Detection and Quantitation of Trehalose and Other Sugars

Home , Trehalose

DETECTION AND QUANTITATION OF TREHALOSE AND OTHER SUGARS

IN RICE AND ARABIDOPSIS USING GC-MS

A Thesis

Presented to the

Faculty of

California State Polytechnic University, Pomona

In Partial Fulfillment

Of the Requirements for the Degree

Master of Science

Chemistry

Elizabeth N. Martinez

2016

SIGNATURE PAGE

THESIS: DETECTION AND QUANTITATION OF TREHALOSE AND OTHER SUGARS IN RICE AND ARABIDOPSIS USING GC- MS

AUTHOR: Elizabeth N. Martinez

DATE SUBMITTED: Spring 2016

Chemistry and Biochemistry Department

Gregory A. Barding Jr., Ph.D. Thesis Committee Chair Chemistry and Biochemistry

Lisa A. Alex, Ph.D. Chemistry and Biochemistry

Yan Liu, Ph.D Chemistry and Biochemistry

ACKNOWLEDGEMENTS

There are many people I would like to acknowledge for their continuous support of my educational goals. Most importantly, I would like to thank my mom and dad, for always being there and supporting me through my good days and my bad days, and making sure I always had everything I needed. I would also like to thank my siblings,

Joseph for dealing with me being crazy and always trying to help me, Anthony for being entertaining, and Linda for being the awesome sister to me. Also, my amazing friends for always being there to burn off the stress on the weekends, but also understanding when I could not make it, thank you Elizabeth. I would also like to thank my fellow grad students for keeping me motivated and reassuring me that it will definitely be worth it one day.

Lastly, I would like to thank my advisor, Dr. Gregory Barding Jr. for putting up with my millions of questions, and guiding me to a successful path. I definitely would not have finished without your help and support during this experience.

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ABSTRACT

Trehalose, a disaccharide commonly found in many organisms, is an important sugar often associated with increased tolerance to a variety of stressors. Understanding how trehalose levels change in plants during abiotic stress will enhance our understanding of how plants cope with increasing stresses associated with climate change. For this study, trehalose sucrose, glucose and fructose, were quantified in submergence tolerant and intolerant rice varieties to better understand carbon metabolism during complete flooding and subsequent recovery. In plants, trehalose is present in exceedingly low quantities making it difficult to detect and quantify, especially due to its low relative abundance compared with sucrose, fructose and glucose. The analysis was done using Gas chromatography – mass spectrometry (GC-MS) equipped with a quadrupole detector, a powerful technique for the sensitive detection of gas-phase analytes. Because of the low concentration of trehalose present in plants, it was important to clean up the samples by removing anionic metabolites through weak anion exchange

(WAX) solid phase extraction (SPE). For data analysis of metabolites were derivatized with a suitable reagent, such as MSTFA, followed by GC-MS experiments carried out using a full scan (60-600 m/z) to identify each sugar using library matching as well as an in-house library generated with standards, and then selected ion monitoring (SIM) was used to further reduce the background associated with unwanted signals by only detecting specific ions associated with each sugar. The ions chosen were 307, 319, 323, and 361,

13 and used to quantify (fructose, glucose, C6 glucose, and trehalose) the specific ions to the sugars. In addition to rice, different variations of Arabidopsis thaliana were evaluated for sugar content using SIM to quantify trehalose and a full scan to quantify sucrose,

iv glucose, and fructose. It was determined the LOD and LOQ for trehalose in the rice samples are 0.640 ng, and 1.01 ng of trehalose per g of tissue, respectively, The LOD and

LOQ for trehalose in the Arabidopsis samples are 0.744 ng, and 0.825 ng, respectively.

This resulted in an average concentration of trehalose in M202 to be 11.5 ng of trehalose per g of tissue, in IR64 to be 1.3 ng of trehalose per g of tissue, and in Arabidopsis thaliana to be 17.9 μg of trehalose per g of tissue.

TABLE OF CONTENTS

SIGNATURE PAGE ...... ii ACKNOWLEDGEMENTS ...... iii ABSTRACT ...... iv LIST OF TABLES ...... viii LIST OF FIGURES ...... xii CHAPTER 1: INTRODUCTION ...... 15 1.1 Metabolomics ...... 15 1.2 Gas Chromatography (GC) ...... 19 1.3 Mass Spectrometry (MS) ...... 25 1.4 Growth conditions, extraction, and sample preparation ...... 32 1.5 Data analysis ...... 35 1.6 Application of GC-MS in biological systems ...... 37 1.7 Objectives of study ...... 40 1.8 REFERENCES ...... 42 CHAPTER 2: METHODS AND MATERIALS ...... 45 2.1 Tissue extraction and preparation of rice tissue samples ...... 45 2.2 Tissue extraction and preparation of Arabidopsis tissue sample ...... 46 2.3 Sample derivatization ...... 46 2.4 Sample injection and GC ...... 47 2.5 Mass spectrometry data analysis ...... 47 2.6 Sugar identification ...... 49 2.7 Quantitation of sugars ...... 49 2.8 Retention indices (RI) ...... 50 2.9 Retention time lock (RTL) ...... 50 2.10 Rice tissue bulking and Seed bulking ...... 51 2.11 REFERENCES ...... 55 CHAPTER 3: QUANTITATION OF TREHALOSE AND OTHER SUGARS IN SUBMERGENCE RESISTANCE RICE ...... 56 3.1 M202 and M202(Sub1) Results ...... 56 3.2 IR64 Results ...... 66 3.2.1 Discussion ...... 74 3.3 Conclusion ...... 85

3.4 REFERENCES ...... 90 CHAPTER 4: QUANTITATION OF TREHALOSE AND OTHER SUGARS IN DROUGHT TOLERANT ARABIDOPSIS PLANTS ...... 91 4.1 Introduction ...... 91 4.2 Results and Discussion ...... 92 4.3 Conclusion ...... 111 4.4 REFERENCES ...... 114 CHAPTER 5: SUMMARY AND CONCLUSION ...... 115 5.1 REFERENCES ...... 119 CHAPTER 6: FUTURE WORK ...... 120 6.1 REFERENCES ...... 122

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LIST OF TABLES

Table 2.1 M202 submergence tolerant and intolerant rice samples used in study ...... 53

Table 2.2 IR64 submergence tolerant and submergence intolerant rice samples ...... 54

Table 2.3 Arabidopsis drought tolerant and intolerant samples ...... 54

Table 3.1 Ratios of M202 to M202(Sub1) for control samples. Samples were harvested at noon (t=0 hr post submergence), dusk (t=6 hr post submergence), midnight (t=12 hr post submergence), Dawn (t=18 hr, post submergence), and 24 hr (t=24 hr post submergence). The asterisks indicates samples that are significantly different at the 90% confidence limit...... 56

Table 3.2 Ratios of M202 to M202(Sub1)after the indicated recovery period. Samples were harvested at noon (t=0 hr post submergence), dusk (t=6 hr post submergence), midnight (t=12 hr post submergence), Dawn (t=18 hr, post submergence), and 24 hr (t=24 hr post submergence). The asterisks indicates samples that are significantly different at the 90% confidence limit...... 56

Table 3.3 Ratios of control to submerged concentrations for sucrose, glucose, fructose and trehalose for M202 and M202(Sub1). Samples were harvested at noon (t=0 hr post submergence), dusk (t=6 hr post submergence), midnight (t=12 hr post submergence), Dawn (t=18 hr, post submergence), and 24 hr (t=24 hr post submergence). The asterisks indicates samples that are significantly different at the 90% confidence limit...... 57

Table 3.4 Average concentrations (ng/g D.W.) of trehalose in control samples for M202 and M202(Sub1) ...... 57

Table3.5. Average concentrations (ng/g D.W.) of trehalose in recovery samples for M202 and M202(Sub1) ...... 58

Table 3.6 Average concentrations (mg/g D.W.) of sucrose in control samples for M202 and M202(Sub1) ...... 59

Table 3.7 Average concentrations (mg/g D.W.) of sucrose in recovery samples for M202 and M202(Sub1) ...... 60

Table 3.8 Average concentrations (ng/g D.W.) of glucose in control samples for M202 and M202(Sub1) ...... 62

Table 3.9 Average concentrations (ng/g D.W.) of glucose in recovery samples for M202 and M202(Sub1) ...... 63

Table 3.10 Average concentrations (ng/g D.W.) of fructose in control samples for M202 and M202(Sub1) ...... 64

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Table 3.11 Average concentrations (ng/g D.W.) of fructose in recovery samples for M202 and M202(Sub1) ...... 65

Table 3.12 Ratios of control samples from IR64 submergence intolerant rice to genetically modified submergence tolerant IR64 rice. The asterisks indicate samples that are significantly different at the 90% confidence limit...... 66

Table 3.13 The effect of submergence treatment represented as the ratio of concentrations for the IR64 submergence intolerant rice compared to the genetically modified submergence tolerant IR64 rice. The asterisks indicates samples that are significantly different at the 90% confidence limit...... 67

Table 3.14 Ratios of each rice variety comparing concentrations of control to submerged samples. The asterisk indicates samples that are significantly different at the 90% confidence limit...... 67

Table 3.15 Average concentrations (ng/g D.W.) of trehalose in control and submerged samples for each rice variety used in study...... 68

Table 3.16 Average concentrations (ng/g D.W.) of sucrose in control and submerged samples for each rice variety used in study...... 69

Table 3.17 Average concentrations (ng/g D.W.) of glucose in control and submerged samples for each rice variety used in study...... 70

Table 3.18 Average concentrations (ng/g D.W.) of fructose in control and submerged samples for each rice variety used in study...... 71

Table 3.19 Limit of detection and limit of quantification calculated from the standard deviation and slope of standard curves...... 76

Table 3.20 For each metabolite used in this study, a mean percent - RSD and range of RSD values (representing the lowest and highest RSD) is calculated from the calibration curve...... 76

Table 4.1 Limit of detection and limit of quantification calculated from the standard deviation and the slope of the standard curves. All values are in ng ...... 94

Table 4.2 From the calibration curve for each sugar, the mean percent - RSD and the range percent - RSD is calculated...... 94

Table 4.3 Average concentrations of trehalose (μg of trehalose per a g of tissue). The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control...... 95

Table 4. 4 Ratios of the average trehalose concentrations comparing air to submerged samples for the t = 4, 48 and 52 hr time points. The asterisk indicates samples that are significantly different at the 90% confidence limit...... 96

Table 4.5 Ratios of trehalose concentrations comparing the three different genotypes. The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. The asterisk indicates samples that are significantly different at the 90% confidence limit...... 96

Table 4.6 Average concentrations of sucrose (μg of sucrose per a g of tissue). The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. . 99

Table 4.7 Ratios of average sucrose concentrations comparing the different genotypes. The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. The asterisk indicates genotypes that are significantly different at the 90% confidence limit...... 100

Table 4.8 Ratios of the average sucrose concentrations comparing air to submerged samples for the t = 4, 48 and 52 hr time points. The asterisk indicates samples that are significantly different at the 90% confidence limit...... 100

Table 4.9 Average concentrations of glucose (mg of glucose per a g of tissue). The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. 103

Table 4.10 Ratios of the average glucose concentrations comparing air to submerged samples for the t = 4, 48 and 52 hr time points. The asterisk indicates samples that are significantly different at the 90% confidence limit...... 103

Table 4.11 Ratios of average glucose concentrations comparing the different genotypes. The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. The asterisk indicates samples that are significantly different at the 90% confidence limit...... 104

Table 4.12 Average concentrations of fructose (μg of fructose per g of tissue). The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. 107

Table 4.13 Ratios of the average fructose concentrations comparing air to submerged samples for the t = 4, 48 and 52 hr time points. The asterisk indicates samples that are significantly different at the 90% confidence limit...... 107

Table 4.14 Ratios of average fructose concentrations comparing the different genotypes. The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. The asterisk indicates samples that are significantly different at the 90% confidence limit...... 108

Table 4.15 Metabolite identification and corresponding retention times used to investigate the biological variance in the Arabidopsis samples...... 110

LIST OF FIGURES

Figure 1.1 Schematic of injector with a packed glass wool injection liner ...... 21

Figure 1.2 Schematic of a quadrupole mass analyzer...... 28

Figure 1.3 Schematic of a triple quadrupole mass analyzer...... 29

Figure 1.4 Schematic of a time of flight mass analyzer ...... 31

Figure 1.5 Solid phase extraction schematic...... 34

Figure 1.6 Derivatization reaction of glucose, resulting in TMS derivative...... 34

Figure 3.1 A comparison of the concentrations of trehalose in M202 and M202(Sub1). The error bars represent the standard deviation of each set of replicates...... 59

Figure 3.2 A comparison of the concentrations of sucrose in M202 and M202(Sub1). The error bars represent the standard deviation of each set of replicates...... 61

Figure 3.3 A comparison of the concentrations of glucose in M202 and M202(Sub1). The error bars represent the standard deviation of each set of replicates...... 64

Figure 3.4 A comparison of the concentrations of fructose in M202 and M202(Sub1). The error bars represent the standard deviation of each set of replicates...... 66

Figure 3.5 A comparison of trehalose concentrations for the IR64 rice verities. The varieties of IR64 with the different incorporated traits are indicated in the figure, with the IR64 plant being the intolerant variety. The error bars represent the standard deviation of each set of replicates...... 68

Figure 3.6 A comparison of the concentrations of sucrose in IR64 rice varieties. The varieties of IR64 with the different incorporated traits are indicated in the figure, with the IR64 plant being the intolerant variety. The error bars represent the standard deviation of each set of replicates...... 69

Figure 3.7 A comparison of the concentrations of glucose in IR64 rice varieties. The varieties of IR64 with the different incorporated traits are indicated in the figure, with the IR64 plant being the intolerant variety. The error bars represent the standard deviation of each set of replicates...... 71

Figure 3.8 A comparison of the concentrations of fructose in IR64 rice varieties. The varieties of IR64 with the different incorporated traits are indicated in the figure, with the IR64 plant being the intolerant variety. The error bars represent the standard deviation of each set of replicates...... 72

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Figure 3.9 A comparison of the variation of the internal standard peak area in the M202 samples ...... 73

Figure 3.10 A comparison of the variation of the internal standard peak area in the IR64 samples ...... 74

Figure 3.11 Calibration curve of Trehalose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement...... 76

Figure 3.12 Results from the trehalose spiking experiment in M202 rice sample. (a) sample analyzed without addition of trehalose and (b) sample analyzed with a 0.6 ng addition of trehalose from a stock solution...... 78

Figure 3.13 Calibration curve for sucrose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement...... 80

Figure 3.14 Calibration curve for glucose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement...... 82

Figure 3.15 Calibration curve for fructose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement...... 84

Figure 3.16 SIM traces in submergence intolerant M202 rice (a) trehalose (b) sucrose, and (c) fructose and glucose ...... 88

Figure3.17. SIM traces in submergence tolerant M202(Sub1) rice (a) trehalose (b) sucrose, and (c) fructose and glucose ...... 89

Figure 4.1 Calibration curve for trehalose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement...... 93

Figure 4.2 A comparison of the average concentrations of trehalose in Arabidopsis. The error bars represent the standard deviation of each set of replicates...... 94

Figure 4.3 Calibration curve for sucrose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement...... 97

Figure 4.4 A comparison of the average concentrations of sucrose in Arabidopsis. The error bars represent the standard deviation calculated from each set of replicates...... 98

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Figure 4.5 Calibration curve for glucose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement...... 101

Figure 4.6 A comparison of the average concentrations of glucose in Arabidopsis. The error bars represent the standard deviation calculated from each set of replicates...... 102

Figure 4.7 Calibration curve for fructose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement...... 105

Figure 4.8 A comparison of the average concentrations of fructose in Arabidopsis. The error bars represent the standard deviation calculated from each set of replicates...... 106

Figure 4.9 Peak area of methyl stearate using the SIM method. Samples 1-28 are OST-A, samples 29-56 are OST-B, and samples 57-84 are Col-0 ...... 109

Figure 4.10 Peak of methyl stearate using the full scan method. Samples 1-28 are OST- A, samples 29-54 are OST-B, and samples 55-82 are Col-0 ...... 109

Figure 4. 11 TIC of OST-A, Air samples at time zero (a) bio rep 1, A01, (b) bio rep 2, A02, (c) bio rep 3, A03, (d) bio rep 4, A04 ...... 113

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CHAPTER 1: INTRODUCTION 1.1 Metabolomics

Metabolomics is a field of study that focuses on small molecule metabolites in complex biological systems, the metabolites being the end product of cellular regulatory processes.1 Metabolomics experiments are commonly carried out to better understand complex biological problems and systemic responses due to genetic modification or environmental stressors.1 Metabolomics is a constantly expanding field of research which includes a wide selection of analytical techniques and instrumentation.2 The most common instrumentation used in metabolomics includes nuclear magnetic resonance

(NMR), capillary electrophoresis coupled with mass spectrometry (CE-MS), high performance liquid chromatography coupled with mass spectrometry (HPLC-MS), and gas chromatography coupled with mass spectrometry (GC-MS).3 Each approach has specific advantages and disadvantages.

NMR is a commonly used analytical technique for metabolite profiling because it is nondestructive to the sample and requires minimal sample preparation with no derivatization being required, however, it suffers from low sensitivity and spectral convolution.4 The main advantage of NMR in metabolomics is that it is absolutely quantitative. For example, De Graaf et al. performed a study with human plasma using

1H NMR, and were able to identify 28 different metabolites, but faced difficulties from spectral overlap due to the presence of different metabolites.5 Bouatra et al. also recently reported the urine metabolome in which 179 metabolites could be detected by NMR but only 85 quantitatively due to convolution and limited sensitivity.6

In contrast, mass spectrometry used in metabolimics usually associates with a separation process, such as CE, HPLC, and GC, prior to MS detection, which significantly reduces spectral convolution, and is considerably more sensitive than NMR.

