genetic architecture of wheat (triticum aestivum l.) phenology …€¦ · wheat production inthe...

Genetic Architecture of Wheat (Triticum aestivum L.) Phenology to Maximize Yield in Water

Limited Environments

MIRZA ABU NASER NAZIM UD DOWLA

A thesis submitted to Murdoch University in fulfilment of the

requirements for the degree of Doctor of Philosophy

Western Australian State Agricultural Biotechnology Centre School of Veterinary and Life Sciences

Murdoch University Perth, Western Australia

August 2017

iii

THESIS DECLARATION

I, Mirza Abu Naser Nazim Ud Dowla, 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 Murdoch University 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.

Signature: Mirza Abu Naser Nazim Ud Dowla

Date: 25/08/2017

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Acknowledgements

Although my name alone is printed on the front cover of this thesis, in fact it is not only my

but the cumulative efforts of many people from various disciplines. This thesis would not

have been possible without their cordial help and support to plan and implement the entire

task to make this project successful. It might not be possible or I might forget to mention all

the names here but to all I am very much grateful.

Firstly, I would like to extend my sincere gratitude to esteemed members of my supervisory

team, Professor Wujun Ma, Adjunct Professor Ian Edwards and Dr. Graham O’Hara who

took over all supervising responsibilities midway in my Ph.D. journey.

I must express special thanks to Professor Wujun Ma for his priceless suggestions, valuable

input and support to my project. I owe him lots of gratitude for showing me a new area of

research, his dynamic view on research and his provision of resources. I must thank him for

giving me freedom to manage this research while providing guidance when needed, and a

research assistantship which have helped me immensely.

I am forever indebted to Adjunct Professor Ian Edwards for his unstinting cooperation,

advice, encouragement, motivation and enormous logistic support for field experiments from

the start of my Ph.D. project as a volunteer advisor, and afterward as a co-supervisor. It

would not have been possible to complete this thesis without his successful endeavour to

pursue an industry scholarship to support my survival in Australia after end of my university

scholarship. His immense knowledge of plant breeding, his strategic thinking and, his

encouraging personality are valuable addition to my future research career.

My cordial thanks to Dr. Graham O’Hara for his brilliant contribution to my project even

though it was outside of his research focus. I appreciate all of his contributions of time, ideas,

productive discussions, constructive comments and guidance in the preparation of this thesis.

I also acknowledge Associate Professor Katia Stefenova for helping me to design the glass

house and field experiments, and also to analyze experimental data. My heartfelt thanks to

Dr. Timothy March, University of Adelaide and Dr. Adrian Turner, John Inn Centre, UK for

providing seeds of DH population and NIL’s., respectively. Many thanks to Professor

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Richard W. Bell and members of his land management group for their cordial suggestions

about how to manage the drought experiments in the glasshouse. I also express my gratitude

to Professor Richard Trethowan for managing one year of field trials in PBI, Narrabri.

I am grateful for the financial support from Murdoch University for providing the

postgraduate scholarship. I am also thankful to Elders and Edstar Genetics for financially

supporting my research project and the State Agricultural Biotechnology Centre (SABC) for

providing me with contemporary molecular level research facilities. My special thanks to

UWA Institute of Agriculture for providing a Travel Award to attend the “InterDrought-V”

conference.

It was really nice to come in to contact with some wonderful people during my PhD that have

made this journey smoother. I am grateful to Richard Owen who used to take me far away to

my research field when I was a novice driver and also helped me a lot in conducting field

experiment. I also thank Mr. Ian Mckernan and Mr. Jose Minetto for their relentless support

in managing my glass house experiments and Mr. Gordon Thomson for his assistance in

microscopy.

I would also like to extend my thanks to all the excellent people at the SABC especially Dr

Dave Berryman, Bee Lay Addis, Frances Brigg and Professor Mike Jones for administrative

and technical assistance during my research work in the laboratory and all fellow students

working together in the lab, for having fun and for the wonderful time spent together.

I am forever grateful to my parents and family members who are the everlasting source of

inspiration and emotional support for my higher studies and research career. I would also like

to pay high regards to my parents-in-law who are always supportive and sympathetic to me.

Finally, and most importantly, my heartfelt thanks and indebtedness are to my beloved wife

“Silvee” for sacrificing her bright future at Sydney University and joining me here at

Murdoch University to accompany me on the long journey. I feel guilty for the decision we

made but I would not have coped without you as “I am fated to journey hand in hand with

you”. I would not have been able to reach this far without you. I acknowledge your

encouragement and endeavour to bring me out of my shell. Last but not least, I would have

never been able to accomplish this task without God allowing me to succeed.

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Abstract

Water deficiency during the critical growth stages is one major concern affecting successful

wheat production in the rainfed agriculture systems used in Australia and other semi-arid

regions of the world. Bypassing the sensitive growth phases from a water stress period can

result in higher grain yields in water limited environments. Therefore, this study was

concentrated on the adjustment of phenology genes to synchronize the critical growth phases

with the rainfall pattern or water availability of target environments. Vernalization (Vrn),

photoperiod (Ppd) and earliness per se are the three major components of flowering pathway

in wheat. A series of experiments were conducted to determine the role of allelic variations

of vernalization and photoperiod genes in heading and subsequent grain production. The first

experiment with double haploid (DH) lines differing for winter/spring alleles of Vrn-1 loci

(vrn-A1, vrn-B1 and vrn-D1) revealed that combination of all three spring alleles confers the

earliest flowering while the combination of all three winter alleles results in the latest

flowering. On the other hand a combination of any one winter allele with two spring alleles

results in intermediate heading but performed better in terms of a higher number of grain per

spike, thousands kernels weight and test weight. Inclusion of a photoperiod insensitive allele

with any combination of Vrn-1 loci reduces the heading time further by at least 20 days

except for the combination with all winter alleles. The results also revealed that allelic

variation in the Vrn and Ppd genes also alters the water requirement rate and total water

consumption by modifying the duration of the growth phases. Investigation of the role of

photoperiod sensitive/insensitive alleles of Ppd-D1 on heading and spike development

revealed the relationship of photoperiod insensitive alleles Ppd-D1a with the gibberellin

regulated pathway in addition to the effects of day length on flowering. Field experiments

with selected advanced lines also revealed this relationship where variation in the days to

heading and grain number/spike have been observed due to changes in the dwarfing genes

along with the alleles of Vrn and Ppd genes. Variations for yield and protein contents of the

genotypes in different locations indicated that expression of the same set of gene combination

differs with the altering environmental conditions.

The final experiment investigating leaf proteome dynamics during the event of flowering

recognized 88 unique differentially expressed proteins out of 165 identified proteins between

the two varying heading time DH lines. It revealed that a number of proteins, including sugar

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metabolism, stress related and, most importantly Glycine Rich RNA binding (GRP) and cold

shock domain proteins (CSD) are involved in the regulation of the flowering pathway. The

outcomes of this study provide new insights into the control of the flowering pathway in

wheat, that along with the previously reported Vrn and Ppd genes provide opportunities for

further in depth investigation to fine tune the flowering time for better yields in water limited

environments.

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Table of Contents

THESIS DECLARATION....................................................................................................... iii

Acknowledgements .................................................................................................................... v

Abstract .................................................................................................................................... vii

Table of Contents ...................................................................................................................... ix

List of Tables ......................................................................................................................... xiii

List of Figures .......................................................................................................................... xv

List of Appendices ................................................................................................................. xvii

Chapter 1 .................................................................................................................................... 1

1 General Introduction and Literature Review ..................................................................... 1

1.1 Introduction ................................................................................................................. 1

1.2 WHEAT: Origin, genome structure, and distribution ................................................. 3

1.3 Drought........................................................................................................................ 4

1.4 Drought adaptation mechanism in cereals .................................................................. 5

1.5 Phenology Genes ......................................................................................................... 6

1.5.1 Vernalization genes .............................................................................................. 6

1.5.2 Photoperiod genes ................................................................................................ 7

1.5.3 Earliness per se (eps) genes ................................................................................. 9

1.6 Molecular Intervention of Phenology Genes ............................................................ 10

1.7 Dwarfing Genes......................................................................................................... 11

1.8 Physiological Aspects of Phenology and Dwarfing Genes ....................................... 13

1.9 Conclusion ................................................................................................................. 19

1.10 Thesis outline and objectives .................................................................................... 19

1.11 References ................................................................................................................. 20

Chapter 2 ................................................................................................................................ 29

2 Allelic Variants at the Vrn-1 Locus of Bread Wheat for Enhanced Yield in Water-

Limited Environments ............................................................................................................. 29

2.1 Introduction ............................................................................................................... 29

2.2 Materials and Methods .............................................................................................. 31

2.2.1 Plant materials .................................................................................................... 31

2.2.2 Experimental design and description of data ..................................................... 32

2.2.3 Genotyping of the plant materials ...................................................................... 36

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2.2.4 Statistical model and data analysis .................................................................... 37

2.3 Results ....................................................................................................................... 38

2.3.1 Allelic variation of phenology genes within the DH population ....................... 38

2.3.2 Linear mixed model analysis of the allelic combination effects on agronomic

traits …………………………………………………………………………………40

2.3.3 Effects of allelic combination on phenology and agronomic traits ................... 41

2.3.4 Effects of allelic combinations and water stress interactions on agronomic traits

…………………………………………………………………………………51

2.3.5 Relationships among agronomic traits in the WS treatment .............................. 54

2.3.6 Allelic effects on water consumption by genotypes .......................................... 55

2.4 Discussion ................................................................................................................. 56

2.4.1 Allelic effects of vernalization and photoperiod genes on days to heading ...... 56

2.4.2 Allelic effects of vernalization and photoperiod genes other agronomic traits . 58

2.5 Conclusion ................................................................................................................. 60

2.6 References ................................................................................................................. 61

Chapter 3 .................................................................................................................................. 65

3 Effects of Photoperiod Gene Ppd-D1 in the Spike Developments and Heading of Wheat

…………………………………………………………………………………………..65

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


3.3 Results ....................................................................................................................... 66

3.3.1 Ppd-D1 effects on plant growth ......................................................................... 66

3.3.2 Ppd-D1 effects on reproductive development ................................................... 67

3.4 Discussion ................................................................................................................. 72

3.5 Conclusion ................................................................................................................. 74

3.6 References ................................................................................................................. 74

Chapter 4 .................................................................................................................................. 77

4 Phenology and Dwarfing Genes Effects on Adaptation of Advanced Lines across

Diverse Water-Limited Environments of Western Australia ................................................... 77

4.1 Introduction ............................................................................................................... 77


4.2.1 Plant materials .................................................................................................... 79

4.2.2 Field experiments ............................................................................................... 80

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4.2.3 Agronomic Traits ............................................................................................... 80

4.2.4 Genotyping of the plant materials ...................................................................... 81

4.2.5 Statistical analysis .............................................................................................. 81

4.3 Results ....................................................................................................................... 82

4.3.1 Allelic distribution at the Vrn-1 and Ppd-1 loci................................................. 82

4.3.2 Environmental effects on yield and protein content .......................................... 84

4.3.3 Environmental and allelic combination effects on yield and protein content .... 84

4.3.4 Allelic combination effects on agronomic traits ................................................ 87

4.4 Discussion ................................................................................................................. 90

4.4.1 Allelic diversity in the advance lines ................................................................. 90

4.4.2 Allelic combination effects on agronomic traits ................................................ 91

4.4.3 Environment and allelic combination effects on yield and protein content ....... 92

4.5 Conclusion and Recommendations ........................................................................... 93

4.6 References ................................................................................................................. 94

Chapter 5 .................................................................................................................................. 97

5 Changes in Differential Protein Expression of Wheat during Phenological Development

…………………………………………………………………………………………..97

5.1 Introduction ............................................................................................................... 97


5.2.1 Plant materials .................................................................................................... 99

5.2.2 Plant morphological parameters ........................................................................ 99

5.2.3 Protein extraction ............................................................................................... 99

5.2.4 2DE Separation ................................................................................................ 100

5.2.5 Nano-HPLC-MS/MS ....................................................................................... 100

5.3 Results ..................................................................................................................... 101

5.3.1 Plant agronomic traits ...................................................................................... 101

5.3.2 The proteomic profile differences between wheat DH lines at different growth

stages ………………………………………………………………………………..101

5.3.3 Quantitative analysis of the differentially expressed proteins between the two

lines ………………………………………………………………………………..104

5.3.4 Protein identification and functional distribution ............................................ 106

5.3.5 Expression profiles of the proteins with putative function in flower

development and growth ................................................................................................ 125

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5.4 Discussion ............................................................................................................... 126

5.4.1 Proteins involved in photosynthesis and energy metabolism .......................... 127

5.4.2 Proteins involved in metabolism and protein synthesis ................................... 128

5.4.3 Protein involved in stress and redox homeostasis............................................ 129

5.4.4 RNA binding proteins and diverse role in plant growth and development ...... 129

5.5 Conclusion ............................................................................................................... 131

5.6 References ............................................................................................................... 131

Chapter 6 ................................................................................................................................ 137

6 Summary and Recommendations .................................................................................. 137

6.1 Significance of the work ......................................................................................... 137

6.2 Future recommendations ......................................................................................... 141

6.3 References ............................................................................................................... 143

7 Appendices ..................................................................................................................... 147

xiii

List of Tables

Table1.1 Major wheat producers of the world: five year (2008-2012) averages of production,

area harvested and area irrigated for wheat ............................................................................... 3

Table 1.2 Current status of the identified alleles for Vrn1 and Ppd1 loci ................................. 8

Table 1.3 PCR markers for the different vernalization and photoperiod response allele ....... 15

Table 2.1 Summary of the trials .............................................................................................. 36

Table 2.2 Primers for identification of different alleles of Vrn-1 gene ................................... 38

Table 2.3a Description of the fitted model for the glasshouse experiment during 2013 ........ 43

Table 2.3b Description of the fitted model for the glasshouse experiment during 2014……44

Table 2.3c Description of the fitted model for the field experiment during 2013………….45

Table 2.3d Description of the fitted model for the field experiment during

2014………………………………………………………………………………………......46

Table 2.4a Predicted means of the traits against different allelic combinations of

vernalization and photoperiod genes of the 2013 experiments under both glasshouse and field

conditions ................................................................................................................................. 49

Table2.1b Predicted means of the traits against different allelic combinations of vernalization

and photoperiod genes of the 2014 experiments under both glasshouse and field conditions 50

Table 2. 5 Interaction of allelic combinations and water stress treatment in GH14 trial ........ 54

Table 4.1 Environmental condition of the three trial sites during 2014 along with sowing and

harvesting time ......................................................................................................................... 80

Table 4.2 Allelic composition of the advanced lines .............................................................. 83

Table 4.3 Location and allelic combination interaction effects on yield and protein content 85

Table 5.1 Results of analysis of variance for the two lines for different agronomic traits ... 102

Table 5.2 Identified protein in wheat leaves by MS ............................................................. 108

Table 5.3 Number of the differentially expressed proteins between the lines across four

growth stages. ........................................................................................................................ 125

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List of Figures Figure 2. 1 Possible allelic combinations considering spring/winter alleles in Vrn-1 loci ..... 32

Figure 2.2 Pot set-up for controlled water stress treatment .................................................... 33

Figure 2.3 Environmental conditions of the trial sites a) average temperature of glasshouse in

the year 2013 and 2014, b) rainfall, minimum and maximum temperature of Narrabri during

2014 and c) rainfall, minimum and maximum temperature of todays during 2013 ................ 35

Figure 2.4 Mean Heading days of the DH lines with parents and checks in the controls (blue)

and water stress (red) treatments.............................................................................................. 39

Figure 2.5 PCR amplification products demonstrating the presence of spring or winter alleles

of Vrn-A1, Vrn-B1 and Vrn-D1 and, photoperiod insensitive allele of Ppd-B1 ...................... 40

Figure 2.6 Response of different allelic combinations to stress treatment for (a) heading

(SED 1.43), (b); plant height (SED 3.2); (c) tiller number per plant (SED 0.87); (d) seed

number per spike (SED 2.88); (e) thousand-kernel weight (SED 2.45); and (f) test weight

(SED 1.59) ............................................................................................................................... 53

Figure 2.7 Relationships among agronomic traits in the water-stressed treatment ................ 55

Figure 2.8 (a) Variation in water consumption during the growing season among genotypes

(V_1 to V_16) due to developmental differences; (b) differences in total water consumption

among genotypes (V_1 to V_16) throughout the growing season with significant (p<0.05)

ranking ..................................................................................................................................... 57

Figure 3.1 Differences between the photoperiod insensitive (W9331) and sensitive (W9333)

for root length (A), shoot length (B) and tiller number (C) ..................................................... 68

Figure 3.2 A-C Sequential development of reproductive organs on the same day for the

Photoperiod insensitive (W9331) and sensitive (W9333) lines A) apical dome; B) double

ridge stage and C) faster growth of spikelet primordia in W9333 ........................................... 69

Figure 3.3 A and B faster growth of spikelet primordia in W9333 ........................................ 70

Figure 3.4 A-C Sequential development of reproductive organs on the same day for the

Photoperiod insensitive (W9331) and sensitive (W9333) lines A) Terminal spikelet stage of

W9333; B) terminal spikelet stage of W9331; and C) anthesis of W9331 .............................. 71

Figure 3.5 Differences between the Photoperiod insensitive (W9331) and sensitive (W9333)

lines for spike length, spikelet number and grain number ....................................................... 72

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Figure 4.1 Predicted changes in temperature (a) and rainfall (b) from 2000 to 2050 in the

cropping area of southwest Australia (adopted from Niel et al., 2011) ................................... 78

Figure 4.2 Location means of grain yield and protein content for the 23 lines grown at the

three trial sites. ......................................................................................................................... 84

Figure 4.3 GGE biplot for allelic combination and environmental interaction on (A) yield

and (B) protein content ............................................................................................................ 87

Figure 4.4 Comparison of the allelic combination effects on (a) days to heading; (b) plant

height; (c) spike length; (d) seed number per spike; (e) spikelet fertility; (f) thousand-kernel

weight; (g) aspect ratio; and (h) roundness. ............................................................................. 89

Figure 5.1 2D electrophoresis gels of wheat leaf proteome of Line A (left panel) and Line B

(right panel) at seedling (top) and tillering (bottom) growth stages, respectively. ................ 103

Figure 5.2 2D electrophoresis gels of wheat leaf proteome of Line A (left panel) and Line B

(right panel) at AR2 (top) and AR10 (bottom) growth stages, respectively. ......................... 104

Figure 5.3 Enlarged views of 2DE gel cuttings showing the presence and absence leaf

protein in two lines (Line A at left panel and Line B at right panel) at different stages ....... 105

Figure 5.4 Classifications of the identified proteins based on putative biological functions 107

Figure 5.5 Protein-protein interaction network analysed by STRING including all

differentially expressed proteins. Different line colours represent the types of evidence used

in predicting the associations: gene fusion (red), neighbourhood (green), co-occurrence across

genomes (blue), co-expression (black), experimental (purple), association in curated

databases (light blue) or co-mentioned in PubMed abstracts (yellow). The densely clustered

protein nodes include proteins involved in photosynthesis. Protein names against protein ID

have been presented in the following table. ........................................................................... 122

Figure 5.6 Protein networks generated by Bingo A) Biological pathway and B) Molecular

function. GO categories of TAIR homologous proteins are presented for wheat. The size of

the node is related to the number of proteins and the colour intensity represents the p-value

for the statistical significance for overrepresented GO term. ................................................ 124

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List of Appendices Appendix A Soil properties of field-collected soil from Merredin ....................................... 147

Appendix B Amount and composition of Fertilizers used in the experiments ..................... 148

Appendix C1 Lay out of glass house experiment 2013 ........................................................ 149

Appendix C2 Lay out of glass house experiment 2014 ........................................................ 149

Appendix C3 Lay out of filed experiment 2013 and 2014 at Toodyay ................................ 149

Appendix D Biological pathways generated for the identified proteins ............................... 150

Chapter-1

1

Chapter 1

1 General Introduction and Literature Review

1.1 Introduction

Cereals constitute a prime global human food source. Among them, wheat (Triticum aestivum

L.) is ranked as the second most important food after rice and is the most widely cultivated

cereal in the world. It is one of the central pillars of food security, supplying 20% of total

calories and a similar portion of total protein to the world’s population (Braun 2010).

Globally, the average wheat yield is 3.3 tonnes/ha; but it varies widely with regional averages

ranging from 1.7 tonnes/ha in Australia to a potential of up to 9 tonnes/ha in other parts of the

world (FAO 2015a). The yield penalty is usually due to different environmental stresses that

reduce yield potential by 69.1% (Boyer 1982). In most developed countries, wheat is grown

mainly on rainfed marginal land where inadequate and erratic rainfall limits yield (Table 1).

Drought is a key stress that constrains wheat production on about 65 million hectares of land

worldwide (Zaynali Nezhad et al., 2012) and reduces yield by up to 50% (Byerlee and Morris

1993). Modelling exercises have revealed that water stress in marginal wheat growing

environments reduces 50 to 90% their yield potential under irrigated conditions (Morris

1991). During 2012, the overall global wheat production was lowered by 1.4% mainly due to

severe drought in USA, Europe and central Asia (FAO 2013). Australian wheat yield dropped

by 46 % in 2006 compared to the previous yield trend of previous the 50-years and accounted

for billion dollar losses to the wheat industry (FAO 2012).

The impacts of future drought episodes on wheat production are expected to deteriorate due

to the effects of climate change on temperature and precipitation. It is estimated that 1 oC

increase of temperature during the last 29 years has resulted in 6% reduction of wheat yield

compared to yield with no global warming effects (Lobell and Gourdji 2012). According to

the fifth Assessment Report of Intergovernmental Panel on Climate Change (IPCC), global

mean temperature will increase by 3.7 oC by the end of this century, where incidents of

numbers of hottest days and coolest nights will occur 50% more frequently than the present

(IPCC 2014). Changes in the precipitation pattern coupled with increasing temperature would

affect the major crop production of the world and wheat production could decline by 23.2%

to 27.2% by 2050 unless protective measures for limiting global warming or appropriate

Chapter-1

2

cultivars and crop management practices are adopted (Nelson et al., 2009). Wheat production

in low latitude sites would be more vulnerable with the rise of 3-5 oC temperature scenario

compared to the high latitude regions, and yields could decline by up to 40 % with an

increase of 2 oC (Reynolds 2010). In Australia wheat belt region have a typical

Mediterranean climate, most precipitation occurs in winter followed by less frequent rain

during spring, and summer is the driest season. Thus, water stress in spring is the major factor

limiting yield improvement in these regions and often coincides with stem elongation,

flowering and grain filling (Turner 2004). In these environments, terminal heat often

combines with drought during the grain filling period and further limits grain yield (Nachit

1998). The IPCC in their fifth assessment report predicted that an increase in mean annual

temperature by 2.2-5 oC with +5% to -30% change in precipitation pattern will result in the

expansion of drought affected areas by 5.4%, 4.6% and 3.8% by 2030, 2050 and 2070,

respectively (IPCC 2014). In this situation, plant breeders have to be well-prepared to

embrace the challenges of climate change and feed the world by developing varieties better

adapted to water limited environments. Better utilization of available genetic resources of

wheat is essential to maintain and maximize wheat yield potential in water limited

environments, and optimization of phenology is one of the most effective ways to achieve

this goal.

Phenology is the key factor for crop adaptation to a particular environment. A proper

understanding of the genetic control of phenological traits will enable breeders to develop

crops better adapted to a specific environment. It is well documented that yield loss due to

drought depends on the growth stage of its occurrence, as well as the duration and intensity of

the stress (Lopez et al., 2003, Serraj et al., 2005). Spike development, from terminal spikelet

initiation to anthesis, is the most important phase in determining grain yield as it has been

observed that a heavier spike at anthesis is positively correlated with grain yield (Slafer et al.,

2001) and can be manipulated without affecting other phases (Whitechurch and Slafer 2002).

Therefore, adverse effects of drought could be minimized by ensuring that the most sensitive

developmental stages do not occur during stress periods (Saini and Westgate 1999). Hence,

fine tuning of flowering and the duration of developmental phases are advocated for better

adaptation of wheat in water limited environments or to escape from these constraints

(Richards 1991, Worland 1996, Debaeke 2004, Cockram et al., 2007). Phenology genes also

Chapter-1

3

Table1.1 Major wheat producers of the world: five year (2008-2012) averages of production, area harvested and area irrigated for wheat

COUNTRY Production (m tonnes)

Area harvested (‘000 Ha)

Area Irrigated (‘000 Ha)

Australia 24.51 13650 -

Canada 26.22 9200 -

China, mainland 116.15 24110 -

France 38.37 5540 30.24

India 84.36 28640 -

Pakistan 23.40 8860 7335

Russian Federation 52.19 24090 -

Turkey 19.99 7980 -

Ukraine 20.34 6480 46.9

United States of America 60.91 20060 1662

World 678.02 220400 13241.5

Source: FAOSTAT (2015b)

regulate the physiological development of wheat (Barrett et al., 2002) and some morpho-

physiological traits have been identified as effective in breeding drought adaptive varieties

(Acevedo 1987, Reynolds et al., 2009). Taking account of many important traits and their

interactions in stress environments, a sound understanding of the genetic control and

physiological basis of drought tolerance would facilitate the improvement of yield in water

limited environments.

1.2 WHEAT: Origin, genome structure, and distribution

Wheat (Triticum ssp.) is a prominent member of cereals and belongs to the Graminae family

under the tribe Triticeae. From the sign of earliest crop domestication, it can be assumed that

cultivation of wheat began around 7500 to 8000 BC i.e., 10,000-12,000 years ago in the

Fertile Cresent of the Near East. Recent phytogeographical, molecular, archeobotanical, and

genetic evidence has pointed to a small ‘core area’ within the Fertile Cresent, in present day

southeastern Turkey and northern Syria as the cradle of agriculture (Salamini et al., 2002,

Abbo et al., 2006, Peleg et al., 2011).

Chapter-1

4

The domestication process of cultivated wheat involved polyploidization among different

wild diploid ancestors of A, B and D genomes, where selection for nonshattering, free

threshing, non-brittle rachis and hull less spike, and for higher yield were the driving force

for genetic changes (Carver 2009, Bonjean and Angus 2001). Aegilops tauschii is the early

progenitor of the ‘DD’ genome and Triticum urartu is donor for the ‘AA’ genome to

allotetraploid T. turgidum, while A. speltoides ssp. Speltoides, has been recognized as origin

of then ‘BB’ genome, albeit it is still controversial (Huang et al., 2002). Most of the cultivars

of modern hexaploid bread wheat [T. aestivum (2n=6x=42; BBAADD)] and tetraploid durum

wheat [T. turgidum ssp. Durum (2n=4x=28; BBAA)] have been evolved from allotetraploid

wild emmer wheat [Triticum turgidum sspdicoccoides (2n=4x= 28; genome BBAA)] (Peleg

et al., 2011)

The initially domesticated wheat and their wild diploid ancestors were winter annual long day

plant, adapted to cool temperate regions. It was mainly adapted to the region between 300 N

and 600 N in the northern hemisphere but introduction of insensitivity to day length and

temperature has spread these limits to between 200 S and 400 S (Díaz et al., 2012, Thomas

and Vince-Prue 1996, Sivertsen 1999). Thus, wheat is grown over a wide range of

elevations, climatic conditions and soil fertility, from sea level to more than 3000 masl with a

temperature range of 3 oC to 32 oC (Curtis et al., 2002, Bushuk 1998). It can be grown in

areas with average precipitation ranges from 250 mm to 1750 mm, while 75% of wheat

growing areas receive an average of 375 to 875 mm rainfall (Curtis et al., 2002).

1.3 Drought

Drought is becoming a major concern of agricultural scientists due to alarming patterns of

climate change and increasing occurrence on crop lands every year. In general, drought refers

to an acute or severe shortage of water due to prolonged absence or marked deficiency of

precipitation that limits agricultural, urban or environmental water supply (Gibbs and Maher

1967; Bureau of Meteorology 1989; Heim, 2002).

Drought has a large impact on the environment, agriculture, farm income levels, and thus the

socio-economic conditions at local and national levels. However, it is the agriculture which is

clearly first to feel the pressure of drought as drought impedes crop yields, kills animals, and

reduces the productivity of farms. Accordingly, Foley (1957) defined drought broadly as

Chapter-1

5

‘dryness due to lack of rain’, but acknowledged the concept of the amount of ‘effective’ (for

germination) or ‘influential’ (for growth) rainfall required to maintain soil moisture above the

wilting point for herbage plants.

Drought in a particular environment is related to soil properties, temperature and radiation,

vapour pressure deficit, and is compounded with other biotic and abiotic stresses like disease

and salt. Available soil water changes in different times of season, thus, can develop early,

mid and late stress during crop development stages. However, rainfall distribution and

drought severity varies from season to season, and year to year for a given location.

Moreover, cultivars adapted to certain drought conditions may not perform alike in other

types of drought (Fukai and Cooper 1995). So, it is important to identify the timing and

severity by drought for a particular location for successful crop breeding program.

1.4 Drought adaptation mechanism in cereals

Active growth of our agricultural plants is stunted when the soil moisture falls to half of the

field capacity and none of them, and few semi desert plants resist drought in a true sense. For

successful crop production not only survival but also reasonable yield is desirable for

economic purpose and subsistence (Fukai and Cooper 1995). However, different groups of

plants have different adaptation mechanism to drought, such as drought escape or drought

resistance mechanisms, with resistance further classified into drought avoidance and drought

tolerance (Levitt, 1980; Price et al., 2002).

Drought escape is described as the ability of plants to complete the life cycle before severe

stress sets in, i.e. growing the crop during the period of high rainfall and high soil water

availability. Drought avoidance is the maintenance of high tissue water potential in a few

ways such as extracting more water under stress by improved root traits, the capacity of plant

cells to hold acquired water, and reducing water loss through reduced epidermal conductance

reduced radiation absorption and reduced leaf area (Price et al., 2002). Drought tolerance is

the ability to withstand and maintain metabolism even at low tissue water potential during

water deficit (Ingram and Bartels, 1996). Dehydration tolerance helps to continue metabolic

activity for a few additional days and translocation of assimilates to fill grain, hence

important for terminal drought stress.

Chapter-1

6

Drought is conditioned by many components and traits along with plant water relations and

plant function during stress (Blum.1996; Blum, 1999; McWilliam, 1989). Therefore,

developing a suitable variety for a stress environment necessitates deep understanding of

genetic basis of drought resistance. The cereal crops are generally able to utilize drought

avoidance strategies based on maintaining cell water potential to minimize the drought effects

(Kosova et al., 2014). Additionally, modification of growth phases by phenological

adjustment would allow them to save the critical growth stages from a severe drought period.

1.5 Phenology Genes

Wheat is adapted to a wide range of agricultural environments (Curtis et al., 2002).

Synchrony of flowering to a wider range of climatic conditions is largely controlled by i)

vernalization (Vrn) genes (exposure to cold temperature requirement), ii) photoperiod (Ppd)

genes (photoperiod sensitivity) and iii) autonomous earliness per se (Eps) genes (Kato and

Yamagata 1988). Hence, the adaptation of a genotype to a particular environment depends on

the interaction of these three groups of genes.

