The Pennsylvania State University

The Graduate School

College of Agricultural Sciences

NUTRITIONAL AND GENETIC ARCHITECTURE OF ROOT TRAITS IN

RICE (Oryza sativa)

A Dissertation in

Horticulture

Phanchita Vejchasarn

2014 Phanchita Vejchasarn

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

May 2014

The dissertation of Phanchita Vejchasarn was reviewed and approved* by the following:

Kathleen Brown Professor of Plant Stress Biology Dissertation Advisor Chair of Committee Graduate Program Officer

Jonathan Lynch Professor of Plant Nutrition

Dawn Luthe Professor of Plant Stress Biology

Yinong Yang Associate Professor of Plant Pathology

*Signatures are on file in the Graduate School

iii

ABSTRACT

As the global population continues to grow, especially in the developing nations,

about 870 million people are food insecure and experiencing chronic malnutrition. Rice

is the most important cereal crop of the developing world and a staple food source of

more than half of the world’s population, providing 20-70% of total daily caloric intake.

Rainfed lowland rice is the dominant rice production system in areas of greatest poverty:

South Asia, parts of Southeast Asia, and essentially all of Africa, where its production is

limited by multiple abiotic stresses, uncertain moisture supply and decreasing soil

fertility. Phosphorus deficiency is considered to be one of the major constraints limiting

rice production in those areas. Most farmers still plant traditional rice varieties that

produce poor plant growth and low yields. Adding to the problem is that resource-

poor farmers are largely unable to afford the cost of fertilizers, thus, they remain trapped

in poverty. An alternative strategy is to develop phosphorus efficient varieties with

enhanced genetic adaptation to phosphorus-stressed soils, defined as improved yield ability in low-input agricultural systems coupled with better phosphorus acquisition and use efficiency.

Plant roots display a wide array of adaptations to low phosphorus stress, including

changes in root anatomy, morphology and architecture as well as increased production and secretion of root exudates. Plastic root responses to low phosphorus availability show promising benefits to improve phosphorus acquisition efficiency. The first experiment was undertaken to investigate the effect of low phosphorus availability on root morphological, anatomical and architectural characteristics among diverse rice

iv genotypes. This work serves as the basis of further understanding of various root traits controlled by low phosphorus availability in rice. Our results show that rice genotypes varied considerably in root traits. Low phosphorus availability has significant effects on the majority of root traits evaluated. Several root traits such as root hairs and root cortical aerenchyma are considered important in phosphorus acquisition.

In addition, genetic mechanisms determining natural phenotypic variation of root hairs, lateral root branching, root anatomical features, and nodal root growth angle were studied. Traits were evaluated on ~335 accessions from the O. sativa diversity panel. To identify loci underlying such traits, genome-wide association (GWA) analyses were performed using 36,901 single nucleotide polymorphisms (SNPs). We identified significant associations for all root traits. Significant loci associated with these root traits would be useful for plant breeders by incorporating them into new rice cultivars. This study represents an essential step toward genetic improvement strategies with the final goal of producing improved cultivars that enhance acquisition efficiency of soil resources, especially phosphorus, in the low-input agricultural system.

TABLE OF CONTENTS

List of Figures ...... viii

List of Tables ...... xiii

Chapter 1 Introduction ...... 1

Low soil phosphorus availability: a major limiting factor for plant productivity ...... 1 The importance of rice, rice production and ecosystems ...... 2 Structure of the rice root system ...... 3 Adaptive root traits for better phosphorus acquisition ...... 4 References ...... 8

Chapter 2 Effect of Phosphorus on Root Hairs and Anatomical and Architectural Traits in Rice Roots (Oryza sativa) ...... 14

Abstract ...... 14 Introduction ...... 15 Materials and Methods ...... 17 Plant cultivation and phosphorus treatments ...... 17 Root and shoot growth measurements ...... 18 Root hair measurement ...... 18 Lateral branching measurement ...... 19 Root anatomical traits measurement ...... 19 Tissue phosphorus content ...... 20 Experimental design and data analysis ...... 20 Results...... 21 Phenotypic variation of root hair traits within the root systems ...... 21 Phenotypic variation of root cortical aerenchyma (RCA) within the root systems ...... 22 Genotypic differences in root morphological and anatomical characteristics as affected by low phosphorus availability ...... 23 Discussion ...... 26 References ...... 67

Chapter 3 Genome-wide Association Mapping of Root Hair Traits in Rice (Oryza sativa) ...... 74

Abstract ...... 74 Materials and Methods ...... 77 Plant cultivation ...... 77 Root hair measurement ...... 78 Experimental design and data analysis ...... 78

Genome-wide association (GWA) analysis ...... 79 Results...... 80 Phenotypic variation of root hair traits ...... 80 Identification of loci underlying root hair traits through genome-wide association (GWA) study ...... 81 Discussion ...... 82 References ...... 95

Chapter 4 Genome-wide Association Mapping of Root Anatomical Traits in Rice (Oryza sativa) ...... 99

Abstract ...... 99 Introduction ...... 100 Materials and Methods ...... 103 Plant cultivation ...... 103 Root anatomical traits measurement ...... 104 Experimental design and data analysis ...... 105 Genome-wide association (GWA) analysis ...... 105 Results...... 106 Phenotypic variation of root anatomical traits ...... 106 Detecting loci controlling root anatomical traits through genome-wide association (GWA) mapping ...... 108 Discussion ...... 109 References ...... 138

Chapter 5 Genome-wide Association Mapping of Lateral Root Traits in Rice (Oryza sativa) ...... 144

Abstract ...... 144 Introduction ...... 145 Materials and Methods ...... 147 Plant cultivation ...... 147 Root system architectural traits measurement ...... 148 Experimental design and data analysis ...... 149 Genome-wide association (GWA) analysis ...... 149 Results...... 150 Phenotypic variation of root system architectural traits ...... 150 Identification of loci controlling root system architectural traits through genome-wide association (GWA) mapping ...... 151 Discussion ...... 152 References ...... 166

Chapter 6 Genome-wide Association Mapping of Root Growth Angle in Rice (Oryza sativa) ...... 170

vii

Abstract ...... 170 Introduction ...... 171 Materials and Methods ...... 174 Mapping population ...... 174 Plant cultivation ...... 174 Measurement of root growth angle ...... 175 Statistical analysis of phenotypic data ...... 175 Genome-wide association (GWA) analysis ...... 176 QTL co-localization and candidate gene analysis ...... 177 Results...... 178 Phenotypic variation of root growth angle ...... 178 Identification of loci associated with root growth angles and lateral root branching ...... 178 Discussion ...... 180 References ...... 222

Chapter 7 Summary ...... 226

Appendix A The curvature effect on root hair density...... 228 Appendix B Lists of O. sativa accessions ...... 229

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LIST OF FIGURES

Figure 1.1: Rice embryonic (left) and postembryonic (right) root system, showing the radicle (primary root), the embryonic nodal roots (seminal roots), the postembryonic nodal roots, and the large lateral roots...... 7

Figure 2.1: Greenhouse setup with drip irrigation system for root morphological and anatomical study...... 35

Figure 2.2: Quantitative analysis of root hair density (left) and root hair length (right) using ImageJ software. Roots were stained with Toluidine Blue dye and root hairs were observed on 20-25 cm-long nodal roots...... 36

Figure 2.3: Representative images of root hairs of rice indica cultivar IR64 (left) and tropical japonica cultivar Moroberekan (right) showing genotypic difference in root hair length. Plants were grown under high phosphorus. Roots were sampled, stained with Toluidine Blue and root hairs were observed on nodal roots at 5 – 10 cm from the root apex...... 39

Figure 2.4: Distribution of mean root hair length by sampling positions (5, 10 and 15 cm from the root apex)...... 40

Figure 2.5: Distribution of mean root hair density by sampling positions (5, 10 and 15 cm from the root apex)...... 41

Figure 2.6: Cross sections of nodal roots of 56 days old rice cultivar Azucena (O. sativa spp. japonica cv. Azucena) showing the distribution of root cortical aerenchyma (RCA) along the root axis. Root cortical aerenchyma was observed at 5, 10 and 15 cm from the root apex and at 1 cm from the root base...... 42

Figure 2.7: Distribution of mean aerenchyma area by sampling positions (5, 10 and 15 cm from the root apex and at 1 cm from the root base) ...... 43

Figure 2.8: Effect of phosphorus treatment on shoot dry weight. Values shown are means of 3 replicates ± SD...... 51

Figure 2.9: Effect of phosphorus treatment on shoot phosphorus content. Values shown are means of 3 replicates ± SD...... 52

Figure 2.10: Effect of phosphorus treatment on tiller number. Values shown are means of 3 replicates ± SD...... 53

Figure 2.11: Effect of phosphorus treatment on root to shoot ratio. Values shown are means of 3 replicates ± SD...... 54

Figure 2.12: The phenotypic correlation between shoot dry weight under high phosphorus (x-axis) and low phosphorus availability (y-axis) of 15 O. sativa accessions. Genotypes with high shoot dry weight values under both low and high phosphorus (upper right quadrant) are the most phosphorus efficient...... 55