CE utilizes the different migration rates of charged species under an electric field to separation target analytes. Compared with other separation techniques, CE provides higher separation efficiency due to the plug electroosmotic flow (EOF) inside the capillary. At the end of the capillary the separated analytes are introduced into the mass spectrometer. One disadvantage of CE-MS is the deleterious effects from the high ionic strength in the running buffer for CE. In addition, only metabolites with charges can be analyzed.7,8 In a study by Acunha and coworkers, anionic metabolites instead of more common cationic metabolites were profiled. To facilitate the analysis of anionic metabolites, the MS detector, which was usually located at the cathode, was moved to the anode. 87 metabolites were detected in orange juice using CE-MS, and this method was claimed to be comparable to HPLC-MS.8

HPLC-MS is a very sensitive method with a wide range of applications. In HPLC, the sample is forced by a mobile phase at high pressure through a packed column, the stationary phase.9 After this separation, the sample is then introduced to the mass spectrometer, commonly by electrospray ionization, which results in gas phase ions. The disadvantage of HPLC-MS is that several different methods are needed to analyze different classes of compounds. For example, a highly polar and hydrophilic analyte will be poorly retained in reversed-phase (RP) HPLC–MS, so analysis using hydrophilic interaction chromatography–MS (HILIC–MS) would allow profiling of polar compounds but not hydrophobic compounds.8 Also, a large range of suitable solvents are desired to

16 increase the chromatographic resolution of the analytes.10 A study conducted by

González-Peña et al. used HPLC-MS to identify the metabolites present in onions, and their effects of a high cholesterol diet in mice. They found using HPLC-MS, 64 known metabolites were identified and quantified. The study was carried out by doing two analyses of each sample at both negative and positive ESI, and found a low relative standard deviation (RSD) when quantifying their data, which suggests high reproducibility for their method. The active biological ingredients found suggests that they play a role in higher cholesterol levels, and related complications through the altered metabolic pathways. 11

GC-MS is a widely used technique in metabolomics experiments because of the high sensitivity and reproducible fragmentation characteristics of electron ionization (EI) in MS analysis as well as reproducible retention and elution of metabolites from GC separation.3 In this approach, the sample is introduced to the instrument by a syringe injection into the injection port where the sample is vaporized so the sample is in gas phase prior to introduction onto the GC column. 1 The separated analytes in the sample are then transferred to the mass spectrometer where the analytes are ionized and separated based on their mass to charge ratio, m/z, resulting in a mass spectrum. The mass spectrum represents characteristic fragmentation patterns for each individual metabolite, facilitating identification. GC-MS has become a powerful tool for identification and quantification of analytes in metabolomics. Fiehn et al. used the step toward functionally characterizing GC-MS chromatograms by injecting an Arabidopsis leaf sample, which was carefully extracted and derivatized prior to injection. This resulted in over 160 peaks,

17 and by using different ion chromatograms to deconvolute the data, they were able to detect and quantify 326 different metabolites. 12

For more complex samples, two-dimensional gas chromatography coupled with mass spectrometry (GC × GC-MS) has been used in metabolomics. This method involves the sample being vaporized and eluting through a conventional column typically found in

GC, followed by a second column that is much shorter and thinner. The two columns are connected by a modulator, which fractionally collects the analyte from the first column and pulses the analyte into the second column. Bressanello et al. completed a study of urine from mice with highly concentrated fat and sugar diets. They used GC × GC–MS, because of the excellent separation power, sensitivity and resolution. They reported peaks spanning as small as 0.6 s and RSD% all being smaller than 8%, which suggests their method is highly reproducible. The disadvantage of this method lies in balancing and fine tuning both columns, making the separation of the analytes on the stationary phase difficult and resulting in irreproducible retention times.13

Because of the versatility, sensitivity, and reproducibility, GC-MS was chosen for the sensitive detection of the disaccharide, trehalose, which is present in very low concentrations in Oryza sativa ssp. Japonica cv.M202 rice, Oryza sativa ssp. Indica cv.

IR64 rice and Arabidopsis thaliana shoot tissue. Trehalose has been associated to have minimal changes in concentrations when faced with environmental stressors such as a drought or a flood, but can play a significant role in carbohydrate consumption, and plant growth.14 Along with trehalose, other carbohydrates including glucose, fructose, and sucrose were quantified in this study. This study aims to determine if this method

18 provides accurate quantification, reproducibility, and to later determine if trehalose plays a role in the differential regulation of carbon metabolism.

1.2 Gas Chromatography (GC)

Gas chromatography coupled with mass spectrometry is a hyphenated technique that involves two separate systems. In GC volatile sample is initially vaporized and the carrier gas moves the sample along the column and the components within the sample are separated based on physical properties and boiling point. After the components are separated, they enter into a detector and a chromatogram is produced.15,16

1.2.1 Sample introduction

Before the sample enters the GC column for separation, the components are introduced to the instrument by injection from a syringe, usually 1 μL of sample volume, and into an inlet. There are typically two types of sample introduction methods used with capillary columns, split and splitless. The choice of the two methods will depend on the concentration of the sample being injected and can influence the reproducibility of the method. Inappropriate injection will result in either not enough or too much sample reaching the detector, causing ion suppression or dirtying the source causing it fail. For both methods, the injector is maintained at a constant temperature so that the sample is vaporized immediately upon injection, referred to as an isothermal injection method.

Split injections are commonly used for concentrated samples that are greater than

0.1% (volume/volume of solvent), or samples with minimal amounts of solvent present.

In split mode, the sample is vaporized after injection and a stream of the carrier gas,

19 typically helium, travels through the inlet in a preset split ratio (Figure 1.1). This results in small amount of the sample being sent to the column for sampling and the rest of the sample is vented out of the instrument. An advantage of a split injection is it acts as a dilution of the sample.17 A split injection, however, can decrease precision because it is difficult to ensure the sample is split perfectly after injection. To avoid the variance introduced by splitting the injection, sample can be diluted for a splitless injection.

Splitless mode is more commonly used for samples being analyzed for trace amounts of an analyte or low concentrations less than 0.1% (volume/volume of solvent).

In a splitless method, the sample is added to the injector but the splitter vent is not opened, and the sample is introduced directly on the column. After a certain amount of time, the injector is vented by opening the splitter to purge the injector of any remaining solvent or sample to avoid contaminating subsequent injections. The timing of the purge is important because if vented too early, some of the analyte may be lost and if the splitter is opened too late then there will be chromatographic tailing of the analytes. This method requires a lower initial temperature of the column since the solvent will be present in higher concentrations and the initial temperature needs to be below the boiling point of the solvent, with an initial solvent delay added to the method to prevent the detector from being saturated. In splitless mode, solvent trapping is used to re-concentrate the bands at the column inlet, resulting in narrower peaks. Experiments ran without solvent trapping will exhibit wider and distorted peaks.

Figure 1.1 Schematic of injector with a packed glass wool injection liner

For either injection method, an appropriate injector liner needs to be chosen.

There are several experimental considerations that will influence the choice of injector liners, such as splitless or split injection, the volume and concentration of the sample being injected, and the quality or volatility of the sample. Typically, packed glass liners are used to prevent nonvolatile samples from leaving the injector, which could result in a clogged or contaminated column.17 Glass liners are also better at preventing sample decomposition. Packed glass wool liners have been found to better reduce standard deviation when completing large amounts of sampling,18 and readily retain un-vaporized sample components so they do not move on to the column.19

1.2.2 Separation of components

Most biological samples are complex matrices, making a separation prior to the detection essential for accurate quantitation of metabolites. Components are separated by a column located inside a temperature-controlled oven. The column is most often a fused silica capillary column functionalized depending on the application. The column contains

21 a stationary phase where the sample is introduced and retained based on the interaction of the chemical properties of both the analyte and selected stationary phase in addition to differences in boiling points. Analytes are eluted from the column using a carrier gas as the mobile phase, and elution order is based on both physical (analyte boiling point) and chemical (analyte-column interaction) properties. Analyte elution can be fine-tuned by careful control of the oven temperature and selection of an appropriate column stationary phase. The sample is then transferred to the mass spectrometer.15,17

1.2.3 Retention time lock (RTL)

Retention time lock (RTL) for the GC is a technique used to standardize the carrier gas flow to keep analyte retention time consistent from sample to sample, even after extensive column degradation. This allows for inter- and intra- laboratory retention time reproducibility.20 An RTL is accomplished by injection of a standard, frequently myristic acid-d27, multiple times at different carrier gas flow rates. After running the sample, the peak is integrated and the desired retention time is set to 16.75. The software automatically calculates the appropriate flow rate based on the data collected at various flow rates.

1.2.4 Retention indices (RI)

Kovàts system of retention indices (RI) is used to standardize the retention time of compounds for a specific stationary phase to allow for the universal comparison of GC data. Typically, retention time of a specific compound is dependent on different experimental conditions such as column type, column length, carrier gas flow and

22 temperature gradient.17 Although the retention times will be different, the relationship between the elution time of the analyte and a known standard will allow retention indices to be calculated. The resulting numerical value is unique for each analyte but independent of changes or differences in columns as long as the temperature gradient is linear or an isothermal separation is used. The retention indices are most useful when used with databases such as NIST, where analyte identification based on spectral matching might fail due to common fragment patterns of isomers.21,22 Spectral patterns combined with RI data provide an extra identification characteristic unique to each molecule. Retention indices are created by analyzing 10-15 n-alkanes or fatty acid methyl esters (FAMEs), which have a predictable elution spacing based on the number of carbons in each compound. If a linear temperature gradient is used, the relationship between the retention times of the analyte and alkanes/FAMEs can be described by:

푡푟(푢푛푘푛표푤푛) − 푡푟(푛) 푅퐼 = 100 × [푛 + ] 푡푟(푁) − 푡푟(푛)

This method takes advantage of the linear relationship of tr to the number of carbon atoms present in a molecule. In this equation tr is the retention time, N is the number of carbons in the larger n-alkene/FAME, n is the number of carbons in the smaller n-alkane/FAME, and unknown is the target analyte.

1.2.5 Injection variability and quantitation

Despite highly reproducible elution times associated with GC analysis, injection variability can significantly influence quantitative analysis even when an autosampler is

23 used. To correct for injection error, internal standards are added to each sample prior to derivatization. For the best result, the internal standard is added at known concentrations and elutes in a region of the chromatogram that does not overlap with any other signals.

The concentration of the internal standard should correspond to an intensity that is strong enough to integrate.16 Moreover, the internal standard is used to normalize the data to remove the irregularities due to injection error. Normalized peak area is the intensity or integrated peak area of the compound of interest divided by the internal standard peak area. Normalization to an internal standard will not affect quantitation as long as the same concentration of internal standard is used for each sample.

There are several different methods of quantitation for metabolomics experiments.

The most common is relative quantitation, where the results for one experiment are compared to another and general trends are reported (i.e., increase in a metabolite in one group compared to another). In most cases, relative quantitation is sufficient to answer the biological question being probed. If relative quantitation is insufficient, absolute quantitation is used. There are several different methods for absolute quantitation. For example, the use of external standards is one such method. In mass spectrometry and other techniques excluding NMR, the quantification of target analytes will be carried out following a set of calibration curves. Calibration curves are generated using varying concentrations of the analyte, ideally producing a linear curve with no fewer than six data points. With this, a calibration curve is constructed using the normalized values of the analyte as a function of concentration. Each concentration needs to be analyzed in triplicate (at least) to evaluate the precision of the method.16

1.3 Mass Spectrometry (MS)

For metabolomics experiments, mass spectrometry is a commonly used detector because it is sensitive, reproducible, and in many cases, amenable to library generation, simplifying analyte identification. All mass spectrometers have five basic components: a sample inlet, ionization source, mass analyzer, detector, and vacuum.16 Each of these components will have a significant influence on the outcome of a metabolomics experiment. Here, the components of a mass spectrometer used in conjunction with a GC as the sample inlet are considered.1

1.3.1 Electron ionization

Electron impact ionization or electron ionization (EI) is a typical ionization source used when coupled with GC. Electron ionization is a hard ionization source commonly used for small molecules (less than 1000 m/z).16 After separation from the GC, the analytes are carried to the ionization source, where they encounter an electron beam maintained at 70 eV. The electron beam is produced by a filament and as the analyte moves across the electron beam path, the electrons will collide with the analyte, removing an electron in the outer shell. The electrons continue through to the positive ion trap. The analyte or resulting fragmentation products, encounter an acceleration plate, making a 90 degree angle path change. The analyte and/or fragments are focused using electric fields and then exit to the mass analyzer.16 The large excess energy involved with

EI results in fragmentation and begins when a high energy electron collides with the molecule. This removes an electron from the outer shell producing a radical cation if the internal energy is high enough, the molecules will fragment, producing a new radical and

25 cation of lower internal energy than the initial ionized molecule. Multiple fragmentation events can happen until the internal energy of the molecule reaches a minimum, producing multiple fragments which result in a mass spectrum. Each mass spectrum is unique to each analyte, which is important in identification. The resulting cations are separated by the mass analyzer and are transferred to the detector.

Despite the inter- and intra-laboratory reproducibility of EI, one major disadvantage is an exceedingly low ionization efficiency. Electron ionization has an overall efficiency of about 0.01% to 0.001%, meaning greater than 99.99% of the analyte is lost prior to detection.16 Despite the inefficiency, EI-MS is still far more sensitive than non-MS based techniques, such as NMR, making it not only practical, but essential for metabolic profiling.2 Additionally, the reproducibility of EI allows the generation of large commercial libraries, simplifying identification.17

In addition to EI, another ionization method, chemical ionization (CI), may be coupled with GC. CI is a lower energy process and yields less fragments compared to EI.

In CI, the analytes are ionized by a collision with ions of a reagent gas present in the ion source. The commonly used reagent gas includes methane, ammonia and isobutene. CI generates an easily identifiable molecular ion, which helps determining the molecular mass of a compound but does not provide unique fragmentation patterns to facilitate identification.16

1.3.2 Mass analyzers

Prior to detection, each of the different mass-to-charge ratios produced from ionization has to be analyzed. There are several different types of mass analyzers;

26 however quadrupole (Q), triple quadrupole (QQQ), and time-of-flight (TOF) are the three main types of mass analyzer commonly used in mass spectrometry.

Of the three, the quadrupole is the most commonly used mass analyzer (Figure

1.2). It is composed of four cylindrically shaped rods, with electrodes inside producing an electric field. The rods are mounted together in a square configuration, comprised of both an AC and DC component, and the rods oriented opposite of each other have the same potential charge. The ions traveling through the rods will encounter the electric fields produced by the oscillating currents, and only those ions whose trajectories are stable through the field will reach the detector. Because of the relationship between the ion m/z, strength of the field and the frequency of oscillation, usually only ions of the same m/z will traverse the quadrupole at a time in a corkscrew like path, these are the resonant ions, and will exit the quadrupole to the detector. The ions of m/z that are not currently being scanned for, non-resonance ions, will crash out and never reach the detector. An advantage of the quadrupole is the fast scan times, enabling the entire spectrum to be sampled multiple times a second, and it is considerably less expensive compared to other mass analyzers. A major disadvantage of quadrupole mass spectrometers is the limited m/z range, and larger mass ions generally above 3000 m/z cannot be measured and scan ranges are limited to 2000 m/z.16

Figure 1.2 Schematic of a quadrupole mass analyzer.

The triple quadrupole mass analyzer is simply three quadrupoles arranged linearly and allows for several distinct advantages over a single quadrupole (Figure 1.3). As the analyte leaves the ionization source, it enters the first quadrupole as described previously.

The first quadrupole mass analyzer can be set to scan a desired m/z range, or a specific m/z. However, instead of exiting into the detector analytes will enter into the second quadrupole, which can act as either a second mass filter or a collision cell. If used as a second mass filter, erroneous ions are further removed, reducing background and increasing sensitivity. If used as a collision cell, it is filled with an inert gas, most commonly argon or nitrogen, and the analyte will collide with the inert gas and cause further fragmentation, potentially aiding in the identification of the analyte. The rods in quadrupole collision cell in the second section only have AC voltage applied, which help in guiding the ions to the third quadrupole. The sample exits into the third quadrupole which behaves similarly to the first quadrupole and is responsible for scanning the spectrum, sending only specific m/z to the detector. The advantage of the triple quadruple mass spectrometer is the high sensitivity and selectivity.23

Figure 1.3 Schematic of a triple quadrupole mass analyzer.

Another commonly used mass analyzer is time-of-flight (TOF) (Figure 1.4). The time-of-flight mass analyzer is a relatively simple method based on the relationship between mass, kinetic energy, and velocity. Instead of using oscillating currents to allow specific ions to traverse the analyzer, ions are now separated based on their m/z in a drift tube. In TOF the ions are guided from the ionization source to an acceleration region through an octapole. In the acceleration region the ions are all given the same kinetic energy by an applied voltage. After leaving the acceleration region the ions enter a field free drift path where the ions separate based on their masses, where the heavier ions will have slower velocities resulting in a longer drift time. Some TOF instruments commonly use reflectors in the field free region to increase the separation of ions, which will increase the resolution of the mass spectrum. Refletectron TOF also has the ability to correct for angular spread and spatial distribution. Multiple reflectors can be used in a

TOF instrument.

