Assessment of differential responses of rice varieties under drought stress : a value addition to rice phenomics

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ASSESSMENT OF DIFFERENTIAL RESPONSES OF RICE VARIETIES UNDER DROUGHT STRESS: A VALUE ADDITION

TO RICE PHENOMICS

ANUPAMA

KUSUMA SCHOOL OF BIOLOGICAL SCIENCES INDIAN INSTITUTE OF TECHNOLOGY DELHI

AUGUST 2019

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©Indian Institute of Technology Delhi (IITD), New Delhi, 2019

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ASSESSMENT OF DIFFERENTIAL RESPONSES OF RICE VARIETIES UNDER DROUGHT STRESS: A VALUE ADDITION TO

RICE PHENOMICS

by

ANUPAMA

Kusuma School of Biological Sciences

Submitted

In fulfilment of the requirements of the degree of Doctor of Philosophy

to the

INDIAN INSTITUTE OF TECHNOLOGY DELHI

AUGUST 2019

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i CERTIFICATE

This is to certify that the thesis titled “Assessment of Differential Responses Of Rice Varieties Under Drought Stress: A Value Addition To Rice Phenomics”, being submitted by Ms.

Anupama to the Indian Institute of Technology Delhi for the award of the degree of “Doctor of Philosophy” is a record of the bonafide research carried out by her, which has been prepared under our supervision and guidance in conformity with the rules and regulations of the Indian Institute of Technology Delhi, India. The results prescribed in it have not been submitted in part or full to any other University for the award of any degree or diploma.

Prof. Brejesh Lall (Co-Supervisor) Dr. Archana Chugh (Supervisor)

Professor Associate Professor

Department of Electrical Engineering Kusuma School of Biological Sciences Indian Institute of Technology Delhi Indian Institute of Technology Delhi

New Delhi 110016, India New Delhi 110016, India

Date:

New Delhi

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ii ACKNOWLEDGEMENTS

“What you GET by achieving your goals is not as important as what you BECOME by achieving your goals.”

Henry David Thoreau

It is my immense pleasure to express my gratitude to all those people who have helped me steer through my journey of PhD with ease. First and foremost, with great sense of respect I would like to express thanks to my PhD supervisor, Dr. Archana Chugh for her constant support throughout this academic voyage. Her nurture and reassurance in times of distress has always made me see a motherly figure in her. Her freedom and support with work has been a persistent inspiration to excel as a researcher and most importantly as a good human being. She provided me with all the laboratory facilities and materials needed for my research. I am deeply indebted to her for the trust and confidence on me. Apart from research and science, I have also learnt to be a good human from her, which, I strongly believe, made me an improved person.

I am extremely thankful to my co-supervisor Prof. Brejesh Lall and my project head Prof. Santanu Chaudhury for their valuable insights and meticulous guidance. Prof. Brejesh Lall has always been very supportive and encouraged me time to time. His valuable suggestions whether academic or life always encouraged me to outperform myself. I would like to convey my special thanks to the student research committee members, Prof. James Gomes, Prof. C.S Dey and Dr. Ritu Kulshreshtha (Department of Biochemical Engineering and Biotechnology, IIT Delhi) for their regular inputs which help me refine my work everytime. I express my sincere thanks to Dr. Jitender Thakur (National Institute of Plant Genome research, New Delhi) for his valuable suggestions that

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iii strengthened my work. I would also like to thank all the faculty members (Prof. Seyed E Hasnain, Prof. Jayaram, Prof. Aditya Mittal , Prof Tapan Kumar Chaudhuri, Dr. Vivekanandan Perumal, Dr. Manidipa Banerjee and Dr. Ashok Kumar Patel) of Kusuma School of Biological Sciences (KSBS) for their support and encouragement while providing various facilities and equipments necessary whenever needed, which helped me immensely to accomplish my work. I specially wish to thank Prof. Aditya Mittal for encouraging me since day one of my PhD as well as sharing his knowledge of statistics through Biometry as a course work that helped me immensely in my PhD result analysis. I am also thankful to Prof. Viswanathan Chinnusamy for providing rice plant samples and Dr. P.K Mandal for providing WinRhizo software.

My sincere thanks to the staff members of KSBS, Ms. Mini, Mr. Praveen, Mrs. Pushpalatha, Ms.Punita, Mr. Mukesh, Mr. Vijay Pal and our store purchase officer, Mr. Inderjeet, Mr. Rakesh Garg for assisting me in various administrative processes. I would like to specially thank Mr.

