Exploring The Effects of Abiotic Stress on Plants Using

EXPLORING THE EFFECTS OF ABIOTIC STRESS ON PLANTS 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

In

Chemistry

By Ambria Pogue

2021

ii

SIGNATURE PAGE

THESIS: EXPLORING THE EFFECTS OF

ABIOTIC STRESS ON PLANTS USING GC-MS

AUTHOR: Ambria Pogue DATE SUBMITTED: Spring, 2021

Department of Chemistry and Biochemistry Gregory A. Barding Jr. Ph.D. ____________________________________ Thesis Committee Chair Chemistry and Biochemistry Kathryn M. McCulloch Ph.D. ____________________________________ Chemistry and Biochemistry Yan Liu Ph.D. ____________________________________ Chemistry and Biochemistry

iii

ABSTRACT

As food insecurity persists worldwide, finding ways to enhance crop production

in adverse conditions is increasingly important. Plants are faced with numerous

environmental stressors, such as aluminum toxicity and flooding. Gas chromatography-

mass spectrometry (GC-MS) provides both a sensitive and reproducible method to study

plants exposed to these stressors and identify biochemical markers significant for

survival. Through GC-MS, root tissue, root exudate, and shoot apical meristem extracts

were analyzed to explore the ethylene hormone cascade, quantify organic acids in a

phosphoenoloyruvate carboxylase (PPC) knockdown in relation to aluminum toxicity,

and study the effects of submergence on both submergence tolerant and intolerant rice

variants respectively. A GC-MS method was first developed by analyzing several

standards, including sugars and TCA cycle intermediates. Using retention times and

characteristic ions of those standards, a selected ion monitoring (SIM) method was

developed to improve the sensitivity for the targeted analytes. After development of the

GC-MS method, 1-aminocyclopropane-1-carboxylic acid (ACC), an ethylene precursor,

was first quantified in root tissue extracts. Although ACC was readily identified in the

standard, it was not detected in the root tissue, suggesting that the observed phenotype

was not due to the ethylene hormone cascade, but another stress response. The roots

were also investigated for their role in resistance to aluminum toxic soils, including

relationship of PPC (an important enzyme in the production of TCA cycle intermediates)

to root exudation of the organic acids malate and citrate. After PPC was knocked down,

malate showed a decrease in exudate concentration from an average of 44.99 µM in the

wild type to 14.90 µM in the knockdown. Citrate, however, showed no significant

iv

difference in concentration between the two genotypes, suggesting that malate is a key

player in aluminum resistance and its exudation is directly affected by PPC. Finally, the

effects of submergence stress on the shoot apical meristem of rice plants were also

studied. The two genotypes evaluated were the M202 (submergence intolerant) and

M202(sub1) (submergence tolerant) genotypes, which were analyzed after a 4-day

submergence period followed by a 1-day recovery. Several analytes including citrate,

malate, sucrose, glucose, fructose, trehalose, GABA, 4-hydroxybenzoate, 5-oxoproline,

and hydroxyproline were identified and their relative concentrations were compared

among the two genotypes. Although no difference was observed between the two

genotypes, detection of these compounds in the shoot apical meristem suggests that the

developed GC-MS method is sufficient to study metabolism of the shoot apical meristem

(SAM) in response to environmental stressors.

v

TABLE OF CONTENTS

SIGNATURE PAGE--------------------------------------------------------------------------------ii

ABSTRACT-----------------------------------------------------------------------------------------iii

LIST OF TABLES---------------------------------------------------------------------------------vii

LIST OF FIGURES-------------------------------------------------------------------------------viii

CHAPTER 1: INTRODUCTION------------------------------------------------------------------1

1.1 Plant Metabolomics--------------------------------------------------------------------1

1.2 ACC and Ethylene----------------------------------------------------------------------2

1.3 Aluminum Toxicity and PPC---------------------------------------------------------2

1.4 Flooding and the Shoot Apical Meristem (SAM) ---------------------------------6

1.5 GC-MS-----------------------------------------------------------------------------------9

1.5.1 Instrumentation------------------------------------------------------------9

1.5.2 Derivatization------------------------------------------------------------10

1.5.3 Separation-----------------------------------------------------------------11

1.5.4 Ionization-----------------------------------------------------------------11

1.5.5 Mass Analyzer-----------------------------------------------------------13

1.6 Overview-------------------------------------------------------------------------------14

1.7 References------------------------------------------------------------------------------16

CHAPTER 2: MATERIALS AND METHODS-----------------------------------------------23

2.1 Materials--------------------------------------------------------------------------------23

2.2 Preparation of Standards and Samples---------------------------------------------23

2.2.1 Standards--------------------------------------------------------------------23

2.2.2 Root Exudate and Root Tissue-------------------------------------------24

vi

2.2.3 Shoot Apical Meristem----------------------------------------------------24

2.3 Derivatization--------------------------------------------------------------------------25

2.4 Instrumentation and Experimental Parameters------------------------------------25

2.5 Data Analysis--------------------------------------------------------------------------27

CHAPTER 3: RESULTS AND DISCUSSION------------------------------------------------28

3.1 Standard Quantification--------------------------------------------------------------28

3.2 Development of a SIM Method for Targeted Metabolite Profiling-------------30

3.3 ACC Quantification in the Root Tissue--------------------------------------------33

3.4 Analysis of the Root Exudate and PPC Knockdown-----------------------------35

3.5 Analysis of the Shoot Apical Meristem--------------------------------------------39

3.6 Summary-------------------------------------------------------------------------------42

3.7 References------------------------------------------------------------------------------43

CHAPTER 4: CONCLUSIONS------------------------------------------------------------------50

4.1 References------------------------------------------------------------------------------53

CHAPTER 5: FUTURE WORKS----------------------------------------------------------------60

vii

LIST OF TABLES

Table 2.1 The specific time and ions selected for each quantified standard while using the SIM method-------------------------------------------------------------------------------------27

Table 3.1 The retention times and characteristic ions of the highest intensity for the standards---------------------------------------------------------------------------------------------29

Table 3.2 Peak areas and concentrations for both malate and citrate in both the PPC knockdown and wild type plant samples contained in the root exudate as determined by GC-MS-----------------------------------------------------------------------------------------------39

viii

LIST OF FIGURES

Figure 1.1 The chemical structures of 1-aminocyclopropane-1-carboxylic acid (ACC), the ethylene precursor, and ethylene---------------------------------------------------------------2 Figure 1.2 PEP is converted to oxaloacetate via PPC. Oxaloacetate then directly feeds into the TCA cycle by producing either citrate or malate---------------------------------------6 Figure 1.3 Silylation of malate. MSTFA is used to add silyl groups onto the labile hydrogens of malate to make the metabolite volatile enough to be ran through GC-MS-----------------------------------------------------------------------------------------------10 Figure 1.4 a) Schematic of electron ionization in which the electron beam hits the molecule to form a molecular ion, and then the molecular ion interacts with more electrons to create reproducible fragmentation. The charges species of the fragmentation are then sent to the mass analyzer. b) Schematic of chemical ionization in which the reagent gas interacts with an electron beam and becomes ionized. The ionized reagent gas then interacts with the molecule to create a molecular ion that can be detected by the mass analyzer----------------------------------------------------------------------------------------13 Figure 3.1 Extracted ion chromatogram (ion 233 m/z) of the malate standard with the corresponding MS spectrum results that represent the reproducible m/z ratios created by electron ionization----------------------------------------------------------------------------------29 Figure 3.2 A comparison of extracted ion chromatograms for the SIM and scan modes. a) Scan mode EIC (m/z 273) for citrate compared to b) the EIC SIM chromatogram (m/z 273) for citrate, c) scan mode EIC (m/z 233) for malate compared to d) the EIC SIM chromatogram (m/z 233) for malate, e) scan mode EIC (m/z 113) for oxaloacetate compared to f) the EIC SIM chromatogram (m/z 133) for oxaloacetate, and g) scan mode EIC (m/z 117) for butyrate compared to h) the EIC SIM chromatogram (m/z 117) for butyrate-----------------------------------------------------------------------------------------------31 Figure 3.3 A bar graph demonstrating the quantifiable difference in signal between the EIC from the scan mode and EIC from the SIM chromatograms for citrate (m/z 273), malate (m/z 233), oxaloacetate (m/z 133), and butyrate (m/z 117) --------------------------33 Figure 3.4 The quantification of an ACC standard and plant tissue extract. A) is an extracted ion chromatogram (m/z 128) of the derivatized blank, b) is an extracted ion chromatogram (m/z 128) of the standard ACC, and c) is an extracted ion chromatogram (m/z 128) of the plant tissue which does not contain ACC. The chromatogram shown in this figure has three peaks around the retention time of ACC. This is because the instrument was in a mode that alternated between the total scan and SIM method--------34 Figure 3.5 EICs from root exudate samples for both ion m/z 233 (malate) and 273 (citrate). Part a) and c) represent the PPC knockdown whereas b) and d) represent the wild type---------------------------------------------------------------------------------------------38