TOF takes advantage of the following two equations:

1 퐸 = 푒푧푈 = 푚 푣2 = 퐸 푒푙 2 푖 푘푖푛

2푒푧푈 푣 = √ 푚푖

If all ions are given the same initial kinetic energy, then the velocity of each ion will be dependent on its mass-to-charge ratio. As each m/z travels through the drift tube, they will separate and hit the detector at different times allowing identification of different mass-to-charge ratios. Because of the relationship between drift length and m/z separation, TOF analyses must be performed under high vacuum to ensure an elongated mean free path. This will reduce the possibility of collisions between the ions and other molecules, while going through the mass analyzer, increasing resolution by decreasing energy losses through interaction with surrounding molecules. The advantages of TOF are that it is a highly sensitive method capable of analyzing compounds of almost unlimited molecular weight, and the design is relatively sensitive and the construction is comparatively simple and inexpensive. However, one disadvantage is that ions can be lost in the drift tube due to interactions with the walls based on angular spread.16,17

Figure 1.4 Schematic of a time of flight mass analyzer

1.3.3 Limit of detection (LOD) and limit of quantification (LOQ)

The terms, limit of detection (LOD) and limit of quantification (LOQ), can be measured by the signal to noise ratio in real time or by an equation. LOD is the lowest amount of analyte necessary to obtain a signal which can be clearly distinguished from the background noise for identification, typically defined as three times the signal to noise ratio. The LOQ is found in higher concentrations than the LOD, which is defined as ten times the signal to noise ratio that is clearly distinguished from the background noise for quantification.24

Experimentally, the LOQ can be found by creating a set of four to five standards of increasing concentrations above the LOD (commonly spanning two or three orders of magnitude). LOD and LOQ are more commonly calculated from the slope of the calibration curves using the following relationships:

푆푡푎푛푑푎푟푑 푑푒푣𝑖푎푡𝑖표푛표푓 푡ℎ푒 푙표푤푒푠푡 푐표푛푐푒푛푡푟푎푡𝑖표푛 퐿푂퐷 = 3 × 푆푙표푝푒 표푓 푡ℎ푒 푐푎푙𝑖푏푟푎푡𝑖표푛푐푢푟푣푒

푆푡푎푛푑푎푟푑 푑푒푣𝑖푎푡𝑖표푛 표푓 푡ℎ푒 푙표푤푒푠푡 푐표푛푐푒푛푡푟푎푡𝑖표푛 퐿푂푄 = 10 × 푆푙표푝푒 표푓 푡ℎ푒 푐푎푙𝑖푏푟푎푡𝑖표푛 푐푢푟푣푒

The resulting calibration curve should result in a linear response. The sample concentrations must remain within the range of the standards. This is done to prevent saturation of overabundant analytes, which will result in underestimating the concentrations at the upper limits, and overestimating at the lower limits. This may occur from contaminations of previous sample injections or underestimation can occurs due to loss of absorption.16,24

1.4 Growth conditions, extraction, and sample preparation

Because of the complexity of biological samples, it is important to have the appropriate sample preparation techniques to be able to target specific analytes, for reproducible quantification.

1.4.1. Extraction

In metabolomics, it is essential to distinguish between concentrations of specific metabolites from sample to sample. Because biological systems are highly variable, proper sample preparation is important to minimize introduction of analytical variation.

To dried and homogenized tissue, metabolites are extracted by adding organic solvent, which can quench the enzymatic activity and breakup cell walls, and water to extract

32 water soluble metabolites. Methanol or a methanol water mixture is commonly used for most metabolites.25 Alternatively, chloroform, a non-polar organic solvent, is used to extract lipophilic components.26 From the extraction a quantitative aliquot is taken to ensure steady concentrations of metabolites in different samples, which is known as a fractionation method.1

1.4.2 Solid phase extraction

Solid phase extraction is a technique generally used to clean samples or select for specific functional groups and it can increase overall sensitivity by enriching specific analytes while decreasing background.27 There are several types of ion exchange stationary phases available for selective isolation of either cationic or anionic analytes: weak-anion exchange (WAX), strong-anion exchange (SAX), weak-cation exchange

(WCX), and strong-anion exchange (SCX).28 The chemistry of solid phase extraction are based on a reversible exchange of ions with like charges between the solution and a functionalized solid phase (Figure 1.5). The solid phase must be an open permeable molecular structure so that the ions in the solvent of the sample can move freely in and out.29

Figure 1.5 Solid phase extraction schematic.

1.4.3. Derivitization

Figure 1.6 Derivatization reaction of glucose, resulting in TMS derivative.

One limitation of GC–MS is that the analytes need to be volatile. Since the majority of plant metabolites are not volatile (such as sugars, amino acids, and organic acids), the samples must be derivatized prior to injection.17 This is done in a two-step process (Figure 1.6). The first step is a methoxyimation of ketones and aldehydes.3,30 This step also helps when performing an analysis of reducing sugars such as fructose and glucose, which can produce multiple peaks as a result of it cyclizing between the open chain form and the cyclic form. The methoximation with methoxyamine hydrochloride

34 will prevent cyclization of monosaccharides; this is due to the lack of rotation because of the new carbon-nitrogen double bond that is produced. The second step is silylation with

N-Methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), replacing all labile hydrogens with a trimethylsilyl group. A combination of these two steps will increase volatility by removing labile hydrogens and reduce spectral complexity by eliminating cyclization reactions.31

1.5 Data analysis

Metabolite identification was simplified due to the NIST program which contains nearly 300,000 spectra of different analytes as reported in 2008.17 There are multiple other programs that analyze data to simplify the analysis. The quantitation of the analyte is done by taking a single ion and obtaining the EIC for that ion, and then integrating the peak area. The peak area is then compared to an internal standard of known concentrations.

1.5.3 Full scan and selected ion monitoring (SIM)

When quantifying specific analytes, a method using selected ion monitoring

(SIM) is preferred due to increased sensitivity.32 Instead of scanning the entire mass spectrum, a selected number of ions are monitored increasing the dwell time for each ion.

Alternatively, a full scan is usually chosen when trying to identify components within a pre-defined mass range. For metabolite profiling, the typical scan range of a quadrupole is 60-600 m/z. While the SIM method increases sensitivity through increased ion count of the selected ion and eliminating ion count from the background, analyte identification

35 must be known in advance because the full spectrum for each compound is not being detected.17 The information obtained from the SIM method is just as useful as a full scan when quantifying analytes, even though a mass spectrum is not produced. Since SIM is only monitoring for a few selected m/z and not the entire range, the noise in the gaps between the peaks are reduced to zero. In some cases, this will actually increase the signal to noise ratio for those specific peaks compared to full scan data. The sensitivity is observed to increase anywhere from a factor of 10 to 100. However, this is only true for scanning instruments such as magnetic sectors and quadrupole mass analyzers. The time of flight mass analyzer does not have an increased sensitivity associated with SIM, because the dwell time spent on ions selected will be almost identical as a full scan cycle time, which would cancel out any sensitivity increase.16

1.5.4 Quantitation

Quantitation is done by taking the absolute intensities from the signal each analyte produced. From the signal that each analyte produces the EIC is taken from the ion being quantitated. Since the ionization method used a specific fragment pattern that is produced in the same ratios every time, quantitation is done with a single ion of each compound.16

This is done with the internal standard initially used to normalize the data and then the external standards to build a calibration curve of multiple concentrations, resulting in a curve of normalized peak areas versus concentrations. Integration using the EIC is also used when analytes have overlapping signals. By using the EIC and selecting an ion that only appears in one of the analytes of the overlapping peak, the analyte-specific peak can be integrated and used for quantitation.

1.6 Application of GC-MS in biological systems

Metabolomics is the measurement of metabolites; these metabolites are the productsof biochemical pathways in complex biological systems. The metabolites being explored are fructose, glucose, sucrose and trehalose which are carbohydrates present in rice and Arabidopsis plants. Through a study sampling at a variety of time points, and submergence stress, the consumption or accumulation of these sugars are analyzed by

GC-MS. Concentrations of sugars are studied because carbohydrate consumption is essential to the process of plant growth and development, which is associated with survival when under stress from a flooding event.33 When crops are exposed to flooding, the plant will typically consume its energy reserves to elongate to maintain the photosynthetic tissue above the air-water interface, but this can be problematic if the plant consumes all the energy reserves prior to resumption of photosynthesis.34

1.6.1 Rice

Rice Submergence. This study is focused on the biochemical pathways associated specifically with the shoots of submergence intolerant and tolerant rice, Oryza sativa ssp. Japonica cv.M202 and cv. M202(Sub1), respectively.3 The purpose was to quantify trehalose, sucrose, glucose, and fructose after submergence by comparison with a control plant growing at the same time. The SUBMERGENCE 1A-1 (SUB1A) gene, contributes to prolonged tolerance in submergence. A diurnal study was done to help better understand the effects of the SUB1A gene by using both, M202 submergence intolerant rice and M202(Sub1), submergence tolerant rice. The rice varieties were

37 submerged for three days and allowed to recover until dusk, midnight, dawn, and 24 hours after reoxygenation. At each harvest a non-submerged control sample was harvested.3,4

Carbohydrate consumption is known to be associated with plant growth. In ideal aerobic conditions, ethylene, a plant hormone, diffuses through the cells, and regulates the abscisic acid (ABA) concentrations, which regulates the gibberellic acid (GA) concentrations. GA is responsible for carbohydrate consumption and elongation.

Commercially grown varieties of rice elongate during submergence stress, consuming their carbohydrate stores in hopes of breaking out of the water and resuming photosynthesis. During submergence, intolerant plants also entrap ethylene due to significantly slower diffusion rates. This will result in a significant decrease in ABA bioactivity and no longer regulation of GA, resulting in uncontrolled carbohydrate consumption. However, the presence of SUB1A triggers a hormone signaling pathway that underlies a different submergence survival strategy. The SUB1A gene is an ethylene response (ERF) transcription factor, which in the presence of increasing concentration of ethylene in the submerged tissue triggers a hormone signaling cascade that will increase the interaction between ABA and GA, which normally limits cell elongation and carbohydrate consumption. With the SUB1A gene, submerged plants will accumulate the ethylene, which induces the transcription of SUB1A-1 . Subsequently, the SUB1A transcription factor induces production of the SLENDER RICE1 (SLR1) and SLENDER

RICE-LIKE 1 (SLRL1) genes, which, when translated, acts as negative regulators to GA to limit carbohydrate consumption and restrict flowering during submergence.34

Another submergence resistance line, IR64, was also studied. IR64 has the submergence intolerant SUBMERGENCE 1A-2 gene, which is located on the same chromosome as the SUBMERGENCE 1A-1 gene, but does not make the rice submergence tolerant.35 In this study submergence experiments were carried out with

IR64, IR64-Sub1, IR64-AG1, IR64-Sub1+AG1, and IR64-Sub1+AG1+Saltol. IR64-

Sub1. The IR64 rice was studied with the submergence tolerant Sub1-A1 gene, this is the same gene M202 is studied with. IR64-AG1, is a submergence tolerant with an anaerobic germination (AG1) trait which is useful for flooding events during the germination period of growth.36 A combination of genes, IR64-SUB1+AG1 and IR64-Sub1+AG1+Saltol, are considered mega varieties of rice. SALTOL is a gene which confers tolerance to high salinity and in combination with presence of the SUB1-A1 and the AG1, this variety is favored in the areas of rising sea levels.37

1.6.2 Arabidopsis

Arabidopsis thaliana is a frequently studied plant due to its rapid growth characteristics. Columbia-0 (Col-0) is used as a wild type, this strain is commonly used for cross breading and as a control.38,39 Arabidopsis tissue has been produced to express the OTS-A and OTS-B gene. Both these genes have been studied in depth in tobacco and tomato plants. It has been found that both of these genes once expressed result in a drought tolerance and salt tolerance. Both also are found to result in physical changes during high levels of stress events, such as the plant begins to show a yellowish color, and the roots and leaves start to show uncharacteristic growth in shape and size.40

With the genetic modifications made, OTS-A and OTS-B are commonly associated with higher concentrations of carbohydrates when stressed comparing to the control. In a study done by Cortina and Culianez-Macia of tomato plants expressing these genes, they found that trehalose concentrations serves as a positive regulator of stress tolerance, and increases by a factor of 2.5 to 3.0 after a drought like event.14 In this study, the concentration of various carbohydrates in the Arabidopsis thaliana mutants will be determined by GC-MS.

1.7 Objectives of study

Metabolomics is a useful tool to understand biological systems through analytical techniques. Carbohydrate consumption and accumulation is associated with growth and elongation, therefore trehalose, sucrose, glucose, and fructose are quantified to help better understand the effect of the Sub1A gene. This study aims to determine if this method provides accurate quantification, reproducibility, and to later determine if trehalose plays a role in the differential regulation of carbon metabolism. Because of the low levels of trehalose, a GC-MS analysis was carried out due to its highly sensitive nature.

The objective of this study is to quantify trehalose, sucrose, glucose, and fructose in submergence tolerant and intolerant rice varieties, as well as, drought tolerant

Arabidopsis thaliana. The specific rice varieties being studied include, M202 rice, with the Sub1 gene, and IR64 rice with the Sub1, AG1, Sub1+AG1, Sub1+AG1+Saltol genes.

Arabidopsis thaliana is studied with the Col-1, OST-A, and OST-B genes. The analysis will be carried out using GC-MS, with an EI, ionization source and quadrupole mass

40 analyzer. In this study, absolute quantitation is performed, and the LOQ is taken into consideration.

1.8 REFERENCES

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CHAPTER 2: METHODS AND MATERIALS

2.1 Tissue extraction and preparation of rice tissue samples

The following method is adapted from Delatte et al. 20 mg of dried plant tissue was incubated with 0.8 mL chloroform:acetonitrile (3:8, v/v) on a shaker (5 min) to disrupt cell walls The sample was then extracted twice with 0.8 mL water by shaking for five minutes followed by centrifugation at 12,000 x g (4 min). The aqueous layers were transferred into a clean 2.0 mL Eppendorf tube, discarding the organic layer. The sample was dried via vacuum centrifuge. Anionic metabolites were extracted from each sample using a weak-anion exchange solid phase extraction (SPE).2 The SPE cartridge was first conditioned with 1.0 mL of methanol followed by 1.0 mL of water containing 5%

NH4OH (v/v) (discarded). The dried extracts were reconstituted with 2.0 mL of water and loaded on to the cartridge, preceded by a wash with 1.0 mL of water and 1.0 mL of methanol. This first 4.0 mL fraction was collected as the cartridges were allowed to run dry, and eluted into another Eppendorf tube and labeled fraction one. The SPE cartridge was then loaded with 2.0 mL of 80/20 water/methanol containing 5% formic acid, and eluted into a 2.0 mL Eppendorf tube and labeled as fraction two. The samples were then dried by vacuum centrifuge. To the dried fraction one sample, 1.5 mL of pure water was added to reconstitute the sample. Then a 1:10 dilution was made into water, and a 60 µL

13 aliquot was dried with 20 µM D-Glucose C6 added as an internal standard. This dilution was used for analysis of fructose, glucose, and trehalose. Then from the 1:10 dilution a

1:100 dilution of the sample was made into water, and a 60 µL aliquot was dried with 20

13 µM D-Glucose C6. This dilution was used for analysis of sucrose. The final

45 concentrations in each vial were 1 mg of tissue per mL of sample, and 0.01 mg of tissue per mL of sample, respectively. The samples were then dried by vacuum centrifuge.

2.2 Tissue extraction and preparation of Arabidopsis tissue sample

The tissue extracts were made according to Barding et al.3 In a 2.0 mL Eppendorf tube, a sample of dried plant tissue (8 mg) was mixed with 80:20 methanol:water, v/v

(1.5 mL). The sample was shaken for 5 minutes on a vortex shaker, and then centrifuged at 12,000 × g for 4 min. A 188 µL aliquot was placed in 1.5 mL Eppendorf tube, along with 20 μL of 0.02 g/L methyl stearate as the internal standard. The final concentration in each vial was 1 mg of tissue per mL or sample. The samples were then dried using the vacuum centrifuge.

2.3 Sample derivatization

The derivatization of the dried tissue extracts was performed according to the method the adapted from Lee and Fiehn.4 A 20 mg/µL of methoxyamine in pyridine solution was prepared and heated for 20 min at 37 ˚C on a block heater. To the dried sample, 10 µL of the methoxyamine/pyridine solution was added, and then heated for 90 minutes at 37 ˚C. This was followed by the addition of 45 µL of N-Methyl-N-

(trimethylsilyl) trifluoroacetamide (MSTFA). The sample was heated for 30 min at 37 ˚C.

The 50 μL sample was then transferred into glass vial for analysis.

2.4 Sample injection and GC

A 1 μL injection of the derivatized sample was added to using the automatic liquid sampler and 10 μL Agilent syringe, which was washed with one portion of ethyl acetate, one portion of methylene chloride, and another portion of ethyl acetate prior to each injection. The sample was injected into a splitless injector liner with glass wool heated at 250 °C. The separation was done using Agilent 6890N gas chromatograph equipped with an Agilent J&W GC-capillary column, 0.25 mm i.d. and 30 m length with an additional 10 m integrated guard column. For samples analysis using selective ion monitoring (SIM), the 1 μL injection of the sample was added the initial oven temperature that was set at100 °C and held for 1 min, then ramped 20 °C/min to 210°C, 3

°C/min to 226°C, 20 °C/min to 285°C, 3 °C/min to 290°C, 20 °C/min to a final temperature of 320 °C, and was held for 5 minutes. This resulted in a finish run time of

22.95 minutes. Alternatively, full scans were collected using the same 1μL injection of the derivatized sample. After the sample was introduced to the oven with an initial temperature of 60 °C and held for 1 min, then a constant temperature ramp of 10 °C/min was done for the method until a final temperature of 320 °C, and held for 5 min. This resulted in a total run time of 32 minutes. All instrument operation was done using

MSDChemstation software.

2.5 Mass spectrometry data analysis

The sample was the transferred from the GC to the MS through a transfer line kept at 300°C. The electron impact ionization was used at 70 eV, with a source temperature of 230°C.