Figure 2.13: Images of nodal roots of rice cultivars Azucena (tropical japonica), Dular (aus) and Nipponbare (temperate japonica) showing variation in large lateral root length. Small lateral roots are not visible in this image. Plants were grown under high phosphorus (100 µM P) for 56 days...... 56

Figure 2.14: Effect of phosphorus treatment on total small lateral root length. Values shown are means of 3 replicates ± SD...... 57

Figure 2.15: Effect of phosphorus treatment on total large lateral root length. Values shown are means of 3 replicates ± SD...... 58

Figure 2.16: Effect of phosphorus treatment on proportion of small to total lateral root length. Values shown are means of 3 replicates ± SD...... 59

Figure 2.17: Effect of phosphorus treatment on total root cross-section area (RXSA). Values shown are means of 3 replicates ± SD...... 60

Figure 2.18: Effect of phosphorus treatment on total root cortical area (TCA). Values shown are means of 3 replicates ± SD...... 61

Figure 2.19: Effect of phosphorus treatment on total root stele area (TSA). Values shown are means of 3 replicates ± SD...... 62

Figure 2.20: Representative images of root hairs of rice indica cultivar IR64 (left) and tropical japonica cultivar Azucena (right) showing genotypic difference in stele area...... 63

Figure 2.21: Effect of phosphorus treatment on TCA:RXSA ratio. Values shown are means of 3 replicates ± SD...... 64

Figure 2.22: Effect of phosphorus treatment on percentage of aerenchyma (%AA). Values shown are means of 3 replicates ± SD...... 65

Figure 2.23: Effect of phosphorus treatment on meta-xylem vessel area (MXA). Values shown are means of 3 replicates ± SD...... 66

Figure 3.1: Quantitative analysis of root hair density (left) and root hair length (right) using ImageJ software. Roots were stained with Toluidine Blue dye and root hairs were observed on 20-25 cm-long nodal roots, at 5-10 cm from the root apex...... 86

Figure 3.2: Frequency distribution of root hair length (A, top) and density (B, bottom) across 335 accessions containing 52 aus, 67 indica, 11 aromatic, 74 temperate japonica, 80 tropical japonica, and 51 highly admixed accession...... 88

Figure 3.3: Box plots illustrating the variation in root hair length (A, top) and density (B, bottom) across 335 O. sativa accessions consisting of 52 aus, 67 indica, 11 aromatic, 74 temperate japonica, 80 tropical japonica, and 51 highly admixed accession. (ARO = aromatic, TEJ = temperate japonica, TRJ = tropical japonica, AUS = aus, IND = indica, ADMIX = admixture)...... 89

Figure 3.4: The phenotypic correlation between root hair length (y-axis) and root hair density (x-axis) across 335 O. sativa accessions consisting of 52 aus, 67 indica, 11 aromatic, 74 temperate japonica, 80 tropical japonica, and 51 highly admixed accession. (TEJ = temperate japonica, AROMATIC = aromatic, ADMIX = admixture, TRJ = tropical japonica, IND = indica, AUS = aus)...... 90

Figure 3.5: Genome-wide association study of root hair length (A), and root hair density (B). Quantile-Quantile plots for the mixed linear models for root hair length and density (left panel). P-Values are shown from the mixed linear models for root hair length and root hair density (right panel). The X axis shows the SNPs on each chromosome, y axis is the –log10 (P-Value) for the association...... 91

Figure 4.1: Image of rice root cross-section showing aerenchyma lacunae, epidermis, endodermis, cortex, stele and xylem vessels...... 124

Figure 4.2: Frequency distribution of seven root anatomical traits across 336 O. sativa accessions consisting of 52 aus, 67 indica, 11 aromatic, 76 temperate japonica, 80 tropical japonica, and 50 admixed accessions...... 125

Figure 4.3: Box plots showing the phenotypic variation in seven root anatomical traits across 336 O. sativa accessions containing 52 aus, 67 indica, 11 aromatic, 76 temperate japonica, 80 tropical japonica, and 50 admixed accessions. (ARO = aromatic, TEJ = temperate japonica, TRJ = tropical japonica, AUS = aus, IND = indica, ADMIX = admixture)...... 129

Figure 4.4: Principal component analysis (PCA) biplot of seven root anatomical traits in 336 O. sativa accessions. The x and y axes are components 1 and 2, respectively. The cumulative percentage of the total variance explained by each PC is shown on each axis...... 133

red horizontal line depicts the significant threshold (P ≤ 10-4). The X axis shows the SNPs on each chromosome, y axis is the –log10 (P-Value) for the association...... 134

Figure 5.1: An example of a single nodal root of rice bearing large and small lateral roots...... 157

Figure 5.2: Images of nodal roots of rice cultivars Azucena (tropical japonica), Dular (aus) and Nipponbare (temperate japonica) showing genetic variation in lateral root length. Each image shows one nodal root bearing large and small lateral roots...... 158

Figure 5.3: Frequency distribution of small lateral root length (A), large lateral root length (B) and total lateral root length (C) across 333 accessions containing 52 aus, 67 indica, 11 aromatic, 73 temperate japonica, 80 tropical japonica, and 50 admixed accessions...... 159

Figure 5.4: Phenotypic variation of small lateral root length (A), large lateral root length (B) and total lateral root length (C) across 333 accessions containing 52 aus, 67 indica, 11 aromatic, 73 temperate japonica, 80 tropical japonica, and 50 admixed accessions. (ARO = aromatic, TEJ = temperate japonica, TRJ = tropical japonica, AUS = aus, IND = indica, ADMIX = admixture). Box plot shows the median and range of phenotypic variation for each O. sativa subpopulation independently ...... 161

Figure 5.5: Genome-wide association study of small lateral root length, large lateral root length, and total lateral root length. Quantile-Quantile plots for the mixed linear models for root system architectural characteristics (left panel). P-Values are shown from the MLM for root system architectural characteristics (right panel). The X axis shows the SNPs on each chromosome, y axis is the –log10 (P-Value) for the association...... 163

Figure 6.1: Natural phenotypic variation in root growth angle for shallow (left) and deep (right) genotypes of rice (Oryza sativa) grown in the fields for 80- 90 days in high phosphorus conditions...... 186

Figure 6.2: Frequency distribution of root growth angle (degrees from horizontal) under control (A) and phosphorus-stressed (B) conditions across 196 O. sativa accessions consisting of 57 aus, 70 indica, and 69 tropical japonica. Larger angles signify deeper roots...... 187

Figure 6.3: Box plots showing the phenotypic variation in root growth angle 196 O. sativa accessions consisting of 57 aus, 70 indica, and 69 tropical japonica. (AUS = aus, IND = indica, TRJ = tropical japonica)...... 188

xii

Figure 6.4: Genome-wide association (GWA) study of root growth angle in field- grown plants from three subpopulations. Quantile-Quantile plots are shown for the mixed linear models for root growth angle (left). P-Values are shown from the mixed linear models for root growth angle (right). The x axis shows the SNPs on each chromosome, y axis is the –log10 (P-Value) for the association...... 190

Figure 6.5: Significant SNPs indentified for root growth angle on rice chromosome 9 were compared to previously identified QTL stored in the TropgeneDB database (top). A linkage disequilibrium (LD) block containing IAA26 is shown in the expanded significant region on rice chromosome 9 (bottom)...... 191

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LIST OF TABLES

Table 2.1: Rice accessions (Oryza sativa) used in this study...... 34

Table 2.2: Root anatomical traits evaluated in this study, listing abbreviation and explanation of traits...... 37

Table 2.3: Analysis of variance table for the effects of axial position, genotype, and sub-population on root hair traits and (in the lower part of the table) means and standard deviations (SD) for root hair traits measured at different axial positions (tip (0-5 cm), middle (5-10 cm), and base (10-15 cm) from the root apex)and in different subpopulations. Different letters indicate significant differences among axial positions and sub-populations by the least significant difference (LSD) test (P<0.05)...... 38

Table 2.4: Analysis of variance table for the effects of axial position, genotype, and sub-population on aerenchyma area and (in the lower part of the table) means and standard deviations (SD) for aerenchyma measured at different axial positions (5, 10 and 15 cm from the root apex and at 1 cm from the root base) and in different subpopulations. Different letters indicate significant differences among axial positions and sub-populations by the least significant difference (LSD) test (P<0.05)...... 44

Table 2.5: Pearson correlation coefficients among all traits measured under high phosphorus (HP). (PH = plant height; SPC = shoot phosphorus content; SDW = shoot dry weight; RHL = root hair length; RHD = root hair density; SLR = small lateral root length; LLR = large lateral root length; RXSA = root cross- section area; TCA = total cortical area; TSA = total stele area; AA = aerenchyma area; %AA = percentage of aerenchyma; MXV = number of meta-xylem vessels; MXA = median meta-xylem vessel area)...... 45

Table 2.6: Pearson correlation coefficients among all traits measured under low phosphorus (LP). (PH = plant height; SPC = shoot phosphorus content; SDW = shoot dry weight; RHL = root hair length; RHD = root hair density; SLR = small lateral root length; LLR = large lateral root length; RXSA = root cross- section area; TCA = total cortical area; TSA = total stele area; AA = aerenchyma area; %AA = percentage of aerenchyma; MXV = number of meta-xylem vessels; MXA = median meta-xylem vessel area)...... 46