1.5.1 Vernalization genes

Vernalization promotes the switching of the plant vegetative phase to the reproductive phase

by inducing floral primordia from leaf primordia in the shoot apical meristem (Trevaskis

2010, Gouis et al., 2012). In wheat three genes determine the vernalization requirement;

Vrn1, Vrn2 and Vrn3 (Pugsley 1971, Dubcovsky et al., 1998, Yan et al., 2006). The three

orthologous Vrn1 genes; Vrn -A1, Vrn-B1, and Vrn-D1 are located on long arms of the

homoeologous chromosomes 5A, 5B, and 5D in common wheat, respectively, and mainly

control the vernalization requirement (Pugsley 1971, Law 1976, Galiba et al., 1995,

Dubcovsky et al., 1998). The Vrn2 gene is also located on the long arm of 5A and Vrn3 is

located on short arm of chromosome 7B (Law and Wolfe 1966, Yan et al., 2003, Yan et al.,

2004, Yan et al., 2006). Winter wheat varieties require a certain period of cold to induce

flowering, whereas those varieties that flower without vernalization are referred to as spring

types. The dominant alleles of Vrn-A1, Vrn-B1, Vrn-D1 and Vrn3 are responsible for the

spring growth habit, and thus, a dominant allele at any of the three Vrn1 loci confers a spring

type. On the other hand, Vrn2 is dominant for the winter type and epistatic to dominant

alleles of Vrn1 (Stel'makh 1987, Dubcovsky et al., 1998, Tranquilli and Dubcovsky 2000,

Chapter-1

7

Yan et al., 2004a). Vrn2 is a floral repressor that delays flowering, but vernalization under

long days suppresses the expression of Vrn2 and enhances expression of Vrn1 (Trevaskis et

al., 2007). Multiple alleles of Vrn1 with different levels of responses to vernalization and

effects on flowering have been identified (Table 2) (Tsunewaki 1961, Roberts 1984, Koval

SF 1998 , Santra et al., 2009), and have an adaptive value (Gotoh 1979, Stelmakh 1990,

Goncharov 1998, Stelmakh 1998, Iwaki et al., 2000, Iwaki et al., 2001). The extent of

flowering depends on the basal level of Vrn1 expression (Trevaskis et al., 2003) and some

alleles of Vrn1 are expressed without prior cold treatment, thus allowing flowering without

vernalization (Danyluk et al., 2003, Trevaskis et al., 2003, Yan et al., 2003). Mutations in the

promoter or deletion in the first intron of the Vrn1 gene cause expression of Vrn1 without

vernalization and the alleles lacking the larger section are more active during earlier

flowering without vernalization (Yan et al., 2003, Yan 2004b, Fu et al., 2005, Szűcs et al.,

2007, Hemming et al., 2009). On the other hand, varieties of wheat and barley flower early

without vernalization when they lack a functional copy of the Vrn2 gene (Dubcovsky et al.,

1998, Yan et al., 2004a). The Vrn3 gene also express at a high level when Vrn2 is absent, and

active alleles of Vrn3 accelerate flowering irrespective of day length or vernalization (Yan et

al., 2006). Accordingly, five loci of Vrn genes influence flowering by controlling

vernalization requirement of wheat cultivars in different parts of the world (Pugsley 1971,

McIntosh 1998, Goncharov 2003, Yan et al., 2006).

1.5.2 Photoperiod genes

Wheat is a long day plant, requiring exposure to long days (>14 hrs light) for flowering,

whereas photoperiod insensitive varieties flower early in short days (10 hrs or less light)

(Foulkes et al., 2004, Beales et al., 2007, Kumar et al., 2012). This photoperiod sensitivity is

controlled by the semi-dominant homoeologous Ppd1 gene on the short arm of chromosome

2, and as is the case with Vrn1, the dominant allele confers photoperiod insensitivity (Welsh

1973, Law 1978, Börner et al., 1993, Worland et al., 1998, Snape et al., 2001). The effects of

the photoperiod insensitive allele Ppd1 were studied thoroughly by Worland and Sayer

(1995) over a fourteen-year period in different wheat growing regions, and revealed that

insensitive Ppd1 advances flowering time by 9-15 days, and this earliness can be utilized to

obtain yield advantages in water limited environments by drought avoidance. The early Ppd1

gene has also some pleiotropic effects including reduced plant height and number of tillers,

Chapter-1

8

and less spikelets per ear (Worland et al., 1998). However, an increase in spikelet fertility can

compensate for the yield penalty (Snape et al., 2001). It is clear that Ppd1 insensitivity brings

forward the time of terminal spikelet formation, thus advancing the flowering time by

reducing the number of spikelets in the ear. However, it does not influence the rates of leaf

and flower primordial production. There is also variation among the potency of three Ppd1a

loci where plants with Ppd-A1a and Ppd-D1a are earlier in flowering than the plants with

Ppd-B1a (Díaz et al., 2012). In the same way that a number of Vrn-1 alleles have been

identified, so too recently a number of alleles and their haplotypes have also been identified

for all the three homeologous loci of Ppd-1 gene (Table 2) in both bread and durum wheat,

and have a great agronomic importance for deployment in breeding programs (Nishida et al.,

2013, Takenaka and Kawahara 2012, Guo et al., 2010, Muterko et al., 2015, Beales et al.,

2007, Cane et al., 2013).

Table 1.2 Current status of the identified alleles for Vrn1 and Ppd1 loci

Gene Allele Sequence variation from wild type Sensitivity to

light/temperature

Reference

Vrn-A1 Vrn-A1a 231-bp and 140-bp insertions in the

promoter region of common wheat

Insensitive (Yan 2004)

Vrn-A1b 20-bp deletion in the promoter region

of common wheat


Vrn-A1c 7222 bp deletion in intron 1 Insensitive (Fu et al., 2005)

Vrn-A1d 32-bp deletion in the promoter region

of tetraploid wheat


Vrn-A1e 54-bp deletion in the promoter region

of tetraploid wheat


vrn-A1 Wild type Sensitive (Yan 2004)

Vrn-B1 Vrn-B1a 6850 bp deletion in intron 1 Insensitive (Fu et al., 2005)

Vrn-B1b 6850 bp and 36 bp deletion in intron 1 Insensitive (Santra et al., 2009)

Vrn-B1c 817 bp deletion and 432 bp duplication

in intron 1

Insensitive (Milec et al., 2012)

vrn-B1 Wild type Sensitive (Fu et al., 2005)

Vrn-D1 Vrn-D1a 4235 bp deletion in intron 1 Insensitive (Fu et al., 2005)

Chapter-1

9

Gene Allele Sequence variation from wild type Sensitivity to

light/temperature

Reference

Vrn-D1b C replaced by A at translation site in

CArG-box of wild type

facultative (Zhang et al., 2012)

vrn-D1 Wild type Sensitive (Fu et al., 2005)

Ppd-A1 Ppd-A1a1 1085 bp deletion in the promoter

region

Insensitive (Nishida et al.,

2013)

Ppd-A1a2 1027 bp deletion in the promoter

region

Insensitive (Wilhelm et al.,

2009a)

Ppd-A1a3 1117 bp deletion in the promoter

region

Insensitive (Wilhelm et al.,

2009a)

Ppd-A1a4 684 bp deletion in the promoter region Insensitive (Muterko et al.,

2015)

Ppd-A1b Wild type Sensitive (Wilhelm, et al.,

2009b)

Ppd-B1 Ppd-B1a.1 308 bp insertion in the promoter region Insensitive (Nishida et al.,

2013)

Ppd-B1a.2 Four copy of Ppd-B1 Insensitive (Díaz et al., 2012)

Ppd-B1a.3 3 copy of Ppd-B1 Insensitive (Díaz et al., 2012)

Ppd-B1a.4 2 copy of Ppd-B1 Insensitive (Díaz et al., 2012)

Ppd-B1e Null - (Cane et al., 2013)

Ppd-B1b Wild type Sensitive (Díaz et al., 2012)

Ppd-D1 Ppd-D1a.1 2089 bp deletion in the promoter

region

Insensitive (Beales et al., 2007)

Ppd-D1a.2 5 bp deletion in exon 7 Intermediate (Guo et al., 2010)

Ppd-D1b.1 Wild type Sensitive (Beales et al., 2007)

Ppd-D1b.2 Insertion of transposable element in the

intron 1

Sensitive (Guo et al., 2010)

1.5.3 Earliness per se (eps) genes

The earliness per se (eps) genes control flowering time independent of temperature and

photoperiod. To date, very few eps genes have been identified in wheat, but several QTL

Chapter-1

10

studies revealed that most of the chromosome groups carry such genes and they are present as

QTL effects rather than as major genes in the Ppd and Vrn pathways (Chen et al., 2010, Law

et al., 1998, Snape et al., 2001, Kulwal et al., 2003, Tóth et al., 2003, Hanocq et al., 2007,

Griffiths et al., 2009). The eps genes are involved in the fine tuning of flowering time

(Hoogendoorn 1985), and hence can be utilized for adaptation to specific climatic conditions

following precise characterization of such loci.

1.6 Molecular Intervention of Phenology Genes

In concordance with studies on Arabidopsis, important progress has been made at the

molecular level to elucidate the flowering pathway in wheat. Molecular and sequence

analysis revealed that Vrn1 encodes a MADs-box transcription factor similar to the

Arabidopsis meristem identity gene APETALA1 (AP1), CAULIFLOWER (CAL) and

FRUITFULL (FRU), that regulates the shoot apical meristem to determine the transition from

vegetative to reproductive development (Yan et al., 2003). Insertions, deletions and mutations

in the promoter region are associated with allelic variation of Vrn1 (Yan 2004). Following

this, a series of molecular markers have been developed (Table 3) and successfully utilized to

identify allele frequency of the local wheat cultivars as well as cultivars from the CIMMYT

world collection (Eagles et al., 2009, Fu et al., 2005, Chen et al., 2013, Iqbal et al., 2007). The

Vrn2 encodes a zinc finger-CCT domain transcription factor and is a floral repressor, down

regulated by both vernalization and short day length (Yan et al., 2004). Vrn2 plays a very

similar role to that of FLOWERING LOCUS C (FLC) in Arabidopsis but actually has no

orthologues suggesting an independent evolution of the vernalization pathways (Dubcovsky

et al., 2006). Vernalization gene Vrn3 is similar to Arabidopsis FLOWERING LOCUS (FT)

and the dominant allele is associated with a retro element insertion in the TaFT promoter,

resulting in early flowering (Yan et al., 2006). Recent screening of a set of Chinese wheat

cultivars led to the discovery of two more dominant alleles of Vrn3, where 80 days variation

in heading has been observed due to their action (Chen et al., 2013). Allelic variations of

these Vrn1 genes quantify the vernalization effects, and determine flowering time by

interacting with photoperiod gene Ppd1. The latter is a member of a pseudo response

regulator (PRR) gene family where insensitivity is associated with deletion or transposon

insertion within the promoter region and also copy number variation (Beales et al., 2007,

Díaz et al., 2012). In wheat, Ppd-1directly regulates the Flowering Locus T1 (FT1) and

Chapter-1

11

mutants with promoter deletions result in the overexpression of FT1 causing early flowering

(Muterko et al., 2015). Markers have been developed to identify the Ppd-1 mutants with

different promoter deletions (Table 2) that will facilitate their effects on the flowering time of

wheat (Muterko et al., 2015, Nishida et al., 2013, Cane et al., 2013, Beales et al., 2007).

The complicated interaction of these phenology genes has resulted in two conflicting models

of flowering regulatory network where the first model designated as Vrn2 to FT (Distelfeld

and Dubcovsky 2010) recommends that Vrn2 represses FT expression but vernalization

during winter slightly up regulates Vrn1 causing down regulation of Vrn2 and the release of

FT expression. Then this FT interacts with Ppd1 and again up regulates the Vrn1 beyond the

threshold to initiate flowering under long day length (Distelfeld, Li, and Dubcovsky 2009).

By contrast, the second model known as FT to Vrn2 proposed by Shimada et al., (2009)

suggests that Vrn1 promotes FT transcription which down regulates Vrn2 to initiate flowering

based on the fact that the maintained vegetative phase (mvp) mutants lacking Vrn1 fail to up

regulate FT. Subsequently, detailed experimentation by Distelfeld and Dubcovsky (2010)

with the mvp mutants segregating for Vrn1 and Vrn2 deletions found evidence to contradict

both of the previously proposed models and these authors suggested that more investigation

should be conducted to elucidate the flowering network of wheat and that this may lead to

identification of more genes that interact in the flowering pathway.

1.7 Dwarfing Genes

The introduction of dwarfing genes into cereals, including wheat, was a key driver of the

green revolution. Since then, Rht-B1b and Rht-D1b previously known as Rht1 and Rht2

respectively are the most commonly adopted dwarfing genes in wheat breeding programs

throughout the world (Rebetzke et al., 2012). These two semi-dwarfing genes together

produce the dwarf phenotype whereas alone in combination with their counterpart Rht-B1a or

Rht-D1a produce semi-dwarf plants in nature. The plants with these genes are less prone to

lodging and are more effective in partitioning assimilates to the grain. Some workers have

suggested that the improved yield potential of such varieties is limited only to a favourable

growth environment (Waddington et al., 1986, Chapman et al., 2007). However, these

specific dwarfing genes are insensitive to endogenous gibberellins, and produce shorter plants

with smaller cells (Keyes et al., 1989). These smaller size cells are consequently responsible

for the shorter coleoptile length, less early vigour, smaller leaf area, lower water use

Chapter-1

12

efficiencies and poor seedling establishment, especially in water limited environments

(Donald 1975, Allan 1989, Richards 1992b, Rebetzke et al., 2004, Botwright et al., 2005).

The insensitivity to gibberellins of both Rht-B1b and Rht-D1b alleles is due to single

nucleotide substitutions that create a translational stop codon TGA, reducing the plant’s

ability to respond to gibberellins (Peng et al., 1999).

Most of the world’s wheat is grown without irrigation, and because of the dependence on

seasonal rainfall the potential yield is often hampered by water scarcity. About 50% of the

rain water can be lost directly by soil evaporation, whereas early vigour can increase water

use efficiency by 25% and thus improve yield (Leuning et al., 1994, Siddique et al., 1990,

Regan et al., 1992, López-Castañeda and Richards 1994). Again, deep sowing of longer

season varieties is often recommended to obtain yield benefits in dry areas, as occur in

southern Australia, but seedling establishment is impaired when dwarf/semi dwarf varieties

are sown more than 5 cm deep (Allan 1989). Consequently farmers wait until the first rains

before sowing and in that case between 140 to 330 kg yield loss per week per hectare has

been reported in Australian wheat crops (Doyle and Marcellos 1974, Shackley and Anderson

1995). Wheat varieties with longer coleoptiles are able to emerge sooner when sown deep

and have greater early vigour (Hadjichristodoulou et al., 1977, Gan et al., 1992). Moreover,

early vigour and longer coleoptiles help plants avoid phytotoxic effects of residual herbicides,

compete against weeds and reduce evaporative water loss by shading. Hence, breeding for

vigorous seedling growth and longer coleoptiles are the prime objectives for better adaptation

of wheat in water limited environments (Whan 1976, Schillinger et al., 1998, Richards et al.,

2002), and a project with these objectives is currently underway at CIMMYT, Mexico (I.B

Edwards- personal communication).

On the other hand, a number of dwarfing genes such as Rht 7, Rht 8, Rht 9, Rht 13 and Rht 14

have been reported, which have potential in reducing plant height without affecting seedling

vigour and tissue response to gibberellins (Rebetzke and Richards 1999, Ellis et al., 2004,

Rebetzke et al., 2007). In stress conditions taller varieties store assimilates in the stem and do

not depend entirely on current assimilation for grain filling (Borrell et al., 1989). Several

studies across many favourable and unfavourable environments demonstrated that plants with

heights of between 70 and 100 cm are better yielders than those that are taller or shorter than

this limit (Richards 1992a, Flintham et al., 1997). Therefore, accumulations of minor

Chapter-1

13

dwarfing genes or combination with one of the gibberellins insensitive genes for shorter plant

height are desirable (Flintham et al., 1997, Rebetzke et al., 1999). This is depicted by

different studies that used Rht8 and/or Rht13 alleles with Rht1 and/or Rht2 to maximize

yields compared to other dwarf/semi dwarf varieties (Rebetzke et al., 2012). Markers linked

to these dwarfing alleles make it easier to select both alleles simultaneously across a large

population (Korzun et al., 1998, Ellis et al., 2007).

1.8 Physiological Aspects of Phenology and Dwarfing Genes

Grain yield is strongly influenced by timing of developmental stages in a particular

environment, making crop phenology a critical component for yield physiology (Slafer G.A.

2009). Moisture stress at the reproductive stage, especially that period from a few weeks

before anthesis to a few days after anthesis, has the most critical effect on crop yields in water

limited environments (Fischer 1985, Reynolds et al., 2009). Passioura (1977) emphasized the

importance of water use, water use efficiency and harvest index for crop yields in dry areas.

In dry environments an important portion of soil moisture that could be available for

transpiration is evaporated from a barren soil surface, thus indirectly affecting dry matter

accumulation through limiting water availability to roots, and modifying canopy temperature

(Jamieson et al., 1995). In this situation faster early seedling growth is beneficial to prevent

evaporation by shading. Moreover, late flowering cultivars continue to produce tillers until

they get the signal for reproductive development and many of them cannot produce fertile

spikes but put pressure on the available soil moisture through normal transpiration. In this

regard, heading date and effective tiller number should be additional considerations to

improve water use efficiency in varieties being developed for drought environments.

Moreover, water requirement varies throughout the growth period and is higher during seed

setting and development stages. Hence, there is an opportunity to improve yield through

changes in crop development. The synchronization of crop developmental stages by

phenological adjustment with seasonal moisture availability should be the most important

target for new wheat varieties being developed for water limited environments such as occur

in Mediterranean climate regions.

Harvest index (HI) and ultimately final grain yield largely depend on pre and post anthesis

biomass production, mobilization of assimilates to florets, and the pattern of water supply

during the life cycle (Araus 2002, Richards et al., 2002). One strategy to raising harvest index

Chapter-1

14

might be increasing assimilate movement to developing florets, which will prevent floret

abortion before anthesis. This can be done by increasing the duration of spike growth with a

reduction in the earlier period for larger ear development (Slafer et al., 2001). Moreover, this

larger ear will also contribute more photosynthate during grain filling along with the flag leaf

thereby increasing HI. Studies on two alternative spring alleles of Vrn-A1 have shown their

significant influence on the variation in root and vegetative morphology like rosette growth

habit, plant height and leaf length (Roberts 1990). Recently, a significant relation has been

observed between the duration of pre-anthesis growth phases with tillering and dry matter

accumulation (Borràs-Gelonch et al., 2012). A detailed study of Australian wheat cultivars

over years and locations has revealed that cultivars with one spring allele in any of the three

Vrn1 loci are the earliest in heading compared to cultivars having two spring alleles (Eagles

et al., 2010). Again, spring alleles in all three loci have very small effects in forwarding the

heading date suggesting the presence of epistatic or overdose effects. This study also showed

that Vrn-B1 has a weaker effect on the reduction of heading time compared with Vrn-A1 or

Vrn-D1. Recently, however, it has been shown that Vrn-B1 has greatest effect on grain yield

(Eagles et al., 2014).

Semi-dwarf varieties with RhtD1b are advantageous over RhtB1b in environments with high

maximum temperatures and lower rainfall during the flowering and grain-filling periods as

RhtD1b is associated with less leaf porosity in plants relative to RhtB1b leading to slow

transpiration before heading and leaving more soil moisture for later use (Rebetzke et al.,

2013, Eagles et al., 2014). Plants reduce water use during drought stress by accelerated leaf

desiccation and death, which causes a reduction of current photosynthate (Asana 1966,

Brooks et al., 1982, Aggarwal and Sinha 1984, Blum 2009). Consequently, stem reserves

become an important source of carbohydrate for grain filling (Blum et al., 1991, Kiniry 1993,

Schnyder 1993) However, RhtB1b and RhtD1b dwarfing genes reduce stem reserves by 35%

and 39%, respectively (Borrell et al., 1993) and hence taller varieties often perform better in

stress environments when there is a shortage of assimilates compared to the modern dwarf

cultivars.

Chapter-1

15

Table 1.3 PCR markers for the different vernalization and photoperiod response allele

Allele Primer Primer Sequence (5ʹ-3ʹ) Annealing Temperature (oC)

Product size Reference

Vrn-A1a VRNA1F GAAAGGAAAAATTCTGCTCG 50 965+876 Yan (2004)

Vrn-A1b 714

Vrn-A1c VRN1-INT1R TGCACCTTCCC(C/G)CGCCCCAT 734

vrn-A1 734

Vrn-A1 BT706 CATTGTTCCTTCCTGTCCCACCC 63 1431 (Eagles et al., 2011)

BT750 ATTACTCGTACAGCCATCTCAGCC

Vrn-A1c (Langdon) Ex1/C/F GTTTCTCCACCGAGTCATGGT 55.6 522 Fu et al., (2005)

Intr1/A/R3 AAGTAAGACAACACGAATGTGAGA

Vrn-A1c (IL 369) Intr1/A/F2 AGCCTCCACGGTTTGAAAGTAA 58.9 1170

Intr1/A/R3 AAGTAAGACAACACGAATGTGAGA

vrn-A1 Intr1/C/F GCACTCCTAACCCACTAACC 56 1068

Intr1/AB/R TCATCCATCATCAAGGCAAA

Vrn-B1a Intr1/B/F CAAGTGGAACGGTTAGGACA 58 709

Intr1/B/R3 CTCATGCCAAAAATTGAAGATGA

vrn-B1 Intr1/B/F CAAGTGGAACGGTTAGGACA 56.4 1149

Intr1/B/R4 CAAATGAAAAGGAATGAGAGCA

Vrn-D1 Intr1/D/F GTTGTCTGCCTCATCAAATCC 61 1671

Intr1/D/R3 GGTCACTGGTGGTCTGTGC

Chapter-1

16



vrn-D1 Intr1/D/F GTTGTCTGCCTCATCAAATCC 61 997

Intr1/D/R4 AAATGAAAAGGAACGGAGCG

Vrn-D1a VRN1DF CGACCCGGGCGGCACGAGTG 65 612 Zhang et al., (2012)

VRN1-SNP161CR AGGATGGCCAGGCCAAAACG

Vrn-D1b VRN1DF CGACCCGGGCGGCACGAGTG

VRN1-SNP161AR AGGATGGCCAGGCCAAAACT

Vrn-3 FT-B-INS-F CATAATGCCAAGCCGGTGAGTAC 63 1200 Yan et al., (2006)

FT-B-INS-R ATGTCTGCCAATTAGCTAGC

vrn-3 FT-B-NOINS-F or FT-B-NOINS-F2

ATGCTTTCGCTTGCCATCC or GCTGTGTGATCTTGCTCTCC

57 1140 or 691

FT-B-NOINS-R CTATCCCTACCGGCCATTAG

Ppd-D1a Ppd-D1_F ACGCCTCCCACTACACTG 54 228 Beales et al., (2007)

Ppd-D1_R2 AND CACTGGTGGTAGCTGAGATT

ppd-D1b Ppd-D1_F ACGCCTCCCACTACACTG 54 414

Ppd-D1_R1 and GTTGGTTCAAACAGAGAGC

16 bp deletion in exon 8 Ppd-D1exon8_F1 GATGAACATGAAACGGG 52 320 or 326+22, 257,69 and 22

Ppd-D1exon8_R1 GTCTAAATAGTAGGTACTAGG

Ppd-B1 Ppd-B1exon3SNP_F1 AGACGATTCATTCCGCTCC 55 471, 328, 155

Ppd-B1exon3SNP_R1 TCTGAATGATGATACACCATG

Chapter-1

17



Ppd- B1_2ndcopy_ F1 TAACTGCTCGTCACAAGTGC 55 475

Ppd- B1_2ndcopy_R1 CCGGAACCTGAGGATCATC

5bp deletion

Exon 7

D5-1F GAATGGCTTCTCCTGGTC 50 1,032 or 1,027 Guo et al., (2010)

D5-1R GATGGGCGAAACCTTATT

D5-2F GTGTCCTTTGCGAATCCTT 53 184 or 179

D5-2R TTGGAGCCTTGCTTCATCT

A TE insertion

Intron 1

D520F AGGTCCTTACTCATACTCAATCTCA 50 2,612

D520R CTCCCATTGTTGGTGTTGTTA

D78F CCATTCGAGGAGACGATTCAT 55 1,005

D78R CTGAGAAAGAACAGAGTCAA

Truncated Ppd-B1 gene in the ‘Chinese Spring’ allele

219H05F2 TAACTGCTCCTCACAAGTGC 56 425 Díaz et al., (2012)

97J10R2 CCGGAACCTGAGGATCATC

Intact Ppd-B1 copies in the ‘Chinese Spring’ allele

Ppd-B1_F25 AAAACATTATGCATATAGCTTGTGTC 58 994

Ppd-B1_R70 CAGACATGGACTCGGAACAC

Intact Ppd-B1 copies in the ‘Sonora64’/‘Timstein’ allele

Ppd-B1_F31 CCAGGCGAGTGATTTACACA 58 223

Ppd-B1_R36 GGGCACGTTAACACACCTTT

Vrn-A1b Vrn_P2 CCTGCCGGAATCCTCGTTTT 63 147 or 167 Chen et al., (2013)

CTACGCCCCTACCCTCCAACA

Vrn-B1b Vrn-P7 CCAATCTCACATGCCTCCAA 59 215 or 252

Chapter-1

18



ATGCGCCATGAACAACAAAG

Vrn-B3c GCTTTGAACTCCAAGGAGAA 52 1401

Vrn-P14 ATAATCAGCAGGTGAACCAG

vrn-B3/Vrn-B3 ACTCATCATCACCACTTCCT 51 1499

Vrn-P15 TAATGCTTAATTCGTGGCTG

Vrn-B3 promoter GTCCATACAAATCATGCCAC 51 491

Vrn-P16 TTCTGACAGTTTTAGTTGCG

Vrn-B3 promoter Vrn-P17 GCTTTCGCTTGCCATCCCAT 62 898

GCGGGAACGCTAATCTCCTG

Vrn-B3 promoter TTTGAGACAGGAGATTAGCG 53 1131

ACCATCATGAGGCACCATTA

GCTTTGAACTCCAAGGAGAA 52 1425

ATAATCAGCAGGTGAACCAG

CCGTTCACCATCTATTGCTC 55 1259

CACCCAAATCCTTCATCTCA

Vrn-B3-RT GGAGGTGATGTGCTACGAGA 55 147

TTGTAGAGCTCGGCGAAGTC

Chapter-1

19

1.9 Conclusion

Drought is a major threat to world agriculture and predicted to worsen in the near future due

to climate change. Wheat is the most widely grown cereal crop in the world and is vital for

worldwide food security. Altering the developmental stages and maturity is one of the best

ways to combat drought without compromising yield. However, current knowledge about the

numbers of genes that control flowering and maturity in wheat is limited. Based on

knowledge obtained from the model plant species Arabidopsis, where more than 80 genes

have been reported to control flowering, it is logical to conclude that many new genes and

genetic pathways for wheat flowering and maturity are yet to be discovered. But the fact is

that different genetic pathways finally converge, interact and ultimately lead to the activation

of floral identity genes in the floral primordia (Mouradov et al., 2002) and these interacting

networks that promote flowering are yet to be unveiled. As significant research efforts are

currently underway, knowledge on wheat flowering genes and pathways will increase over

time and will require advances in computational biology to integrate and interpret this

information. In addition, future international collaboration will help to combine the

cumulative efforts of the research underway in different research groups and disciplines, and

the challenge for the breeders will be to integrate this work into new genetic combinations.

1.10 Thesis outline and objectives

My PhD program broadly aimed to investigate the better adaptation of wheat to water limited

environments through phenological adjustment. Firstly, using a double haploid population

segregating only for Vrn-1 gene a series of glasshouse and multi-location field trials had been

conducted with the objective of quantifying individual and combination effect of

vernalization gene on days to flowering, maturity and other yield components (Chapter 2).

Similarly, two near isogenic lines of Ppd-D1 were studied to understand the effect of the

photoperiod gene on flower development (Chapter 3). A multi-location field trial was also

conducted, including some elite Australian cultivars and advanced lines to investigate the

interaction patterns of vernalization and photperiod genes in different environments with

regard to days to flowering, grain yield and other yield components (Chapter 4). Finally, two

double haploid lines were studied with the objective of understanding the changes in protein

expression during phenological development (Chapter 5).

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Chapter 2

2 Allelic Variants at the Vrn-1 Locus of Bread Wheat for Enhanced Yield

in Water-Limited Environments

2.1 Introduction

Wheat grown under rain-fed conditions is often affected by adverse environmental conditions

during the growing season, especially by drought, which can happen at any growth and

developmental stage, but mainly at later stages of grain-filling, commonly known as terminal

drought. The risk of drought in wheat production is one of the major concerns of wheat

producer and world traders, as 50% of the world wheat production area is regularly affected

by drought (Pfeiffer 2005) and climate change due to global warming is expected to increase

the incidence of drought throughout the world in the near future. The Intergovernmental

Panel on Climate Change (IPCC 2014) in a recent report predicted that the mean global

temperature will increase by 3.7 oC by the end of this century and will dry up the land surface

by increasing the rates of pan evaporation and evapotranspiration. Presumably, mean

precipitation will decrease at mid-latitudes and subtropical dry regions while it is likely to

increase in high latitudes. In terms of precipitation alone it is expected that an additional 5 to

45% of the land surface area will be affected by drought (Seneviratne and Reichstein 2012 ),

including vast areas of the Mediterranean, Central America, Brazil, South Africa, and

Australia (IPCC 2013). Drought in these areas will severely affect crop production, with

wheat production predicted to decline by 23% to 27% by 2050 unless protective measures

for limiting global warming or better adapted cultivars and crop management practices have

been adopted before then (Nelson et al., 2009). Therefore, development of varieties adapted

to changing climatic condition is of utmost priority for sustainable world wheat production

and food security.

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Cereals like wheat deal with water scarcity mainly by means of drought avoidance

mechanisms that allow plants to maintain cell water potential by maximizing water uptake

and minimizing transpiration (Kosova et al., 2015). Not all developmental stages contribute

to grain yield significantly, hence when breeding for new varieties we could also consider

drought escape mechanisms based on protecting critical growth stages from the detrimental

effects of drought. Among these, phenology plays an important role and is considered a key

factor in successful adaptation of plants to specific environments. Phenology is controlled by

vernalization (Vrn) genes (exposure to cold temperature requirement), photoperiod (Ppd)

genes (photoperiod sensitivity), and autonomous earliness per se (Eps) genes (Kato and

Yamagata 1988). In wheat, the vernalization requirement is mainly determined by the three

loci of the Vrn gene, namely Vrn-1, Vrn-2 and Vrn-3 (Milec et al., 2012, Dubcovsky et al.,

1998). Similarly, three homeologous loci of the Ppd-1 gene regulate the photoperiod response

during the reproductive developmental phase of wheat (Welsh 1973, Snape et al., 2001).

Several recent studies have identified a number of alleles and haplotypes of the Vrn-1 and

Ppd-1 genes, and specific markers are now available to accurately diagnose each allele for

both genes (Yan et al., 2003, Yan 2004, Fu et al., 2005, Beales et al., 2007, Díaz et al., 2012,

Guo et al., 2010, Nishida et al., 2013, Muterko et al., 2015).

Using the allelic diversity of vernalization and photoperiod genes we can generate a wide

spectrum of heading and flowering times, which can be exploited to fine-tune the adaptation

of varieties to a wide range of growing regions and environments (Pugsley 1971). These

alleles can modify not only heading time but have a large influence on other agronomic traits,

including yield. Several glasshouse and field studies have been conducted to determine the

effects of different photoperiod alleles on heading time, floret fertility, spike length, plant

height, and yield (Foulkes et al., 2004, Worland 1996, González et al., 2003, Cane et al.,

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2013, Guo et al., 2010). On the contrary, information about the effects of vernalization genes

on these traits is very limited. It is generally assumed that vernalization genes affect only the

vegetative phase up to initiation of the reproductive phase (Robertson et al., 1996). But

studies in the model plant Arabidopsis have revealed that the interaction of both the

photoperiod and vernalization pathways with other gene networks determines flowering time

(Wellmer and Riechmann 2010, Mouradov et al., 2002). Hence, the authors believe that

variation in the allelic combinations of vernalization and photoperiod genes could be utilized

to enable wheat plants escaping periods of drought by modifying the flowering time to match

the rainfall patterns and water availability of the target environments.

This study was aimed at determining the allelic effects of the Vrn-1 gene on heading date and

to assess the effect of allelic variants of Vrn-1 under water-stressed conditions on other

agronomic traits and seed physical quality parameters.

2.2 Materials and Methods

2.2.1 Plant materials

Pugsley (1971) developed near-isogenic lines (NILs) of wheat harbouring five different

combinations of winter alleles of the three homoeologous Vrn-1 loci using the variety Triple

Dirk as a common genetic background. NILs were developed by performing a minimum of

three backcrosses, to reconstitute the Triple Dirk genetic background to about 94%. A

population of 210 doubled haploids (DH) developed from crosses of two NILs contrasting for

Vrn-1 alleles across the three genomes (AABBDD) was kindly provided by Dr Timothy

March, University of Adelaide. Three lines from each of eight possible Vrn-1 allelic

combinations were randomly selected for the experiments (Figure 2.1). Hence, 24 DH lines

contrasting for Vrn-1 alleles along with their parents and two checks (Westonia and

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Wyalkatchem) were planted in a glasshouse as well as under rainfed conditions in the field in

2013 (Table 2.1). The following year experiments were repeated with one line from each of

eight possible Vrn-1 gene combinations and with two additional checks supplied by Edstar

Genetics Pty. Ltd., namely B53 and Tenfour. Two additional lines, selected from the same

group based on differences in heading time in the previous year's experiments, were added to

the 2014 experiment (Table 2.1).