2.5.12 Selected ion monitoring

For the SIM method the filament was turned on after the initial 5 min, and for the undiluted rice samples a solvent delay was added at 16.40 to 17.10 min, to prevent the ionization of the highly concentrated sucrose. The SIM method for rice samples, recorded mass spectra data for two different groups, in group one m/z 307, 319, and 323 were used until 14 min for analysis of glucose, fructose and the internal standard labeled carbon 13 glucose. Then group two m/z 316 and 451, were used until the method was completed for analysis of sucrose and trehalose. The SIM method for the Arabidopsis samples, recorded mass spectra data for three different groups, in group one was set to monitor for m/z 307 and 319, which used until 14 min for analysis of glucose and fructose. The second group was set to monitor for m/z 255, which was used until 20 min, for analysis of the internal standard methyl stearate. Group three was set to monitor for m/z 316 and 451, which was used until the method was completed for analysis of sucrose and trehalose. For both SIM methods all ions were scanned at a rate of 1 scan per sec.

2.5.1 Full scan

Then the full scan method had an 8 min solvent delay added at the start of the method. The full scan method recorded the mass spectra from m/z 60-600, at a rate of

2.71 scans per sec. The data was initially translated by MSD ChemStation Data Files, and then the samples were analyzed using Agilent Mass Hunter Qualitative Analysis B.07.00.

Mass Hunter was used to collect the integration values for each metabolite of interest.

2.6 Sugar identification

A sample made up of glucose, sucrose and trehalose were first analyzed by GC-

MS to identify each of the sugars retention times, and then the plant tissue was ran again to confirm retention time, relative abundance, and mass spectral data. NIST MS search

2.2 was used confirm the spectrum of each sugar. Because of the similarity in elution time with another disaccharide, trehalose was confirmed by spiking a tissue sample with

85μL of 0.015 μM stock solution, approximately doubling its concentration. From the mass spectra data, the sugar specific ions were then selected for SIM. The ions chosen

13 were 317, 319, 323, 361, and 255, and used to quantify (fructose, glucose, C6 glucose, trehalose, and methyl stearate).

2.7 Quantitation of sugars

Each sugar was quantified using a calibration curve with sugar solutions of known concentration. The calibration curve for the rice samples were ran using SIM and the calibration curve for the Arabidopsis tissue was ran using SIM for trehalose and full scan for fructose, glucose, and sucrose. Fructose and glucose had a concentration range of

0.176 to 8 μM, sucrose had a concentration range of 1 to 30 μM, and trehalose had a concentration range of 0.0064μM to 0.555 μM. Using MassHunter, an extracted ion chromatogram (EIC) was plotted for each sugar and integrated.. It was determined that the calibration curve had a best fit line of a quadratic equation.

2.8 Retention indices (RI)

An initial sample of 98 μL of chloroform and 2 μL of a standard FAMEs mixture in chloroform, containing linear carbon chain lengths, C8, C9, C10, C12, C14, C16, C18,

C20, C22, C24, C26, C28, and C30, was injected prior to starting each sequence and ran again at the completion of each sequence. The standard FAMEs mixture was prepared in chloroform, with C8-C16 concentrations of 0.8 mg/mL, C18-C24 concentrations of 0.4 mg/mL, and C26 – C28 concentrations of 0.8 mg/mL, and C30 concentration of 1.2 mg/mL. After all samples in sequence were completed the same FAMEs mixture was analyzed again. From this AMDIS was used to calculate the RI using an internal standard library and a calibration standard library.

2.9 Retention time lock (RTL)

A 5μL aliquot of d-27myristic acid, provided in Agilent GC-MS metabolism standard kit, was dried in vacuum centrifuge, and then added to 90μL of MSTFA, and heated for 30 min at 37 °C. The sample was then run under Acquire RTL data, with the sample being injected multiple times at different flow rates. After the sample was analyzed, the peak was automatically integrated and the optimal pressure setting was determined by the software to ensure RT locking at 16.752 min. The standard was analyzed again to confirm the appropriate flow rate that was selected, within a 0.02 min deviation. The new flow rate was then manually adjusted on all remaining methods.

2.10 Rice tissue bulking and Seed bulking

2.10.1 Seed sterilization.

Approximately 50 seeds of a single genotype were placed in a 50 mL falcon tube with 25mL of bleach, 25 mL of water and a small amount of dish soap. The tube was then inverted approximately every 10 minutes for 40 minutes. The seeds were then rinsed with deionized water excessively until the odor of bleach was gone. The seeds were then placed into a new 50 mL falcon tube, filled with deionized water, and left in the refrigerator overnight. This process was repeated with the second genotype.

2.10.2 Seed germination.

A paper towel was placed in a Pyrex dish, and saturated in deionized water. The seeds were placed on the paper towel evenly spaced out. Water was added until the seeds were approximately half way submerged, and then the Pyrex dish was covered with plastic wrap. The seeds were placed inside the growth chamber, on the top shelf with fluorescent light only, for 6 days.

2.10.3 Iron sulfate and fertilizer preparation

In a 25 mL falcon tube, 30 g of iron sulfate was weighed out, and added slowly to

700 mL of water on the stir plate. After the iron sulfate was completely dissolved it was then five times diluted by adding 2.4 L of water to the 0.7 L of iron sulfate. Then, from the fertilizer in the greenhouse 230 g, or 1/8 of a cup, of the 12% nitrogen fertilizer, was dissolved in 1.75 L of water, followed by a 1:5 dilution, resulting in a 400 ppm nitrogen containing mixture.

2.10.4 Potting

Small pots were taken and filled with soil, and then saturated with water. Then each pot was layered with 100 mL of iron sulfate, and 50 mL of fertilizer. For seed bulking, one germinated seed was placed in each pot, and for tissue bulking, 15 germinated seeds were placed in each small pot. The germinated seeds were placed by using a pencil to create a small hole into which the seeds were placed with the root pointing down. After the seedlings were potted, the pots were placed into a plastic tray and watered and fertilized twice a week. Additional iron sulfate was added approximately once a month, as needed.

2.10.5 Bulk tissue harvest

The rice plants were allowed to grow for five weeks from germination before being harvested. The plants were harvested according to Barding et al.5 Taking one pot at a time, the aerial tissue was cut, about one inch above the soil, with a razor and placed into a container filled with ultrapure water. After the tissue was rinsed and dried using paper towels, the tissue was quickly placed into a 50 mL falcon tube. The falcon tube was submerged in liquid nitrogen using metal tongs flash freeze the tissue, then removed and the top was screwed on the falcon tube loosely, and returned to the liquid nitrogen. This process was repeated for every pot. Between genotypes everything was cleaned and the water was changed. The tissue was kept -80 °C until it was lyophilized overnight. The dried tissue was ground into a fine powder with the use of the Quantachrom tissue homogenizer set at 0.50 min, and a frequency of 30 sec-1, and stored -20 °C.

2.10.6 Seed bulking and harvesting

After five weeks of growth, the plants were transplanted into a larger pot. The pots were filled with soil and saturated with water as before. The iron sulfate and fertilizer, as prepared before, were layered on top of the soil using about 200 mL and 150 mL respectively. The plant were then removed from the small pot, and placed in the large pot and filled with more soil around the plant. Around 20 weeks after transplanting, the plants started to flower. Using self-pollinating bags, the pinnacles were bagged individually, as they began to develop. After a total of six and a half months of growing the seeds turned to a light brown, and were ready for harvesting. The seeds were harvested using the razor, and cut off the plant. They were placed into a small container and moved to the lab then laid out on the lab bench to dry for a week. Once dried the seeds were the collected and placed into an envelope that was placed into a container with desiccant.

Table 2.1 M202 submergence tolerant and intolerant rice samples used in study

Treatment M202 M202(sub1) Control 1 – 6 7 – 12 Submerged 13 – 18 19 – 24 Dusk Control 25 – 30 31 – 36 Dusk Recovery 37 – 42 43 – 48 Midnight Control 49 – 54 55 – 60 Midnight Recovery 61 – 66 67 – 72 Dawn Control 73 – 78 79 – 84 Dawn Recovery 85 – 90 91 – 96 24 hr Control 97 – 102 103 – 108 24 hr Recovery 109 – 114 115 – 120

Table 2.2 IR64 submergence tolerant and submergence intolerant rice samples

Sample ID Air Submerged IR64 – Sub1+AG1+Saltol 363, 389, 427, 437 369, 394, 432, 442 IR64 – Sub1+AG1 362, 388, 426, 436 368, 393, 431, 441 IR64 – AG1 360, 386, 424, 434, 406 366, 391, 429, 439, 409 IR64 359, 385, 423, 433, 405 365, 390, 428, 438, 408 IR64 – Sub1 361, 387, 425, 435, 407 367. 392, 430, 440, 410

Table 2.3 Arabidopsis drought tolerant and intolerant samples

Genotype OTS-A OTS-B Col-1 treatment SUB AIR SUB AIR SUB AIR t = 0 A01 B01 C01 A02 B02 C02 A03 B03 C03 A04 B04 C04 t = 4 A4S1 A4A1 B4S1 B4A1 C4S1 C4A1 A4S2 A4A2 B4S2 B4A2 C4S2 C4A2 A4S3 A4A3 B4S3 B4A3 C4S3 C4A3 A4S4 A4A4 B4S4 B4A4 C4S4 C4A4 t = 48 A48S1 A48A1 B48S1 B48A1 C48S1 C48A1 A48S2 A48A2 B48S2 B48A2 C48S2 C48A2 A48S3 A48A3 B48S3 B48A3 C48S3 C48A3 A48S4 A48A4 B48S4 B48A4 C48S4 C48A4 t = 52 A52S1 A52A1 B52S1 B52A1 C52S1 C52A1 A52S2 A52A2 B52S2 B52A2 C52S2 C52A2 A52S3 A52A3 B52S3 B52A3 C52S3 C52A3 A52S4 A52A4 B52S4 B52A4 C52S4 C52A4

2.11 REFERENCES

(1) Delatte, T. L.; Selman, M. H. J.; Schluepmann, H.; Somsen, G. W.; Smeekens, S. C. M.; de Jong, G. J. Anal. Biochem. 2009, 389 (1), 12–17.

(2) Barding, G. a.; Orr, D. J.; Larive, C. K. Encycl. Magn. Reson. 2011, 4, 1–7.

(3) Barding, G. a.; Béni, S.; Fukao, T.; Bailey-Serres, J.; Larive, C. K. J. Proteome Res. 2013, 12 (2), 898–909.

(4) Lee, D.; Fiehn, O. Plant Methods 2008, 4 (1), 7.

(5) Barding Jr., G. a; Fukao, T.; Beni, S.; Bailey-Serres, J.; Larive, C. K. J Proteome Res 2012, 11 (1), 320–330.

CHAPTER 3: QUANTITATION OF TREHALOSE AND OTHER

SUGARS IN SUBMERGENCE RESISTANCE RICE

3.1 M202 and M202(Sub1) Results

Submergence experiments were carried out to evaluate concentrations of the carbohydrates, such as glucose, fructose, sucrose and trehalose in aerial tissue of M202 and M202(Sub1). Rice varieties were submerged for three days and immediately harvested (noon or noon control) or allowed to recover until dusk, midnight, dawn, and

24 hours after post submergence. At each harvest, a non-submerged control sample was harvested as well.

M202/M202(Sub1) Noon Dusk Midnight Dawn 24hr Sucrose 1.15 0.92 1.13 1.05 1.01 Glucose 1.20 1.14 0.84 0.92 1.49 Fructose 1.13 1.07 1.02 0.95 1.36* Trehalose 0.99 1.00 1.02 0.98 1.01

M202/M202(Sub1) Noon Dusk Midnight Dawn 24hr Recovery Recovery Recovery Recovery Recovery Sucrose 0.66 1.17 1.29 1.43 0.93 Glucose 0.89 0.88 0.82 1.09 0.72 Fructose 0.92 0.85 0.82 0.92 1.17 Trehalose 0.95 0.98 0.99 1.01 0.92*

Noon Dusk Midnight Dawn 24hr control/ control/ control/ control/ control/ Noon Dusk midnight Dawn 24hr Sucrose M202 4.95* 1.27 1.95* 1.21 2.41* Sub1 2.83* 1.38 2.21* 1.64 2.20* Glucose M202 1.56 1.12 0.79 1.04 0.67* Sub1 1.16 0.77 0.74 1.22 0.44* Fructose M202 1.39* 1.07 0.98 1.00 0.65* Sub1 1.14 0.85 0.78 0.97 0.56* Trehalose M202 1.03 1.04 1.03 1.01 1.08 Sub1 1.02 1.02 1.00 1.04 1.04

3.1.2 Trehalose

Comparing the M202 and M202(Sub1) control group (untreated), there were no significant differences in trehalose concentrations (Table 3.1). The concentrations of trehalose in the M202 control group ranged from 11.6 to 12.2 ng of trehalose per g tissue, and in the M202(Sub1) control group trehalose concentration ranged from 11.6 to 12.0 ng/g D.W. (Table 3.4).

Table 3.4 Average concentrations (ng/g D.W.) of trehalose in control samples for M202 and M202(Sub1)

Noon Dusk Midnight Dawn 24hr Control Control Control Control Control M202 11.6 12.0 11.8 11.8 12.2 Sub1 11.7 11.9 11.6 12.0 12.0

For the submergence-recovery experiments, there was a slight increase in trehalose concentrations in plants containing the SUB1 gene (Tables 3.2 and 3.5). The only time point that had a statistically significant difference in trehalose concentrations

57 between the two genotypes was the 24 hr time point (Table 3.2). In M202, the concentrations of samples from the recovery experiments ranged from 11.2 to 11.6ng/g

D.W., and for M202(Sub1) the trehalose concentrations ranged from 11.3 to 11.6 ng/g

D.W. (Table 3.5).

Table3.5 Average concentrations (ng/g D.W.) of trehalose in recovery samples for M202 and M202(Sub1)

Noon Dusk Midnight Dawn 24hr Recovery Recovery Recovery Recovery Recovery M202 11.2 11.5 11.5 11.6 11.3 Sub1 11.4 11.6 11.6 11.5 11.6

Minor differences in trehalose concentrations are found comparing the control and recovery samples for the M202 genotype. The largest change is seen in the 24 hr samples, where the concentration decreased during submergence. In the submergence sample, the concentration was 11.2 ng/g D.W. and in the noon control sample the concentration is

12.2 ng/g D.W., with an overall 1.08 fold difference (Table 3.3, Table 3.4). For the other samples, the concentrations decrease when under submergence by 0.1 to 0.5 ng/g D.W., with the total concentrations of trehalose in the control samples ranging from 11.5 to 12.2 ng/g D.W., and the recovery samples ranging from 11.2 to 11.6 ng/g D.W..

For the M202(Sub1) genotype, the trehalose concentration changed even less between the control and submerged samples. Trehalose concentrations decreased with treatment except at midnight where there was no concentration change. Trehalose control samples ranged in concentration of 12.0 to 11.5 ng/g D.W., and submerged samples ranged in concentrations of 11.3 to 11.6 ng/g D.W. (Table 3.5). The samples harvested at midnight stayed constant at 11.6 ng/g D.W..

0.015 Trehalose

g tissue 0.01

0.005 trehalose/ trehalose/ μg 0 Noon Noon Dusk Dusk Midnight Midnight Dawn Dawn 24hr 24hr Control Recovery Control Recovery Control Recovery Control Recovery Control Recovery

M202 Sub1

Figure 3.1 A comparison of the concentrations of trehalose in M202 and M202(Sub1). The error bars represent the standard deviation of each set of replicates.

3.1.3 Sucrose

The two genotypes only had minor sucrose concentration differences at each harvest time. In the control samples, sucrose concentrations in M202 ranged from 12.6 to

26.3 mg/g D.W., and M202(Sub1) ranged from 14.9 to 28.7 mg/g D.W. (Table 3.6).

Table 3.6 Average concentrations (mg/g D.W.) of sucrose in control samples for M202 and M202(Sub1)

Noon Dusk Midnight Dawn 24hr Control Control Control Control Control M202 21.4 26.3 21.8 13.6 22.8 Sub1 20.2 28.7 21.8 14.9 22.8

As with the control samples, the sucrose concentrations in the recovery samples were similar in both genotypes. In the recovery samples, the dusk and 24 hr had higher concentration of sucrose in M202(Sub1), while for samples harvested at midnight and dawn the concentrations of sucrose were higher in M202. The differences in concentrations varied from 4.5 to 20.6 mg/g D.W. in M202 and 7.1 to 21.1 mg/g D.W. in

M202(Sub1) (Table 3.3, Table 3.7).. Interestingly, sucrose concentrations in the noon

59 recovery samples was higher for the M202(Sub1) genotype compared with the M202 genotype, while the subsequent dusk, midnight, and dawn samples had higher concentrations of sucrose in the M202 genotype (Table 3.2), although none of the time points were significantly different.

Table 3.7 Average concentrations (mg/g D.W.) of sucrose in recovery samples for M202 and M202(Sub1)

Noon Dusk Midnight Dawn 24hr recovery Recovery Recovery Recovery Recovery M202 4.5 20.6 9.6 10.1 7.0 Sub1 7.1 21.1 8.7 8.4 8.8

Differences between the recovery and control samples are quite distinct. In the submergence intolerant M202, the sucrose concentrations had a fold change as large as

4.95 and as low as 1.21 (Tables 3.3 and 3.6). All control samples had higher sucrose concentrations compared with the corresponding recovery time point. The most significant changes took place at noon, midnight, and 24 hr. These had differences in sucrose concentration ranging from 12.2 to 16.9 mg/g D.W. The most significant was the noon treatment samples, which had a concentration ratio of 4.95 comparing the control with recovery samples (Table 3.3). The dusk and dawn samples were not as different, with fold differences of 1.21 and 1.27 (control to recovery), respectively.

