intraspecific variation in the populus balsamifera drought response: a systems … ·...

Intraspecific variation in the Populus balsamifera drought response: A systems biology approach

by

Erin T. Hamanishi

A thesis submitted in conformity with the requirementsfor the degree of Doctor of Philosophy

Faculty of ForestryUniversity of Toronto

© Copyright by Erin T. Hamanishi 2013

ii

Intraspecific variation in the Populus balsamifera drought

response: A systems biology approach

Erin T. Hamanishi

Doctor of Philosophy

Faculty of Forestry

University of Toronto

2013

Abstract

As drought can impinge significantly on forest health and productivity, the mechanisms by which

forest trees respond to drought is of interest. The research presented herein examined the intra-

specific variation in the Populus balsamifera drought response, examining the potential role of the

transcriptome to configure growth, metabolism and development in response to water deficit.

Amassing evidence indicates that different species of Populus have divergent mechanisms and Three

lines of inquiry were pursued to investigate the intraspecific variation the drought response in P.

balsamifera.

First, the transcriptome responses of six genotypes of P. balsamifera were examined using

Affymetrix Poplar GeneChips under well-watered and water-deficit conditions. A core species-

level transcriptome response was identified. Significantly, intraspecific variation in the drought

transcriptome was also identified. The data support a role for genotype-derived variation in the

magnitude of P. balsamifera transcriptome remodelling playing a role in conditioning drought

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responsiveness.

Second, the impact of drought-stress induced declines in stomatal conductance, as well as an

alteration in stomatal development in two genotypes was examined. Patterns of transcript

abundance of genes hypothesised to underpin stomatal development had patterns congruent with

their role in modulation of stomatal development. These results suggest that stomatal development

may play a role as a long-term mechanism to limit water loss from P. balsamifera leaves under

conditions of drought-stress.

Finally, the drought-induced metabolome of six P. balsmaifera genotypes was interrogated.

Metabolite profiling reveled amino acids such as isoleucine and proline and sugars such as galactinol

and raffinose were found with increased abundance, whereas TCA intermediates succinic and malic

acid were found with decreased abundance in response to drought. Comparative analysis of the

metabolome and the transcriptome revealed genotypic-specific variation in energy and carbohydrate

metabolism.

Taken together, the findings reported in this thesis form a foundation to understand the basis

of intraspecific variation in the drought response in P. balsamifera. Transcripts and metabolites

that contribute to within-species differences in drought tolerance were defined. These molecular

components are useful targets for both future study, as well as efforts aimed at protecting and

growing trees of this important species under challenging environmental conditions.

iv

Acknowledgments

First and foremost, I would like to extend my heartfelt gratitude to my supervisor, Dr. Malcolm

Campbell, for accepting me as his graduate student all those many years ago. I am forever grateful

for his support and guidance throughout this academic journey. Whether it was in the lab, or

walking through field trials in Oregon, or basking in the beauty of network plots, Malcolm’s

enthusiasm has been of a great inspiration to me. I would also like to thank my supervisory

committee, Dr. Nick Provart and Dr. Sean Thomas, for all their support and excellent academic

advice over the years.

The research presented in this thesis would not have been possible without help from so many

people. Over the years I have grown hundreds of poplar trees, and I am so thankful for the

technical support of Bruce Hall, John McCarron and Andrew Petrie. I am also thankful to

Matthew Hoskins for spending many hours counting epidermal and stomatal cells with me. I have

benefited from collaborating with many colleagues, including Genoa Barchet, Dr. Shawn Mansfield,

Dr. Aine Plant and Dr. Barb Thomas. I thank them for their advice, support and collaborations in

my scientific endeavors.

I would like to express my appreciation for my fellow graduate students, many of which have been

important sources of inspiration and support. Most notably, I would like to thank Liz Nelson

and Agnieszka Sztaba for their unconditional friendship and support, both academically and non-

academically. My fellow Campbell lab-mates have always been willing to provide feedback and have

been a source of excellent support and friendship over the years. To Katharina Bräeutigam, Thomas

Canam, Michael Prouse, Sherosha Raj, Julia Romano, Joseph Skaf, Michael Stokes, Heather

Wheeler and Olivia Wilkins, I extend my sincere gratitude. I am particularly thankful to Joan

Ouellette for always ensuring everything ran smoothly in the lab, and for always lending a helping

hand when needed.

Lastly, I am indebted to my family, for everything. I am grateful to my sister, Sarah, for always

being there when I needed her and for our countless Skype study dates. Finally, I would like to

thank my parents, Joan and Russel, who have provided never-ending support and encouragement

over the years, without which I would likely not be where I am today. In addition, I must also

thank them for undoubtedly instilling in me a keen sense of curiosity and love for all things

“science.”

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Table of Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xviiList of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xixChapter 1: Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Research Hypotheses and Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Chapter 2: Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1 Responses of forest trees to drought . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Modulation of stomatal development in response to environmental change . . . . 7

2.3 Plant perception of water status and downstream signalling pathways . . . . . . . . 8

2.4 Molecular outputs in response to water-deficit signalling . . . . . . . . . . . . . . . . 10

2.5 Early identification of drought-responsive genes in forest trees . . . . . . . . . . . . 12

2.6 Genome-wide dissection of forest tree drought responses—whole transcriptome

analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.7 From drought transcriptome to drought proteome . . . . . . . . . . . . . . . . . . . . . 17

2.8 The metabolic drought response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.9 Recent advances in genome analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.10 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.11 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Chapter 3: Intraspecific variation in the Populus balsamifera drought transcriptome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.3.1 Plant Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.3.2 Physiological and growth traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.3.3 RNA extraction, microarray hybridisation and analysis . . . . . . . . . . . . . . . . 28

3.3.4 Single-feature polymorphism (SFP) analysis . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.3.5 DNA extraction and simple-sequence repeat (SSR) analysis . . . . . . . . . . . . . 29

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3.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.4.1 There is intraspecific variation in the productivity and physiological responses in

Populus balsamifera following water deficit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.4.2 Water deficit conditions elicit significant responses within the P. balsamifera

transcriptome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.4.3 There is a common P. balsamifera drought transcriptome . . . . . . . . . . . . . . . 38

3.4.4 There is a notable significant variation in the drought transcriptome across P.

balsamifera genotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.4.5 Time of day shapes the P. balsamifera drought transcriptome . . . . . . . . . . . . . 44

3.4.6 The extent of transcriptome-wide transcript abundance change enables the P.

balsamifera to sustain growth under water-deficit conditions . . . . . . . . . . . . . . . . . . 44

3.4.7 The extent of differences in drought-responsive transcriptomes between P.

balsamifera clones positively correlated with the extent of intraspecific DNA sequence

variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49


3.7 Supplementary Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.8 Supplementary Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Chapter 4: Drought induces alterations in the stomatal development program in Populus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.3 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.3.1 Plant material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.3.2 Physiological measurements and stomatal quantification . . . . . . . . . . . . . . . 68

4.3.3 Gene selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.3.4 Targeted transcript abundance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.3.5 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.4.1 Stomatal conductance (gs) and relative water content (RWC) in response to water-

deficit stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

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4.4.2 Stomatal quantification following water-deficit stress . . . . . . . . . . . . . . . . . . 72

4.4.3 Populus homologues of genes implicated in stomatal development . . . . . . . . . 72

4.4.4 Developmental variation in transcript abundance of stomatal development genes

after water deficit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

4.4.5 Genes acting as positive regulators in stomatal development have correlated

transcript profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.5.1 Drought response varied between Populus balsamifera genotypes over time . . . 81

4.5.2 Transcript abundance of the Populus homologues of key stomatal development

regulatory genes varied through leaf development in a manner consistent with their

proposed molecular functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.5.3 Elevated Populus ERECTA (ER) transcript abundance early in development

corresponded with decreased stomatal indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.5.4 STOMATAL DENSITY AND DISTRIBUTION 1 (SDD1) and genotype-

specific control of stomatal development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.5.5 Stomatal development and the regulation of Populus STOMAGEN and FAMA

transcript abundance in response to water deficit . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87


4.8 Supplementary Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.9 Supplementary Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Chapter 5: Integrated analysis of the drought metabolome and transcriptome in Populus balsamifera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

5.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.3 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.3.1 Plant material and experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.3.2 Non-targeted metabolic profiling by gas chromatography/mass spectrometry . 106

5.3.3 Metabolome: data processing and statistics . . . . . . . . . . . . . . . . . . . . . . . . . 107

5.3.4 RNA isolation and transcriptome analysis . . . . . . . . . . . . . . . . . . . . . . . . . 107

5.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

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5.4.1 Populus balsamifera genotypes were subjected to water withdrawal to induce a

drought response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.4.2 Variation in Populus balsamifera metabolite profiles was evident . . . . . . . . 109

5.4.3 A Populus balsamifera drought metabolome was identifiable . . . . . . . . . . . 114

5.4.4 The drought metabolome varied among P. balsamifera genotypes . . . . . . . . . 121

5.4.5 There were correlations between drought-responsive metabolites and specific

components of transcriptome remodelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

5.4.6 Energy metabolism and secondary metabolite biosynthesis varied in a genotypic-

dependant manner in response to drought . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

5.4.7 Network analysis illuminated the nature of genotype-specific responses to drought

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

5.4.8 AP-1006 had a genotype-specific transcriptome response to drought . . . . . . . 137

5.4.9 There were strong correlates between specific transcript-metabolite pairs in response

to drought in AP-1006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Chapter 6: Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . 1421.1 Major Findings and Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

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Figure 3.1 Source of origin of the six P. balsamifera genotypes examined in this study . . . . . . . . 27

Figure 3.2 Above ground biomass (a), plant height (b) and stem circumference (c) of six genotypes

of P. balsamifera were calculated 15 d after the onset of the water-withdrawal experiment for both

well watered (blue bars) and water deficit treated (orange bars) plants. Significant differences

between genotypes and treatments (P < 0.05) are denoted by small letters for all variables. Mean

values and SE bars are represented. Figure originally published in black and white. . . . . . . . . . . 31

Figure 3.3 Box plot of the variation in midday leaf stomatal conductance for six P. balsamifera

genotypes: (a) AP-947 (b) AP-1005 (c) AP-1006 (d) AP-2278 (e) AP-2298, and (f ) AP-2300.

Midday stomatal conductance for well watered plants (blue boxes) and plants grown under water-

deficit conditions (orange boxes) are represented. Asterisks indicate significant difference between

well-watered and water-deficit-treated plants: *P < 0.1; **P < 0.05; ***P < 0.001. WD, Water-deficit

treatment.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Figure 3.4 Heat maps representing transcript abundance of all drought responsive probe sets in

six P. balsamifera genotypes: AP-947, AP-1005, AP-1006, AP-2278, AP-2298 and AP-2300. Only

probe sets that are significant for treatment main effect, irrespective of time of day or genotype,

and are differentially expressed relative to a given threshold are represented (n = 280; FDR = 0.05,

log2(fold-change)-cutoff = 2.0) for both time points: (a, b) mid day, and (c, d) pre-dawn. Row

normalized, transcript abundance for all drought responsive probe sets at (a) mid day and (c) pre-

dawn. Each column represents a biological sample, and all treatments are represented in triplicate

replicates. Red indicates increased transcript abundance; blue indicates decreased transcript

abundance. Data are row normalized. Heat maps representing mean relative fold-change transcript

abundance for all genotypes at (b) mid day and (d) pre-dawn. Dark blue indicates increased

mean transcript abundance in water-deficit treated samples relative to well-watered samples; white

indicates decreased mean transcript abundance in water-deficit treated samples relative to well-

watered samples. Rows are clustered using Pearson correlation for all heat maps. . . . . . . . . . . . 37

Figure 3.5 Pearson correlation coefficient (PCC) heat map representing the P. balsamifera drought

List of Figures

x

transcriptome responses. Differential transcript abundance between well watered and water-deficit

samples for the six genotypes for the drought responsive probe sets are represented (Treatment

main effect; FDR = 0.05, log2(fold-change) cutoff = 2.0, n = 280 probe sets). Differential transcript

abundance was calculated as the mean log2(fold-change) between well watered and water-deficit

samples for a given probe-set. The PCC was determined for each pair-wise comparison, and is

represented by the colour in the corresponding cell. All samples are represented on both the x- and

y-axis, in the same order. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 3.6 Box plot illustrating the interplay of genotype and treatment in shaping the drought

transcriptome of six P. balsamifera genotypes. The average log2(fold-change) between well watered

and water-deficit treated samples for all genes identified as significantly differentially expressed

for treatment main effect (FDR = 0.05, log2(fold-change)-cutoff = 2.0, n = 280 probe sets) for

probe sets with (a) decreased transcript abundance in response to WD and (b) increased transcript

abundance in response to WD at the midday time point. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Figure 3.7 The relationship between the magnitude change in gene expression and the difference

in plant height between well watered and water-deficit treated P. balsamifera trees. Linear regression

analysis revealed a significant relationship between these two variables (P = 0.02033). The

coefficient of determination (R2) is shown in the figure panel. . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Supplementary Figure S3.1 (a) Correlation between historic climatic variables and observed

phenotypic traits for the six. balsamifera genotypes. (b) Correlation between absolute magnitude

change in gene expression of probe sets identified as significant for treatment main effect in response

to WD conditions and historic climatic variables, phenotypic traits and physiological response. 52

Supplementary Figure S3.2 Bar graphs representing the functional categories represented by genes

that are differentially expressed for treatment main effect (n = 280, FDR = 0.05, log2(fold-change)

cutoff = 2.0) for (a) increased transcript abundance, and (b) decreased transcript abundance in

response to water-limitation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53


that are significantly differentially expressed between WD and WW conditions. The proportion of

probe sets identified classified for each GO biological process functional category is represented as

the percentage of total genes differentially expressed for treatment main effect increased transcript

xi

abundance and decreased transcript abundance; FDR = 0.05, log2(fold-change) cutoff = 2.0, n =

280), and each individual genotype when analysed individually as a 2 x 2 factorial (FDR = 0.05). 54

Supplementary Figure S3.4 Quantitative reverse transcription PCR validation of transcript

abundance levels of selected genes from microarray data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Figure 4.1 The stomatal development signalling network, based on current literature. Arrows

represent positive regulation; whereas, blocked lines represent negative regulation. Question marks

represent unknown interactions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Figure 4.2 Variation in the physiological response to drought stress in genotype AP-1005 and AP-

1006. Box plot of the variation in midday stomatal conductance for (a) AP-1005 and (b) AP-1006

for well-watered (blue boxes) and water-deficit-treated (orange boxes) samples. Response of intrinsic

water use efficiency (WUEi; A/gs) across well-watered and water-deficit-treated samples for (c) AP-

1005 and (d) AP-1006 and photosynthesis (A) for (e) AP-1005 and (f ) AP-1006 at days 0, 5, and

15 after the onset of water withdrawal. Error bars represent the standard error of the mean. . . . 71

Figure 4.3 Variation in leaf epidermis between genotype AP-1005 and AP-1006 under (a, b)

well-watered and (c, d) water-deficit conditions, on day 30. White scale bar=50 µm. (e, f ) Box

plot of variation in stomatal index for well-watered (blue box) and water-deficit-treated (orange

box) samples for leaves that were fully developed prior to the onset of the drought experiment (leaf

A) and for those that developed during the drought experiment (leaf B). A significant reduction

in stomatal index is observed in leaves that developed during the experimental period (leaf B) for

each genotype (e) AP-1005 and (f ) AP-1006; however, no significant variation in stomatal index

is observed for leaves that developed prior to the experiment (leaf A), and the onset of water-

deficit conditions. The midline of the box represents the median value for stomatal index (e-f ) or

stomatal density (g-h), the upper and lower bounds of the box represent the interquartile range,

and the whiskers extend to the most extreme values that are not outliers. No signficant change in

stomatal density in response to water-deficit conditions for genotype (g) AP-1005 and (h) AP-1006.