Table 2.7: Analysis of variance (ANOVA) table showing F and P values for the effects of genotype, sub-population, and phosphorus treatment on shoot and root morphological traits in 15 O. sativa accessions, and (in the lower part of the table) means and standard deviation (SD) values for shoot and root morphological traits evaluated under high (100 µM; HP) and low phosphorus (2 µM; LP) levels...... 47

xiv

Table 3.1: Analysis of variance (ANOVA) table for the effects of genotype, replicate, and sub-population on root hair characteristics, and (in the lower part of the table) means and standard errors (SE) of root traits by sub- populations. Different letters indicate significant differences between sub- populations by the least significant difference (LSD) test (P<0.05)...... 87

Table 3.2: Summary of significant associations between genetic markers and root hair length and density, listing the associated trait, SNP name, chromosome, position, P value, additive contribution to the phenotype, and broad-sense 2 heritability (hB )...... 93

Table 4.1: Root anatomical characteristics evaluated in this study, listing abbreviation and explanation of traits...... 117

Table 4.2: Summary of descriptive statistics for root anatomical characteristics examined in 336 accessions from the O. sativa diversity panel...... 118

Table 4.3: Analysis of variance (ANOVA) table showing F and P values for the effects of genotype, sub-population, and replication on root anatomical traits in 336 O. sativa accessions used in this study...... 119

Table 4.4: Summary of mean and standard deviation (SD) values for root anatomical traits detected in each sub-population. Different letters indicate significant differences among sub-populations by the least significant difference (LSD) test (P<0.05). AUS = aus; IND = indica; TRJ = tropical japonica; TEJ = temperate japonica; AROMATIC = aromatic; ADMIX= admixture. The rice diversity panel consists of 52 aus, 67 indica, 11 aromatic, 76 temperate japonica, 80 tropical japonica, and 50 admixed accessions...... 120

Table 4.5: Pearson correlation coefficients among root anatomical traits in the O. sativa diversity panel consisting of 336 accessions...... 121

Table 4.6: Loading scores for principal component analysis (PCA) of root anatomical traits examined in 336 O. sativa accessions...... 122

Table 4.7: Summary of significant associations between genetic markers and root anatomical traits, listing the associated trait, SNP name, chromosome, position, P value, additive contribution to the phenotype, and broad-sense 2 heritability (hB )...... 123

populations by the least significant difference (LSD) test (P<0.05).Different letters indicate significant differences among sub-populations...... 156

Table 5.2: Summary of significant associations between genetic markers and lateral root traits, listing the associated trait, SNP name, chromosome, position, P value, additive contribution to the phenotype, and broad-sense 2 heritability (hB )...... 165

Table 6.1: Analysis of variance (ANOVA) table for the effects of genotype, sub- population and phosphorus treatment on root growth angle, and (in the lower part of the table) means and standard errors (SE) of root traits by phosphorus treatment and sub-population. Different letters indicate significant differences between treatments or sub-populations by the least significant difference (LSD) test (P<0.05)...... 185

Table 6.2: Summary of significant associations between genetic markers and root growth angle, listing the associated trait, SNP name, chromosome, position, P value, and additive contribution to the phenotype...... 189

Table 6.3: List of annotated genes within the LD blocks of significant SNPs. Strong candidate genes are highlighted in yellow...... 192

Chapter 1

Introduction

Low soil phosphorus availability: a major limiting factor for plant productivity

Phosphorus (P), classified as a macronutrient, is essential for plant growth

(Marschner, 1995), however, its availability in many soils is insufficient for optimal

growth (Lynch and Deikman, 1998; Abel et al., 2002). Phosphorus is a nonrenewable

resource and it is estimated that by 2050, world resources of inexpensive phosphorus may

be depleted (Vance et al., 2003). Low soil phosphorus availability is one of the primary

limiting factors for crop production in most soils throughout the world, especially in

weathered mineral soils, forest soils and humid tropics (Walker, 1965; Lynch and

Deikman, 1998; Lynch and Brown, 2008).

Most phosphorus is generally found bound to soil chemical and biological

components and exists as either organic phosphate or insoluble inorganic phosphate (Pi)

which is taken up by plant roots (Smith et al. 2003). The availability of Pi in the total

phosphorus content of the soil is extremely low and movement of Pi in the soil solution is

slow, therefore, making it almost unavailable to growing plants (Comerford, 1998; Lynch

and Brown, 2008). Application of phosphorus fertilizers is one solution to correct this

problem, however, this is uncommon in the low-input agricultural systems and has several drawbacks. Firstly, over 80% of the phosphorus fertilizer can become immobile because of precipitation, adsorption or conversion to the organic form (Holford, 1997).

2 Secondly, excessive use of phosphorus fertilizer causes soil and water pollution and

ecosystem damage (Runge-Metzger, 1995). Therefore, crop improvement for

phosphorus efficiency would be more practical and economically feasible, especially for

developing countries where fertilizer use is negligible.

The importance of rice, rice production and ecosystems

Rice is the world’s most important crop and is the staple food for 2.5 billion

people, almost half of the total population in the world (FAO, 2004). It supplies 21% of

global human per capita energy and 15% of per capita protein (Maclean et al., 2002).

More than 90% of the world’s rice is produced in Asia, where 60% of the earth’s

population lives (Subudhi et al., 2006). Rice belongs to the grass family Gramineae like

other cereals such as maize, sorghum, wheat, rye and oats, and the genus Oryza that

includes two cultivated species and about 20 other wild species. Two cultivated species

of rice, Asian cultivated rice (Oryza sativa) grown worldwide, and African cultivated rice

(Oryza glaberrima) grown in parts of West Africa, are the most important cereals for

human nutrition (Subudhi et al., 2006).

Within O. sativa, five sub-populations (tropical japonica, temperate japonica, aromatic, aus, and indica) have been identified using SSR and SNP markers (Garris et al., 2005). Of these, the tropical japonica, temperate japonica and aromatic sub-

populations cluster within the japonica group, and the indica and aus sub-populations

cluster within the indica group (Garris et al., 2005). Of the numerous rice varieties, only

two major varieties are considered agronomically important, japonica, adapted to tropical

3 uplands and the temperate regions, and indica, adapted to the tropics (Maclean et al.,

2002). Rice ecosystems can be classified into four broad ecosystems used by IRRI

(International Rice Research Institute, Philippines): upland, rainfed lowland, irrigated, and flood-prone.

Phosphorus deficiency is considered a major limiting factor for rice production, especially in upland production systems (Kirk et al., 1998). Upland soils range from

Oxisols and Ultisols in Latin America, to Ultisols and badly leached Alfisols in Asia and

West Africa (Okada and Fischer, 2001). Unlike upland rice ecosystem, irrigated rice soil constraints are less crucial because flooded conditions increase nutrient availability and stabilize soil pH (Okada and Wissuwa, 2003). Therefore, understanding of the ecosystem’s constraints, cultivar development, and sustainable and cost effective approaches may be an appropriate solution for rice production on phosphorus stressed soils.

Structure of the rice root system

The root system is essential for many different functions, including absorption and translocation of water and nutrients and structural support. Root systems are determined by endogenous genetic controls as well as by soil properties and other external environmental factors, including biotic and abiotic stresses (Rebouillat et al.,

2009) and may differ in: (i) length, diameter, and density relationships; (ii) architecture, the spatial configuration and branching pattern, which determine soil exploration capacity, and (iii) anatomical characteristics that determine ability to transport water and

4 nutrients (Nicotra et al., 2002) and affect the metabolic cost of the root system (York and

Lynch, 2013).

Rice root architecture is characterized by having a fibrous and compact root

system comprised of five different types of roots with complicated age distribution: the radicle (primary root), the embryonic nodal roots(seminal roots), the postembryonic nodal roots, and the large lateral roots, and the small lateral roots (Hochholdinger et al.,

2004; Rebouillat et al., 2009) (Figure 1.1). The radicle emerges from the coleorhiza. A

few days after germination, the embryonic nodal roots emerge from the coleoptile during

the first and second leaf stages. The postembryonic nodal roots, exhibited at later growth

stages, emerge from the nodes on the stem. The lateral roots, which correspond to root-

borne roots, are by far the most numerous and can be classified into two main types, large

and small lateral roots. Any primary root of the other root classes can form 1st-order

lateral roots (large lateral roots) and 1st-order lateral roots produce 2nd-order lateral roots,

and so on. Small lateral roots never bear lateral roots. Higher-order lateral roots can be

observed on the nodal roots which emerge at later growth stages (Kawata and Soejima,

1974; Rebouillat et al., 2009).

Adaptive root traits for better phosphorus acquisition

The root systems perform many different adaptive functions including anchorage

to the soil and translocation of water and nutrients. The bioavailability of soil nutrients

may determine root growth and root proliferation. Changes in the architecture of the

root, therefore, can enhance the adaptation of plants to soils in which the availability of

5 nutrients are limited (Lopez-Bucio et al., 2003). Phosphorus is among the nutrients that

have a critical impact in determining root developmental processes (Lopez-Bucio et al.,

2003).