The M202(Sub1) genotype behaved similarly as the M202 genotype All samples taken from submerged plants saw a decrease in sucrose compared with the corresponding control samples. The concentrations decreased by 14.0 to 6.5 mg/g D.W. in the submerged samples (Table 3.7). The highest flux in sucrose concentration was the noon, midnight, and 24 hr time points, with differences ranging from 13.1 to 14.0 mg/g D.W., and fold changes of 2.83 to 2.20 (control to recovery, Table 3.3). For the dusk and dawn

60 time points, the differences in sucrose content become less significant, ranging from a difference of 7.6 and 6.5 mg/g D.W., respectively. Dusk and dawn samples had control/recovery ratios of 1.38 and 1.64, respectively.

Sucrose 4000 3500 3000 2500 2000 1500 1000

μg sucrose/g tissue μg sucrose/g 500 0 Noon Noon Dusk Dusk Midnight Midnight Dawn Dawn 24hr 24hr Control Recovery Control Recovery Control Recovery Control Recovery Control Recovery

M202 Sub1

Figure 3.2 A comparison of the concentrations of sucrose in M202 and M202(Sub1). The error bars represent the standard deviation of each set of replicates.

3.1.4 Glucose

Glucose concentration in the control samples varied when comparing M202 and

M202(Sub1). In M202 control samples, the glucose concentrations ranged from 6.3 to

11.9 ng/g D.W., while for the M202(Sub1) control samples, the glucose concentrations ranged from 6.1 to 8.0 ng/g D.W. (Table 3.8). For the noon, dusk, and 24 hr control time points, glucose concentrations were higher in M202 and for samples harvested at the midnight and dawn control time points, glucose concentrations were higher in

M202(Sub1). The most significant difference was found in the 24 hr control time point, where the M202 glucose concentration was 11.9 ng/g D.W. and the M202(Sub1) glucose

61 concentration was 8.0 ng/g, resulting a ratio of 1.49 (M202/M202(Sub1), Table 3.1).

This was also a clear statistical difference.

Table 3.8 Average concentrations (ng/g D.W.) of glucose in control samples for M202 and M202(Sub1)

Noon Dusk Midnight Dawn 24hr Control Control Control Control Control M202 9.2 7.0 6.3 7.3 11.9 Sub1 7.7 6.1 6.5 7.9 8.0

In recovery samples, glucose concentrations vary significantly. For the dawn recovery time points, glucose had a higher concentration in M202 in contrast to with the noon, dusk, midnight, and 24 hr recovery time points in which glucose concentrations were higher in the M202(Sub1). For samples harvested at the dawn recovery time point, glucose concentration in M202 was 7.0 ng/g D.W. and in M202(Sub1), the glucose concentration was 6.4 ng/g D.W., which resulted in a ratio of 1.09 (Table 3.2, Table 3.9).

For the M202 genotype, the glucose concentrations for noon, dusk and midnight recovery time points ranged from 5.9 to7.3 ng/g D.W. and in M202(Sub1), the glucose concentrations ranged from 6.6 to 8.8 ng/g D.W.. The fold difference between M202 and

M202(Sub1) ranged from 0.82 to 0.89 for the noon, dusk, and midnight recovery time points. The most significant concentration difference is the 24 hr recovery time point, where M202 had a glucose concentration of 14.4 ng/g D.W. and M202(Sub1) had a glucose concentration of 20.0 ng/g D.W., this resulted in a ratio of M202 to M202(Sub1) of 0.72.

Table 3.9 Average concentrations (ng/g D.W.) of glucose in recovery samples for M202 and M202(Sub1)

Noon Dusk Midnight Dawn 24hr Recovery Recovery Recovery Recovery Recovery M202 5.9 6.6 7.3 7.0 14.4 Sub1 6.6 7.5 8.8 6.4 20.0

There were significant differences between the recovery and control samples for the M202 genotype. At noon, dusk, and dawn time points, there were higher glucose concentrations in the control samples, and at the midnight and 24 hr time points there were higher glucose concentrations in the recovery samples. One of the most significant differences is comparing the noon control to noon recovery time points, with a fold difference of 1.56 (Table 3.3). Additionally, the 24 hr recovery and control samples were statistically different, with the control sample differing from the recovery sample by a factor of 0.67.

M202(Sub1) also had significant variations in glucose concentrations. For samples harvested at dusk, midnight, and 24 hr, there were higher glucose concentrations in the recovery samples compared with the control, while the noon and dawn time points had higher glucose concentrations in the control samples. The most significant difference in glucose concentration was the 24 hr treatment time point, with the control sample differing from the treatment sample by a factor of 0.44.

Glucose 0.3 0.25 0.2 0.15 0.1

μg glucose/g tissue μg glucose/g 0.05 0 Noon Noon Dusk Dusk Midnight Midnight Dawn Dawn 24hr 24hr Control Recovery Control Recovery Control Recovery Control Recovery Control Recovery

M202 Sub1

Figure 3.3 A comparison of the concentrations of glucose in M202 and M202(Sub1). The error bars represent the standard deviation of each set of replicates.

3.1.5 Fructose

Fructose concentrations differed slightly between the two genotypes. In the control samples, all except the dawn time point have higher concentrations in M202 compared with M202(Sub1). The most significant difference between the genotypes was in the 24 hr control where fructose was 1.49 times greater in M202 compared with

M202(Sub1).

Table 3.10 Average concentrations (ng/g D.W.) of fructose in control samples for M202 and M202(Sub1)

Noon Dusk Midnight Dawn Control 24hr Control Control Control Control M202 1.39 1.16 1.14 1.18 1.67 Sub1 1.24 1.09 1.12 1.24 1.23

Differences between the genotypes during recovery were even less extreme. With the exception of the 24 hr recovery time point, fructose content hovered around 1 ng/g

D.W. (Table 3.11). For the 24 hr recovery time point, the fructose concentration was 2.57

64 ng/g D.W. in M202 and 2.46 ng/g D.W. in M202(Sub1) (Table 3.2, Table 3.11). For the noon, dusk, midnight, and dawn time points, fructose had higher concentrations in

M202(Sub1) than in M202, with the differences ranging from 0.26 to 0.09 ng/g D.W., resulted in ratios ranging from 0.83 to 0.92.

Table 3.11 Average concentrations (ng/g D.W.) of fructose in recovery samples for M202 and M202(Sub1)

Noon Dusk Midnight Dawn 24hr Recovery Recovery Recovery Recovery Recovery M202 1.00 1.09 1.17 1.18 2.57 Sub1 1.09 1.28 1.43 1.34 2.46

As expected, the largest concentration differences were found comparing the control and recovery experiments. In the M202 genotype, the midnight and 24 hr time points were found to have higher concentrations in recovery samples, while the noon and dusk time points had higher fructose concentrations in the control samples.

In M202(Sub1), fructose levels were generally higher in the recovery group than the control group, with the exception of the noon time point. One of the most significant differences is found in the 24 hr time point, where the fructose concentration for the 24 hr control and recovery time points had concentrations of 1.23 ng/g D.W. and 2.46 ng/g

D.W., respectively, with a factor of 0.65 less fructose in the control samples. These were the only samples found to be statistically different in a comparison of M202(Sub1) samples.

Fructose 0.035 0.03 0.025 0.02 0.015 0.01 0.005 μg fructose/ g tissue μg fructose/ 0 Noon Noon Dusk Dusk Midnight Midnight Dawn Dawn 24hr 24hr Control Recovery Control Recovery Control Recovery Control Recovery Control Recovery

M202 Sub1

Figure 3.4 A comparison of the concentrations of fructose in M202 and M202(Sub1). The error bars represent the standard deviation of each set of replicates.

3.2 IR64 Results

Using IR64 rice that is commercially available, a variety of modifications were made through cross breeding to incorporate traits expected to increase drought and submergence tolerance. For each of these genotypes, the concentrations of trehalose, sucrose, glucose, and fructose were quantified.1 The concentrations of these carbohydrates are compared before and after submergence, as well as with and without these genetic modifications. The traits introduced to IR64 rice include SUBMERGENCE

1A (Sub1), anaerobic germination (AG1), and Saltol. IR64 was studied with the absence of any modification, and also with Sub1, AG1, Sub1+AG1, and Sub1+AG1+Saltol.

IR64/Sub1 IR64/AG1 IR64/Sub1+AG1 IR64/Sub1+AG1+Saltol Trehalose 0.91 1.00 0.91 1.00 Sucrose 0.66 0.81 0.78 0.75 Glucose 0.77 0.63 0.88 0.51 Fructose 0.80 1.13 0.96 2.20

IR64/Sub1 IR64/AG1 IR64/Sub1+AG1 IR64/Sub1+AG1+Saltol Trehalose 0.73 0.85 0.81 0.73* Sucrose 1.36 0.99 0.60 1.99 Glucose 0.50 0.38 0.43 0.27 Fructose 1.22 0.79 0.89 0.53*

Table 3.14 Ratios of each rice variety comparing concentrations of control to submerged samples. The asterisk indicates samples that are significantly different at the 90% confidence limit.

control/submerged IR64 Sub1 AG1 Sub1+AG1 Sub1+AG1+Saltol Trehalose 0.88* 0.75 0.78* 0.78 0.67* Sucrose 0.50 1.03 0.61 0.38 1.33 Glucose 4.02* 2.61 2.38 1.95 2.11 Fructose 4.89* 7.46* 3.43* 4.57* 1.60

3.2.1 Trehalose

Very few of the genotypes exhibited statistically significant differences in trehalose concentration. For the control samples, none of the genotypes were different

(Table 3.12, Table 3.15). For submergence treated plants, only one significant difference between the IR64 intolerant and crossbred varieties was found, with trehalose having a factor of 0.73 less in the IR64 variety compared with the IR64+Sub1+AG1+Saltol (Table

3.13, Table 3.15, Figure 3.5). The effect of submergence within each genotype is even more significant, with all but the Sub1 and Sub1+AG1 varieties being statistically different from the control samples (Table 3.14, Figure 3.5). Interestingly, for all genotypes, the control samples had less trehalose compared with the submergence-treated samples.

Table 3.15 Average concentrations (ng/g D.W.) of trehalose in control and submerged samples for each rice variety used in study.

IR64 Sub1 AG1 Sub1+AG1 Sub1+AG1+Saltol Control 1.0 1.1 1.0 1.1 1.0 Submerged 1.1 1.5 1.3 1.4 1.5

Trehalose 0.025

0.02

0.015

0.01

0.005 μg trehalose/g tissue μg trehalose/g 0 IR64 Sub1 AG1 Sub1+AG1 Sub1+AG1+Saltol

Control Submerged

3.2.2 Sucrose

Unlike the M202 varieties (Section 3.1), there were no statistically significant differences in sucrose concentration at any point of the study (Tables 3.12, 3.13, and 3.14 and Figure 3.6.) despite the wide variations in control and submerged concentrations

(Table 3.16). The lack of difference can be attributed to biological variance, which will be discussed in section 4.2.5.

Table 3.16 Average concentrations (ng/g D.W.) of sucrose in control and submerged samples for each rice variety used in study.

IR64 Sub1 AG1 Sub1+AG1 Sub1+AG1+Saltol Control 36.5 54.8 44.9 46.5 48.7 Submerged 73.1 53.4 73.4 121.0 36.7

Sucrose 250

200

150

100

50 μg sucrose/ g tissue μg sucrose/

0 IR64 Sub1 AG1 Sub1+AG1 Sub1+AG1+Saltol

Control Submerged

3.2.3 Glucose

Glucose concentrations were statistically different in several of the studied IR64 varieties. In the submergence-treated IR64 rice, glucose concentrations were higher in submergence tolerant varieties than in the submergence intolerant IR64. The submergence intolerant IR64 glucose concentration was 13.2 μg/g D.W., and in the submergence tolerant varieties glucose ranged from 26.2 to 49.7 μg/g D.W. (Table 3.17).

The most significant glucose concentration difference is in IR64-Sub1+AG1+Saltol, with a ratio comparing IR64 to IR64-Sub1+AG1+Saltol of 0.27 (Table 3.13).

Glucose concentrations in the control samples were found to be similar to the submerged samples. The lowest concentration is found in the IR64 submergence intolerant samples, with a concentration of 53.1 μg/g D.W. The submergence tolerant

IR64 rice had concentrations in the control samples ranging from 60.1 to 105.0 μg/g

D.W. The most significant concentration difference is in IR64-Sub1+AG1+Saltol, the glucose concentration was 105.0 μg/g D.W., which resulted in a fold difference of 0.51

(Table 3.12).

Table 3.17 Average concentrations (ng/g D.W.) of glucose in control and submerged samples for each rice variety used in study.

IR64 Sub1 AG1 Sub1+AG1 Sub1+AG1+Saltol Control 53.1 68.6 84.0 60.1 105.0 Submerged 13.2 26.2 35.2 30.8 49.7

Comparing control and submerged samples of each variety, glucose levels were generally lower in the submerged samples. The most significant difference was found in

IR64 (intolerant) genotype, with a ratio of 4.02 for control to submergence (Table 3.14).

This is over twice the fold difference for the IR64-Sub1+AG1 (1.95 control:submerged).

Glucose 2.5

1.5

0.5

μg glucose / g tissue / μg glucose 0 IR64 Sub1 AG1 Sub1+AG1 Sub1+AG1+Saltol -0.5

Control Submerged

3.2.4 Fructose

Fructose concentrations were not significantly different between the genotypes and the only genotype that differed among the submergence samples was the IR64

(intolerant) and the IR64 (Sub1+AG1+Saltol) during submergence. The intolerant variety had a factor of 0.54 less fructose compared with the IR64 +Sub1+AG1+Saltol.

The concentration ranged from 1.2 ng/g D.W. to 7.2 ng/g D.W. (Table 3.18), but because of the biological variance, only one time point was significantly different.

Table 3.18 Average concentrations (ng/g D.W.) of fructose in control and submerged samples for each rice variety used in study.

IR64 Sub1 AG1 Sub1+AG1 Sub1+AG1+Saltol control 5.8 7.2 5.1 6.0 3.6 Submerged 1.2 1.0 1.5 1.3 2.2

The difference in fructose concentration between control and submergence within each genotype was statistically significant for all samples except

IR64+Sub1+AG1+Saltol, higher in the control samples by as much as a factor of 7.5

(Tables 3.18 and 3.14 and Figure 3.8)

Fructose 0.12 0.1 0.08 0.06 0.04 0.02

μg fructose/ g tissue μg fructose/ 0 IR64 Sub1 AG1 Sub1+AG1 Sub1+AG1+Saltol -0.02

Control Submerged

13 3.1.6 Internal standard C6 glucose

13 Fully labeled C6 glucose was used as the internal standard to account for injection variability. However, there was a significant amount of variance in raw peak areas for the IS, ranging from approximately 1x107 to 2x107 for the M202 rice varieties

(Figure 3.9). This resulted in an average peak area of 1,580,000 and an RSD of 23.7% despite the presence of one outlier. The peak areas were integrated using the SIM method and an m/z of 323.

Internal standard for M202 samples 4500000 4000000 3500000 3000000 2500000 2000000

Peak area Peak 1500000 1000000 500000 0 0 20 40 60 80 100 120 Sample number

Figure 3.9 A comparison of the variation of the internal standard peak area in the M202 samples

The IS for the IR64 samples varied significantly as well, with peak areas ranging from 6x106 to 1.4x107 (Figure 3.10) and an RSD of 28.9%. The general areas of the IS for the IR64 samples are lower compared with the M202 samples and the (Figures 3.9 and 3.10), however the difference in RSD can be attributed to a smaller sample pool for the IR64 samples.

Internal Standard for IR64 samples 1800000 1600000 1400000 1200000 1000000 800000

Peak area Peak 600000 400000 200000 0 0 10 20 30 40 50 60 Number of samples

Figure 3.10 A comparison of the variation of the internal standard peak area in the IR64 samples 3.2.1 Discussion

Trehalose is found in submergence tolerant and intolerant rice varieties at very low concentrations.2 Because of the low concentrations, trehalose is not easily quantifiable by NMR, therefore the focus of this study is to determine if trehalose and other abundant sugars can be quantified accurately and reproducibly by GC-MS. One approach is to evaluate the LOD, LOQ and the corresponding calibration curves of the analysis. Carbohydrates are studied in depth to understand the SUBMERGENCE 1A gene because their regulation are essential processes underlying plant growth and development and can be associated with survival when under stress from a flooding event.3 In this study, M202 and M202(Sub1) are ideal systems to help understand the effects of oxidative stress on the carbohydrate concentrations. M202 is a submergence intolerant variety, and does not contain the SUB1A gene while the M202(Sub1) variety does.4 IR64 is also a submergence intolerant rice variety; however it contains the

74 inactive SUB1-A2 gene. The other IR64 rice varieties that are evaluated include those with the SUB1-A1 gene, AG1 trait and Saltol gene.

3.2.2 Trehalose

For all samples, trehalose is found at low concentrations. The LOD of trehalose was calculated to be 0.297 ng, and the LOQ was calculated to be 0.989 ng (Table 3.19).

The LOD was determined by using the calibration curve using the following equation:

푆푡푎푛푑푎푟푑 푑푒푣𝑖푎푡𝑖표푛 표푓 푡ℎ푒 푙표푤푒푠푡 푐표푛푐푒푛푡푟푎푡𝑖표푛 퐿푂퐷 = 3 × 푆푙표푝푒 표푓 푡ℎ푒 푐푎푙𝑖푏푟푎푡𝑖표푛 푐푢푟푣푒

The LOQ was the determined using the data from the calibration curve and the following equation:

In the resulting standard curve, the standard deviation of the lowest data point was considerably small, equaling 0.00094. Trehalose levels in the biological samples were above the LOQ. Absolute quantitation was carried out by use of a standard curve generated from measuring six samples (in triplicate), ranging in concentration from 64.2 nM to 5.5 μM,. This resulted in a standard curve with a corresponding correlation coefficient of 0.9823 (Figure 3.9). The mean percent-RSD for the calibration curve was found to be 8.64% (Table3.20), however, trehalose did have a high range of RSD values

(25.1%) which was attributed to the third data point, but we are uncertain as to the reason.