Asterisks represent significant variation between well-watered and water-deficit-treated plants. *P

<0.05 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 4.4 Heat map of transcript abundance across a range of tissues for Populus homologues of

genes implicated in stomatal differentiation and patterning. Transcript accumulation for the 14

xii

Populus homologues that had differential transcript abundance across the dataset, was derived from

the PopGenExpress microarray compendium made available via http://bar.utoronto.ca (Wilkins

et al., 2009a). As per the scale provided, elevated transcript abundance is represented by red and

diminished transcript abundance is represented by green. The highest levels of transcript abundance

for this group of genes are in young leaves in contrast to other tissue types. Each column represents

a discrete biological sample, and data are represented as biological triplicate replicates for each tissue

type. Data are row normalized. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Figure 4.5 Variation in transcript abundance between well-watered and water-deficit-treated trees

at six time points (days 5, 10, 15, 20, 25, and 30) for genotype AP-1005 (yellow) and AP-1006

(green) represented by the log2(fold change transcript abundance) for genes involved in stomatal

development. A positive log2(fold change transcript abundance) value is an indicator of higher

transcript abundance in water-deficit-treated samples, whereas a negative log2(fold change transcript

abundance) value is an indicator of lower transcript abundance in water-deficit-treated samples. 76

Figure 4.6 Pearson correlation coefficient (PCC) heat map representing the transcript abundance

profiles across AP-1005 and AP-1006 and six time-points. The PCC was determined for each pair-

wise comparison (gene–gene), and is represented by the colour in the corresponding cell. All genes

are represented in the same order on the x- and y-axes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Supplementary Figure S4.1 Experimental design to test the change in transcript abundance

through time and across a developmental series in Populus balsamifera. The first fully expanded

leaf (red circle) and first expanding leaf (red arrow) was marked at the onset of the water-deficit

experiment (day 0), these leaves were subsequently followed throughout the experimental period

(30 days). This enabled collection of leaf tissue that developed throughout the water-deficit

experimental at day 5, 10, 15, 20, 25 and 30. The first fully expanded leaf at day 0 represented a

leaf that was fully developed prior to the onset of water-deficit treatment. . . . . . . . . . . . . . . . . . 89

Supplementary Figure S4.2 Variation in the relative transcript accumulation of aPopulus TMM

homologue as determined by qRT-PCR. Transcript abundance calculated relative to ACT-7

transcript abundance levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Supplementary Figure S4.3 Variation in the relative transcript accumulation of a Populus YODA


xiii


Supplementary Figure S4.4 Variation in the relative transcript accumulation of aPopulus ERECTA



Supplementary Figure S4.5 Variation in the relative transcript accumulation of aPopulus

STOMATAL DENSITY AND DISTRIBUTION-1 homologue as determined by qRT-PCR.

Transcript abundance calculated relative to ACT-7 transcript abundance levels. . . . . . . . . . . . . . 93

Supplementary Figure S4.6 Variation in the relative transcript accumulation of aPopulus FAMA


transcript abundance levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94


STOMAGEN homologue as determined by qRT-PCR. Transcript abundance calculated relative to

ACT-7 transcript abundance levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Supplementary Figure S4.8 Pearson correlation coefficient (PCC) heat map representing the

transcript accumulation profiles and stomatal indices in P. balsamifera at (a) 5 d, (b) 10 d, and (c)

15 d after the imposition of water-deficit stress. The Pearson correlation coefficient (r; top) and

P-value (bottom) are indicated within each square. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Figure 5.1 Box-plot representing net photosynthetic rate (µmol CO2 m-2 s-1) for genotype AP-947,

AP-1005, AP-1006, AP-2278, AP-2298 and AP-2300. Well-watered samples represented by blue

boxes; water-deficit-treated samples represented by orange boxes (n=3 per treatment per genotype).

The midline of the box represents the median value for photosynthesis, the upper and lower bounds

of the box represent the interquartile range, and the whiskers extend to the most extreme values that

are not outliers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Figure 5.2 Dendrogram obtained after hierarchal clustering analysis (HCA) of the metabolic

profiles of the six P. balsamifera genotypes under well-watered and water-deficit conditions at mid-

day and pre-dawn time point. Rows represent specific metabolites (n=87). Columns represent

mean intensity of all replicates for each genotype, treatment and time of day sample. Plotted values

are the mean of n = 4-10 replicates for each sample. Metabolite classes: AA = Amino Acid; C =

xiv

Carbohydrate; P = Phenolic, SA = Sugar Alcohol. NI = Not Identified. . . . . . . . . . . . . . . . . . . 112

Figure 5.3 HCA reveals 13 significant clusters (P < 0.05). Significant clusters are labeled with

unique colours and numbered (I through XIII) for identification. Hierarchical clustering was done

using pvclust (Suzuki & Shimodaira 2006), with a correlation distance measure and a complete

agglomerative clustering method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Figure 5.5 Metabolite accumulation levels for treatment main effect and treatment x genotype

interaction. (a) Hierarchally clustered metabolites that are significant for treatment main effect

across all genotypes at two different time-points [pre-dawn (PD) and mid-day (MD)]. (b) Venn

diagram demonstrating the number of metabolites that are significant for treatment main effect or

a 2-way interaction. (c) Mean log2(fold-change) of metabolite abundance for metabolites that are

significant for treatment main effect only. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Figure 5.6 Variation in the drought metabolome among six genotypes of P. balsamifera represented

by a Pearson correlation coefficient (PCC) heatmap. Differential abundance [log2(fold-change)] . .

for metabolites significant for treatment main effect (ANOVA, P < 0.05) are represented. The PCC

value was calculated for each pair-wise comparison among genotypes, and is represented by the

colour in the given cell. All genotypes are represented on both the x- and y-axis in the same order.

123

Figure 5.7 Box-plot illustrating the interplay of genotype and treatment in shaping the drought

metabolome and transcriptome of six P. balsamifera genotypes. The average absolute log2(fold-

change) between well-watered and water-deficit-treated samples for all (a) metabolites (n=40; P <

0.05) and (b) transcripts (n = 2636; P < 0.05) with significant variation in their abundance between

treatment conditions at the mid-day (MD) time point. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Figure 5.8 Heatmap of drought responsive transcript and metabolite correlations. Of all the

drought responsive transcripts, 747 unique transcripts were correlated with at least one metabolite

(|r| > 0.6; P<0.05). The rows in the heatmap represent metabolites, and the columns represent

transcripts. The columns are clustered based on their expression across samples, and the metabolites

are grouped according to functional categories. Pearson correlation coefficient (r) are represented

for each pair-wise M:T comparison, and were calculated using R. . . . . . . . . . . . . . . . . . . . . . . 127

xv

Figure 5.9 A heatmap of representative functional classes (transcripts) from the correlation data.

The averaged Spearman correlation value is represented for significant functional class: metabolite

comparisons (coloured squares). Red indicates positive correlation, whereas blue indicates negative

correlation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Figure 5.10 Pathway analysis related to the galactose metabolism. (a) Pathway map displays

selected steps from galactose metabolism pathway. Colours indicate fold-change in transcript

or metabolite abundance between water-deficit and well-watered treated samples for all six

genotypes; red indicates higher abundance in water-deficit-treated samples and blue indicates lower

abundance in water-deficit-treated samples. Enzymes are given as EC numbers. EC 2.4.1.123,

galactinol synthase; EC:2.4.1.82, raffinose synthase; EC:2.4.1.67, stachyose synthase. (b) Heatmap

representing Spearman correlation values among transcripts related to galactose metabolism and

raffinose or galactinol. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Figure 5.11 Pathway analysis related to the citric cycle (TCA cycle). (a) Correlation among select

transcripts and metabolites from the KEGG pathway pop00020 ‘Citrate cycle (TCA cycle)’ for

genotype AP-1006. Colors represent Pearson correlation value. Red indicates positive correlation

and blue represents negative correlation values. (b) Map displays selected steps from citrate cycle

pathway. Colours indicate fold-change in transcript or metabolite abundance between water-

deficit and well-watered treated samples for genotype AP-1006; red indicates higher abundance in

water-deficit-treated samples and blue indicates lower abundance in water-deficit-treated samples.

Enzymes are given as EC numbers. EC 1.1.1.37, malate dehydrogenase; EC:1.1.1.41, isocitrate

dehydrogenase (NAD+); EC:1.3.5.1, succinate dehydrogenase; EC:2.3.3.1, citrate synthase;

EC:5.2.1.2, fumarate hydratase, EC: 5.2.1.3, aconitate hydratase, EC: 6.2.1.5, succinate-CoA ligase,

beta subunit. Pearson correlation and pathway maps for other genotypes can be found in Appendix

Figure A.4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Figure 5.12 Transcript correlation networks obtained from WGCNA for (a) AP-947, (b) AP-1005,

(c) AP-1006, (d) AP-2278, (e) AP-2298 and (f ) AP-2300. The top 1000 interactions for each

genotype are represented. Nodes in the graphs represent individual transcripts that connect via

edges to other transcripts. Each node is colored according to the modules defined in Table 5.3. 136

Figure 5.13 Overrepresentation of GO terms associated with transcripts that have (a) decreased

xvi

transcript abundance in AP-1006 and (b) increased transcript abundance in AP-1006. Figures

generated using AgriGO (http://bioinfo.cau.edu.cn/agriGO). Significant overrepresentation is

represented by darker coloured boxes (P < 0.05). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

xvii

Table 3.1 Location and climate variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Table 3.2 Total number of single-feature polymorphisms (SFPs) were identified in all probe sets

on the Affymetrix Poplar GeneChip using SNEP (P < 0.05; Fujisawa et al. 2009). Genes that were

identified as significantly differentially expressed (FDR = 0.05; log2(FC) cutoff = 2.0) and genes

whose expression is not significantly different among genotypes were surveyed for SFPs and the

proportion was calculated based on the total number of probe sets examined, respectively. . . . . 48

Supplementary Table S3.1 The microsatellite loci used to fingerprint the six P.

balsamifera genotypes in this study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Supplementary Table S3.2 Relative Water Content (RWC) calculated for each of the six P.

balsamifera genotypes after 15 days of water-deficit treatment. . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Supplementary Table S3.3 Probe sets with significant main effects or interactions for (a) all

genotypes (FDR = 0.05, log2(fold-change) cutoff = 2.0) (b) all genotypes (FDR = 0.05, no

minimum threshold) and (c) all pair-wise genotype comparisons (FDR = 0.05). . . . . . . . . . . . . 59

Table 4.1 Mean cumulative transcript abundance across all time-points for genotype AP-1005 and

AP-1006 in well-watered and water-deficit-treated samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Table 4.2 Mean plant height (in cm) on day 30 ±standard error of the mean, n ≥6 . . . . . . . . . . 84

Supplementary Table S4.1 Primers used for qRT-PCR analysis.. . . . . . . . . . . . . . . . . . . . . . . . 98

Supplementary Table S4.2 Relative water content (RWC) on day 30. . . . . . . . . . . . . . . . . . . . 99

Supplementary Table S4.3 ANOVA results: transcript abundance. . . . . . . . . . . . . . . . . . . . . 100

Table 5.1 Number of metabolites with significant main effects or interactions (n=87 metabolites).

Padj-value cutoff = 0.05 (Benjamini-Hochberg). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Table 5.2 Metabolites with significantly different abundance levels in response to drought

List of Tables

xviii

(ANOVA, Padj-value < 0.05). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Table 5.3 Module membership in the drought transcriptome network of AP-1006 and preservation

of drought modules among the other genotypes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Table 5.4 Module-treatment or -time of day relationships of the P. balsamifera (AP-1006) drought

transcriptome. Columns 2 and 3 represent the correlation between the mean expression of the

module and the experimental factor (T or D). Significant values are in bold (P-value < 0.05). 135

xix

List of Abbreviations

A Carbon assimilation

AA Amino acid

AB Alberta

ABA Abscisic Acid

ABF ABRE-binding factor

ABRE ABA-responsive element

AFLP Amplified fragment length polymorphism

ANOVA Analysis of variance

AP Alberta Pacific

At Arabidopsis thaliana

BCAA Branched chain amino acid

bHLH Basic-helix-loop-helix

BLAST Basic local alignment search tool

C Carbohydrate

cDNA Complementary DNA

cm Centimeter

CO2 Carbon dioxide

d Days

D Time-of-day

DNA Deoxyribonucleic acid

DRE Drought-responsive element

DW Dry weight

EPF EPERDIMAL PATTERNING FACTOR

ER ERECTA

ERD EARLY RESPONSE TO DROUGHT

EST Expressed sequence tag

FDR False discovery rate

Fs Fagus sylvatica

FW Fresh weight

xx

G Genotype

GC Gas chromatography

GO Genome ontology

gs Stomatal conductance

HAB1 HYPERSENSITIVE TO ABA 1

HCA Hierarchal clustering analysis

HIC HIGH CARBON DIOXIDE

HKT1 A high-affinity K transporter

HTP High-throughput

Ile Isoleucine

IRGA Infrared gas analyzer

KEGG Kyoto encyclopedia of genes and genomes

LD Linkage disequilibrium

LEA Late embryogenesis-abundant

LI Licor

LPI Leaf plastochron index

LRR Leucine rich repeat

MAP Mitogen activated protein

MD Midday

MK Mitogen activated protein kinase

MKK Mitogen activated protein kinase kinase

MS Mass spectrometry

MYB Myeloblastoma

NE Nebraska

NI Not identified

P Phenolic

P. Populus or Pinus

PCC Pearson correlation coefficient

PD Pre-dawn

PHYB PHYTOCHROME B

PIF4 PHYTOCHROME INTERACTION FACTOR 4

PIP Plasma membrane intrinsic protein

xxi

PP2C Protein phosphatase 2C

Pro Proline

PYR/PRL PYRABACTIN RESISTANT / PYR-like

PYR1 PYRABACTIN RESISTANCE 1

qPCR Quantitative polymerase chain reaction

QTL Quantative trait loci

RD22 RESPONSIVE TO DESICCATION 22

RFO Raffinose family oligosaccharides

RNA Ribonucleic acid

RT-PCR Reverse transcriptase polymerase chain reaction

RWC Relative water content

SA Sugar alcohol

SDD1 STOMATAL DENSITY AND DISTRIBUTION 1

SFP Single-feature polymorphism

SNEP Simultaneous detection of nucleotide and expression polymorphisms

SSR Simple-sequence repeat

T Treatment

TCA Tricarboxylic acid cycle

TMM TOO MANY MOUTHS

Tp Time point

Tx Treatment

USA United States of America

Val Valine

WA Washington

WD Water-deficit

WGCNA Weighted gene correlation network analysis

WUE Water use efficiency

WW Well-watered

YDA YODA

1

Chapter 1: Overview

2

Chapter 1: Overview

As long-lived sessile organisms, forest trees are exposed to a variety of deleterious environmental

conditions and must develop mechanisms in order to avoid, tolerate or adapt to the consequences

of these stresses. Although trees have evolved to contend with the adverse environmental conditions

that occur over their lifetimes, forests are threatened by rapid changes in climate that are occurring

on a global scale. Widespread forest mortality has been recently been attributed to global climate

change, including drought stress (Allen & Breshears 1998; Breshears et al. 2005; Bonan 2008; van

Mantgem et al. 2009; Allen et al. 2010). The frequency and severity of future droughts is predicted

to increase (Intergovernmental Panel on Climate Change 2007; Allen et al. 2010), negatively

impacting many facets of forest productivity and ecosystem function (Fischlin et al. 2007; Adams et

al. 2010).

In Canada, forest trees, including those of the genus Populus are ecologically and economically

important. The continued survival and productivity of such trees is highly correlated with

water availability. In hybrid poplar plantations, decreased water availability negatively impacts

productivity (Silim et al. 2009). Due to the negative impact of drought stress on forest health and

productivity, it is becoming increasingly important to understand the mechanisms that underpin the

drought response in trees.

1.1 Research Hypotheses and Aims

The main aim of the research presented in this thesis is to explore various aspects of the intraspecific

variation in the drought response among Populus balsamifera genotypes. In addition, this research

aims to increase our understanding of the molecular underpinnings that allow sessile organisms,

such as Populus trees to contend with the impact of drought stress. When the research presented

herein began, very little was known about the extent of intraspecific variation at the transcriptome-

level in a single species of Populus. Early studies focused on transcriptome-level responses to water-

deficit stress in individual or between different Populus hybrids (Street et al. 2006; Wilkins et al.

2009). More specifically, the research presented in this thesis focused on a single species, Populus

balsamifera, and tested the following hypotheses:

1. Intraspecific variation in phenotypic responses to drought stress among six genotypes

3of Populus balsamifera are underpinned by significant differences in their drought-responsive

transcriptomes.

2. Drought-induced modification of the transcription of genes implicated in the stomatal

development regulatory network are linked to changes in stomatal density.

3. There are transcript-metabolite relationships that occur in response to drought in P.

balsamifera that vary in a genotype-dependant manner.

