CHARACTERISATION OF ROOT ARCHITECTURAL RESPONSES OF ... · CHARACTERISATION OF ROOT ARCHITECTURAL...

CHARACTERISATION OF ROOT

ARCHITECTURAL RESPONSES OF MUNGBEAN

TO WATER DEFICIT

Michael Dodt

BASc(Biotechnology), BBiomed(Hons)

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Science and Engineering Faculty

Queensland University of Technology

2017

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Keywords

Plants, root systems architecture, drought, photosynthesis, pre-treatment, food security,

transcriptome, RNA-Seq, modelling, APSIM, GxExM, agriculture, crop improvement,

production, abiotic stress, tolerance, seed, legume, mungbean, Vigna radiata

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Abstract

Plant root systems are much more complex than simple anchors in the soil. Although this is itself a deceptively

important function, plants perform a plethora of additional roles in sensory perception, signal transduction,

nutrient and water acquisition as well as acting as hosts to a range of beneficial symbiotic microorganisms. In

many plants the root system has a relatively high level of plasticity which allows plants to sense and respond to

their edaphic environment. It follows that the importance of plant roots becomes magnified when plants are

subjected to environmental abiotic stresses to which, as sessile organisms, they would otherwise be highly

sensitive. Root system developmental plasticity governed at the morpho-anatomical, physiological and

molecular level gives rise to complex architectural arrangements which vary considerably across environments

and between species and this root plasticity is of growing interest for crop improvement. Especially in the case

of drought stress – responsible for an estimated 50% of global crop losses and by far the most pervasive of all

the abiotic stresses. The significance of drought tolerant crops comes from a need to increase the efficiency

with which agriculture utilizes water resources. As the single largest consumer of global freshwater reserves,

the agricultural sector has a significant responsibility to ensure that its practices are as efficient as possible.

It is in the developing countries of the world with increasing populations, incidence of food shortages and

inaccessibility of clean drinking water, where the resounding impacts of water use inefficacy are most greatly

experienced. Therefore it is the staple crops of these regions on which research should be focussed to have the

most positive impact on society and relevance. For instance pulses, which form the foundation of diets in the

Indian subcontinent, the Asia Pacific region and the middle east, are consumed relatively much less in the

western world, but nonetheless are produced in vast amounts and predominantly exported. Around 95% of

mungbean produced in Australia fits this description and is exported (mostly to India) as is the case with

chickpea and other pulses. There is enormous potential to expand Australian production of pulses including

mungbean as our climate is perfectly suited year round to their cultivation and they attract very competitive

prices for farmers. Not surprisingly there is increasing interest in pulse production from the Australian pulse

industry following the recognition of their potential to grow the Australian economy and fulfil the seemingly

insatiable demand of our international neighbours for pulses.

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Historically increasing production was a simple matter of resource intensification and expansion of agricultural

production sites. However these strategies are no longer viable as we are approaching capacity in terms of

global freshwater availability and arable land. Therefore the solution for reaching our ambitious food

production targets for 2050 will either require totally new approaches to food production such as vertical

agriculture and urban farming, or by increasing efficiency of our agricultural methods. The former has

certainly been more romanticised in the media and is suitably a very exciting avenue. However the latter has

ushered in the introduction of precision agriculture, robotics as well as advanced sensing and biotechnologies

into the agricultural sector which is undergoing a major facelift in recent years. Advances in computing power

have facilitated a much wider adoption of molecular breeding techniques and allowed a more technical

approach to crop improvement. Similarly, with development of advanced imaging techniques for root system

studies, plant root systems – which have been somewhat of an enigma for researchers in the past – are now

increasingly being focussed on in literature. So much so, that it is now becoming popular in literature to refer

to root systems traits as “…traits of the second green revolution”. About half a century ago the first green

revolution represented a critical step toward contemporary agriculture and introduced widespread

mechanisation, development of high-yielding crops and management schemes such as irrigation and

fertilisation. The impact of the green revolution was enormous such that it is now difficult to even imagine

conventional agriculture without these elements. It is the opinion of many researchers at present that a better

utilization of plant root systems in agriculture could lead to a ‘blue revolution’ referring to water use efficiency

and bring about changes of similar magnitude in the near future.

This project evaluated a novel chemical treatment referred to as ‘ATW1124‘as a tool for enhancing drought

tolerance of mungbean. The chemical treatment was optimized as a seed pre-treatment with the intention of

aligning with current industrial practices and to allow rapid integration into industry in the future.

Morphological analyses and root imaging revealed that treatment significantly increased root development of

commercial verities of mungbean and led to higher survivability rates under drought due to improved root

water capture. Non-destructive imaging of plant roots through X-ray tomography revealed that spatial root

distribution of treated plants was significantly deeper enabling ATW-treated plants to extract soil moisture

more effectively under drought conditions. It was found that the combination of these traits led to a higher

efficiency of photosynthetic carbon assimilation under drought stress and increased survival rates.

Root-specific effects of ATW1124 resulted in a shift toward architectural arrangements previously reported in

literature to enhance drought tolerance. These included longer but fewer lateral roots, steeper branch angles,

deeper maximum taproot penetration and increased root volume. Additionally these root traits were

accompanied by an increased photosynthetic rate suggesting an indirect effect of ATW1124 on carbon

assimilation and thus yield under stress. Similar observations were reported by collaborators also working on

ATW1124 in a number of other species including chickpea, cotton, rice and a number of Australian forestry

eucalypt species. This discovery of a chemical means of enhancing drought tolerance of commercial

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mungbean varieties could become valuable tool for Australian producers who frequently suffer significant

yield losses from drought stress inherent in the Australian climate.

Crop modelling of ATW1124 effects revealed that target production regions within the Australian Northern

Grains region which would benefit most from application of ATW1124 were those with low soil-moisture

availability and shallow soil profiles. Simulated field trials across forty five Australian production sites

predicted reliable yield increases of around 10% in drought affected sites. Crop biomass, transpiration and

water use were also elevated in ATW1124 treated plants indicating that treatment reduced some of the

negative impacts of drought on commercial varieties currently used by Australian farmers. Glasshouse

analyses confirmed that these traits were a combination of enhanced root development and photosynthetic

efficiency under drought stress.

Bioinformatic analysis using RNA-seq revealed that ATW1124 treatment resulted in an over-representation of

a number of plant defence, structural and regulatory genes previously reported to regulate drought tolerance.

While some differentially expressed genes were identified under hydrated conditions, substantially more were

found in plants subjected to drought stress at the same stage. This led to the discovery that the root-enhancing

and photosynthetic effects of ATW1124 had, at least in part, a molecular basis that was antagonized by

drought stress. This would mean that ATW1124 treatment could be an effective strategy of enhancing drought

tolerance of mungbean crops via modification of root architecture and photosynthetic processes.

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Table of Contents

Keywords ................................................................................................................................................. i

Abstract ................................................................................................................................................... ii

Table of Contents .................................................................................................................................... v

List of Figures ...................................................................................................................................... viii

List of Tables ......................................................................................................................................... xi

Statement of Original Authorship ......................................................................................................... xii

Acknowledgments ................................................................................................................................ xiii

1 CHAPTER 1: INTRODUCTION ............................................................................................... 1

1.1 Background .................................................................................................................................. 1

1.2 Focus and aims ............................................................................................................................. 2

1.3 Aims ............................................................................................................................................. 2

1.4 Significance ................................................................................................................................. 3

2 CHAPTER 2: LITERATURE REVIEW ................................................................................... 5

2.1 Future of food security and significance of pulses ....................................................................... 5 2.1.1 Climate change and the future of global food security .................................................... 5 2.1.2 Growing significance of pulses ......................................................................................... 6

2.2 Production landscape: challenges and opportunities .................................................................... 9 2.2.1 Australian mungbean production ..................................................................................... 9 2.2.2 Biotic stresses limiting Australian mungbean production .............................................. 11 2.2.3 An introduction to GEM modelling ................................................................................. 14

2.3 Drought: a major limitation to productivity ............................................................................... 16 2.3.1 Abiotic stress and drought .............................................................................................. 16 2.3.2 Drought tolerance........................................................................................................... 17 2.3.3 Plant responses to drought ............................................................................................ 19 2.3.4 Signalling and molecular responses to drought.............................................................. 21

2.4 Plant root systems ...................................................................................................................... 25 2.4.1 Lateral root development and patterning ...................................................................... 25 2.4.2 Root contribution to plant performance under stress ................................................... 28 2.4.3 Root systems architecture .............................................................................................. 33

2.5 Implications ............................................................................................................................... 35

3 GENERAL MATERIALS AND METHODS ............................................................................ 36

3.1 General materials ....................................................................................................................... 36 3.1.1 Novel chemical ATW1124 ............................................................................................... 36 3.1.2 WinRHIZO root imaging methods ................................................................................... 36 3.1.3 Leaf gas exchange and photosynthesis monitoring equipment ..................................... 36 3.1.4 Plant growth facilities ..................................................................................................... 36

3.2 General Methods ....................................................................................................................... 39 3.2.1 Tissue culture protocol ................................................................................................... 39 3.2.2 Methods for quantifying shoot responses of mungbean to drought ............................. 41 3.2.3 Quantification of morphological responses to drought and ATW1214 treatment ........ 41

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3.2.4 General methods for measuring responses of photosynthesis and chlorophyll fluorescence ................................................................................................................... 46

3.3 Molecular techniques to assess transcriptional response ......................................................... 51 3.3.1 RNA Extraction from root and shoot tissue .................................................................... 51 3.3.2 RNA quality control ......................................................................................................... 52

4 CHAPTER 4 – OPTIMISATION AND EVALUATION OF MORPHOPHYSIOLOGICAL

EFFECTS OF ATW1124 .............................................................................................................. 53

4.1 Introduction ................................................................................................................................ 53

4.2 Materials and Methods .............................................................................................................. 53 4.2.1 Selection process and criteria for starting material ....................................................... 53 4.2.2 in vitro assessment of ATW1124 treated mungbean root systems architecture ........... 59 4.2.3 Effects of ATW1124 on radicle development and WinRHIZO verification ..................... 59 4.2.4 Developmental Stage Specificity WinRHIZO anlaysis ..................................................... 60 4.2.5 Xray tomographical assessment of mungbean root architectural effects of ATW1124 . 60 4.2.6 QCDF mungbean glasshouse ATW1124 drought assay .................................................. 64

4.3 Results ........................................................................................................................................ 68 4.3.1 In vitro ATW1124 assay .................................................................................................. 68 4.3.2 Effects of ATW1124 on radicle development and WinRHIZO verification ..................... 68 4.3.3 Developmental stage specificity WinRHIZO analysis ...................................................... 73 4.3.4 Xray tomography of ATW1124 treatment effects .......................................................... 75 4.3.5 P9 Glasshouse experiment #1: Optimising ATW1124 concentration ............................. 77

4.4 Chapter summary ...................................................................................................................... 88

5 CHAPTER 5 – TRANSCRIPTOME ANALYSIS OF DIFFERENTIALLY DROUGHT

TOLERANT MUNGBEAN TREATED WITH ATW1124 ......................................................... 90

5.1 Introduction ................................................................................................................................ 90

5.2 Materials and Methods .............................................................................................................. 91 5.2.1 Identification of differentially expressed genes from RNA-Seq data .............................. 91 5.2.2 Pipeline from differential gene expression to gene ontology enrichment analysis ....... 96

5.3 Results ........................................................................................................................................ 97 5.3.1 Interaction I – Effects of drought on the mungbean shoot transcriptome under

hydrated conditions. ..................................................................................................... 101 5.3.2 Interaction III – Transcriptomic differences between differentially drought tolerant

genotypes ..................................................................................................................... 104 5.3.3 Interaction IV – Effects of ATW1124 treatment on mungbean shoot transcriptome

under hydrated conditions ........................................................................................... 104 5.3.4 Interaction V – Effect of ATW1124 treatment on mungbean shoot drought response105

5.4 Chapter summary .................................................................................................................... 107

6 CHAPTER 6 – FIELD EVALUATION OF ATW1124 AND MODELLING TO DETERMINE IMPLICATIONS FOR PRODUCTION ........................................................................................................................ 110

6.1 Introduction ............................................................................................................................. 110

6.2 Materials and Methods ............................................................................................................ 111 6.2.1 APSIM simulated effects of ATW1124 on mungbean and implications for production111 6.2.2 Experimental verification of the impacts of ATW1124 on crop lower limit (CLL) and

water extraction ........................................................................................................... 112 6.2.3 ATW1124 field trial #1 – Hermitage, QLD ..................................................................... 113 6.2.4 ATW1124 rainout shelter trial – Kingaroy, QLD ............................................................ 115

6.3 Results ...................................................................................................................................... 118 6.3.1 APSIM simulated effects of enhancing crop lower limit ............................................... 118 6.3.2 Experimentally derived and APSIM simulated effects of ATW1124 treatment on water

extractive capacity ........................................................................................................ 119

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6.3.3 Hermitage field trial evaluation of the effects of ATW1124 on mungbean ................. 122 6.3.4 Kingaroy ATW1124 rainout shelter field trial results ................................................... 123

6.4 Chapter summary .................................................................................................................... 128

7 CHAPTER 7: GENERAL DISCUSSION AND CONCLUSIONS ........................................................... 130

7.1 Ideal root traits for abiotic stress tolerance ............................................................................ 132

7.2 Major findings .......................................................................................................................... 133 7.2.1 ATW significantly enhanced root growth and longevity under mild drought stress .... 133 7.2.2 ATW1124 modulated shoot morphophysiological traits contributing to enhanced

performance under drought stress............................................................................... 137 7.2.3 Application of ATW1124 may improve productivity and improve efficiency of water use

in arid environments..................................................................................................... 140

7.3 Conclusion ................................................................................................................................ 142

BIBLIOGRAPHY ....................................................................................................................... 144

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List of Figures

Figure 1: Heat map depicting areas drought-declared during Queensland’s 2014 drought ................... 1

Figure 2 – Global population growth distribution trends approaching 2050 (FAO, 2009)...................... 8

Figure 3: Major mungbean seed defects affecting quality-related yield loss ((Cumming, 2013b). ..... 10

Figure 4: Performance of the mungbean model (observed versus simulated grain yield in g/m2) against test datasets reported by Robertson et al. (2002). .......................................................... 15

Figure 5: Transcriptional regulatory networks of plant stress and gene expression. ........................... 23

Figure 6 – Schematic overview of amino acid, polyamine, and glycine betaine metabolism. .............. 24

Figure 7 – Representation of root developmental regions and tissue organization ............................. 27

Figure 8: Schematic of root responses to abiotic stresses at the whole plant level. ............................ 28

Figure 9: Non-invasive imaging of maize roots and soil water using CT-MRI (A) and 2D image of Lupinius albus roots and soil generated using light emission through a thin rhizotron (B). ........ 34

Figure 10: (A) Photo depicting the inside of the Kingaroy rainout shelter facility. ............................... 38

Figure 11 – in vitro mungbean seedling 21 days after seed treatment housed in a growth chamber . 40

Figure 12: Sample image scan of three week old glasshouse grown mungbean roots illustrating root trace analysis ................................................................................................................................ 42

Figure 13: Sample image depicting positioning of leaves for image-based leaf area calculation ......... 45

Figure 14: Photo depicting physiological monitoring setup and location. ............................................ 49

Figure 15 Scatterplot of temporal dynamics of photosynthetic rate and stomatal conductance of mungbean grown under hydrated conditions in the QCDF glasshouse. ...................................... 50

Figure 16 Summary of key phenological and yield data of NMIP breeding lines from which genotypes included in the study were derived. ............................................................................................. 57

Figure 17 Radicle development 5DAG following seed imbibition for 1hr in varying concentrations of ATW1124. ..................................................................................................................................... 62

Figure 18 Differences in root length between mungbean seed pre-treatments .................................. 63

Figure 19 – Photo illustrating placement of drip irrigation and staking to prevent lodging of mungbean grown in PC2 glasshouse facilities at the Queensland Crop Development Facility in Brisbane. ....................................................................................................................................... 66

Figure 20: Photo illustrating use of LI-6400XT systems to monitor photosynthetic responses of mungbean leaves.......................................................................................................................... 67

Figure 21: Tissue culture evaluation of ATW1124 seed pretreatment of Crystal cultivar mungbean (n = 10). ............................................................................................................................................. 68

Figure 22: Representative illustration of root morphological assessment using the WinRHIZO root phenotyping platform. ................................................................................................................. 69

Figure 23 Graphical representation of radicle development 5DAG. ..................................................... 70

Figure 24: Comparison of untreated and ATW1124 treated mungbean roots grown in soil. .............. 70

Figure 25 Box and whisker plot illustrating the effect of seed pre-treatment of mungbean seed with 100mM ATW1124. Plants grown in a growth chamber, destructively sampled for root development 7 days after sowing. ............................................................................................... 72

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Figure 26: Root development of mungbean treated with 100mM ATW1124 versus H2O controls assessed with the WinRHIZO imaging platform ........................................................................... 74

Figure 27: Representative X-ray tomographic reconstruction of ATW1124-plant root architecture versus controls given 7 days of soil drying ................................................................................... 75

Figure 28 Scatterplot of X-ray tomography derived spatial root volume ............................................. 76

Figure 29: Effects of seed pre-treatment with ATW1124 on total root length at flowering ................ 79

Figure 30: Effect of mild (DS1) and severe (DS2) drought on (a) root surface area and (b) total root length of mungbean treated with ATW1124................................................................................ 80

Figure 31 Comparison of root and shoot biomass dynamics at flowering with ATW1124 treatment under different irrigation regimes ................................................................................................ 81

Figure 32 Effects of ATW1124 and drought on morphological indicators at physiological maturity. .. 83

Figure 33 Photosystem II photochemical yield (PSII) dynamics under mild and severe drought stress.87

Figure 34 Representative nucleotide contribution of short sequence read data obtained in CLC Genomics ...................................................................................................................................... 92

Figure 35 Representative quality distribution histogram illustrating typical PHRED quality scores of RNA sequencing runs obtained using CLC Genomics. .................................................................. 99

Figure 36: Representative summary of read count statistics obtained by mapping total RNA sequences against a published reference mungbean genome. Top: number of total fragments which mapped (mapped); bottom: composition of mapped fragments according to their type. Figure obtained from CLC Genomics. ......................................................................................... 100

Figure 37 Systematic representation of GOEA conducted on ‘interaction I’ to decipher the impact of drought stress on the mungbean shoot transcriptome under hydrated conditions. ................ 102

Figure 38 Graphical representation of GOEA conducted on interaction I to decipher the impact of drought stress on the mungbean shoot transcriptome under hydrated conditions. ................ 103

Figure 39 Graphical representation of GOEA conducted on interaction IV to decipher the impact of ATW1124 treatment on shoot transcriptomes of mungbean plants under hydrated conditions.105

Figure 40 Graphical representation of GOEA conducted on interaction V to determine the impact of ATW1124 treatment on shoot transcriptomes of mungbean plants under drought stressed conditions. .................................................................................................................................. 106

Figure 41: Representative image depicting severely wilted mungbean at which periodic rewatering and analysis commenced for calculation of CLL. ........................................................................ 112

Figure 42 APSIM simulated yield (kg/ha) of Emerald mungbean cultivated at Roma (blue line), Goondoowindi (yellow) and Hermitage (red). ........................................................................... 114

Figure 43: Photograph depicting ATW1124 rainout shelter trial at Kingaroy (QLD) ........................... 117

Figure 44: Experimental layout of rainout shelter field trial design conducted at Kingaroy (QLD) .... 117

Figure 45 Exceedance plot comparing hypothetically improved varieties of mungbean with 6% (red) and 12% (blue) improved crop lower limit (CLL) compared with commercial crystal mungbean (green line). ................................................................................................................................ 118

Figure 46 - A: Comparison of permanent wilting point and soil moisture dynamics of mungbean plants treated with either H2O (control), ATW1124 (100mM) or thiourea (10µm foliar spray).120

Figure 47 Heat map comparing KL-improved mungbean production across 45 Australian locations varying in soil-moisture availability ............................................................................................ 121

Figure 48: Effect of seed pre-treatment with different regimes of ATW1124 on Crystal mungbean seed harvest index. ..................................................................................................................... 124

Figure 49 Bar graph comparing commercial and ATW1124 treated field grown mungbean vegetative biomass at flowering. ................................................................................................................. 125

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Figure 50: Bar graph illustrating the effects of ATW1124 on harvest index of filed grown mungbean.126

Figure 51: Representative photo comparing treatment effects of ATW1124 to thiourea. ................ 127

Figure 52 Root system of an 18 day old white lupin. All of the primary basal lateral roots have become proteoid roots in response to P deficiency. .................................................................. 136

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List of Tables

Table 1: Common mungbean, adzuki and navy bean pests impacting production. ............................. 13

Table 2: Classification of drought-inducible gene products functioning in response and tolerance .... 22

Table 3: Comparison of treatment conditions between the two runs of the experiment .................... 40

Table 4: Description of main parameters selected for analysis in WinRHIZO ....................................... 42

Table 5 – Phenology and physiological evaluation of 25 mungbean varieties ...................................... 55

Table 6 – Phenology and physiological evaluation of 25 mungbean varieties ...................................... 56

Table 7 – Mungbean varieties utilised in the present study and their sources .................................... 58

Table 8 Summary of mean root morphological data comparing ATW1124-plants with controls 7DAS in a growth chamber. Statistical analysis derived using Minitab 16. ........................................... 73

Table 9 Summary of X-ray tomography-derived RSA characteristics of ATW1124-plants vs H2O controls subjected to one week soil drying .................................................................................. 76

Table 10: Effects of transitioning into anthesis on photosynthetic apparatus of mungbean subjected to different degrees of drought stress. ........................................................................................ 86

Table 11: Effects of ATW1124 on mean physiological responses to drought stress. Figures in bold denote statistically significant differences (p < 0.05). .................................................................. 86

Table 12: Summary of interactions included in gene ontology enrichment analyses .............................. 94

Table 13 Summary and details of treatment groups included in the ATW1214 rainout shelter field trial at Kingaroy (QLD). ............................................................................................................... 116

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Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best of my

knowledge and belief, the thesis contains no material previously published or written by another

person except where due reference is made.

Signature:

Date: 12/12/2017

Date of lodgement: 02/08/2017

Date published: 12/12/2017

QUT Verified Signature

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Acknowledgments

First and foremost I’d like to thank my principal Professor Sagadevan Mundree for his generosity in

offering me this opportunity. The mentorship and support provided by Sagadevan was exceptional and

made my cross-disciplinary transition from health seamless. I’d like to especially thank Dr. Brett

Williams who has been a fantastic supervisor and friend throughout this entire journey. My external

supervisors Dr’s Rex Williams and Yash Chauhan who were both sources of great wisdom and advice

and contributed significantly with their unique perspectives and experience.

I would like to thank members of the Queensland Government Department of Agriculture and Fisheries

who provided support for the project and access to facilities. The entire former Tropical Pulses Team and

the Abiotic Stress Team who I had the pleasure to work alongside – they have been an endless source of

knowledge and their passion for research and science continues to inspire me. It was truly an honour to

have the opportunity to work with such a diverse and cohesive team. My many friends and colleagues

I’ve met along the way and of course my family without whom none of this would have been remotely

possible.

There are such an enormous number of people to acknowledge that it would take an additional thesis to

include them all – thanks to everyone for making this journey possible.

Michael

Chapter 1: Introduction

1

1Chapter 1: Introduction

1.1 BACKGROUND

Drought is the largest cause of crop losses globally. With global population set to reach 9 billion

by 2050 it is estimated that food production will need to increase by 70% in order to keep pace with the

growing food demand (FAO, 2009). Agriculture is already approaching capacity in terms of the

availability of both arable land and freshwater, therefore in order to ensure food security in the future there

needs to be a recognition of the impact agriculture has on the environment and a drastic improvement in

water use efficiency. Climate models are predicting less rainfall and an expansion of areas that are

considered ‘drought stressed’. In Queensland, 2014 marked the most significant drought event on record

with almost 80% of the state drought-declared (figure 1) (Hough & Rogers, 2014). As one of the

agricultural centres of Australia, Queensland is a

perfect location to conduct research into

generating drought tolerant crops. As the

primary means by which plants sense and

acquire nutrients and water from their

environment, plant root systems have a pivotal

role in drought tolerance. However despite

concerted efforts, there is a lack of information

on root traits of many crop species largely due to

the inherent difficulties of studying root

responses. As a high-throughput method of root

phenotyping is currently unavailable to the

majority of plant breeding programs, drought

tolerance is under-represented when compared

to other factors such as disease resistance.

Techniques for studying root traits in situ do

exist but they remain expensive and impractical

for the larger scales on which they would be

required. As a result molecular techniques are

now becoming more common such as marker

assisted selection through QTL mapping. These techniques can be performed on plants in the early stages

of development, which greatly reduces time which would normally be required for conventional

phenotyping. However, whether or not a plant possesses a particular trait depends on more than genetics

alone. Complex interactions occurring between environmental and genetic factors combine with

Figure 1: Heat map depicting areas drought-declared

during Queensland’s 2014 drought


2

agronomic management practices to determine crop phenotype and performance. Therefore to accurately

predict plant performance in a given environment, there needs to be a great impetus toward integration of

Genetic, Environmental and Management (GEM) interactions. Computer modelling is currently the most

efficient method for interrogating GEM networks, as a simulated experiment may take minutes to hours,

while a conventional equivalent would take months to years. Simulated data does still, however, require

validation using conventional methods; and although it is a vastly more time-efficient approach, it is highly

unlikely to ever become a replacement. Additionally, with Next-Generation Sequencing becoming

increasingly more price competitive and accessible to researchers, it is now feasible to unite the previously

isolated fields of molecular genetics, physiology and GEM modelling into a unified investigation into the

mechanisms underpinning drought tolerance. This forms the focus of the present study.

1.2 FOCUS AND AIMS

The major focus of the study was to gain insights into the responses of the legume crop Vigna radiata

(mungbean) to water deficit stress with an emphasis on the role of root architecture. This comprises three

main aims:

1) Characterisation of morphological and physiological responses of mungbean root architecture

and vegetative tissues to water deficit stress and chemical treatment with the novel chemical

ATW1214;

2) Transcriptome and pathway analysis of root and shoot tissues under water deficit stress treated

with ATW1124;

3) Field evaluation and interrogation using the agricultural production systems simulator (APSIM)

to assess implications in target production environments.

1.3 AIMS

The aims of this study were to delineate the complex mechanisms of drought response in

mungbean and provide insights leading to improved reliability of grain yields under climatic conditions

with scarce water availability. Investigations of morphological and physiological factors were compiled

with transcriptome data to assess drought responses at a whole plant level and align these results to

generate an integrated view of the effects of drought on mungbean. A novel pre-treatment of seed was

investigated as an enhancer of root architecture and thus drought tolerance, and through the utilization of

powerful in silico modelling techniques the project was able to study industrial scale impacts in a range of

environments. The overarching aim of the project was to inform the crop production industry on new

technology and information on how best to face the looming challenge of food insecurity in a future

forecast to have increasingly variable climatic conditions, limited arable land and freshwater availability.


3

Research into plant root systems remains relatively uncommon mainly due to the inherent

difficulties related to their study. Nonetheless there is overwhelming support in literature for the idea that a

better understanding of plant root systems would have drastic implications on farming practices in the

future and play a pivotal role in the future of food security. As such the aim of this project was to provide

insight into the enigmatic ‘hidden half’ of plants and shed light on some innovative new technologies

which may help improve crop production going forward.

1.4 SIGNIFICANCE

Research into improving water use efficiency of agricultural systems is gaining attention in recent

years as the majority of climate change scenarios paint a deteriorating picture of fresh water availability.

Agriculture accounts for in-excess of 70% of the worlds’ freshwater usage (Morison et al., 2008) and this

figure is set to increase by another ~19% by 2050. Lack of freshwater availability has been described as

the single biggest problem in meeting the ever-increasing global food requirement (Moffat, 2002;

Allahmoradi et al., 2011; Goswami et al., 2013). Other abiotic stresses leading to crop losses include

salinity, temperature, and chemical toxicity. However, of these, drought and salinity (often occurring in

conjunction) are the most costly. Climate change scenarios within Australia suggest an increasing

frequency of exceptionally hot and dry periods, an increase in annual evaporation (2-4%) and a reduction

of annual rainfall (3-5%) by 2030 (Hennessy et al., 2008).

Exceptionally hot periods have increased in frequency in recent decades and this trend is expected

to continue (Kimura, 2011). According to a 2008 report by Australia’s Department of Agriculture,

Fisheries and Forestry (Hennessy et al., 2008) 84% of primary producers involved in dryland cropping

(e.g. cereals and legumes) indicated that they were affected by adverse weather conditions including

drought. The vast majority of the respondents from other farming practices (e.g. dairy, livestock and

forestry etc.) were also affected. Analysis of Australian market data (Kimura, 2011) reveals that the major

risks to mixed-broadacre farming is rainfall deficit and the associated low production and profitability. To

protect against these risks farmers employ diversification strategies (e.g. production diversification and off-

farm income streams), production management (e.g. conservation of soil moisture) and price risk

management (e.g. storage, forward contracts and price pooling). In 2007 – 2008, 23% of Australian farms

received drought assistance with some requiring income support continuously since 2002. More recently,

2014 marked the worst drought event on record in Queensland (Hough & Rogers, 2014). There is a

general trend emerging in broadacre farming whereby fewer farmers are relying on their farm business to

generate all of their income (Government Drought Support, Report No. 46, 2009). This is attributed to

unreliable crop production due to effects of adverse weather conditions which by no means are unique to

Australia. What is vitally important is to identify crops with high potential to benefit from drought

tolerance and capitalise on them.


4

Vigna radiata (mungbean or ‘green gram’) is one of the most important pulse crops (e.g.

chickpeas, mungbean, lentils, beans etc.) in the world. Pulses are one of the most economical sources of

protein available, containing 22-24% protein, high levels of dietary fibre, essential amino acids including

methionine and lysine, vitamins, minerals and contain only a small amount of oil (Gowda et al., 2013).

They also form a large component of the predominantly vegetarian diet of India and continue to be in high

demand. Mungbeans have been commercially grown in Australia since the late 1960s and 1970s and

annual production has increased dramatically throughout the 2000s (GRDC, 2011). Breeding programs

have since become a priority for industry, which aims to improve stress tolerance (biotic and abiotic) and

improve grain yield predominantly by generating new genotypes. An advantage for growers to include

pulses such as mungbean in crop rotations is the large amounts of residual nitrogen left in the soil, reducing

the need for nitrogenous fertilisers and their associated costs (environmental and financial). Pulse rotations

have also helped farmers mitigate biotic stress on other crops such as crown rot in wheat and grass weeds

(Daniel et al., 2011).

Breeding programs have systematically been increasing mungbean yield over the years by

selecting for disease resistant and high yielding varieties, however yield potential has still not been

achieved because of the large numbers of crop losses occurring as a result of severe abiotic stresses. There

is enormous potential to increase mungbean production in Australia where current annual production over

the last 10 years has ranged from 50-70,000 tonne per annum (Cumming, 2014) to around 100,000 tonne

in 2015/16. More recently, the Australian Mungbean Association has set the target to increase mean annual

production to170, 000 tonne by 2020 (Australian Mungbean Association, 2014). However one of the

limitations currently faced by industry is that mungbean yields are notoriously erratic between seasons and

there is relatively little information regarding best practices of the crop. In order to improve production we

need a better understanding of how these crops respond to environmental stresses. This could facilitate

generation of region-specific varieties appropriately suited to their target production environments leading

to dramatically better outcomes for producers.

Chapter 2: Literature review

5

2Chapter 2: Literature review

2.1 FUTURE OF FOOD SECURITY AND SIGNIFICANCE OF PULSES

Food security is a major global concern and a multifaceted complex issue which would require a

great number of dissertations from a number of disciplines to effectively cover all aspects. Section 2.1

provides a brief overview of some key points with an emphasis on the role of agricultural production as it

constitutes a major component of the rationale underpinning this project.

2.1.1 Climate change and the future of global food security

As we approach 2050 world agriculture as a whole is set to face a number of great challenges.

