photosynthetic acclimation to lower light intensity in arabidopsis thaliana

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PHOTOSYNTHETIC ACCLIMATION TO

LOWER LIGHT INTENSITY IN

ARABIDOPSIS THALIANA

A thesis submitted to The University of Manchester

for the degree of DOCTOR OF PHILOSOPHY

in the Faculty of Life Sciences

2014

Furzani Pa’ee

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Table of Contents

List of Figures ................................................................................................................... 6

List of Tables..................................................................................................................... 8

Abstract ............................................................................................................................. 9

Declaration ...................................................................................................................... 10

Copyright Statement ....................................................................................................... 10

List of Abbreviations....................................................................................................... 12

Acknowledgements ......................................................................................................... 15

1.0 INTRODUCTION ................................................................................................... 16

1.1 Changes in the climate due to changing weather pattern .......................................... 17

1.1.1 Types of climate variation that affects plant’s performance .............................. 18

1.1.2 The importance of studying how plants regulate photosynthesis process under

acclimation .................................................................................................................. 22

1.2 Photosynthesis ........................................................................................................... 23

1.2.1 Photosynthetic organelle - Chloroplast .............................................................. 26

1.2.2 Light capture ...................................................................................................... 26

1.2.3 Electron transport chain (ETC) .......................................................................... 28

1.2.4 Carbon fixation................................................................................................... 31

1.2.5 Starch and Sugar synthesis ................................................................................. 34

1.3 Plant’s responses to environmental stress ................................................................. 36

1.3.1 Photoinhibition ................................................................................................... 36

1.3.2 The production of reactive oxygen species (ROS) ............................................ 37

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1.4 Changes to changing light condition ......................................................................... 40

1.4.1 Short-term responses to changing light condition .............................................. 42

1.5 Photoacclimation - long-term response to changing light condition ....................... 45

1.6 Aims and Objectives ................................................................................................. 49

2.0 MATERIALS & METHODS ................................................................................. 52

2.1 Plant material ............................................................................................................ 53

2.2 Gas exchange ............................................................................................................ 54

2.2.1 Light response curve measurement .................................................................... 54

2.2.2 Photosynthetic capacity measurement .............................................................. 54

2.2.3 Chlorophyll fluorescence measurement ............................................................. 55

2.2.4 Chlorophyll extraction and analysis ................................................................... 57

2.3 Microarray analysis ................................................................................................... 58

2.3.1 RNA extraction .................................................................................................. 58

2.3.2 Microarray procedure ......................................................................................... 58

2.3.3 RT-PCR .............................................................................................................. 59

2.4 Statistical analyses .................................................................................................... 61

2.5 QTL analysis ............................................................................................................. 62

2.5.1 Plant growth for recombinant-inbred (RI) lines ................................................. 62

2.5.2 Physiological measurement of recombinant-inbred (RI) lines........................... 63

2.5.2.1 Photosynthetic capacity measurement of RI lines ...................................... 63

2.5.2.2 Chlorophyll fluorescence measurement of RI lines .................................... 63

2.5.2.3 Chlorophyll extraction and analysis of RI lines .......................................... 63

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2.5.3 QTL analysis with WinQTL Cartographer ........................................................ 63

3.0 PHYSIOLOGICAL RESPONSES OF ARABIDOPSIS THALIANA TO

DECREASES IN GROWTH IRRADIANCE ............................................................ 64

3.1 Introduction ............................................................................................................... 65

3.2 Results ....................................................................................................................... 67

3.2.1 Light intensity determination from light response curve in WS ........................ 67

3.2.2 Changes in maximum photosynthetic capacity in WS, WS-gpt2 and Col-0

during acclimation following a transfer from high to low light .................................. 69

3.2.3 Changes in chlorophyll content and composition during acclimation to low light

in WS, WS-gpt2 and Col-0 ......................................................................................... 74

3.2.4 Photosynthetic acclimation of WS and WS-gpt2 under fluctuating light

condition in Winter 2010-2011 ................................................................................... 77

3.2.5 Photosynthetic acclimation of WS and WS-gpt2 under fluctuating light

condition in Winter 2011-2012 ................................................................................... 80

3.3 Discussion ................................................................................................................. 83

4.0 MICROARRAY ANALYSIS ................................................................................. 90

4.1 Introduction ............................................................................................................... 91

4.2 Results ....................................................................................................................... 92

4.2.1 Changes in GPT2 expression in WS following acclimation to low light .......... 92

4.2.2 Microarray analysis on photosynthetic acclimation in Arabidopsis thaliana of

WS ............................................................................................................................... 93

4.2.3 Average Profile Cluster analysis on genes in high to low light acclimation and

in the reverse acclimation ......................................................................................... 101

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4.2.4 Gene Ontology (GO) annotation and analysis ................................................. 105

4.3 Discussion ............................................................................................................... 108

5.0 QUANTITATIVE TRAIT LOCI (QTL) ANALYSIS ........ ............................... 113

5.1 Introduction ............................................................................................................. 114

5.2 Results ..................................................................................................................... 116

5.2.1 Physiological assessment in recombinant-inbred (RI) lines of Col-4 x Ler-0

population in low to high light acclimation ............................................................. 116

5.2.2 Quantitative trait loci (QTL) mapping ............................................................. 122

5.2.2.1 Single-marker analysis .............................................................................. 122

5.3 Discussion ............................................................................................................... 127

6.0 GENERAL DISCUSSION ................................................................................... 128

7.0 REFERENCES ...................................................................................................... 133

8.0 APPENDIX ............................................................................................................ 142

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List of Figures

Figure 1.1: The light response curve of photosynthesis. ................................................. 24

Figure 1.2: The visible range in light spectrum. ............................................................. 27

Figure 1.3: The photosynthetic electron transport chain. ................................................ 29

Figure 1.4: Benson Calvin cycle. .................................................................................... 32

Figure 1.5: Starch synthesis. ........................................................................................... 34

Figure 1.6: Sucrose synthesis. ......................................................................................... 35

Figure 1.7: A cyclic electron flow in PSI. ....................................................................... 40

Figure 2.1: An illustration of a typical fluorescence. ...................................................... 56

Figure 3.1: A light response curve of WS. ...................................................................... 68

Figure 3.2: A time-course acclimation of maximum photosynthetic capacity ............... 75

Figure 3.3: A time-course acclimation of photosystem II (PSII) efficiency ................... 76

Figure 3.4: A time-course acclimation of non-photochemical quenching (NPQ). ......... 77

Figure 3.5: Total chlorophyll content.............................................................................. 75

Figure 3.6: Chl a/b of WS and WS-gpt2 .......................................................................... 80

Figure 3.7: Photosynthetic measurement of WS and WS-gpt2 in Winter 2010/2011 ..... 82

Figure 3.8: Chlorophyll content measurement of WS and WS-gpt2 plants during Winter

of 2010 to 2011. .............................................................................................................. 83

Figure 3.9: Photosynthetic measurement of WS and WS-gpt2 plants during Winter of

2011 to 2012 .................................................................................................................... 85

Figure 3.10: Chlorophyll content measurement of WS and WS-gpt2 plants during

Winter of 2011 to 2012 ................................................................................................... 86

Figure 4.1: A gel showing GPT2 expression in WS plants during acclimation from high

to low light. ..................................................................................................................... 96

Figure 4.2: Schematic representation of microarray analysis. ........................................ 98

Figure 4.3: Average profile cluster of 1,2,3,4,5 and 6. ................................................ 108

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Figure 4.4: A gene ontology (GO) representation from the GO analysis using the web

interface AGRIGO. ....................................................................................................... 112

Figure 4.5: A color-coded diagram showing the significance levels and arrow types. 112

Figure 4.6: A graphical result from the GO analysis based on the biological processes

....................................................................................................................................... 113

Figure 5.1: Phenotypic distribution of maximum photosynthetic capacity, ɸ PSII and

NPQ for recombinant inbred lines of Col-4 x Ler-0. .................................................... 124

Figure 5.2: Phenotypic distribution of transpiration, stomatal conductance and internal

CO2 concentration for recombinant inbred lines of Col-4 x Ler-0 ............................... 125

Figure 5.3: Phenotypic distribution of chlorophyll a/b, chl a and chl b for recombinant

inbred lines of Col-4 x Ler-0 ......................................................................................... 126

Figure 5.4: Phenotypic distribution of total chlorophyll content for recombinant inbred

lines of Col-4 x Ler-0. ................................................................................................... 127

Figure 5.5: A single-marker analysis on all 5 chromosomes of Arabidopsis thaliana

using 10 phenotypic traits ............................................................................................. 130


using Internal CO2 parameter as phenotypic trait ......................................................... 131


using NPQ parameter as phenotypic trait...................................................................... 132

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List of Tables

Table 2.1: Primer sequences for GPT2 and Act2. ........................................................... 61

Table 4.1: The difference in the mean fold change of gene expression based on top 20

most induced genes in Day 0 ........................................................................................ 102


most repressed genes in Day 0 ...................................................................................... 103


most induced genes in Day 1. ....................................................................................... 105


most repressed genes in Day 1 ...................................................................................... 106

Table 4.5: The top 20 most induced and repressed genes in Day 0 and its profile cluster.

....................................................................................................................................... 103


....................................................................................................................................... 110

Table 8.1: The 331 differentially expressed genes that are shared between Day 0 and

Day 1. The genes were ranked from the most repressed to the most induced. ............. 144

Word Count: 28,357

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Abstract

Institution: The University of Manchester Name: Furzani Pa’ee Degree Title: PhD in Plant Sciences Thesis Title: Photosynthetic Acclimation To Lower Light Intensity In Arabidopsis thaliana Date: 2014

Photoacclimation is a process by which photosynthetic capacity is regulated in response to environmental adjustments in terms of light regime. Photoacclimation is essential in determining the photosynthetic capacity to optimize light use and to avoid potentially damaging effects.

Previous work in our laboratory has identified a gene, gpt2 (At1g61800) that is essential for plants to acclimate to an increase in growth irradiance. Furthermore, we observed that the accession Columbia-0 (Col-0) is unable to respond to increases in light. Therefore, a Quantitative Trait Locus (QTL) mapping analysis was performed in Landsberg erecta (Ler)/Columbia (Col) recombinant inbred line population to identify novel genes responsible for this variation to acclimation.

In order to investigate the photoacclimation in Arabidopsis thaliana, photosynthetic capacity was measured in plants of the accession Wassileskija (WS) and in plants lacking expression of the gene At1g61800 (WS-gpt2) during acclimation from high to low light. Plants were grown for 6 weeks under high light (400 µmol.m-2.s-1) and half of them were transferred to low light (100 µmol.m-2.s-1) after six weeks. Gas exchange measurements were performed in order to measure the maximum capacity for photosynthesis. Acclimation to a decrease in light resulted in a decrease in the photosynthetic capacity in WS and WS-gpt2 plants. This shows that under lower or limiting light, photosynthesis was slowed down.

Chlorophyll fluorescence analysis was carried out to measure changes in the quantum efficiency of PSII (ΦPSII) and nonphotochemical quenching (NPQ) during acclimation. ΦPSII decreased in both WS and WS-gpt2 plants showing that under low light, PSII is more saturated However, it was found that there was no significant changes in NPQ level for both WS and WS-gpt2.

To estimate the total chlorophyll and chl a/b ratio, a chlorophyll composition analysis was performed. There was no significant changes in the total chlorophyll for both WS and WS-gpt2. However, the chlorophyll a/b ratio was seen to be decreased in low light plants representing an increase in light harvesting complexes relative to reaction centre core.

Plants of WS and WS-gpt2 were also grown under natural variable light in an unheated greenhouse in Manchester, UK. This experiment was carried out to study the photosynthetic acclimation of plants under fluctuating light condition.

A preliminary work on gene expression of gpt2 was conducted by doing reverse transcriptase PCR (RT-PCR). It shows that the gene expression of gpt2 decreased following transfer to low light plants in WS. Microarray analysis was also performed to investigate the role of GPT2 (if any) and to identify any potential gene that is important in high to low light acclimation.

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Declaration

No portion of the work referred to in the thesis has been submitted in support of an

application for another degree or qualification of this or any other university or other

institute of learning.

Copyright Statement

i. The author of this thesis (including any appendices and/or schedules to this thesis)

owns certain copyright or related rights in it (the “Copyright”) and he has given The

University of Manchester certain rights to use such Copyright, including for

administrative purposes.

ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic

copy, may be made only in accordance with the Copyright, Designs and Patents Act

1988 (as amended) and regulations issued under it or, where appropriate, in accordance

with licensing agreements which the University has from time to time. This page must

form part of any such copies made.

iii. The ownership of certain Copyright, patents, designs, trademarks and other

intellectual property (the “Intellectual Property”) and any reproductions of copyright

works in the thesis, for example graphs and tables (“Reproductions”), which may be

described in this thesis, may not be owned by the author and may be owned by third

parties. Such Intellectual Property and Reproductions cannot and must not be made

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available for use without the prior written permission of the owner(s) of the relevant

Intellectual Property and/or Reproductions.

iv. Further information on the conditions under which disclosure, publication and

commercialisation of this thesis, the Copyright and any Intellectual Property and/or

Reproductions described in it may take place is available in the University IP Policy

(see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=487), in any relevant

Thesis restriction declarations deposited in the University Library, The University

Library’s regulations (see http://www.manchester.ac.uk/library/aboutus/regulations) and

in The University’s policy on Presentation of Theses.

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List of Abbreviations

[H+] Proton

1O2 Singlet oxygen

ADP Adenosine diphosphate

APX Ascorbate peroxidase

ATP Adenosine-5'-triphosphate

BPG 1,3-biphosphoglycerate

CO2 Carbon dioxide

Col-0 Columbia (ecotype of A.thaliana)

Cvi Cape Verde Islands

Cyt b6f Cythocrome b6f

DHAP Dihydroxyacetone phosphate

DTT Dithiothreitol

EL Excess light

ETC Electron transport chain

F6P Fructose 6-phosphate

FNR Ferredoxin NADP Reductase

G1P Glucose 1-phosphate

G3P Glyceraldehydes 3-phosphate

G6P Glucose 6-phosphate

GPT Glucose-6-phosphate/phosphate translocator

GST Glutathione-S-transferase

H2O2 Hydrogen peroxide

HPLC High-performance liquid chromatography

Ler Landsberg erecta

Mn Manganese

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Mt Martuba

NADP+ Nicotinamide adenine dinucleotide phosphate

NADPH Reduced form of nicotinamide adenine dinucleotide phosphate

No Nossen

NPQ Nonphotochemical quenching

O2 Oxygen

O2- Superoxide

O3 Ozone or trioxygen

OEC Oxygen evolving complex

Oy Oystese

P680 Photosystem II primary donor

P700 Photosystem I primary donor

PCR Polymerase chain reaction

PGA 3-phosphoglycerate

PQH2 Plastoquionol

PsbS A protein associated with PSII

PSI Photosystem I

PSII Photosystem II

Qb Plastoquinone

qE High-energy-state quenching

ROS Reactive oxygen species

RT-PCR Reverse transcription PCR

Ru5P Ribulose 5-phosphate

RuBP Ribulose 1,5-biphosphate

SOD Superoxide dismutase

UDP Uridine diphosphate

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UTP Uridine-5'-triphosphate

VDE Violaxanthin de-epoxidase enzyme

WS Wassilewskija

WS-gpt2 A mutant which lacks gpt2 gene

∆pH pH gradient

ΦPSII Phi PSII

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Acknowledgements

Bismillah ir-Rahman ir-Rahim (In the name of God, most Gracious, most Compassionate).

In the completion of this thesis, I have received a very tremendous amount of help from various

people in my life. Therefore, I would like to express my gratitude to these people. First and

foremost, I would like to thank my supervisor, Dr Giles Johnson who have been very patient in

guiding me through my PhD years. Besides my supervisor, I would also like to thank my

advisor, Dr Anil Day who had always critically advised me on my work and progression.

I would also like to acknowledge my fellow colleagues who were very dear to me, especially

Sashila, Beth, Yaomin and Xun and in general to everyone else in our Plant Sciences

department. They have been giving me an enormous help and care during my ups and downs

and without them, I may not able to enjoy my university life as much. Also, my deepest

appreciation to my fellow Malaysian friends where we have support groups that made me felt

closer to God and become more optimistic towards the life He laid down for me. In addition to

that, I would also like to thank my financial supporter Majlis Amanah Rakyat (MARA) for

giving me the opportunity to further my studies in the first place. Without all these support, it

would be hard for me to have the courage to complete my studies at the University of

Manchester.

Finally, my loving parents, Pa’ee Kassim and Nazlizah Hassan, and in laws, Mohd Zairi Serlan

and Wan Norziah Wan Othman, who have been very patient with me and provided lots of

encouragement. They had always reminded me about the goals of life and to complete this study

as soon as possible. Throughout my PhD journey, I am very thankful to have gained a husband

and later on, a son. Mohd Naqiuddin Mohd Zairi, my husband, being my best friend was always

there for me and every time I had a meltdown, his advice and little Muadz’s laugh always made

it all okay. I love them both so much.

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Faculty of Life Sciences

Chapter 1

Introduction

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1.1 Changes in the climate due to changing weather pattern

Due to the changing weather patterns, plants experience difficulties in surviving, so

much so it could lead to stress conditions. Stress can be defined as an external factor

that has detrimental influence on plants. Stress conditions can cause damage to plants

and eventually can lead to death. To minimise the effects of stress, most plants are able

to undergo a process named acclimation. This acclimation process will be discussed in

detail later in this thesis (Section 1.5).

Briefly, the main focus of this thesis is to study the light effect on plants specifically on

the photosynthesis process. Light is vital for plants in which sufficient light is needed

for the aforementioned process which is photosynthesis and also for growth. However,

plants can be affected if excess light is received and caused several problems such as

photoinhibition and the production of reactive oxygen species (ROS). Therefore, it is

essential to study on how acclimation works and its benefits to plants. Besides, by

understanding how to deal with climate variation, many crop plants can produce higher

yield to meet the world’s population demand.

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1.1.1 Types of climate variation that affect plant performance

In order for plants to undergo acclimation, they have to identify what sort of problems

the environment is causing. Therefore, this section will discuss the most frequent types

of climate variation, which include water availability, temperature variation and most

importantly light intensity and accessibility.

Undoubtedly, water is needed by plants. Plants get a water supply from the soil via roots

and the water is transported though the xylem. The xylem is responsible for transporting

water and essential nutrients from roots to other parts of the plants. Water loss through

the stomata, known as transpiration, induces the capillary suction of water from soils to

roots and finally throughout the rest of the plants (McCulloh, Sperry et al. 2003).

When the availability of water to plants is limited, plants undergo several changes,

including stomatal closure (Lizana, Wentworth et al. 2006), in order to reduce water

loss. In a study by Lizana, Wentworth et al. (2006), the study compared the responses of

two different varieties of common bean, Arroz and Orfeo, to abiotic stresses,

specifically high irradiance and drought stress. The studies found that Arroz type was

more sensitive to water stress and Orfeo type was more tolerant to stress conditions

such as high light, high temperature and drought. Orfeo type was faster in closing the

stomata to retain the water under drought stress (Lizana, Wentworth et al. 2006).

Therefore, Orfeo is more dynamic in controlling the stomatal conductance under stress

condition compared to Arroz.

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When water is limited, plants close the stomata in order to ensure no transpiration

occurs. However, by closing the stomata, it also disables gas diffusion into the leaf, in

particular carbon dioxide (CO2) which is required for photosynthesis. Therefore,

drought limits the photosynthesis rate in plants.

As weather changes in countries where four seasons occur, temperature is one of the

parameters that is most affected. Temperature is important to plants since it helps to

manipulate flowering and reproduction (Craufurd and Wheeler 2009). If the temperature

is right, plants can perform photosynthesis and respiration at the maximum rate.

Many studies have examined the effects of low temperature on photosynthesis or

growth. In many studies, maize has been used as a model plant. In temperate climates,

maize needs to cope with low temperatures in early stages of growth. Other studies have

examined how maize responds to changing temperature while grown in a tropical or

temperate climates (Pietrini, Iannelli et al. 1999). In a study by Massacci, Iannelli et al.