ix

Figure 3.6 Scatter plots of normalized area vs. timepoints for reps 1-3 of the shoot apical meristem of various metabolites, sugars, and amino acids. Standard deviation shown by black error bars--------------------------------------------------------------------------------------41

1

CHAPTER 1: INTRODUCTION

1.1 Plant Metabolomics

Due to increasing global food demands, improving crop production can have a

significant impact on food stability. However, otherwise-arable land is susceptible to

climate change and soil acidification. Common stressors encountered by farming include

flooding, drought, soil acidity, and sub-optimal temperatures.1 Because of the increasing

hardships food crops encounter, it is important to understand how certain plants adapt and

what modifications can be made to optimize crop production throughout the world. In

order to make favorable modifications to food crops, plant metabolomics is a key tool

that is pivotal to understanding the underlying mechanisms involved in plant growth.

Metabolomics can be defined as the analysis of small molecules, also known as

metabolites, to determine various interactions in any given organism.2 Identifying the

changes and differences among these metabolomic processes can lead to establishing

important metabolomic markers that can then be used to assess a stress response.3 These

metabolomic markers can then serve as a starting point to incorporate favorable traits into

food crops to enhance growth in unfavorable conditions.4,5 In response to stressors,

plants often adapt a novel homeostasis involving new pathways to aid in survival. When

adapting, alternate signaling pathways can be activated as soon as the plant detects a

change in the environment.6 The adaptive signaling pathways that respond initially to the

stress would be different from the normal signaling pathways, resulting in a change of

end products.6 This could lead to a variety of changes in a variety of different metabolites

produced by the plant, including those in the energy producing cycles involved in the

plant growth.6 These metabolites can be detected through a variety of analytical methods,

2

such as nuclear magnetic resonance (NMR) or various mass spectrometry methods such

as GC-MS, LC-MS, and CE-MS.3

1.2 ACC and Ethylene

Ethylene is a hormone found in plants that is linked to several developmental factors

such as growth and sex determination, and it has been demonstrated that ethylene

biosynthesis occurs at a much faster rate when a plant is exposed to various stressors.7

Ethylene is produced from 1-aminocyclopropane-1-carboxylic acid (ACC). 8 Both of

these structures are shown in figure 1.1.The reaction from ACC to ethylene is oxygen

dependent, however the formation of ACC itself does not require oxygen, suggesting that

ACC is an important metabolomic marker in determining the amount of ethylene that

may be produced in response to stress.7 Determining the amount of ACC present in our

plants can suggest whether the ethylene hormone cascade is responsible for the stress

response or if there is another hormone cascade involved. Quantifying ACC is essential

in understanding the stress response of these plants and if it involves an ethylene pathway

or if it is independent of ethylene.

Figure 1.1. The chemical structures of 1-aminocyclopropane-1-carboxylic acid (ACC), the ethylene precursor, and ethylene.

1.3 Aluminum Toxicity and PPC

Aluminum toxicity is a global problem affecting a large portion of the land used

to grow crops and can occur in acidic soils, usually in pH of less than 5.5. Aluminum is

naturally present and bound to different components in the soil such as organic matter,

3

but when the soil becomes too acidic, aluminum becomes soluble which increases the

concentration of the toxic 𝐴𝐴𝐴𝐴3+ cation.9 This can hinder the growth of plants by

disrupting both cell division and cell growth.9,10 Cells undergo repeated division in the

root apex in order for the root to grow, and previous studies have shown that the primary

site of inhibition to root growth occurs when Al3+ interacts with the root apex.11

Aluminum poisoning has been found to affect around 50 percent of the Earth’s arable

land and causes up to an 80 percent crop yield decrease in those areas.12 There are

various methods to mitigate aluminum toxicity, such as the use of lime and phosphate in

the soil.13 The combination of these two treatments provides nutrition to plants in

aluminum toxic soils as well as increases the pH of the soil and prevents the formation of

Al3+. Although use of lime and phosphate has resulted in an increase in crop production,

it is largely unrealistic and expensive to use the amounts of lime and phosphorus needed

in order to affect the large portion of land hindered by aluminum toxicity and is only

feasible for developed countries. Since the plants affected by aluminum poisoning

include food crops, finding a survival mechanism to this stressor is significant in

stabilizing food production across the world.12

Currently, plants have adopted both tolerance and avoidance survival strategies to

mitigate the effects of aluminum toxicity. The tolerance strategy centers around the plant

taking up and storing the Al3+ cation either into the vacuole or the cell wall so the toxic

effects do not hinder the root apex.14 For the avoidance strategy, studies have shown that

organic acid exudation can combat the toxic soil, leading to greater crop yields in areas

affected by Al.15 One study has shown that for Al-resistant genotypes, malic acid

presence in the root exudate increased by 10-fold when compared to its Al-sensitive

4

counterpart.16 Another study demonstrates a similar trend but with citrate rather than

malate in maize crop, showing a greater citrate exudation in Al-tolerant genotypes when

compared to Al-sensitive genotypes.17 These natural survival strategies provide insight to

genetic modifications that can be carried out to help Al-sensitive crops combat toxicity

and continue to thrive in their current environment. Because of this, studying the root

exudate of these plants is imperative to understanding the organic acids and other

important metabolites being expelled from the plant during times of stress. The root

exudate is also important to study because it is influenced by the environment, allowing

researchers to understand nutrient content and soil components that the plant was grown

in.18

One enzyme that is of significant interest to genetically modify plants exposed to

unfavorable soil conditions is phosphoenolpyruvate carboxylase (PPC). PPC is an

enzyme that catalyzes the reaction between phosphoenolpyruvate (PEP) and HCO3− to

produce oxaloacetate and inorganic phosphate.19 As shown in Figure 1.1, oxaloacetate

then goes on to either produce citrate or malate, both of which are involved in the

tricarboxylic acid (TCA) cycle. The TCA cycle is one of the most important metabolic

pathways in aerobic organisms since it is the main energy producing cycle.20 Pyruvate is

another important metabolite to consider since it is a precursor to PEP and can help

determine the activity of PPC. Since PPC has a significant role in the TCA cycle due to

its contribution of intermediates, other studies have looked at this enzyme in various plant

species responding to stress.21,22 One study done in rapeseed plants demonstrated that

when silencing the gene encoding for PPC, there was an increase in osmotic sensitivity

when compared to the wild type. However, there was no difference among the two

5

genotypes when not exposed to osmotic stress.21 This shows that PPC may play a

significant role in increased resistance to stressors such as osmotic stress. Another study

showed that transgenic maize plants containing the PPC gene had increased drought

resistance compared to the control group that did not contain this gene.22 In addition to

the production of oxaloacetate, PPC also plays a role in providing malate as a means of

cellular respiration. As previously mentioned, malate and other organic acids help to

combat the harmful effects of aluminum toxicity in the soil. One experiment found that

exposure to aluminum causes a decrease in enzymes related to the TCA cycle, which

would include PPC, suggesting that a decrease in these enzymes may be responsible for

the stunted plant growth when exposed to aluminum.23 Another study showed that

increased levels of PPC correlates with increased resistance to abiotic stressors, and that

decreased levels of PPC correlates with decreased resistance to stressors.19 Together,

these studies suggest a modification to the gene encoding PPC that leads to an increase in

enzyme production may help facilitate increased plant growth and crop production for

those plants grown in aluminum toxic soils.