First and foremost global population is predicted to exceed 9 billion people – a 2 billion increase in around

30 years – putting immense pressure on the agricultural industry to meet demand. Food production needs

to increase by 70% in order to keep pace with this population growth; and this is further complicated by

decreasing availability of arable land and freshwater which are virtually at capacity. Therefore the increase

in food production required will need to occur largely through innovations such as urban agriculture

(Johnson et al., 2015) or by increasing production efficiency to maximize output from land currently being

utilized, rather than agricultural expansion. Furthermore, although food production is a major concern in

the context of climate change, the exact impacts a changing climate may have remain unknown (Mimura,

2013). It is widely reported in climate modelling scenarios that the future will bring an increased incidence

of extremes in weather, including drought, which have already been reported in Europe and the USA

(Vicente-Serrano et al., 2014; C. et al., 2015; Diffenbaugh et al., 2015; Smirnov et al., 2016). An analysis

of the factors surrounding the devastating California (USA) drought which has now spanned four years,

has linked extremely warm conditions to ground water overdraft, critically low streamflow and acute water

shortages (Diffenbaugh et al., 2015).

In 2014 Queensland (AUS) experienced the worst drought event in recorded history with in-

excess of 80% of the state drought declared, leaving only a narrow coastal strip from Rockhampton

through to Cape York not drought stricken (Hough & Rogers, 2014). Impacts of extremes in weather are

further exacerbated by simultaneous occurrence of indirect factors such as disease outbreak, pests and an

increased risk of wildfires (Mimura, 2013; Diffenbaugh et al., 2015). In the case of the Queensland

drought of 2014, rising electricity cost became a major indirect impact which had significant impacts on

producers in the region. The negative impacts of climate change are well documented; however,

contrastingly, it also reported that due to the fertilizing effect of atmospheric CO2 on plants, certain

production regions may actually benefit from elevated atmospheric CO2 – at least in the short term. It is

thought that agricultural production potential would increase up to a mean global temperature rise of 3oC.


6

Beyond that the effects would be reversed and global production potential would suffer a considerable

decline (Mimura, 2013).The issue of the future of food security is further complicated as it also depends

heavily on socioeconomic developments (Mimura, 2013). Currently there are over 820 million people

suffering from malnutrition, and perhaps surprisingly, this is more the result of inadequate access to food,

rather than inadequate food production. According to IPCC Special Report on Emissions Scenarios,

assuming no climate change it is predicted that by 2080 the number of those suffering from malnutrition

should decrease to 100-770 million. Considering socioeconomic development generally improves quality

of life and reduces risk of hunger, this is unsurprising. However scenarios including climate change predict

figures ranging from 100 – 1300 million people. This highlights the vast uncertainty surrounding the

impacts of climate change on food security and the need to deepen our understanding of how crop plants

respond to abiotic stress.

2.1.2 Growing significance of pulses

As the majority of population expansion is expected to occur in the developing world (Figure 2),

these regions are a particular focal point in literature. Some of the regions of particularly rapid population

expansion include the Indian subcontinent (India, Pakistan, Sri Lanka and Bangladesh) and the Asia-

Pacific region as well as the Middle East. The strengthening of these developing economies is expected to

usher in a larger proportion of middle class citizens and an equivalent increase in the demand for high-

value protein-rich foods. Furthermore due to the predominantly vegetarian predisposition (est. 65% in

India) of these regions there will be an increase in demand for vegetarian sources of food. One such source

are pulses (grain legumes such as mungbean, chickpea and lentils etc.). Nutritionally pulses are

complementary to cereals as they contain high levels of protein, vitamins, minerals, important amino acids

(lysine and methionine) and relatively low levels of fats and oils. Consumption is predominantly localised

to the Indian sub-continent, the Middle East and Asia where they are very accessible as a major source of

food – especially for the economically vulnerable social classes. India is currently the largest producer,

consumer and importer of pulses in the world earning it a pivotal role in shaping the decisions of all other

pulse markets (Chandrashekhar G., 2016). In 2012 annual consumption of pulses in India was estimated at

17 million tonnes, predominantly by the lower economic classes of society more so than the middle

classes. For this reason the Indian government implements corrective measures to ensure that the market

price of pulses remains affordable through large scale imports via trading organizations (Point, 2012).

Private firms in India are also allowed to import from sources of their choosing given they have an import

permit from the Ministry of Agriculture. During the 2015/16 financial year India imported 5.8 million

tonnes of pulses including yellow peas (2.4M tonnes), lentils (1.2M tonnes) chickpeas (1M tonnes), pigeon

pea, urad and mungbean (collectively 1.2M tonnes). This was a 26% increase from the previous 2014/15

year which saw 4.6M tonnes of imported pulses (Chandrashekhar G., 2016). An increased reliance on

import to satisfy food demand in the future has been recommended as a key strategy to meet increasing


7

food demand (Mimura, 2013) therefore these figures are expected to increase even further in the future. As

the third largest exported of pulses to India, Australian exports constitute approximately 9% of the total

(behind Canada (40%), Myanmar (27%) and followed by the USA (6%)).


8

Figure 2 – Global population growth distribution trends approaching 2050 (FAO, 2009).


9

2.2 PRODUCTION LANDSCAPE: CHALLENGES AND OPPORTUNITIES

Australia is a major pulse producer and competes very well in the global market against much

larger international entities. The Australian climate is well-suited to cultivation of pulses however there are

a number challenges limiting production. Following is a review of the Australian pulse production

landscape with an emphasis on mungbean.

2.2.1 Australian mungbean production

Despite the large volumes of pulse trade already in motion, there is still an enormous and growing

demand for pulses from India presenting an excellent opportunity for Australia. Recent production in India

has suffered due to severe weather conditions – including drought and floods – leading to a domestic

shortage of pulses (Point, 2012) and a spike in the value of Australian exports. In Australia there are six

major pulses produced commercially: chickpea, faba/broad bean, field pea, lentil, lupin and mungbean

(Pulse Australia 2016). And of particular interest in Queensland is mungbean (Vigna radiata), which due

to an especially high market price and low input costs for production has experienced enormous industry

growth in recent years. Queensland mungbean production constitutes 90% of the total in Australia and is

valued at $100M. There has been considerable growth in Queensland production in recent years from 50k

tonne in 2013 to 100k tonne in 2016 and the production target for 2020 has been set at 170k (AMA, 2014).

The Australian market price of mungbean at the time of writing ranges from $800 to $1400 per tonne

(2016). By extrapolating market trends, achieving the 2020 production target of 170k tonnes would equate

to an annual turnover of $184.3 million – assuming current market price. It is near-impossible to make

accurate predictions on market price, however price is known to fluctuate with market demand as well as

seed quality which is governed by a set of parameters summarized concisely by the Australian Mungbean

Association (2013):

1. Pod scale defects: Any blemish of the seed coat that retains the white or grey coloured

lining of the pod on the seed coat, covering more than 25% of the surface area.

2. Seed coat damage: Any environmental or mechanical damage that creates cracks or

removes any portion of the seed coat to expose the kernel. Any portion of the seed kernel that

is missing is considered defective.

3. Staining: Any coating of vegetable gum that reduces the lustre or provides a

contact adhesion for plant material to the seed coat. Discolouration of the white hilum,

speckling of vegetable matter and a fresh ‘grass like smell’ are indications of staining.

4. Wrinkling: Damage sustained from weather events that creates wrinkles in any

direction over the seed coat.


10

Mungbean production is almost exclusively rainfed in Australia due to a number of factors. The first

is that irrigation is a limited resource and producers often have a fixed amount they are willing or able

to allocate to any particular crop and are forced to prioritise. The second in that irrigation is simply

not feasible to many producers due to decreasing availability of freshwater in production regions.

Nonetheless the general consensus is that development of an efficient irrigation system for mungbean

would be readily adopted by industry. The short growth cycle of mungbean (<90 days) in combination

with comparatively low irrigation requirements – 3.5-4.5 ML/ha – (Australian Mungbean

Association) – compared with other major crops such as sorghum (5 ML/ha), sunflower (4.5-7.5

ML/ha) soybean 6-8 ML/ha) and maize (8-9 ML/ha) certainly make irrigating a financially cost

effective option. However due to freshwater constraints, cost and concerns of sustainability of

irrigation, research is trending toward alternate methods of improving yields such as optimising

planting times, row spacing, planting density, disease management and breeding.

As Australian mungbean crops are machine dressed, there are a series of standards in place to ensure

uniformity in the grain quality (Cumming, 2013a). The principal method by which grain quality is assessed

is by visual examination against a series of photographic charts. Grain which does not meet the standards is

discarded – a process which accounts for considerable yield losses. Of the many factors influencing seed

quality, pest-induced damage and associated management practices are among the most widely practiced

by industry. Many voracious pests and ‘beneficials’ (typically arthopods of predatory or competitive nature

to pests) have been identified with varying impacts depending on insect load (No. pests / 1m2) and

developmental stage of the plant at which they are encountered (Brier et al., 2012). Table 1 below

summarises some of the major pests relevant to Australian mungbean production. Yield loss associated

with these pests can be significant (e.g. 35kg/ha per heliocoverpa larva per m2) as can associated financial

costs of control which range from $15 – $60/ha.

Pod scale Damaged seed coat

Wrinkling Staining

Figure 3: Major mungbean seed defects affecting quality-related yield loss ((Cumming, 2013b).


11

In Australia mungbean is a tropical summer grain legume cultivated during the summer months

spanning two planting windows from September - October and/or December – January. Optimum

temperature is around 30oC with rainfall (>30mm) occurring at planting. Most commonly growers wait for

a suitable rainfall event before sowing, which can cause significant variability in the planting time between

locations. In terms of nutrient requirements mungbean is fairly robust and, as with other legumes, acquires

nutrients from its very large cotyledons for the initial stages of growth leading to a very rapid and efficient

germination. Furthermore mungbean forms complex interactions with nitrogen fixing rhizobia and

mycorrhizae of which comparatively little is known. This symbiosis promotes the formation of root

nodules which a composed of around 80% microorganisms and facilitate enhanced uptake of immobile

and often deficient nutrients (e.g. phosphorous) from the soil. Nodulation has been reported to be enhanced

in phosphorous and/or nitrogen depleted soils not unlike the formation of proteoid roots in members of the

family Proteaceae (Watt & Evans, 1999a; Niu et al., 2013) – and appears to serve an orthologous function

in legumes.

2.2.2 Biotic stresses limiting Australian mungbean production

Other key factors limiting mungbean production are disease causing microorganisms including

viruses, fungi and bacterial pathogens. Impacts of pathogens is often highly varied and although may not

directly reduce seed quality, most often impairs some aspect of plant development or function and can thus

have significant impacts on yield. Control for disease is much more difficult to achieve as there is

comparatively little information available to producers. One reason is the continual emergence of new

strains of pathogens which often have very limited control options. For example Fusarium oxysporum –

the fungus responsible for a range of symptoms including wilting, chlorosis and premature senescence in a

number of crop species – which in 2016 suddenly become a major concern for Australian mungbean

producers. Historically incidence of fusarium in mungbean crops has been low at around 1-10%, however

recently this has risen alarmingly to as high as 70% in some cases (AMA, 2016). Studies have shown that

upon infection with fusarium oxysporum mungbean can suffer considerable tissue damage and exhibit

significantly reduced stand (Anderson, 1985). Unfortunately very few effective control options are

available. Control of fusarium in tomato (solanum lycopersicum) – a major host – has been somewhat

achieved but is very time and financially intensive such as through breeding for resistant varieties, raising

soil pH or fumigation (Larkin & Fravel, 1998). Genetic resistance is perhaps the most effective method of

control, as is the case with most other diseases, however it requires a significant amount of research and

time to establish.

There are three main diseases known to effect mungbean on the commercial scale in Australia:


12

Powdery mildew – a disease caused by a range of fungal species in the order Erysiphales that

predominantly affects foliar tissues and it known to infect a number of plant species. If contracted early in

the growth cycle powdery mildew can lead to foliar damage and a reduction in photosynthetic potential of

the plant. However there are several effective anti-fungal agents available commercially.

Tan Spot – A foliar disease characterised by irregular diamond or ovoid necrotic lesions. The

pathogen has been identified as the fungus Pyrenophora tritici-repentis and symptoms include necrosis

and/or chlorotic foliar tissues (See et al., 2016). Infection is facilitated by the germination of ascospores on

moistened leaf tissue which then penetrate through stomata or epidermal cells into the mesophyll.

Fungicides in the class strobilurin and triazole are the typical treatment.

Halo blight – A bacterial disease caused by the pathogen Xanthomonas campestris pathovar

phaseoli which initially infects soaked foliar tissues of bean plants (Victorian Department of Environment

and Primary Industries, 1999). Symptoms include angular spots that grow into larger necrotic circular

lesions characterised by a very narrow ‘halo’ of yellow tissue. In severe infections leaf tissue can appear

burnt but does not typically senesce. Pods may also contain dark green spots which gradually become red-

brown with maturity. The disease is typically antagonised by humid conditions and in extreme cases

infected tissues are covered in bacterial ‘ooze’. Although the disease can infect via wounds, the major

cause of dispersion is via contaminated seed harbouring the pathogen asymptomatically. Control options

are very limited including using disease-free seed, genetic resistance obtained through breeding, avoiding

overhead irrigation (humidity) and application of broad spectrum bactericides.

Breeding for resistant varieties is generally considered the most effective and favoured approach to

tackling these pathogens. As such, generation of new varieties in breeding programs is heavily driven by

crop performance under disease pressure. Resistance is typically scored according to level of infection,

tissue damage and maintenance of yield in highly regulated disease nurseries. However advances in

genomics facilitated largely by increased accessibility to Next-Gen sequencing technologies are becoming

more widespread which has drastically changed the breeding landscape in recent years. Identification of

quantitative trait loci (QTLs) for particular traits can now be used as ‘biomarkers’ in order to screen large

populations of new varieties very rapidly. In conjunction with nested association mapping populations

(NAM), breeders now have access to enormous germplasm diversity for use as parent lines for new

varieties. QTLs for biotic and abiotic stress tolerance are at the forefront of this effort which can help to

screen for tolerant genotypes and transfer these traits into commercial varieties.


13

Table 1: Threshold values given in ‘green vegetable bug adult equivalents (GVBAEQ)/m2

indicate levels where intervention is required. Those in CAPS are particularly significant.

Common mungbean, adzuki and navy bean pests impacting production


14

The main benefit of marker assisted selection is a significantly more rapid rate of new trait

integration into breeding lines. There is much enthusiasm in literature regarding this new technology and

its profound potential, although they are not the sole panacea for the development of new crops. One of the

issues with QTL driven selection is that the presence of a QTL does not necessarily guarantee target gene

expression much less manifestation of the desired plant phenotype. There are many complex interactions

which govern whether a plant will contain a particular set of traits and many more still governing how that

genotype will perform in the field. Rather it is the interplay between genetics, environmental and on-farm

management practices which determine crop performance – a field termed ‘GEM’. Studies of GEM are

valuable for improving practices and resource efficiency, especially water.

2.2.3 An introduction to GEM modelling

As the majority-user of freshwater globally (>70% total freshwater usage), agriculture as a whole

must become more efficient with water use and refrain from unnecessary freshwater consumption

wherever possible. Possible avenues for achieving this are though the development of drought tolerant

crop varieties, biotechnology, genome editing and ‘Next-Gen breeding’ such as marker assisted selection

of drought tolerant lines. These longer term solutions are crucial to securing the future of food security

globally. Therefore although mungbean is often considered one of the more water use efficient (WUE)

crops (Bourgault & Smith, 2010) which could benefit from an irrigation strategy, further consumption of

freshwater is an unlikely solution to reaching production targets as it is simply unsustainable. The crop has

an inherent affinity for rainfed production and with proper management there is enormous potential to

increase crop yields through other means. Not that irrigation should be avoided, rather that it should be

considered after establishment of proper management practices, which have far longer-reaching

applications. Ideally we would have region-specific management packages available for producers which

could guide on-farm decisions in real-time. Such technology is already in development and improving

rapidly. One such example discussed further in a later chapter is the crop modelling platform ‘APSIM’

which has already generated spin-off technologies aimed at producers. For example ‘Yield Prophet ™’

which provides precursory yield forecasts before the planting season in order to guide crop selection and

management practices.

APSIM serves primarily as a research tool to investigate relationships between plant traits,

environmental conditions and management practices to develop an optimum production system. One of

the main advantages of an in silico platform is that it is possible to generate large amounts of production

data in a much shorter timeframe than conventional field trials. That is not to suggest that a modelling

platform should replace experimental data but rather that modelling should be used as a tool to hone the

focus of field trials and refine practices. When considering drought tolerance in agricultural crops, survival

without a rational yield is insufficient. Only genotypes exhibiting higher grain yield and survival under


15

water stress are truly commercially viable ((Fukai & Cooper, 1995; Kiliç & Yağbasanlar, 2010). Grain

yield represents a proportion of plant total biomass, which itself is dependent on a vast array of complex

interactions between the plant and its environment. Due to the sheer number of interactions involved a

computer model is essential.

Researchers ascribe values to key parameters and model them to better understand these

interactions. As an example, soil moisture that is able to be harnessed by a particular plant is a fundamental

parameter and can be described as the plant available water (PAW). This is the volume of water contained

within the drained upper limit (DUL or field water capacity (%)) and crop lower limit (CLL – threshold

water volume (%) below which a particular plant cannot utilize soil moisture) (Keating et al., 2003; Tolk

& Evett, 2012). CLL has been defined as the water content that results in plant dormancy (incipient wilting

point); or as permanent wilting point (PWP) described as the wilting of lower leaves and their failure to

recover under humid conditions (Tolk & Evett, 2012). We can therefore influence crop PAW by either

increasing water (e.g. through irrigation) or by lowering the CLL by improving root systems (e.g. root

phenotyping in breeding programs,

transgenic approaches or modulating root

architecture chemically). Water lost

through transpiration is another important

factor relating to WUE and it is governed

largely by stomatal aperture, which itself is

driven by a variety of determinants

including internal leaf CO2 concentration,

cellular solutes, specific ions, pH and ABA

production (Blum, 2009).

There are a seemingly endless

number of interactions affecting plant

growth simultaneously; and to determine

the impact and magnitude of a singular

parameter is a very difficult task for

conventional field studies. However

advances in computing power and

modelling have recently facilitated great progress on this front. One such computer model which was been

in development for the past 20 years is the Agricultural Production Systems Simulator (APSIM), which

integrates genotypic, environment and management (GxExM) parameters to predict what impact certain

parameters have on crop performance. The model has been optimized for a variety of different crops

including mungbean under a range of different conditions (Keating et al., 2003; Chauhan et al., 2010;

Mace et al., 2013). The mungbean model (Robertson et al., 2000, 2002) has not received the same level of

Figure 4: Performance of the mungbean model

(observed versus simulated grain yield in g/m2)

against test datasets reported by Robertson et al.

(2002). The 1:1 line depicts the point where

experimental and simulated data are congruent.

The fitted trendline shows actual model accuracy.


16

attention as other crop models such as sorghum or wheat, however simulation data is still often remarkably

accurate (Figure 4).

APSIM predicts how changes in particular parameters will impact crop production by way of an

output file containing a vast amount of data. The user stipulates field starting conditions, relevant

meteorological data and management practices and runs the simulation for a given timeframe. Output data

is given for selected time points (e.g. phenological events, dates, harvest etc.) for whichever parameters

were of interest. Simulation runtime therefore depends on the complexity and volume of data stipulated by

the researcher and can range from an instant to several hours. The main benefit of APSIM is that it allows

researchers to identify specific parameters which significantly impact crop performance in the field in a

very time efficient manner. Researchers are able to assess hundreds of parameters in a very short space of

time and determine the best farming practices for a particular season. In contrast, assessing impacts of

individual parameters using conventional field trials would take months or even years. Growers could also

benefit from APSIM, for instance as a guide for how to manage row spacing, shade, irrigation, fertilizer

etc., and indeed there have already been spin-off technologies made available for growers (Holzworth et

al., 2014). There is, however, still some scepticism regarding how well the model performs in predicting

real-world agriculture (Keating et al., 2003) and as a result improvements are constantly being

implemented by parameterising new genotypes and locations with soil and weather data and verifying the

model with experimental data.

2.3 DROUGHT: A MAJOR LIMITATION TO PRODUCTIVITY

By far the most pervasive constraints to crop production globally are those of abiotic origin –

drought, salinity, temperature extremes, chemical toxicity and water-logging. And of these, drought and

salinity – often occurring in conjunction – have the largest impacts on crop production, not only in

Australia but on a global scale (Zhu et al., 2010; Jiang et al., 2011; Roy et al., 2011; Chimungu et al.,

2014). Following is a review of literature surrounding abiotic stress tolerance with an emphasis on drought

and including intracellular signalling and biochemical pathways known to be involved in drought response.

2.3.1 Abiotic stress and drought

It has been estimated that abiotic stress reduces yield for most major crop plants, on average, by

>50% (Greco et al., 2012a). Drought is currently the leading limitation to global food supply and has

proven difficult to achieve tolerance for since it elicits diverse phenotypic responses on plants varying with

stress intensity, environment, species and developmental stage (Budak et al., 2013). Crop varieties used

for agriculture have typically been selectively bred over time to be high yielding, easy to harvest and for

their taste, which, unfortunately, has led to a significant decrease their resilience to abiotic stress. In

contrast, wild varieties of the same species not subject to this intense artificial selection are usually highly


17

resilient, can sometimes be toxic and in appearance are often almost unrecognizable as members of the

same species. Wild relatives of modern wheat (Triticum aesitivum) varieties – (Emmer wheat (T.

dicoccoides) – have previously been pursued as a genetic resource of drought tolerance (Budak et al.,

2013). Following the identification of transferable genetic elements contributing to tolerance, breeding or

biotechnology can facilitate the transfer of these beneficial traits into commercial crop varieties. This

‘omics’ strategy is common in literature and has led to the identification of QTLs and genes related to

drought and osmotic stress tolerance in a range of species with proven success (Mahajan & Tuteja, 2005;

Lopes et al., 2011; Moumeni et al., 2011; Yu et al., 2013a; González-Guzmán et al., 2014). The majority

of agricultural crops maintain their tissue relative water content (RWC) above 85% during their active

growth (Gaff & Oliver, 2013) and generally begin to show signs of tissue damage when RWC falls within

60 – 30% (Vance & Zaerr, 1991; Gaff & Oliver, 2013). Not surprisingly, crop losses due to drought are

extremely common and are expected to increase given that incidence of drought is forecast to increase in

the future (Vicente-Serrano et al., 2014; C. et al., 2015; Diffenbaugh et al., 2015; Smirnov et al., 2016).

2.3.2 Drought tolerance

When plants experience water deficit, there is typically a physiological shift in the plant toward

conserving water and minimizing loss. Responses include stomatal closure, repression of cell growth,

photosynthesis and respiration (Shinozaki & Yamaguchi-Shinozaki, 2007). These are elicited by an

increase in stress-inducible genes encoding products which can be divided into two groups (Kreps et al.,

2002; Seki et al., 2002) - those that prevent cellular injury (osmoprotectants, metabolites, late

embryogenesis abundant proteins and detoxification enzymes); and regulators of gene expression. A 2002

study by (Seki et al., 2002), identified 277 drought inducible genes which were shown to cross-talk heavily

with cold and salinity response pathways. Genes shown to be up- or down-regulated by drought, cold or

salinity included 40 transcription factors, 24 involved in cellular metabolism, 20 in carbohydrate

metabolism, and 37 in photosynthesis (Seki et al., 2002). Modulation of some of these genes through

biotechnology has been shown to be effective in enhancing drought tolerance (Pilon-Smits et al., 1998; Yu

et al., 2013a, 2013b; Zhao et al., 2016) however it has also been demonstrated that transgenic plants often

exhibit retarded growth and atypical phenotypes (Liu et al., 1998; Pilon-Smits et al., 1998; Mizoi et al.,

2013). Therefore it appears that integration of tolerance-enhancing traits often comes at the expense of

those which are generally sought after for agricultural purposes – yield, low processing, high biomass,

rapid growth etc. The challenge is therefore to increase tolerance without sacrificing agricultural suitability.

Traits known to confer drought tolerance vary considerably between species and environments,

however those which recur frequently in literature include an increase in root to shoot ratio, a reduction of

biomass, leaf area and thickening of root secondary xylem (Gregory, 2006). Studies aimed at introducing

or enhancing these traits between plants requires first the determining their molecular basis. Researchers


18

are then able to target these molecular elements to improve tolerance such as through chemical

intervention, gene transfer or modulating expression levels. For example Yu et al., (2013) uncovered an

Arabidopsis transcription factor that, when transformed into rice (Oryza sativa), produced transgenic plants

with more extensive root systems, reduced stomatal density, higher water use efficiency (WUE) and higher

grain yield (Yu et al., 2013a). Targeting transcription factors to modify expression of native genes in plants

is a common method in biotechnology to enhance abiotic stress tolerance (Shinozaki & Yamaguchi-

Shinozaki, 2007; Doheny-Adams et al., 2012; Yu et al., 2013a). This has shown great potential and

continues to surface in literature as a viable approach for developing more resilient crops with higher

WUE. Indeed high WUE has been the goal of a large body of research (Manavalan et al., 2009; Eneji,

2011; Lopes et al., 2011; Yu et al., 2013a). However there are conflicting opinions in literature. A review

by Blum (2009) argued that when breeding for water-limited conditions, selection for high WUE would

actually reduce yield and drought tolerance in the majority of environmental scenarios (Blum et al., 2005;

Blum, 2009). The authors propose a shift toward ‘effective use of water’ (EUW) rather than WUE

principally by maximizing water capture for transpiration and reduced non-stomatal transpiration and soil

evaporation.

There are many factors which invariably surface in literature as key factors driving plant

performance under water limited conditions including harvest index (HI), water uptake (WU) by roots and

stomatal regulation. Harvest index describes how efficient the plant is in terms of partitioning

photosynthate toward reproductive structures𝑅𝑒𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑒 𝑏𝑖𝑜𝑚𝑎𝑠𝑠

𝑇𝑜𝑡𝑎𝑙 𝑎𝑏𝑜𝑣𝑒 𝑔𝑟𝑜𝑢𝑛𝑑. It should be noted that no cases were

identified in literature where harvest index accounted for root mass. Under water limiting conditions plants

exhibiting high HI are favourable as they require less vegetative biomass in order to produce grain and

suffer relatively less transpirative water loss. There are therefore two ways of achieving a high harvest

index: by reducing vegetative tissues or by increasing yield. As such a high harvest index does not

necessarily guarantee that a particular crop will be high yielding, only that grain yield constitutes a high

proportion of the total biomass. Plants with high harvest index can still be low yielding in instances when

total biomass is low, as is the case with most drought tolerant landraces.

Naturally resilient wild varieties of crops have evolved to suit their environments and acquired

tolerance to their environmental pressure. In Australia, the extremely harsh climate of the vast interior of

the continent is an environment in which very few plant species are able to survive. The few plant species

that can survive in these environments are typically extremely resilient, small and slow growing. An

example are the class of plants referred to as desiccation-tolerant or ‘air dry’ plants able to tolerate extreme

loss of RWC to as low as 5-13% (Gaff, 1977; Gaff & Oliver, 2013). These species retain most of their

water until soil moisture has been totally exhausted. Their tissue water content then rapidly drops and the

plants enter a state of almost total metabolic dormancy. The plants then rehydrate very rapidly over a series


19

of hours or days to a fully metabolically active state. Importantly, desiccation tolerant plants suffer

relatively little dehydration-related tissue damage under moderate to severe dehydration and are able to

restore dehydrated tissue to its original hydrated, metabolically active form. This has led to the coining of

their colloquial name – ‘resurrection plants’. Although the mechanisms of this phenomenon have proven

elusive to researchers, several have been proposed. One notable hypothesis is that autophagic recycling of

cytoplasmic contents plays a critical role in preventing tissue damage of plants under abiotic stresses

including drought and salinity (Liu et al., 2009; Kuzuoglu-Ozturk et al., 2012; Budak et al., 2013). A

recent study has linked accumulation of the sugar trehalose to the onset of autophagy which then facilitates

removal of cellular toxins thereby suppressing apoptosis and delaying senescence. Research into these

resurrection plants could prove extremely valuable for the generation of drought tolerant crops, given that

they share some genetic similarities with monocotyledonous crop cereals including rice, sorghum and

maize. Furthermore, as extreme examples of drought tolerance, resurrection plants may provide insights

into potential mechanisms of drought tolerance in other species including crop plants. Therefore not

surprisingly, studies into dissecting the basis of tolerance in resurrection plants continues to provide an

extremely valuable and growing resource for the generation of drought tolerant crops (Oliver, 1991; Gaff

& Oliver, 2013; Tobergte & Curtis, 2013). The extent of similarity in drought tolerance mechanisms of

resurrection plants compared with crop plants remains largely unknown.

2.3.3 Plant responses to drought

Traditionally plant responses to drought have been divided into three categories: escape, avoidance and

tolerance (Jenks & Hasegawa, 2005). Escape refers to processes which, upon detection of water deficit,

hasten the plants’ growth cycle toward reproduction before the stress becomes terminal. This can involve

forward shifts in normal phenology such as anthesis, duration of reproduction and physiological maturity.

Escape processes tend to be genetically encoded, thus the only practical methods of modifying these traits

are through breeding, chemical application or genetic engineering. Avoidance refers to processes which

conserve water by reducing transpiration and promote water uptake (WU) in order to avoid extreme

changes in water potential and the damage that often accompanies it. The role of drought avoidance in

plants under mild drought stress has been the focus of many molecular and physiological studies

(Kirkham, 2004; Des Marais & Juenger, 2010). Key parameters measured include stomatal aperture,

stomatal density and leaf transpiration. Root responses to drought are far less characterised, however the

general consensus is that there is great potential to improve drought tolerance by targeting root systems. So

much so, that root traits which enhance abiotic stress tolerance have been termed the “…traits of the

second green revolution” (Lynch, 2007; Den Herder et al., 2010).

Drought tolerance is defined as the ability of plants to cope with reduced tissue water potential. Drought

tolerance involves adaptive responses in morphology, metabolism or cellular structure (Jenks &


20

Hasegawa, 2005). A common feature of abiotic stress tolerant plants is the ability to down-regulate

metabolism and overall growth to conserve water which, from an agricultural perspective, usually also

means a reduction in grain yield. This stunting is widely reported in transgenic plants with engineered

tolerance (Liu et al., 1998; Pilon-Smits et al., 1998; Mizoi et al., 2013). Methods of improving stress

tolerance while circumventing this yield penalty are in high demand. Studies have shown that plants

exhibiting delayed leaf senescence – also known as ‘stay green mutants’ – are able to tolerate post-

flowering drought stress and increase yields under water limiting conditions (Subudhi et al., 2000; Thomas

& Howarth, 2000; Hörtensteiner, 2009). More recently, Australian researchers have demonstrated that

‘stay green’ sorghum achieved increased yields under water-limiting field conditions (Jordan et al., 2012).

However contrasting results were reported in a 2016 paper in PNAS which demonstrated that enhanced

leaf senescence under the control of a stress inducible promotor (RD29A) can actually promote extreme

drought stress tolerance in both Arabidopsis and rice (Zhao et al., 2016). It was reported that ABA-induced

leaf senescence generated an osmotic potential gradient in transgenic plants overexpressing the ABA

receptor PYL9 – pyrabactin resistance 1-like abscisic acid (ABA) receptor gene – causing water to

preferentially flow into developing tissues (Zhao et al., 2016). However this latter article dealt with

extreme drought stress imparted within tightly regulated experimental conditions, while the former dealt

with field trials which had little control over stress levels and were conducted in actual production

environments following industry practices. It is clear from literature that plant traits that confer drought

tolerance can have considerable variation between species, environmental conditions and severity of stress.

The majority of plant water loss occurs via the stomata as a result of the high evaporative demand

of ambient air (even under mild conditions). As a result stomatal aperture is one of the most extensively

studied drought avoidance strategies in plants along with stomatal density, both of which strongly

influence WUE (Yoo et al., 2009; Kim et al., 2014). Stomata typically open in the morning around dawn

when light levels begin to rise and close rapidly at night with the falling light levels. Light is therefore a

direct factor regulating stomatal aperture however the discovery that isolated epidermal strips or guard cell

protoplasts responded to CO2 revealed a crucial role of CO2 assimilation in regulating stomatal aperture

(Kriedemann et al., 1999). Additionally, stomata have a tendency of ‘leaking’ water at night which can

significantly contribute plant water loss (Blum, 2009). As photon irradiance increases, photosynthesis

increases throughout the day, decreasing the intercellular partial pressure of CO2 (pi) as it is fixed in the

thylakoid membranes of the chloroplast. Therefore the opening of stomata to permit CO2 passage also

permits the carbon fixation. Stomata respond to the pi rather than to partial pressure of atmospheric CO2

(pa). Furthermore, stomatal size has previously been strongly negatively correlated with stomatal density

and plants with low stomatal density are considered better adapted to arid climates (Des Marais & Juenger,

2010). Water loss can also as a result of leaf permeability – i.e. through the cuticle, which is water loss

without the added benefit of CO2 fixation (Blum et al., 2005).