Figure 2. 1 Possible allelic combinations considering spring/winter alleles in Vrn-1 loci

2.2.2 Experimental design and description of data

Glasshouse experiments were conducted at Murdoch University in 2013 and 2014 with two

treatments, control and water-stressed. Four seeds were sown in 6L (230mmx210mm) plastic

pots and thinned to two plants per pot after one week of emergence. In 2013 pots were filled

with 7 kg of air-dried and sieved soil with the characteristics of heavy clay (Appendix A)

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collected from the Dryland Agricultural Institute, Merredin, WA. Before planting, pots were

pasteurised in a steel chamber at 65oc for 2 h, and fertilizers were added as per farmers’

practice (Appendix B) based on pot surface area. One gram of urea per pot was top-dressed

during tillering stage. In 2014, Murdoch potting mix was used to fill the pots. A 20-mm

diameter pierced PVC pipe was placed in the middle of each pot to ensure even distribution

of water throughout the pot (Figure 2.2).

Field capacity was determined by weighing thoroughly wetted pots after allowing them to

drain out for 48 h. It was determined that 1 L of water per 7 kg of air-dried soil was needed to

reach field capacity. Controls were watered daily up to field capacity. The water stress

treatment pots were maintained at field capacity up to tiller initiation stage and then stressed

to 60% of field capacity till maturity by weighing and watering the pots daily in the morning.

Figure 2.2 Pot set-up for controlled water stress treatment

In the field trials all the 24 DH lines, including parents and checks, were planted in a farmer’s

field in Toodyay in 2013 and 2014 and, in NSW at the IA Watson GrainsResearch Centre at

Narrabri in 2014. In both trials plants were grown under rainfed conditions, with the NSW

trial site receiving slightly more rainfall and lower temperatures than the Toodyay site

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(Fig. 2.2). At Toodyay, 20 seeds were sown in one meter long, four-row plots with 20 cm row

spacing. A 50 cm distance was kept between plots. Wheat variety Westonia was sown as a

buffer surrounding the whole experimental set-up. Plants were fertilized with urea and mixed

fertilizer as practised by the farmers (Appendix B).

Row-column designs were generated using mainly DiGGer software (Coombes, 2002) for the

glasshouse and field experiments in Toodyay (Appendix C 1, 2 & 3), while the 2014 field

experiment at Narrabri was designed as an incomplete block design, with each block

consisting of six columns. Two-directional blocking was used for the 2014 glasshouse

experiments and one-directional blocking for the 2013 glasshouse and field experiments. The

2013 trials consisted of 28 lines, including 24 DH lines, two parents and two controls. In

2014 glasshouse trials consisted of 16 lines, including 10 DH lines, two parents and four

controls, but field experiments consisted of 30 lines with two additional controls, as

mentioned above. The number of columns varied between 4 and 30 , and the number of rows

between 2 and 21 (Table 2.1).

Glasshouse (GH) data were taken from the main tiller of each seedling that was tagged during

tillering initiation. Data for tillering (TL), heading (HD), anthesis (ANTH), physiological

maturity (PM), and plant height (PH) were taken for the GH trials. Heading time was

recorded when 50% of spikes had emerged from the flag leaf, and anthesis was determined

when 50% of the spikes had extruded anthers. Physiological maturity was recorded when

50% of spikes turned yellowish. PH was measured from the soil surface to the top of the

spike without including the awn. Number of effective tillers (TL) was counted for individual

plants at the late reproductive stage of growth. After harvesting, spikes were hand-threshed

and data for spike length (SL) and seed number per spike (SN/S) were recorded for the main

tillers of plants grown in the glasshouse trials.

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Figure 2.3 Environmental conditions of the trial sites a) average temperature of glasshouse in the year 2013 and 2014, b) rainfall, minimum and maximum temperature of Narrabri during 2014 and c) rainfall, minimum and maximum temperature of todays during 2013

a

b

c

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In the field trials only data for HD, PH and PM were collected. During harvest 10 spikes were

randomly selected and hand-threshed to measure SL and SN/S. Moreover, data for thousand-

kernel weight, test weight (hectolitres), seed length, width and roundness were taken for both

the GH and Field trials using a digital seed image analyser (SeedCount™ version 2.4.0) in

the seed testing laboratory of DAFWA, South Perth, WA.

Table 2.1 Summary of the trials

Experiments Location Entries Treatment Rep Row Column

Glasshouse-2013

Murdoch University

28 (DH-24, parents-2 and control-2)

2 (control and water stressed)

3 21 8

Field-2013 Toodyay 30 (DH-24, parents-2 and control-2)

Rain fed DH-2

Control-4

2 30

Glasshouse-2014

Murdoch University

16 (DH-10, parents-2 and control-4)

2 (control and water stressed)

2 16 4

Field-2014 Narrabri, NSW 30 (DH-24, parents-2 and control-4)

Rain fed 2 7 12

2.2.3 Genotyping of the plant materials

Genomic DNA was extracted from leaf tissues of 10-day-old pot-grown seedlings of each

DH line, parents and check varieties, using an SDS extraction protocol. Presence of the

homeologous alleles of the Vrn-1 gene were identified as described by Yan (2004) and Fu et

al., (2005) but 1% polyvinylpyrrolidone was used in the PCR mix to improve band

resolution. Primer sequences for the different Vrn-1 alleles are presented in Table 2.2. After

conducting the GH 2013 trials all the lines were also tested for the photoperiod gene (Ppd-1)

alleles using primers developed by Beales et al., (2007), Díaz et al., (2012), and Muterko et

al., (2015). PCR products were visualized in 1.2% (w/v) agarose gels in 1X TAE buffer.

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37

In the discussion “S” and “W” are used to denote spring and winter-type alleles of the Vrn-1

gene, and “P” for the photoperiod insensitive alleles of the Ppd-1 gene. No notation was used

for the photoperiod-sensitive allele. Allelic variants of Vrn-A1 with no photoperiod

insensitive allele were represented simply as “S” and “W”, and the addition of a first “P”

represented the presence of Ppd-B1 while a second “P” represented the presence of Ppd-D1.

2.2.4 Statistical model and data analysis

A linear mixed model was formulated for each trial using a randomization-model based

approach (Smith et al., 2005). The model for each trait and each trial includes random

blocking terms to account for the randomization process and additional terms to model the

extra sources of variation such as spatial trends and extraneous variation. The allelic

combinations (AC) and treatment effects and their interactions were fitted as fixed effects.

Due to the nature of the trials, identifying and modelling of any spatial trends was crucial for

data interpretation. For the analysis was adopted the approach of Gilmour et al., (1997),

followed by additional diagnostics for the adequacy of the spatial model (Stefanova et al.,

2009). The initial baseline mixed model for each trait comprised a random replicate, fixed

AC effect and a separable (column by row) autoregressive process of first-order component

to account for the local spatial trend. After fitting this model, the residuals were checked

(residual plots, variogram and variogram faces with 95% coverage intervals) to model

additional spatial variation (global trend) and/or extraneous variation, and the best-fit model

was identified based on lowest AIC value (Table 2.3).

The aim of the analysis was to adjust for the spatial variation (by fitting autocorrelations for

the local trend and regressions on row/column number for the global row/column trends,

respectively), to correct for any positional bias within the experimental set-up.

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Water consumption of line entries was further analysed using the statistical package

“agricolae” (Mendiburu 2014) in the R version 3.0.3 (Team 2014).

Table 2.2 Primers for identification of different alleles of Vrn-1 gene

Primers Sequence (5¢ to 3¢) Size (bp)

Ann. Temp. (0C)

Allele Reference

VRN1AF

VRN1INT1R

GAAAGGAAAAATTCTGCTCG

GCAGGAAATCGAAATCGAAG

965, 876, 714, 734

55.0 Vrn-A1a, Vrn-A1b and Vrn-A1

Yan et al., (2004)

Intr1/C/F Intr1/AB/R

GCACTCCTAACCCACTAACC

TCATCCATCATCAAGGCAAA

1068 56.0 Vrn-B1 Fu et al (2005)

Intr1/B/F Intr1/B/R3

CAAGTGGAACGGTTAGGACA

CTCATGCCAAAAATTGAAGATGA

709 58.0 Vrn-B1 Fu et al (2005)

Intr1/B/F Intr1/B/R4

CAAGTGGAACGGTTAGGACA

CAAATGAAAAGGAATGAGAGCA

1149 56.4 Vrn-D1 Fu et al (2005)

Intr1/D/F Intr1/D/R3

GTTGTCTGCCTCATCAAATCC

GGTCACTGGTGGTCTGTGC

1671 61.0 VrnD1 Fu et al (2005)

Ppd-B1_F31

Ppd-B1_R36

CCAGGCGAGTGATTTACACA

GGGCACGTTAACACACCTTT

223 58 Díaz et al., (2012)

2.3 Results

2.3.1 Allelic variation of phenology genes within the DH population

A water-stressed and a control treatment were carried out in the GH using 24 DH lines that

included three randomly selected biological replicates of each Vrn-1 allele combination along

with their parents and two checks. A large variation was observed among the DH population

for heading dates, up to 50 days between different allelic groups and up to 22 days within the

same allelic group (Figure 2.4). These intra-group variations indicated interactions with other

phenology genes in the population, and as a result all the lines were also tested for the

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39

presence of photoperiod gene alleles. Genotyping results revealed the presence of a “Sonora

64”/”Timestein” type allele of Ppd-B1 (Díaz et al., 2012) in one of the parents and this allele

segregated randomly among the DH population (Figure 2.5), thus constituting an additional

source of variation within the same allelic combination for Vrn-1 genes (Figure 2.4). As a

result, considering all possible combinations of the three alleles of Vrn-1 along with Ppd-B1

there were twelve allelic groups within the DH population, although we had originally

expected only eight possible combinations based on the allelic combinations of the three Vrn-

1 homeologs.

Figure 2.4 Mean Heading days of the DH lines with parents and checks in the controls (blue) and water stress (red) treatments

Chapter-2

40

Figure 2.5 PCR amplification products demonstrating the presence of spring or winter alleles of Vrn-A1, Vrn-B1 and Vrn-D1 and, photoperiod insensitive allele of Ppd-B1

2.3.2 Linear mixed model analysis of the allelic combination effects on agronomic

traits

The traits measured in GH and field experiments were separately analysed for every

experiment. The analyses provided best linear unbiased estimates (BLUEs) of the allelic

combination (AC) effects while accounting for the spatial variation.

The results from these analyses showed a weak presence of local trends, with rather low row

and column autocorrelations. For the GH 2013 trial AR1row was in the range of 0.02-0.21 and

AR1col range was 0.03-0.25 (Table 2.3a). The GH 2014 trial behaved similarly except for

strong row trends for SL and local column trends for HD and SL (Table 2.3a). For both the

glasshouse trials the global linear trends along the rows and columns were not significant,

which indicates the proper ventilation and arrangement of the pots in the glasshouse. The

local trend pattern identified in the field 2013 trial was similar to the GH trials where AR1row

and AR1col ranged from 0.02-0.30 and 0.06-0.19, respectively (Table 2.3c). An identity was

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41

fitted in a few cases. Spatial analysis was not performed for field 2014 trials, where we used

rather a simple linear mixed model to analyse the AC effects on response variables (Table

2.3d). Tables 3a, b, c and d contain a full description of the models for each trait measured in

the glasshouse (GH) and field experiments in 2013 and 2014. The tables present the results

with AC fitted instead of variety. AC main effects were significant for the traits analysed in

both GH and field trials.

The results for the 2013 GH experiment show that the treatment main effect was not

significant for traits SL, seed TW and RN (Table 2.3). More importantly, the AC-by-

treatment interaction was not significant only for seed AR and SL as well.

The pattern of significant effects for GH 2014 experiments is different from that of GH 2013,

where the treatment main effect was not significant for the traits SL, SN/S, TKW, TW, and

RN. Similarly, the AC-by-treatment interaction was not significant for SN/S, AR, TW, and

RN.

2.3.3 Effects of allelic combination on phenology and agronomic traits

Allelic combinations for the control were analysed separately to eliminate other genetic

background effects on the phenology of the DH population. Lines with recessive alleles of

the Vrn-1 gene on all three chromosomes (WWW) were latest in heading in all the

experiments (Table 2.4a and 2.4b), but the differences between glasshouse and field trials

were large in 2013, being 107 under glass house to 136 days in the field at trial at Toodyay

(Table 2.4a) whilst the differences were similar in 2014. Lines with dominant alleles in all

three homeologous positions were not the earliest in heading (Table 2.4a and 2.4b). In all

trials, the earliest genotypes were rather those with double dominant Vrn-A1 and Vrn-D1

alleles, followed by double-dominant genotypes for the Vrn-A1 and Vrn-B1 alleles (2.4a and

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42

2.4b). The HD for the controls ranged from 56 to 120 days across the trials. Unfortunately,

there were no lines with double-dominant Vrn-B1 and Vrn-D1 alleles among the DH

population. In all cases it was observed that addition of a dominant Ppd-B1 allele with any

combination of Vrn-1 alleles advanced the heading date by up to 25%, except for the lines

with winter alleles on all chromosomes (WWW), although there was a slight reduction in

heading date observed in all the trials in the winter type lines due to the presence of Ppd-B1.

Plant height (PH) was also influenced by the allelic combinations of phenology genes.

Likewise, heading double -dominant genotypes for Vrn-A1 and Vrn-D1 produced the shortest

phenotypes in all the trials except for field 2013, followed by genotypes double-dominant for

Vrn-A1 and Vrn-B1. PH range for the DH population and controls ranged from 96.2 to 135.3

and 51.8 to 82.0 cm, respectively (Table 2.4a and 2.4b).

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Table 2.3a Description of the fitted model for the glasshouse experiment during 2013

EXP Response Variable Fixed Terms AR1row AR1col

Error Variance Deviance AIC

GH 2013 HEADING AC***+TRT***+AC.TRT* 0.078 0.117 5.258 417.22 425.22

PLANT HEIGHT AC***+TRT***+AC.TRT*** ID 0.032 26.67 646.81 652.81

TILLER/PLANT AC***+TRT***+AC.TRT** 0.214 ID 2.134 287.82 293.82

SPIKE LENGTH AC***+TRT+AC.TRT 0.046 0.084 0.574 108.43 116.43

SEED/SPIKE AC***+TRT***+AC.TRT*** 0.059 ID 20.32 608.4 614.4

SEED/UNIT SPIKE AC***+TRT***+AC.TRT** 0.086 0.079 0.131 -99.13 -91.13

TKW AC***+TRT***+AC.TRT** ID ID 10.08 314.05 318.05

ASPECT RATIO AC***+TRT**+AC.TRT 0.096 0.119 0.004 -341.24 -333.24

TW AC***+TRT+AC.TRT*** 0.131 0.253 4.712 244.94 252.94

ROUNDNESS AC***+TRT+AC.TRT*** 0.024 0.167 0.0002 -605.47 -597.47 ***significant at p<0.001, **significant at p<0.01 * significant at p <0.05

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Table 2.3b Description of the fitted model for the glasshouse experiment during 2014

EXP Response Variable Fixed Terms AR1row AR1col

Error Variance Deviance AIC

GH 2014 HEADING AC***+TRT***+AC.TRT*** ID 0.864 0.843 28.49 32.49

PLANT HEIGHT AC***+TRT***+AC.TRT*** ID ID 2.344 84.93 88.93

TILLER/PLANT AC***+TRT***+AC.TRT** 0.365 0.226 0.988 56.54 50.54

SPIKE LENGTH AC***+TRT+AC.TRT** 0.525 0.516 0.279 2.67 8.67

SEED/SPIKE AC***+TRT+AC.TRT ID 0.256 14.75 142.11 178.11

TKW AC***+TRT+AC.TRT*** 0.149 ID 1.766 76.01 72.01

ASPECT RATIO AC***+TRT***+AC.TRT ID ID 0.0013 -157.94 -155.94

TW AC***+TRT+AC.TRT 0.202 ID 3.335 92.03 98.03

ROUNDNESS AC***+TRT+AC.TRT ID ID 0.00007 -249.76 -247.76 ***significant at p<0.001, **significant at p<0.01 * significant at p <0.05

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Table 2.3c Description of the fitted model for the field experiment during 2013

EXP Response Variable Fixed Terms AR1row AR1col Error Variance

Deviance AIC

Toodyay-2013

HEADING AC*** ID 0.186 1.973 93.22 97.22

PLANT HEIGHT AC*** ID ID 27.45 213.13

SPIKE LENGTH AC*** 0.303 ID 0.197 -10.89 -6.89

SEED/SPIKE AC*** 0.023 ID 26.66 211.80 217.80

SEED/UNIT SPIKE AC*** ID 0.056 0.191 -10.61 -6.61

TKW AC*** 0.347 0.152 12.29 173.55 179.55

ASPECT RATIO AC** 0.164 0.198 0.006 -172.01 -166.01

ROUNDNESS AC** 0.051 0.1667 0.00001 -355.34 -349.34

***significant at p<0.001, **significant at p<0.01, * significant at p <0.05

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Table 2.3d Description of the fitted model for the field experiment during 2014

EXP Response Variable Fixed Terms Error Variance DEVIENCE AIC

2014 SYDNEY NOT SPATIAL

HEADING AC*** 2.576 105.08 109.08

PLANT HEIGHT AC*** 32.22 215.75 219.75

SPIKE LENGTH AC*** 0.358 19.56 23.56

SEED/SPIKE AC*** 16.94 190.56 194.56

TKW AC*** 6.49 146.41 150.41

ASPECT RATIO AC** 0.005 -173.92 -169.92

TW AC*** 1.605 85.27 89.27

***significant at p<0.001, **significant at p<0.01 * significant at p <0.05

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PH for all the genotypes was shorter in the GH trials than in the field trials across both years.

Presence of Ppd-B1 with any allelic combinations of Vrn-1 resulted in shorter PH in

comparison to the same Vrn-1 allelic combinations without Ppd-B1.

Data for effective tiller number per plant was only collected from GH experiments and it was

observed that results were not consistent across years. In 2013 genotypes with all dominant

alleles in the Vrn-1 locus produced eight tillers per plant, while genotypes with all recessive

alleles produced only five tillers per plant. The allelic combination SSWP produced a

maximum of nine tillers per plant but other allelic combinations produced a similar number

of tillers. On the other hand, in GH 2014 genotypes with all recessive alleles in the Vrn-1

locus produced three more tillers per plant than the genotypes having three dominant alleles

of the Vrn-1 gene on all chromosomes (Table 2.4 a and 2.4b).

For spike length (SL), significant variation was observed between the winter-type (recessive

allele in all three positions) and spring-type (at least one dominant Vrn-1 allele) genotypes

when taking into account only the Vrn-1 alleles, but no significant variation was found

among the spring-type allelic combination except for SWS, which produced longer spikes in

most trials. The allelic combinations of Vrn-1 associated with the Ppd-B1 allele resulted in

significantly smaller spikes across the trials. Similarly, no significant variation for seed

number (SN) was found within the spring-type allelic combination of Vrn-1 alleles but

significant variation for SN was observed between the winter-type combinations and the

spring type with or without Ppd-B1.

Allelic combinations had a significant effect on TKW, whereby winter-type combinations

always exhibited significantly lower values than the spring-type allelic combinations, ranging

from 22.76 g in field 2013 to 43.05 g in GH 2014 (Table 2.4a and 2.4b). A similar level of

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variation was also observed among the spring-type combinations, where genotypes with all

the dominant Vrn-1 alleles had significantly lower TKW than the other spring-type genotypes

and genotypes with double-dominant Vrn-1 alleles, which consistently achieved higher TKW

across the trials. In field trials carried across both years it was observed that double dominant

allelic combinations Vrn-A1 and Vrn-D1 or Vrn-D1 and Vrn-B1 or even single dominant for

either allele had consistently higher SN and TKW, while double-dominant genotypes Vrn-A1

and Vrn-B1 had always lower SN and TKW than the other spring-type combinations (Table

2.4 c and 2.4d). Again, a combination of the double-dominant variant of Vrn-A1 and Vrn-B1

with the photoperiod insensitive allele Ppd-B1 led to higher SN and TKW in both field trials.

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Table 2. 4a Predicted means of the traits against different allelic combinations of vernalization and photoperiod genes of the 2013 experiments under both glasshouse and field conditions

EXP TRAITS ALLELIC COMBINATIONS

SED SSWPP (C1) WSWP (C2) SSS SSSP SSW SSWP SWS SWSP SWW WSSP WSWP WWSP WWW WWWP

GH 2013

HD 56.38 57.73 83.97 61.12 87.04 62.70 79.89 59.09 86.29 62.72 69.00 61.16 107.73 105.81 1.005

PH 60.72 51.76 114.54 97.68 117.75 100.79 110.47 96.23 116.68 98.60 104.91 100.59 105.17 101.78 2.311

TL 6.35 7.32 8.11 7.60 7.56 9.05 7.94 7.67 7.75 7.38 8.78 7.30 5.49 5.88 0.624

SL 8.73 6.88 13.67 8.43 13.87 10.25 14.02 8.97 14.19 8.58 9.95 9.60 13.46 11.12 0.336

SN 45.41 36.26 51.74 33.45 46.90 37.89 47.15 35.42 47.84 29.93 39.06 34.87 45.58 30.89 2.005

SN/S 5.22 5.28 3.80 3.95 3.38 3.73 3.37 3.97 3.37 3.49 3.93 3.65 3.38 2.75 0.159

TKW 38.18 41.33 39.04 46.90 42.50 46.45 45.15 46.55 43.97 47.85 50.65 48.78 36.66 37.28 1.735

TW 79.39 81.03 68.86 76.82 69.99 74.38 68.35 72.33 68.55 75.77 75.00 75.75 68.71 70.76 1.105

AR 1.937 1.892 1.892 1.913 1.913 1.886 1.856 1.863 1.870 1.834 1.869 1.870 1.912 2.071 0.035

RN 0.642 0.661 0.648 0.647 0.646 0.650 0.655 0.650 0.641 0.665 0.651 0.648 0.629 0.621 0.007

FIELD 2013

HD 113.82 120.08 133.50 117.57 131.20 113.61 133.20 123.59 132.45 124.15 124.36 123.74 136.29 135.47 1.063

PH 78.75 68.75 112.50 107.50 115.00 120.00 120.00 112.50 112.50 114.17 110.83 108.33 107.50 105.00 4.148

SL 10.88 8.43 10.78 10.11 11.76 11.77 12.87 9.63 11.33 10.46 10.38 11.37 10.82 9.92 0.336

SN/S 6.26 6.30 4.97 4.73 4.42 4.44 4.41 5.03 4.43 4.09 4.54 4.55 4.27 3.31 0.345

SN 68.41 52.85 53.10 48.03 51.34 52.81 56.35 47.54 51.06 43.49 48.24 52.98 46.33 31.77 4.085

TKW 36.40 31.73 25.64 37.86 27.55 37.92 31.22 33.64 34.69 34.69 34.96 36.20 22.76 30.20 2.567

AR 1.986 2.127 2.237 2.082 2.178 2.059 2.067 2.167 2.031 2.109 2.094 2.080 2.113 2.207 0.056

RN 0.602 0.620 0.600 0.619 0.607 0.610 0.599 0.599 0.603 0.621 0.596 0.604 0.599 0.610 0.007

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Table 2.2b Predicted means of the traits against different allelic combinations of vernalization and photoperiod genes of the 2014 experiments under both glasshouse and field conditions

EXP TRAITS ALLELIC COMBINATIONS

SED SSWPP (C1) WSWP

(C2) WWSP (C3)

SSWPP (C4) SSS SSSP SSW SSWP SWS SWSP SWW WSSP WSWP WWSP WWW WWWP

SYDNEY HD 94.50 94.50 97.00 110.00 104.25 96.50 104.83 97.00 101.75 94.50 105.17 96.50 100.83 98.17 113.75 109.00 1.294

PH 82.00 72.00 95.00 77.50 135.25 129.00 130.33 125.00 129.50 124.75 124.17 133.33 128.67 126.00 121.25 125.50

0.046

SL 10.75 8.55 9.93 10.48 9.97 9.58 10.49 10.45 11.11 9.85 10.68 9.60 9.64 9.89 9.90 10.33

0.482

SN 60.44 49.25 58.05 57.25 44.58 40.07 42.07 39.90 44.48 44.40 41.75 38.32 39.43 40.48 37.20 42.85

3.318

TKW 33.80 31.40 36.20 36.30 35.58 40.75 35.72 39.60 39.90 40.35 40.30 44.02 42.47 42.05 31.78 34.55 2.054

AR 2.015 2.110 1.855 1.945 2.035 2.015 2.068 2.045 1.953 1.995 1.998 1.930 1.980 1.968 2.048 2.155 0.054

GH 2014

SSWPP (C1) WSWP (C2)

WWSP (C3)

SSWPP (C4) SSS SSSP

SSW (P1) SSW SSWP SWS SWSP SWW WSSP WSWP WWSP (P2) WWW

HD 71.00 69.22 76.37 63.28 92.80 69.56 104.65 100.98 72.50 93.04 71.22 91.92 74.39 84.64 73.65 112.79

0.43

PH 70 55 80 65 136.25 107.5 138.75 142.5 112.5 135 107.5 135 117.5 117.5 108.75 126.25

1.083

SL 9.92 7.99 10.53 9.29 10.71 9.45 11.26 11.78 10.73 11.36 9.95 12.40 9.35 8.87 10.29 12.68

0.271

SN 51.26 42.12 60.10 47.62 45.88 32.80 46.73 49.03 39.75 45.82 37.55 56.56 38.42 40.63 38.08 48.93 2.649

TL 9.21 8.56 9.13 8.21 10.85 12.12 11.60 10.34 8.73 9.31 11.14 9.97 9.86 12.41 10.35 13.77

0.618

TKW 45.03 48.59 47.70 47.61 39.97 49.96 44.72 49.98 50.82 50.36 50.26 48.87 53.21 48.86 55.69 43.05 0.92

TW 81.02 82.83 84.91 82.97 78.25 81.65 79.73 80.09 80.89 76.69 81.11 77.13 79.73 80.79 80.46 76.96

0.92

AR 1.878 1.920 1.683 1.758 1.918 1.948 1.870 1.898 1.838 1.900 1.865 1.903 1.793 1.860 1.908 1.870

0.025

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2.3.4 Effects of allelic combinations and water stress interactions on agronomic traits

AC and WS interaction effects on the HD, PH, SN, TL, TKW, and TW were observed in GH

2013, where the greatest interaction found for TL and TKW (Figures 2.6c and 2.6e). WS

advanced the HD of all combinations except for SSSP genotypes while a significant

reduction in heading date was observed for two spring-type combinations (SSS and SSW)

and two winter-type combinations (WWW and WWWP) when considering only the Vrn-1

allelic variants (Figure 2.6a). When taking into account the presence of Ppd-B1 in

combination with Vrn-1 alleles, significant interactions were found only in one spring-type

(WSWP) combination. PH was significantly reduced as a result of the WS treatment in three

spring-type combinations (SSS, SWW and SSW) and the winter-type combination when only

Vrn-1 alleles were taken into account (Figure 2.6b). WS had a large effect on TN per plant

(Figure 2.6c) and WS significantly affected all genotypes, including the DH population and

controls. SN in two spring-type and in the winter-type allelic combinations were most

significantly affected by the WS treatment, and overall WS had a small negative effect on the

remaining combinations. Surprisingly, increased SN was observed for both controls although

interactions were not significant. Interestingly, TKW increased significantly for most spring-

type combinations but decreased in the winter-type combination of the Vrn-1 alleles (Figure

2.6e). WS had a small effect on seed TW although significant interactions were observed for

two spring-type combinations and in the winter-type combination, as for most genotypes

tested (Figure 2.6f).

In the GH 2014 trial, significant interaction between AC and WS treatment was observed for

HD, PH, TL, SL and TKW. Imposing a water stress resulted in a significant decrease in days

to heading, irrespective of spring or winter-type allelic combination except for SSSP.

Likewise, a significant reduction in plant height occurred irrespective of allelic combination

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of the DH population, spring or winter type, but controls were not affected at all. TN per

plant values were significantly reduced in all allelic combinations, including controls, but

effects were not as drastic as those seen in the previous year GH trial. Unlikely the GH trial

in the previous year, significant interactions for spike length were observed only for two

allelic combinations (SSS and SSW), where spike length increased as a result of the stress

treatment. In the GH 2014 trial TKW increased in the winter-type allelic combination but

decreased in some spring-type combinations. The allele combination SSW was the most

affected by the WS treatment.

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53

Figure 2.6 Response of different allelic combinations to stress treatment for (a) heading (SED 1.43), (b); plant height (SED 3.2); (c) tiller number per plant (SED 0.87); (d) seed number per spike (SED 2.88); (e) thousand-kernel weight (SED 2.45); and (f) test weight (SED 1.59)

a

b

c f

e

d

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Table 2. 5 Interaction of allelic combinations and water stress treatment in GH14 trial

Allelic combinations

Heading days Plant height (cm)

Tiller number Spike length (cm)

TKW (gm)

Control Stress Control Stress Control Stress Control Stress Control Stress

SSS 93.74 91.87 140 132.5 12.35 9.36 10.27 11.15 39.61 40.33

SSSP 69.61 69.51 110 105 14.03 10.22 9.45 9.44 49.32 50.60

SSW 102.22 99.75 150 135 11.33 9.36 10.99 12.57 54.56 45.40

SSWP 75.13 69.87 120 105 10.66 6.79 10.53 10.94 51.78 49.87

SWS 94.18 91.90 140 130 9.66 8.96 10.47 12.25 49.60 51.12

SWW 93.34 90.50 140 130 12.32 7.62 12.39 12.41 47.06 50.67

WSSP 76.31 72.47 120 115 11.82 7.91 9.63 9.07 51.44 54.97

WSWP 87.56 81.71 122.5 112.5 14.10 10.71 8.98 8.76 49.36 48.36

SWSP 72.96 69.49 110 105 13.69 8.59 9.93 9.97 51.14 49.38

WWW 113.87 111.70 130 122.5 17.65 9.90 12.78 12.58 45.99 40.10

SSW (P1) 107.63 101.66 145 132.5 14.06 9.15 11.16 11.35 45.50 43.93

WWSP (P2) 74.66 72.64 112.5 105 12.43 8.28 10.55 10.03 53.81 57.57

SSWPP (C1) 71.63 70.36 70 70 11.89 6.52 9.83 10.02 44.09 45.98

WSWP (C2) 70.86 67.59 55 55 10.16 6.97 7.71 8.27 50.21 46.97

WWSP (C3) 80.36 72.37 80 80 10.37 7.90 10.02 11.03 47.69 47.70

SSWPP (C4) 63.94 62.62 70 60 10.45 5.98 9.48 9.09 47.31 47.91

SED ±0.52 ±1.53 ±0.89 ±0.39 ±1.31

2.3.5 Relationships among agronomic traits in the WS treatment

For the GH 2013 trial correlation among the response variables showed that HD had a positive

relationship with PH and spike length (Figure 2.7). On the other hand, HD had a strongly

negative relationship with TW and a moderately negative relationship with TKW, RN and TL

(effective) though actual tiller number per plant had a strongly positive relationship with HD

(data not shown). PH was positively associated with longer spikes (SL) but had negative

relationship with TW. Positive association was also observed among the traits TKW, TW and

RN. However, no associations were found with AR and the other variables. In the same way RN

was not associated with PH and SN (Figure 2.7).

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Figure 2.7 Relationships among agronomic traits in the water-stressed treatment

2.3.6 Allelic effects on water consumption by genotypes

The amount of water replenished every day to maintain 60% of field capacity in the water-

stressed treatment was recorded for all genotypes. Water consumption varied depending on the

allelic composition of the genotypes, with variation becoming noticeable from week 9 since

initiation of tillering (Figure 2.8a). The B53 control genotype (C3) consumed more water during

the early vegetative stages than the other controls (Westonia (C1), Wyalkatchem (C2) and

Tenfour (C4)) but all controls behaved similarly in terms of water consumption at the later

stages of growth. Similar variation was also observed in the case of DH lines, where lines with

all recessive Vrn-1 alleles and also lines with double-dominant Vrn-1 alleles for the A and B

genome required continuously more water than the other lines. Association of photoperiod allele

Ppd-B1 with the Vrn-1 allelic combinations made lines consume less water, especially during the

later stages of growth. As a result, DH lines 2, 4, 7, 9, and 12 required a similar amount of water

as the controls from week 9. These lines were significantly distinct from the other Vrn-1 variants

regarding total water consumption during their life cycle, consuming less than 14 litres of water

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56

overall (Figure 2.8b). On the other hand, lines with all winter-type alleles of Vrn-1 required more

than 20 litres of water, which is almost double compared to the least water-consuming controls

(Figure 2.8b).

