are several critical factors in metabolomics studies, since the metabolome is sensitive

to perturbation. Plant cultivation, sampling, extraction and derivatization must all be

carefully standardised and strategies need to be incorporated to minimize variations

(Fukusaki and Kobayashi, 2005).It is generally accepted that a single analytical technique

will not provide sufficient visualization of the metabolome and therefore, multiple

technologies are needed for a comprehensive view and sometimes a combination of

techniques are to used (Le Gall et al.,2005).The selection of the most suitable technology is

generally a compromise between speed, selectivity and sensitivity (Sumner et

al.,2003).Metabolic profiling is complex and it involves various techniques to cover a wide

range of metabolites. Each metabolite should be considered according to its characteristics

in the following categories: hydrophilic, hydrophobic, small molecule, large molecule,

charged, uncharged and combinations of these (Schauer and Fernie, 2006). Metabolites vary

due to volatility, polarity, solubility and chromatographic behaviour mean that multiple

methods will need to be deployed to analyse different subsets of metabolites (Ward et al.,

2002).A major technological challenge encountered in metabolomics is dynamic range.

Dynamic range defines the concentration boundaries of an analytical determination over

which the instrumental response as a function of analyte concentration is linear. The

dynamic range of many techniques can be severely limited by the sample matrix or the

presence of interfering and competing compounds. In other words, the presence of some

excessive metabolites can cause significant or severe chemical interferences that limit the

range in which other metabolites may be successfully profiled .For example, high levels of

primary metabolites such as sugars often interfere with the ability to profile secondary

31

metabolites such as flavonoids. This is one of the most difficult issues to address in

metabolomics (Sumner et al.,2003).There is another drawback where metabolomics is a

snapshot and thus is not particularly suited to studying the rapid kinetics of metabolic

processes and requires measurement of fluxes (a series of snapshots), which represents a

major challenge (Kim et al.,2011).A truly comprehensive analysis of the metabolome is

currently not feasible and the number of the primary and secondary metabolites in any given

plant species is still uncertain. This is the current inability and limitation of metabolomics.

Metabolomics is not a goal in itself; it is a tool for improving our understanding of the

metabolism and biochemistry of organisms. To provide a deeper biological meaning,

metabolomic studies should deal with identified metabolites instead of unknown signals: the

number of signals is less important than the number of identified metabolites. To facilitate

identification, an important issue and a great challenge will be to build a common public

database to share information within scientific communities (Kim et al.,2011)

Present study

The recent progress about the pathways, control of ascorbate metabolism and the potential

roles of intracellular and long distance transport as factors determining ascorbate

concentration has helped in understanding plant ascorbate to a certain extent. There are also

certain areas of ascorbate metabolism which require a deeper understanding. Much remains

to be learned about the contribution of alternative ascorbate biosynthetic pathways in each

plant tissue and the regulation of metabolic fluxes, and many important genes have not yet

been identified. There are many important questions that still needs to be explored about

ascorbate metabolism and the ascorbate-deficient mutants provide an opportunity to

32

investigate the following questions such as-What is the effect of each specific mutation on

metabolism and can the function of VTC3 identified? Ascorbate has proposed roles as an

antioxidant and as a cofactor for dioxygenase enzymes that are involved in synthesis of a

variety of plant hormones and secondary compounds (e.g. phenolic compounds involved in

defence against pathogens).Therefore can the effect of ascorbate deficiency on plant

function, as visualised by the metabolome, provide information on the function(s) of

ascorbate? Ascorbate mutants are more sensitive to environmental stresses and, if so, can

this be detected by changes in their metabolome compared to wild-type plants. Profiling of

all the metabolites contained in the ascorbate deficient mutants will offer much useful

information about metabolism and manipulation. The main objectives of the present study

are: 1.to use NMR as an analytical tool for profiling the vtc mutants and Wild type plants to

produce NMR data 2. to use software packages MestRe-C to perform spectral processing to

study the ascorbate NMR data 3.to look for differences in spectral processed data at the

metabolite levels across the plant types (Wild type/ mutants and alleles), growth stages

(rosette and flowering) 4.to verify the discriminating bins from the statistical analysis with

the original spectra for the presence of peaks.

33

Chapter 2

MATERIALS AND METHODS

Arabidopsis thaliana ecotype Columbia-0 seeds (Wild type) and ascorbate-deficient mutants

namely vtc1, vtc2, vtc3 and vtc4 were chosen for the present study (Table 2.1). All the

mutants have the same genetic background.

Table 2.1 Plant types used for the study

The seeds were grown, seedlings transplanted to soil, harvested, prepared and extracted for

NMR analysis in The National Centre for Plant and Microbial Metabolomics, at Rothamsted

Research. U.K. The protocols followed for each of the above stages are given below:

Germination of Arabidopsis thaliana seeds on agarose:

Before starting the procedure Laminar Flow Hood was turned on and the working surface

were wiped with 70% Ethanol.The hood was left running in LAF purge mode for 10 minutes

and then turned to red to normalise the flow for working.