21

Reduction in transpiration is a common feature of many reportedly drought tolerant varieties.

Regulation of stomatal aperture in response to dehydration is perhaps the most well documented response

of plants to dehydration. One of the main regulators of stomatal aperture is the plant hormone abscisic acid

(ABA) which also regulates a number of other adaptive stress responses (Cutler et al., 2010) and has been

a focal point in the literature as far back as1965 when its structure was first determined. In 2010, a new

model for ABA action (Cutler et al., 2010) was proposed which unified previous studies on individual

components into a core signalling network. It is now known that ABA is a promoter of root growth at low

water potential, in contrast to its inhibitory role when applied to unstressed plants (Kriedemann et al.,

1999). Inhibitory in the sense that ABA regulates stomatal aperture (Zhang et al., 2001; Mustilli et al.,

2002) thereby effectively halting carbon fixation. However under water limiting conditions, stomatal

closure and resulting decrease in transpirational water loss can be extremely beneficial in ensuring plant

survival under drought stress. Another effect of ABA is the induction of lateral root quiescence which has

recently been shown to be intricately linked to the ABA-receptor PYL9 (Zhao et al., 2016). It has now

been shown that through close association with auxin responsive genes, PYB8 and PYB9 are important in

recovery of lateral roots following ABA-induced quiescence during drought stress (Xing et al., 2016).

Lifting the shroud surrounding these biochemical pathways involved in plant drought stress response is

critical for improving how we manage plants in stressful environments.

2.3.4 Signalling and molecular responses to drought

Signalling networks involved in plant stress perception and response are expansive and involve a

large amount of crosstalk between biotic and abiotic stress pathways. The interaction between biotic and

abiotic is heavily dependent on hormone signalling – particularly ABA – which can be antagonistic. These

interactions can be beneficial or negative, in that responses to abiotic stress can both enhance or reduce

tolerance to biotic stresses, and vice versa (Greco et al., 2012a). Coordination of this crosstalk is mediated

through signalling via transcription factors, kinase cascades and ROS production. Studies of gene

expression have revealed hundreds of differentially expressed genes in plants under drought stress (Ashraf,

2010; Chan, 2012; Nitsch et al., 2012; Sreenivasulu et al., 2012). An eloquent review of the gene networks

involved in drought stress response and tolerance was provided by Shinozaki (2007). The majority of the

studies describing genes and gene products involved in drought stress response have focussed on the

model species Arabidopsis thaliana and rice (Oryza sativa) and were laregly based on GeneChip

microarrays. Of the thousands of stress-inducible genes identified in this way, more than half of those

induced by drought were also induced by high salinity and/or ABA treatment (Seki et al., 2002; Shinozaki

& Yamaguchi-Shinozaki, 2007). This highlights the large amount of crosstalk between drought and

salinity pathways. In contrast, only 10% of drought-inducible genes were involved in cold stress (Figure

5). Drought inducible gene products are divided into two groups:


22

Table 2: Classification of drought-inducible gene products functioning in response and tolerance

(Shinozaki & Yamaguchi-Shinozaki, 2007)

Group 1: Functional proteins Group 2: Regulatory proteins

Detoxification enzymes Transcription factors (DREB2, AREB, MYC,

MYB, bZIP, NAC, HB, etc.)

Water channels, transporters Protein kinases, phosphatases

Protection factors of macromolecules (LEA

proteins, chaperones)

Phospholipid metabolism

Key enzymes for osmolyte biosynthesis (proline,

sugars)

ABA biosynthesis

Proteases

Introduction of some of these genes via plant transformation has led to generation of transgenic

plants with demonstrably enhanced abiotic stress tolerance (Zhang et al., 2004; Bartels & Sunkar, 2005;

Umezawa et al., 2006). Transgenes utilized in these studies encoded for biosynthesis of proline and other

amino acids, various amines (e.g. glycinebetaine and polyamines), sugars and sugar alcohols (e.g.

trehalose, raffinose, mannitol and galactinol). Proline is believed to play an important role in plant abiotic

stress tolerance and serves a number of roles including stabilisation of sub-cellular structures, free radical

scavenging, redox buffering and triggering gene expression (Kaur & Asthir, 2015). As such genetic

engineering of proline content is considered a potential avenue for achieving plant stress tolerance.

However the intricate network underpinning plant stress pathways makes it difficult to predict the final

outcome of modifying any one of pathways’ constituents. There is a large body of research aimed at

clarifying the role of the many aspects of the stress response pathway which continues to answer many

questions regarding the role of various genes, but also raises many more. For instance downregulation of

SSADH (succinic semialdehyde dehydrogenase) – results in increased ROS production and impaired plant

development under stress. In Arabidopsis the SSADH gene is involved in oxidation of succinic

semialdehyde (SSA) to succinate which then enters the tricarboxylic acid (TCA) cycle of cellular

respiration (Krasensky & Jonak, 2012a) (Figure 6). However whether overexpression SSADH is able to

elevate succinate levels and increase TCA cycle efficiency, or even whether an increase in cellular

respiration under stress would be beneficial remains unknown. Our view of stress response pathways is far

from complete and as such, modifications made to intermediates in these pathways can often have

unforeseen implications.


23

Figure 5: Transcriptional regulatory networks of plant stress and gene expression.

Of the six signal transduction pathways involved in drought, cold and salinity, three are involved in ABA

biosynthesis and three are ABA-independent. The products generated by these processes are governed by

a complex network of genes, transcription factors and products all of which contribute to a plant’s ability

to tolerate stress (Shinozaki & Yamaguchi-Shinozaki, 2007).


24

Figure 6 – Schematic overview of amino acid, polyamine, and glycine betaine metabolism.

Plants with enhanced or reduced activity of the indicated enzymes show altered tolerance to abiotic

stress. ApGSMT, Aphanothece halophytica glycine sarcosine methyl transferase; ApDMT,

Aphanothece halophytica dimethylglycine methyltransferase; codA, choline oxidase; betA, choline

dehydrogenase; betB, betaine aldehyde dehydrogenase; GAD, glutamate decarboxylase; GABA-T, 4-

aminobutyrate aminotransferase; SSADH, succinic semialdehyde dehydrogenase; P5CS, 1-pyrroline-

5-carboxylate synthetase; P5CR, pyrroline-5-carboxylate reductase; ProDH, proline dehydrogenase,

P5CDH, 1-pyrroline-5-carboxylate dehydrogenase; d-OAT, ornithine d-aminotransferase ; ADC,

arginine decarboxylase; ODC, ornithine decarboxylase; SPDS, spermidine synthase; SPMS, spermine

synthase; PAO, polyamine oxidase; DAO, diamine oxidase (Krasensky & Jonak, 2012b).


25

There have also been efforts to enhance stress tolerance through exogenous application of plant

stress pathway intermediates to plants grown in stressful environments (Peleg & Blumwald, 2011). For

example researchers at the University of California have recently discovered a synthetic variant of ABA –

Quinabactin (González-Guzmán et al., 2014) – with a similar mode of action, but much lower production

costs. This synthetic analogue of ABA exhibited ‘ABA like potency’ by interacting with dimeric ABA

receptors presumptively by possessing structural similarity with the ABA receptor binding site. Plants

treated with Quinabactin exhibited a range of altered phenotypes including stimulation of guard cell

closure and suppression of water loss thereby improving drought tolerance in soy bean (Glycine max) and

Arabidopsis. Implications of this type of chemical intervention are generally well received by industry as

they have fairly low ethical concerns and a comparatively rapid timeframe from development to

implementation. Of particular relevance to drought stress tolerance are similar methods which enhance

root development and improve nutrient acquisition including water.

2.4 PLANT ROOT SYSTEMS

Plant roots constitute a significant proportion of total biomass of a plant and contribute

enormously to productivity of crop species. However comparatively little information is available as they

are enigmatically difficult to investigate. Below is a review of literature on root development, particularly

lateral roots for their involvement in drought tolerance; involvement of plant roots in plant abiotic stress

tolerance; and root systems architecture.

2.4.1 Lateral root development and patterning

In water limiting environments, plant roots are a critical sensing organ which can trigger a wide

range of altered plant processes including the generation of new root tissue. Studies have shown that the

proper flow of non- cell-autonomous factors and their corresponding downstream processes via the PD is

essential for healthy development of meristems and meristemoids (Benitez-Alfonso et al., 2013). Included

in this is the development of lateral root meristems, the spatial patterning of which governs the emergence

of lateral root primordia (LRP) and, ultimately, post-embryonic lateral root architecture. Development of

LRP has been divided into seven stages (I – VII) and is now known to be initiated in the xylem-pole

pericycle (XPP) cells in the lateral root forming region of the primary root axis. Studies have shown that a

balance of the plant hormones auxin/cytokinin and auxin/indole-3-acetic acid proteins regulate expression

of genes required for development and emergence of lateral root meristems (Gonzalez-Rizzo et al., 2006;

Peleg & Blumwald, 2011). The characteristic patterning of lateral roots – alternating left/right along the

primary root axis – is believed to be the result of a corresponding pattern of auxin maxima along the basal

meristem of the primary root and intercellular transport of other signalling molecules. The transfer of these

signalling molecules including gene products of GATA23 and DR5 depends on PD-mediated symplastic

connectivity of cells proximal to LRP initiation sites. Recently studies have linked regulation of PD


26

aperture to the periodic deposition and degradation of the sugar callose – a β-1-3 glucan. Misregulation

callose at the PD has been shown to have deleterious effects on lateral root morphology, including

clustering of LRP sites leading to an increase in lateral root density and even lateral root fusion (Benitez-

Alfonso et al., 2013). Despite the evident significance of PD to plant development – particularly with

respect to abiotic stress tolerance and root development – many questions still remain with respect to their

mechanics.


27

Figure 7 – Representation of root developmental regions and tissue organization

Abbreviations: qc, quiescent centre; Ep, epidermis; C, cortex, E, endodermis; P, pericycle.

Numbering indicates distribution of the seven stages of lateral root formation along the

length of an Arabidopsis thaliana primary root (Malamy & Benfey, 1997).


28

2.4.2 Root contribution to plant performance under stress

The ability of plants to harness soil moisture and nutrients depends extensively on root physiology

and architecture. For plants to survive anchored in a constantly fluctuating environment requires that roots

are able to respond to nutrient and water deficiencies as well as other harmful conditions such as toxic

solutes (Zhu et al., 2010; Postma & Lynch, 2011a; Jaramillo et al., 2013). Although roots do not directly

participate in the reproductive process or carbon fixation they are essential to plant survival and contribute

significantly in stress tolerance (Jenks & Hasegawa, 2005). A significant portion of plant photosynthates

are invested in roots and it is estimated that, in terrestrial plants, the total surface area of fine roots matches

and in some cases even exceeds, the surface area of photosynthesising above ground tissues in a variety of

ecosystems (Jackson et al., 1997). Much of the reason plants are able to adapt to different environments is

due to a high level of root developmental plasticity and genotypic variation (Watt & Evans, 1999a; de

Dorlodot et al., 2007), and these root stress responses are a growing focus for crop improvement. Higher

drought tolerance in rice has been attributed to several root characteristics including thickness, density and

branching (Gregory, 2006).

Figure 8: Schematic of root responses to abiotic stresses at the whole plant level.

(A) White circles represent toxic substances which are being excluded and avoided by root placement.

Grey circles represent a limited nutrient which is being acquired by root foraging. (B) Schematic of root

responses to abiotic stress at the cellular level. Stress perception by roots leads to tissue specific

responses in morphology, organisation and root-shoot signalling (Jenks et al. 2013).


29

In other species including maize, wheat and other cereals, root foraging has been revealed as a key

trait in stress avoidance (Wang et al., 2005; Gregory, 2006; Zhu et al., 2010; Flavel et al., 2012; Renton &

Poot, 2014). Nutrient acquisition is directly related to the ability of roots to forage through soil layers

(Manavalan et al., 2009; Postma & Lynch, 2011a, 2011b; Roy et al., 2011; Jaramillo et al., 2013; Lynch,

2013). Specifically it is the foraging capacity of lateral roots which contribute most to nutrient uptake,

especially for nitrogen and phosphorous. Wang et al. (2005) showed that when plants are grown in soil

with a fixed-patchy water availability (cf. uniform water availability from conventional watering) plants

tend to selectively place their roots in the wetter parts of the pot (Wang et al., 2005). They attributed the

more extensive roots in their experiments to partial root drying (PRD), which, they propose, caused an

influx of several stress response factors commonly associated with improved tolerance (Peleg &

Blumwald, 2011; Nitsch et al., 2012; Sreenivasulu et al., 2012). The authors postulate a ‘priming’

mechanism whereby PRD-induced generation of stress response factors elicited enhanced drought

tolerance. Furthermore PRD treated plants developed 10% more shoot biomass as a result of the more

extensive root system. It has also been proposed that PRD improves nutrient acquisition and reduces

fertiliser demand (Wang et al., 2013). This utilisation of root traits to improve plant performance has

become popular in literature and there continues to be large scale, widespread projects targeting a range of

agricultural and horticultural crops (Dry et al., 2000; Santos et al., 2005; Sepaskhah & Ahmadi, 2010;

Romero et al., 2014; Degaris et al., 2016).

Nutrient availability, principally NPKS, is well known to govern plant development including the

root system. The traditional perspective is that a deficiency of each of these nutrients has a specific effect

on root architecture. Due to the complexity of nutrient signalling in plants, investigations into the effects of

nutrient deficiencies have largely focussed on deficiency of key nutrients independently – i.e.

combinations of deficiencies or the possibility of co-dependencies were disregarded. This approach is

logical and demonstratively effective, however as nutrient pathways are often intricately related these

studies provide a simplified perspective. More recently researchers have identified a plethora of cross talk

between nutrient dependencies that govern RSA suggesting a more complex inter-connected network of

signalling events. Investigations into the effects of multiple nutrient deficiencies on root development are

very limited thus our understanding of their networks remains poor. In response to this, systematic inquiry

into this area is now emerging in literature (Kellermeier et al., 2014). Studies in Arabidopsis have shown

that lateral root emergence and elongation are particularly affected by the supply and transport of nitrogen.

Furthermore, the molecular mechanisms underlying these responses have begun to be interrogated (Little

et al., 2005; Damiani et al., 2016; Yu et al., 2016). A better understanding of the role of nutrient

dependencies on root development would be useful in deciding how best to manage plants in suboptimal

environments.


30

There have been many studies linking dry soil conditions to an increase in plant root development

(Moroke et al., 2002; Schenk & Jackson, 2005; Gregory, 2006; Morison et al., 2008); and it appears that

this relationship is conserved in many species. This type of root plasticity – that is, the dynamic nature of

the root system – particularly a tendency toward an increased root:shoot ratio under drought stress,

contributes greatly to plant survival under stress, however there is relatively sparse discussion of the

mechanisms underlying these responses in literature. In a 2014 study (Chimungu et al., 2014), it was found

that maize (Zea mays L.) landraces with reduced root cortical cell file number (CCFN) – that is, the

number of parenchyma cells contained within the maize root cortex – exhibited enhanced drought

tolerance (Chimungu et al., 2014). The study was founded on earlier work which correlated drought

tolerance with the number of root cortical aerenchyma (RCA) – a parameter referred to as cortical burden.

Their findings suggest that a deep root phenotype has, at least in part, a structural basis. In reduced CCFN

plants, living cortical tissue was converted to air space by RCA which lowered metabolic cost of root

exploration improving plant growth, nutrient acquisition and reproduction under edaphic stress including

drought (Lynch, 2003, 2007; Zhu et al., 2010; Postma & Lynch, 2011b; Jaramillo et al., 2013). Plant

parameters which showed improvement included CO2 assimilation, shoot biomass at flowering, deeper

rooting, leaf RWC and stomatal conductance. Isotopic analysis of xylem water revealed a heavier reliance

on deep (>30cm) water containing a lower ratio of δ18O (rain water), a technique typically used to analyse

water flow (Hsieh et al., 1998; Gazis & Feng, 2004) (e.g. glacial ice mixing with oceanic or rainwater).

Given that soil water tends to accumulate in the deeper reaches of the soil horizon, plants with

longitudinally distributed root architectures are advantaged in water-limiting environments.

Roots are highly sensitive to nutrient supply however there tends to be extensive response

plasticity (Yu et al., 2014; Araya et al., 2016; Li et al., 2016). Two of the most significant nutrients with

respect to root development are nitrogen and phosphorous – both of which are widely applied in fertilizers

(Wang et al., 2015b). Determination of optimal root traits for N uptake is difficult since root morphology is

strongly affected by both GE interactions and nutrient supply. Nonetheless identification of QTLs to

maximize N uptake continues to be a focal point in breeding research (Yu et al., 2014; Li et al., 2016). For

instance it was shown that plants transferred from N-replete to N-depleted conditions had their root growth

restricted. It appeared this served as a preventative mechanism to avoid extension into a nutrient poor

environment (Araya et al., 2016). However in the soil, N can occur in a number of different forms each

with its own distinct effect on plant roots. Excess N in the form of nitrate (NO3-) causes roots to become

inhibited, particularly the length of lateral roots. While excess ammonium (NH4+) inhibits primary root

growth but stimulates lateral root branching, resulting in densely branched but short lateral roots. Similar

effects are elicited by glutamate (Araya et al., 2016). As such root responses to even single nutrients are

highly variable. Nonetheless there have been momentous efforts to establish modelling platforms to model

root systems incorporating these vastly complex interactions with remarkable effectiveness (Lynch et al.,

1997; Araya et al., 2016).


31

Phosphorous deficiency is one of the other major abiotic limitations to crop production. As such

application of fertilisers containing P-compounds has become extensively widespread in modern

agricultural and has resulted in large economic benefits. However the process is highly inefficient since as

much as 90% of added P in fertilizer becomes sorbed to soil particles, making it effectively unusable to the

plant and causing it to become eroded and lost as run-off (Wang et al., 2015b). In this way high P input has

resulted in many environmental concerns including eutrophication and hypoxia of waterways. Plants have

evolved mechanisms to cope with low P such as through mycorrhizal associations and exudation of

organic acids into the rhizoshpere to increase uptake. Further research into these mechanisms and

generation of plants with improved capacity for P extraction would reduce the need for P fertililzation.

Organic acid exudation by certain plant species has been shown to dramatically improve plant

performance under P-deficient conditions (Aono et al., 2001; Kania et al., 2007; Wang et al., 2015b). One

species especially well adapted to low P is white lupin (Lupinus albus L.), whose success is predominantly

the result of formation of highly specialised proteoid root structures. In P-limited conditions, proteoid roots

exude P-mobilizing carboxylates (citrate and malate), phosphatase enzymes, phenolics and protons into the

rhizosphere and dramatically increase P uptake (Aono et al., 2001; Kania et al., 2007). This release occurs

as an exudative burst of very highly concentrated (<2 µmol citrate

1𝑔 𝑟𝑜𝑜𝑡 𝑓𝑟𝑒𝑠ℎ 𝑤𝑒𝑖𝑔ℎ𝑡 𝑝𝑒𝑟 ℎ𝑜𝑢𝑟) citrate from mature cluster

roots for several days. This highly effective adaptation is unfortunately also equally uncommon in the plant

kingdom. However similar mechanisms have been in other plant species such as production and exudation

of strigolactones from plant roots into the rhizosphere (Kapulnik & Koltai, 2016). Strigolactones (SLs) are

plant hormones known to regulate development of shoots by regulating axillary bud outgrowth and roots

by altering architecture and root hair morphology. In the rhizosphere, following exudation, SLs have also

been linked to hyphal branching of mycorrhizal fungi and are involved in communication in the

rhizoshepere. Under P-limiting conditions SL biosynthesis and exudation has been shown to increase and

has been associated with ‘fine-tuning’ of root responses through auxin transport to improve P uptake.

Root mediated modification of the rhizosphere is a common mechanism by which plants cope

with stress as they are sessile organisms and lack any real mobility. Many examples have been identified in

literature including release of water and protons, exudation of carboxylic acids, nucleases and acid

phosphatases (Wang et al., 2015b). A comprehensive review of the role of organic acids in the rhizosphere

was provided by Jones et al. (1998) describing their involvement in a diverse array of processes including

metal detoxification, alleviation of anaerobic stress in roots, mineral weathering and pathogen attraction

(Jones, 1998). Studies have since shown that exogenous application of many of these compounds can elicit

beneficial effects on plants under stress. Similar approaches led to the revolutionary discovery of the role

of ABA in regulating stomatal aperture and lateral root formation (Melcher et al., 2010; Tworkoski et al.,

2011; Zhao et al., 2016).


32

Generally RSA has been considered to have a genetic basis over which on-farm intervention can

exert little control. However that is now coming into question with studies demonstrating that application

of a range of chemicals can have large impacts on root development. Chemical pre-treatment of plants has

been used to improve plant stress tolerance since the 1930s; however the mechanisms underlying these

phenomena have only recently begun to be explained. Mungbeans subjected to various seed-priming

techniques have been shown to elicit better establishment and higher yield (Farooq et al., 2008; Umair et

al., 2011). These findings are supported by reports of seed priming improving emergence and stand

establishment – two of the most important factors affecting crop yield in mungbean (Khan & Al, 2008).

We now know that the plants treated with growth regulators before onset of stress, can enhance their

tolerance to subsequent stress (Peleg & Blumwald, 2011; Nitsch et al., 2012). Treatment with abscisic acid

by way of root drench has previously been shown to enhance drought tolerance in apple (Malus domestica)

by reducing transpiration and thus conserving water (Tworkoski et al., 2011; Chan, 2012; Nitsch et al.,

2012). Guo et al. (2009) showed that exogenous application of ABA to peanut seedlings resulted in

elevated AhNCED1 expression – a key gene in ABA biosynthesis. Involvement of phytohormones in

regulating plant development is widely known.

However, interestingly, in some cases these hormones can originate from avirulent rhizobacterial

infection which then trigger diverse plant responses through the salicylic acid dependant systemic acquired

resistance (SAR) pathway (Beneduzi et al., 2012). Only around 1-2% of soil rhizobia are considered to be

plant growth promoting rhizobacteria (PGPR), and of those Basillus and Pseudomonas spp. predominate

(Beneduzi et al., 2012). PGPR often produce compounds required by the plant such as phytohormones or

improve nutrient uptake and can lessen the effects of other plant pathogens through the production of

siderophores, bacteriocins, and antibiotics (Beneduzi et al., 2012). Following isolation of ACC deaminase

from the soil, in their pioneering work Mayak et al. (2004) deduced that bacteria isolated from the Arava

region of southern Israel, may have potential in enabling cultivation of plants in water limited conditions.

They demonstrated that the infection with PGPR can improve plant drought tolerance of tomato and

peppers. This form of acquired abiotic tolerance has been described as a variation of the more common

‘systemic acquired resistance’ (SAR) to biotic stress – referred to as induced systematic tolerance (IST)

(Marulanda et al., 2009; Yang et al., 2009; Beneduzi et al., 2012). Furthermore it was reported that

inoculation of common bean (Phaseolus vulgaris) with certain PGPR elicited increased nodulation,

augmented plant height and shoot dry weight (Yang et al., 2009). PGPR were involved in eliciting

production of antioxidants for ROS scavenging, cytokinins which promote shoot development, ACC

deaminase which reduces ethylene levels, indole-3-acetic acid (IAA) that stimulates root proliferation and

volatiles which facilitate mobilisation of soil nutrients (Yang et al., 2009; Glick, 2014). Furthermore it has

been shown PGPR-induced tolerance has a transcriptional basis and can be systemically acquired (Maleck

et al., 2000). The effects of PGPR are diverse and could provide a highly cost effective method of inducing

a range of highly beneficial plant traits to increase plant stress tolerance.


33

2.4.3 Root systems architecture

Root architecture – explicitly, the spatial distribution of roots in soil – is thought to be

predominantly governed by genetics. As such the combination of ‘genotype x environment’ interactions

have a far stronger influence on RSA than management (𝐺𝑥𝐸

𝑀< 1). However studies have shown that there

are certain aspects of RSA that can be manipulated chemically or by controlling the plants’ local

environment in order to achieve predetermined modifications of RSA in a range of conditions. A 2009

study showed that exogenous application of ABA promotes primary root elongation and inhibits lateral

root (LR) growth thereby reducing root density but increasing depth of penetration (Guo et al., 2009).

Therefore exogenous application of ABA is one (fairly costly) method of generating plants with deeper

root penetrative capacity. Improved access to deep soil moisture facilitated by a deeper primary root

resulted in a net improvement to WUE in spite of reduced lateral root elongation. However there is

overwhelming consensus in literature that lateral roots contribute a great deal to achieving drought

tolerance. For example it has long been documented that plants with steep branching angles (relative to the

vertical plane) tend to perform better than those with shallower rooting angles under water or nutrient

limiting conditions (Lynch et al., 1997; Kato et al., 2006; Lopes et al., 2011; Lynch, 2013; Renton & Poot,

2014).

Root angle is considered an accurate, quantifiable measure of the general distribution of a root

system and is widely used to categorise root systems; however there are two definitions of root angle

which surface in literature and careful distinction must be made. ‘Root growth angle’ refers to the angle of

root growth relative to the horizontal plane, a certain distance from the parent root. While ‘branch angle’

refers to the angle of root growth at the point it emerges from the parent root. Some of the most

informative research pertaining to root angle has been conducted using classical manual tracing of roots

grown in flat transparent culture boxes or ‘rhizotrons’ (de Dorlodot et al., 2007; Clark et al., 2013). The

major advantages of which – over, for instance, artificial media or hydroponics – are that plants can be

grown in soil and can therefore encompass microbial associations (Endlweber & Scheu, 2007). However

the majority of rhizotron–based methods rely on constraining root growth into two dimensions for

visualisation. Incidentally results from these studies can only accurately provide snapshots of the complete

root system and are confounded by dense or overlayed root structures. To account for this many rhizotrons

are very large, in some cases even comprising enormous underground facilities such as the Houghton

Rhizotron in Michigan (USA). In order to make these methods less labour intensive, some researchers

have developed root tracing software aimed at digitizing entire rhizotron-grown root systems using

vectorization (Diggle, 1988; Lobet & Draye, 2013).Vectorization allows for in silico modelling of root

structures and generation of predictive root models. However it requires destructive root extraction and is

thus itself only able to provide rough estimations of spatial root distribution and requires continual


34

validation as with any modelling platform. Nonetheless root modelling platforms developed in this way are

impressive in their accuracy and provide an extremely valuable tool for gaining insights into RSA (Diggle,

1988; Lynch et al., 1997; Lobet et al., 2011).

More recently researchers have studied root architecture in situ by generating 2D and 3D

reconstructions of root systems using non-destructive and digital techniques (Clark et al., 2011).

Furthermore automation of these technologies is a key focus in literature which would certainly have

enormous implications on high throughput screening of root phenotypes (Clark et al., 2013). Recent

advances in plant phenomics have facilitated visualisation of plant roots using ultraviolet light (10 –

400nm wavelength). The major advantage of UV is that it is able to analyse cross-sectional areas through

opaque media, allowing for 3D visualisation of plant roots while maintaining native RSA. Micro-CT is one

such technique now commonly used to study root systems and is essentially a derivative of CT scanners

used in medical radiography. CT has been used to scan a broad range of samples but has only recently

been applied to plant root systems. This has largely been due to the difficulties in distinguishing roots from

the surrounding soil matrix – as virtually indistinguishable in terms of chemical composition, water content

and density. The rapid advances in computing power and software have since overcome this problem but

there are strict conditions which must be adhered to in order to achieve high quality results. Plants must be

grown in containers of specific diameter in soil with preferably low amounts of organic contents and water,

as these generate ‘noise’ in the soil matrix. Since plant roots are organic and contain large amounts of

water, addition of these substrates interferes with the density-based threshold segmentation of roots from

surrounding soil matrix which is required for visualisation. The major benefit of micro-CT is that it enables

successive temporal measurements of RSA in three dimensions without extraction, leaving plants roots to

forage in three dimensions within the confines of container size.

Figure 9: Non-invasive imaging of maize roots and soil water using CT-MRI (A) and 2D image of

Lupinius albus roots and soil generated using light emission through a thin rhizotron (B). Image from

(de Dorlodot et al., 2007).


35

The resolution of CT images decreases with scanning path-length (i.e. container diameter).

Therefore in order to resolve fine roots, the maximum sample diameter is limiting. In order to visualise

fine root features of around 300µm, a resolution limit of ~73µm would be required, this equates to

container diameter limit of approximately 65mm – providing low signal/noise ratio. X ray CT is therefore

practically restricted to plants in the early stages of development or to plants of small stature such as

grasses. Nonetheless the technique is among the most advanced technologies currently available to

researchers in this field and has become an integral part of a large number of publications (Tracy et al.,

2010; Flavel et al., 2012; Helliwell et al., 2013). Other imaging techniques include neutron radiography,

which allows investigators to trace the flow of water within plants (Zarebanadkouki et al., 2013); and

magnetic resonance imaging (MRI) which is suited to automated measurements and can accommodate

much larger pots – up to 8.6 L (van Dusschoten et al., 2016) (Figure 9). One of the principle goals of this

rapidly developing field of non-destructive imaging is the development of a high-throughput root

phenotyping method for screening for advantageous root traits (Clark et al., 2013; Topp et al., 2013;

Bucksch et al., 2014). The potential for this technology to provide insights into whole-plant responses to

stimuli is enormous, however acquiring the required equipment and resources can be very costly. Research

employing these types of techniques is highly varied ranging from medical disciplines, vertebrate

evolution, shelf-life studies of food items, phytoremediation, biofuel production and plant-derived

bioplastics.

2.5 IMPLICATIONS

A key theme in literature is the increasing emergence of cross-disciplinary approaches to problems enabled

largely through advances in computing power and analysis of big data. This includes genomics, high

resolution imaging, biotechnology and computer modelling. Establishing networks across these many

disciplines has empowered researchers with new tools and provided new perspectives. The challenge is to

unify traditional techniques with the more recent technologies in integrated studies, as the potential impact

of such efforts on industry would be enormous.

General Materials and methods

36

3General Materials and methods

3.1 GENERAL MATERIALS

3.1.1 Novel chemical ATW1124

[Chemical composition and molecular characteristics remain at the present time commercial

in confidence]

ATW1124 treatment was prepared as an aqueous solution at specified osmolarities using

ultrapure Mili-Q® (EMD Milipore) water. The rate of dissolution was increased using a

magnetic stirrer.

3.1.2 WinRHIZO root imaging methods

WinRHIZO (Regent Instruments, Québec) root imaging was conducted within PC2 laboratories at

the Queensland University of Technology. Scanning parameters were set at manufacturers

recommendations unless otherwise specified. Scanning parameters included but was not limited to total

root length, root volume, root diameter distribution and surface area. Analysis was conducted on the

accompanying WinRHIZO software package according to manufacturer’s guidelines unless otherwise

specified.

3.1.3 Leaf gas exchange and photosynthesis monitoring equipment

Two systems from LI-COR Biosciences were utilized for the monitoring of leaf gas exchange

and photosynthetic responses of mungbean to drought and ATw1124 treatment. Both the

LI6400 and the newer model LI6400XT were provided by the CTCB at the Queensland

University of Technology. These devices were portable and so were transported to the

necessary locations as required.

3.1.4 Plant growth facilities

Plants were housed in a number of specialty facilities across a number of locations including

a number of growth chambers, glasshouses and field locations.


37

Tissue culture

Tissue culture media preparation was conducted in aseptic laminar flow hoods in autoclaved

glassware prior to incubation. Tubes were sealed prior to transport to the growth cabinet for

sterility. Media used for tissue culture was sterile ½ Muraskige & Skoog Basal Medium

prepared by dissolving 4.43g MS medium with vitamins and 30g sucrose in 2L dH2O and

pH-adjusting to 5.5 using 1M potassium hydroxide : 1M Hydrochloric acid. Agar was then

added at 8g/L and the solution autoclaved for 20mins at 121oC. Once media had cooled to

approximately 40oC it was poured into autoclaved glass 20ml tissue culture tubes sealed with

a plastic lid and allowed to solidify in a laminar flow hood. Incubation of media inoculated

with treated seeds took place in a growth cabinet with highly regulated lighting (maintained

at ~200 PAR) and temperature (25oC +/- 1oC).

Glasshouse culture

Two glasshouses were utilized throughout the course of the project at two separate locations.