2.4 Discussion

2.4.1 Allelic effects of vernalization and photoperiod genes on days to heading

Ensuring favourable environmental conditions during anthesis is one of the major aims for

successful crop production in water-stressed environments. The most important period for yield

determination in wheat stretches from a few weeks prior to a few days post anthesis (Fischer

1985, Reynolds et al., 2012). Taking this into account, the first objective was to quantify the

allelic effects of the Vrn-1 gene on heading. This study demonstrated that heading date varied by

one to three weeks, depending on whether glasshouse or field trials were conducted and based on

allelic variants of Vrn-1 and combinations thereof. When a photoperiod insensitivity allele was

combined with different vernalization alleles, heading time could be reduced by another few

weeks using the same allelic Vrn-1 variants. This result resembled the observation of Grogan et

al., (2016) that flowering is more strongly influenced by photoperiod than vernalization genes.

Considering only the Vrn-1 variants, with little exception due to year-to-year variation and

location, winter alleles at all the three homeologous loci of Vrn-1 took the longest time to flower

but replacement with one or two spring alleles had greater effect on shortening heading time

than replacement with all the three spring alleles. Regarding replacement of two alleles, it has

been found that replacement of both the Vrn-A1 and Vrn-D1 loci together had the largest effect

on reduction compared to replacing Vrn-A1 and Vrn-B1 or Vrn-B1 and Vrn-D1 together. When

substituting only one spring allele, the largest effect was found for Vrn-D1 followed by Vrn-A1.

These results were in concordance with previous studies of vernalization gene effects on heading

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57

Figure 2.8 (a) Variation in water consumption during the growing season among genotypes (V_1 to V_16) due to developmental differences; (b) differences in total water consumption among genotypes (V_1 to V_16) throughout the growing season with significant (p<0.05) ranking

a

b

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58

(Stelmakh 1992, Eagles et al., 2010). The variation in heading time due to variants of the

Vrn-1 gene supported the fact that vernalization can modify not only the duration of

vegetative but also late reproductive stages of development (Rodrigues et al., 2014,

Whitechurch, Slafer, and Miralles 2007). Therefore, different alleles of the Vrn-1 gene with

varying degrees of insensitivity could be identified and incorporated into wheat cultivars to

fine-tune flowering time and thus achieve the best possible local environment-specific

adaptation.

2.4.2 Allelic effects of vernalization and photoperiod genes other agronomic traits

Study of the allelic effects of Vrn-1 was extended beyond heading date to include other

agronomic performance indicators like plant height, effective tillers per plant, spike length,

kernel number per spike, and seed quality parameters like TKW, TW, AR, and RN, as heads

and kernels per square, and seed weight are also the important yield determinants. In this

study, heading time had a significant positive relation to PH. It became apparent that

genotypes that headed later were taller than early-flowering lines and also produced more

tillers. But in the case of effective tillers per plant, heading was negatively correlated because

most of the late tillers could not produce functional spikes. Spike length had a positive

correlation with heading date and seed number, although heading was weekly correlated to

seed number. Thus, the allelic variants responsible for early heading had shortened plant

height, smaller spikes and less seeds per spike as well. When considering the ratio of seed

number and spike length as a measure of spikelet fertility, moderately early-heading lines

were significantly more fertile than the earliest and latest lines. These results suggest that the

earliest lines did not have enough time to accumulate sufficient resources for spike

development, while the latest heading lines experienced floret abortion due to a disruption of

the supply of resources by environmental stresses. On the other hand, a longer duration of

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59

heading had a negative correlation with TKW, TW and RN. This suggested that there was

limited nutrient availability to the late-heading lines for grain filling due to resource capture

by the infertile tillers.

The allelic combinations and water stress treatment interaction strongly influence important

yield contributing components like tiller number, seed number, and TKW. Some results in the

GH 2014 trial varied from those in the GH 2013 trial, which can be explained by

environmental conditions across years and also the potting media used in the two trials. But it

was generally observed that tiller and seed numbers were affected by all allelic combinations.

TKW increased as a result of the WS treatment for most allelic variants except genotypes

with all the three winter alleles. This result suggests that plants can fill the grain more

efficiently by avoiding competition from surplus tillers. This result is similar to that obtained

by Yang and Zhang (2005) in rice, who reported that controlled soil drying can lead to faster

and better remobilization of stored nutrients from the stem to grains. However, a few allelic

variants of Vrn-1, when combined with Ppd-B1 (such as SSSP and SWSP) minimized the

reduction of tillers and seed number while maintaining higher kernel weight.

Field study of the allelic effects also supported that genotypes with one or two spring alleles

at the homeologous Vrn-1 loci maintained a higher kernel weight and number of seeds than

lines with all three winter or spring alleles. Low seed number and TKW of the winter-type

lines could be explained by the effects of environmental stresses but the latter might also be

the consequence of inadequate growth and development due to accelerated maturity caused

by the three spring alleles at the Vrn-1 loci. It was also observed in the glasshouse that too

early lines always had weak and thin stems with smaller spikes compared to other genotypes.

The field studies demonstrated that allelic variants with double dominant alleles Vrn-A1 and

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60

Vrn-D1 or Vrn-B1 and Vrn-D1 were more effective in maintaining greater SN and TKW, two

important yield components, compared to double dominant alleles Vrn-A1 and Vrn-B1.

This present study clearly showed that allelic variants at the homeologous Vrn-1 loci do

significantly influence heading date and other agronomic traits. More interestingly, this study

also demonstrated that allelic variants can modify the timing of increased water uptake as

well as total water consumption by modifying the duration of different developmental stages,

whereby some allelic variants consume up to half less water than other genotypes. In terms of

total water consumption control genotype B53 (C3) was not water efficient as compared to

the other controls but the weekly consumption graph clearly showed that it required an

equivalent amount of water during the later stages of growth which is also the critical time in

terms of water-stressed environments. Among the DH population, allelic combination WSWP

showed a similar pattern of water consumption to B53, where plants used more water during

early stages and less at later stages. On the other hand, some allelic variants required

continuously less water, like the control genotype Westonia (C1). This result could be

successfully applied to generate new varieties directed to specific environments, depending

on local water availability and rainfall patterns.

2.5 Conclusion

This study was undertaken to assess the allelic effects of Vrn-1 variants using a doubled

haploid population derived from near-isogenic lines of the well-studied genotype Triple Dirk,

and assuming that background effects had been eliminated. The presence of variation in

heading time within the same allelic variant combination prompted us to search for the

presence of other phenology genes, which led us to the identification of a photoperiod

insensitive allele of Ppd-B1 in one of the parents. This photoperiod insensitive allele

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61

segregated randomly and combined with different allelic variants of Vrn-1. As a result we

analysed our response variables for 12 allelic variant combinations instead of eight possible

allelic variants of Vrn-1 alone, while still missing some allelic variants of Vrn-1. Despite of

this fact our study clearly demonstrated the allelic effects of Vrn-1 on wheat flowering and

other yield components. Additionally, interaction effects of Vrn-1 variants with Ppd-B1 were

estimated in this study. Glasshouse trials identified that double-dominant or single-dominant

Vrn-1 allelic variants associated with Ppd-B1 can better withstand water stress, as evidenced

by the better field performance of those allelic variants. Therefore, strategic deployment of

such allelic variants in breeding programs could minimize yield losses in water-limited

environments.

2.6 References

Beales, J., A. Turner, S. Griffiths, J.W. Snape, and D.A. Laurie. 2007. "A pseudo-response regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat (Triticum aestivum L.)." Theoretical and Applied Genetics no. 115 (5):721-733.

Cane, Karen, H. A. Eagles, D. A. Laurie, Ben Trevaskis, Neil Vallance, R. F. Eastwood, N. N. Gororo, Haydn Kuchel, and P. J. Martin. 2013. "Ppd-B1 and Ppd-D1 and their effects in southern Australian wheat." Crop and Pasture Science no. 64 (2):100-114.

Díaz, Aurora, Meluleki Zikhali, Adrian S. Turner, Peter Isaac, and David A. Laurie. 2012. "Copy Number Variation Affecting the Photoperiod-B1and Vernalization-A1 Genes Is Associated with Altered Flowering Time in Wheat (Triticum aestivum)." PLoS ONE no. 7 (3):e33234.

Dubcovsky, J, D Lijavetzky, L Appendino, and G Tranquilli. 1998. "Comparative RFLP mapping of Triticum monococcum genes controlling vernalization requirement." Theoretical and Applied Genetics no. 97 (5-6):968-975.

Eagles, H.A., K. Cane, H. Kuchel, G.J. Hollamby, N. Vallance, R.F. Eastwood, NN Gororo, and PJ Martin. 2010. "Photoperiod and vernalization gene effects in southern Australian wheat." Crop and Pasture Science no. 61 (9):721-730.


Foulkes, MJ, R. Sylvester-Bradley, AJ Worland, and JW Snape. 2004. "Effects of a photoperiod-response gene Ppd-D1 on yield potential and drought resistance in UK winter wheat." Euphytica no. 135 (1):63-73.


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González, Fernanda G., Gustavo A. Slafer, and Daniel J. Miralles. 2003. "Grain and floret number in response to photoperiod during stem elongation in fully and slightly vernalized wheats." Field Crops Research no. 81 (1):17-27.

Grogan, Sarah M., Gina Brown-Guedira, Scott D. Haley, Gregory S. McMaster, Scott D. Reid, Jared Smith, and Patrick F. Byrne. 2016. "Allelic Variation in Developmental Genes and Effects on Winter Wheat Heading Date in the U.S. Great Plains." PLoS ONE no. 11 (4):e0152852.

Guo, Zhiai, Yanxia Song, Ronghua Zhou, Zhenglong Ren, and Jizeng Jia. 2010. "Discovery, evaluation and distribution of haplotypes of the wheat Ppd-D1 gene." New Phytologist no. 185 (3):841-851.

IPCC. 2013. "Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.".

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Kosova, Klara, Pavel Vitamvas, Milan Oldrich Urban, Jana Kholova, and Ilja Tom Prasil. 2015. Breeding for Enhanced Drought Resistance in Barley and Wheat - Drought-Associated Traits, Genetic Resources and their Potential Utilization in Breeding Programmes.

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hexaploid wheat (Triticum aestivum L.), and their effect on heading time." Molecular Breeding no. 31 (1):27-37.

Pfeiffer, W. H., R. M. Trethowan, M. van Ginkel, M. I. Ortiz, S. Rajaram 2005. "Breeding for abiotic stress tolerance in wheat, pp. 401-489 in Abiotic Stresses: Plant Resistance through Breeding and Molecular Approaches., edited by M. Ashraf, and P. J. C. Harris. The Haworth Press, Inc., NY."


Reynolds, M., J. Foulkes, R. Furbank, S. Griffiths, J. King, E. Murchie, M. Parry, and G. Slafer. 2012. "Achieving yield gains in wheat." Plant, Cell & Environment.

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Chapter 3

3 Effects of Photoperiod Gene Ppd-D1 in the Spike Developments and

Heading of Wheat

3.1 Introduction

Successful grain yield in wheat relies on the timely onset of its reproductive development,

especially the stem elongation period under suitable environmental conditions as the numbers

of fertile florets at anthesis are determined during this stage (Fischer, 1985). Spike dry weight

at anthesis has a positive effect on the final grain yield through increasing grain numbers per

spike (Slafer et al., 2001). The event of spike and stem growth coincidence restricts grain

yield due to competition for assimilates ((Kirby, 1988). On the contrary, an excess supply of

assimilates beyond the demand of developing grains has been reported in some other studies

(Langer and Hanif, 1973), where sink strength could be increased by the extra numbers of

florets per spike (Miralles and Slafer, 2007). Extending the duration of the stem elongation

phase through manipulation of photoperiod results in an increased number of grains at

anthesis (Miralles et al., 2000), suggesting that photoperiod alters the duration of stem

elongation. The photoperiod sensitivities of different developmental phases have been found

to be independent of each other, and could be manipulated independently to improve grain

yield by manipulating photoperiod (Miralles and Richards, 2000; Slafer et al., 2001). A series

of homoeologous loci of photoperiod genes located on group 2 chromosomes (Ppd-D1, Ppd-

B1 and Ppd-A1) control the photoperiod sensitivity in wheat (Law, 1978), where each of the

alleles responds differently with day length. Therefore, proper knowledge on the effects of

photoperiod alleles in spike development would provide an opportunity for the genetic

manipulation of reproductive development phases, thereby obtaining yield gains. Several

studies have illustrated the impact of different Ppd-1 genes on the duration of pre-anthesis

phases, anthesis, heading time and yield (Foulkes et al., 2004; Law and Worland, 1997;

Whitechurch and Slafer, 2002; Worland, 1996). An intensive investigation has been made in

this study to find out how the sensitive and insensitive alleles of Ppd-D1 modify the pre-

anthesis reproductive phases, heading date and finally grain number per spikes. Plant

phenotyping was also carried out to quantify the effects of Ppd-D1 sensitivity on vegetative

growth in relation to reproductive development.

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Ppd-D1 near isogenic lines (NILs) of Mercia received from the John Innes Centre (UK) were

used in this study to quantify the effects of the Ppd-D1 allele. Mercia isogenic line W9333 is

a winter wheat having the photoperiod sensitive allele of Ppd-D1a, while the photoperiod

insensitive Ppd-D1b allele was introgressed in the isogenic line W9331 from cv. Hope. A

glasshouse experiment was conducted at Murdoch University during May to November’

2015 under natural light conditions. Murdoch potting mix was used to fill the pots. Seeds

were vernalized for three weeks and four seedlings were planted in each 6L

(230mmx210mm) plastic pot, and thinned to two plants per pot one week after germination.

Forty pots were planted for each line following a completely randomized block design, where

five pots for each line were maintained separately as five replications for taking phenotypic

and agronomic notes. The plants in the rest of the pots were used for microscopic

observation. The main shoots from three randomly selected plants of each line were harvested

twice a week commencing one week after planting (29.05.2015) and dissected for

microscopic observation of apical development. At the later stages of development (after

terminal spikelet stage) observations were made weekly. Dates for the important apical

changes such as the formation of a double ridge (DR) and terminal spikelet (TS) initiation

were recorded. Data were also taken for root length, shoot length, leaf number and tiller

numbers per plant.

3.3 Results

3.3.1 Ppd-D1 effects on plant growth

Seeds were soaked in water and vernalized for three weeks at 4oC in the dark. During

transplanting into the pots it was observed that seedlings of the photoperiod insensitive line

W9331 had a comparatively longer plumule and radicle than the sensitive line W9333 (data

not shown). Root length and shoot length were recorded twice a week until the initiation of

the flag leaf, and line W9331 had consistently longer root and shoot lengths than line W9333

(Figure 3.1 A and B). Average root length for line W9331 at the final measurement was

20.33cm, while in line W9333 it was 16.13 cm. A point to note is that apparently line W9333

had a greater root mass at physiological maturity but measurements were not taken due to of

difficulties separating root mass from the soil media. Average shoot lengths were 51 cm and

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45.50 cm in lines W9331 and W9333, respectively. On the contrary, line W9333 always

produced a greater tiller number than line W9331 (Figure 3.1C), while the final tiller numbers

were 11.67 and 14.33 in W9331 and W9333, respectively. It is noteworthy that in both these

lines not all tillers produced spikes, and generally only three to five were effective. Line

W9333 produced 11 to 12 leaves per plant before producing the flag leaf compared to 9

leaves on line W9331, though both lines had an average 8 leaves at the terminal spikelet stage

of spike development.

3.3.2 Ppd-D1 effects on reproductive development

Microscopic observations of apical development were made twice a week to determine the

differences between the two lines in reproductive development. Line W9333 (photoperiod

sensitive) had more leaf primordia than line W9331 (photoperiod insensitive) though both

lines reached the double ridge stage in the same week at around 28 days (Figure 3.2 B). The

subsequent rate of spike development (i.e. spikelet primordia differentiation) was faster in

line W9333 than in line W9331 (Figure 3.2 C and Figure 3.3 A and B). During this stage line

W9333 produced more spikelet primordia than the other line. Accordingly, line W9333

reached the terminal spikelet (TS) stage one week before line W9331, at around 90 days, and

by the time line W9331 reached the TS stage line W9333 was more advanced and in the

floral primordia stage (Figure 3.4 A and B). Interestingly, internode elongation in line W9331

commenced at 45 days despite the spikelet development rate being slower. Line W9333 at the

TS stage had only two shorter internodes, while at the same stage line W9331 had three

longer internodes. Thus after reaching the terminal spikelet stage the photoperiod insensitive

line W9331 quickly reached anthesis twelve days before than the photoperiod sensitive line

W9333, at around 130 days, though it had a comparatively shorter spike length than line

W9333 (Figure 3.3 C). At maturity the average spike length of W9333 was 4cm longer than

in line W9331, and the photoperiod sensitive line had at least two spikelets more than line

W9331 (Figure 3.3). However, the photoperiod insensitive line W9331 produced more seeds

than line W9333 (Figure 3.5) due to a higher spikelet fertility.

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Figure 3.1 Differences between the photoperiod insensitive (W9331) and sensitive (W9333) for root length (A), shoot length (B) and tiller number (C)

0.00

10.00

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0.00

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A

B

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Figure 3.2 A-C Sequential development of reproductive organs on the same day for the Photoperiod insensitive (W9331) and sensitive (W9333) lines A) apical dome; B) double ridge stage and C) faster growth of spikelet primordia in W9333

A

B

C

W9331 W9333

0.5 mm 0.5 mm

0.5 mm 0.5 mm 0.5 mm

0.5 mm

0.5 mm

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Figure 3.3 A and B faster growth of spikelet primordia in W9333 compared to W9331

A

B

1 mm

1 mm 1 mm

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Figure 3.4 A-C Sequential development of reproductive organs on the same day for the Photoperiod insensitive (W9331) and sensitive (W9333) lines A) Terminal spikelet stage of W9333;B) terminal spikelet stage of W9331; and C) anthesis of W9331

B

C

A

1 mm 1 mm

2 mm

1 mm

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Figure 3.5 Differences between the Photoperiod insensitive (W9331) and sensitive (W9333) lines for spike length, spikelet number and grain number

3.4 Discussion

Grain yield is ultimately dependent on the number of grains per m2 (Fischer, 1985), and the

number of grains per spike is determined during the stem elongation phase. Therefore,

reproductive development and its consequences for grain yield in relation to photoperiod

sensitivity have been recognized as important aspects of research in the past few years. In

concordance with the few previous studies (Gonzalez et al., 2002; González et al., 2003) this

current study demonstrated that photoperiod sensitivity not only controls the stem elongation

phase but also the early reproductive and vegetative growth phases including leaf emergence

and tiller development. Until the 8th leaf stage the photoperiod insensitive line W9331

produced longer shoots and roots, while the photoperiod sensitive line produced more tillers

with a relatively similar number of leaves. Compared to the sensitive line overall photoperiod

insensitivity advanced the flowering time by 13 days. Similar results were reported by

Foulkes et al., (2004) where photoperiod insensitivity advanced flowering time by 8-15 days

depending on year. Longer root and shoot growth of the photoperiod insensitive line could be

explained by the shortening of life cycle whereas profuse tillering of the sensitive line might

be due to a more prolonged life cycle. Variation in the final leaf number was observed

between the lines, with the photoperiod sensitive line having increased numbers of leaves

after the TS stage (average 8 leaves till TS) as phyllochron (thermal time interval between

0

10

20

30

40

50

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70

SPIKE LENGTH SPIKELET No. Grain No.

W9331 W9333

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emergence of two successive leaves) during the late reproductive stage is responsive to

photoperiod (González et al., 2003; Miralles and Richards, 2000).

In accordance with the previous studies (Miralles and Richards, 2000; Slafer et al., 2001)

photoperiod sensitivity affected the late reproductive stage (TS to anthesis), however, this

study also showed that photoperiod sensitivity can modify the early reproductive stage (DR

to TS), with the photoperiod sensitive line reaching the TS stage one week earlier than the

insensitive line. In contrast, the photoperiod insensitive line passed through the floret

primordia stage very quickly and, reached heading and anthesis 13 days earlier than the

sensitive line even though it was late reaching TS. This evidence strengthens the hypothesis

that the photoperiod sensitivity of different developmental phases is independent of each

other and could be manipulated independently to improve grain yield (Halloran and Pennell,

1982; Slafer et al., 2001). It is noteworthy that elongation of the internode started well before

the TS stage with the insensitive line, while internode elongation was delayed in the sensitive

line until after TS stage. The results indicate that the photoperiod pathway of flowering

control interlinked with the gibberellic acid biosynthetic pathway of plant development, and

earlier heading of the photoperiod insensitive line might be due to the combined effects of

both pathways. In some previous studies gibberellins have been reported to control internode

elongation in a number of cereals including wheat (MacMillan, 1987; Phinney, 1985) and to

promote a number of plant developmental processes from germination to seed development

including flowering (Sun and Gubler, 2004). Rebetzke et al., (2012) also depicted the linkage

of the photoperiod insensitive allele Ppd-D1b with the reduced height genes in wheat.

Consistent with previous studies with an isogenic line of the photoperiod allele (Foulkes et

al., 2004; A. J. Worland et al., 1998) the photoperiod sensitive line in this study had longer

spike length and greater spikelet number than the insensitive line. However the lower number

of grains in the sensitive line compared to the insensitive line is inconsistent with those

previous studies and this difference might be due to the effects of environmental conditions.

The late flowering of the sensitive line exposed it to relatively high temperature and it is well

documented that high temperatures during the late reproductive phase causes kernel damage

and sterile grains (Tashiro and Wardlaw, 1990).

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3.5 Conclusion

This study clearly demonstrated the effects of Ppd-D1b and Ppd-D1a on the vegetative and

reproductive development of wheat. Photoperiod insensitivity by Ppd-D1b was associated

with earlier flowering but smaller spikes, photoperiod sensitivity by Ppd-D1a was associated

with delayed flowering and larger spikes. At the same time it has revealed the relationship of

photoperiod insensitivity with meristem tissue development due to gibberellin effects on

earlier flowering. The photoperiod sensitive line had larger spikes with greater spikelet

number but a lower number of elongated internodes while the insensitive line flowered with

smaller spikes but more developed internodes. The interaction of vernalization and

photoperiod genes is well established but the interacting relationship between photoperiod

and the gibberellin pathway should also be explored further to fine tune the control of

flowering time and environmental adaption of wheat. Similarly expression studies of the

other genes in relation to photoperiod and vernalization might provide new insights into the

control of flowering time in wheat. Moreover, different photoperiod alleles could behave

differently in different genetic backgrounds and environmental conditions and this should be

tested accordingly to gain the further yield benefits under adverse environmental effects.

3.6 References

Fischer, R. A. (1985). Number of kernels in wheat crops and the influence of solar radiation and temperature. The Journal of Agricultural Science, 105(02), 447-461.

Foulkes, M., Sylvester-Bradley, R., Worland, A., & Snape, J. (2004). Effects of a photoperiod-response gene Ppd-D1 on yield potential and drought resistance in UK winter wheat. Euphytica, 135(1), 63-73.

Gonzalez, F. G., Slafer, G. A., & Miralles, D. J. (2002). Vernalization and photoperiod responses in wheat pre-flowering reproductive phases. Field Crops Research, 74(2), 183-195.

González, F. G., Slafer, G. A., & Miralles, D. J. (2003). Grain and floret number in response to photoperiod during stem elongation in fully and slightly vernalized wheats. Field Crops Research, 81(1), 17-27.

Halloran, G. M., & Pennell, A. L. (1982). Duration and Rate of Development Phases in Wheat in Two Environments. Annals of Botany, 49(1), 115-121.

Kirby, E. J. M. (1988). Analysis of leaf, stem and ear growth in wheat from terminal spikelet stage to anthesis. Field Crops Research, 18(2–3), 127-140.

LANGER, R. H. M., & HANIF, M. (1973). A Study of Floret Development in Wheat (Triticum aestivum L.). Annals of Botany, 37(4), 743-751.

Law, C., & Worland, A. (1997). Genetic analysis of some flowering time and adaptive traits in wheat. New Phytologist, 137(1), 19-28.

Law CN, S. J. a. W. A. (1978). A genetic study of day-length response in wheat. . Heredity, 41, 185–191. doi: 110.1038/hdy.1978.1087.

MacMillan, J. (1987). Gibberellin-Deficient Mutants Of Maize And Pea And The Molecular Action Of Gibberellins Hormone Action in Plant Development–Acritical Appraisal (pp. 73-87): Butterworth-Heinemann.

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Miralles, D., & Richards, R. (2000). Responses of leaf and tiller emergence and primordium initiation in wheat and barley to interchanged photoperiod. Annals of Botany, 85(5), 655-663.

Miralles, D. J., Richards, R. A., & Slafer, G. A. (2000). Duration of the stem elongation period influences the number of fertile florets in wheat and barley. Functional Plant Biology, 27(10), 931-940.

MIRALLES, D. J., & SLAFER, G. A. (2007). PAPER PRESENTED AT INTERNATIONAL WORKSHOP ON INCREASING WHEAT YIELD POTENTIAL, CIMMYT, OBREGON, MEXICO, 20–24 MARCH 2006 Sink limitations to yield in wheat: how could it be reduced? The Journal of Agricultural Science, 145(02), 139-149.

Phinney, B. O. (1985). Gibberellin A1 dwarfism and shoot elongation in higher plants. Biologia Plantarum, 27(2), 172.

Rebetzke, G., Bonnett, D., & Ellis, M. (2012). Combining gibberellic acid-sensitive and insensitive dwarfing genes in breeding of higher-yielding, sesqui-dwarf wheats. Field Crops Research, 127, 17-25.

Slafer, G. A., Abeledo, L. G., Miralles, D. J., Gonzalez, F. G., & Whitechurch, E. M. (2001). Photoperiod sensitivity during stem elongation as an avenue to raise potential yield in wheat. Euphytica, 119(1-2), 191-197.

Sun, T. P., & Gubler, F. (2004). Molecular mechanism of gibberellin signaling in plants. Annu Rev Plant Biol, 55, 197-223. doi: 10.1146/annurev.arplant.55.031903.141753

Tashiro, T., & Wardlaw, I. (1990). The Response to High Temperature Shock and Humidity Changes Prior to and During the Early Stages of Grain Development in Wheat. Functional Plant Biology, 17(5), 551-561.

Whitechurch, E. M., & Slafer, G. A. (2002). Contrasting Ppd alleles in wheat: effects on sensitivity to photoperiod in different phases. Field Crops Research, 73(2–3), 95-105.

Worland, A. J. (1996). The influence of flowering time genes on environmental adaptability in European wheats. Euphytica, 89(1), 49-57.

Worland, A. J., Börner, A., Korzun, V., Li, W. M., Petrovíc, S., & Sayers, E. J. (1998). The influence of photoperiod genes on the adaptability of European winter wheats. Euphytica, 100(1-3), 385-394.

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Chapter 4

4 Phenology and Dwarfing Genes Effects on Adaptation of Advanced

Lines across Diverse Water-Limited Environments of

Western Australia

4.1 Introduction

Australia is the fourth largest wheat exporter of the world, with 50% of its production coming

from Western Australia. As in many other countries, wheat is grown under rain-fed

conditions in Australia. Therefore, good production depends mainly on the environmental

conditions during the growing season, which include temperature and rainfall, as well as heat

and frost events. Generally, in Western Australia wheat is sown after the first flush of rain in

late autumn or early winter seeking to sow late enough to escape frost damage during

flowering in spring but early enough to allow plants to reach physiological maturity before

the beginning of the dry hot summer (Pugsley, 1983). Control of phenological development is

governed mainly by vernalization and photoperiod responsive genes which play key roles for

the successful adaption to the target environments. South-western Australia is characterised

by a Mediterranean climate, classified as semi-arid dryland (Turner, 2004). Late maturing tall

wheat varieties were confined to wetter long growing season areas a long time ago. These

varieties have been replaced by photoperiod and gibberellin insensitive varieties suited to

Australian environments and which have allowed wheat to be grown in drier environments

(Eagles et al., 2009; Pugsley, 1983).

Due to global warming, the climate of south-western Australia is expected to become warmer

and drier by the end of this century, with mean annual rainfall predicted to decrease by 20-40

mm with the increase of temperature by 1.25-1.75 °C (Figure 4.1) (Turner et al., 2011). The

cumulative effects of changing temperature and rainfall will increase the frequency of

drought episodes and affect wheat production in low latitudes, including Australia, more than

in high latitudes. A 2 oC increase in temperature could depress wheat yields by up to 40%

(Reynolds, 2010) unless we adopt appropriate, improved varieties and good management

practices in a timely fashion and as a matter of urgency. Improved varieties will require fine-

tuning of phenological development in order to adapt wheat production to future changing

climate conditions in southwest Australia.

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Flowering in wheat is primarily controlled by at least five vernalization loci (Vrn-A1, Vrn-B1,

Vrn-D1, Vrn-2, and Vrn-3), three homeologous loci of photoperiod genes (Ppd-D1, Ppd-B1

and Ppd-A1), and earliness per se genes (Law, 1978; A. Pugsley, 1971). With recent

advances in molecular biology a number of alleles at the Ppd-1 and Vrn-1 loci including

haplotypes and copy number variations have been identified as being responsible for

affecting heading date by modifying phenological development phases and other agronomic

traits (Beales et al., 2007; Chen et al., 2009; Díaz et al., 2012; Foulkes et al., 2004; Guo et al.,

2010; Muterko et al., 2015; Nishida et al., 2013; Worland, 1996; L. Yan et al., 2006; Yan et

al., 2004; Yan et al., 2003; Yan, et al., 2004). This allelic variation is associated with

insertions, deletions and mutations in the promoter region of Vrn-1, and deletion or

transposon insertion within the promoter region and also copy number variation for the Ppd-1

gene (Beales et al., 2007; Díaz et al., 2012; Yan, et al., 2004). All these alleles of

vernalization and photoperiod genes respond differently to environmental stimuli and act

initially within separate pathways which converge at a point to produce flowers (Mouradov et

al., 2002; Wellmer and Riechmann, 2010). Thus, each of these alleles has adaptive value to

specific environments, whereby 70-75%, 20-25% and 5% genetic variability has been

attributed to vernalization, photoperiod and earliness per se genes, respectively (Stelmakh,

1998). On the other hand, the dwarfing genes (Rht), acknowledged as the genetic basis of the

green revolution during the 1970s, are also known to interact with the phenology genes in

determining yield (Grogan et al., 2016). The availability of molecular markers for those

alleles makes it easy to identify and trace them in breeding populations (Eagles et al., 2009;

Figure 4. 1 Predicted changes in temperature (a) and rainfall (b) from 2000 to 2050 in the cropping area of southwest Australia (adopted from Niel et al., 2011)

1.00 - 1.25 1.25 - 1.50 1.50 - 1.75 1.75-2.00

–120 - –100 (mm) –100 - –80 –80 - –60 –60 - –40 –40 - –20 –20 - 0 0 - 20 20 - 40 40 - 60 60 - 80 80 - 100 100 - 120

Chapter- 4

79

Fu et al., 2005). Quantification of the effects of different allelic combinations of Vrn, Ppd and

Rht genes on heading date could provide a guideline for the strategic breeding of wheat

varieties for specific water-limited environments via drought stress avoidance.

The overall objective of this study was to identify and estimate the interaction effects of Vrn-

1 and Ppd-1 allelic combinations on heading date and yield parameters in selected advanced

breeding lines developed from diverse parents. In the analysis all the three homeologous loci

of Vrn-1 and Ppd-1 along with Rht-1 and Rht-2 genes were included to obtain accurate

estimates of the genetic and environmental interaction effects of different alleles on heading

date and other agronomic traits with the goal of providing useful data for wheat breeding

programs targeting specific water-limited environments.



Nineteen advanced lines representing five different genetic pools were used for this study. All

had been previously tested in multi-location trials and merited inclusion in the advanced

variety trial of Edstar Genetics Pty. The five genetic pools represented the crosses made on

material from Spain, Queensland, Victoria, CIMMYT (Mexico), and Winter X Spring crosses

using UK winter varieties. Four local check cultivars were also included namely

Wyalkatchem and Magenta (Australian premium white classification) and, Mace and Bonnie

Rock (Australian hard wheat classification).