Step 1: Preparing 1 litre of Arabidopsis growth media: 1Litre Duran bottle was labelled

as M+S + 3% Sucrose, and with the date and the name of the operator and 4.4g of

Murashige-Skoog media 30g sucrose (3% final concentration), 7g Agarose Type PGP,

supplied by Park Scientific Ltd (0.7% final concentration) was weighed into a 1L Duran

Wild type plant

Ascorbate-deficient Mutants

Wt

vtc1

vtc2

vtc3

vtc4

34

bottle.1 litre of deionised polished water was added to the bottle. The growth media was

mixed well in the solution by inverting the bottle three times. The solution was adjusted to

pH 5.6 with 1M KOH. The lid of the Duran bottle was slightly loosened and a strip of

autoclave tape was place over the top of the lid. The bottle was autoclaved using the 2100

Classic Autoclave supplied by Jencons-PLS. (Autoclaving takes approximately 30 minutes

but the programme was automatic and timing depended on whether the machine was starting

from cold, or has recently been used).Once the autoclave programme finished, the Duran

bottle was removed, the lid was tightened and placed in the Laminar Flow Hood to cool.

(The following steps 2, 3 and 4 were carried out in a Laminar Flow Cabinet)

Step 2: Pouring Agarose plates: Using a black marker pen the outside of the bottom half of

sterile petri dishes were labelled with details: M+S+3% Sucrose, name of the seed to be

plated, date and name of the operator. Once the agarose was cooled enough to handle

(usually 30-60 minutes after removal from the autoclave) it was poured directly from the

bottle into the labelled triple vent 90mm petri dishes until agarose 2/3rds fill the bottom of

the dishes. The petri dishes were pushed to the back of the flow hood and the lids of the petri

dishes were left slightly open (offset by approximately 5mm), with the opened lid facing the

filter at the back of the flow hood, to prevent condensation while minimising contamination.

The plates were allowed to stand for one hour. Before carrying out sterilisation of seeds,

10% bleach solution was made in a 100ml Duran bottle using Parazone Jeyes Active Power

Thick Bleach and deionised water (v/v). All the equipment needed viz., seeds, eppendorf

tubes, bleach solution, sterile water, pipettes (P1000 and P20, pipette tips (1ml and 10-100µl

Barky Ultipette Capillary tips), parafilm and scissors - were transferred to the Laminar Flow

Hood.

35

Step 3: Sterilising Arabidopsis seeds: The seeds to be plated were transferred to an

eppendorf tube labelled with the seed name.1ml of 10% bleach (v/v in water) was added to

sterilise the seeds. Each eppendorf tube was inverted five times in 3 minutes, to ensure all

the seeds are exposed to the bleach. The seeds in bleach were left not for longer than 3

minutes as it would cause seed coat breakdown. The bleach solution was pipetted off and

1ml of sterile deionised water was added to remove residual bleach from the seeds. The tube

was inverted five times to ensure thorough washing of the seeds. The water was removed

with a pipette and repeated the sterile water wash. The majority of the water was removed,

leaving a small volume in the base of the tube to facilitate plating of seeds.

Step 4: Plating out of Arabidopsis seeds: The lid of the petri dish was lifted, of the agarose

plate to be plated, and angled towards the back of the flow hood, to help minimise

contamination. Using capillary tips and a P20 Gilson Pipette, a 20µl aliquot of seeds was

taken from the appropriate eppendorf tube. The pipette tip was inserted with the seeds under

the lid to distribute the seeds individually at regular intervals over the plate. A maximum of

50 seeds were plate per plate. The plates were sealed by wrapping a 1cm strip of parafilm

around the petri dishes where the top and bottom of the dishes meet. The petri dishes were

place flat, in a cabinet where ensuring the conditions were set at 24 hours light and

22°C.The plates were left in the tissue culture cabinet 3 for 10 days or until the seedlings

have 2-4 leaves.

Step 5: Recording Information: The seeds plated were listed on a record of Arabidopsis

thaliana plant growth sheet. The sheet was completed with the following sections for

referencing: description of the ecotype, seed name and number for identification and date

plated.

36

Transferring Arabidopsis thaliana seedlings to soil

The seedlings were transferred 10 days after plating to the soil (75% medium grade peat,

12% screened sterilised loam, 3% medium grade vermiculite, 10% grit (5mm screened, lime

free), 3.5kg osmocote plus ¾ month per m3, 0.5kg PG mix per m3, Lime to pH 5.5-6.0 and

wetting agent.).The plants are usually at the 2-4 leaf stage. On day of transferral the soil

trays were made up according to number required. The soil is watered from above (from a

height of approximately 50cm) using a hose with a rose attached, so as not to cause breakup

of the soil surface. Once the surface looks wet some water was added to the bottom of the

trays. Each tray was labelled with: the seed number, date of plating, and date of soil transfer,

tray number and the name of the operator. The petri dishes containing seedlings were

collected from cabinet where they were kept for germination. The seedlings were carefully

picked off of the agarose plates with a sterilised cocktail stick and laid on the soil surface.

Using the cocktail stick they were then gently pushed so that all the roots under the surface

of the soil, leaving the seedling’s leaves resting on top of the soil. The trays were covered

with an incubation lid, ensuring all vents are closed; the trays then were transferred to

controlled environment growth room with growth conditions specific for Arabidopsis

growth viz., Long day conditions: 16 hour day(Day- Light: 00:00-16:00 hrs, Humidity 75

%,Temperature 23ºC; Night16:00-00:00 hrs, 80 %,18ºC).