The first was a PC2 glasshouse facility at the Queensland University of Technology equipped

with artificial lighting and stringent temperature regulation. The second was at the

Queensland Crop Development Facility (QCDF Redlands, Brisbane) equipped with

evaporative cooling facilities and a centrally controlled automatic watering system (Figure

10B). Regulated deficit irrigation (RDI) at the QUT glasshouse was conducted manually by

handwatering according to a watering schedule; while at the RQCF RDI was implemented

via the automatic watering system modified with 7 day irrigation timers.

Field locations

Field trials were conducted at two DAF Research Station field sites at Kingaroy, QLD and

Hermitage, QLD. Tillage and sowing was mechanised at the Hermitage location while these weren’t

feasible at the Kingaroy location due to the trial plots being housed under a rainout shelter. Incidentally all

tillage, weed control and pest management was manual at this location. Irrigation was available at both

facilities through trickle tape supplied with tank water. Sampling was conducted by hand according to the

experimental requirements, which typically involved biomass sampling from 1m beginning 0.5m from the

ends of rows. For sampling at maturity grain was separated from vegetative biomass manually and stored

separately for further processing.


38

Figure 10: (A) Photo depicting the inside of the

Kingaroy rainout shelter facility. Access to

machinery was not possible thus all handling of

the trial was by hand. (B) Photo depicting

automatic watering system utilised at the

Queensland Crop Development Facility in which a

number of experiments were housed.


39

3.2 GENERAL METHODS

3.2.1 Tissue culture protocol

In order to determine an optimum concentration of ATW1124 to treat mungbean seed an

in vitro tissue culture system was developed to qualitatively assess RSA in transparent artificial

growth medium. First seeds were surface sterilized in 1% (v/v) sodium hypochlorite (2ml in

200ml H2O) for 5mins then rinsed with deionised H2O four times for 2mins per wash. While

media was setting sterile seeds were soaked in an aqueous solution of ATW1124 at a

concentration of 0mM (dH2O only), 1mM, 2.5mM, 5mM and 10mM. An untreated control was

also included to reveal effects of imbibing seeds with dH2O. Seeds were then rinsed 3 times in

dH2O with shaking for 2mins per wash and inoculated onto the cooled sterile ½ MS media

ensuring correct orientation (radicle orientated into the media) with half the seed submerged and

stored in an incubator kept at 25oC for 21 days. Mungbean seeds taper toward the point of radicle

emergence therefore seeds were inserted with this region facing down to ensure proper hypocotyl

and epicotyl development. Only 50% of the seed was immersed in growth medium to allow

oxygenation.

In instances of improper seed orientation, growth defects were very apparent. As embryonic

tissues emerged, they twisted to orient themselves according to their respective tropisms.

Embryonic root tissues corrected their course of development back into the growth media;

while shoot tissues grew contralateral to roots but also toward the light source. Orientation-

induced augmentation in seedling morphology was minimal. Culture tubes were placed in a

growth chamber set at 25oC 8/12hr day/night cycle for 21 days. It should be noted that

optimal temperature (30oC) could not be used as the facility was not exclusive to mungbean

and housed a number of other plant species with a range of thermal requirements. In order to

accommodate them all there was restriction on regulation of temperature. Previous studies

have shown, however, that 25oC is a suitable temperature for mungbean cultivation under

similar condition (M. K. Khatun, M. S. Haque, S. Islam, 2008). Comparisons between

treatments were made using high resolution photos taken using a DSLR camera which were

used to establish if ATW1124 had any effect on mungbean root development – as was

reported in earlier work by collaborators on other species. A range of concentrations were

included as treatment effects were previously reported to occur in a concentration-dependant

manner. The experiment was repeated in two separate instances (n=10, n=15) with slight

modifications outlined in Table 4. These experiments formed the foundation on which more


40

rigorous treatment optimization was based. A detailed description of observations including

justification for these modifications is included in Chapter 4 of the results section.

Table 3: Comparison of treatment conditions between the two runs of the experiment

Run 1 Run 2

Treatment composition

0mM (dH2O only), 1mM, 2.5mM,

5mM and 10mM

n=10

2hr seed soak

0mM (dH2O only), 5mM and

10mM

n=15

1hr seed soak

Figure 11 – in vitro mungbean seedling 21 days after seed treatment housed in a growth chamber.

This system illustrates the method used to compare the various seed-treatments as described in a later

chapter. A series of high resolution photographs were obtained in likeness to that depicted and

compared visually.


41

3.2.2 Methods for quantifying shoot responses of mungbean to drought

Quantifying stress response involved measuring a number of key morphologic and physiological

parameters including plant height, leaf area, specific leaf area, shoot weight, harvest index and relative

water content. Methods for measuring number of physiological indicators of drought stress are also

described here including leaf gas exchange with the Li-6400XT system by Li-COR as well as measuring

chlorophyll fluorescence using the MINI-PAM photosynthesis yield analyser by Heinz Walz GmbH. Key

root specific parameters included total root length, volume, surface area as well as root to shoot ratio.

Unique methods not covered in this section are included in respective results sections below.

3.2.3 Quantification of morphological responses to drought and ATW1214 treatment

WinRHIZO root scanning

In preparation for scanning, roots were destructively extracted from soil by upending the

pots then separated from bound soil using a 0.5mm sieve. Roots were then washed in H2O to

remove any residual soil and stored in plastic snap-lock bags filled with 50mL of water and kept

at 4oC for storage. Prior to scanning roots were more thoroughly washed using a steady stream of

water for 3-4 mins or until all visible contaminants were removed. Plastic scanning trays were

then filled with dH2O and the roots submerged and carefully unwoven using needle nose forceps

such to minimize clustering of root branches which could otherwise confound subsequent root

trace analyses in the WinRHIZO software. In order to investigate distribution of root thickness,

root diameter was included in the analysis and divided into 0.2mm categories and colour coded

for root tracing. For instance roots with diameter within the range 0.0 – 0.2mm were traced in

red; while those falling within 0.21-0.4 were traced in yellow et cetera. Following scanning the

resulting images were examined in ‘edit mode’ for contaminants which were then removed.

Scanning settings unique to particular experiments are explained in further detail in the respective

results sections below. The key output parameters are summarised in Table 4.

A number of extraneous parameters were included in the output file by default however these

were of little significance. For instance “NCross” and “NFork”, which would generally only

be applied to non-destructively extracted two-dimensional young root systems. Similarly, the

destructive nature of root extraction used in these methods renders “NTips” equally irrelevant

as any severed roots would lead to falsely positive measures of NTips. WinRHIZO allows for

analysis of specific regions of the root system as illustrated in the green border of Figure 12.


42

However as the present study was concerned primarily with the morphological characteristics

of the whole root system, analysis was applied to the entire image. Data from the analysis

Table 4: Description of main parameters selected for analysis in WinRHIZO

Parameter Description

Total root length (cm) Sum of total root length

Root surface area (cm2) Sum of root surface area

Root volume (cm3) Sum of total root volume

Average root diameter (mm) Recalculated as projected area/ length

Figure 12: Sample image scan of three week old glasshouse grown mungbean roots

illustrating root trace analysis bordered in green, the list of output parameters on the far left

in blue and an example of contaminants indicated by the black arrows. Note the colouring of

the root trace which distinguishes roots by diameter. “Rgn” denotes features of the

analysed region, Len – total root length (cm), SA – surface area (cm2), PA – projected area

(cm2), Vol – total root volume (cm3), AvgD – average diameter (mm), NTips – number of root

tips, NForks – number of root forks, NCross – number of crossings, NNodules. Nodulation

must be inputted manually but is nonetheless included as an output parameter.


43

was outputted as a TXT file for visualisation in Microsoft Excel and statistical analysis in

Minitab 16. Statistical analysis comparing output parameters between treatments was carried

out by way of a one-way ANOVA in Minitab 16 implementing Tukey’s HSD post-hoc test to

identify statistically significant differences.

Assessing morphologic responses of vegetative shoot tissues to drought and ATW1124

Effects of drought stress and ATW1124 treatment on mungbean shoot morphology were

determined by monitoring the dynamics of a series of key parameters. Parameters encompassed

whole plant characteristics such as height and biomass; leaf specifics including leaf number, leaf

area, leaf biomass and specific leaf area; and root specifics including root biomass and

WinRHIZO measurements discussed above. As the foci of each experiment was distinct, so too

were the subset of parameters selected in each. Below is a more detailed explanation of the

methods used for measuring each parameter.

3.2.3.1.1 WHOLE PLANT RESPONSES:

3.2.3.1.1.1 Plant height

Plant height was defined as the shortest distance between the upper boundary of

photosynthetic tissues and the soil surface, expressed in cm (Heijden, 2015). This was measured

using a retractable measuring tape with millimetre increments. Under field conditions plant height

was estimated for the whole sampled region (typically 1m row segments), as individual plant

measurements were impractical and unfeasible.

3.2.3.1.1.2 Biomass

Total biomass referred to the vegetative above ground tissues, as root tissues were

analysed separately. Specifically, the tissue was as above for height measurements excluding

pods, as they were also analysed separately for yield determination. For glasshouse experiments,

above ground shoot biomass was severed at the base of the stem using secateurs at the soil

interface, and immediately weighed using at least 2-decimal digital analytical balances. Under

field conditions biomass was sampled as above from 1m row segments, separated from pods then

placed in paper bags and transported before being weighed. The method of sampling differed


44

slightly between field experiments as outlined in the respective results sections below. In order to

obtain dry biomass measurements, samples were dried in a PC1 drying oven at 60oC for 72 hours.

Once dry, samples were weighed as above using on 2-decimal digital analytical balances. Water

relations, root:shoot ratios and harvest indices were based upon data obtained using these

methods.

3.2.3.1.1.3 Leaf specific parameters

Drought is reported to affect the leaves of many plant species in a number of ways

including reduced tissue water content, dry matter and area. Mungbean leaves are particularly

symptomatic of drought stress and have been shown to elicit a number of morphologic and

physiological responses as described previously (Nahar et al., 2015). Examination of a selection

of these responses under drought stress could therefore serve as a diagnostic to the level of

drought stress experienced by the plant. This method featured in many of the experiments of the

present study which sought to investigate responses of mungbean to drought stress and

ATW1124 treatment. Parameters included whole leaf biomass – calculated as total biomass of the

one of the uppermost fully expanded lateral trifoliates, expressed in grams; fresh leaf biomass –

measured at the time of sampling (typically at anthesis and maturity); dry leaf biomass –

calculated by weighing oven-dried leaf samples at 60oC for 3 days on a 2-decimal digital

analytical balance; turgid leaf weight – calculated by immersing excised leaves in H2O for 24

hours, blotting dry then weighing as above. Relative water content (RWC) was based upon data

obtained using these methods and expressed as a relative measure of leaf tissue water content.

Leaf area was measured from HD images captured using a Canon EOS1000D DSLR with

resolution set at 72dpi and pixel dimensions 3888 x 2592 px. These were kept constant for all

images in order to enable determination of leaf area based on pixel density using the image

analysis suite GIMP 2. Excised leaves were positioned on grid paper to allow scale calibration,

and then the number of pixels per cm2 was calculated to serve as an area scale. Number of pixels

comprising the area of each leaf was then calculated using a pixel density histogram and divided

by the pixel density of the 1cm2 scale to derive area (cm2) of each leaf. In some instances grid

paper was replaced by a singular 1cm3 square cube which provided comparable scaling but had

the advantage of faster processing in GIMP 2. As drought stress was reported to reduce leaf area

in mungbean, leaf area could be used as a surrogate for the level of stress experienced by the

plant. This technique is based on the assumption that leaf area was not the basis of tolerance,

which, for ATW1124 treatment – based on current results – holds true.


45

Figure 13: Sample image depicting positioning of leaves for image-based leaf area

calculation using the image analysis platform GIMP 2. Grid squares of 1cm2 were used for

software calibration and downstream extrapolation into total leaf area (cm2).


46

3.2.3.1.1.4 Root specific parameters

The principal method of assessing responses in root morphology used throughout the

present study was image based WinRHIZO analysis as described above. Image based analyses

were preferred wherever possible as they provided a highly accurate measure of the general

structure of root architecture and allowed more rigorous analysis for instance of, root diameter,

surface area and volume. Additionally, as water content of root tissues can vary considerably –

even between roots of the same plant – an image based approach suffers less interference from

water content than mass based analyses. Nonetheless root biomass was also included for the

determination of root:shoot ratios which are known to show some alteration with drought stress.

For this measure roots were destructively extracted from potted plants by upending pots,

removing soil then thoroughly but delicately washing roots with running water to remove

contaminants. Roots were then oven dried at 60oC for 72 hours and weighed using a 2-decimal

digital analytical balance for dry weight biomass (g) determination.

Root:shoot (R:S) ratio was determined as follows:

𝑅 ∶ 𝑆 = 𝑅𝑜𝑜𝑡 𝑑𝑟𝑦 𝑤𝑒𝑖𝑔ℎ𝑡 (𝑔)

𝑆ℎ𝑜𝑜𝑡 𝑑𝑟𝑦 𝑤𝑒𝑖𝑔ℎ𝑡 (𝑔)

The result of resolving the above equation yields the proportion of root tissues of a plant

relative to the total vegetative dry biomass. Higher values of R:S have been reported as a

common feature in drought tolerant species (Lopes et al., 2011; Greco et al., 2012b) –

incidentally it was a common feature of analyses in the present study. Unique methods are

explained further in the relevant results sections of later chapters.

3.2.4 General methods for measuring responses of photosynthesis and chlorophyll

fluorescence

In order to assess the photosynthetic and chlorophyllic responses to drought stress and

ATW1124 treatment, a series of measurements were taken at key time-points with highly

regulated supply of CO2 and H2O. Photosynthetic measurements were facilitated through the use

of a LI 6400XT and a LI 6400 (LI-COR Biosciences), while chlorophyllic responses were

measured on a MINI-PAM photosynthesis yield analyser (Heinz Walz GmbH). Data was

transferred from the devices using an RS-232 cable with LI-6400 software obtained from the LI-


47

COR website. Data was then stored in Microsoft Excel and analysed in Minitab 16 by way of

one-way ANOVA and Tukey’s HSD post hoc test to identify statistically significant differences

between treatments.

LI-COR general protocol

Environmental conditions within the infrared gas analyser (IRGA) head were matched as

closely as possible to ambient conditions in the growth facility. Both the LI-6400 and the LI-

6400XT were fitted with a CO2 mixer attachment to supply pressurised CO2 to the IRGA head

chamber. Atmospheric carbon dioxide (CO2) levels were maintained this way to a constant level

of 400ppm (approximately 775 mg/m3) by adding pressurised CO2 via the mixer then scrubbing

excess CO2 with soda lime. Relative humidity (RH) was regulated in a similar fashion by adding

moisture via the wetted soda lime and reducing levels by scrubbing with desiccant. Relative

humidity was maintained at 60% (+/- 5%) such that each measurement was taken from plants

equilibrated to uniform CO2 and H2O levels. For fine adjustment of RH where scrubbing was too

crude, airflow rate was controlled electronically on the LI-COR unit.

Photosynthetically active radiation (PAR) was regulated via an LED attachment set at a

constant 600PAR to match, as closely as possible, with the ambient conditions of growth facility.

Light levels in the growth facility were checked using a PAR sensor housed on the exterior of the

LI-COR devices which revealed a peak at around 600PAR. Both devices were calibrated before

each series of measurements by running diagnostics for each of the components and confirming

that similar readings were obtained from each device prior to commencement. Optimum timing

window for taking measurements was determined by running a longer term reading and

monitoring temporal dynamics of photosynthetic rate and stomatal conductance (Figure 15). All

readings were taken between 9:30am and 2pm to ensure minimal interference from circadian

natural rhythms of stomatal aperture identified from this longer-term run. Note the unusual

depression in both conductance and photosynthetic rate beginning at 13:45 (Figure 15) was

attributed to a shade cover of the glasshouse being engaged automatically. Stomatal conductance

responded very sensitively to this event, but also in response to changing cloud cover which was

experienced throughout the day. All physiological measurements were carried out on the upper-

most fully expanded centre trifoliate at the time of sampling.


48

MINI-PAM general protocol

The photosynthesis yield analyser MINI-PAM was used for analysis of photosystem II by

quantifying photosynthetic responses to drought stress and ATW1124 treatment. The two main

output parameters analysed were maximum and effective photochemical yield of photosystem II

(FV/FM and Y(II) respectively. FV/FM was calculated by dark-adapting the desired region of leaf

tissue with the aptly named ‘dark-adaptor clips’ to unsaturated photoreceptors of photosystem II,

then subject them to saturating light pulses to obtain maximum yield of the plants’ photosynthetic

apparatus (Figure 14). Measurement of Y(II) was achieved by measuring photochemical yield

without dark adaptation to obtain ‘actual’ yield values. Comparison of Y(II) with FV/FM described

the efficiency of photosystem II relative to maximum photochemical yield potential. This

technique provided an indication of the impact of stress on photosynthetic apparatus between

treatments. Key input parameters included actinic light intensity, duration of saturating light

pulses, frequency and duration of readings with settings following manufacturer

recommendations. Output data was transferred to Microsoft Excel for storage and imported into

Minitab 16 for statistical analysis.


49

Figure 14: Photo depicting physiological

monitoring setup and location. (A) an LI-COR IRGA

head clamped on to the upper most centre fully

expanded trifoliate leaf of a mungbean plant

approximately 40 days after sowing. (B) Photo depicting

usage of the portable MINI-PAM photosynthesis yield

analyser; and (C) the QCDF facility in which a number

of experiments were housed.

A B

C


50

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synth

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Temporal dynamics of photosynthetic rate and stomatal conductance under hydrated conditions

Photosynth

Conductance

Figure 15 Scatterplot of temporal dynamics of photosynthetic rate and stomatal conductance of mungbean

grown under hydrated conditions in the QCDF glasshouse. Data used to confirm suitability of measurement times. All

LI-COR readings were kept within a range ensuring fairly consistent photosynthetic rate and conductance.


51

3.3 MOLECULAR TECHNIQUES TO ASSESS TRANSCRIPTIONAL RESPONSE

To determine the molecular basis of observed drought and ATW1124-mediated responses, RNA

was isolated for sequencing and subsequently analysed to identify differentially expressed genes and their

corresponding biochemical pathways. Below are the details of the general methods used for extraction of

RNA from root and shoot tissue, quality control by way of gel electrophoresis, NanoDrop

spectrophotometry and analysis on Bioanlyser at QUTs Central Analytical Research Facility in Brisbane.

3.3.1 RNA Extraction from root and shoot tissue

For RNA extraction 100mg of tissue was collected from both excised leaves and washed root

samples, wrapped in aluminium foil, snap frozen in liquid nitrogen then kept on dry ice for transport.

Samples were then kept in an ultrafreezer at -80oC until further processing. RNA was extracted using a

QIAGEN RNeasy Plant Mini Kit according to manufacturer’s specifications with slight modification for

root RNA extraction. Grinding of root tissue in a liquid nitrogen-cooled mortar and pestle was replaced

with a QIAGEN tissue lyser using sterile leadshot beads contained in 2ml conical cryovials. Tissue lyser

racks were cooled to -80oC for 30mins prior to use to avoid RNase-mediated RNA degradation. Samples

were homogenised using a TissuelLyser (QIAGEN, Netherlands) in a two-step process: the first consisting

of a 30sec run at maximum frequency on frozen samples to form a fine powder, at which point 700µl of

extraction buffer (RLT supplied with QIGEN RNeasy kit) was added to subdue RNase activity, vortex

mixed for 20sec and then run for a further 60 seconds at maximum frequency. Samples were then

incubated at room temperature for 5 mins. Remainder of the extraction protocol was carried out according

to QIAGEN RNeasy plant mini kit manufacturer recommendations. Following extraction, concentration of

nucleic acids was estimated using a NanoDrop2000 spectrophotometer (Thermo Scientific - USA)

measuring the absorbance at 260nm; and purity of nucleic acid against protein and phenolic content

contamination checked by reading absorbance ratio of 260/280nm and 260/230nm respectively. Expected

values for both ratios were above 2.00 and nucleic acid concentrations ideally above 150ng/µl. Viable

samples were then aliquotted to obtain 500ng RNA and electrophoresed on agarose gels of 1% (w/v)

prepared using agarose (Roche diagnostics -USA) dissolved in 1 X TAE buffer containing 0.5 X SYBR

Safe DNA gel stain (Invitrogen - USA).

Gels were cast and run in an EasyCast Mini Gel System loaded with 500ng of extracted RNA

combined with 1/6 volume of loading dye 6 X. A molecular weight marker (Marker X – Roche

Diagnostics - Switzerland) was added to one lane to determine the approximate sizes of the PCR products.

Electrophoresis was carried out at 120 V and in 1 X TAE buffer, for 30 min. The gel was viewed and

photographed using a Syngene Geldoc system (G-box and GenSnap version 6.07) (Syngene - UK)

assessing the banding pattern quality represented by the 28S and 18S ribosomal subunits to infer level of


52

degradation and sample purity. Remaining samples were stored in an ultrafreezer at -80oC until further

processing.

3.3.2 RNA quality control

In order to confirm extracted total RNA concentration and purity, 2µl aliquots of sample were sent

to the Central Analytical Research Facility at the QUT to run on an Agilent 2100 Bioanalyser. Samples

were considered viable for RNA sequencing if their RNA integrity number (RIN) scores were above 6.5

and concentrations were above 100ng/µl. Further quality control was carried out on the CLC Genomics

Workstation (QIAGEN) once sequencing had been completed including read-trimming of 13bp from the 3'

of short sequence reads to remove adaptor sequences, examining GC content and checking overall trends

of sequences graphically. Details of additional downstream approaches are included in relevant results

sections of later chapters.

Chapter 4 – Optimisation and evaluation of morphophysiological effects of ATW1124

53

4Chapter 4 – Optimisation and evaluation of

morphophysiological effects of ATW1124

4.1 INTRODUCTION

Root developmental plasticity governed at the morpho-anatomical, physiological and molecular

level gives rise to complex architectural arrangements which vary considerably (Watt & Evans, 1999a;

de Dorlodot et al., 2007) and is of growing interest for the field of crop improvement (Li et al.,

2016; Topp, 2016). Furthermore this is one of the main underlying principles for using exogenous

chemical treatments to augment root development for improved abiotic stress tolerance. Additionally, as

roots are the primary means by which plants perceive soil moisture depletion, stress signals often originate

in the root system and are translocated via plant vasculature to other organs where stomatal reactions and

leaf growth occur (Jeschke et al., 1997). Many of these root-shoot stress signals are hormonal in nature and

have been of significant interest for biologists striving to enhance crop abiotic stress tolerance (Xiong et

al., 2006; Shinozaki & Yamaguchi-Shinozaki, 2007; Cominelli et al., 2013; Helander et al., 2016). Aside

from the five classical phytohormones known to elicit plant growth regulatory effects under abiotic stress –

abscisic acid (ABA), ethylene, cytokinin (CK), auxin (IAA), giberellin (GA) and jasmonate (JA) – many

novel plant growth regulators continue to emerge in literature and it is likely that many more remain yet

undiscovered (Peleg & Blumwald, 2011).

Agrochemical development serves as a viable option for enhancing crop abiotic stress tolerance

with a relatively rapid uptake and implementation into existing infrastructure, producer values and current

industrial management strategies. Results presented in this and following chapters presents some

promising results of pre-treating mungbean seed with the novel chemical ATW1124 demonstrated to

enhance root development and improve water capture under drought conditions.

4.2 MATERIALS AND METHODS

4.2.1 Selection process and criteria for starting material

Starting material was focused on commercial genotypes of mungbean to maximize industrial

translatability. Through close association with mungbean breeders an extensive mungbean germplasm was

available offering diverse genotypic variability. Phenological, physiological and yield data obtained from

the National Mungbean Improvement Program (NMIP) were used as the basis for selection. Performance

was assessed based on data collected from 25 breeding lines grown at the Queensland locations of Emerald

and Warwick (


54

Table 6). The aim was to identify a subset of varieties exhibiting differential drought sensitivity,

inferred via a number of surrogate traits included in the breeding program. These included timing of key

phenological events (flowering, maturity etc.) grain yield, harvest index (HI), dry matter production, as

well as susceptibility to abiotic and biotic stress. Yield was normalised against DTM to provide the

standardised measure of ‘yield accumulated per day’ in the farthest right column of

Table 5. Crop growth cycle was divided into vegetative and reproductive and plotted graphically

against yield (Figure 16).

Varieties with average yield below 1000kg/ha were discarded, in accordance with widely accepted

industry benchmarks below which conclusions become unreliable (Conference proceedings, AMA

General Meeting 2015). Criteria included that lines reached anthesis and maturity in approximately 45 and

65 DAS, respectively. Additional parameters included susceptibility to lodging, senescence, Powdery

Mildew, Tan Spot and Halo Blight; all of which were assessed in collaboration with the mungbean breeder

Col Douglas. A description of specific varieties is provided overleaf.


55

Table 5 – Phenology and physiological evaluation of 25 mungbean varieties (courtesy of DAF) at

the location of Warwick (QLD) which formed the basis of genotypic selection for the study.

Days to flowering

Days of reproduction

Days to maturity Yield

Yield normalised

Genotype DTF dr DTM kg/ha kg.ha/day

AusTRC324137 44 42 85 853 10.00

AusTRC324185 44 35 79 1188 14.98

AusTRC324187 48 38 85 537 6.29

AusTRC324244 37 31 68 337 4.98

AusTRC324255 37 29 66 502 7.61

AusTRC324270 45 40 85 1290 15.24

AusTRC324294 44 43 86 1355 15.70

AusTRC324330 47 39 86 1190 13.89

AusTRC324366 38 47 85 1059 12.46

Berken 42 33 75 1045 13.93

Celera II-AU 43 33 76 1085 14.28

Crystal 44 43 87 1268 14.52

King 43 43 86 975 11.29

M10403 50 49 99 1494 15.09

M11047 43 42 85 1562 18.38

M11236 43 34 77 1122 14.57

M11262 49 48 100 1420 14.25

M12038 42 32 74 1197 16.18

M12065 41 39 80 1291 16.20

M12068 39 38 77 1050 13.58

M12120 39 35 74 1062 14.42

M12130 38 37 75 1177 15.69

M12263 43 34 77 1312 16.97

M12398 48 52 100 1261 12.65

Putland 56 48 105 1459 13.94


56

Table 6 – Phenology and physiological evaluation of 25 mungbean varieties (unpublished data

from Queensland Government Department of Agriculture and Fisheries 2014) at the location of

Emerald (QLD) which formed the basis of genotypic selection for the study.

Days to

flowering Days of

reproduction Days to maturity Yield Yield per day

Genotype # days # days # days kg/ha kg.ha/day

AusTRC324137 37 26 63 642 10.24

AusTRC324185 47 16 63 969 15.37

AusTRC324187 42 23 65 683 10.51

AusTRC324244 32 26 59 310 5.27

AusTRC324255 31 24 55 359 6.53

AusTRC324270 40 25 64 861 13.38

AusTRC324294 36 26 62 692 11.16

AusTRC324330 41 27 68 1003 14.74

AusTRC324366 36 27 64 1182 18.57

Berken 38 25 63 899 14.26

Celera II-AU 39 24 63 856 13.58

Crystal 38 27 65 1137 17.48

King 39 25 64 949 14.90

M10403 44 25 69 1380 19.90

M11047 36 31 67 957 14.36

M11236 39 27 66 1054 15.97

M11262 46 24 71 941 13.31

M12038 35 27 62 829 13.44

M12065 35 27 62 792 12.84

M12068 34 29 64 690 10.84

M12120 34 26 60 423 7.05

M12130 35 28 63 726 11.58

M12263 38 25 63 1065 16.90

M12398 40 27 67 1029 15.36

Putland 55 27 82 577 7.05


57

Figure 16 Summary of key phenological and yield data of NMIP breeding lines from which genotypes

included in the study were derived. Unpublished data obtained from the National Mungbean Improvement

Progra. ‘dr’ represents length (days) of reproductive phase; DTF represents days to flowering (left Y-axis);

yield presented in kg/ha (right Y-axis


58

Crystal – a variety reported to have notably high quality grain and resistance to various key

pathogens listed above. Improving disease resistance formed the main impetus of the NMIP and had a

large influence over which varieties enter the commercial domain. Nonetheless the details of these

pathogens escape the scope of this study but warrant mention as starting germplasm was likely biased for

these traits from the outset. Furthermore there have been reports that selection for biotic stress resistance

may in fact hinder abiotic stress tolerance inadvertently (Greco et al., 2012a). However this conditional

and abiotic stress tolerance can also benefit inadvertently in much the same way under certain conditions.

Berken – one of the oldest commercial Australian varieties primarily used for sprouting, reported

for its susceptibility to virtually every pathogen and abiotic stress known to affect mungbean. Due to its

increased time in commercial circulation, Berken has been subjected to less selection from breeders and is

therefore much more biotic and abiotically sensitive, providing a contrast to the newer varieties.

Jade-Au, a very recent addition to commercial circulation in Australia was much more resilient

than its predecessors. This variety was not included in the 2014 trial summarised above (Figure 16,

Table 6) however was included following recommendation from agronomists and breeders for its

consistently high performance throughout arid years and high quality grain.

A number of less established breeding lines listed above (“AusTRC” prefixed lines: Table 5, Table

6 and Figure 16) were also examined in preliminary in vitro assays using ATW1124 however these were

excluded from further study due to their inconsistency in preliminary studies and lack of supplemental

reference data. An initial glasshouse ATW1124 drought trial was run employing the commercial variety

‘Emerald’, however there appeared to be little distinction between ‘Crystal’ and ‘Emerald’ in terms of

phenology or drought responsiveness thus the variety was removed from further study. Suppliers of seed

outlined in Table 7.

Table 7 – Mungbean varieties utilised in the present study and their sources

Genotype Supplier

Berken Bean Growers Australia

Crystal Queensland Government Department of Fisheries

(DAF) Hermitage Research Station

Emerald Bean Growers Australia


59

Jade-Au Bean Growers Australia

Breeding lines DAF Hermitage Research Station

4.2.2 in vitro assessment of ATW1124 treated mungbean root systems architecture

In order to ascertain whether ATW1124 elicited any root-specific effects on mungbean, a tissue

culture evaluation was conducted according to the general tissue culture protocol outlined in chapter 3. Six

seed pre-treatments (n = 10) were prepared as follows:

1. Untreated dry mungbean seeds to reveal any effects of H2O imbibition

2. H2O imbibed mungbean seed as a control for ATW1124 treatment

3. Varying concentrations of ATW1124 treatment solution [1mM, 2.5mM, 5mM, 10mM]

Seeds were imbibed for 2hrs then rinsed prior to inoculation into media. Incubation took place in a

plant growth cabinet for 21 days at 25oC with 16/8hr day night cycle at which point images were captured

on a Canon DSLR camera.

4.2.3 Effects of ATW1124 on radicle development and WinRHIZO verification

To investigate temporal dynamics of post-embryonic root development after treatment, an

experiment was conducted assessing radical emergence 5 days after germination in vitro. Treatments

included control (dH2O) 1hr seed soak, 10mM, 25mM, 50mM, 100mM and 200mM (Figure 17). Seeds

were then germinated on wetted 3mm Whatman filter paper in 250ml plastic tissue culture jars. Five days

after germination the ten longest roots from each treatment were collected and analysed by way of an

ANOVA of mean radicle length. Radicle length was found to increase with increasing ATW1124

concentration up to 100mM where it plateaued. Radicle length was measured as the distance from the root

crown from the point the radicle emerges from the seed to the root tip. Lengths were averaged and

analysed by way of an analysis of variance (unstacked ANOVA – Minitab 16) in conjunction with a

Tukey’s post-hoc test (HSD) to identify any statistically significant differences between treatments.

For root imaging a WinRHIZO Epson Perfection V700 modified flatbed scanner and software

platform (REGENT Instruments, Canada) were employed to standardise quantification of roots and allow

quantification of finer aspects of the root system including total root length (cm), root volume (cm3),

surface area (cm2) and root diameter (mm). Methodology was as above with the exception that growth

chamber temperature was raised to 30oC. ATW1124 concentration was 100mM. Analysis was conducted

using Minitab 16 by way of an ANOVA in conjunction with Tukey’s HSD post-hoc test in order to

distinguish statistically significant means between treatments.


60

4.2.4 Developmental Stage Specificity WinRHIZO anlaysis

In order to more clearly define the temporal effectiveness of ATW1124 treatment, an

experiment was fashioned in likeness of the above, modified such that root development was

examined 3, 6 and 13 days after sowing (DAS). Plants were housed in a growth chamber with

temperature set at 30oC, 8/16hr day night cycle and relative humidity (RH), although

unregulated, remaining fairly constant at around 60%. Statistical analysis was carried out as

above using Minitab 16.