In order to test these hypotheses three experiments were undertaken. First, as described in Chapter

3, six genotypes of P. balsamifera were grown in a common garden experiment and exposed to

a period of drought stress (15 days). The transcriptomes of the six genotypes in response to

well-watered and water-deficit conditions were interrogated using Affymetrix Poplar GeneChip

technology at both a pre-dawn and mid-day time point. Second, as described in Chapter 4, the

expression of genes involved in the stomatal development pathways were examined in two P.

balsamifera genotypes (AP-1005 and AP-1006) in conjunction with phenotypic analysis of stomatal

development under well watered and water deficit conditions throughout the experimental period

(exposure to drought stress; days 0 through 30). Finally, as described in Chapter 5, a non-targeted

metabolome analysis was performed on the six P. balsamifera genotypes to identify the drought

responsive metabolites. This was then compared to the drought transcriptomes in P. balsamifera to

identify relationships between the transcriptome and metabolome.

As summarized in Chapter 6, the results of the research described herein contribute to our

understanding of the intraspecific variation in the drought response in P. balsamifera at the

molecular-level.

4

Chapter 2: Literature Review

Contents of this chapter have been published in Forestry an International Journal of Forest

Research: Erin T. Hamanishi and Malcolm M. Campbell. 2011. Genome-wide responses to

drought in forest trees. Forestry. 84: 273-283

The published paper can be found online at

http://forestry.oxfordjournals.org/content/84/3/273.full?sid=c51c6426-0679-4a21-890c-

8bc3e06b96f6

The material in this chapter is © by Oxford University Press, 2011.

5

Chapter 2: Literature Review

Alterations in global climate and precipitation regimes strongly influence forest distribution and

survival (Allen & Breshears 1998; Shaw et al. 2005; Engelbrecht et al. 2007). While forest trees

are sessile organisms, they possess many attributes that allow them to contend with variable water

availability, within limits (Ingram & Bartels 1996; Chaves et al. 2003). Nevertheless, expected

rates of global climate change are unprecedented, with longer and more severe periods of drought

predicted (Intergovernmental Panel on Climate Change 2007). This may have a profound effect on

forest health, as water limitation is one of the leading contributors to forest declines globally (Bigler

et al. 2006; van Mantgem et al. 2009; Allen et al. 2010). For example, in western Canadian aspen

forests, drought negatively impacted growth and survival after a severe regional drought during

the 2001–2002 growing season (Hogg et al. 2008), with similar reductions in forest productivity

observed in Europe (Ciais et al. 2005).

Drought is a multidimensional environmental factor; affecting tree responses from the molecular

level to the forest stand level. Interpretation of the drought response at the stand and tree level is

complex because it involves consideration of the stress effects and responses (Yordanov et al. 2000).

Nevertheless, negative impacts of drought are observed in many facets of forest health, including

seedling recruitment, productivity, susceptibility to pathogen or insect attack and fire susceptibility

(Hogg & Wein 2005; van Mantgem et al. 2009; Zhao & Running 2010). Consequently, there is

considerable incentive to better understand the means by which forest trees respond to drought,

so as to develop strategies for preservation of forest tree growth and survival against this particular

environmental threat.

Given recent advances in genome biology, there is great scope to develop a more comprehensive

mechanistic understanding of forest tree drought responses. In keeping with this, over the past

decades, advances made in genetics, molecular biology, genomics, proteomics and bioinformatics

have provided ever-growing insights into how forest trees respond to drought. This review considers

the emergence of those insights and how they might shape the protection of forest trees from

drought in the future. Readers interested in a broader consideration of the genomics of forest tree

responses to abiotic stress or the modification of specific genes as a means by which to achieve

drought tolerance are directed to excellent reviews elsewhere [for review, see: Polle et al. (2006) and

Fischer and Polle (2010)].

6

2.1 Responses of forest trees to drought

A reduction of available water impinges on trees’ ability to grow and transpire by affecting the soil–

plant water continuum. Often, in response to declines in plant water potential, in order to reduce

water loss under drought stress, forest trees reduce transpiration by closing their stomatal pores, at

the expense of CO2 assimilation (Jarvis & Jarvis 1963; Cowan & Farquhar 1977). Additionally,

water fluxes in a tree can further be disrupted through cavitation or xylem embolism at high xylem

tensions induced by water stress. Cavitation, or xylem embolism, is the filling of xylem with air or

water vapour instead of water, leading to a reduction in the water conductivity of the plant. The

reduction of water conductance within a tree can, in turn, limit growth (Tyree et al. 1994; Rice et al.

2004).

Forest trees utilize a range of strategies to contend with drought. A series of molecular, biochemical,

physiological and morphological changes underpin plant response to water deprivation, and the

extents of these responses are highly variable and complex [for review, see Chaves et al. (2003) and

Ingram & Bartels (1996)]. The variability in drought responses is a function of the severity and

duration of the drought stress (Chaves et al. 2003; Yan et al. 2012), superimposed upon genetic

variation at the individual, population and species levels (Zhang et al. 2004; Wilkins, Waldron, et

al. 2009b; Hamanishi et al. 2010). Foresters have known for many years that different tree species

have variable responses to drought. Almost 50 years ago, in a cross-species examination of tree

seedlings under drought conditions, Jarvis and Jarvis (1963) concluded that Pinus spp. were the

most drought-tolerant species, whereas Populus tremuloides was the most susceptible.

Variation in the drought response is not only seen between forest tree species but also seen

intraspecifically. For example, Pseudotsuga menziesii (Douglas fir) seedlings, from distinct geographic

regions, exhibited variable drought resistance when grown under water-deficit conditions in a

greenhouse (Ferrell & Woodard 1966). Similarly, variation in response to water availability has

been observed in progeny and provenance testing in Pinus taeda (loblolly pine) (Teskey et al. 1987).

These early genetic studies, aimed at examining drought tolerance, focused on the relationship

between genotype and environment and established the importance of genetic variation in drought

tolerance both inter- and intraspecifically among forest trees. Nevertheless, the mechanisms that

underpinned drought tolerance and resistance in forest trees and their molecular basis were much

less well understood.

7Forest trees posses a wide array of traits that confer drought tolerance. The ability to avoid drought

stress is dependent on the trees’ ability to minimize water loss and maximize water uptake (Chaves

et al. 2003). For example, some forest trees can increase water uptake through more extensive and

deeper roots (Nguyen & Lamant 1989). In order to minimize water loss under drought conditions,

forest trees can utilize a variety of traits including altered leaf morphology [e.g. cuticular wax

(Hadley & Smith 1990)], reduction in leaf area [e.g. increased leaf abscission (Munne-Bosch &

Alegre 2004)] and reduction in stomatal conductance. In response to drought, stomatal closure

is an effective mechanism to limit water loss and prevent desiccation. For example, in potatoes,

leaf water potential was maintained under a period of slowly developing drought stress, and

the transpiration rate was regulated by stomatal aperture (Kopka et al. 1997). Within a plant,

stomatal control plays a particularly important role in regulating water balance and, in turn, CO2

assimilation.

2.2 Modulation of stomatal development in response to environmental change

The development of stomata on the leaf surface is regulated by developmental and environmental

cues. Much is known about the stomatal development in Arabisopsis thaliana, including many

of the regulatory components and networks that underlie stomatal differentiation [for review, see

Bergmann and Sack (2007), Casson and Heatherington (2010)]; however, the modulation of this

pathway in response to environmental cues is largely unknown. In Arabidopsis, stomatal density in

new, developing leaves is adjusted with the environment sensed by mature leaves (Lake et al. 2001;

Miyazawa et al. 2006). Increasing light availability is correlated with higher stomatal density, and

is modulated through the function of PHYTOCHROME B (PHYB) and a transcription factor,

PHYTOCHROME INTERACTION FACTOR 4 (PIF4; Casson et al. 2009). Whereas, elevated

atmospheric CO2 levels are associated with a decline in stomatal density (Woodward 1987). HIGH

CARBON DIOXIDE (HIC) modulates stomatal density in response to changing atmospheric CO2

in Arabidopsis (Gray et al. 2000). In response to drought stress, modification to stomatal density is

variable among plant species and is dependant on the severity of drought. In Arabidopsis mutants

with increased decreased stomatal density demonstrated improved drought tolerance (Yu et al. 2008;

Yoo et al. 2010). Alterations to stomatal development, including the reduction of stomatal density,

resulting from the exposure to drought stress may represent a long-term strategy to contend with

8water-deficit stress; however, it may also be a limiting factor to future productivity.

Although stomatal regulation is an efficient mechanism to contend with water shortages by limiting

water loss through the stomatal pores (Froux et al. 2005), it cannot solely prevent water balance

decline in trees. Consequently, forest tree growth and survival under drought stress is frequently

dependent on many of the aforementioned strategies acting in concert. Although inroads have been

made in dissecting the physiological responses to drought in forest trees, understanding the depths

of the molecular underpinnings is scant in forest trees relative to herbaceous annual plant species.

2.3 Plant perception of water status and downstream signalling pathways

Some of the means by which plants sense water-deficit conditions and subsequently induce

downstream molecular signalling response cascades have been elucidated [for review, see Shinozaki

and Yamaguchi-Shinozaki (2007)]. In order to mount any response to drought, plants must first

sense water-deficit conditions. Although the precise mechanism underlying drought perception

in plants is not well understood, there are multiple hypotheses related to how roots sense drought

conditions in the soil. Under conditions of decreased soil water, the plant phytohormone abscisic

acid (ABA) accumulates in the soil solution. The increase in soil ABA concentration may act as

a mechanism by which roots sense reduced soil water (Slovik et al. 1995; Hartung et al. 1996).

Drought may also be perceived through a reduction in turgor by osmosensors. Urao et al. (1999)

identified an Arabidopsis thaliana transmembrane histidine kinase, AtHK1, which has putative

function as an osmosensor. AtHK1 senses osmotic changes and transmits a stress signal to

downstream mitogen-activated protein kinase signalling cascades, which in turn induce drought-

responsive gene expression (Urao et al. 1999). In red river gum (Eucalyptus camaldulensis), Liu et al.

(2001) identified two HKT1 homologues that can sense changes in solute concentration, similar to

AtHK1. Eucalyptus HKT1 homologues altered sodium and potassium transport in Xenopus oocytes,

suggesting a role in osmoperception and osmoregulation. These homologues are strong candidates

for tree proteins that play a key role in the perception of water limitation leading to drought

response signalling.

Downstream of water-deficit sensing, the steps in the drought-responsive pathway proceed via one

of two signalling pathways: the ABA-dependent and the ABA- independent pathways (Shinozaki &

9Yamaguchi-Shinozaki 1996). Under drought conditions, increasing levels of ABA are observed in

the roots and shoots; ABA is thought to play an important role in root to shoot signalling (Walton

et al. 1976; Zeevaart & Creelman 1988; Davies & Zhang 1991). The ABA signal is modified

through changes to xylem or apoplastic pH, influencing the signalling process by moderating

sensitivity and availability of ABA in planta (Wilkinson et al. 1998; Bahrun et al. 2002; Sobeih

et al. 2004). ABA not only acts in the signalling of drought stress but also plays a central role in

regulating drought response in plants.

One of the most prominent roles of ABA is in the regulation of stomatal movement in response to

drought [for review, see Belin et al. (2010), Wilkinson and Davies (2002) and Popko et al. (2010)].

Mediating stomatal aperture under drought conditions allows plants to limit water loss and regulate

water balance during periods of water deficit. Another important role of ABA-mediated drought

response in plants is the maintenance of root growth under mild or moderate drought stress,

whereas leaf growth under drought conditions is restricted (Hsiao & Xu 2000). Under drought

conditions, growth allocation patterns in plants are altered and the variable growth rates observed

in roots and shoots are correlated with ABA levels; however, the regulation of growth rates may be

mediated through another plant hormone, ethylene (Sharp et al. 2004).

Despite what is known about the role of ABA in mediating plant response to drought stress,

little was known about ABA perception by the plant cell until recently. Initial reports of putative

ABA receptors were considered controversial because of limited evidence of central roles in ABA

perception and response (McCourt & Creelman 2008). More recently, using a chemical screen

technique, Park et al. (2009) identified a protein, PYRABACTIN RESISTANT 1 (PYR1), which is

involved in ABA signalling. PYR1 and PYR1-like receptors are necessary for many plant responses

to ABA. Members of the PYRABACTIN RESISTANT / PYRABACTIN RESISTANT-LIKE

(PYR/PRL) family of receptor proteins interact downstream with HAB1, a 2C protein phosphatase

(PP2C). PP2Cs negatively regulate ABA signalling(Saez et al. 2004). Through proteomic

approaches, another group simultaneously identified the ABA receptor, RACR1, which belongs to

the same PYR/PYL family of receptors (Ma et al. 2009). This family of receptor proteins appears

to be highly conserved across crop plants, and recent work is aimed at elucidating members in tree

species [for review, see Klingler et al. (2010)]. Saavedra et al. (2010) identified a PP2C homologue

from beech tree (Fagus sylvatica) that is a negative regulator of ABA signalling and showed that

10FsPP2C interacts with Arabidopsis PYR7 and PYR8. Identification of ABA receptor protein

homologues is important for understanding the perception of ABA and its involvement in the

drought response in trees.

2.4 Molecular outputs in response to water-deficit signalling

Whole-plant responses to ABA are underpinned by ABA- dependent changes in gene expression

that are mediated through the action of ABA-inducible transcription factors controlling the

expression of genes containing cis-acting ABA response elements (ABREs) [for review, see Ingram

and Bartels (1996)]. The ABRE is stereotypically found in the upstream regulatory regions of

drought-responsive genes (Giuliano et al. 1988; Bray 1994). ABRE-like sequences have also been

identified in the upstream regulatory regions of drought-inducible genes, including the G-box

sequence (Williams et al. 1992; Shen et al. 1996). Members of the bZIP protein family are known

to bind to ABRE and ABRE-like sequences and, in turn, activate ABA-dependent gene expression

(Guiltinan et al. 1990; Choi et al. 2000; Uno et al. 2000).

Among the best-characterized drought-induced genes, RESPONSIVE TO DESSICATION 22

(RD22) has ABA-regulated transcription. ABA-mediated regulation of RD22 transcription requires

the synthesis of an MYC (rd22BP1/AtMYC2) and an MYB (AtMYB2) transcription factor, both

of which are induced by ABA. AtMYC2 and AtMYB2 act as transcriptional activators and bind

cis- elements in the promoter of RD22 (Abe et al. 1997; 2003). AtMYC2 and AtMYB2 also are

involved in ABA-dependent gene expression of other ABA-inducible genes (Abe et al. 2003).

While many drought responses are mediated by ABA, plants also have ABA-independent responses

to drought conditions. Several genes are induced under drought conditions that are not dependent

on ABA (Shinozaki & Yamaguchi-Shinozaki 1996). Often these genes contain a conserved

dehydration-responsive element (DRE) in their upstream gene regulatory region, which functions

to recruit transcription factors that are not regulated by ABA. Many of the non-ABA abiotic stress

signalling pathways are complex, and it is hypothesized that the DRE cis-acting element plays a role

mediating different stress-signalling cascades, resulting in an overall plant response to abiotic stress

(Knight & Knight 2001).

Integration of the ABA-dependent and ABA-independent signalling cascades also occurs through

11downstream gene regulation. For example, the gene RD29A contains both an ABRE and a DRE

within its cis-acting upstream gene regulatory region. In the initial stages of drought stress,

expression of RD29A is independent of ABA but later is dependent on ABA for gene expression

(Shinozaki & Yamaguchi-Shinozaki 2000).

Through molecular analysis of multiple plant species, insights have been gained into a range of

proteins that are induced under water-deficit conditions [for review, see Ramanjulu and Bartels

(2002)]. For example, the hydrophobic late-embryogenesis-abundant (LEA) proteins accumulate

under drought stress and are commonly associated with tolerance to water-deficit conditions

(Welin et al. 1994). Recent evidence suggests that LEA proteins may have an important role in

the stabilization of other proteins and membranes, as well as the prevention of protein aggregation

during periods of water deficit (Close 1996; Goyal et al. 2005). In a poplar clone (P. euramericana

cv Dorskamp), the rapid induction of a LEA family protein, dehydrins, gene expression was

observed after osmotic stress was imposed on the clones (Caruso et al. 2002). Similar increases in

transcript or protein levels of LEA family proteins in other forest trees, such as spruce, have been

observed (Blodner et al. 2007).

Aquaporins are another major class of proteins that play a key role in the water-deficit response.