(1995) using two different maize genotypes, it was found that the A-619 type was low

in photosynthesis, stomatal conductance and fluorescence properties under low

temperature. This is more likely to happen in plants when normal conditions are

disturbed. Thus, it leads to physiological effects on plants that could eventually impact

on growth.

Conversely, high temperature has effects on crops to some extent. It has been estimated

that for each increase in degree Celsius, about a 17% decrease in crop yield occur.

Besides, high temperature has an effect on rate of photosynthesis too. A temperature

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between 35oC and 40oC reduces the rate of photosynthesis but does not damage

photosystem II (PSII) (Sharkey 2005). It is now established that damage to PSII only

occurs at temperatures above 45ºC (Yamane et al 1998). Moreover, Weis (1980) found

that rubisco activity is reduced under moderate high temperature. The deactivation of

rubisco activity leads to three hypotheses (Sharkey 1998) :

1. Rubisco activase is very sensitive and cannot perform its normal activities at

high temperature.

2. At high temperature, photorespiration is favored over photosynthesis. Thus, it

would be detrimental to plants if the plants keep metabolizing phosphoglycolate

while the carbon fixation is low.

3. High temperature can cause detrimental effects on plants. Therefore, by

deactivating the rubisco, it can prevent damage to, for example, thylakoid

membrane.

Higher light intensities have effects on the flowering time too. According to Lacey

(1988), flowering time is determined by growth rate and plant size. However, they

found that three types of A. thaliana (Col, chl-1 and late flowering mutants) started to

flower at an early age. This shows that high irradiance affects and accelerates the flower

induction in plant. At high irradiance, A. thaliana produces shorter petioles and grows

more compactly. This is probably to avoid the excess light. Meanwhile, under low

irradiance, it has longer petiole and grows bigger, in order to allow plants to absorb

more light (Moharekar, Tanaka et al. 2007).

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As shown in an experiment by Rossel, Wilson et al. (2002) using HPLC of pigments

extracted from treated Col-0 leaves, plants were stressed more under high light

treatment than at high temperature. During the life of a plant, it can be exposed to an

array of different irradiances, changing on timescales from seconds to days. When

plants are growing at high light, they need to efficiently use the light, by increasing the

limit of the enzymes associated with carbon fixation (Boardman 1977, Anderson, Chow

et al. 1995). It is to their advantages if plants can acclimate to the changing condition

and so increase photosynthetic efficiency. This has been observed in many plants,

especially in tomato (Charles-Edwards and Ludwig 1975). Therefore, plants with high

photosynthetic capacity are known to use high light more efficiently.

On the other hand, there are some incidences where plants cannot use light efficiently

and other factors limit their photosynthetic capacity. The excess light can damage the

plants for example it can damage the reaction centre of photosystem II. As suggested by

Osmond, Bjorkman et al. (1981), this kind of damage is due to the inability of plants to

use the light efficiently. Under such conditions, plants are exposed to the full amount of

light while the enzymatic reactions are not fully active. Therefore, plants have a very

limited capacity for photosynthesis while the reaction centres are fully active and

reduced. This can lead to photoinhibition (Somersalo and Krause 1989).

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1.1.2 The importance of studying how plants regulate photosynthesis

process under acclimation

Some of the abiotic stresses that plants encounter may include drought, temperature and

variation in irradiance (Lizana, Wentworth et al. 2006). Fortunately, plants can develop

a defence mechanism that alter their metabolism which is termed acclimation. In

particular, the pathway of photosynthesis is known to undergo acclimation to changes in

the environment. According to Murchie and Horton (1997), there are two levels of

acclimation: leaf level and chloroplast level acclimation. At the leaf level, changes in

leaf thickness, total chlorophyll content and total number of chloroplasts can occur.

Photosynthetic parameters such as the ratio of chl a to b, Photosystem II/Photosystem I

(PSII/PSI) ratio and changes in the maximum photosynthetic capacity are examples of

chloroplast level acclimation.

Leaf thickness somewhat varies among species in different climate. For plants living in

shaded places, leaves are typically thin and have a large surface area. This is due to the

low amount of light available to the plants and by having larger surface area, it would

increase in absorbing light. Therefore, shade plants must possess a whole range of

adaptations to optimize the use of the light and to maximize photosynthetic efficiency

(Boardman 1977, Anderson, Chow et al. 1995). However, the exposure of shade plants

to high irradiance can cause the plants to undergo photoinhibition (Kok 1956, Critchley

and Smillie 1981, Fetcher, Strain et al. 1983, Langenheim, Osmond et al. 1984). As for

plants living in sunny places, their leaves tend to be thicker that also acts as a protective

layer. This is to avoid damaging the leaf cells by being over exposed to the light.

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It has been shown that different climate variations as discussed above may affect plants

in the environment. When plants are affected due to climate variations, plants do

response by performing acclimation. These responses are made mostly because it is

important for their photosynthesis process. Photosynthesis is a process which is

essential to all plants and even a slight change in the key elements needed for

photosynthesis will result in the inefficiencies of the photosynthesis process. Light

quality and quantity and also carbon dioxide (CO2) availability are among the key

elements of photosynthesis. These elements need to be maintained at an optimum level

to ensure photosynthesis to be working properly. It is very important to ensure the

efficiency of photosynthesis so that it can help in the plant’s growth. In a bigger picture,

eventually it will lead in an increasing yield crop production.

1.2 Photosynthesis

Fundamentally, photosynthesis is a process where light energy is absorbed by

chlorophylls and some of that energy is used to remove electrons from water to produce

oxygen, sugar and other primary compounds such as NADPH and ATP. This process is

essential to plants to provide energy for growth and reproduction.

Photosynthesis is a light-driven process. Thus, it is extremely important for plants to

absorb the right amount of quantity and quality of light. As shown in Figure 1.1, light

absorption of plants is linear to the increase of irradiance. Therefore, as the light

intensity increases, plants will absorb more light to drive photosynthesis. However, as

photosynthesis saturates, the absorption of excess light will result in excess excitation

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energy. This can be harmful to plants as it can damage the leaf and lead to

photoinhibition. For this reason, plants acclimate by optimising their growth or

metabolism to survive in the new changing environment.

Figure 1.1: The light response curve of photosynthesis. As the light intensity

increases, the rate of light absorption is also increased which results in the increase in

photosynthesis. Unfortunately, excess excitation energy is obtained when plants keep

absorbing the unnecessary light while photosynthesis is already saturated. This incident

is damaging to plants in which it could initiate a production of harmful events such as

photoinhibition and reactive oxygen series (ROS). Redrawn from

http://www.pnas.org/content/109/39/15533/F1.expansion.html.

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According to Maxwell and Johnson (2000), light that is absorbed by chlorophyll can go

to each of the following three processes;

i. it can be used for photosynthesis, or

ii. excess energy can be dissipated as heat, or

iii. the light can be re-emitted as light (chlorophyll fluorescence)

It is known that the higher the probability of one process is happening, the lower the

probability of other processes occurring. In one of the processes in which the absorbed

light can be re-emitted as light is termed chlorophyll fluorescence. Chlorophyll

fluorescence can be measured by one of the photochemical quenching parameters which

is the measurement of the efficiency of photosystem II (PSII). This parameter measures

the proportion of absorbed light by chlorophyll associated with PSII being used for

photochemistry processes such as photosynthesis. Indirectly, this measurement gives an

indication of overall photosynthesis. Meanwhile, non-photochemical quenching (NPQ)

is one of the non-photochemical quenching parameters in chlorophyll fluorescence.

NPQ can be measured based on the efficiency of dissipating light as heat. Besides, NPQ

also acts as a regulatory mechanism to protect plants from damaging the reaction centre

due to high light absorption (Carbonera, Gerotto et al. 2012).

Photosynthesis is sensitive to environmental changes. Besides light availability, changes

in temperature may have an effect on photosynthesis too. Optimum temperature is

needed for plants to be able to drive its photochemical processes most efficiently. If the

temperature is below or above the favourable temperature, it may lead to

photoinhibition state. It was found that the assimilation of B.vulgaris decreased when

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the temperature was either lower or higher than 25oC. At the lowest temperature (5oC),

they found that the NPQ value was the highest as the thermal dissipation represent the

main heat dissipative system in plants.

1.2.1 Photosynthetic organelle – Chloroplast

The important photosynthetic organ that initiates the whole process of photosynthesis is

the chloroplast. Chloroplasts are abundant in the spongy parenchyma and palisade

parenchyma. A chloroplast is surrounded by two membranes known as the outer and

inner membrane. Besides those two membrane systems, chloroplasts also possess a third

membrane system which is known as the thylakoid membrane. Proteins and pigments

(chlorophylls and carotenoids) required for photosynthesis are located in the thylakoid

membrane.

1.2.2 Light capture

Chlorophyll pigments from chloroplasts are responsible for light capturing for the use of

photosynthesis. These chlorophyll pigments capture light energy in a specific and

narrow range of light spectrum which usually in the visible light range.

The range of the visible light is from 400nm to700nm within the electromagnetic

spectrum (Figure 1.2). The longer the wavelength, the less energy the light possesses.

Light in this wavelength is more readily available to plants than any other wavelengths.

27

Figure 1.2: The visible range in light spectrum. In PSI, the longest wavelength absorption

occurs at wavelength of 700nm. Meanwhile, in PSII, the longest wavelength absorption occurs

at 680nm. Redrawn from http://www.punaridge.org/doc/factoids/light/default.htm.

In a work by Kreslavski, Shirshikova et al.(2013), plants of Arabidopsis thaliana wild-

type (Col-0) were pre-illuminated with a range of visible lights ranging from

550-730 nm wavelength. These plants were studied for the effect of visible light pre-

illumination on photosynthesis and activity of PSII, the content of photosynthetic

pigments and H2O2 and the peroxidase activity in response to UV-A radiation. Under

the radiation of UV-A, Col-0 had a decreased PSII activity as well as the content of chl

a, chl b and carotenoids pigment. Meanwhile, peroxidase activity and H2O2 levels were

increased under UV-A radiation. When the plants were pre-illuminated with red light

(λmax = 664 nm), photosynthesis and PSII activity was increased. However, when plants

were pre-illuminated with red light and then a far red light (λmax = 727 nm), the effect of

red light on photosynthesis and PSII activity was inhibited and the value was of similar

to the effect of UV-A radiation only. It was suggested that an active form of

phytochrome (PFR) was involved in these processes.

28

1.2.3 Electron transport chain (ETC)

In light reactions, light energy absorbed by chlorophylls is transferred between

molecules within photosystems. Photosystems converts light energy into chemical

energy. Two different types are found in plants - photosystem I (PSI) and photosystem

II (PSII). The latter occurs first in the electron transport chain. PSI and PSII contain

special chlorophylls known as P700 and P680 respectively because they absorb the

longest light wavelengths of 700nm and 680nm.

Before transferring molecules to Photosystem I (PSI) in the electron transport chain

(ETC), Photosystem II (PSII) is also responsible for splitting water (Baker 1996). When

a photon of light strikes, it is converted into a chemical energy. This process triggers the

water-splitting complex in which two molecules of water are split into one oxygen

molecule and four protons with the assistance of 4 molecules of manganese (Mn).

However, the chemical reactions involved in the water splitting can be harmful to the

plants (Baker 1996). In order to extract the electrons from the water, it needs a very high

oxidizing potential that could lead to other dangerous oxidation reactions.

29

Figure 1.3: The photosynthetic electron transport chain. Then electron transport

chain is initiated when a photon of light strikes the P680 chlorophyll in photosystem II.

The electrons are transported through series of reaction involving carrier molecules and

enzyme. Eventually, the electron transport chain provides an electrochemical gradient

essential for NADPH and ATP synthesis. The electron transport chain resulted in

ultimate reduction of NADP to NADPH. Meanwhile, a proton gradient is created across

the chloroplast membrane and is used by ATP synthase to create ATP from ADP.

Redrawn from http://www.cell.com/cms/attachment/591673/4552861/gr3.jpg.

Excitation energy from chlorophyll light absorption can be transferred from one

chlorophyll to another. When P680 is excited, an electron is transferred to a mobile

carrier molecule, plastoquinone (Figure 1.3). Besides accepting electrons from P680,

plastoquinone also accepts two protons available from the stroma. Since plastoquinone

carries two electrons from P680, P680 loses electrons which are replaced by the water-

splitting process.

30

Plastoquinone carries the two electrons and two protons to a cytochrome complex called

cytochrome b6f. Cytochrome b6f acts as an electron mediator between PSII and PSI.

When plastoquinone reaches the complex, it releases the protons to the lumen and

transfers the electrons into the cytochrome b6f complex. Then, the electrons are

transferred to another mobile carrier protein, plastocyanin. In the meantime, cytochrome

b6f complex pumps additional protons across the thylakoids membrane to the lumen

and creating a proton gradient. This proton gradient is needed to drive the ATP

synthesis

Plastocyanin transfers one electron to the reaction centre in PSI. In PSI, the electrons are

re-energized by a photon of light. The energized and excited electrons are then

transferred to ferredoxin. Ferredoxin is responsible for transferring each electron to

another protein, Ferredoxin NADP Reductase (FNR). After the process is complete,

they are coupled with another proton and a molecule NADP+. Eventually, NADP+ is

reduced by adding two electrons and one proton creating NADPH.

Light-driven charge separation reactions initiate the electron transport that eventually

produces NADPH and O2. Electron transport also generates a proton motive force

across the thylakoid membrane which drives the synthesis of ATP. ATP is synthesized

by an enzyme called the ATP synthase. ATP synthase is embedded in a thylakoid

membrane where the unit that is responsible for ATP synthesis is situated in the stroma.

ATP is synthesized from adenosine diphosphate (ADP) and inorganic phosphate (Pi)

and this synthesis needs energy to drive it. The energy comes from the electrochemical

gradient from the movement of proton, H+ from lumen to stroma.

31

1.2.4 Carbon fixation

The electron transport chain produces NADPH and ATP for use in plant cells. These are

high-energy compounds, and are of intermediate stability so cannot be used for long-

term storage. Therefore, the energy needs to be converted into a more stable form.

Normally, plants convert the energy into sugar or complex carbohydrates such as starch.

The pathway that incorporates carbon into plants by reducing the CO2 is called the

Benson-Calvin cycle (Figure 1.4).

In the Benson-Calvin cycle, there are three phases: carboxylation, reduction and

regeneration (Heldt 1997, Sharkey 1998, Taiz and Zieger 1998, Martin, Scheibe et al.

2000). In the carboxylation phase, 3-phosphoglycerate (PGA) is produced from ribulose

biphosphate (RuBP). Meanwhile, in the reduction phase, NADPH and ATP produced

by the ETC are used to generate triose phosphate. Triose phosphate will be used for the

regeneration of ribulose 1,5-biphosphate (RuBP) in the regeneration phase. A

proportion of triose phosphate is used for starch and sugar synthesis.

32

Figure 1.4: Benson Calvin cycle. The Benson Calvin cycle consists of 3 phases which are the

carboxylation, reduction and regeneration phase. The ATP and NADPH produced from the

electron transport chain are used in the Benson Calvin cycle. Redrawn from

http://www.mhhe.com/biosci/genbio/enger/student/olc/art_quizzes/genbiomedia/0158.jpg.

33

In the carboxylation phase, an enzyme called ribulose-biphosphate

carboxylase/oxygenase (Rubisco) is used. RuBP binds to the active site and it forms

enediol intermediate. CO2 is then added to produce 2-carboxy-3-ketoarabinitol-1,5-

biphosphate intermediate. This intermediate is then hydrolyzed to form 2 molecules of

PGA. These PGA molecules will be used later in the reduction phase.

In the reduction phase, PGA is phosphorylated to 1,3-biphosphoglycerate (BPG) using

ATP by an enzyme called phosphoglycerate kinase. The next step is the reduction

process of BPG to glyceraldehyde 3-phosphate (G3P). This process involves the

oxidation of NADPH to NADP+ by the enzyme, NADP-glyceraldehyde 3-phosphate

dehydrogenase (GAPDH). The overall reaction of this phase is the conversion from

carboxylic acid to an aldehyde. In addition, the enzyme triose phosphate isomerase

catalyzes the equilibrium between G3P and its isomer, dihydroxyacetone phosphate

(DHAP). These rapidly equilibrate and so act as a pool which is known as the triose

phosphate pool. Triose phosphates are the starting molecule for the regeneration of

RuBP in the regeneration phase.

In the regeneration phase, the three-carbon triose phosphate is regenerated to five-

carbon sugar ribulose 5-phosphate (Ru5P) through several intermediates. Then, Ru5P is

phosphorylated using ATP to produce RuBP using an enzyme, phosphoribulokinase.

The regenerated RuBP is then ready to be used in the Calvin cycle again.

34

1.2.5 Starch and Sugar synthesis

Besides using triose phosphate for RuBP regeneration, it can also be used for starch and

sucrose synthesis. For starch synthesis, the first reaction is triose phosphate is converted

to fructose 1, 6-biphosphate and eventually to fructose 6-phosphate (F6P) (Figure 1.5).

By using an enzyme hexose phosphate isomerase, F6P is isomerized to glucose 6-

phosphate (G6P). Then, G6P is converted to glucose 1-phosphate (G1P) by

phosphoglucomutase. G1P is activated by ATP and ADP-glucose pyrophosphorylase to

release ADP-glucose and pyrophosphate. Pyrophosphate is then hydrolyzed by

pyrophosphatase to form the ADP-glucose. Finally, the ADP-glucose is attached to a

starch chain by starch synthase.

Figure 1.5: Starch synthesis. The starting molecule for starch synthesis is the triose phosphates

produced in the CO2 fixation process, the Calvin cycle. Starch synthesis occurs in the

chloroplast. Redrawn from https://encrypted-

tbn0.gstatic.com/images?q=tbn:ANd9GcR5rdKbO3bLpnapZsuKYP3_ltnxb-

msXFOKoITic8NYl2Y7HfpwVw.

35

To synthesize sucrose, the early reactions from triose phosphate to G1P are chemically

identical but occur in the cytosol. G1P is then converted to UDP-glucose, instead of

ADP-glucose by a nucleotide, uridine triphosphate (UTP) and UDP-glucose

pyrophosphorylase (Figure 1.6). UDP-glucose is condensed with F6P to form sucrose

6-phosphate using sucrose phosphate synthase. Ultimately, sucrose 6-phosphate is

hydrolyzed to form the sucrose. Sucrose synthesis takes place in the cytosol, from triose

phosphate exported from the chloroplast via a triose phosphate translocator (TPT).

Figure 1.6: Sucrose synthesis. The starting molecule for sucrose synthesis is the triose

phosphates produced in the CO2 fixation process, the Calvin cycle. Sucrose synthesis occurs in

the cytosol. Redrawn from https://www.jic.ac.uk/STAFF/trevor-

wang/images/full/starchpath2.jpg.

36

1.3 Plant’s responses to environmental stress

Undesirable changes in light quality or quantity is an external factor that may have a

detrimental influence on plants. These light changes may cause damage to plants and

may lead to death. If the plant is able to cope with the changes, the plant undergoes a

process called acclimation. However, if plants are not able to cope with the extreme

changes, the excess stress could lead to damage.

1.3.1 Photoinhibition

Photoinhibition is defined as light induced damage to the photosynthetic apparatus

leading to a decrease in the efficiency of photosynthesis. Light damage is mostly

associated with Photosystem II (PSII). During photoinhibition, the reaction centre of

PSII is inactivated and the D1 polypeptide forming part of the core of PSII is damaged.

The PSII of oat (Avena sativa L. cv. Prevision) plants were clearly affected in its PSII

activity when oat plants were exposed to high light intensity. The plants showed a

decreased in the quantum yield of PSII, in the capacity of photochemical quenching and

an increase in non-photochemical quenching (Quiles and López 2004). The non-

photochemical quenching value was increased as a result of the photoinhibition that

occurred in PSII. It was also shown that PSI was more stable than PSII and it was

suggested the reason might be because of the photoprotective role in Photosystem I

(PSI). Therefore, it was shown that PSII was more prone to damage due to

photoinhibition compared to PSI.

