Characterization of drought tolerance in bread wheat ... · Characterization of drought tolerance...

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Characterization of drought tolerance in bread wheat (Triticum

aestivum L.) using genetic and transcriptomic tools

Md Sultan Mia

BSc in Agriculture

MS in Genetics and Plant Breeding

This thesis is presented for the degree of

Doctor of Philosophy

at The University of Western Australia

School of Agriculture and Environment

Faculty of Science

February 2019

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THESIS DECLARATION

I, Md Sultan Mia, certify that:

This thesis has been substantially accomplished during enrolment in the degree.

This thesis does not contain material which has been accepted for the award of any other degree or

diploma in my name, in any university or other tertiary institution.

No part of this work will, in the future, be used in a submission in my name, for any other degree or

diploma in any university or other tertiary institution without the prior approval of The University of

Western Australia and where applicable, any partner institution responsible for the joint-award of this

degree. This thesis does not contain any material previously published or written by another person,

except where due reference has been made in the text.

The work(s) are not in any way a violation or infringement of any copyright, trademark, patent, or

other rights whatsoever of any person.

Part of the works described in this thesis was funded by the Global Innovation Linkages program

(GIL53853), Australian Department of Industry, Innovation and Science.

This thesis contains published work and/or work prepared for publication, some of which has been co-

authored.

Signature:

Date: 15 February 2019

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ABSTRACT

Wheat production, especially in rain-fed farming like in Australia, often experiences drought, which

causes severe yield loss. The experimental procedures of this research project mainly focused on

understanding the mechanism of drought tolerance in wheat at two critical growth stages: early seedling

and post-anthesis stages using genetic, transcriptomic and bioinformatic tools. Initially, early stage root

trait variability under water deficit was investigated in a set of germplasm. A wide range of variation

were observed among the genotypes for the studied root traits under both well-watered and water-

stressed conditions. Based on stress tolerance index, Abura, Erechim, Hybride 38, Kenya Bongo and

Funello were ranked as the top five most tolerant genotypes whereas Aus 12671, Changli, Philippines

7, W96 and GBA Sapphire as the most susceptible genotypes. Expression patterns of drought-

responsive genes, such as TaSnRK2.4, TaMYB3R and TaMYBsdu1 in the contrasting genotypes were

significantly different, indicating the suitability of the screening technique used to discriminate the

genotypes.

Once the contrasting genotypes were identified, tolerant genotypes Abura and susceptible genotype

AUS12671 were subjected to RNA-sequencing to compare the transcriptome aiming to understand

their response mechanism under stress. Under stress, differentially expressed genes (DEGs) were

significantly more abundant in the susceptible genotype AUS12671 than the tolerant genotype Abura.

Gene ontology enrichment highlighted upregulation of “metabolic process”, “cellular process”,

“response to stimuli”, “cell”, “membrane”, “catalytic activity” and “binding” related genes in the

susceptible genotype. In contrast, these terms were more abundant for downregulated genes in the

tolerant genotype. KEGG pathway analysis also confirmed the energy-conserving strategy of the

tolerant genotype as evidenced from the downregulation of the key pathways. Also, transcription factor

(TF) families like MYB, and NAC contributed tolerance of Abura to water stress by regulating the

expression of stress-related genes.

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While previous two experiments investigated early growth stage drought, comparative performance

and nature of gene action of a diallel population were analyzed under post-anthesis water stress.

Parental genotypes Babax (B) and Westonia (W) performed better compared to C306 (C) and Dharwar

Dry (D) with respect to the relative reduction in grain yield and related traits under stressed condition.

Direct cross B×D and reciprocal cross W×C produced genotypes that were more tolerant to water

stress, while cross between C306 and Dharwar Dry, either direct or reciprocal, produced more sensitive

genotypes. Combining ability analysis revealed that both additive and non-additive gene action were

involved in governing the inheritance of the studied traits, with the predominance of non-additive gene

action for most of the traits. The results of the study suggested that specific cross combinations with

high SCA involving better-performing parents with high GCA may generate hybrids as well as

segregating populations suitable for further breeding.

Finally, C306 × Dharwar Dry hybrids, developed in the last experiment, were used for fast track

development and characterization of near-isogenic lines (NILs) targeting wheat 4BS QTL hotspot in

C306 background for post-anthesis drought tolerance. Mean grain yield per plant of the +NILs and -

NILs showed significant differences ranging from 9.61 to 10.81 g and 6.30 to 7.56 g, respectively,

under water-stressed condition, whereas a similar grain yield was recorded between the +NILs and -

NILs under well-watered condition. Confirmed isolines of +NIL and -NIL pairs showed similar

physiological traits at the beginning of the stress. However, as the stress period progressed, the +NILs

showed significantly higher SPAD (12%), photosynthesis (66%), stomatal conductance (75%), and Tr

(97%) than the -NILs. Quantitative RT-PCR analysis targeting the MYB transcription factor gene

TaMYB82, within this genomic region, retrieved from the wheat reference genome TGACv1, also

revealed differential expression in +NILs and –NILs under stress.

In summary, this research identified contrasting wheat genotypes differing in root trait variability for

early-stage water stress. Comparative transcriptomic analysis of two contrasting genotypes revealed

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their molecular mechanism of adaptation under early-stage water stress. The predominance of non-

additive gene action was found for yield and related traits in a full diallel analysis. The confirmed NIL

pairs developed in this study would be valuable resources for fine-mapping the targeted QTL, and also

for cloning and functional characterization of the gene(s) responsible for drought tolerance in wheat.

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TABLE OF CONTENTS

THESIS DECLARATION .................................................................................................................................... i

ABSTRACT ......................................................................................................................................................... ii

TABLE OF CONTENTS ...................................................................................................................................... v

LIST OF FIGURES ............................................................................................................................................. ix

LIST OF TABLES .............................................................................................................................................. xii

LIST OF ABBREVIATIONS ............................................................................................................................ xiv

ACKNOWLEDGEMENTS ............................................................................................................................... xvi

AUTHORSHIP DECLARATION: CO-AUTHORED PUBLICATIONS ....................................................... xvii

CHAPTER 1 ........................................................................................................................................................ 1

1. General Introduction ........................................................................................................................................ 2

1.1 Background of the research .................................................................................................................. 2

1.2 Objective of the research ...................................................................................................................... 4

1.3 Outline of the thesis structure ............................................................................................................... 5

CHAPTER 2 ........................................................................................................................................................ 8

2 Literature Review ............................................................................................................................................ 9

2.1 Introduction ........................................................................................................................................... 9

2.2 Wheat: taxonomy, origin, and distribution ........................................................................................... 9

2.3 Wheat: morphology and growth stages ............................................................................................... 10

2.4 Economic importance of wheat: from global to local perspective ...................................................... 12

2.5 Crops adaptation to drought stress ...................................................................................................... 13

2.6 Effects of drought in different growth stages ..................................................................................... 14

2.7 Breeding strategies for drought tolerance ........................................................................................... 16

2.8 Traditional breeding approaches for improving drought tolerance in wheat ...................................... 17

2.9 Molecular breeding approaches for improving drought tolerance in wheat ....................................... 18

2.9.1 Molecular markers ...................................................................................................................... 19

2.9.2 Quantitative trait loci (QTL) related to drought tolerance .......................................................... 20

2.9.3 Marker-assisted selection ............................................................................................................ 20

2.9.4 Drought-responsive proteins, genes and transcription factors .................................................... 21

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2.10 Transcriptomics for drought tolerance ................................................................................................ 24

CHAPTER 3 ...................................................................................................................................................... 26

3 Characterizing root trait variability for early-stage water stress tolerance in wheat: from morphology to

gene expression analysis ................................................................................................................................ 27

3.1 Abstract ............................................................................................................................................... 28

3.2 Introduction ......................................................................................................................................... 29

3.3 Materials and Methods ........................................................................................................................ 31

3.3.1 Germplasm used ......................................................................................................................... 31

3.3.2 Experimental set-up and treatment application ........................................................................... 33

3.3.3 Scanning of root samples ............................................................................................................ 34

3.3.4 Stress tolerance index (STI) ........................................................................................................ 34

3.3.5 Drought responsive gene expression........................................................................................... 35

3.3.6 Statistical analysis ....................................................................................................................... 37

3.4 Results ................................................................................................................................................. 37

3.4.1 Root traits variability and the effects of stress application ......................................................... 37

3.4.2 Correlation among the root traits ................................................................................................ 39

3.4.3 Principal component analysis (PCA) of the root traits................................................................ 41

3.4.4 Ranking of the genotypes to stress application ........................................................................... 44

3.4.5 Drought responsive gene expression between tolerant and susceptible genotypes .................... 46

3.5 Discussion ........................................................................................................................................... 48

3.6 Conclusions ......................................................................................................................................... 51

3.7 Competing interests ............................................................................................................................ 51

3.8 Funding ............................................................................................................................................... 51

3.9 Authors’ contributions ........................................................................................................................ 51

3.10 Acknowledgements ............................................................................................................................. 51

3.11 Supplementary file (Table S3.1) ......................................................................................................... 52

CHAPTER 4 ...................................................................................................................................................... 53

4 Root transcriptome profiling of contrasting wheat genotypes reveals mechanism of tolerance to water

deficit ............................................................................................................................................................. 54

4.1 Abstract ............................................................................................................................................... 55

4.2 Introduction ......................................................................................................................................... 56

4.3 Materials and methods ........................................................................................................................ 57

4.3.1 Plant materials, growth condition, treatment application and tissue sampling ........................... 57

4.3.2 Total RNA extraction, library preparation and sequencing ........................................................ 58

4.3.3 Differential gene expression analysis ......................................................................................... 59

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4.3.4 Functional annotations, GO enrichment and Pathway analysis .................................................. 59

4.4 Results ................................................................................................................................................. 59

4.4.1 Abura (ABU) and AUS12671 (AUS) differ in morphological and physiological traits under

deficit .................................................................................................................................................... 59

4.4.2 Transcriptome quality and mapping statistics ............................................................................. 62

4.4.3 Differential gene expression in response to water deficit ........................................................... 65

4.4.4 Gene ontology analysis of DEGs ................................................................................................ 68

4.4.5 KEGG pathway enrichment analysis .......................................................................................... 70

4.4.6 Transcription factor (TF) encoding genes ................................................................................... 71

4.5 Discussion ........................................................................................................................................... 74

4.6 Acknowledgments .............................................................................................................................. 77

4.7 Author Contributions .......................................................................................................................... 77

4.8 Competing Interests: ........................................................................................................................... 77

4.9 Supplemetary file (Table S4.1) ........................................................................................................... 78

4.10 Supplemetary file (Table S4.2) ........................................................................................................... 79

CHAPTER 5 ...................................................................................................................................................... 81

5 Response of wheat to post-anthesis water stress, and the nature of gene action as revealed by combining

ability analysis ............................................................................................................................................... 82

5.1 Abstract ............................................................................................................................................... 83

5.2 Introduction ......................................................................................................................................... 84

5.3 Material and methods .......................................................................................................................... 85

5.3.1 Materials ..................................................................................................................................... 85

5.3.2 Methods ...................................................................................................................................... 86

5.3.3 Data collection ............................................................................................................................ 88

5.3.4 Statistical analysis ....................................................................................................................... 88

5.4 Results ................................................................................................................................................. 89

5.4.1 Phenotypic differences under imposed water treatments were significant but variable in the

wheat diallel crosses ................................................................................................................................... 89

5.4.2 Yield attributes were associated with biomass and chlorophyll content ..................................... 93

5.4.3 Combining ability analysis revealed complex gene action, and heritability of the studied traits

in two water regimes ................................................................................................................................... 94

5.5 Discussion ......................................................................................................................................... 102

5.6 Acknowledgments ............................................................................................................................ 105

CHAPTER 6 .................................................................................................................................................... 106

6 Multiple near-isogenic lines targeting a QTL hotspot of drought tolerance showed contrasting performance

under post-anthesis water stress ................................................................................................................... 107

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6.1 Abstract ............................................................................................................................................. 108

6.2 Introduction ....................................................................................................................................... 109

6.3 Materials and Methods ...................................................................................................................... 110

6.3.1 Plant materials ........................................................................................................................... 110

6.3.2 Heterogeneous inbreed families (HIF) method ......................................................................... 112

6.3.3 Embryo culture based fast generation technique ...................................................................... 112

6.3.4 Genotyping of the segregating populations and NILs .............................................................. 113

6.3.5 Phenotyping of the NILs (F8) .................................................................................................... 114

6.3.6 Physiological traits .................................................................................................................... 115

6.3.7 RNA extraction and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR)

Analysis .................................................................................................................................................. 116

6.3.8 Statistical analysis ..................................................................................................................... 117

6.4 Results ............................................................................................................................................... 117

6.4.1 Development of the near-isogenic lines .................................................................................... 117

6.4.2 Putative isolines varies for grain yield and grain weight under post-anthesis water stress ...... 117

6.4.3 Other phenological traits of the confirmed NILs ...................................................................... 119

6.4.4 Physiological traits of the confirmed NILs ............................................................................... 121

6.4.5 Differential expression of TaMYB82 gene between the isolines .............................................. 122

6.5 Discussion ......................................................................................................................................... 123

6.6 Authors’ Contributions ..................................................................................................................... 126

6.7 Acknowledgments ............................................................................................................................ 126

CHAPTER 7 .................................................................................................................................................... 127

7 General discussion ....................................................................................................................................... 128

7.1 Introduction ....................................................................................................................................... 128

7.2 Key research findings ....................................................................................................................... 128

7.2.1 Tolerance to early seedling stage water stress .......................................................................... 129

7.2.2 Tolerance to post-anthesis water stress ..................................................................................... 131

7.3 Future directions and implications .................................................................................................... 133

REFERENCES ................................................................................................................................................. 135

APPENDICES .................................................................................................................................................. 174

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LIST OF FIGURES

Figure 1.1 Schematic representation of the experimental approaches followed in this research. .......... 7

Figure 2.1 Schematic diagram of wheat growth stages from sowing to maturity adapted from Zadoks

et al. (1974) and www. wheat-training.com ....................................................................... 11

Figure 2.2 Schematic representation of drought stress signal perception and gene expression with

implication in plant breeding .............................................................................................. 23

Figure 3.1 Principal component biplot showing genotypic grouping under well-watered (A) and water-

stressed (B) conditions ........................................................................................................ 43

Figure 3.2 Expression patterns of drought-responsive genes in roots of contrasting wheat genotypes in

response to the PEG-simulated water stress. (S= susceptible genotypes, T=tolerant

genotypes, WW= well-watered, WS= water-stressed). The 2-ΔΔCT method was used to

measure the relative expression level of each gene. Expression of each gene in susceptible

genotypes under WW condition was considered as the standard. Bars represented mean with

standard deviation. * denotes significant at p≤0.05 ............................................................ 47

Figure 4.1 Effect of water stress on root morphology. A) Root length of the contrasting tolerant

genotype ABU and susceptible genotype AUS under control (left, blue) and stressed (right,

orange) conditions; B) Root biomass of the contrasting genotypes under control (WW) and

stressed conditions (DD). Values in white boxes show per cent reduction due to stress. .. 60

Figure 4.2 Effect of water stress on gas exchange parameters. A) Net photosynthetic rate (A); B)

Stomatal conductance (Gs) and C) transpiration rate (Tr) of the contrasting genotypes ABU

(tolerant) and AUS (susceptible) under control and stressed conditions. ........................... 61

Figure 4.3 Number of genes with different FPKM (Fragments Per Kilobase of transcript Per Million)

value in the 24 RNA-seq samples ....................................................................................... 64

Figure 4.4 Pearson’s correlation heatmap for all the 24 samples showing consistency among the

biological replicates. The colour intensity represents degree of correlation. ..................... 65

Figure 4.5 Differential gene expressions (control vs stressed) in the two genotypes, Abura (ABU) and

AUS12671 (AUS) in two time points of stress period (6 & 48 hours). A) Volcano plots of

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gene expression in the tolerant genotype at 6h (left) and 48 h (right), showing that DEGs

were greater in number at 48h; B) Scatter plots of gene expression in the susceptible

genotype at 6h (left) and 48 h (right), showing that DEGs were greater in number at 48h;

and C) Venn diagram showing the number of upregulated (left) and downregulated genes

(right) in different combination of treatment-time points. .................................................. 66

Figure 4.6 Number of differentially expressed genes (DEGs) enriched with different Gene Ontology

(GO) terms in the tolerant genotype Abura (ABU, in blue ) and susceptible genotype

Aus12671 (AUS, in orange). The GO terms were grouped into three categories: i) biological

process, ii) cellular component, and iii) molecular function. ............................................. 69

Figure 4.7 Number of differentially expressed genes (DEGs) enriched with different KEGG pathways

in the tolerant genotype Abura (ABU, in blue ) and susceptible genotype Aus12671 (AUS,

in orange). Pathways were grouped into six categories i) cellular process, ii) environmental

information processing, iii) genetic information processing, iv) disease-related processes,

v) metabolism, and vi) organismal systems ........................................................................ 70

Figure 4.8 Distribution of DEGs in different Transcript factors (TF) families considering the whole

transcriptome. ..................................................................................................................... 72

Figure 4.9 Transcription factors encoding DEGs (genotype specific and consistently expressed) and

their distributions. i) Venn diagram showing the number of transcription factors (TF)

encoding DEGs in different treatment–time points combination; ii) Distribution of the

consistently expressed and tolerant genotype specific DEGs encoding different TFs; iii)

Distribution of the consistently expressed and susceptible genotype specific DEGs encoding

different TFs. ...................................................................................................................... 73

Figure 5.1 Soil water content (% field capacity) of the pots in well-watered (WW) and water stress

(WS) conditions from anthesis (Day 0) to 10 DAA (days after anthesis). In WS treatment,

watering was withheld for 7 days from anthesis followed by rewatering on 7 DAA close to

80% FC until maturity, while WW treatment was maintained at around 80% FC until

maturity. Each data point represents mean ± standard error (n=48). .................................. 87

Figure 5.2 Effect of post-anthesis water deficit on a) fertile tiller number b) thousand grain weight and

c) grain yield in 4×4 full diallel crosses including parents, direct crosses and reciprocal

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crosses. Paired bars represent means ± standard errors (n=3). Least significant difference

lsd) values across genotype (G), treatment (T), and interaction (G×T) effect are at 95% level

of significance. B= Babax, C=C306, D=Dharwar Dry, W=Westonia................................ 90

Figure 6.1: Schematic representation of the development of near-isogenic lines following

heterogeneous inbreed family (HIF) method coupled with embryo culture based fast

generation technique ......................................................................................................... 111

Figure 6.2 A) QTL hotspot in wheat chromosome 4BS and the marker (circled in green) used for

selection, adapted from Kadam et al. (2012), B) selection of different progeny types using

molecular marker, C) culture of young embryos on petri-plates with sterile media, D) young

seedlings from embryo culture growing in plant growth chamber, E) NIL pair at anthesis in

glasshouse, F) NIL pair at physiological maturity, G) hundred seeds of a NIL pair ........ 113

Figure 6.3 Different phenological traits of the confirmed NIL pairs. qDSI.4B1_2, qDSI.4B1_3,

qDSI.4B1_6, and qDSI.4B1_8 are denoted as NIL 2, NIL 3, NIL 6 and NIL 8 respectively.

a= NIL with C306 background (+NIL), b= NIL with Dharwar Dry background (-NIL), * =

significant at P≤0.05, ** = significant at P≤0.01, *** = significant at P≤0.001 .............. 120

Figure 6.4 Chlorophyll content, photosynthesis (A; µmolm-2 s−1), stomatal conductance (Gs; mmolm-

2 s−1) and transpiration rate (Tr; µmolm-2 s−1) of isolines of the confirmed NILs under

post-anthesis water stress. Data points are mean ± SD (n=12). a = NIL with C306

background (+NIL), b = NIL with Dharwar Dry background (-NIL) .............................. 122

Figure 6.5 Relative expression of TaMYB82 gene in +NIL and –NIL during stress period. Mean fold

change in gene expression (2-ΔΔCT) of – NIL was set to 1 at each time-point to determine

relative expression in +NIL. Each value represents mean ± SD of three biological replicates

with three technical replicates. ns = non-significant at P≤0.01; * = significant at P≤0.01

.......................................................................................................................................... 123

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LIST OF TABLES

Table 3.1 Wheat genotypes used for characterizing early stage water deficit tolerance ...................... 31

Table 3.2 Primers of the selected drought-responsive genes and their references ............................... 36

Table 3.3 Descriptive statistics, heritability and two-way ANOVA of the root traits in 50 wheat

genotypes (SD= standard deviation, CV=coefficient of variation, T=treatment, G=Genotype,

T*G=treatment genotype interaction. .................................................................................. 38

Table 3.4 Pearson’s correlation matrix for the root traits in 50 genotypes under well-watered (above

diagonal) and water-stressed (below diagonal) conditions .................................................. 40

Table 3.5 Variable loading scores of the root traits and the proportion of variation in each principal

component under well-watered (WW) and water-stressed (WS) conditions ....................... 42

Table 3.6 Means of different root traits of 5 best and 5 worst performing wheat genotypes under well-

watered (WW) and water-stressed (WS) conditions. Full table containing all the 50

genotypes are in the supplementary Table S1 ...................................................................... 45

Table 4.1 Summary of the transcriptome sequencing and quality ....................................................... 62

Table 4.2 Mapping summary of the transcriptome ............................................................................... 63

Table 4.3 Differentially expressed gene counts in contrasting genotypes at different time points of

stress ..................................................................................................................................... 67

Table 5.1 Combined analysis of variance for various traits of parents and their 4×4 full diallel crosses

under well-watered and water stress treatment .................................................................... 89

Table 5.2 Comparison of relative tolerance of the diallel crosses on the basis of rank summation index

.............................................................................................................................................. 92

Table 5.3 Association of the investigated traits in well-watered (above diagonal) and water stress

(below diagonal) condition. (Numbers in bold are significant at P≤0.05) .......................... 93

Table 5.4 Analysis of Variance for combining ability ......................................................................... 95

Table 5.5 Estimate of general combining ability effects of parents for different traits ........................ 97

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Table 5.6 Estimate of specific combining ability effects of F1 crosses ............................................... 98

Table 5.7 Estimate of reciprocal combining ability effects of reciprocal crosses .............................. 100

Table 5.8 Estimates of various genetic parameters and heritability of different traits under two water

treatments ........................................................................................................................... 101

Table 6.1 Grain yield (g/plant) and 100 grain weight (g) of the developed NIL pairs. The significant

difference was calculated using student’s t-test ................................................................. 118

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LIST OF ABBREVIATIONS

A Assimilation rate

ABU Abura

ANOVA Analysis of variance

AUS Australia

cDNA Complementary deoxy-ribonucleic acid

cM Centi Morgan

CV Coefficient of variation

DAA Days after anthesis

DAS Days after stress

DEG Differentially expressed genes

DRG Drought responsive genes

F1 First filial generation

F2 Second filial generation

FC Field capacity

FGCS Fast generation cycling system

FKPM Fragments Per Kilobase per Million

GCA General combining ability

GO Gene ontology

GS Stomatal conductivity

h2 Heritability

HIF Heterogeneous inbreed family

KEGG Kyoto Encyclopedia of Genes and Genomes

LSD Least significant difference

mRNA messenger ribonucleic acid

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NIL Near-isogenic line

ORF Open reading frame

PCA Principal component analysis

PCR Polymerase chain reaction

PE Paired-end

PEG Polyethylene glycol

qRT-PCR Quantitative reverse transcriptase polymerase chain reaction

QTL Quantitative trait loci

r Correlation coefficient

RCA Reciprocal combining ability

RIL Recombinant inbred lines

SCA Specific combining ability

SD Standard deviation

SSR Simple sequence repeat

SNP Single nucleotide polymorphism

STI Stress tolerance index

TF Transcription factors

TILLING Targeting induced local lesion in genomes

Tr Transpiration rate

WS Water stressed treatment

WW Well-watered treatment

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ACKNOWLEDGEMENTS

It is unbelievable to me that I am here, writing this acknowledgement, just a few days before submitting

my thesis. It has been a rewarding and challenging journey so far. I most certainly, could not have done

any of it without the help of some special people along the way. First of all, I would like to thank the

authority of Endeavour postgraduate scholarship of the Australian Government for sponsoring my PhD

study at UWA. I would like to express my gratitude to my enthusiastic supervisor Prof. Dr. Guijun Yan

for giving me the opportunity to explore this exciting research over the last four years, for supporting

me during my critical moments and for accepting my strengths and weaknesses. Moreover, I am

thankful to him for his valuable guidance and constant encouragement I received throughout the last

four years. Secondly, I would like to extend my sincere gratitude to my co-supervisor Dr Hui Liu, in

particular for her patience, intellect, generosity guidance and endless encouragement.

Furthermore, I am thankful to all my friends, so many to name, colleagues and support staff of the

school for their continuous assistance and for creating such a pleasant atmosphere in the lab and the

office.

Finally, I would like to thank my parents. Without all of their unconditional support and belief from

my very childhood, I would not be able to achieve this today. I deeply miss my father, who is not with

me to share this achievement. I have nothing but admiration for you both, and I am so very proud to

be your son. I must also thank my elder sisters, who provided great support during my challenging

periods. I am also very thankful to all of my family members for the boundless generosity and love

they gave throughout my entire life.

Above all, my utmost gratitude is toward the Almighty for the endless bounty and privilege that enable

me to commence and complete this exciting endeavour successfully.

xvii

AUTHORSHIP DECLARATION: CO-AUTHORED PUBLICATIONS

This thesis contains work that has been published and/or prepared for publication.