Plants exhibit a wide range of adaptations to low phosphorus availability,

including changes in root architecture and morphology to improve soil exploration

(Bonser et al., 1996; Nielsen et al., 1998; Fan et al., 2003; Zhu et al., 2006; Lynch and

Brown, 2008), increased secretion of P-mobilizing root exudates (Hinsinger, 2001),

increased root hair elongation and proliferation (Bates and Lynch, 1996), increased root

elongation (Nielsen et al., 2001), increased adventitious root development and enhanced

lateral root formation (Miller et al., 2003), and enhanced root cortical aerenchyma (RCA)

formation to reduce root respiration and the metabolic cost of soil exploration (Fan et al.,

2003, Zhu et al., 2010). According to Kirk et al. (1998), the major factors that contribute

to phosphorus uptake efﬁciency in rice production systems are: (i) root geometry effects

– differences in root total length and density, root hair length and density, root diameter,

etc.; (ii) mycorrhizal effects – differences in the extent or rate of infection; and (iii)

solubilization effects – differences in phosphorus solubility arising from root-induced changes in soil chemical conditions.

Genetic and molecular mechanisms controlling the rice root development, either under control or abiotic stress conditions, has been well studied in rice (Oryza sativa)

mainly through quantitative trait loci (QTL) analysis. Many QTLs associated with small-

to-medium effects on root length, root number, root thickness, stele and xylem structures,

and root biomass have been identified (Zheng et al., 2000; Kamoshita et al., 2002; Zheng

6 et al., 2003; Steele et al., 2006; Qu et al., 2008; Uga et al., 2008; Rebouillat et al., 2009).

However, these traits are difficult to evaluate because root systems are exceedingly complex structures and exhibit high phenotypic plasticity. This strategy, therefore, requires a large population and progeny testing to define the positions of QTL more accurately (Rebouillat et al., 2009).

Genome-wide association (GWA) mapping is a powerful tool used to identify underlying genes controlling inherited traits in many plant species. As an alternative to traditional linkage mapping of quantitative trait loci (QTL), association mapping is now widely used for the dissection of complex agronomic traits, gene discovery and exploring the links between genotype and phenotype. Association mapping has the advantages of increased mapping resolution and the ability to examine more than two alleles at the same time (Yu and Buckler, 2006).

The goals of this study are to examine the effect of phosphorus deficiency on root architectural, morphological and anatomical traits among diverse accessions of rice

(Oryza sativa) and identify molecular markers associated with genes controlling root anatomical, morphological and architectural traits in rice. Knowledge and information generated from this study provide a better understanding of genetic control of such traits in rice and opens a possibility of crop yield increase through genetic improvement.

7 Figure 1.1: Rice embryonic (left) and postembryonic (right) root system, showing the radicle (primary root), the embryonic nodal roots (seminal roots), the postembryonic nodal roots, and the large lateral roots.

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Chapter 2

Effect of Phosphorus on Root Hairs and Anatomical and Architectural Traits in Rice Roots (Oryza sativa)

Abstract

Phosphorus is one of the most limiting nutrients for agricultural productivity

worldwide. Almost 50% of rice production areas are considered phosphorus deficient.

This, in turn, calls for more phosphorus-tolerant varieties as well as potential strategies

for improving phosphorus acquisition efficiency. Plastic root responses to low

phosphorus stress can be adaptive strategies to enhance phosphorus acquisition. This

study assessed the phenotypic variation and effect of phosphorus deficiency on shoot

growth, and root morphological, architectural and anatomical traits among 15 diverse rice

(Oryza sativa) accessions. Rice plants were grown in diffusion-limited phosphorus using

solid-phase buffered Al-P to mimic realistic phosphorus availability conditions. Overall,

results revealed substantial genetic variation in all root traits measured. Shoot dry

weight, tiller number, plant height, and shoot phosphorus content were reduced under low

phosphorus availability. Phosphorus deficiency significantly increased root hair length

and density, but reduced small and large lateral root length in all genotypes. Root

diameter measured as total root cross-section area is significantly lower under low

phosphorus availability. Phosphorus deficiency caused an increase in percent of

aerenchyma by 20%. Total stele area and meta-xylem vessel area responses to low phosphorus stress differed significantly among genotypes. We suggest that some root

15 traits such as root hair length and density, total root cross-section area, and root cortical aerenchyma, which clearly showed response to low phosphorus availability, could be important in phosphorus acquisition.

Introduction

The on-going increase of human population in developing countries requires an increase in crop yields to meet the growing demands for food. In these countries, however, agricultural productivity is limited by soil infertility. Although phosphorus (P) is essential for plant growth (Marschner, 1995), it is one of the least available nutrients principally because phosphorus is bound to soil chemical and biological components

(Sample et al., 1980; Barber, 1995; Zhu et al., 2010), making it almost unavailable to growing plants (Comerford, 1998; Lynch and Brown, 2008). This problem can be corrected through intensive phosphorus fertilization. Not only is this solution inefficient because of phosphorus immobilization by the soil, but excessive use of phosphorus fertilizer causes soil and water pollution and ecosystem damage (Runge-Metzger, 1995).

Crop improvement for phosphorus efficiency would be more practical and economically feasible, especially for developing countries where fertilizer use is negligible.

Rice is the most important staple food crop of more than half of the world’s population. Two cultivated species of rice, Asian cultivated rice (Oryza sativa) grown worldwide, and African cultivated rice (Oryza glaberrima) grown in parts of West

Africa, are the most important cereals for human nutrition (Subudhi et al., 2006). Within

O. sativa, five sub-populations (tropical japonica, temperate japonica, aromatic, aus,

16 and indica) have been identified using SSR and SNP markers (Garris et al., 2005). Rice

ecosystems can be classified into four broad ecosystems used by IRRI (International Rice

Research Institute, Philippines): upland, rainfed lowland, irrigated, and flood-prone.

Phosphorus deficiency is considered a major limiting factor for rice production,

especially in upland production systems (Kirk et al., 1998). Upland rice, which is

primarily grown as a subsistence crop, is the dominant rice production system in Latin

America and West Africa, where most of the soils are affected by low phosphorus

availability (Okada and Fischer, 2001). Most existing rice cultivars produce low yields in

phosphorus stressed soils. Adaptive root traits known to confer better performance of other crops in such conditions include greater root hair length and density (Bates and

Lynch, 1996), modification of root architecture to improve soil exploration (Lynch and

Brown, 2001), increased root elongation (Nielsen et al., 2001), increased adventitious root development and enhanced lateral root formation (Miller et al., 2003), and enhanced root cortical aerenchyma (RCA) formation to reduce root respiration and the metabolic cost of soil exploration (Fan et al., 2003, Zhu et al., 2010). In rice, genotypic variation in root growth under phosphorus stressed conditions has been observed (Wissuwa and Ae,

2001a; Ismail et al., 2007) and major QTL related to maintenance of root growth under phosphorus deficiency has been identified (Wissuwa and Ae, 2001b, Ismail et al., 2007).

It is likely that root hairs and many of the architectural and anatomical traits

studied in other species are important for phosphorus acquisition in rice. Root traits may

also be plastic, i.e. change value under low phosphorus availability. However, the effects

of phosphorus on these traits in rice, and particularly comparing genotypes from the five

different sub-populations, have not been well studied. The overall goal of this study is to

17 examine the effect of phosphorus deficiency on root architectural, morphological and

anatomical traits among accession of rice (Oryza sativa) with different genetic

backgrounds.

Specific objectives include:

1. Examine distribution and phenotypic variation of root hair traits and root cortical

aerenchyma within the root systems.

2. Determine whether there is genetic variation for root hairs and anatomical and

architectural traits, using selections from each of the five subpopulations.

3. Examine phosphorus effects on root anatomical, morphological (root hair length

and density) and architectural (lateral branching) traits.

Materials and Methods

Plant cultivation and phosphorus treatments

The experiments were carried out in a greenhouse located on the campus of the

Pennsylvania State University, University Park, PA (40º48’ N, -77º51’ W). Figure 2.1

shows greenhouse setup for phenotyping rice root systems. The study made use of fifteen O. sativa accessions (Table 2.1) from five sub-populations. Three replications were grown per accession, and replications were staggered in time. Rice seeds were surface sterilized with 10% bleach and pre-germinated prior planting. For pre-

germination, seeds were sown on moist paper towel soaked with 0.5 mM CaSO4 for 72

hours at 28 oC in the germination incubator. Plants were cultivated in 10.5 L pots (21 cm

18 x 40.6 cm, top diameter x height, Nursery Supplies Inc., Chambersburg, PA, USA.

Healthy germinated seeds were transplanted to 10.5 L pots (21 cm x 40.6 cm, top

diameter x height, Nursery Supplies Inc., Chambersburg, PA, USA) filled with a mixture

(volume based) of 40% medium size (0.3-0.5 mm) commercial grade sand (Quikrete

Companies Inc., Harrisburg, PA, USA), 60% horticultural vermiculite (Whittemore

Companies Inc.), and 1% solid-phase buffered phosphorus (Al-P, prepared according to

Lynch et al., 1990) providing a constant availability of low (2 µM) and high (100 µM)

phosphorus concentration in the soil solution. Plants were irrigated once per day with

phosphorus-free Yoshida nutrient solution (Yoshida et al., 1976) via drip irrigation.

Root and shoot growth measurements

Plants were harvested at the 8th leaf stage. At harvest, the number of tillers and shoot height were recorded. Root systems were excavated, washed and preserved in 70% ethanol until the time of processing and analysis. Shoots and roots were dried at 65oC for

72 hours prior to dry weight determination.