37

There are two types of photoinhibition identified which are dynamic photoinhibition

and chronic photoinhibition (Osmond, Baker et al. 1994). Dynamic photoinhibition

occurs under moderate excess light and the maximum photosynthetic capacity remains

the same. This type of photoinhibition is only temporary and the effects can be reversed

by decreasing the photon flux below the saturation level. Meanwhile, when plants are

exposed to high levels of excess light, chronic photoinhibition occurs. This is when the

photosynthetic mechanism is damaged and photosynthetic capacity is reduced. This is a

long-term photoinhibition that could last for a certain period of time.

Photoinhibition occurs when the rate of photodamage exceeds the capacity of

chloroplast repair process (Melis 1999). A functional PSII will be photodamaged under

an incident light intensity and the PSII becomes disassembled. D1 protein is degraded

and de novo biosynthesis occurs. Then, the PSII gets re-assembled again.

1.3.2 Reactive oxygen species (ROS)

Plants growing under natural variable light are more prone to absorbing more light

energy than needed for the photochemistry processes. This excess energy is known to

be burdening and damaging the cell by increasing the risk of reactive oxygen

production. Since photosynthesis is one of the photochemistry processes that is light-

driven, the absorption of excess light energy can actually limit photosynthesis process.

38

Excess lights can induce the production of reactive oxygen species (ROS) in the

chloroplasts such as hydrogen peroxide (H2O2), superoxide (O2-), hydroxyl radicals

(OH·) and singlet oxygen (1O2). According to Niyogi (1999), ROS can be produced

when excess light is absorbed through three sites in the photosynthetic apparatus which

are the light-harvesting complex associated with PSII, the reaction centre of PSII and

also PSI. Since ROS are very reactive components, their production can cause damage

to the plants in a way that they induce the oxidation of lipids, proteins and enzymes

essential in the functions of the plants. Besides light, other conditions can also amplify

the production of ROS such as the O3, salt, toxic metals and temperature (Conklin and

Last 1995, Richards, Schott et al. 1998, Shinozaki and Yamaguchi-Shinozaki 2000).

These conditions need to be controlled so that the plants can function properly and

efficiently.

When the rate of photosynthetic electron transport in plants exceeds its metabolic

capacity, an alternative pathway for electrons is needed, primarily to protect PSII from

photoinhibition. As suggested by Mehler (1951) in the Mehler reaction, the reduction of

oxygen molecule may provide an alternative. However, the reaction is known to form

radical molecules such as H2O2. H2O2 and other radical molecules are very harmful and

can damage PSII as well as PSI. Therefore, damage to both photosystems may offset the

benefits of oxygen molecule reduction. In order to decrease the number of radical

molecules, a desirable amount of enzymes needs to be maintained and this process

requires an enormous energy. Thus, it is better to minimize the opportunity of producing

the radical molecules as prevention is better than cure.

39

There are several defence systems developed in plants to protect from the damage

provided by ROS. These include many enzymatic processes in plants to prevent damage

(Asada 2000). However, these processes are energetically expensive and high

concentrations of antioxidants and enzymes are needed. Antioxidants, including

ascorbate and glutathione are needed in plants since it can dissipate the excess heat so

much so it prevents the oxidation of lipids by ROS. Meanwhile, enzymes that are used

in this defense system are superoxide dismutase (SOD), ascorbate peroxidase (APX)

and glutathione-S-transferase (GST) (Wetzel, Harmacek et al. 2009). These enzymes are

known to dismutate the radicals and to eliminate other ROS.

Besides, as suggested by (Clarke and Johnson 2001), cyclic electron flow in PSI could

provide an alternative pathway for the electrons (Figure 1.7). The electrons from

ferredoxin is transported back to cytochrome b6f via plastoquinone. They are then

returned to PSI again via plastocyanin. This flow results in proton gradient that is

important for ATP production by ATP synthase. Cyclic electron flow is beneficial for

plants when there is a necessity to produce more ATP while maintaining the amount of

NADPH. The ATP and NADPH molecules can be used in the Calvin cycle.

40

Figure 1.7: A cyclic electron flow in PSI. Cyclic electron flow involves in making more ATP

than NADPH. This is important for Calvin cycle in which the Calvin cycle uses more ATP than

NAPDH. Instead of passing the electron in ferredoxin to ferredoxin NADP reductase, the

electron is transferred back to cytochrome b6f. Redrawn from

https://learning.uonbi.ac.ke/courses/SBT306/scormPackages/path_2/photo_phosphorylation.htm

l.

1.4 Changes to changing light condition

Depending on the time of the year, plants might experience a condition where the light

availability is either too high or too low. Either of these conditions can be stressful to

plants. Often, stress conditions affect important metabolic process in plants, in

particular photosynthesis (Doubnerová and Ryšlavá 2011).

41

There are some conditions where plants cannot use light efficiently and other factors

limit their photosynthetic capacity and this results in the leaf absorbing excess light.

Excess light can damage the plant, in particualr it can damage the reaction centre of

photosystem II. This kind of damage is due to the inability of plants to use the light

efficiently (Osmond, Bjorkman et al. 1981). Plants can be exposed to the full sunlight

while the enzymatic reactions are not fully active. Therefore, plants have a very limited

capacity for photosynthesis, while the reaction centres are fully active and reduced. This

situation can lead plants to photoinhibition (Somersalo and Krause 1989).

In the experiments in this thesis, care was taken to make sure that the plants were not

suffering stress. There are many studies designed to see how plants change when grown

under different light intensities. (Bailey, Walters et al. 2001) grew plants in a range of

light conditions to assess them in terms of their capacity for photosynthetic acclimation.

It was found that, as the light intensity increased, the photosynthetic capacity and Chl

a/b increased as well. This suggests that, as light intensity increases, plants respond by

altering their chlorophyll-containing components such as the light-harvesting complexes

of photosystem II (LHC-II) and the amount of reaction centres.

When plants are continually exposed to changing light environments, they develop

regulatory mechanism to inhibit any photodamage processes happening (Roach and

Krieger-Liszkay 2012). These include the short-term responses and long-term responses

to the changing light which will be discussed in the next section.

42

1.4.1 Short-term responses to changing light condition

There are several short-term responses by plants when they are exposed to stress factors,

for example changes in light availability. Plants that are exposed to changing lights may

induce the mechanisms of state transitions and non-photochemical quenching (Kanervo,

Suorsa et al. 2005). Besides, the short-term responses also include feedback de-

excitation (xanthophyll cycle), D1 protein synthesis and chloroplast movement. These

short-term responses are detailed in the next paragraphs.

The first short-term response to changing in light conditions is a process called state

transition. The turnover of PSI and PSII need to be balanced to allow the flow of

electrons through both photosystems. State transitions allow plants to ensure a correct

imbalances in excitation between the photosystems. State transitions act as a mechanism

to distribute excitation light energy between PSII and photosystem I (PSI) (Leoni,

Pietrzykowska et al. 2013). State transitions are well established in red algae and green

algae. Although the light-harvesting complex is quite different between some algae and

plants, it is believed that the mechanism for balancing the energy in the photosynthetic

apparatus is similar. When illumination conditions occur in plants that lead to excess

excitation of PSII compared to PSI, a reduction of the plastoquinone (PQ) pool activates

a kinase (stn7) which phosphorylates the light harvesting LHCII. This induces a

migration of LHCII from PSII and PSI resulting in a state termed State 2. When PSI is

over-excited, LHCII is dephosphorylated, returning to State 1 which diverts the excess

absorbed energy to PSII. These state transitions allow plants to balance the excitation

energy under changing light regimes (van Thor, Mullineaux et al. 1998, Allen and

Forsberg 2001).

43

Secondly, the short-term response also includes the ability of plants to dissipate excess

heat through a process called non-photochemical quenching (NPQ). When the light

harvesting complexes absorb more energy than needed, a photoprotective mechanism in

plants is switched on to prevent damage to the reaction centres. The major component

for NPQ is high energy state quenching, qE, a ∆pH-dependent NPQ. qE is induced

when the pH gradient across the thylakoid membrane is high. When the qE is induced, it

de-excites singlet excited chlorophyll in PSII antenna and dissipates the excess energy

as heat.

One of the processes inducing qE is the conversion of the xanthophyll violaxanthin to

zeaxanthin by the enzyme violaxanthin deepoxidase (VDE) in xanthophyll cycle

(Niyogi, Li et al. 2005). This feedback de-excitation is another short-term response by

plants when the absorption of light surpasses the capacity for CO2 fixation. Under

excess light or high [H+] level in the thylakoid lumen, violaxanthin is converted to

zeaxanthin via antheraxanthin. Zeaxanthin and antheraxanthin are essential for NPQ

(Gilmore and Yamamoto 1993), (Thiele, Schirwitz et al. 1996),(Jahns and Holzwarth

2012). Under low light, zeaxanthin and antheraxanthin are converted back to

violaxanthin by zeaxanthin epoxidase (ZE). Npq1 mutants are known to lack the VDE

enzyme which they cannot convert violaxanthin to zeaxanthin. Meanwhile, npq4

mutants accumulate zeaxanthin and lack PsbS protein. PsbS is the photosystem II

subunit S which is known to have a vital role in quenching the excess excitation energy

(Crouchman, Ruban et al. 2006). PsbS is also known in participating in the formation of

qE which is important for plants under high or excess light. Therefore, lack of PsbS as

seen in the npq4 mutant has shown that the npq4 mutant experience more

44

photoinhibition (Roach and Krieger-Liszkay 2012). Due to that, PsbS also serves as a

role in photoprotection against excess light.

In order to measure the photoinhibition experienced by plants in field conditions, the

decrease of Fv/Fm in chlorophyll fluorescence can be measured (Kulheim, Agren et al.

2002), (Jiang, Li et al. 2005). The more the Fv/Fm is reduced, the more the plants are

experiencing photoinhibition. When the weather was at full noon, the mutants of npq1

and npq4 were greatly reduced in Fv/Fm compared to the wild-type. Meanwhile during

a cloudy day, there was no photoinhibition occurring in either mutants or wild type.

Therefore, these results suggest that feedback de-excitation, protects plants from

photoinhibition.

It has been shown that damage to D1 protein is directly proportional to light intensity

(Vijayan and Browse 2002). Therefore, the inactivation and recovery process of D1 is

another short-term response of Arabidopsis under changing light condition. Specifically

when plants are exposed to excess light (EL), some proteins are affected in their

synthesis, including the D1 protein of PSII (Shapira, Lers et al. 1997). When the D1

protein is inactivated, it must be repaired instantly in order to restore the PSII activity

by inserting the newly-synthesized D1 into the thylakoid and incorporating it with the

PSII complex (Vijayan and Browse 2002). In C. reinhardtii, the D1 protein synthesis

increases in EL which indicates the D1 protein is rapidly repaired once exposed to EL.

45

A chloroplast avoidance movement is also a plant’s short-term response when the

chloroplasts move to the side of cells in which it is parallel to the direction of light. This

is mainly to avoid over absorption of excess light (EL) and eventually to reduce the

effect of photodamage (Kasahara, Kagawa et al. 2002). This response is mediated by

blue light and its photoreceptor, phototropin. In Arabidopsis, there are two phototropins

which are PHOT1 and PHOT2. From a study done in a molecular genetics area, it was

found that PHOT2 was responsible in regulating the chloroplast avoidance movement

(Jarillo, Gabrys et al. 2001, Kagawa, Sakai et al. 2001, Kasahara, Kagawa et al.

2002).The chloroplasts in phot2 mutants accumulate perpendicular to the light direction

and maximize the interception of light.

On the other hand, acclimation is a long-term response where it involves in the

synthesis and degradation of selective chloroplast components. For example, changes in

the ratio of chlorophyll a to chlorophyll b (chl a/b) indicate changes in the relative

abundance of light harvesting complexes (LHC) compared to reaction centres

(Maenpaa and Andersson 1989). Acclimation towards light which is also termed as

photoacclimation will be discussed thoroughly in the next section.

1.5 Photoacclimation - Long-term response to changing light condition

Sunlight availability changes through the year. Light can fluctuate over long as well as

short time periods. When the amount of light available changes, the plant needs to use

the light efficiently to sustain life. Therefore, plants have evolved to overcome the

problem by performing acclimation. Acclimation takes up to several days and can

46

involve changes in pigments such as chlorophylls, carotenoids and anthocyanins, as

well as of different enzymes involved in photosynthesis and other processes (Wetzel,

Harmacek et al. 2009).

Changes in irradiance, for example, could lead to harmful effects on plants. Therefore,

plants respond to changes by altering their light capture capacity and at the same time

limit potentially damaging effects, such as photoinhibiton and the production of reactive

oxygen species (ROS) (Ballare 1999).

Arabidopsis thaliana is a model plant that has been extensively used in experiments for

light acclimation (Yin and Johnson 2000, Bailey, Walters et al. 2001, Bailey, Horton et

al. 2004, Athanasiou, Dyson et al. 2010). However, most of the experiments have been

done by growing plants from seed in separate growth irradiances, which is termed

developmental acclimation. This means that plants develop different metabolic

capacities (photosynthetic capacities) and different leaf structures. Bailey, Walters et al.

(2001) grew Arabidopsis plants in six different growth irradiances. Measurement of the

maximum photosynthetic rate (Pmax) showed that this increased when growth

irradiance increased. Moreover, all plants at different growth irradiances increased their

PSII efficiency (ɸPSII) (Bailey, Horton et al. 2004).

In natural habitats, where light fluctuates on time scales from seconds to weeks, some

plants have the potential for dynamic acclimation. Dynamic acclimation is where leaves

that mature in one set of condition (e.g high or low light), are able to change their

photosynthetic capacity when transferred to a different set of conditions. As shown by

47

Naramoto, Katahata et al. (2006), Fagus crenata (Japanese beech) plants exposed to

changing conditions are able to acclimate dynamically. F.crenata was grown under low

light (LL) condition before being exposed to 2 different light intensities of medium light

(ML) and high light (HL). Photochemical efficiency of PSII (Fv/Fm) and

photosynthetic capacity were measured and it was found that F.crenata experienced

more photoinhibition under HL. The HL acclimated plant was unable to increase its

photosynthetic capacity compared to the ML. Therefore, it was concluded that a slow

increase of the light intensity plays a key role to have a successful photosynthetic

acclimation (Naramoto, Katahata et al. 2006).

Work by Athanasiou, Dyson et al. (2010) showed that Arabidopsis grown at a low light

intensity (100 µmol m-2 s-1) had the ability to change their photosynthetic capacity when

being transferred to a higher light intensity (400 µmol m-2 s-1). It was also found that the

gene At1g61800, which encodes a Glucose-6-P/phosphate translocator (GPT2) is

essential for this type of acclimation. GPT2 has a primary function of translocating

sugar and phosphates across the chloroplast (Knappe, Flugge et al. 2003).

As suggested by Bowsher, Lacey et al. (2007), the primary function of GPT is that it

imports G6P into plastids of heterotrophic tissue as a precursor for starch biosynthesis.

The GPTs (GPT1 and GPT2) belong to the phosphate translocator family, which

contains six functional phosphate translocators (PTs) in Arabidopsis. These are a triose

phosphate/PT (TPT) (Schneider, Hausler et al. 2002), two phosphoenolpyruvate

(PEP)/PT (Knappe, Flugge et al. 2003), a xylulose-5-phosphate (Xul5P)/PT (Eicks,

Maurino et al. 2002) and two glucose-6-phosphate (Glc6P)/PT (Kammerer, Fischer et

al. 1998). The genes of Glc6P/PT, gpt1 and gpt2, were demonstrated to have different

48

effects on vegetative and generative development. Plants lacking the gpt1 gene have

retarded development of both pollen and embryo sac development (Niewiadomski,

Knappe et al. 2005). However, GPT1 is known not to have an effect in starch

biosynthesis. Thus, pollen and embryo sac development do not require starch for

development (Kunz, Hausler et al. 2010). Meanwhile, the gpt2 gene has been shown to

be essential in the higher irradiance acclimation in Arabidopsis thaliana, as the gpt2-

mutant plants did not acclimate photosynthesis when transferred to high light conditions

(Athanasiou, Dyson et al. 2010). To confirm the impairment in acclimation of

photosynthesis of gpt2 mutants, mutants were complemented with a copy of the gpt2

gene and it was shown that plants were able to acclimate. Therefore, it was concluded

that GPT2 is important in dynamic acclimation to increased light in Arabidopsis.

Previous studies also have shown that the gpt2 gene is also induced during sugar-

feeding and sugar-induced senescence (Gonzali, Loreti et al. 2006, Li, Lee et al. 2006,

Pourtau, Jennings et al. 2006).

According to (Yin and Johnson 2000), many plants have been grown under separate and

static light conditions, but few studies have been carried out when plants were grown

under fluctuating light environments. The light environment variation ranges from

seconds to hours and the light availability will greatly affect plants, especially for

woodland plants. In the study by (Yin and Johnson 2000) Arabidopsis thaliana,

Digitalis purpurea and Silene dioica were grown at different light intensities fluctuating

between 100 µmol m-2 s-1 and 475 µmol m-2 s-1 or 810 µmol m-2 s-1. It was found that

the fluctuating light environment increased the maximum photosynthetic rate for all

species. However, the extent of acclimation responses varied between species in terms

of the cytochrome f content and Rubisco protein.

49

Besides affecting the maximum photosynthetic rate, growth of plants of Arabidopsis

under short and long fluctuating light treatment may involve in the reorganization of

pigment-protein complexes and enhancement of photoprotective mechanisms (Alter,

Dreissen et al. 2012). 7 ecotypes of Arabidopsis were treated with short and long

sunflecks and it was found that all plants had an increased in NPQ. This shows that

these plants were unable to utilize the light efficiently, even under short sunflecks.

Besides NPQ, the short and long sunflecks resulted in a decrease in chlorophyll content,

an increase in the de-epoxidation of violaxanthin to zeaxanthin and antheraxanthin,

upregulation of the amount of PsbS protein and of superoxide dismutase activity (Alter,

Dreissen et al. 2012).

1.6 Aims and Objectives

Plants have been studied for their response of photosynthetic capacity under fluctuating

light regime. In order to study the plant mechanism under fluctuating light, the direction

of light changing was studied separately as from low to high light and high to low light.

This is to give better insights to how plants respond to each of set of light condition.

In contrast to the work of (Athanasiou, Dyson et al. 2010), this work was designed to

understand the acclimation of Arabidopsis when plants were grown to maturity under

high light condition and then transferred to a lower light condition. Plants were

monitored by following the photosynthetic rate of low light plants upon transfer up to 9

days of acclimation. By doing the reverse acclimation, one question would be posed: Is

this a simply the reverse of low to high light acclimation?

50

The amount of light used in high light and low light was determined by previous work

so that it would not put the plants in a stress condition. Besides, the high light condition

(400 µmol m-2 s-1) and low light condition (100 µmol m-2 s-1) are set where the

acclimation is clearly seen and maximum photosynthesis (Pmax) rate is achieved at this

growth light intensity.

In this study, the photosynthetic capacity in WS and WS-gpt2 mutants in A.thaliana was

examined during acclimation from high light to low light. Responses of gas exchange

and chlorophyll fluorescence (ΦPSII and NPQ) were investigated as well as the

chlorophyll composition (total chlorophyll and chl a/b ratio). The gas exchange

measurements were performed to measure the photosynthetic capacity of plants during

acclimation from high to low light. Simultaneously, there were two parameters in the

chlorophyll fluorescence analysis that were measured; ΦPSII and nonphotochemical

quenching (NPQ). In order to measure the chlorophyll composition in plants (total

chlorophyll and chl a/b ratio), chlorophyll extractions were performed. Besides

physiological analysis, a molecular biology approach (RT-PCR and microarray) is being

carried out too to study the expression of gpt2 gene. The data so far obtained provide an

initial insight on the changes in the system during acclimation.

To carry out the above experiments, plants were grown in a controlled laboratory

condition in which only the light quantity was altered. In addition, an outdoor project

was carried out at the FIRS Botanical Garden in Manchester to investigate the changes

in plants in terms of fitness under natural variable light. The WS and WS-gpt2 plants

were sown in autumn season and grown through winter and spring. Then, the plants

were collected and measured in terms of their photosynthetic capacity.