The incubation lids were left on for 2-3 days, until seedlings have grown and look healthy,

and removed when the plants were grown the required developmental stage for harvest. The

date of transferral of seeds to soil was recorded on the relevant record of Arabidopsis

thaliana growth sheet.

37

Harvesting of Arabidopsis thaliana plants for analysis

50ml centrifuge tubes were labelled with sample number, date of harvest and the operator's

name. Separate tubes were used for different plant types and different trays of the same plant

type. A minimum of three holes were made in the centrifuge tube caps with a pair of

scissors. The plant samples were harvested at two growth stages namely rosette (5.10) and

flowering (6.1-6.5) (Boyes et al.,2001).Harvesting of the plants were started at 14 hours into

the growth day (2pm). A labelled 50ml centrifuge tube was dipped into a flask containing

liquid. The plants were cut, using scissors, immediately below the rosette, as close to the soil

surface as possible. The harvested plant was then put immediately using tweezers, into the

centrifuge tube containing liquid nitrogen, so that it was frozen instantly, thus minimising

any alteration of metabolites after harvest. Three to six plants were put in each tube. The cap

of the tube was replaced, ensuring it has holes in it and immerse it in the flask of liquid

nitrogen to ensure samples are kept frozen. The numbers of plants harvested were recorded

for reference. The tubes of plant material was then transferred to a plastic bag labelled with

date and operator name and stored at –80°C until preparation for analysis can be carried out.

Preparing Arabidopsis thaliana plants for NMR analysis

A pestle and mortar was chilled in a –80°C freezer for 30 minutes. A polystyrene box was

filled with ice, a well was made in the ice and the mortar was placed in the well. Little

Liquid Nitrogen was poured into the mortar and allowed to evaporate. The plant material

from the –80°C freezer was removed and the plant material of the same ecotype and tray

number were batched by emptying all centrifuge tubes containing the specified material into

the pre chilled mortar. The liquid nitrogen was poured into the mortar and the pestle was

used to grind the plant material. Grinding was continued until the material was fine and

38

homogenous. Any excess liquid nitrogen was allowed to evaporate off. A glass vial was

taken, respectively labelled and the lid of vial was removed and the ground material was

transferred to the vial, with a spatula. The material was retained at –80°C until further

analysis.

Analysing Arabidopsis thaliana plants by NMR

Step 1: Plant Extraction: A solution of 0.05% TSP w/v 80:20 D2O:CD3OD (pH typically

around 6.5) was made up. Three 1.5ml eppendorf tubes were labelled with the name of the

plant material and the replications to be analysed. The material stored at -80°C was removed

from storage and allowed stand on the laboratory bench for one hour, to allow the plant

material to return to room temperature.15mg ± 0.03mg of plant material were weighed out,

into each labelled eppendorf tube. The water bath was turn set to 50°C and 90°C and

allowed to reach the required temperature. The extraction method was a hot ethanol

extraction and hence two temperatures were used for effective extraction of samples. To

each eppendorf tube of plant material 1ml of the solvent, 0.05% TSP w/v 80:20

D2O:CD3OD, made in step 1.1 was added. Each tube was mixed, using a bench top whirl

mix, until all the plant material was held within the solvent. The samples were then heated at

50°C in a water bath for 10 minutes. A second set of eppendorf tubes were labelled with the

same descriptions as the first set in the water bath. The samples from the water bath were

removed and centrifuged for 5 min in a bench top centrifuge. The supernatant were then

transferred, using a 1ml Gilson pipette and a clean tip for each sample, to the clean,

appropriately labelled eppendorf tube. The supernatant was heated at 90°C in a water bath

for 2 minutes and the samples were removed from the water bath and transferred to a

39

refrigerator and store for 30 minutes. Clean NMR tubes were labelled with the same

descriptions as those on the eppendorf tubes. The samples were then removed from the

refrigerator and centrifuged for 5 minutes, in a bench top centrifuge. 750µl of the

supernatant was transfer, using a 1ml Gilson Pipette and a clean tip for each sample, from

each eppendorf tube to the appropriately labelled NMR tube and cap the tube. To get good

quality NMR spectra, there should be enough solution in the NMR tube to span the “active

volume” region. For a 5mm NMR tube the active volume was normally in excess of

500microlitres.750 µl were used because that was approximately the amount of clean

supernatant that can reproducibly collect from the extraction protocol.

Step 2: NMR Analysis: The samples were loaded on to the NMR instrument which was run

to the program set, selecting the appropriate solvent, (D2O) and experiment (Watersup.lars

2K) and giving each sample a title. The data was collected by NMR acquisition Software

and then was used for further analysis.The parameters set for data collection is tabulated as

shown in Table 4.