Kuldeep Sharma (Department of Textile Technology) for providing the facility of Scanning Electron Microscopy, which helped me in obtaining good quality images that enhanced the quality of my work. I thank Mr. Subodh, MALDI facility in-charge, Kusuma School of Biological Science for helping me in protein identification.

I would like to acknowledge Council of Scientific and Industrial Research (CSIR), New Delhi, India for providing me the Junior and Senior Research Fellowship during the tenure of my Ph.D.

I am grateful to Indian Institute of Technology Delhi for providing financial assistance to present my work at an international conference in Belgium, 2015, Department of Biotechnology (DBT) for an international conference in Prague, 2016 and Indian Council of Agricultural Research (ICAR) for international conference in Chennai, 2014. I would like to further acknowledge the kind help and support provided by my labmates, Dr. Aastha Jain, Dr. Nisha Ponnappan, Dr.

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iv Deepthi Poornima B, Harsha Rohira, Vivek Kumar, Pankhuri Narula Prothi, Anjali Priya, S.

Sujithra, Saurabh Saraswat, Saiguru Sekharan, Tushar, Mudit Mishra, Sheeba Zarin and Dr.

Anusha Aditya and the new kids of our lab, Aditi Arora and Prasanjeet Kaur, hope they cherish and spread happiness in lab.

I take this as opportunity to owe my sincere appreciation to my friend and labmate Dr. Nirupama Gangopadhyay, for her selfless company. I have been blessed with a joyful friend Dr. Priyam Narain with whom I had a lot of fun and she was also very supportive during my PhD. I am thankful to Swati Bhugra my friend, my batchmate and without whom the sample collection at IARI as a part of my research work wouldn’t have been possible. I would like to thank my friend Dr. Shikha Chawla for her support in my professional and personal life. I would like to thank Dravika Sharma, Rohit Chabbra, Mansi Magoo, Sreekanth S.H. and Neeti Chawla, my besties who have been a great support during all the phases of my life. I would also like to mention my special thanks to my loving and caring hostel mates Komal Tripathi, Neelam Verma and Abeer with whom my encounter was at the last stage of my PhD (August 2018) but I wish I had met them before.

Also, with lots of love and respect, I would like to thank my family for the unconditional support throughout my tenure. I am forever grateful to my parents, who believed and took pleasure in my work as much as I did. Their belief in their daughter will always be my greatest strength. They are the whole and sole inspiration behind each and every achievement of my life. No words are enough to express my love for my brother Lohit Rathee, who supported whatever I did without a doubt. I am also grateful to my lovely cousin Rachita and Ayush who held my hand throughout the tenure.

She (Rachita) is a friend and one of the greatest treasures God has bestowed me with.

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v Last, but not the least, I would like to thank Almighty for vesting wisdom to all wishes, standing by me at every step and for all of what I am today.

Anupama

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vi ABSTRACT

Rice is a global staple crop that requires ample amount of water for growth and development. Drought is one of the severe abiotic stresses that affects the productivity of rice.

Therefore, there is need to select drought tolerant rice varieties that can efficiently grow in drought-prone areas. The traits responsible for enhancing or adapting drought resistance in rice plants can be selected and studied to improve their growth under stress conditions. Remarkable progress has been made in the direction of trait selection criteria.

Comparative analysis of various traits has been conducted on drought tolerant and drought sensitive rice varieties. In our study, we have studied morphological, physiological, anatomical, biochemical and molecular variation of different rice varieties. Various root related biochemical and morphological traits such as root length, diameter, area, volume, Relative Water Content (RWC), xylem number, xylem area, proline content, Malondialdehyde (MDA) content, protein and gene expression have been investigated for a comparative study of the plant response to drought stress in different rice varieties. In addition, we implemented novel automated frameworks based on computer vision algorithms for high-throughput quantification of xylem and stomata. This task has been accomplished using Scanning Electron Microscopy images.

The results of transcriptome analysis revealed variation in expression of Med37, RSOsPR10, OsPIP2;5 and OsNIP2;1 genes among various rice varieties. The differential proteome analysis using 2-D followed by MALDI-based identification shows role of chitinases in drought tolerance. The learning generated both, through computational/digital image analysis as well as biochemical studies can contribute significantly towards the field of rice phenomics.