51

Results of a microarray analysis are also described, to study the expression of many

genes, including GPT2 in a high to low light acclimation. The microarray results were

compared to the results by (Athanasiou, Dyson et al. 2010) in which the experiments

done by them were reversible to this experiment.

Based on a previous work, it has been shown distinctly that Lansberg erecta (Ler) has

the ability to acclimate but that Col-0 does not acclimate (Athanasiou, Dyson et al.

2010). The study was done in a manner in which the plants were acclimated to high

light. Therefore, a quantitative trait locus (QTL) analysis was carried out to find loci in

their progeny that contributes to the different abilities to acclimate.

The aim of this work was to investigate the extent of acclimation of Arabidopsis

thaliana under high to low light acclimation. A physiology work was carried out as

stated above to study the changes in plants during the acclimation. Besides, molecular

and genetic work was performed as well to find possible explanation to the

physiological changes under high to low light acclimation.

52


Chapter 2

Materials and Methods

53

2.1 Plant material

Wild type seeds of Wassilewskija-2 (WS-2) and Columbia-0 (Col-0) and mutant seeds

of WS-gpt2 were sown onto soil and then placed at 4oC for two days before being

transferred to 20 ºat low light (100 µmol m-2 s-1). The seedlings were left in low light for

7 days before being transferred to high light (400 µmol m-2 s-1). This was to avoid stress

to the small seedlings.

After germination, plants were grown in a growth cabinet (EJ Stieel, Glasgow,UK) with

light being provided by high frequency fluorescent lamps. The plants were put under

high light condition (400 µmol m-2 s-1) for six weeks. All plants were grown under eight

hours light at 20 ± 2 oC and sixteen hours dark at 16 ± 2 oC. The plants were grown in

such condition to delay flowering. After six weeks, half of the plants were transferred to

low light (100 µmol m-2 s-1). Control plants were kept at 400 µmol m-2 s-1.

For an outdoor fitness experiment, the seedlings were grown in an unheated greenhouse

in Manchester, UK without supplementary lighting during the periods of October 2010

to January 2011 and October 2011 to February 2012.

After the plants were put under treatment, plants were brought to the measurement room

for gas exchange measurement.

54

2.2 Gas exchange

2.2.1. Light response curve measurement

Photosynthesis measurements were carried out using a CIRAS 1 portable infra-red gas

analyser (PP systems, Amesbury, MA, USA). In order to determine which light

intensity is sufficient to saturate photosynthesis, a light response curve was measured.

Plants of Arabidopsis thaliana were grown in high light (HL) condition for 6 weeks and

then half were transferred to low light (LL) conditions. After 9 days of exposure to LL,

the photosynthetic rate of plants were measured at 100, 200, 400, 800, 1200 and 1600

µmol m-2 s-1. Plants were illuminated at 1600 µmol m-2 s-1 for the first 20 min and

continued for 5 min at different light intensities which were at 100, 200, 400, 800 and

1200 µmol m-2 s-1. Measurements were performed at a CO2 concentration of 2000 ppm.

The actinic light used was provided by a red Luxeon LXHL-LD3C LED (Philips

Lumileds, California) in a laboratory built lamp.

2.2.2. Photosynthetic capacity measurement

The maximum capacity for photosynthesis was measured as the rate of photosynthesis

at 1600 µmol m-2 s-1 light and at 20oC. Measurements were carried out at 2000 ppm

CO2. Immediately after the plant was removed from the growth cabinet, it was placed

into a CIRAS 1 standard broad leaf chamber (area 2.5 cm2). The plants were left in the

chamber for 5 min until a steady-state of gas exchange level was reached. Afterwards,

55

the plant was illuminated with an actinic light for 20 min, after which the value of

photosynthetic capacity was recorded.

2.2.3. Chlorophyll fluorescence measurements

At the same time as photosynthetic capacity measurements, chlorophyll fluorescence

analysis was performed using a PAM 101 chlorophyll fluorometer (Walz, Effeltrich,

Germany). This analysis was performed to measure the chlorophyll fluorescence

parameters of photosystem II efficiency (ΦPSII) and non-photochemical quenching

(NPQ). Data were recorded on a PC using a National Instruments M series data

acquisition card and running software written using Labview (National Instruments,

Austin, US).

Prior to each chlorophyll fluorescence measurement, a plant was taken out of the growth

cabinet and a full-size mature leaf was placed in the CIRAS 1 chamber while still

attached to the plant. The leaf was left for 5 min in the chamber to equilibrate with the

chamber environment. The fluorometer measuring beam was switched on to measure Fo

and the leaf was exposed to a saturating flash of 7500 µmol m-2 s-1 to determine the

value of Fm (Figure 2.1). Afterwards, actinic light at 1500 µmol m-2 s-1 was given for

the next 20 minutes. During the 20 min interval, a saturating flash was given to the leaf

every 120 sec to measure changes in Fm’ over time.

56

The data from the fluorescence analysis was calculated for Φ PSII and NPQ using

Equations 1 and 2.

∅��

� (1)

��

� (2)

Figure 2.1: An illustration of a typical fluorescence. The Fo was set 5 sec after the recording

started. 10 sec later, the Fm was measured. A saturating flash was given at every 120 sec for 20

minutes to measure Fm’. Meanwhile, the Ft was recorded as the yield of fluorescence just before

the saturating flash. Fm = maximum fluorescence, Fm’ = fluorescence maximum in light, Ft =

steady state fluorescence yield in light

57

2.2.4. Chlorophyll extraction and analysis

After the measurements of photosynthesis, the same leaf was detached from the plant

and the leaf area was measured by scanning using a Canon LiDE 20 scanner, with the

leaf images being analysed using Scion Image (Scion Corp., Maryland, USA). The leaf

was ground in a pestle and mortar in 80% (v/v) acetone. The extract centrifuged using a

microfuge (Progen) at full speed (16,000 g) for 5 minutes. The absorbance of the

supernatant was measured using a USB2000 spectrophotometer (Ocean Optics,

Dunedin, USA) and the absorbance value at 646.6 nm, 663.5 nm and 750 nm were

recorded. The chlorophyll content was calculated according to Porra et. al. (1989) as

shown in Equation 3 and 4.

)(

10)(85.2)(71.13)/(

27506.6467505.6632

cmareaLeaf

mLAAcmngalCh

×−= −− (3)

)(

10)(42.5)(39.22)/(

27505.6637506.6462

cmareaLeaf

mLAAcmngbChl

×−= −− (4)

58

2.3 Microarray analysis

2.3.1 RNA extraction

The RNA was extracted from leaves using a Qiagen RNAeasy Kit (Qiagen, Crawley,

UK) and the protocols used were as recommended by the manufacturer. Leaves were

harvested from growth conditions and immediately flash-frozen in liquid nitrogen

before being ground. This step is crucial as the RNA in plant tissue is liable to change

rapidly in response to changing conditions.

The extracted RNA was quantified using an Eppendorf Spectrophotometer (Eppendorf

AG, Hamburg, Germany) by adding 2µl of RNA in 58µl of sterilized distilled water.

Using a plastic disposable Eppendorf UVette, the absorbance of the RNA samples was

measured at 260 nm and 280 nm. The purity of the extracted RNA was determined from

the ratio of A260/A280 which should be in the range of 1.8-2.0.

2.3.2 Microarray procedure

The extracted RNA from Section 2.3.1 was also used in microarray analysis. To

perform the microarray analysis, the GeneChip® Arabidopsis ATH1 Genome Array

(Affymetrix, Santa Clara, California, USA) was used for gene expression analysis.

Biotinylated cDNA was synthesized from the total RNA and was hybridized to an

Arabidopsis oligonucleotide array according to the manufacturer’s instructions. The

arrays were read by means of an Agilent Gene Array scanner 3000 7G using Affymetrix

GCOS (GeneChip® Operating Software) V1.4. Quality control was performed using

dChip software. Robust Multichip Average (RMA) was used to carry out the

59

normalisation and expression analysis. Principal component analysis (PCA), using a

covariance dispersion matrix was used for the assessment of experiment. A list of

differentially expressed genes was obtained from a modified t-test on logarithmically

scaled data using Cyber-T. The list was generated based on a p value less than 0.01,

mean fold change greater than 2 and mean expression level greater than 100 in one

condition. The gene annotation used was derived from The Arabidopsis Information

Resource (TAIR). The Affymetrix chip analysis was performed at the Microarray

Facility of the Faculty of Life Sciences in the University of Manchester, UK.

2.3.3 RT-PCR

The extracted RNA was also used to perform the reverse transcriptase polymerase chain

reaction (RT-PCR).

The cDNA synthesis steps were carried out using a recommended protocols provided by

SuperScriptTM III Reverse Transcriptase kit (Invitrogen). 1 µl of 200-500 ng of oligo

(dT)12-18, 1 µl 10mM dNTP Mix (10 mM each dATP, dGTP, dCTP and dTTP at neutral

pH), 5 µg total RNA and a volume of sterile, distilled water to make up to 13 µl were

added to a nuclease-free microcentrifuge tube for a 20-µl reaction volume. The mixture

was heated in a water bath of 65oC for 5 min and was incubated on ice for at least 1

min. Then, 4 µl of 5X First-Strand Buffer, 1 µl of 0.1M DTT, 1 µl of RNaseOUT and 1

µl of SuperscriptTM III RT (200 units/µl) were added into the mixture. The mixture was

then placed in MWG AG Biotech Primus 96 Plus PCR Thermocycler (Ag-Biotech,

California) to proceed with the cDNA synthesis. The cDNA synthesis was performed by

60

incubating the mixture at 50oC for 60 min. Then, the temperature was increased to 70oC

for 15 min to inactivate the reaction.

To amplify the cDNA, a PCR reaction was performed by using PCR Master Mix

(ABgene, Epsom, UK) which contains 10x PCR buffer, MgCl2, dNTP mix and Taq

DNA polymerase. 2µl of DNA template was added to 25µl of PCR Master Mix, 1µl of

each forward primers for GPT2 and Act2 (Table 2.1), 1µl of each reverse primers for

GPT2 and Act2 (Table 2.1) and 21µl of sterilized distilled water to make up to 50µl in

total. The PCR reaction was carried out in MWG AG Biotech Primus 96 Plus PCR

Thermocycler (Ag-Biotech, California) with the setting of 2 minutes at 94oC followed

by twenty-five cycles of 30 seconds at 94oC, 30 seconds at 58oC and 40 seconds at

72oC. Then, the reaction continued for 5 minutes at 72oC and maintained at 4oC.

Gene expression was verified using agarose gel electrophoresis. A 2% agarose gel was

made with 0.5x TBE (Tris base;MW 121.14, Boric acid;MW 61.83, 0.5 M EDTA pH

8.0) and 1.5µl of ethidium bromide (EtBr) 10 mg/mL. 5µl of PCR product was mixed

with 1µl of loading buffer. The mixture was loaded into the gel and 5µl of Hyperladder

IV (Bioline, London, UK) was loaded on both sides of the samples. The gel was run at

40mA for 60 minutes. Then, the gel was imaged using a UV transilluminator (Personal

Gel Imaging System, Cell Biosciences).

61

Table 2.1: Primer sequences for GPT2 and Act2. The primers were designed by obtaining the

sequence from SIGNAL and were inserted into Primer3. The resulting design primer were

ordered and provided by Eurogentec (Hampshire, UK)

At1g61800

GPT2F 5' -CCGTTATTGTTGCATCGATCA- 3'

GPT2R 5' -GCAGCACCGAGGGCATTA- 3'

At3g18781

Act2F 5' -GATTCAGATGCCCAGAAGTCTTG- 3'

Act2R 5' -TGGATTCCAGCAGCTTCCAT- 3'

2.4 Statistical analyses

A data management software and statistics package SPSSv15 (IBM Inc. Chicago,

Illinois, USA) was used to conduct a statistical analysis on the data. To test the data

significance, a simple t-test and one-way ANOVA analysis where appropriate were

carried out. The one-way ANOVA result was then followed with a Tukey’s post hoc

test with a significance level at 0.05.

62

2.5 QTL analysis

2.5.1 Plant growth for RI lines

A core population of 305 recombinant inbred (RI) lines, derived from crosses between

the Arabidopsis ecotypes Col-4 and Ler-0 with Columbia as the male parent, was

examined. 30 RI lines are recommended for mapping, as these lines have been selected

as having the highest frequency of recombination over five chromosomes. Therefore, it

would be the most informative and helpful for mapping purposes. The population seeds

was obtained from The European Arabidopsis Stock Centre (http://arabidopsis.info/).

Due to the failure of seed germination, only 24 RI lines were germinated and tested.

The RI lines seeds were sown onto soil and then placed in the fridge of 4oC for two days

before being transferred to low light (100 µmol m-2 s-1). The seedlings were placed in

low light for a week before being pricked out.

After germination, plants were grown in a growth cabinet (EJ Stieel, Glasgow,UK) with

light being provided by high frequency fluorescent lamps. The plants were put under

low light conditions (100 µmol m-2 s-1) for six weeks. All the plants were grown under

eight hours light at 20 ± 2 oC and sixteen hours dark at 16 ± 2 oC. After six weeks, half

of the plants were transferred to high light shelf (400 µmol m-2 s-1) before 10 am to start

the treatment. Meanwhile, control plants were kept at 100 µmol m-2 s-1.

63

2.5.2 Physiological measurement of recombinant-inbred (RI) lines

2.5.2.1 Photosynthetic capacity measurement of RI lines

Plants were analysed as described in Section 2.2.2.

2.5.2.2 Chlorophyll fluorescence measurement of RI lines


2.5.2.3 Chlorophyll extraction and analysis of RI lines


2.5.3 QTL analysis with WinQTL Cartographer

WinQTL Cartographer software was used for QTL mapping. WinQTL Cartographer

provides a user-friendly tool to map quantitative trait loci (QTL) from inbred lines. In

addition, WinQTL Cartographer allows us to present mapping results using its powerful

graphic tool. Besides, it allows us to import and export source data in a variety formats.

64


Chapter 3

Results

Physiological Responses Of Arabidopsis

thaliana To Decrease In Growth

Irradiance

65

3.1 Introduction

In this chapter, the main focus was on the investigation of the physiological responses

of several ecotypes of Arabidopsis thaliana towards a lowered light intensity. As

described in Chapter 2 of Materials and Methods, these Arabidopsis ecotypes were

grown fully under high light condition and were given a treatment of a lower light

condition.

This experiment from high light to low light condition was chosen mostly due to the

previous established experiment which was done in the reverse order of low light to

high light condition. In the low to high light condition, (Athanasiou, Dyson et al. 2010)

have found several significant changes. The maximum photosynthetic capacity (Pmax)

showed a significant increase in the WS ecotype and also a significant increase in the

chl a/b ratio which marked a clear acclimation response. Therefore, these findings posed

a question whether the reverse process in high to low light condition would also give

reversible result?

In order to answer this question, the ecotypes of Arabidopsis used were Wassilewskija

(WS), WS-gpt2 and Colombia -0 (Col-0). WS and Col-0 are the two wild types used in

this experiment. Col-0 was used many by researchers to conduct experiments on

acclimation such as the cold acclimation (Le, Engelsberger et al. 2008, Fursova,

Pogorelko et al. 2009) and light acclimation (Kouřil, Wientjes et al. 2013), (Wientjes,

van Amerongen et al. 2013). Col-0 was found to be not acclimating in the low to high

light acclimation (Athanasiou, Dyson et al. 2010). Thus, it was of the interest to include

66

Col-0 ecotype in this high to low light acclimation to measure the acclimation response.

Meanwhile, the WS-gpt2 plants were found to be not acclimating in the low to high light

plants as it was found that gpt2 gene is essential in higher light intensity acclimation. As

a result, the WS-gpt2 was also included in this high to low light experiment to

investigate if the gpt2 gene is also responsible and important in lower light intensity

acclimation.

Besides measuring the physiological responses of Arabidopsis plants in controlled

conditions in the laboratory, this chapter also covered an outdoor experiment done

under fluctuating natural light condition. This experiment was done over a time span

from early Autumn to early Spring season in 2010 and 2011. Under fluctuating natural

light condition, plants have to cope with the natural weather and find ways to survive

through acclimation. Thus, WS and WS-gpt2 plants were chosen for this outdoor project

as to investigate the photosynthetic capacity of plants under changing light condition.

67

3.2 Results

3.2.1 Light intensity determination from light response curve in WS

According to (Athanasiou, Dyson et al. 2010), WS plants acclimated from low to high

light showed a significant increase in their photosynthetic capacity. For comparison, we

investigated the photosynthetic capacity of plants acclimated from high to low light.

A light response curve was measured on the control (HL) and treated (LL) plants of

WS. The actual rate of photosynthesis, PSII efficiency (ɸ PSII), and non-photochemical

quenching (NPQ) were measured at different range of irradiances.

At lower irradiance (100 and 200 µmol m-2 s-1), the LL plants had a higher

photosynthetic rate compared to the HL plants (Figure 3.1 A). However, as the

intensity increased, the HL plants increased their photosynthetic rate up to a point until

it started to saturate at 1500 µmol m-2 s-1. In view of these data, a light intensity at 1500

µmol m-2 s-1 was used for subsequent experiments as a saturating irradiance.

Measurement of ɸ PSII indicate that PSII is more efficient in utilizing the absorbed light

for photochemistry processes at lower irradiance (Figure 3.1 B). As the irradiance

increased, the PSII efficiency decreased. This is due to PSII reaction centres becoming

saturated at higher light intensity. Meanwhile, the NPQ measurement increased when

the irradiance increased (Figure 3.1 C).

68

02468

1012141618

0.0

0.2

0.4

0.6

0.8

0 200 400 600 800 1000 1200 1400 1600 18000.0

0.5

1.0

1.5

2.0

2.5

3.0 C

B

Pm

ax (

µmol

CO

2 m-2 s

-1)

A

φ P

SII

NP

Q

Irradiance (µmol m-2 s-1)

Figure 3.1: A light response curve of WS. (A) Maximum photosynthetic capacity of

WS against irradiance in plants grown at 400 µmol m-2 s-1 (High Light; HL; open

circle) for six weeks after which half were transferred to low light at 100 µmol m-2 s-1

(LL; black circle) for 7 days. The measurement was taken after 9 days of acclimation.

All data are mean ± SE for at least 3 biological replicates. (B) The maximum quantum

efficiency (ɸ PSII) of WS was measured in plants grown for six weeks at 400 µmol m-2

s-1 (High Light; HL; open circle) after which half were transferred to low light at 100

µmol m-2 s-1 (LL; black circle) for 7 days. (C) Non-photochemical quenching (NPQ)

of WS. All data are mean ± SE for at least 3 biological replicates.

69

3.2.2 Changes in maximum photosynthetic capacity in WS, WS-gpt2

and Columbia-0 (Col-0) during acclimation following transfer

from high to low light

To investigate the changes in the maximum photosynthetic capacity during the

acclimation process further, a time-course experiment was conducted by measuring WS

plants at different times after transfer to LL.

Both HL and LL plants of WS showed some changes in Pmax through the experiment

(Figure 3.2 A). The LL plants showed significant changes in Pmax starting on Day 3

(p<0.05) compared to the HL. Therefore, WS plants had the ability to change their

photosynthetic capacity when the growth condition was altered to low light. At the same

time, plants of HL and LL showed some differences in the PSII efficiency with the HL

plants having a higher ɸ PSII value (Figure 3.3 A). This shows that the WS plants had

the ability to acclimate to the new environment as early as 24 hours after transfer. This

shows that the WS plants were prone to oversaturation by high light when transferred to

a lower light intensity. Meanwhile, the NPQ value of both HL and LL plants tends to

decrease over the week (Figure 3.4 A). The NPQ was measured to give an idea on how

much heat was dissipated in both HL and LL plants. At 1500 µmol m-2 s-1 light no

difference in NPQ was detected between HL and LL plants.

70

Microarrays have identified that GPT2 gene as the most up-regulated in Arabidopsis

leaves transferred from low to high light and it was shown that this is essential for

dynamic acclimation to increased light (Athanasiou, Dyson et al. 2010). To test whether

GPT2 also plays a role in acclimation from high to low light, plants of WS-gpt2 grown

at HL and transferred to LL were analysed in terms of their photosynthetic capacity.