6

Figure 1.2 PEP is converted to oxaloacetate via PPC. Oxaloacetate then directly feeds into the TCA cycle by producing either citrate or malate.

1.4 Flooding and the Shoot Apical Meristem (SAM)

Another prominent stressor to crop production is flooding. Irrigated rice fields

constitute the majority of crop production worldwide, however rainfed lowlands as well

as deep-water rice fields also supply a significant amount of the total rice area.24 These

areas are predominantly located in South Asia as well as Southeast Asia and account for

approximately 35% of the world’s rice farmlands.25,26 The rainfed areas are typically

flooded with up to 25 cm of water, but they are also prone to submergence events due to

monsoons and other forms of flooding that can completely submerge the plant and hinder

crop growth.1 One of the most common types of flooding is short-term inundation,

otherwise known as flash floods.24 These submergence events can last up to two weeks

and subject fields to 50 cm or more of water, completely submerging plants.1,24 Another

type of flooding, stagnant flooding, can result from accumulating water and result in

depths of over 50 cm. 24

7

Survival of rice during flooding events is crucial to crop production. When

submerged, both aerobic respiration as well as photosynthesis are inhibited due to lack of

oxygen and CO2 diffusion.27 Depletion of the plant carbohydrate stores during prolonged

submergence could lead to both tissue starvation as well as tissue death.28 To combat

flooding, rice crops have two main survival strategies dependent on the genetic

background of the plant. The first strategy, escape, occurs when the plant is completely

submerged, entrapping the plant hormone ethylene, causing an increase in the

responsiveness of gibberellic acid (GA).5,29 A study carried out by Raskin and Kende

showed that when ethylene was present in non-submerged plants, a lower concentration

of GA was favored.30 This increase in responsiveness, along with the degradation of the

GA antagonist abscisic acid (ABA), leads to carbohydrate consumption within the

plant.29 Subsequently, when ethylene is entrapped during submergence, the bioactivity of

ABA is decreased and a decrease of up to 75% in the level of ABA can occur within 3

hours when both flooding and ethylene entrapment are a factor.31 This strategy is known

as an escape strategy since the plant attempts to consume carbohydrates at a rate rapid

enough to grow above the water line and resume photosynthesis and photorespiration.

However, if the plant is unable to grow above the water, it dies within 10-14 days.29

The second strategy, quiescence, allows plants to survive while completely

submerged by reserving their carbohydrates for respiration.32 This strategy, as opposed to

the escape strategy, allows the plant to restrict elongation as to conserve energy until the

water subsides. Rice that undergo the quiescence strategy were found to contain the

active form of the SUBMERGENCE1A (SUB1A). Expression of this gene has been

shown to contribute to decreased plant growth and slower carbohydrate consumption.27,29

8

As mentioned previously, the escape strategy leads to elongation via carbohydrate

consumption through the ethylene hormone cascade. In the presence of SUB1A,

however, GA activity is downregulated resulting in slower carbohydrate consumption

and overall decreased plant growth.1 SUB1A inhibits ethylene, resulting in an increased

level of ABA activity. Since ABA is an antagonist to GA, the increase of ABA results in

decreased levels of GA.1 In plants that express this gene, increased survival during

prolonged flooding events as well as better recovery after submergence were seen.5 Thus,

understanding how the SUB1A gene affects plant survival is imperative to food crop

security. Using metabolomics, we can discern the downstream impact the presence of

this single gene can have on plant survival. Previously, metabolites have been profiled

via NMR and GC-MS while studying the M202 (submergence intolerant) vs. M202

(Sub1) (submergence tolerant) genotypes. The shoot tissue was analyzed and significant

differences were seen in various organic acids and amino acids between the two

genotypes such as TCA intermediates, GABA, and alanine. These findings support the

regulation of a stress response by Sub 1 gene expression.5 Previous studies such as the

one mentioned above have either focused on shoot or root tissue. However, another part

of the plant, the shoot apical meristem (SAM), has yet to be characterized with

metabolomic markers in response to stress. The SAM is a part of the shoot in plants that

contains undifferentiated cells that can undergo both differentiation and cell division.

Cells from the SAM can become any part of the plant that is above the ground.33 As it is

such a unique portion of the plant, the SAM would be expected to have its own gene

regulation, and therefore a unique stress response. It is hypothesized that starch is

consumed in the SAM via anaerobic respiration when the plant is submerged, leading to

9

shoot elongation. The Sub 1 gene is thought to limit that consumption and therefore

conserving carbohydrates. Thus, the SAM of both the M202 and M202 (Sub 1) variant

will be analyzed by measuring metabolite levels after a period of flooding and a period of

recovery.

1.5 GC-MS

1.5.1 Instrumentation

Gas chromatography-mass spectrometry (GC-MS) is a popular analytical

technique for metabolite profiling due to its sensitivity and selectivity.34 Identification of

many different analytes has proven to be sufficient through this analytical method. Such

analytes include lipids, alcohols, ketones, aldehydes, hydrocarbons and sulfides that are

already volatile as well as metabolites that can be made volatile via derivatization such as

amino acids, organic acids, peptides and sugars.35 GC-MS demonstrates selective mass

detection among ions with a relatively low mass, which helps to profile metabolites, and

further build large libraries as a database for metabolite profiling. Selectivity is

demonstrated by the reproducible fragmentation patterns created as a result of electron

ionization. Sensitivity is also important in analyzing organic acids, and GC-MS provides

the advantage of a selected ion monitoring (SIM) mode when using a quadrupole as the

mass analyzer which will be explored in section 1.5.5. GC-MS is also advantageous in

that only small volumes are required to get a response from the GC-MS. Other analytical

methods used in this field include liquid chromatography- mass spectrometry (LC-MS)

and nuclear magnetic resonance (NMR) to detect metabolites. GC-MS will be used

instead of NMR in this study as NMR is not quite as sensitive as GC-MS.36

10

1.5.2 Derivatization

In order to be suitable for analysis by GC-MS, the samples must be volatile or be

made volatile. Since many metabolites are not volatile as they contain either polar

functional groups or labile hydrogen, they must be derivatized prior to analysis.37 A

common derivatization technique used in metabolomics is silylation.37 In this technique,

silyl moieties are added the molecule to make it volatile enough for the sample to be

analyzed. Figure 1.3 illustrates this process as the derivatization agent, N-methyl-

trimethylsilyl-trifluoroacetamide (MSTFA), is reacted with malate to create a TMS

derivative that is volatile enough to be analyzed with GC-MS. The malate TMS

derivative no longer contains labile hydrogens that can form hydrogen bonds with other

molecules as they were replaced by silyl moieties through the reaction. However, some

compounds, such as amino acids and organic acids, can result in unstable derivatives. 38

A study carried out to demonstrate the stability of metabolites after derivatization found

that seven of fifteen amino acids experienced changes to their peaks, representing a

decline in stability over a three day period.39 Despite this drawback, GC-MS is a useful

analytical technique to explore the metabolic stress response in a variety of organisms,

including plants if derivatization and analysis are carried within 24 hours.

Figure 1.3. Silylation of malate. MSTFA is used to add silyl groups onto the labile hydrogens of malate to make the metabolite volatile enough to be ran through GC-MS.