4.2.5 Xray tomographical assessment of mungbean root architectural effects of ATW1124

In order to overcome many of the limitations of destructive sampling, a non-destructive method of

quantifying root traits was employed via X-ray computerised tomography. The technique allowed for

imaging of native RSA in soil and therefore provided a higher level of analytical rigour. However it was

restricted in its capacity to distinguish roots from surrounding soil matrix due to high compositional

similarities of water content and density of organic contents. Therefore stringent sample dimensional, soil

and water content restrictions were required in order to obtain the required level of detail. Resolution of CT

images decreased with scanning path-length, therefore container size was restricted. In order to visualise

fine root features of around 300µm, a resolution limit of ~73µm was required. This equated to a container

diameter limit of 65mm – assuming low signal/noise ratio. Adhering to these conditions, an experiment

was designed to assess root spatial distribution and root mass density of ATW-plants subjected to 7 days of

soil drying.

Seeds were treated as above then sown in 800 cm3 of soil acquired from Kingaroy, Queensland

(1:1 v/v red ferrosol:fine sand). Water was administered by hand watering daily for 14 days then withheld

for the remaining 7 days prior to imaging. Plants were housed in a temperature controlled PC2 glasshouse

at 25oC with a 16/8hr day night cycle and around 60% RH. Twenty-one DAS samples were transported to

the Australian Plant Phenomics Facility at Armidale (NSW) for imaging in a Vtomexs scanner (GE

Phoneix) as previously described (Flavel et al., 2012). Three seed pre-treatments were included:

1. H2O imbibed control 1hr

2. 5mM ATW1124 1hr

3. 10mM ATW1124 1hr

Each treatment was replicated four times giving a population size of 12 plants (n=4). Output data

comprised of root surface area measurements obtained from cross-sectional images at depth-wise intervals

of ~73µm for the length of the 260mm PVC column. Two-dimensional images were processed as


61

previously described (Flavel et al., 2012) such as to generate 3D digital image reconstructions (Figure 27)

and spatial-volumetric measurements of RSAs with depth. Length of each column was stratified into three

equal layers denoting deep, mid and shallow roots which provided a means of assessing the proportion of

each class in the whole root system (Figure 28). A summary of key data are provided overleaf in Table 9.

Results revealed that root length was the most responsive parameter to ATW1124, evidenced by a

statistically significant (p < 0.05) increases compared with control plants (Figure 18). Given that the

treatment was prepared as an aqueous solution, the effects of H2O were accounted for by inclusion of an

H2O-only control. Priming with H2O alone elicited very little changes, albeit a statistically insignificant

increase in germination and early root initiation. Of the seven parameters listed above, four exhibited large

amounts of unexpected variability attributed to glasshouse inconsistency suggesting that the experimental

design (RCD) required revision. As such a RCB design was developed and implemented in future

experiments. Parameters exhibiting unexpectedly high variability included pod number, root and shoot dry

weight, and shoot fresh weight.

Variability was assessed by first conducting a test for normality (Minitab 16 > Stat > Normality

test). Data were considered normally distributed if the null hypothesis of the normally test was accepted (p

> 0.05). Subsequent to this, a test for equal variance was conducted using both the Levene’s and Bartlett’s

test for equal variance in Minitab (Stat > ANOVA > Test for equal variance). Providing the data passed

these quality control tests, it was analysed by way of an ANOVA in conjunction with Tukey’s post-hoc

test to allow differentiation between treatments (Stat > ANOVA > One-way (Unstacked/Stacked –

depending on layout). Means were considered significantly different if the null hypothesis was rejected (p

< 0.05).

Data for most parameters were found to have equal variance with the exception of shoot dry

weight which, interestingly, yielded conflicting results between Levene’s (0.541) and Bartlett’s (p = 0.040)

tests. This was attributed to the higher sensitivity of Bartlett’s test to violations of normality, since it was

found that both roots and shoot dry weight data did not follow a normal distribution (normality test p >

0.05) and had irregularly high levels of variability. As such, despite the putative positive regulatory role on

shoot length, the trend was not supported statistically. It was concluded that the completely randomised

experimental design was inadequate to accurately capture differences in these parameters, whereas for

more consistent parameters (e.g. total root length), a completely random design was sufficient (Figure 18).

All analysis and graphical illustrations generated using Minitab 16. Results revealed that priming of

mungbean seed with ATW1124 for1hr prior to sowing enhanced maximum rooting depth by 44.16%

compared with control plants. Furthermore, a positive effect of ATW1124 was observed on root and

shoots dry weight, as well as shoots length, although these observations required additional verification


62

Figure 17 Radicle development 5DAG following seed imbibition for 1hr in

varying concentrations of ATW1124.

with a RCB design. Yield remained unaffected by treatment, although pod-set was considered too low and

was later assessed more rigorously in field trials described in chapter 6.


63

A B

C D

a a

ab bc

c

Figure 18 Differences in root length between mungbean seed pre-treatments including dry untreated seed, H2O

control, A3 (2.5mM), A4 (5mM) and A7 (10mM) ATW1124. A – Probability plot illustrating normality, p > 0.05 for

each plot. B – Individual value plot illustrating raw data spread. C – Boxplot illustrating quartile distribution and outliers.

D – Cumulative distribution plot illustrating overall effect of each treatment on root length. Experiment conducted in

glasshouse at QUT, analysis carried out in Minitab 16.


64

4.2.6 QCDF mungbean glasshouse ATW1124 drought assay

To further investigate the effects of ATW1124 on mungbean, a glasshouse experiment was

developed building upon the findings of the previous experiment with a more rigorous experimental

design. Two genotypes of mungbean with contrasting drought sensitivity (cultivars Jade and Berken) were

compared in their responsiveness to ATW1124 under drought stress. Sampled was carried out at anthesis

(DTF) and maturity by way of morphology, physiology and molecular responses. Morphology was

assessed via destructive tissue sampling of root and shoot biomass, root development (WinRHIZO) and

leaf area (DSLR images and GIMP 2.0 software analysis). Physiological responses were assessed using the

LI-6400XT portable Photosynthesis system (Li-COR, USA, Figure 20) by measuring photosynthetic rate,

stomatal conductance, transpiration and vapour pressure deficit; as well as using the MINI-PAM portable

fluorometer (Walz, Germany) to assess chlorophyll integrity by comparing actual (YII) with maximum

fluorescence (FV/FM). Molecular responses were assessed by RNA-seq analysis of total RNA, which will

be discussed in detail in chapter 5.

Following treatment with ATW1124 seeds were sown in propagation trays containing Searles

premium potting mix and raised for 7 days prior to transplanting into larger 20cm diameter pots containing

UC mix at the QCDF. Irrigation was automated using an established dripper feed system. Watering

schedule was regulated using digital 7day timers such that there were three groups:

Well-watered control (WW): Watered three times weekly

Drought stress 1 (DS1): Watered once weekly

Drought stress 2 (DS2): Watered once per fortnight

Water was supplied using 8 polypipe drip lines (flow rate of 12.5mL/min) allowing application of

water directly in the root zone. Watering schedule was dynamic and dependant on the plant developmental

stage to accommodate transpirative demand from larger leaves and an increase in biomass in larger plants:

Developmental stage Irrigation schedule Total volume (ml) per watering

Vegetative 3mins twice daily 75

Post-anthesis 4mins twice daily 100

Pod filling 5mins twice daily 125

Drought stress was facilitated by digital timers, programmed to reduce watering frequency to once

per week and once per fortnight for mild (DS1) and severe drought stress (DS2), respectively. The


65

composition of this soil medium and its nutritional supplements consisted of a mixture originally

developed by the University of California, aptly named UC mix, containing:

Component Parts 300

Sand 80

Peat 120

Gravel 100

Supplemented with a nutrient profile according to the following:

Nutrient Parts 2.48

Blood and Bone 0.400

Micromax 0.100

Potassium Sulphate 0.040

Potassium Nitrate 0.040

Superphosphate 0.400

Hydrated Lime 0.300

Dolomite 1.200

Temperature was maintained at 30oC using a balance of heating and evaporative cooling. Relative

humidity (RH) ranged from 40% to 60% however, as with light supply, was unregulated. Experimental

design was a randomized complete block design with 5 blocks per glasshouse bench, which separated each

of the f groups (WW, DS1 and DS2) according to their watering schedule. Treatments comprised of both

mungbean genotypes (G1 – Berken and G2 – Jade) given either 100mM ATW1124 or H2O such that there

were four possible treatments. Each block was comprised of three plants from each treatment giving a

total of 12 plants per block with 5 blocks total:

Treatments: G1-, G1+, G2-, G2+

Blocks: 5, three plants per treatment per block

Total population size: 180 plants with an additional 138 edge plants

Each individual plant was neighboured by 8 other plants and given a single drip irrigation line inserted

directly to the root zone (Figure 19).


66

Figure 19 – Photo illustrating placement of drip irrigation and staking to prevent lodging of mungbean

grown in PC2 glasshouse facilities at the Queensland Crop Development Facility in Brisbane.


67

Figure 20: Photo illustrating use of LI-6400XT systems to monitor photosynthetic responses of

mungbean leaves.


68

4.3 RESULTS

4.3.1 In vitro ATW1124 assay

Roots of ATW-treated mungbean grown in vitro appeared slightly more abundant in ATW-plants

compared with controls, with the highest ATW1124 concentrations (5mM and 10mM) eliciting the most

pronounced effects (n=10). No consistent differences were observed between 1mM or 2.5mM compared

with controls (Figure 21). As verification this experiment was repeated with an increased sample size (n =

15) but with treatment concentrations streamlined to 5mM, 10mM as well as a H2O control. Length of

imbibition was also reduced to 1h to reduce swelling and cotyledon fragility. Results confirmed previous

observations of ATW-plants exhibiting enhanced root development.

4.3.2 Effects of ATW1124 on radicle development and WinRHIZO verification

It was evident that ATW1124 positively regulated radicle development with the most efficient

concentration identified as 100mM (Figure 23). Treatment concentrations above 200mM elicited

comparable effects as 100mM. Visual examination revealed an elongation in radicle morphology

following treatment with ATW1124 as well as a reduction in diameter. This observation was in alignment

with previous reports that ATW1124 treatment induces an increase in fine root development which

appears exacerbated by mild drought stress. Given the untranslatability of results from an in vitro drought

stress assay, a subsequent experiment was designed which utilised soil-raised specimens.

Dry Control 1mM 2.5mM 5mM 10mM H2O only

Figure 21: Tissue culture evaluation of ATW1124 seed pretreatment of Crystal cultivar mungbean (n = 10).


69

100mM ATW1124

Control

Figure 22: Representative illustration of root morphological assessment using the WinRHIZO

root phenotyping platform. Plants treated with 100mM ATW1124 (bottom) exhibited

significantly more root tissue compared with (controls) at 7DAS. Six seeds per 500mL

punnet per treatment (n=3) containing soil.


70

0

5

10

15

20

25

30

35

40

45

50

H2O_C 10mM 25mM 50mM 100mM 200mM

Me

an r

adic

le le

ngt

h (

mm

)

Treatment

Effect of ATW1124 on radicle length 5 days after germination

a a

b c

d d

Figure 23 Graphical representation of radicle development 5DAG. Concentrations

that do not share a letter are statistically significantly different from each other.

H2O_C represents seeds imbibed with H2O for 1hr (n = 10).

Figure 24: Comparison of untreated and ATW1124 treated mungbean roots

grown in soil. H2O imbibed (left) and 100mM ATW-plant (right) development 7 DAS

in soil in a growth chamber. Plants were well-watered for the duration of the

experiment (n=5).

Untreated Treated


71

ATW1124-plants exhibited significantly enhanced root development compared with controls.

One-way ANOVA with Tukey’s HSD post-hoc test revealed statistically significant

differences in mean maximum root length 7 DAS compared with controls. Maximum total

root length was measured at 79.80mm versus 59.80mm for ATW1124-plants and controls,

respectively (p=0.001, n=5). Graphical representation generated using Minitab 16 illustrated

a fairly precise spread of the data (Figure 25).

Results revealed a potential prolonged effect of seed pre-treatment, which raised the question

as to whether the effect persisted for the entirety of the growth cycle or whether it was

developmental stage-specific.

WinRHIZO images indicated that total root length and surface area increased without an

increase in volume, suggesting higher fine root RSA compositions 7 DAS. Root architecture

was predominated by a single primary tap root emerging from the crown, 5-8 lateral roots

with significant branching and a small number of adventitious roots. Results revealed a

statistically significant increase in total root length and surface area 7DAS. Statistically

significant mean differences were revealed in total root length (p=0.035) and surface area

(p=0.041).


72

Figure 25 Box and whisker plot illustrating the effect of seed pre-treatment of mungbean

seed with 100mM ATW1124. Plants grown in a growth chamber, destructively sampled for root

development 7 days after sowing.


73

Table 8 Summary of mean root morphological data comparing ATW1124-plants with controls 7DAS in a

growth chamber. Statistical analysis derived using Minitab 16.

Parameter (means) H2O imbibed

control

100mM

ATW1124

Relative difference

(%)

P-

value

Total root length

(cm) 150.04 (SD +/-7.66)

188.05 (SD +/-

17.75) 122.88 0.035

Root volume (cm3) 0.21 (SD +/- 0.02) 0.284 (SD +/- 0.06) 135.24 0.104

Surface area (mm2) 19.306 (SD +/- 1.00) 25.85 (SD +/- 3.69) 133.89 0.041

Average diameter

(mm) 0.39 (SD +/- 0.02) 0.43 (SD +/- 0.04) 108.61 0.140

4.3.3 Developmental stage specificity WinRHIZO analysis

At 3 DAS mean total root length was significantly increased by ATW1124 treatment by

21.2% (0 = 0.006, n = 3). Although surface area exhibited a slight increase (12.5%), the

effect was non-significant (0 = 0.197). Average diameter was decreased by ATW1124 by

10.0% however this effect was, similarly, non-significant (p = 0.108). No observable effect

on mean root volume was observed using WinRHIZO. At 6 DAS mean total root length

appeared to be the only statistically effected parameter by 100mM ATW1124 with 10.5%

increase compared with control (p = 0.083, n = 3). No recordable differences were observed

in mean SA, average diameter or root volume. A notable observation in RSA was an

elongation in taproot length with ATW1124 treatment (Figure 26). However given the

susceptibility of tap-roots to shearing during destructive sampling, comparison based on

rooting depth with this method was not considered viable. At 13DAS RSA was comparable

between treatments, however at this stage the plants had become restricted by small container

size.


74

H2O ONLY 3 DAYS 100mM ATW 3 DAYS

H2O ONLY 6 DAYS 100mM ATW 6 DAYS

H2O ONLY 13 DAYS (DS for 7 days) 100mM ATW 13 DAYS (DS for 7 days)

Figure 26: Root development of mungbean treated with 100mM ATW1124 versus H2O controls

assessed with the WinRHIZO imaging platform at 3, 6 & 13 DAS in a plant growth chamber. Drought

stress induced from 6 DAS. Plants contained in individual 500mL pots containing potting mix.


75

4.3.4 Xray tomography of ATW1124 treatment effects

Results revealed that ATW1124 enhanced root development in a concentration dependant manner.

There was a marked increase in the prominence and developmental maturity of the primary taproot system,

lateral root branch length, penetrative depth and total root volume. Mid-level roots were the most

responsive to the treatment with a total root volume increase of 93% and 680% for the 5mM and 10mM

treatments, respectively. Both the 5mM and 10mM ATW1124 treatment increased mean branch length by

33% however the effect was statistically insignificant (p = 0.113). Mean root volume, calculated as a

derivative of surface area with depth, was found to be strongly statistically different between all three

treatments suggesting that raising the concentration of ATW1124 may continue this trend. Total root

volume, calculated as a summation of approximately 2800 image cross sections for each plant (n=4) gave a

statistically significant (p=0.033) increase of 183% and 304% for 5mM and 10mM ATW1124,

respectively (Table 9).

Figure 27: Representative X-ray tomographic reconstruction of

ATW1124-plant root architecture versus controls given 7 days of

soil drying

Control 5mM 10mM


76

Table 9 Summary of X-ray tomography-derived RSA characteristics of ATW1124-plants vs H2O

controls subjected to one week soil drying

Mean branch

length (mm)

Mean max

depth (mm)

Total root

Volume (cm3)

Shallow

roots (cm3)

Mid roots

(cm3)

Deep roots

(cm3)

Control 5.80 83.8 0.304 0.2526 0.04823 0.00318

5mM 7.76 152.2 0.820 0.72094 0.09366 0.005067

10mM 7.72 230.8 1.172 0.73198 0.3766 0.06347

Figure 28 Scatterplot of X-ray tomography derived spatial root volume against depth below soil

surface (mm) – generated in Minitab 16. Thresholds refer to the classification of roots according to

their depth in the soil.


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4.3.5 P9 Glasshouse experiment #1: Optimising ATW1124 concentration

In order to ascertain whether tissue culture effects translated into plants grown in a soil medium,

an experiment was developed to test the effects of seed pre-treatment of mungbean seed with a range of

ATW1124 concentrations. Mungbean seeds (Crystal) were treated for 2 hours, rinsed three times in dH2O,

then planted 1cm below the soil surface and grown in a glasshouse with 8/16hr day/night cycle at 25oC.

Seed treatments were:

Treatment ID Composition

A0 Untreated

A1 dH2O only

A3 2.5mM

A4 5mM

A7 10mM

Plants were grown in 15cm diameter fluted citrus pots containing 3.7L of Searles premium potting

mix supplemented with a three month supply of micronutrients. Showerhead irrigation was administered

manually three times per week to drainage for vegetative growth phase. At the stage 50% of the population

had reached anthesis, drought stress commenced by withholding watering for 20 days. Experimental

design selected was completely randomized design with 15 replicates (n=15). Drought responses were

assessed by way of the following parameters:

1. Root length (cm): Defined as distance from tip of longest root to the root crown

2. Shoot length (cm): Defined as distance from soil surface to uppermost stem node

3. Root fresh weight (g): Obtained at time of sampling on a 2-decimal digital balance

4. Shoot fresh weight (g): Obtained at time of sampling on a 2-decimal digital balance

5. Root dry weight (g): Obtained after 72hours vacuum-oven drying at 70oC

6. Shoot dry weight (g): Obtained after 72hours vacuum-oven drying at 70oC

7. Mature pod number


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QCDF ATW1124 mungbean glasshouse assay

Results were divided into morphologic and physiological responses of mungbean to ATW1124

treatment and drought stress. This section describes those results according to the methods described above

(Chapter 3 and Section [4.9.1]). Statistical analysis and graphs were generated using Minitab 16 or

Microsoft Excel unless otherwise specified.

4.3.5.1.1 MORPHOLOGICAL RESPONSES

Morphology was assessed at flowering (DTF) and physiological maturity (DTM) for both above-

ground and root parameters with WinRHIZO analysis performed only at flowering. The following sections

describe those results.

4.3.5.1.1.1 Morphological responses at Anthesis

Morphological responses to drought at anthesis were critical as this is one of the most water

sensitive stages of mungbean development. Concentration of ATW1124 used in the experiment was

100mM, consistent with previous experiments which identified this as optimum. ANOVA of total root

length comparing untreated G1 (Berken) and G2 (Jade) revealed that the two varieties did not differ in

terms of root morphology under hydrated (p = 0.158), mild drought (p = 0.179) or severe drought (p =

0.240) conditions in the absence ATW1124 treatment. However ATW1124 statistically significantly

increased total root length of G2 under mild drought stress by 17% (p = 0.032) (Figure 29 and Figure 30).

Fine roots – defined as roots with diameters of less than 0.5mm – were the most enhanced by treatment,

while those with diameters above 0.5mm remained unaffected.

Under severe drought stress both genotypes exhibited a reduction in mean root surface area

(Figure 29, (B) ‘30’), presumably brought about by dehydration and abscission of finer roots constituting

the majority of total root surface area. This was supported by the finding that ATW1124 promoted fine

root development, thus the reduction in root surface area of ATW-plants subjected to severe drought stress

in Figure 29 may have been due to the dehydration of those fragile tissues. As expected, data followed a

normal distribution and exhibited acceptable levels of variability indicating that the RCB design was

appropriate (Figure 29, Figure 31). Root biomass consistently increased with ATW1124 treatment, with

the most pronounced increase occurring under drought conditions (Figure 31). In conjunction with an

increased root biomass, shoot biomass was found to decrease giving a net increase to r/s ratio of 15%.

Findings were later verified in field trials at the two QLD locations of Warwick and Kingaroy.


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Figure 29: Effects of seed pre-treatment with ATW1124 on total root length at flowering

under mild drought stress. G1 refers to ‘genotype 1’ (Berken cv), G2 refers to ‘genotype 2’ (Jade cv).

+/- refers to ATW1124 treatment. 100, 60 and 30 denote well-watered conditions, mild drought stress and

severe drought, respectively. Data from all treatments was found to follow a normal distribution with equal

variance (n=4). Brackets denotes the most significant interactions of Jade cultivar (G2) treated with ATW1124

under mild drought stress, which exhibited root length and SA increases of 17.5% (p = 0.032) and 26.6% (p =

0.025), respectively. Error bars represent SEM.


80

.

Figure 30: Effect of mild (DS1) and severe (DS2) drought on (a) root surface area and (b) total

root length of mungbean treated with ATW1124. Representative images collected using WinRHIZO

presented in (c) untreated and (d) treated samples. Error bars represent SEM.


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Figure 31 Comparison of root and shoot biomass dynamics at flowering with ATW1124 treatment

under different irrigation regimes: 100 (well-watered control), 60 (mild drought stress) and 30 (severe

drought stress). G1 and G2 refer to the mungbean genotypes Berken and Jade respectively. Probability

plots were used to test for normality and found that root biomass was generally less consistent than shoot

data, thus WinRHIZO analysis was the preferred method of root analysis. Shoot biomass was slightly

reduced with treatment while root tissue was increased leading to a net increase in root:shoot ratio of ~15%

under drought conditions (n=4).


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4.3.5.1.1.2 Morphological responses at Maturity

At maturity plants had been recovered for approximately two weeks post drought stress. Results

indicated that plants performed comparably across treatments although G2 shoot biomass slightly

decreased with ATW1124 under mild drought stress treatment compared with controls (Figure 32).

However very low recovery was noted across all treatments under severe drought stress resulting in sample

size ranges of 1-3 for those treatment groups (Figure 32, ‘30’). As such morphological data at maturity was

disregarded. Drought was found to reduce shoot biomass by approximately 15% while root biomass

remained fairly consistent. Genotypic comparisons under hydrated conditions without ATW1124 revealed

that Jade cultivar mungbean developed 30% increased shoot biomass (p = 0.018, n = 5) and 20% higher

root biomass (p = 0.186, n = 5). Interestingly, RWC remained fairly constant for both genotypes and was

maintained remarkably consistently across all hydration levels – approximately 63-66% at flowering plants

and 73-77% at maturity. It was hypothesised that this may have been facilitated at the expense of shoot

biomass via leaf senescence in order to maintain RWC within the normal range. This was supported by the

observation that, when faced with dehydration, plants may sacrifice leaves proximal to the root crown as a

water conservation strategy. Indeed visual observations confirmed a frequent and stark dichotomy between

hydration level of lower and upper leaves during dehydration. Given that the RWC measurements were all

collected from the uppermost mature centre tri-foliate, it was unsurprising to observe such consistency.

4.3.5.1.1.3 Leaf area

Leaf area (LA) analysis calculated using intelligent edge fitting area extraction and pixel density in

GIMP2 from DSLR images (methods in Section [3.2.3.1.1.3]) revealed no significant differences in LA

between treatments. However mean LA measurements were decreased in G1 and G2 by 18.1% and

12.4%, respectively. Leaf number per plant was unchanged with treatment suggesting that reduction in

shoot FW biomass may be at least in part attribute to a reduction in leaf area in ATW-plants.


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Figure 32 Effects of ATW1124 and drought on morphological indicators at physiological

maturity. Treatment refers to genotype and ATW1124 status, ‘Bench’ refers to drought status (60 - mild,

100 - hydrated). Error bars represent standard error.


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4.3.5.1.2 PHYSIOLOGICAL RESPONSES

To determine whether transpiration and photosynthetic efficiency was affected by ATW1124

treatment and to investigate any interaction this may have on drought stress, two LI6400XT’s (LICOR,

USA) and a MINIPAM system (Waltz, Germany) were employed as described in Section [3.1.3]. All

measurements were taken on the same day between 9:30am and 2pm – this range was identified in

preliminary experiments as the time plants were most photosynthetically active (Figure 15). Preliminary

results were obtained by taking 12hr readings of photosynthetic dynamics to ascertain optimum

measurement times. The midday depression in photosynthetic activity seen in many plant species was not

observed, likely due to high humidity of the glasshouse, which may have mitigated humidity deficit-

induced stomatal closure (Koyama & Takemoto, 2014) and caused plants to continue their photosynthetic

activity during this time. Additionally, humidity in the LICOR chamber head, in accordance with

preliminary results, was kept at 60% RH with gas scrubbers and flowrate regulation to similar effect.

Physiological readings were taken over a 4 week period, once per week on the uppermost mature

centre trifoliate. Each plant was measured periodically beginning with once under well-watered conditions

(week 1), followed by two readings while under its respective irrigation schedule (weeks 2 and 3) and

finally one reading after a recovery period of one week (week 4). The major photosynthetic determinants

examined were photosynthetic rate, transpiration rate, leaf vapour pressure deficit and stomatal

conductance. Three plants were analysed per treatment replicated 3 times (3 blocks in RCB design) once

for each irrigation schedule (WW – well watered, DS1 – mild drought stress and DS2 – drought stress 2).

Variance was pooled for well-watered data as watering schedule at this stage was identical across groups.

Similarly, for mild drought stress data obtained in week 2, mild and severe drought stress variances were

pooled as length of drought stress was consistent across groups (7 days). For all other time points data

were grouped independently with separate variance. A significant proportion of plants subjected to severe

drought stress did not recover thus recovery data have been omitted for this group due to low sample size.

Results are presented below.

4.3.5.1.2.1 Leaf gas exchange responses (LICOR)

To quantify the level of drought stress the plants were subjected to, leaf vapour pressure deficit

(VpdL) was measured and compared between watering schedules. Under well-watered conditions VpdL

was similar across treatments with a mean of 1.4kPa. On average drought elicited a 26% increase and 71%

in VpdL for mild and severe stress, respectively, indicating that a substantial level of drought stress was

being induced on those groups during physiological measurements. However under well-watered

conditions there was also a decrease in a number of these parameters between week 1 and 2 marked by the

transition to the reproductive developmental stage. Contrastingly, chlorphyll fluorescence (Fv/Fm) of well-


85

watered controls remained consistent across all four weeks physiological measurements were collected and

remained unaffected by the transition into reproduction.

Physiological effects of transitioning into anthesis were dwarfed by the marked effects of drought

(Table 10). Perception of drought drastically reduced photosynthetic activity to a minimum. However

ATW-G2 (Jade) was able to maintained statistically significantly higher levels of photosynthetic rate (p =

0.028) and stomatal conductance (p = 0.05) under mild drought stress. Proportionally this equated to

approximately double the photosynthetic activity than untreated controls. Values were still reduced

compared with well-watered controls, however results showed that ATW1124 reduced the degree to which

drought was hindering photosynthesis. Comparison of genotypic differences in photosynthetic rate

revealed that the contrasting varieties perceived the drought stimulus at different rates. The more tolerant

G2 began slowing its photosynthetic rate by 11.4% per day after induction, whilst G1 (sensitive)

photosynthetic rates reduced by 8.55%. Additionally upon restoring watering for recovery, all plants

exhibited a lag phase of 2 weeks before returning full function of photosynthesis.

4.3.5.1.2.2 Chlorophyll fluorescence responses (MINI-PAM)

Comparison of maximum photochemical yield (Fv/Fm or PSII yield) dynamics revealed that both

genotypes exhibited remarkable resilience under even severe drought stress. Transition into anthesis had

no effect under hydrated conditions. While the reduction attributed to mild drought stress was calculated to

be 5.13% averaged for all treatments; however the effect was not detected until the recovery phase 7 days

after cessation of stress. Severe drought stress reduced PSII yield significantly more by 14.1% averaged for

all treatments. Results suggested that drought-induced degradation of photosystem efficiency had a lag

phase of up to 7 days. Similarly, PSII did not return to full function until plants were recovered for 2

weeks. Under severe drought stress, PSII yield reduced to a significantly greater extent in G2 (Jade)

compared with G1 (Berken) indicating a greater degree of PSII plasticity. G2 PSII found was -13.0% of

G1 PSII (p = 0.004) under severe drought stress highlighting a key genotypic difference in their

responsiveness to severe drought stress. Recovery after mild drought stress was slightly enhanced in

ATW-G2 plants (+3.69%, p = 0.167) however the effect was insignificant. Overall there were minimal

effects of ATW1124 on PSII yield – the factors with the greatest impact were genotype and water supply.


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Table 10: Effects of transitioning into anthesis on photosynthetic apparatus of mungbean

subjected to different degrees of drought stress.

Effect of anthesis (%) and drought stress on mean physiological responses

Parameter Anthesis Mild drought Severe drought

Photosynthetic rate -11% -68% -99.90%

Stomatal conductance -54% -88% -100%

Transpiration rate -42% -83% -100%

Leaf vapour pressure deficit +4.20% +25.50% +71%

Table 11: Effects of ATW1124 on mean physiological responses to drought stress. Figures in

bold denote statistically significant differences (p < 0.05).

Impact of ATW1124 (%) on mean physiological responses under drought stress

Well-watered Mild drought Severe drought

Parameter G1 G2 G1 G2 G1 G2

Photosynthetic rate -3.76% +3.98% = +193% * = =

Stomatal conductance -13.9% -8.06% -11.9% +231% * = =

Transpiration rate -20.1% +7.17% -20.8% +71.17% = =

Leaf vapour pressure deficit = = -1.74% -4.68% = =


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0.6

0.65

0.7

0.75

0.8

0.85

WW WW WW WW

ϕP

SII

Treatment

Well watered

G1(-)

G1(+)

G2(-)

G2(+)

0.6

0.65

0.7

0.75

0.8

0.85

WW DS1 RW RW

ϕP

SII

Treatment

Mild drought

G1(-)

G1(+)

G2(-)

G2(+)

0.6

0.65

0.7

0.75

0.8

0.85

WW DS1 DS2

ϕP

SII

Treatment

Severe drought

G1(-)

G1(+)

G2(-)

G2(+)

Figure 33 Photosystem II photochemical yield (PSII) dynamics under mild and

severe drought stress. WW, DS1 and DS2 denotes well-watered, mild and severe drought

stress conditions (n=3).


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4.4 CHAPTER SUMMARY

In conjunction these results highlight a role for ATW1124 in enhancing root development under mild

drought stress. Root modifications appeared to shift morphology toward the ideal root ideotype for abiotic

stress tolerance commonly referred to in literature as “steep, cheap and deep” (Lynch, 2013) referring to a

deeper inclination of overall RSA and a lower metabolic cost of foraging. The former was certainly

observed in the X-ray tomograph (Figure 27) and the latter was implied due to the significantly higher

composition of shallow and mid-level roots. This suggests that ATW-plants may exhibit elevated drought

tolerance and a higher efficiency for nutrient scavenging when under stress. In accordance with previous

studies the observed significant increase in lateral root development of ATW-plants would be particularly

beneficial for acquisition of immobile nutrients such as phosphorous and potassium from the soil (Postma

& Lynch, 2011b; Chimungu et al., 2014). While deeper RSA arrangements have previously been shown to

improve nitrogen uptake (Postma & Lynch, 2011a).

The finding that ATW1124 enhances rate of radicle development was significant, since this parameter

underpins germination and emergence rates which relate to seed vigour. Vigour refers to germination

efficiency under sub-optimal soil conditions and is considered closely by the pulse industry when

evaluating commercial or breeding lines. Thus further investigation into the relationship between

ATW1124 treatment and seed vigour is a recommended goal for future work. This could potentially also

be extend into nutrient deficiencies, cold and drought stress vigour testing.