Root hair measurement

Root hair length and density (number of root hairs per mm2 root surface area) were

observed on approximately 20-cm-long nodal roots, at 5, 10, 15 cm from the root apex.

Roots were instantaneously immersed in 0.5% Toluidine Blue, which allowed to a clear

observation of root hairs. The stained roots were observed under a dissection microscopy

19 (SMZ-U, Nikon, Tokyo, Japan) at 40x magnification, equipped with digital camera

(NIKON DS-Fi1, Tokyo, Japan.). Two different images for length and density were

captured on each sampling location. Image J software (National Institute of Mental Health,

Bethesda, Maryland, USA.) was used for quantitative analysis of root hair length and

density (Figure 2.2).

Lateral branching measurement

A 30-cm-long nodal root was collected from each plant to assess small and large

lateral root length. Roots were scanned using a flatbed scanner at a resolution of 600 dpi

(HP ScanJet II, Hewlett Packard, USA). Root analysis software WinRhizo (Regent

Instruments, Quebec, Canada) was used to determine lateral root branching, which was

categorized using two diameter classes of < 0.10 mm (2nd order or small laterals) and

0.10 – 0.70 mm (1st order or large laterals).

Root anatomical traits measurement

Root anatomical structures were observed on 20-cm-long nodal roots, at 5, 10 and

15 cm from the root apex and at 1 cm from the basal end of the root. Preserved roots

were freehand-sectioned using Teflon-coated double-edged stainless steel blades

(Electron Microscopy Sciences, Hatfield, PA, USA) and stained with 0.5% Toluidine

Blue. Transverse sections were examined under a Diaphot inverted light microscope

(Nikon, Chiyoda-ku, Japan). The three best root cross-sections were selected and images

20 captured with a black and white XC-77 CCD Video Camera (Hamamatsu, Iwata-City,

Japan). The image analysis software ‘RootScan’ (Burton et al., 2012) was used for quantitative analysis of cortical area, aerenchyma area, stele area, and xylem traits (Table

2.2).

Tissue phosphorus content

Dry samples of root and shoot tissue were ground and ashed at 495oC for 12 hours. The ash was dissolved by adding 8 mL of 100 mM HCl and analyzed for phosphorus concentration spectrophotometrically according to the Murphy and Riley method (Murphy and Riley, 1962).

Experimental design and data analysis

A randomized complete block design was used with the time of planting between each replication as a block effect. Statistical analyses were performed using package R, version 3.0.2 (R Foundation for Statistical Computing, Vienna, Austria) and Minitab version 16.2 (Minitab Inc., University Park, USA). For the root hairs and aerenchyma distribution data, collected data was analyzed with one- and two-way analyses of variance (ANOVA) to determine the main effects and interactions between genotype and sampling position. Low phosphorus treatments were not included in the sampling position study. In the low phosphorus study, samples for root hair and anatomical analysis were collected 5-10 cm from the nodal root tip. One- and two-way analyses of

21 variance (ANOVA) were used to examine the influence of phosphorus treatments on

dependent variables and interactions between genotype (G) and phosphorus level (P) (G x

P), and sup-population (S) and phosphorus level (S x P).

Results

Phenotypic variation of root hair traits within the root systems

Signiﬁcant differences in average root hair length (P<0.001) and root hair density

(P<0.001) of the fifteen accessions were observed among different axial positions

(Figures 2.4, 2.5 and Table 2.3). Figure 2.3 shows typical root hair characteristics in two contrasting genotypes when examined on nodal roots at middle position, 5-10 cm from the root apex. Across all axial positions, genotypes and sub-populations, root hair length ranged from 0.18 mm to 0.32 mm and root hair density ranged from 183 hairs.mm-2 root

to 238 hairs.mm-2 root. Axial position, genotype and sub-population significantly

affected root hair length and density and a significant interaction for root hair length was

observed between axial position and genotype (Table 2.3). Among sub-populations, the

indica varieties had greater mean root hair length, whereas the tropical japonica varieties

had greater mean root hair density (Figures 2.4, 2.5 and Table 2.3).

Root hair traits were measured at three different axial positions: tip (0-5 cm from

the root apex), middle (5-10 cm from the root apex), and base (10-15 cm from the root

apex). Comparison of the three positions within each accession showed that the mean

root hair length and density were greater in the middle and basal axial positions than in

22 the apical position (Figures 2.4, 2.5 and Table 2.3). Root hairs at the apical position were not fully developed, while those in the basal position were sometimes less dense than

those at the middle position. Therefore, we selected the middle region as the most

representative of mean root hair characteristics.

Phenotypic variation of root cortical aerenchyma (RCA) within the root systems

Since aerenchyma formation occurs in older segments of the root, in the zone

where lateral roots form (Figures 2.6, 2.7), we examined phenotypic variation in root

cortical aerenchyma (RCA) along the axis of nodal roots. Our result shows that RCA

formation was first visible 4-5 cm from the root tip and was fully developed by 15 cm

from the root tip. Across all axial positions, genotypes, and sub-populations, the

percentage of RCA ranged from 8 to 30%. Axial position had a strong and significant

effect on the absolute area and percent of RCA (Figure 2.7 and Table 2.4).

In all accessions, the greatest mean RCA area (0.349 mm2) was found at 15 cm

from the root apex (Figure 2.7). RCA was 80% and 39% greater at 15 cm than at the

apical (5 cm) and basal regions, respectively. The amount of RCA was significantly

lower at the basal region (1-2 cm below root-shoot junction), which is consistent with

other studies in maize (Bouranis et al., 2006; Siyiannis et al., 2012; Burton et al., 2012).

Therefore, we chose 15 cm from the root apex as the sampling position for further

research on RCA and other root anatomical traits. Among sub-populations, the aromatic

varieties had greater RCA area, whereas accessions with least mean values were from the

indica and temperate japonica sub-populations (Table 2.4).

23 Genotypic differences in root morphological and anatomical characteristics as affected by low phosphorus availability

In this study, rice plants were cultivated in diffusion-limited phosphorus using solid-phase buffered Al-P method (Lynch et al., 1990), which produces realistic phosphorus availability conditions. Low phosphorus availability inhibited shoot growth as shown by the lower shoot biomass, tiller number, plant height and shoot phosphorus content (Figures 2.8, 2.9, 2.10 and Table 2.7), showing that the phosphorus treatments were effective in producing phosphorus deficiency. Phosphorus-stress treatment significantly reduced shoot biomass and tiller number by 42% and 41%, respectively.

Shoot phosphorus content was reduced by 71% under phosphorus deficiency (Table 2.7).

Phosphorus deficiency did not affect root biomass, but significantly increased root:shoot ratio in all genotypes (Table 2.7). Differences among accessions and sub-populations were observed for shoot biomass, tiller number and plant height (Table 2.7).

Additionally, significant genotype (G) x phosphorus treatment (P) interactions were observed for shoot biomass, number of tillers, and shoot phosphorus content which indicated different phosphorus efficiencies among the genotypes (Table 2.7). Root to shoot biomass ratios of the phosphorus deficient plants was relatively larger than that of the phosphorus sufficient plants (Figure 2.11). Overall, high correlations were observed among shoot variables (Tables 2.5, 2.6). The aus cultivar Jhona 349 had superior ability to maintain shoot growth under low phosphorus availability (Figure 2.12).

Root hair characteristics

Root hair length and density were significantly affected by genotype, subpopulation, and phosphorus treatment (Table 2.7). Low phosphorus availability

24 promoted root hair development as indicated by greater mean values of root hair length

and density under low phosphorus stress. Phosphorus deficiency increased root hair

length and density by 15% and 10%, respectively. However, there was no significant

genotype (G) x phosphorus treatment (P) interaction for either root hair length or density,

indicating no significant difference in phosphorus response among these genotypes. A

significant negative relationship between root hair length and density was observed under high phosphorus, but this correlation was not significant under low phosphorus availability (Tables 2.5, 2.6). Root hair length and density did not correlate with shoot traits (Tables 2.5, 2.6).

Lateral root branching

Small and large lateral roots were significantly affected by genotype, subpopulation, and phosphorus treatment (Figures 2.13, 2.14, 2.15 and Table 2.7).

Across all accessions, the aus cultivar Kasalath had the greatest mean values of both

small and large lateral root length. Low phosphorus availability caused a reduction in

small and large lateral root length in all genotypes. Under phosphorus stress, small and

large lateral root lengths were reduced by 48% and 41%, respectively (Table 2.7). There

is a significant genotype (G) x phosphorus treatment (P) interaction for small lateral root

length (Table 2.7).

Low phosphorus availability had a significant effect on the proportion of small to

total lateral root length (Table 2.8, Figure 2.16). There was a significant genotype (G) x

phosphorus treatment (P) interaction for proportion of small to total lateral root length.

Proportion of small to total lateral root length of some genotypes increased under low

phosphorus availability, while others decrease (Figure 2.16).

25 Root anatomical characteristics

The total root cross-section area (RXSA), varied greatly among genotypes and

sub-populations (Figure 2.17, Table 2.9). Significant differences were observed between

phosphorus levels for RXSA. Phosphorus stress caused a reduction in RXSA by 15% on

average. The interaction between genotype (G) and phosphorus treatment (P) was

significant for RXSA.