11

1.5.3 Separation

The gas chromatography portion of GC-MS separates the sample into individual

components. The sample solution is first injected into the instrument on the order of

microliters (usually 0.1-1 µL) and immediately vaporized in the injector liner.35 After

injection, the sample is pushed through the column using an inert carrier gas such as

helium, which is referred to as the mobile phase. The stationary phase, which includes

chemicals bound to the column, interacts with the sample that is being moved through the

column by the mobile phase.35 These chemical interactions between the stationary phase

and sample, along with the boiling point of each component, determines the separation.

The boiling point affects the separation based on how much time each analyte spends in

the gas phase. Lower boiling point compounds with higher vapor pressures are more

likely to elute first. In addition, compounds that have the least chemical interactions with

the column will also elute out first. The combination of these two factors ultimately

determines when each analyte will elute from the column. The column being used in this

experiment is a DB-5ms, which means the stationary phase embedded in the column is

non-polar consisting of 5% phenyl/ 95% dimethyl siloxane.35

1.5.4 Ionization

After GC separation, the components are moved to the ionization portion of the

instrument. Components must be ionized prior to mass detection, and there are several

different methods of ionization used in mass spectrometry. Two of the most common

used with gas chromatography are electron ionization (EI, also known as electron impact)

and chemical ionization (CI).40 For EI, the sample encounters an electron beam which

causes the molecules to lose an electron, becoming the molecular ion.35 EI is considered a

12

hard ionization source as it transmits such a large amount of energy that it usually causes

the molecular ion to further fragment. A schematic of this process can be seen in Figure

1.4a. This fragmentation is reproducible across several instruments regardless of the mass

analyzer, creating a “fingerprint” for each specific molecular structure.41 Through the

reproducibility in the fragmentation patterns, development of reference libraries are

possible.37,41 Since EI is suited for small molecules, it is used extensively in the study of

metabolites. EI is beneficial to metabolomics since the reference libraries allow for

accelerated identification of components within the sample. The other common ionization

source used in GC-MS, CI, is a soft ionization source as it utilizes less energy resulting in

less fragmentation. Chemical ionization works by using a reagent gas that is first ionized

by EI and then comes into contact with the sample.41 The ionized reagent gas then

interacts with the analyte to form the molecular ion. Figure 1.4b demonstrates this

process of creating a charged species that can then be detected by the mass analyzer.

Fragmentation in this ionization technique can be controlled by selecting reagent gasses

with different proton affinities.41 However, the fragmentation is not as extensive as EI

due to being a soft ionization source and is not as extensively used for metabolite

profiling.

13

Figure 1.4. a) Schematic of electron ionization in which the electron beam hits the molecule to form a molecular ion, and then the molecular ion interacts with more electrons to create reproducible fragmentation. The charges species of the fragmentation are then sent to the mass analyzer. b) Schematic of chemical ionization in which the reagent gas interacts with an electron beam and becomes ionized. The ionized reagent gas then interacts with the molecule to create a molecular ion that can be detected by the mass analyzer. 1.5.5 Mass Analyzer

Once ionized, the analyte can proceed to the mass analyzer to be separated based

on mass-to-charge (m/z) ratios. There are several mass analyzers used in a GC-MS

instrument, although the most widely used is the quadrupole. Other mass analyzers

include time-of-flight (TOF), ion trap, magnetic sector, and orbitrap.40 In TOF analysis,

ions are accelerated through an electric field where low mass ions reach the detector

faster as those ions can reach higher velocities. The time taken to reach the detector from

the time the ion was formed demonstrates the m/z ratio for each specific ion. Although

e- e- e-

e- e-

Reagent gas (in excess)

M (molecule)

M + H+ [MH]+

e- e- e-

e- e-

M (molecule)

M+• + 2e-

Mn+• + n

Mn + n+•

e-

a)

b)

14

this method offers exceptional mass accuracy, the cost of this instrument is prohibitive.35

The next method, ion trap, does not provide the same accuracy as the TOF method,

although it requires less maintenance and has similar sensitivity when compared to other

mass analyzers.42

As previously mentioned, the quadrupole is the most widely used mass analyzer

in GC-MS. The system of the quadrupole consists of four rods that are parallel to each

other, where the opposite rods are connected electrically. This electrical connection

between the rods creates an oscillating electric field that the ions move through. Only

specific m/z ratios at specific voltages can pass through to the detector.35 Using a

quadrupole, up to 2000 amu’s can be scanned in the full scan mode. The sensitivity of

this analyzer, however, can be significantly improved through the selected ion monitoring

(SIM) mode. This SIM mode only scans for ions that have been selected, reducing the

amount of m/z ratios being scanned at any given time. This allows the detector to spend

more time transmitting the selected ions, which increases the signal for those specific

ions.

1.6 Overview

This experiment was carried out in collaboration with biology labs at UC Riverside

who provided the plant material. To combat aluminum toxicity, one lab prepared the PPC

knockdown in A. thaliana and supplied both root exudate and root tissue for this study.

In studying the efficiency and effects of the SUB1 response to flooding, another lab

supplied rice plant tissue from plants that had been subject to submergence. These plants,

including the M202 and M202 (Sub1) genotypes, were submerged for 4 days. In addition,

a 4-day control that was not submerged as well as a 1 day recovery sample were also

15

provided for each genotype. The same lab then prepared the SAM of these samples for

analysis. This study uses GC-MS to determine metabolomic markers and establish if

either method is viable to reducing the negative stress caused by aluminum toxicity and

flooding.

16

1.7 References

(1) Bailey-Serres, J.; Fukao, T.; Ronald, P.; Ismail, A.; Heuer, S.; Mackill, D.

Submergence Tolerant Rice: SUB1 ’s Journey from Landrace to Modern Cultivar. Rice

2010, 3 (2), 138–147.

(2) Huberty, M.; Choi, Y. H.; Heinen, R.; Bezemer, T. M. Above-Ground Plant

Metabolomic Responses to Plant–Soil Feedbacks and Herbivory. Journal of Ecology

2020, 108 (4), 1703–1712.

(3) Hong, J.; Yang, L.; Zhang, D.; Shi, J. Plant Metabolomics: An Indispensable

System Biology Tool for Plant Science. International journal of molecular sciences

2016, 17 (6).

(4) Beale, D. J.; Link to external site, this link will open in a new window; Pinu, F.

R.; Kouremenos, K. A.; Poojary, M. M.; Narayana, V. K.; Boughton, B. A.; Link to

external site, this link will open in a new window; Kanojia, K.; Dayalan, S.; Jones, O. A.

H.; Link to external site, this link will open in a new window; Dias, D. A.; Link to

external site, this link will open in a new window. Review of Recent Developments in

GC–MS Approaches to Metabolomics-Based Research. Metabolomics; Heidelberg 2018,

14 (11), 1–31.

(5) Barding, G. A.; Béni, S.; Fukao, T.; Bailey-Serres, J.; Larive, C. K. Comparison

of GC-MS and NMR for Metabolite Profiling of Rice Subjected to Submergence Stress.

Journal of proteome research 2013, 12 (2), 898–909.

(6) Shulaev, V.; Cortes, D.; Miller, G.; Mittler, R. Metabolomics for Plant Stress

Response. Physiologia Plantarum 2008, 132 (2), 199–208.

17

(7) Iqbal, N.; Trivellini, A.; Masood, A.; Ferrante, A.; Khan, N. A. Current

Understanding on Ethylene Signaling in Plants: The Influence of Nutrient Availability.

Plant Physiology and Biochemistry 2013, 73, 128–138.