Details of the work:

Mia Md Sultan, Liu Hui, Yan Guijun. 2019. Characterizing root trait variability for early-stage water

stress tolerance in wheat: from morphology to gene expression analysis. To be submitted to Frontiers in

Plant Science

Location in thesis:

Chapter 3

Student contribution to work:

90%

Co-author signatures and dates: (The coordinator supervisor signed on behalf of all the co-authors)


Mia Md Sultan, Liu Hui, Wang Xingyi, Zhang Chi, Yan Guijun. 2019. Root transcriptome profiling

of contrasting wheat genotypes reveals mechanism of tolerance to water deficit. Submitted to

Scientific Reports Journal on 6 February 2019

Location in thesis:

Chapter 4


85%


xviii


Mia Md Sultan, Liu Hui, Wang Xingyi, Lu Zhanyuan, Yan Guijun. 2017. Response of wheat to post-

anthesis water stress, and the nature of gene action as revealed by combining ability analysis. Crop and

Pasture Science 68, 534-543. https://doi.org/10.1071/CP17112

Date of publishing: 26 July 2017

Location in thesis:

Chapter 5


85%



Mia Md Sultan, Liu Hui, Wang Xingyi, Yan Guijun. 2019. Multiple near-isogenic lines targeting a

QTL hotspot of drought tolerance showed contrasting performance under post-anthesis water

stress. Frontiers in Plant Science 10:271. doi: 10.3389/fpls.2019.00271

Date of publishing: 08 March 2019

Location in thesis:

Chapter 6


85%

dinator supervisor signed on behalf of all the co-authors)

https://doi.org/10.1071/CP17112

xix

Student signature

Date: 14 February 2019

I, Guijun Yan, certify that the student statements regarding their contribution to each of the works listed

above are correct

Coordinating supervisor signature:

Date:

1

CHAPTER 1

2

1. General Introduction

1.1 Background of the research

Wheat (Triticum aestivum L.) is one of the major cereal crops of the world in terms of production and

area coverage. It is the second widely cultivated grain crop in Bangladesh, next to rice, but in Australia,

it ranks the first in production (FAO, 2016). Wheat is considered as the most important grain crop in

Australia for both domestic consumption and export (Balouchi, 2010). Australia is one of the top-

ranked wheat exporters in the global market. From 2014 to 2017, Australia exported an average of

about 18 million tons of bulk wheat with a net worth of AUD 5.5 billion. Although Australia

contributes only a small portion (3.2%) of the world wheat production, it is responsible for 13% of

global wheat trade, as more than quarter of its locally produced wheat is exported (AEGIC, 2018).

Within Australia, Western Australia is the largest producing and exporting state, which contributed to

almost half of the total wheat produced in Australia (ABARES, 2017).

Growing wheat under reduced soil moisture or drought stress has always been a challenge to the

growers of Bangladesh, Australia and other parts of the world. It is projected that wheat production

throughout the world is under serious threat due to recent changes in climate. These climatic changes

will have a significant impact on temperature and precipitation pattern, resulting in increased incidence

and severity of drought (Barros et al., 2014). Drought is by far the most detrimental abiotic stress

limiting the yield potential of wheat globally, especially in rain-fed farming like here in Australia. For

example, wheat production is adversely affected by drought in 50% of the production area in the

developing countries (Ashraf, 2010). To combat this global climate change effect, breeding stress

resilient crop is a must to ensure successful crop production in adverse climatic conditions (Curtis and

Halford, 2014).

3

Tolerance to drought is defined as the ability of a plant to overcome the effect of water shortage and to

live, grow and reproduce satisfactorily (Turner, 1979). Drought tolerance is a complex quantitative

character controlled by several genes, and environment also plays a pivotal role in the expression of

those genes (Fleury et al., 2010b; Turner et al., 2014). This is particularly so in wheat because of the

size and complexity of its genome. Besides, high genotype × environment interaction and variation in

environmental conditions have hindered breeders’ efforts to select for drought tolerance. Besides, the

interaction of different morphological, physiological and biochemical traits makes studying drought

tolerance even more complicated (Mitra, 2001 ).

Conventional plant breeding method uses the existing variations in germplasm by crossing and then

selecting individuals with desirable traits. Significant improvement in genetic gain for yield potential

has been achieved in wheat over the last five decades through traditional plant breeding methodologies

(William et al., 2007). Although this approach has been extraordinarily successful for traits like

reduced plant height and vernalization requirement, little success was reported in case of drought

tolerance related traits. Compared to the traditional approaches, molecular genetics and omics

approaches offer unprecedented opportunities for dissecting complex quantitative traits and allow for

identifying individual genetic determinants of those traits (Varshney et al., 2018). Furthermore, high-

throughput phenotyping platforms and speed breeding via fast generation cycling system have all

added new opportunities for deciphering and manipulating the genetic basis of drought tolerance

(Zheng et al., 2013; Ayalew et al., 2015; Watson et al., 2018a).

Phenotyping of core germplasm under stress using high-throughput phenotyping platforms will help

to identify extreme genotypes precisely. Sequencing and bioinformatics analysis of those extremes

may lead to the identification of differentially expressed genes and their relevant pathways for stress

tolerance. Previous molecular marker and trait association studies in wheat lead to identification of

many drought-related quantitative trait loci (QTLs), most of which have been identified through yield

4

and yield component measurements under water-limited conditions (Quarrie et al., 2006; McIntyre et

al., 2010; Pinto et al., 2010a; Gupta et al., 2017). Genetic analysis of the population resulted from the

crossing of genotypes reported to have major drought tolerance related QTLs will provide valuable

information on gene action for drought-related traits. Developing near-isogenic lines (NILs)

harbouring major yield-related QTL and comparing their morphological, physiological traits and gene

expression profiles under stressed and stress-free environments will reveal the underlying mechanism

of tolerance in wheat for later stage drought resistance.

In wheat, Early establishment period and post-anthesis period are the two most critical growth stages

under reduced soil moisture condition (Blum et al., 1980; Fukai and Cooper, 1995). Therefore, the

experimental procedures of this research project mainly focus on understanding the mechanism of

tolerance in the wheat genotypes at different growth stages using genetic, transcriptomic and

bioinformatics tools.

1.2 Objective of the research

The overall objectives of this research project were to investigate drought tolerance in bread wheat,

focusing mainly on two stages of life cycle: early seedling stage and post-anthesis. To achieve the

major objectives mentioned above, the following specific objectives were targeted:

(1) Identify root trait variability by phenotyping a collection of wheat germplasm for early seedling

stage water stress tolerance

(2) Examine the whole transcriptome of roots of wheat seedlings contrasting for early seedling stage

water stress tolerance

(3) Investigate the effect of post-anthesis water stress on yield and yield-related traits of a full-diallel

cross and Identify the nature of gene action for those traits

5

(4) Generate near-isogenic line targeting yield-related major QTL to simplify the genetic study of this

complex trait

1.3 Outline of the thesis structure

This thesis is presented as a series of scientific papers following the regulation of the Graduate

Research School (GRS) of The University of Western Australia. The whole thesis contains seven

chapters. The first two chapters comprise a general introduction and a literature review. The findings

of the four experiments investigating the targeted objectives are presented in Chapter three to six,

respectively. Each of those research chapters was presented in the form of research papers addressing

the relevant chapter topics and contained an independent introduction, review of the relevant literature,

experimental procedure and method, key results, discussion of the major findings, and a summary.

Therefore, these four research chapters can be read as a part of the whole thesis or as separate original

research articles. The final chapter includes an overall summary, general discussion, and concluding

remarks.

Chapter 1 named as “General Introduction” outlined the background information and rationale of this

research. This chapter put forwarded some research questions and targeted some research objectives

aimed at answering those questions. Finally, it outlined the overall structure of the thesis.

Chapter 2 presented a brief overview of the literature relevant to this study.

Chapter 3 investigated root trait variability of a collection of wheat germplasm to test their tolerance

for early-stage water deficit and to identify the contrasting genotypes. It also includes outcomes from

a follow-up experiment where the expression patterns of five selected drought-responsive genes were

investigated in the contrasting genotypes.

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Chapter 4 described the next-generation whole transcriptome resequencing of two wheat genotypes

contrasting in their tolerance to early-stage PEG-simulated water stress. Genes expressed differentially

between the two genotypes under stress were identified, and their relevant pathways were also

investigated to explore their mechanism of tolerance.

Chapter 5 focused on the post-anthesis drought in bread wheat. A full-diallel population were produced

using four parents having a varying degree of stress tolerance and their responses to post-anthesis water

stress were measured in terms of grain yield and other yield-related traits. In this study, the nature of

gene action was investigated using the combining ability analysis.

Chapter 6 reported detailed experimental procedure of creating near-isogenic lines targeting major

yield-related QTL under drought where an embryo culture based fast generation cycle system (FGCS)

coupled with molecular marker-based selection was practised during segregating generations. These

putative NILs were evaluated for several morphological, physiological traits under post-anthesis water

stress to identify true NILs. Gene expression analysis was performed to further validate the identified

NILs.

Chapter 7 contained a general discussion of the key findings of this research and their significance. It

also discussed the challenges faced during experimentation, along with their solutions. Finally, it ended

up with the scope of future investigations.

The experimental approaches followed during the whole investigation period (from 2015 to 2018)

have been summarized in Figure 1.1.

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Figure 1.1 Schematic representation of the experimental approaches followed in this research.

8

CHAPTER 2

9

2 Literature Review

2.1 Introduction

Crop improvement through plant breeding requires a detailed understanding at different levels

(population, individual, tissue, cell and gene), the surrounding environment and their interaction.

Ascertaining plant responses under stress environment requires the utilisation of various

morphological, physiological, genetic and omics approaches. It is beyond the scope of this chapter to

review all those approaches. Instead, this chapter is intended to review the literature relevant to the

current study with some background information on wheat, its importance in the scenario of water

deficit environment. The following sections of this chapter cover different breeding strategies for

drought tolerance using traditional and modern approaches. Moreover, experiment-specific literature

is reviewed in the following relevant chapters.

2.2 Wheat: taxonomy, origin, and distribution

Wheat, one of the first domesticated food crops, is a cereal and belongs to the genus Triticum, tribe

Triticeae of the grass family Poaceae. The tribe Triticeae consists of more than 15 genera and many

domesticated species, such as bread wheat, durum wheat, barley and rye (Sears, 1969; Gupta et al.,

1991; Caligari and Brandham, 2001). Bread wheat is considered as the most prominent member of the

tribe. From archeological and molecular studies, it is predicted that bread wheat originated nearly

10,000 years ago along the Fertile Crescent of the Middle East (Eckardt, 2010). The modern common

wheat, usually referred to as bread wheat, arose from the evolutionary process of multiple

polyploidization events of closely related grass species of the Triticum and Aegilops genera, making it

an allohexaploid of three closely related genomes (Petersen et al., 2006). The first hybridization event

occurred about 400,000 years ago between two wild grass species, a diploid einkorn wheat T. urartu

and an unspecified progenitor of B-genome to form a primitive tetraploid emmer wheat (T. turgidum

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ssp. dicoccoides) (Huang et al., 2002; Dvorak et al., 2006). Modern durum wheat, widely known as

pasta wheat, is the domesticated version of the wild emmer wheat. Bread wheat or common wheat is

the product of the second natural hybridization event between the tetraploid wild grass species, T.

turgidum L. (genome AABB; 2n=4x=28) and a diploid wild grass species A. tauschii Coss. (syn. Ae.

squarrosa; genome DD; 2n=2x=14) (Matsuoka, 2011).

Wheat is cultivated across a wide range of climatic conditions globally, covering from the northern

latitude of China and Canada to the southern regions of Australia and South America. It is adapted to

the considerable diversity of climatic conditions and soil fertility. Its optimum productivity is attained

in regions with cool, moist climate followed by dry, warm conditions with rainfall ranging from 250

to 1750 mm (Curtis et al., 2002). In Australia, wheat is predominantly grown under rainfed condition

ranging from latitude 22ºS to 38ºS, well known as the “wheat belt”. The rainfall frequency and

distribution pattern vary extensively over those wheat-growing areas, with most areas receiving less

than 250 mm of rainfall during the whole growing season.

2.3 Wheat: morphology and growth stages

An understanding of the relationships between different growth stages and plant response to stress is

essential to predict precisely the impact of various biotic and abiotic stresses, such as diseases, insects,

frost, heat and drought. Moreover, decision making for several management practices, such as

weeding, irrigation and fertilization requires proper identification of critical growth stages.

Morphologically, wheat is an annual grass of determinate flowering habit. Figure 2.1 shows a

schematic diagram of different growth stages of wheat according to Zadoks scales (Zadoks et al.,

1974).

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Figure 2.1 Schematic diagram of wheat growth stages from sowing to maturity adapted from Zadoks

et al. (1974) and www.wheat-training.com

12

Three major stages of bread wheat include: 1) the vegetative stage, periods when the crop grows

vegetatively starting just after emergence to before the onset of floral initiation; 2) the reproductive

stage, starting from floret differentiation and development to fertilization; and 3) the grain-filling stage,

from endosperm cells formation to physiological maturity (Miralles and Slafer, 1995). The time-span

of each growth stages varies depending upon genotype, sowing date and other climatic conditions, such

as temperature and day-length (Stapper and Fischer, 1990).

Based on the growth habit and adaptation to climatic condition, bread wheat is categorized into major

types: “spring” and “winter”. Spring wheat has a comparatively shorter life cycle as they do not need

cold treatment or vernalization. However, winter wheat requires cold treatment to induce flowering

and needs to undergo a slow vegetative stage during winter to flower in spring. Australian wheat are

mostly spring wheat. In most growing areas, seed sowing generally starts from April to June (late

autumn to mild winter), and the final harvest is in November and December.

2.4 Economic importance of wheat: from global to local perspective

In terms of dietary intake, wheat is the second most important cereal after rice (FAO, 2016),

contributing about 19% of the calories and 21% of the protein of daily human dietary requirements

globally (Braun et al., 2010). Out of the two most cultivated wheat, bread wheat accounts for more

than 90% of total production while durum wheat covers only 5% of the total production. According to

Shiferaw et al. (2013), wheat is the staple food for 40% of the world population. It has been predicted

that the demand for wheat will increase by approximately 50% by 2030 while the world population is

expected to reach about 9 billion by the end of the 21st century (Tilman et al., 2002; Foley et al., 2011).

The demand for wheat in developing countries is predicted to rise even further (Mottaleb et al., 2018).

On average, annual global wheat production is around 650 million metric tonnes, out of which about

25 million metric tonnes are produced in Australia (ABARES, 2017). The value of Australia’s annual

13

wheat production is about $5 billion, and more than about 65-75% of the total wheat production is

exported each year, especially in Asia and the Middle East (ABARES, 2017). In Australia, wheat is

mainly grown as a rain-fed crop in a narrow crescent famously known as the ‘grain belt’, spreading

from central Queensland through New South Wales, Victoria and southern South Australia and south-

western Australia and Western Australia, with Western Australia being the largest exporting state

(AEGIC, 2018). In Western Australia, wheat grows in approximately 5 million hectare area,

representing 30% of the Australian grain belt (ABARES, 2017) with an annual wheat production worth

AUD 1.8 billion (AEGIC, 2018). More than one-third of yearly rainfall in WA is received from May

to October and more than 65% of which is received during winter (Ludwig et al., 2009). Annual rainfall

in this area has been predicted to a 60% reduction by 2050 (Whetton et al., 1993). Due to the low

rainfall and high temperatures, increasing frequency of drought spells and low yield years has been

predicted in the Western Australian grain belt (Farre and Foster, 2010).

2.5 Crops adaptation to drought stress

Resistance to drought is generally defined as the ability of a plant to perform satisfactorily in growth

and development in limited water environments or under periodic conditions of water deficit (Turner,

1979). According to Turner (1979), crops to be termed as drought-resistant should be able to produce

a harvestable yield in addition to their ability to survive under drought. Therefore, drought resistant in

the context of agriculture can be redefined as the ability of a crop to generate yield with a minimum

loss under water-stressed condition compared to the well-watered condition. Improving water-stress

resistant in crops is one of the most challenging tasks for breeders since traits related to water-stress

resistance are mostly quantitative, often with low heritability, and highly influenced by genotype and

environment (G×E) interactions (Barnabás et al., 2008; Fleury et al., 2010b). However, researchers

identified some adaptive strategies or mechanisms in plants to survive water stress, namely drought

escape, dehydration avoidance and dehydration tolerance (Blum, 2018).

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Drought escape is the ability of a crop to complete its life cycle before the development of severe water

deficit (Chaves et al., 2003). Drought escape is achieved by rapid growth and development; increased

assimilate reserves and partitioning; and shortening life cycle by early flowering, and earlier maturity

(Blum, 1998). Drought escape by early flowering is an effective breeding strategy for Mediterranean

environments where crops are frequently exposed to terminal water deficit (Cattivelli et al., 2008).

However, this might pose the risk of frost damage to the reproductive organs.

Dehydration avoidance is the plant’s ability to sustain a relatively higher level of water status under

conditions of soil or atmospheric water stress. The mechanism of dehydration tolerances is exerted

either by reducing the loss of water via stomata resistance, reduction in absorbed radiation and

evaporative surfaces or via increased root density, rooting depth and hydraulic conductance. This is

considered the most effective water-stress resistance mechanism, indicating the importance of

identifying the traits that enhance this mechanism (Blum, 2011b).

Dehydration tolerance is the ability of a plant to endure periods of water deficit or to postpone further

dehydration by maintaining turgor via osmotic adjustment, increase of cell wall elasticity or a decrease

in cell size. Plants exert this mechanism by producing and distributing different plant hormones and

other secondary metabolites at tissue, cellular and subcellular levels, or by regulating the concentration

of different osmolytes in the cellular compartment under stress (Ingram and Bartels, 1996).

Jones (2013) proposed the term ‘drought tolerance’ instead of ‘drought resistance’ and suggested that

the latter refers to the ability of plants to survive drought, whereas the former describes all mechanisms

that plant maintains survival or productivity under stressed conditions.

2.6 Effects of drought in different growth stages

Drought is considered as the single most serious obstacle for crop production in the world at large.

Depending on the stage of crop growth, the severity of the stress, and the duration of the stress, effect

15

of drought on crop production varies considerably (Passioura, 2012; Tuberosa, 2012). Fukai and

Cooper (1995) identified three broad periods during plant development that are useful when

considering the effects of water deficit. These are: 1) early-season drought at the seedling stage; 2)

water deficit at the vegetative and reproductive development stage; 3) late water deficit during the post-

anthesis or grain-filling period.

Studies reported that the field capacity of -0.01 and -0.03 MPa are optimum for plant growth and soil

microbial respiration (Krzic et al. 2004). Stored soil moisture is continually taken-up by plants during

their life cycle and with no additional water being added, the water potential of the soil might fall to

about -1.5 MPa and can cause permanent wilting (Krzic et al., 2004). Usually, drought tolerance in a

germinating seed is found to be relatively high, which later deteriorates with the plant’s advancement

to the seedling stage (Blum, 2005). Evaluation of drought tolerance at the early seedling growth stage

is reported to be pivotal in distinguishing between drought resistant and susceptible genotypes (Degu

and Fujimura, 2010; Ayalew et al., 2015; Govindaraj et al., 2015; Ayalew et al., 2016a). During the

early seedling stage, the early vigour, acute root angle and prolific root growth of plants become very

crucial. Rapidly expanded leaves at the early stage tend to cover the soil surface and reduce

evaporation, whereas longer roots with acute angle ensure the better acquisition of stored soil moisture

(Richards, 1991). Therefore, the speed of plant growth is a key factor at this stage. Other traits that

contribute to increased seedling vigour included broad seedling leaves, large embryo size, specific leaf

area and large coleoptiles (Richards, 1991). Genotypes with longer coleoptiles are suitable for sowing

deeper and thus facilitate better use of stored soil moisture (Rebetzke et al., 2007).

Water stress at vegetative stage limits leaf expansion and photosynthesis (Taiz et al., 2015). With the

increased intensity of water stress, mesophyll metabolism is impaired. Dehydration of mesophyll cells

inhibits photosynthesis, and thus usually decreases water use efficiency (Olsovska et al., 2016). Traits

like pale colour of leaves, wax deposition on the leaf surface, and optimized leaf angle are considered

beneficial at this stage as they would affect the temperature on the leaf surface and thereby prevent

16

rapid evapotranspiration (Lun, 1998). Blum (1996) reported that plants with large biomass are more

susceptible to dehydration compared to those with smaller biomass.

Although drought may affect wheat growth during all developmental stages, the reproductive and

grain-filling phases are generally considered the most sensitive stages (Pradhan et al., 2012). Shortage

of water during reproductive and grain-filling stages causes substantial yield reductions in wheat.

Water stress during meiosis causes impaired development of reproductive organs, including nonviable

pollen grains, sterile florets that produce shrivelled grains and low grain numbers, and thereby reduces

grain yield (Reynolds et al., 2002; Ji et al., 2010; Dolferus et al., 2011; Onyemaobi et al., 2017). Water-

deficit during anthesis can cause pollen sterility and reduced receptivity of stigma that prevents

pollination and reduces the final grain number (Dong et al., 2017). Under severe stress, assimilate

partitioning is directly affected during the grain filling period. The ability to continue partitioning

assimilates under drought is a key factor of plant tolerance (Taiz et al., 2015).

2.7 Breeding strategies for drought tolerance

According to literature, breeding for improved tolerance to drought can be categorized into four basic

approaches. The first approach includes breeding for high yield under favourable conditions assuming

that under stress conditions this might provide a yield advantage. However, the suitability of this

approach is limited by the fact the strong G × E interactions limit the high performance of genotypes

under stress condition. Blum (1988) pointed out that the solution to yield improvement under stress

cannot be sustained only by improving yield potential. Bidinger and Witcombe (1989) proposed the

second approach as breeding for maximum yield in the target environment. However, drought in stress-

prone areas is highly variable over time, and the expression of variability for yield and related traits in

such conditions is quite low. Hence, selected genotypes should be able to produce outstanding yield

under not only sub-optimum condition but also the optimum condition (Rosielle and Hamblin, 1981).

17

Mitra (2001) reviewed the third approach as simultaneous selection for yield in both stressed and non-

stress conditions for ensuring yield stability. This approach involved the development of genotypes for

the water-deficit condition through traditional breeding programs by selection and incorporation of

physiological and morphological mechanisms to establish a drought tolerance trait that will provide

yield advantage under stress (Reynolds et al., 2005). The fourth and most recent approach is a holistic

approach which involved the morphological and physiological dissection of crop drought avoidance

and tolerance traits using various multidisciplinary techniques, including genetic and genomics tools

(molecular markers, marker-assisted selection, QTL mapping) and various agronomic practices that

ensure better utilization and conservation of stored soil moisture in the target environment (Serraj et

al., 2005).

2.8 Traditional breeding approaches for improving drought tolerance in wheat

The fundamental basis of traditional breeding is the selection of high yielding genotypes of desirable

traits and assemble more desirable gene combinations via hybridization. The primary goal is to

recombine favourable genes from different sources in new varieties (Hussain et al., 2015). The

presence of ample genetic variation in wheat germplasm is very crucial to identify the contrasting

parents for hybridization program (Shelden and Roessner, 2013). Exploring the existing genetic

variability in a crop species is the foundation for a successful breeding program. The classical way of

achieving this is through phenotypic selection in collections of germplasm or in segregating

populations in field or glasshouse screening trials. Genetic variability existing in the current germplasm

can be directly used to select drought-tolerant varieties (Moose and Mumm, 2008) or selected lines

from the screening experiment can be hybridized to create more genetic variation and to harvest

heterotic vigour (Hallauer et al., 2010; Whitford et al., 2013). Synthetic hexaploid wheat produced by

hybridization of wheat progenitors can also offer extra genetic variation and helps produce drought

18

tolerant wheat with introgression of several novel drought tolerance genes (Placido et al., 2013). For

example, synthetic wheat produced from T. durum cv. Langdon and Ae. tauschii showed drought

resistance for biomass and grain yield (Song et al., 2017). Becker et al. (2016) also reported root traits

of synthetic wheat contributed to drought tolerance.

Plant breeders use several crossing populations including biparental, poly cross, three-way, topcross,

North Carolina (I, II, III), diallel cross and line×tester for estimating combining abilities (Griffing,

1956; Kearsey and Pooni, 1996; Hallauer et al., 2010). The principal reason is to generate information

for breeders on the genetic control of the trait of interest and to develop a meaningful breeding strategy

(Hayman, 1954; Mather and Jinks, 1982). To identify combining ability, the diallel cross is one of the

most common methods that has been used to study various traits in wheat and other crop species.

(Borghi and Perenzin, 1994; Glover et al., 2005; El-Rawy and Hassan, 2014).

2.9 Molecular breeding approaches for improving drought tolerance in wheat

Traditional plant breeding approaches have contributed significantly to improve different traits in

wheat such as height, yield and disease resistance (Ashraf, 2010). However, improvement of drought

tolerance related trait following this approached has not been so encouraging (Ribaut et al., 2010;

Blum, 2011a). In contrast, molecular breeding provides useful and powerful tools and techniques to

complement phenotypic selection of conventional breeding. Recent progress in genotyping and

sequencing technology has unlocked a new age of genomics research for wheat improvement (Uauy,

2017). Comparative genomics, involving wheat and other major grasses, has been used to study

evolutionary relationships of the traits of interest. Functional genomics approaches including RNA

interference, epigenetics and TILLING (Targeting Induced Local Lesion In Genomes) have been used

to identify functions of individual genes in wheat (Uauy et al., 2009; Uauy, 2017; Uauy et al., 2017).

https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/drought

https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/grain-yield

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2.9.1 Molecular markers

The use of classical markers like biochemical, cytological or morphological markers is very limited in

genetic studies due to their finite number, environmental dependence and growth stage specificity

(Jiang, 2013). The technological advancement in the field of molecular biology facilitates the

availability of various kinds of molecular markers. Molecular markers have been routinely used in

various genetic analysis such as genetic linkage mapping, diversity analysis, gene pyramiding and map-

based cloning (Collard et al., 2005; Collard and Mackill, 2009; Xue et al., 2010), Xu 2010). A

molecular marker is derived from a small segment of DNA sequence, which shows polymorphism

between individuals within a species. In general, the wide range of molecular markers available to

breeders can be divided into two major types: 1) hybridization-based markers, 2) PCR-based markers

and 3) sequence-based markers as reviewed by Nawaz et al. (2018). A marker can be termed as

dominant or codominant based on its ability to differentiate between individuals of homozygote or

heterozygote. The most common and frequently used molecular markers are: restriction fragment

length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs), simple sequence

repeats (SSRs), diversity array technologies (DArT) and single nucleotide polymorphisms (SNP).

AFLP and SSR DNA markers are reported to be more robust, highly polymorphic and reproducible

and therefore, they have been used extensively in many genetic mapping and plant breeding studies

(Gupta et al., 1999; Gupta and Varshney, 2000; Zhou et al., 2002; Jaiswal et al., 2017). The advantages

of SSRs over other markers are their reliability, co-dominance, specificity, abundance and uniform

dispersal through plant genomes. In hexaploid bread wheat, SSRs are particularly valuable as they

showed a much higher level of polymorphism than any other marker system (Gupta and Varshney,

2000). However, the popularity of SNP markers is increasing day by day in wheat breeding because of

their easy abundance in the huge wheat genomes and their applicability in the high-throughput

platforms.