40

Table 2.2 Parameters for NMR Data Collection

Instrument Parameters

Instrument Bruker Biospin, Advance 400

Probe 5mm broad band multinuclear probe (BBO Z8248/0048)

Proton Frequency 400.1299232 MHz

Sample Introduction System BACS

Automation Control Software ICON-NMR

NMR acquisition Software X win-NMR 9 (v3.5)

NMR tube size 5mm

Acquisition Parameters

Parameter Set Watersup.roth

Pulse Sequence zgpr (low power pre-saturation pulse followed by high power 90˚ pulse)

Presaturation pulse 50.0 dB for 5 s

90˚ pulse -6 dB for 7.9 µs

Sweep width 12.1084 ppm

Time Domain size 32k data points

Temperature 300 K

Irradiation Frequency/

transmitter offset

4.7ppm

Number of Scans 2048

Lock Automated locking on D2O

Sample Shimming tune xyz (all under automation)

Sample Spinning 20 Hz

Receiver Gain Optimized for each sample by the automation program au_zg (using

command RGA)

other details

sample temperature 27ºC

T1 values not measured for the sample

Time Delay 5s( combines with 3.4s acquisition is enough for adequate relaxation)

spectral processing 0.5Hz line broadening

41

NMR Spectral processing with MestRe-C

A commercial processing software, MestRe-C, which is particularly designed to process

NMR data is used (Figure 3).MestRe-C can display and manipulate single or multiple 1D

spectrum and allows for detailed analysis and interpretation of NMR spectra. It is able to

handle data formats from a wide variety of instrument vendors and it automatically

recognises and converts data to the MestRe-C format when opened.

The data file taken from the spectrometer called the raw free induction decay (fid) were

imported in the MestRe-C and all the fids for all the samples involved in the present study

and their respective biological replications and technical replications were given a new

appropriate name for identification(e.g. WTR1 refers to wild type, rosette, biological

replication number 1.)Full Fourier Transformation (FT) was selected to get the proton

spectrum onto the interface and to convert NMR signals from time domain to frequency

domain which was done automatically with the help of option button on the software. Phase

correction was done next on to the spectra on the screen to get the phase corrected by

Automatic phase correction (Global method). Baseline correction was performed next on the

phase corrected spectra. Two baseline corrections are used with a) multipoint baseline

correction with linear picking and b) Auto baseline correction, to smooth the outliers to

obtain a smooth baseline. Referencing was done to the spectra with Trimethylsilane (TMS)

which was used as internal standard and was referenced to a tsp value of zero by expanding

the spectrum and dragging the TMS icon manually and the spectra was calibrated

(referenced). Bucket/binning, a commonly used technique for digitizing a spectrum into a

row vector was done with a bucket width of 0.01 ppm and 0.04 ppm was applied, the spectra

42

was split into 1000 and 250 integrated buckets respectively and the resultant data (as

absolute normalized values).The generated ASCII files was then imported to Microsoft

EXCEL for the addition of labels saved for further analysis. All the samples were subjected

to similar data processing procedures and all the resultant files are copied to a single

Microsoft EXCEL spread sheet and all the three technical replications for each single

sample are averaged up to give rise to biological replications. The processed data was also

saved as text files for statistical analysis.

Figure 2.1 Spectral processing with MestRe-C. (A) Raw NMR data exported into MestRe-C as

FIDs;(B) Fourier transformation;(C,D) Phase correction before and after;(E)Auto-baseline correction

(F,G) Baseline correction by linear correction before and after;(H)Calibration/Referencing with TMS

shift value 0;(I) Bucketing Integration and integrals imported as ASII files.

Data Analysis

The pre-processed from MestRe-C are then further altered by removing water

regions(bins)450-560 from 0.01ppm bucketed data and regions(bins)116-154 from 0.04 ppm

bucketed spectra to eliminate any variability in suppression of the water signal.

43

The statistical data analyses and visualisation were performed using MeV. Two types of

statistical analysis were done to the data; Analysis of Variance (ANOVA) and Hierarchical

clustering (HCA).The statistical analysis ANOVA in MeV was carried out by uploading the

data file and the numbers of groups was assigned followed by the P-value parameters and

Bonferroni correction which gives a stringent condition. No correction gives a least stringent

condition. The results are displayed in an open window which gives the values for the

significant bins and non-significant bins. The significant bins after from ANOVA were

chosen for performing clustering analysis of the data.HCA was chosen with Pearson's

correlation with average linking clustering as linkage method. The results are clusters which

were visualized as Gene Tree and heat maps. The schema of data processing and data

analysis is given in Figure 2.2.

Figure 2.2 Schema of the data analysis. Wild type and four vtc mutants have two stages (rosette

and flowering), two baseline correction (auto, linear), two band width (0.01 and 0.04 ppm), two

normalizations with control (Total area and HPV (HPV=High Peak value) and statistically analyzed

(HCA=Hierarchical clustering analysis, ANOVA=analysis of variation)

44

Chapter 3

RESULTS and DISCUSSION

NMR SPECTRAL FEATURES OF ASCORBATE- DEFICIENT MUTANTS

Four ascorbate-deficient mutants (vtc1, vtc2, vtc3 and vtc4) were employed in the current

study. All mutants contained different levels of ascorbate as described in the Chapter 1 as

these mutants have genetic defect in ascorbate biosynthesis pathways (Conklin, 1999;