Both HL and LL plants of WS-gpt2 showed some changes in Pmax (Figure 3.2 B) and

ɸPSII (Figure 3.3 B) starting at Day 1 showing that WS-gpt2 plants has the ability to

acclimate to lower light. By the end of the acclimation period, changes between HL and

LL plants were seen to be greatest in the maximum photosynthetic capacity and PSII

efficiency. However, the value of NPQ decreased towards Day 7 in both WS and WS-

gpt2 (Figure 3.4 B).

Col-0 accession was grown in a same manner as the WS and WS-gpt2 . When Col-0

plants were transferred to a low light intensity (100 µmol m-2 s-1), they were able to

acclimate by lowering their photosynthetic capacity (Figure 3.2 C). Consistent with

Pmax, ɸ PSII was also decreased (Figure 3.3 C). However, Col-0 plants did not show

any significant changes for the measurement of NPQ, total chlorophyll or chl a/b ratio

(Figure 3.4 C, Figure 3.5 C, Figure 3.6 C).

71

0

1

13

14

15

16

17

18

19

20

0 1 2 3 4 5 6 7 80.0

10

12

14

16

18

20

22

***

*

*

Ass

imila

tion

WS

( µ

mol

CO

2 m-2 s

-1)

*

0

2

4

6

8

10

12

14

16

18

HL LL

Ass

imila

tion

Col

-0 (

µmol

m-2 s

-1)

Col-0

A

B

C

Ass

imila

tion

WS

-gpt

2 (µ

mol

CO

2 m-2 s

-1)

Time (Day)

Figure 3.2: A time-course acclimation of maximum photosynthetic capacity in (A) WS and (B) WS-gpt2. Plants were measured at different times following a transfer from HL to LL. Plants were grown at 400 µmol m-2 s-1 (High Light; HL; open circle) for six weeks and half were transferred to low light at 100 µmol m-2 s-1 (LL; black circle). Plants were measured at an actinic light of 1500 µmol.m-2.s-1 and CO2 concentration at 2000ppm. Maximum photosynthetic capacity of (C) Col-0 were also measured with an actinic light at 1500 µmol m-2 s-1 and CO2 concentration at 2000 ppm. Plants were grown at 400 µmol m-2 s-1 (High Light; HL; hatched bar) for six weeks and half were transferred to low light at 100 µmol m-2 s-1 (Low light; LL; white bar). The measurements were taken after 9 days of acclimation. All data are mean ± SE for at least 3 biological replicates.

72

0.00

0.08

0.09

0.10

0.11

0.12

0.13

0.14

0.15

0.16

0 1 2 3 4 5 6 7 80.00

0.07

0.08

0.09

0.10

0.11

0.12

0.13

0.14

0.15

WS

φ P

SII

0.00

0.05

0.10

0.15

HL LL

Col

-0 φ

PS

II

Col-0

A

B

C

**

WS

-gpt

2 φ

PS

II

Time (Day)

*

Figure 3.3: A time-course acclimation of photosystem II (PSII) efficiency in (A) WS and (B) WS-gpt2. Plants were measured at different times following a transfer from HL to LL. Plants were grown at 400 µmol m-2 s-1 (High Light; HL; open circle) for six weeks and half were transferred to low light at 100 µmol m-2 s-1 (LL; black circle). Plants were measured simultaneously with the maximum photosynthetic capacity measurement at an actinic light of 1500 µmol.m-2.s-1 and CO2 concentration at 2000ppm. PSII efficiency of (C) Col-0 were also measured simultaneously with maximum photosynthetic capacity measurement at an actinic light at 1500 µmol m-2 s-1 and CO2 concentration at 2000 ppm. Plants were grown at 400 µmol m-2 s-1 (High Light; HL; hatched bar) for six weeks and half were transferred to low light at 100 µmol m-2 s-1 (Low light; LL; white bar). The measurements were taken after 9 days of acclimation. All data are mean ± SE for at least 3 biological replicates.

73

0.0

2.0

2.1

2.2

2.3

2.4

2.5

2.6

0 1 2 3 4 5 6 7 80.0

1.9

2.0

2.1

2.2

2.3

2.4

2.5

2.6

WS N

PQ

0.0

0.5

1.0

1.5

HL LL

Col

-0 N

PQ

Col-0

A

B

C

WS-g

pt2

NPQ

Time (Day)

*

Figure 3.4: A time-course acclimation of non-photochemical quenching (NPQ) in (A) WS and (B) WS-gpt2. Plants were measured at different times following a transfer from HL to LL. Plants were grown at 400 µmol m-2 s-1 (High Light; HL; open circle) for six weeks and half were transferred to low light at 100 µmol m-2 s-1 (LL; black circle). Plants were measured simultaneously with the maximum photosynthetic capacity measurement at an actinic light of 1500 µmol.m-2.s-1 and CO2 concentration at 2000ppm. PSII efficiency of (C) Col-0 were also measured simultaneously with maximum photosynthetic capacity measurement at an actinic light at 1500 µmol m-2 s-1 and CO2 concentration at 2000 ppm. Plants were grown at 400 µmol m-2 s-1 (High Light; HL; hatched bar) for six weeks and half were transferred to low light at 100 µmol m-2 s-1 (Low light; LL; white bar). The measurements were taken after 9 days of acclimation. All data are mean ± SE for at least 3 biological replicates.

74

3.2.3 Changes in chlorophyll content and composition during

acclimation to low light in WS, WS-gpt2 and Columbia-0 (Col-0)

Chlorophyll content analysis was performed to calculate the total chlorophyll and chl

a/b ratio according to (Porra, Thompson et al. 1989). For the total chlorophyll content in

WS, there was no changes seen between HL and LL plants (Figure 3.5 A). Meanwhile,

the chl a/b ratio in this experiment showed a decrease over the week in LL plants as

shown in Figure 3.6 A. The chlorophyll content of WS-gpt2 plants was observed and

there was no significant changes found in the total chlorophyll (Figure 3.5 B). While

the total chlorophyll did not show any changes, the chl a/b ratio in WS-gpt2 plants

showed significant (p<0.05) changes when measured during the acclimation period of 7

days (Figure 3.6 B).

75

Figure 3.5: Total chlorophyll content of (A) WS (B) WS-gpt2 and (C) Col-0 in a

time-course experiment. The total chlorophyll content was calculated at different times

after transfer to LL. Plants were grown for six weeks at 400 µmol m-2 s-1 (High Light;

HL; open circle) and then half of the plants were transferred to a lower light intensity at

100 µmol m-2 s-1 (LL; black circle). The leaf used for maximum photosynthetic capacity

measurement was used to estimate the chlorophyll content. The total chlorophyll

content of (C) Col-0 were also calculated according to (Porra, Thompson et al. 1989) in

Equation 2. Plants were grown at high light – hatched bars (400 µmol m-2 s-1) for six

weeks and half were transferred to low light – white bars (100 µmol m-2 s-1) for 9 days.

All data are mean ± SE for at least 3 biological replicates.

76

Figure 3.6: Chl a/b of (A) WS (B) WS-gpt2 and (C) Col-0 in a time-course

experiment. The chl a/b was calculated at different times after transfer to LL. Plants

were grown for six weeks at 400 µmol m-2 s-1 (High Light; HL; open circle) and then

half of the plants were transferred to a lower light intensity at 100 µmol m-2 s-1 (LL;

black circle). The leaf used for maximum photosynthetic capacity measurement was

used to estimate the chlorophyll content. The chl a/b of (C) Col-0 were also calculated

according to (Porra, Thompson et al. 1989) in Equation 2. Plants were grown at high

light – hatched bars (400 µmol m-2 s-1) for six weeks and half were transferred to low

light – white bars (100 µmol m-2 s-1) for 9 days. All data are mean ± SE for at least 3

biological replicates.

77

3.2.4 Photosynthetic acclimation of WS and WS-gpt2 under

fluctuating light condition in Winter 2010-2011

In this experiment, WS and WS-gpt2 plants were grown in an unheated greenhouse in

Manchester. Photosynthetic measurements such as Pmax, ɸ PSII, NPQ and chlorophyll

analysis such as total chlorophyll and chl a/b were taken and measured.

In the year of 2010-2011, plants were measured at one time-point only as the plants

flowered earlier than expected. It was found that there was no significant changes

between WS and WS-gpt2 plants in maximum photosynthetic capacity (Figure 3.7 A)

and ɸ PSII (Figure 3.7 B). Therefore WS and WS-gpt2 plants had equal capacity to

survive under natural variable light. Furthermore, both plants of WS and WS-gpt2 plants

did not have the ability to quench excess excitation energy as there was no significant

change in NPQ (Figure 3.7 C). In terms of chlorophyll analysis, similarly there was no

significant change in the total amount of chlorophyll (Figure 3.8 A) and the ratio of

chlorophyll a to chlorophyll b (Figure 3.8 B).

78

0

2

4

6

Pm

ax (

µmol

CO

2 m-2 s

-1)

Ws Ws-gpt2

0.00

0.05

0.10

φ P

SII

0

1

2

C

B

A

NP

Q

Accession

Figure 3.7: Photosynthetic measurement of WS and WS-gpt2 in Winter 2010/2011

(A) Pmax, (B) ɸ PSII and (C) NPQ for WS and WS-gpt2 plants during Winter of 2010

to 2011. The plants were sowed in the lab and germinated in the greenhouse. After 12

weeks of growing in the greenhouse, the plants were taken to the lab to be measured.

79

Figure 3.8: Chlorophyll content measurement of WS and WS-gpt2 plants

during Winter of 2010 to 2011 (A) total chlorophyll and (B) chl a/b for WS

and WS-gpt2 plants during Winter of 2010 to 2011. The plants were sowed in

the lab and germinated in the greenhouse. After 12 weeks of growing in the

greenhouse, the plants were taken to the lab to be measured. The same leaf for

photosynthetic measurement was used for this chlorophyll measurement

80

3.2.5 Photosynthetic acclimation of WS and WS-gpt2 under

fluctuating light condition in Winter 2011-2012

In the meantime, in the year of 2011-2012, plants were measured at 5 different time-

points which were at week 8, 9, 11, 12 and 13. The Pmax value was still low, however

there was still no significant difference between WS and WS-gpt2 plants during the

course of experiment (Figure 3.9 A). Similarly, the value of ɸ PSII (Figure 3.9 B) had

no difference but NPQ (Figure 3.9 C) decreased over the week.

As for the chlorophyll analysis the total value of chlorophyll was slightly lower than the

previous year (Figure 3.10 A). The value of total chlorophyll content in this year did

not significantly differ between plants and during the course of treatment. Meanwhile,

the chl a/b ratio (Figure 3.10 B) of WS and WS-gpt2 showed no difference over the

course of measurement.

81

0

6

8

10

12

0.04

0.06

0.08

0.10

0.12

0 6 8 10 12 141.7

1.8

1.9

2.0

2.1

2.2

2.3

Pm

ax (

µmol

CO

2 m-2 s

-1)

WS WS-GPT2

φ P

SII

C

B

AN

PQ

Time (Week)

Figure 3.9: Photosynthetic measurement of WS and WS-gpt2 plants during Winter of 2011 to 2012 (A) Pmax, (B) ɸ PSII and (C) NPQ for WS and WS-gpt2 plants during Winter of 2011 to 2012. The plants were sowed in the lab and germinated in the greenhouse. After the plants were mature, the plants were taken for measurement at week 8, 11, 12 and 14.

82

Figure 3.10: Chlorophyll content measurement of WS and WS-gpt2 plants during

Winter of 2011 to 2012 (A) total chlorophyll and (B) chl a/b for WS (open circle) and

WS-gpt2 (black circle) plants during Winter of 2011 to 2012. The plants were sowed in the

lab and germinated in the greenhouse. After the plants were mature, the plants were

taken for measurement at week 7,8, 11, 12 and 14. The same leaf for photosynthetic

measurement was used for this chlorophyll measurement.

83

3.3 Discussion

Photosynthetic capacity in WS using mature leaves was found to be decreased in low

light plants (Figure 3.2 A). Initially, plants were grown under high light which is

available for conducting their photochemistry processes. However, when light becomes

a limiting factor (under low light condition), there is a limited energy to drive the

photochemistry processes optimally. Since photosynthesis is a light-dependent reaction,

insufficient light limits the overall rate of photosynthesis.

When (Athanasiou, Dyson et al. 2010) measured changes in the photosynthetic capacity

following acclimation from low to high light, it was found that there was an increase in

the Arabidopsis accession WS. The extent of change and the values of photosynthetic

rate obtained were similar to those seen here in plants transferred from HL to LL,

suggesting the processes might simply be the reversal of one another. However, whilst

the WS-gpt2 plants did not acclimate when moved from low to high light, they did when

transferred from HL to LL. The gpt2 mutants were complemented with a functional

gene of gpt2 and it was found that it restored the ability to acclimate. Therefore, it was

concluded that GPT2 is essential for acclimation from low to high light (Athanasiou,

Dyson et al. 2010). In the acclimation from high to low light, WS-gpt2 plants had the

ability to acclimate (Figure 3.2 B). This suggests that the acclimation from high to low

light in WS-gpt2 is partially but not completely inhibited.

84

Acclimation from high to low light was also examined in the accession Col-0. It was

found that Col-0 did have the ability to acclimate to low light just like WS (Figure 3.2

C). However, (Athanasiou, Dyson et al. 2010) found that Col-0 did not have the ability

to acclimate to high light condition. This suggests that the lesion in Col-0 that prevents

acclimation may be at a level that is common to allow acclimation in one direction only,

just like GPT2 which may only be required for acclimation to high light. Therefore, a

QTL mapping analysis can be used to find any potential QTL associated with this

phenotype.

Since the low light plants have a larger antenna, they possess more chlorophylls per

reaction centre so the rate at which light energy arrives at the reaction centre is faster at

any given light intensity. This means that reaction centres work more efficiently at low

light but they are more vulnerable to an oversaturation of PSII. When PSII is

oversaturated, the electron transport will be less efficient. PSII will also be more

vulnerable to photoinhibition. As a result, CO2 fixation will be decreased. The

observation that ΦPSII is lower in low light acclimated plants is consistent with the idea

that the antenna size of PSII increases when plants acclimate to low light. There is

however also a decrease in overall photosynthetic capacity at low light. Previously,

(Athanasiou 2008, Athanasiou, Dyson et al. 2010) observed no consistent changes in

ΦPSII during low to high light acclimation, suggesting that acclimation from high to

low light is not simply the reverse of acclimation from low to high.

When plants are exposed to excess light, one of the short-term responses is non-

photochemical quenching (NPQ). This response is switched on within seconds after the

light exposure. When a low pH builds up in the thylakoid lumen, it switches the antenna

85

into heat dissipation rather than trying to utilize the excess light (Kulheim, Agren et al.

2002). From the data (Figure 3.4 A and 3.4 B, respectively), it seems that the low light

acclimated plants in both WS and WS-gpt2 had no difference in the NPQ value

indicating that both WS and WS-gpt2 plants had the same capacity to quench excitation

energy under low light condition. However, it was seen that the NPQ value in both WS

and WS-gpt2 was decreasing over the week of measurement. This could be due that as

the plants were still acclimating to lower light intensities, there was less heat dissipation

of excess light energy. Previously, in the reverse acclimation to high light, no

significant changes were found in terms of ΦPSII and NPQ (Athanasiou 2008).

In terms of total amount of chlorophyll (chlorophyll a and b), there was no significant

change seen during acclimation to low light in both WS and WS-gpt2 plants. (Figure

3.5 A and 3.5 B). Similarly, there was no significant change in chlorophyll content

upon acclimation from low to high light (Athanasiou, Dyson et al. 2010).

In many species, depending on the light condition, differences in chl a/b ratio have

frequently been reported (Moharekar, Tanaka et al. 2007, Pantaleoni, Ferroni et al.

2009). Hence, it has been taken as an indicator of a simple light acclimation response

(Akoumianaki-Ioannidou, Georgakopoulos et al. 2004). In this study, it was found that

the chl a/b ratio decreased in plants transferred from high to low light, compared to

plants kept in high light (Figure 3.6 A and 3.6 B). This is most likely due to an increase

in the light harvesting complexes relative to reaction center core. Reaction center cores

contain only chlorophyll a. Associated with the reaction centers are the light harvesting

complex which contain both chlorophyll a and b. Thus, the expansion of the complexes

86

results in an increase in chlorophyll b and decrease in the chl a/b ratio in low light

plants. The ability of plants to change the amount of light harvesting complexes has

been claimed to determine the plant’s ability to change in response to light environment

(Akoumianaki-Ioannidou, Georgakopoulos et al. 2004). In contrast, acclimation from

low to high light resulted in only very marginal changes in chl a/b, suggesting that this

form of acclimation involved only small changes in antenna size. This is consistent with

the observation that ΦPSII changes markedly during high to low but not low to high

acclimation and reinforces the notion that these forms of acclimation are at least

somewhat distinct processes.

In the accession of Col-0, the total chlorophyll and the chl a/b ratio were seen not to

change significantly (Figure 3.5 C and 3.6 C). These changes were in concordance

with the results found in the acclimation to high light (Athanasiou, Dyson et al. 2010).

In this outdoor project, plants of WS and WS-gpt2 were grown in an unheated

greenhouse and without any lighting at the experimental ground in Manchester over the

winter season. The project was carried out in two consecutive years of 2010 to 2011

(2010/2011) and 2011 to 2012 (2011/2012). In 2010/2011, only one measurement was

successfully performed because the plants were already flowered by the time of the

measurement. Flowering in plants marks the transition phase from vegetative phase to

reproductive phase (Lokhande, Ogawa et al. 2003). This transition is sensitive to any

environmental stresses including chilling, drought and high light stresses. In 2010/2011,

the mean temperature (oC) during winter season was lower than the next year of

2011/2012. Therefore, it was shown that the winter of 2010/2011 was markedly colder

than 2011/2012. Besides, vernalization which is plants exposure to a certain period of

87

time to cold condition can promote flowering but this process is not required for

Arabidopsis thaliana (Engelmann and Purugganan 2006). However, stratification which

is seeds exposure to cold condition for a certain period of time can have similar effect

on flowering to most but not all ecotypes of Arabidopsis thaliana. Therefore, plants of

WS and WS-gpt2 in 2010/2011 winter flowered earlier than 2011/2012 due to the

chilling stress since the mean temperature were fluctuating.

The reproductive stage of plants can influence the senescing stage in leaves or the whole

plant (Escobar-Gutiérrez and Combe 2012). Therefore, making the measurement on

plants that are already flowered would not indicate an accurate value. This is why the

Pmax value was very low compared to the value in 2011/2012. Besides, low

temperature is one of the main important factors affecting plant performance,

specifically photosynthesis (Stitt and Hurry 2002). However, Arabidopsis and other

cold-hardy herbaceous species have the ability to acclimate to cold condition. Thus,

plants WS and WS-gpt2 had a low value of all photosynthetic parameters (Pmax, ɸ PSII,

and NPQ) due to the senescing factor but also those plants had the ability to survive

under the fluctuating temperature and light. According to (Athanasiou, Dyson et al.

2010), the WS plants had the ability to acclimate to higher light intensities but WS-gpt2

did not. However, in this experiment, it has been shown that both WS and WS-gpt2 can

acclimate to lower light intensities. However, in this fluctuating light condition, there

was no differences between these plants of WS and WS-gpt2. Besides, according to

metoffice, the mean temperature of winter season in 2010/2011 was even below the

average. It was only two weeks before the measurement, the mean temperature rose

above 0oC but still below the average temperature. The fluctuating in temperature might

88

indicate that the plants had a very limited sunlight and thus lowering the maximum

photosynthetic capacity.

In the reproductive and senescing phase, chlorophyll breakdown is a very common

event happening in plants (Lokhande, Ogawa et al. 2003). Besides, during acclimation

process, chloroplast also undergo molecular re-arrangements involving the chloroplast

composition. The changes in the chloroplast composition in terms of chl a/b shows a

clear acclimation response along with the maximum photosynthetic capacity value

(Bailey, Horton et al. 2004). Due to that, it was found that there was no difference in the

total chlorophyll and chl a/b ratio in both WS and WS-gpt2 plants. These data were

consistent with the no significant changes in the Pmax as well. However, the value of

chl a/b of WS and WS-gpt2 plants were quite similar to the value of total chlorophyll of

LL plants grown in the laboratory condition. In the laboratory condition, the chl a/b of

LL plants were decreased indicating that the chl b was increased compared to chl a.