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2.9.2 Quantitative trait loci (QTL) related to drought tolerance

Understanding the underlying genetic basis of tolerance in crops is fundamental to enable breeders to

develop stress-resilient new varieties. However, most of the traits related to drought tolerance and

yield are controlled by several loci, each contributing to a part of the phenotypic expression of the

related traits. Genetic linkage maps allow the identification of regions associated with traits related to

drought tolerance. QTL mapping analysis is a statistical procedure to locate genomic regions

associated with trait of interest by using genetic linkage maps and phenotypic trait values. F2

populations, doubled haploids (DHs) and recombinant inbred lines (RILs) are the often used mapping

populations for QTL identification (Budak et al., 2013). The advent of PCR-based molecular markers

has enhanced identification of quantitative traits related to drought tolerance in wheat (Quarrie et al.,

1994; Quarrie et al., 2005; Avise, 2012). Some of these QTL might correspond to genes acting directly

or in regulatory functions for drought tolerance (Kobayashi et al., 2010; Iehisa et al., 2014; Barakat

et al., 2015). Despite the identification of numerous QTLs related to drought tolerance traits, their use

in gene tagging and positional cloning is very limited because of the large interval of the identified

QTLs, usually more than 10 cM (Sehgal et al., 2016). A fine mapping experiment with an increased

number of molecular markers and large mapping population, especially from near-isogenic lines

(NILs) developed from targeting QTLs detected in the previous mapping experiments is necessary

to refine the size of the regions. NIL-derived populations differing for a targeted genomic location,

allow the conversion of QTL into Mendelian factor, thus making accurate positioning of a QTL

possible (Habib et al., 2015).

2.9.3 Marker-assisted selection

Marker-assisted selection (MAS) involves the utilization of genotyping values to infer phenotyping

performances of plants from the marker-trait association. It is considered as more effective, efficient,

21

rapid, reliable and cost-effective than the conventional phenotypic selection. Numerous studies

reported that combining marker-assisted selection with conventional breeding programs increases the

efficiency of breeding programs by increasing overall selection gain. In marker-assisted selection

procedures, genotyping can be performed at any growth stage, even traits with low heritability can be

selected effectively, and particular traits can be examined with the omission of phenotypic screening

(Collard et al., 2005; Sehgal et al., 2016). However, successful implementation of MAS largely

depends on the distance of the flanking markers from the target QTLs/genes so that no crossover occurs

between the flanking marker and the target QTL (Collard and Mackill, 2009; Jiang, 2013). Before

utilization of the identified markers for MAS, the mapped QTL should be validated in different genetic

backgrounds other than the genetic background of the original mapping population. This validation

trial of the mapped QTLs can be performed by using independent populations or by using near-isogenic

lines (NILs) developed from parents harbouring the desired QTL and marker (Collard et al., 2005;

Collard and Mackill, 2009). However, high QTL × environment interaction shown by the majority of

drought tolerance related QTLs is one of the major difficulties in enhancing drought tolerance through

MAS (Ribaut et al., 2010).

2.9.4 Drought-responsive proteins, genes and transcription factors

Under stress condition such as drought, the plant undergoes a series of changes in molecular, cellular

and physiological processes to better adapt to the situation. Previous studies reported several gene

networks related to these processes (Atkinson et al., 2015; Takahashi et al., 2018). The drought-

inducible genes and their products identified in the literature can be categorized into two groups

(Shinozaki et al., 2003). The first group includes genes that are supposed to function directly to protect

against abiotic stresses. These include genes that code for enzymes required for the biosynthesis of

various osmoprotectants, late-embryogenesis-abundant (LEA) proteins, mRNA-binding proteins,

water channel proteins, antifreeze proteins, chaperones, sugar and proline transporters etc. The second

22

group includes genes that code for proteins (transcription factors) that regulate signal transduction and

the expression of other genes in response to stress. Enzymes such as protein kinases, protein

phosphatases and phosphoinositide metabolism-related enzymes are also included in this group

(Shinozaki and Yamaguchi-Shinozaki, 2007a). It is worth mentioning that most of the studies of

drought-responsive genes have not been investigated in field-grown material and their relevance to

‘field drought’ therefore remains unidentified. Researchers warned that many of those genes are likely

to relate to desiccation tolerance and survival, and their economic relevance should be further verified

in field condition.

Under stress, plant biochemical responses are characterized by the accumulation of high concentrations

of osmoprotectants. Osmoprotectants are small, nontoxic at molar concentrations and electrically

neutral molecules that serve for osmotic adjustment under stress. These are of three types: betaines and

related compounds like choline-O-sulfate; amino acids such as fructan, proline and extoine; and sugars,

such as glucose, fructose, sucrose and trehalose (Sharma et al., 2011). Studies showed that

overexpression of some LEA genes has resulted in enhanced tolerance to dehydration, although the

mechanism of tolerance is yet to be identified (Cheng et al., 2002). Ashraf and Foolad (2007) reported

accumulation of various amino acids in response to drought, such as alanine, arginine, glycine, serine,

leucine and proline. Mwadzingeni et al. (2016) identified a wide range of genetic variation in wheat in

terms of the accumulation of proline in response to drought and reported a positive correlation between

grain yield and proline content under drought.

Transcription factors (TFs) play crucial roles in regulating the expression of downstream target genes

that are involved in the drought stress response and tolerance. These proteins are classified into two

major subfamilies: 1) the drought-responsive element binding factor (DREB) subfamily (group A) and

2) ethylene-responsive factor (ERF) subfamily (group B). Each of them is further divided into six

subgroups, A1-A6, and B1-B6, depending on their conserved domain (Sakuma et al., 2002). In wheat,

23

both DREB1A and DREB2A were reported to be involved in drought-inductive gene expression

(Kumar et al., 2016; Liu et al., 2018). Rong et al. (2014) reported that TaERF enhanced drought

tolerance in wheat with higher proline and chlorophyll levels. Other TFs, reviewed by Gahlaut et al.

(2016), can be involved in the breeding of drought tolerant wheat including bZIP, MYB/MYC, NAC

and WRKY. Figure 2.2 shows a schematic representation of drought stress signal perception and gene

expression via ABA-dependent and ABA-independent pathways in plants and their utilization in the

development of drought-tolerant wheat cultivars using marker-assisted selection is also shown

(Adapted from Gahlaut et al. (2016).

Figure 2.2 Schematic representation of drought stress signal perception and gene expression with the

implication in plant breeding

Phytohormones such as abscisic acid (ABA), ethylene, brassinosteroids (BRs2), jasmonates (JA) and

salicylic acid (SA) are also actively involved as signalling molecules in response to drought stress

(Yoshida et al., 2006). ABA was found to be associated with stomatal response in regulating water use

24

under terminal drought in wheat (Saradadevi et al., 2017). Under water deficit, ethylene is found to be

involved in polyamines biosynthesis and abdominal phloem tissues characters of wheat caryopsis

during grain filling stage (Yang et al., 2017a).

2.10 Transcriptomics for drought tolerance

The genetic mechanisms underlying the crop response to drought often involve complex biochemical

pathways and the responses of many genes and regulatory regions that vary depending on timing, the

severity of stress, plant organ and growth stages. Therefore, identification of relevant mechanisms is a

critical challenge for the researchers investigating stress responses. Transcriptome profiling has

provided a means to investigate various genes and their related pathways with the potential of

involvement in stress response and a better understanding of the underlying tolerance mechanisms.

Microarray-based hybridization or sequence-based approaches are the two leading technologies have

been used to quantify the transcriptome (Wang et al., 2009). Both of the techniques require extraction

and isolation of high-quality mRNA from plant tissues followed by the synthesis of complementary

DNA (cDNA) using an enzyme called reverse transcriptase (Hoheisel, 2006). Microarray-based

method has been extensively used in gene expression studies of wheat under drought after the

commercial release of wheat Affymetrix Gene Chip® (Santa Clara, CA, USA) (Aprile et al., 2009b;

Ergen et al., 2009; Liu et al., 2016). However, this technique relies heavily on the existing knowledge

about genome sequence to develop species- or transcript-specific probes. Other difficulties, such as

background noise, signals saturation and complicated normalization methods have limited their use

(Ozsolak and Milos, 2010).

In contrast, RNA-Seq is a recently developed approach to transcriptome profiling that uses next-

generation deep-sequencing technologies. Unlike microarrays, RNA-seq technology does not require

any prior information on genomic sequences and can detect a higher percentage of differentially

25

expressed genes, including genes with low expression. Detection of novel transcripts, gene fusions,

single nucleotide variants, and small insertions and deletion (indels) variants give it an unprecedented

advantage over microarray in gene expression analysis.

The new era of next-generation transcriptome sequencing has certainly empowered researchers to

improve drought tolerance in wheat with more specific goals. Attention should be paid to make the

most use of the recently released fully annotated wheat genome in transcriptomics studies (Deyholos,

2010). However, transcript abundance is not always correlated with the final activity of a gene due to

various post-transcriptional regulatory influences over the final gene product. Besides, the poor

experimental design of RNA-seq experiment might lead researchers to the ambiguous outcome.

Therefore, validation by phenotyping and physiological experiment plays a critical role in applying

RNA-seq results into plant breeding.

26

CHAPTER 3

27

3 Characterizing root trait variability for early-stage water stress

tolerance in wheat: from morphology to gene expression analysis

Md Sultan Mia1,2, Hui Liu1*, and Guijun Yan1*

1UWA School of Agriculture and Environment, Faculty of Science, and The UWA Institute of

Agriculture, The University of Western Australia, Perth, WA 6009, Australia. 2Department of Plant

Breeding, Bangladesh Agricultural Research Institute, Joydebpur, Gazipur 1701, Bangladesh.

* Correspondence:

Hui Liu

[email protected]

Guijun Yan

[email protected]

(This chapter has been submitted to BMC Plant Biology journal on 31 January 2019)

mailto:[email protected]

28

3.1 Abstract

Water shortage during the early growth period is a critical limiting factor for successful crop production

in wheat. Effective root phenotyping of diverse genotypes in the limited water environment is crucial

to explore trait variability essential for developing stress-resilient crop varieties. In this study, early

stage root trait variability under water deficit was investigated in a set of germplasm, which was

previously characterized for reproductive stage water stress. Results of our study demonstrated a wide

range of variation among the genotypes for the studied root traits under both well-watered and water-

stressed conditions. Water stress at the seedling stage significantly reduced most of the root traits

investigated. Based on stress tolerance index, Abura, Erechim, Hybride 38, Kenya Bongo and Funello

were ranked as the most tolerant genotypes whereas Aus 12671, Changli, Philippines 7, W96, GBA

Sapphire as the most susceptible genotypes. Gene expression pattern of TaSnRK2.4, TaMYB3R and

TaMYBsdu1 genes in the contrasting genotypes were significantly different, indicating the suitability

of the screening technique used to discriminate the genotypes. Contrasting genotypes resulted from the

present investigation could be used as parents for hybridization in a breeding program or for

comparative transcriptomic study to identify the mechanism of tolerance to water stress.

Keywords: wheat, root trait variability, water stress, gene expression, phenotyping

29

3.2 Introduction

Water shortage during the life cycle of wheat is a critical limiting factor for successful crop production.

Particularly, for rain-fed wheat production areas, stored soil moisture is crucial for the early

establishment, plant growth and final harvest. However, intermittent rainfall and elevated temperature

worsen wheat production in those areas, causing a serious threat to global food security. In this context,

the primary challenge for plant breeders is to breed for stress-resilient crop varieties better adapted to

climate change. Therefore, exploitation of available genetic variability to select for better performing

genotypes under limited water environment would be an effective approach.

Water stress tolerance in wheat is a complex, polygenetic trait, influenced by the surrounding

environment. The trait-based selection has been suggested as an effective strategy to improve this

complex trait in wheat (Wasson et al., 2012). Root is the primary organ that senses a shortage of

available moisture in the soil profile. Studies reported that roots contribute most to drought adaptation

by signal transduction to the aerial parts for early responses and producing stress-related

phytohormones. For this reason, root system related traits are the prime target of modern breeding

programs to develop stress-resilient varieties (Del Bianco and Kepinski, 2018). Genotypic variation

for different root traits and their implications for better water and nutrient uptake under limited water

conditions have been reported for different crop species (Abenavoli et al., 2016; Avramova et al., 2016;

Jin et al., 2017). In wheat, the root parameters that are desirable for stress tolerance include rooting

depth, root length, root elongation rate, root to shoot ratio, root diameter, and surface area of roots

(Manschadi et al., 2007; Comas et al., 2013).

The critical challenge for using root traits as selection criteria lies in the difficulty of phenotyping

procedure (Richards 2008). Large-scale root screening for water stress tolerance under field condition

is very cumbersome, offering limited flexibility over experimental design and treatment application.

30

However, an effective alternative often practised for large scale screening, especially for stress

tolerance during early growth stage is hydroponics (Tavakkoli et al., 2012). Previous studies on

seedling stage water stress resistance showed the suitability of hydroponics based screening procedure

in wheat (Balouchi, 2010; Ayalew et al., 2015).

It is well-documented that water deficit at the root growing area triggers expression of drought-

responsive genes (Janiak et al., 2016). Many of those genes encode transcription factors that regulate

the expression of downstream genes and modulates plant response to stress. It was reported that

transcription factors, such as DREB, MYB, bZIP, ERF, HD-ZIP, NAC, and WRKY were differentially

expressed in tolerant genotypes compared to the susceptible genotypes under stress (Ergen and Budak,

2009; Nguyen et al., 2015). These reports strongly suggest the potential of correlating stress tolerance

with expression patterns of drought-responsive genes.

Breeding for improving drought tolerance in wheat during early growth stage tend to receive less

attention than reproductive stress breeding, which is directly associated with yield. Precise root

phenotyping of the available germplasm under stress could lead to the selection of genotype that copes

well with the stress. This study aimed at investigating root trait variability of a collection of wheat

genotypes under both normal and stressed conditions to determine their relative tolerance. These

genotypes showed wide variation when characterized by Onyemaobi et al. (2017) for reproductive

stage water stress tolerance and thus provides a rationale for examining their early stage water stress

tolerance, which has not been characterized. To validate the characterization technique, drought-

responsive gene expression pattern among the contrasting genotypes was also investigated. Superior

genotypes with desirable traits for early and later growth stage tolerance could serve as parents in

breeding programs.

31

3.3 Materials and Methods

3.3.1 Germplasm used

Plant materials (seeds) used for this study were procured from the germplasm collection of Australian

Grains Genebanks (AGG), Horsham, Victoria, Australia. Fifty genotypes of bread wheat of diverse

origin, coming from a range of geographical backgrounds covering 21 countries of seven continents,

were selected for this study (Table 3.1). Out of them, forty-six were previously characterized by

Onyemaobi et al. (2017) for their tolerance to meiotic stage water deficit. Four Australian cultivars,

well-known for their adaptation to the dry sowing and low rainfall ecosystem were also included,

leading to a total of 50 genotypes screened for their early stage water stress tolerance.

Table 3.1 Wheat genotypes used for characterizing early stage water deficit tolerance

S. No Name Country of Origin Continent Type

1 Abura Japan Asia Spring

2 AUS12351 Argentina South America Spring

3 AUS12671 Mexico North America Winter

4 Axe Australia Australia Spring

5 Beijing8 China Asia Winter

6 Belgrade7 Yugoslavia Europe Winter

7 Canary4 Spain Europe Spring

8 Changli China Asia Spring

9 Diamanteinta Argentina SouthAmerica Spring

10 Drysdale Australia Australia Spring

11 EmuRock Australia Australia Spring

12 Erechim Brazil SouthAmerica Spring

13 Espada Australia Australia Spring

32


14 Fang60 Thailand Asia Spring

15 Flaminio Italy Europe Spring

16 Florida301 USA North America Winter

17 Funello Italy Europe Winter

18 Gail South Africa Africa Winter

19 GalaxyH45 Australia Australia Spring

20 GBA Sapphire Australia Australia Spring

21 Gilat182 Israel Middle East Spring

22 Giza150 Egypt Africa Spring

23 Halberd Australia Australia Spring

24 Hybride38 India Asia Spring

25 India344 India Asia Spring

26 IsraelL224 Israel Middle East Spring

27 Janz Australia Australia Spring

28 Jimai20 China Asia Spring

29 Kenya1877 Kenya Africa Spring

30 Kenya Bongo Kenya Africa Spring

31 Kukri Australia Australia Spring

32 Mace Australia Australia Spring

33 Magenta Australia Australia Spring

34 Morocco426 Morocco Africa Spring

35 Nobre Brazil North America Spring

36 Norin10 Japan Asia Winter

33


37 Persia123 Iran Middle East Spring

38 Philippines7 Philippines Asia Spring

39 Punjab8A India Asia Spring

40 Riddley India Asia Spring

41 RL6019 Canada North America Spring

42 Sakha8 Egypt Africa Spring

43 Spoetnik South Africa Africa Spring

44 Tammarin Rock Australia Australia Spring

45 Thatcher USA North America Spring

46 W96 Pakistan Asia Spring

47 Westonia Australia Australia Spring

48 Wyalkatchem Australia Australia Spring

49 Yaqui50 Mexico North America Spring

50 Yitpi Australia Australia Spring

3.3.2 Experimental set-up and treatment application

The experiment was conducted in a plant growth chamber at the School of Agriculture and

Environment, The University of Western Australia, Australia. Healthy uniform seeds were surface

sterilized with 1% sodium hypochlorite for 10 minutes and then rinsed 3 times with de-ionized water.

Sterilized seeds were placed in Petri dishes lined with two layers of moist filter paper for two days in

dark in a 20°C plant growth chamber. After germination, seeds were transferred to a customized

hydroponic system, each unit containing 3L of half-strength Hoagland’s solution with pH set at ~5.9.

The whole set of the hydroponic system was housed in the plant growth chamber set at a constant

34

temperature of 22±2 ºC with 16/8 hr photoperiod supplied with cool fluorescent lamps providing light

intensity of 300 µmol m-2 s-1 at canopy level. Sprouted seeds were allowed to grow in half-strength

Hoagland solution for 10 days (2-3 leaf stage, Zadok’s scale 12-13) and afterwards, stress treatments

were applied using 20% PEG6000 that produced osmotic stress of about -0.5±2 MPa measured with a

Psychrometer (Wescor Inc.). The system was constantly aerated using an electric air bubbler and

growing medium containing both Hoagland solution and PEG6000, or only Hoagland solution was

changed in every 72 hrs. After seven days of the stress period, both controlled and stressed plants were

harvested and separated into roots and shoots to measure their fresh weight. Root length (from the base

of the seedling to the tip of the longest root) was measured with a graduated ruler.

3.3.3 Scanning of root samples

Roots were scanned with a scanner (Epson perfection v700), and images of the scanned roots were

analyzed using WinRHIZO Pro software (Regent Instruments Inc., Quebec City) for different root

morphological characters, namely, total root length (sum of the lengths of all roots in the root system),

total root surface area, total root volume, and mean root diameter. Root and shoot were then air-dried

in an oven at 65°C for 72 hours, and dry weights were measured. The root-shoot ratio was calculated

as the ratio of root dry weight to shoot dry weight.

3.3.4 Stress tolerance index (STI)

Stress tolerance index (STI) was calculated for each genotype as described in Li et al. (2015a) and

Tahjib-Ul-Arif et al. (2018) using the following formula:

STI = (trait value in WS condition/trait value in WW condition) × 100

STI values close to 100 or higher indicate tolerance, whereas lower values specify the susceptibility of

the genotypes.

35

3.3.5 Drought responsive gene expression

For the expression analysis, a separate experiment was established following the similar procedure of

the previous experiment with only the five most tolerant and most susceptible genotypes (in terms of

overall mean STI values) grown in triplicates. However, the stress treatment applied this time was 48

h. Roots were harvested from both well-watered (WW) and water-stressed (WS) plants and separated

into two groups for each condition: tolerant (T) and susceptible (S). Whole roots of the individual

seedling of each genotype within each group were bulked, and there were three biological replicates

for each group. Samples were immediately snap-frozen in liquid nitrogen for later use. About 100g of

frozen root samples were ground and homogenized with a mortar and pestles in liquid nitrogen. RNA

was extracted from the homogenized tissues using the Qiagen Plant mini kit with an on-column DNase

treatment to avoid DNA contamination. The concentration of the extracted RNA was checked by

Nanodrop, and quality was checked by denatured gel electrophoresis and Agilent bioanalyser. Real-

time RT PCR was performed as followed: qualified RNA samples were reverse transcribed to cDNA

with SensiFAST cDNA Synthesis Kit, and then quantitative PCR was performed with SensiFAST

SYBR master mix in an Applied Biosystems® 7500 fast real-time PCR machine. The amplification

protocol was: 95°C for 3 min (1 cycle), 95°C for 15 s, 60°C for 1 min (39 cycles), and melting curve

analysis with an increase in temperature of 0.5°C per second from 65 to 95°C. Three technical

replicates were carried out for each of the three biological replicates tested. Expression analysis was

performed for five genes (Table 3.2) previously reported for differential expression in roots of wheat

seedlings under PEG simulated water stress. A wheat Tubulin gene was used as a reference gene, and

relative expression was calculated using the 2-ΔΔCT method (Livak and Schmittgen, 2001; Schmittgen

and Livak, 2008).

36

Table 3.2 Primers of the selected drought-responsive genes and their references

Gene name Forward Primer Reverse Primer Reference

Wheat tubulin 5’-AGCGCCTTTGAGCCTTCGTCC-3’ 5’-TCATCGCCCTCATCACCGTCC-3’ Vaseva et al. (2010)

TaMYBsdu1 5’-CAGTTCGATCTCCCGTTCTC-3’ ATCATGCATTTCTTCCGAGTTC-3’ Rahaie et al. (2010)

TaMYB3R1 5’-ACGAGAAAGACCGACACCTGC-3’ 5’-AACCCAGTGACAGAAAGGAAGCA-3’ Cai et al. (2011)

TaSnRK2.4 5′-GGTTCATGCAAGCGGAGAGC-3′ 5′-AACCAAAACCAAACAGAAGCAAAC-3′ Mao et al. (2010)

TADHN 5’-TGGGACGGGCTCAGTGCT-3’ 5’-ATGGGCGGGAGGAGGAAG-3’ Yu et al. (2017)

TaWRKY2 5’-TCGATCGCCATGTCCTCCTC-3’ 5’-AGCGACTCGACGAACATGTCG-3’ Yu et al. (2017)

37

3.3.6 Statistical analysis

The experiment was laid out in a two-factorial (genotype and treatment) randomized block design with

five blocks with five replications. Two-way analyses of variance (ANOVA) for root traits were

performed using GenStat 18th edition (Payne et al., 2017). Water treatments were considered to have

fixed effects, while genotype effects were considered as the random following (Mathew et al., 2018).

Means were separated using the least significant difference (LSD) at p≤0.05. Association between traits

were determined by calculating the Pearson correlation coefficient. The principal component analysis

was performed using XLSTAT (Avramova et al., 2016). Heritability of each studied trait was measured

according to Acquaah (2012) using the following formula, ℎ𝑏2 =

σ2g

σ2p; where, σ2g=genotypic variance,

and σ2p =phenotypic variance.

3.4 Results

3.4.1 Root traits variability and the effects of stress application

A total of 10 root traits were investigated to screen the studied genotypes for early seedling stage water

stress tolerance. These are : root length (RL), fresh root weight (FRW), dry root weight (DRW), fresh

root to shoot ratio (FR/S), dry root to shoot ratio (DR/S), total root length (TRL), root surface area

(RSA), mean root diameter (MRD) and total root volume (TRV). Two-way analysis of variance

showed the significant effect of the genotype, water treatments and their interaction for all the studied

traits (Table 3.3). A wide range of variation was observed among the studied genotypes for each of the

studied traits under both well-watered (WW), and water-stressed (WS) condition (Table 3.3). Our

results showed that stress treatment application caused a significant reduction in most of the measured

parameters. In well-watered condition, longest root length ranged from 12.5 to 36 cm with an average

of 21.05 cm, which reduced to 13.76 cm in stressed condition.

38

Table 3.3 Descriptive statistics, heritability and two-way ANOVA of the root traits in 50 wheat genotypes (SD= standard deviation,

CV=coefficient of variation, T=treatment, G=Genotype, T*G=treatment genotype interaction.

Root traits Treatment Min Max Mean Median SD CV

(%)

Heritability

(%)

Two-way

ANOVA

T/G/T*G

Longest Root length (cm) WW 12.5 36 21.05 20.17 4.69 22 93

0.00/0.00/0.00 WS 6.8 29 13.76 4.1 0.26 30 90

Fresh root weight (mg) WW 166.5 870 370.1 359.4 101.7 27 88

0.00/0.00/0.00 WS 15 198 75.39 68.70 34.28 45 91

Fresh root to shoot ratio WW 0.31 1.03 0.62 0.6 0.14 23 88

0.00/0.00/0.00 WS 0.17 0.78 0.44 0.43 0.11 26 91

Dry root weight (mg) WW 5.54 30.82 14.31 13.61 4.71 33 84

0.00/0.00/0.00 WS 3.64 28.70 10.26 9.27 3.95 38 88

Dry root to shoot ratio WW 0.17 0.92 0.37 0.35 0.10 27 72

0.00/0.00/0.00 WS 0.14 0.7 0.34 0.33 0.09 25 80

Root growth rate (cm/day) WW 0.89 2.57 1.5 1.44 0.34 22 93

0.00/0.00/0.00 WS 0.49 2.07 0.98 0.93 0. 29 30 90

Total root length (cm) WW 103 415.56 238.43 227.92 64.41 27 91

0.00/0.00/0.00 WS 43.12 258.30 115.45 109.37 35.23 30 79

Root surface area (cm2) WW 3.8 42.04 23.05 22.22 6.77 29 92

0.00/0.00/0.00 WS 3.8 22.53 10.58 9.94 3.46 34 86

Mean root diameter (mm) WW 0.22 0.40 0.31 0.31 0.03 9 93

0.00/0.00/0.00 WS 0.18 0.36 0.29 0.29 0.03 11 93

Total root volume (cm3) WW 0.03 0.37 0.18 0.17 0.06 33 92

0.00/0.00/0.00 WS 0.02 0.18 0.08 0.07 0.03 37 89

39

Although, root to shoot ratio for both fresh and dry weight seemed least affected due to stress

application, the genotypes showed wide ranges for fresh root weight and dry root weight in both

conditions, as evident from relatively large coefficient of variation (CV) value (27 and 33 respectively

under normal condition, 45 and 38 under stressed condition). Average root growth rates declines nearly

33% from 1.5 cm/day to 0.93 cm/day under stress. Similarly, PEG treatment caused more than 50%

reduction in total root length. Other root traits, such as root surface area was in the range of 3.8-42 cm2,

mean root diameter 0.22-0.4 mm, and total root volume 0.03-0.37 cm3 in well-watered condition,

whereas in the stressed condition they were in the range of 3.8-22.53 cm2, 0.18-0.36mm, and 0.02-0.18

cm3, respectively. Heritability of the root traits in WW condition ranged from 84-93%, of which RL,

RGR, RSA, MRD and TRV had a heritability of >90%, while in WS condition it ranged from 79-93%.

Interestingly, TRL having higher heritability in WW condition became lowest in WS condition.