Substantial genetic variation was observed for total areas of root cortex (TCA)

stele (TSA), and TCA:RXSA ratio (Figures 2.18, 2.19, 2.20 and Table 2.9). Low phosphorus availability had a significant effect on TCA and TCA:RXSA ratio, but not

TSA (Figure 2.21). TCA decreased by 18% under phosphorus stress. However, the interaction between genotype (G) and phosphorus treatment (P) was significant for both

TCA and TSA. TSA of some genotypes increases under low phosphorus availability, while others decrease (Figures 2.19, 2.20). For the total cortical area (TCA), Bico

Branco and Azucena cultivars are the most affected genotypes under phosphorus stress

(Figure 2.16).

Significant genetic effects were observed for both absolute area (AA) and percentage (%AA) of aerenchyma (Figure 2.22 and Table 2.10). Low phosphorus availability significantly affected %AA, but not AA. Phosphorus deficiency caused an increase in %AA by 20%.

Significant differences among genotypes and sub-populations were observed for both number (MXV) and area (MXA) of meta-xylem vessels (Table 2.10). The cultivar

Azucena had the greatest mean values for MXA (Figure 2.23). MXV did not differ

26 between two phosphorus levels. However, there was a significant interaction between genotype (G) and phosphorus treatment (P) for MXA.

Discussion

Historically, Asian cultivated rice (Oryza sativa) has been divided into two

varietal groups, Indica and Japonica, based on various classification systems such as

geographic origin, ecological adaptation, morphological characteristics (Oka, 1988;

Famoso et al., 2011), isozyme markers (Glaszmann, 1987; Li and Rutger, 2000; Lafitte et

al., 2001) and various molecular markers (Wang and Tanksley, 1989; Dally and Second,

1990; Zhang et al., 1992; Kojima et al., 2005; Uga et al., 2009). Recently, Garris et al.

(2005) has further classified these two varietal groups into five sub-populations, indica,

aus, tropical japonica, temperate japonica, and aromatic, based on SSR and SNP

markers (Garris et al., 2005; Famoso et al., 2011). This study was conducted to elucidate

phenotypic variation in lateral root branching, root hair traits and root anatomical traits of

fifteen O. sativa accessions, three from each sub-population, under high and low

phosphorus availability.

Low soil phosphorus availability is a primary nutrient constraint limiting plant

growth and crop productivity worldwide, and almost 50% of rice soils are considered

phosphorus deficient (Pariasca-Tanaka et al., 2009). Plants have developed different

adaptive responses to minimize the impact of phosphorus deficiency (Raghothama and

Karthikeyan, 2005; Li et al., 2009). We found that low phosphorus availability affected

overall plant growth, as shown by the reduction in phenotypic values of shoot dry matter,

27 plant height, and number of tillers (Figures 2.8, 2.9, 2.10 and Table 2.7). Phosphorus stress caused an increase in root to shoot dry weight ratio (Figure 2.11). Additionally, we observed considerable genetic variation for phosphorus efficiency (Figure 2.12). The most efficient genotypes (Jhona 349, Dular and Kasalath) are from aus sub-population.

The aus genotypes had a smaller change in their root to shoot ratios under low phosphorus availability when compared to others, probably resulting from a lesser degree of stress as indicated by their higher shoot phosphorus content under low phosphorus

(Figure 2.9).

Plant roots exhibit highly plastic responses to low phosphorus availability and have evolved a series of adaptive strategies to enhance phosphorus acquisition. Root hairs are sub-cellular extensions of root epidermal cells that play an important role in the acquisition of relatively immobile nutrients such as phosphorus (Clarkson, 1985;

Peterson and Farquhar, 1996; Jungk, 2001; Gahoonia et al.,1999; Zhu et al., 2010).

Genotypic variation for root hair traits is evident in several crop species including wheat, barley, clover, bean, turfgrass and soybean (Caradus, 1979; Green et al., 1991; Yan et al.,

1995; Gahoonia and Nielsen, 1997; Wang et al., 2004; Brown et al., 2013). Root hair length and density regulation by phosphorus deficiency has been demonstrated in

Arabidopsis (Bates and Lynch, 1996), and several agronomic crops (Fohse and Jungk,

1983; Hoffmann and Jungk, 1995; White et al., 2005; Brown et al., 2013). An increase in root hair length and density is a metabolically efficient way to improve phosphorus acquisition by increasing the absorptive surface area of the root, which enables a greater soil volume explored. Despite extensive study of root hair characteristics in other species, knowledge on the phenotypic diversity and phosphorus response of root hair

28 traits in Oryza sativa is very limited. Our results demonstrate substantial genetic variation for root hair length and density in Oryza sativa genotypes, and the influence of phosphorus and axial position (Tables 2.3, 2.11). Fully elongated root hairs were present at 5-10 cm from the root apex and they slightly decreased toward basal region, which suggest that they may be shed from the root along with the epidermal cells when their function declines (Rebouillat et al., 2009). The indica accessions had greater mean root hair length, whereas the tropical japonica accessions had greater mean root hair density

(Table 2.3). Our result shows that rice responds to low phosphorus availability by increasing root hair length and density, which is consistent with the previous study in rice reported by Rose et al. (2013). Additionally, root hairs could be selected under either control or suboptimal phosphorus conditions since all accessions respond similarly.

Lateral roots, which arise from anticlinal symmetrical divisions in the pericycle and endodermal cells, can be classified into two main types: small and large lateral roots

(Sasaki et al., 1981; Rebouillat et al., 2009). Lateral branching would be expected to increase phosphorus efficiency by increasing soil exploration (Zhu et al., 2005), phosphorus solubilization (Lynch, 2007), and the absorptive surface of the root system

(Perez-Torres et al., 2008; Nui et al., 2013). Prior studies have shown that phosphorus deficiency increased elongation and density of lateral roots in Arabidopsis (Williamson et al., 2001; Linkohr et al., 2002; Reymond et al., 2006; Nui et al., 2013) and in seedlings of some maize genotypes (Zhu et al., 2004). However, our results demonstrated that low phosphorus availability reduced small and large lateral root length in all genotypes. The contrasting results may be due to treatment and plant age. In this study, rice plants were cultivated to the V-8 stage (well beyond the seedling stage) in diffusion-limited

29 phosphorus using solid-phase buffered Al-P (Lynch et al., 1990), which produces realistic

phosphorus availability conditions. Consistent with our results, Mollier and Pellerin

(1999) detected a reduction rather than an increase in lateral root branching under

phosphorus deficiency in maize, and low phosphorus similarly reduced lateral root

density in common bean (Borch et al., 1999). Substantial phenotypic variation for lateral

root branching was observed among accessions and sub-populations (Table 2.7). Greater

lengths of small and large lateral roots were observed in the aus sub-population, which

originates from a region in India with infertile and drought-prone soils (Londo et al.,

2006; Haefele and Hijmans, 2006; Gamuyao et al., 2012). Lateral root development is an

adaptation that enhances phosphorus acquisition. Specifically, the aus-type cultivar

Kasalath, which is a donor of phosphorus deficiency tolerant allele named phosphorus- starvation tolerance 1 (PSTOL1), shows greatest lengths of small and large lateral roots in the present study (Figures 2.14, 2.15), which may contribute to its phosphorus stress tolerance. Small to total lateral root length ratio was significantly affected by low phosphorus availability (Figure 2.16), however genotypes showed a variable response to phosphorus stress. We would expect large lateral roots to be more important than small lateral roots under phosphorus stress, since large lateral roots would have a strong role in soil exploration for more phosphorus.

Root cortical aerenchyma (RCA) consists of large air-filled intercellular spaces or

lacunae in the root cortex (Esau, 1977). In rice, the formation of aerenchyma is

associated with cell lysis and likely to be coordinated by a programmed cell death (PCD)

mechanism (Kawai et al., 1998; Parlanti et al., 2011). The constitutive presence of

aerenchyma in rice is a common adaptive response to soil hypoxia and anoxia, since it

30 provides a pathway of low resistance to diffusion of atmospheric oxygen to the root tips

(Justin and Armstrong, 1987; Colmer, 2003; Suralta and Yamauchi, 2008). Although

RCA formation is induced by water-logging, RCA can also be stimulated by a variety of edaphic stresses, including low phosphorus, nitrogen, sulfur, high temperature, and drought (Drew et al., 1989; Przywara and Stepniewski, 2000; Bouranis et al., 2003;

Evans, 2003; Zhu et al., 2010; Lynch et al., 2013). RCA formation under nutrient stress has been shown to reduce the metabolic cost of soil exploration by replacing metabolically active cortical cells with air-filled lacunae (Lynch and Brown, 2008; Lynch et al., 2013). In this study, RCA as well as other anatomical root features were examined under well-drained soil conditions at different axial positions with high and low phosphorus availability.

In the present study, considerable phenotypic variation was observed for anatomical root characteristics (Tables 2.9, 2.10). Both the absolute amount and the percentage of RCA varied significantly among genotypes and axial positions, consistent with the previous study in maize (Burton et al. 2012). Root diameter, measured as the total root cross-section area (RXSA) varied greatly among genotypes and sub- populations. The aromatic and tropical japonica varieties had thicker root diameter.