Conklin, 2000; Dowdle et al, 2007).These mutants are phenotypically different on the basis

of their size, as previously found ascorbate is an important regulator of growth (Kercheve et

al, 2011).The aim of the current study is to investigate the effect of ascorbate deficiency on

metabolic changes analysed by NMR.NMR was used as an analytical tool to analyse the

differences at metabolite level in two growth stages such as rosette and flowering in WT and

vtc mutants. There are different techniques used for plant metabolic profiling as described in

Chapter 1, but NMR spectroscopy is a very powerful technique which gives the structural

elucidation of metabolites which are useful for identification. For the past many years, NMR

has been found very useful technique because of its unique advantages viz., non-destructive,

non-biased, easily quantifiable, requires no or little separation and needs no derivatization

(Wisahart, 2008).NMR has played a central role in understanding the metabolic changes in

different strains. The limitation of NMR is that it is unable to detect the low abundant

metabolites and also provide no information about retention time (RT) and mass spectral.

45

Figure 3.1 represents the one dimensional H-NMR spectra from Arabidopsis thaliana. A

total of 45 NMR spectra representing Wild type and four mutants of two different growth

stages (rosette and flowering) were collected and spectral processed in a commercial

software MestRe-C for exploratory analysis. The spectrum of both rosette and flowering

shows a dominance of signals in the carbohydrate region and well defined signals both in

aromatic and aliphatic regions (Fig 3.2a and 3.2b).

Figure 3.1 1H-NMR spectra of the Arabidopsis thaliana (Wild type) extracted with in

D2O:CD3OD. The bottom spectrum represents the original spectra and the spectra in the box shows

the extended region of the original spectra.

46

Figure.3.2a 1H-NMR spectra of Arabidopsis thaliana at rosette stage. Spectra here represent

different vtc mutants. A: Wild type B: vtc1, C: vtc2, D: vtc3, E: vtc4.

Figure.3.2b 1H-NMR spectra of Arabidopsis thaliana strains at flowering stage. Insert represents

different vtc mutants. A: Wild type B: vtc1, C: vtc2, D: vtc3, E: vtc4.

47

NMR DATA PRE-PROCESSING

Pre-processing of data is a major challenge for metabolomics study as spectral processing

influences the outcome for the data (Schleif, 2007). The strains in the study were subjected

to different steps normally involved in data pre-processing i.e., Fourier transformation,

phase correction, baseline correction, peak alignment (referencing to internal standard) and

bucketing (binning). Many of these steps are necessary, because the crude NMR data show

artefacts due to physicochemical differences (Torgrip et al.,2006).(1) Two baseline

correction in MestRe-C namely the auto baseline and linear baseline corrections were used

in the present study.(2) Binning is another crucial step in data pre-processing, as it is an

approach to solve the unmanageable dimensionality of the data and the inter individual

differences in peak locations caused by slight variations in the sample environment (Meyer,

2008).Two different bin widths (0.04 ppm and 0.01ppm) were applied to divide the spectral

regions into equal sized bins. The intensity values of each bin were integrated and annotated

to the bin. The bins were then compared across the different spectra.(3)All the bin intensity

were then normalised. Normalization is a row operation that is applied to the processed data

from each sample and comprises methods to make the data from all samples directly

comparable (Craig et al., 2006). (a) The binned data were normalized with total area where

the bin values were converted as a fraction of the total spectrum area. (b) They were also

normalized using highest peak value where the integrals were individually divided by the

highest peak value (here the reference peak).

All the above mentioned parameters were applied in the present study while processing the

NMR spectra and also after obtaining the processed data to see if there are any influences of

these different parameters on the outcome of the data and to choose the best combination for

further analysis.

48

MULTIVARIATE ANALYSIS OF ASCORBATE- DEFICIENT MUTANTS

Many approaches have been used for the analyses of NMR data. The most common methods

used for the analysis of metabolites data are the multivariate data analysis such as principle

component analysis (PCA) and the hierarchical clustering (HCA) clustering(Deferenez and

Colquhoun, 2003). In the current study, the analysis of variance (ANOVA) was performed

to analyse the variability of the data between different strains. Each strain contained three

biological replicates and three technical replicates. The ANOVA was calculated on all the

strains against WT (control) in flowering and rosette separately. Bonferroni correction was

used, which kept the false positives to a minimum as hundreds of bins (integrated buckets)

were being analysed. The p < 0.05 was selected as the significant values. The significant

data (p < 0.05) from both stages of growth used in the current study was combined and

subjected to HCA.

HCA clusters calculated the differences between biological replicates and strains. It also

clustered features together which are behaving in a similar way on the basis of their

chemical nature. HCA was performed using Pearson's correlation and average linkages, the

most commonly used methods for the metabolomic and genomic analysis. The resulted

dendrogram (rosette and flowering), which separates the data into branches and showed the

variability between strains and the biological replicates. The horizontal (x-axis) represents

the biological replicates of each strain and two growth stages while vertical (y-axis) shows

the features (metabolites). The features which are similar in nature were clustered together

as shown in Figure 3.3. The dendrogram showed all the biological replicates from rosette

were clustered together but unfortunately, the biological replicates from flowering were far

away from each other and not clustered properly (Figure 3.3).