3.4.2 Correlation among the root traits

Results from Pearson correlation analysis of the studied traits revealed that longest root length was

strongly (P<0.01) correlated with fresh root weight (r=0.48), dry root weight (r=0.60), root growth rate

(r=0.99) and total root length (r=0.37) in normal condition, however, under stress the magnitude of

association was higher in most cases (Table 3.4). Although fresh root weight found to be negatively

associated with fresh root to shoot ratio (r=-0.32) under WW condition, significantly strong positive

correlation (r=0.65, p<0.001) was reported between them under WS condition. Surprisingly, no

significant correlation was observed between dry root to shoot ratio and any other parameters except

for fresh root to shoot ratio and dry root weight in either condition. In contrast, dry root weight was

significantly (p<0.05) associated all the measured traits except for mean root diameter in normal

condition. Both total root length and root surface area showed a significant positive correlation

(p<0.05) with all traits except dry root to shoot ratio and mean root diameter in both conditions. While

40

total root volume had no significant correlation with LRL, DRSR and RGR in WW condition, all the

traits except DRSR found highly associated (r>0.45) with root volume in WS condition.

Table 3.4 Pearson’s correlation matrix for the root traits in 50 genotypes under well-watered (above

diagonal) and water-stressed (below diagonal) conditions

Root traits LRL FRW FR/S RDW DR/S RGR TRL RSA MRD TRV

LRL 1 0.48 0.12 0.60 0.11 0.99 0.37 0.30 -0.03 0.23

FRW 0.57 1 0.10 0.74 -0.32 0.48 0.47 0.48 0.19 0.47

FR/S 0.37 0.65 1 0.41 0.36 0.12 0.63 0.68 0.28 0.67

RDW 0.52 0.80 0.35 1 0.35 0.60 0.54 0.56 0.28 0.55

DR/S 0.16 0.11 0.39 0.48 1 0.11 0.05 0.05 0.09 0.06

RGR 0.99 0.57 0.37 0.52 0.16 1 0.37 0.30 -0.03 0.23

TRL 0.59 0.83 0.55 0.59 -0.01 0.59 1 0.93 0.04 0.82

RSA 0.61 0.86 0.53 0.69 0.05 0.61 0.94 1 0.31 0.96

MRD 0.00 0.21 0.03 0.37 0.05 0.00 0.03 0.23 1 0.52

TRV 0.62 0.84 0.46 0.78 0.13 0.62 0.83 0.95 0.45 1

Values in bold font are significant at P<0.01, bold and italic are significant at P<0.05, normal fonts

are non-significant

41

3.4.3 Principal component analysis (PCA) of the root traits

Ten root traits were included in the principal component analysis (PCA) for well-watered and water-

stressed treatments (Table 3.5). In both treatments, the three principal components were reported with

eigenvalue > 1, explaining more than 80% of the total variability in the studied root parameters of fifty

wheat genotypes (Table 3.5). In WS condition, the first component (PC1) was the most influential,

representing 57.26% of the total variability, marginally higher than the PC1 of WW condition

(47.36%), accounted for most of the root traits except dry root to shoot ratio and mean root diameter.

The second component (PC2, 12.16% variation) accounted primarily for mean root diameter under

stress. However, in WW condition, PC2 represented slightly higher variability (19.8%) with higher

loading value of root length, root growth rate and mean root diameter. In both conditions, variable dry

root to shoot ratio had high loading into the third component (PC3), which represented 13.58% and

12.19% of the total variation in WW and WS condition, respectively.

Distribution of the fifty genotypes with respect to PCA regression scores is further shown by the

principle component biplots in Figure 3.1A & 3.1B for WW and WS condition, respectively. The

relative distance among the fifty genotypes is illustrated for each combination of root parameters. The

genotypes were more concentrated under the stressed condition, whereas more number of genotypes

were allocated in negative quadrat in WW condition. Abura, Emu Rock, Flaminio, Spoetnik, Mace,

Magenta, and Yaqui 50 were assigned in positive quadrat for both WW and WS condition. Among

them, Abura appeared to have higher loading score evident from its relative position in both WW and

WS conditions.

42

Table 3.5 Variable loading scores of the root traits and the proportion of variation in each principal

component under well-watered (WW) and water-stressed (WS) conditions

WW WS

Parameters PC1 PC2 PC3 Parameters PC1 PC2 PC3

LRL 0.62 0.72 0.18 LRL 0.77 -0.42 0.15

FRW 0.67 0.35 -0.49 FRW 0.92 0.06 -0.10

FR/S 0.65 -0.51 0.30 FR/S 0.63 0.01 0.36

RDW 0.83 0.24 0.17 RDW 0.82 0.37 0.15

DR/S 0.16 -0.15 0.94 DR/S 0.24 0.38 0.87

RGR 0.62 0.72 0.18 RGR 0.77 -0.42 0.15

TRL 0.85 -0.19 -0.12 TRL 0.88 -0.18 -0.21

RSA 0.89 -0.34 -0.15 RSA 0.93 -0.01 -0.24

MRD 0.34 -0.41 -0.05 MRD 0.25 0.78 -0.33

TRV 0.87 -0.42 -0.16 TRV 0.93 0.18 -0.23

Eigenvalue 4.74 1.98 1.36 Eigenvalue 5.73 1.31 1.22

Variability (%) 47.36 19.80 13.58 Variability (%) 57.26 13.12 12.19

Cumulative

Variability %

47.36 67.16 80.75

Cumulative

Variability %

57.26 70.38 82.57

43

Figure 3.1 Principal component biplot showing genotypic grouping under well-watered (A) and water-stressed (B) conditions

44

3.4.4 Ranking of the genotypes to stress application

The fifty genotypes were ranked according to their mean stress tolerance index (STI) values of all the

root parameters. Table 3.6 representing the top five and bottom five genotypes. Full list containing all

the fifty genotypes can be found in the supplementary Table S1. Based on the mean STI score genotype

Abura, Erechim, Hybride 38, Kenya Bongo and Funello suffered the least having higher mean STI

score of 74 or more, indicating their “adaptive fitness” to PEG-simulated stress. Especially, PEG

treatment caused a lower reduction of TRV and relatively low RSA, TRL, RGR, FRW and RL in

genotype Abura. Similarly, Kenya Bongo experienced the smallest reduction of RL, RGR and TRL

under stress. In contrast, root traits of Aus 12671, Changli, Philippines 7, W96, GBA Sapphire

exhibited stern decline due to stress indicating their “maladaptive fitness” to early-stage drought. In

particular, genotype Philippines exhibited the highest reduction for RL, FRW and RGR (lowest STI

value). GBA sapphire ranked last due to a sharp reduction of DR/S, TRL and TRV under stress. Water

stress caused the highest reduction for FR/S, TRV in Aus 12671 having a mean STI score of 52.

45

Table 3.6 Means of different root traits of 5 best and 5 worst performing wheat genotypes under well-watered (WW) and water-stressed

(WS) conditions. Full table containing all the 50 genotypes are in the supplementary Table S1

Genotype

LRL FRW FR/S RDW DR/S RGR TRL RSA MRD TRV Mean

STI WW WS STI WW WS STI WW WS STI WW WS STI WW WS STI WW WS STI WW WS STI WW WS STI WW WS STI WW WS STI

Top five genotypes……………………………………………………………………………………………………………………………………………………………….…………………………….

Abura 29.5 25.3 86 421 152 36 0.50 0.55 110 23.5 20.0 85 0.50 0.40 36 2.10 1.81 86 199 169 0.85 19.8 18.4 93 0.35 0.32 91 0.16 0.13 81 83

Erechim 18.6 15.0 81 379 92 24 0.59 0.58 98 12.3 11.0 89 0.29 0.39 74 1.33 1.07 81 264 168 0.64 24.3 14.6 60 0.29 0.28 95 0.18 0.10 57 78

Hybride 38 16.1 12.4 77 331 124 38 0.55 0.67 121 12.7 9.9 78 0.42 0.31 82 1.15 0.89 77 180 143 0.80 14.0 10.2 73 0.25 0.23 94 0.09 0.06 67 78

Kenya Bongo 13.9 12.9 93 305 69 23 0.45 0.42 95 11.1 8.0 72 0.35 0.29 89 2.10 1.81 86 199 169 0.85 14.2 10.6 75 0.32 0.28 87 0.16 0.13 81 77

Funello 25.2 23.0 91 458 146 32 0.67 0.55 82 19.6 16.2 83 0.38 0.34 80 1.33 1.07 81 264 168 0.64 19.8 18.4 93 0.35 0.32 91 0.18 0.10 57 74

Bottom five genotypes ……………………………………………………………………………………………………………………………………………………………………….…….………….

Aus 12671 20.0 10.8 54 400 81 20 0.95 0.48 51 16.9 11.0 65 0.37 0.36 97 1.43 0.77 54 356 104 29 35.7 10.0 28 0.32 0.31 96 0.28 0.08 28 52

Changli 18.8 9.9 53 375 55 15 0.67 0.38 57 14.6 7.8 53 0.35 0.29 84 1.34 0.71 53 234 86 37 19.8 18.4 35 0.33 0.32 96 0.20 0.07 33 51

Phillipines 7 26.7 13.2 49 542 58 11 0.57 0.28 49 16.9 8.8 52 0.26 0.24 93 1.90 0.94 49 255 108 42 24.3 14.6 38 0.32 0.31 96 0.20 0.07 35 51

W96 22.5 11.8 52 320 35 11 0.70 0.31 45 13.2 6.1 46 0.39 0.29 74 1.61 0.84 52 228 94 41 14.0 10.2 37 0.31 0.28 92 0.18 0.06 34 48

GBA Sapphire 24.4 11.1 46 411 61 15 0.85 0.44 52 19.8 8.1 41 0.42 0.31 73 1.74 0.79 46 298 100 34 14.2 10.6 30 0.32 0.28 88 0.24 0.06 26 45

LSD (5%) 3.1 3.0 81 25 0.11 0.04 4.1 3.1 0.10 0.08 0.22 0.22 48 34 4.8 2.9 0.02 0.02 0.04 0.02

46

3.4.5 Drought responsive gene expression between tolerant and susceptible genotypes

To validate the screening procedure, transcripts expression of the drought-responsive genes (DRGs)

were also tested using qRT-PCR. Analysis of relative expression levels of the selected DRGs between

tolerant and susceptible genotypes revealed relatively higher expressions under 48 hrs of PEG stress

(Figure 3.2). However, out of five DEGs, the expression level of TaMYBsdu1, TaMYB3R1, TaSnRK2.4

and TaDHN was significantly different between tolerant and susceptible genotypes under stress. Gene

TaMYBsdu1, TaMYB3R1, TaSnRK2.4 were expressed in higher level in tolerant genotypes, whereas

TaDHN expression was significantly higher in susceptible genotypes. Interestingly, TaWRKY

expression was not significantly different between tolerant and susceptible genotypes in either

condition.

47

Figure 3.2 Expression patterns of drought-responsive genes in roots of contrasting wheat genotypes in

response to the PEG-simulated water stress. (S= susceptible genotypes, T=tolerant genotypes, WW=

well-watered, WS= water-stressed). The 2-ΔΔCT method was used to measure the relative expression

level of each gene. Expression of each gene in susceptible genotypes under WW condition was

considered as the standard. Bars represented mean with standard deviation. * denotes significant at

p≤0.05

48

3.5 Discussion

Effective root phenotyping of germplasm in the limited water environment is very crucial for

developing stress-resilient crop varieties. In this study, early stage root trait variability under water

deficit was investigated in a set of germplasm, which were previously characterized for reproductive

stage water stress. The main aim of this investigation was to evaluate whether the characterization of

root responses under stress at the early growth stage offers possibilities to screen extremes genotypes

suitable for water stress tolerance breeding. Results of the study clearly showed that PEG simulated

water stress at the seedling stage significantly reduces most of the root parameters investigated. This

is consistent with the previous studies by (Dodig et al., 2014; Ji et al., 2014; Robin et al., 2015;

Mwadzingeni et al., 2016). However, the genotypes responded variably and the range of the responses

was quite wide under both WW and WS environment, suggesting that the genotypes used were rich in

terms of genetic diversity and the experiment was suitable for screening germplasm for early-stage

water stress tolerance. The significant effects of genotypes, environment and their interaction as

observed in the current investigation matched our hypothesis that germplasm from the diverse origin

will vary in terms of root traits, which are mostly quantitatively inherited, thereby responded

differentially to different environmental conditions.

In this experiment, a stress tolerance index was adopted for screening the wheat genotypes that

categorizes Abura, Erechim, Hybride 38, Kenya Bongo and Funello as the most tolerant genotypes and

Aus 12671, Changli, Philippines 7, W96, GBA Sapphire as the most susceptible genotypes (Table 3.6).

Detailed analysis of the root traits of the susceptible genotypes confirmed a sharp decline in root length,

total root length, root surface area and diameter under stressed condition. This was also represented by

the corresponding reduction in fresh and dry root biomass due to stress. Genotype Abura and Erechim,

tolerant for early-stage water stress, was also reported for their tolerance to moisture deficit at

reproductive stage. These two genotypes maintained comparatively higher seed set percentage and

49

yield in a condition where irrigation was withheld during meiotic cell division as reported by

Onyemaobi et al. (2017). In that study, they also categorized AUS 12671, GBA sapphire and W96

among the sensitive genotypes which aligned with the outcome of the present investigation. This

implies that some correlation might exist between early- and late-stage drought tolerances in those

wheat genotypes. Several previous studies have demonstrated that trait variability at earlier stages can

be related to the grain yield under stress (Singh et al., 1999; Moud and Maghsoudi, 2008; González

and Ayerbe, 2011). In wheat, STI of seedling traits under PEG-induced water stress was found

significantly correlated with the STI from grain yield (Dodig et al., 2014). Furthermore, Grzesiak et al.

(2012) showed a strong correlation between drought tolerance for seedling dry weight and grain yield

in maize and triticale. Hence, comparative studies of molecular responses to drought among the

contrasting genotypes will help identify the molecular processes responsible for drought tolerance at

both stages.

Water stress in crops leads to an array of biochemical changes including altered expression of numerous

genes and transcription factors to protect from stress damage and maintain growth (Joshi et al., 2016;

Kulkarni et al., 2017b). Mao et al. (2010) reported an SNF1-type serine/threonine protein kinase of

wheat, TaSnRK2.4 conferred tolerance to water stress by enhancing root growth, strengthening water

retention ability and decreasing rate of water loss. Cai et al. (2011) identified TaMYB3R gene involving

in drought stress in wheat, indicating their potential role in regulating genes of cell cycle and therefore,

affecting overall plant growth under stress. Rahaie et al. (2010) examined differential regulation of

TaMYBsdu1 in the contrasting genotype under PEG-simulated water stress. The present investigation

also reported differential regulation of TaSnRK2.4, TaMYB3R, TaMYBsdu1 genes in the contrasting

genotypes under water stress indicating their roles for stress tolerance in those genotypes. Although

Niu et al. (2012) and Gao et al. (2018) identified TaWRKY2 as a regulator of downstream drought-

responsive genes, our study did not find any significant differences in the expression level of TaWRKY2

50

between the contrasting genotypes, suggesting that it might not involve in the tolerance mechanism in

the studied genotypes. Interestingly, gene expression pattern from our study showed more than 2-fold

higher expression of TaDHN in susceptible genotypes, which requires further investigation to explore

the underlying cause.

Being a complex trait, root system architecture requires a better understanding of the association among

different root parameters (Zhu et al., 2011). In this study, Pearson’s correlation analysis and principle

component analysis explored the multiple associations among the root traits and the relative

contribution of each root traits. The strong correlation between longest root length and other traits, like

root dry weight, root growth rate, total root length, root surface area and total root volume under stress

supports the importance of root proliferation under a limited environment in stress tolerance, as found

in Zaidi et al. (2016). Our study also found a highly significant association of root dry weight with all

the measured traits under stress indicating that root biomass at an early stage can be used as selection

criteria as suggested in Wasson et al. (2012) and Riaz et al. (2013). Under WS condition, high loading

score of FRW, RDW, TRL, RSA and TRV into the first principle component implied that these traits

have much influence on overall variability, therefore, can be selected simultaneously due to their direct

influence on each other. This could be another explanation of the fact that the genotypes with higher

STI values for these traits are more tolerant.

Effective root phenotyping is very challenging in soil, particularly for a large number of genotypes

differing in early vigour and rate of development. Heterogeneity, inconsistent water potential, and

varying degree of soil drying throughout the soil profile etc. greatly affect the screening procedure and

results (Munns et al., 2010). To overcome those challenges, hydroponics procedure is often preferred

for large-scale screening (Price et al., 1997; Szira et al., 2008). The consistency in the response of the

contrasting genotypes in terms of morphological traits and gene expression from our study and the

51

study of Onyemaobi et al. (2017) indicates that this procedure was successful in categorizing genotypes

for PEG-simulated early stage water stress.

3.6 Conclusions

In conclusion, wheat genotypes showed a wide range of variability in root system architectural traits

in response to the PEG-simulated water stress. Genotypes were ranked based on their relative stress

tolerance to identify tolerant and susceptible genotypes. Expression analysis of the drought-responsive

genes in contrasting genotypes further validated their characterization. Contrasting genotypes resulted

from the present investigation could be used for the comparative transcriptomic study to identify the

mechanism of tolerance to water stress.

3.7 Competing interests

The authors declare that they have no competing interests.

3.8 Funding

Md Sultan Mia highly acknowledges the Endeavour postgraduate scholarship from the Australian

Government for funding his PhD study. This study is also supported by the Global Innovation Linkages

program (GIL53853), Australian Department of Industry, Innovation and Science.

3.9 Authors’ contributions

MSM, GY and HL conceived and designed the experiments with planned scientific objectives. MSM

conducted the experiments, collected and analyzed the experimental data. MSM wrote the manuscript;

GY and HL critically reviewed the manuscript. All authors read and approved the final manuscript for

submission.

3.10 Acknowledgements

We thank Dr. Habtamu Ayalew for his technical support regarding the hydroponics system set-up. We

also thank Dr. Olive Onyemaobi for providing the seeds and relevant data.

52

3.11 Supplementary file (Table S3.1)

53

CHAPTER 4

54

4 Root transcriptome profiling of contrasting wheat genotypes reveals

mechanism of tolerance to water deficit

Md Sultan Mia1,2,3, Hui Liu1,2*, Xingyi Wang1,2, Chi Zhang4 and Guijun Yan1,2*

1UWA School of Agriculture and Environment, Faculty of Science, The University of Western

Australia, Perth, WA, Australia

2The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia

3Department of Plant Breeding, Bangladesh Agricultural Research Institute, Gazipur, Bangladesh

4 Beijing Genomics Institute-Shenzhen, Shenzhen 518083, China

*Correspondence:

Hui Liu [email protected]

Guijun Yan [email protected]

(This chapter has been submitted to Scientific Reports Journal on 6 February 2019)


55

4.1 Abstract

Water deficit limits plant growth and productivity in wheat. This study attempted comparative

transcriptome profiling of the tolerant (Abura) and susceptible (AUS12671) genotypes under PEG-

simulated water stress via genome-wide RNA-seq technology to understand the dynamics of tolerance

mechanism. Morphological and physiological analyses indicated that the tolerant genotype Abura had

a higher root growth and net photosynthesis, which accounted for its higher root biomass than

AUS12671 under stress. Transcriptomic analysis revealed a total of 924 differentially expressed genes

(DEGs) in response to water stress that were unique in the contrasting genotypes across time points,

with susceptible genotype AUS12671 having slightly more abundant DEGs (505) than the tolerant

genotype Abura (419). Gene ontology enrichment and pathway analyses of these DEGs highlighted

the contrasting mechanism of tolerance of the two genotypes. Genotype AUS12671 was hypersensitive

in response to water deficit as suggested by the predominant upregulation of genes involved in various

metabolic pathways. In contrast, Abura adopted an energy conserving strategy mostly by

downregulating the expression of genes of key pathways, such as global and overview maps,

carbohydrate metabolism, and genetic information processing. In addition, transcription factors (TF)

families like MYB, NAC contributed tolerance of Abura by regulating expressions of stress-related

genes and assisting in maintaining root growth under stress. Gene encoding transcription factors TIFY

were only differentially expressed between stressed and non-stressed conditions in the sensitive

genotype. DEGs and the tolerance mechanism identified in this study provides an insight for improving

seedling stage water deficit tolerance in wheat.

Keywords: comparative transcriptome, wheat, root, RNA-seq, water deficit

56

4.2 Introduction

Wheat is one of the most important cereal crops in terms of area of cultivation and production

throughout the world (FAO, 2016). A common feature of global wheat production is that wheat is

largely grown under rain-fed condition (Lobell et al., 2015). Limited and erratic pattern of rainfall

poses high risk for successful crop production throughout their life cycle, especially in the

Mediterranean environment (Tataw et al., 2016). Available soil moisture plays very crucial role during

early seedling growth for successful crop establishment in those areas. Water deficit during this stage

limits crop growth and development. Several previous studies have demonstrated that plants, when

subjected to water deficit during early stage, their root growth is restricted severely (Hsiao and Xu,

2000; Ji et al., 2014; Ayalew et al., 2015; Robin et al., 2015; Ayalew et al., 2018).

Roots are the primary organs that perceive the signals of water deficit and produce responses at cellular

and molecular such as genomic, transcriptomic and metabolic levels. Maintaining root growth under

low water potential is considered as effective adaptive response. Tolerant genotypes have the ability to

maintain root growth at low water potential that enables them to maintain an adequate water supply.

Therefore, root growth under water deficit is an effective indicator of plant adaptation and this has been

exploited in trait-based drought-tolerance breeding programs for different crops (Sharp et al., 2004;

Wasson et al., 2012; Comas et al., 2013).

In general, plants when subjected to water deficit exhibit adaptive mechanism involving certain

morphological, physiological and molecular processes (Fleury et al., 2010a; Kaur and Asthir, 2017;

Mia et al., 2017). It has been reported that these processes involve enhanced or reduced expression of

related genes to compensate for the stress damage (Shinozaki and Yamaguchi-Shinozaki, 2007b).

Some of those genes, such as dehydration-responsive-element-binding (DREB) genes, encode

“effector protein” rendering direct defense whereas others act passively by encoding regulatory

proteins in the form protein kinase and transcription factors that regulate the expression of stress-related

57

genes (Janiak et al., 2016; Yang et al., 2017b). With the advancement of next-generation sequencing

technology and dramatic reduction in associated cost, transcriptome profiling is a highly effective way

to investigate expression of stress related genes and their related pathways. Comparative transcriptome

profiling between contrasting genotypes has been studied to elucidate different molecular mechanism

of stress tolerance in various crops (Walia et al., 2005; Moumeni et al., 2011; Singh et al., 2014; Zhang

et al., 2015; Zhang et al., 2016b). However, in wheat most of those studies focused mainly on the water

deficit tolerance at the later stage of life cycle and many of them were accomplished using above

ground tissues (Aprile et al., 2009a; Aprile et al., 2013; Ma et al., 2017). In this study, next-generation

transcriptome sequencing was applied to elucidate how water deficit causes significant changes in gene

expression in roots of the two contrasting genotypes and to reveal the underlying mechanisms that play

crucial role at early growth stage water deficit tolerance.

4.3 Materials and methods

4.3.1 Plant materials, growth condition, treatment application and tissue sampling

Two genotypes of bread wheat, Abura and AUS 12671, hereafter termed as ABU and AUS, were

selected for this study. ABU, the tolerant genotype and AUS, the susceptible one, were selected based

on their contrasting performances (per cent root length reduction) under early-stage PEG-simulated

water deficit from a previous screening experiment comprising of fifty genotypes of diverse origins.

For transcriptomic study, healthy, uniform sized, surface sterilized seeds were germinated on soaked

filter paper and grown in a hydroponic system (pH set at 5.7-5.9) housed in a controlled plant growth

chamber (temperature of 22±2℃ with 16:8 hr light :dark cycle, light intensity of 300 µmol m-2s-1) at

the University of Western Australia. Twenty seedlings from each genotype were grown in half-strength

Hoagland solution for the initial 10 days after germination, and afterwards, stress treatments were

applied by supplementing the growing medium with 20% PEG6000 (osmotic potential of -0.50±2

MPa), whereas controlled plants received no supplement. Whole roots were collected from randomly

58

selected individual seedlings of each genotype in three biological replicates from both control and

stress treatment at 6 h and 48 h of stress imposition, frozen immediately in liquid nitrogen and stored

at -80℃ until RNA extraction. Before root sampling, gas exchange parameters (net photosynthesis

rate, transpiration rate, stomatal conductance) were also measured using a portable photosynthesis

system (LI-6400, Li-COR Inc., Lincoln, NE, USA) at those time points with the following settings:

2×3 EB opaque cuvette, block temperature (20ºC), CO2 concentration (400 µmols-1) and LED light

intensity (1000 µmolm-2 s-1). Seedlings were then grown up to 7days of stress period and harvested on

the seventh day to examine the morphological differences in root traits. Root length, per sent root

length reduction under stress, and dry root biomass (oven dried at 65 C for 72 h) were measured for

each genotype-treatment combination.

4.3.2 Total RNA extraction, library preparation and sequencing

Total RNA was extracted from 24 samples (2 genotypes × 2 treatments × 2 time-points × 3 replicates)

using RNeasy Plus Plant mini kit (Qiagen) with an on-column DNase treatment. Concentration of the

extracted RNA was checked by NanoDrop 2000 (ND-2000, Thermo Fisher Scientific, Inc., CA, USA),

and quality was checked by 1% (w/v) denatured gel electrophoresis and Agilent 2100 Bioanalyzer

(Agilent Technologies, Inc., CA, USA). The samples were then sent to Beijing Genomics Institute

(BGI), China for sequencing. In short, mRNAs were isolated from total RNA with oligo (dT) method

and fragmented, which were then used for cDNA synthesis. 150-bp paired-end sequencing libraries

were prepared and sequenced using HiSeq X Ten (Illumina, San Diego, USA) according to

manufacturer’s standard protocols. Raw sequencing data were processed by removing adapters, reads

with unknown bases (N’s>5%) and low quality (% bases with Phred score < 15 is greater than 20%)

to generate “clean data” as FastQ files using SOAPnuke software (version：v1.5.2) (Chen et al.,

2018b). Q20, Q30 and GC contents of the clean data were also calculated. All downstream analyses

were performed on clean data of those 24 libraries.