Aquaporins are channel proteins that are found in cellular membranes and are responsible for water

flux and are crucial for maintaining proper water balance [for review, see Maurel et al. (2008)].

There are two major groups of aquaporins: those found specifically in plasma membranes are plasma

membrane intrinsic proteins (PIPs), whereas those found in the tonoplast are known as tonoplast

membrane intrinsic proteins. Both classes of water transport proteins are important for maintaining

water status in the plant, which is vital for photosynthesis and subsequently growth. In Eucalyptus,

PIPs are essential for normal growth; a reduction in PIPs resulted in a suppression of growth

(Tsuchihira et al. 2010). The expression of aquaporins is dynamic in response to plant water status.

Under drought stress conditions, the expression of Plasma-membrane-Intrinsic-Protein (PIP)-type

aquaporins was reduced in tobacco plants, hypothesized to decrease water transport (Mahdieh et al.

2008). In white poplar (Populus alba L.), Berta et al. (2010) identified five transcripts for aquaporin

proteins that were up-regulated in following re-watering in trees that experienced drought stress.

Accumulation of aquaporins following re-watering may be integral to restoration of water transport

of plants under well-watered conditions. In poplar trees, members of the PIP1 family of aquaporins

12are important for recovery from xylem embolism Populus (Secchi & Zwieniecki 2010). The

functional variability and importance of aquaporins in a trees’ response to drought is reflected in the

distinct responses of aquaporins in trees with different drought response strategies. Under drought

conditions, the drought responsiveness of specific aquaporin family members varied between two

poplar clones (P. balsamifera and P. simonii × P. balsamifera) that had contrasting drought response

strategies (Almeida-Rodriguez et al. 2010). The variability in aquaporin response may reflect the

different roles of aquaporins, with respect to water transport, in trees.

Over the past decades, examination of the drought response in herbaceous annual plant species has

revealed many details about the molecular pathways involved in the drought response. This had

led to the identification of many important proteins that accumulate under drought conditions,

including transporter proteins, messenger RNA- binding proteins, proteases and many others

involved in regulation and signal transduction [for review, see Ingram and Bartels (1996)]. More

recently, progress has been made uncovering the molecular mechanisms underpinning these

pathways and, in turn, drought tolerance and resistance in forest trees. Currently, genomic

approaches are being brought to bear the drought responses that enable the integration of

knowledge of tree-level responses with gene expression and function.

2.5 Early identification of drought-responsive genes in forest trees

Prior to the genomic era, foresters and plant biologists alike were limited to studying the function

of one or a few genes at a time. Early studies in trees revealed that drought- responsive genes

initially identified in herbaceous annual plants, such as dehydrins and heat-shock proteins, and had

homologues expressed in the bark tissue of various woody plants (Wisniewski et al. 1996). Insights

into the molecular response of trees to drought began to improve through the identification of

genes induced by drought in trees. For example, a number of drought-induced genes were first

identified in Pinus taeda through comparative analysis of complementary DNA (cDNA) clones

whose expression was induced under water-deficit conditions (Chang et al. 1996). Chang et al.

(1996) were able to further characterize four water-deficit-induced cDNAs, providing insight into

their sequences and patterns of expression. Based on sequence similarity to characterized genes in

other plant species, the majority of these genes were thought to function in cell wall reinforcement

and hypothesized to participate in the adaptation of the cells to water-deficit stress (Chang et al.

1996). To identify larger numbers of drought-responsive genes without basing discovery on a

13priori knowledge, Dubos and Plomion (2003) pioneered the use of cDNA-Amplified Fragment

Length Polymorphism (AFLP) to identify drought-responsive genes in roots and then the needles of

Pinus pinaster (maritime pine). Dubos et al. (2003) identified 48 putative genes that were drought

responsive in Pinus pinaster seedlings. Of these 48 genes, many corresponded to proteins of known

function with roles in photosynthesis, carbohydrate metabolism, cell wall synthesis and plant

defence; however, a relatively high proportion corresponded to genes of unknown function(Dubos

et al. 2003). Similar experiments using a cDNA-AFLP technique with almond (Prunus amygdalus)

identified drought-responsive genes in young leaves of different cultivars with variable drought

response (Campalans et al. 2001).

The breadth and depth of gene discovery in forest trees was expanded through partial sequencing

of transcribed cDNA libraries of loblolly pine (Allona et al. 1998) and poplar (Sterky et al. 1998)

to generate compendia of expressed sequence tags (ESTs). These pioneering EST efforts focused

almost exclusively on genes involved in wood formation but generated information about gene

expression and coding sequences in what were, at that time, almost completely uncharacterized

genomes. These initial efforts in gene discovery, although modest by today’s standards, were ground

breaking and provided important foundations for future studies.

Following the initial efforts in pine and poplar, the number of reported ESTs from forest tree

species, including birch, pine and eucalyptus, increased year by year (Strabala 2004; Li et al. 2009;

Wang et al. 2010). Nevertheless, many of these efforts continued to focus ESTs that were related

to wood formation. With an increasing desire to gain insights into stress responses in forest trees,

biologists carried out EST analysis on other tissues under various treatment regimes. Ujino-Ihara et

al. (2000) identified ~1400 ESTs from the inner bark from a sugi tree (Cryptomeria japonica), which

was felled 2 days prior to EST analysis in order to enrich the EST library in drought, wounding

and other stress-related genes. In order to directly identify water-responsive genes in loblolly pine,

Lorenz et al. (2006) subjected seedlings to various watering regimes and generated an EST library

from the root tissues. In these studies, some of the ESTs identified were homologous to genes

previously identified as drought responsive in herbaceous plants, such as LEAs and dehydrins.

Although there was some degree of similarity between ESTs identified in trees and previous attempts

in other plant species, many of the transcripts identified in trees were of unknown function.

Although the identification of drought-induced ESTs in forest trees was important for early gene

14identification and transcriptome studies, the EST libraries generated from these studies played key

roles in founding broader analyses of tree transcriptome activity (Nagaraj et al. 2007).

2.6 Genome-wide dissection of forest tree drought responses—whole transcriptome analyses

During the course of EST discovery efforts, it was clear that the identification of subsets of drought-

responsive genes was insufficient to fully understand the complexities of the drought response in

forest trees. cDNA-AFLP techniques and EST frequencies revealed basic data with respect to gene

expression; however, genome-wide analysis techniques, such as microarray analysis, had the potential

to reveal global gene expression patterns. Early microarray platform experiments investigating the

gene expression patterns in hybrid aspen, based on a small set of ESTs identified in wood formation

(Sterky et al. 1998), revealed unique tissue-specific transcript profiles in differentiating xylem

(Hertzberg et al. 2001). Some of the earliest insights into transcriptome responses to drought in

forest trees was determined using cDNA microarray based on ESTs libraries from specific tissues,

such as xylem, shoot tips or pollen. Heath et al. (2002) used a 384 pine cDNA microarray to

investigate the adaptation to mild drought in pine seedlings. Although the number of genes being

investigated was limited, the importance of molecular chaperones and membrane transport proteins

was revealed. These particular proteins are postulated to be vital in cell maintenance and repair and

therefore necessary for forest trees to cope with mild drought stress (Heath et al. 2002).

Early microarray experiments, aimed at investigating the molecular basis of a given trait or response,

had potential; however, they were constrained by the number of genes under investigation. As

development of genetic resources for forest trees continued, more comprehensive microarrays were

established that enabled relationships between physiological responses and genome-wide gene

expression profiles to be investigated. Watkinson et al. (2003) used a microarray consisting of

~2100 cDNA clones to examine the gene expression in drought-stressed loblolly pine that revealed

that alterations in gene expression patterns in response to drought in loblolly pine were not only

qualitative but also quantitative. The increased number of transcripts examined, representing 15

functional categories, allowed the authors to correlate patterns of expression with acclimation to

mild or severe drought and define roles for specific groups of genes (Watkinson et al. 2003).

Although EST sequencing and early microarray experiments provided significant insights into

15groups of drought- responsive genes, the sequencing of the complete black cottonwood (P.

trichocarpa Torr. & Gray) genome represented a significant milestone in the ability to explore the

drought transcriptome in its entirety in a tree (Tuskan et al. 2006). The Populus genome sequence

was not only integral in the development of more comprehensive whole-genome microarray

platforms but also important for comparative analyses with other plant genomes, such as

Arabidopsis. In the ‘post-genomic’ era, a number of microarray resources were developed for poplar

species using the available sequence data. POP2, a spotted cDNA array representing more than

100 000 ESTs and ~40 per cent of predicted gene models from the Populus genome, was used to

investigate the global drought response in black cottonwood (P. trichocarpa) and eastern cottonwood

[P. deltoides Bart.; (Sterky et al. 2004; Street et al. 2006)]. Street et al. (2006) identified genes with

contrasting responses to drought in the two Populus species and hypothesized that the control of

gene expression may be an important process in species divergence.

In addition to cDNA microarrays, two short oligo-nucleotide microarrays were developed for

Populus: the Affymetrix GeneChip Poplar Genome Array (www.affymetrix.com) and the Nimblegen

Populus whole-genome array (http://www.nimblegen.com/products/exp/ eukaryotic.html). Both

of these microarrays were designed based on the gene model sequences from the poplar genome

sequence (Tuskan et al. 2006), as well as available publicly available EST sequences. Using

Affymetrix GeneChip Poplar Genome Arrays, Wilkins et al. (2009b) were able to identify divergent

responses in gene expression profiles in response to drought between two poplar hybrids, suggesting

that it is difficult to capture a genome-wide drought response with one or a few Populus genotypes.

They also showed that transcriptional responses to drought are time of day dependent in hybrid

poplars, indicating that any investigation into the molecular-level responses to drought should factor

time of day in order to fully grasp the molecular basis of such a response.

The ability to identify a Populus-specific drought transcriptome is becoming increasingly more

difficult as whole-genome arrays uncover variation in the transcriptomes within a given species,

P. balsamifera (balsam poplar; Hamanishi et al. 2010; Chapter 3, this volume). Hamanishi et al.

(2010) compared phenotypic traits with gene expression profiles, showing that balsam poplar

genotypes that exhibited increased magnitude change in gene expression were also able to sustain

growth under drought conditions. Differences in the drought-responsive transcriptomes among

balsam poplar genotypes was related to the extent of intra-specific DNA sequence variation,

16suggesting that genetic relatedness is likely an indicator of a shared drought response.

Until recently, many gene expression studies on forest trees focused on leaves or roots provided

insights into transcriptome response in a given organ; however, the subtleties of the drought

response at the cellular level could not be identified from these experiments. Berta et al. (2010)

examined the transcriptome response to water deficit in the wood-forming tissue in white poplar

(Populus alba). This study investigated the transcriptome response to drought, as well as the

interplay between wood formation and drought response in trees. Many drought-responsive gene

networks were shared between different tissues (e.g. leaves and roots), but some transcripts were

identified that may have had specific roles in modulating wood formation under drought stress

(Berta et al. 2010). Together, these studies on Populus reveal the complexities in the genome-

wide drought response. In potato, Kopka et al. (1997) investigated guard cell-specific patterns of

transcript abundance. Guard cell-specific transcript levels indicated a systemic drought response

signal, leading to long-term changes in transcript abundance (Kopka et al. 1997). In forest trees,

the variability in the drought transcriptomes on a temporal and spatial scale, as well as the variability

that is present among various individuals, can be exploited for breeding and selection of drought-

resistant stock.

Genome-wide dissection of forest tree drought responses—quantitative trait locus mapping and association studies

The response of forest trees to drought stress at the morphological and transcriptome level is

complex and highly variable, both intra- and inter-specifically. In order to dissect such a complex

trait, biologists have often employed quantitative genetic techniques to reveal genetic intervals

to which variability in the trait can be ascribed. Quantitative trait locus (QTL) analysis is

advantageous for analysing traits, where a priori knowledge of molecular underpinnings or genes

is elusive. Genetic maps of many forest trees have been generated for forest trees, and many QTLs

have been identified for drought-related traits. In rapidly growing willow hybrids (Salix dasyclados

× Salix viminalis), Ronnberg-Wästljung et al. (2005) identified a few QTLs that had a significant

effect on water use efficiency (WUE), and the authors noted that the analysis revealed the complex

nature of drought tolerance in willow. Similarly, in pedunculate oak (Quercus robur L.), Brendel

et al. (2008) identified 10 QTL for WUE, where only a few QTL were responsible for the larger

proportion of the clonal variation. Generally, in forest tree species, the number of QTL identified,

17and the amount of variation that is explained by any given QTL reveals the complexity of drought

tolerance or WUE. The knowledge gained from QTL analysis in tree species is useful for tree

breeding; however, these gains have been restricted by difficulties and time-consuming nature

of identifying genes or genes located at a given QTL for species with limited genomic sequence

availability.

In order to overcome the obstacles associated with QTL mapping experiments, biologists have

used association or linkage disequilibrium (LD) mapping in order to determine the genetics

underpinning complex traits, as it is proposed to be more efficient than QTL mapping (Hall et al.

2010). Association mapping relies on the association of genomic regions containing genetic markers

with complex traits. With time, the availability of genetic markers has improved allowing the use

of association or LD mapping to become more prevalent. Using a candidate gene loci approach,

Gonzalez-Martinez et al. (2006) estimated the LD estimate for 18 drought-tolerance candidate

genes in loblolly pine (P. taeda). A majority of the drought-tolerance candidate genes showed

neutral selection, with the exception of CCoAOMT-1 and EARLY RESPONSE TO DROUGHT 3

(ERD3) (Gonzalez-Martinez et al. 2006).

An alternative association mapping approach is a whole-genome scan. In white spruce (Picea

glauca), single- nucleotide polymorphisms (SNPs) were identified in expressed genes and used

as genetic markers for mapping purposes (Namroud et al. 2008). Although the authors noted

limitations in their methods, Namroud et al. (2008) found potential associations between local

adaptation of candidate genes and phenotypic attributes of populations. The benefits of identifying

genes under potential selection for drought tolerance in non-model tree species through association

mapping has the potential to be very useful for tree breeding strategies in the future.

2.7 From drought transcriptome to drought proteome

Studies unveiling the drought-responsive transcriptome in trees have provided a wealth of

information regarding the molecular underpinnings of the drought response; however, analysis of

the proteome reveals the abundance of final gene products that may be important in understanding

the down stream drought response. The drought proteome has been examined in several tree

species, including poplar (Plomion et al. 2006) and spruce (Blodner et al. 2007). Initial drought

studies examining both gene and protein expression in poplar revealed limited overlap between

18drought-related transcripts and proteins, suggesting the need for complementary approaches to

unveil the mechanisms and molecular plasticity that control drought responses in trees (Plomion et

al. 2006).

Many proteome studies focused on drought response in forest trees assess the role of genotype

in shaping the response. Proteome analysis of eight P. x euramericana genotypes with varying

intrinsic water use efficiencies revealed a number of proteins with significant genotype by treatment

interaction. A large majority of these proteins were found to be chloroplastic in nature and

involved in control of carbon fixation (Bonhomme et al. 2009). While comparing two native

poplar species from China, Yang et al. (2010) examined the combined effect of physiological

and proteome response to drought stress. Although the two species of poplar differed in their

responses, to drought stress, it is evident that physiological and proteomic processes are important

for maintenance of cellular homeostasis under drought conditions. Responses to drought stress are

not limited to the species level; Zhang et al. (2010) identified sex-specific variation in the expression

of drought-responsive proteins in P. cathayana. Many photosynthetic-related and stress-responses

proteins have a significant sex by drought interaction effect (Zhang et al. 2010). The differences

in the proteomic response to drought observed between the sexes may provide insight into the

variability in their productivity as well as their response to drought stress.

Investigations into proteomic variation among genotypes provide excellent insights into the

variability in drought- stress response among trees; however, investigations within a given

individual will shed light on the dynamic nature of plant stress responses. Pechanova et al. (2010)

examined the proteome within apoplastic continuum of P. deletoides. Using a systems approach,

the dynamic nature of the proteome between leaves and stems was investigated and many stress-

responsive proteins were identified. Interestingly, a large constituent of diverse peroxidases thought

to play a role in cell wall modifications were identified. Complexities in the proteome among and

within individual trees highlight the diversity in the drought response. Using system approaches,

combining genomic and proteomic methods of investigation will increase our ability to understand

the complex and dynamic responses of forest trees to drought stress.