49

This may be due to variability in biological replicates from flowering which needs further

investigation. Interestingly, we observed a prominent difference in metabolites intensity

between WT and the vtc mutants. The differences were also observed in the flowering and

the rosette in the accumulation of metabolites. The results indicated that spectra produced by

NMR for metabolites intensity are able to primarily show difference between vegetative and

flowering stages.

Sixty four combinations from two different growth stages were used, including two different

baseline corrections, two bin widths, two different normalisations methods along with the

non-normalised. The data were obtained after performing data pre-processing and post

processing using different parameters as described earlier are presented in Tables 3.1-3.4.

As described before we observed variation between biological replicates in flowering

compared to rosette. We are unable to investigate the variability between biological replicate

in flowering in the current study which needs further work. So, for the further analysis here,

we selected the rosette data which showed very close behaviour in biological replication.

50

Figure 3.3 HCA showing the difference in growth stages (rosette and flowering) and WT and

the vtc mutants. Boxes and ellipses show the clustering of biological replicates in rosette stage the

flowering stage respectively. M=mutants, R=rosette, F=flowering, WT= wild type. The scale shows

intensity of colours from highest to lowest. Red = Highest intensity, Green = Lowest intensity. The

significant difference was calculated by ANOVA (p < 0.05).

51

Table 3.1 Clustering analyses of all plant types in rosette stage with 0.01 bin width and

different processing combinations. Cluster No = number of clusters where all the three biological

replicates within and between strain clustered together, Strains = represents the strains in which are

all the biological replicates are clustered close to each other.

STAGE

BIN

WIDTH

(PPM)

BASELINE

CORRECTION NORMALIZATION

CLUSTERING

ANALYSIS

CLUSTERS

(No) STRAINS

ROSETTE

0.01

linear

non-normalized

HCA 1 wt

HCA/ ANOVA

/no correction

2 wt,vtc1

HCA/ANOVA

/Bonferroni 3 wt, vtc 1, vtc 2

HPV

HCA

1 wt

HCA/ ANOVA

/no correction

4 wt, vtc 1, vtc 2, vtc

3

HCA/ANOVA

/Bonferroni 5

w, vtc 1, vtc 2, vtc

3, vtc 4

Total Area

HCA 2 wt, vtc 1

HCA/ ANOVA

/no correction

2 wt, vtc 1

HCA/ANOVA

/Bonferroni 5

wt, vtc 1, vtc 2, vtc

3, vtc 4

auto

non-normalized

HCA 2 wt, vtc 1

HCA/ ANOVA

/no correction

4 wt, vtc 1, vtc 2, vtc

3,

HCA/ANOVA

/Bonferroni 3 wt, vtc 1, vtc 4

HPV

HCA 2 wt, vtc 1

HCA/ ANOVA

/no correction

4 wt, vtc 1, vtc 2,

vtc 3,

HCA/ANOVA

/Bonferroni 2 wt, vtc 1

Total Area

HCA 2 wt, vtc 1

HCA/ ANOVA

/no correction

2 wt, vtc 1

HCA/ANOVA

/Bonferroni

5 wt, vtc 1,v2, vtc 3,

vtc 4

52

Table 3.2 Clustering analyses of all plant types in rosette stage with 0.04 bin width and

different processing combinations. Cluster No = number of clusters where all the three biological

replicates within and between strain clustered together, Strains = represents the strains in which are

all the biological replicates are clustered close to each other

STAGE

BIN

WIDTH

(PPM)

BASELINE

CORRECTION NORMALIZATION

CLUSTERING

ANALYSIS

CLUSTERS

(No) STRAINS

ROSETTE 0.04

linear

non-normalized

HCA 2 vtc 1, vtc 2

HCA/ ANOVA

/no correction

4 wt, vtc 1, vtc 2,

vtc 3

HCA/ANOVA

/Bonferroni

correction

3 wt, vtc 1, vtc 4

HPV

HCA 2 vtc 1, vtc 2

HCA/ ANOVA

/no correction

4 wt, vtc 1, vtc 2,

vtc 3

HCA/ANOVA

/Bonferroni

correction

2 wt, vtc 2

Total Area

HCA 2 vtc 1, vtc 2

HCA/ ANOVA

/no correction

2 vtc 1, vtc 2

HCA/ANOVA

/Bonferroni

correction

5 wt, vtc 1, vtc 2,

vtc 3, vtc 4

auto

non-normalized

HCA 2 vtc 1, vtc 2

HCA/ ANOVA

/no correction

4 wt, vtc 1, vtc 2,

vtc 3

HCA/ANOVA

/Bonferroni

correction

3 wt, vtc 1, vtc 4

HPV

HCA 2 vtc 1, vtc 2

HCA/ ANOVA

/no correction

4 vtc 1, vtc 2, vtc 3,

vtc 4

HCA/ANOVA

/Bonferroni - 5

wt, vtc 1, vtc 2,

vtc 3, vtc 4

Total Area

HCA 2 vtc 1, vtc 2

HCA/ ANOVA

/no correction

4 vtc 1, vtc 2, vtc 3,

vtc 4

HCA/ANOVA

/Bonferroni - 5

wt, vtc 1, vtc 2,

vtc 3, vtc 4

53

Table 3.3 Clustering analyses of all plant types in flowering stage with 0.01 bin width and

different processing combinations. Cluster No = number of clusters where all the three biological

replicates within and between strain clustered together, Strains = represents the strains in which are

all the biological replicates are clustered close to each other

STAGE

BIN

WIDTH

(PPM)