59

4.3.3 Differential gene expression analysis

Processed high-quality paired-end reads from each library were mapped to the bread wheat reference

(ftp://ftp.ensemblgenomes.org/pub/plants/release-39/fasta/triticum_aestivum/dna/) sequence using

HISAT2 (Hierarchical Indexing for Spliced Alignment of Transcripts) software, version v2.0.4 (Kim

et al., 2015). Reads were then aligned to the reference sequence using Bowtie2 (Langmead and

Salzberg, 2012), and gene expression level was calculated using RSEM (RNA-Seq by Expectation

Maximization) software Version: v1.2.12 (Li and Dewey, 2011) with default parameter. Pearson

correlation between all samples was calculated using ‘cor’ function in R software. DEGs were detected

with DEGseq as described in Wang et al. (2010) with the following parameters: Fold Change (control

vs stressed) >= 2 and Adjusted Pvalue <= 0.001.

4.3.4 Functional annotations, GO enrichment and Pathway analysis

Gene ontology (GO) classification and functional enrichment analysis were performed using

hypergeometric test (phyper), with the selected DEGs. DEGs with false discovery rate (FDR) not

larger than 0.01 were defined as significantly enriched. With the KEGG annotation result, DEGs were

classified according to official classification, and pathway functional enrichment was also performed

using phyper. FDR for each p-value was calculated.

Getorf (Rice et al., 2000) were used to find open reading frame (ORF) of each DEG. ORFs were then

aligned to transcription factor (TF) domains from PlntfDB using hmmsearch (Mistry et al., 2013) to

identify TF encoding genes from the selected DEGs.

4.4 Results

4.4.1 Abura (ABU) and AUS12671 (AUS) differ in morphological and physiological traits

under deficit

Application of stress treatment resulted in significant reduction (p<0.05) in root length and root

biomass in both ABU and AUS (Figure 4.1). However, the tolerant genotype ABU suffered less,

60

showed only about 14% reduction in root length and nearly 18% decline in root biomass under stress.

On the other hand, percent reduction in root length and biomass in the sensitive genotype AUS was

about 4-fold and 2-fold greater, respectively, than the tolerant counterpart (Figure 4.1).

Figure 4.1 Effect of water stress on root morphology. A) Root length of the contrasting tolerant

genotype ABU and susceptible genotype AUS under control (left, blue) and stressed (right, orange)

conditions; B) Root biomass of the contrasting genotypes under control (WW) and stressed conditions

(DD). Values in white boxes show per cent reduction due to stress.

The contrasting genotypes also differ significantly in terms of gas exchange parameters under stress.

Net photosynthetic rate (A), stomatal conductance (Gs) and transpiration rate (Tr) declined sharply due

to stress in both the genotypes (Figure 4.2). At 6h of stress, ABU and AUS differ significantly (p<0.05)

only for net photosynthetic rate. However, with the longer stress period (48 hours), marked differences

were observed for all the three measured parameters, where the tolerant genotype had nearly 2-fold

higher photosynthesis rate and 3-fold higher stomatal conductance and transpiration rate than the

susceptible genotype (Figure 4.2).

P=0.032

P=2.88E7

14.24%

58.56%

0

5

10

15

20

25

30

35

ABU AUS

Ro

ot

len

gth

(cm

)

P=0.034

P=0.011

17.69%

32.22%

0

5

10

15

20

25

30

ABU AUS

Ro

ot

bio

mas

s (m

g)

WW DD

61

Figure 4.2 Effect of water stress on gas exchange parameters. A) Net photosynthetic rate (A); B) Stomatal conductance (Gs) and C)

transpiration rate (Tr) of the contrasting genotypes ABU (tolerant) and AUS (susceptible) under control and stressed conditions.

62

4.4.2 Transcriptome quality and mapping statistics

A total of 216 Gb 150-bp paired-end (PE) reads were generated through deep whole transcriptome

sequencing on a total of 24 root samples (2 genotypes × 2 treatments × 2 time-points × 3 replicates)

from stressed and non-stressed (control) wheat seedling after quality control (Table 4.1). On average,

about 60 million (M) clean reads were obtained from each library with sizes ranged from 7-15 Gb

(Table S4.1). The reads were of high quality; nearly 98% and 95% of the clean reads had quality score

of Q20 and Q30, respectively. Additionally, the GC% of each library was about 57 (Table 4.1).

Table 4.1 Summary of the transcriptome sequencing and quality

Sum of Total

Clean Reads(M)

Sum of Total

Clean

Bases(Gb)

Mean

Q20 (%)

Mean

Q30 (%)

Mean

GC (%)

ABU 768.58 115.3 98.32 94.83 57

WS 371.08 55.66 98.32 94.84 57

WW 397.5 59.64 98.32 94.83 57

AUS 673.65 101.05 98.38 95.01 57

DDWS 318.93 47.84 98.30 94.75 57

WW 354.72 53.21 98.46 95.27 56

Grand Total 1442.23 216.35 98.35 94.92 57

ABU: The tolerant genotype, Abura, AUS: The susceptible genotype, AUS12671, DD: PEG-treated water stressed

condition, WW: Well-watered or control condition

More than three-quarters of the total reads were mapped to the wheat reference genome, including

around 50% with a perfect match and about 21% with unique matches (Table 4.2). A multi-position

match of 57.5 and 58.2 % was observed in control (WW) and treated (DD) sample of ABU,

63

respectively, whereas in the AUS genotype it was 54.8 and 56%, respectively. Total number of

transcripts detected in each library ranged from 88,225 to 104,995, accounting for nearly 70% of all

wheat genes (Table S4.1). Moreover, the genes with FPKM (transcript abundance of the gene) value

of one or higher accounted for around 60% (Figure 4.3) of all wheat genes. Pearson’s correlation

coefficients among the three biological replicates for each combination ranged from 0.86 to 0.99

(Figure 4.4), indicating the consistency of the three replicates.

Table 4.2 Mapping summary of the transcriptome

Reads

ABU AUS

DD (%) WW (%) DD (%) WW (%)

Total Mapped 79.2 78.1 77.0 75.6

Perfect Match 52.2 51.5 49.5 48.0

Mismatch 27.0 26.5 27.5 27.6

Unique Match 20.9 20.6 21.0 20.7

Multi-position Match 58.2 57.5 56.0 54.8

Unmapped 20.8 21.9 23.0 24.4

64

Figure 4.3 Number of genes with different FPKM (Fragments Per Kilobase of transcript Per Million)

value in the 24 RNA-seq samples

65

Figure 4.4 Pearson’s correlation heatmap for all the 24 samples showing consistency among the

biological replicates. The colour intensity represents degree of correlation.

4.4.3 Differential gene expression in response to water deficit

Transcriptome analysis of the two contrasting genotypes revealed significant differences in terms of

initial and adaptive responses as evident from the gene expression pattern during early growth period

water deficit. At 6 h of stress, a considerably higher number of genes (6077) were upregulated in the

susceptible genotype AUS than the tolerant genotype ABU (Table 4.3, Figure 4.5).

66

Figure 4.5 Differential gene expressions (control vs stressed) in the two genotypes, Abura (ABU) and

AUS12671 (AUS) in two time points of stress period (6 & 48 hours). A) Volcano plots of gene

expression in the tolerant genotype at 6h (left) and 48 h (right), showing that DEGs were greater in

67

number at 48h; B) Scatter plots of gene expression in the susceptible genotype at 6h (left) and 48 h

(right), showing that DEGs were greater in number at 48h; and C) Venn diagram showing the number

of upregulated (left) and downregulated genes (right) in different combination of treatment-time points.

Table 4.3 Differentially expressed gene counts in contrasting genotypes at different time points of

stress

Genotype 6h 48h

ABU (WWvsDD) Up

Down

3823

4974

9201

7564

AUS (WWvsDD) Up

Down

6077

4599

8727

7231

In contrast, number of upregulated genes were slightly higher in tolerant genotype at 48 h stress period.

However, higher number of DEGs were observed at both 6h and 48h time point in the tolerant

genotype. In general, number of DEGs (both upregulated and downregulated) were higher when the

stress period was longer (48hr). To understand the adaptive mechanism in the tolerant and susceptible

genotypes, particular attention were given to the genes whose differential regulations were genotype

specific and consistent in both 6h and 48h of stress. In the tolerant genotype, the number of genotype-

specific genes that were consistently upregulated and downregulated across time points under stress

were 159 and 260, respectively (Figure 4.5 C). In contrast, 309 and 196 genes were found to be

consistently upregulated and downregulated, respectively, under stress in the susceptible genotype

across time points (Figure 4.5 C).

68

4.4.4 Gene ontology analysis of DEGs

With genotype-specific and consistently expressed DEGs, upregulated and downregulated DEGs were

functionally categorized into three principal categories: biological process, cellular component and

molecular function (Figure 4.6). Under biological process category, most of the genes were associated

with the GO terms fell in the subcategories of “metabolic process”, “cellular process”, and “single

organism process” in both the tolerant and susceptible genotypes. However, the most striking

difference between the two genotypes was the number of upregulated and downregulated genes under

each of these subcategories, with the susceptible genotype having nearly twice the number of

upregulated genes (107, 91 and 68 respectively) than that (50, 39, and 33 respectively) in the tolerant

genotype. In contrast, considerably higher number of downregulated genes were associated with these

three terms in the tolerant genotypes (79, 61, and 47 respectively) than in the susceptible genotype (51,

50 and 30 respectively). Moreover, the GO term “biological regulation” were enriched almost equally

in both genotypes, but considerably higher number of upregulated genes (26) were associated with the

term “localization” in the susceptible genotype than in the tolerant genotype (Figure 4.6).

For cellular component categories, “cell”, “cell part”, “membrane”, “membrane part” and “organelle

part” were the most frequent GO term subcategories in both the tolerant and susceptible genotypes

with latter having considerably higher number of upregulated genes in each subcategory (96, 96, 80,

54 and 50, respectively) (Figure 4.6). Noticeably, no downregulated genes were enriched with term

“membrane” in the susceptible genotype. Furthermore, upregulated genes associated with terms “cell

junction” (5), and “symplast” (5) were assigned exclusively in the susceptible genotype, but in case of

downregulated genes they were enriched only in the tolerant genotype.

“Catalytic activity”, “binding” and “transporter activity” were the most abundant terms under

molecular function category. In the sensitive genotype, 117 and 78 upregulated genes were enriched

with the term “catalytic activity” and “binding”, respectively, nearly 2-fold higher than the tolerant

counterpart. However, in case of downregulated genes, these two terms were more frequent in the

69

tolerant genotype (92, and 60 respectively) than the susceptible genotype (52, 34 respectively).

Moreover, only 5 upregulated genes gave the term “transporter activity” in the tolerant genotypes,

about 4-times lesser than the sensitive genotype (19), whereas 14 and 6 downregulated genes were

enriched with this term in the tolerant and susceptible genotypes, respectively (Figure 4.6).

Figure 4.6 Number of differentially expressed genes (DEGs) enriched with different Gene Ontology

(GO) terms in the tolerant genotype Abura (ABU, in blue ) and susceptible genotype Aus12671 (AUS,

in orange). The GO terms were grouped into three categories: i) biological process, ii) cellular

component, and iii) molecular function.

70

4.4.5 KEGG pathway enrichment analysis

Pathway enrichment analysis assigned genotype-specific and consistently expressed DEGs across time

points to different pathways belonging to six major categories, including “environmental information

processing”, “genetic information processing”, “cellular processes”, “metabolism”, “organismal

systems’, and “disease-related” (Figure 4.7).

Figure 4.7 Number of differentially expressed genes (DEGs) enriched with different KEGG pathways

in the tolerant genotype Abura (ABU, in blue ) and susceptible genotype Aus12671 (AUS, in orange).

Pathways were grouped into six categories i) cellular process, ii) environmental information

processing, iii) genetic information processing, iv) disease-related processes, v) metabolism, and vi)

organismal systems

71

Interestingly, no enrichment of “transport and catabolism” pathway of cellular process was observed

in the tolerant genotype. Although, the “signal transduction” and “transcription” pathways of AUS and

ABU were upregulated to the similar frequency, AUS showed 4-5 fold enrichment of upregulated

genes influencing “translation” and “replication and repair” related pathways. In both the tolerant and

susceptible genotypes about 60-70% genes accounted for pathways related to metabolism categories.

However, except for pathways related to “biosynthesis of secondary metabolites” and “metabolism of

cofactors and vitamins”, all the pathways of this category were upregulated largely in the sensitive

genotype than the tolerant genotype. Similarly, number of upregulated genes annotated in

“environmental adaptation” was 2-fold higher in the sensitive than the tolerant genotype. However,

this pathway was downregulated to the same degree in both genotypes. (Figure 4.7).

Among the downregulated “metabolism” pathways, the top three pathways, “global and overview

maps”, “carbohydrate metabolism” and “biosynthesis of secondary metabolites”, were enriched to a

greater extend in the tolerant genotype (Figure 4.7). In contrast, no other pathway of this category

accounted for major changes in number of downregulated genes between AUS and ABU, except for

“nucleotide metabolism” which showed a seven-fold greater enrichment in the susceptible genotype.

Whereas, “translation”, “transcription”, “folding, sorting and degradation”, “membrane transport” and

“transport and catabolism” were downregulated with a higher frequency in the tolerant genotype than

the susceptible genotype.

4.4.6 Transcription factor (TF) encoding genes

Transcription factors play a vital role as molecular switches controlling the expression of certain genes

and regulating plant growth and development under certain environmental conditions. Extensive

database searches of all the DEGs predicted a total 6,088 TFs to be differentially expressed. These TFs

could be grouped into 60 families. MYB and MYB-related TFs were the highest among the identified

TF families. Other TFs with larger number of encoding genes (~400) were NAC, FAR1, and BHLH

(Figure 4.8).

72

Figure 4.8 Distribution of DEGs in different Transcript factors (TF) families considering the whole

transcriptome.

Sixteen TF families encoded by 40 DEGs were expressed uniquely in the tolerant genotype across time

points (Figure 4.9 i). These were MYB, G2-like, MADS, bHLH, AP2-EREBP, NAC, CPP, MYB-

related, TCP, HSF, LOB, GRAS, OFP, GRF, mTERF, and C2H2 (Figure 4.9 ii). Of them, MYB were

encoded by the highest number of DEGs (6) (Figure 4.9 ii). In contrast, Tify, Bhlh, MADS, AP2-

EREBP and Mterf were the mostly encoded TFs in the sensitive genotype (Figure 4.9 iii).

73

Figure 4.9 Transcription factors encoding DEGs (genotype specific and consistently expressed) and

their distributions. i) Venn diagram showing the number of transcription factors (TF) encoding DEGs

in different treatment–time points combination; ii) Distribution of the consistently expressed and

tolerant genotype specific DEGs encoding different TFs; iii) Distribution of the consistently expressed

and susceptible genotype specific DEGs encoding different TFs.

74

4.5 Discussion

Several previous studies indicated that plants subjected to water deficit exhibit wide ranges of

responses including molecular expression (Rampino et al., 2006), biochemical processes (Abid et al.,

2018) and involving various genes and pathways (Kulkarni et al., 2017a). In this study, transcriptomic

changes in wheat roots during early seedling growth stages (Zadoks scale 11-12) were investigated in

contrasting genotypes differing in root morphology under water deficit condition. It was hypothesized

that these differences when linked with differences in gene expressions between the contrasting

genotypes, would allow us to explore the underlying mechanism, identify the important genes,

transcription factors and complex pathways that play critical roles for water deficit tolerance at early

growth stage in wheat.

We found that relatively longer roots of the tolerant genotype ABU enabled it to uptake more water

under stress resulting in comparatively higher transpiration and stomatal conductance, subsequently

better photosynthesis leading to higher root biomass than the susceptible genotype AUS.

Transcriptomic analysis of the two contrasting genotypes also revealed that despite subjecting to the

same extent of water deficit, the responses differed greatly between the tolerant and the susceptible

genotypes in terms of number of genes expressed (Figure 4.5), pathways (Figure 4.7) and transcription

factors (Figure 4.9) involved. A significantly higher number of genotype-specific and consistently

expressed DEGs between stressed and non-stressed conditions were detected in the susceptible

genotype AUS than those in the tolerant genotype ABU. Fracasso et al. (2016) also reported higher

expression of drought-related genes in the susceptible genotype than the tolerant genotype.

Predominance of upregulation of genes in the susceptible genotype (Figure 4.6) indicated

hypersensitivity of this genotype in response to water deficit. In Contrast, downregulation of genes was

more prominent in the tolerant genotype. Therefore, it could be hypothesized that the tolerant genotype

adopted a defensive approach by downregulating key stress-related gene to minimize the adverse effect

75

of stress. Pathway analysis also supported our findings that the two genotypes adopted a contrasting

metabolic strategy to survive under early-stage water deficit. In response to the stress, AUS became

over-reactive and followed a faster energy utilization strategy by markedly upregulating genes related

to carbohydrate metabolism, lipid metabolism, amino acid metabolism and so on (Figure 4.7).

Downregulation of the key metabolic pathway related genes in the tolerant genotypes suggested an

energy conserving approach through metabolic regulation under stress in ABU.

Previous studies hypothesized that drought leads to accumulation of reactive oxygen species (ROS),

which causes oxidative stress in plants (Niinemets, 2016). Secondary metabolites, such as

phenylpropanoids and flavonoids act as antioxidant defence to protect plants from damaging effects of

oxidative stress (Hernández et al., 2009; Tattini et al., 2015). In wheat and barley, accumulation of

phenylpropanoids and flavonoid was observed in response to drought stress together with upregulation

of genes involved in the phenylpropanoids and flavonoid biosynthetic pathway (Ma et al., 2014;

Piasecka et al., 2017). This study confirmed that a total of thirteen genes involving those pathways

(Table S4.2) were upregulated in the tolerant genotype rendering additional defence against oxidative

stress. Seven upregulated genes that were unique to tolerant genotype across time point were found to

be involved in plant signal transduction pathways, two of which encoding SAUR (small auxin up-

regulated RNA) protein responsible for cell enlargement and plant growth (Table S4.2). Stortenbeker

and Bemer (2018) reviewed the role of SAUR gene family for adaptation of plant growth and

development. Li et al. (2015b) reported that SAUR proteins promoted plant growth in Arabidopsis.

It was reported that MYB transcription factors control many crucial biological processes under limited

water condition (Zhang et al., 2012). This study also revealed that MYB TF families were highly

enriched in tolerant genotype ABU across time points. MYB96, a modulator of the ABA signalling

pathway in Arabidopsis, was reported to be involved in restricting initial lateral root elongation so that

primary root growth was maintained and accessed deeper soil moisture under stress (Janiak et al.,

76

2016). RNA-seq analysis by Zhao et al. (2018) indicated TaMYB31, an ortholog of AtMYB96,

functioned as a positive regulator of drought resistance through up-regulation of wax biosynthesis

genes and drought-responsive genes. Expression of TaMYB3R was found nearly 6-fold higher under

6h of PEG treatment compared to the control in roots of wheat seedling (Cai et al., 2011). Another

MYB transcription factors, TaMYB sdu1 was also reported to be upregulated in roots of PEG-6000

treated wheat seedlings (Rahaie et al., 2010). Besides MYBs, several NAC transcription factors were

also consistently expressed in tolerant genotype AUS. In PEG-treated wheat seedlings, overexpression

of TaRNAC1, a predominantly root-expressed NAC transcription factor, conferred increased root

length, biomass and dehydration tolerance (Chen et al., 2018a). In another study, it was reported that

under water-limited conditions up-regulation of TaNAC69-1 acted as a transcriptional repressor of the

root growth inhibitory genes, thus enabling root elongation, enhancing biomass in wheat under stressed

condition (Chen et al., 2016). All the above-mentioned reports suggest that MYB and NAC TFs might

have played a crucial role in root growth, root system proliferation and overall biomass in the tolerant

genotypes under stress.

In Summary, high-throughput RNA-seq technology was employed in this study to characterize the root

transcriptome of the contrasting genotypes under early seedling stage water deficit. The differential

response of the two genotypes was evident from their root morphological features and physiological

measurements under stress. The sensitive genotype AUS suffered significantly due to early-stage water

deficit. These were further characterized by the root transcriptome analysis where sensitive genotype

showed a higher number of DEGs. Upregulation of DEGs related to all major key pathways

demonstrated the hypersensitive response of the susceptible genotype AUS, whereas the tolerant

genotype was generally less affected. Additionally, secondary metabolites such as phenylpropanoids

and flavonoid and transcription factors MYB, NAC acted as a defence mechanism in the tolerant

genotype.

77

4.6 Acknowledgments

An Endeavour Postgraduate Scholarship from the Australian Government funded Md Sultan Mia’s

PhD study. The authors would like to thank the financial support from the Global Innovation Linkage

project of Australian Government (GIL53853) and acknowledge the support and resources provided

by the Pawsey Supercomputing Centre with funding from the Australian Government and the

Government of Western Australia. Nectar Research Cloud, a collaborative Australian research

platform supported by the National Collaborative Research Infrastructure Strategy (NCRIS) is also

acknowledged. Authors express their gratitude to Ms Pratima Gurung for her help during root sampling

and preparations.

4.7 Author Contributions

MSM, HL and GY conceived and designed the experiment. MSM set up the experiment; performed

the phenotypic and physiological characterization and data scoring with occasional help from XW.

MSM and XW did RNA isolation and sample preparation. CZ of BGI prepared sample libraries,

conducted sequencing and subsequent processing. MSM and CZ performed relevant data analysis and

prepared the figures. MSM wrote the manuscript with feedbacks from HL and GY. All authors

reviewed the manuscript before submission.

4.8 Competing Interests:

The authors declare no conflict of interest.

78


Table S4.1: Sample-wise read quality and mapping statistics of the whole transcriptome of Abura and AUS12671

Sample Reads ( million) Bases (Gb) Q20 (%) Q30 (%) Number of genes mapped Number of transcripts mapped

1-AUS-WW-6H 64 10 99 96 73424 95594

2-AUS-WW-6H 67 10 99 96 76042 98792

3-AUS-WW-6H 53 8 98 95 76094 97628

4-AUS-WW-48H 60 9 98 95 75731 96845

5-AUS-WW-48H 65 10 98 95 74751 95278

6-AUS-WW-48H 45 7 98 95 75641 98734

7-AUS-DD-6H 56 8 98 95 73347 91699

8-AUS-DD-6H 58 9 98 95 77330 102107

9-AUS-DD-6H 51 8 98 95 76820 99141

10-AUS-DD-48H 54 8 98 94 76378 97658

11-AUS-DD-48H 53 8 98 95 75012 94935

12-AUS-DD-48H 47 7 98 95 77751 101532

13-ABU-WW-6H 57 9 98 95 77437 101704

14-ABU-WW-6H 48 7 98 95 76672 100306

15-ABU-WW-6H 64 10 98 95 78311 103907

16-ABU-WW-48H 102 15 98 95 77709 101043

17-ABU-WW-48H 73 11 98 95 75331 96224

18-ABU-WW-48H 54 8 98 95 77428 102013

19-ABU-DD-6H 54 8 98 95 71758 88225

20-ABU-DD-6H 78 12 98 95 76959 100075

21-ABU-DD-6H 55 8 98 95 75763 97963

22-ABU-DD-48H 69 10 98 95 79372 104995

23-ABU-DD-48H 56 8 98 95 74187 92919

24-ABU-DD-48H 59 9 98 95 74062 92480

Average 60 9 98 95 75971 97992

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Table S4.2 Upregulated genes related to KEGG pathway in the tolerant genotype

Pathway

DEGs with

pathway

annotation

(100)

P value Q value Pathway ID Level 1 Level 2

Flavonoid biosynthesis 7 (7%) 2.49E-05 0.001347 ko00941 Metabolism Biosynthesis of other secondary metabolites

Biosynthesis of secondary metabolites 27 (27%) 0.000277 0.00749 ko01110 Metabolism Global and overview maps

AGE-RAGE signalling pathway in diabetic complications 3 (3%) 0.002981 0.051689 ko04933 Human Diseases Endocrine and metabolic diseases

Isoquinoline alkaloid biosynthesis 3 (3%) 0.003829 0.051689 ko00950 Metabolism Biosynthesis of other secondary metabolites

Isoflavonoid biosynthesis 3 (3%) 0.009054 0.087111 ko00943 Metabolism Biosynthesis of other secondary metabolites

Tyrosine metabolism 3 (3%) 0.009679 0.087111 ko00350 Metabolism Amino acid metabolism

Metabolic pathways 31 (31%) 0.023747 0.183192 ko01100 Metabolism Global and overview maps

Stilbenoid, diarylheptanoid and gingerol biosynthesis 3 (3%) 0.028216 0.190455 ko00945 Metabolism Biosynthesis of other secondary metabolites

Benzoxazinoid biosynthesis 2 (2%) 0.050663 0.303978 ko00402 Metabolism Biosynthesis of other secondary metabolites

Cutin, suberine and wax biosynthesis 2 (2%) 0.091412 0.425833 ko00073 Metabolism Lipid metabolism

Plant hormone signal transduction 7 (7%) 0.093392 0.425833 ko04075 Environmental Information Processing Signal transduction

Phenylalanine metabolism 2 (2%) 0.095481 0.425833 ko00360 Metabolism Amino acid metabolism

Steroid biosynthesis 2 (2%) 0.102515 0.425833 ko00100 Metabolism Lipid metabolism

Phenylpropanoid biosynthesis 6 (6%) 0.111964 0.431862 ko00940 Metabolism Biosynthesis of other secondary metabolites

Anthocyanin biosynthesis 1 (1%) 0.171989 0.578239 ko00942 Metabolism Biosynthesis of other secondary metabolites

Flavone and flavonol biosynthesis 1 (1%) 0.182932 0.578239 ko00944 Metabolism Biosynthesis of other secondary metabolites

Tryptophan metabolism 2 (2%) 0.193795 0.578239 ko00380 Metabolism Amino acid metabolism

Circadian rhythm - plant 2 (2%) 0.196675 0.578239 ko04712 Organismal Systems Environmental adaptation

Tropane, piperidine and pyridine alkaloid biosynthesis 1 (1%) 0.244642 0.578239 ko00960 Metabolism Biosynthesis of other secondary metabolites

RNA degradation 3 (3%) 0.260082 0.578239 ko03018 Genetic Information Processing Folding, sorting and degradation

Phenylalanine, tyrosine and tryptophan biosynthesis 1 (1%) 0.273259 0.578239 ko00400 Metabolism Amino acid metabolism

Ubiquitin mediated proteolysis 3 (3%) 0.282681 0.578239 ko04120 Genetic Information Processing Folding, sorting and degradation

Pentose and glucuronate interconversions 2 (2%) 0.297781 0.578239 ko00040 Metabolism Carbohydrate metabolism

beta-Alanine metabolism 1 (1%) 0.316466 0.578239 ko00410 Metabolism Metabolism of other amino acids

Ether lipid metabolism 1 (1%) 0.325517 0.578239 ko00565 Metabolism Lipid metabolism

Linoleic acid metabolism 1 (1%) 0.329104 0.578239 ko00591 Metabolism Lipid metabolism