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vii Highlights

The salient findings of this work are as follows:

1. Reduction in xylem area as well as xylem number in drought tolerant varieties under drought stress.

2. Lower stomatal density in drought tolerant varieties.

3. Significant correlation of aquaporin genes with the root length.

4. Significant higher root length, area, diameter and volume of root in drought tolerant varieties.

5. Upregulation of OsPIP2;5 and downregulation of OsNIP1;2 was observed in roots of drought tolerant variety.

6. Proline content in the roots found to be significantly higher in tolerant variety.

7. Negative correlation between proline content and RWC.

8. MDA content in tolerant variety under drought is significantly lower.

9. Five proteins (including chitinase) identified that are induced during drought stress.

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"सूखे तनाव के तहत चावल की ककस्मों के ववभेदकों का आकलन: चावल फेनोममक्स के मलए एक अततरिक्त मूल्य"

साि

चावल एक वैश्ववक प्रधाान फसल है श्ससे ववकास के मलए पयााप्त मात्रा में पानी की आववयकता

होती है। सूखा एक गंभीि असैववक तनाव है सो चावल की उत्पादकता को प्रभाववत किता है।

इसमलए, सूखा सहहष्णु चावल ककस्मों का चयन किने की आववयकता है सो कुशलता से

सूखाग्रस्त क्षेत्रों में ववकमसत हो सकते हैं। चावल के पौधाों में सूखा प्रततिोधा को बढाने या

अपनाने के मलए श्सम्मेदाि लक्षणों को चुना सा सकता है औि तनाव की श्स्ितत में उनके

ववकास को बेहति बनाने के मलए अध्ययन ककया सा सकता है। ववशेषता चयन मानदंडों की

हदशा में उल्लेखनीय प्रगतत की गई है।

सूखा सहहष्णु औि सूखा संवेदनशील चावल ककस्मों पि ववमभन्न लक्षणों का तुलनात्मक वववलेषण ककया गया है। हमािे अध्ययन में, हमने ववमभन्न चावल ककस्मों के ातपात्मक, शािीरिक, सैव

िासायतनक औि आणववक मभन्नता का अध्ययन ककया है। एक मूल लंबाई, व्यास, क्षेत्र, आयतन, सापेक्ष सल सामग्री (RWC), साइलम संख्या, साइलम क्षेत्र, प्रोमलन सामग्री, मालोंडडयलडडहाइड (एमडीए) सामग्री, प्रोटीन औि सीन अमभव्यश्क्त सैसे ववमभन्न सड़ संबंधाी सैव िासायतनक औि

ातपात्मक लक्षणों की तुलनात्मक सांच की गई है। ववमभन्न चावल ककस्मों में सूखे के तनाव के मलए पौधाे की प्रततकिया का अध्ययन। इसके अलावा, हमने साइलम औि स्टोमेटा की उच्च- थ्रूपुट मात्रा का ठहिाव के मलए कंप्यूटि दृश्ष्ट एल्गोरिदम पि आधाारित उपन्यास स्वचामलत फ्रेमवका लागू ककया। यह काया स्कैतनंग इलेक्रॉन माइिोस्कोपी छववयों का उपयोग किके पूिा

ककया गया है।

रांसश्स्िप्टम वववलेषण के परिणामों से Med37, RSOsPR10, OsPIP2; 5 औि OsNIP2;1 की

अमभव्यश्क्त में मभन्नता का पता चला; चावल की ववमभन्न ककस्मों में 1 सीन। MALDI- आधाारित पहचान के बाद 2-डी के उपयोग से अंति प्रोहटओम वववलेषण ने सूखा सहहष्णुता में चचहटनासेस

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की भूममका को दशााया है। कम्प्यूटेशनल / डडश्सटल इमेस वववलेषण के साि-साि सैव

िासायतनक अध्ययनों के माध्यम से चावल के कफनोममक्स के क्षेत्र में महत्वपूणा योगदान दे

सकता है।

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viii

TABLE OF CONTENTS

Title Page No.