Table 3.19 Limit of detection and limit of quantification calculated from the standard deviation and slope of standard curves.

Limit of Detection Limit of Quantification Glucose 1.39 ng 4.64 ng Sucrose 7.62 ng 25.4 ng Fructose 6.85 ng 22.8 ng Trehalose 0.297 ng 0.989 ng

Table 3.20 For each metabolite used in this study, a mean percent - RSD and range of RSD values (representing the lowest and highest RSD) is calculated from the calibration curve.

Mean Percent - RSD Range RSD Glucose 21.5 10.9 Sucrose 37.9 48.3 Fructose 28.2 42.7 Trehalose 8.64 25.1

Trehalose Standard Curve y = 0.1629x + 0.0347 R² = 0.9823 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Normalized Peak Area Peak Normalized 0.1 0 0 1 2 3 4 5 6 Concentratipon (μM)

Figure 3.11 Calibration curve of Trehalose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement.

Since a SIM method was used to help accurately quantify the data by monitoring only specific ions, an incomplete mass spectrum was produced. After identifying each sugar with a prepared stock solution, the rice samples were analyzed using the SIM 76 method. However, rice samples contained two adjacent peaks where trehalose was expected to elute in the SIM trace. A trehalose spiking experiment was carried out to better determine which peak is trehalose. An identical sample was split and analyzed first at natural abundance and then after spiking the second samples with 0.6 ng of trehalose (Figure 3.10). Figure 3.10(a) is the SIM trace of the two peaks that appear in the rice samples, and in figure 3.10(b) is after the trehalose spike, where there is an obvious increase in the second peak resulting in a larger ratio of the second peak to the first peak.

The peak height in (a) is 222 counts with a final concentration of 7.53 ng/g D.W., and the peak height in (b) is 872 counts with a final concentration of 26.8 ng/g D.W., while the first peak remained nearly constant. The internal standard, carbon – 13 labeled glucose, also remained constant in both samples, having a constant peak area and retention time.

Because the internal standard remained constant, there was little injection variability in the two samples. This means the second peak is trehalose at a retention time of 17.2 min, which is constant in the biological samples.

(a)

(b)

In the biological samples, there was only one statistically significant difference between the M202 and M202(Sub1) genotypes at the 24 hr recovery time point for trehalose. Comparing concentrations of trehalose in M202 to M202(Sub1) in the control samples, there were no significant differences. In a comparison of the control samples to the submerged samples for both genotypes, there was only a small variation, with no significant differences. The mean RSD for the M202 and M202(Sub1) samples ranged from 0.5 - 14.2%, indicating that trehalose concentrations were stable across all samples and not significantly affected by submergence. Trehalose was found to generally be

78 higher M202(Sub1) concentration, then M202 directly after submergence with no recovery time.

In IR64 rice trehalose concentrations were generally higher in the submerged sample for all IR64 varieties. A comparison of control and submerged samples of the submergence tolerant IR64-Sub1+AG1+Saltol were the only samples found to be statistically different. However, in the submerged samples, the error bars were significantly larger than in the control samples, with a mean RSD ranging from 1.9 to

61.8% (Figure 3.5). The high RSD indicates that either treatment was increasing the variability of trehalose concentrations or subtle variations in growing conditions were influencing the results.

3.2.3 Sucrose

Sucrose was found to have very high concentrations in rice, especially in M202 and M202(Sub1). Due to the high concentrations of sucrose, a 1:100 dilution was performed in order to accurately quantify sucrose without causing ion suppression or compromising the ionization source. The LOD was calculated to be 7.62 ng and the LOQ was 25.4 ng (Table 3.19). The LOD and LOQ are calculated using the same equation used in section 3.2.2. The sucrose calibration curve was produced by analyzing a series of six samples (in triplicate), ranging in concentration from 1.0 to 30.0 μM. The calibration curve resulted in a mostly linear response, with the correlation coefficient of 0.9781

(Figure 3.11). From the calibration curve, the mean percent - RSD and range RSD was calculated to be 37.9% and 48.3%, respectively. Such a high standard deviation complicates the quantitation of sucrose, however, most of the variance is attributed to the

3rd and 5th data points. The calibration curve was repeated multiple times to elucidate the origin of the errors, however the magnitude of variation would not change significantly.

After further experimentation, it was concluded that the higher concentrations of sucrose were being degraded in solution prior to analysis. Care will be taken in the future to minimize such errors.

y = 0.5636x + 0.1079 Sucrose Standard Curve R² = 0.9781 7

2 Normalized Peak Area Peak Normalized 1

0 0 1 2 3 4 5 6 7 8 9 Concentration (μM) Figure 3.13 Calibration curve for sucrose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement.

As indicated in the results section, there were significant differences in sucrose concentration for the study involving the M202 varieties. Comparing the submergence samples for both M202 and M202(Sub1), there was a significantly higher concentration of sucrose in the control samples compared with submerged. The noon, midnight and 24 hour time points were statistically different between the genotype. There were no significant differences comparing M202 to M202(Sub1) at the same treatment groups. In

M202 rice, sucrose levels are generally higher in samples with the Sub1 gene in the submerged samples. Additionally, the samples were found to have large error bars,

80 indicating that the RSD for the biological samples was also high, ranging from 13.8 to

55.8% (Figure 3.2).

In IR64 varieties of rice, there were no significant differences comparing the sucrose concentrations since the error bars overlapped in every comparison (Figure 3.6).

However, sucrose levels were found to be generally lower in the submergence intolerant

IR64 to the submergence tolerant IR64 varieties, specifically in the control samples.

3.2.4 Glucose

Glucose was quantified at relatively high abundances in rice, especially in the

IR64 varieties. The LOD and LOQ for glucose was 1.39 and 4.64 ng, respectively (Table

3.19). The LOD and LOQ were determined with the equations in section 3.2.2. The glucose calibration curve was generated by using a series of six samples (in triplicate) measuring the concentration range of 0.176 to 8.0 μM. Glucose had the most linear response in the calibration curve with a correlation coefficient of 0.9968 (Figure 3.12).

From the calibration curve, glucose had a mean RSD of 21.5%, and a range RSD of

10.9%. The glucose mean RSD was rather large, considering the first three data points had small standard deviations and the last three data points had larger standard deviations, meaning at lower concentrations of glucose this is a more precise and reproducible method.

y = 0.1077x + 0.0337 Glucose Standard Curve R² = 0.9968 1.2

0.8

0.6

0.4

0.2 Normalized Peak Area Peak Normalized

0 0 1 2 3 4 5 6 7 8 9 Concentration (μM)

Figure 3.14 Calibration curve for glucose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement.

Glucose concentrations varied widely for each sample treatment in the M202 rice varieties. The single statistically significant time point was a when comparing the control and submerged samples at the 24 hr time point for both M202 and M202(Sub1) (Figure

3.3). With the exception of the dawn time point, there was a higher concentration of glucose had higher levels in the submerged samples than in the control samples. There was no significant difference except in the 24 hr recovery sample where there was also a large increase in glucose levels.

In IR64 rice, there was a higher concentration of glucose after submergence for all of the submergence tolerant varieties compared to the intolerant variety. In the presence of the SUB1 gene, there was a higher glucose concentration in the submerged plants compared with the intolerant genotype.

In the M202 project, glucose had significantly smaller standard deviations than in the IR64 project. In IR64 rice, there are significantly higher concentrations of glucose

82 than in M202 rice. Even though the same calibration curve was used for both M202 and

IR64 samples, the concentration of glucose for the M202 varieties was found to be at the end of the calibration curve, where the results are significantly more reproducible. Unlike

IR64, the concentrations were found at the higher end of the calibration curve, where the calibration curve was not as reproducible.

3.2.5 Fructose

Fructose was found to have slightly lower levels in rice than glucose, in both

M202 and IR64 rice varieties. The LOD and LOQ for fructose was 6.85 and 22.9 ng, respectively (Table 3.19). The LOD and LOQ were determined using the equations from section 3.2.2. The fructose calibration curve was generated measuring six samples (in triplicate) ranging in concentrations from 0.176 to 8.0 μM. The calibration curve for fructose was very linear, with a correlation coefficient for the fructose calibration curve was 0.9832 (Figure 3.13). The calibration curve for fructose, like glucose, was also found to have larger standard deviations for the last three data points, with a mean percent RSD of 28.2% and a range of 42.7%.

y = 0.0158x + 0.006 Fructose Standard Curve R² = 0.9832 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 Normalized Peak Area Peak Normalized 0.02 0 0 1 2 3 4 5 6 7 8 9 Concentration (μM)

Figure 3.15 Calibration curve for fructose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement.

In the M202 rice varieties, (Sub1) there is generally higher concentrations of fructose in M202(sub1) directly after submergence. From a comparison of M202 to

M202(Sub1) in the control samples, fructose had a higher concentration in M202. Also, comparing the control to submerged samples, there is significantly more fructose at 24 hr recovery than 24 hr control, for both M202 and M202(Sub1). For this project, the concentrations for fructose were found to be lower than expected and therefore ended up being on the lower end of the calibration curve and significantly more reproducible.

In IR64 rice, fructose was not higher in the presence of the SUB1 gene while under submergence, as with trehalose and glucose. However, fructose was generally higher in the submergence tolerant IR64 varieties, after submergence. In the IR64 samples, there were significantly high error bars that made it difficult to determine if any samples could be significantly different (Figure 3.8). In this project, the fructose concentrations were much higher in comparison to the M202 project. While still within the concentration

84 range of the calibration curve, the values tend to be closer to the last three data points, indicating the technical variance may increase the overall RSD.

13 3.2.6 Internal standard C6 glucose

The internal standard is used to normalize the peak areas of the metabolites to account for any possible error found with injection variability. Both M202 and IR64 samples were measured using the same internal standard at the same concentration. The

M202 samples resulted in a RSD of 23.7% and the IR64 samples resulted in a RSD of

28.9% (Figure 3.9, Figure 3.10). Because both M202 and IR64 were ran using the exact same concentration comparing the average peak area of the two sets 1,500,000 and

1,000,000, respectively shows there is some sort of error, not just injection variability.

The IR64 samples were analyzed at a much later time, allowing the possibility that the IS degraded during the delay. Because the IS is reproducible within the same set of sample data, it still fulfilled its purpose of accounting for injection variability.

3.3 Conclusion

There were some significant differences in sugar content with various samples in this study. GC-MS has been used in many metabolomics studies and is beneficial for analytes found in extremely low abundances such as trehalose. All samples were found to have concentrations above the LOD and LOQ, and mostly linear responses from the calibration curve. Even though calibration curves were linear, there are still rather large standard deviations in specific data sets. This issue was emphasized from calculating the

RSD. The RSD was found to be exceedingly high, except for trehalose, and the range of

RSD values was high for sucrose, glucose and fructose. This means the calibration curves may not be very reproducible and contribute to the overall RSD. Interestingly, trehalose had a RSD of 8.64%, indicating that the overall error was primarily biological in its origin.

GC-MS also faces difficulty in analysis of metabolites because sugars are not generally volatile. Also, there was a longer dwell time used than intended for each ion, possibly contributing to undersampling of each peak.

The SUB1 gene currently accounts for 69% of submergence tolerant rice varieties, and the AG1 trait currently accounts for 33% of the submergence tolerant rice varieties.5 The

SUB1 gene, which is found in other plants, not only rice, has been found to survive much longer when under stressed environments. The longer survival times are known to be associated with starch and carbohydrate consumption being regulated during periods of elongation.6 This study found that there is a positive trend associated with the SUB1 gene and higher carbohydrate concentration when submerged. In M202 rice, the sub1 gene directly after submergence generally has higher concentrations of trehalose, sucrose, glucose and fructose. In the IR64 rice, the sub1 gene is found to generally have higher trehalose and glucose concentrations, but not sucrose and fructose.

Trehalose, being the most significant part of the study, is found to have mostly insignificant differences in concentration in M202 rice. However, in IR64 rice there were interesting comparisons where there is a higher trehalose concentration in the submergence tolerant varieties used in this study. In the M202 rice project, there were relatively small error bars representing the standard deviation. In M202 rice, trehalose was found to be reproducible, as well as in the calibration curve. In the IR64 project, the

86 trehalose error bars representing the standard deviation was in some cases larger than the actual averages. This means that trehalose is better regulated biologically in M202 rice than in IR64 rice.

(a)

(b)

(c)

Figure 3.16. SIM traces in submergence intolerant M202 rice (a) trehalose (b) sucrose, and (c) fructose and glucose

(a)

(b)

(c)

Figure3.17 SIM traces in submergence tolerant M202(Sub1) rice (a) trehalose (b) sucrose, and (c) fructose and glucose

3.4 REFERENCES

(1) Septiningsih, E. M.; Collard, B. C. Y.; Heuer, S.; Bailey-serres, J. 2013, No. July 2015.

(2) Barding, G. a.; Béni, S.; Fukao, T.; Bailey-Serres, J.; Larive, C. K. J. Proteome Res. 2013, 12 (2), 898–909.

(3) Fukao, T. Plant Cell Online 2006, 18 (8), 2021–2034.

(4) Xu, K.; Xu, X.; Fukao, T.; Canlas, P.; Maghirang-Rodriguez, R.; Heuer, S.; Ismail, A. M.; Bailey-Serres, J.; Ronald, P. C.; Mackill, D. J. Nature 2006, 442 (7103), 705–708.

(5) Toledo, A. M. U.; Ignacio, J. C. I.; Casal Jr, C.; Gonzaga, Z. J.; Mendioro, M. S.; Septiningsih, E. M. Plant Breed. Biotechnol. 2015, 3 (2), 77–87.

(6) Bailey-Serres, J.; Fukao, T.; Gibbs, D. J.; Holdsworth, M. J.; Lee, S. C.; Licausi, F.; Perata, P.; Voesenek, L. a. C. J.; van Dongen, J. T. Trends Plant Sci. 2012, 17 (3), 129–138.

CHAPTER 4: QUANTITATION OF TREHALOSE AND OTHER

SUGARS IN DROUGHT TOLERANT ARABIDOPSIS PLANTS

4.1 Introduction

In this study Arabidopsis thaliana is used because it is considered a model plant due to its very short life cycle, only requiring a six week period from germination to flowering.1 Because the genome is well known and a wide variety of mutants are available for purchase, Arabidopsis is an ideal plant model to study the effects of abiotic stress, such as submergence, drought, and salinity. In this study, the effects of submergence were studied on a variety of Arabidopsis thaliana mutants. The plants were submerged for 4 hours, 48 hours, and 52 hours and then harvested and compared to a set of control plants (no submergence). Both the submerged and control groups were harvested simultaneously to avoid circadian-induced differences. Metabolites were subsequently extracted and analyzed by GC-MS. This study was focused on three genotypes of Arabidopsis: OST-A, OST-B, and Col-0. The OST-A and OST-B genotypes are drought tolerant, while OST-A is also salt tolerant.2 The OST-A and OST-B genotypes were compared to the commonly usedwildtype Columbia or Col-0.3 The effects of submergence on trehalose, sucrose, glucose and fructose are described for the three different genotypes of Arabidopsis, as well as the different treatment time points. In

Arabidopsis, glucose is found in very high abundances therefore a dilution was carried out to avoid overloading the column and detector and to ensure quantitative results. As a result, glucose, sucrose, and fructose were quantified in the diluted samples while trehalose was quantified in the undiluted samples.

Despite the significant differences in sugar content between genotypes at specific treatments, there were large variations within each sample group. After careful examination of the data, the variation was attributed to biological variances. For data collected using the full scan method, other metabolites (non-sugar containing compounds) were selected to compare analytical reproducibility; if the error was due to technical preparation or analysis, a high variance would be expected for all compounds, not just sugars. Indeed, for each replicate there were metabolites that had similar peak areas, indicating biological variance was the primary driver for variation in sugar content.

Because this study is more focused on developing a method for trehalose quantitation, the discussion will be focused primarily on method development.

4.2 Results and Discussion

4.2.1 Trehalose

Due to the significantly lower abundance of trehalose compared to sucrose, glucose, and fructose, accurate quantitation of trehalose required a separate analysis using a SIM method. The only ions included in the analysis were m/z 361 for trehalose and m/z

255 for methyl stearate. The first indication that methyl stearate was not an appropriate internal standard was comparing the standard curves of trehalose for the Arabidopsis

(Figure 4.1) and rice experiments (Figure 3.11). The trehalose standard curve for the

Arabidopsis experiments normalized to methyl stearate produced a second degree polynomial fit (Figure 4.1) and a correlation coefficient of 0.9955, which means the equation, characterized the data well. The calibration curve was generated by measuring six data points, ranging in concentrations from 6.42 nM to 0.555µM and the polynomial

92 equation was used for quantitation. A derivative of the equation is used to determine the slope, in order to calculate the LOD and LOQ. The calculated LOD for trehalose is

0.0877 ng and the calculated LOQ is 0.292 ng (Table 4.1). The sample concentrations were found to be above the LOD and LOQ. From the data generated for the calibration curve, the mean RSD was found to be high, at 37.4%, with a range of 70.2% (Table 4.2).

The mean RSD has a significantly wide range, because the RSD of the fifth data point was over 70%. Because of the high RSD of many of the data points, accurate quantitation is difficult at best and can be attributed (at least in part) to the variation of methyl stearate

(Figure 4.2).

y = 1.8556x2 + 0.3536x + 0.0416 Trehalose Calibration Curve R² = 0.9955 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Normalized Peak Area Peak Normalized 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 Concentration μM

Figure 4.7 Calibration curve for trehalose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement. Due to the high variance of the internal standard, trehalose measurements in

Arabidopsis plants was highly variable. The Col-0 52 hr air sample was the most reproducible, having an RSD of 17.3%. The rest of the samples have an RSD of greater than 40%, with five sample sets well above 100%. The high variance of the each sample

93 set can be seen on the error bars of Figure 4.4. Trehalose is quantified using a SIM method to better help with accuracy, and since trehalose is in significantly lower abundance, ion suppression was not thought to contribute to the highly variable data.