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23

CHAPTER 2: MATERIALS AND METHODS

2.1 Materials

For sample and standard derivatization, methyl-trimethylsilyltrifluoroacetamide in

1% trimethylchlorosilane (MSTFA) purchased from Thermo Fisher Scientific (Waltham,

MA) was used. Methoxyamine hydrochloride was also used in derivatization and was

purchased from MP Biomedicals, LLC (Irvine, CA) and pyridine 99% was purchased

from Acros Organics (Fair Lawn, NJ). Fatty acid methyl esters (FAMEs) of carbon

chains C8-C16 and C-18-C20 were dissolved in chloroform 99.9% purity purchased from

Acros Organics and used as retention indices. The standards of alanine, pyruvate, and

lactate were purchased from Acros Organics as were the TCA cycle intermediates (citric

acid, malic acid, oxalic acid, isocitrate, and alpha-ketoglutarate). The wash solvents used

for the instrument, hexane, chloroform, and dichloromethane, were purchased from

Thermo Fisher Scientific. Sugar standards sucrose and glucose were purchased from

Acros Organics and trehalose was purchased from Thermo Fisher Scientific. Arabinose

and galactose samples were obtained from our collaborators at UCR.

2.2 Preparation of Samples and Standards

2.2.1 Standards

Standards consisting of TCA cycle intermediates, alanine, pyruvate, lactate,

glucose, sucrose, trehalose, arabinose, and galactose were prepared to be a final

concentration of 100 µM using ultra pure water as the solvent. The prepared standards

were then stored in the freezer at -20 °C for future use. Standards were dried for

derivatization using the LABCONCO CentriVap Console at a temperature of 70 °C for

45 minutes, or until completely dry.

24

2.2.2 Root Exudate and Root Tissue

The root exudate and root tissue were prepared by our collaborators at UCR. After

receiving the dried exudate, it was reconstituted in 1 mL of a 50/50 methanol in ultrapure

water. Subsequently, 500µL in two 250 µL aliquots were transferred into a 300 µL glass

insert placed inside of a 1.5 mL centrifuge tube. The first 250 µL aliquot was dried using

the LABCONCO CentriVap Console at a temperature of 70°C for 1 hour. The second

aliquot was then pipetted into the same glass insert as the first aliquot and dried to

completion. The samples were immediately derivatized (see section 2.3).

Root tissue samples were received dried. Samples were first run through a bead

homogenizer from Quantachrome Instruments and then reconstituted in a 1 mL 50/50

Methanol/UP water solution. The tissue was then vortexed for 4 minutes at 1325 rpm

using a digital vortex mixer and then centrifuged for 4 minutes at 12000 rpm using a

microcentrifuge to pellet the cellular debris. Subsequently, 100 µL of the supernatant

was transferred to a 300 µL glass insert in a 1.5 mL centrifuge tube to be dried using the

LABCONCO CentriVap Console at a temperature of 70°C for 1 hour, or until dry. Both

the root exudate and tissue were derivatized for GC-MS analysis.

2.2.3 Shoot Apical Meristem

The SAM tissue was provided by UC Riverside after submergence for 4 days for

both the M202 and M202 (sub1) genotypes. Additionally, a control sample was provided

(no submergence) as well as a sample taken after 24 hours of recovery (post-

submergence) for each genotype. Samples were received dried. Metabolites were

extracted after weighing 10 mg of tissue into a 1.5 mL centrifuge tube and mixed with 1

mL of a 50/50 MeOH/water. These samples were then vortexed and centrifuged

25

following the same procedure as above. A total of 50 µL of the supernatant was then

transferred into a 300 µL glass insert in a 1.5 mL centrifuge tube to be dried using the

LABCONCO CentriVap Console at a temperature of 70°C for 1 hour. Once completely

dry, the samples were derivatized as described below and analyzed by GC-MS.

2.3 Derivatization

Derivatization via silylation was used to prepare samples for GC-MS analysis.

After samples and standards were dried, 20 µL of a 20 mg/mL methoxyamine

hydrochloride in pyridine solution was added into the glass insert of the previously

prepared and dried sample. This solution was then heated for 30 minutes at 40 °C on the

Fischer Scientific Isotemp hot plate. After heating, 80 µL MSTFA and 2 µL FAMES

were added to the solution. This was heated for another 15 minutes at 40 °C. The glass

insert with the prepared sample was then placed in a VWR International (Radnor, PA)

standard opening crimp top GC vial, capped with an 11 mm crimping tool, and ran within

24 hours of preparation.

2.4 Instrumentation and Experimental Parameters

The instrument used in this experiment is an Agilent Technologies 7890B GC-

MS. The Agilent 7693A Autosampler used a 10 µL syringe to inject 1 µL of sample into

the instrument. After injection, the syringe was washed with the wash solvents

chloroform and dichloromethane. The sample was injected into an EPC Split-Splitless

Inlet operated in the pulsed splitless mode with helium as the carrier gas. The injection

pulse pressure was held at 25 PSI for 0.5 minutes and the purge flow to the split vent was

100 mL/min at 1 minute. An Agilent DB-5ms column used to separate the components of

the sample and has capillary dimensions of 30.0 m by 250 µM by 0.25 µM. The

26

experiment was set at a constant rate of 0.734 mL/min. For separation, the initial oven

temperature was set to 60 °C and held for 1 minute, after which the temperature increased

at 10°C per minute until a final temperature of 320°C was reached and held for 5 minutes

for a total run time of 32 minutes.

Sample components were moved from the GC portion of the instrument to the MS

portion at a MSD transfer line temperature of 280 °C. The ionization source used was

electron impact (EI) and was set at 70 eV. A solvent delay of 6.5 minutes was

implemented, after which the filament was turned on. The method scanned at a rate of 1.4

scans/second from 60 to 600 m/z. The applied EM voltage of the detector was set at 899

V.

The selected ion monitoring (SIM) method was utilized in order to increase the

sensitivity. This method was developed by first quantifying standards in the full scan

method, by determining their retention time and significant ions. These parameters, that

can be seen in table 2.1, were added into the SIM method, which only scans for the

specific ions at the specific retention time indicated. For the SIM method, the same

instrument specifications were used, as well as a dwell time of 100 ms and significant

m/z ions for each metabolite was entered into the parameters. The ENV mode was set to

absolute and the absolute voltage was 1671 V.

27

Table 2.1. The specific time and ions selected for each quantified standard while using the SIM method.

Standard Scan Time (min) Ion 1 (m/z) Ion 2 (m/z) Ion 3 (m/z)

Butyrate 6.6-7.1 117 191 233 Oxaloacetate 7.2-7.9 133 220 235

ACC 8.0-11.6 128 202 - Malate 11.7-13.4 189 233 245

Alpha Ketoglutarate 13.5-15.5 156 198 288 Citrate 15.5-16.5 273 465 -

2.5 Data Analysis

The Automated Mass Spectral Deconvolution and Identification System (AMDIS,

NIST, Gaithersburg, MD) was used as the main method of identification. In the full scan

method, ions of the highest intensity were used to identify each compound. Ions 73 and

147 were excluded from this identification process as they are the result of the

derivatization method. Peaks of interest were integrated to find the peak area, and

approximate concentrations were determined by comparing the area to the area of the

peaks from the standards with known concentrations as seen in the equation below.