Shoot physiology appeared to also be effected by ATW1124 by way of an increased efficiency of

photosynthetic rate under mild drought stress (Table 11 and Figure 32). Interestingly photosynthetic rate

recovered more rapidly with ATW1124 treatment in the drought tolerant mungbean variety (G2) but not in

the sensitive variety (G1). This highlights one area of difference between the genotypes and may also

provide insights into potential mechanisms underlying those differences. This is discussed at length in

Chapter 7. Physiological measurements were collected at the developmental stage of anthesis which marks

the transition from vegetative to reproductive phases. This was controlled for by monitoring a number of

physiological indicators of a fully irrigated block as it transitioned. The effects of anthesis on

photosynthetic rate were therefore accounted for in subsequent measurements during drought stress and

recovery stages. Anthesis was found to reduce leaf physiological activity (Table 10) for both drought

sensitive and tolerant varieties fairly equally. However these effects were dwarfed considerably by those of

drought stress. As an example: as well-watered plants reached anthesis, photosynthetic rate reduced by

around 11%; while for drought, reductions were around 68% for mild and 99.9% for severe drought stress.

Treatment with ATW1124 was found to counter reductions from drought stress considerably (Table 11).


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The drought sensitive variety (G1) exhibited no significant responses to ATW1124 treatment while the

tolerant variety appeared to be substantially more responsive. It was found that the ability of G2 ATW-

plants (G2+) to maintain higher physiological activity under drought stress came at the expense of

physiological activity in preceding well-watered (WW) stages (Table 11). Under well-watered conditions

photosynthetic rate was reduced by around 3.8% (both genotypes) but then maintained 193% higher than

controls under mild drought stress. G2+ stomatal conductance was reduced in preceding WW stages by

10% then maintained 231% higher than untreated controls under mild drought stress. G2+ transpiration

rate was 7.17% higher under WW conditions and under drought stressed conditions by around 70%

compared with controls.

Interestingly the leaf vapour pressure deficit (essentially leaf hydration status or VdpL) was found to be

4.68% lower in G2+ plants than controls indicating that despite maintaining higher physiological activity

under drought stress, the severity of stress they were experiencing was actually more severe. The results

indicate that ATW1124 elicits ‘preparatory’ reductions in physiological activity under well-watered

conditions, which allow maintenance of photosynthetic activity at higher rates when drought is

subsequently imposed. Additionally the lack of photosynthetic plasticity of G1 drought sensitive plants

was identified as a potential mechanism underlying differential drought sensitivity between genotypes.

It is therefore inferred that reductions in photosynthetic activity of ATW-plants under hydrated conditions

may reduce vegetative biomass production. Reduced vegetative biomass could enhance drought tolerance

by reducing transpirative water demand. Additionally if reduction in vegetative biomass is found to be

independent of yield then this may also improve harvest index and overall performance on water limited

soils. These hypotheses are tested and described in detail in subsequent chapters.

Chapter 5 – Transcriptome analysis of differentially drought tolerant mungbean treated with ATW1124

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5Chapter 5 – Transcriptome analysis of differentially

drought tolerant mungbean treated with ATW1124

5.1 INTRODUCTION

Recent advances in bioinformatics now allow high throughput comparisons of genetic information

using high performance computers (HPC) capable of processing vast amounts of information in a fraction

of the time compared to their conventional counterparts. A technique known as RNA sequencing (RNA-

Seq) utilises high throughput sequencing technology for RNA and has many advantages over more

conventional methods for transcriptome analysis (Wang et al., 2009; Haas & Zody, 2010; Nagalakshmi et

al., 2010). The process generates expression data at a much higher resolution than conventional (Sanger

sequencing and microarray) techniques and, importantly, can quantify expression levels of novel genes

(Nagalakshmi et al., 2010). RNA-Seq is also commonly used to compare transcriptional structure of genes

for example splicing, coding regions and posttranslational modification between different sets of

experimental conditions (Wang et al., 2009).

Broadly speaking, using RNA-Seq research can compare sample transcriptomes in order to identify

differentially expressed genes (DEGs). These can provide some indication of what plant processes or

biochemical pathways are involved in a particular treatment. Gene expression is quantified using

specialized computer software (e.g. CLC Genomics, Galaxy, Bowtie) by mapping short-read sequence

data against an annotated reference. If a reference is unavailable then one can be assembled de novo from

short sequence read data, albeit with some inherent difficulties. Once identified, DEGs can be ascribed

gene ontology (GO) terms which can be acquired from publicly available datasets of GO annotated

references (De Wit et al., 2012). GO terms describe DEG functions, which can be very useful in early

stage investigations aimed at uncovering the molecular mechanisms of a particular treatment or process.

Lists of GO terms describing transcriptomes of interest can be classified according to biological process,

molecular function and cellular component, allowing enrichment analysis of GO terms (GOEA). This can

provide information on whether a dataset is functionally enriched in any way. For instance a dataset from

drought stressed tissues could be found to be functionally enriched for antioxidant enzymes or peroxidases

in response to oxidative stress. The process requires that a sample dataset is compared against an annotated

reference genome or transcriptome. Generation of a reference genome de novo is a laborious and

extremely difficult procedure which fortunately, was previously completed for mungbean by researchers in

Seoul, South Korea and made publicly available for use in this project.

This chapter describes the pipeline from RNA-Seq to gene ontology enrichment analysis (GOEA); and

details of GOEA results obtained in this way for a number of interactions between drought stress and


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ATW1124 treatment of mungbean. The main objectives were to gain insights into the impact ATW1124

treatment has at the transcriptome level; to compare and contrast transcriptomes of two differentially

drought sensitive mungbean varieties; and to identify key GO terms those interactions to infer underlying

molecular mechanisms.


This section describes the materials and methods used to full the above objectives. Molecular

techniques and quality control have been previously described in Chapter 3.

5.2.1 Identification of differentially expressed genes from RNA-Seq data

Tissue samples from which RNA was extracted for sequencing were collected as part of the

glasshouse experiment conducted at the Queensland Crop Development Facility (QCDF) described in

detail in Chapter 4. Two separate groups of plants were used to obtain unstressed and drought-stressed

tissue samples to avoid any developmental bias. Tissue described as ‘shoot’ refers to 100mg of leaf tissue

collected from the uppermost mature centre trifoliate. 100mg of root tissues were sampled 2cm from the

growing tip from destructively extracted and washed root systems. Once collected samples were

immediately wrapped in aluminium foil and snap frozen in liquid nitrogen. Once all samples had been

snap frozen they were placed on dry ice for 30mins for transport from the QCDF to a -80oC freezer at the

Queensland University of Technology (QUT). Samples were stored for approximately 6 weeks at -80oC

until total RNA was extracted using QIAGEN RNEasy kit.

Once extracted, RNA quality was assessed using a Thermoscientific NanoDrop2000, Agilent

2100 Bioanalyzer and RNA gel electrophesis (see Chapter 3). RNA concentrations were considered viable

for sequencing when concentrations were above 150ng/ul. RNA samples were then sent to the Central

Analytical Research Facility at QUT (CARF) for cDNA library construction. In total there were 16

libraries with three biological reps (three individual plants) of which there were three technical reps

(separate samples from the same leaf). Sequencing was carried out on an Illumina NextSeq 500 at the

CARF at QUT employing single-end 75 bp reads. Resulting Short Sequence Read (SSR) data was

imported into the software platform CLC Genomics for quality assessment, read trimming and mapping to

a reference mungbean genome obtained from NCBI (assembly Vradiata_ver6). Average read length

before trimming was 75 nt, after trimming this was reduced to 60 nt. The 15 nucleotides which were

trimmed comprised 13 nt from the 5’ end to remove universal adaptor sequences attached during

sequencing; 2 nt were trimmed from the 3’ end to remove a small peak miscalling artefact which was

evident in all samples (Figure 34). Read quality was assessed using the Phred score cut-off of 20, with

sample scores ranging from 30-35 indicating high quality data. Additionally GC content was consistently


92

found to be normally distributed indicating no nucleotide bias was present with the number of ambiguous

bases found to be negligible (Figure 34).

After trimming sequences were mapped to a reference mungbean genome (Kang et al., 2014) to

generate read count data of differentially expressed genes arising from each treatment. Treatments

Figure 34 Representative nucleotide contribution of short sequence read data obtained in CLC

Genomics. Inconsistencies appearing at position 1-13 denote universal adaptor sequences; inconsistencies

appearing at position 73-75 were attributed to base-miscalling artefacts of the sequencing technique present at

all samples.


93

included drought stress (DS), which was imposed as a 7 day water-withholding period prior to sampling;

and ATW1124 seed pre-treatment at a concentration of 100mM 1hr seed soak prior to sowing (+), or a

H2O seed soak as a control (-). Two genotypes were used distinguished from each other by their drought

sensitivity. The drought sensitive variety was ‘Berken’ cultivar mungbean (G1)) and the tolerant variety

was ‘Jade-AU’ (G2) - selected according to the method described in section [4.2.1]. Root and leaf tissue

samples were also included giving a total of 16 possible treatment combinations:

cDNA library tissue origin

Well-watered Drought stressed

G1- root G2- root G1- root G2- root

G1+ root G2+ root G1+ root G2+ root

G1- shoot G2- shoot G1- shoot G2- shoot

G1+ shoot G2+ shoot G1+ shoot G2+ shoot

N = 3 biological replicates each with 3 technical reps

For each sequencing cDNA library (n = 3) there were an average of 25 – 30 million reads with

around 88% mapping leaving 12% unmapped sequence attributed to genotypic differences between

reference and sample species. Of the total mapped fragments around 95% mapped to gene sequences with

the remainder mapping to intergenic regions or introns. All 16 cDNA libraries were analysed in CLC

genomics to identify the number of differentially expressed genes (DEGs) resulting from the respective

treatment. It was found that root transcriptomes were not the main sites of differential gene expression

despite being morphologically very responsive to ATW1124 treatment. Similarly, the variety G1 was

considerably less responsive to ATW1124 treatment than G2, supported by results in chapter 4 which

showed that G1 was also physiological less responsive. As such, the main focus of the RNA-seq based

analysis remained on G2 shoot tissue. The analysis was based on comparing transcriptomes from the

above table to reveal the impacts of certain treatments. For instance to determine the impact of drought

stress, the two libraries compared were:

(1) G2- WW shoots

(2) G2- DS shoots

Where (1) represents genotype 2 (Jade), negative for ATW1124 treatment (-), from well-watered (WW)

shoot tissue; and (2) differed only in the level of water the plant received. Both were from different plants

of the same developmental stage such that any differential gene expression between the two libraries

would be attributed to dehydration. This same principle was applied to investigate a number of interactions

described in Table 12.


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Table 12: Summary of interactions included in gene ontology enrichment analyses

Interaction examined Purpose

I

G2- WW shoots VS G2- DS shoots:

To determine the transcriptomic impact of drought stress

II

G2- WW roots VS G2+ WW roots

To determine whether ATW1124 had any effect on the root

transcriptome in the absence of drought stimulus

III

G1- WW shoots VS G2- WW shoots

To determine the transcriptomic differences between

drought sensitive and tolerant genotypes

IV

G2- WW shoots VS G2+ WW shoots

To determine what impact ATW1124 has on shoot

transcriptome under hydrated conditions

V

G2- DS shoots VS G2+ DS shoots

To reveal whether ATW1124 augments drought responses

in the shoot transcriptome

VI

G2- DS roots VS G2+ DS roots

To reveal whether ATW1124 augments root transcriptomic

drought responses

G1 = Berken cultivar; G2 = Jade cultivar; WW = well-watered; DS = drought stressed; -/+

referring to ATW1124 status

Output of these experiments took the form of a list of genes accompanied by read counts illustrating the

fold change differences in gene expression between treatments from the interactions above. Raw read

count data was then normalized in CLC Genomics by using scale normalization by mean, which

essentially multiplied original expression values by a constant such that the normalized values shared the

same mean. This ensured that expression data for each sample was comparable even between transcripts of

highly dissimilar abundance. A Student’s T-test was then performed on normalized (Gaussian) expression

values to enable filtering of DEGs according to their statistical significance (p < 0.05). Due to the large

number of simultaneous tests (thousands of genes) being analysed in this form of expression data, family

error or false discovery rate (FDR) becomes significant (Azzabou & Paragios, 2008). Put simply, the

higher the number of simultaneous hypothesis tests, the higher the chance of false positives. Therefore p-


95

values had to be adjusted to account for FDR which was done using the statistics function in CLC

Genomics. Next DEGs were subsetted to a list containing only those exhibiting:

Expression fold change (on normalized values) greater than |2|;

Difference between mean expression values greater than |5|; and

FDR corrected p-values less than 0.05.

This filtration method selected for DEGs exhibiting reasonably high levels of differential

expression in response to their respective treatments (i.e. drought, ATW1124 treatment or genotype

selection). Resulting lists of genes involved in each respective interaction were then exported as a plain

text file for additional processing in other software platforms. It should be noted that given the high level

of stringency of DEG filtering methods described, each DEG entry was enlisted with a very high level of

confidence. Incidentally it was likely that the more subtle differences in gene expression were filtered out.

Lists of DEGs associated with all six of the interactions described in Table 12 were then clustered

according to their gene ontologies (GO terms) using the David Bioinformatics Database (Huang et al.,

2008, 2009) with the exception of interactions II and VI which had gene lists of less than 10 – the

minimum requirement for gene ontology clustering in DAVID. Analysis of gene ontology enrichment of a

gene set can provide a holistic perspective of the transcriptome by identifying any key plant processes or

pathways which can later be explored using more in-depth molecular techniques.


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5.2.2 Pipeline from differential gene expression to gene ontology enrichment analysis

Functional analysis of those genes was then performed by inputting DEG IDs into the DAVID

Bioinformatics Database (Huang et al., 2008, 2009) and BLASTed against an archived GO-annotated

mungbean reference genome to generate a list of associated GO terms. As very scarce GO annotation was

available for mungbean, the genome array of a closely related species, medicago truncatula (Mt), was

downloaded from NCBI and used as a reference. As Mt is a model organism for which there is a wider

selection of genomic resources available, the MT genome array provided a source of abundant GO

annotation. Mungbean DEG lists were first converted into Mt orthologues by BLASTing the mungbean

reference transcriptome (CDS region of genome) against an Mt genome array to produce a list describing

the entire mungbean genome as a list of Mt gene orthologues. This was carried out using a high

performance computing cluster (Lyra) at QUT using the following scripts:

1. Generation of a local Mt database based on reference sequence (.fasta) was executed using the

‘makeblastdb’ application of the Blast+ package:

## makeblastdb -in MT_CDS_goodformat.fasta -dbtype nucl -out medicago_goodformat_db

Where ‘in’ refers to the mungbean references sequence input; -‘dbtype’ indicates a nucleotide

sequence; and ‘out’ refers to the name of the output database.

2. Execution of BLAST alignment was carried out using the following CMD script:

module load blast+

blastn -query Transcript_sequences_clean.fa -db ~/medicago_goodformat_db -out

MT_goodformat_blast.xml -outfmt 5 -word_size 7 -show_gis -max_target_seqs 5

num_threads 8

Where ‘query’, ‘db’ and ‘out’ refer to input, database and output filenames, respectively; and the

remainder refer to alignment specificity and formatting parameters.

3. The outputted extensible mark-up language (.xml) file was then parsed (txt) for visualisation using

the following Perl script:

# perl BlastParse.pl MT_goodformat_blast.xml > MT_goodformat_blast.txt


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4. Finally gene lists from the original mungbean reference transciptome along with their

corresponding Mt orthologues were entered into Microsoft Excel. Mungbean DEGs were then

extracted from the Mt gene list using Excel’s VLookup function.

The resulting lists of Mt gene orthologues were then formatted according to input requirements of DAVID

Bioinformatics Resources as well as the online tool AgriGO for visualisation developed by the Zhen Su

Bioinformatic Center – China Agricultural University. AgriGO accepts lists of GO terms (corresponding

to DEG lists as above) as inputs and compares the level of representation of the inputted lists against that

of a known reference. For the purposes of this study the references sequence to which enriched GO terms

were compared was the Mt genome array version 4.0 (Tang et al., 2014). The results from these two tools

were composed of a series of graphs and statistical tests for significance (t-tests) describing enriched GO

terms describing modulations in gene expression exhibited in the various interactions listed above (see

Results sections 5.3.1 through 5.3.4). The AgriGO analysis was based on a Fisher statistical test method

using a Bonferroni FDR correction method with a significance level of p<0.05 using the Medicago

Truncatula (Mt) genome array V4.0 (Tang et al., 2014) as a reference background.

5.3 RESULTS

To investigate the effects of ATW1124 on gene expression a series of RNA-seq experiment were

conducted on respective pairs of cDNA libraries listed above. RNA quality assessed using CLC was found

to be of a very high standard (Figure 35) with a very high degree of mapping (Figure 36). Effects of

drought were included to give a sense of scale to the magnitude of gene expression changes, as drought is

known to have a significant impact on a vast array of gene expression networks (Shinozaki & Yamaguchi-

Shinozaki, 2007). Data presented in previous chapters revealed that of the two genotypes included, G2

(Jade cultivar mungbean – tolerant variety) exhibited higher responsiveness to ATW1124 in both

morphology and physiology. Photosynthesis was found to be higher in G2 in response to ATW1124

treatment and root/shoot morphology were also found to be higher. Therefore the scope of GOEA was

focussed primarily on G2although a genotypic comparison was included in order to determine whether

differences in drought sensitivity and/or ATW1124 responsivity had a transcriptomic basis. Visualisation

of GOEA in AgriGO could only be performed on DEG lists of 10 or greater which excluded 2 out of the 6

total interactions. These were:

Interaction II in Table 12 – the effect of ATW1124 on WW roots – which revealed 8 DEGs

encoding transcription factors, potassium transporters, protein transporters and structural

transmembrane proteins. However no functional clusters were identified. This suggested that

although root morphology was a major affected site of ATW1124 treatment, this modification was

not reflected in the root transcriptome of those tissues. An explanation for this may be that signals


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responsible for the ATW1124-induced root morphological changes originated in shoot tissues and

were translocated to roots, or that they were post-translational in nature. Studies have shown that

post-translational modification (PTM) of proteins has a major role in root architectural

modification (Miura et al., 2007a). Additionally it has been reported in other species including

Medicago truncatula that post-translational reversible conjugation of the small ubiquitin-related

modifier (SUMO) peptide to protein substrates (‘sumolation’) is a major regulatory process in

plants and other eykaryotes. SUMO conjugation/deconjugation systems are conserved in many

plant species including Arabidpsis, rice, tomato and Medicago truncatula, in which a number of

SUMO-related enzymes have been linked to regulation of drought tolerance (Zhang et al., 2013),

root meristem development (Zhang et al., 2010), the temporal dynamics of flowering (Miura et

al., 2007a), phosphate starvation (Miura et al., 2005), cold stress signalling and tolerance (Miura et

al., 2007b; Miura & Hasegawa, 2008). In order to determine whether PTM contributes to the root

morphological effects of ATW1124, future studies may wish to include a proteomic analysis – as

has recently proven useful in other species such as barley (Hordeum vulgare) (Wang et al.,

2015a).

Interaction VI in Table 12 – the effect of ATW1124 on DS roots – which could identify no

statistically significantly differentially expressed genes in response to ATW1124 treatment on

mungbean under drought stress compared with controls. As discussed above, it may be that root

tissues may not have actually been the sites of differential gene expression in spite of being sites of

primary morphological effect. Additionally, as ATW1124 was found to exhibit only minor effects

on root-borne gene expression under hydrated conditions, it was unsurprising that a similar trend

was observed under drought stress. However it should again be emphasised that filtering

stringency was high and only DEGs with high level of confidence were enlisted. Therefore if the

molecular effects of ATW1124 were only subtle, or even transient, then they would not be

reflected in the present cDNA libraries which were filtered for only major fold-changes (< |5|) in

gene expression. In order to capture transient expression changes, a tissue culture method

employing polyethylene glycol (PEG) treatment (common in vitro method of imposing drought)

may be preferred (Cao et al., 2014), as this would enable more stringent control over when stress

was imposed and thus sampling could be carried out immediately after stress induction to capture

transiently expressing genes. However such an experiment would require highly artificial

laboratory conditions and cell cultures or protoplasts, while the scope of this study remained on

generating data highly relevant to industry. As such an investigation into transient molecular

effects of ATW1124 could be of greater relevance to future studies.


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With these two exceptions, the remaining interactions listed in Table 12 were examined in greater depth

using gene ontology enrichment analyses. Lists of differentially expressed genes identified in CLC

Genomics were first inputted into the DAVID bioinformatics tool to ascribe gene ontology terms to those

genes, then categorised into clusters to determine whether any patterns or general trends were evident in

the types of genes being differentially expressed.

Figure 35 Representative quality distribution histogram illustrating typical PHRED

quality scores of RNA sequencing runs obtained using CLC Genomics. Scores of above

20 were considered high quality.


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Figure 36: Representative summary of read count statistics obtained by mapping total RNA

sequences against a published reference mungbean genome. Top: number of total fragments which

mapped (mapped); bottom: composition of mapped fragments according to their type. Figure

obtained from CLC Genomics.


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5.3.1 Interaction I – Effects of drought on the mungbean shoot transcriptome under

hydrated conditions.

GOEA results pertaining to the transcriptomic effect of drought stress identified 1426 unique

DEGs which were then functionally classified using the DAVID Database according to common GO

terms for functional enrichment. GOEA compared hydrated and drought stressed untreated control plants.

Results revealed that the most significantly enriched GO terms were ‘membrane’ (21.9% of total genes),

‘transmembrane’ (21.7%), ‘transmembrane helix’ (21.2%), ‘transferase’ (13.5%) and ‘coiled coil’

(10.5%). Functional annotation clustering was then performed in order to group GO terms which shared a

common theme. This function measured the relationships among annotation terms based on their level of

association between genes and grouped similar, redundant and heterogeneous annotation contents (Huang

et al., 2008) to enable a more focussed interpretation. This process identified 74 annotation clusters, the

most significantly enriched of which were:

Annotation cluster 1: Enrichment score 4.3: The cluster was composed of kinases (126 counts), receptors

(82 counts) and transferases (192 counts), which clustered most significantly by molecular function, which

was predominantly either ATP binding or in protein kinase activity.

Annotation cluster 2: Enrichment score: 4.27: The cluster was enriched for GO terms related to both

nucleotide-binding (83 counts) and ATP binding (74 counts) sharing the predominant molecular function

of ATP binding.

Annotation cluster 3: Enrichment score 4.12: The cluster was enriched for GO terms related to a number

of nuclear elements and transcription regulatory elements which clustered according to the biological

process of DNA-templated transcription and the molecular function of DNA-binding.

Analysis of the remaining functional annotation clusters revealed a pattern of enrichment in terms related

to maintenance of structural integrity of cellular (e.g. plasma membranes and cell walls) and molecular

(nucleic acid) components. Terms related to the structural component of cell walls were a prominent

feature, appearing as variations in numerous clusters which exhibited enrichment scores of 3.71 and above

– notably highlighting proline-rich repeats and extensins, which have previously been reported to play key

roles in cell wall signal transduction and stress tolerance (Kavi Kishor et al., 2015). Main GO terms

showing down regulation included an over representation of genes annotated under ‘cellular processes’ (a

diverse array of processes predominated by protein synthesis and translation) and ‘binding’ (referring

mainly to ATP, nucleic acid, protein and mitochondrial). In contrast the most notable upregulation was in

catalytic activity (Figure 38). It should be noted that the level of drought stress imposed on these

specimens was considered mild (7 days water withholding) such that any radical gene expression shifts


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would be unexpected. Upon comparing both DAVID Bioinformatics and AgriGO, it was evident that the

former provided significantly greater analytical depth and formed the foundation of the analysis.

Figure 37 Systematic representation of GOEA conducted on ‘interaction I’ to decipher the impact

of drought stress on the mungbean shoot transcriptome under hydrated conditions. Yellow boxes indicate

statistically significant terms (p < 0.05) while non-significant terms are shown in white. Solid, dashed and

dotted lines represent 2, 1 & 0 enriched terms at the connected ends; rank direction runs from top to

bottom. Figure generated using the online tool AgriGO.


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Figure 38 Graphical representation of GOEA conducted on interaction I to decipher the impact of

drought stress on the mungbean shoot transcriptome under hydrated conditions. Figure generated using the

online tool AgriGO


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5.3.2 Interaction III – Transcriptomic differences between differentially drought tolerant

genotypes

Founded on the observations of previous chapters that the drought sensitive and tolerant varieties

exhibited differential responsiveness to ATW1124 treatment, GOEA was used to compare their expression

profiles to ascertain whether the underlying mechanism could be detected at the transcriptome level. Both

genotypes were fully hydrated and negative for ATW1124 treatment thus any DEGs were attributed to

genotypic differences. A total of 35 DEGs were identified, some of which were grouped into a single

cluster related to transmembrane functions. Analysis on DAVID revealed that the two genotypes differed

in biological processes related to cell wall organisation as well as molecular functions related to structural

constituents of the cell wall. Functions of DEGs included but were not limited to phospholipid transport,

phototropic responsive proteins, a range of transcription factors including WRKY and extension-like

regions. Some of these elements have previously been reported to be involved in abiotic stress responses

including the WRKY transcription factors (Seki et al., 2002; Yu et al., 2013a) as well as cell wall

extension proteins (Gall et al., 2015). Furthermore although there was some overlap between some of these

DEGs and physiological results of previous chapters, such as differences in photosynthetic responses and

drought sensitivity, no unifying GO annotation clustering was identified in either DAVID or AgriGO.

5.3.3 Interaction IV – Effects of ATW1124 treatment on mungbean shoot transcriptome

under hydrated conditions

Following observations of previous chapters that ATW1124 treatment was only effective under

mild drought stress, GOEA was used to compare mungbean shoot transcriptomes under hydrated

conditions. Both control and ATW-plants were fully hydrated such that any DEGs were attributed to

ATW1124 treatment. Results identified 31 DEGs which were grouped into a single cluster of GO terms

related to transmembrane proteins and helixes. DEGs were also found to cluster according to the cellular

components that were integral components of the plasma membrane. However it was evident in the

DAVID functional annotation carried out that a number of those DEGs were annotated under processes

which aligned with previous experimental observations. For instance a significantly upregulated gene

previously identified as an auxin responsive gene (Medtr5g093520; gene ID LOC106764438 in

mungbean) which may have roles in root development (Young et al., 2011). Additionally, a WRKY

transcription factor was significantly upregulated, however this was found to be associated with a KEGG

Pathway involved with plant-pathogen interaction rather than abiotic stress. Furthermore a DEG annotated

as a dehydration-responsive element-binding protein was found to be significantly upregulated, which,

considering the sample came from a fully irrigated plant, was atypical. This may indicate some level of

dehydration in unstressed control plants. As water movement through a soil medium is non-uniform it is

possible that some areas of the root system became dehydrated and perceived drought stress while others


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did not. This is an inherent challenge associated with drought assays which could not be eliminated.

Visualisation in AgriGO revealed underrepresentation of GO terms related to biological regulation,

regulation of biological processes, catalytic activity and binding compared with controls. Collectively

results indicate enrichment of genes involved in cell redox homeostasis, protein catabolism, and cytoplasm

and plasma membrane regulation.

5.3.4 Interaction V – Effect of ATW1124 treatment on mungbean shoot drought response

Founded on results of previous chapters that the effects of ATW1124 treatment was antagonised

by drought stress, GOEA was used to determine the molecular mechanisms underpinning those

observations. Both control and ATW-plants were subjected to drought stress thus any DEGs were

attributed to ATW1124 treatment. A total of 143 DEGs were identified which were functionally classified

into 10 annotation clusters with enrichment scores ranging from 0.3 to 0.8. These revealed an upregulation

of a number of GO terms including ATP- and nucleic acid-binding, transcription regulation, metal and ion

binding – especially zinc (appearing in two clusters), and plant defence. It was found that GO clustering

excluded a number of s GO terms which did not group into any given ‘clusters’. Therefore these

Figure 39 Graphical representation of GOEA conducted on interaction IV to decipher the impact

of ATW1124 treatment on shoot transcriptomes of mungbean plants under hydrated conditions. Figure

generated using the online tool AgriGO.


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unclustered entries were examined more closely to identify potential trends. Analysis revealed a large

number of additional significantly enriched GO terms including DNA repair, structural regulation of the

plasma membrane, calcium binding, metabolic pathways and lipid catabolic processes.

Visualisation in AgriGO revealed that certain functions shown to be downregulated under

hydrated conditions became overrepresented under drought stress, including metabolic processes and

catalytic activity (Figure 40). This suggested that ATW1124-induced repression under hydrated conditions

may have enabled higher levels to be maintained under stress. Furthermore minor reductions were

identified in GO terms related to structural elements of the cytoplasm.

Figure 40 Graphical representation of GOEA conducted on interaction V to determine the impact

of ATW1124 treatment on shoot transcriptomes of mungbean plants under drought stressed conditions.

Figure generated using the online tool AgriGO.


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5.4 CHAPTER SUMMARY

In summary these results suggest that despite ATW1124 not eliciting any observable

morphological effects under hydrated conditions, regulation at the transcriptomic level was strongly

apparent. Given the findings of previous chapters that ATW-plants exhibited lower physiological activity

under well-watered conditions it was surprising that this was not a prominent feature in transcriptome

analysis. Although it was evident that drought had clearly amplified the effects of ATW1124 and elicited

upregulation of a number of relevant gene functions in ion binding and plant defence which aligned very

closely with results of previous chapters. These plant defence genes may be related to the reduction in

physiological activity described in Chapter 4. Ion binding may relate to oxidative stress response which

may suggest a ‘priming’ mechanism of ATW1124 toward subsequently encountered drought stress.

Involvement of both auxin-dependant and independent genes was revealed suggesting that

ATW1124 may have some effect on root development at the transcriptome level. Therefore a closer

investigation of the interaction between auxin signalling and ATW1124 pathways may be a possible

avenue for future work. This also relates to shoot responses as hormonal signals are systemic and involve

shoot and root transport networks. GO terms related to photosynthesis were prominent in the interaction

between ATW1124 and drought stress, particularly with respect to light harvesting, thylakoid membrane

integrity and pathways involved in photosynthetic antenna proteins. In conjunction with the data of

Chapter 4 this suggests that ATW1124 has a significant impact on physiological processes udner both

hydrated and drought stressed conditions. The effect seems to first appear under hydrated conditions by

way of a reduction in overall activity, allowing for maintenance at higher levels under stress. This finding

confirms previous work by collaborators investigating the effects of ATW1124 on Australian and

Canadian woody tree species who reported higher levels of photosynthesis in ATW-plants. Root

transcriptomes exhibited little to no response to ATW1124 treatment under either hydrated or drought

stressed conditions suggesting that although treatment significantly modified root morphology (see

Chapter 4), there appears to no observable transcriptomic basis of this in the roots themselves.

Investigation of the impact of ATW1124 on hydrated shoot transcriptomes identified

downregulation of biological regulation, regulation of biological processes and catalytic activity.

Downregulation of these processes may have reduced respiratory and thus water demand of shoot cells

which may be related to ATW1124-induced activation of stress responsive genes under unstressed

conditions. It should be noted however that even with stringent irrigation management the possibility exists

that some area of the root-zone could become dehydrated and activate these genes – as root placement

cannot be controlled by the operator. GOEA results may therefore suggest that plants were primed against

stress following treatment with ATW1124 by upregulating various plant defence elements and reducing

overall plant metabolism. However, if this was the case a higher degree of upregulation would be expected,


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thus it is recommended that this hypothesis is subject to additional validation. It should be emphasised,

however, that all genes enlisted as DEGs were done so with very high level of significance (FDR corrected

p value < 0.05) which limited the number of interactions but dramatically improved confidence. Any lesser

stringency may have yielded a higher number of DEGs, however this would have come at the expense of

confidence, which was of higher priority.

Comparisons of leaf transcriptome profiles of drought tolerant and sensitive varieties revealed

significant differences in phototropic responsive proteins which may explain, at least in part, the

differential photosynthetic responses presented in previous chapters. The drought sensitive G1 variety

consistently exhibited a lower level of photosynthetic plasticity in response to drought and ATW1124.

Lack of photosynthetic plasticity has been previously recognised as a prominent feature of drought tolerant

wild crop varieties and identified as an important trait for drought tolerance (Saha et al., 2016; Picorel et

al., 2017). Differential composition of phototropic responsive proteins functioning in light harvesting

complexes of photosystem II may be a factor contributing to the genotypic differences in this plasticity.

Another difference in transcriptome profiles between genotypes was the expression of WRKY

transcription factors and extension-like regions which are known to regulate downstream target genes

involved in drought stress response and tolerance (Umezawa et al., 2006) – these could explain some of

the differential drought sensitivity features between genotypes. Under hydrated conditions it was found

that ATW1124 induced repression of a significant number of biological processes and their regulation

including catalytic activity which was reversed under drought stress (Figure 39 and Figure 40). This

provided support for the findings of Chapter 4 that ATW-induced reductions in physiological activity

under well-watered conditions, which were similarly reversed under drought stress compared with

untreated controls.