Trehalose

130

110

30 µg of Trehalose l g of tissue l g of Trehalose µg of

-10 0 4-Submerged 4-Air 48-Submerged 48-Air 52-Submerged 52-Air

OST-A OST-B Col-0

Figure 4.8 A comparison of the average concentrations of trehalose in Arabidopsis. The error bars represent the standard deviation of each set of replicates. Table 4.4 Limit of detection and limit of quantification calculated from the standard deviation and the slope of the standard curves. All values are in ng

Limit of detection Limit of quantification Trehalose 0.0877 0.292 Sucrose 24.4 81.4 Glucose 2.97 9.91 Fructose 3.22 10.7

Table 4.5 From the calibration curve for each sugar, the mean percent - RSD and the range percent - RSD is calculated.

Mean percent - RSD Range percent - RSD Trehalose 37.4 70.2 Sucrose 52.7 84.6 Glucose 31.5 32.1 Fructose 32.0 30.7

Trehalose was significantly different in the three Arabidopsis genotypes. For the

OST-A plants, trehalose concentrations in the control group (untreated) samples ranged from 9.1 to 42.4 μg/g, with the lowest concentration found in the 4 hr air sample, and the highest concentration in the 52 hr air sample (Table 4.2). For the submerged samples, the trehalose concentrations ranged from 1.2 to 27.5 μg/g, with the lowest concentration found in the 48 hr submerged sample, and the highest found in the 52 hr submerged sample. In OST-B, the trehalose concentrations in the air samples ranged from 1.7 to 66.2

μg/g and the submerged samples ranged from 2.6 to 14.2 μg/g. For both treatment conditions, the lowest concentration of trehalose was found at the 48 hr time point and the highest concentration was found at the 52 hr time point. Lastly, in Col-0 the trehalose concentrations in the air samples ranged from 9.0 to 110.1 μg/g, and in the submerged samples the concentrations ranged from 1.2 to 17.5 μg/g. As with the OST-B genotype, the lower concentrations were both found to be at the 48 hr time point and the higher concentrations are found to be at the 52 hr time point for Col-0.

Table 4.6 Average concentrations of trehalose (μg of trehalose per a g of tissue). The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control.

t = 4 t = 48 t = 52 Genotyp t = 4 t = 48 t = 52 t = 0 Submerge Submerge Submerge e Air Air Air d d d OST-A 2.7 4.7 9.1 1.2 19.9 27.5 42.4 OST-B 3.0 3.8 11.1 2.6 1.7 14.2 66.2 Col-0 1.0 8.1 19.6 1.2 9.0 17.5 110.1

Generally, higher concentrations of trehalose were detected in the air samples compared with the submerged samples. For the OST-A samples, the ratio of trehalose in air and submerged samples ranged from 1.93 to 16.21 (Table 4.3). There was a similar

95 trend for Col-0, where the ratio of trehalose for the air and submerged samples ranged from 2.43 to 7.45, peaking at t = 48 hr. Interestingly, the trends for OST-A and Col-0 are similar, where the concentration of trehalose is greater at t = 48 hr in the air samples followed by a decrease, while OST-B samples appear to decrease from t = 4 to t =48 hr, and rise significantly at t = 52 hr.

Table 4. 7 Ratios of the average trehalose concentrations comparing air to submerged samples for the t = 4, 48 and 52 hr time points. The asterisk indicates samples that are significantly different at the 90% confidence limit.

Genotype t = 4 t = 48 t = 52 OST-A 1.93 16.21* 1.54 OST-B 2.91 0.65 4.66 Col-0 2.43 7.45* 6.31

To better understand the unique response of trehalose for each genotype during treatment, a comparison of each genotype and treatment condition was carried out (Table

4.5). Interestingly, the only samples significantly different between the genotypes occurred in the control experiments either at t = 48 hr (Col-0/OST-B, OST-A/OST-B) or t

= 52 hr (Col-0/OST-A).

Table 4.8 Ratios of trehalose concentrations comparing the three different genotypes. The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. The asterisk indicates samples that are significantly different at the 90% confidence limit.

t = 4 t = 4 t = 48 t = 48 t = 52 t = 52 Genotype t = 0 Submerged Air Submerged Air Submerged Air Col-0/ 0.37 1.71 2.15 0.98 0.45 0.64 2.60* OST-A Col-0/ 0.33 2.11 1.77 0.46 5.22* 1.23 1.66 OST-B OST-A/ 0.89 1.24 0.82 0.46 11.57* 1.93 0.64 OST-B

4.2.2 Sucrose

Sucrose was quantified extracting m/z 361 from a full scan (60-600 m/z) of a diluted sample due to its high abundance relative to trehalose. To quantify the data, a calibration curve with concentrations of 1 µM to 30 µM was used, resulting in a linear trend line with a correlation coefficient of 0.9898 (Figure 4.3). Despite the linearity, the mean RSD of the curve increased in each of the third, fourth and fifth data points. The overall mean RSD of the sucrose calibration curve was 52.7%, and the range RSD was

84.6% (Table 4.2). From the standard curve the LOD and LOQ were calculated to be

24.4 ng and 81.4 ng, respectively (Table 4.1). Sucrose had the lowest LOD and LOQ of all sugars quantified in this study.

Sucrose Calibration Curve y = 0.4489x - 0.3211 R² = 0.9894 16 14 12 10 8 6 4 Normalized Peak Area Peak Normalized 2 0 0 5 10 15 20 25 30 35 Concentration μM

Figure 4.9 Calibration curve for sucrose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement.

Interestingly, sucrose varied the most within sample replicates. The only statistically significant difference was found comparing the air samples of OST-A and

OST-B, where OST-A has higher sucrose concentrations (Figure 4.4). The few biological differences can be due to the high RSD of sucrose within sample replicates. The error bars, which represent the standard deviation of the three replicates, are in some cases larger than the actual averages. In this data set, the lowest RSD is 33% in OST-A sample at 48 hr air, and the rest ranged from 52% to well over 100%.

Sucrose

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Figure 4.10 A comparison of the average concentrations of sucrose in Arabidopsis. The error bars represent the standard deviation calculated from each set of replicates.

Sucrose changed significantly during the course of the treatment. In OST-A, the sucrose concentrations for the air samples ranged from 122.4 to 663.5 μg/g, and the submerged samples ranged from 5.2 to 213.9 μg/g (Table 4.6). The highest concentration was found at t = 4 hrs (air), and the lowest sucrose concentration was at the t = 48 hrs submerged and the t = 52 hrs air time points. In OST-B, the sucrose concentrations for the air samples range from 51.9 to 316.3 μg/g, and in the submerged sample the concentrations ranged from 9.2 to 404.0 μg/g. For the air and submerged OST-B, the

98 lowest concentration was found at the 52 hrs time point and the highest concentration was found at the 4 hr time point. For the Col-0 genotype, sucrose concentrations in the air samples ranged from 12.5 to 213.5 μg/g, and the submerged samples ranged from 11.8 to

206.3 μg/g. The 52 hrs air and the 4 hrs submerged time points had the highest concentrations, while the 4 hr air and the 52 hr submerged had lowest sucrose concentrations.

Table 4.9 Average concentrations of sucrose (μg of sucrose per a g of tissue). The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control.

t = 4 t = 4 t = 48 t = 48 t = 52 t = 52 Genotype t = 0 Submerged Air Submerged Air Submerged Air OST-A 420.8 213.9 663.5 5.2 193.6 153.8 122.4 OST-B 170.4 404.0 316.3 75.0 145.8 9.2 51.9 Col-0 180.0 206.3 12.5 37.4 155.7 11.8 213.5

Comparing each genotype, the only statistically significant difference due to submergence was found at the t = 52 hr time point between Col-0 and OST-A. There were only 3 statistically significant differences comparing the air control time points between the genotypes. The Col-0 genotype compared with the OST-A and OST-B genotypes had only a factor of 0.02 and 0.04 of glucose concentration, respectively, for the t = 4 hr time point. Between the OST-A and OST-B genotypes, sucrose levels were generally higher in OST-A air control samples, with a ratio of 2.47, 2.10, 1.33, and 2.36 for the t= 0 hr, 4 hr, 48 hr and 52 hr, respectively (Table 4.7). Comparing the air and submerged samples, the ratios ranged from 37.24 to 0.006 with only one statistically significant difference at the t = 48 hr time point (Table 4.8).

Table 4.10 Ratios of average sucrose concentrations comparing the different genotypes. The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. The asterisk indicates genotypes that are significantly different at the 90% confidence limit.

t = 4 t = 4 t = 48 t = 48 t = 52 t = 52 Genotype t = 0 Submerged Air Submerged Air Submerged Air Col-0/ 0.43 0.96 0.02* 7.19 0.80 0.08* 1.74 OST-A Col-0/ 1.06 0.51 0.04* 0.50 1.07 1.29 4.11 OST-B OST-A/ 2.47* 0.53 2.10 0.07 1.33 16.74 2.36 OST-B

Table 4.11 Ratios of the average sucrose concentrations comparing air to submerged samples for the t = 4, 48 and 52 hr time points. The asterisk indicates samples that are significantly different at the 90% confidence limit.

Genotype t = 4 t = 48 t = 52 OST-A 3.10 37.24* 0.80 OST-B 0.78 1.94 5.65 Col-0 0.06 4.17 18.07

4.2.3. Glucose

Because glucose is present at high concentrations in Arabidopsis extracts, a dilution was made to be able to more accurately quantitate the metabolite without risking overloading the detector causing ion suppression. Also, due to the high abundances of glucose, the internal standard was changed and methyl stearate was used instead of labeled glucose to avoid quantitation errors from ion suppression. Glucose was quantified from the 319 m/z EIC taken from the full scan method (60-600 m/z). The glucose standard curve resulted in a reasonably linear line with a correlation coefficient of 0.9716

(Figure 4.1). The glucose standard curve was produced with six samples analyzed, ranging in concentrations of 0.176 µM to 8.0 µM, with a mean RSD of 31.5% and a

100 range of32.1% (Table 4.2). The RSD for glucose was high because the third fourth and fifth data points have large standard deviations. From the calibration curve, the LOD and

LOQ of glucose were found to be 2.97 and 9.91 ng, respectively (Table 4.1). The LOD and LOQ of glucose was significantly higher than the other sugars because of the higher standard deviation of the first data point (lowest concentration), which is used for the calculations.

y = 3.3632x + 1.0032 Glucose Calibration Curve R² = 0.9716 35

10 Normailzed Peak Area Peak Normailzed 5

0 0 1 2 3 4 5 6 7 8 9 Concentration μM

Figure 4.11 Calibration curve for glucose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement. As with sucrose, glucose was also found to be highly variable between replicates of the Arabidopsis samples. The most reproducible samples were found in OST-A 48 hr air, 48 hr submerged and 52 hr submerged. All three of these sets of sample replicas have

RSD of 22%. The rest of the sets of sample replicas had RSD of 40% and higher. There are three sets that result in RSD of greater than 100% (Figure 4.6).

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Figure 4.12 A comparison of the average concentrations of glucose in Arabidopsis. The error bars represent the standard deviation calculated from each set of replicates. For all measured sugars, glucose had the most significant differences between genotypes and treatments. In OST-A, the glucose concentrations in the air samples ranged from 80.9 to 141.4 mg/g and in the submerged samples ranged from 37.8 to 118.5 mg/g (Table 4.9). In OST-A, both air and submerged, glucose had higher concentrations at the 52 hr sample, however, for samples grown in air, the lowest concentration of glucose was in the 4 hr sample, and in the submerged sample the lowest concentration was at the 48 hr time point. In OST-B, the glucose concentrations for the air samples ranged from 26.0 to 73.9 mg/g and in the submerged samples ranged from 10.7 to 18.7 mg/g with the lowest concentration of glucose in 4 hr air and 52 hr submerged, and the highest glucose concentrations were at the 52 hr air and four hr submerged time points.

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Table 4.12 Average concentrations of glucose (mg of glucose per a g of tissue). The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control.

t = 4 t = 4 t = 48 t = 48 t = 52 t = 52 Genotype t = 0 Submerged Air Submerged Air Submerged Air OST-A 64.1 39.6 80.9 37.8 96.9 118.5 141.4 OST-B 25.2 18.7 26.0 11.0 38.8 10.7 73.9 Col-0 44.9 62.7 24.4 34.7 49.7 22.6 43.2

A comparison of the air and submerged samples shows higher concentrations of glucose in the air samples compared with the submerged samples (Table 4.10). For the

OST-A genotype, the ratio of glucose in air and submerged samples ranged from 1.19 to

2.57. For the OST-B genotype, the ratio of glucose in the air and submerged samples ranged from 1.39 to 6.88, and for Col-0, ranged from 0.39 to 1.91.The ratio of glucose in the air and submergence treated samples were statistically significant for the Col-0 genotype (t = 4) and OST-A genotype (t = 48), indicating submergence treatment had a significant effect on central carbon metabolism.

Table 4.13 Ratios of the average glucose concentrations comparing air to submerged samples for the t = 4, 48 and 52 hr time points. The asterisk indicates samples that are significantly different at the 90% confidence limit.

Genotype t = 4 t = 48 t = 52 OST-A 2.04 2.57* 1.19 OST-B 1.39 3.53 6.88 Col-0 0.39* 1.43 1.91

To better understand how glucose is different between the genotypes, a table of ratios was generated to compare each genotype during the time course (Table 4.11).

Interestingly, glucose levels in the submergence-treated OST-B were consistently lower compared with submergence treated OST-A and Col-0. For example, Col-0/OST-B ratios ranged from 2.10 to 3.35 and OST-A/OST-B ranged from 1.91 to 11.0. The genotypes also differed in the corresponding controls. The Col-0/OST-A comparison

103 shows more glucose is significantly lower in the Col-0 at the t = 4, 48, and 52 hr air samples while the Col-0/OST-B comparison had no significant differences in the control samples. The OST-A/OST-B air control comparison only had one significant time point at t = 48 hr, where OST-A had a factor of 2.49 more glucose than OST-B.

Table 4.14 Ratios of average glucose concentrations comparing the different genotypes. The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. The asterisk indicates samples that are significantly different at the 90% confidence limit.

t = 4 t = 4 t = 48 t = 48 t = 52 t = 52 Genotype t = 0 Submerged Air Submerged Air Submerged Air Col-0/ 0.70 1.58 0.30* 0.92 0.51* 0.19* 0.31* OST-A Col-0/ 1.78 3.35* 0.94 3.15* 1.28 2.10 0.58 OST-B OST-A/ 2.54 2.12 3.11* 3.43 2.49* 11.04* 1.91 OST-B

4.2.4 Fructose

Fructose was detected using a full scan method, scanning 60-600 m/z, and was then quantified with the EIC of the 307 m/z from diluted samples. The calibration curve for fructose did result in a reasonably linear trend line with a correlation coefficient of

0.9751 (Figure 4.7). The calibration curve was made by a series of six samples ranging in concentrations from 0.176 µM to 8.0 µM with a mean RSD of 32.0% and a range of

30.7% (Table 4.2). The LOD and LOQ of fructose were calculated from the standard curve, resulting in an LOD of 3.22 and an LOQ of 10.7 ng (Table 4.1). The LOQ and

LOD of fructose were the highest of all quantified sugars in this study.

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y = 1.4757x + 0.5899 Fructose Calibration Curve R² = 0.9751 14

Normailzed Peak area Peak Normailzed 2

0 0 1 2 3 4 5 6 7 8 9 Concentration μM

Figure 4.13 Calibration curve for fructose. Each point was measured and triplicate and reported as the average and the error bars represent standard deviation of the measurement. As with sucrose and glucose, fructose also varied significantly in a majority of the samples. The most reproducible sample set was OST-A at time zero and 48 hr air samples, which resulted in a RSD of 14%. The RSD for the rest of the sample sets was

30% - 100%. The samples that are over 100% are Col-0 48 hr air and 48 hr submerged.

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200 µg fructose / g of tissue / µg fructose 0 0 4-Submerged 4-Air 48-Submerged 48-Air 52-Submerged 52-Air -200 OST-A OST-B Col-0

Figure 4.14 A comparison of the average concentrations of fructose in Arabidopsis. The error bars represent the standard deviation calculated from each set of replicates.

Fructose concentrations change significantly between genotypes and treatment conditions. In OST-A, the average fructose concentrations in the air samples ranged from

162.4 to 234.0 μg/g, and the average fructose concentrations in the submerged samples ranged from 32.8 to 781.1 μg/g (Table 4.12). In OST-A, there was higher concentration of fructose in the 52 hr submerged and air samples, and lower concentrations were in the

4 hr air and 48 hr submerged. OST-B is similar to OST-A, except the ranges of fructose concentration in the air samples were 63.4 to 164.2 μg/g, and in submerged samples 43.6 to 99.9 μg/g. In Col-0 fructose concentrations in the air sample range from 70.0 to 131.4

μg/g, and in the submerged samples the concentrations range from 111.9 to 238.2 μg/g.

The highest concentrations of fructose were found in t = 4 hr (submerged) and t = 48 hr

(air) samples, and the lowest concentrations of fructose were found in in t = 4 hr (air) and t = 52 hr (submerged).