2.8 The metabolic drought response

Like the drought-responsive proteome, the complement of metabolites that are identified under

19drought conditions likely play an important role in acclimation process, helping trees tolerate

or avoid stress. Non-targeted profiling of metabolites in biological samples is therefore highly

complementary to transcriptome and proteomic methods in the study of drought responses in

plants (Weckwerth 2003). Examination of the metabolome provides a snapshot of the patterns

of accumulation of metabolites in response to a given biological condition, providing insight into

variation due to treatment or genotype.

Osmotic adjustment is one of the initial responses to water-deficit stress, and metabolites such as

polyols manitol and sorbitol, sugars such as sucrose or raffinose family oligosaccharides (RFOs),

or amino acids such as proline are thought to be of particular importance, serving as osmolytes

or osmoprotectants under drought stress (Seki et al. 2007; Krasensky & Jonak 2012). Small

molecules such as anthocyanins and carotenoids, which accumulate under conditions of drought,

are hypothesized to protect plant tissue from damage caused by reactive oxygen species (Shulaev et

al. 2008). Other small organic molecules, including ABA [see: ‘Plant perception of water status and

downstream signaling pathways’] and jasmonic acid (Ollas et al. 2012)accumulate under drought

stress and serve as signalling molecules activating downstream drought responses.

Plant metabolism is complex and highly dynamic; regulation of metabolic processes occurs at many

different levels (Sweetlove & Fernie 2005). Understanding the regulatory networks underpinning

any aspect of plant metabolism will undoubtedly require detailed analyses of all cellular processes.

Although characterization of the whole plant metabolome is not yet possible, the integration of

comprehensive metabolomic data with other whole-genome platforms will help uncover important

metabolic pathways involved in the drought response (Sweetlove & Fernie 2005; Schauer &

Fernie 2006; Guy et al. 2008). In Arabidopsis, an integrated approach was used to evaluate the

effects of nutritional stress on gene-metabolite networks(Hirai 2005). Correlation networks

revealed specific responses to nutritional deficiencies, including the coordinated modulation of

genes and metabolites in the glucosinolate metabolic pathway (Hirai 2005). Similar approaches

in Populus, uncovered some of the perturbations to molecular networks preceding the onset of

winter (Hoffman et al. 2010). Pathway analysis has also proven fruitful identifying variations in

stress tolerance mechanisms between two poplar species with varying salt-tolerances with respect to

both metabolites and patterns of transcript accumulation (Janz et al. 2010). Although integrated

approaches will ultimately provide a more holistic view of the complex and dynamic metabolic

20networks in trees, improved metabolite profiling techniques and data integration methods are

required for a deeper understanding of these complex networks.

2.9 Recent advances in genome analysis

In the field of genomics, many milestones have been passed, including the sequencing of whole

genomes, which have opened many doors to our understanding of tree molecular biology [for

review, see Deschamps and Campbell (2009)]. The next-generation high-throughput (HTP)

sequencing technologies offer more opportunities to ask other, more complex, biological questions

(Mardis 2008). While the use of poplar as a model species has been highly beneficial to the

understanding of biology and molecular underpinnings of trees to stress, technology is moving

at such a pace that forest biologists can now investigate different tree species with many of the

genomic and technological advantages of a model species. The ability to rapidly sequence genomes

at increased depth and speed allows for the rapid increase in available genomic resources, including

sequence data, physical maps and molecular genetic markers. All these advantages will improve

marker-aided tree breeding and tree improvement methods.

Next-generation HTP sequencing technology not only provides better insight into sequence

variation but also gives us the ability to investigate epigenetic modifications. Epigenetic

modifications, such as DNA or histone modifications, play key roles in regulating gene expression

and, therefore, plant growth and development. Under stress conditions, epigenetic modifications

play important roles regulating the expression of stress-induced genes (Boyko & Kovalchuk 2008;

Chinnusamy & Zhu 2009). Divergent drought transcriptomes and differences in global DNA

methylation in Populus trees of the same genotype is observed between clones propagated in

different geographic locations (Raj et al. 2011). Variation in epigenome reprogramming resulting

in altered gene expression may enable long-lived organisms, such as trees, to better acclimate to

environmental fluctuations (Raj et al. 2011). Some epigenetic modifications are heritable and

may provide a sort of ‘stress-memory’ to plants, allowing them to better cope with future stress

conditions; however, the benefit of being better able to cope with these conditions may be at the

expense of growth (Chinnusamy & Zhu 2009). While genotypic variation for DNA methylation

is observed among poplar hybrids under drought stress and is correlated to productivity under

non-stress conditions (Gourcilleau et al. 2010), understanding the role and the mechanisms by

which epigenetic modifications regulate gene expression under stress conditions is of increasing

21importance.

Synergistic approaches, including the integration of whole-genome expression data with genotyping

data, such as SNP marker analysis, similarly offer the opportunity to link sometimes apparently

disparate biological mechanisms to derive a more holistic description of tree responses to a drought

stimulus. Advances in the next-generation sequencing technology opens doors for the ability to

examine genomic sequences for non-model tree species, as well as many individuals of the same

species to gain insight into sequence variation, as well as epigenetic modifications. These new

technologies create opportunities to increase our wealth of information about stress adaptation and

to delineate the relationships among phenotypic, genetic and epigenetic variation in forest trees.

2.10 Perspectives

As climate and precipitation regimes change and impinge on forest productivity, it is becoming

increasingly clear that understanding how trees adapt and survive under adverse conditions is

important for many reasons. Increased periods of drought stress may limit the ability for trees to

survive; however, with new found knowledge of molecular responses to drought in forest trees, we

may be able to equip ourselves with the ability to plant more resilient stock that is best suited for

future conditions.

Over the past few decades, we have accelerated from the initial discovery of individual genes

involved in a drought response to variations observed at whole transcriptome level. Microarray

studies and other HTP transcriptome analyses have revealed many complexities in the drought

response among forest trees. Links between phenotypic observations and transcriptome responses

reveal potential mechanisms for adaptation to drought. With further efforts in other “-omics”

platforms, investigators had the ability to also examine proteomic and metabolic responses to

drought in trees. Although the response at any given molecular level reveal much information about

how trees respond to drought, the integration of the many various high-throughput platforms may

uncover many complex molecular mechanisms and pathways that underpin the drought response.

A more holistic or systems biology approach will be important in order to understand the relative

importance of various pathways and mechanisms. For example, transcriptome studies reveal many

genes involved in the synthesis of raffinose and galactinol sugars are found with higher transcript

abundance in drought-treated trees (Shinozaki & Yamaguchi-Shinozaki 2007; Hamanishi et al.

222010). These metabolites are thought to have an osmoprotectant role under drought conditions.

Understanding the mechanisms and molecular plasticity of each level of this pathway could be

important to exploit this innate drought protection mechanism in trees.

The ability to capitalize on the new genomics technologies has the potential to lead to strategies

to better preserve existing tree populations, as well as improve the productivity of new stands and

plantations under changing climates. One might make use of these technologies, based on whole-

genome approaches or multi-pronged systems biology approaches in order to identify genes and

gene products related to drought responses. The identified genes, or gene products, can be used for

strategies for selection or directed modification of trees with enhanced capacity to tolerate drought.

For example, identification of drought-resistant QTLs in rice (Bernier et al. 2009) has played an

important role in the marker-aided selection of drought-tolerant rice varieties (Steele 2009). The

identification of genes, such as homologues of the AtMYB61 gene in Arabidopsis thaliana involved in

the closure of stomata, and therefore regulation of water loss (Liang et al. 2005), can be used for the

future modification of tree stocks with enhanced drought tolerance. Using bioinformatic methods,

the relationship of genes from the herbaceous annual Arabidopsis thaliana can be transferred to forest

trees, such as Populus (Wilkins et al. 2009a), and the roles of genes, such as MYB61, can be inferred.

Using these tools, we can engineer trees with enhanced drought tolerance through combination of

genomic, bioinformatics and prior knowledge of drought responses in plants. Improved planting

stock helps improve or maintain productivity in areas influenced by increasing levels of drought.

As well, knowledge of the molecular responses to drought will facilitate in the identification of

naturally occurring variation in the drought response. This variation can be selected for or used as

a focus for conservation in forests. In this period of uncertainty about our climatic future, genomic

approaches that enable us to enhance and increase precision and improve rates of identification of

resilient individuals will be of paramount importance.

2.11 Acknowledgements

We are most grateful for very useful comments on the draft manuscript provided by two anonymous

reviewers. Research in the Campbell laboratory is generously supported by the Natural Science and

Engineering Research Council of Canada (NSERC), the Canada Foundation for Innovation (CFI),

the Ontario Research Fund (ORF), Genome Canada and the University of Toronto.

23

Chapter 3: Intraspecific variation in the Populus balsamifera drought transcriptome

Contents of this chapter have been published in Plant, Cell and Environment: Erin T. Hamanishi,

Sherosha Raj, Olivia Wilkins, Barb R. Thomas, Shawn D. Mansfield, Aine L. Plant, Malcolm M.

Campbell. 2010. Intraspecific variation in the Populus balsamifera drought transcriptome. Plant,

Cell and Environment. 33: 1742-1755

Contributions: ETH, SDM, ALP and MMC designed research; ETH, SR, BT, SDM, ALP and

MMC organised experimental logistics including transfer and establishment of biological materials;

ETH, SR, and OW performed research; ETH, OW, and MMC analysed data; ETH and MMC

wrote manuscript with editorial assistance from SR, OW, BT, SDM, ALP and MMC.

The published paper and supplementary files can be found online at

http://onlinelibrary.wiley.com/doi/10.1111/j.1365-3040.2010.02179.x/suppinfo

Supplemental tables and figures are numbered in the order in which they appear online and in the

published paper. Supplemental figures S3.1 through S3.3 and supplemental tables S3.1 through

S3.3 are also included at the end of this chapter.

The material in this chapter is © by the Wiley-Blackwell Publishing Limited.

24

Chapter 3: Intraspecific variation in the Populus balsamifera drought transcriptome

3.1 Abstract

Drought is a major limitation to the growth and productivity of trees in the ecologically and

economically important genus Populus. The ability of Populus trees to contend with drought is

a function of genome responsiveness to this environmental insult, involving reconfiguration of

the transcriptome to appropriately remodel growth, development and metabolism. Here we test

hypotheses aimed at examining the extent of intraspecific variation in the drought transcriptome

using six different Populus balsamifera L. genotypes and Affymetrix GeneChip technology. Among

genotypes there was a positive correlation between the magnitude of water-deficit induced changes

in transcript abundance across the transcriptome, and the capacity of that genotype to maintain

growth following water deficit. Genotypes that had more similar drought-responsive transcriptomes

also had fewer genotypic differences, as determined by microarray-derived single feature

polymorphism (SFP) analysis, suggesting that responses may be conserved across individuals that

share a greater degree of genotypic similarity. This work highlights the fact that a core species-level

response can be defined; however, the underpinning genotype-derived complexities of the drought

response in Populus must be taken into consideration when defining both species- and genus-level

responses.

3.2 Introduction

Trees of the genus Populus, which include poplars, aspen and cottonwoods (herein collectively

referred to as poplars), are found primarily throughout the northern hemisphere (Dickmann 2001),

and have many favourable attributes which have lead to their widespread use in both forestry and

agriculture (Brunner et al. 2004). Occupying both a large geographical region and a diverse array of

habitats, poplar trees must contend with a variety of environmental conditions in order to survive.

Along with other environmental stresses, such as insect defoliation and rust cankers, drought is a

major factor impinging on poplar growth, productivity and survival throughout its range (Hogg et

al. 2002; van Mantgem et al. 2009). The drought sensitivity of poplar trees, which are a prominent

species in many temperate forests, has posed increased concern for the future amidst predictions of

changing climate and water shortages, as well as during periods of episodic drought (Schindler &

25Donahue 2006).

In response to water deficit, poplar trees generally display alterations in plant water status resulting

in suppression of stomatal conductance and declines in productivity; however, considerable

variation in drought response and tolerance has been observed within the genus Populus (Gebre

& Kuhns 1991; Tschaplinski et al. 1994; Chen et al. 1997; Monclus et al. 2006; Bonhomme et al.

2009). The ability of trees to adapt and survive diverse environmental variables, such as drought,

is a consequence of a variety of biochemical and physiological processes, many of which are the

result of stress signal perception leading to alterations in the transcriptome, resulting in an adaptive

response (Dickmann 2001; Kozlowski & Pallardy 2002; Lei et al. 2006). The variability in response

to drought observed at the morphological and physiological level suggests that poplar trees are an

excellent organism to study the molecular underpinnings of the drought response and the variation

in this adaptive response to such an environmental insult.

Variation in gene expression observed among populations is heritable (Oleksiak et al. 2002; Brunner

et al. 2004; Whitehead & Crawford 2006) and, therefore, examination of the variability in the

transcriptome response to drought may provide insight into the diversity and adaptation to such a

response. Previous investigations dissecting the molecular underpinnings of the drought response

in the genus Populus have focused on transcriptional differences among various poplar species or

hybrids (Street et al. 2006; Wilkins et al. 2009b). This indicates that poplar trees, regardless of

species or hybrid, likely have a variety of mechanisms governing the drought response, and that this

response is highly dependent on genotype. Although there is evidence that intra-specific variation

in drought response can be observed among growth rates and physiological traits within a species of

Populus (Schindler & Donahue 2006; Lu et al. 2009), drought-induced variation in gene expression

within a given species of Populus has not yet been investigated. Here, we explore the variation

in transcriptome responses to drought within the species Populus balsamifera L. spp. balsamifera

(balsam poplar). P. balsamifera is a dominant tree species within North America’s boreal ecosystems

whose range is transcontinental, and can be found growing on upland riparian sites (Gebre &

Kuhns 1991; Tschaplinski et al. 1994; Chen et al. 1997; Monclus et al. 2006; Bonhomme et al.

2009; USDA-NRCS 2009). On account of its similarity to black cottonwood (Populus trichocarpa

Torr. & Gray) and the wealth of available genomic tools (Tuskan et al. 2006), P. balsamifera

represents an ideal species for studying intra-specific variation in the drought response.

26In this study, Affymetrix GeneChip technology was used to study the drought responsive

component of the Populus transcriptome. Six P. balsamifera genotypes from various geographical

regions (Figure 3.1) were grown in a common growth chamber environment and the variation in

gene expression in trees responding to drought was examined. These experiments aimed to test the

hypothesis that there are significant differences in the trancriptomes of P. balsamifera genotypes

in response to water-deficit conditions; however a common species-level response could also be

assessed. We expect that the knowledge about transcriptome variation within a given species of

poplar will contribute to our understanding of the adaptive responses to drought and the molecular

underpinnings of these responses.

3.3 Materials and Methods

3.3.1 Plant Material

Dormant, 25 cm, un-rooted hardwood cuttings of six P. balsamifera genotypes (AP-947, AP-1005,

AP-1006, AP-2278, AP-2298, AP-2300) were obtained from Alberta-Pacific [Forest Industries

Inc. (Al-Pac), Boyle, AB, Canada]. Cuttings were imbibed for 48 h prior to planting (Desrochers

& Thomas 2003) into Sunshine Mix-1 (Sun Gro Horticulture Inc, Bellevue, WA, USA; http://

www.sungro.com) in 1 m opaque pots (10.5 cm diameter). The plants were grown in a climate-

controlled growth chamber under long day conditions (16 h photoperiod, light intensity: 178-

220 µmol m-2 s-1), with a maximum day temperature of 22 °C and a minimum night temperature

of 17 °C throughout the experiment. All plants were watered every 2 to 3 d to field capacity and

fertilized (20:20:20, N-P-K, 1.5 g L-1, 600 mL plant-1) every 3 weeks. All plants were grown for 9

weeks prior to the onset of the water-withholding experiment, at which point they were divided

into two groups, well watered (WW; n = 27–35 per genotype) and water deficit (WD; n = 27–35

per genotype). Water deficit conditions were imposed on dry plants by withholding water; wet

plants were regularly watered every 2 to 3 d to maintain water status. Fifteen days following the

water withholding, the first fully expanded leaf was harvested from three individual trees from each

genotype for both well watered and water-deficit treatments at two time points: midday (MD;

middle of the light period) and pre-dawn (PD; 1 h before the lights were turned on). Leaves were

pooled and immediately flash frozen in liquid nitrogen for subsequent analysis. This was repeated

three times in order to achieve triplicate replicates for each genotype-treatment combination at each

27

AP 1005, AP 1006

AP 947

AP 2298AP 2278

AP 2300

Figure 3.1 Source of origin of the six P. balsamifera genotypes examined in this study

28time point.