Genotypes also differed in terms of cell wall structure and transmembane composition which has

previously been identified as a key factor determining drought tolerance (Picorel et al., 2017). Membrane

fluidity, governed in part by plasma membrane phospholipid and transmembrane composition, has been

previously identified as a major factor determining abiotic stress (mainly heat and light but also other

oxidative stress inducers such as drought) tolerance (Yamamoto, 2016); and these processes were highly

prominent areas of differentiation between genotypes in the present study. In the context of previous

studies and results of previous chapters, results suggest that ATW1124 treatment may have primed plants

against drought stress by reducing overall physiological activity which, at least in part, had a

transcriptomic basis. The obvious question as to whether this had any negative impacts on grain yield was

the focus of following chapter.

The RNA-Seq data generated during the course of this chapter can be used as a resource for

downstream bioinformatics analyses in future studies. Some recommendations for future work include


109

analysing the involvement of unannotated genes via computational co-expression analysis. This could

identify what other proteins are associated with unknown gene products to infer their functions.

Additionally unannotated gene sequences could be analysed for sequence similarity to any previously

characterised homologues or orthologues of which function and gene ontologies are already known. This

may provide a deeper understanding of the molecular mechanisms involved in ATW1124 treatment. It is

also recommended that future studies include some peptide prediction studies based on primary sequence

utilizing expected motifs or secondary structures to predict function for pathway analysis. The combination

of these efforts would complement the data reported above and could all be performed using the datasets

generated during the present study.

Chapter 6 – Field evaluation of ATW1124 and modelling to determine implications for production

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6Chapter 6 – Field evaluation of ATW1124 and

modelling to determine implications for

production

6.1 INTRODUCTION

Precision agricultural systems models are powerful tools for crop improvement with important

roles in breeding, government policy, climate mitigation and food security (Holzworth et al., 2014).

Development of crop varieties tolerant to drought, high temperatures and/or low nutrient availability is

essential to maintain sustainable crop production in the future (Falcon et al., 2008; Peskin et al., 2009).

Investigation of these interactions using conventional field methods is very time- and resource intensive;

however modelling can significantly streamline these efforts. In APSIM, the Agricultural Production

Systems Simulator, root traits are described in terms of water extraction (KL), root exploration (XF),

wilting point (CLL) and root length velocity (RLV - coded in the .ini file of the crop module) (APSIM

developer documentation www.APSIM.info). One of the best methods of modelling root development is

through the use of these fixed rate constants (e.g. fixed growth rate (mm/day) multiplied by water

availability constants per soil layers (KL)). As such modelling strategies employed in this study focused

primarily on two key parameters:

1. KL – A fixed rate constant related to the efficacy of the root system in extracting water from

a particular layer. It describes the upper rate limit of water uptake by the root system and is most often

utilized to simulate deterioration or impediment of root development by environmental stressors such as

nematodes, disease or soil compaction. For instance nematode-induced tissue damage could inhibit root

water uptake through necrotic lesions or disrupting xylem transport, thereby lowing the KL of that crop.

Conversely, increased KL would suggest that the root system of that crop is stimulated thus raising the

upper rate limit of water extraction. For instance by increased root mass density at depth or in certain soil

layers (Manschadi et al., 2006). Additionally KL can be ascribed to certain soil layers, which allows

interrogation of layer-specific changes in KL, for example enabling broad comparisons between shallow or

deep RSA arrangements. It should be noted however that KL is a ‘maximum’ not an actual rate. Therefore

it provides a limited generalisation of root water extraction which cannot be altered during a simulation in

response to temporal shifts in either root development or environmental conditions. Information

summarised from APSIM developer documentation (www.APSIM.info).


111

2. CLL – Abbreviated from ‘Crop Lower Limit’ describes the fraction of relative soil water

content (SWC) below which plants are no longer able to extract water and is marked by the onset of

permanent wilting. CLL varies predominantly across soil types but also between species and represents a

network of highly complex interactions between crop physiology, root development and environmental

factors. Linking root traits to other physiological processes related to drought adaptability (Hammer et al.,

2009) remains a major focus. The objectives of this chapter were to simulate the effects of ATW1124 in

APSIM to determine impacts on broadacre production focussed specifically on Australian agro-ecological

regions; to validate these findings experimentally by targetting various key APSIM parameters; and to

assess the translatability of ATW1124 treatment effects in a series of field trials.


6.2.1 APSIM simulated effects of ATW1124 on mungbean and implications for

production

This section describes a number of simulations developed in APSIM aimed at modelling root

architectural effects of ATW1124 on mungbean and investigating impacts on production in water limited

environments. The main objective was to model some treatment effects of ATW1124 and to simulate

production in a range of differentially drought stressed locations around Australia. It was hypothesised that

the altered root phenotypes of ATW-plants described above may have some effect on crop lower limit

(CLL or permanent wilting point). CLL is an input parameter in APSIM which measures water extractive

capacity of a plant for a particular soil type and is governed by length, density and osmotic potential of the

root system at a given soil layer.

A simulation was developed to test the effects of improved crop lower limit (CLL) on mungbean yields

from the arid location of Roma (QLD). This was further assessed experimentally in a glasshouse trial run

in parallel. Results revealed an improved capacity for soil water extraction (KL) of ATW-plants under

severe drought stress which led to the development of another simulation to test the impacts of this on

productivity in 40 different Australian locations. Additionally, field trials were conducted at the two QLD

locations of Warwick and Kingaroy aimed at investigating whether treatment effects translated into a field

environment. The remainder of this chapter describes experimental design of those experiments, results

and analyses carried out in Excel, Minitab 16 and/or R Studio.

Following the findings of Chapter 4 of the root promotive effects of ATW1124, it was

hypothesised that crop lower limit (CLL or permanent wilting point) may also be improved with treatment.

In order investigate this in silico, an APSIM simulation was developed comparing hypothetical CLL-

improved varieties with 6% and 12% improved CLL grown at the arid QLD location of Roma. Soil type

was a brown clay vertosol (APSoil No. 063) with initial soil moisture set at 100%. Rainfall and other


112

climatic data were obtained from the Agricultural Production Systems Research Unit (APSRU) and

integrated for the years spanning 1889 – 2014 for Roma, QLD (Station ID 42091). Emerald cultivar

mungbean seeds were sown with a planting density of 20 plants/m2 at a sowing depth of 10mm with row

spacing of 500mm. Initial surface residue was set at 1000 kg/ha giving a C:N ratio of 80. Output

parameters included yield, biomass, flowering and maturity time.

6.2.2 Experimental verification of the impacts of ATW1124 on crop lower limit (CLL)

and water extraction

In order to assess the impact of ATW1124 on crop lower limit (CLL or permanent wilting point),

a simulation was developed based on observations that treated plants exhibited enhanced root development

and water extractive capacity (KL). The objectives were to determine whether improved-KL varieties

would exhibit any advantages in large scale production across a number of Australian production regions.

Field sites were selected based on previous work by Chauhan et. al 2014, which divided the Australian

Northern Grains region into agro-ecological regions according to their drought portfolios. Every agro-

ecological region identified in that study was represented in the simulation to encompass all environments

typically used for mungbean production.

Jade cultivar mungbean (Beangrowers, Kingaroy) was treated with either H2O, 100mM

ATW1124 or the previously patented RSA

enhancer thiourea (TU) as a positive control as

described previously (Ku, 1976). Glasshouse

conditions and growth medium was as described in

Section [4.8.1]. Pot weights were measured at field

capacity and then periodically at every sampling

event for the remainder of the trial to monitor soil

moisture depletion. Plants were fully hydrated until

anthesis at which point they were divided into their

respective irrigation groups (n =5 per treatment

(total 15 plants)) in order to impose various lengths

of drought stress. Sampling was conducted once

severe wilting was observed in upper leaves

(Figure 41) which occurred at 21, 24, 26, 28 and

31 days after drought stress. This involved

weighing each individual pot to determine soil

moisture and taking a series of photographs to act

as a photographic chart for subsequent

Figure 41: Representative image depicting

severely wilted mungbean at which periodic

rewatering and analysis commenced for

calculation of CLL.


113

experiments. Each plant was recovered for 7 days by returning to full water supply. Plants were considered

recovered with the return of leaf turgor and resumption of new tissue growth. The length of drought stress

at which # 𝑝𝑙𝑎𝑛𝑡𝑠 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑒𝑑

𝑇𝑜𝑡𝑎𝑙 # 𝑜𝑓 𝑝𝑙𝑎𝑛𝑡𝑠 = < 50% was calculated as the CLL or permanent wilting point below which

plants could not survive. Relative soil moisture was then calculated for each CLL based on pot weights to

reveal volumetric water content thresholds for each treatment.

6.2.3 ATW1124 field trial #1 – Hermitage, QLD

In order to test translatability of ATW1124 treatment effects from a glasshouse to a field

environment an ATW1124 field trial was conducted at the Queensland Government Hermitage Research

Station. This field trial was aimed at assessing the impacts of ATW1124 under hydrated conditions as the

site was considered ideal for mungbean cultivation in terms of soil moisture (Figure 42). A number of key

agronomic parameters were included to allow comparison to previous years of field data made available

from researchers at the site.

The main objectives were to test the translatability of the effects of ATW1124 observed in

previous chapters both in vitro and in glasshouse experiments. Given the favourable nature of the site, a

subsequent field trial was also conducted to impose drought stress using a rainout shelter. Soil at this site

was a cracking clay black vertisol with mean monthly rainfall for the trial months (Jan – Mar) of 78.5mm

andmean monthly rain days of 8.4 days. Mean min and max temperatures (°C) for those months ranged

from 16.2 to 28.9 (°C), respectively. The site was considered favourable for the cultivation of mungbean

with yield potential examined using APSIM found to reach up to 2500 kg/ha which was significantly

higher than reported typical commercial yields which scarcely exceeded 1500 kg/ha. As such the site was

ideal for this experiment which was intended to test the effects of ATW1124 under hydrated field

conditions in order to ascertain whether treatment had any negative impacts on production in the absence

of drought stress. A second field trial was later conducted employing a rainout shelter to simulate drought

and determine whether drought amplified treatment effects as observed in previous chapters. Experimental

procedures began with Crystal mungbean seeds (n = 4 plots per treatment) being treated with one of the

following five seed pre-treatments (ATW1124 concentration 5mM):

T0 - Untreated control

T1 - T1dH2O 1hr

T2 - 5mM ATW1124 1h

T3 - 5mM ATW1124 1hr + drying

T4 - 5mM ATW1124 2hrs


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For drying, seeds were air dried for several hours before sowing. Seeds were machine sown on the

17th Jan 10cm apart in 4m rows with row spacing of 1m. Flowering was recorded at 44 DAS and

physiological maturity at 72 DAS indicating no unusual deviation from normal phenology in any of the

treatments. Two sampling events were conducted, the first at flowering then again at maturity. This

involved collecting whole above ground biomass from 1m segments of each row, weighing the material to

obtain fresh weight biomass (g), oven drying for 3 days at 72oC and weighing again to quantify total dry

matter (g). Plant height defined as the vertical length from the soil surface to the uppermost foliar tissue

was recorded for each row (n = 4) using a 1m notched ruler. Furthermore number of surviving plants for

each row was recorded at each sampling point to monitor overall population viability. The table below

provides a summary of parameters:

Figure 42 APSIM simulated yield (kg/ha) of Emerald mungbean cultivated at Roma (blue

line), Goondoowindi (yellow) and Hermitage (red). Simulations based on meteorological data

spanning 1989 – 2014.

Simulated yields of commercial Emerald mungbean grown at three

QLD field sites of Roma, Goondoowindi and Hermitage


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6.2.4 ATW1124 rainout shelter trial – Kingaroy, QLD

In order to test whether ATW1124 treatment enhanced drought tolerance under field conditions, a

field trial was developed which imposed drought stress via a rainout shelter. This was conducted at the

Queensland Government’s Research Station at Kingaroy (QLD) who facilitated the construction of a semi-

permanent rainout shelter (ROS). The site of Kingaroy was selected based on climatic data indicating it

was likely site for drought studies. Plant parameters were selected with high relevance to commercial

production such as yield and biomass which allowed for comparatively large sample sizes. The main

objective of this experiment was to determine whether the root promotive effects of ATW1124 observed in

previous chapters translated into drought tolerance in the field. Biomass production was also included

following findings of previous chapters that ATW1124 may reduce vegetative tissue biomass; however the

focus remained predominantly on grain yield.

The ROS was constructed from a transparent, watertight plastic which excluded rainfall but

permitted sufficient light for normal plant development to occur without removal of the cover. Light

irradiance beneath the shelter was comparable to that of the interior of a glasshouse. Ventilation was

mediated via openings at each end of the shelter to prevent overheating. Atmospheric and soil temperatures

were tracked using a data logger equipped with a digital thermometer. Mean daily min / max atmospheric

temperatures ranged from 16.7 oC / 34.84 oC, while mean soil temperatures ranged from 23.13 oC / 26.54

oC, respectively. Irrigation was administered via trickle tape run along the length of each row. Drought

stressed plants were grown under rainfed conditions as soil water level was sufficient for establishment (21

Parameter Details

Germination (DAS) Time to reach 50% germination

Days to flowering (DAS) Time to reach 50% anthesis

Biomass at flowering (kg) Gravimetric vegetative biomass at anthesis

Days to maturity (DAS) Time to reach 50% physiological maturity

Biomass at maturity (kg) Gravimetric vegetative biomass at maturity

Total reproductive biomass (kg) Gravimetric biomass of mature seed + pod

Grain yield (kg) Calculated per 1m row sampling at harvest


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DAS). The trial area was tilled and sown by hand across two days spanning Jan 12-13 2016. A previous

run of the experiment was planned for December but was prevented by significant amount of rainfall prior

to the construction of the ROS which saturated the trial.

Treatment groups for the experiment were comprised of two contrasting mungbean genotypes (G1 -

Berken & G2 - Jade) each treated with either H2O only or with 100mM ATW1124 for 1h. Additionally,

the trial plot was divided into well-watered (WW) and drought stressed (DS) halves such that there were 8

possible treatment combinations (Table 13). Experimental design was developed with advice from DAF

biometrician Gabriela Borgognone and consisted of a randomized complete block (RCB) design. Blocks

consisted of one 2m row of each treatment. In total there were six blocks which were duplicated for each

water treatment giving a total of 12 blocks (6 DS; 6 WW). Rows were separated lengthwise by a 25cm

spacer row with inter-row (parallel) spacing of 0.5m. Each watering group (WW or DS) was then bordered

by a 1m edge-row to mitigate the effects of wind and edge-bias. Watering groups were separated by an

additional 2m spacer row to ensure no run-off irrigation reached drought stressed plants (Figure 44). Seeds

were sown 20mm below soil surface at a planting density of 5 plants / 10cm arrangement (Figure 43)

which was thinned by hand at 14 DAS to 1 plant / 10cm.

Table 13 Summary and details of treatment groups included in the ATW1214 rainout shelter field

trial at Kingaroy (QLD).

Treatment ID Description

WWG1- Irrigated Berken mungbean treated with H2O

WWG1+ Irrigated Berken mungbean treated with 100mM ATW1124

WWG2- Irrigated Jade mungbean treated with H2O

WWG2+ Irrigated Jade mungbean treated with 100mM ATW1124

DSG1- Drought stress Berken mungbean treated with H2O

DSG1+ Drought stressed Berken mungbean treated with 100mM ATW1124

DSG2- Drought stressed Jade mungbean treated with H2O

DSG2+ Drought stressed Jade mungbean treated with 100mM ATW1124


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Figure 43: Photograph depicting ATW1124 rainout shelter trial at Kingaroy (QLD) 14 DAS.

Outermost rows were edge rows to mitigate edge-bias; ropes mark 25cm intervals between rows; rows

spaced by 0.5m; and original planting density (depicted) of 5plants / 10cm was thinned to 1 plant / 10cm at

this time of 14 DAS. Image taken prior to severe rainfall event which partially inundated the trial area.

Figure 44: Experimental layout of rainout shelter field trial design conducted at Kingaroy (QLD). G1 and

G2 refer to either Berken or Jade cultivar mungbean, respectively; while -/+ refers to ATW1124 status. Trial plot

was divided in two for each water treatment group (WW or DS) separated by 2m of spacer rows (n = 6 for each

water treatment group).


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6.3 RESULTS

6.3.1 APSIM simulated effects of enhancing crop lower limit

Results of simulated ATW-induced enhancement to crop lower limit revealed that in the majority

of years, yields were reliably increased by around 7% and 15% in with 6% and 12% improved CLL,

respectively (Figure 45). At 10cm depth increments below the soil surface beginning at 0cm CLL was 0.2,

0.22, 0.24, 0.26 then 0.28 – illustrating that the plants experienced greater difficulty in extracting soil

moisture at depth. Improved varieties had each of these values modified by either 6% or 12% but were

identical in all other parameters such that any differences in productivity could only be attributable to CLL.

Improvements were ascribed arbitrarily but in accordance with trends observed in previous chapters of

increased spatial root mass in ATW-plants. The aim of this modelling approach was therefore to determine

the potential value of CLL-improvement. Data revealed that the yield benefits of improving CLL were

mainly effective within the functional yield range of 1200-1800 kg/ha. As yields exceeded 1800 kg/ha

there was little observable differences between groups. CLL-induced yield increases of 7% to 15%

signified that it was indeed a key trait for increasing mungbean yields in arid regions. Therefore to quantify

the extent ATW1124 alters CLL experimentally an experiment was designed as outlined below.

Figure 45 Exceedance plot comparing hypothetically improved varieties of mungbean with 6%

(red) and 12% (blue) improved crop lower limit (CLL) compared with commercial crystal mungbean

(green line). Simulation was based on 25years of climatic data at the QLD location of Roma. X-axis

represents mungbean yields (kg/ha); Y-axis represents probability (%) of yields ‘being above’ the

corresponding value.


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6.3.2 Experimentally derived and APSIM simulated effects of ATW1124 treatment on

water extractive capacity

In order to assess the impact of this observation on a broadacre scale, this trait was modelled

in APSIM in the form of improved plant water extractive capacity (KL) which was then run

across 45 Australian locations with varying moisture availability and soil profiles

(deep/shallow). Improved-KL plants exhibited consistent yield increases of around 10%

across the majority of locations included in the simulated scenarios with the most pronounced

increases found in sites with water-limited shallow soil profiles (Figure 46). Simulated plant

total biomass and maximum water use were similarly increased in KL-improved plants with

the most pronounced effects occurring in arid regions (E.g. Roma in Figures 37-39). This was

illustrated as a heat map generated in R-studio with tiles coloured according to treatment

deviation from controls (%) ranging from white (negative or no effect) to red (highest effect).

Production data were averaged for the years spanning 1900 – 2014 and presented as a ratio:

Heatmap tiles = 𝑀𝑒𝑎𝑛 𝐾𝐿−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟

𝑀𝑒𝑎𝑛 𝐶𝑜𝑚𝑚𝑒𝑟𝑐𝑖𝑎𝑙 𝐽𝑎𝑑𝑒−𝐴𝑈 𝑚𝑢𝑛𝑔𝑏𝑒𝑎𝑛

This relationship gives KL-plant data relative to commercial mungbean data, with values typically falling

between 0.8 – 1.5 for each parameter. This circumvented the need for range normalisation and allowed

heatmapping as a single matrix with minimal processing. Data were formatted into tab delimited [.txt] and

imported into R-Studio for visualisation. The following R script was used to generate the heatmap which

relied only on inbuilt R functions:

KL_heatmap <- heatmap(heatmap_matrix, Rowv=NA, Colv=NA, col = heat.colors(256, ),

scale="column", margins=c(5,10))

row.names(mina_kl) <- mina_kl$Location

The object [heatmap_matrix] refers to a table of ratios (KL-improved / commercial) comparing yield,

biomass, transpiration and water use for each location. Results revealed that sites showing least distinction

between improved-KL and control plants generally tended to be high yielding sites with deep soil profiles

not generally limited by drought. Incidentally it was concluded that yield advantages of ATW-plants were

restricted to arid regions and had little benefit in favourable sites where soil water was abundant. Soil

nutrients and compositional variability between sites was considered likely to have accounted for some

differences between sites, however the interaction between drought and nutrient bioavailability escaped the

scope of the study.


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Figure 46 - A: Comparison of permanent wilting point and soil moisture dynamics of mungbean plants

treated with either H2O (control), ATW1124 (100mM) or thiourea (10µm foliar spray). Wilting point was defined as

the time at which less than 50% of the population recovered and is denoted by the dotted line. Both measurements

were conducted on the same set of plants; B-D: Cumulative probability plot (ECDF) of yield (B), biomass (C) and

maximum water use (D).


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Figure 47 Heat map comparing KL-improved mungbean production across 45 Australian locations

varying in soil-moisture availability. Data simulated in APSIM and visualised in R-studio. Columns

represented in unitless ratios corresponding to KL-improved data normalised against commercial Jade-AU

mungbean to give relative yield, biomass (bio), water use (WU) and transpiration (trans).


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6.3.3 Hermitage field trial evaluation of the effects of ATW1124 on mungbean

Results from the Hermitage field trial identified a number of significant effects of ATW1124 as

well as the general practice of pre-treating seed. Inclusion of the treatment T0 controlled for the effects of

the aqueous solvent in the treatment solution – i.e. dry untreated seed. Comparison of T0 to T1 (H2O only)

revealed that the absence of any pre-treatment regime had detrimental effects on virtually every parameter.

Furthermore, the T0 population decreased by around 25% at flowering compared with other treatments

indicating that pre-treatment is a necessary step to maximizing yield. Comparisons of all other treatments

were made with T1 (H2O control) to account for solvent effects.

Analysis of vegetative biomass at flowering revealed no statistical differences between treatments

although ATW1124 treatment consistently reduced vegetative biomass by 15% compared with controls.

Analysis of grain yield revealed no treatment effects, consistent with previous glasshouse data. Integration

of both these parameters into harvest indices (grain yield / vegetative biomass), revealed that ATW1124

elicited a preferential allocation of plant resources into grain rather than vegetative tissues. HI increases

induced by T2 and T4 were found to be 8% (p = 0.006) and 21% (p = 0.004), respectively. Significance

tests were conducted by way of an ANOVA in conjunction with Tukey’s post hoc test. T3 did not confer

any such effects, suggesting that the drying stage which characterised this treatment may have introduced

other factors which negatively impacted productivity. No differences were recorded in phenology

(including germination, flowering time or maturity) indicating that incorporation of treatments would not

require any major revisions in agronomic practices were they to be adopted in their current state.

Under favourable water-abundant field conditions ATW1124 treatment had no observable adverse

effects on productivity. It was considered possible that the reduction in vegetative biomass at flowering

elicited by treatments T2 and T4 may have enhanced drought tolerance due to a reduction in transpirative

water loss, which was investigated in a subsequent rainout shelter field trial. In conjunction with the sizable

amount of support presented in previous chapters of the root-enhancing properties of ATW1124, it was

inferred that root/shoot ratio may have also increased. This being a common mechanism reported in a

variety of plant species as a coping strategy for drought stress (Champoux et al., 1995; Vandeleur et al.,

2009; Eneji, 2011; Wang et al., 2013). The lack of any detrimental effects under hydrated conditions

indicated that application of ATW1124 in unstressed conditions would not cause any negative impacts.

The main research question remaining at this stage was whether drought stress antagonised the effects

ATW1124. Therefore a subsequent field trial was conducted at Kingaroy (QLD) aimed at deciphering the

interaction between ATW1124 treatment and rainout shelter induced drought stress in the field.


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Results revealed a number of effects of ATW1124 which mimicked those seen in previous

chapters. Analysis of vegetative dry biomass at flowering (DTF) revealed that G1 and G2

both exhibited an 18.66% and 15.31% reduction due to ATW1124, respectively. Although

statistical analysis by way of ANOVA in conjunction with Tukey’s post-hoc test revealed

that the effect was insignificant (p = 0.452). A similar yet less pronounced trend was

observed in fresh biomass measurements both G1 (-11.21%) and G2 -2.37%) (Figure 49).

Analysis of fresh biomass at harvest showed that ATW-plants exhibited a reduction of 1.40%

and 4.04% for G1 and G2, respectively. Following a similar trend to the data collected at

flowering, dry biomass was reduced in ATW-plants by 10% and 5% for G1 and G2

respectively. Once again these trends, although fairly consistent, remained statistically

insignificant. Grain biomass remained unaffected by ATW1124, with both genotypes

achieving fairly typical yields of between 1.5 – 1.6 tonne per hectare. As previously

described, vegetative and grain biomass values were integrated into harvest indices which

revealed a clear distinction in ATW1124 between genotypes (Figure 50). While G1 showed

little response in HI, G2 exhibited a statistically significant 23.5% increase (p = 0.031; n = 6).

6.3.4 Kingaroy ATW1124 rainout shelter field trial results

Despite all contingencies and forecasting for best weather conditions, the field site received

record rainfall (10 year record high) during the season leading to partial inundation of the

field. This resulted in significant soil saturation which did not dehydrate sufficiently to

impose drought stress. As such both the irrigated and drought stressed ends of the trial area

were considered to be ‘well-watered’ raising the sample size to 12 (n = 12). The objective of

the experiment was therefore restricted to investigating whether the treatment effects of

ATW1124 translated to a field environment. Soil analysis conducted by the DAF team at the

site revealed that soil type was a red scrub ferrosol with top soil (0-10cm depth) pH of 6.4

containing normal amounts of N and S but very low amounts of P (0.073%) and K (0.28%).

Alkalinity slightly increased at depths of 20 – 30 cm to pH 6.7. Additionally N, P and S

levels were acceptable while K levels (0.18%) were again low. At depth of 50 – 60cm the

NPKS profile remained unchanged but pH returned to 6.4.


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Figure 48: Effect of seed pre-treatment with different regimes of ATW1124 on Crystal mungbean

seed harvest index. Harvest index presented as grain biomass (kg) as a proportion of total plant biomass

(kg). Higher values are typically indicative of genotypes exhibiting high carbon-grain partitioning (n = 4).

T1 – H2O 1hr seed soak; T2 -


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Figure 49 Bar graph comparing commercial and ATW1124 treated field grown mungbean

vegetative biomass at flowering. Error bars represent one standard error form the mean (n = 6).


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Figure 50: Bar graph illustrating the effects of ATW1124 on harvest index of filed grown

mungbean. Error bars represent one standard error from the mean (n = 6).


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Results from the experimental crop lower limit or permanent wilting point assay revealed that both

ATW1124 and thiourea (TU) extended the time mungbean could tolerate severe drought stress by 22.7%

and 4.6%, respectively (Figure 51). It was noted however, that foliar tissues treated with TU according to

published methods (see above) proved to be caustic, inflicting severe damage to treated leaves and in some

cases senescence. This pre-stress reduction in vegetative biomass may have accounted for some degree of

TU-plant drought tolerance. Analysis of soil water content dynamics over the 31 day drought period

revealed that ATW-plants maintained a higher level of soil moisture compared with controls and TU-

plants. WinRHIZO analysis of RSA revealed total length increases of 12.5% (p = 0.132) and 25.0% (p =

0.012) for ATW1124 and TU, respectively. Higher soil water content was attributed to more efficient

water capture of treated plants exhibiting enhanced root development. Results also revealed that both

ATW1124 and TU did not directly reduce CLL as expected, but rather increased water use through

enhanced water capture which significantly increased tolerance under severe drought stress.

Control ATW

TU

Figure 51: Representative photo

comparing treatment effects of ATW1124 to

thiourea. Image depicting mungbean plants 45 days

after sowing after a 24 day drought stress period.

TU plants treated with foliar application of

thiourea. Each treatment was replicated 5 times (n

= 5).


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6.4 CHAPTER SUMMARY

In combination both field trials suggest that ATW1124 may reduce vegetative biomass by around

15-20% without yield penalty, thereby increasing partitioning toward grain production. This would be of

major benefit to production regions limited in water availability, as target yields could be achieved with a

reduction in transpirative water demand from vegetative tissues. This finding supports previous results of

Chapter 4 which identified a reduction of shoot biomass in ATW-plants of similar magnitude. Furthermore

the reduced biomass was genotype dependant as was also observed in previous glasshouse experiments

with respect to root-enhancing properties of ATW1124. As vegetative biomass depends heavily on

photosynthetic efficiency, it was inferred that the differential shoot morphology between genotypes may be

related to photosynthetic plasticity. This was supported by findings from Chapter 5 that GO terms relating

to these aspects of plant development were significant areas of genotypic difference. Moreover, this may

provide further support for the hypothesis that ATW1124 primes plants against subsequent drought stress

by downregulating metabolism, catabolic pathways and modulating photosynthesis. One of the limitations

of the field trials conducted in this project were that due to extremes in weather experienced in the trial

region, drought stress could not be sufficiently imposed in the field. As such, the inference that ATW1124

treatment enhances drought tolerance remains speculative and should be confirmed in future field trials.

Assessment of the impact of ATW1124 on mungbean permanent wilting point dynamics in the

glasshouse revealed that ATW-plants retained a higher level of net soil moisture compared with controls

under severe drought stress (Figure 46, A). The significance of this is that those ATW-plants were able to

acquire higher levels of soil moisture and retain it for longer than untreated plants. Furthermore, in

comparison to the positive control for root-enhancement, thiourea, ATW1124 was considerably more

effective on mungbean. Admittedly the foliar application of thiourea was tailored specifically for a

different species (maize) with considerably different leaf morphology. Thiourea was a class six hazardous

chemical with considerable health and environmental risks if not handled appropriately. Mungbean leaves

treated with thiourea exhibited a severe caustic burn which led to necrosis of those tissues, but also elicited

a substantial boost to root development. It was likely that the increased survivability of TU-plants under

drought stress was partly due to the localised loss of a portion of those treated foliar tissues in confuction

with enhanced root development. Therefore although the chemical composition of the two chemicals was

drastically different, they both elicited some reduction in vegetative biomass and enhanced root

development. Comparison of root development between the two treatments revealed that thiourea had a

more pronounced promotive effect on root development, although ATW1124 proved more effective at

increasing longevity and soil moisture retention without tissue damage.

Results also identified no major phenological effects indicating that application of ATW1124 on a

larger commercial application would not require specialised agronomy. One aspect which would require


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investigation, with respect to commercialisation, would be the interaction with rhizobia and beneficial root

symbionts which contribute enormously to the overall performance of legumes in the field. Overall the

field trial results were in agreeance and provided support for the hypothesis that ATW1124 increases HI

under hydrated conditions thereby reducing nutrient (including water) demand of vegetative tissues. In

conjunction with enhanced root development revealed in previous chapters, this new data suggests that

ATW1124 may be a viable means of increasing drought tolerance of mungbean crops. Root morphology

could not be assessed in the field as the specialised machinery typically used for sampling of that nature is

very expensive and was unfortunately unavailable at the sites on which the trials were conducted.

Modelling of ATW1124’s treatment effects in APSIM revealed that a significant portion of field

sites around Australia would benefit considerably by treating their mungbean crops with ATW1124. Sites

most likely to benefit from treatment generally included those with limited water availability and shallow

soil profiles. Whilst sites where soil moisture was non-limiting and/or soil profiles were deeper produced

similar yield irrespective of ATW1124 treatment. Therefore the impact of traits enhancing root

development and soil moisture acquisition were region specific and would be recommended only to sites

fitting the portfolio described above. APSIM simulations revealed that maximum water use and

transpiration were higher in ATW-plants when grown in arid regions or shallow soil sites. Shallow soil

profiles generally result in faster drainage to subsoil layers beyond the reach of the root system, thus the

term is synonymous with ‘water-limited’ in this context. Higher water use was therefore an indicator that

ATW-plants were able to acquire moisture more effectively than untreated varieties which translated into

uniform yield increases of around 10%. Vegetative biomass was also increased by a similar amount which

contradicted previous results, however the simulation provided a simplified perspective of treatment, as it

was based only KL. A more complex simulation would require a significant amount of other variables to

be incorporated which would involve parameterisation and a significant time investment which was not

possible during this study.

Chapter 7: General Discussion and Conclusions

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7Chapter 7: General Discussion and Conclusions

The functions of plant root systems extend far beyond their role as simple anchors in the soil.