BASELINE

CORRECTION NORMALIZATION

CLUSTERING

ANALYSIS

CLUSTERS

(No) STRAINS

FLOWERING 0.01

linear

non-normalized HCA 1 vtc 3

HCA/ ANOVA /no

correction

1 vtc 3

HCA/ANOVA

/Bonferroni 1 wt

HPV HCA 1 vtc 3

HCA/ ANOVA /no

correction

1 vtc 3

HCA/ANOVA

/Bonferroni 0 -

Total Area HCA 1 vtc 3

HCA/ ANOVA /no

correction

1 vtc 3

HCA/ANOVA

/Bonferroni 1 wt

auto

non-normalized HCA 1 vtc 3

HCA/ ANOVA /no

correction

2 vtc 1, vtc 3

HCA/ANOVA

/Bonferroni 0 -

HPV HCA 1 vtc 3

HCA/ ANOVA /no

correction

1 vtc 1

HCA/ANOVA

/Bonferroni 0 -

Total Area HCA 1 vtc 3

HCA/ ANOVA /no

correction

1 vtc 3

HCA/ANOVA

/Bonferroni 2 wt, vtc 1

54

Table 3.4 Clustering analyses of all plant types in flowering stage with 0.04 bin width and

different processing combinations. Cluster No = number of clusters where all the three biological

replicates within and between strain clustered together, Strains = represents the strains in which are

all the biological replicates are clustered close to each other. *Equal refers to the clusters which

presented no hierarchy.

STAGE

BIN

WIDTH

(PPM)

BASELINE

CORRECTION NORMALIZATION CLUSTERING ANALYSIS

CLUSTER

S (No) STRAINS

FLOWERING

0.04

linear

non-normalized HCA 1 vtc 2

HCA/ ANOVA /no

correction

2 vtc 1, vtc

3

HCA/ANOVA

/Bonferroni - -

HPV HCA 1 vtc 2

HCA/ ANOVA /no

correction

1 vtc 2

HCA/ANOVA

/Bonferroni 5

EQUAL

*

Total Area HCA 1 vtc 2

HCA/ ANOVA /no

correction

2 vtc 2, vtc

3

HCA/ANOVA

/Bonferroni 0 -

auto

non-normalized HCA 1 vtc 2

HCA/ ANOVA /no

correction

2 vtc 1, vtc

3

HCA/ANOVA

/Bonferroni 0 -

HPV HCA 1 vtc 2

HCA/ ANOVA /no

correction

2 vtc 1, vtc

3

HCA/ANOVA

/Bonferroni 5

EQUAL

*

Total Area HCA 1 vtc 2

HCA/ ANOVA /no

correction

2 vtc 1, vtc

3

HCA/ANOVA

/Bonferroni 0 -

55

HCA was also performed on non-significant data (no ANOVA was performed here) as listed

in the Tables 3.1 to 3.4. We observed the biological replicates were not clustered together,

which makes sense because ANOVA is able to calculate the variability of the data and

filtered out all the features which might be noise. Good clustering shows good experimental

replication and consistency of the replicates (Ward et al., 2003).In general normalised data

showed better clusters than non-normalized data and both the normalizations on the data

were comparable in terms of clusters showing that the two different types of normalizations

did not outperform each other, although total area normalization gave slightly increase in the

number of clusters.

Definite differentiation was observed in the rosette stage where the separation between wild

type and four mutants were obvious which also shows the reliability of all the three

biological replicates and hence the rosette stage was chosen than flowering stage for further

analysis to determine the influence of different baseline correction and bin widths on the

data.

Four combinations from rosette stage were used to carry on further analysis: (1) Total area

normalisation, (2) ANOVA (Bonferroni corrected bins), (3) auto and (4) linear baseline

correction. The baseline correction was performed with 0.01 and 0.04 ppm bin widths. The

results were listed in Tables 3.1 and 3.2.Auto baseline correction combination in 0.01 ppm

bin width although comparable discriminated few more bins than linear baseline correction

whereas they were equal in 0.04 ppm. It was concluded, that bin width 0.01 ppm

discriminated more metabolites than 0.04 ppm, as bin width 0.01 ppm separated two to four

times more integrated buckets (12-29 in numbers) than 0.04ppm (7 in numbers).

56

Wild type and mutants classified themselves as five crisp clusters and in all the four

combinations studied. In all four combinations chosen as mentioned before, the vtc2

grouped itself as a separate group from the other strains (Figure 3.4). The separate grouping

of vtc2 suggested it shows the different behaviour in metabolic level, this may be due to

lowest ascorbate level. As mentioned Chapter 1 the ascorbate levels in vtc 2 mutants are 10-

15 % of Wild type as compared to other which contained about 50 -70 % for vtc 3 and 4

(Conklin et al 2000; Dowdle et al, 2007).