Sesquiterpenoid and triterpenoid biosynthesis 1 (1%) 0.339752 0.578239 ko00909 Metabolism Metabolism of terpenoids and polyketides

80

Pathway

DEGs with

pathway

annotation

(100)

P value Q value Pathway ID Level 1 Level 2

Pyrimidine metabolism 3 (3%) 0.340657 0.578239 ko00240 Metabolism Nucleotide metabolism

Basal transcription factors 1 (1%) 0.341511 0.578239 ko03022 Genetic Information Processing Transcription

Protein processing in endoplasmic reticulum 5 (5%) 0.356991 0.578239 ko04141 Genetic Information Processing Folding, sorting and degradation

Purine metabolism 3 (3%) 0.367826 0.578239 ko00230 Metabolism Nucleotide metabolism

MAPK signaling pathway - plant 4 (4%) 0.369109 0.578239 ko04016 Environmental Information Processing Signal transduction

Spliceosome 3 (3%) 0.374684 0.578239 ko03040 Genetic Information Processing Transcription

RNA polymerase 2 (2%) 0.377871 0.578239 ko03020 Genetic Information Processing Transcription

Ubiquinone and other terpenoid-quinone biosynthesis 1 (1%) 0.383171 0.578239 ko00130 Metabolism Metabolism of cofactors and vitamins

Arachidonic acid metabolism 1 (1%) 0.394587 0.578239 ko00590 Metabolism Lipid metabolism

Diterpenoid biosynthesis 1 (1%) 0.396201 0.578239 ko00904 Metabolism Metabolism of terpenoids and polyketides

RNA transport 3 (3%) 0.422223 0.600001 ko03013 Genetic Information Processing Translation

N-Glycan biosynthesis 1 (1%) 0.45081 0.608511 ko00510 Metabolism Glycan biosynthesis and metabolism

Glycine, serine and threonine metabolism 1 (1%) 0.455194 0.608511 ko00260 Metabolism Amino acid metabolism

Amino sugar and nucleotide sugar metabolism 2 (2%) 0.462018 0.608511 ko00520 Metabolism Carbohydrate metabolism

Plant-pathogen interaction 6 (6%) 0.497803 0.618859 ko04626 Organismal Systems Environmental adaptation

Terpenoid backbone biosynthesis 1 (1%) 0.504516 0.618859 ko00900 Metabolism Metabolism of terpenoids and polyketides

mRNA surveillance pathway 3 (3%) 0.513527 0.618859 ko03015 Genetic Information Processing Translation

Porphyrin and chlorophyll metabolism 1 (1%) 0.519525 0.618859 ko00860 Metabolism Metabolism of cofactors and vitamins

Indole alkaloid biosynthesis 1 (1%) 0.527176 0.618859 ko00901 Metabolism Biosynthesis of other secondary metabolites

Glycerophospholipid metabolism 1 (1%) 0.576894 0.654078 ko00564 Metabolism Lipid metabolism

Homologous recombination 1 (1%) 0.581403 0.654078 ko03440 Genetic Information Processing Replication and repair

ABC transporters 1 (1%) 0.605896 0.667722 ko02010 Environmental Information Processing Membrane transport

Ascorbate and aldarate metabolism 1 (1%) 0.662229 0.715207 ko00053 Metabolism Carbohydrate metabolism

Glycerolipid metabolism 1 (1%) 0.752733 0.797012 ko00561 Metabolism Lipid metabolism

Cyanoamino acid metabolism 1 (1%) 0.797996 0.828688 ko00460 Metabolism Metabolism of other amino acids

Biosynthesis of amino acids 1 (1%) 0.936693 0.936952 ko01230 Metabolism Global and overview maps

Starch and sucrose metabolism 1 (1%) 0.936952 0.936952 ko00500 Metabolism Carbohydrate metabolism

81

CHAPTER 5

82

5 Response of wheat to post-anthesis water stress, and the nature of

gene action as revealed by combining ability analysis

Md Sultan Mia1,2, Hui Liu1, Xingyi Wang1, Zhanyuan Lu3*and Guijun Yan1*

1UWA School of Agriculture and Environment, Faculty of Science, and The UWA Institute of

Agriculture, The University of Western Australia, Perth, WA 6009, Australia. 2Plant Breeding

Division, Bangladesh Agricultural Research Institute, Joydebpur, Gazipur 1701, Bangladesh. 3Inner

Mongolia Academy of Agriculture and Animal Husbandry, 22 Zhaojun Road, Yuquan District,

Huhehot 010031, China

* Correspondence:

Guijun Yan

[email protected]

Zhanyuan Lu

[email protected]

(This chapter has been published in Crop and Pasture Science on 26 July 2017

https://doi.org/10.1071/CP17112)



83

5.1 Abstract

Post-anthesis water stress is a major limitation to wheat grain yield globally. Understanding the nature

of gene action of yield-related traits under post-anthesis water stress will help to breed stress-resilient

genotypes. Four bread wheat genotypes having varying degree of drought tolerance were crossed in a

full-diallel fashion and the resultant crosses along with the parental genotypes were subjected to water

stress after the onset of anthesis in order to investigate their comparative performance and nature of

gene action. Parental genotypes Babax (B) and Westonia (W) performed better compared to C306 (C)

and Dharwar Dry (D) with respect to relative reduction in grain yield and related traits under stressed

condition. Direct cross B×D and reciprocal cross W×C were more tolerant to water stress, while cross

between C306 and Dharwar Dry, either direct or reciprocal, produced more sensitive genotypes.

Combining ability analysis revealed that both additive and non-additive gene action were involved in

governing the inheritance of the studied traits, with predominance of non-additive gene action for most

of the traits. Among the parents, Babax and Westonia were better combiners for grain yield under stress

condition. B×D in stressed condition, and C×W in both stressed and stress-free conditions were the

most suitable specific crosses. Moreover, the specificity of parental genotypes as female parents in

cross combination was also evident from the significant reciprocal combining ability (RCA) effects of

certain traits. Low to medium narrow sense heritability and high broad sense heritability were observed

for most of the studied traits in both well-watered and water stress conditions. The results of the study

suggested that specific cross combinations with high SCA involving better performing parents with

high GCA may generate hybrids as well as segregating populations suitable for further breeding

programs.

Keywords: combining ability, diallel cross, gene action, water stress, wheat.

84

5.2 Introduction

Wheat (Triticum aestivum L.) is one of the major cereal crops throughout the world in terms of

production and area coverage (FAO, 2016). In Australia, wheat is considered to be the most important

grain crop for both domestic consumption and export markets (Balouchi, 2010). However, every year

global wheat production is affected by a number of abiotic stresses and among them drought is the

major stress factor causing serious damage (Araus et al., 2008; Balouchi, 2010; Comas et al., 2013).

With intermittent rainfall pattern and rising temperature, the extent and frequency of drought are likely

to increase in future due to global climate change (Turner and Asseng, 2005; Wheeler and von Braun,

2013). More than half of the world wheat growing area is affected by drought of varying degree during

their growth period (Rajaram, 2001; Pfeiffer et al., 2005).

Effect of drought can be evident in various degrees during different growth periods of wheat including

early establishment period (Tigkas and Tsakiris, 2015), pre-anthesis period (Abid et al., 2016), as well

as post-anthesis periods (Kobata et al., 1992; Loss and Siddique, 1994; Pradhan et al., 2012). Post-

anthesis water deficit is one of the major limitations to yield in areas where crop water use relies

strongly on stored soil moisture (Chenu et al., 2011). Post-anthesis shortage of soil moisture causes

floral abnormalities, low pollen viability, and grain abortion resulting in reduced kernel number

(Dolferus et al., 2011). This also affects grain filling leading to shrivelled kernels (Rajala et al., 2009;

Mitchell et al., 2013) and consequently, to reduced yield (de Oliveira et al., 2013). In Australia, severe

drought can cause wheat grain yield reduction by more than 50% (Australian Grains Report, 2009).

Comprehensive knowledge of the genetic control of quantitative traits is useful in designing successful

breeding strategy for those traits. The diallel crossing designs and their modifications, proposed by

(Hayman, 1954; Jinks, 1954; Gardner and Eberhart, 1966; Mather and Jinks, 1982) are frequently used

by plant breeders to obtain detailed information regarding the genetic architecture and mode of gene

action in the manifestation of various quantitative traits. The diallel cross design provides information

on estimates of general combining ability (GCA), specific combining ability (SCA) and heritability

85

(Baker, 1978; El-Maghraby et al., 2005; Iqbal et al., 2007). The combining ability analysis is useful to

understand the breeding value of the parental lines and the roles of additive and non-additive gene

effects in expression of quantitative traits (Madić et al., 2014). Successful use of combining ability in

identifying superior genotypes in earlier generation has been reported in many cereals including wheat

(Zhang et al., 1994; Joshi et al., 2004; Krystkowiak et al., 2009; Amegbor et al., 2016). Combining

ability analysis has been effectively used to select excellent parents and better hybrid combinations in

various stress tolerance studies, including drought tolerance (Farshadfar et al., 2011; Ayalew et al.,

2016b), heat tolerance (Celliers et al., 1999), salinity tolerance (Chen et al., 2008), pre-harvest

sprouting tolerance (Fakthongphan et al., 2016), waterlogging tolerance (Zhou et al., 2007), disease

resistance (Danehloueipour et al., 2006; Hei et al., 2015) and insect resistance (Mohammed et al.,

2016). However, most drought tolerant research has focused on early growth stages and the nature of

gene action of bread wheat under post-anthesis water-stress condition needs to be characterized. An

understanding of the inheritance of yield and related traits of wheat under post-anthesis water-stress

will contribute to developing suitable strategy for breeding drought-tolerant wheat genotypes.

Therefore, this study investigated the phenotypic differences of diallel crosses under both well-watered

and post-anthesis water stress conditions to understand the underlaying mechanism, to explain the

association among different traits, and to determine the nature of gene action of those traits using

combining ability analysis.

5.3 Material and methods

5.3.1 Materials

Four bread wheat genotypes, Babax (B), C306 (C) and Dharwar Dry (D), and Westonia (W) were used

for this study. Babax, C306, and Dharwar Dry were reported to have varying degree of drought

tolerance in several previous studies (Kirigwi et al., 2007; Pinto et al., 2010b; Kadam et al., 2012), and

Westonia is a popular Australian wheat variety adapted to the low and medium rainfall in Western

86

Australia (Conocono, 2002). Twelve F1 hybrids including reciprocals were obtained from the bi-

parental crosses of full diallel design following a standard crossing procedure (Simmonds N.W., 1999).

5.3.2 Methods

Four parents and their twelve hybrids were grown in a controlled environment (set at 22/15°C day/night

temperature) glasshouse at The University of Western Australia, Crawley, Western Australia (31°59’

S, 115°49’ E). Seeds were surface sterilized with 1% sodium hypochlorite for 10 minutes and then

rinsed 3 times with de-ionized water. Sterilized seeds were placed in Petri dishes lined with two layers

of moist filter paper for two days in dark in a 20°C plant growth chamber. Three healthy and evenly

germinated seedlings were planted in each cylindrical plastic pots of 9cm inner diameter and 50cm

height, closed at the base with a cover with holes to facilitate water drainage. Filter paper was placed

at the bottom of the inside part of each pot, which were then filled with 2.5 kg of sterilized pot mixture

(5:2:3 fine composted pine bark:coco peat:brown river sand, pH~6.0). After establishment, seedlings

were thinned out leaving a single plant per pot. A 20mm thick layer of white plastic beads was placed

on the soil surface of each pot to reduce evaporative water loss. Pots were fertilized with greenhouse

grade water-soluble fertilizer (N:P:K:Ca:Mg 15:2.2:12.4:5:1.8) on weekly basis after 21 days of

sowing. Pots were arranged in a completely randomized block design with three replicates. There were

three pots (replicates) per genotype per treatment totalling 96 pots. For the measurement of field (pot)

capacity of the soil media, three free draining pots, each containing 2 kg of air-dry soil media, were

inundated with water and allowed to drain for 48 hours. Two samples from each pot were taken, and

their fresh weight and dry weight were measured using a balance before and after oven-drying,

respectively. The per cent water content of the media at filed capacity was calculated as 28% (w/w),

using the following formula: % soil water content = FW−DW

DW× 100, where, FW= fresh weight, and

87

DW= dry weight of the samples. Pots were watered manually on each alternate days close to 80% field

capacity (FC) by weighing until anthesis, Zadoks growth scale Z60 for cereals (Zadoks et al., 1974).

Pots were also rearranged weekly to avoid any positional effect (Fang et al., 2010). Main stem of

individual plant of each pot was tagged before anthesis. Anthesis date was recorded on the day when

anthers had emerged from the flowers of the main stem of the individual plants and two water

treatments were imposed: (i) pots in the well-watered (WW) treatment were weighed and watered every

day at 10.00am to maintain soil moisture at 80% field capacity (FC) and continued until plants reached

physiological maturity; (ii) pots in the water stress (WS) treatment were weighed but not watered for

a period of 7 days from anthesis. On 7 DAA (days after anthesis) pots of WS treatments were rewatered

as per WW treatment and maintained close to 80% field capacity (FC) until maturity (Figure 5.1).

Figure 5.1 Soil water content (% field capacity) of the pots in well-watered (WW) and water stress

(WS) conditions from anthesis (Day 0) to 10 DAA (days after anthesis). In WS treatment, watering

was withheld for 7 days from anthesis followed by rewatering on 7 DAA close to 80% FC until

maturity, while WW treatment was maintained at around 80% FC until maturity. Each data point

represents mean ± standard error (n=48).

88

5.3.3 Data collection

Fertile tiller (having spikes with grain) per plant were counted. Chlorophyll content of the plants was

measured three times on the middle section of the flag leaf using a handheld portable chlorophyll meter

(SPAD-502Plus; Konica Minolta, Osaka) on fifth day of water stress both in water-stressed and well-

watered treatments. For biomass, root and shoot of individual plants were collected after harvesting at

physiological maturity and were dried at 60˚C until the samples attained a constant weight. Grain yield

per plant was recorded by weighing of grains.


The experiment was designed in a two-factorial (genotype and treatment) randomized block design

with three replications. Two-way analyses of variance (ANOVA) were performed using GenStat (17th

Version for windows) software. Comparison of genotypic means was conducted using least significant

difference (LSD) at p≤0.05. Association between traits were determined by calculating phenotypic

correlation coefficient. Griffing’s method I of model I (Griffing, 1956) were used in calculating general

and specific combining abilities, in statistical software R 3.2.3 using the R package “DiallelAnalysisR”

(Yaseen, 2009). Broad-sense and narrow-sense heritability were calculated according to the following

formulae (Wray and Visscher, 2008): Broad-sense heritability, ℎ𝑏2 =

σ2a+σ2d

σ2p ; narrow-sense

heritability, ℎ𝑛𝑠2 =

σ2a

σ2p , where, σ2a=additive variance; σ2d=dominance variance; and σ2p

=phenotypic variance.

89

5.4 Results

5.4.1 Phenotypic differences under imposed water treatments were significant but variable in

the wheat diallel crosses

Withdrawal of water at anthesis caused significant reduction (p<0.001) of the studied traits, including

fertile tiller number per plant, thousand grain weight and grain yield in parental genotypes as well as

in direct and reciprocal crosses (Figure 5.2a to 5.2c) as revealed by the analysis of variance, which

showed highly significant differences among full diallel crosses for all the traits measured (Table 5.1).

Significant variation for the two water treatments in the studied traits denoted the differential responses

of the genotypes to the imposed water treatments. Moreover, the interaction between diallel crosses

(genotype) and treatments was also highly significant (p<0.001).

Table 5.1 Combined analysis of variance for various traits of parents and their 4×4 full diallel crosses

under well-watered and water stress treatment

Source of

variation

df Fertile tiller

no./plant

Biomass Chlorophyll

content

Thousand

grain

weight

Grain yield

Replication 2 5.01NS 44.54** 1.79NS 0.46NS 2.24NS

Genotype (G) 15 35.75*** 404.48*** 30.74*** 115.13*** 24.03***

Treatment (T) 1 330.15*** 1103.26*** 391.93*** 1251.37*** 402.052***

G×T 15 4.24** 28.86*** 0.77NS 19.16** 5.13***

Error 62 2.52 6.53 0.66 6.96 1.60

‘*p<0.05; ‘**P< 0.01; ‘***P<.001, NS denotes no significant difference, df denotes degree of freedom

90

Figure 5.2 Effect of post-anthesis water deficit on a) fertile tiller number b) thousand grain weight and

c) grain yield in 4×4 full diallel crosses including parents, direct crosses and reciprocal crosses. Paired

bars represent means ± standard errors (n=3). Least significant difference lsd) values across genotype

(G), treatment (T), and interaction (G×T) effect are at 95% level of significance. B= Babax, C=C306,

D=Dharwar Dry, W=Westonia

91

5.4.1.1 Effective tiller per plant

Due to imposed water stress, parental genotype Westonia underwent highest reduction in fertile tiller

number from 17 (WW condition) to 10 (WS condition) followed by Dharwar Dry loosing

approximately 26% fertile tiller, whereas C306 and Babax were consistent with highest and lowest

fertile tiller number in both conditions, respectively (Figure 5.2a). Reciprocal cross DB and D×C

suffered more compared to the direct cross B×D and C×D under stressed condition. Cross C×W and

its reciprocal was the best performer with highest number of fertile tiller in both WW and WS

conditions.

5.4.1.2 Thousand grain weight

Post-anthesis water stress caused nearly 42% reduction (P=0.013) in thousand grain weight in C306,

followed by Dharwar Dry (26%) and Westonia (25%), whereas Babax, with highest thousand grain

weight in both treatments, faced only about 14% reduction (P=0.043) as compared to well-watered

condition (Figure 5.2b & Table 5.2).

5.4.1.3 Grain yield

Among the parents, Dharwar Dry produced highest grain yield in normal condition, but in stressed

condition it suffered seriously and ranked last in terms of relative grain weight (Figure 5.2c & Table

5.2). In contrary, Babax was the least suffered genotype in reproductive stage moisture deficit with

consistent grain yield in both conditions. While the direct cross C×W and reciprocal cross D×B

produced highest grain yield under normal condition, B×W and its reciprocal was the best in

maintaining grain yield under stressed condition. Both direct and reciprocal crosses of B×C were the

worst with lowest grain yield in both conditions.

Parental genotypes and their direct and reciprocal crosses were assigned a ranking score based on their

magnitude of reduction in each yield component under post-anthesis water stress compared to well-

watered condition (least reduction = 1) (Table 5.2). Genotypes with lower ranking score are more

tolerant to the reproductive stage water stress, and those with higher rankings are more sensitive.

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Table 5.2 Comparison of relative tolerance of the diallel crosses on the basis of rank summation index

Genotypes

% reduction Rank score Rank

summation

index

Fertile

tiller

no./plant

Thousand

grain

weight

Grain

yield

Fertile

tiller

no./plant

Thousand

grain

weight

Grain

yield

Parents

Babax (B) 11.48 14.36 13.97 1 1 1 3

C306 (C) 12.94 42.10 30.77 2 4 3 9

Dharwar Dry (D) 26.21 26.25 52.83 3 3 4 10

Westonia (W) 37.24 25.16 28.64 4 2 2 8

Direct Cross

B×C 12.56 16.47 33.07 1 4 5 10

B×D 16.64 6.70 26.00 2 1 2 5

B×W 17.80 17.85 24.43 3 5 1 9

C×D 26.36 28.86 49.34 5 6 6 17

C×W 24.56 7.77 28.84 4 2 4 10

D×W 34.89 15.85 26.90 6 3 3 12

Reciprocal Cross

C×B 20.56 18.18 16.94 1 4 2 7

D×B 43.23 11.72 34.72 6 2 5 13

D×C 35.59 29.96 41.47 5 6 6 17

W×B 23.68 14.09 22.97 2 3 3 8

W×C 24.11 6.24 10.69 3 1 1 5

W×D 28.87 25.64 33.80 4 5 4 13

B= Babax, C=C306, D=Dharwar Dry, W=Westonia

There was only small reduction in thousand grain weight in the direct and reciprocal crosses of B×D

and C×W due to water stress condition, but relatively larger reduction occurred in the direct and

reciprocal crosses of C×D.

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5.4.2 Yield attributes were associated with biomass and chlorophyll content

In both stressed and stress-free conditions, grain yield was positively correlated with both chlorophyll

content and thousand grain weight (Table 5.3). There was a weak nonsignificant negative correlation

between thousand grain weight and fertile tiller number across two water regimes (r=-0.11in WW, and

r=-0.05 in WS condition). Under both WW and WS conditions, fertile tiller number per plant was

positively correlated with biomass (r=0.44, and r=0.48 respectively at P<0.01) and chlorophyll content

(r=0.42, and r=0.28 respectively at P<0.05). While correlation between biomass and thousand grain

weight (r=-0.06) was negative in WS condition, there was nonsignificant positive correlation (r=0.13)

among them in WW condition. However, no significant correlation was observed for fertile tiller

number and biomass with grain yield across treatments.

Table 5.3 Association of the investigated traits in well-watered (above diagonal) and water stress

(below diagonal) condition. (Numbers in bold are significant at P≤0.05)

Traits Fertile tiller

no./plant Biomass

Chlorophyll

content

Thousand

grain

weight

Grain

yield

Fertile tiller no./plant 1 0.44 0.42 -0.11 0.22

Biomass 0.48 1 0.11 0.13 0.18

Chlorophyll content 0.28 0.26 1 0.22 0.52

Thousand grain weight -0.05 -0.06 0.04 1 0.44

Grain yield 0.23 0.02 0.60 0.58 1

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5.4.3 Combining ability analysis revealed complex gene action, and heritability of the studied

traits in two water regimes

ANOVA for combining ability revealed that the variances due to general combining ability (GCA) and

specific combining ability (SCA) of all the traits were highly significant in both WW and WS

conditions (Table 5.4). The significant GCA and SCA indicated the importance of both additive and

non-additive nature of gene action in controlling most of the traits under both WW and WS conditions.

The non-significant mean sum square of reciprocal combining ability (RCA) of chlorophyll content

and grain yield suggested no cytoplasmic effect for determining of the traits irrespective of treatment

conditions.

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Table 5.4 Analysis of Variance for combining ability

Source

Well-watered Water stress

GCA SCA RCA GCA SCA RCA

Fertile tiller no./plant 26.58** 4.50** 5.86** 8.17** 5.03** 7.09**

Biomass 42.9** 58.48** 108.83** 63.18** 47.51** 93.71**

Chlorophyll content 7.67* 7.66* 0.82 9.15* 8.55* 0.8

Thousand grain

weight

7.28* 20.76** 8.51** 45.81** 48.53** 7.56*

Grain yield 9.43** 8.40** 1.48 9.22** 6.65** 0.41

*, ** - significant at 0.05 and 0.01 probability levels, respectively; GCA=general combining ability, SCA=specific combining ability, RCA=reciprocal combining ability

96

Estimate of GCA effects of the parents differed in WW and WS treatments. For example, Babax had

significant positive GCA effect (1.16) only for thousand grain weight in WW condition, whereas in

WS condition significant positive GCA effect was found for biomass (2.94), thousand grain weight

(2.77) and grain yield (0.39) (Table 5.5). In case of Westonia, significant positive GCA effect was

observed for chlorophyll content (1.13) and thousand grain weight (1.17) under stressed condition.

However, it was a better combiner for grain yield under both conditions. Dharwar Dry had highly

positive GCA effects for grain yield (0.84) and biomass (3.61) in WW and WS condition, respectively.

On the other hand, the GCA effects of C306 was positively significant only for fertile tiller number

across treatments.

Except for fertile tiller number in WW condition, the direct cross C×W showed significant positive

SCA effects in both conditions, ranged from 1.11 (for chlorophyll content) to 6.73 (for biomass) in

WW condition, and from 1.07 (for chlorophyll content) to 8.65 (for thousand grain weight) in WW

condition (Table 5.6). It is the best specific combiner among the direct crosses across treatments. By

comparison, B×D showed no significant SCA effects for any trait in WW condition, but in WS

condition it had significant SCA effects for all the traits excluding chlorophyll content. B×W had

preferred SCA only for thousand grain weight (2.78) and grain yield (1.53) in stress free condition.

Under stressed condition, none of the C×D and D×W crosses had preferred SCA for yield contributing

traits. B×C was the worst specific combiner across treatments with significant negative SCA effects

for all the studied traits, ranged from -3.76 (for thousand grain weight) to -2.48 (for fertile tiller

no./plant) in WW condition, and from -6.57 (for biomass) to -2.4 (for fertile tiller no./plant) in WS

condition.

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Table 5.5 Estimate of general combining ability effects of parents for different traits

*, ** - significant at 0.05 and 0.01probability levels, respectively; WW=Well-watered condition, WS=Water stress condition;

Traits

Babax C306 Dharwar Dry Westonia

WW WS WW WS WW WS WW WS

Fertile tiller no./plant -2.44** -0.9** 1.35* 1.31** -0.35 -0.69* 1.44** 0.27

Biomass -2.64** 2.94** 0.32 -0.61 -2.46** 3.61** 0.83 -1.97**

Chlorophyll content -1.13** -0.5 0.75 0.88 -1.23** -0.51 0.62 1.13*

Thousand grain

weight

1.16* 2.77** -0.66 -2.36** -0.88 -1.57** 0.39 1.17*

Grain yield -0.66** 0.39* -1.18** -0.89** 0.84* -0.84** 1.00** 1.34**

98

Table 5.6 Estimate of specific combining ability effects of F1 crosses

*, ** - significant at 0.05 and 0.01probability levels, respectively: WW=Well-watered condition, WS=Water stress condition; B=Babax, C=C306, D=Dharwar Dry,

W=Westonia

Traits

B×C B×D B×W C×D C×W D×W

WW WS WW WS WW WS WW WS WW WS WW WS

Fertile tiller no./plant -2.48** -2.4** 0.23 0.94* 0.27 0.48 0.6 -0.77* 1.15 1.27** -0.98 -0.73*

Biomass -2.65* -6.57** -1.79 1.59* 1.81 -0.64 -5.61** -4.38** 6.73** 5.36** -3.17* -1.64**

Chlorophyll content -3.46** -3.89** 0.28 0.49 0.14 0.62 1.52** 0.89 1.11** 1.07* -1.53** 1.48**

Thousand grain weight -3.76** -2.49** 0.23 2.91** 2.78* 0.80 -1.82 -2.46** 3.42** 8.65** 0.41 -0.33

Grain yield -3.13** -2.59** 0.88 1.09** 1.53* 0.58 3.88** -0.95* 4.64** 1.57** -0.8 0.18

99

In both the stressed and stress-free conditions, D×C showed preferred RCA effects (1.33 and 1.17,

respectively) for fertile tiller number per plant (Table 5.7). For biomass, significant positive RCA

effects were observed in W×B (5.15) under WS condition, in W×D (2.56) under WW condition, and

in D×C and W×C under both conditions. The reciprocal crosses C×B and D×C in both conditions, and

W×D in stressed condition had preferred RCA effects for thousand grain weight.