Consistent with this result, Lafitte et al. (2001) previously reported that thicker root diameter is a characteristic of rice cultivars from upland japonica group. For total stele area (TSA), our result demonstrates that aromatic and tropical japonica accessions had larger stele transversal area (TSA) (Figure 2.19), which agrees with the previous study reported by Kondo et al. (2000) and Uga et al. (2008).

31 Low phosphorus availability increased %AA by 20%, but did not affect the absolute amount of aerenchyma (AA) (Figure 2.22 and Table 2.10). Previous research showed increased aerenchyma formation under low phosphorus in maize, rice and common bean (He et al., 1992; Fan et al. 2003; Insalud et al., 2006). Fan et al. (2003) showed that increased aerenchyma formation reduces root phosphorus content and the respiratory cost of constructing and maintaining roots, thereby improving soil exploration at minimal nutrient and carbon costs (Fan et al., 2003; Lynch, 2011). Low phosphorus availability reduced the total root cross-section area (RXSA) and total cortical area

(TCA) by 14% and 18%, respectively. Since low phosphorus availability has a direct effect on reducing TCA, this might explain why %AA is significantly higher but not AA.

Thinner root diameter, a process termed “root etiolation”, can result from low phosphorus availability, and has been suggested to reduce metabolic cost of soil exploration (Morrow de la Riva, 2010; Lynch et al., 2011). In particular, a smaller area of living cells in the cortex was associated with reduced root metabolic cost and improved drought tolerance in maize (Jaramillo et al., 2013). Rice showed a reduction in TCA across all genotypes, while stele area (TSA) showed variable responses to low phosphorus, indicating that rice may reduce its metabolic cost by increasing aerenchyma and reducing living cortical area under low phosphorus stress.

Among root anatomical characteristics, the size (MXA) and number (MXN) of meta-xylem vessels are directly associated with axial conductivity for water transport

(Kondo et al., 2000; Uga et al., 2008), and can affect plant productivity under drought

(Zimmermann, 1983; Tyree et al., 1994; Comas et al., 2013). Genetic variation for root xylem features has been observed in wheat (Richards and Passioura, 1981), maize

32 (Burton et al., 2013), and rice (Kondo et al., 2000; Lafitte et al., 2001, Uga et al., 2008).

Overall, our result shows that genotype has the biggest effect on MXA and MXV. The

tropical japonica accessions had larger area (MXA) and greater number (MXV) of meta-

xylem vessels, correlated with the larger diameter of roots (Tables 2.5, 2.6). Consistent

with this result, Kondo et al. (2000) and Uga et al. (2008) reported that traditional upland

japonica cultivars were characterized by larger xylem structures and thicker roots. Low

phosphorus availability had no significant effect on MXV. However, phosphorus

treatment effects on MXA varied among genotypes. Reducing xylem vessel area in some

genotypes should reduce root axial hydraulic conductivity, which is believed to reduce

leaf area in phosphorus-stressed plants (Radin and Eidenbock, 1984). This allows

resources to be diverted to roots for greater soil exploration.

The present study provides the basis of further understanding of various root traits

controlled by low phosphorus availability in rice. Our results demonstrate that rice

genotypes differ considerably in root traits. Low phosphorus availability has significant

effects on root hairs, lateral root branching, total root cross-section area (RXSA), total cortical area (TCA), total stele area (TSA), and meta-xylem vessel area (MXV). While phosphorus deficiency increased root hair length and density as well as aerenchyma

(%AA) in most genotypes, it reduces the lengths of small and large lateral roots. Several well-known phosphorus-efficient genotypes (i.e., Kasalath and Dular) have superior ability to maintain shoot growth under low phosphorus stress. We suggest that variation for root hair length and density, total root cross-section area (RXSA), and aerenchyma

(%AA) among genotypes that clearly showed response to low phosphorus availability could be used for improving phosphorus acquisition efficiency in phosphorus-deficient

33 soils. Further research is also required to study the value of lateral root branching and xylem features in this species under low phosphorus availability.

Table 2.1: Rice accessions (Oryza sativa) used in this study.

Accession Name Country of Origin Sub-population Assignment Kasalath India aus Jhona 349 India aus Dular India aus IR 64 Philippines indica Pokkali Sri-Lanka indica Patnai 23 India indica Dom-sofid Iran aromatic Basmati Pakistan aromatic BicoBranco Brazil aromatic Moroberekan Guinea tropical japonica Azucena Philippines tropical japonica Cocodrie United States tropical japonica Leung Pratew Thailand temperate japonica Aichi Asahi Japan temperate japonica Nipponbare Japan temperate japonica

Figure 2.1: Greenhouse setup with drip irrigation system for root morphological and anatomical study.

Table 2.2: Root anatomical traits evaluated in this study, listing abbreviation and explanation of traits.

Trait Description RXSA Root cross-section area (mm2) TCA Total cortical area (mm2) TSA Total stele area (mm2) AA Aerenchyma area (mm2) %AA Percent of cortex as aerenchyma MXV Number of meta-xylem vessels MXA Median meta-xylem vessel area (mm2)

d.f. Root Hair Length (mm) Root Hair Density (hairs/mm2) F P F P Axial Position 2 20.959 <0.001 11.523 <0.001 Genotype 14 22.129 <0.001 2.585 0.003 Sub-population 4 37.704 <0.001 6.944 <0.001 Axial Position*Genotype 28 3.015 <0.001 0.399 0.996 Axial Position*Sub-population 8 0.317 0.958 0.277 0.972 Axial Positions Mean SD Mean SD Tip (5 cm) 0.225 0.033a 208.496 14.632a Middle (10 cm) 0.264 0.032b 221.059 15.828b Base (15 cm) 0.263 0.034b 222.431 15.046b Sub-populations Mean SD Mean SD aus 0.263 0.030b 215.987 16.075ab indica 0.291 0.019c 206.582 15.833a aromatic 0.228 0.030a 219.604 15.421b temperate japonica 0.261 0.028b 216.654 12.964ab tropical japonica 0.213 0.019a 227.814 14.723c

Figure 2.4: Distribution of mean root hair length by sampling positions (5, 10 and 15 cm from the root apex).

______temperate japonica tropical japonica aromatic indica aus

Figure 2.5: Distribution of mean root hair density by sampling positions (5, 10 and 15 cm from the root apex).

______temperate japonica tropical japonica aromatic indica aus

5cm 10cm 15cm [base]

Figure 2.7: Distribution of mean aerenchyma area by sampling positions (5, 10 and 15 cm from the root apex and at 1 cm from the root base)

______temperate japonica tropical japonica aromatic indica aus

Table 2.4: Analysis of variance table for the effects of axial position, genotype, and sub- population on aerenchyma area and (in the lower part of the table) means and standard deviations (SD) for aerenchyma measured at different axial positions (5, 10 and 15 cm from the root apex and at 1 cm from the root base) and in different subpopulations. Different letters indicate significant differences among axial positions and sub- populations by the least significant difference (LSD) test (P<0.05).

d.f. Aerenchyma Area Percent Aerenchyma (mm2) F P F P Axial Position 3 57.206 <0.001 189.859 <0.001 Genotype 14 6.916 <0.001 0.932 0.526 Sub-population 4 15.323 <0.001 1.441 0.222 Axial Position*Genotype 42 2.229 <0.001 3.149 <0.001 AxialPosition*Sub-population 12 2.108 0.019 2.962 0.001 Axial Positions Mean SD Mean SD 5 cm 0.070 0.062a 8.581 6.080a 10 cm 0.253 0.109b 30.884 5.650b 15 cm 0.349 0.120c 44.872 6.217c [base] 0.213 0.108b 29.723 10.237b Sub-populations Mean SD Mean SD aus 0.195 0.099ab 27.171 13.807 indica 0.144 0.084a 25.715 15.082 aromatic 0.337 0.171c 33.227 14.561 temperate japonica 0.159 0.095a 26.994 15.400 tropical japonica 0.272 0.151bc 29.463 15.085

Table 2.5: Pearson correlation coefficients among all traits measured under high phosphorus (HP). (PH = plant height; SPC = shoot phosphorus content; SDW = shoot dry weight; RHL = root hair length; RHD = root hair density; SLR = small lateral root length; LLR = large lateral root length; RXSA = root cross-section area; TCA = total cortical area; TSA = total stele area; AA = aerenchyma area; %AA = percentage of aerenchyma; MXV = number of meta-xylem vessels; MXA = median meta-xylem vessel area)

PH -0.406** SPC 0.319* 0.257ns SDW 0.369* 0.430** 0.619** RHL 0.426** -0.318* -0.118ns -0.117ns RHD -0.406** 0.389** 0.069ns 0.126ns -0.471** SLR 0.219ns 0.350* 0.408** 0.591** 0.100ns 0.095ns LLR 0.209ns 0.417** 0.357* 0.567** 0.054ns 0.111ns 0.804** RXSA -0.329* 0.376* 0.050ns 0.141ns -0.522** 0.283ns 0.024ns 0.228ns TCA -0.332* 0.360* 0.034ns 0.129ns -0.523** 0.289ns 0.004ns 0.217ns 0.999** TSA -0.292ns 0.434** 0.130ns 0.213ns -0.533** 0.231ns 0.064ns 0.194ns 0.900** 0.886** AA -0.262ns 0.313* 0.127ns 0.193ns -0.653** 0.356* 0.012ns 0.214ns 0.858** 0.856** 0.813** %AA 0.085ns -0.073ns 0.178ns 0.140ns -0.398** 0.181ns -0.033ns 0.004ns -0.001ns -0.005ns 0.105ns 0.501** MXV -0.504** 0.680** 0.162ns 0.347* -0.506** 0.402** 0.222ns 0.241ns 0.368* 0.354* 0.480** 0.341* 0.058ns MXA -0.122ns 0.256ns 0.179ns 0.015ns -0.326* 0.227ns 0.061ns 0.191ns 0.670** 0.668** 0.606** 0.530** -0.086ns 0.323* TN PH SPC SDW RHL RHD SLR LLR RXSA TCA TSA AA %AA MXV

*, significant at p-levels of 0.05. **, significant at p-levels of 0.01. ns, not significant.