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Table 4.15 Average concentrations of fructose (μg of fructose per g of tissue). The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control.

t = 4 t = 4 t = 48 t = 48 t = 52 t = 52 Genotype t = 0 Submerged Air Submerged Air Submerged Air OST-A 199.3 130.4 162.4 32.8 227.9 781.1 234.0 OST-B 89.8 61.1 63.4 43.6 119.8 99.9 164.2 Col-0 108.7 238.2 70.0 134.3 131.4 111.9 100.4

The changes in fructose concentration as a function of submergence or air treatment become even more exaggerated when comparing air and submergence treated plants (Table 4.13). The OST-B genotype was found to have higher concentrations of fructose in the air samples compared with the submerged samples, resulting in ratios ranging from 1.04 to 2.75. In contrast, ,Col-0 had lower fructose concentrations in the air controls compared with the submergence treated plants, ranging from a factor of 0.29 to

0.90.

Table 4.16 Ratios of the average fructose concentrations comparing air to submerged samples for the t = 4, 48 and 52 hr time points. The asterisk indicates samples that are significantly different at the 90% confidence limit.

Genotype t = 4 t = 48 t = 52 OST-A 1.25 6.95* 0.30* OST-B 1.04 2.75 1.64 Col-0 0.29* 0.98* 0.90*

Comparing the average fructose concentrations of Col-0 to OST-A air samples,

OST-A generally has higher fructose concentrations than Col-0 with ratios ranging from

0.43 to 0.55 (Table 4.14). In contrast, Col-0 has equal or higher concentrations of fructose compared with OST-B, with the exception of t = 52 hr. The amount of fructose in the submergence-treated Col-0 and OST-B is also significantly higher in the Col-0, indicating a different mechanism of carbon metabolism. A comparison of the two drought resistant genotypes, OST-A and OST-B air samples, indicates there are higher

107 concentrations of fructose in OST-A than in OST-B, with ratios ranging from 1.43 to

2.56.

Table 4.17 Ratios of average fructose concentrations comparing the different genotypes. The time points are as follows: t=0 (initial control), and t=4, 48, 52 hr submerged or air control. The asterisk indicates samples that are significantly different at the 90% confidence limit.

t = 4 t = 4 t = 48 t = 48 t = 52 t = 52 Genotype t = 0 Submerged Air Submerged Air Submerged Air Col-0/ 0.55 1.83* 0.43 4.09 0.58 0.14* 0.43* OST-A Col-0/ 1.21 3.90* 1.10 3.08 1.10 1.12 0.61 OST-B OST-A/ 2.22 2.14 2.56* 0.75 1.90* 7.82* 1.43 OST-B

4.2.5 Internal standard methyl stearate

The internal standard (IS) methyl stearate was used to normalize for injection variability. However, when the areas of the internal standard were plotted for each sample there appeared to be a significant amount of variance across the sample set when compared with 13C-glucose (Figure 3.9, Figure 3.10) in both full scan and SIM methods.

For the SIM method (m/z 255), the peak area of the IS for each sample ranged from approximately 28000 to 870,000 (Figure 4.1), resulting in an average peak area of

210,000 and a mean RSD of greater than 100%. The diluted injections using the full scan method (60-600 m/z) also showed a wide range of injection variability when methyl stearate was the internal standard. The peak areas of the internal standard (integrated from the EIC for 255 m/z) ranged from 7,400 to 130,000 (Figure 4.2), resulting in an average peak area of 30,000 and an RSD of 98.9%. The high RSD of stearic acid associated with both types of analysis indicates there may have been errors other than injection variability associated with the measurements. Despite the high IS variability,

108 there is still a clear indication that a significant portion of the sample variance is due to biological variability.

Peak area of internal standard in SIM samples 1000000

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0 0 10 20 30 40 50 60 70 80 90 Peak area of internal standard internal area of Peak Number of samples

Figure 4.15 Peak area of methyl stearate using the SIM method. Samples 1-28 are OST- A, samples 29-56 are OST-B, and samples 57-84 are Col-0

Peak area of internal standard in full scan samples 140000

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Figure 4.16 Peak of methyl stearate using the full scan method. Samples 1-28 are OST- A, samples 29-54 are OST-B, and samples 55-82 are Col-0

4.2.6 Biological variance

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Although it was clear there were problems with methyl stearate as an IS, the contribution of biological variance to the overall analysis can be evaluated. To do this, changes in peak area for other metabolites can be compared across sample replicates.

Peak areas from 10 other metabolites (serine, oxoproline, threonic acid, malic acid, glutamate, citrate, asparagine, glutamine, myo-inositol, and palmitate) were compared across replicates analyzed by the full scan method.

Table 4.18 Metabolite identification and corresponding retention times used to investigate the biological variance in the Arabidopsis samples.

Metabolite Retention time (min) Serine 11.087 Oxoproline 13.233 Threonic acid 13.614 Malic acid 12.754 Glutamic acid 14.168 Citric acid 16.560 Asparagine 14.979 Glutamine 16.099 Myo-inositol 19.259 Palmitic acid 19.689

Comparison of OST-A samples, t = 0 to evaluate biological variance

To determine if biological variance was contributing to the overall variance of the analysis, the mean – RSD was calculated considering both raw peak areas and normalized peak areas (normalized to methyl stearate) of oxoproline, glutamine, sucrose, glucose, and fructose (Figure 4.11). It was found that glutamine had stable concentrations in the four replicates for the normalized peak areas, but was less reproducible when considering the raw peak areas. The glutamine concentration had a mean - RSD of 8.9% when normalized to the IS, and is 20.4% without normalization. Oxoproline had the largest difference in each sample with an RSD of 61.1% with and without normalizing

110 the peak areas. Fructose, glucose, and sucrose had an RSD of 14.1, 20.2, and 52.1%, respectively when normalized. The mean – RSD of the raw peak areas for fructose, glucose, and sucrose was found to be 20.1, 27.4, and 46.9%, respectively. Because the overall RSD of each measurement was usually lower in the normalized data, it would appear that normalizing to the IS did not increase the variance but actually decreased it.

Comparing the intra-sample abundances of glutamine to fructose, the ratio ranged from

1.1 to 1.6, this narrow range means the ratios were reproducible in the four replicates.

However, comparing glucose to glutamine, the intra-sample ratio ranged from 5.5 to 8.7, and comparing glutamine with sucrose the intra-sample ratio range from 0.5 to 2.1. Taken together with the relatively low RSD of glutamine (for both raw and normalize peak areas), the high variation of the sugars and low variation of glutamine (and other metabolites) indicate that there was a significant amount of biological variance in these samples.

4.3 Conclusion

Quantifying trehalose and the other major abundant carbohydrates, glucose fructose, and sucrose by GC-MS is not trivial. Despite careful experiment design for sample analysis, biological variance can greatly affect the experimental outcome. For these experiments, biological variance was significantly high enough that very few biological conclusions can be made. To quantify the variance, metabolites from the full scan diluted samples were quantified with the goal of measuring sample uniformity across biological replicates. Some metabolites, such as citric acid, palmitic acid, serine,

111 myo-inositol, glutamic acid, glutamine, and asparagine were found to have significantly lower variation across replicates compared with sucrose, fructose, and sucrose.

In addition to high biological variance, the technical variance was found to be high for the quantitation of sucrose, glucose, fructose, and trehalose. The standard curves for all sugars had large relative standard deviations that would limit the reproducibility. A significant portion of the variability can be attributed to variance in the internal standard, methyl stearate. Comparing the 13C-labeled glucose areas from the rice experiments with methyl stearate from the Arabidopsis experiments revealed that glucose had more consistent peak areas while methyl stearate varied significantly. Although the reasons are unclear, methyl stearate is known to be a common oil found on human fingers and it is possible that either the glassware, samples, or other part of the apparatus were contaminated with oil from the skin.

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(a)

(b)

(c)

(d)

Figure 4. 117 TIC of OST-A, Air samples at time zero (a) bio rep 1, A01, (b) bio rep 2, A02, (c) bio rep 3, A03, (d) bio rep 4, A04

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4.4 REFERENCES

(1) Steven D. Rounsley, Maarten Koornneef, David W., M. J. M. C. C. D. Science (80-. ). 1998, 282 (5389), 662–682.

(2) Fahlgren, N.; Montgomery, T. a.; Howell, M. D.; Allen, E.; Dvorak, S. K.; Alexander, A. L.; Carrington, J. C. Curr. Biol. 2006, 16 (9), 939–944.

(3) Boyes, D. C.; Zayed, a M.; Ascenzi, R.; McCaskill, a J.; Hoffman, N. E.; Davis, K. R.; Görlach, J. Plant Cell 2001, 13 (7), 1499–1510.

(4) Penna, S.; Atomic, B.; Teixeira, J. a; Retired, S. 2006, No. November 2015.(what type of journal)

(5) Müller, J.; Aeschbacher, R. a; Wingler, a; Boller, T.; Wiemken, A. Plant Physiol. 2001, 125 (2), 1086–1093.

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CHAPTER 5: SUMMARY AND CONCLUSION

In this project the purpose was to focus on the sensitivity, detection, and quantification of trehalose and other largely abundant sugars in submergences resistance rice and Arabidopsis tissue by GC-MS. GC-MS is a widely used technique in metabolomics because of the high sensitivity, reproducible fragmentation patterns and reproducible retention times of the analytes. These are qualities that are helpful when trying to quantify low abundance metabolites, making GC-MS the preferred method for the trace analysis of trehalose. Trehalose is of interest in plants because in previous studies it has been associated with stress protection.1,2

The M202 and M202(Sub1) rice were grown and submerged for three days and were harvested directly after submergence, or were allowed to recover and harvested at either dusk, midnight, dawn, or noon post submergence. The submerge samples were grown alongside a set of control samples used for comparison and harvested simultaneously. The IR64 rice was harvested directly after submergence. After the rice was grown the tissue was harvested, dried, and extracted. In addition, weak anion SPE was carried out to help further clean up the samples. To increase volatility, all samples were derivatized prior to injection onto the GC-MS. Due to the high abundance of sucrose in rice, two samples were ran at different concentrations both using a SIM method. Trehalose, glucose and fructose were quantified in a 1 mg of tissue per mL of sample, and sucrose was quantified in a 10 µg of tissue per mL of sample.

Arabidopsis thaliana was grown and submerged for different time periods. The

Arabidopsis plants were submerged for four hours, 48 hours and 52 hours, while an air sample and a zero time sample were grown for comparison. These plants were then

115 harvested directly after submergence. After the plant tissue was dried, the metabolites were extracted and derivatized for GC-MS analysis. The Arabidopsis samples were ran in two different concentrations due to the overwhelming abundance of glucose, sucrose, and fructose. The samples were analyzed using an SIM method to quantify trehalose where the concentration in each vial was 1 mg of tissue per mL of sample, and glucose, fructose and sucrose was quantified using a full scan method where the final concentration in each vial was 10 µg of tissue per mL of sample.

The standard curves were generated by measuring a series of six samples of different concentrations. The standard curves produced normalizing to 13C - labeled glucose was used to correct for injections errors for the rice samples and resulted in linear trend lines for all four sugars. Trehalose was found to be the most reproducible with a mean RSD of 8.64%, typically accepted as very good for measurements acquired in mass spectrometry, unlike sucrose, glucose, and fructose which had RSD of 37.9, 21.5 and

28.2%, respectively. These higher values suggest that there is some error associated with these measurements. The standard curves generated by normalizing to methyl stearate to quantify the Arabidopsis samples did not produce linear trend lines for all sugars.

Trehalose produced a second degree polynomial curve while sucrose, glucose and fructose all had linear trend lines. None of the mean - RSD of the standard curves are in the acceptable range for measurements taken using mass spectrometry. The trehalose standard curve resulted in a RSD of 37.4% while sucrose, glucose and fructose had an

RSD of 52.7, 31.5, and 32.0%, respectively. Such high RSD was surprising for a standard curve as having a low RSD is important to show reproducibility at these concentrations.

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Because of the low concentration of trehalose, the LOD and LOQ were important to understand and to ensure accurate quantitation of the analytes. Different internal standards were used for the rice and Arabidopsis projects, therefore an LOD and LOQ was calculated for each. The LOD and LOQ for trehalose in the rice were determined to be 0.297 ng/g and 0.989 ng/g, respectively, and in the Arabidopsis project the LOD and

LOQ were determined to be 0.087 µg/g and 0.292 µg/g, respectively. For glucose the

LOD and LOQ for the rice project was determined to be 1.39 ng/g and 4.64 ng/g, and in the Arabidopsis project the LOD and LOQ were determined to be 2.97 µg/g and 9.91

µg/g. For fructose the LOD and LOQ for the rice project was determined to be 6.85 ng/g and 22.8 ng/g, and for the Arabidopsis project the LOD and LOQ was determined to be

3.22 µg/g and 10.7 µg/g. For sucrose, the LOD and LOQ for the rice project was determined to be 7.62 ng/g and 25.4 ng/g, and the LOD and LOQ for the Arabidopsis project was determined to be 24.4 ng/g and 81.4 ng/g. Concentrations of the sugars in all plants were well above the LOQ.

M202 and M202(Sub1) rice tissue were found to have the highest reproducibility of all the projects. Trehalose had a mean RSD ranging 0.36 to 10.2%, but averaging an overall RSD of 3.20%. Fructose had a mean RSD ranging 4.33 to 40.4%, and averaging an overall RSD of 13.4%. Glucose had a mean RSD ranging from 3.6 to 55.2%, and averaging an overall RSD of 21.5%. Sucrose was found to be the most unreproducible having a mean RSD ranging from 15.8 to 55.8%, and averaging an overall RSD of 36.4%

These relatively high RSD suggests biological variance played a role in the precision of the analysis.

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IR64 rice was found to have very poor reproducibility in this project. Trehalose was found to have the best mean RSD of all the sample replicas ranging from 1.9 to

61.8% and resulted in an overall average of 19.7%. Glucose had a mean RSD, ranging from 26.7 to greater the 100%, and resulted in an overall average RSD of 68.1%. Sucrose had a mean RSD ranging from 36.7 to 91.1%, and averaging an overall RSD of 64.5%.

Fructose was the least reproducible in the IR64 rice project with a mean RSD ranging from 40.8 to greater than 100%, and an overall average RSD of 76.1%. These results show that very few of these samples were very reproducible and can be attributed to biological variance.

In the Arabidopsis project, there was very poor reproducibility for all four of the quantified sugars. For each set of four sample replicas, trehalose resulted in a mean RSD ranging 17.3 to greater than 100%, glucose had a mean RSD ranging from 22.0 to grater than 100%, fructose had a mean RSD ranging from 14.8 to greater than 100%, and sucrose resulted in a mean RSD ranging from 33.0 to greater than 100%. Trehalose had an overall average RSD of 83.3%, glucose had an overall average RSD of 59.4%, fructose had an overall average RSD of 56.5%, and sucrose had an overall average RSD of 83.7%. Because of the poor performance of the internal standard, various metabolites were used to evaluate whether the error source was technical or biological. Citric acid, palmitic acid, serine, myo-inositol, glutamic acid, glutamine, and asparagine were found to be significantly more consistent across the samples, suggesting that the variations in sugar content are mostly attributed to biological variance.

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5.1 REFERENCES

(1) Penna, S.; Atomic, B.; Teixeira, J. a; Retired, S. 2006, No. November 2015.

(2) Müller, J.; Aeschbacher, R. a; Wingler, a; Boller, T.; Wiemken, a. Plant Physiol. 2001, 125 (2), 1086–1093.

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CHAPTER 6: FUTURE WORK

Because of the variance associated with this project for both rice and Arabidopsis projects, as well as in the standard curves, significant method development is needed to assess and eliminate sources of error. One approach would be to use a different technique for quantification, such as analysis by NMR spectroscopy of the more abundant sugars, to re-analyze the Arabidopsis samples by GC-MS with a different internal standard, and decrease the dwell time for the SIM method to increase sampling rate.

A previous study done by Barding et al. determined that the glucose and sucrose concentrations are quantifiable in M202 and M202(sub1) rice by 1H NMR, however trehalose is only quantifiable by GC-MS.1 NMR is known to be one of the more regularly used methods in metabolomics because of its highly quantitative nature, and the results are very reproducible. By using NMR to quantify these metabolites we will be able to determine if the relative standard deviations calculated are actually that significantly different or instrumental error played a factor. If the concentrations of the samples are confirmed, the NMR analysis will demonstrate the reproducibility and quantitation properties of GC-MS suggesting the error

Arabidopsis samples were analyzed with methyl stearate as the internal standard.

13 Originally C6 glucose was used as the internal standard, but because of the large abundances of glucose in the samples, methyl stearate was used to not interfere with ionization or cause ion suppression. However, methyl stearate was found to fluctuate significantly. The internal standard fluctuated between 28,000 and 820,000 counts in the

SIM method when quantifying for trehalose. Comparing the calibration curves in chapter

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3 and chapter 4, the samples were analyzed with fructose, glucose, sucrose and trehalose as well as both internal standards labeled glucose and methyl stearate. Normalizing the data to the different internal standard was clearly causing some of the error. When normalized to carbon - 13 labeled glucose, the calibration curve for trehalose is linear, and when normalized to methyl stearate the calibration curve was a polynomial curve.

Also, the glucose and fructose standard curves are more linear when normalized to labeled glucose, and have smaller error bars. The standard curves with the methyl stearate as the internal standard had larger error bars of standard deviation, which also resulted in significantly higher limits of detection and limits of quantifications.

Lastly, the dwell time for the SIM methods need to be shortened. It was determined after the completion of the project that the ion dwell time was actually higher in the full scan method then in the SIM method. This could have played a significant factor into the poor results, and low reproducibility of the project because the peaks were not being sampled enough.

In this project the results were not very reproducible, especially in the

Arabidopsis project. Therefore future work would include changing the internal standard for something other than methyl stearate, decreasing the ion dwell time on the instrument and complete reanalysis of the samples by GC-MS. Also, future work would include 1H

NMR of the samples for the more abundant glucose and sucrose to evaluate sources of error.

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6.1 REFERENCES

(1) Barding, G. a.; Béni, S.; Fukao, T.; Bailey-Serres, J.; Larive, C. K. J. Proteome Res. 2013, 12 (2), 898–909.

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