In general, most of the studied characters showed higher SCA variance (σ2g) compared to GCA

variance (σ2s) for both WW and WS conditions (Table 5.8). Traits having higher σ2g indicates

contribution of additive gene action in manifestation of those traits, on the other hand higher σ2s

suggests non-additive gene effects. Only fertile tiller per plant in well-watered condition was recorded

with higher variance due to GCA than that due to SCA, suggesting that this trait was governed by

additive gene action. By contrast, the rest of the studied traits, both in WW and WS conditions, were

mostly controlled by non-additive gene action as they had lower GCA variance and higher SCA

variance i.e. low GCA/SCA ratio. Except for chlorophyll content, all the traits in both treatments were

recorded with greater dominant variance than additive variance. Estimates of narrow sense heritability

of the studied traits were low to moderate, ranged from 0.03 to 0.63 in WW condition, and 0.12 to 0.69

in WS condition. On the other hand, very high broad sense heritability was evident in both conditions,

ranged from 0.92 to 0.98, and 0.86 to 0.98 in WW and WS conditions, respectively. Although most of

the studied characters exhibited moderate to high heritability across treatments, low narrow sense

heritability was observed for thousand grain weight under both conditions.

100

Table 5.7 Estimate of reciprocal combining ability effects of reciprocal crosses

Traits

C×B D×B D×C W×B W×C W×D

WW WS WW WS WW WS WW WS WW WS WW WS

Fertile tiller no./plant -0.33 0.17 -0.33 3.50** 1.17* 1.33* -3.50** -1.67* -0.83 -0.67** -0.33 -0.67

Biomass 2.86 -1.77 -0.47 -1.71 14.26** -13.66** -0.34 5.15** 10.33** 7.62** 2.56** 2.01

Thousand grain weight 2.37** 2.26* -1.97 -0.81 3.39** 2.59* 0.27 -0.58 1.69 1.25 1.28 2.89**

*, ** - significant at 0.05 and 0.01probability levels, respectively; WW=Well-watered condition, WS=Water stress condition; B=Babax, C=C306, D=Dharwar Dry,

W=Westonia

101

Table 5.8 Estimates of various genetic parameters and heritability of different traits under two water treatments

Traits

Fertile tiller

no./plant Biomass

Chlorophyll

content

Thousand

grain weight Grain yield

WW WS WW WS WW WS WW WS WW WS

σ2g 3.22 0.92 5.05 7.66 0.94 1.11 0.64 5.42 1.09 1.10

σ2s 2.94 3.31 56.02 45.57 7.49 8.3 18.63 46.06 6.31 5.20

σ2r 2.04 2.52 52.94 45.89 0.32 0.27 3.19 2.55 0.69 0.02

σ2e 0.81 0.87 2.47 1.94 1.88 2.22 2.13 2.47 0.69 0.38

σ2a 6.44 1.83 10.11 15.31 7.49 8.3 1.28 10.84 2.18 2.20

σ2d 2.94 3.31 56.02 45.57 0.18 0.26 37.26 92.12 6.31 5.20

σ2p 10.19 6.01 68.59 62.83 9.55 10.78 40.67 105.43 9.19 7.78

hns2 0.63 0.30 0.15 0.24 0.20 0.21 0.03 0.10 0.24 0.28

hb2 0.92 0.86 0.96 0.97 0.98 0.98 0.95 0.98 0.92 0.95

GCA/SCA 1.10 0.28 0.09 0.17 0.13 0.13 0.03 0.12 0.17 0.21

σ2g=GCA variance; σ2s =SCA variance; σ2r= reciprocal variance; σ2e=environment variance; σ2a=additive variance; σ2d=dominance variance; σ2p

=phenotypic variance; hns2= narrow sense heritability; hb

2= broad sense heritability; GCA/SCA= variance ratio. WW=Well-watered condition, WS=Water

stress condition

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5.5 Discussion

Diversity of the four parents and their crosses in response to the post-anthesis water stress indicated

the existence of genetic potential in the studied material for the improvement of post-anthesis

drought tolerance. Among the parental genotypes, performance of Babax was stable across

treatments with regard to most of the studied traits. Under stressed condition, it maintained its fertile

tiller numbers with lowest reduction in grain weight leading to only 14% grain yield reduction

compared to the well-watered condition (Figure 5.2, & Table 5.2). This result is in agreement with

the studies reported by Olivares-Villegas et al. (2007) and Lopes et al. (2013b), where higher yield

performance of Babax under drought has also been reported. Under stress, yield penalty for

Westonia was comparatively less despite having lowest fertile tillers among the parental genotypes.

Moreover, combining ability study revealed that both Babax and Westonia genotypes possessed

significant positive GCA effects for grain yield under drought condition. Therefore, to develop

hybrids with higher grain yield under stressed condition these two parents can be included in the

breeding program.

In contrast, yield penalty for Dharwar Dry under stress was 53% with serious reduction of fertile

tiller, grain weight and grain yield (Table 5.1). Under stressed condition, late tillers died

prematurely causing reduced net tillers at maturity (Blum and Pnuel, 1990). Likewise, reduced grain

number might resulted from the pollen inviability, spikelet sterility and grain abortion (Ji et al.,

2010; Onyemaobi et al., 2017), whereas reduced grain weight might be due to low assimilate supply

to the developing grain due to lower chlorophyll content of flag leaf under stressed condition (Inoue

et al., 2004). Although combining ability analysis suggested Dharwar Dry and C306 was the best

combiners in terms of biomass production and fertile tiller per plant, respectively, under stress

condition, both of them show negative GCA effects for grain yield. Hence, these genotypes may

not be good parents to use in post-anthesis drought tolerance breeding program targeting grain

yield.

103

In general, direct crosses and reciprocal crosses involving at least one better performing parents

with higher GCA effect (Babax or Westonia) suffered less under stress. For example, significant

positive SCA effects of the direct cross B×D and reciprocal cross C×W denotes their ability to

perform better in specific cross combination and should be accommodated in recombination

breeding program. It could be hypothesized that favorable alleles from better performing parent

masked the deleterious effect of unfavorable alleles of the underperforming parent. Cross

combination of C306 and Dharwar dry, the two underperforming genotypes with relatively lower

GCA effects, suffered most, probably due to combinations of unfavorable alleles from both the

parents (Zhang et al., 2014).

In our study, analysis of variance for combining ability revealed the significance of general

combining ability and specific combining ability for all the studied traits in both stressed and stress-

free conditions, indicating interplay of both additive and non-additive (dominant) gene effects for

expressing those traits. Moreover, significance of reciprocal combining ability (RCA) for some of

the traits indicated that cytoplasmic factors also played a major role in the expression of those traits

under stress. Estimating RCA effect is also important in hybridization breeding program, as it helps

to detect a desirable female parent for producing commercial hybrids. Our study found that Dharwar

Dry and Westonia perform better as female parent than as pollen parent based on the RCA effects

of the crosses involving those parents. The influence of cytoplasm on quantitative traits including

grain yield has also been reported in a number of studies (Fan et al., 2008; Yao et al., 2013; Fan et

al., 2014). Results of those studies indicated that heterosis might occur due to the interaction of

nuclear genes and the genes in mitochondria and chloroplasts. Therefore, for formulating

meaningful breeding strategy for improvement of grain yield under drought stress, these

interactions need to be considered carefully.

In this study, chlorophyll content was found highly associated with fertile tiller number per plant

and grain yield in both stressed and stress-free conditions (Table 5.3). According to Yang et al.

(2001), water stress at anthesis triggers rapid relocation of metabolites from leaves and stems to

104

developing grains causing quick loss of chlorophyll from leaves. The use of chlorophyll content as

selection criteria under stressed condition will give better results in determining better-performing

genotypes as in our study we found comparatively higher correlation with grain yield in reduced

water condition as compared to well-watered condition. Yildirim et al. (2010) and Pradhan et al.

(2012) also found high correlation of chlorophyll content with grain yield under stressed condition.

Therefore, chlorophyll content measured under stress conditions can be reliably used to select

favorable genotypes for drought tolerance. Similarly, thousand grain weight were also significantly

correlated with grain yield under both conditions. The correlation coefficient of thousand grain

weight with grain yield under reduced water condition was higher than that of under well-watered

condition. Under terminal drought condition, significant correlation of grain weight and grain yield

has also been reported by Elhani et al. (2007). Therefore, thousand grain weight can also be used

as a selection criterion in breeding strategies targeting grain yield improvement under drought

condition.

Estimates of various genetic parameters of the traits suggested that most of the traits were governed

by non-additive gene action. The presence of relatively higher non-additive gene effects in the

control of quantitative traits in wheat under drought condition has also been reported by Dhanda et

al. (2004) and Shahbazi et al. (2012). Narrow sense heritability was low to moderate for most of

the traits. This is quite common in quantitative characters since the interaction of favorable genes

and environment play a major role in the manifestation of those traits (Shi et al., 2009; Mohammed

et al., 2015). However, high broad sense heritability and moderate narrow sense heritability for

grain yield (Table 5.8) indicate the possibility of improvement for yield in wheat under post-

anthesis water stress.

This study was conducted in controlled glasshouse facilities, which provided stability, control and

precision for better management and treatment imposition. However, further investigation of the

better performing cross combinations in field condition in drought-prone areas will provide

additional insights to scale up between level of organization (Passioura, 2010).

105

Conclusion

The present study was carried out to identify parental genotypes and their crosses that perform

better under post-anthesis water stress and to determine their nature of gene action through

combining ability analysis. Water stress applied at anthesis caused significant reduction in yield in

the 4×4 full diallel crosses, although their responses varied considerably based on their genetic

make-up. Combining ability analysis for gene action study confirmed that both additive and non-

additive gene effects were involved in governing the inheritance of the studied traits, but non-

additive gene action was the predominant role player. Therefore, recombination breeding targeting

specific crosses with high SCA, involving parents with greater GCA would be a worthwhile

strategy to develop better performing genotypes under post-anthesis water stress. High non-additive

gene actions and RCA effects also suggest that development of hybrid wheat cultivars through

heterosis breeding might be another possible pathway.

5.6 Acknowledgments

Md Sultan Mia acknowledges the Endeavour Postgraduate Scholarship from the Australian

Government for sponsoring the PhD study. Authors also acknowledge The University of Western

Australia for funding the operational cost of the research. Authors would like to thank Dr. Habtamu

Ayalew for his help with data analysis.

106

CHAPTER 6

107

6 Multiple near-isogenic lines targeting a QTL hotspot of drought

tolerance showed contrasting performance under post-anthesis

water stress

Md Sultan Mia1,2,3, Hui Liu1,2, Xingyi Wang1,2, and Guijun Yan1,2*

1School of Agriculture and Environment, Faculty of Science, The University of Western Australia,

Perth, WA, Australia

2The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia

3Plant Breeding Division, Bangladesh Agricultural Research Institute (BARI), Gazipur,

Bangladesh

* Correspondence:

Guijun Yan

[email protected]

(This chapter has been published in Frontiers in Plant Science journal,

doi: https://doi.org/10.3389/fpls.2019.00271)


https://doi.org/10.3389/fpls.2019.00271

108

6.1 Abstract

The complex quantitative nature of drought-related traits is a major constraint to breed tolerant

wheat varieties. Pairs of near-isogenic lines (NILs) with a common genetic background but

differing in a particular locus could turn quantitative traits into a Mendelian factor facilitating our

understanding of genotype and phenotype interactions. In this study, we report our fast track

development and evaluation of NILs from C306 × Dharwar Dry targeting a wheat 4BS QTL hotspot

in C306, which confers drought tolerance following the Heterogeneous Inbreed Family analysis

coupled with immature embryo culture-based fast generation technique. Molecular marker

screening and phenotyping for grain yield and related traits under post-anthesis water stress

confirmed four isoline pairs, viz. qDSI.4B.1-2, qDSI.4B.1-3, qDSI.4B.1-6 and qDSI.4B.1-8. There

were significant contrasts of responses between the NILs with C306 QTL (+NILs) and the NILs

without C306 QTL (-NILs). Among the four confirmed NIL pairs, mean grain yield per plant of

the +NILs and -NILs showed significant differences ranging from 9.61 to 10.81 g and 6.30 to 7.56

g, respectively, under water-stressed condition, whereas a similar grain yield was recorded between

the +NILs and -NILs under well-watered condition. Isolines of +NIL and -NIL pairs showed similar

chlorophyll content (SPAD), assimilation rate (A) and transpiration rate (Tr) at the beginning of the

stress. However, the +NILs showed significantly higher SPAD (12%), A (66%), stomatal

conductance (75%), and Tr (97%) than the -NILs at the seventh day of stress. Quantitative RT-PCR

analysis targeting the MYB transcription factor gene TaMYB82, which was retrieved from the

wheat reference genome TGACv1, also revealed differential expression in +NILs and –NILs under

stress. These results confirmed that the NILs can be invaluable resources for fine mapping of this

QTL, and also for cloning and functional characterization of the gene(s) responsible for drought

tolerance in wheat.

Keywords: Drought tolerance; Near-isogenic lines; Quantitative trait loci; Bread Wheat; Gene

expression

109

6.2 Introduction

Global wheat production is often hampered by biotic and abiotic stresses like heat, drought, salinity,

insects and diseases. Among those stresses, drought is by far the most detrimental limiting the yield

potential of wheat, particularly in rain-fed and limited irrigation environments (Akpinar et al., 2013;

Budak et al., 2013). Effect of drought can be detected in various degrees during different growth

periods including early establishment (Ayalew et al., 2015; Ayalew et al., 2016a), pre-anthesis

(Onyemaobi et al., 2017), and post-anthesis grain filling (Mia et al., 2017). Understanding the

underlying mechanism of tolerance and identifying candidate genes and proteins will help to breed

stress-resilient genotypes (Budak et al., 2015).

Although drought tolerance is a complex quantitative trait, a number of drought tolerance QTLs in

wheat have been reported in various previous studies, most of which have been identified through

grain yield and related component measurements under limited water conditions (Quarrie et al.,

2006; Maccaferri et al., 2008; Mathews et al., 2008). However, large genomic intervals associated

with those QTLs make them unsuitable for direct use in a breeding program. Characterizing those

QTLs and identifying the causal genes, despite large genomic regions of interest, is quite a

formidable task. This can be solved by developing near-isogenic lines (NILs) with different

flanking markers of the respective QTL.

NILs have otherwise identical genetic backgrounds except at one or a few genetic loci and have

been used intensively for detailed mapping and characterization of individual loci. Multiple pairs

of isolines can offer a common genetic background to assess the phenotype conditioned by a

genomic locus. Traditionally, NILs are developed through backcross introgression method

(Kaeppler et al., 1993; Monforte and Tanksley, 2000; Babu et al., 2017). Alternatively, they can be

generated following a selfing and selection scheme called heterogeneous inbreed family (HIF)

analysis, which utilizes molecular markers linked to QTL of interest to identify heterozygous

individuals, from which NILs can be extracted that are isogenic, but differ for certain genomic

locations (Afanador et al., 1994; Tuinstra et al., 1997). Despite their usefulness for genetic and

110

physiological studies of QTL, the considerable amount of time and effort needed to develop NILs

have limited their use (Yan et al., 2017). However, a recently developed accelerated breeding

technique for wheat called fast generation cycling system (FGCS) offers a suitable solution to this

problem (Yan et al., 2017). Molecular marker assisted development of NILs following HIF analysis

alone (Barrero et al., 2015), or coupled with FGCS, has been reported for some major QTLs in

wheat (Ma et al., 2011; Wang et al., 2018) and barley (Habib et al., 2015). However, no such study

has been reported for drought-tolerance related QTLs.

In this study, we utilize FGCS for fast track development of NILs targeting a QTL hotspot

conferring drought tolerance following HIF analysis. Subsequently, those putative isolines were

phenotyped for grain yield and related traits to characterize and confirm the NIL pairs. Several

physiological parameters and relative gene expression were also evaluated for further confirmation

and to explore the underlying mechanism of water stress tolerance during post-anthesis period in

wheat.

6.3 Materials and Methods

6.3.1 Plant materials

Two bread wheat varieties of spring type growth habit, C306 and Dharwar Dry, were crossed to

produce hybrids and subsequent segregating generations for this study. Several previous studies

reported C306 (RGN/CSK3//2*C591/3/C217/N14//C281) as a drought tolerant variety containing

major-effect drought yield QTL (Aggarwal and Sinha, 1987; Sharma and Kaur, 2008; Kadam and

Chuan, 2016). During crossing, C306 served as female parent, and Dharwar Dry was used as the

male parent. NILs were developed from this cross targeting a QTL hotspot of 12 cM interval in

C306 background by the heterogeneous inbreed family (HIF) method (Tuinstra et al., 1997),

coupled with immature embryo culture based fast generation cycling system (FGCS) (Zheng et al.,

2013; Yan et al., 2017). The procedure is summarized in Figure 6.1.

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Figure 6.1: Schematic representation of the development of near-isogenic lines following

heterogeneous inbreed family (HIF) method coupled with embryo culture based fast generation

technique

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6.3.2 Heterogeneous inbreed families (HIF) method

HIF analysis as described in Tuinstra et al. (1997) was followed for identifying NILs that differ for

selected markers linked to a QTL of interest. In short, segregating generations from the biparental

hybrids were advanced following single seed descent method till F4, where heterozygous plants

were identified using the linked marker and selfed to produce seeds for next generation. For this

study, marker gwm368 linked to the QTL qDSI.4B.1 (Kadam et al., 2012) was used to identify

heterozygous individuals for the targeted QTL in advancing generations. Six to eight plants derived

from each of the heterozygous plants were used for the next round of selection, genotyped with the

linked marker and only a single heterozygous plant from each progeny line was selected and selfed.

This process of selecting heterozygous individuals and selfing was repeated from F4 to F7. In F7,

two isolines, homozygous but having different parental alleles, were isolated from the individual

heterozygous plant progenies. These isolines served as putative NIL pairs for further phenotypic

and physiological characterization in F8 (Figure 6.1).

6.3.3 Embryo culture based fast generation technique

For rapid generation advancement, an embryo culture based fast generation cycling system (FGCS)

as described in Yan et al. (2017) was followed from F3 to F7 (Figure 6.1 & 6.2). In each generation,

about 12-14 days after anthesis (DAA), young embryos were harvested from sterilized developing

grains aseptic conditions and cultured on suggested medium. Cultured embryos in petri plates were

kept in a specially designed plant growth chamber in the dark to germinate and were then transferred

into a 22°C constant temperature room with 16 h light (fluorescent lamps) period for rooting. When

the roots of the young seedlings were about 2.0 cm long, they were transferred into 30-well Kwikpot

trays (Rite-Gro Kwikpots, GardenCity Plastics) containing growing media in plant growth

chambers until the soft dough (Zadoks growth scale Z85) stage, when grains were ready for next

cycle of embryo culture (Figure 6.2 C&D).

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Figure 6.2 A) QTL hotspot in wheat chromosome 4BS and the marker (circled in green) used for

selection, adapted from Kadam et al. (2012), B) selection of different progeny types using molecular

marker, C) culture of young embryos on petri-plates with sterile media, D) young seedlings from

embryo culture growing in plant growth chamber, E) NIL pair at anthesis in glasshouse, F) NIL

pair at physiological maturity, G) hundred seeds of a NIL pair

6.3.4 Genotyping of the segregating populations and NILs

Genomic DNA was isolated from leaf tissues collected from seedlings of 2-3 leaves stage following

the CTAB method with necessary modifications (Dreisigacker et al., 2017). Genomic RNA

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contamination was removed from the extracted DNA by treating with RNaseA. The quality of the

treated DNA was assessed by a NanoDrop 2000 spectrophotometer (ND-2000, Thermo Fisher

Scientific, Inc., USA) and concentration was adjusted as per requirement. Integrity of the DNA

was also checked by 1% agarose gel electrophoresis. Polymerase chain reaction (PCR)

amplification was performed in an Eppendorf Mastercycler with 15 µl reaction volumes for each

sample containing 50 ng template DNA, 200 nM of each primer, 1.5 µl 10 × PCR buffer, 2 mM

MgCl2, 0.2 mM dNTPs, and 1 unit TaqDNA polymerase (Fisher Biotec) with initial denaturation

at 94°C for 3 min, followed by 40 cycles of 94°C for 30 sec, annealing at 58°C for 30 sec, and 72°C

for 30 sec, with a final extension at 72°C for 5 min. PCR products were electrophoresed in 2.5%

agarose gel, stained with ethidium bromide and visualized with UV trans-illuminator. All plants

were genotyped with the gwm 368 marker and grouped according to the genotypes ‘+ +’, ‘+ −’, and

‘− −’, where ‘+’ represents allele of C306 type and ‘−’ represents allele of Dharwar dry type. The

DNA profile from each marker was scored in each generation and only heterozygous plants (+ −)

were selected for the next cycle, except for the last cycle (F7) when homozygous progeny (‘+ +’,

and ‘− −’) from each of those heterozygous plants were selected as NIL pairs (Figure 6.2).

6.3.5 Phenotyping of the NILs (F8)

NILs were phenotyped following the procedure as described in Mia et al. (2017) in a temperature-

controlled and naturally-lit glasshouse at The University of Western Australia, Crawley, Western

Australia (31°59’ S, 115°49’ E) in 50 cm × 9 cm cylindrical pots containing 2.5 kg of soil media

(5:2:3 compost: peat: sand, pH~6.0). The experiment was arranged in a completely randomized

block design with three replicates. Field (pot) capacity of the soil media was initially measured in

three free draining pots, each containing 2.5 kg of air-dry soil media, by inundating the pots with

water and allowing them to drain for 48 hours. Three random samples from each pot were taken,

and their weights were measured using a balance before and after oven-drying to calculate per cent

water content of the media at filed capacity using the following formula: % soil water content =

FW−DW

DW× 100, where, FW = fresh weight, and DW = dry weight of the samples. Pots were watered

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and maintained at around 80% field capacity (FC) by weighing and manual watering on each

alternate days until anthesis, Zadoks growth scale Z60 for cereals (Zadoks et al., 1974). At anthesis,

two water treatments were implemented, where soil moisture in the well-watered (WW) treatment

was maintained at about 80% field capacity (FC) by daily weighing and watering and continued

until plants reached physiological maturity (Z91). In contrast, plants in the water stress (WS)

treatment were weighed but not watered for a period of 7 days from anthesis, with watering

resuming on 7 DAA as per WW treatment and maintained until physiological maturity. At

physiological maturity, plants were harvested manually and separated into shoots and roots. Grains

were collected and dried at 35°C for 72 hours and the rest of the shoots were oven-dried at 65°C

until constant weight. Roots from individual pots were washed thoroughly and then oven-dried at

65°C until they reach a constant weight. Data on plant height at maturity, days to anthesis, days to

maturity, spikelet numbers per spike, fertile tiller (having spikes with grain) per plant, shoot

biomass, root biomass, harvest index, hundred grain weight and grain yield per plant were recorded.

6.3.6 Physiological traits

The confirmed NIL pairs were also characterized for physiological traits under both stressed and

stress-free conditions. Chlorophyll content of the isolines were also measured at those times using

a handheld portable chlorophyll meter (SPAD-502Plus; Konica Minolta, Osaka) just before

treatment imposition and on 4 and 7 days after stress (DAS) treatment. Leaf gas exchange

measurements (net photosynthesis rate, transpiration rate and stomatal conductance) were also

measured during those time points using a portable photosynthesis system (LI-6400, Li-COR Inc.,

Lincoln, NE, USA) with a LED light source on the leaf chamber. In the LICOR cuvette, CO2

concentration was set to 400 µmols-1 and LED light intensity was 1500 µmolm-2 s-1.

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6.3.7 RNA extraction and quantitative reverse transcriptase polymerase chain reaction

(qRT-PCR) Analysis

Flag leaf tissues from the NILs were snap frozen in liquid nitrogen and stored in -80°C for later

use. For RNA extraction, leaf tissues from isolines with C306 background were bulked in one

sample and those of Dharwar Dry background were bulked in another. There were three

independent biological replicates, each with three technical replicates for both stressed and stress-

free conditions. Total RNA was extracted using RNeasy® Plant mini kit (Qiagen) with DNase

digestion to eliminate genomic DNA contamination. Total RNA was assayed qualitatively and

quantitatively by Nanodrop 2000, denatured gel electrophoresis, and LabChip® GX Touch

capillary electrophoresis (PerkinElmer). The cDNA was synthesized using a SensiFast cDNA

Synthesis Kit (Bioline) following manufacture’s protocol. Quantitative real-time polymerase chain

reaction (qRT-PCR) was carried out with an ABI 7500 Fast system using SensiFast syber kit

(Bioline). For gene expression analysis, Triticum aestivum MYB 82 (TaMYB82) gene, repeatedly

found in this genomic region, were selected as target gene (Kadam et al., 2012; Liu et al., 2015).

Primers (Forward 5'-TCGTCGGGTTCGTTCACATC-3', Reverse 5'-

GGTCGACGTGGAAAAGACCA-3') were developed form the highly conserved exonic region of

the MYB transcription factor gene, retrieved from chromosome 4BS of wheat reference genome

TGACv1, using Geneious software version11.1.2 (Kearse et al., 2012). Wheat actin gene (Forward

5'-CTCCCTCACAACAACCGC-3', Reverse 5'-TACCAGAACTTCCATACCAAC-3') was used

as endogenous control (Yu et al., 2017). Amplification was performed in a 20 μl final reaction mix

containing 100 ng cDNA, 10μl of 2X SensiFast SYBR Lo-ROX mix and 0.8 μl of each primer (10

μM) with the following protocol: 95⁰C for 2 min (1 cycle), 95⁰C for 5 s and 62⁰C for 30 s (40 cycle),

melt curve analysis concluding with a 4°C hold. Relative gene expression was determined by the

comparative Ct method (Livak and Schmittgen, 2001; Schmittgen and Livak, 2008).

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Statistical analysis was performed using GenStat software, 17th edition (Payne et al., 2017). t-test

was used to compare the difference between various means. Graphs were produced using statistical

software R 3.5.1 with R package ‘ggplot2’ and ‘gridExtra’ (R core Team, 2013; Wickham, 2016).

6.4 Results

6.4.1 Development of the near-isogenic lines

Twenty-one F1 seeds were generated from the cross between C306 and Dharwar Dry. Hybridity of

the F1 seeds was confirmed using the flanked marker gwm368. A total of 310 seeds from the best

performing hybrid were grown from F2 to F4 following single seed descent method. 275 F4 plants

were screened with the QTL flanking SSR marker gwm368 and 21 were found to be heterozygous.