In 2011/2012 winter project, plants of WS and WS-gpt2 were measured at 5 different

time point starting at after 7 weeks of germination. At this stage, the leaves are mature

enough to be measured. Similarly, in 2011/212, there was no significance difference in

Pmax between the WS and WS-gpt2. Moreover, the value ɸPSII was also shown no

difference in both WS and WS-gpt2 indicating that there was no difference in the PSII

efficiency since both plants had no difference in the capacity of light absorption.

However, the value of Pmax in winter 2011/2012 was higher than the value in winter

2010/2011. Meanwhile, the NPQ was also shown no difference between WS and WS-

gpt2 but it showed that the NPQ tends to decrease over the course of measurement. The

major contributor for NPQ is known to be a high energy state quenching (qE). qE is

89

essential in plants in order to protect plants from damage due to strong light (Maxwell

and Johnson 2000). Thus, since the light availability to plants was very limited at this

time of the year, the qE was less induced and eventually it lead to less excess energy

quenching.

The total chlorophyll content and chlorophyll composition in terms of chl a/b were also

had no difference between WS and WS-gpt2. When there was no difference in the chl

a/b ration, it indicates that there was also no changes in the size of PSII light harvesting

antennae (Leong and Anderson 1984), or the reaction centre content such as the number

of PSII (Evans 1987). However, in week 14, there was a small difference in the total

chlorophyll content between WS and WS-gpt2. This could be due to the temperature

that started to rise. Therefore, under high light irradiance, plants of WS had more

chlorophyll content to support the higher rate of photosynthesis (Bailey, Walters et al.

2001).

90


Chapter 4

Results

Microarray Analysis

91

4.1 Introduction

Microarray analysis is a method commonly used by many researchers nowadays.

Primarily, microarray method is chosen due to its ability to measure the level of

expression of enormous numbers of genes at the same time. Generally, the concept of

how microarray works and determines the expression level is by looking at the labelled

cDNA. The cDNA is prepared and labelled with fluorescence dye. Then the

fluorescently labelled cDNA is added to a chip consisting of single-stranded DNA. If

the fluorescently labelled cDNA bind to any single-stranded DNA on the chip, it will

fluoresce. The chip is then scanned and analysed to find out the intensity of each

fluoresced which measures the relative abundance of the gens.

In a dynamic acclimation from low light to high light, it was found that At1g61800

which encodes glucose 6 phosphate/phosphate translocator, GPT2 to be up-regulated

with 34 fold change in Day 1 (Athanasiou, Dyson et al. 2010). The gpt2 knockout

mutants were managed to be isolated and grown in the WS background. Since Col-0

was found to be not acclimating to high light condition, the gpt2 knockout mutants in

Col-0 background was not able to classify any specific roles for this gpt2 gene.

This Ws-gpt2 mutants did not have the ability to acclimate to higher light intensity and

it was concluded that GPT2 is important in acclimation to high light. Therefore, in this

chapter, microarray method was used to identify the role of GPT2, if any and to find

any potential genes that might be responsible in the ability of plants to acclimate to

lower light intensity.

92

4.2 Results

4.2.1 Changes in GPT2 expression in WS following acclimation to low light

In an acclimation of low to high light, a protein called GPT2 was highly expressed and

induced based on a microarray analysis. Therefore, to investigate the GPT2 expression

during the acclimation from high to low light, a reverse transcriptase PCR (RT-PCR)

analysis was performed.

The tissues used for this analysis were taken from plants after four hours of exposure to

low light. From this, it was seen that the GPT2 expression could be seen under high

light conditions. Under low light condition, the detected expression was substantially

lower (Figure 4.1).

Figure 4.1: A gel showing GPT2 expression in WS plants during acclimation from high to low light. A housekeeping gene, act2 was used as a control. The size of GPT2 is 500bp. A 2% agarose gel was made with 0.5x TBE (Tris base;MW 121.14, Boric acid;MW 61.83, 0.5 M EDTA pH 8.0) and 1.5µl of ethidium bromide (EtBr) 10 mg/mL. 5µl of PCR product was mixed with 1µl of loading buffer. The mixture was loaded into the gel and 5µl of Hyperladder IV (Bioline, London, UK) was loaded on both sides of the samples. The gel was run at 40mA for 60 minutes. Then, the gel was imaged using a UV transilluminator (Personal Gel Imaging System, Cell Biosciences). (Keyword: Act=actin; HL=high light; LL=low light).

93

4.2.2 Microarray analysis on photosynthetic acclimation in Arabidopsis thaliana of WS

Microarray analysis was performed on Arabidopsis thaliana WS ecotype to firstly

investigate the role of GPT2 in this high to low light acclimation. This is due because in

the reverse low to high acclimation, GPT2 was found to be the most essential gene for

acclimation to higher light (Athanasiou, Dyson et al. 2010). Secondly, the microarray

analysis was carried out to identify any potential gene that might be important to the

lower light acclimation.

From the analysis, it was found that there were 22,747 genes that were involved in this

high to low light acclimation. From these genes, there were 1,798 differentially

expressed genes that met these 3 criteria:

1) The p-value must be below 0.01, p<0.01,

2) The mean fold change of these genes must be at 2 or greater and

3) The mean expression level must be >100 in at least one condition.

The 1,798 genes were divided into two time points of Day 0 and Day 1 (Figure 4.2).

Day 0 represents plants that were exposed to low light for 4 hours before being taken

and measured. Meanwhile, Day 1 represents plants after 28 hours exposure to low light

condition. From 1,798 genes, most of the genes were differentially expressed in Day 0

than Day 1. In Day 0, there were 1,362 genes were differentially expressed and from

that, 548 genes were induced. The remaining of 814 genes were the one that were

repressed. Meanwhile, in Day 1, there were 436 genes that were differentially

94

expressed. From that total, 258 genes were induced and 178 genes were repressed. From

all 1,798 differentially expressed genes, there were 331 genes that were shared between

Day 0 and Day 1. The remaining, 1,031 genes were expressed only in Day 0 and 105

genes were expressed only in Day 1.

Figure 4.2: Schematic representation of microarray analysis. This Venn Diagram

shows the number of differentially expressed genes in Arabidopsis WS plants in Day 0

(four hours after exposure to LL condition) and Day 1 (28 hours after exposure to LL

condition). The total number of differentially expressed genes were divided according to

changes in Day 0 and Day 1 and the number of induced (yellow arrow) and repressed

(purple arrow) genes were also shown. Moreover, the number of shared genes between

Day 0 and Day 1 was also shown.

95

From 548 genes that were induced in Day 0, the top 20 most induced genes are shown

in Table 4.1 . From the 20 genes, 10 of those genes were either putative, unknown or

expressed protein. The top most induced gene is At5g49360 which encodes a

bifunctional -D-xylosidase/{alpha}-L-arabinofuranosidase (beta-xylosidase 1) with

36.2-fold increase in the expression, a 31.8-fold increase of gene expression for

At4g27450 which encodes a putative protein, a 23-fold increase in gene expression for

At4g35770 which encodes a senescence-associated protein (atsen1) and a 22.7-fold

increase in gene expression for At5g20250 which is induced in senescing leaves. The

At4g27450 gene was also the most up-regulated in Day 1. Besides At4g27450 and

At5g49360, there were 8 other genes that were shared in Day 0 and Day 1 and met all

three criteria. The genes were At3g58120 encodes a member of the BZIP family which

had a fold change of 20 in Day 0 to 5.8 fold change in Day 1, At3g15450 which is an

unknown protein which had 18.4 fold change in Day 0 to 6 fold change in Day 1,

At2g33830 which is associated with dormancy/auxin family protein had a 17.9 fold

change in Day 0 to 11.2 fold change in Day 1, At3g06070 which is an unknown protein

had a 17.4 fold change in Day 0 to 8.8 fold change in Day 1, At3g62950 which is a

thioredoxin superfamily protein had a 14.4 fold change in Day 0 to 7.8 fold change in

Day 1, At1g13700 which is an unknown protein had a 11.5 fold change in Day 0 to 6.4

fold change in Day 1, At2g45170 which is a putative protein had a 11.2 fold change in

Day 0 to 5.6 fold change in Day 1 and At4g17245 which is an expressed protein had a

11 fold change in Day 0 to 5.2 fold change in Day 1. In the low to high light

acclimation, there were 8 genes that were down-regulated at both time points of

equivalent Day 0 and Day 1. However, these genes were up-regulated only in either Day

0 or Day 1 in this high to low acclimation. The genes that were only up-regulated in

Day 0 high to low light acclimation but down-regulated at both time points in low to

high light acclimation were At3g15450 and At5g22920. Meanwhile, there were 5 genes

96

that were up-regulated only in Day 1 of high to low light acclimation but down-

regulated at both time points in low to high acclimation which were At1g74670,

At2g40610, At2g22980, At2g15890 and At1g72150. There was only one gene that were

up-regulated in both time points of high to low light acclimation and down-regulated at

both time point in low to high light acclimation which was At2g33830.

Meanwhile, from 814 genes that were repressed in Day 0, the top 20 most repressed

genes were illustrated as in Table 4.2. 8 genes were also reported to be either putative,

unknown, expressed or hypothetical protein. The top most down-regulated gene is

At4g15210 which encodes for beta-amylase (BAM5/BMY1) with a factor of 32,

At4g14690 which is an expressed protein with a factor of 17 and At1g61800 which

encodes of glucose-6-phosphate/phosphate-translocator precursor with a factor of 14.2.

From the list of genes listed in Table 4.2, there were 6 genes that were also most up-

regulated genes in low to high light acclimation. Besides the At4g15210 and At1g61800

genes, the other genes were At4g01080 which is a hypothetical protein, At4g16590

which encodes a cellulose synthase like protein, At1g57590 which encodes

pectinacetylesterase family protein and At1g56650 which encodes a putative MYB

domain involved in anthocyanin metabolism. Besides, there were 8 genes that were

shared in both Day 0 and Day 1 of high to low light acclimation. One of the genes that

were shared was At4g14690 was down-regulated in Day 0 with a factor of 17 and in

Day 1, the gene expression was more repressed with a factor of 32.6. The other seven

shared genes had an increased gene expression from Day 0 to Day 1 which was from the

factor of 32 to 19.7 in At4g15210, from the factor of 12.1 to 5 in At4g01080, from the

factor of 12 to 8.4 in At4g16590, from the factor of 11.5 to 8 in At1g57590, from the

factor of 10.2 to 5.4 in At1g56650, from the factor of 9.6 to 6 in At4g36010 and from

the factor of 9.6 to 5.9 in At1g24020.

97

Table 4.1: The difference in the mean fold change of gene expression in Day 0 and Day 1 based

on top 20 most induced genes in Day 0

Transcript ID

Description Mean fold change in

Day 0

Mean fold change in

Day 1

Difference in mean fold

change At5g49360 xylosidase 36.1859 6.61014 -29.57576

At4g27450 putative protein stem-specific protein 31.8035 11.6165 -20.187

At4g35770 senescence-associated protein sen1 23.043 11.2476 -11.7954

At5g20250 seed imbitition protein-like 22.7364 3.63282 -19.10358

At5g57560 TCH4 protein _AF367262 22.281 2.03252 -20.24848

At3g58120 putative protein basic leucine zipper transcription activator

19.6992 5.84923 -13.84997

At3g15630 unknown protein 19.0168 3.07337 -15.94343


At5g22920 PGPD14 18.161 4.87914 -13.28186

At2g33830 putative auxin-regulated protein 17.9072 11.1643 -6.7429


At3g62950 glutaredoxin -like protein 14.355 7.81792 -6.53708

At5g44020 vegetative storage protein-like 13.1983 3.23366 -9.96464

At3g13750 galactosidase, 13.0382 3.14268 -9.89552

At5g21170 AKIN beta1 12.7571 4.88041 -7.87669



At2g45170 putative microtubule-associated protein

11.2 5.59197 -5.60803

At4g17245 Expressed protein 10.975 5.22504 -5.74996

At1g02640 beta-xylosidase 10.7734 1.32925 -9.44415

98


on top 20 most repressed genes in Day 0

Transcript ID

Description Mean fold change in

Day 0

Mean fold change in

Day 1


change At4g15210 beta-amylase -31.9914 -19.7693 -12.2221 At4g14690 Expressed protein -16.9468 -32.5882 15.6414

At1g61800 glucose-6-phosphate/phosphate-translocator precursor

-14.1867 -11.581 -2.6057

At4g01080 hypothetical protein -12.1126 -5.05318 -7.05942 At4g16590 cellulose synthase like protein -12.0848 -8.4351 -3.6497 At1g57590 pectinacetylesterase precursor -11.5173 -7.9878 -3.5295 At5g52320 cytochrome P450 -10.8835 -2.066 -8.8175 At3g44750 putative histone deacetylase -10.6201 -1.90313 -8.71697 At1g56650 anthocyanin2 -10.1841 -5.41248 -4.77162 At2g27840 unknown protein -9.96942 -3.09984 -6.86958 At1g06000 unknown protein Ceres:1040. -9.87827 -3.27024 -6.60803 At5g50800 MtN3-like protein -9.60082 -2.79855 -6.80227 At4g36010 thaumatin-like protein -9.59743 -6.06308 -3.53435 At1g24020 pollen allergen-like protein -9.5695 -5.89316 -3.67634 At3g57490 40S ribosomal protein -8.79995 -2.55085 -6.2491

At3g44990 xyloglucan endo-transglycosylase

-8.36401 -3.18727 -5.17674

At4g27570 UDP rhamnose -8.18136 -4.1911 -3.99026 At5g48850 putative protein -8.04129 -2.84333 -5.19796 At5g62165 Expressed protein -7.94431 -3.82042 -4.12389 At1g61870 unknown protein -7.73961 -1.63037 -6.10924

99


on top 20 most induced genes in Day 1

Transcript ID

Description Mean fold

change Day 1

Mean fold change Day 0


change At4g27450 putative protein 11.6165 31.8035 20.187

At2g33830 putative auxin-regulated protein

11.1643 17.9072 6.7429

At3g06070 unknown protein 8.82052 17.3728 8.55228

At3g62950 glutaredoxin -like protein 7.81792 14.355 6.53708

At5g49360 xylosidase 6.61014 36.1859 29.57576


At2g40610 putative expansin 6.30018 6.84143 0.54125



At1g72150 cytosolic factor 5.90624 7.4325 1.52626

At3g58120 putative protein 5.84923 19.6992 13.84997

At2g44740 putative PREG1-like 5.81916 8.43655 2.61739

At3g61060 putative protein 5.61865 8.05139 2.43274

At2g45170 putative microtubule-associated protein

5.59197 11.2 5.60803

At1g74670 GAST1-like proteinMar 5.58294 6.21237 0.62943

At5g63470 transcription factor Hap5a-like protein

5.33715 5.2081 -0.12905

At2g22980 putative serine carboxypeptidase I

5.28407 6.70809 1.42402

At2g32100 hypothetical protein predicted by genscan

5.28365 5.93396 0.65031

At4g17245 Expressed protein 5.22504 10.975 5.74996

At5g40890 anion channel protein 4.94895 9.7709 4.82195

100


on top 20 most repressed genes in Day 1

Transcript ID

Description Mean fold

change Day 1

Mean fold change Day 0


change At4g14690 Expressed protein -32.5882 -16.9468 -15.6414

At4g15210 beta-amylase -19.7693 -31.9914 12.2221

At4g16590 cellulose synthase like protein -8.4351 -12.0848 3.6497

At5g58310 polyneuridine aldehyde esterase-like

-8.33758 -1.94927 -6.38831

At1g57590 pectinacetylesterase precursor -7.9878 -11.5173 3.5295

At2g27420 cysteine proteinase -7.19351 -4.93126 -2.26225

At2g16890 putative glucosyltransferase -6.29153 -5.52554 -0.76599

At2g40100 putative chlorophyll a/b binding protein

-6.17497 1.06064 -7.23561

At4g36010 thaumatin-like protein -6.06308 -9.59743 3.53435

At1g24020 pollen allergen-like protein -5.89316 -9.5695 3.67634

At1g62710 beta-VPE -5.86117 -5.43442 -0.42675

At5g13930 chalcone synthase -5.85746 -2.33206 -3.5254

At1g76530 unknown protein -5.73763 -4.39502 -1.34261

At1g56650 anthocyanin2 -5.41248 -10.1841 4.77162

At4g01390 hypothetical protein -5.33276 -4.95016 -0.3826

At1g56430 nicotianamine synathase -5.30504 -3.67114 -1.6339

At2g29090 putative cytochrome P450 -5.14903 -5.56974 0.42071

At4g01080 hypothetical protein -5.05318 -12.1126 7.05942

At4g17090 putative beta-amylase -4.67542 -2.2509 -2.42452

At5g05270 putative protein -4.56798 -3.32484 -1.24314

101

4.2.3 Average Profile Cluster analysis on genes in high to low light acclimation

As we have shown that many genes expression were shared in the high to low light

acclimation and in the reverse acclimation. Average profile cluster analysis was done to

categorize these genes into profiles according to their expression pattern. As shown in

Figure 4.3, there were 6 average profile clusters were made and these profiles contain

differentially expressed genes from low to high light acclimation and the reverse

acclimation. Most of the top up-regulated genes in high to low light acclimation in Day

0 were clustered into the average profile cluster 6 in which there was a clear pattern

showing that the same genes were down-regulated in the low to high light acclimation

(Table 4.5). Meanwhile, in the average profile cluster 2, most of the repressed genes in

Day 0 were up-regulated in low to high light acclimation but down-regulated in high to

low light acclimation.

In the average profile cluster 1, the genes were repressed in the low to high light

acclimation and induced in high to low light acclimation. However, the genes in the low

light acclimated plants were not greatly induced compared to the genes in low light

plants as controls. On the other hand, in average profile cluster 3, the genes in the low

light acclimated plants were more induced than the control plants in low light.

Meanwhile, in average profile cluster 4, the genes in high light control plants were more

induced than the genes in high light acclimated plants. The genes in low light control

plants were even more repressed than the genes in low light acclimated plants. In

average profile cluster 5, the genes in high light acclimated plants and high light control

plants were both induced at a very similar level. However, the genes in the low light

plants were expressed at different level and pattern.