100 𝑢𝑢𝑢𝑢 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑐𝑐𝑐𝑐𝑠𝑠𝑐𝑐𝑐𝑐𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑐𝑐𝑐𝑐𝑠𝑠𝑆𝑆𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑝𝑝𝑐𝑐𝑠𝑠𝑝𝑝 𝑠𝑠𝑠𝑠𝑐𝑐𝑠𝑠

=𝑈𝑈𝑠𝑠𝑝𝑝𝑠𝑠𝑐𝑐𝑈𝑈𝑠𝑠 𝑠𝑠𝑠𝑠𝑠𝑠𝑝𝑝𝐴𝐴𝑐𝑐 𝑐𝑐𝑐𝑐𝑠𝑠𝑐𝑐𝑐𝑐𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑐𝑐𝑐𝑐𝑠𝑠

𝑆𝑆𝑠𝑠𝑠𝑠𝑝𝑝𝐴𝐴𝑐𝑐 𝑝𝑝𝑐𝑐𝑠𝑠𝑝𝑝 𝑠𝑠𝑠𝑠𝑐𝑐𝑠𝑠

28

CHAPTER 3: RESULTS AND DISCUSSION

3.1 Standard Quantification

The first step to develop a method that is both sensitive and reliable for

quantification by GC-MS is to analyze standards. This is to determine experimental

parameters as well as establish retention times and significant ions for quantitation. Each

standard was analyzed using a full scan method (60-600 m/z), which produces a total ion

chromatogram (TIC). From the TIC, specific ions can be plotted as a function of time to

create an extracted ion chromatogram (EIC) of the standard. Figure 3.1a shows an

example of a standard for malate that shows the EIC of ion 233 m/z in which the peak for

malate can be seen at retention time 11.81 minutes. Figure 3.1b shows the mass spectrum

for the peak at 11.81 minutes corresponding to malate. In this case, ions m/z 233, 245,

and 189 can be seen with a significantly larger signal than the remaining ions in the

spectra. Therefore, these ions can be used in conjunction with the retention time to

identify malate in any given sample. As mentioned previously, ions m/z 73 and 147 are a

result of the derivatization method and are not used when identifying analytes because

they are ubiquitous. Table 3.1 shows the specific standards that were analyzed with their

unique retention times and ions. The identity of each standard was confirmed by

reference to the NIST database (Gaithersburg, MD). These parameters were also used to

develop the SIM method discussed in section 3.2.

29

Table 3.1. The retention times and characteristic ions of the highest intensity for the standards.

Name Retention Time (min)

Ion 1 (M/Z)

Ion 2 (M/Z)

Ion 3 (M/Z)

Butyrate 6.72 117 191 233 Oxaloacetate 7.21 133 220 235

ACC 8.16 128 202 - Malate 11.81 189 233 245

Methionine 12.20 176 232 293 Alpha Ketoglutarate 13.55 156 198 288

Citrate 15.59 273 465 - Aspartic Acid 12.21 232 218 100

Trehalose 24.89 361 191 217 Sucrose 24.10 361 217 437

Counts vs. M/Z

a)

b)

Figure 3.1. a) Extracted ion chromatogram (ion 233 m/z) of the malate standard with the corresponding b) MS spectrum results that represent the reproducible m/z ratios created by electron ionization.

30

3.2 Development of a SIM Method for Targeted Metabolite Profiling

Because this was largely a targeted method, we developed a SIM method using

the most intense ions from each analyte. This method is expected to increase signal

because it only scans for the selected ions, rather than scanning for all 540 ions of the

total scan method. Scanning for fewer ions increases the dwell time for each selected ion,

potentially increasing the signal to noise ratio while decreasing the background. The

decreased background is important when analyzing plant samples because it makes each

metabolite easier to quantify as opposed to searching through hundreds of peaks to find a

specific metabolite. For the SIM method, 2-3 ions were selected for each standard and

analyzed using the selected ion monitoring mode rather than the total ion scan mode.

Between two to three ions were chosen for each analyte to help confirm the identity of

the analyte. Some standards have similar m/z ions, so providing multiple ions for each

standards further validates the identity of the analyte. These ions, as well as the retention

time for each metabolite, were confirmed with spectrum matching using the total scan

mode. The SIM method was then used for quantification. As seen in Figure 3.2, the

signal increases when plotting the EIC from the SIM mode compared to the EIC

chromatogram plotted from the scan mode for each citrate, malate, oxaloacetate, and

butyrate. The signal increase is further characterized in Figure 3.3, which directly

compares the peak height for each chromatogram. Nearly a two-fold increase can be

seen in the peak height of the SIM for citrate and butyrate. The signal for citrate

increased from 1.8x104 to 4.5x104 for m/z 273 and butyrate from 3.4x105 to 5.0x105 for

m/z 117. A smaller increase is seen in malate from 7.0 x104 to 1.0x105 for m/z 233, and

31

for oxaloacetate from 4.0x105 to 4.5x105 for m/z 133. This method developed to analyze

the standards can be directly transitioned into analyzing plant samples.

Citrate

Malate

a)

c)

d)

b)

32

Butyrate

Oxaloacetate

g)

e)

f)

h)

Figure 3.2. A comparison of extracted ion chromatograms for the SIM and scan modes. a) Scan mode EIC (m/z 273) for citrate compared to b) the EIC SIM chromatogram (m/z 273) for citrate, c) scan mode EIC (m/z 233) for malate compared to d) the EIC SIM chromatogram (m/z 233) for malate, e) scan mode EIC (m/z 113) for oxaloacetate compared to f) the EIC SIM chromatogram (m/z 133) for oxaloacetate, and g) scan mode EIC (m/z 117) for butyrate compared to h) the EIC SIM chromatogram (m/z 117) for butyrate.

33

1.80

E+04

7.00

E+04

4.00

E+05

3.40

E+05

4.50

E+04

1.00

E+05

4.50

E+05

5.00

E+05

C I T R A T E M A L A T E O X A L O A C E T A T E B U T Y R A T E

COMPARING PEAK HEIGHT IN THE EIC OF THE SCAN MODE VS. SIM

EIC EIC SIM

Figure 3.3. A bar graph demonstrating the quantifiable difference in signal between the EIC from the scan mode and EIC from the SIM chromatograms for citrate (m/z 273), malate (m/z 233), oxaloacetate

(m/z 133), and butyrate (m/z 117).

3.3 ACC Quantification in the Root Tissue

When plants are exposed to stressors, they can display a number of stress

responses. One of the well-known hormone cascades involved with stress is centered

around ethylene. ACC has previously been identified as a precursor to ethylene.8 Since

ethylene is already volatile prior to derivatization, it is not detectable by the method used

in this study using GC-MS. As a result, the quantification of ACC is significant in

determining if the plant samples being studied are utilizing the stress response employed

by ethylene. In order to identify ACC, a derivatized blank was first run (Figure 3.4a).

Figure 3.4b then shows the signal of an ACC standard followed by the same analysis of a

plant sample in Figure 3.4c. Although the chromatogram appears choppy due to the

alternation between the SIM and scan modes, ACC can still be identified as shown in

Figure 3.4b at 8.16 min. Root tissue was then analyzed to determine if ACC was present

34

in the sample that had been exposed to stress. Figure 3.4c shows the derivatized A.

thaliana root tissue extract, and when m/z 128 is plotted, ACC does not appear in the

sample. Since ACC is a precursor to ethylene, its absence demonstrates that A. thaliana

is using a stress response independent of ethylene. This shows that the need for novel

metabolomic stress markers is increasingly important in understanding how the plant

system responds to stress, since the plant extract being studied may not be responding

through the ethylene hormone cascade..

a)

b)

c)

Figure 3.4. The quantification of an ACC standard and plant tissue extract. A) is an extracted ion chromatogram (m/z 128) of the derivatized blank, b) is an extracted ion chromatogram (m/z 128) of the standard ACC, and c) is an extracted ion chromatogram (m/z 128) of the plant tissue which does not contain

35

ACC. The chromatogram shown in this figure has three peaks around the retention time of ACC. This is because the instrument was in a mode that alternated between the total scan and SIM method.

3.4 Analysis of the Root Exudate and PPC Knockdown

The root is especially important when exploring the systemic response to stressors

in the soil, such as aluminum toxicity. In particular, understanding how the root responds

to the presence of aluminum can provide insight as to how specific plants can grow in

toxic soils. One method of survival is to increase the excretion of organic acids into the

surrounding soil to chelate the aluminum, preventing uptake by the roots. Additionally,

the root exudate can also influence the content and amount of nutrients in the soil. 18 For

example, an exudate sample in which the soil composition was low in phosphorus will be

reflected by an increasing amount of organic acids in the exudates. 18 Plants grown

during a drought will reflect a lack of water in the root exudate as shown by an increase

in excretion of secondary metabolites, which is hypothesized to be a reflection of the

plant composition at the time of the stress. 18 Understanding the composition of the root

exudate is essential in studying a stress response to aluminum toxicity.