3.3.2 Physiological and growth traits

A portable infrared gas analyser (IRGA; LI-6400, LI-COR Biosciences Inc., Lincoln, NE, USA) was

used for measuring photosynthesis, stomatal conductance (gs) and transpiration. Beginning at the

start of the water-withdrawal experiment, 9 weeks after planting, measurements were taken daily

throughout the experimental period (n = 4–7 individuals per genotype/treatment). Measurements

were on mature, fully expanded leaves at the midday time-point. Productivity and relative water

content (RWC) measurements were made 15d after the onset of the water- withdrawal experiment

on both well-watered and water-deficit-treated plants. Productivity was measured by determination

of tree height, stem circumference and total aboveground dry-weight biomass. Data analysis was

performed using R (R Development Core Team 2009). Means were calculated with their standard

error (SE), and compared using a two-way ANOVA. Genotype and treatment were considered as

the main factors; differences between treatments and among genotypes were determined using a

TukeyHSD test. Leaf RWC was calculated on a mature fully expanded leaf (n = 5 individuals per

genotype/treatment). Fresh weight (FW) was recorded, and the leaf was allowed to rehydrate in

distilled H2O for 24 h in the dark in order to obtain turgor weight (TW). Leaf dry weight (DW)

was obtained after the leaf was dried at 70 °C for 48 h. RWC was calculated according to Barrs &

Weatherley (1962) as: RWC (%) = (FW - DW) * 100%/(TW - DW).

3.3.3 RNA extraction, microarray hybridisation and analysis

Total RNA was isolated from fully expanded leaves of P. balsamifera and hybridized to an Affymetrix

Poplar GeneChip (Affymetrix, Santa Clara, CA, USA) at the Center for the Analysis of Genome

Evolution & Function (CAGEF) at the University of Toronto as described by Wilkins et al.

(2009b). GeneChip expression analysis was performed using the Bioconductor (Gentleman et

al. 2004) software package AFFY (Gautier et al. 2004) in (R Development Core Team 2009) as

described in Wilkins et al. (2009b). All 72 arrays were pre-processed together using GC-robust

multi-array analysis (gcrma; Wu et al. 2004). Expression data was filtered to eliminate probe sets

with low levels of variation across samples and low levels of expression according to Wilkins et al.

(2009b). The preprocessed data was analysed as a 6x2x2 factorial ANOVA design (six genotypes,

two treatments and two time points) using the linear models for microarray package (LIMMA;

29Smyth 2004) package in R (R Development Core Team 2009). Treatment, genotype and time point

were considered the main factors. Differential expression in response to water deficit was determined

using an empirical-Bayes moderated t-statistic with a Benjamini and Hochberg adjustment to

control the false discovery rate (adjusted P value cut-off of 0.05; (Smyth 2004). In order to take into

consideration the magnitude of differential expression for genes that are significantly differentially

expressed for treatment main effect only, probe sets were filtered according to a t-test threshold,

which corresponds to a minimum fold-change of 2.0 (TREAT; McCarthy & Smyth 2009). Genes

were annotated using the Annotation for Probe Sets in PLEXdb (Wise et al. 2007) and Annotation

Batch Function in PopGenie (Sjodin et al. 2009). All samples were uploaded to Gene Expression

Omnibus (http://www.ncbi.nlm.nih.ov/geo/); accession number GSE21171.

3.3.4 Single-feature polymorphism (SFP) analysis

SFPs were identified using pair-wise comparisons between genotypes using Affymetrix Poplar

GeneChip arrays for well-watered, midday poplar samples according to Fujisawa et al. (2009). The

number of SFPs were identified for all probe sets that passed through the filtering criteria, as well as

for probe sets that were either significantly differentially expressed for genotype main effect, or not.

3.3.5 DNA extraction and simple-sequence repeat (SSR) analysis

Total DNA was extracted according to Doyle & Doyle (1990). Seven SSR microsatellite loci were

used to fingerprint the six P. balsamifera genotypes. Five of the seven loci mapped to distinct linkage

group in the Populus genome (Tuskan et al. 2004); however, the remaining two have no informative

mapping information. Electrophoresis-based SSR genotyping was performed by The Centre for

Applied Genomics, The Hospital for Sick Children, Toronto, Canada.

3.4 Results and Discussion

3.4.1 There is intraspecific variation in the productivity and physiological responses in Populus balsamifera following water deficit

To investigate the intraspecific variation of P. balsamifera in response to water deprivation, a multi-

factorial experiment was conducted using six P. balsamifera genotypes. The six genotypes examined

originated from five distinct geographic regions in Western Canada (Figure 3.1), with varying

30climatic histories (Table 3.1). Each P. balsamifera genotype was genetically unique based on SSR

microsatellite fingerprinting (Supplementary Table S3.1).

After 15 d of withholding water, decreased productivity (Figure 3.2) and stomatal conductance

(Figure 3.3) were observed between well-watered plants and those deprived of water. Multi-

factor ANOVA analysis for aboveground biomass, plant height and stem circumference revealed

a significant genotype effect for all three variables; whereas, only significant treatment effect for

aboveground biomass (P = 0.05, data not shown). In response to water-deficit, all six genotypes

exhibited significant differences in midday stomatal conductance (gs); however, genotype AP-1006

had significant differences as early as five days after the onset of the water-deficit treatment (P =

0.1), whereas genotype AP-2300 did not exhibit significant differences until 11 d after the onset of

water-deficit conditions (Figure 3.3). Genotypes AP-1006 and AP-2278 showed striking differences

in gs, between well watered plants and plants grown under water-deficit conditions at day 15. The

differences in gs between well-watered and water-limited plants observed in other genotypes, such

as AP-947 and AP-1005, was less marked. By fifteen days after the onset of the water-withholding

experiment, relative water content (RWC) was significantly lower in genotypes AP-1005, AP-

1006, AP-2298 and AP-2300 (P = 0.05, Supplementary Table S3.2). Phenotypic responses, both

physiological and morphological, to water-deficit treatment in P. balsamifera showed no significant

correlation with historic climatic conditions and geographic origin (Supplementary Figure S3.1).

This may reflect the high level of variation among these genotypes in their ability to respond to

environmental stimuli regardless of their origins. Although their responses do not reflect historic

origins, the variability observed may an important trait for survival in fluctuating environments

(Clark 2010). Moreover, the variation in such adaptive traits may be particularly important in P.

balsamifera as population genotypic variation and effective population size is considered low (Olson

et al. 2010).

Similar reductions in gs and RWC, as well as photosynthetic capacity, have been demonstrated in

poplar and other plant species under drought stress conditions (Duan et al. 2005; Giovannelli et al.

2007); however, in poplar, suppression of gs and photosynthesis occur well before changes in whole

leaf water status are observed (Tardieu & Simonneau 1998; Giovannelli et al. 2007). The regulation

of stomatal conductance and water status in poplar may represent an important trait for survival

under fluctuating environments, particularly the extreme stresses induced by episodic drought.

31

0.00

1.00

2.00

3.00

4.00

5.00

6.00

AP-947 AP-1005 AP-1006 AP-2278 AP-2298 AP-2300

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

AP-947 AP-1005 AP-1006 AP-2278 AP-2298 AP-2300

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

AP-947 AP-1005 AP-1006 AP-2278 AP-2298 AP-2300

Abov

egro

und

biom

ass

(g D

W)

Plan

t hei

ght (

cm)

Stem

circ

umfe

renc

e (m

m)

abc abc

ac

abc

abcabc abc

abd

b b

d

c

e efefg efg efg efg

ef fef ef

gg

hi hi

hihi

hihi

hi

h hh

h

i

Figure 3.2 Above ground biomass (a), plant height (b) and stem circumference (c) of six genotypes

of P. balsamifera were calculated 15 d after the onset of the water-withdrawal experiment for both

well watered (blue bars) and water deficit treated (orange bars) plants. Significant differences

between genotypes and treatments (P < 0.05) are denoted by small letters for all variables. Mean

values and SE bars are represented. Figure originally published in black and white.

32

0.0

0.1

0.2

0.3

0.4

0.5 AP-947

0.0

0.1

0.2

0.3

0.4

0.5 AP-2278

0 1 3 5 7 9 11 13 15

AP-1005

AP-2298

AP-1006

AP-2300

0 1 3 5 7 9 11 13 15 0 1 3 5 7 9 11 13 15

*

*

**

*

*

*

** * **

*******

***

******

******

**** *

Con

duct

ance

(mol

H2O

m-2 s

-1)

Days since the onset of WD

Figure 3.3 Box plot of the variation in midday leaf stomatal conductance for six P. balsamifera

genotypes: (a) AP-947 (b) AP-1005 (c) AP-1006 (d) AP-2278 (e) AP-2298, and (f ) AP-2300.

Midday stomatal conductance for well watered plants (blue boxes) and plants grown under water-

deficit conditions (orange boxes) are represented. Asterisks indicate significant difference between

well-watered and water-deficit-treated plants: *P < 0.1; **P < 0.05; ***P < 0.001. WD, Water-deficit

treatment.

33Table 3.1 Location and climate variables

Clone Lattitude LongitudeElevation (m)

Mean Annual Temperature (°C)

Mean Annual Precipitation (mm)

Degree Days (>5°C)

AP-947 55° 38' 3 .13" 113° 23' 53 .68" 786 1 .8 565 1193AP-1005 55° 24' 25 .95" 114° 36' 19 .67" 630 1 .8 538 1258AP-1006 55° 24' 25 .95" 114° 36' 19 .67" 630 1 .8 538 1258AP-2278 58° 46' 13" 123° 4' 21" NA* -0 .2 533 1294AP-2298 59° 11' 19" 122° 46' 35" NA* -0 .9 468 1255AP-2300 58° 51' 14" 122° 31' 28" NA* -1 .1 439 1241

Location details and historic climatic variables adjusted for specific location and elevation using the

Climate BC model described by Wang et al. (2006). * no elevation data available.

34The marked differences in gs at day 15 suggest variability in the regulation of stomatal control and

physiological response to drought. Intraspecific variation in acclimation strategies in response to

drought has been observed for many other tree species (Beikircher & Mayr 2009). Regulation of

morphological and physiological parameters in response to water-deficit may provide insight into

the hydraulic strategies of the various P. balsamifera genotypes. The observed variation in stomatal

conductance, and other physiological and morphological variables in response to water-deficit,

suggested that these genotypes deployed various drought tolerance and acclimation strategies. To test

this hypothesis, the variation in the transcriptome response to water-deficit conditions among the

six P. balsamifera genotypes was examined.

3.4.2 Water deficit conditions elicit significant responses within the P.

balsamifera transcriptome

Transcriptome analysis can provide insights into similarities and differences in the mechanisms

underpinning the response to water-deficit between groups of individuals. Variation in the

molecular mechanism regulating the drought response in Populus suggests that genotype plays an

important role in shaping the drought transcriptome (Street et al. 2006; Wilkins et al. 2009b).

Comparison of the drought transcriptome of two Populus hybrid genotypes indicate that there

is indeed a level of conservation in the transcriptome response; however, the variable response of

a given genotype cannot be overlooked (Wilkins et al. 2009a). In this study we hypothesize that

conserved transcriptome level responses to drought will be observed, and that the differences

observed in the drought transcriptomes that are specific to an individual genotype may provide

valuable insight into the molecular basis of ecologically important variation in the drought response.

Using Affymetrix Poplar GeneChip microarrays, we investigated the transcript-level response to

water-deficit among six P. balsamifera. Employing a multi-factorial ANOVA design (adjusted P

< 0.05), 280 probe sets reported on genes with significant differential transcript accumulation in

response to water-deficit conditions, irrespective of the effect of genotype or sampling time (Figure

3.4; Supplementary Table S3.4). Many more probe sets were considered differentially expressed

when no minimum threshold cutoff was applied; however, many of those probe sets had very low

levels of differential accumulation of transcripts in response to water deficit. Filtering significant

probe sets using a minimum threshold cutoff allows the identification of genes that are consistently

differentially expressed and may prove to be more biologically meaningful (McCarthy & Smyth

35

AP947.Wet.MDAP947.Wet.MDAP947.Wet.MDAP947.Dry.MDAP947.Dry.MDAP947.Dry.MDAP1005.Wet.MDAP1005.Wet.MDAP1005.Wet.MDAP1005.Dry.MDAP1005.Dry.MDAP1005.Dry.MDAP1006.Wet.MDAP1006.Wet.MDAP1006.Wet.MDAP1006.Dry.MDAP1006.Dry.MDAP1006.Dry.MDAP2278.Wet.MDAP2278.Wet.MDAP2278.Wet.MDAP2278.Dry.MDAP2278.Dry.MDAP2278.Dry.MDAP2298.Wet.MDAP2298.Wet.MDAP2298.Wet.MDAP2298.Dry.MDAP2298.Dry.MDAP2298.Dry.MDAP2300.Wet.MDAP2300.Wet.MDAP2300.Wet.MDAP2300.Dry.MDAP2300.Dry.MDAP2300.Dry.MD

AP2300.MD

AP2298.MD

AP947.MD

AP1005.MD

AP1006.MD

AP2278.MD

−20

2R

ow Z

−Sco

re

Col

or K

ey

Valu

e1

23

4

Col

or K

ey

(b) Relative fold-change transcript abundance at mid day

(a) Relative transcript abundance at mid day

36

AP2300.PD

AP2298.PD

AP947.PD

AP1005.PD

AP1006.PD

AP2278.PD

−20

2R

ow Z

−Sco

re

Col

or K

ey

Valu

e1

23

4

Col

or K

ey

AP947.Wet.PDAP947.Wet.PDAP947.Wet.PDAP947.Dry.PDAP947.Dry.PDAP947.Dry.PDAP1005.Wet.PDAP1005.Wet.PDAP1005.Wet.PDAP1005.Dry.PDAP1005.Dry.PDAP1005.Dry.PDAP1006.Wet.PDAP1006.Wet.PDAP1006.Wet.PDAP1006.Dry.PDAP1006.Dry.PDAP1006.Dry.PDAP2278.Wet.PDAP2278.Wet.PDAP2278.Wet.PDAP2278.Dry.PDAP2278.Dry.PDAP2278.Dry.PDAP2298.Wet.PDAP2298.Wet.PDAP2298.Wet.PDAP2298.Dry.PDAP2298.Dry.PDAP2298.Dry.PDAP2300.Wet.PDAP2300.Wet.PDAP2300.Wet.PDAP2300.Dry.PDAP2300.Dry.PDAP2300.Dry.PD

(d) Relative fold-change transcript abundance at pre-dawn

(c) Relative transcript abundance at pre-dawn

37

Figure 3.4 Heat maps representing transcript abundance of all drought responsive probe sets in

six P. balsamifera genotypes: AP-947, AP-1005, AP-1006, AP-2278, AP-2298 and AP-2300. Only

probe sets that are significant for treatment main effect, irrespective of time of day or genotype,

and are differentially expressed relative to a given threshold are represented (n = 280; FDR = 0.05,

log2(fold-change)-cutoff = 2.0) for both time points: (a, b) mid day, and (c, d) pre-dawn. Row

normalized, transcript abundance for all drought responsive probe sets at (a) mid day and (c) pre-

dawn. Each column represents a biological sample, and all treatments are represented in triplicate

replicates. Red indicates increased transcript abundance; blue indicates decreased transcript

abundance. Data are row normalized. Heat maps representing mean relative fold-change transcript

abundance for all genotypes at (b) mid day and (d) pre-dawn. Dark blue indicates increased

mean transcript abundance in water-deficit treated samples relative to well-watered samples; white

indicates decreased mean transcript abundance in water-deficit treated samples relative to well-

watered samples. Rows are clustered using Pearson correlation for all heat maps.

382009). Consistent with previous findings among species and hybrids of Populus (Street et al. 2006;

Wilkins et al. 2009b), a larger proportion of genes (~5000 probe sets) had significant differences in

transcript abundance for the main effect of genotype relative to the effect of water-deficit treatment

alone or the genotype x treatment interaction together. Genotype was known to play an integral role

in shaping the drought response in Populus between species or hybrids (Street et al. 2006; Wilkins

et al. 2009b). The current study extends this finding, and highlights the importance of genotype in

shaping the water-deficit response within a given Populus species.

3.4.3 There is a common P. balsamifera drought transcriptome

Previously, identification of a common drought transcriptome in the genus Populus was challenging

because of extensive variation in the transcriptome between hybrid genotypes, as well as variation in

the transcriptome-level water-deficit response that is time of day dependent (Wilkins et al. 2009b).