102

LH_C LH_D1 HL_C HL_D0 HL_D1

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5


-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5


-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0


-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5


-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5


-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Average profile cluster 1






Figure 4.3: Average profile cluster of 1, 2, 3, 4, 5 and 6. The functional genes were

classified in a clustering analysis. LH_C = low to high light acclimation, control;

LH_01 = low to high light acclimation, Day 1; HL_C = high to low light acclimation,

control; HL_00 = high to low light acclimation, Day 0; HL_01 = high to low light

acclimation, Day 1

103


Transcript ID (induced)

Description Cluster Transcript

ID (repressed)

Description

Cluster

At5g49360 xylosidase 6 At4g15210 beta-amylase N/A

At4g27450 putative

protein stem-

specific protein

6 At4g14690 Expressed protein 4

At4g35770 senescence-

associated

protein sen1

6 At1g61800 glucose-6-

phosphate/phosphate-

translocator precursor

2

At5g20250 seed imbitition

protein-like 6 At4g01080 hypothetical protein 2

At5g57560 TCH4 protein

_AF367262 6 At4g16590

cellulose synthase like

protein 5

At3g58120

putative

protein basic

leucine zipper

transcription

activator

6 At1g57590 pectinacetylesterase

precursor 2

At3g15630 unknown

protein 6 At5g52320 cytochrome P450 5

At3g15450 unknown

protein 6 At3g44750

putative histone

deacetylase 2

At5g22920 PGPD14 3 At1g56650 anthocyanin2 2

At2g33830 putative auxin-

regulated

protein

1 At2g27840 unknown protein 5

At3g06070 unknown

protein 3 At1g06000

unknown protein

Ceres:1040. 5

At3g62950 glutaredoxin -

like protein 6 At5g50800 MtN3-like protein 2

At5g44020 vegetative

storage

protein-like

6 At4g36010 thaumatin-like protein 2

At3g13750 galactosidase, 6 At1g24020 pollen allergen-like

protein 4

At5g21170 AKIN beta1 6 At3g57490 40S ribosomal protein 5

At1g79700 unknown

protein 6 At3g44990

xyloglucan endo-

transglycosylase 2

At1g13700 unknown

protein 6 At4g27570 UDP rhamnose 2

At2g45170

putative

microtubule-

associated

protein

3 At5g48850 putative protein N/A

At4g17245 Expressed

protein 6 At5g62165 Expressed protein 4

At1g02640 beta-xylosidase 6 At1g61870 unknown protein 5

104


Transcript ID (induced)

Description Cluster Transcript

ID (repressed)

Description Cluster

At4g27450 putative protein 6 At4g14690 Expressed protein 4

At2g33830 putative auxin-

regulated protein 1 At4g15210 beta-amylase 5

At3g06070 unknown protein 3 At4g16590 cellulose synthase

like protein 5

At3g62950 glutaredoxin -like

protein 6 At5g58310

polyneuridine

aldehyde esterase-

like

4

At5g49360 xylosidase 6 At1g57590 pectinacetylesterase

precursor 2

At1g13700 unknown protein 6 At2g27420 cysteine proteinase 5

At2g40610 putative expansin 3 At2g16890 putative

glucosyltransferase 4

At2g15890 unknown protein 1 At2g40100 putative chlorophyll

a/b binding protein 4

At3g15450 unknown protein 6 At4g36010 thaumatin-like

protein 2

At1g72150 cytosolic factor 1 At1g24020 pollen allergen-like

protein 4

At3g58120 putative protein 6 At1g62710 beta-VPE 5

At2g44740 putative PREG1-like 3 At5g13930 chalcone synthase N/A

At3g61060 putative protein 6 At1g76530 unknown protein 4

At2g45170 putative

microtubule-

associated protein

3 At1g56650 anthocyanin2 2

At1g74670 GAST1-like

proteinMar 3 At4g01390 hypothetical protein 4

At5g63470 transcription factor

Hap5a-like protein 3 At1g56430

nicotianamine

synathase 4

At2g22980 putative serine

carboxypeptidase I 3 At2g29090

putative

cytochrome P450 2

At2g32100 hypothetical

protein predicted

by genscan

3 At4g01080 hypothetical protein 2

At4g17245 Expressed protein 6 At4g17090 putative beta-

amylase 2

At5g40890 anion channel

protein 6 At5g05270 putative protein 4

105

4.2.4 Gene Ontology (GO) annotation and analysis

Gene ontology (GO) database was used to provide tools to analyse the function of large

numbers of genes (Hayes, Castrillo et al. 2007). GOstat (http://gostat.wehi.edu.au/cgi-

bin/goStat.pl) was used to analyse the function of genes and the gene ontology website

(http://bioinfo.cau.edu.cn/agriGO/ ) was used to produce schematic diagram of the gene

functions (Figure 4.4).

The gene in question, At1g61800 which were up-regulated in low to high light

acclimation but down-regulated in high to low light acclimation was involved in hexose

phosphate transport and glucose-6-phosphate transmembrane transporter acitivity

(Figure 4.6).

106

Figure 4.4: A gene ontology (GO) representation from the GO analysis using the web

interface AGRIGO.

Figure 4.5: A color-coded diagram showing the significance levels and arrow types.

This diagram is to be used with Figure 4.10.

107

Figure 4.6: A graphical result from the GO analysis based on the biological processes. The graph contains all statistically significant terms

which are shown in Figure 4.9

108

4.3 Discussion

gpt2 gene expression was seen to decrease during high to low light acclimation (Figure

4.1). Although this preliminary result might not be significant, at least it showed some

changes in the expression of gpt2. Microarray analysis was performed on WS plants to

identify potential genes which involve in the process of acclimation to lower light

intensities. Besides, it was much of our interest to investigate the expression level of

GPT2 in high to low light acclimation compared to the GPT2 expression in low to high

light acclimation.

From the microarray data, it was found that the number of differentially expressed genes

that met all three criteria was higher in high to low light acclimation compared to low to

high light acclimation. In the high to low light acclimation, there was 1,798

differentially expressed genes whereas there was just 951 differentially expressed genes

in low to high acclimation. The number simply shows that there were more genes that

changed in low light acclimation compared to high light acclimation. Moreover, the

number of genes were twice as many in low light acclimation than high light

acclimation. Besides, the number of shared genes in Day 0 and Day 1 of low light

acclimation was even higher than the high light acclimation. There were 331 shared

genes in low light acclimation and 210 shared genes.

GPT2 is able to translocate sugar phosphates such as Glucose-6-P and triose-P in

exchange for phosphates (Knappe, Flugge et al. 2003). Previous studies also have

shown that gpt2 gene is induced during sugar-feeding and sugar-induced senescence

109

(Gonzali, Loreti et al. 2006, Li, Lee et al. 2006, Pourtau, Jennings et al. 2006). GPT2 is

probably required during the acclimation itself, not for the steady-state photosynthesis

following acclimation (Athanasiou, Dyson et al. 2010). Therefore, one specific role of

GPT2 is probably on balancing sugar phosphate and phosphate pools in the cell.

Besides that, glucose 6-phosphate is the precursor for starch biosynthesis. When the

gpt2 gene was expressed constitutively, it could rescue the plants with low starch

phenotype (Plaxton 2006).

At1g61800 was greatly down-regulated in Day 0. At1g61800, which is also known as

glucose 6-phosphate/phosphate translocator (GPT2) involves in the transporter activity.

This GPT2 is clustered into the average profile cluster 2 in which GPT2 was up-

regulated in low to high light acclimation and down-regulated in high to low light

acclimation. In high light acclimation, when there is more light available to plants, the

rate of photosynthesis and electron transport rate would be increased so much so there is

a need to transport glucose 6-phosphate and triose phosphate in exchange for

phosphates (Athanasiou, Dyson et al. 2010). Moreover, At4g15210 encodes for

Arabidopsis thaliana beta-amylase (BAM5), was also down-regulated in high to low

light acclimation but up-regulated in low to high acclimation. BAM5 is expressed in

rosette leaves such as Arabidopsis thaliana and BAM5 is inducible by sugars. One of

the products of Calvin cycle is carbohydrates which can be retained in the chloroplast or

transported in the form of transitory starch as a precursor for sucrose biosynthesis.

During the day, triose phosphate/phosphate translocator (TPT) is responsible in

transporting the transitory starch. However, during night or when the light is limited, the

transitory starch is degraded by beta-amylase to produce sucrose and maltose (Schmitz,

Schoettler et al. 2012).

110

At4g14690 gene which encodes for early light induced proteins (ELIPs) was down-

regulated in Day 0 and the ELIPs were even more repressed more in Day 1. ELIPs are

involved in the chlorophyll-binding complexes which affects the synthesis and

assembly specific photosynthetic complexes (Tzvetkova-Chevolleau, Franck et al.

2007). When the ELIPs were down-regulated, it was shown that the chlorophyll

synthesis pathway was increased. This led to the increase in the chlorophyll availability

for photosynthesis.

In Day 0, there were two genes that were up-regulated and only induced in the

senescing stage which were At4g35770 and At5g20250. Senescing is always associated

with flowering stage when the plants undergo transition from vegetative phase to

reproductive phase. Reproduction is the most factor associated with senescing (Causin,

Jauregui et al. 2006). However, the plants used were not flowering as they were

carefully selected. On the other hand, light also plays role in inducing senescence. When

plants are being exposed to changing in light availability, the senescing symptoms

might occur (Causin, Jauregui et al. 2006). Therefore, these could explain how

senescing related genes were mostly up-regulated in this high to low light acclimation.

The most up-regulated gene is the At5g49360 which encodes for bifunctional -D-

xylosidase/{alpha}-L-arabinofuranosidase (beta-xylosidase 1). According to the gene

ontology database, At5g49360 has a function in carbohydrate metabolic process which

might be involved in starch synthesis in Arabidopsis thaliana. Starch synthesis is

strongly associated with the rate of photosynthesis and the electron transport rate. In

high to low light, the photosynthetic rate was slowed down and thus the electron

transport rate was also lowered. Therefore, it was expected that the starch synthesis

111

would be low as well. However, since At5g49360 gene was up-regulated just after 4

hours of transfer to low light, it could be that the acclimation response was slow and

that the gene was expressed when the plants were in the high light condition. This could

be shown by the gene expression level in Day 1 which was decreased than the level in

Day 0.

Besides that, At3g62950 was also found to be induced in both Day 0 and Day 1.

At3g62950 is a thioredoxin superfamily protein which has functions in electron carrier

activity. According to the average profile cluster 6, the gene was induced on Day 0 and

continued to Day 1 with lowered gene expression level. Similarly to At5g49360, the

gene expression indicates the electron carrier activity when the plants were still in the

high light growth condition.

There were also two genes which had an increased in the gene expression in Day 0 but

had a reduced gene expression in the low to high light acclimation. At3g15450 and

At5g22920 genes were important to low light acclimation but not in the high light

acclimation. At3g15450 which encodes for aluminium induced protein with YGL and

LRDR motifs involves in the light and sugar responses. When Arabidopsis thaliana was

grown under a very low light condition for 16 hours, gene associated with light and

sugar responses were the most repressed (Kittang, Winge et al. 2013). Meanwhile,

At5g22920 involves in protein degradation were also induced under low light

acclimation. When plants of Arabidopsis thaliana were acclimated to a low light

condition where the light is limited, the limited electron flow induced rapid D1 protein

degradation (Keren, Berg et al. 1997).

112

At1g72150 (PATL1) gene was induced in Day 1 in high to low light acclimation but

repressed in low to high light acclimation. At1g72150 gene involves in membrane

trafficking (Peterman, Sequeira et al. 2006) and according to the GO database,

At1g72150 functions in transporter activity. This gene started to be up-regulated in Day

1 indicating that this is a late response in low light acclimation.

113


Chapter 5

Results

Quantitative Trait Loci (QTL) analysis

114

5.1 Introduction

In order to understand plants better at the genetic level, a phenotypic characterization is

required that allows a better insight to the genetic variants. Frequently, genetic

variations are examined, based largely on the laboratory-induced mutants (Alonso-

Blanco and Koornneef 2000). These forward or reverse genetic approaches use

biological agents or chemicals to induce mutations. However, if these approaches are

used in a large-scale project, large numbers of genes must be disrupted. Thus, it leads to

a limitation due to a limited number of genetic background.

An alternative, to study the link between the natural genetic variation and the

phenotypic distribution, is an approach called quantitative trait loci (QTL) mapping.

QTL is a genomic tool used by researches to understand the genetic basis of natural

variation. Many researchers use Arabidopsis thaliana as their model plant system as

Arabidopsis possess a lot of natural variation for a wide variety of evolutionary and

agriculturally relevant traits (Maloof 2003).

In order to detect the location of loci responsible for a desired quantitative variation, it

an F2 generation of a segregating population is needed alongside molecular markers.

The traits of interest are scored and the link between the genotypes and phenotypes of

the traits are examined by using specific statistical methods, depending on the software

used. To date, there are a lot of mapping softwares available that are widely used and

each software has its very own mapping system. MAPMAKER/QTL (Lincoln, Daly et

al. 1992) needs its companion program of MAPMAKER/EXP (Lander, Green et al.

115

1987) because MAPMAKER/QTL can perform the mappings and tests but it cannot

format the data and calculate the marker maps (Manly and Olson 1999). Meanwhile,

QTL Cartographer (Basten, Weir et al. 1997) is more user-friendly in which it allows

users to use it on almost any operating system. In addition, MapQTL and PLABQTL

are also operating in a similar system like QTL Cartographer. For all mapping software,

source files need to be in a certain format and Map Manager QT (Manly and Elliott

1991) provides a data-preparation program. Map Manager QT can be used to prepare

the source file for other mapping software and also it can be used as a mapping program

itself. For this project, QTL Cartographer software was chosen since the interface is

more menu-driven and the source file used is in text (.txt) format.

There are several recombinant inbred line (RIL) pairs which are commonly used for

QTL mapping. Specifically in Arabidopsis, there are two commonly used RIL pairs

which are Ler x Col (Lister and Dean 1993) and Ler x Cvi (Alonso-Blanco, Peeters et

al. 1998). In a recent years, there is a third pair of RIL has been introduced which is

Bay-0 x Shahdara (Loudet, Chaillou et al. 2002). According to (Athanasiou, Dyson et

al. 2010), Col-0 did not have the ability to acclimate to higher light intensity but Ler-4

did acclimate. Therefore, for this project, the Ler-4 x Col-0 pair was used for QTL

analysis to identify candidate genes at which locus or loci that are associated with the

ability to acclimate to higher light intensities.

116

5.2 Results

5.2.1 Physiological assessment in recombinant-inbred (RI) lines

of Col-4 x Ler-0 population in low to high light acclimation

From the 305 RI lines derived from the Col-4 x Ler-0 population, 24 RI lines were

chosen for the QTL mapping as these RI lines were selected and recommended by The

European Arabidopsis Stock Center (http://arabidopsis.info/).

These RI lines were acclimated from low to high light to identify at which locus it is

responsible for the ability to acclimate to higher light intensities. In the maximum

photosynthetic capacity measurement, Col-4 did not have the ability to increase its

Pmax value when transferred to high light condition (Figure 5.1 A). However, Ler-0

had the ability to acclimate to the high light condition by increasing the Pmax. Across

the 24 RI lines, all of them were behaving like their parent, Ler-0, by having the ability

to acclimate to higher light intensities by increasing their maximum photosynthetic

capacity.

In consistent with the Pmax data, all 24 RI lines had an increased in PSII efficiency (ɸ

PSII) for high light plants (Figure 5.1 B). However, there was one RI line (N 1953)

which had a decreased ɸ PSII for the high light plants. In terms of the ability to quench

excess excitation energy, there were seven RI lines (N1953, N1913, N1969, N1985,

N1990, N1963, N1900) which had a decreased NPQ value just like the parent, Col-4.

Meanwhile, the other 17 RI lines increased their NPQ value when transferred to high

light condition, just like Ler-0 (Figure 5.1 C).

117

Besides that, transpiration, stomatal conductance and internal CO2 parameters were also

measured. There were several lines which did not have the value for these parameters

because these parameters were not included in the early experiment. All RI lines had

some changes in the transcription (Figure 5.2 A), stomatal conductance (Figure 5.2 B)

and internal CO2 (Figure 5.2 C).

Besides photosynthetic measurement, the chlorophyll content and composition were

also measured and calculated. Again, there were several lines which did not have the

values as these parameters were not included in the early measurement. Most of the RI

lines had some changes in the total amount of chlorophyll (Figure 5.4) which consisted

of the amount of chl a (Figure 5.3 B) and chl b (Figure 5.3 C). At the same time, the

chl a/b ratio in all RI lines were also changed due to the transfer to high light condition

(Figure 5.3 A). This shows that upon acclimation to high light, plants changed its

chlorophyll composition to accommodate the acclimation process.

118

0

5

10

15

20

25

30

Pm

ax (

µmol

m-2 s

-1) LL

HL

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Φ P

SII

LL HL

Col-4Ler-

0

N 1911

N1974

N 1948

N 1966

N 1978

N 1953

N 1971

N 1913

N 1984

N 1969

N 1985

N 1990

N 1963

N 1900

N 1975

N 1986

N 1968

N 1903

N 1910

N 1925

N 1936

N 1987

N 1988

N 1994

0.0

0.5

1.0

1.5

2.0

2.5

NP

Q

LL HL

A

B

C

RI lines

Figure 5.1: Phenotypic distribution of (A) maximum photosynthetic capacity,

Pmax, (B) ɸ PSII and (C) NPQ for recombinant inbred lines of Col-4 x Ler-0. The

RI lines were grown at 100 µmol m-2 s-1 for six weeks after which half were

transferred to high light at 400 µmol m-2 s-1. The measurement was taken after 9 days

of acclimation. All data are mean ± SE for at least 3 biological replicates.

119

0

1

2

3

4

Tra

nspi

ratio

n (m

ol m

-2 s

-1)

LL HL

050

100150200250300350400450

Sto

mat

al c

ondu

ctan

ce (

mm

ol m

-2 s

-1)

LL HL

Col-4Le

r-0

N 1911

N1974

N 1948

N 1966

N 1978

N 1953

N 1971

N 1913

N 1984

N 1969

N 1985

N 1990

N 1963

N 1900

N 1975

N 1986

N 1968

N 1903

N 1910

N 1925

N 1936

N 1987

N 1988

N 1994

0

500

1000

1500

2000

RI lines

Inte

rnal

CO

2 co

ncen

trat

ion (µ

mol

mol

-1)

LL HL

A

B

C

Figure 5.2: Phenotypic distribution of (A) transpiration, (B) stomatal conductance

and (C) internal CO2 concentration for recombinant inbred lines of Col-4 x Ler-0.

The RI lines were grown at 100 µmol m-2 s-1 for six weeks after which half were

transferred to high light at 400 µmol m-2 s-1. The measurement was taken after 9 days of

acclimation. All data are mean ± SE for at least 3 biological replicates.

120

Figure 5.3: Phenotypic distribution of (A) Chl a/b, (B) Chl a and (C) Chl b for

recombinant inbred lines of Col-4 x Ler-0. The RI lines were grown at 100 µmol m-2 s-1 for

six weeks after which half were transferred to high light at 400 µmol m-2 s-1. The measurement

was taken after 9 days of acclimation. All data are mean ± SE for at least 3 biological replicates.

121

Figure 5.4: Phenotypic distribution of total chlorophyll content for recombinant

inbred lines of Col-4 x Ler-0. The RI lines were grown at 100 µmol m-2 s-1 for six

weeks after which half were transferred to high light at 400 µmol m-2 s-1. The

measurement was taken after 9 days of acclimation. All data are mean ± SE for at least

3 biological replicates.

122

5.2.2 Quantitative trait loci (QTL) mapping

5.2.2.1 Single marker analysis

Single-marker analysis was performed on all chromosomes to find the best possible

QTLs. In order to find the significance of this analysis, a simple linear regression model

was used to assess the segregation of a trait phenotype with respect to a marker

genotype.

A total of 10 traits were used in this analysis to find the association of marker genotypes

with phenotypic trait. A possible QTL would be indicated to be near to the marker locus

if the marker-trait association is there.

From the analysis, it was found that there were 406 possible QTLs which were

significant in all 5 chromosomes of Arabidopsis thaliana. The most number of possible

QTLs were found on chromosome 1 with 149 possible QTLs. Meanwhile, there were

13 possible QTLs found in chromosome 2, 126 possible QTLs found in chromosome 3,

51 possible QTLs found in chromosome 4 and 66 possible QTLs found in chromosome

5.

The single-marker analysis produced a graph illustrating the genetic distance of markers

(in cM) as the x axis and LOD (logarithm of the odds) score profile as the y axis. In

Figure 5.5, the graph showed QTL mapping for all 5 chromosomes and all 10

phenotypic traits. The horizontal line on the graph was used to identify significant

marker if the peak is above the threshold line. If the threshold line was set lower, it

123

could give results of false positives. However, if the threshold line was set higher, it

could miss significant possible QTLs. It can be seen that there were 2 significant QTLs

that the peaks were above the threshold line. The first significant QTL was found to be

in the marker gene associated with internal CO2 parameter in chromosome 1 (Figure

5.6). The second significant QTL was found to be in the marker gene associated with

NPQ parameter in chromosome 4 (Figure 5.7).

124

Figure 5.5: A single-marker analysis on all 5 chromosomes of Arabidopsis thaliana using 10 phenotypic traits. The phenotypic traits

were color-coded as shown. The horizontal line on the graphs represents the threshold line

125

Figure 5.6: A single-marker analysis on all 5 chromosomes of Arabidopsis thaliana using Internal CO2 parameter as phenotypic

trait. The phenotypic traits were color-coded as shown. The horizontal line on the graphs represents the threshold line

126

Figure 5.7: A single-marker analysis on all 5 chromosomes of Arabidopsis thaliana using NPQ parameter as phenotypic trait. The

phenotypic traits were color-coded as shown. The horizontal line on the graphs represents the threshold line.