Although this function itself is essential for crop harvestability and to minimize lodging, there are a

plethora of additional roles for roots in sensory perception, signalling, hormone biosynthesis as well as

acting as hosts to a range of beneficial symbiotic microorganisms. This last role is especially significant for

members of the legume family which form root nodules for nitrogen fixation – a useful quality for

replenishing soil N for subsequent crops – making legumes an extremely valuable option for farmers as a

rotation crop. Similarly some legumes such as Lupinus albus (white lupin) have been shown to form

specialised appendages to increase phosphorous uptake in response to detecting P deficiency. These

structures are referred to as proteoid or cluster roots (Figure 52) and have been shown to dramatically

improve P-deficiency tolerance as well as drought tolerance (Watt & Evans, 1999b; Kania et al., 2007).

The formation of these structures is intricately dependant on a balance of tricarboxylic acid (TCA)

cycle intermediates such as citrate and malate which results in an exudative burst from roots into the soil

which increases availability of scarce nutrients such as phosphorous (Watt & Evans, 1999b). Given the

ubiquitous nature of the TCA throughout the plant kingdom, it may be possible to elicit similar responses

in other species either through chemical treatments or genetic manipulation. However no such examples

were encountered at the current time of writing in the literature. It is possible that the introgression of an

exudative burst mechanism or the formation of proteoid roots into other species would require additional

energy and may come at the expense of foliar tissues or yield. It is certainly a commonly reported issue in

the field of transgenics that elite varieties exhibit accompanying stunting or yield penalties in conjunction

with their introduced traits, with few exceptions (Yu et al., 2013a). However the now very popular

CRISPR/Cas9 targeted gene editing tools available to researchers may allow the removal of those penalties

and thus a strengthening of genetic strategies in the near future. As a chemical means of achieving drought

tolerance, chemicals like ATW1124 may provide an alternative option. However the potential outcome of

sacrificing yield for elevated drought tolerance poses another challenge. Certainly plants that survive a

drought would yield more highly than plants which did not; however the implementation of such a product

would require an accurate method of predicting rainfall patterns and would surely be met with scepticism

from industry. A probabilistic approach such as this may be well coupled with a predictive platform such

as ‘Yield Profet ®’, a commercialised product arising from research in APSIM. The combination of both

these technologies could potentially receive more support in combination than independently.

A predictive approach is likely a viable option when considering the variability and dynamic

nature of plant development. Many plants exhibit high degree of developmental plasticity in their root


131

system which allows plants to distinguish harmful from beneficial features in the soil. Research has shown

that this contributes significantly to the overall survivability of plants under stress (Des Marais & Juenger,

2010; Yu et al., 2014; Li et al., 2016). However this also makes it very challenging to describe root

architectural features of plants since they are constantly in flux. Root system plasticity is governed at the

morpho-anatomical, physiological and molecular level which results in arrangements that vary

considerably across environments, climatic conditions, soil types and on-farm management practices (Watt

& Evans, 1999a; de Dorlodot et al., 2007). However root architecture and its regulation is of growing

interest to the field of abiotic stress tolerance in recent years. For instance it was shown that one of the

major determinants of drought tolerance in Zea mays (maize) is a structural root trait known as root

cortical burdon (Jaramillo et al., 2013). Increased drought tolerance was facilitated by enhanced root

foraging and deeper architectural arrangements, which had a structural basis. Reduced root cortical burden

– or root cortical cell file number (RCCFN) greatly improved the ability of the root system to forage and

remain mobile in a dehydrating soil environment. Cortical burden refers to the diameter (number of cells)

contained in the maize root cortex. Varieties with reduced RCCFN tended to have a higher number of

aerenchyma cells (essentially air channels) which lowered the metabolic cost of root foraging giving those

plants a major advantage under drought stress (Zhu et al., 2010; Postma & Lynch, 2011a, 2011b; Jaramillo

et al., 2013; Chigmungu et al., 2014; Chimungu et al., 2014). The abiotic stress tolerance benefits of

reduced RCCFN also included enhanced tolerance to soil deficiency in nitrogen, phosphorus and

potassium (NPK).

However it was on the impacts of drought stress that the present study was most focussed. It is

estimated that drought stress is responsible for 50% of global crop losses and is by far the most pervasive

of all the abiotic stresses impeding our ability to reach global food production targets (FAO, 2009). The

significance of drought tolerant crops comes from a need to increase the efficiency with which agriculture

utilizes water resources. Less than 3% of the world’s water is freshwater, and agriculture is responsible for

using upwards of 70% of that 3%. As the single largest consumer of global freshwater reserves (FAO,

2009), the agricultural sector has a significant responsibility to ensure that its practices are as efficient as

possible. The importance of increasing water use efficiency of agriculture was accurately described in an

address at the UNIS ‘South Summit’ in Cuba on increasing equity of the global economy (Annan, 2000):

“…the more than 1 billion people who lack access to safe drinking water live overwhelmingly in

developing countries. It is for their sake that we must stop the unsustainable exploitation of water

resources. And it is for the sake of the poor and hungry that we need a "Blue Revolution" in

agriculture, focused on increasing productivity per unit of water, or "more crop per drop"…”.

– Secretary General of Ghana – Kofi Annan, 2000


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There is a large and varied body of research aimed at trying to achieve this ‘blue revolution’ and

increasingly the focus is shifting toward gaining a better understanding of plant root systems. As the

primary means by which plants acquire water from the soil, our understanding of plant root systems could

reduce the need for irrigation and streamline our on-farm management approaches. About half a century

ago the first green revolution represented a critical step toward contemporary agriculture (Den Herder et

al., 2010) and ushered in widespread mechanisation, utilisation of high-yielding crops and management

schemes such as irrigation and fertilisation. The impact of the green revolution was enormous such that it

is now difficult to even imagine conventional agriculture without these elements. It is becoming popular in

literature in recent times to refer to root systems traits as ‘…traits of the second green revolution” (Lynch,

2007; Den Herder et al., 2010; Long et al., 2015).

7.1 IDEAL ROOT TRAITS FOR ABIOTIC STRESS TOLERANCE

Despite a general consensus in literature that root systems have a central role in the efforts of

improving water use efficiency, there is a clear scarcity of information relating to agricultural crop root

characteristics and how to best capitalise on them. One of the reasons for this is the unavailability to

breeders of high-throughput root phenotyping methods. The development and implementation of such a

high throughput system would enable breeders to screen their germplasms for desired root traits and

incorporate them into commercial lines. However this is a rare practice at the present time and root traits

are largely excluded from breeding program selection criteria. In response to this problem, an increasing

number of novel imaging and phenotyping platforms have begun emerging in literature with this objective

in mind (Clark et al., 2011, 2013; van Dusschoten et al., 2016). Breeding programs tend to focus heavily

on selecting for yield and biotic stress resistance (i.e. pests and disease) for which they are now highly

effective. However generally they tend to neglect abiotic stress tolerance traits, as they are very difficult to

define and screen for, especially root-specific traits. However new research has begun defining root traits

for optimized nutrient and water acquisition – and even coined the phrase ‘…steep, cheap and deep…’ in

reference to an ideal root architectural ideotype for abiotic stress tolerance (Lynch, 2013; Yu et al., 2014).

Steep referring to root angle (Singh et al., 2010), cheap referring to metabolic cost (i.e. reduction of root

cortical burden for metabolically cheaper foraging (Chigmungu et al., 2014)) and deep referring root

architectural arrangements which allow plants to access subsoil moisture in arid climates more effectively

(Wasson et al., 2012; Comas et al., 2013).

One challenge to these recommendations is that root traits are heavily dependent on soil

characteristics and therefore geolocation of the production region. For instance for varieties with deep root

traits to have any advantage, water would need to be scarce in topsoil and more abundant at depth.

Therefore production of these varieties on sites with uniform water distribution such as on heavy clay soils

would likely not result in many, if any, advantages. Similarly, varieties with a high component of deep

roots at the expense of shallow/mid roots would have enhanced access to subsoil moisture but would likely


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underperform in soils depleted of immobile nutrients such as phosphorus or potassium (Postma & Lynch,

2011a). For those lacking in immobile nutrients, lateral root foraging would be much more desirable than

‘steep and deep’ RSA arrangements which would contribute little to their acquisition (Yu et al., 2014).

Therefore different locations would require a different blend of each of the three components. Ideally

producers would have access to locally adapted varieties developed to capitalise on soil and environmental

differences. This is one of the benefits of introgressing local wild crop relatives into breeding germplasm,

although the focus tends to remain on above ground traits. Inclusion of root-based selection in breeding

programs – weather by developing high-throughput root phenotyping technologies or molecular breeding

for instance – in these programs could be highly beneficial.

As roots serve as the primary means by which plants perceive soil moisture depletion, stress

signals often originate in the root system and are translocated via plant vasculature to other organs where

stomatal reactions and leaf growth occur (Jeschke et al., 1997). Many of these root-shoot stress signals are

hormonal in nature and have been of significant interest for biologists striving to enhance crop abiotic

stress tolerance (Xiong et al., 2006; Shinozaki & Yamaguchi-Shinozaki, 2007; Cominelli et al., 2013;

Helander et al., 2016). Aside from the five classical phytohormones known to elicit plant growth

regulatory effects under abiotic stress – abscisic acid (ABA), ethylene, cytokinin (CK), auxin (IAA),

giberellin (GA) and jasmonate (JA) – many novel plant growth regulators continue to emerge in literature

and it is likely that many more remain yet undiscovered (Peleg & Blumwald, 2011). This thesis details the

assessment of a novel chemical pre-treatment, ATW1124, to improve root growth. The molecular

mechanics of ATW1124-mediated root growth were also investigated. Prior to this, studies on ATW1124

were limited to treatment of forestry trees and a few horticultural crops by root drench with little to no

physiological or molecular assessment of the process. Although effective the use of a root drench is not

commercially scalable. Therefore the first aim of this project was to demonstrate basic proof of principle

for improved root growth following ATW1124 pre-treatment of seeds.

7.2 MAJOR FINDINGS

7.2.1 ATW significantly enhanced root growth and longevity under mild drought stress

The most consistent effect of ATW1124 was an increase in root development under mild drought

stress. This effect was originally reported in previous work by collaborators investigating effects of

ATW1124 on Australian and Canadian woody tree species, thus the findings that ATW1124 also elicits a

similar response in mungbean provides support for that observation. The treatment method was modified

from that previously used on tree species in order to accommodate treatment of seeds of legumes.

However similar concentrations of ATW1124 were employed which resulted in similar root and shoot


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morphological effects as those reported in previous work by collaborators. For instance enhanced radicle

development which was of particular interest for its close relationship to germination and emergence

(Fyfield & Gregory, 1989; Bradford & Haigh, 1994) which underpin seed vigour. Seed vigour relates to

germination efficiency under stressful conditions and has been previously correlated to field performance

(Botwright et al., 2002). In this context the finding that ATW1124 enhances radicle development may also

suggest that ATW1124 can improve seed vigour. The X-ray tomography experiment (Figure 27) utilized a

soil-type conducive to soil compaction and therefore served as a pseudo-vigour test, revealing dramatic

increases in root development in ATW-plants. This provided additional support for a putative effect of

ATW1124 on seed vigour. However in order to conclusively ascertain whether this is the case, a number

of field trials would need to be conducted in future work spanning assessment of germination efficiency

under a range of stressful environments.

X-ray tomography data were by far the most distinct example of the root enhancing effects of

ATW1124 (up to 300% increase in root volume) and also had the earliest sampling time (21 DAS). This

suggested that the root-enhancing properties of ATW1124 occur pre-anthesis (Figure 27) and continue

until plants enter reproductive stages (Figure 29, Figure 30). Additionally the differences between ATW-

plants and controls seem to diminish as plants mature perhaps due to a shift in dry matter priority toward

grain rather than vegetative tissues. The finding that root enhancement extended beyond the seedling stage

(approximately 21 days) was significant, since studies have shown that early root traits identified in the lab

generally will not translate into mature traits in the field (Wasson et al., 2012). Furthermore, flowering

stage has been shown to be the most drought sensitive point of plant development (Yue et al., 2006). With

respect to the impact of ATW-induced root modifications on physiology: higher total root length and

surface area would likely facilitate increased water acquisition efficiency during vegetative growth stages

and flowering – which was indeed observed in the wilting point assay in Chapter 6 (Figure 46). Although

it is possible that the observed increases in water retention may have also had a shoot physiological basis in

addition to the increases in root development. The destructive nature of this experiment precluded temporal

monitoring of physiology, however previous experiments in Chapter 4 provided additional support. This

included enhanced root development of ATW-plants accompanied by suppression of vegetative shoot

biomass and photosynthetic rates under unstressed conditions. Ultimately ATW-plants were able to

survive 16% longer than control plants which proved a significant increase in drought tolerance compared

with untreated plants of the same variety. This finding would ideally be tested in a rainout shelter field

experiment. However the two attempts made during this project at simulating drought stress in the field

were compromised due to extreme weather. Thus it is recommended that it is reattempted in future work.

This finding that ATW-plants retained higher levels of soil moisture and exhibited drought tolerance traits

at flowering has major implications, as this stage is reported in literature as the point of highest drought

sensitivity in mungbean, as with most other crops (Bourgault & Smith, 2010). Facilitating access to higher


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levels of soil water during this time would likely improve plant performance during grain filling. Previous

studies have illustrated the benefits of deep RSA arrangements in acquiring limited and/or deep soil

moisture using oxygen isotopic composition analysis (Gazis & Feng, 2004). Based on WinRHIZO (Figure

30) and X-ray tomography (Figure 27) results, ATW-induced increases in root depth did not compromise

shallow or mid-level root development. Recalling the ideal ‘steep, cheap and deep’ root ideotype for

abiotic stress tolerance (Lynch, 2013), this becomes a key observation, as shallow/mid-level roots are

critical for acquisition of limited immobile nutrients. As such it is possible that the observed ATW-induced

increases in depth, surface area and volume may also improve performance under phosphorous and

potassium deficiency – which may provide a potential avenue for future study. Studies have shown that the

drought tolerance associated traits of depth and steep root angle also significantly enhance nitrogen uptake

(Manschadi et al., 2006; Lynch, 2013; Araya et al., 2016).

Another key nutrient for its involvement in RSA determination is phosphorous. It has been reported in

literature that phosphorous deficiency has a major impact on root development (Lambers et al., 2003). In

certain species of lupin (Lupinis albus) phosphorous deficiency elicits formation of specialised root

appendages called ‘proteoid roots’ (Figure 52) which form intricate mycorrhizal symbioses and drastically

improve phosphorous uptake (Kania et al., 2007). It has been proposed that the signals involved in

proteoid root development probably involve auxins (Watt & Evans, 1999b); and interestingly, there are

very strong similarities between the compounds involved in these processes and ATW1124. These

similarities in conjunction with the recorded root-promotive effects of ATW1124 may be an indication that

treatment could aid in root acquisition of phosphorous. It is recommended that this be investigated this in

future work.


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Figure 52 Root system of an 18 day old white lupin. All of the primary basal lateral roots have

become proteoid roots in response to P deficiency. Scale bar 1cm (Watt & Evans, 1999b).


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7.2.2 ATW1124 modulated shoot morphophysiological traits contributing to enhanced

performance under drought stress

In addition to the root enhancing properties, treatment with ATW1124 was also found to elicit

changes in a number of shoot parameters indicative of enhanced drought tolerance. One of the most

significant of these was a reduction in vegetative biomass under hydrated conditions. In the context of the

enhanced root development observed in those same plants, it was hypothesised that this may be the result

of shifting in dry matter priority toward roots. The combination of decreased shoot vegetative biomass and

increased root development resulted in a net increase in root:shoot ratio (R:S), which was consistently

observed in glasshouse experiments as a feature of ATW-plants. Importantly, previous studies have linked

high R:S to improved drought tolerance (Lloret et al., 1999; Karcher et al., 2008; Chang et al., 2016; Dash

et al., 2017). Furthermore results of a recent study by Berrached et al., 2017 imply that drought tolerance

traits of roots and shoots can no longer be considered in isolation as they are interdependent on one another

in this way. For instance deep root phenes have been shown to delay flowering (Berrached et al., 2017),

highlighting the substantial yet indirect relationship between RSA modification and agronomy. Flowering

of course representing the critical shift from vegetative to reproductive stages and can dramatically affect

commercial management strategies. By delaying flowering deep root phenes are therefore also able to shift

the time at which plants are most sensitive to drought. This suggests that introgression of deep root phenes

may allow some degree of control over the time at which plants are exposed to drought stress – if not

tolerance per se. This would be of particular significance to efforts aimed at generating locally adapted

varieties as another means of effecting phenology. An investigation of the impact of ATW1124 on

flowering dynamics may provide an avenue for future work.

Another major finding was that ATW1124 treatment resulted in higher physiological activity

under drought stress compared with untreated controls. This appeared to come at the expense of pre-stress

physiological activity which was maintained at lower levels than untreated plants. Although still

significantly reduced compared with their hydrated rates, the impact of drought stress was markedly lower

in ATW-treated plants. Reduction in these physiological indicators is a hallmark of plant drought

responses (Lopes et al., 2011), therefore a slighter reduction suggests that those plants were perceiving that

same stress as having a lower severity. Contrastingly, repression of these biological processes under

hydrated conditions may suggest that ATW1124 treatment mimicked stress stimulus in a minor way and

mobilised plant defences against a more severe subsequent stress (Wang et al., 2005, 2014). This was

supported by transcriptome analyses which revealed that ATW-plants exhibited a lower degree of leaf

catalytic activity. This is a strategy which has previously been explored in wheat (Triticum aestivum L.)

and found to be an effective strategy for reducing the negative impacts of drought on yield.

Priming plants with a pre-anthesis mild drought event altered protein expression patterns and

resulted in upregulation of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) small subunit,


138

Rubisco activase and ascorbate peroxidase (Wang et al., 2014). Rubisco plays an important role in carbon

fixation during photosynthesis and, not surprisingly, its upregulation resulted in elevated photosynthetic

rates under drought stress. Contrastingly, ascorbate peroxidase and other peroxidases function in mitigating

the harmful effects of reactive oxygen species generated during osmotic stress including salinity or drought

stress (Mustilli et al., 2002; Goswami et al., 2013; Yamamoto, 2016). At the cellular level drought and

salinity stress often occur in conjunction, as depleting cellular water content increases solute concentrations

and exposes cells to the harmful effects of salinity. Initial exposure of the plants to drought would therefore

reduce stomatal aperture and photosynthesis; however the subsequent higher abundance of stress response

proteins would alleviate the harmful effects of future stress. Whether or not this mechanism was involved

in the treatment effects of ATW1124 remains an interesting question which may be investigated in future

work. Engineering crops for photosynthetic efficiency is certainly considered a viable strategy for

developing drought tolerant crops (Long et al., 2015). Maintenance of photosynthetic capacity in barley

(Hordeum vulgare L) and wheat under salinity stress (another form of osmotic stress in some ways similar

to drought) was associated with a low cytosolic Na+/K+ ratio in the cytoplasm (James et al., 2006).

Additionally previous studies have shown that lower levels of photosynthetic capacity are indicative of

higher production of reactive oxygen species (ROS) and growth rates (Munns & Tester, 2008). It follows

that maintenance of photosynthetic rates under drought stress is a useful diagnostic for assessing tolerance.

In the context of these previous studies, data obtained in this project suggest that ATW1124 increases mild

drought tolerance of mungbean.

Maintaining a lower basal photosynthetic rate under hydrated conditions may have enabled

ATW-plants to either preserve soil moisture by reducing transpirative water loss, or water demand of foliar

tissues by reducing biomass. The net effect was ultimately that ATW1124 treatment increased water

content of plants without significant changes in morphology (Figure 46). Furthermore stomatal aperture

was significantly maintained at higher levels under drought stress in ATW-plants indicating a higher level

of resilience to osmotic stress. Stomatal aperture, regulated via guard cell swelling and shrinking has been

shown in previous studies to be highly sensitive to plant osmotic stress (Zhang et al., 2001; Mustilli et al.,

2002; Doheny-Adams et al., 2012). Differences in regulation of these processes would therefore likely

result in differential drought sensitivity. As stomata largely govern the rate of transpiration, regulation of

stomata could have a large impact on plant water requirements and drought sensitivity.

Phototropic response proteins as well as transmembrane protein composition were identified as areas of

disparity drought sensitive and tolerant varieties. This may explain their differences in photosynthetic

plasticity and efficiency. Furthermore it has been previously reported, that breeding for disease resistance

can either induce or antagonize abiotic stress tolerance factors (Greco et al., 2012a). In this case it appears

to be the former, that the more drought tolerant Jade (G2) variety – as a newer variety having been

subjected to around ten years of additional artificial selection than the drought sensitive Berken (G1) –


139

inadvertently was also more adapted to drought stress. In this way, breeding for drought tolerance was

achieved as an accessory to disease resistance, an area in which the two varieties also differ significantly.

Selection of drought tolerance in breeding programs remains largely absent, however this will likely

change in the near future due to the increasing accessibility to molecular technologies. Recent studies have

identified a number of micro RNAs known to be modulated in response to drought stress in both wheat

(Giusti et al., 2017) and alfalfa (Medicago sativa) (Arshad et al., 2017). Notably, overexpression of

miR156 in alfalfa resulted in enhanced stomatal conductance and reduced water loss by silencing the

SPL13 which is involved in DNA and zinc binding. Although achieving lower water loss as a result of

higher stomatal conductance seems contradictory, the effect was accompanaied by accumulation of

compatible solutes (proline), abscisic acid (ABA) and antioxidants (Arshad et al., 2017). When challenged

with drought stress the study reported an overall increase in survival rates under drought stress.

Interestingly, ATW-plants exhibited similar responses in stomatal conductance (Table 11), water retention

(Figure 46 A) and gene expression (Figure 39). When subjected to drought stress ATW-plants exhibited an

over representation of GO terms related to zinc finger proteins (ZFPs) which are stress inducible DNA-

binding proteins (Chang et al., 2016). A number of root-specific ZFPs previously identified in wheat

(TaZFP22, TaZFP34 and TaZFP46) were found to be upregulated in response to salinity, dehydration

oxidative and cold stresses. Chang et. al. showed that overexpression of TaZFP34 in wheat resulted in

reduction in negative root regulator homologues. Additionally, a number of shoot growth-related genes

(e.g. GA3-ox and expansins) were downregulated in shoot tissues which resulted in a net increase in R:S

ratio and drought tolerance.

The strong similarities between the transcriptonal effects of ATW1124 on drought stressed mungbean and

those previously published in recent key papers (Chang et al., 2016; Arshad et al., 2017) suggest that

treatment effects may be the result of an increase in transcript abundance of ZFP transcriptional repressors

which may themselves be regulated by miRNA. This would explain the increases in root tissue

development and the reduction in vegetative biomass under hydrated conditions. The net effect would be

an increase in R:S ratio and an enhanced level of drought tolerance in treated plants. There did not appear

to be any negative impacts on grain yield perhaps because the carbon cost of increased root tissues came at

the expense of shoots, rather than reproductive biomass. However overall the transcriptional effects of

ATW1124 on ZFP transcript abundance were mild compared to those in previously reported studies which

employed constitutively overexpressing transgenics. This relationship between ATW1124 treatment and

ZFP transcript abundance would require validation in the future as the results obtained in this project were

insufficient to permit such a conclusion. However with the potential outcome of establishing a

commercially viable means of modifying plant drought tolerance in vivo, this hypothesis may provide an

attractive avenue for future work.


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7.2.3 Application of ATW1124 may improve productivity and improve efficiency of water

use in arid environments

It was important in this project to establish early whether ATW1124 has commercial value before

conducting more rigorous investigations in future work. This was predominantly achieved through a

combination of glasshouse and field trials, then imputing treatment effects into the Agricultural Production

Systems Simulator (APSIM) to determine what impacts treatment may have on large scale commercial

production systems. APSIM simulations were based on forty five locations from across the Australian

Northern Grains region which represented every class of drought stress likely to be experienced by

Australian growers (Chauhan & Rachaputi, 2014). The range included ideal production sites through to

chronically drought afflicted areas to determine not only if treatment would have production value, but

specifically on which sites it has the most value. Perhaps the most consequential treatment effect of

ATW1124 on agronomy and commercial application was the finding that ATW1124 improves harvest

index – a shift in dry matter priority toward grain rather than vegetative tissue formation (Morison et al.,

2008). This increases efficient use of water (EUW – distinct from WUE) which, especially for dryland

agriculture can result in maximum soil capture for transpiration which is a major target for yield

improvement in drought afflicted environments (Blum, 2009). It should be noted that plants with high HI

(and indeed water WUE) can still be low yielding, i.e. where both grain yield and total biomass are low,

although these phenotypes are typically avoided in breeding programs as high yield is always paramount.

The distinction between EUW and WUE is a relatively recent argument which points out some

important nuances in the field of plant stress physiology which impact on the results obtained in this

project. Essentially the argument is that as long as the biochemistry of photosynthesis cannot be genetically

improved, genotypic water-use and transpiration efficiency (TE) are achieved ultimately by reducing

transpiration and water use; effectively by hindering plant production (Blum, 2009). The premise that

photosynthesis cannot yet be genetically improved within a crop species (Horton, 2000; Mitchell &

Sheehy, 2006; Raines, 2011) remains a major hindrance to improving crops for arid environments (Flexas,

2016). Although a recent study founded on 87 years of genetic improvement of soybean (glycine max) in

northern China has linked the genetically encoded root traits of ‘bleeding sap mass’ and ‘root activity’ to

enhanced net photosynthetic rate (Cui et al., 2016). This suggests that it may soon be possible to alter

photosynthetic efficiency genetically within a given species in the near future. In the context of results of

the present study, the link between photosynthetic rate and root development under stress may suggest that

roots may play a role in increasing photosynthetic efficiency in the future.

However in the absence of a genetic method of improvement of photosynthesis, an alternative

solution for the time being may be to select for traits which maximize soil moisture capture for

transpiration and reduce plant water loss through non-transpirative processes; to effectively increase water

use and transpiration (Blum, 2009). This somewhat counterintuitive perspective has its basis in the concept


141

that fixated CO2 is the engine for enhancement of production under water limitation. In this context

increasing transpiration refers to mitigating undesirable sources of non-transpirative water loss (i.e. leaky

stomata, evaporation directly from soil, transcuticular water loss etc.) which ultimately enhances stomatal

aperture and photosynthetic rates under stress (Blum, 2009). Although the current method of selecting for

reduced transpiration would increase TE, it will also very likely reduce grain yield under water limited

conditions as it would result in a reduction in carbon assimilate available for grain filling. Increasing soil

moisture capture could be achieved by reducing evaporative water loss (e.g. through row spacing and

canopy management), which in the Australian continent, is responsible for an astounding 40% of total

available soil moisture (Siddique et al., 1990). Interestingly results obtained in this study revealed that

enhanced water capture was one of the effects treatment with ATW1124 (Figure 46, A). Data suggest that

the basis for this was an enhancement of root development (Figure 29) and a reduction of vegetative

biomass (Figure 49). Another strategy for reducing evaporative water loss is by breeding for early vigour

to form a canopy as quickly as possible. For this reason early vigour traits has long been a staple goal of

the Australian wheat breeding program (Rebetzke & Richards, 1999). Enhanced vigour was detected in

vitro following ATW1124 treatment although this was not evaluated in the field during this project. This

could provide an opportunity for future work.

When discussing commercial applicability two important considerations are varietal selection and

agronomy. The two differentially drought sensitive varieties used in this project exhibited some key

differences in ATW1124 responsivity, photosynthetic plasticity and gene expression which may have

eventuated from their breeding strategies. Previous studies have shown that the selective pressure for high

yielding traits in commercial varieties may inadvertently lead to an increased (or decreased) level of

drought sensitivity (Lobell et al., 2014). This would mean that varieties subjected to less artificial selection

for yield (i.e. G1 Berken) should have higher drought tolerance than newer varieties (i.e. G2 Jade).

However the reverse was found to be true, that the more tolerant of the two genotypes used in this study

had been subjected to >10 years more selection and was not surprisingly higher yielding. A possible

explanation for the differential drought tolerance between the two varieties was proposed by Atkinson et

al., who suggested that breeding for biotic stress tolerance may also enhance abiotic stress (Greco et al.,

2012a). Their work highlighted that the orchestration of this synergy between abiotic and biotic stress

tolerance may be the result of hormonal cross-talk in signalling pathways. The key elements of this

crosstalk were found to be transcription factors, kinase cascades, reactive oxygen species, heat-shock

factors and small RNAs (Endlweber & Scheu, 2007).

This raises the question as to whether the increased drought tolerance exhibited in ATW-plants

also promotes biotic stress resistance to, for instance, root diseases such as fusarium root rot which have

been known to reduce mungbean yields globally for over 30 years (Anderson, 1985) and to which there

have been very few answers. Although fusarium oxysporum typically has a very low incidence in


142

Queensland, in 2016 it resurfaced in a dramatic way causing extensive damage with incidence exceeding

70% (GRDC, 2016). This overlap between abiotic and biotic stress response pathways may prove useful in

unifying efforts toward biotic and abiotic stress tolerance, which occur in relative isolation from one

another. The advancement of GxExM modelling is assisting enormously with this unification, although no

examples were encountered during the course of this project, of modelling platforms incorporating equally

abiotic and biotic components. Similarly, root modelling efforts generally occurs independently from these

larger modelling approaches despite having been well established for over 20 years (Lynch et al., 1997;

Zhu et al., 2010). Future work in this area of crop improvement would likely benefit from molecular

breeding strategies such as QTL mapping, which could further unify these independent efforts.

7.3 CONCLUSION

In conclusion the results obtained during this project suggest that ATW1124 enhances a number of

traits which may lead to more efficient use of water and mild drought tolerance. The main effects included

enhancement of root development during vegetative stages up until flowering stages; a reduction in

photosynthesis under hydrated condition which led to relatively higher photosynthetic rates under drought

stress; and over representation of a number of plant defence factors which may have contributed to

enhanced drought tolerance. Treatment was also found to enhance soil moisture capture which was

associated with a survival rate increase of 16% in ATW-plants compared with their untreated commercial

counterparts. More efficient soil moisture capture was attributed to enhanced root development which

likely resulted in higher amount of water available for transpiration, explaining the higher rates of stomatal

conductance and maintenance of photosynthesis under drought stress. Harvest index was found to be

increased in ATW-plants which resulted in a reduction in vegetative biomass rather than an increase in

yield. However the finding that comparable yield was achieved in ATW-plants with less vegetative

biomass than untreated plants may be the result of a lower level of photosynthetic assimilate being

preferentially partitioned toward reproductive rather than vegetative tissues. This would manifest as an

increased harvest index (HI), which was recorded in both field trials conducted during this study.

Modelling of the effects of ATW1124 indicated that areas which would most likely benefit from

enhanced soil moisture capture would be those with limited water availability and shallow soil profiles.

The results indicated that there would likely be little advantage of treatment under conditions where soil

moisture is abundant or where soil profiles were deeper and thus had lower rates of drainage. There was an

indication of a potential role of ATW1124 on seed vigour which may indicate that treatment promoted

early vigour however this would require additional validation as the effect was only observed in vitro.

Attempts at simulating drought stress in the field were hampered by extremes levels of rainfall, thus a

simulated drought experiment in the field utilizing a rainout shelter is strongly recommended for future

validation of ATW1124’s commercial applicability.


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Transcriptome analysis revealed that although roots were the major sites of morphological effects

of ATW1124, they did not reflect those changes at the transcriptomic level. Rather there were significant

transcriptomic changes in shoot tissues which were amplified by mild drought stress. In the context of

relevant literature the results of the project suggest that miRNA regulation of zinc finger proteins may have

some role to play in the drought tolerance-enhancing properties of ATW1124. Specifically miR156 was

shown in literature as a potential candidate miRNA with this function therefore future work may consider

using this as a starting point for assessing miRNA regulation. The silencing of the gene SPL13 was also

reported in literature to exhibit similarity to the effects of treatment with ATW1124 thus this is another

transcriptional element which may be useful in future work.

Treatment of ATW1124 was soley administered as a seed pre-treatment in this project – building

on previous work by collaborators and colleagues who experimented with alternative treatments. However

it is recommended that a foliar application be investigated as the effects appear to be, at least partly,

photosynthetic in nature. In this way ATW1124 treatment could be used in response to drought stress,

rather than as a pre-emptive seed-pre-treatment. Both seed pre-treatment and foliar sprays are very familiar

to industry so these practices would not require major revisions in current practices to be incorporated

effectively. However a foliar application widens the number of species outside of legumes, which have

quite robust seed structures. Additionally it is recommended that the number of species on which

ATW1124 is administered is widened. There have already been some investigations into treatment of

species other than mungbean by colleagues and collaborators and the list now includes chickpea,

mungbean, cotton as well as a number of Australian and Canadian woody tree species. Thus it is possible

that a number of receptive species have not yet been discovered. This could provide an additional avenue

for future work.

Bibliography Page 144

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