By contrast, comparison of the drought transcriptome across six genotypes of P. balsamifera, at two

time points, revealed a common transcriptome-level response to water-deficit treatment within this

species (Figure 3.4, Supplementary Tables S3.3 and S3.4). The common response genes that were

identified in this comparison had a significant change in transcript abundance in response to water

deficit that was genotype independent (i.e. corresponded to main effect of treatment irrespective of

genotype in ANOVA).

The functional roles of the probe sets that are significant for the treatment main effect for all

genotypes (FDR = 0.05) and also show differential transcript abundance in response to water deficit

according to a minimum threshold cutoff [log2 (fold-change) of 2.0] were classified using GO

categories (Berardini et al. 2004; Supplementary Figure S3.2). Overall, the largest proportion of

probe sets with increased transcript abundance under water deficit conditions were those categorized

as ‘other cellular processes’. By contrast, probe sets with decreased transcript abundance under water

deficit conditions largely fell into the ‘protein metabolism’ category. Interestingly, for both probe

sets, with increased and decreased transcript abundance under water deficit conditions, a large

proportion were categorized as ‘response to abiotic or biotic stimulus or stress’, in keeping with their

involvement in the water deficit response.

Many of the genes comprising the common water-deficit response, genotype-independent P.

balsamifera transcriptome corresponded to genes previously identified as drought responsive in other

39plants (Kreps et al. 2002; Bray 2004; Street et al. 2006; Bogeat-Triboulot et al. 2007; Wilkins et al.

2009a). Of the 280 probe sets that were significant for the main effect of treatment irrespective of

genotype, with a minimum log2 fold-change of 2.0, 29% corresponded to water-deficit-responsive

genes identified in a similar experiment conducted with two hybrid poplar genotypes (Wilkins et

al. 2009b). In comparison to other high-throughput experiments examining the drought response

in Populus (for example, see: Brosche et al. 2005; Street et al. 2006; Bogeat-Triboulot et al. 2007) a

much more limited shared response was identified for P. balsamifera, as was observed by Wilkins et

al. (2009b).

Of the 98 probe sets that reported increased transcript abundance in response to water-deficit in

this study, several of particular interest include those involved in the production of galactinol and

stachyose, including GALACTINOL SYNTHASE and STACHYOSE SYNTHASE. The expression

of genes encoding enzymes involved in the production of sugars from the raffinose family of

oligosaccharides was also water-deficit responsive in hybrid poplar (Wilkins et al. 2009b). Raffinose-

derived oligosaccharides are believed to function as osmoprotectants during drought stress (Taji et

al. 2002; Nishizawa et al. 2008). Increased transcript abundance of genes encoding enzymes that

produce these compounds is consistent with a plant that is attempting to counteract water deficit.

In keeping with a plant mounting a water-deficit response, the common P. balsamifera water-deficit

transcriptome also included genes homologous to gene families in Arabidopsis with key roles in

adjusting water balance in response to water-deficit including ABA RESPONSIVE ELEMENT

BINDING FACTOR 4 (ABRE4) and EARLY RESPONSIVE TO DEHYDRATION 7 (Bray 2004).

The phytohormone abscisic acid (ABA) has an extremely well established role in plant drought

signalling (Bray 2004; Mahajan & Tuteja 2005). The increased transcript abundance of genes

implicated into the ABA signalling pathway in P. balsamifera in response to water deficit emphasizes

the central role of this compound in the drought response across diverse taxa.

A large number of probe sets with decreased transcript abundance in response to water-deficit in

the common P. balsamifera water-deficit transcriptome were homologous to genes involved in cell

wall modification, including pectin esterases and endoxyloglucan transferases. Decreased transcript

abundance of these classes of genes is thought to decrease cell wall extensibility by promoting cell

wall loosening or stiffening (Micheli 2001) and by controlling the cleavage of xyloglucan chains

(Hyodo et al. 2003), respectively. Genes involved in cell wall modification have previously been

40identified as drought responsive in A. thaliana (Bray 2004). A large proportion of genes identified in

the conserved P. balsamifera drought response have unknown function based on homology to other

plant species. These genes that appear to be drought responsive in P. balsamifera may represent a

specific water-deficit response for this species.

3.4.4 There is a notable significant variation in the drought transcriptome across

P. balsamifera genotypes

While a core set of probe sets representing a common species-level response to water-deficit

treatment was identified, many probe sets reported differential expression between P. balsamifera

genotypes. That is, when the water-deficit transcriptomes of the six P. balsamifera genotypes

were compared using a Pearson correlation based on all drought-responsive probe sets (FDR =

0.05), some genotypes were more closely related than others with respect to their water-deficit

transcriptome (Figure 3.5). The similarity between all genotypes was still quite high, with minimum

Pearson correlation coefficient values for any given pair-wise comparison > 0.6. Genotypes AP-

1005, AP-1006 and AP-2278 had the most similar drought transcriptomes; whereas, genotype AP-

2300 was the most distinct with respect to the transcriptome response to water-deficit relative to the

other genotypes.

Various patterns of gene expression in response to drought were identified: genes with increased

levels of transcript abundance for all genotypes, those with decreased transcript abundance across

all genotypes and those with differential drought responsiveness between the six P. balsamifera

genotypes. However, in the conserved set of probe sets that were drought responsive regardless

of genotype (treatment main effect), notable differences in the mean log2 fold-change between

well watered and water-deficit treated samples were observed. The magnitude of change in gene

expression in response to water-deficit treatment varied considerably between genotypes (Figure

3.6). For example, genotypes AP-1006 and AP-2278 had significantly larger fold-changes in

transcript abundance levels relative to other genotypes for those genes with significant differences in

transcript abundance in response to water deficit. This suggests that there was significant variation in

not only transcript abundance of P. balsamifera genes that exhibited a significant treatment-genotype

interaction, but also in the magnitude of intraspecific differences in gene expression for those genes

whose change in transcript abundance was attributable to treatment main effect alone. This is to say

that the variation in P. balsamifera water-deficit transcriptomes across the species is attributable to

41

AP-2

300

AP-9

47

AP-2

298

AP-1

005

AP-1

006

AP-2

278

AP-2300

AP-947

AP-2298

AP-1005

AP-1006

AP-2278

0.6 0.7 0.8 0.9 1Value

Colour Key

Figure 3.5 Pearson correlation coefficient (PCC) heat map representing the P. balsamifera drought

transcriptome responses. Differential transcript abundance between well watered and water-deficit

samples for the six genotypes for the drought responsive probe sets are represented (Treatment

main effect; FDR = 0.05, log2(fold-change) cutoff = 2.0, n = 280 probe sets). Differential transcript

abundance was calculated as the mean log2(fold-change) between well watered and water-deficit

samples for a given probe-set. The PCC was determined for each pair-wise comparison, and is

represented by the colour in the corresponding cell. All samples are represented on both the x- and

y-axis, in the same order.

42

AP-947 AP-1006 AP-2298

−8

−6

−4

−2

0

2

Genotype

−2

0

2

4

6

8

Genotype

log 2

(FC

)

AP-2300AP-2278AP-1005 AP-947 AP-1006 AP-2298 AP-2300AP-2278AP-1005

log

(FC

)2

(a) (b)

Figure 3.6 Box plot illustrating the interplay of genotype and treatment in shaping the drought

transcriptome of six P. balsamifera genotypes. The average log2(fold-change) between well watered

and water-deficit treated samples for all genes identified as significantly differentially expressed

for treatment main effect (FDR = 0.05, log2(fold-change)-cutoff = 2.0, n = 280 probe sets) for

probe sets with (a) decreased transcript abundance in response to WD and (b) increased transcript

abundance in response to WD at the midday time point.

43both qualitative and quantitative changes in transcript abundance.

Qualitative variation was observed among the genotypes in the representation of genes in different

functional categories in the drought transcriptomes. Qualitative differences (i.e. individual gene

identities) in the transcriptomes of each genotype were analysed as a two-factor ANOVA, where

treatment and time point were the two main factors. Genotype-specific responses to water deficit

treatment emerged from these analyses (Supplementary Table S3.3). Notably, across all genotypes, a

large number of genes were predicted to be involved in protein metabolism, or response to biotic or

abiotic stimulus in each genotype, similar to the results observed for the treatment main effect across

all six genotypes (i.e. the common transcriptome). Nevertheless, across genotypes there was variation

in the representation of given GO functional categories, with some GO categories more populated

by drought transcriptome genes of some genotypes relative to other genotypes. This underscores

the fact that there were qualitative differences in the nature of the drought transcriptomes across

genotypes, with each genotype having a ‘GO fingerprint’ that was broadly similar to the other

clones, but still relatively unique.

While natural variation in the transcriptome response to various environmental stimuli has not been

documented in poplar, it has previously been described in A. thaliana (Kreps et al. 2002; Hannah et

al. 2006; van Leeuwen et al. 2007). Variation in the transcriptome response to cold stress between

various accessions of A. thaliana highlighted the complexities of such a response. These stress-

induced alterations in gene expression suggested that not only differential expression of genes, but

also the variation in the magnitude of expression is likely to influence the variation in acclimation

capacity of these accessions (Hannah et al. 2006). Consistent with this, analysis of the A. thaliana

salicylic acid response revealed extensive transcriptome variation, where relatively few genes

responded similarly across the A. thaliana accessions (van Leeuwen et al. 2007). These findings

suggested that A. thaliana ecotypes differentiate to a greater extent in terms of environmental

responsiveness, such that each ecotype is well matched to local environmental conditions. The

higher level of commonalities in the P. balsamifera water-deficit transcriptome response is likely a

consequence of the relatively low population genetic variation found in P. balsamifera (Olson et al.

2010). The higher level of commonalities in the P. balsamifera water-deficit transcriptome response

may be reinforced by the fact that a broad range, long-lived species, like P. balsamifera, must retain

a more generalist response to environmental stimuli across its range. Future studies could test this

44hypothesis by examining the intraspecific variation in P. balsamifera transcriptome responses to a

wider variety of environmental stimuli.

3.4.5 Time of day shapes the P. balsamifera drought transcriptome

The P. balsamifera water-deficit transcriptome is not only shaped by genotype, but also by the

time of day. The interaction between time point and water-limitation revealed 129 probe sets with

significant differences in transcript abundance (Supplementary Table S3.3); however, when the

interaction between the time of day and treatment was assessed within each individual genotype,

particular genotypes, such as AP-1006, had a larger cohort of probe sets with differential transcript

abundance relative to others, such as AP-2298. As previously observed with hybrid poplar

genotypes, the transcriptome-level response to water-deficit conditions were influenced by time of

day, and time of day was an important factor when considering the conserved drought response in

Populus (Wilkins et al. 2009b). However, in P. balsamifera, the time of day treatment interaction

was less significant than that observed previously between the hybrid poplar genotypes (Wilkins et

al. 2009b). Hybrid phenotypes that are more extreme than the parental phenotypes is defined as

transgression (deVicente and Tanksley 1993). The magnitude of transcriptome differences observed

with the hybrid poplar genotypes relative to that observed between the P. balsamifera genotypes

may be attributable to transgressive effects. Transgressive effects have been observed in interspecific

hybrids in other plant genera (Lai et al. 2006), and might be expected in the hybrid poplars, but

would be lacking in the pure P. balsamifera genotypes.

3.4.6 The extent of transcriptome-wide transcript abundance change enables the

P. balsamifera to sustain growth under water-deficit conditions

This study demonstrates the complexities of the drought response within a given species of Populus.

Genotypes with strong physiological responses to water-deficit conditions tended to have increased

magnitude change in expression of genes that were significant for the treatment main effect (Figure

3.6, Supplementary Figure S3.1b). Genotype AP-1006 had the most rapid decline in stomatal

conductance in response to the imposition of water-deficit conditions, and also showed the largest

mean log2(fold-change) between well-watered and water-deficit treated samples for all probe

sets significant for treatment main effect. Correlation between magnitude of cold tolerance and

amplitude of gene expression [log2(fold-change)] has been observed among Arabidopsis accessions

45(Hannah et al. 2006). It has been hypothesized that increased capacity for cold acclimation may be

related to the observed changes in the transcriptome; however, supporting evidence revealed that

reduced cold acclimation in accessions with reduced capacity for cold tolerance was not supported

by metabolic activity. This suggests that the mechanisms that form the foundations of complex

phenotypic traits, such as cold tolerance, or drought acclimation are likely controlled by a large

number of transcriptome changes, rather than individual genes.

Tree growth, another complex phenotypic trait, is also underpinned by genetic factors that respond

to environmental stimuli (Grattapaglia et al. 2009). Consistent with this, the capacity of P.

balsamifera to sustain growth during drought was positively correlated (R2 = 0.776, P = 0.02) with

the magnitude of change in transcript abundance across the remodeled transcriptomes (Figure 3.7).

This suggests that it is not merely the nature of genes that enables plant growth during drought,

but also the magnitude of change in transcript abundance for genes that are drought responsive.

While most studies emphasise the importance of changes in the specific ‘cohort’ of genes expressed

in response to a stress stimulus, the results presented here indicate that the magnitude of change in

transcript abundance for all genes across the transcriptome is every bit as important in buffering

the response. It is noteworthy that sustained growth under drought conditions and the magnitude

of drought-induced, transcriptome-wide changes transcript abundance were the only two factors

that showed a significant correlation in this study (Supplementary Figure S3.1). This underlines

the often-overlooked role of magnitude of transcriptome-wide changes in transcript abundance

as a capacitor for growth in response to key environmental stimuli, and provides a balanced

counterpoint to the focus on the role of individual genes.

3.4.7 The extent of differences in drought-responsive transcriptomes between P. balsamifera clones positively correlated with the extent of intraspecific DNA sequence variation

The differences and commonalities in water-deficit-induced transcript abundance patterns may be

attributable to sequence variants from one P. balsamifera genotype to another. One advantage of

Affymetrix GeneChip data of the sort described herein is that probe-level data provide a relatively

simple means by which to assess sequence polymorphisms between pairs of genotypes. Genome-

wide sequence polymorphisms, known as single feature polymorphisms (SFPs) can be identified

using Affymetrix GeneChip data (Luo et al. 2007; Gupta et al. 2008; Fujisawa et al. 2009). When

46

Difference in Plant Height (cm)

Abso

lute

mag

nitu

de c

hang

e in

gene

exp

ress

ion

(Log

2(fol

d-ch

ange

))

0.0

1.0

2.0

3.0

4.0

0 1 2 3 4 5

AP-947AP-1005AP-1006AP-2278AP-2298AP-2300

Genotype

R2 = 0 .776

Figure 3.7 The relationship between the magnitude change in gene expression and the difference

in plant height between well watered and water-deficit treated P. balsamifera trees. Linear regression

analysis revealed a significant relationship between these two variables (P = 0.02033). The

coefficient of determination (R2) is shown in the figure panel.

47pair-wise comparisons of the single-probe level hybridisation data for the six P. balsamifera genotypes

were examined, across all probe sets on the Affymetrix Poplar whole-genome GeneChip, the number

of SFPs found in any given pair-wise comparison between genotypes varied from approximately

3100 to 13 000 (Table 3.2). Genotypes that appeared to be most divergent based on increased SFP

occurrence between the genotypes also showed decreased commonalities in drought-responsive

genes. For example, genotype AP-947 and AP-2300 were divergent with respect to their drought

transcriptomes (Figure 3.5) and also had >10 000 SFPs. Conversely, genotype AP-1006 and AP-

2278 were more closely related with a high degree of similarity for genes significantly expressed in

response to water-deficit, and also had the least number of SFPs between them.

Notably and importantly, transcript abundance differences observed between genotypes were not

attributable to the number of SFPs identified for a given pair-wise comparison. The proportion

of SFPs identified for any given pair-wise comparison was the same for genes with significant

differences in transcript abundance in response to water-deficit in combination with genotype (i.e.

they had a significant genotype-treatment interaction) and for those where genotype played no role

in water-deficit-induced changes in transcript abundance (i.e. determined by treatment main effect

only; Table 3.2). These data are important in that they reveal that the differences in transcriptome

observed between two genotypes were not attributable to differences in hybridization on account

of sequence polymorphism, but rather most of the intraspecific differences in water-deficit-induced

changes to the P. balsamifera transcriptome were likely attributable to non-coding cis-acting

sequences.