5.3 Discussion

In order to do QTL mapping, both parental strains of Col-0 and Ler-4 were crossed to

produce RI lines. These RI lines contained different fractions of the genome of each

parental strain. The genotype markers and phenotype of each of these RI lines were

assessed to yield quantitative data essential for mapping.

In this QTL experiment, Col-0 and Ler-4 parental strains were selected because this pair

was one of the commonly used pair for QTL mapping. Col-0, Ler-4 and 24 other RI

lines were put on an acclimation to high light because it was found that Col-0 did not

have the ability to acclimate to high light but Ler-4 did have the ability (Figure 5.1).

Meanwhile, all 24 RI lines did have the ability to acclimate to high light, just like its

parent Ler-4.

From the single-marker analysis, it was found that there were 406 possible QTLs. These

possible QTLs were obtained from 10 phenotypic traits and it was also found that from

these 406 possible QTLs, there were only 2 QTLs that were significant. The

significance of QTLs was determined by using the threshold line which was set at 2.5.

By selecting threshold line at 2.5, a lot of QTLs were eliminated and the effects of ghost

could be reduced. Further analysis is needed to obtain thorough and accurate result of

significant QTL associated with light acclimation.

128


Chapter 6

General Discussion

129

6.1 General Discussion

The success of crop production is depending on the current climate variation. Climate

variation may include an increase in the temperature and CO2 level and the frequency

and intensity of extreme weather such as warming. Some crop plants may benefit from

this climate variation in which it can lead to quicker growth and higher yield. However,

there are a few crop plants that may not survive this harsh climate variation and

consequently lead to reduced yield production. Hence, changes in climate condition can

significantly impact crop yields.

Therefore, this work was based on trying to find the solution due to the unfavourable

effects of light variation on plants. It has been established that plants have the ability to

do acclimation as one of the solutions to encounter problems with changing

environment. In this work, the main focus was on to understand the dynamic

acclimation as opposed to developmental acclimation. Developmental acclimation

happens when plants were grown from seeds at different sets of growing conditions

which enable plants to develop different metabolic capacities. Meanwhile, dynamic

acclimation happens when plants are grown from seeds to mature at one set of growing

conditions and the condition is altered to measure the ability of the mature plants to

change their metabolic capacities.

The dynamic acclimation to light in Arabidopsis was tested by growing WS plants

under HL condition and transferring the plants to LL plants when the plants were

mature. Besides WS, plants lacking GPT2 expression in the background of WS (WS-

130

gpt2) were also grown to verify its ability to acclimate to LL condition. During time-

course experiment where plants of WS and WS-gpt2 were taken for photosynthetic

capacity measurement on daily basis, the acclimation responses to LL can be seen as

early as Day 1 upon transfer to low light. Similarly, (Athanasiou, Dyson et al. 2010)

observed that a small extent of acclimation occurred within the first day at HL in WS-

gpt2. Therefore, it was concluded that Arabidopsis of WS accession can

photoacclimate when the light was increased and decreased. However, GPT2 was found

to be non-essential in a decreasing light acclimation but essential in an increasing light

acclimation.

Knowing GPT2 is not important in HL to LL acclimation, a microarray analysis was

carried out to search for other genes that might be responsible for the acclimation to LL.

One of the advantages of doing microarray is that it is a technique that allows for the

comparison of thousands of genes from one experiment. From the microarray analysis,

the mean fold change of GPT2 increased in the higher light intensity acclimation but

decreased in the lower light intensity. Thus, it supports the conclusion from the

physiological work in which GPT2 is essential in the higher light intensity acclimation

but not in the lower light intensity acclimation.

In the higher light intensity acclimation, it was found that Col-0 did not have the ability

to acclimate but Landsberg erecta (Ler) did have the ability to acclimate. Therefore, this

work also included this information to further analyze using Quantitative Trait Loci

(QTL) mapping. The work of QTL mapping was only at the preliminary stage where

several recombinant inbred (RI) lines were measured on determined phenotypic traits.

Therefore, more work need to be done in order to achieve the objective of doing the

131

QTL mapping. The most difficulty encountered during this mapping was to find a

suitable software for the mapping. Thus, a further analysis needs to start by finding the

right mapping software that is easily downloadable and easy to comprehend. An in

depth understanding on what and how quantitative trait loci (QTL) works is also needed

in order to achieve the objectives of doing QTL. Besides, more recombinant inbred (RI)

lines need to be tested to represent a bigger population.

Although extensive work has been done to study the photosynthetic acclimation to

lower light intensity in Arabidopsis thaliana, there is still room for future work that can

be done to support existing data.

During the physiological analysis of photosynthetic capacity measurement, the light

intensity was the main variable tested in this experiment. In contrast to only one set of

changing light condition, a fluctuating light regime would be the next interesting project

to carry out in Ws and Ws-gpt2 ecotype of Arabidopsis thaliana in a controlled

condition. The data can be coupled with the information gained from the acclimatory

responses to fluctuating light environment in other accessions (Alter, Dreissen et al.

2012).

Besides physiological work, a molecular level work was also carried out. A preliminary

screen on the result of microarray was done but it is insufficient. A reverse-transcriptase

polymerase chain reaction (rt-PCR) needs to be done next in order to validate the

findings from the microarray experiment. Thus, it can validate the result of the gene in

question which is GPT2 that was found to be repressed. Besides GPT2, it would be

132

beneficial to investigate other induced genes that might be essential to acclimation to

lower light intensity. Besides rt-PCR, an analysis through GOstat and MapMan can also

be helpful to understand the microarray result better.

In a conclusion, this work was done in a hope to get a better understanding on how

plants cope with changing conditions especially during this global warming issues. It is

very important for crop plants to find solution to survive during this harsh environment

as crop plants is needed by the world population.

133


Chapter 7

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acclimation processes in Arabidopsis thaliana L. (Heynh.)." J. Exp. Bot. 60(6): 1715-1727.

Wientjes, E., et al. (2013). "LHCII is an antenna of both photosystems after long-term

acclimation." Biochimica et Biophysica Acta (BBA) - Bioenergetics 1827(3): 420-426.

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Chapter 8

Appendix

144

8.0 APPENDIX

Table 8.1: The 331 differentially expressed genes that are shared between Day 0 and Day 1.

The genes were ranked from the most repressed to the most induced.

Transcript ID Target Description Fold change

At4g15210 beta-amylase -31.9914

At4g14690 Expressed protein -16.9468

At4g01080 hypothetical protein -12.1126

At4g16590 cellulose synthase like protein -12.0848

At1g57590 pectinacetylesterase precursor -11.5173

At1g56650 anthocyanin2 -10.1841

At2g27840 unknown protein contains non-consensus donor splice site AT at exon2 and acceptor splice site AC at exon3. -9.96942

At5g50800 MtN3-like protein -9.60082

At4g36010 thaumatin-like protein thaumatin-like protein -9.59743

At1g24020 pollen allergen-like protein -9.5695

At3g57490 40S ribosomal protein S2 homolog 40S ribosomal protein S2 -8.79995

At4g27570 UDPrhamnose--anthocyanidin-3-glucoside rhamnosyltransferase -8.18136


At3g13230 unknown protein -7.35661

At5g52310 low-temperature-induced protein 78 -6.58957

At5g49480 NaCl-inducible Ca2+-binding protein-like; calmodulin-like -6.15912

At5g19470 putative protein thiamin pyrophosphokinase -5.65416

At2g29090 putative cytochrome P450 -5.56974

At2g16890 putative glucosyltransferase -5.52554

At3g55940 phosphoinositide-specific phospholipase C -5.50598

At4g25630 fibrillarin 2 (AtFib2) -5.50311

At1g62710 beta-VPE -5.43442


At4g17340 membrane channel like protein -5.07758


At2g27420 cysteine proteinase -4.93126

145

At4g39210 glucose-1-phosphate adenylyltransferase (APL3) -4.89111

At3g23810 S-adenosyl-L-homocysteinas -4.83796

At5g62440 putative protein -4.75798

At5g11590 transcription factor -4.67537

At1g02820 late embryogenis abundant protein -4.50482


At1g73600 phosphoethanolamine N-methyltransferase -4.41108


At2g32220 60S ribosomal protein L27 -4.23999

At4g24010 putative protein cellulose synthase catalytic subunit (Ath-A) -4.10573


At1g32900 starch synthase -3.9978

At2g22200 AP2 domain transcription factor -3.95345



At1g78440 gibberellin 2- oxidase -3.92683

At1g48100 polygalacturonase PG1 -3.91594

At1g51090 proline-rich protein -3.86976

At1g30530 UDP glucose:flavonoid 3-o-glucosyltransferase -3.86158

At3g51240 flavanone 3-hydroxylase (FH3) -3.81605



At1g56430 nicotianamine synathase -3.67114


At3g14720 putative MAP kinase -3.62684

At3g20240 mitochondrial carrier protein -3.60372



At5g08640 flavonol synthase (FLS) -3.46445

At1g19640 floral nectary-specific protein -3.43006

At3g15650 putative lysophospholipase -3.42773

At2g20450 60S ribosomal protein L14 -3.40836

146

At4g23990 cellulose synthase catalytic subunit -3.37816


At5g47060 putative protein similar to unknown protein -3.37135


At3g28500 acidic ribosomal protein P2b (rpp2b) -3.36455

At2g32990 putative glucanse -3.35599



At3g21890 zinc finger protein -3.31332

At5g15740 putative protein auxin-independent growth promoter -3.31085

At2g42540 cold-regulated protein cor15a precursor -3.29251

At2g22360 putative DnaJ protein -3.28358

At1g64780 ammonium transporter -3.26986


At3g03770 hypothetical protein may contain C-terminal ser/thr protein kinase domain -3.21295

At2g34850 putative UDP-galactose-4-epimerase -3.20279

At2g18230 putative inorganic pyrophosphatase -3.11641


At1g75270 GSH-dependent dehydroascorbate reductase 1 -3.07403

At4g36360 beta-galactosidase like protein -3.02596


At3g13310 DnaJ protein -2.88133

At4g35320 putative protein predicted protein -2.84869


At5g13750 transporter-like protein -2.81555

At4g24960 abscisic acid-induced - like protein abscisic acid-induced protein HVA22 -2.81434

At1g69870 putative peptide transporter -2.81374

At3g01820 putative adenylate kinase -2.80624


At5g14760 L-aspartate oxidase -like protein L-aspartate oxidase -2.77689

At5g50720 putative protein similar to unknown protein -2.77159

147

At5g37980 quinone oxidoreductase -2.74384

At1g67360 stress related protein -2.73323

At1g17100 SOUL-like protein -2.72884

At4g34590 bZIP transcription factor ATB2 -2.72029

At2g15310 putative ADP-ribosylation factor -2.69987

At4g20170 putative protein gene -2.69632


At3g15020 mitochondrial NAD-dependent malate dehydrogenase -2.66068


At4g34740 amidophosphoribosyltransferase 2 precursor -2.63067

At1g62570 flavin-containing monooxygenase -2.58762

At2g01290 putative ribose 5-phosphate isomerase -2.57217



At2g15320 putative leucine-rich repeat disease resistance protein -2.51118



At3g14650 putative cytochrome P450 -2.40268

At5g60540 imidazoleglycerol-phosphate synthase subunit H -2.37399

At3g50970 dehydrin Xero2 -2.3662

At3g22840 early light-induced protein identical to early light-induced protein -2.33326

At4g31870 glutathione peroxidase -2.31611

At4g37980 cinnamyl-alcohol dehydrogenase ELI3-1 -2.29107

At1g72230 blue copper protein -2.28477

At4g17880 bHLH protein -2.25394

At4g17090 putative beta-amylase -2.2509

At1g07890 L-ascorbate peroxidase i -2.23106





At2g39700 putative expansin -2.17213

148



At3g46670 glucosyltransferase-like protein UDP-glucose glucosyltransferase -2.11385


At3g58070 zinc finger-like protein several zinc finger proteins -2.07759


At5g44670 putative protein strong similarity to unknown protein -2.06187


At3g61220 putative protein carbonyl reductase (NADPH) -2.05022

At3g25570 S-adenosylmethionine decarboxylase -2.01975


At3g10740 putative alpha-L-arabinofuranosidase 1.11457

At4g01130 putative acetyltransferase 1.82727

At5g63800 beta-galactosidase 2.00914

At2g03310 hypothetical protein 2.04114

At1g22640 putative myb-related transcription factor 2.07044

At1g02300 cathepsin B-like cysteine protease 2.07695

At1g73750 unknown protein 2.09121

At5g08520 putative protein 2.09148

At5g48930 anthranilate N-benzoyltransferase 2.09628

At4g36040 DnaJ-like protein DnaJ-like protein 2.09666

At4g35440 putative protein hypothetical protein F22O2.23 2.10638

At3g47160 RNA-binding protein-like protein various RNA-binding proteins 2.13371

At4g39640 putative gamma-glutamyltransferase gamma-glutamyltransferase 2.14939

At1g74840 myb-related transcription activator 2.16299

At1g69850 nitrate transporter (NTL1) 2.16459

At3g23080 unknown protein C-term 2.17883

At3g45260 zinc finger protein zinc finger protein ID1 2.18784

At3g28860 P-glycoprotein 2.18944

At1g17990 12-oxophytodienoate reductase 2.19383


149

At1g68560 alpha-xylosidase precursor 2.20126


At2g32010 putative inositol polyphosphate 5'-phosphatase 2.20996

At3g28910 MYB family transcription factor (hsr1) 2.21802

At4g38850 small auxin up RNA (SAUR-AC1) 2.23187

At3g04910 putative mitogen activated protein kinase 2.27573

At1g34760 14-3-3 protein GF14omicron (grf11) 2.28085

At5g19140 aluminium-induced protein - like aluminium-induced protein 2.28465

At3g26320 cytochrome P450 2.29064

At5g39080 acyltransferase 2.29091

At5g47440 putative protein strong similarity to unknown protein 2.3138





At4g23300 serine/threonine kinase 2.36602


At3g62550 putative protein ER6 protein - Lycopersicon esculentum 2.38986



At5g63620 alcohol dehydrogenase 2.44588

At3g26740 light regulated protein 2.45368

At3g53260 phenylalanine ammonia-lyase 2.45834



At1g29460 auxin-induced protein 2.52191

At5g25280 serine-rich protein 2.53287

At3g13690 protein kinase 2.53571

At5g49730 FRO2-like protein; NADPH oxidase-like 2.54128

At4g38690 putative protein phospholipase C (EC 3.1.4.3) precursor,phosphatidylinositol-specific - 2.58101

At1g32540 zinc-finger protein 2.58135

At5g06870 polygalacturonase inhibiting protein 2.5859

150





At5g60680 putative protein predicted proteins 2.62861


At5g16180 putative protein hypothetical proteins 2.63944

At2g01420 putative auxin transport protein 2.63984


At5g25900 cytochrome P450 GA3 2.66612

At5g02150 putative protein Hsp70 binding protein HspBP1 - Homo sapiens 2.69488


At4g32340 putative protein predicted proteins 2.71024

At3g01490 putative protein kinase similar to ATMRK1 2.72162


At5g67520 adenylylsulfate kinase 2.73

At1g71030 putative transcription factor 2.74161


At3g16857 ARR1 protein 2.75111

At5g35790 glucose-6-phosphate dehydrogenase 2.75219

At4g16520 symbiosis-related like protein 2.75707


At5g05690 cytochrome P450 90A1 (sp|Q42569) 2.81749

At1g11530 thioredoxin h 2.83866



At3g09580 putative oxidoreductase 2.87028

At1g64720 membrane related protein CP5 2.87952


At4g11360 RING-H2 finger protein RHA1b 2.9146

At4g05070 coded for by A. thaliana 2.9284

At5g16030 putative protein with poly glutamic acid stretch hypothetical

2.92926

151

protein

At1g21920 phosphatidylinositol-4-phosphate 5-kinase 2.93955


At4g03510 RMA1 RING zinc finger protein identical to RMA1 2.96226



At1g35670 calcium-dependent protein kinase 3.06966




At4g17810 SUPERMAN like protein 3.12158

At2g05540 putative glycine-rich protein 3.12407


At4g33666 Expressed protein 3.13205


At1g69530 expansin (At-EXP1) 3.15478

At5g65110 acyl-CoA oxidase 3.15593

At5g18630 triacylglycerol lipase-like protein triacylglycerol lipase 3.17676

At1g09390 putative lipase 3.20443

At4g13830 DnaJ-like protein DnaJ-like protein 3.22777

At3g53800 putative protein Hsp70 binding protein HspBP1 3.22872

At3g30180 cytochrome P450 homolog 3.35628





At3g16770 AP2 domain containing protein RAP2.3 3.41214

At4g37260 myb-related protein 3.42054

At4g38470 protein kinase like protein protein kinase 6 3.45532

At2g39400 putative phospholipase 3.45713

At4g30690 putative protein translation initiation factor, IF3 3.48908

At3g51840 acyl-coA dehydrogenase Mus musculus glutaryl-CoA dehydrogenase precursor encoded by GenBank 3.511

152

At1g22550 peptide transporter 3.52743



At1g68190 putative zinc finger protein 3.59674

At4g17460 homeobox-leucine zipper protein HAT1 (hd-zip protein 1) 3.66907

At2g43820 putative glucosyltransferase 3.71535




At1g75220 integral membrane protein 3.86361


At2g18300 hypothetical protein predicted by genscan 3.9022

At1g63800 E2, ubiquitin-conjugating enzyme 5 (UBC5) 3.92437

At2g28630 putative fatty acid elongase 3.92626

At2g25900 putative CCCH-type zinc finger protein 3.95761


At2g43520 putative trypsin inhibitor 3.98702



At3g60290 SRG1 - like protein SRG1 protein 4.19201



At1g09570 putative phytochrome A 4.25093

At5g63190 topoisomerase 4.29513

At1g12780 uridine diphosphate glucose epimerase 4.37451

At4g24800 putative protein apoptosis gene MA3 4.3796



At4g36670 sugar transporter like protein 4.47004

At4g23400 water channel 4.49815


At1g77210 sugar carrier protein 4.68968

153


At4g03110 putative ribonucleoprotein 5.02362

At1g60140 trehalose-6-phosphate synthase 5.05877

At5g49450 putative protein contains similarity to bZIP transcription factor 5.08943

At5g06690 thioredoxin-like 5.19893

At5g63470 transcription factor Hap5a 5.2081

At1g70290 trehalose-6-phosphate synthase 5.21157

At1g68840 putative DNA-binding protein (RAV2-like) 5.24635



At2g16660 nodulin-like protein 5.43601



At1g55960 membrane related protein CP5 5.58286

At5g28770 bZIP transcription factor family protein 5.63207


At5g40450 putative protein microtubule-associated protein homolog 5.6631





At1g74670 GAST1-like protein 6.21237

At5g25190 ethylene-responsive element 6.66808


At1g80920 J8-like protein 6.78779

At2g40610 putative expansin 6.84143

At4g20260 endomembrane-associated protein 6.9896

At1g80440 unknown protein contains two Kelch motifs 7.00214


At3g61060 putative protein hypothetical proteins 8.05139

At5g61590 ethylene responsive element binding factor 8.0591

At5g14120 nodulin-like protein 8.07975

154

At2g44740 putative PREG1-like negative regulator 8.43655

At2g18700 putative trehalose-6-phosphate synthase 8.48154

At1g11260 glucose transporter 8.60143


At5g40890 anion channel protein 9.7709



At2g45170 putative microtubule-associated protein 11.2


At5g21170 AKIN beta1 12.7571

At3g13750 galactosidase 13.0382

At3g62950 glutaredoxin -like protein glutaredoxin 14.355


At2g33830 putative auxin-regulated protein 17.9072

At5g22920 PGPD14 protein 18.161


At3g58120 putative protein basic leucine zipper transcription activator shoot-forming PKSF1 19.6992

At5g20250 seed imbitition protein-like seed imbitition protein Sip1 22.7364

At4g27450 putative protein stem-specific protein 31.8035

At5g49360 xylosidase 36.1859

photosynthetic acclimation to lower light intensity in arabidopsis thaliana

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