Certificate i

Acknowledgements ii

Abstract vi

Table of contents viii

List of Figures xiii

List of Tables xvii

Abbreviations xviii

Symbols xxv

Chapter 1, Introduction and Objectives 1-14

1.1 Rice is life 2

1.2 Environmental stress: a limiting factor 3

1.2.1 Abiotic stress: Drought stress 4

1.3 Phenomics: a holistic approach 5

1.3.1 Characteristics of phenomics 7

1.3.2 Various phenotyping platforms 10

1.4 Objectives 12

Chapter 2, Review of Literature 15-40

2.1 Rice- a leading cereal crop 16

2.2 Drought resistant mechanism 17

2.3 Water uptake in rice plants under drought stress 19 2.3.1 Role of xylem in plant water uptake 21

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ix

2.3.2 Stomata 22

2.4 ROS and antioxidants during stress 24

2.4.1 Production of ROS 24

2.4.2 Role of antioxidants 25

2.4.3 Role of proline on plant physiology during stress 25

2.4.4 Malondialdehyde (MDA) 28

2.5 Role of drought induced genes in plants 29

2.5.1 Aquaporins 30

2.5.2 Probable mediator of RNA polymerase II transcription subunit 37c

33

2.5.3 RSOsPR10 33

2.6 Role of drought stress related proteins in plant 34

2.6.1 Chitinase 34

2.6.2 ATP-dependent DNA helicase 2 subunit KU70 isoform X2

35

2.6.3 ELMO domain containing protein A isoform X2 36 2.7 Role of omics in rice yield improvement 37

2.7.1 Plant phenomics 37

2.7.2 Tools and software 37

2.7.3 Image processing tasks 39

Chapter 3, Materials and Methods 41-57

3.1 Materials 42

3.1.1 Plant growth condition and stress imposition 42

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x

3.2 Methods 44

3.2.1 Root morphology study 44

3.2.1.1 k-means clustering method 44

3.2.1.2 WinRhizo software 45

3.2.2 Anatomical analysis using Scanning Electron Microscopy

46

3.2.2.1 Xylem number and diameter 46

3.2.2.2 Stomata count 48

3.2.3 Physiological analysis through relative water content

48

3.2.4 Biochemical analysis 49

3.2.4.1 Proline content 49

3.2.4.2 Malondialdehyde (MDA) content 49 3.2.5 Quantification of gene expression 50

3.2.5.1 Total RNA extraction 50

3.2.5.2 cDNA 51

3.2.5.3 Real Time PCR (qPCR) 51

3.2.6 Protein identification 53

3.2.6.1 Protein extraction 53

3.2.6.2 SDS gel electrophoresis and IEF 54

3.2.6.3 Trypsin treatment 55

3.2.7 Statistical analysis 56

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xi Chapter 4, To evaluate morphological and anatomical response of

different rice varieties under drought stress.

57-71

4.1 Introduction 57

4.2 Results 60

4.3 Discussion 69

4.4 Conclusion 71

Chapter 5, To evaluate response of different rice varieties during drought stress at physiological and biochemical level.

72-83

5.1 Introduction 73

5.2 Results 74

5.3 Discussion 81

5.4 Conclusion 83

Chapter 6, To evaluate response of different rice varieties at molecular level under drought stress.

84-98

6.1 Introduction 85

6.2 Results 87

6.3 Discussion 95

6.4 Conclusion 98

Summary 99-101

References 102-125

Annexures 126-132

Annexure 1 List of chemicals 127

Annexure 2 Preparation of buffers 129

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xii

Annexure 3 MATLAB code 132

List of publications and conferences 134-136

Author’s resume 137-139

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xiii

LIST OF FIGURES

Figure No. Title Page No.

1.1 The parameters considered for study of morphological, anatomical, physiological, biochemical and molecular level response.

7

1.2 Characteristics of phenomics. 8

2.1 Rice production in Asia region. 17

2.2 Strategies adopted by crop plants under drought stress. 18 2.3 The cross-sectional structure of rice root depicted through SEM

images.

20

2.4 Stomata structure of monocot crops (wheat and rice) depicted through SEM images.

23

2.5 The imbalance between AOX and ROS during oxidative stress. 25

2.6 Role of proline during drought stress. 27

2.7 Chemical reaction depicting the MDA formation. 28

2.8 Effect of ROS production on cell membrane. 29

2.9 The Genes investigated in our study. 30

2.10 Various types of aquaporin present in the rice. 32

3.1 Schematic diagram of the study carried out. 42

3.2 Methodology for determination of root length where the root segmentation carried out through k-means algorithm has been analyzed by Image J tool.

45

3.3 Illustrative root image analysis through WinRhizo software displaying the measurement of root morphological traits.

46

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xiv

3.4 Automatic detection of xylem. 47

3.5 Stomata detection. 48

3.6 Methodology for gene expression studies. 53

3.7 Methodology for identifying differential protein from roots. 56 4.1 Root images of drought-tolerant and drought-sensitive rice

varieties captured as colored images through canon EOS60D camera with resolution of 18 megapixels and converted into binary images.

61

4.2 Change in root length of drought-tolerant and drought-sensitive rice varieties under drought stress condition with respect to control.