Mace has a Wyalkatchem background but higher grain yield than the parent, which has led to

a rapid uptake across the environments of WA. Mace also provides good disease resistance,

grain quality and better tolerance to sprouting compared to Wyalkatchem and Magenta.

Wyalkatchem is the most widely adapted variety and good yielder in the water-limited

regions of WA, and has a good level of tolerance to acid soils. Wyalkatchem is resistant to

yellow spot, hence suitable for wheat-on-wheat systems. Magenta is a mid-long maturing

variety and is best suited to early sowing. Its yield is similar to Wyalkatchem; it is also

resistant to yellow spot and has good early vigour due to a longer coleoptile. Bonnie Rock is

Chapter- 4

80

also a popular variety, appreciated for its protein quality. It has also a good resistance level to

stem rust.

4.2.2 Field experiments

Field experiments were conducted in 2014 and comprised the 23 lines being grown under

rainfed conditions in three locations across WA, namely Kojonup (32.7°S-117.4°E), Corrigin

(32.3°S-117.8°E) and Toodyay (31.4°S-116.5°E). The lines were sown immediately after the

first flush of rain in 6.0x1.35-m plots employing a randomized complete block design. Daily

meteorological data were obtained from the nearest BOM station (Table 4.1). Nutrient and

pest management practices were done according to local farmers’ standard practice.

Table 4.1 Environmental condition of the three trial sites during 2014 along with sowing and harvesting time

Month Kojonup Corrigin Toodyay

Mean max

temp.

Mean min

temp.

Total rain

Mean max

temp.

Mean min

temp.

Total rain

Mean max

temp.

Mean min

temp.

Total rain

January 31.8 13.8 0.5 34.1 17.4 4.4 35.1 18.6 1.2 February 29.6 13.1 0 32.3 16.1 5 34.3 17.8 2.4 March 28 13.2 0.7 30.3 15.2 2.6 32.1 16.4 0.6 April 24 11.2 10.2 25.7 11.9 90.6 27.1 12.9 48.8 May 18.4 10.3 91 19.5 9.9 47.1 20.7 10.4 77.2 June 16.3 7 24.7 17.3 4.2 25.1 18.3 4.3 45.2 July 14.6 6.9 75.6 16.2 5.7 58.8 17.2 5.5 87

August 17 7.2 60.7 19.8 7.6 39.6 20.7 7.3 42 September 18.9 7.2 50.7 22.2 7.8 27.4 22.4 8.4 43

October 22.9 8.9 52.1 26 10.4 68 26.7 10.5 50.8 November 26.7 10 18.6 29.1 12.3 28.5 29.1 12.6 8.6 December 28.2 11.3 12.2 31 13.4 0 32.3 15.1 0

Sowing Last week of May Third week of May Third week of May Harvesting Last week November Mid of December Mid November

4.2.3 Agronomic Traits

In the field trials, grain harvested (Table 4.1) form each plot was converted to tons/ha yield.

Data for heading, plant height and physiological maturity were recorded for the Toodyay site.

Heading time was recorded when 50% of spikes had emerged from the flag leaf, and anthesis

was determined when 50% of the spikes had extruded anthers. Physiological maturity was

Chapter- 4

81

recorded when 50% of the culm below the spikes had turned yellowish. Plant height was

measured from the soil surface to the top of the spike without including the awn.

Twenty main heads were harvested from each plot and spikes were measured and threshed

manually to obtain the data for spike length and grains per spike. Then data for thousand-seed

weight, test weight (hectolitre), seed length, width, plumpness, and roundness were taken

using a digital seed image analyser (SeedCount™ version2.4.0) in the seed testing laboratory

of Department of Agriculture and Food (DAFWA), South Perth, WA.

4.2.4 Genotyping of the plant materials

Genomic DNA was extracted from leaf tissues of 10-day old seedlings of each line including

controls using SDS extraction protocol. Vrn-1 gene alleles were identified using the primers

described by Yan, et al., (2004) and Fu et al., (2005). Ppd-1 alleles were identified using the

primers developed by Beales et al., (2007), Díaz et al., (2012) and Muterko et al., (2015).

Presence of the dwarfing genes was determined using primers and protocol followed by (Ellis

et al., 2002). In all cases 1% (w/v) polyvinylpyrrolidone was added to the PCR mix to

improve band resolution. PCR products were visualized in 1.2% (w/v) agarose gels run in

1X TAE buffer.

4.2.5 Statistical analysis

A more complex linear mixed model was adopted in the current research where the GxE

effect was modelled using a Multiplicative Mixed Model (MMM), more specifically, this is a

Factor Analytic (FA) model (Smith et al, 2005), accounting for GxE and for heterogeneous

genetic variance and covariance between trials.

In the current study, the data for yield and protein did not have a complete spatial

configuration, therefore a general LMM model (Smith et al., 2005) was used to model GxE

interactions. The latter involves a variance component model fitting environment and

variety/AC main effects and varietal/AC interactions with the environment (trial), referred to

as GxE. Some of the trials were affected by ryegrass weed and a covariate RYE was include

in the model to account for the extent to which each plot was affected.

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82

All single-site and MET analyses involved model selection based on the Log Likelihood and

Akaike Information Criterion (AIC).

The dataset was analysed using GenStat 18 and ASREML-R (Butler et al, 2010) which

facilitates joint modelling of blocking structure, spatial variation, treatment effects, and

extraneous variation. The latter was run in R environment (R Core development Team, 2009).

4.3 Results

4.3.1 Allelic distribution at the Vrn-1 and Ppd-1 loci

The nineteen advanced lines and four local checks were genotyped to determine the

individual allelic combination of phenology and dwarfing genes. The spring allele Vrn-A1a

was identified in 10 lines using the primer combination VRN1AF and VRN-INT1R (Table

4.2). All the lines were tested using three pairs of primers to distinguish between the presence

of the dominant allele Vrn-A1c and recessive allele Vrn-A1v. The primer pair Intr1/A/F2 and

Intr1/A/R3 did not produce bands in any of the lines while one line (Vic-3) produced a 522-

bp product with primer pair Ex1/C/F and Intr1/A/R3, indicating the presence of a Langdon-

type Vrn-A1c allele (Fu et al., 2005). On the other hand, a 1068-bp fragment was amplified

by the primer pair Intr1/C/F and Intr1/AB/R in the remaining lines, indicating the presence of

the recessive Vrn-A1v allele. The dominant Vrn-B1a allele was identified in 19 lines using the

primer pair Intr1/B/F and Intr1/B/R3, and the remaining four lines showed an 1149-bp

fragment characteristic of the recessive Vrn-B1v allele. Amplification of a 1671-bp fragment

using primer pair Intr1/D/F and Intr1/D/R3 indicated the presence of the dominant Vrn-D1a

allele in eight lines while a 997-bp product characteristic of recessive Vrn-D1v was generated

in the remaining 15 lines using the primer pair Intr1/D/F and Intr1/D/R4. Multiplex PCR with

primers Ppd-A1proF / durum_Ag5del_F2 / durum_Ag5del_ R2 (Muterko et al., 2015)

generated a 452 bp fragment characteristic of the recessive Ppd-A1b allele (Table 4.2). For

the identification of the Ppd-B1 allele the lines were tested against two sets of primers

according to Díaz et al., (2012), where only one line (UK-4) produced a 994-bp fragment

characteristic

Chapter- 4

83

Table 4.2 Allelic composition of the advanced lines

SL. NO

.

Line Name

Vernalization loci Photoperiod loci Reduced height loci

VRN A1 VRN B1 VRN D1 PPD-A1 PPD-D1 PPD-B1 Rht-1 Rht-2

1. SP-1 Vrn-A1a Vrn-B1v Vrn-D1a Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1b Rht-D1a

2. SP-2 Vrn-A1a Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1a Rht-D1b

3. SP-3 Vrn-A1v Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1b Rht-D1a

4. SP-4 Vrn-A1v Vrn-B1a Vrn-D1a Ppd-A1b Ppd-D1a Ppd-B1a Rht-B1b Rht-D1a

5. UK-1 Vrn-A1a Vrn-B1v Vrn-D1v Ppd-A1b Ppd-D1b Ppd-B1a Rht-B1a Rht-D1b

6. UK-2 Vrn-A1a Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1b Ppd-B1a Rht-B1a Rht-D1b

7. UK-3 Vrn-A1v Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1b Ppd-B1b Rht-B1b Rht-D1a

8. UK-4 Vrn-A1a Vrn-B1v Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1c Rht-B1b Rht-D1a

9. VIC-1 Vrn-A1a Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1a Rht-D1b

10. VIC-2 Vrn-A1a Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1a Rht-D1b

11. VIC-3 Vrn-A1c Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1a Rht-D1b

12. CMT-1 Vrn-A1v Vrn-B1a Vrn-D1a Ppd-A1b Ppd-D1b Ppd-B1a Rht-B1b Rht-D1a

13. CMT-2 Vrn-A1v Vrn-B1a Vrn-D1a Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1b Rht-D1a

14. CMT-3 Vrn-A1v Vrn-B1a Vrn-D1a Ppd-A1b Ppd-D1a Ppd-B1a Rht-B1b Rht-D1a

15. CMT-4 Vrn-A1a Vrn-B1a Vrn-D1a Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1b Rht-D1a

16. QLD-1 Vrn-A1v Vrn-B1v Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1b Rht-D1a

17. QLD-2 Vrn-A1v Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1b Rht-D1a

18. QLD-3 Vrn-A1v Vrn-B1a Vrn-D1a Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1b Rht-D1a

19. QLD-4 Vrn-A1a Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1b Rht-D1a

20. Mace Vrn-A1v Vrn-B1a Vrn-D1a Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1a Rht-D1b

21. Wyalkatchem

Vrn-A1v Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1a Rht-D1b

22. Magenta Vrn-A1v Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1a Rht-D1b

23. Bonnie Rock Vrn-A1a Vrn-B1a Vrn-D1v Ppd-A1b Ppd-D1a Ppd-B1b Rht-B1b Rht-D1a

Designation of the vernalization, photoperiod and reduced height alleles was adopted from Cane et al., (2013); H.A. Eagles et al., (2009); Fu et al., (2005) and Ellis et al., (2002)

of the four-copy Ppd-B1 of Chinese Spring with primer pair Ppd-B1_F25 and Ppd-B1_R70.

On the other hand, a 223-bp fragment characteristic of the three-copy allele Ppd-B1a of

Sonora 64 was produced in five lines using primer pair Ppd-B1_F31 and Ppd-B1_R36 (Table

4.2). Alleles of Ppd-D1 were identified using multiplex PCR with primers Ppd-D1_F/ Ppd-

Chapter- 4

84

D1_R1/ Ppd-D1_R2 (Beales et al., 2007) where 19 lines produced a 218-bp fragment of the

photoperiod insensitive Ppd-D1a allele and the remaining four lines produced a 414-bp

product of the photoperiod sensitive Ppd-D1b allele (Table 4.2).

Two sets of primers were used to identify the alleles for reduced height at the Rht-B1 and

Rht-D1 loci (Ellis et al., 2002). Fourteen lines had the Rht-B1b and the remaining nine lines

had the Rht-D1b allele (Table 4.2).

4.3.2 Environmental effects on yield and protein content

All 23 lines were grown in three different locations in Western Australia. Mean grain yield

across the locations ranged from 2.99 t/ha to 4.95 t/ha and protein content ranged from

10.32% to 11.82%. Grain yield at the trial site in Kojonup was 4.95 t/ha and significantly

(p<0.05) higher than the other trial sites at Toodyay and Corrigin, which had similar yields

(Figure 4.2). Protein content at Kojonup and Toodyay was statistically similar but Corrigin, at

10.32%, was significantly lower than the other sites (Figure 4.2).

Figure 4.2 Location means of grain yield and protein content for the 23 lines grown at the three trial sites.

4.3.3 Environmental and allelic combination effects on yield and protein content

Considering the dominant and recessive alleles at the Vrn-1, Ppd-1, Rht-B and Rht-D loci the

23 lines investigated were grouped into 15 classes, where the control Bonnie Rock shared a

similar allelic combination with only one line; both Wyalkatchem and Magenta shared a

Chapter- 4

85

similar allelic combination with another line, while Mace and another nine lines showed

unique allelic combinations (Table 4.3). For ease of the discussion allelic combinations have

been represented by seven letters, where the first three letters designate spring (S) or winter

(W) alleles at the Vrn-A1, Vrn-B1 and Vrn-D1 loci, respectively, the next two letters

designate the dominant (S) or recessive (W) alleles at the Ppd-D1 and Ppd-B1 loci,

respectively, and last two letters designate dwarf (D) or tall (T) alleles at the Rht-B and Rht-D

loci. Since all lines were recessive for the Ppd-A1 locus this information was not included in

the analysis.

Mean grain yield (GY) at Kojonup ranged from 4.56 to 5.37 t/ha for different allelic variants,

followed by Toodyay, ranging from 2.72 to 4.07 t/ha, and Corrigin from 2.33 to 3.65 t/ha

(Table 4.3). Mean GY significantly varied among the different allelic variants for each trial

site

Table 4.3 Location and allelic combination interaction effects on yield and protein content

ALLELIC COMBINATION

Number of lines YIELD (ton) PROTEIN (%)

Corrigin Kojonup Toodyay Corrigin Kojonup Toodyay

SSSSWDT 1 (CMT-4) 2.84 4.87 3.30 10.53 11.57 12.72

SSWSWDT 2 (QLD-4 and Bonnie Rock

2.98 4.75 2.86 10.32 12.77 11.36

SSWSWTD 4 (SP-2, VIC-1, VIC-2 and VIC-

3)

3.19 5.27 4.03 10.24 11.81 10.74

SSWWSTD 1 (UK-2) 3.37 5.32 2.90 9.87 11.70 11.79

SWWSSDT 1 (UK-4) 3.04 5.15 3.08 10.20 10.70 11.90

SWWWSTD 1 (UK-1) 2.95 4.77 2.94 9.80 11.17 11.83

SWSSWDT 1 (SP-1) 3.00 4.60 3.01 11.57 13.27 11.42

WSWSWDT 2 (QLD-2 and SP-3)

2.74 5.37 3.16 10.50 11.87 11.33

WSWSWTD 2 (Wyalkatchem and Magenta)

3.20 5.15 3.03 10.26 12.08 12.25

WSWWWDT 1 (UK-3) 2.88 4.62 2.72 10.37 10.90 11.26

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86

ALLELIC COMBINATION

Number of lines YIELD (ton) PROTEIN (%)

Corrigin Kojonup Toodyay Corrigin Kojonup Toodyay

WSSSSDT 2 (SP-4 and CMT-3)

2.70 4.93 3.21 10.42 11.98 11.65

WSSSWDT 2 (CMT-2 and QLD-3)

2.90 5.19 3.22 10.53 11.92 11.60

WSSSWTD 1 (Mace) 3.65 5.04 4.07 9.50 12.53 10.48

WSSWSDT 1 (CMT-1) 3.15 4.56 3.04 9.93 11.93 11.33

WWWSWDT 1 (QLD-1) 2.33 4.66 3.16 10.70 11.13 11.09

SED± 0.2929 0.6729

and also among the trial sites for each allelic group. Control Mace was the highest yielder in

Corrigin and Toodyay with a mean GY of 3.65 t/ha and 4.07 t/ha, respectively. Mean GY of

Mace in Kojonup was 5.04 t/ha which was insignificantly lower than the highest mean GY

5.37 t/ha by the allelic variant WSWSWDT although the same allelic variant gave low GY in

Corrigin at 2.74 t/ha (Table 4.3). The mean GY for the allelic combination SSWSWTD

across the three locations, Corrigin, Kojonup and Toodyay, was 3.19, 5.27 and 4.03 t/ha,

respectively, where the difference was insignificant compared to the highest yielder at the

respective location. In contrast, the allelic combination WWWSWDT gave significantly

lower GY compared to the highest yielder in all the three locations, 2.33, 4.66 and 3.16 t/ha

for Corrigin, Kojonup and Toodyay, respectively.

Protein content across the trial sites ranged from 9.5 to 11.57% in Corrigin, 10.70 to 13.27%

in Kojonup, and 10.48 to 12.72% in Toodyay (Table 4.3). Significant variation in protein

content was observed among the allellic groups within and across trial sites. The allelic

combination SWSSWDT had the highest protein content in Corrigin and Kojonup, 11.57 and

13.27% respectively, and also close to the highest in Toodyay. The highest protein content

obtained in Toodyay was for the allelic combination SSSSWDT at 12.72%.

A biplot was constructed to obtain the allelic combination by location interaction effects for

both yield and protein content (Figure 4.3 A & B). The GY positive and negative values in

both axes indicated that some allelic combinations had a positive interaction with one or two

locations and a negative interaction with others. From the biplot it was observed that the best

three allelic combinations for each site were: WSWSWDT, SSWWSTD and SSWSWTD for

Contd.

Chapter- 4

87

Kojonup; WSSSWTD, SSWWSTD and WSWSWTD for Corrigin and WSSSWTD,

SSWSWTD and WSSSSDT for Toodyay (Figure 4.3A). An allelic combination and location

interaction was also observed for protein content. The biplot showed that the best allelic

combinations for each site were: SSSSWDT, WSWSWTD and SWWWSTD for Toodyay;

SWSSWDT, WWWSWDT and SSSSWDT for Corrigin; and SWSSWDT, SSWSWDT and

WSSSWTD for Kojonup (Figure 4.3B).

Figure 4.3 GGE biplot for allelic combination and environmental interaction on (A) yield and (B) protein content

4.3.4 Allelic combination effects on agronomic traits

Data for days to heading, plant height and other agronomic traits were recorded from the

Toodyay trial site and analysed to obtain any significant differences among the allelic groups

for the traits of interest. Days to heading (DH) ranged from 93 to 110 days, with most allelic

combinations having a DH value of 102 days (Figure 4.4A). Allelic combination SSWSWTD

took 93 days to heading, followed by WSWSWDT (96 days), SSWSWDT and WSSSWTD

(97 days). On the other hand allelic combination WSWWWDT took 110 days to heading,

followed by SWWWSTD (107 days). Plant height of the allelic variants ranged from 70 to

100 cm, with more than half of the allelic groups having a height of 83-85 cm (Figure 4.4B).

Two allelic groups, SSWWSTD and WSWWWDT, were significantly the shorter with

heights of 70 and 75 cm, respectively. In contrast, the four allelic groups WSSWSDT,

Chapter- 4

88

WSWWDT, SSSSWDT and WSSSSDT were significantly taller than the average with

heights greater than 95 cm.

Spike length of the allelic groups ranged from 7.6 to 10.9 cm, whereby four allelic groups

namely SSSSWDT, WSSSWDT, SWSSWDT and WSWSWDT, had values above 10.5 cm

and two allelic groups, SWWSSDT and WSWWWDT, were shorter, 8.7 and 7.5 cm,

respectively (Figure 4.4C). Seed number per spike ranged from 43 to 76 among the allelic

groups, whereby allelic groups SWSSWDT and SSWSWTD had the highest number of seeds

per spike and allelic groups SSWSWDT and WSWWWDT had lowest number of seeds per

spike (Figure 4.4D).

Seed number per spike and spike length ratio were calculated to estimate spikelet fertility. It

was observed that seeds per unit spike length ranged from 4.7 to 8.2, whereby SSWSWTD

had the highest value, followed by SWSSWDT and, SSWSWDT had the lowest value

followed by WSSWSDT (Figure 4.4E). Thousand-kernel weight (TKW) for the allelic groups

ranged from 31.3 to 41.8 g, whereby most groups had values within a range of 35 g to 39 g

(Figure 4.4F). Aspect ratio of the allelic group ranged from 1.8 to 2.1, and roundness ranged

from 0.59 to 0.97 (Figure 4.4 G & H).

Chapter- 4

89

Figure 4.4 Comparison of the allelic combination effects on (A) days to heading; (B) plant height; (C) spike length; (D) seed number per spike; (E) spikelet fertility; (F) thousand-kernel weight; (G) aspect ratio; and (H) roundness.

H G

E F

C D

A B

Chapter- 4

90

4.4 Discussion

4.4.1 Allelic diversity in the advance lines

This study focused on the effects of allelic interactions at the vernalization (Vrn-1),

photoperiod (Ppd-1) and reduced height (Rht-1 and 2) loci for adaptation to three different

wheat growing environments of Western Australia in a set of 19 advanced breeding lines and

four local checks. Each genotype was characterized for the allelic variant combination at the

four loci. For the Vrn-A1 locus, the temperature insensitive Vrn-A1a and the sensitive Vrn-

A1v alleles were almost equally distributed among all lines except for one which had a

Victorian germplasm genetic background carrying with the weaker spring allele Vrn-A1c,

characteristic of Langdon-type genotypes (Table 4.2). The varieties with this allele are

derived from synthetic hexaploids in which the durum wheat variety Langdon was used. In

the case of the Vrn-B1 locus it was observed that Vrn-B1a was the most frequently used

spring allele regardless of background. Only four lines from four different backgrounds had

the winter allele Vrn-B1v. Regarding the Ppd-A1 locus, without any exception all the lines

had the recessive Ppd-A1b allele. Most of the lines had the recessive Ppd-B1b allele at the

Ppd-B1 locus except for six lines. Among these lines the (four-copy) Chinese Spring-type

Ppd-B1c allele was identified in only one line with a UK genetic background, while five

other lines had the (three-copy) Sonora 64-type Ppd-B1a allele. In regards to the Ppd-D1

locus, it was observed that Ppd-D1a was the most commonly used photoperiod-insensitive

allele in all lines except for the UK genetic background line and one line with a CIMMYT

background. For the reduced-height genes only Rht-B1 (Rht-1) and Rht-D1 (Rht-2) loci were

considered in this study, and it was observed that all the lines had either a dominant Rht-B1b

or Rht-D1b allele along with a recessive Rht-D1a or Rht-B1a, respectively. Great variation

was observed regarding allelic variants in the lines from different genetic backgrounds except

for the lines with the Victorian background. All the lines with a Victorian background had the

same dominant and recessive allelic combinations for all the loci, although only one line had

a different spring allele at the Vrn-A1 locus (Table 4.2).

Chapter- 4

91

4.4.2 Allelic combination effects on agronomic traits

In this study the allelic combinations effect of phenology and reduced height genes was

investigated for the Toodyay site on heading and a few more agronomic traits. Final yield is

the cumulative result of several successful events during the plant’s developmental phases.

Heading date, which is one of the most important considerations for water-limited

environments, varied significantly among the allelic combinations studied. It was observed

that lines with the spring allele Vrn-A1a in combination with Vrn-D1a/Vrn-B1a along with

Ppd-D1a and Rht-D1b were the earliest in terms of heading. This result is in accordance with

previous individual studies where genotypes with Vrn-A1a, Ppd-A1a and Rht-D1a alleles

resulted in early flowering (Foulkes et al., 2004; Grogan et al., 2016; Iqbal et al., 2007).

Plant height is also an important trait for drought adaptability, as the stem supplies stored

carbohydrate assimilates to grains during drought (Yang et al., 2000). All the lines in this

study were semi-dwarfs with an intermediate plant height of 70 to 100 cm (Fig 4.4B). This

result indicated that selection of breeding lines for WA environments was in accordance with

previous findings that a plant height of 70-100 cm maximized yield across environments,

whether favourable or unfavourable (Richards, 1992). It was also observed that allelic groups

having similar alleles for Rht-B1 and Rht-D1 but varying for Vrn-1 and Ppd-1 had significant

differences in plant height. These results revealed the interactions of these three

developmental pathways in determining plant height and thus yield.

Grain number, determined by spike length and seeds per spike, is the most important yield

determining factor (Fischer, 1985). Different allelic groups showed significant variation in

spike length and seeds per spike. It was also noticeable that the allelic groups with the larger

spikes did not always result in the highest number of seeds per spike, and the early flowering

allelic groups, except for SSWSWDT, resulted in the highest number of seeds per spike.

Similarly, except for the SSWSWDT allelic combination, most early flowering allelic groups

had the highest ratio of seed and per unit spike length, an indicator of spikelet fertility, as

opposed to the late flowering allelic combination group. This was probably due to the effect

of drought stress on the late flowering lines, because of floret abortion and/or sterile grain. A

similar trend was observed for the thousand-kernel weight where the allelic group with two

spring alleles at the homeologous Vrn-1 loci produced more grain than the allelic group with

two or three winter alleles.

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A comparison between the two allelic groups SSWSWTD and SSWSWDT, which differed

only for the reduced height allele, identified the superiority of Rht-D1b over Rht-B1b for

early flowering, plant height, seed number per spike and spikelet fertility, and that was also

reflected in the plot yield at the Toodyay trial site. This result was in agreement with H. A.

Eagles et al., (2014) who reported that the Rht-B1a/Rht-D1b combination was advantageous

in lower rainfall areas where drought stress and high temperatures coincide during flowering

and grain-filling periods.

4.4.3 Environment and allelic combination effects on yield and protein content

A great variation in yield performance and protein content was observed for all allelic groups

across locations, whereby Kojonup results were highest and Corrigin the lowest among the

three environments (Figure 4.2). There was not much variation in terms of total rainfall

during the cropping season among the three locations. Kojonup and Corrigin received almost

the same amount of rainfall while Toodyay received only 15 mm more rainfall than the two

other sites. Regarding the monthly average temperature, it was observed that Toodyay had

the highest monthly average temperature, closely followed by Corrigin and Kojonup with

almost 1.5oC less than other two locations (Table 4.1). The higher temperature in Corrigin

and Toodyay led to higher pan evaporation and evapotranspiration and aggravated the

drought effects in these two locations. Thus, the yield benefits at the Kojonup site could be

explained by these temperature differences. Again, as Toodyay received more rainfall than

Corrigin but had a similar range in temperature, the former produced a better yield than the

latter.

Performance of the same allelic group for yield and protein also varied across environments

as indicated by differential expression of the allelic combination as a result of varying

environmental stimuli like temperature, soil moisture, and day length (Table 4.3 and Figure

4.3). In this study, it was found that the interaction of Rht-D1b with photoperiod insensitive

Ppd-D1a and at least two spring type alleles of Vrn-1 loci performed best for grain yield

across the environments with few exceptions (Table 4.2). It is interesting to note that the lines

with two dominant alleles at the homeologous Vrn-1 loci and one dominant allele at the Ppd-

1 loci or vice-versa, in combination with the reduced height allele Rht-B1b (SWWSSDT and

SWSSWDT) had considerably more stable yields across environments (Table 4.2). These

observations are supported by the previous study of Eagles et al., (2014), who concluded that

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genotypes with Rht-B1a/Rht-D1b are advantageous for yield in most environments. In

addition to this, Ppd-D1a and Vrn-A1a have been reported to induce early flowering (Foulkes

et al., 2004; Iqbal et al., 2007), which might result in better yield due to greater incident

radiation during grain-filling and avoidance of terminal drought. In contrast, lines with two or

three winter alleles at the Vrn-1 loci (WWWSWDT, WSWWWDT and SWWWSTD) in

combination with the same photoperiod and reduced-height alleles produced lower yields

across environments. This might be the consequence of later heading in those lines due to

longer vernalization period requirements by the winter alleles and assumed to be affected by

terminal drought stress. Higher yields of lines with winter alleles at the Vrn-A1 and Vrn-D1

loci in combination with Ppd-A1a and Rht-B1b (WSWSWDT) at Kojonup could be explained

by better environmental conditions at that site, which would explain the significantly lower

yields at the other two sites.

Protein content also varied significantly among trial sites as well as among the different

allelic combinations group (Table 4.2). Higher protein content in the Kojonup trial could be

explained by more suitable environmental conditions during the growing season. Protein

content for different allelic groups varied significantly and revealed the influence of

phenology and reduced height genes in determining protein content of a cultivar, although no

obvious pattern of variation was apparent. There was the suggestive high protein content of

the previously mentioned low-yielding allelic group, but some high-yielding lines also had a

high protein content. Therefore, more work will be required in future to determine the

contribution of phenology genes to protein content as well as yield of the cultivars by

including known protein-related genes in the analysis along with phenology and reduced

height genes.

4.5 Conclusion and Recommendations

This study characterized a set of advanced lines developed from diverse genetic backgrounds

along with a few local checks. The focus was on important loci known to affect growth and

development via vernalization, photoperiod and reduced-height pathways. It also investigated

the interaction effects of these three pathways on yield and yield components of wheat. The

study indicated that allelic variants at those loci interacted in complex ways to determine

yield and protein content of wheat. Allele combinations are most probably expressed

differentially under varying environmental conditions. The results obtained in the current

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study would allow breeders to select appropriate vernalization and photoperiod allelic

combinations along with reduced height alleles to maximize yield potential in target

environments in Western Australia. However, this study was not able to identify all available

allelic variants at the Vrn-1 and Ppd-1 loci, which suggest those missing alleles, should be

introduced into current breeding populations and tested for their effects in target

environments. That might contribute toward improving wheat productivity under future

climate change scenarios.

4.6 References

Beales, J., Turner, A., Griffiths, S., Snape, J. W., & Laurie, D. A. (2007). A pseudo-response regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 115(5), 721-733.

Cane, K., Eagles, H. A., Laurie, D. A., Trevaskis, B., Vallance, N., Eastwood, R. F., . . . Martin, P. J. (2013). Ppd-B1 and Ppd-D1 and their effects in southern Australian wheat. Crop and Pasture Science, 64(2), 100-114.

Chen, Y., Carver, B., Wang, S., Zhang, F., & Yan, L. (2009). Genetic loci associated with stem elongation and winter dormancy release in wheat. Theoretical and Applied Genetics, 118(5), 881-889.

Díaz, A., Zikhali, M., Turner, A. S., Isaac, P., & Laurie, D. A. (2012). Copy Number Variation Affecting the Photoperiod-B1 and Vernalization-A1 Genes Is Associated with Altered Flowering Time in Wheat (Triticum aestivum). PLoS ONE, 7(3), e33234.

Eagles, H. A., Cane, K., Trevaskis, B., Vallance, N., Eastwood, R. F., Gororo, N. N., . . . Martin, P. J. (2014). Ppd1, Vrn1, ALMT1 and Rht genes and their effects on grain yield in lower rainfall environments in southern Australia. Crop and Pasture Science, 65(2), 159-170.

Eagles, H. A., Cane, K., & Vallance, N. (2009). The flow of alleles of important photoperiod and vernalisation genes through Australian wheat. Crop and Pasture Science, 60(7), 646-657.

Ellis, M., Spielmeyer, W., Gale, K., Rebetzke, G., & Richards, R. (2002). " Perfect" markers for the Rht-B1b and Rht-D1b dwarfing genes in wheat. Theoretical and Applied Genetics, 105(6), 1038-1042.



Fu, D., Szűcs, P., Yan, L., Helguera, M., Skinner, J. S., Von Zitzewitz, J., . . . Dubcovsky, J. (2005). Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat. Molecular Genetics and Genomics, 273(1), 54-65.

Grogan, S. M., Brown-Guedira, G., Haley, S. D., McMaster, G. S., Reid, S. D., Smith, J., & Byrne, P. F. (2016). Allelic Variation in Developmental Genes and Effects on Winter Wheat Heading Date in the U.S. Great Plains. PLoS ONE, 11(4), e0152852.

Guo, Z., Song, Y., Zhou, R., Ren, Z., & Jia, J. (2010). Discovery, evaluation and distribution of haplotypes of the wheat Ppd-D1 gene. New Phytologist, 185(3), 841-851.

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Iqbal, M., Navabi, A., Salmon, D. F., Yang, R.-C., Murdoch, B. M., Moore, S. S., & Spaner, D. (2007). Genetic analysis of flowering and maturity time in high latitude spring wheat. Euphytica, 154(1), 207-218.

Law CN, S. J. a. W. A. (1978). A genetic study of day-length response in wheat. . Heredity, 41, 185–191.

Mouradov, A., Cremer, F., & Coupland, G. (2002). Control of Flowering Time: Interacting Pathways as a Basis for Diversity. The Plant Cell Online, 14(suppl 1), S111-S130.

Muterko, A., Kalendar, R., Cockram, J., & Balashova, I. (2015). Discovery, evaluation and distribution of haplotypes and new alleles of the Photoperiod-A1 gene in wheat. Plant Molecular Biology, 88(1-2), 149-164.

Nishida, H., Yoshida, T., Kawakami, K., Fujita, M., Long, B., Akashi, Y., . . . Kato, K. (2013). Structural variation in the 5′ upstream region of photoperiod-insensitive alleles Ppd-A1a and Ppd-B1a identified in hexaploid wheat (Triticum aestivum L.), and their effect on heading time. Molecular Breeding, 31(1), 27-37.