Six to eight seeds from each of those 21 plants were grown and screened with the marker to select

only heterozygotes until F7 where two homozygous plants, one with + allele and another with -

allele, from the same progeny lines were selected. At the end of F7, 14 putative isoline pairs were

recovered and 10 pairs having similarity in flowering time and morphology were selected for

phenotyping at F8.

6.4.2 Putative isolines vary for their grain yield and grain weight under post-anthesis water

stress

Grain yield and grain weight of the 10 putative NIL pairs under well-watered and post-anthesis

water stress condition is given in Table 6.1. In general, post-anthesis water stress caused significant

reduction in both grain weight and grain yield, though there was sharp contrast in plants’ responses

between the NIL with C306 background (termed as +NIL) and the corresponding NIL with Dharwar

Dry background (-NIL) of the same pair. In the well-watered, the grain yield per plant and 100

grain weight of the NILs ranges from 10.56 to 15.52 g and 4.45 to 5.60 g, respectively. By contrast,

the grain yield per plant and 100 grain weight of the NILs ranges from 4.57 to 10.81 g and 3.19 to

5.13 g, respectively under stressed condition. On average, post-anthesis water stress caused 42.57%

reduction in grain yield and 12.51% in grain weight.

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Table 6.1 Grain yield (g/plant) and 100 grain weight (g) of the developed NIL pairs. The significant

difference was calculated using student’s t-test

NIL pairs

Grain yield (g/plant) 100 grain weight (g)

Well-

watered p

Water-

stressed p

Well-

watered p

Water-

stressed p

qDSI.4B.1-1(+) 12.48±0.74 ns

7.30±0.58 ns

5.04±0.21 ns

4.33±0.17 ns

qDSI.4B.1-1(-) 11.74±0.68 6.65±0.52 4.46±0.43 5.00±0.59

qDSI.4B.1-2(+) 13.85±0.22 ns

9.71±0.35 s

4.92±0.30 ns

4.72±0.30 s

qDSI.4B.1-2(-) 13.20±0.39 6.30±0.36 5.21±0.22 3.91±0.22

qDSI.4B.1-3(+) 13.56±0.95 ns

9. 61±0.56 s

5.191±0.21 ns

5.13±0.21 s

qDSI.4B.1-3(-) 13.51±0.70 6.49±0.38 4.876±0.27 3.72±0.27

qDSI.4B.1-4(+) 12.09±1.00 ns

7.35±0.15 ns

4.91±0.22 ns

5.13±.0.11 s

qDSI.4B.1-4(-) 11.69±0.55 6.63±0.34 5.02±0.13 3.72±0.17

qDSI.4B.1-5(+) 11.80±1.23 ns

6.58±0.11 ns

5.12±0.41 ns

4.40±0.18 ns

qDSI.4B.1-5(-) 11.22±0.80 5.94±0.51 5.42±0.10 4.83±0.24

qDSI.4B.1-6(+) 15.52±0.47 ns

10.81±0.29 s

5.614±0.13 ns

4.75±0.13 s

qDSI.4B.1-6(-) 14.19±1.07 7.06±0.24 5.061±0.24 3.72±0.24

qDSI.4B.1-7(+) 11.25±0.14 ns

6.15±0.11 ns

4.96±0.16 ns

4.94±0.04 ns

qDSI.4B.1-7(-) 10.56±1.09 5.59±0.04 4.91±0.12 4.30±0.51

qDSI.4B.1-8(+) 15.31±0.61 ns

10.71±0.37 s

5.28±0.18 ns

4.74±0.18 s

qDSI.4B.1-8(-) 14.24±0.20 7.56±0.43 5.26±0.27 3.65±0.27

qDSI.4B.1-9(+) 12.84±0.16 s

8.81±0.15 s

4.45±0.18 ns

4.12±0.21 ns

qDSI.4B.1-9(-) 14.21±0.66 5.78±0.11 4.68±0.16 4.57±0.07

qDSI.4B.1-10(+) 11.20±0.08 s

4.47±0.05 s

4.527±0.13 s

3.19±0.16 s

qDSI.4B.1-10(-) 14.60±0.52 9.16±0.16 4.84±0.15 4.37±0.14

* ns = non-significant at P≤0.05; s=significant at P≤0.05; + = isoline with C306 background, - =

isoline with Dharwar Dry background; pairs in bold are confirmed NILs

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No significant difference was observed between the majority of the NILs of the well-watered

condition. However, in the stress treatment, significant differences between the NILs were observed

in most of the cases. We were particularly interested in the isoline pairs which showed similar

responses under stressed-free condition but varied significantly under stressed condition. Out of 10

putative NILs pair, only 4 pairs, viz. qDSI.4B.1-2, qDSI.4B.1-3, qDSI.4B.1-6 and qDSI.4B.1-8

showed such significant difference between the isolines for both grain yield and grain weight and

were considered as confirmed NIL pairs. For example, +NIL and -NIL of qDSI.4B.1-3 was

recorded with mean grain yield per plant of 13.56 and 13.50 g respectively under well-watered

condition, whereas in the stressed condition mean grain yield per plant of -NIL was 6.49g, about

32% less than that of the corresponding +NIL (9.61 g). Similarly, +NIL produced 38% higher grain

weight than the corresponding -NIL (3.72 g) under stress.

Difference between the isoline pairs of qDSI.4B.1-4 and qDSI.4B.1-9 were significant only for

either grain weight or grain yield and therefore were not considered as true NIL pairs. In most cases

NILs with C306 background out-yielded the corresponding NIL with Dharwar Dry background.

However, isoline qDSI.4B.1-10(-), which has an allele from Dharwar Dry, outperformed the

corresponding isoline with C306 allele under both stressed and stress-free conditions.

6.4.3 Other phenological traits of the confirmed NILs

NIL pairs commenced anthesis at around 68 days after sowing (Figure 6.3). However, under

stressed treatment they reach physiological maturity at around 103 DAS, about one week earlier

than that under well-watered treatment (110 DAS). NIL pairs of qDSI.4B1-6 in the well-watered

treatment and those of qDSI.4B1-3 in the stressed treatment differed significantly for days to

maturity. Significant difference in plant height was observed only between the NIL pairs of

qDSI.4B1-3 and qDSI.4B1-2 in well-watered treatment and stressed treatment, respectively. In both

cases +NIL was taller than the corresponding -NIL. NIL pairs did not differ significantly in terms

of effective tiller number, and spikelet number across conditions (Figure 6.3).

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Figure 6.3 Different phenological traits of the confirmed NIL pairs. qDSI.4B1_2, qDSI.4B1_3,

qDSI.4B1_6, and qDSI.4B1_8 are denoted as NIL 2, NIL 3, NIL 6 and NIL 8 respectively. a= NIL

with C306 background (+NIL), b= NIL with Dharwar Dry background (-NIL), * = significant at

P≤0.05, ** = significant at P≤0.01, *** = significant at P≤0.001

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In both stressed and non-stressed conditions, isoline qDSI.4B.1-2(+) and qDSI.4B.1-8(+), each of

which has an allele form C306, showed higher shoot biomass than their corresponding isoline

qDSI.4B.1-2(-) and qDSI.4B.1-8(-), respectively. By contrast, root biomass and harvest index were

significantly lower in isolines with Dharwar Dry background in almost all of the four NIL pairs

under stressed treatment. For example, under stressed condition, root biomass and harvest index of

+NIL of qDSI.4B1-3 were about 40 and 27% lower, respectively, under stressed condition, than

the corresponding –NIL, respectively (Figure 6.3).

6.4.4 Physiological traits of the confirmed NILs

Results indicated that isolines of the NIL pairs responded variably in terms of physiological

parameters when post-anthesis water stress was applied (Figure 6.4). The magnitude of reduction

varied between isolines of the NIL pairs with the increase of the stress period. Just prior to

beginning of the stress period (0DAS) both the isolines had similar chlorophyll content, assimilation

rate (A), stomatal conductance (gs), and transpiration (Tr). However, with the progression of the

stress period, both +NIL and -NIL showed significant decrease in A, gs, SPAD units and Tr. Overall,

the +NIL maintained comparatively higher A, Chlorophyll content, Tr and gs than the –NIL during

the stress period.

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Figure 6.4 Chlorophyll content, photosynthesis (A; µmolm-2 s−1), stomatal conductance (gs;

mmolm-2 s−1) and transpiration rate (Tr; µmolm-2 s−1) of isolines of the confirmed NILs under

post-anthesis water stress. Data points are mean ± SD (n=12). a = NIL with C306 background

(+NIL), b = NIL with Dharwar Dry background (-NIL)

6.4.5 Differential expression of TaMYB82 gene between the isolines

Relative expression of TaMYB82 gene was significantly different between +NIL and –NIL under

post-anthesis water stress at both time points (4 DAS and 7 DAS) (Figure 6.5). However, under

well-watered condition (0 DAS), no significant difference was observed between the expression

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level of TaMYB82 gene in +NIL and –NIL. At 4 DAS, there was about 3 fold difference in relative

expression level of TaMYB82 gene between them. This difference was reduced but still significant

at 7 DAS.

Figure 6.5 Relative expression of TaMYB82 gene in +NIL and –NIL during stress period. Mean

fold change in gene expression (2-ΔΔCT) of – NIL was set to 1 at each time-point to determine

relative expression in +NIL. Each value represents mean ± SD of three biological replicates with

three technical replicates. ns = non-significant at P≤0.01; * = significant at P≤0.01

6.5 Discussion

In this study, we reported the development of 10 putative pairs of NILs targeting a major locus for

drought tolerance. Molecular screening and phenotyping of those NILs confirmed four true NILs

which showed differential responses under well-watered and water-stressed conditions, as

hypothesized. Among those, NILs having alleles from the C306 background showed improved

performances in terms of grain yield and grain weight. This is because the nearby marker used for

screening were tightly linked with the QTL identified for drought tolerance in parent C306, which

may harbor some key genes responsible for grain yield and grain weight under post-anthesis

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stressed condition (Kadam et al., 2012). Several previous studies also reported that this genomic

region is a rich hub for grain yield and related traits in spring wheat (Marza et al., 2006; Yang et

al., 2007; Mathews et al., 2008; Pinto et al., 2010b; Cabral et al., 2018).

Our study also reported that the NILs having allele from the C306 background are better performers

in terms of physiological traits under post-anthesis water stress. NILs with C306 background

maintained comparatively higher photosynthesis, chlorophyll content, stomatal conductance and

transpiration rate compared to the corresponding NILs with Dharwar Dry background. One possible

explanation of this might be the higher chlorophyll content and root biomass of the +NILs compared

to the -NILs. Comparatively higher root biomass provides higher chances of accessing available

soil moisture under stress (Wasson et al., 2012; Maeght et al., 2013). Kumar et al. (2012) reported

several QTLs for photosynthesis and chlorophyll content in C306 background. Moreover, several

previous studies indicated positive correlation of chlorophyll content and gas exchange parameters

(Chen et al., 2015; Wang et al., 2016). This might be another possible explanation of the higher

grain yield and grain weight in the tolerant NILs under stress.

Gene expression analysis of NILs revealed that TaMYB82 was markedly down-regulated in the

better performing NILs with C306 background when compared with the expression pattern of the

NILs with Dharwar Dry background. This suggests that TaMYB82 acted as a negative regulator in

response to the post-anthesis water stress. The role of negative regulators of MYB transcription

factors under drought has also been reported by Jaradat et al. (2013) and Cui et al. (2013).

Furthermore, Zhao et al. (2017) indicated the role of TaMYB82 in an ABA-dependent signalling

transduction pathway in response to abiotic stress. This supports the idea of involvement of MYB

transcription factors in drought response mechanisms in wheat (Baldoni et al., 2015).

Although we identified four confirmed NIL pairs based on phenotyping and genotyping evaluation,

there were six pairs of NILs, which were not in agreement with marker-trait association. The

possible explanation for this could be the recombination events between the targeted QTL and the

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nearby marker used for screening (Peleman et al., 2005). For example, in qDSI.4B.1-10, the isoline

with Dharwar Dry background showed improved performance with respect to grain yield and grain

weight compared to its counterpart, the NIL with the C306 background, under both well-watered

and water stressed conditions. Moreover, despite having different genetic background, the isolines

of NIL pairs qDSI.4B.1-1, qDSI.4B.1-5 and qDSI.4B.1-7 did not differ significantly in terms of

grain yield and grain weight across conditions. Wang et al. (2018) also reported such phenomena

in wheat during NIL production following HIF. Access to a higher resolution genetic map of this

QTL locus saturated with more nearby markers would have solved this challenge. Yadav et al.

(2011) described the utilization of multiple QTL-NILs for validation of drought tolerance QTL and

how they were used for developing and mapping new gene-based markers in that genomic region.

Therefore, fine mapping of this QTL using the confirmed NIL pairs reported in this study will

provide an excellent opportunity to identify more closely linked markers, which will ensure higher

selection efficiency (Gupta et al., 2017).

Transcriptomic analysis of multiple NILs enabled Barrero et al. (2015) to identify the candidate

genes for a targeted QTL in wheat. Habib et al. (2018) also identified two key genes for a major

dormancy QTL in wheat using similar multiple QTL-NILs RNA sequencing approaches. Following

these examples, next-generation transcriptome sequencing of the confirmed NILs under contrasting

water regimes can be pursued as a future direction of the current study in order to identify the

candidate genes in qDSI.4B.

Development of isolines for cereal crops requires a substantial investment of time, regardless of

whether the traditional backcrossing or HIF method being followed. However, for this study, we

utilized a rapid breeding technique, which dramatically shortened the life-cycles of segregating

generations as reported in Zheng et al. (2013). Some drawbacks of this technique include dissecting

of young embryo individually and culturing them in-vitro, which requires considerable amount of

effort and labor. Additionally, a sterile environment must be maintained during embryo culture.

However, a recent discovery regarding shortening the life cycle of wheat by utilizing the longer

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photoperiod (22hr) with specialized LED lights might allow researchers to avoid the exploitation

of immature embryo culture (Watson et al., 2018b). Another limitation was the use of a single

marker during marker-assisted selection. Between the two flanking markers (barc20 and gwm368)

of the targeted QTL, only gwm368 was found to be polymorphic between the two parents. The

other flanking maker barc20 was not polymorphic while the next closest neighbouring marker

(wmc125) was too far away (nearly 25cM) from the targeted QTL. Hence, only gwm368 was used

in the current study. Wang et al. (2018) and Habib et al. (2015) also successfully used one single

marker to develop near-isogenic lines in wheat and barley, respectively, following heterogeneous

inbreed family (HIF) method.

In summary, the present study confirmed the importance of the 4BS QTL from the C306

background in post-anthesis drought tolerance. The confirmed NILs identified in this study is a

valuable resource for future fine mapping of this QTL and cloning and functional characterization

of the gene(s) responsible for post-anthesis drought tolerance.

6.6 Authors’ Contributions

MM, HL and GY conceived and designed the study. MM carried out the experimental procedures

at plant growth chambers and greenhouse. MM and XW performed an immature embryo culture

and molecular marker-assisted selection. MM collected all relevant data and performed analysis

with occasional help from XW. MM prepared the manuscripts with inputs from XW, HL and GY.

All the authors reviewed the manuscript and approved the submission.

6.7 Acknowledgments

Md Sultan Mia acknowledges the Endeavour Postgraduate Scholarship from the Australian

Government for sponsoring his PhD study. Authors express their heartfelt thanks to Ms Pratima

Gurung for her cordial help during data collection. Authors also acknowledge the University of

Western Australia for funding the operational cost of the research.

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CHAPTER 7

128

7 General discussion

7.1 Introduction

With the changing climatic condition, global wheat growing areas, especially the arid, semi-arid

and rain-fed Mediterranean-type climatic regions similar to South-Western Australia frequently

experience a shortage of available soil moisture during the cropping season. Water deficit during

critical stages of crop growth drastically reduces final yield. This research utilizes a combination of

conventional and modern tools and techniques to characterize drought tolerance in two critical

growth stages of bread wheat: early seedling growth stage (Zadocs Scale 12-13) and post-anthesis

stage (Zadocs Scale 61-69). Root system architectural traits were the primary target for early stage

drought tolerance characterization, whereas grain yield and yield-related traits were the main focus

for post-anthesis water stress tolerance.

7.2 Key research findings

The present study has contributed to the existing knowledge with the following key findings:

i. Significant root trait variability has been identified with potential for early stage water stress

tolerance in a collection of germplasm adapted to the Mediterranean and semi-arid

environments across the world.

ii. A reliable and quick root phenotyping procedure for early seedling stage water stress

tolerance based on a hydroponic system and subsequent image processing techniques has

been established and validated by gene expression analysis.

iii. Under early stage water stress, a tolerant genotype Abura adopted an energy conserving

strategy by downregulating the expression of genes of major pathways, whereas a

susceptible genotype AUS12671 was hypersensitive by upregulating genes involving in

various metabolic pathways.

iv. Yield and yield-related traits under post-anthesis water stress were governed by both the

additive and non-additive gene actions.

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v. Pairs of near isogenic lines (NILs) was developed using a rapid breeding technology and

marker-assisted selection targeting a yield-related QTL under water stress. Confirmed NIL

pairs which differed in the genomic location of that QTL showed contrasting responses

when characterizing for morpho-physiological traits and drought-responsive gene

expression under post-anthesis water deficit.

7.2.1 Tolerance to early seedling stage water stress

In our first experiment (Chapter 3), wheat genotypes coming from diverse genetic backgrounds

were evaluated for their early stage tolerance to water stress. Traits related to the root system

architecture were selected to identify their relative responses and levels of tolerance in the form of

stress tolerance index (STI). The hypothesis behind this was that the genotypes with longer, thicker

and deeper roots covering more areas in the soil profile have the potential to maintain cellular

hydration and growth when experienced dry spell at the start of the cropping season. Deep and

profuse root system is also helpful for successful crop establishment under dry environment as this

assists in maintaining early vigour and counterbalancing water loss by preventing soil

evapotranspiration. We tested this hypothesis in a phenotyping experiment where root trait

variability was investigated under PEG-simulated water stress using a specialized hydroponic

system and subsequent 2D imaging.

The precision of a phenotyping experiment largely depends on the number of germplasm used and

the screening procedure followed. Inclusion of germplasm from more diverse source and utilization

of advanced imaging procedure like non-destructive 2D imaging is essential for developing a

reliable and rapid root phenotyping technique. In our phenotyping experiment, genotypes Abura,

Erechim, Hybride 38, Kenya Bongo and Funello were the top five most tolerant genotypes based

on their overall mean STI scores of different root parameters. In contrast, Aus 12671, Changli,

Philippines 7, W96, GBA Sapphire were ranked as the most susceptible genotypes. When we

compared our result with a previous study by Onyemaobi et al. (2017), some genotypes (Abura and

Erechim,) were found tolerant for both early seedling stage and meiotic stage drought. This implies

130

the importance of drought tolerance at early seedling stage as a selection criterion for predicting

later stage response to drought. However, in order to explore such relationship further

comprehensive experimentation is needed.

There is controversy regarding the use of PEG to simulate water stress environment. However, the

use of PEG-simulated water stress in the hydroponic system at early stage has a long history backed

by various researchers (Ji et al., 2014; Robin et al., 2015; Yang et al., 2017b; Ayalew et al., 2018).

Moreover, in order to check the effectiveness of the screening procedure of our experiment, we

tested the expression pattern of the drought-responsive genes between the extreme genotypes.

Expression patterns were significantly different between tolerant and susceptible genotypes for the

most of the drought-responsive genes tested, which corroborated with many previous studies (Mao

et al., 2010; Rahaie et al., 2010; Cai et al., 2011).

In the next experiment (chapter 4), we focused on the transcriptomic differences between the two

genotypes (Abura and AUS12671) under early-stage PEG-simulated water stress. These two

genotypes were from the most tolerant and susceptible group, respectively, of the previous

experiment. Responses of Abura and AUS12671 under early-stage PEG-simulated water stress

were significantly different both morphologically (root traits) and physiologically (gas exchange

parameters). It is hypothesized that comparative analysis of root transcriptome profiles would

uncover the underlying mechanisms of their differential adaptation under stress. To test this

hypothesis, we sequenced the whole root transcriptome and analyzed the differentially expressed

genes (DEGs) due to stress in the two contrasting genotypes. Gene ontology and the pathway

analysis of the DEGs supported our hypothesis that these two genotypes adopted two different

strategies of adaptation under stress. We found that the tolerant genotype Abura implemented an

energy conserving approach when subjected to water stress by downregulating the genes of the

relevant pathways. We also found secondary metabolites, such as phenylpropanoids and flavonoids

and transcription factors, such as MYB, NAC, and SAUR conferred additional defence against

131

water stress. In contrast, the susceptible genotype was found metabolically over-reactive due to

stress as evident from the predominance of upregulation of genes in key metabolic pathways.

7.2.2 Tolerance to post-anthesis water stress

Breeding for later-stage drought tolerance mainly targets grain yield and yield-related traits as

selection criteria (Richards, 1996). Wheat genotypes varied considerably in their responses when

subjected to drought at later stage. Once genotypes with promising genetic variation for later-stage

drought tolerance is identified, it is necessary to investigate the heritability of the existing variation

and how it is controlled genetically. Even in this era of next-generation sequencing, making crosses

between promising genotypes and subsequent quantitative genetic analysis of traits of interest is

still essential for gene action studies. We studied responses of post-anthesis water stress on four

wheat genotypes and their direct and reciprocal crosses. Additionally, we quantitatively analyzed

gene actions controlling grain yield and related traits under post-anthesis stress in those full diallel

cross population (Chapter 5). Analysis of variation confirmed the diversity of the parental and

progeny lines in response to post-anthesis water stress treatment. The responses of the parental

genotypes were in agreement with the previous studies, except Dharwar Dry, which suffered most

in terms of grain yield and yield-related traits (Kadam et al., 2012; Lopes et al., 2013a; Tahmasebi

et al., 2014; Zhang et al., 2016a). Developing biparental or multi-parent mapping population from

these genotypes with contrasting grain yield and related traits will facilitate the identification and

genetic mapping of QTL of these traits under post-anthesis water stress. We confirmed that both

additive and dominant gene effects played a significant role in the control of grain yield and related

traits. However, non-additive (dominant) gene effects contributed more to grain yield and related

traits under stress than the additive gene effects suggesting complementarity of the additive genes.

We also observed heterosis for grain yield in the crosses C306 × Westonia, Dharwar Dry ×

Westonia and Westonia × Babax; for thousand grain weight in the crosses Babax × Dharwar Dry,

Dharwar Dry × Babax, and Dharwar Dry × C306 under post-anthesis water stress. These hybrids

showed heterosis greater than the better-performing parents suggesting the presence of

132

overdominance. Sio-Se Mardeh et al. (2006) and Li et al. (2008) also suggested the importance of

overdominance in the inheritance of grain yield and yield-related traits in wheat and rice,

respectively.

Previous studies have identified several QTL in wheat that are related to tolerance to drought at the

later stage (Farooq et al., 2014). Genetic dissection of QTL related to yield and yield-related traits

under stress is essential to identify the candidate gene linked to the underlying mechanisms of

tolerance. However, the complex quantitative nature of these traits makes this task quite formidable.

Near-isogenic lines (NILs) can effectively be utilized for the detailed phenotyping and

characterization of an individual locus (Venuprasad et al., 2011; Wang et al., 2018). In the final

experiment (Chapter 6), we characterized the effect of a 4BS QTL hotspot for increased drought

tolerance which was verified by creating NIL pairs segregating in this QTL region, comparing them

for various morphological, physiological and yield-related traits under well-watered (WW) and

under drought stressed conditions. For creating the NIL pairs, we successfully used the alternative

method of heterogeneous inbreed family combined with immature embryo culture based fast

generation cycling system. Results from the gene expression studies of a transcription factor

(TaMyb82) encoded within this region gave support on the effectiveness of the QTL and also

validated the procedure of selecting confirmed NIL pairs. The similarity of the genetic backgrounds

of the NIL pairs is theoretically estimated to be 99.61% for the F8 generation. However, the exact

similarity/difference can be evaluated by genetic sequencing of the near-isogenic lines (NILs),

which could be a focus for future research.

During morphological characterization of the NIL pairs, we noticed very few cases of significant

differences in phenology within NIL pairs even under WW conditions. These rare differences might

be due to the residual differences in the genetic background, which can be elucidated after fine

mapping or genome sequencing. Currently, we do not have enough evidence to claim that the

significant differences within the NIL pairs even under WW conditions are due to genes from the

4BS QTL. Although the confirmed NIL pairs were not tested in field condition, characterization of

133

the NILs for this study was performed with three replicates in a semi-automated glasshouse with

precise controlled over weather parameters and treatment applications. Therefore, the results are

expected to be reproducible and accurate.

7.3 Future directions and implications

i. The germplasm screening technique optimized and validated in this research can be utilized

to explore additional sources of variation for early-stage drought tolerance. Genotypes

identified with improved adaptability from this screening technique can be further trialled

under filed condition. Incorporation of these genotypes in the drought tolerance genetics

and pre-breeding programs will ensure a better outcome. Results from our study indicated

that there might be some correlation of early-stage drought tolerance with later-stage

drought tolerance. Detailed study of the adaptive mechanism of the genotypes that are

reported to be tolerant and sensitive, irrespective of their growth stages, may provide us

with solution for durable tolerance. This will also help us answer the research question if

there is any link between drought tolerance mechanisms at the early seedling stage and later

reproductive-stage with respect to final grain yield.

ii. Comparative proteomic and metabolomics studies of the contrasting resistant and

susceptible genotypes may provide additional information and thus facilitate better

understanding of their complex adaptive mechanism, which was identified in the

transcriptomics study of the current investigation.

iii. Hybrid wheat breeding targeting the genotypes contrasting in their water stress tolerance

can be emphasized as large differences were observed among the extreme pools in our study.

Moreover, both additive and non-additive gene action was reported for later-stage drought

with a predominance of dominant gene effect. Therefore, hybrid breeding might have the

potential to improve water stress tolerance by the exploitation of hybrid vigour.

iv. The fast generation cycling system (FGCS), coupled with molecular marker-assisted

selection, has been proven to be significant for developing near-isogenic lines. Exploitation

134

of these techniques in developing QTL pyramiding lines combining two or more QTL will

help to understand the effect of different QTLs combination and their interaction critical for

obtaining higher yield advantage under stress.

v. Fine mapping and positional cloning of yield-related QTL using multiple contrasting near-

isogenic lines (NILs) developed from this research may facilitate pinpointing the gene(s)

from the large genomic interval.

135

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APPENDICES

Appendix 1 Chapter five published as original research article (first page)

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Appendix 2 Chapter six published as an original research article (first page)

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Appendix 3 Chapter four submitted as an original research manuscript (email confirmation)

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