62

4.3 Measurements of the acquired root images of different rice varieties under control and stress condition were performed using WinRhizo.

63

4.4 Scanning Electron Micrograph (SEM) of rice root section depicting distribution of xylem vessels under control and drought stress condition where magnification was X 800.

65

4.5 Scanning Electron Micrograph (SEM) of rice root section depicting distribution of xylem vessels under control and drought stress condition where magnification was X 800.

66

4.6 Scanning Electron Micrograph (SEM) of rice leaf section showing distribution of stomata under control and drought stress condition where magnification was X 700.

68

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xv 5.1 Relative water content (RWC) of roots under drought stress

condition.

75

5.2 Relative water content (RWC) of root under drought stress condition.

76

5.3 Change in proline content under drought stress with respect to control in drought-tolerant and drought-sensitive rice varieties.

77

5.4 Free proline content (µmol g-1 FW) in the roots of drought-tolerant and drought sensitive varieties treated for five days under control and drought condition.

78

5.5 Change in proline content in root under drought stress with respect to control.

79

5.6 Change in MDA content under drought stress with respect to control in drought-tolerant and drought-sensitive varieties.

80

5.7 MDA content (µmol g-1 FW) in roots of drought-tolerant and drought-sensitive rice varieties under control and drought stress.

81

6.1 Relative gene expression of OsPIP2;5 in drought-tolerant (Sahabhagidhan) and drought-sensitive (IR64, and MTU1010) under drought stress condition for five days.

88

6.2 Relative gene expression of OsNIP2;1 in drought-tolerant (Sahabhagidhan) and drought-sensitive (IR64, and MTU1010) under drought stress condition for five days.

89

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xvi 6.3 Relative gene expression of Probable mediator of RNA polymerase

IItranscription subunit 37c (Os01g0840100) in PB6, Moroberakan and Way Rarem under drought stress condition for four days.

91

6.4 Relative gene expression of Major Allergen Dau c1 (Os12g0555000) in PB6, Moroberakan and Way Rarem under drought stress condition for four days.

92

6.5 Proteins extracted from rice roots were loaded at a concentration of a 10 µg/µl and were separated by SDS gel electrophoresis and detected by CBB staining.

93

6.6 Comparative protein profiling of rice roots during control (A, B, C) and stress (A’, B, C’) conditions.

95

7.1 Diagrammatic view of work carried out in the present study. 101

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xvii

LIST OF TABLES

Table No. Title Page No.

3.1 Summary of sample collection involved in our study. 44 3.2 Primer sequence used in qRT-PCR studies. 51 6.1 Relationship between OsPIP2;5, OsNIP2;1 and root length

under drought stress with respect to control condition in drought- tolerant (Sahabhagidhan) and drought-sensitive (IR64, and MTU1010).

90

6.2 List of proteins identified through MALDI-TOF MS analysis. 94

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xviii ABBREVIATIONS

a Red-green axis

ACN Acetonitrile

AOX Antioxidant

APPF Australian plant phenotyping facility

APX Ascorbate peroxidase

AQP Aquaporin

Ar/R Aromatic/arginine

ARF ADP-ribosylation factor

ASH Ascorbic acid

ATP Adenosine triphosphate

ATAF Arabidopsis transcription activation factor

b Blue layer

bgh Blumeria graminis f.sp. hordei

BCM Billion cubic meter

CAM Crassulacean acid metabolism

CAT Catalase

cDNA Complementary DNA

CDPK Calcium-dependent protein kinases

CHAPS 3-[(3-Cholamidopropyl)dimethylammonio]-1-propanesulfonate hydrate CLAHE Contrast limited adaptive histogram equalisation

CT Computed tomography

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xix

CUC Cup-shaped cotyledon

DEG Differentially expressed genes

DEPC Diethylpyrocarbonate

DNA Deoxy ribonucleic acid

dNTPs Deoxynucleotide-5’-triphosphates

DREB Dehydration responsive element-binding protein DST Drought and salt tolerance

DTT 1,4-Dithiothreitol

DW Dry weight

EDTA Ethylenediamine tetra acetic acid

ELMO Engulfment and motility

ELMOD ELMO domain containing protein EPF Epidermal pattering factor

ER Endoplasmic reticulum

ERF ETS2 repressor factor

Etbr Ethidium bromide

FA Formaldehyde

FP Forward primer

FRET Fluorescence resonance energy transfer

FW Fresh weight

GB Glycine betaine

GC Guard cell

GIP GlpF-like intrinsic protein

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xx GlcNAC b-1,4-N-acetylglucosamine