Pugsley, A. (1971). A genetic analysis of the spring-winter habit of growth in wheat. Australian Journal of Agricultural Research, 22(1), 21-31.

Pugsley, A. T. (1983). The impact of plant physiology on Australian wheat breeding. Euphytica, 32(3), 743-748.

Reynolds, M. P. (2010). Climate Change and Crop Production: CABI. Richards, R. (1992). The effect of dwarfing genes in spring wheat in dry environments. II.

Growth, water use and water-use efficiency. Australian Journal of Agricultural Research, 43(3), 529-539.

Stelmakh, A. F. (1998). Genetic systems regulating flowering response in wheat. Euphytica, 100(1-3), 359-369.

Turner, N. C. (2004). Sustainable production of crops and pastures under drought in a Mediterranean environment. Annals of Applied Biology, 144(2), 139-147.

Turner, N. C., Molyneux, N., Yang, S., Xiong, Y.-C., & Siddique, K. H. M. (2011). Climate change in south-west Australia and north-west China: challenges and opportunities for crop production. Crop and Pasture Science, 62(6), 445-456.

Wellmer, F., & Riechmann, J. L. (2010). Gene networks controlling the initiation of flower development. Trends in Genetics, 26(12), 519-527.


Yan, L., Fu, D., Li, C., Blechl, A., Tranquilli, G., Bonafede, M., . . . Dubcovsky, J. (2006). The wheat and barley vernalization gene VRN3 is an orthologue of FT. Proceedings of the National Academy of Sciences, 103(51), 19581-19586.

Yan, L., Loukoianov, A., Blechl, A., Tranquilli, G., Ramakrishna, W., SanMiguel, P., Dubcovsky, J. (2004). The wheat VRN2 gene is a flowering repressor down-regulated by vernalization. Science Signalling, 303(5664), 1640.

Yan, L., Loukoianov, A., Tranquilli, G., Helguera, M., Fahima, T., & Dubcovsky, J. (2003). Positional cloning of the wheat vernalization gene VRN1. Proceedings of the National Academy of Sciences, 100(10), 6263-6268.

Yan, L. H., M. Kato, K. Fukuyama, S. Sherman, J and Dubcovsky, J. (2004). Allelic variation at the VRN-1 promoter region in polyploid wheat. Theoretical and Applied Genetics, 109(8), 1677-1686.

Yang, J., Zhang, J., Huang, Z., Zhu, Q., & Wang, L. (2000). Remobilization of Carbon Reserves Is Improved by Controlled Soil-Drying during Grain Filling of Wheat. Crop Sci., 40(6), 1645-1655.

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Chapter 5

5 Changes in Differential Protein Expression of Wheat during

Phenological Development

5.1 Introduction

Plant products are the major sources of food for humans and animals. Successful growth and

development of the plant parts of interest that often depend on their growing environments is

critical for humans and animals. Wheat is a nutritionally important and widely cultivated

cereal crop. Wheat yield is adversely affected by environmental stresses, particularly drought

and heat since, 43% of the arable land of the world is arid or semi-arid nature (Jingxiu 2013).

Flowering time in wheat is the most critical stage since coincidence of a few days of drought

and heat stress around anthesis causes severe damage to seed setting and yield (Fischer 1985,

Wheeler et al., 2000, Reynolds et al., 2009). Therefore, enhanced yield potential can be

achieved by fine-tuning flowering time and other developmental stages and matching these to

the local temperatures, precipitation pattern and other environmental conditions.

Flowering time of wheat is largely controlled by vernalization, photoperiod and earliness per

se genes, while vernalization and photoperiod genes interact with growing season

temperature and day length, respectively, the earliness per se genes act independent of

environmental cues (Kato and Yamagata 1988). Vernalization requirements in wheat are

controlled by three groups of loci, where Vrn-1, Vrn-2 and Vrn-3 located on a homoeologous

loci of chromosome 5, 5, and 7, respectively (Distelfeld, et al., 2009). Photoperiod is also

controlled by three homoeologous loci of chromosome 2, Ppd-A1, Ppd-B1 and Ppd-D1

(Cockram et al., 2007). Accorded with the studies on Arabidopsis, several vernalization and

photoperiod genes as well as their allelic variants and haplotypes have been identified in

wheat during the past few years (Yan et al., 2003, Yan et al., 2004, Fu, et al., 2006, Fu et al.,

2005, Beales et al., 2007, Díaz et al., 2012, Muterko et al., 2015, Nishida et al., 2013). Based

on the interaction pattern of vernalization and photoperiod genes, a few flowering models in

wheat have been proposed (Shimada et al., 2009, Distelfeld and Dubcovsky 2010). A recent

study also found that dwarfing genes also modify the effects of vernalization and photoperiod

genes in heading (Grogan et al., 2016). Moreover, apart from the above mentioned genes the

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flowering time pathway is also influenced by nutrient, photoreceptor signalling and other

genetic pathways (Mouradov et al,, 2002).

It is believed that a specific protein produced in leaves based upon expressions of flowering

genes triggered by environmental stimuli, is transported to the apical meristem and induce

flowering (Corbesier et al., 2007, Trevaskis 2010). Proteomic studies can provide more

insights into the underlying genetic control of flowering as such approaches are devoid of the

limitations of post-translational modifications, mRNA differences, and protein turn over

when compared to the DNA or mRNA level studies (Vienne et al., 1999). Remarkable

progresses have been made in the field of proteomic research during the last few years based

on technologies of mass spectrometry (MS) and protein sequencing. Two dimensional-gel

electrophoresis (2DE) technology integrated with protein identification by mass spectrometry

allows us to separate complex mixtures of proteins in a form of individual polypeptide spots,

and then quantify and identify expressed proteins in cells or tissues under certain stages of

development or circumstances (Gygi, et al., 2000, Canovas et al., 2004). These techniques

have been successfully utilized in many plant species including Arabidopsis to understand

molecular and biochemical mechanisms or to identify proteins expressed during plant

development or stress responses (Hurkman and Tanaka 1988, Hajduch et al., 2005, Hajduch

et al., 2006, Gallardo et al., 2002, Finnie et al., 2004, Gallardo et al., 2003). In wheat, 2DE

approaches have been applied to a wide range of studies including characterizing seed storage

proteins and determining the dynamics of leaf proteomes due to heat, drought, or heavy metal

stress (Islam et al., 2002, Majoul et al., 2003, Jingxiu 2013, Li et al., 2013, Bahrman, et al.,

2004).

In the current study, an effort has been made to understand the molecular mechanisms

through identifying the differentially expressed proteins that are related to flowering

development of common wheat. This study focused on two double haploid lines (DH) that

were generated from two near isogenic lines (NILs) of Triple Dirk contrasting for winter

alleles in the three homoeologous loci of Vrn-1 gene which produced all eight possible

winter/spring combinations for the three homoeologous loci (see Chapter 2). The two DH

lines with identical spring type Vrn-1 alleles and having difference in heading time due to the

variation of only one photoperiod allele (Ppd-B1a/PpdB1b) were selected for investigation to

identify other genetic factors involve in the flowering pathway.

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Two double haploid (DH) lines with different heading time developed from NILs of Triple

Dirk (Pugsley 1971) were grown in 6L (230mmx210mm) plastic pots under glass house

conditions with three replications during 2013 and 2014. Before planting, pots were

pasteurised in a steel chamber at 65oC for 2 h, and Murdoch potting mix (Zhang et al., 2008)

was used to fill the pots. One gram of urea per pot was top-dressed during tillering stage.

Three replicate leaf samples from the two lines were collected at the following four

developmental stages including seedling, tillering, AR-2 (flag leaf emerged about 2 cm from

the penultimate leaf), and AR-10 (flag leaf emerged about 10 cm from the penultimate leaf).

5.2.2 Plant morphological parameters

The growth parameters tillering (TL), heading (HD), physiological maturity (PM), and plant

height (PH) were recorded. Heading time was determined when 50% of spikes had emerged

from the flag leaf, and anthesis was determined when 50% of the spikes had extruded anthers.

PH was measured from the soil surface to the top of the spike without including the awn. The

number of effective tillers (TL) was counted for individual plants at the late reproductive

stage of growth. After harvesting, spikes were hand-threshed and data for spike length (SL),

seed number per spike (SN/S), thousand kernels weight (TKW), and mini test weight (mini

TW) were recorded for the main tillers of plants.

5.2.3 Protein extraction

Protein extractions from the leaf samples were performed using TCA/acetone method

following Zhao et al., (2005a) with minor modifications. Leaf samples were ground to

powder using mortar and pestle containing liquid nitrogen and then suspended in precooled

10% TCA in acetone containing 10 mM DDT. Suspensions were incubated overnight at -20 oC, followed by sonication for 10 min. After centrifugation at 15000 g for 15 min, the

supernatant was discarded and the precipitate was rinsed with pre cooled acetone containing

10 mM DTT, 1 mM PMSF and 2 mM EDTA. Finally, the protein pellet was dried in a speed

vac and resuspended in lysis buffer consisting of 7 M urea, 2 M thiourea, 4 % (w/v) 3-[(3-

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cholamidopropyl) dimethylammonio]-1-propanesulfonate (CHAPS) for 5-6 h at room

temperature (Wang et al., 2017). After centrifugation at 14 000 g for 10 min at 24 oC to

precipitate the insoluble materials, protein concentration was determined using a Bradford

assay kit (Bio-Rad, Herculles, CA) with Lambda 25 UV/Vis spectrometer (PerkinElmer) at

an absorbance of 480 nm with bovine albumin (BSA) standard.

5.2.4 2DE Separation

For each sample, the wheat leaf protein extract (900 μg/gel) was mixed with rehydration

buffer containing of 7 M urea, 2 M thiourea, 2 % CHAPS, 65 mM DTT, 2% (v/v)

immobilized pH gradient (IPG) buffer (pH 4−7) and 0.002% bromophenol blue. This solution

was used to rehydrate IPG strips (17 cm, pH 4-7, Bio-Rad). Strips were focussed at 60,000

Vh, with maximum 10,000 V at 20 oC using Protein IEF cell (BioRad). Prior to running SDS-

PAGE, the strips were equilibrated with reducing buffer (50 mM Tris-HCl (pH 8.8), 6 M

urea, 30% (v/v) glycerol, 2% (w/v) SDS and 0.002% bromophenol blue, containing 65 mM

DTT) for 15 min and alkylating buffer (50 mM Tris-HCl (pH 8.8), 6 M urea, 30% (v/v)

glycerol, 2% (w/v) SDS, and 0.002% bromophenol blue and 135 mM iodoacetamide) for

another 10 min. For second dimension electrophoresis, the focused strips were loaded on

12% acrylamide/bisacrylamide (37.5:1) gels, using Protean II Xi cell (Bio-Rad). Protein

standards (Bio-Rad) were used to estimate the molecular size of the proteins. The running

buffer consists of 2.5 mM Tris-Base, 19.2 mM glycine and 0.01% SDS. The gels were run at

5W/gel for 1 h and then 10 W/gel until the dye front reached the bottom of the gel. To

minimize experimental variability, two technical samples were run for each individual

extraction and IEF. In total, 48 gels corresponding to two lines x four developmental stages x

three replications x two gels per extract were produced. The SDS-PAGE, gels were stained

with Coomassie brilliant blue (CBB) G-250 containing 25% (v/v) ethanol and 7.5 % (w/v)

acetic acid, and then destained with the same buffer except CBB. The gel images were

acquired with a 2-D Proteomic Imaging System, Image Lab 5.0 (Bio-Rad). The digital gel

maps of different samples were analysed and compared using PD Quest software (Bio-Rad).

5.2.5 Nano-HPLC-MS/MS

Selected protein spots were manually excised from gels and identified by mass spectrometric

peptide sequencing. The spots were analysed by Proteomics International Ltd. Pty, Perth,

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Australia. Protein samples were trypsin digested and the resulting peptides were extracted

according to standard techniques. Tryptic peptides were loaded onto a C18 PepMap100, 3 µl

(LC Packings) and separated with a linear gradient of water/acetonitrile/0.1% formic acid

(v/v), using an Ultimate 3000 nano HPLC system. The HPLC system was coupled to a

4000Q TRAP mass spectrometer (Applied Biosystems). Spectra were analysed to identify the

proteins of interest using Mascot sequence matching software (Matrix Science) with

taxonomy set to Viridiplantae (Green Plants). All searches used the Ludwig NR. The

software was set to allow 1 missed cleavage, a mass tolerance of ±1.2 Da for peptides and ±

0.6 for fragment ions. The peptide charges were set at 1+, 2+ and 3+, and the significance

threshold at P < 0.05. Generally, a match was accepted where two or more peptides from the

same protein were present in a protein entry in the Viridiplantae database. Proteins were

selected based on careful inspection of MASCOT score, sequence coverage, pI and molecular

weight.

5.3 Results

5.3.1 Plant agronomic traits

Based on the glasshouse experiment of the two DH lines A & B, line B was significantly

earlier in heading than line A by 26 and 24 days in 2013 and 2014, respectively. Line A had a

taller plant type, larger spikes, more tillers than line B during both years (Table 5.1). In

contrast, line B had significantly higher TKW and Mini TW than line A.

5.3.2 The proteomic profile differences between wheat DH lines at different growth

stages

Initially the leaf proteins at seedling, tillering, AR2 and AR10 stages were examined in a

range of pH 3-10 followed by second dimension separation on 12% polyacrylamide gel. Most

of the protein spots appeared between the pH range 4.5 and 7. Consequently, a narrow pH

range 4-7 was selected for further experiments to get better separation of the proteins.

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Table 5. 1 Results of analysis of variance for the two lines for different agronomic traits

EXPERIMENTS 2013 2014

TRAITS MEAN

P VALUE MEAN

P VALUE LINE A LINE B LINE A LINE B

DAYS TO HEADING 86.67 60.33 0.001 ** 93.67 69.33 0.000 ***

PLANT HEIGHT 113.33 95.00 0.001 ** 138.33 106.67 0.003 **

SPIKEL ENGTH 14.25 8.67 0.000*** 11.58 9.67 0.024 *

SEED NO./SPIKE 55.33 33.00 0.007 ** 44.67 33.67 0.043 *

TILLER NO. 11.00 9.33 0.038 * 11.17 10.33 0.13ns

TKW 37.30 45.47 0.012 * 39.67 50.57 0.003 **

TEST WEIGHT 65.477 77.80 0.001 ** 77.83 82.30 0.027 *

***significant at p<0.001, ** significant at p <0.01,* significant at p <0.05, ns non-significant

The distribution pattern of the high and medium abundance protein spots was similar between

the two lines at different growth stages (seedling, tillering, AR2 and AR10) (Figure 5.1 and

5.2). The numbers of detected protein spots in line A were 380±8, 304±11, 369±23 and

349±20 at seedling, tillering, AR2 and AR10 growth stages, respectively. For line B, they

were 349±28, 326±26, 357±10 and 411±11 at seedling, tillering, AR2 and AR10 growth

stages, respectively. The results revealed that the total numbers of protein spots in the

tillering stage were less than the other growth stages for both lines. However, the AR10 stage

of line B had the highest number of expressed proteins among all stages of the two lines.

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Figure 5.1 2D electrophoresis gels of wheat leaf proteome of Line A (left panel) and Line B (right panel) at seedling (top) and tillering (bottom) growth stages, respectively.

4 pH 7 4 pH 7

15 KDa

20 KDa

25 KDa

37 KDa

50 KDa

75 KDa

15 KDa

20 KDa

25 KDa

37 KDa

50 KDa

75 KDa

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Figure 5.2 2D electrophoresis gels of wheat leaf proteome of Line A (left panel) and Line B (right panel) at AR2 (top) and AR10 (bottom) growth stages, respectively.

5.3.3 Quantitative analysis of the differentially expressed proteins between the two

lines

The prime objective of this study was to identify the differentially expressed proteins, which

are potentially related to the variation of flowering dates. Out of the 224 selected spots, a total

of 189 unique proteins were identified. Among these, 87 proteins were appeared as

quantitatively differentially expressed (at 1% significant level) between the two lines across

the four stages. A total of 48 proteins were unique to the line B, of which fourteen, seven,

fifteen and twelve proteins have been isolated from seedling, tillering, AR2 and AR10 stages,

respectively. On the other hand, 28 proteins were unique to line A, of which nine, five, ten

4 pH 7 4 pH 7

15 KDa

20 KDa

25 KDa

37 KDa

50 KDa

75 KDa

15 KDa

20 KDa

25 KDa

37 KDa

50 KDa

75 KDa

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105

and four spots were isolated from seedling, tillering, AR2 and AR10 stages, respectively

(Table 5.3).

In both the lines, a number of protein spots were highly differentially expressed across all

four stages. At seedling, tillering, AR2 and AR10 stages, eight, twelve, fourteen and ten

proteins showed >3-fold higher expression in line A than line B (Table 5.3). In contrast, four,

ten, thirteen and sixteen spots were highly expressed (>3-fold) in line B compared to line A

during those growth stages, respectively (Table 5.3).

Figure 5.3 Enlarged views of 2DE gel cuttings showing the presence and absence leaf protein in two lines (Line A at left panel and Line B at right panel) at different stages

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5.3.4 Protein identification and functional distribution

A total of 224 spots were selected for protein identification based on i) high abundancy

during all the stages in both the lines, ii) expressed differentially by three folds and iii) highly

significance of variance (1% level). Out of the total of 224 spots, 162 were identified in the

protein database (Table 5.1). Stringent criteria of MASCOTTM protein score no less than 100

and minimum 15% protein coverage was followed as the threshold of protein identification.

Irrespective of growth stages and plant genotypes, a highly abundant horizontal streak around

50 kDa with multiple pl was identified as RuBisCo large subunit and/or Ribulose proteins,

which masked a few other low abundant proteins. Multiple proteins were identified from a

single spot in a few cases (spot 128, 179, 198 and 213) where the RuBisCo masking was

occurred. The identified proteins were categorized into several groups based on their putative

biological function using the Mercator web application (Lohse et al., 2014) (Figure 5.4).

Results revealed that one fourth of the identified proteins were involved in the process of

photosynthesis followed by the group which determines protein functionality such as protein

folding, synthesis and degradation. Photosynthesis group of protein included a large number

of RuBisCo proteins and the proteins like ATP synthase, oxygen evolving enhancer, glycine

cleavage system and a few uncharacterized proteins. Approximately one fourth of the

proteins were constituted as stress related proteins (9%) glycolysis (7.5%) and different

metabolism related proteins (9%). Approximately 7% proteins were involved in RNA

synthesis and signalling where few of them have a role in the process of phenological

development. This group included low temperature responsive RNA binding protein, Ps16

protein, cold shock domain protein, Obg-like ATPase and two uncharacterized proteins

previously found in maize and wheat. However, approximately 5% proteins could not be

assigned to any functional group due to lack of information in the available databases.

Protein-protein interactions analysis was carried out using the software “STRING 10”

(Szklarczyk et al., 2015) from differentially expressed proteins identified (Table 5.3) together

with the proteins that have known roles in flowering. The identified proteins were blasted

against Arabidopsis TAIR 10 protein databases to obtain functions of their Arabidopsis

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107

Figure 5.4 Classifications of the identified proteins based on putative biological functions

orthologues. The biological pathways and molecular functions were predicted accordingly by

using “Cytoscape plugin BiNGO” (Maere, Heymans, and Kuiper 2005). Figure 5.6 shows

the functional links between the proteins and their expressions. The highly interacting

proteins are mostly involved in energy metabolism. The PPI networks also revealed that low

temperature responsive RNA binding protein mostly interacts with the photosynthesis and

photosystem related proteins. On the other hand, cold shock domain proteins mostly interact

with energy metabolism and stress related proteins.

The identified proteins were also analysed with BiNGO to get the over or under represented

GO categories of biological process and molecular functions of the differentially expressed

proteins between two lines (Figure 5.5). The analysis results showed that most significantly

overrepresented biological pathways are response to stress (P=3.55e-22). The other major

groups are response to metal ion (P=5.3e-19), temperature stimulus (P=1.76e-17), cold

(P=3.95e-16), osmotic stress (P=2.69-e9), photosynthesis and light reaction (P=8.35e-3) and

carbon fixation (P=3.82e-3) (Appendix D). Translation of elongation factor activity (1.33e-

06) was the most highly enriched molecular functions followed by copper ion binding (4.72e-

06) and oxidoreductase activity (2.66e-04).

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Table 5.2 Identified protein in wheat leaves by MS

SPOT NO. UNIPORT ID PROTEIN NAME SPECIES COVERAGE% pI Mr SCORE Upregulation

C1 METABOLISM

135 A0A077S2R7 Chromosome 3B, genomic scaffold T. aestivum 22 6.42 110974 699

134 M7Z1X3 Glycine dehydrogenase T. urartu 30 6.01 85808 712

38 A0A0C4BJE5 Serine hydroxymethyltransferase T. aestivum 23 8.18 56087 447

198 A0A0C4BJE5 Serine hydroxymethyltransferase T. aestivum 20 8.18 56087 354 AMINO ACID METABOLISM

192 D6QX85 Cysteine synthase T. aestivum 36 6.35 40163 391 191 M8CF13 Cysteine synthase A. tauschii 63 5.63 34051 953 18 W5CM54 Cysteine synthase T. aestivum 65 5.51 32250 955 120 W5CM54 Cysteine synthase T. aestivum 59 5.51 32250 823 181 W5CM54 Cysteine synthase T. aestivum NAa NAa 32250 1051

49 M7ZHT1 5methyltetrahydropteroyltriglutamatehomocysteine methyltransferase

T. urartu 31 5.74 84499 1017

187 M7ZHT1 5methyltetrahydropteroyltriglutamatehomocysteine methyltransferase

T. urartu 33 5.74 84499 1156

157 A0A077RWS5 Sadenosylmethionine synthase T. aestivum 36 5.61 42821 566 92 A0A077RWS5 S-adenosylmethionine synthase T. aestivum 46 5.61 42821 616

93 W5H103 S-adenosyl methionine synthase T. aestivum 10 5.42 39387 129

157 A0A077RWS5 Sadenosylmethionine synthase T. aestivum 36 5.61 42821 566

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109

SPOT NO. UNIPORT ID PROTEIN NAME SPECIES COVERAGE % pI Mr SCORE Upregulation

158 A0A077RWS5 Sadenosylmethionine synthase T. aestivum 21 5.61 42821 270 194 M7YK23 S‐adenosylmethionine synthase T. urartu 46 5.51 42739 670 ATP SYNTHESIS

17 M8B1Z5 Lactoylglutathione lyase A. tauschii 13 5.43 32547 164

105 N1QXW8 NADH‐ubiquinone oxidoreductase 75 kDa subunit A. tauschii 9 5.06 76165 231 LINE B

5 R7WAZ3 ATP synthase subunit delta’, mitochondrial A. tauschii 28 5.21 23715 114 LINE B

81 W5AFY5 Uncharacterized protein T. urartu 16 5.85 23330 107 10 W5AWB7 Uncharacterized protein T. aestivum 31 5.87 19518 251 CELL DIVISION

99 M4PS99 Tubulin Beta 5 C. arbutifolia 17 4.77 49751 484 LINE B

304 Q9ZRB1 TUBULIN BETA T. aestivum 39 4.68 50232 909

50 P33631.1 Tubulin beta2 Anemia phyllitidis 23 4387 46187 421

168 E6Y289 Translationally controlled tumor protein T. aestivum 32 4.55 18766 365 LINE A

95 M0Z4U7 Uncharacterized protein H. vulgare 33 6.23 36939 328 LINE A

100 K4AA03 Uncharacterized protein Saria italica

26 4.77 49845 608

208 W5B9Q2 Uncharacterized protein T. aestivum 11 6.14 30571 103

CHO METABOLISM

16 M7ZY47 Putative glucose-6-phosphate 1-epimerase

T. urartu 20 5.72 34402 276

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110


184 M7Y857 Lidonate 5dehydrogenase T. urartu 23 6.27 37350 361 LINE B

84 M8C200 PHOSPHO GLYCOLATE PHOSPHATGE A. tauschii 40 4.93 33384 500 LINE B

FERMENTATION

164 M7ZUG3 Aldehyde dehydrogenase family 2 member B7 T. urartu 25 5.84 59570 541

GLYCOLYSIS

109 B6T3P9 Enolase Zea mays 27 5.59 48100 614 30 M4QAZ4 Enolase T. aestivum 48 5.59 48017 745 LINE A

110 M4QAZ4 Enolase T. aestivum 42 5.59 48017 718 26 W5HZ47 Fructose-bisphosphate aldolase T. aestivum 26 6.38 37868 363 LINE B

62 W5DTC2 Fructose bisphosphate Aldolase T. aestivum 36 5.94 41946 719

63 W5G4A2 Fructose‐bisphosphate aldolase T. aestivum 40 6.08 41609 641

124 F2CR16 Fructose-bisphosphate aldolase H. vulgare 19 6.06 37874 479

129 W5G4A2 Fructose-bisphosphate aldolase T. aestivum 36 6.08 41609 533

153 W5G4A2 Fructose‐bisphosphate aldolase T. aestivum 32 6.08 41609 461

231 F2ELD1 Fructose bisphosphate aldolase H. vulgare 43 6.78 41931 622

123 N1R101 Ferredoxin‐‐NADP reductase A. tauschii 34 8.48 39725 555

177 A0A096UTL2 Glyceraldehyde‐3‐phosphate dehydrogenase T. aestivum 26 6.40 36555 222

193 W5HZ47 Fructose‐bisphosphate aldolase dehydrogenase T. aestivum 10 168 37868 168

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27 F2D714 Glyceraldehyde 3 phosphate Dehydrogenase H. vulgare 32 7.6 42673 430 LINE A

96 F2CWJ3 glyceraldehyde-3-phosphate dehydrogenase H. vulgare 28 6.03 46843 678

121 W5ATV6 Glyceraldehyde‐3‐phosphate dehydrogenase

T. aestivum NAa NAa 43344 339

203 W5ATV6 Glyceraldehyde‐3‐phosphate dehydrogenase

T. aestivum NAa NAa 43344 503

156 F2CWJ3 Glyceraldehyde3phosphate dehydrogenase

H. vulgare 30 46843 6.03 769

224 F2CS69 Pyrophosphate‐‐fructose 6‐phosphate 1‐phosphotransferase subunit beta

H. vulgare 20 6.22 60583 445

212 Q43772 UC/UTP glucose1phosphate Uridylyltransferase

H. vulgare 30 5.2 51612 568/

51 M7ZR60 Phosphoglycerate kinase T. urartu 54 5.42 48886 1227

128 M0Y9H9 Phosphoglycerate kinase H. vulgare 11 5.79 42135 156

152 M0Y9H9 Phosphoglycerate kinase H. vulgare 42 5.79 42135 578 LINE A

210 M7ZR60 phosphoglycerate kinase T. urartu 59 5.42 31402 972

202 M7ZR60 Phosphoglycerate kinase T. urartu 59 5.42 48886 1264

52 W5ACW3 Phosphoglycerate kinase T. aestivum 28 4.96 36516 211

19 W5GJ80 Phosphoribulokinase T. aestivum 49 5.72 45049 837

59 W5GJ80 Phosphoribulokinase T. aestivum 35 5.72 45049 551

32 W5D898 Uncharacterized protein T. aestivum 23 5.45 48150 282

33 W5D898 Uncharacterized protein T. aestivum 23 5.45 48150 282

127 W5FPN2 Uncharacterized protein T. aestivum 34 5.49 48056 693

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LIPID METABOLISM

250 A0A077RX33 Chromosome 3B, genomic scaffold T. aestivum 18 5.24 33051 186 MISC

207 W5D4D9 Chromosome 3B, genomic scaffold T. aestivum 53 5.65 23369 359

130 Q8RW03 Glutathione transferase T. aestivum 26 6.35 24994 231 9 Q8RW03 Glutathione transferase T. aestivum 22 6.35 24994 209 LINE B

70 M8AW52 Quinone oxidoreductaselike protein T. aestivum 50 5.48 32317 687

N- METABOLISM

58 G1FFN4 Glutamine synthetase T. urartu 41 5.75 46673 876 67 M0WRL0 Glutamine synthetase H. vulgare 46659 554 180 G1FFN4 Glutamine synthetase T. durum 18 5.75 46673 359 LINE B

NUCLETIDE METABOLISM

201 W5AGK9 Nucleoside diphosphate kinase T. aestivum 39 6.3 16694 320 15 Q5KSL5 N-acetyl glutamate kinase Rice 17 6.08 30050 103 OPP

160 M8ADU2 Putative oxidoreductase GLYR1 T. urartu 52 7.68 27535 380 LINE A

213 M8APV9 Transketolase T. urartu 42 5.36 68425 801

104 M8APV9 Transketolase, chloroplastic T. urartu 49 5.36 68425 794

PROTEIN FATE

178 M8A2B7 20 kDa chaperonin T. urartu 68 5.16 20909 389

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113


204 M8AVR4 20 kDa chaperonin, chloroplastic A. tauschii 59 7.72 25857 700 150 N1QS62 20 kDa chaperonin, chloroplastic A. tauschii 36 5.8 27136 282 LINE A

118 B6T1G9 Elongation factor 1‐beta Zea mays 11 4.55 23383 98 29 F2DXB7 Elongation factor Tu H. vulgare 50 5.99 48262 821 LINE A

103 N1QVD8 Elongation factor G A. tauschii 41 4.97 78732 930 LINE A

197 N1QVD8 Elongation factor G A. tauschii 43 4.97 78732 1148 211 W5F5B3 Elongation factor T. aestivum 25 5.06 105031 896 22 W5G661 Elongation factor T. aestivum 56 4.83 34615 989 23 W5G661 Elongation factor T. aestivum 13 4.83 34615 141 185 W5G661 Elongation factor T. aestivum 52 4.83 34615 755 LINE B

151 Q3S4H9 Eukaryotic translation initiation factor T. aestivum 16 5.76 17371 148

186 M8A8P8 Heat shock cognate 70 kDa protein T . urartu 51 5.14 71000 1235 LINE A

188 M0XLF9 Peptidyl‐prolyl cis‐trans isomerase H. vulgare 11 7.48 24622 143

53 F2CUJ5 Predicted protein H. vulgare 19 5.08 35325 207

195 F2D121 Predicted protein H. vulgare 25 5.75 45657 313

149 M8BTW5 Proteasome subunit alpha type A. tauschii 51 5.58 25784 605 34 C0P530 Putative TCP1 Zea mays 30 5.42 61741 886

183 F2D6W5 Ribosomal protein H. vulgare 36 8.25 37448 528

179 M0WYK5 40S ribosomal protein H. vulgare 27 4.87 32902 407

138 M8BN49 50S ribosomal protein L122 A. tauschii 47 5.35 21824 657

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182 N1QPY8 50S ribosomal protein L4, A. tauschii 32 5.27 31104 454

90 A0A067G4Y2 Uncharacterized protein 6 5.35 46771 69

86 1GV34 Uncharacterized protein B. distachyon 20 5.4 37005 362

8 T1L8G1 Uncharacterized protein T. urartu 34 5.24 26506 221 LINE B 83 T1L8G1 Uncharacterized protein T. urartu 43 5.27 26506 409 LINE A