Table 2.6: Pearson correlation coefficients among all traits measured under low phosphorus (LP). (PH = plant height; SPC = shoot phosphorus content; SDW = shoot dry weight; RHL = root hair length; RHD = root hair density; SLR = small lateral root length; LLR = large lateral root length; RXSA = root cross-section area; TCA = total cortical area; TSA = total stele area; AA = aerenchyma area; %AA = percentage of aerenchyma; MXV = number of meta-xylem vessels; MXA = median meta-xylem vessel area)

PH -0.381** SPC 0.482** 0.065ns SDW 0.583** 0.272ns 0.749** RHL 0.251ns -0.299* 0.024ns -0.110ns RHD -0.288ns 0.333* 0.051ns -0.049ns -0.263ns SLR 0.354* 0.124ns 0.505** 0.461** 0.092ns 0.049ns LLR 0.283ns 0.213ns 0.461** 0.418** 0.058ns 0.202ns 0.829** RXSA -0.139ns 0.437** -0.028ns 0.042ns -0.533** 0.238ns 0.141ns 0.211ns TCA -0.125ns 0.404** -0.025ns 0.041ns -0.525** 0.214ns 0.148ns 0.198ns 0.996** TSA -0.270ns 0.543** -0.020ns 0.077ns -0.536** 0.375* 0.049ns 0.237ns 0.812** 0.768** AA -0.134ns 0.347* 0.063ns 0.129ns -0.552** 0.254ns 0.071ns 0.194ns 0.845** 0.839** 0.737** %AA -0.046ns -0.015ns 0.158ns 0.205ns -0.260ns 0.155ns -0.056ns 0.070ns 0.054ns 0.042ns 0.200ns 0.566** MXV -0.287ns 0.621** 0.256ns 0.355* -0.383** 0.343* 0.334* 0.289ns 0.404** 0.381** 0.517** 0.350* 0.124ns MXA -0.239ns 0.513** -0.179ns -0.098ns -0.432** 0.414** -0.100ns 0.010ns 0.626** 0.592** 0.732** 0.536** 0.069ns 0.372* TN PH SPC SDW RHL RHD SLR LLR RXSA TCA TSA AA %AA MXV

*, significant at p-levels of 0.05. **, significant at p-levels of 0.01. ns, not significant.

d.f. Shoot Biomass Number of Tillers Plant Height Shoot P Content Root Dry Weight (g plant-1) (plant-1) (cm) (mg plant-1) (g plant-1) F P F P F P F P F P Genotype (G) 14 3.807 <0.001 5.285 <0.001 25.907 <0.001 0.848 0.616 5.684 <0.001 Sub-population (S) 4 7.389 <0.001 6.760 <0.001 17.692 <0.001 1.675 0.163 8.867 <0.001 Treatment (P) 1 82.269 <0.001 33.657 <0.001 9.979 0.002 142.801 <0.001 1.874 0.175 G:P 14 4.808 <0.001 7.660 <0.001 0.910 0.553 2.198 0.018 1.630 0.097 P:S 4 0.427 0.789 2.841 0.029 0.109 0.979 1.659 0.168 0.585 0.674 Treatments Mean SD Mean SD Mean SD Mean SD Mean SD HP 6.21 1.56 5.69 2.42 100.44 13.51 14.45 5.04 1.581 0.732 LP 3.61 1.13 3.37 1.13 91.47 13.45 4.59 2.29 1.343 0.909 Mean 4.91 1.88 4.53 2.21 95.96 14.14 9.52 6.30 1.462 0.829 d.f Root Hair Length Root Hair Density Small Lateral Root Large Lateral Root:Shoot Ratio (mm) (hairs mm-2) Length (cm) Root Length (cm) Gentoype (G) 14 7.202 <0.001 1.849 0.047 4.491 <0.001 2.596 0.004 2.272 0.012 Sub-population (S) 4 15.980 <0.001 5.416 0.001 7.560 <0.001 4.047 0.005 1.157 0.336 Treatment (P) 1 26.228 <0.001 47.354 <0.001 37.796 <0.001 67.354 <0.001 14.060 <0.001 G:P 14 0.341 0.985 0.542 0.898 2.655 0.004 1.291 0.240 1.293 0.239 P:S 4 0.239 0.916 1.049 0.387 1.042 0.391 0.879 0.480 0.393 0.813 Treatments Mean SD Mean SD Mean SD Mean SD Mean SD HP 0.214 0.034 213.958 19.285 94.098 38.287 236.289 59.539 0.251 0.081 LP 0.251 0. 035 238.051 13.406 48.537 31.709 140.640 50.670 0.355 0.168 Mean 0.232 0.039 226.005 20.481 71.318 41.792 188.465 73.039 0.303 0.141

Table 2.8: Analysis of variance (ANOVA) table showing F and P values for the effects of genotype, sub-population, and phosphorus treatment on proportion of small to total lateral root length in 15 O. sativa accessions, and (in the lower part of the table) means and standard deviation (SD) values for proportion of small to total lateral root length under high (100 µM; HP) and low phosphorus (2 µM; LP) levels.

d.f. Small:Total Lateral Root Length Ratio

F P Genotype (G) 14 4.801 <0.001 Sub-population (S) 4 5.726 <0.001 Treatment (P) 1 6.806 0.011 G:P 14 2.682 0.004 P:S 4 0.427 0.789 Treatments Mean SD HP 0.289 0.060 LP 0.255 0.067 Mean 0.272 0.066

Table 2.9: Analysis of variance (ANOVA) table showing F and P values for the effects of genotype, sub-population, and phosphorus treatment on root cross-section area (RXSA), total stele area (TSA) and total root cortical area (TCA) in 15 O. sativa accessions, and (in the lower part of the table) means and standard deviation (SD) values for root traits evaluated under high (100 µM; HP) and low phosphorus (2 µM; LP) levels.

RXSA TSA TCA d.f. 2 2 2 TCA:RXSA Ratio (mm ) (mm ) (mm ) F P F P F P F P Genotype (G) 14 44.001 <0.001 27.366 <0.001 34.778 <0.001 2.695 0.003 Sub-population (S) 4 25.127 <0.001 24.154 <0.001 24.125 <0.001 7.641 <0.001 Treatment (P) 1 5.564 0.021 1.886 0.173 6.996 0.010 21.756 <0.001 G:P 14 1.954 0.038 3.727 <0.001 2.041 0.029 1.429 0.168 P:S 4 0.291 0.883 0.860 0.492 0.323 0.862 1.400 0.242 Treatments Mean SD Mean SD Mean SD Mean SD HP 1.035 0.348 0.066 0.024 0.962 0.327 0.929 0.017 LP 0.876 0.288 0.074 0.028 0.796 0.266 0.908 0.025 Mean 0.955 0.327 0.070 0.026 0.879 0.308 0.919 0.024

Table 2.10: Analysis of variance (ANOVA) table showing F and P values for the effects of genotype, sub-population, and phosphorus treatment on aerenchyma area (AA), percentage of aerenchyma (%AA), number of meta-xylem vessels (MXV), and median meta- xylem vessel area (MXA) in 15 O. sativa accessions, and (in the lower part of the table) means and standard deviation (SD) values for root traits evaluated under high (100 µM; HP) and low phosphorus (2 µM; LP) levels.

AA %AA MXA MXV d.f 2 2 (mm ) (percent) (mm ) (counts) F P F P F P F P Gentoype (G) 14 30.346 <0.001 4.225 <0.001 16.118 <0.001 50.071 <0.001 Sub-population (S) 4 24.930 <0.001 2.327 0.063 7.613 <0.001 41.569 <0.001 Treatment (P) 1 0.241 0.625 26.059 <0.001 0.010 0.920 0.256 0.614 G:P 14 0.712 0.754 1.559 0.118 2.197 0.018 0.605 0.850 P:S 4 0.232 0.920 0.396 0.811 0.611 0.656 0.116 0.977 Treatments Mean SD Mean SD Mean SD Mean SD HP 0.306 0.118 31.837 6.156 0.00153 0.000555 5.49 1.31 LP 0.319 0.132 39.991 8.769 0.00155 0.000522 5.36 1.19 Mean 0.313 0.125 35.914 8.577 0.00154 0.000535 5.42 1.25

Figure 2.8: Effect of phosphorus treatment on shoot dry weight. Values shown are means of 3 replicates ± SD.