GOI Gene of interest

GMC Guard mother cell

GPX Glutathione peroxidase

GTPase Guanosine triphosphate HAT Histone acetyletransferase

HDM Histone demethylase

HMT Histone methyltransferase

HCl Hydrogen chloride

HIP Hybrid intrinsic protein

H2O Hydrogen Oxide

H2O2 Hydrogen peroxide

HTPP High-throughput phenotyping

OH^- Hydroxyl radicals

IARI Indian Agricultural Research Institute ICAR Indian Council of Agricultural Research

IEF Isoelectric focusing

IPG Immobilized pH gradient

IRRI International Rice Research Institute

JA Jasmonic acid

kb Kilo base pair

kDa KiloDalton

KCl Potassium chloride

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xxi

L Luminosity

LEA Late embryogenesis abundant

LPO Lipid peroxidation

LRR-RLK Leucine rich repeat receptor-like protein kinase MALDI Matrix-assisted laser desorption ionization MAPK Mitogen-activated protein kinase

MDA Malondialdehyde

Med Mediator

MH Million hectares

MIP Major intrinsic protein

MOPS 3-[N-morpholini] propanesulfonic acid

MQ Millique

MRI-PET Magnetic resonance imaging- Positron emission tomography

MYB Myeloblastosis

NAC NAM, ATAF1,2, CUC2

NADP⁺ Nicotinamide adenine dinucleotide phosphate

NAM No apical meristem

NFY Nuclear transcription factor Y NIP NOD26-like intrinsic protein

NIR Near-Infrared

NPA Asparagine-proline-alanine

NRT Non reverse transcriptase

O_"^# Superoxide anion radicals

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xxii

PB Pusa Basamati

PBS Phosphate buffer saline

PCD Programmed cell death

PCR Polymerase chain reaction

P5CS Pyrroline-5-carboxylate synthetase

PDH Pyruvate dehydrogenase

PGP Plant Genomics and Phenomics

PLD Phospholipase D

PIP Plasma membrane intrinsic protein

PP2A Protein phosphatase 2A

PR Pathogenesis-related

ProDH Proline dehydrogenase

PUFA Polyunsaturated fatty acid

PVPP Polyvinylpolypyrrolidone

QTL Quantitative trait loci RAB Ras-like protein in brain

RAN Ras-like nuclear

RAS Ras sarcoma

RGAP Rice Genome Annotation Project

RGB Red green blue

RHO Ras homologous

RNA Ribonucleic acid

RNase Ribonuuclease

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xxiii

RO Alkoxy radicals

ROP Rho-related GTPases of plants

RP Reverse primer

RSA Root system architecture

RT Room temperature

RT-PCR Real time-PCR

RWC Relative water content

SA Salicylic acid

SAA Systemic acquired acclimation

SD Standard deviation

SDS Sodium dodecyl sulphate

SE Standard error

SEM Scanning electron microscopy

SIP Small and basic intrinsic protein

SMC Subsidiary mother cell

SMP Soil matric potential

SOD Superoxide dismutase

TAE Tris-acetate-EDTA

TBA Thiobarbituric acid

TCA Trichloroacetic acid

TEMED N,N,N’,N’-Tetramethylethylenediamine TIP Tonoplast intrinsic protein

TFA Trifluoroacetic acid

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xxiv Tris Tris(hydroxymethyl)aminomethane

TW Turgid weight

XIP Uncategorized X intrinsic protein WEX Werner syndrome-like exonuclease

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xxv SYMBOLS

Ct Cycle threshold

˚C Degree Celsius

cm² Centimeter square

hr Hour/ Hours

g Gram

kDa Kilodalton

kPa Kilopascal

kX Thousand times

µg Microgram

µL Microliter

µm Micrometer

µmol Micromole

µM Micromolar

mL Milliliter

mg Milligram

MH Million hectares

mM Millimolar

min Minute/Minutes

N2 Nitrogen

ng Nanogram

nm Nanometer

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xxvi

nM Nanomolar

% Percent

r² Coefficient of determination

rpm Revolutions per minute

sec Second/seconds

v/v Volume by volume

w/v Weight by volume

wt Weight

# Significant difference from control at -30kPa

† Significant difference from control at -50kPa

¥ Significant difference from control at -80kPa

& Significant difference from control at >-80kPa

$ Significant difference from stress at -30kPa

€ Significant difference from stress at -50kPa

£ Significant difference from stress at -80kPa µ Significant difference from stress at >-80kPa