PHOTOSYNTHESIS

229 BAJ21369.1 ATP synthase CF1 alpha subunit, P. cristatum NAa NAa 35642 138

57 A0A075VWW4 ATP synthase subunit beta, chloroplastic T. aestivum 70 5.06 53851 1341

176 A0A075VWW4 ATP synthase subunit beta T. timopheevii 74 5.06 53851 1422

165 A0A075VWZ3 ATP synthase subunit alpha, chloroplastic

T. turgidum 25 6.11 55261 555

174 A0A075VWZ3 ATP synthase subunit alpha T. turgidum

24 6.11 55261 429

214 A0A075VWZ3 ATP synthase subunit alpha T. turgidum 43 6.11 55261 1132

144 A0A075VYV9 ATP synthase epsilon chain, chloroplastic A. sharonensis 82 5.20 15208 535

98 A0A075W3Y0 ATP synthase subunit beta T. aestivum

64 5.11 53824 1207 LINE B

2 W5AZN1 Cytochrome b6‐f complex iron‐sulfur subunit

T. aestivum 28 8.47 24114 412 LINE B

136 W5AZN1 Cytochrome b6f complex ironsulfur subunit

T. aestivum 31 8.47 24114 587

45 M7ZNG9 Glyceraldehyde‐3‐phosphate dehydrogenase T. aestivum 23 6.03 46873 598

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115


140 M7Z6F5 Glycine cleavage system H protein T. urartu 55 5.06 17287 225

141 M8C0G1 Glycine cleavage system H protein A. tauschii 62 5.06 17317 316

142 M7Z6F5 Glycine cleavage system H protein T. urartu 55 5.06 17287 225

139 M8C0G1 Glycine cleavage system H protein A. tauschii 67 5.06 17317 391

79 M7YV65 Oxygen‐evolving enhancer protein T. urartu 61 8.94 25486 588

7 M7YV65 Oxygen‐evolving enhancer protein T. urartu 56 NAa 25486 835 LINE B

68 N1R4X3 Oxygen‐evolving enhancer A. tauschii 48 5.75 34407 715

69 N1R4X3 Oxygen‐evolving enhancer A. tauschii 49 5.75 34407 669

85 N1R4X3 Oxygen-evolving enhancer A. tauschii 38 5.75 34407 477

171 N1R4X3 Oxygen-evolving enhancer A. tauschii 48 5.75 34407 660 LINE B

41 N1R4X3 Oxygen evolving enhancer A. tauschii 34 5.75 34407 527

97 C6YBD7 Ribulose-1,5-bisphosphate carboxylase activase T. aestivum 54 6.52 400034 830

114 J7F4U7 Ribulose bisphosphate carboxylase large chain E. canadensis 19 5.96 52788 285

205 M8BIS3 Ribulose bisphosphate carboxylase small chain A. tauschii 50 5.85 14860 263

113 Q539X1 Ribulose bisphosphate carboxylase large chain

NAa NAa 53064 280

126 Q539X0

Ribulose-1,5-bisphosphate carboxylase large subunit

Psathyrostachys fragilis

25 6.13 53067 581 LINE B

143 F2D3J2

Ribulose bisphosphate carboxylase small chain

H. vulgare

39 5.88 15782 225 LINE B

12 W5FC82 Ribulose bisphosphate carboxylase small chain

T. aestivum 34 6.58 15876 206

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116


206 W5FC82 Ribulose bisphosphate carboxylase small chain

T. aestivum 43 6.58 15876 259

40 A0A075VXE0 Ribulose bisphosphate carboxylase large A. speltoides NAa NAa 52817 NAa

42 A0A075VXE0 Ribulose bisphosphate carboxylase large chain

A. speltoides NAa NAa 52817 411 LINE B




A. speltoides NAa NAa 52817 437


A. speltoides NAa NAa 52817 NAa


A. speltoides NAa NAa 52817 NAa

61 A0A0A0YVQ2 Ribulose bisphosphate carboxylase/oxygenase activase A T. aestivum

NAa NAa 42027 1010

116 Q8MCX8 Ribulose‐1,5‐bisphosphate carboxylase/oxygenase

NAa NAa 49916 108

173 A0A0F7C930 Ribulose‐1,5‐bisphosphate carboxylase/oxygenase large subunit

NAa NAa

131 D2KAG0 Uncharacterized protein NAa NAa

28633 127

161 M1PLK2 Ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit Crab grass 29 6.33 47777 483



72 M7ZAC1 Ribulose bisphosphate carboxylase/oxygenase activase A T.urartu 51 6.90 50893 964

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117


71 M8AYP1 Ribulose bisphosphate carboxylase/oxygenase activase A, hl l i

A.tauschii 47 6.90 50907 973

13 M8BIS3 Ribulose bisphosphate carboxylase small chain A.tauschii 50 5.85 14860 345

228 P11383 Ribulose bisphosphate carboxylase large chain

T. aestivum 25 6.22 52817 479

37 P11383 Ribulose bisphosphate carboxylase large chain

T. aestivum 20 6.22 52817 523


Psathyrostachys fragilis 20 6.13 53067 532


Psathyrostachys fragilis 22 6.13 53067 504

89 A0A078BQY4 RUBISCO activase alpha T. aestivum NAa NAa 44554 211

222 A0A078BQY4 RUBISCO activase alpha T. aestivum NAa NAa 44554 1022

21 A0A078BR12 RUBISCO activase beta T. aestivum 56 5.8 41427 1023 LINE A

25 W5E091 Uncharacterized protein T. aestivum 26 6.26 37685 415 LINE A

60 W5DSM4 Uncharacterized protein A.tauschii 46 6.90 50893 627

88 M0YMH1 Uncharacterized protein REDOX

66 B6TDA9 2Cys peroxiredoxin Setaria italica 24 5.97 28032 203 11 C9EF64 Dehydroascorbate reductase T. aestivum 59 5.88 23343 554 133 M8BMC6 Putative Lascorbate peroxidase A.tauschii 34 6.73 57407 733 189 M8C1W9 Lascorbate peroxidase 2 A. tauschii 41 5.1 27642 408 137 Q96123 Superoxide dismutase T. aestivum 47 5.35 20310 421

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118


80 O81480 Thioredoxin peroxidase Secale cereale 28115 493 82 Q8RW03 Glutathione transferase T. aestivum 28 6.35 24994 242 LINE A

4 W5BGL0 Uncharacterized protein T. aestivum 29 6.33 28269 373 LINE A

223 W5BUR9 Uncharacterized protein T. aestivum 40 8.57 37514 458 209 W5G637 Uncharacterized protein T. aestivum 31 6.72 32429 460 RNA SYNTHESIS

78 Q75QN8 Cold shock domain protein T. aestivum 22 5.73 21530 199 LINE A

6 Q75QN9 Cold shock domain protein T. aestivum 21 5.62 19214 132 LINE B

169 Q8LPA7 Cold shock protein T. aestivum 37 5.74 21370 147

1 M4VSR0 Low temperatureresponsive RNAbinding protein

T. aestivum 29 5.54 15874 125 LINE B

145 M4VSR0 Low temperatureresponsive RNAbinding

T. aestivum 42 5.54 15874 355

55 O23798 Ps16 protein T. aestivum 34 4.55 31829 432 119 O23798 Ps16 protein T. aestivum 21 4.55 31829 109 148 O23798 Ps16 protein T. aestivum 51 4.55 31829 813 115 C3V134 cp31BHv T. aestivum 77 4.8/5 18993 529 132 W5GDG4 Uncharacterized protein T. aestivum NAa NAa 27998 220

56 M0UYF5 Uncharacterized protein H. vulgare NAa NAa 30661 87

SIGNALING

87 W5FU56 Obg-like ATPase A. tauschii 16 5.04 40327 270

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119


S ASSIMILATION

196 T2DNG9 ATP sulfurylase 2‐like protein bean NAa NAa 40009 244 SECONDARY MET

14 A0A0A7AMA3 Chalcone-flavonone isomerase family protein T. aestivum 13 5.01 23731 107

35 Q1XIR9 4hydroxy7methoxy3oxo3,4dihydro2H1,4benzoxazin2yl glucoside b D l id 1 hl l i

T. aestivum 24 6.55 64467 519

36 Q1XIR9 4hydroxy7methoxy3oxo3,4dihydro2H1,4benzoxazin2yl glucoside b D l id 1 hl l i

T. aestivum 24 6.55 64467 519 LINE A

107 Q1XIR9 4‐hydroxy‐7‐methoxy‐3‐oxo‐3,4‐dihydro‐2H‐1,4‐benzoxazin‐2‐yl

T. aestivum 17 6.55 64467 293 LINE A

108 Q1XIR9 4‐hydroxy‐7‐methoxy‐3‐oxo‐3,4‐dihydro‐2H‐1,4‐benzoxazin‐2‐yl

T. aestivum 20 6.55 64467 499 LINE A

230 W5BAF7 Uncharacterized protein T. urartu NAa NAa 34750 97 STRESS

166 W5CXB0 Chromosome 3B, genomic scaffold T. aestivum 17 5.69 73254 357

219 M7YZ42 Endoplasmin‐like protein T. urartu 23 4.91 99528 383

170 Q6TM44 Germin-like protein Zea mays 16 6.02 21873 124

221 Q0GR10 Germinlike protein H. vulgare 16 5.68 21801 157

102 C7ENF7 Heat‐shock protein T. aestivum 27 5.06 73496 374 175 C7ENF7 heat shock protein T. aestivum 50 5.06 73496 1438 LINE A

48 C7ENF7 heat shock protein T. aestivum 45 5.06 73496 1549 47 F2DY59 HEAT SHOCK protein H. vulgare 36 4.91 88221 1253

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120


101 M7ZCX6 heat shock protein T. urartu 21 4.79 66287 599 31 M8CPM1 Heat shock protein A. tauschii 34 5.53 72804 1103 LINE B

218 Q2TN84 USP family protein T. aestivum 49 5.78 17853 261 225 A0A0D3H139 Uncharacterized protein Oryza barthii

15 6.01 21847 173

TCA CYCLE

28 A3KLL4 Malate dehydrogenase T. aestivum 27 5.75 35463 301 94 A3KLL4 Malate dehydrogenase n T. aestivum 34 5.75 35463 356 155 R7WD67 Malate dehydrogenase A. tauschii 38 5.26 34646 372 LINE A

215 R7WAR0 Isocitrate dehydrogenase A. tauschii

34 5.99 45841 721

24 W5GHT5 Isocitrate dehydrogenase [NAD] subunit, mitochondrial T. aestivum 19 6.57 39578 283 LINE B

VIT METABOLISM

172 Q69LA6 pyridoxal biosynthesis protein PDX1 Cucumis melo 16 6.11 33536 168

190 M8AMX9 Thiamine thiazole synthase T. urartu 28 5.57 36933 391 LINE B

154 A0A096UMG6 Uncharacterized protein T. aestivum 18 6.19 35676 442 TETRAPYRROLE SYNTHESIS'

46 W5DYL0 Uroporphyrinogen decarboxylase T. aestivum 36 6.56 42923 409

20 N1QZ11 Magnesium-chelatase subunit chlI, chloroplastic

A. tauschii 29 4.98 35778 412 LINE A

NOT ASSAIGNED

122 A0A0C4B2Z2 Uncharacterized protein A. tauschii 34 5.99 45841 721

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121

Notes:

a Unable to extract information due to system error in MASCOT site


3 A7VL25 late embryogenesis abundant protein T. aestivum 30 5.08 18314 278 Line B

54 F2CVK9 Predicted protein H. vulgare 28 6.67 32652 304 64 M0WTH3 Uncharacterized protein H. vulgare 50 8.63 27220 854 65 M0X0Q8 Uncharacterized protein H. vulgare 20 5.08 15268 157 226 M8B6Q5 Uncharacterized protein A. tauschii 14 4.51 48192 162 91 N1QPA0 Uncharacterized protein A. tauschii 20 6.38 31487 156 146 N1QYH0 60S acidic ribosomal protein A. tauschii 48 4.37 11580 344 106 M8A0V0 Dihydroxy‐acid dehydratase T. urartu 15 5.74 54936 291 167 T1LTA5 Uncharacterized protein T. urartu 13 4.68 26038 181 Line A

125 A0A0C4B2Z2 Uncharacterized protein T. aestivum NAa NAa 36637 304 159 A0A0C4B2Z2 Uncharacterized protein T. aestivum 34 5.88 36637 391

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122

Figure 5.5 Protein-protein interaction network analysed by STRING including all differentially expressed proteins. Different line colours represent the types of evidence used in predicting the associations: gene fusion (red), neighbourhood (green), co-occurrence across genomes (blue), co-expression (black), experimental (purple), association in curated databases (light blue) or co-mentioned in PubMed abstracts (yellow). The densely clustered protein nodes include proteins involved in photosynthesis. Protein names against protein ID have been presented in the following table.

B A

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123

Protein ID Protein names Protein ID Protein names Protein ID Protein names ALDH2B4 Aldehyde dehydrogenase family 2

b B7 GSTU19 Glutathione trans ferase emb2726 Elongation factor

AT1G12000

Pyrophosphate‐‐fructose 6‐phosphate 1‐phosphotransferase subunit beta

HSP70 Heat shock cognate 70 kDa protein EULS3 WHEAT Uncharacterized protein

AT1G47480

Chromosome 3B, genomic scaffold IDH-V Isocitrate dehydrogenase [NAD] b i i h d i l

F28P22.20 Germin like protein AT1G56190

3‐phosphoglycerate kinase LOS2 Enolase GAPA-2 Glyceraldehyde‐3‐phosphate d h d AT2G3766

0 TRIUA Uncharacterized protein MBD10 Uncharacterized protein GAPB Glyceraldehyde‐3‐phosphate

d h d AT2G38740

Uncharacterized protein mMDH1 Malate dehydrogenase GLDP2 Glycine dehydrogenase AT3G15670

late embryogenesis abundant protein MTHSC702

Heat shock 70 kDa protein GLYR1 Putative oxidoreductase GLYR1 AT3G17020

USP family protein MTO3 S-adenosylmethionine synthase TL29 THYLAKOID LUMENAL 29 KD AT3G2394

0 Dihydroxy‐acid dehydratase OASA1 Cysteine synthase TUB2 Tubulin beta2

AT3G44590

60S acidic ribosomal protein P40 40S ribosomal protein TUB6 TUBULIN BETA AT3G60750

Transketolase PETC Cytochrome b6‐f complex iron‐sulfur b i

TUB8 Beta tubulin 5 AT4G02930

Elongation factor Tu PGK Phosphoglycerate kinase UGP2 UC/UTPglucose1phosphate U id l l f At5g06290 Uncharacterized protein PGL34 Predicted protein GRP7 Low temperature responsive RNAbi di i AT5G1211

0 Elongation factor 1‐beta PGLP1 Phospho Glycolate Phosphatge GS2 Glutamine synthetase

AT5G47030

ATP synthase subunit delta', i h d i l

PHB3 Uncharacterized protein CPN20 20 kDa chaperonin, hl l i AT5G5197

0 Lidonate 5dehydrogenase PSBO2 Oxygen-evolving enhancer protein 1,

hl l i CPN60B Putative TCP1

AT5G66530

Putative glucose-6-phosphate 1-i

PSBP-1 Oxygen‐evolving enhancer protein 2, hl l i

CSDP1 Cold shock domain protein ATGSTF13

Chromosome 3B, genomic scaffold RABE1b Elongation factor EMB1467 NADH‐ubiquinone id d 75 kD b i ATPA ATP synthase subunit alpha RBP31 Uncharacterized protein SHD Endoplasmin‐like protein

ATPQ Uncharacterized protein RBCS1A Ribulose bisphosphate carboxylase ll h i

TAPX Uncharacterized protein BGLU13 4hydroxy7methoxy3oxo3,4dihydro2

H1,4benzoxazin2yl glucoside b l id 1 hl l i

RBCL Ribulose‐1,5‐bisphosphate carboxylase/oxygenase large subunit

TCTP Translationally controlled tumor protein

BGLU32 Uncharacterized protein RBP31 Ps16 protein THI1 Thiamine thiazole synthase CDSP32 Uncharacterized protein RCA RUBISCO activase alpha cpHsc70-2 Heat‐shock protein cICDH Isocitrate dehydrogenase RGP3 Uncharacterized protein SCO1 Elongation factor G

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Figure 5.6 Protein networks generated by Bingo A) Biological pathway and B) Molecular function. GO categories of TAIR homologous proteins are presented for wheat. The size of the node is related to the number of proteins and the colour intensity represents the p-value for the statistical significance for overrepresented GO term.

A B

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125

Table 5.3 Number of the differentially expressed proteins between the lines across four growth stages.

Description Stages

Seedling Tillering AR 2 Ar 10

Higher expression in Line A 8 12 14 10

Higher expression in Line B 4 10 13 16

Appearance only in Line A 9 5 10 4

Appearance only in Line B 14 7 15 12

5.3.5 Expression profiles of the proteins with putative function in flower development

and growth

Cold shock proteins have been identified in three spots (78, 6 and 169 in Figure 5.) where

two of the spots were upregulated in line A during AR10 and AR 2 stage. Low temperature

responsive RNA binding protein have been identified and observed to express higher in line

B (spot 1 in Fig. 5.2). Ribosomal proteins that also play a role in the flower development

were identified in three spots (149, 182 and 183 in Figure 5.1).

Oxygen evolving enhancer protein were identified in seven spots, of which two spots (7 and

171) exhibited higher expression in line B. Similarly, ATP synthase proteins were identified

in a number of spots and found to express higher in line B for one spot (spot 98). Proteins

related to energy metabolism like enolase, phosphoglycerate kinase, malate dehydrogenase

and Glyceraldehyde-3-phosphate were higher expression in line A (spots 30, 152, 155, and

27). On the other hand, Fructose-bisphosphate aldolase and Isocitrate dehydrogenase were

expressed higher in line B at AR 10 stage (spots 26 and 24).

Line B had high expression for Glutamine synthase, lidonate-5-dehydrogenase, and phospho-

glycolate phosphatase at tillering stage (spots 180, 184 and 84), which are the important

proteins for carbohydrate metabolism. Heat shock proteins were upregulated in Line A and

Line B during tillering and AR 10 stages (spots 175 and 31). Significant difference between

the two lines was observed in the stress related protein expression like cys-peroxiredoxin, L-

ascorbate-peroxide and Endoplasmin (spots 4, 133, and 219).

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5.4 Discussion

The correct timing of flowering maximizes the reproductive success of plants (Bernier 1988).

Thus, heading time is an important yield determinant of wheat and one of the major research

focuses in the current climate changing scenario of the world. Several vernalization,

photoperiod and earliness per se genes have been identified to control the flowering time of

wheat. Genetic studies on the model plant Arabidopsis thaliana revealed that flowering is

controlled by vernalization, photoperiod, the autonomous pathway and the gibberellin acid

(GA) pathway, where vernalization and photoperiod pathways intricate external signals while

the autonomous and GA pathways act independently of environmental stimulus (Mouradov et

al., 2002, Komeda 2004). A number of factors such as nutrient, heat, drought and oxidative

stress, post-transcriptional modification by microRNA and RNA binding proteins have been

reported in regulating those pathways (Simpson and Dean 2002, Simpson et al., 2004,

Lokhande et al., 2003). To get insights in the dynamics of the wheat leaf proteome during

phenological development, a total of 220 protein spots were excised from two DH wheat

lines differing for heading time. The identification of large number of Ribulose and/or

RuBisCo proteins with multiple pI and Mr revealed the high abundancy of that protein at all

the growth stages irrespective of plant genotype. A number of proteins were identified in

multiple spots with different pI and Mr. These might be due to the cases of alternative

splicing or post translational modification events (Agrawal et al., 2008) or protein

degradation during sample preparation (Zhao et al., 2005b).

It is noteworthy to mention that the differently expressed proteins related to vernalization or

photoperiod genes had not been identified in this study. This might be due to their low

abundance and also a drawback of 2DE technique to identify low abundance protein.

Previous studies reported that a large percentage of important proteins such as transcription

factors, signal transduction proteins, and receptors occur in low abundance levels and 2 DE is

of limited use to detect such kind proteins during the analysis of total proteins from leaf

sample (Mesquita et al., 2012). Because RuBisCo is the high abundant protein in leaf sample

that represents 50% of total protein in C3 plants (Feller, Anders, and Mae 2008). Thus the

most predominant proteins like RuBisCo mask the neighbouring low abundant proteins, make

it difficult to isolate and identify, and thereby impede the resolution of 2 DE by liming

loading quantity (Corthals et al., 2000, Cho et al., 2008, Xi et al., 2006, Shaw and Riederer

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2003). Detection of low abundance proteins can be enhanced by depletion of RuBisCo by the

use of specific antibodies and/or pre fractionation targeting specific proteomes although

complete removal is not desirable as it may also eliminate many of the low-abundance

proteins which are associated with the high-abundance counterparts (Garbis, et al., 2005,

Gorg, et al., 2004). Furthermore, isoberg tags for relative and absolute quantification

(iTRAQ) based quantitative proteomic approach could be employed asthat can

simultaneously quantify and identify proteins (Wiese et al., 2007). However, Gene ontology

annotation of the identified proteins for biological function and protein-protein interaction

networks provide an overall insight about the involvement of different proteins in the

phenological development.

5.4.1 Proteins involved in photosynthesis and energy metabolism

A large number of protein spots have been identified as playing roles in photosynthesis, of

which the majority is RuBisCo protein. This protein is the primary enzyme in photosynthesis

involved in carbon dioxide fixation and oxidative fragmentation of the pentose substrate in

the photorespiration process (Bahrman, et al., 2004). In the current study, RuBisCo was

expressed continuously throughout the growth stages in both lines but upregulated in line B at

some stages inferred more photosynthate for its faster growth. In rice RuBisCo was found to

be expressed more when plant needs more in situ photosynthesis (Murchie et al., 2002).

Moreover, an oxygen evolving enhancer protein exhibited higher expression in line B. This

protein acts in photosystem II as a manganese-stabilizing protein involved in PS II core

stability and catalysing the splitting of water (Zadražnik et al., 2013). The increased

expression of this protein is related to higher photosynthetic activity (Bahrman, et al., 2004).

ATP synthase alpha and beta were identified in a number of spots and found to differ

between the lines. ATP synthase is a key enzyme required for the photosynthesis pathway

for oxidative phosphorylation and also related to energy metabolism (Zadražnik et al., 2013,

Bahrman, et al., 2004). The increased expression pattern of this protein could be associated

with higher rate of photosynthesis and more ATP utilization during growth.

Proteins involved in energy metabolism through glycolysis, TCA and Calvin cycle have been

identified differentially between the two lines. Upregulation of enolase, malate

dehydrogenase, phosphoglycerate kinase and few other proteins inferred the more vegetative

growth and delayed flowering in line A but upregulation of fructose bisphosphate aldolase in

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line B implicated demand of sugar product for flower development and vegetative growth is

different or is not so straight forward. Enolase produces phosphoenolpyruvate by catalysing

reversible dehydration of phosphoglycerate (Bahrman, et al., 2004). Malate dehydrogenase

transforms malate to oxaloacetate in the citric acid cycle and also produces pyruvate in the

process of glycolysis (Bahrman, et al., 2004). Phosphoglycerate kinase acts in both the Calvin

cycle and glycolysis pathway to produce 3-biphosphoglycerate from 1,3-biphosphoglycerate.

On the other hand, Fructose-bisphosphate aldolase catalyzes Gleceraldehyde-3-phospahate

from Fructose 1,6-bisphosphate in the glycolysis pathway. Altered sugar metabolism in

relation to transition of flowering has been reported in Arabidopsis thaliana (Ohto et al.,

2001) and suggested a common regulatory pathway between floral transition and starch

accumulation (Eimert et al., 1995). Correlation of sucrose content with the days to spiking

has been observed in Phalaenopsis (Kataoka et al., 2004) and similarly, downregulation of

proteins related to sugar metabolism was observed during flowering phase (Yuan et al.,

2016).

5.4.2 Proteins involved in metabolism and protein synthesis

Variations in the expression of proteins related to primary metabolism like carbon, nitrogen,

lipid and vitamin metabolism have been identified in this study. Line B had high Glutamine

synthase expression, which takes part in the nitrogen metabolism to convert glutamine into

glutamic acid. This glutamic acid is accumulated in the mature seed and considered to be a

protein marker of embryo maturation (Balbuena et al., 2009). High expression of Lidonate-5-

dehydrogenase and phosphoglycerate phosphatase involved in carbohydrate metabolism in

line B might explain the advanced developmental rate of line B compare to line A. Cysteine

synthase responsible for biosynthesis of glutathione through synthesis of cysteine offer

resistance to biotic and abiotic stress (Santos et al., 2002).

Plants require high rates of cell division during growth and this also correlates with the high

rates of protein biosynthesis. A few proteins related to protein synthesis have been identified

in this study. Chaperonins establish normal protein conformations of cell function by

refolding the misfolded proteins, thus play a vital role in defence against stresses (Zadražnik

et al., 2013)

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5.4.3 Protein involved in stress and redox homeostasis

Stress causes protein degradation where heat shock proteins act as metabolic chaperones,

prevent aggregation of denatured protein and facilitate refolding under stress condition

(Zhang, et al., 2006). Redox homeostasis is metabolic interface between stress perception and

physiological responses, and to signal the stress responses reactive oxygen species (Canovas

et al., 2004) with the potential to cause cell damage (Zhang, et al., 2006). Plants eliminate or

reduce ROS level by scavenging them with the production of different peroxides. Superoxide

dismutase scavenges superoxides radicles into molecular oxygen and H2O2 whereas ascorbate

peroxidase involved in the detoxification of H2O2 by donating electron (Navrot et al., 2011).

H2O2 have been found to negatively influence flowering time in Arabidopsis (Chai et al.,

2012, Lokhande et al., 2003). This notion reveals that upregulation of the proteins involved in

H2O2 elimination results in the delayed flowering. Thioredoxin peroxidase through the

involvement in the redox regulation cycle maintains thiol redox state. Together with thiol-

reducing system 2- Cys-peroxiredoxin also plays peroxidase activity. This enzyme has been

identified in the developing barley and wheat endosperm (Finnie et al., 2002, Skylas et al.,

2000) indicates protein marker of endosperm development.

5.4.4 RNA binding proteins and diverse role in plant growth and development

Several cold shock domain (CSD) proteins and low temperature responsive proteins showed

different expression levels between the two lines in the current study. CSD are the member of

a large and diverse zinc finger protein gene family having multiple cellular functions like

transcriptional regulation, RNA binding and protein-protein interactions (Ciftci-Yilmaz and

Mittler 2008). The CSD proteins identified in this study are functionally characterized in

wheat as three C-terminal (CCHC) zinc finger protein that is interspersed with glycine rich

protein and upregulated during cold acclimation (Karlson and Imai 2003), and also reported

to regulate developmental and physiological processes like flowering and circadian rhythm

(Macknight et al., 1997, Heintzen et al., 1997). Low temperature responsive proteins are

characteristics of glycine rich RNA binding proteins (GRP), responding to environmental

cues and involved in plant cold tolerance, flowering time, and circadian timekeeping. This

study identified a RNA binding protein M4VSRO that had upregulated at AR10 stage of line

B, which indicates the early reproductive development in line B compared to line A. RNA

binding proteins are often multifunctional playing roles in cell response to environmental

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signals and developmental cues by interacting with nucleic acids and/or other proteins

beyond its regulatory function of nuclear and cytoplasmic gene expression (Wilkinson and

Shyu 2001). Cold shock proteins act as chaperones, which binds to RNA and DNA to

regulate unwinding of nucleic acid duplex, and also provide cold resistance to plants. RNA

binding proteins have been reported in a number of studies to affect growth and development

of diverse organism including mammals where this protein involved in the embryonic cell

growth and differentiation (Books, and Ley 2005, Roush and Slack 2008).

Differential expression of CSD/GRP proteins observed in this study might have role in the

differences of heading time and spike length. In plants, CSD is expressed highly in the

meristematic and floral organs, regulates developmental processes similar to animal CSD

proteins (Nakaminami et al., 2009, Sasaki, et al., 2007). In Arabidopsis knock down of this

protein resulted in early flowering, reduced number of stamens and number of seeds (Fusaro

et al., 2007) while overexpression of another member of CSD protein group reduced the

silique length (Sasaki, et al., 2013). CSD protein in wheat upregulated during cold

acclimation and collect cold related proteins in crown tissues, thus regulated by stress and

developmental signal (Karlson and Imai 2003). Changes in the expression of several MADS

box (vernalization) and endosperm developmental genes have been observed due to the effect

of CSD proteins (Yang and Karlson 2011). WCSP1 is the first documented CSD/GRP protein

in wheat that binds nucleic acids in vitro and plays a role in the regulation of genes related to

cold acclimation (Karlson et al., 2002).

Moreover, Enolase protein acts as a low expression of osmotically responsive gene 2 (LOS2),

which binds to the zinc finger transcriptional repressor promoter to regulate cold-responsive

gene transcription. Endoplasmin-like protein has a chaperone role in the secreted enzyme and

control apical and floral meristem by regulating CLAVATA proteins. However, difference in

plant height and tiller numbers is obvious when two lines are grown under similar

environmental conditions due to differential protein expressions. It is plausible that when

plants receive similar amount of nutrients but delay in reproductive developments it may

produce more tillers and grow taller. Consequently, when plants flowering late in the

growing season are exposed to high temperatures that hinder the proper grain filling and

development this results in lighter TKW and mini TW.

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5.5 Conclusion

In summary, the analysis of differentially expressed proteins revealed that proteins related to

carbohydrate metabolism, energy metabolism, protein synthesis, stress, and redox

homeostasis are involved in the regulation of flowering.

The proteins, like CSD and/or GRP RNA binding proteins that regulate the flowering time

through influencing major biochemical pathways have showed differential expression

between the lines with contrasting flowering time. the analysis has illustrated the interacting

relationship of different photosynthesis and other metabolism pathways with the flowering

development process. However, the known phenology related genes like vernalization or

photoperiod genes could not be identified in this study might be due to their low abundance

in expression. On the other hand, a few proteins identified in this study were not

characterized yet in wheat but orthologues are oknown in Arabidopsis or other plants have

putative function in regulating growth and development. Further study might be undertaken

to characterize the function those proteins.

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Wiese, S., Reidegeld, K. A., Meyer, H. E. and Warscheid, B. (2007), Protein labeling by iTRAQ: A new tool for quantitative mass spectrometry in proteome research. Proteomics, 7: 340–350.

Wilkinson, M. F., and A. B. Shyu. 2001. "Multifunctional regulatory proteins that control gene expression in both the nucleus and the cytoplasm." Bioessays no. 23 (9):775-87.

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Yan, L., D. Fu, C. Li, A. Blechl, G. Tranquilli, M. Bonafede, A. Sanchez, M. Valarik, S. Yasuda, and J. Dubcovsky. 2006. "The wheat and barley vernalization gene VRN3 is an orthologue of FT." Proceedings of the National Academy of Sciences no. 103 (51):19581-19586.

Yan, L., A. Loukoianov, A. Blechl, G. Tranquilli, W. Ramakrishna, P. SanMiguel, J.L. Bennetzen, V. Echenique, and J. Dubcovsky. 2004. "The wheat VRN2 gene is a flowering repressor down-regulated by vernalization." Science Signalling no. 303 (5664):1640.

Yan, L., A. Loukoianov, G. Tranquilli, M. Helguera, T. Fahima, and J. Dubcovsky. 2003. "Positional cloning of the wheat vernalization gene VRN1." Proceedings of the National Academy of Sciences no. 100 (10):6263-6268.

Yan, S. P., Q. Y. Zhang, Z. C. Tang, W. A. Su, and W. N. Sun. 2006. "Comparative proteomic analysis provides new insights into chilling stress responses in rice." Molecular and Cellular Proteomics no. 5 (3):484-96.

Yang, Yongil, and Dale T. Karlson. 2011. "Overexpression of AtCSP4 affects late stages of embryo development in Arabidopsis." Journal of Experimental Botany no. 62 (6):2079-2091.

Yuan, Xiu-Yun, Shen-Ping Xu, Fang Liang, Su-Hua Jiang, Mo-Fei Wang, Jie Ma, Xian-Yun Zhang, and Bo Cui. 2016. "Comparative proteomic analysis of Phalaenopsis leaves in the vegetative and flowering phase." Acta Physiologiae Plantarum no. 38 (7):175.

Zadražnik, Tanja, Kristin Hollung, Wolfgang Egge-Jacobsen, Vladimir Meglič, and Jelka Šuštar-Vozlič. 2013. "Differential proteomic analysis of drought stress response in leaves of common bean (Phaseolus vulgaris L.)." Journal of Proteomics no. 78:254-272..

Zhang, J., B. Dell, E. Conocono, I. Waters, T. Setter, and R. Appels. 2008. "Water deficits in wheat: fructan exohydrolase (1‐FEH) mRNA expression and relationship to soluble carbohydrate concentrations in two varieties." New Phytologist no. 181 (4):843-850.

Zhao, C., J. Wang, M. Cao, K. Zhao, J. Shao, T. Lei, J. Yin, G. G. Hill, N. Xu, and S. Liu. 2005a. "Proteomic changes in rice leaves during development of field-grown rice plants." Proteomics no. 5 (4):961-72.

Zhao, Caifeng, Jingqiang Wang, Mengliang Cao, Kang Zhao, Jianmin Shao, Tingting Lei, Jianning Yin, Gradford G. Hill, Ningzhi Xu, and Siqi Liu. 2005b. "Proteomic changes in rice leaves during development of field-grown rice plants." Proteomics no. 5 (4):961-972.

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Chapter 6

6 Summary and Recommendations

Plants have evolved a number of mechanisms for protection against drought, of which one of

the fundamental strategies is drought escape, i.e., the development of the most sensitive

growth stages (e.g. the reproductive stage) during a period when drought stress is less likely

to occur. This can be achieved by the fine tuning of flowering to modify the duration of

developmental phases for better adaptation in water limited environments or to escape from

these constraints (Cockram et al., 2007; Debaeke, 2004; Richards, 1991; Worland, 1996).