Figure 3.4 Heatmap for the

1H NMR based metabolic profiles of Arabidopsis thaliana Wild type

and four ascorbate-deficient mutants in rosette. The scale shows the colour intensity from highest

to lowest. Red = Highest, Green = Lowest. Different colours represent different metabolites intensity

which is significantly different between different strains. The wildtype cluster and its corresponding

discriminating resonances (1.32, 1.33) are shown in ellipses.

57

DIFFERENTIALLY EXPRESSED BINS BETWEEN WT AND VTC MUTANTS.

The four combinations from rosette stage the bins separated by ANOVA and HCA were

traced to their resonances and the list of all the resonances responsible for the separation of

classes and groups at spectral level and are tabulated in Table 3.5. Major differences occurred

in the regions 0-6 ppm which corresponds to the aliphatic and carbohydrate regions in the

spectra. The regions where major resonances are discriminated correspond to the regions of

amino acids and fatty acids (Ward et al 2003).The unique resonances that showed visual

differences across the strains from two bin widths and the HCA heatmaps were extracted and

tabulated in Table 3.6. There are also some resonances which clearly might be from the same

peak area. The bin width 0.01 ppm has also picked up resonances from carbohydrate regions

than 0.04 ppm (e.g. 5.25 and 5.80 ppm).

The resonance 1.32, 1.33 that discriminated WT and other mutants were traced in the

original spectra shows the presence of peaks (Figure 3.6). The intensity of both the

resonances shows negative correlation to the ascorbate concentration in the vtc mutants and

was proportional to the ascorbate deficiency with across strains (Figure 3.5). Identifying the

spectral patterns forms the main focus rather identifying and quantifying metabolites. These

resonances (features) can then used to identify the corresponding metabolites from the NMR

database.

58

Table 3.5 List of resonances showing discrimination in the spectral regions.

REGIONS(ppm) RESONANCES(ppm)

0.00-2.00 0.96,0.97,0.98,1.00,1.04, 1.32,1.33,1.36, 1.48,1.81,1.88, 1.89 and 1.90

2.00-3.00

2.10, 2.11, 2.12,2.13, 2.14, 2.15, 2.39,2.40, 2.43, 2.45,2.48,2.46,

2.62,2.71,2.72, 2.91, 2.92,2.93 and 2.94

Table 3.6 Resonances different between strains at spectral level with two bin widths. (Wt=Wild

type, vtc=ascorbate-deficient mutants)

STRAINS 0.01ppm 0.04ppm

wt 1.32,1.33 1.36

vtc1 2.12,2.13,2.14,2.15,2.43,2.45,2.46,2.47,2.72, 5.80 0.99,2.48,2.72

vtc2 2.39,2.72 2.40, 2.48,2.72

vtc3 5.18,5.80 2.40

vtc4 0.96,0.97,0.98,1.00,1.33,2.39 and 2.94 0.99,1.04

The resonances (1.32 and 1.33) were identified using the information from the previous

literature. These resonances (1.32 and 1.33) putatively correspond to threonine and lactate

respectively (Urenjak et al., 1993).Threonine is an amino acid in plants which is involved in

defence mechanism with other defence related metabolites (Gonzales-Vigila, 2011). In plants,

pyruate, the end product of glycolysis, is converted to lactate by an enzyme called lactate

dehydrogenase when oxygen is absent or in short supply (anoxia).Lactate provides the signal

triggering for ethanol production and helps for prolonged survival in the absence of oxygen

(Xia and Saglio 1992, Tadege, et al 1999).We are unable to investigate the nature of

relationship between ascorbate and threonine and lactate at this stage. It needs further

investigation.

59

Figure 3.5 The intensity of the resonances 1.32 and 1.33ppm across the strains. Each dot

represents the intensity of resonance. WT shows the lowest intensity than vtc mutants.

Figure 3.6 Identification of the resonances 1.32 and 1.33 ppm (shown as ellipses) in the

original NMR spectra of one of the vtc mutant. The expanded regions are shown in the box. The

insert is the NMR spectra of Wild type. The 1H-NMR-spectra shows very low signal in WT than vtc

mutants.

Wildtype

vtc1vtc2

vtc3vtc4

Wildtype

vtc1vtc2

vtc3 vtc4

0

5000

10000

15000

20000

25000

30000

Strains

Intensity

1.32ppm

1.33ppm

60

Chapter 4

CONCLUSION

1 H-NMR spectroscopy has proved to be a valuable tool for unbiased metabolite profiling of

Arabidopisis ascorbate-deficient mutants. ANOVA and HCA highlighted differences

between the growth stages and strains. Combinations of different parameters were used to

find the best method for the analysis of NMR-based metabolites data. The method was

developed which allowed making multiple comparison such as two growth stages and five

strains. The vtc2 mutants grouped separately from other strains which contained the lowest

ascorbate suggested ascorbate deficiency might have some effect on the metabolic level.

Two interesting resonances 1.32 and 1.33ppm were separated which were negatively

correlated with the ascorbate levels in the vtc mutants. These resonances (1.32 and 1.33) are

putatively identified as threonine and lactate which needs further validation by running

standards.

61

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