Previous studies have shown that exudation of organic acids, such as citrate and

malate, promote resistance to aluminum toxicity. 16, 17 Finding a way to increase organic

acid exudation in plants exposed to aluminum toxicity should help growth and crop

production in such soils. The TCA cycle is responsible for producing a large amount of

these metabolites, and the PPC enzyme is directly involved in the production of

oxaloacetate, citrate, and malate. Genetic modifications, such as a PPC knockdown,

would help establish the enzyme’s role in the production of these TCA cycle

intermediates and their excretion into the surrounding soil.

36

Root exudate samples were collected by our collaborators at UC Riverside from

two wild type plants and two plants with PPC knocked down. The wild type and

transgenic plant samples were analyzed and compared using the GC-MS method

described in section 3.1. As seen in Figure 3.5, the peak height for malate across all

samples vary significantly. The peak height appears smaller in the PPC knockdown

plants (Figure 3.5a and 3.5c) when compared to the wild type (Figure 3.5b and 3.5d). In

contrast, citrate appears to remain constant across the four samples, suggesting that this

particular PPC knockdown specifically affects malate exudation. To better understand

the relationship between malate and citrate in root extracts, malate and citrate

concentrations were calculated using a single point external standard. In Table 3.2, the

peak area for malate in the wild type is about 3 times greater than that of the PPC

knockdown. The peak area for citrate is consistent with the chromatograms in Figure 3.5,

showing little difference. The concentration of malate in the root exudate also shows a

similar trend to the peak area, as it is about 3 times greater in the wild type. Since PPC

catalyzes the reaction of PEP to oxaloacetate, and then oxaloacetate produces both malate

and citrate, it could be suggested that a PPC knockdown would cause a decrease in both

malate and citrate rather than only malate. One possible explanation for the decrease in

malate with no change in citrate levels could be that the accumulation of citrate comes

from many different metabolomic pathways in many different parts of the plant.43 In

addition to the TCA cycle pathway that PPC is involved in, citrate can be produced from

the glyoxylate cycle as acetate is converted to acetyl-CoA, and then acetyl-CoA is

converted to citrate. 44 Beta oxidation also produces acetyl-CoA, and glycolysis produces

pyruvate which can be converted to acetyl-CoA. Both of these pathways can also lead to

37

the production of citrate. In addition, citrate can be directly produced via the citrate-

pyruvate cycle.14 A knockdown in only one of the pathways may not be significant

enough to cause an overall decrease in the citric acid exudate. Malate, although also

involved with many biochemical pathways, does not have nearly as many as citrate. As a

result, malate levels could be more impacted.

Being able to successfully develop a method to quantify both citrate and malate

was central in determining altering levels of malate in the PPC knockdown

exudate. Since it has been demonstrated that a PPC knockdown decreases the levels of

malate, it can be hypothesized that increasing the activity of PPC could lead to increased

levels of malate exudation. This would be significant in decreasing the toxic effects of the

Al3+ soil as malate is central in the TCA cycle. Having a reliable analytical method

provides opportunity to continue to explore the root exudate and the possibility of

maximizing healthy plant growth using PPC.

38

Malate Citrate

a)

b)

c)

d)

Figure 3.5. EICs from root exudate samples for both ion m/z 233 (malate) and 273 (citrate). Part a) and c) represent the PPC knockdown whereas b) and d) represent the wild type.

39

Table 3.2. Peak areas and concentrations for both malate and citrate in both the PPC knockdown and wild type plant samples contained in the root exudate as determined by GC-MS.

Sample Malate (area) Citrate (area) Malate (µM)

Citrate (µM)

A (PPC knockdown)

1.56x105 2.50x105 14.08 86.61

B (Wild Type) 5.05x105 2.92x105 45.63 101.14 C (PPC knockdown)

1.74x105 3.28x105 15.71 113.75

D (Wild Type) 4.91x105 3.09x105 44.35 107.21

3.5 Analysis of the Shoot Apical Meristem

In addition to aluminum toxicity, crop production is also threatened by flooding

events. During submergence, plants either use carbohydrate stores to grow above the

water line in an escape strategy, or they reserve their carbohydrate stores in a strategy

known as quiescence.29, 32 The escape strategy often leads to plant death if the plant is

unable to successfully rise above the water and resume gas exchange.29 In contrast, the

quiescence strategy slows plant growth and waits for the water levels to drop enough to

resume normal gas exchange. Plants that contain the active form of the gene SUB1A

have been shown to utilize the quiescence strategy, and therefore have a greater chance of

survival following submergence.29, 27 In studying how plants are affected by these

submergence events, the SAM was extracted after a 4 day flooding and 1 day recovery

period for both the M202 and M202 (sub1) genotypes to be analyzed using GC-MS.

Included were two controls for each genotype; one at the start of submergence and one

after the 4 day flooding period. The SAM is expected to have a unique stress response

due to its content of undifferentiated cells and differing gene regulation. Current studies

surrounding the SAM focus mostly on genes and expression of those genes, whereas few

studies have been carried out on the metabolites produced within the SAM. Studying the

40

metabolites in the SAM from plants that undergo either the escape or quiescence strategy

after a flooding period is increasingly important because how metabolites differ in the

SAM is not largely known. A similar study done using a different part of the plant, shoot

tissue, does show significant differences in some metabolites, sugars, and amino acids

between the M202 and M202(sub1) genotypes. Changes were observed in TCA

intermediates, GABA, alanine, and a few other amino acids.5 With the GC-MS method

established in section 3.1, relative quantification of TCA cycle intermediates citrate and

malate, as well as sugars sucrose, glucose, fructose, and trehalose was carried out. In

addition, amino and organic acids such as GABA, 4-hydroxybenzoate, 5-oxoproline, and

hydroxyproline were able to be quantified. This method of quantification determined the

relative differences between the two genotypes, rather than the absolute amount of the

compounds in each sample. Each peak was then integrated and the area was normalized

to the total peak area in the sample using sum normalization. Figure 3.6 shows the

normalized area of each peak during each time point (control, 1 day submergence, 4 day

submergence, and 1 day recovery) as well as the standard deviation (n=3). As can be seen

in the figure, there is no significant difference between the M202 and M202(sub1)

variations across the reps and timepoints. One possible explanation could be the

preparation of the SAM to be analyzed using GC-MS. Since the SAM is such a small part

of the plant, only about 4 mg of tissue was provided for analysis, which may not have

been enough. If the sample was too dilute, the signal might not be strong enough to see a

noticeable difference among the different compounds. Increasing the concentration of the

SAM in the GC vial could lead to more differentiated results, or rule out concentration as

a possibility of why no differences were seen.

41

Figure 3.6. Scatter plots of normalized area vs. timepoints for reps 1-3 of the shoot apical meristem of various metabolites, sugars, and amino acids. Standard deviation shown by black error bars.