Primarily, flowering in wheat is controlled by three major groups of genes: i) vernalization

(Vrn) genes (exposure to cold temperature requirement), ii) photoperiod (Ppd) genes

(photoperiod sensitivity) and iii) autonomous earliness per se (Eps) genes (Kato and

Yamagata, 1988; Snape, et al., 2001). An understanding of the genetic controls of

phenological development and their effects on other morpho-physiological traits will result in

development of wheat cultivars better adapted to specific drought environments. Taking the

above into account, this PhD project was designed to

i) investigate the individual and combination effects of vernalization alleles on days to

flowering, maturity and other yield components;

ii) determine the variable effects of photoperiod genes on flower development; and

iii) understand the changes in protein expression during phenological development.

6.1 Significance of the work

In the first experimental study, the effects of different Vrn-1 allelic combinations have been

quantified, using wheat plants grown in the glasshouse and at different field locations, for

days to heading and the consequent effects of heading on grain and other agronomic traits.

This study showed that lines with all three spring alleles of the Vrn-1 gene (Vrn-A1a, Vrn-

B1a, Vrn-D1a) were the earliest in heading while the combinations with all three winter

alleles (vrn-A1b, vrn-B1b, vrn-D1b) were the latest in flowering and maturity. It is

noteworthy to mention that inclusion of photoperiod insensitive allele Ppd-B1b with the

allelic combinations of the Vrn-1 gene greatly reduced the flowering time except when in

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combination with all winter alleles. However, both the earliest and the latest heading lines

were poor in grain yield in terms of seed number per spike and TKW compared to lines with

one or two spring alleles. These latter lines containing spring alleles were intermediate in

heading time and performed better for grain yield. The superior allelic combinations for grain

yield identified in this study were not always similar to those reported in previous studies

(Eagles et al., 2010; Eagles et al., 2014; Kamran, et al., 2014; Santra, et al., 2009), and the

performance of the desirable allelic combinations were not always consistent across the

locations and years. These outcomes indicate that the expression patterns of the gene

combinations considered appropriate for a particular set of environments can still differ in

response to seasonal variations of the environmental cues. Therefore, this emphasizes the

need to test all available Vrn-1alleles in each specific environment and optimize their allelic

combination to maximize grain yield in water limited environments. The outcomes from this

kind of study can also have implications to escape frost time in the regions where wheat

production is hampered by chilling injury.

This study also showed that differences in the Vrn-1 allelic combinations not only result in

the variation of heading time and other agronomic traits but also alter the rate of water

requirement and consumption during the growing season. Water availability during all

growth phases influences grain yield but water supplies during flower development play a

critical role in determining grain yield. This implies that prudent selection of suitable allelic

combinations that match phenology with the rainfall pattern and/or water availability of a

specific environment would result in yield benefits despite of the drought stress.

The investigation of the difference between photoperiod sensitive and insensitive alleles of

the Ppd-D1 gene in spike development and heading time revealed that the sensitive line

delayed heading until day length increased despite this line having larger and more developed

spikes compared to the insensitive line. Finally, spike length differences between the lines did

not result in much variation in grain number per spike due to the floret abortion resulting

from the delayed flowering of the sensitive line when temperatures were higher. In addition

to these results being similar to those of previous studies (Foulkes, et al., 2004; Worland,

1996) this present study revealed that internode elongation started much earlier in a

photoperiod insensitive line in comparison to a sensitive line. Previous studies also pointed

out the linkage of the photoperiod insensitive allele of Ppd-D1with the reduced height genes

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Rht-B/Rht-D (Rebetzke, et al., 2012). This result suggests that the photoperiod insensitive

allele might be triggered by the expression of genes related to the GA pathway for internode

growth, and also supports the hypothesis of interacting regulatory pathways of vernalization,

photoperiod, earliness per se and gibberellin acid for flower development (Mouradovet al.,

2002). Grain number per square meter is the most important yield determinant for wheat

(Fischer, 1985). The current study showed that drought reduces tiller number and causes

early senescence, i.e., both the source and sink capacity are affected in water limited

environments. However, an optimal balance between photosynthetic source and sink during

pre-anthesis flower development would result in higher grains per spike which would

compensate for reduced tiller numbers. Thus proper spike development for higher grains

number could be ensured through the selection of suitable alleles of phenology genes in the

cultivars for a specific environment.

In this PhD project, efforts were made to study the phenotypic effects of vernalization,

photoperiod and dwarfing genes on the yield performance of selected advanced lines over

several locations. This study revealed that, in most cases, the combination of any two spring

alleles, out of the three Vrn-1 loci, one photoperiod sensitive/insensitive allele and tall/dwarf

allele in either of the two photoperiod and dwarfing loci resulted in higher yield irrespective

of locations. The allelic combination SSWSWTD performed considerably better in all three

locations. Both the dwarfing alleles of Rht-B and Rht-D had similar expression for plant

height and spike length, but the Rht-D dwarfing allele resulted in earlier flowering, higher

grain per spike and greater TKW compare to the Rht-B dwarfing allele while all the

vernalization and photoperiod alleles remained the same. These results indicate the

superiority of the Rht-D dwarfing allele over the Rht-B dwarfing allele in water limited

environments for grain yield and support the observations of a previous study (Eagles et al.,

2014) where the Rht-D dwarfing allele was reported beneficial for lower rainfall areas.

Similar to the results of this study Santra et al., (2009) also observed heading time differences

due to the effect of a reduced height gene, that further reinforces the hypothesis of interacting

regulatory pathways of vernalization, photoperiod, earliness per se and dwarfing genes for

flowering. It is interesting to note that performance of the allelic combination groups varied

among the locations which implies that the expression pattern of the same allelic combination

might be altered by the local environmental conditions such as temperature, day length,

rainfall pattern, soil properties and soil nutrients status. Therefore, flowering is influenced by

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many factors beyond the previously mentioned four major pathways including nutrient

deficiency, heat, drought, oxidative stress, overcrowding, microRNA and RNA binding

protein that have been reported to control flowering time in various plant species including

the model plant Arabidopsis (Chai et al., 2012; Fusaro et al., 2007; Simpson and Dean, 2002;

Yuan et al., 2016). However, a number of genes involved in various signalling pathways that

regulate flowering have been identified through genetic and molecular analyses, which

constitute a complex network that integrates floral genes for flowering through a series of

physiological and biochemical changes (Bernier and Perilleux, 2005; Khan, et al., 2014;

Srikanth and Schmid, 2011).

Proteomic analyses of two DH lines with different heading time identified a number of

deferentially expressed proteins including those for photosynthesis, primary and secondary

metabolism, stress, protein synthesis and RNA synthesis related proteins. Heading time

differences in response to varied expression patterns of the genes implied that the identified

deferentially expressed proteins might have roles in flowering in wheat. Most importantly,

few CSD/GRP RNA binding proteins have been identified with <2 and more fold differences

between the early and late flowering DH lines, which indicates that <2 fold expression

differences might control flowering. This group of proteins are characteristic of zinc finger

group proteins, which act as transcription factors. WCSP1 was the first reported CSD/GRP

protein in wheat that binds nucleic acids in vitro and controls flowering through regulation of

genes related to cold acclimation (Karlson and Imai, 2003). RNA binding proteins are

multifunctional and control both nuclear and cytoplasmic gene expression through synthesis,

transport, translation, storage, stability and degradation of RNA. These proteins also regulate

cell functions by the interaction of nucleic acids and/or other proteins with environmental

cues (Dreyfuss, et al., 2002; Wilkinson and Shyu, 2001). Besides RNA binding proteins a

number of other proteins such as ATP synthase, Enolase, Malate dehydrogenase, Fructose-

bisphosphate aldolase, Theoredoxin peroxidase have been found to express differentially in

this study. These proteins have been reported in previous studies to affect flowering time in

different plant species including Arabidopsis (Bahrman et al., 2004; Balbuena et al., 2009;

Eimert, et al., 1995; Kataoka, et al., 2004; Ohto et al., 2001; Santos, et al., 2002; Yuan et al.,

2016; Zadražnik, et al., 2013).

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Proteins directly related to vernalization and photoperiod genes that are major regulators of

flowering time were not identified, though few differently expressed CSD/GRP RNA binding

proteins obtained between the two DH lines with varying heading time. This result might be

due to their low abundance in comparison to total proteins or their expression timing differing

from the sample collection time. Two-dimensional gel electrophoresis offers the

simultaneous visualization and quantification of thousands of proteins and, the detection of

post-translational modifications. During the analysis of total proteins, it is difficult to detect

all the expressed proteins, especially the transcription factors that occur in low abundance but

in a large percentage (Garbis, et al., 2005). In this present study the high abundant proteins

such as the Rubisco large and small subunits from the leaf samples left a lower percentage of

remaining proteins to be detected as sample volume and total concentration remained the

same. Therefore, depletion of high abundant proteins is required for successful detection of

low abundant proteins, although complete removal is not desirable as they could also trap

some of the low abundance proteins along with their fragments and peptides (Garbis et al.,

2005).

6.2 Future recommendations

This PhD project was aimed at quantifying the effects of the alleles of vernalization (Vrn) and

photoperiod (Ppd) genes, and to determine suitable allelic combinations for better yield

performance in water-limited environments. The outcomes from this study will help plant

breeders to develop cultivars with suitable allelic combinations of vernalization and

photoperiod genes along with right dwarfing genes but deployment of all the appropriate

alleles to fine tune the flowering time for maximum yield benefits under rain fed production

systems requires further research that should emphasize the following:

Several spring type alleles of Vrn-A1 have been identified that can generate a wide spectrum

of vernalization requirements by interacting with spring and/or winter alleles of Vrn-B1 and

Vrn-D1 and enable wheat to grow in a wide range of growing regions and sowing times

(Pugsley, 1971). To quantify Vrn-1 effects on flowering only one spring type (Vrn-A1a)

allele of VRN-A1 was considered in the investigation with DH lines, whereas in the

experiment with advanced breeding lines another spring allele Vrn-A1c was identified in one

line. Previous screening of a wide range of germplasm from the 1970’s also identified only

Vrn-A1a spring allele along with Vrn-B1a and Vrn-D1a and their winter counterparts (Eagles

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et al., 2010; Eagles, et al., 2009). Therefore, it is imperative to test the effects of all other

available spring alleles of vernalization (VRN-A1b, VRN-A1d, and VRN-A1e) in the

Australian environments to ensure the maximum advantage from all Vrn allelic interactions.

Moreover, Vrn-2 and Vrn-3 genes also play a role in flowering, so these genes should be

brought in to the analysis along with alleles of VRN-1.

Introduction of photoperiod insensitivity to the Australian wheat cultivars from Indian

germplasm provided a yield boost and extended wheat cultivation inland to the drier parts of

Australia (Pugsley, 1983). Similar to the vernalization genes, a number of photoperiod alleles

(Ppd-A1a, Ppd-A1b, Ppd-B1a, Ppd-B1b, Ppd-D1a, Ppd-D1b) have been identified while the

photoperiod insensitive allele Ppd-B1 has different versions with varying ranges of response

to day length due to its copy number variation from two to four (Díaz, et al., 2012). In this

PhD project only a few photoperiod insensitive alleles (Ppd- D1b, Ppd-B1b and Ppd-B1c)

were present in a few of the advance lines examined. The presence of Ppd-B1b in the DH

lines segregating for alleles Vrn-1 has shown the photoperiod and vernalization interaction

effects in determining flowering time. Therefore, all the available photoperiod alleles should

also be tested in the water limited environments of Australia to determine the environment

specific photoperiod alleles.

Studies have been carried out on the effects of different allelic combinations on heading and

maturity. Likewise, appropriate timing of flowering tolerance to post anthesis water stress or

terminal drought is also critical to grain yield. During the later stage of growth drought

accompanied by heat stress harness the senescence, thus hampers the photosynthesis and also

carbon supply to developing grain (Rebetzke et al., 2008). Rapid removal of water soluble

carbohydrate or stay green characters could ameliorate the effects of terminal drought.

Therefore, future emphasis should also be put forward to the allelic interaction effects in the

rapid remobilization of the assimilate from the stem to the developing grain as this would

produce cultivars better adapted to water limited environments. Autonomous earliness per se

genes also play important in flowering but only limited numbers of earliness per se QTLs

have been reported to date. Therefore, efforts are needed to explore more earliness per se

QTLs and introgress them in addition to alleles of Vrn and Ppd genes for higher grain yield in

drought prone areas.

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Due to the effect of global warming, the climates of Australia (and other regions of the

world) are gradually becoming more unpredictable (Turner, et al., 2011). To adapt to this

changing climate scenario introgression of new alleles of Vrn and Ppd genes into new

cultivars is necessary. Changes in the expression pattern of the same set of Vrn and Ppd

allelic combinations genes in response to altered temperature and day length have also been

observed in previous studies (Dubcovsky et al., 2006). This reinforces the importance of

testing all available Vrn and Ppd alleles in specific environments and utilizing them in the

breeding population. Global exchange of germplasm containing all the available Vrn and Ppd

alleles is required to develop adaptive cultivars for the water limited environments.

Moreover, an audit of the broad adaptive and strong abiotic resistance genotypes developed

by the International Wheat Centre (CIMMYT and ICARDA) for their major genetic

composition would also greatly assist future research.

6.3 References

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Balbuena, T. S., Silveira, V., Junqueira, M., Dias, L. L. C., Santa-Catarina, C., Shevchenko, A., & Floh, E. I. S. (2009). Changes in the 2-DE protein profile during zygotic embryogenesis in the Brazilian Pine (Araucaria angustifolia). Journal of Proteomics, 72(3), 337-352.

Bernier, G., & Perilleux, C. (2005). A physiological overview of the genetics of flowering time control. Plant Biotechnol J, 3(1), 3-16.

Boyer, J., & Westgate, M. (2004). Grain yields with limited water. Journal of Experimental Botany, 55(407), 2385-2394.

Chai, L., Wang, J., Fan, Z., Liu, Z., Wen, G., Li, X., & Yang, Y. (2012). Regulation of the flowering time of Arabidopsis thaliana by thylakoid ascorbate peroxidase. African Journal of Biotechnology, 11(28), 7151-7157.

Cockram, J., Jones, H., Leigh, F. J., O'Sullivan, D., Powell, W., Laurie, D. A., & Greenland, A. J. (2007). Control of flowering time in temperate cereals: genes, domestication, and sustainable productivity. Journal of Experimental Botany, 58(6), 1231-1244.

Debaeke, P. (2004). Scenario analysis for cereal management in water-limited conditions by the means of a crop simulation model (STICS). Agronomie, 24(6-7), 315-326.

Díaz, A., Zikhali, M., Turner, A. S., Isaac, P., & Laurie, D. A. (2012). Copy Number Variation Affecting the Photoperiod-B1 and Vernalization-A1 Genes Is Associated with Altered Flowering Time in Wheat (Triticum aestivum). PLoS ONE, 7(3).

Dreyfuss, G., Kim, V. N., & Kataoka, N. (2002). Messenger-RNA-binding proteins and the messages they carry. Nat Rev Mol Cell Biol, 3(3), 195-205.

Dubcovsky, J., Loukoianov, A., Fu, D., Valarik, M., Sanchez, A., & Yan, L. (2006). Effect of photoperiod on the regulation of wheat vernalization genes VRN1 and VRN2. Plant Molecular Biology, 60(4), 469-480.

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Eagles, H. A., Cane, K., Kuchel, H., Hollamby, G. J., Vallance, N., Eastwood, R. F., Martin, P. (2010). Photoperiod and vernalization gene effects in southern Australian wheat. Crop and Pasture Science, 61(9), 721-730.

Eagles, H. A., Cane, K., Trevaskis, B., Vallance, N., Eastwood, R. F., Gororo, N. N.,. Martin, P. J. (2014). Ppd1, Vrn1, ALMT1 and Rht genes and their effects on grain yield in lower rainfall environments in southern Australia. Crop and Pasture Science, 65(2), 159-170.

Eagles, H. A., Cane, K., & Vallance, N. (2009). The flow of alleles of important photoperiod and vernalisation genes through Australian wheat. Crop and Pasture Science, 60(7), 646-657.

Eimert, K., Wang, S. M., Lue, W. I., & Chen, J. (1995). Monogenic Recessive Mutations Causing Both Late Floral Initiation and Excess Starch Accumulation in Arabidopsis. Plant Cell, 7(10), 1703-1712.



Fusaro, A. F., Bocca, S. N., Ramos, R. L. B., Barrôco, R. M., Magioli, C., Jorge, V. C., Sachetto-Martins, G. (2007). AtGRP2, a cold-induced nucleo-cytoplasmic RNA-binding protein, has a role in flower and seed development. Planta, 225(6), 1339-1351.

Garbis, S., Lubec, G., & Fountoulakis, M. (2005). Limitations of current proteomics technologies. Journal of Chromatography A, 1077(1), 1-18.

Kamran, A., Randhawa, H. S., Yang, R.-C., & Spaner, D. (2014). The effect of VRN1 genes on important agronomic traits in high-yielding Canadian soft white spring wheat. Plant breeding, 133(3), 321-326.

Karlson, D., & Imai, R. (2003). Conservation of the Cold Shock Domain Protein Family in Plants. Plant Physiology, 131(1), 12-15.

Kataoka, K., Sumitomo, K., Fudano, T., & Kawase, K. (2004). Changes in sugar content of Phalaenopsis leaves before floral transition. Scientia Horticulturae, 102(1), 121-132.

Kato, K., & Yamagata, H. (1988). Method for evaluation of chilling requirement and narrow-sense earliness of wheat cultivars. Ikushugaku Zasshi, 38(2), 172-186.

Khan, M. R., Ai, X. Y., & Zhang, J. Z. (2014). Genetic regulation of flowering time in annual and perennial plants. Wiley Interdiscip Rev RNA, 5(3), 347-359.

Mouradov, A., Cremer, F., & Coupland, G. (2002). Control of flowering time: interacting pathways as a basis for diversity. Plant Cell, 14 Suppl, S111-130.

Ohto, M., Onai, K., Furukawa, Y., Aoki, E., Araki, T., & Nakamura, K. (2001). Effects of sugar on vegetative development and floral transition in Arabidopsis. Plant Physiology, 127(1), 252-261.

Pugsley, A. (1971). A genetic analysis of the spring-winter habit of growth in wheat. Australian Journal of Agricultural Research, 22(1), 21-31.

Pugsley, A. T. (1983). The impact of plant physiology on Australian wheat breeding. Euphytica, 32(3), 743-748.

Rebetzke G. J., van Herwaarden A. F., Jenkins C., Weiss M., Lewis D., Ruuska S., Tabe L., Fettell N. A., Richards R. A. (2008) Quantitative trait loci for water-soluble carbohydrates and associations with agronomic traits in wheat. Australian Journal of Agricultural Research 59, 891-905.

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Rebetzke, G., Bonnett, D., & Ellis, M. (2012). Combining gibberellic acid-sensitive and insensitive dwarfing genes in breeding of higher-yielding, sesqui-dwarf wheats. Field Crops Research, 127, 17-25.

Richards, R. A. (1991). Crop improvement for temperate Australia: Future opportunities. Field Crops Research, 26(2), 141-169.

Santos, A. L. W. d., Silveira, V., Steiner, N., Vidor, M., & Guerra, M. P. (2002). Somatic Embryogenesis in Parana Pine (Araucaria angustifolia (Bert.) O. Kuntze). Brazilian Archives of Biology and Technology, 45, 97-106.

Santra, D. K., Santra, M., Allan, R. E., Campbell, K. G., & Kidwell, K. K. (2009). Genetic and Molecular Characterization of Vernalization Genes Vrn-A1, Vrn-B1, and Vrn-D1 in Spring Wheat Germplasm from the Pacific Northwest Region of the U.S.A. Plant Breeding, 128(6), 576-584.

Simpson, G. G., & Dean, C. (2002). Arabidopsis, the Rosetta stone of flowering time? Science, 296(5566), 285-289.

Snape, J., Butterworth, K., Whitechurch, E., & Worland, A. (2001). Waiting for fine times: genetics of flowering time in wheat. Euphytica, 119(1), 185-190.

Srikanth, A., & Schmid, M. (2011). Regulation of flowering time: all roads lead to Rome. Cell Mol Life Sci, 68(12), 2013-2037.

Turner, N. C., Molyneux, N., Yang, S., Xiong, Y.-C., & Siddique, K. H. M. (2011). Climate change in south-west Australia and north-west China: challenges and opportunities for crop production. Crop and Pasture Science, 62(6), 445-456.

Wilkinson, M. F., & Shyu, A. B. (2001). Multifunctional regulatory proteins that control gene expression in both the nucleus and the cytoplasm. Bioessays, 23(9), 775-787.


Yuan, X.-Y., Xu, S.-P., Liang, F., Jiang, S.-H., Wang, M.-F., Ma, J., Cui, B. (2016). Comparative proteomic analysis of Phalaenopsis leaves in the vegetative and flowering phase. Acta Physiologiae Plantarum, 38(7), 175.

Zadražnik, T., Hollung, K., Egge-Jacobsen, W., Meglič, V., & Šuštar-Vozlič, J. (2013). Differential proteomic analysis of drought stress response in leaves of common bean (Phaseolus vulgaris L.). Journal of Proteomics, 78, 254-272.

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7 Appendices

Appendix A Soil properties of field-collected soil from Merredin

Properties Unit Value

Colour BROR

Gravel % 0.00

Texture 2.00

Ammonium nitrogen mg/Kg 6.50

Nitrate nitrogen mg/Kg 27.50

Phosphorus Colwell mg/Kg 5.00

Potassium Colwell mg/Kg 192.00

Sulphur mg/Kg 30.50

Organic carbon % 1.02

Conductivity dS/m 0.54

pH Level (CaCl2) pH 6.10

pH Level (H2O) pH 6.85

DTPA copper mg/Kg 0.60

DTPA iron mg/Kg 20.38

DTPA manganese mg/Kg 25.05

DTPA zinc mg/Kg 1.43

Exc. Aluminium meq/100g 0.04

Exc. calcium meq/100g 4.36

Exc. magnesium meq/100g 6.28

Exc. potassium meq/100g 0.49

Exc. sodium meq/100g 3.35

Boron hot CaCl2 mg/Kg 4.19

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Appendix B Amount and composition of Fertilizers used in the experiments

Fertilizers Composition Quantity Remarks

Osmocote Nitrogen-16%, phosphorus-1.3%, potassium-9.1%, magnesium-1.8%, iron-2000mg/kg, copper -500mg/kg, manganese-300mg/kg, boron-200mg/kg, molybdenum-100mg/kg, cadmium-0.7mg/kg, lead-9.1mg/kg, mercury-0.3 mg/kg

6 g /pot

Growers Blue Nitrogen-12%, phosphorus-5%, potassium-14%, magnesium-1.2%, sulphur-9.8%, zinc-0.1%, boron-0.02% manganese-0.06%, copper-0.02%

6 g /pot

Dolomite Calcium-21.73%, magnesium-13.18% 3 g /pot

Calcium carbonate Calcium-40% 1.8 g /pot

Gusto Gold Nitrogen-10.2, phosphorus -13.1, potassium- 12.0, sulphur-7.2, copper -0.09 and zinc-0.13

75 kg/Ha

Urea Nitrogen-46% 50 Kg/Ha

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Appendix C1 Lay out of glass house experiment 2013

Appendix C2 Layout of glass house experiment 2014

Appendix C3 Layout of field experiment 2013 and 2014 at Toodyay

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Appendix D Biological pathways generated for the identified proteins

GO-ID Description Adjusted p Value p Value No. of Protein

661 response to stress 3.56E-22 9.53E-25 38 674 response to stimulus 2.55E-21 1.36E-23 45 685 response to cadmium ion 9.43E-21 7.59E-23 20 686 response to abiotic stimulus 1.24E-19 1.33E-21 30 681 response to metal ion 5.30E-19 7.11E-21 20 750 response to inorganic substance 1.25E-18 2.01E-20 21 747 response to temperature stimulus 1.76E-17 3.31E-19 19 748 response to cold 3.95E-16 8.47E-18 16 727 response to chemical stimulus 1.84E-11 4.43E-13 26 762 response to salt stress 1.17E-09 3.13E-11 13 662 response to osmotic stress 2.69E-09 7.93E-11 13 691 response to other organism 1.03E-05 3.33E-07 11 692 response to bacterium 1.33E-05 4.91E-07 8 729 response to biotic stimulus 1.33E-05 4.98E-07 11 723 defense response to bacterium 3.69E-05 1.48E-06 7 641 generation of precursor metabolites and energy 4.24E-05 1.82E-06 7 702 multi-organism process 1.04E-04 4.74E-06 11 677 RNA secondary structure unwinding 1.57E-04 8.36E-06 2 756 DNA geometric change 1.57E-04 8.40E-06 3 757 DNA duplex unwinding 1.57E-04 8.40E-06 3 708 defense response 2.60E-04 1.46E-05 10 643 metabolic process 1.35E-03 8.19E-05 35 689 response to oxidative stress 1.35E-03 8.30E-05 6 699 cellular process 2.82E-03 1.82E-04 36 703 protein folding 2.99E-03 2.00E-04 5 726 glycolysis 3.10E-03 2.16E-04 3 637 cellular metabolic process 3.50E-03 2.53E-04 29 669 organic substance metabolic process 3.82E-03 2.97E-04 2 670 carbon fixation 3.82E-03 2.97E-04 2 638 photosynthesis 4.14E-03 3.33E-04 4 746 response to light stimulus 4.24E-03 3.52E-04 7 687 response to radiation 5.05E-03 4.33E-04 7 754 isocitrate metabolic process 5.11E-03 4.52E-04 2 640 monosaccharide catabolic process 8.35E-03 8.28E-04 3 711 photosynthesis, light reaction 8.35E-03 8.28E-04 3 720 hexose catabolic process 8.35E-03 8.28E-04 3 725 glucose catabolic process 8.35E-03 7.90E-04 3 733 glucose metabolic process 8.90E-03 9.07E-04 3 668 alcohol catabolic process 9.89E-03 1.03E-03 3 667 small molecule catabolic process 1.23E-02 1.32E-03 4

Appendices

151


715 sulfur compound biosynthetic process 1.85E-02 2.03E-03 3 718 DNA conformation change 1.87E-02 2.10E-03 3 719 hexose metabolic process 2.07E-02 2.39E-03 3 654 oxazole or thiazole metabolic process 2.26E-02 2.91E-03 1 712 oxazole or thiazole biosynthetic process 2.26E-02 2.91E-03 1

736 photosynthetic electron transport in cytochrome b6/f 2.26E-02 2.91E-03 1

752 ammonia assimilation cycle 2.26E-02 2.91E-03 1 763 response to nitric oxide 2.26E-02 2.91E-03 1 675 catabolic process 2.31E-02 3.03E-03 7 651 small molecule metabolic process 2.33E-02 3.13E-03 10 760 microtubule-based process 2.35E-02 3.21E-03 3 672 cellular carbohydrate catabolic process 2.37E-02 3.30E-03 3 682 response to zinc ion 2.43E-02 3.45E-03 2 701 carbohydrate catabolic process 2.89E-02 4.19E-03 3 696 sulfur amino acid biosynthetic process 2.94E-02 4.42E-03 2 761 serine family amino acid metabolic process 2.94E-02 4.42E-03 2 639 monosaccharide metabolic process 3.10E-02 4.74E-03 3 698 detoxification of nitrogen compound 3.34E-02 5.82E-03 1 704 'de novo' protein folding 3.34E-02 5.82E-03 1 706 regulation of protein amino acid dephosphorylation 3.34E-02 5.82E-03 1 713 'de novo' posttranslational protein folding 3.34E-02 5.82E-03 1

716 chaperone mediated protein folding requiring cofactor 3.34E-02 5.82E-03 1

728 response to nitrosative stress 3.34E-02 5.82E-03 1 739 chaperone-mediated protein folding 3.34E-02 5.82E-03 1 749 cyanide metabolic process 3.34E-02 5.82E-03 1 753 response to heat 3.75E-02 6.63E-03 3 710 response to high light intensity 3.84E-02 6.99E-03 2 738 response to virus 3.84E-02 6.99E-03 2 737 toxin catabolic process 4.25E-02 7.97E-03 2 759 toxin metabolic process 4.25E-02 7.97E-03 2 741 regulation of dephosphorylation 4.58E-02 8.72E-03 1 657 oxoacid metabolic process 4.72E-02 9.29E-03 6 658 carboxylic acid metabolic process 4.72E-02 9.29E-03 6 678 organic acid metabolic process 4.72E-02 9.36E-03 6 730 cellular ketone metabolic process 4.97E-02 1.00E-02 6 684 cellular amino acid metabolic process 5.23E-02 1.14E-02 4 695 sulfur amino acid metabolic process 5.53E-02 1.24E-02 2 714 sulfur metabolic process 5.54E-02 1.26E-02 3 717 cellular amine metabolic process 6.25E-02 1.52E-02 4 709 response to light intensity 7.47E-02 1.97E-02 2 731 alcohol metabolic process 7.55E-02 2.07E-02 3

Appendices

152


707 amine metabolic process 7.98E-02 2.20E-02 4 705 regulation of protein modification process 8.05E-02 2.31E-02 1 700 carbohydrate metabolic process 8.79E-02 2.61E-02 6 649 cellular nitrogen compound metabolic process 9.17E-02 2.75E-02 9 745 glutamine metabolic process 9.41E-02 2.88E-02 1 697 nitrogen compound metabolic process 1.11E-01 3.48E-02 9 671 cellular carbohydrate metabolic process 1.14E-01 3.62E-02 4 735 glutamate metabolic process 1.20E-01 4.00E-02 1 758 response to toxin 1.37E-01 4.84E-02 1 645 primary metabolic process 1.38E-01 5.19E-02 23

652 cellular amino acid and derivative metabolic process 1.38E-01 5.23E-02 4

676 cellular catabolic process 1.59E-01 6.26E-02 4 732 cellular amino acid biosynthetic process 1.62E-01 6.45E-02 2 693 photosynthetic electron transport chain 1.71E-01 7.04E-02 1 680 secondary metabolic process 1.73E-01 7.18E-02 3 656 amine biosynthetic process 1.81E-01 7.85E-02 2 664 regulation of cellular protein metabolic process 1.85E-01 8.12E-02 1 740 regulation of phosphate metabolic process 1.99E-01 9.45E-02 1 744 regulation of phosphorus metabolic process 1.99E-01 9.45E-02 1 655 cellular nitrogen compound biosynthetic process 2.00E-01 9.81E-02 3 734 glutamine family amino acid metabolic process 2.00E-01 9.72E-02 1 663 regulation of protein metabolic process 2.13E-01 1.13E-01 1 642 electron transport chain 2.48E-01 1.51E-01 1 660 RNA metabolic process 2.80E-01 1.86E-01 3

724 nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 3.20E-01 2.19E-01 5

679 organic acid biosynthetic process 3.22E-01 2.25E-01 2 688 biosynthetic process 3.22E-01 2.25E-01 10 742 carboxylic acid biosynthetic process 3.22E-01 2.25E-01 2 659 cellular macromolecule metabolic process 3.56E-01 2.64E-01 13 665 small molecule biosynthetic process 3.64E-01 2.72E-01 3 683 cellular biosynthetic process 3.70E-01 2.80E-01 9 721 heterocycle biosynthetic process 3.84E-01 2.96E-01 1 644 oxidation reduction 4.10E-01 3.28E-01 1 694 macromolecule metabolic process 4.80E-01 4.12E-01 13 690 cellular protein metabolic process 4.83E-01 4.17E-01 9 751 nucleic acid metabolic process 5.23E-01 4.65E-01 3 646 protein metabolic process 6.20E-01 5.79E-01 9 653 heterocycle metabolic process 6.68E-01 6.32E-01 1 648 regulation of biological process 9.41E-01 9.21E-01 5 647 biological regulation 9.43E-01 9.26E-01 6 666 regulation of cellular process 9.52E-01 9.36E-01 4

Appendices

153


650 regulation of metabolic process 9.99E-01 9.96E-01 1 722 regulation of macromolecule metabolic process 9.99E-01 9.94E-01 1 743 regulation of cellular metabolic process 9.99E-01 9.94E-01 1 755 regulation of primary metabolic process 9.99E-01 9.92E-01 1 673 biological_process 1 1 65

genetic architecture of wheat (triticum aestivum l.) phenology …€¦ · wheat production inthe...

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