42

3.6 Summary

In this study, a successful method for analyzing metabolites was developed using

GC-MS. The method was further improved by inclusion of a SIM method, which

effectively increased the peak height (increasing the signal-to-noise ratio) and decreasing

background. Using the established analytical method based on GC-MS, root tissue, root

exudate, and shoot apical meristem samples were studied. Analysis of the root tissue

confirmed that ACC, the precursor the ethylene, was not present at detectible

concentrations suggesting that ethylene was not the hormone cascade responsible for the

stress response being explored. Understanding plant survival in aluminum toxic soils was

investigated using the root exudate of various plants. Root exudate samples with a PPC

knockdown were determined to have decreased concentrations of malate. This is a

significant finding since PPC is directly involved in the TCA cycle and suggests that it is

a key regulator of plant survival in aluminum toxic soils. In further exploring plant

stressors, metabolite regulation in the shoot apical meristem before, during, and after

submergence was studied. Submergence, in addition to aluminum toxicity, is a significant

stressor that can lead to decreased crop production. The SAM is a unique part of the plant

that contains undifferentiated cells, and metabolomics of this part of the plant is not

widely carried out. Although the SAM data did not show significant differences between

the M202 and M202(sub1) genotypes, the analytical method that was developed using

GC-MS allowed for relative comparisons to be made among metabolites of the different

groups. The GC-MS method was successful in analyzing different parts of plants as well

as identifying and comparing metabolites in the various plant samples.

43

3.7 References



2010, 3 (2), 138–147.



2020, 108 (4), 1703–1712.



2016, 17 (6).







14 (11), 1–31.






44
























45




305.












(6), 1057–1066.







46




1512.



Heidelberg 2008, 279 (4), 339–357.






(1), 3–12.










47



105 (43), 16814–16819.









Pharmacogn Rev 2014, 8 (16), 105–112.







(1), 6.





48






Metabolites 2011, 1 (1), 3–20.







2010, 134 (5), 423–443.








2015, 10 (6), e0126777.



49



50

CHAPTER 4: CONCLUSIONS

The aim of this study was to examine the response plants use to combat various

stressors, such as aluminum toxicity and flooding. Determining metabolomic markers in

these plants was carried out using GC-MS. Gas chromatography-mass spectrometry is a

popular choice for metabolomics due to its sensitivity and selectivity demonstrated by the

reproducible fragmentation patterns created through electron ionization.34 Development

of a method suitable for plant analysis was accomplished by first using different TCA

cycle intermediates and sugar standards. The standards were analyzed with GC-MS to

identify both a retention time and m/z ions of the highest intensity for identification and

quantitation of each compound. After determining these parameters for malate, citrate,

butyrate, oxaloacetate, methionine, α-ketoglutarate, aspartate, trehalose, and sucrose, a

selected ion monitoring method was developed to increase sensitivity. By using this

method, the peak height for malate, citrate, butyrate, and oxaloacetate all significantly

increased. For both citrate and butyrate, nearly a two-fold increase was observed.

Successful development of this method using GC-MS can be directly applied to plant

samples.

Detection of another standard, ACC, was important for understanding whether or

not the plant system was using the ethylene hormone cascade as a response to a specific

stress. ACC has been previously identified as a precursor to ethylene, and therefore

should present in any plant sample using an ethylene centered stress response.8

Identification of the ACC standard was confirmed with a retention time of 8.16 minutes

and m/z 128 and 202 ions of highest intensity. In the sample, no peak was seen at 8.16

minutes with m/z 128 and 202, confirming that ACC was not present in the plant sample.

51

Thus, A. thaliana is using stress response in response to aluminum toxicity that is

independent of ethylene.

Further exploring the response of A. thaliana to aluminum toxicity, a PPC

knockdown was implemented by our collaborators and the dried root exudate was

analyzed by the GC-MS method. Both citrate and malate were detected in the plant

samples, but malate was the only metabolite to differ in quantity between the two

genotypes (wild type and PPC knockdown). In the PPC knockdown, the average

concentration of malate in the two samples was 14.90 µM whereas the average

concentration for the two wild type samples was 44.99 µM. Citrate had an average

concentration for the PPC knockdown of 100.18 µM and an average concentration of

104.16 µM for the wild type. No significant difference was observed for citrate, but a 3-

fold difference was seen in the concentration of malate. This suggests that PPC is a

significant contributor to the total malate exudation in the root exudate. Although PPC

can also lead to the production of citrate, citrate is also involved in several other

pathways and its overall exudate concentration is not significantly affected by the

decrease in PPC activity.43,44 Malate exudation can potentially help mitigate the effects of

aluminum poisoning, as organic acid exudation has been proven to be of significance in

natural stress responses to toxic soils.15 Genetically modifying A. thaliana to have

increased PPC enzyme activity could potentially lead to increased malate exudation, and

a better response to aluminum toxicity.

Also analyzed in this study was the stress response to flooding, another significant

stressor encountered by plants. Both the M202 and M202(sub1) genotypes were prepared

by our collaborators at UCR, exposed to submergence stress for 4 days, and extracted for

52

analysis. The dried shoot apical meristem was then analyzed in this study. Previously, the

SUB1A gene has been identified to promote a quiescence response to flooding rather

than an escape response, which ultimately leads to better survival rates of the plants post-

submergence.27,29 After GC-MS analysis of both genotypes, several compounds were

identified in each sample. These compounds include citrate, malate, sucrose, glucose,

fructose, trehalose, GABA, 4-hydroxybenzoate, 5-oxoproline, and hydroxyproline.

Relative quantification and comparison among the genotypes was carried out and showed

no significant difference between M202 and M202(sub1). This may be due to a low

concentration of metabolites in the limited SAM tissue sample, leading to smaller peak

heights among the analytes detected via this method.

A GC-MS method to detect and identify several different analytes was

successfully developed and applied to various samples. In addition, a SIM method was

developed to increase the sensitivity of the GC-MS method for targeted profiling.

Analysis of root tissue, root exudate, and shoot apical meristem was successful in

identifying several metabolites, amino acids, organic acids, and sugars in each sample

and can continue to be used for future analysis involving plant metabolomics in response

to environmental stressors.

53

4.1 References



2010, 3 (2), 138–147.



2020, 108 (4), 1703–1712.



2016, 17 (6).







14 (11), 1–31.






54
























55




305.












(6), 1057–1066.







56




1512.



Heidelberg 2008, 279 (4), 339–357.






(1), 3–12.










57



105 (43), 16814–16819.









Pharmacogn Rev 2014, 8 (16), 105–112.







(1), 6.





58






Metabolites 2011, 1 (1), 3–20.







2010, 134 (5), 423–443.








2015, 10 (6), e0126777.



59



60

CHAPTER 5: FUTURE WORKS

The purpose of this study was to determine metabolic markers to identify a stress

response in plants experiencing unfavorable growing conditions, such as soil toxicity or

submergence stress. Through identification of these markers, the ultimate goal would be

to incorporate genetic modifications into plants to aid in growth during periods of stress.

An analytical method using GC-MS was developed as well as a SIM method to increase

sensitivity surrounding metabolomic analysis. Applying the SIM method to these plant

extracts in future analysis will help to identify more metabolomic markers and allow for

easier peak identification through the increased sensitivity of the method.

Analysis surrounding the plant extracts with the PPC knockdown provided insight

into the significance of PPC in the TCA cycle. PPC was determined to be critical for

malate production in the root exudate. This finding is significant in moving forward to

help combat the effects of aluminum toxicity. As the goal is to increase organic acid

exudation, the analytical method developed in this study can analyze future genetic

modifications surrounding the increase of the PPC enzyme activity. This modification is

expected to increase levels of malate exudation and lead to a more successful response in

the toxic soils. The response can then be monitored via GC-MS to identify the

components and concentrations of organic acids in the exudation.

Studying how metabolite levels differ in the shoot apical meristem after a period

of submergence, many compounds were identified in both the M202 and M202 (sub1)

genotypes. Although the method of analysis that was developed using GC-MS was

successful in identifying these analytes, no relative difference was seen between the two

genotypes in response to the stress. More work needs to be done in this area to determine

61

why no difference was seen. One approach would be to increase the amount of tissue

available for extraction. Additionally, an added cleanup step with chloroform could help

reduce interferents and increase sensitivity.

Although a robust analytical method was developed, more work still needs to be

done to help in the global food insecurity problem. Further analyzing plant extracts

undergoing stress as well as implementing genetic modifications based on the findings in

this research will hopefully lead to increased crop production worldwide.

Documents

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