Intriguingly, the degree of relatedness between P. balsamifera genotypes, as defined by frequency of

SFPs, did not correspond to the geographical origin of the genotypes. That is, pairs of genotypes

that were acquired nearby had as many pair-wise SFP differences as pairs that were acquired

from two very different locations. This finding has two important implications. First, inasmuch

as the six genotypes reported here were representative of P. balsamifera, SFP-inferred relatedness

does not reflect geographic distance between genotypes. Second, and more importantly, SFP-

inferred relatedness corresponded to transcriptome-level relatedness for the water-deficit-induced

transcriptome. That is, pairs of genotypes with fewer SFPs had more closely related transcriptome

profiles; whereas, pairs with greater numbers of SFPs had more distinct transcriptomes. These

findings suggest that genetic relatedness is likely to be an indicator of a shared water-deficit

48

Number of ProbeSets with 1 SFP

All Probesets (n=61313)

Filtered probesets not significant for Genotype main effect (n=10794)

Filtered probesets significant for Genotype main effect (n=5123)

Genotype 1 Genotype 2AP-947 AP-1005 11663 40 .40 40 .52AP-947 AP-1006 12110 45 .59 45 .81AP-947 AP-2278 13657 46 .90 47 .18AP-947 AP-2298 10943 43 .16 63 .01AP-947 AP-2300 10602 39 .22 39 .72AP-1005 AP-1006 7185 37 .81 38 .16AP-1005 AP-2278 5052 23 .55 24 .54AP-1005 AP-2298 6934 36 .71 37 .81AP-1005 AP-2300 6759 34 .32 34 .86AP-1006 AP-2278 3158 13 .23 13 .65AP-1006 AP-2298 3785 19 .18 20 .11AP-1006 AP-2300 3815 18 .83 19 .13AP-2278 AP-2298 5066 28 .67 29 .22AP-2278 AP-2300 5786 29 .01 29 .77AP-2298 AP-2300 4650 20 .11 20 .03

Table 3.2 Total number of single-feature polymorphisms (SFPs) were identified in all probe sets

on the Affymetrix Poplar GeneChip using SNEP (P < 0.05; Fujisawa et al. 2009). Genes that were

identified as significantly differentially expressed (FDR = 0.05; log2(FC) cutoff = 2.0) and genes

whose expression is not significantly different among genotypes were surveyed for SFPs and the

proportion was calculated based on the total number of probe sets examined, respectively.

49response. Moreover, the findings suggest that while local environments must play a role in the

selection of specific phenotypic responses in P. balsamifera, the responses of some genotypes appear

to be relatively robust across a large geographical distance for this species. Moreover, resent research

suggests most present day populations of P. balsamifera are the result of large-scale range expansions

that occurred since the last glacial maximum (Keller et al. 2010). Three unique sub-populations

have been identified; samples analyzed for this research originate from one sub-population, and

future studies should include sampling across the whole geographic range for P. balsamifera.

However, taken together, this suggests that both local adaptation and phenotypic plasticity might be

underlying factors determining the wide geographical range of P. balsamifera.

3.5 Conclusion

Although there was a common, shared water-deficit induced transcriptome level response for P.

balsamifera, the amplitude of gene expression for the shared water-deficit transcriptome varied

among genotypes. Larger changes in the absolute magnitude of transcript abundance for probe sets

that were significant for treatment main effect were observed for genotypes that had more rapid

declines in their physiological status in response to drought. Phenotypic traits, such as growth, are

correlated with genetic responsiveness to drought. Genotypes that had the capacity to sustain growth

under water-limitation also exhibited increased magnitude change in the remodelled transcriptome.

Genotypes that had greater commonalities in their drought transcriptomes in response to water-

deficit also had fewer SFP differences, suggesting that responses may be conserved across individuals

that share a greater degree of genotypic similarity. Moreover, the lack of correspondence between

pair-wise SFP differences and geographical origin between genotypes suggests that some genotype-

derived responses are locally adapted, while others are spread widely on the landscape. Together,

these findings better define within-species variation in the response of an important genus to a key

environmental challenge, and raise testable hypotheses regarding the mechanisms underpinning the

drought response in poplars, and how these shape the distribution of poplars on the landscape.


We are most grateful to Bruce Hall and Andrew Petrie for excellent greenhouse assistance, John

McCarron for experimental set up, Joan Ouellette for technical assistance, and Dave Kamelchuk

(Al-Pac) for collecting all the plant materials. We are also most grateful for incredibly useful

50comments on the draft manuscript provided by two anonymous reviewers. Research infrastructure

and technical support was generously provided by the Centre for Analysis of Genome Evolution

& Function at University of Toronto. OW was generously supported by a Natural Science and

Engineering Research Council of Canada (NSERC) Canadian Graduate Scholarship (CGSD).

SDM is a Canada Research Chair. This work was generously supported by funding from NSERC,

the Canada Foundation for Innovation (CFI), and the University of Toronto to SDM, ALP and

MMC.

51

3.7 Supplementary Figures

R = 0.267410

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R = 0.030310.0

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Mean Annual Precipitation (mm) Mean Annual Temperature (ºC) Degree Days (> 5ºC)

Num

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f day

s to

sig

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diffe

renc

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gs

Cha

nge

in A

bove

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(g D

W)

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lant

heig

ht (c

m)

Cha

nge

in S

tem

ci

rcum

fere

nce

(mm

)

(a)

52

R = 0.149420.0

1.0

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R = 0.089710.0

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R = 0.572370.0

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R = 0.068940.0

1.0

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R = 0.058510.0

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R = 0.405850.0

1.0

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Mean Annual Temperature (ºC) Degree Days (> 5ºC)

Change in Abovegroundbiomass (g DW)

Change in Stem circumference (mm)

Number of days to significantdifference in gs

Mean Annual Precipitation (mm)

Abso

lute

mag

nitu

de c

hang

e in

gene

exp

ress

ion

(Log

2(FC

))Ab

solu

te m

agni

tude

cha

nge

inge

ne e

xpre

ssio

n (L

og2(F

C))

AP-947AP-1005AP-1006AP-2278AP-2298AP-2300

(b)

Supplementary Figure S3.1 (a) Correlation between historic climatic variables and observed

phenotypic traits for the six. balsamifera genotypes. (b) Correlation between absolute magnitude

change in gene expression of probe sets identified as significant for treatment main effect in response

to WD conditions and historic climatic variables, phenotypic traits and physiological response.

53

0 10 20 30

unknown biological processes transport

transcription signal transduction response to stress

response to abiotic or biotic stimulus protein metabolism

other metabolic processes other cellular processes

other biological processes electron transport or energy

DNA or RNA metabolism developmental processes

cell organization and biogenesis

0 10 20 30

unknown biological processes transport

transcription signal transduction response to stress

response to abiotic or biotic stimulus protein metabolism

other metabolic processes other cellular processes

other biological processes electron transport or energy

DNA or RNA metabolism developmental processes

cell organization and biogenesis

Relative proportion of probe sets

(a)

(b)


that are differentially expressed for treatment main effect (n = 280, FDR = 0.05, log2(fold-change)

cutoff = 2.0) for (a) increased transcript abundance, and (b) decreased transcript abundance in

response to water-limitation.

54


that are significantly differentially expressed between WD and WW conditions. The proportion of

probe sets identified classified for each GO biological process functional category is represented as

the percentage of total genes differentially expressed for treatment main effect increased transcript

abundance and decreased transcript abundance; FDR = 0.05, log2(fold-change) cutoff = 2.0, n =

280), and each individual genotype when analysed individually as a 2 x 2 factorial (FDR = 0.05).

0

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unkn

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to ab

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othe

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proce

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r cellu

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proce

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ransp

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energ

y path

ways

DNA or R

NA meta

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oces

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rganiz

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and b

iogen

esis

DNA or R

NA bind

ing

hydro

lase a

ctivity

kinas

e activ

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nucle

ic acid

bind

ing

nucle

otide

bind

ing

othe

r bind

ing

othe

r enz

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ctivity

othe

r mole

cular

func

tions

prote

in bin

ding

rece

ptor b

inding

or ac

tivity

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tural

molecu

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on fa

ctor a

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trans

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e activ

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to ab

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prote

in meta

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NA bind

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lase a

ctivity

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e activ

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bind

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ctivity

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in bin

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or ac

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tural

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IncreasedTranscript abundance in response to WD

Decreased transcript abundance in response to WD

55

-15

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TMMER

FAMASTOMAGEN

Supplementary Figure S3.4 Quantitative reverse transcription PCR validation of transcript

abundance levels of selected genes from microarray data.

56

3.8 Supplementary Tables

Supplementary Table S3.1 The microsatellite loci used to fingerprint the six P.

balsamifera genotypes in this study.

Clone Locus Allele 1 Allele 2AP- 947 PMGC 2501 234 -AP- 1005 PMGC 2501 208 264AP- 1006 PMGC 2501 218 234AP- 2278 PMGC 2501AP- 2298 PMGC 2501 300 307AP- 2300 PMGC 2501 347 -AP- 947 PMGC 2818 169 176AP- 1005 PMGC 2818 300 209AP- 1006 PMGC 2818 351 351AP- 2278 PMGC 2818AP- 2298 PMGC 2818AP- 2300 PMGC 2818AP- 947 PMGC 2328 266AP- 1005 PMGC 2328 300AP- 1006 PMGC 2328 348AP- 2278 PMGC 2328 299AP- 2298 PMGC 2328 299AP- 2300 PMGC 2328 299AP- 947 ORMP 356 164 177AP- 1005 ORMP 356 210 246AP- 1006 ORMP 356 300AP- 2278 ORMP 356 300AP- 2298 ORMP 356 350AP- 2300 ORMP 356 246 246AP- 947 PMGC 93 328 354AP- 1005 PMGC 93 328 354AP- 1006 PMGC 93 350 354AP- 2278 PMGC 93 186AP- 2298 PMGC 93 350 354AP- 2300 PMGC 93 350

(a)

57

(b)

Locus Reference Linkage group in P. trichocar-pa

Repeat Motif

Size Range (bp)

PMGC 2501 http://ornl .gov/sci .ipgc/ssr_resource .htm III (GA) 208-347PMGC 2818 http://ornl .gov/sci .ipgc/ssr_resource .htm NA (GA) 169-351PMGC 2328 http://ornl .gov/sci .ipgc/ssr_resource .htm NA (GA) 266-348PMGC 93 http://ornl .gov/sci .ipgc/ssr_resource .htm I (CTT) 186-350ORMP 356 Tuskan et al . 2004 IV (AT) 164-350

58Supplementary Table S3.2 Relative Water Content (RWC) calculated for each of the six P.

balsamifera genotypes after 15 days of water-deficit treatment.

Relative water content (%)Genotype Well Watered Water Deficit pAP-947 80 .18 73 .18AP-1005 81 .4 71 .79 *AP-1006 88 .85 70 .03 *AP-2278 87 .87 76 .54AP-2298 81 .86 74 .84 *AP-2300 83 .7 75 .61 *

*p<0 .05, Students t-test

59Supplementary Table S3.3 Probe sets with significant main effects or interactions for (a) all

genotypes (FDR = 0.05, log2(fold-change) cutoff = 2.0) (b) all genotypes (FDR = 0.05, no

minimum threshold) and (c) all pair-wise genotype comparisons (FDR = 0.05).

Number of Significant Probe setsDecreased Transcript Abundance

Increased Transcript Abundance

G .947 .1005 600 741G .947 .1006 630 1224G .947 .2278 735 1121G .947 .2298 343 288G .947 .2300 322 402G .1005 .1006 368 533G .1005 .2278 343 370G .1005 .2298 735 563G .1005 .2300 622 785G .1006 .2278 404 248G .1006 .2298 1423 810G .1006 .2300 1264 1144G .2278 .2298 1155 658G .2278 .2300 1027 1044G .2298 .2300 147 294Treatment 182 98Tx .947 .1005 90 52Tx .947 .1006 155 55Tx .947 .2278 42 51Tx .947 .2298 0 6Tx .947 .2300 0 2Tx .1005 .1006 861 225Tx .1005 .2278 511 225Tx .1005 .2298 102 70Tx .1005 .2300 35 36Tx .1006 .2278 761 230Tx .1006 .2298 175 65Tx .1006 .2300 67 46Tx .2278 .2298 42 60Tx .2278 .2300 15 48Tx .2298 .2300 0 5Time Point 245 481

(a)

60

G .Tx .947 .1005 9 52G .Tx .947 .1006 51 386G .Tx .947 .2278 33 50G .Tx .947 .2298 0 0G .Tx .947 .2300 0 0G .Tx .1005 .1006 16 24G .Tx .1005 .2278 13 4G .Tx .1005 .2298 21 4G .Tx .1005 .2300 189 18G .Tx .1006 .2278 13 4G .Tx .1006 .2298 329 28G .Tx .1006 .2300 686 51G .Tx .2278 .2298 41 19G .Tx .2278 .2300 115 41G .Tx .2298 .2300 0 0G .Tp .947 .1005 129 21G .Tp .947 .1006 217 32G .Tp .947 .2278 154 40G .Tp .947 .2298 26 1G .Tp .947 .2300 64 3G .Tp .1005 .1006 2 0G .Tp .1005 .2278 1 0G .Tp .1005 .2298 22 42G .Tp .1005 .2300 29 11G .Tp .1006 .2278 0 1G .Tp .1006 .2298 42 122G .Tp .1006 .2300 52 30G .Tp .2278 .2298 45 52G .Tp .2278 .2300 58 27G .Tp .2298 .2300 5 1

61

Number of Significant Probe sets

AP-947 WD Response 122Time Point 3040WD x Time Point 86






(b)

62

Chapter 4: Drought induces alterations in the stomatal devel-opment program in Populus

Contents of this chapter have been published in the Journal of Experimental Botany: Erin T.

Hamanishi, Barb R. Thomas and Malcolm M. Campbell. 2012. Drought induces alterations in

the stomatal development program in Populus. Journal of Experimental Botany. 63(13): 4959-4971

The published paper and supplementary files can be found online at

http://jxb.oxfordjournals.org/content/63/13/4959.long

The material in this chapter is © Oxford University Press, 2012.

63

Chapter 4: Drought induces alterations in the stomatal devel-opment program in Populus

4.1 Abstract

Much is known about the physiological control of stomatal aperture as a means by which plants

adjust to water availability. By contrast, the role played by the modulation of stomatal development

to limit water loss has received much less attention. The control of stomatal development in

response to water deprivation in the genus Populus is explored here. Drought induced declines in

stomatal conductance as well as an alteration in stomatal development in two genotypes of Populus

balsamifera . Leaves that developed under water-deficit conditions had lower stomatal indices than

leaves that developed under well-watered conditions. Transcript abundance of genes that could

hypothetically underpin drought-responsive changes in stomatal development was examined, in two

genotypes, across six time points, under two conditions, well-watered and with water deficit. Populus

homologues of STOMAGEN, ERECTA (ER), STOMATA DENSITY AND DISTRIBUTION 1

(SDD1), and FAMA had variable transcript abundance patterns congruent with their role in the

modulation of stomatal development in response to drought. Conversely, there was no significant

variation in transcript abundance between genotypes or treatments for the Populus homologues of

YODA (YDA) and TOO MANY MOUTHS (TMM). The findings highlight the role that could be

played by stomatal development during leaf expansion as a longer term means by which to limit

water loss from leaves. Moreover, the results point to the key roles played by the regulation of the

homologues of STOMAGEN, ER, SDD1, and FAMA in the control of this response in poplar.

4.2 Introduction

Water availability is a key determinant of plant growth and survival. In keeping with this, plants

have evolved mechanisms to modulate physiological and developmental processes so as to match

water use and retention with water availability. Stomata, the pores found on plant surfaces, play a

key role in regulating water movement and retention in response to the prevailing environmental

conditions. For example, episodic water deficit can invoke a decrease in stomatal aperture with a

concomitant decrease in water loss from the plant body (Cowan and Farquhar, 1977; Chaves et

al., 2003). Although reduction in stomatal aperture in response to drought limits photosynthesis

and affects water-use efficiency, it is a short-term response that enables plants to contend with

64fluctuating water supply (Chaves et al., 2003). Under drought conditions, guard cell-specific

signal transduction in potato modulated short-term stomatal movements as well as long-term gene

expression (Kopka et al. 1997). Plants can also mount more lasting stomatal-based responses to

persistent water deficit (i.e. drought) by controlling stomatal density during development. Lower

stomatal density restricts the number of sites for water loss, with an attendant decrease in water loss.

Changes in stomatal density are brought about by modulating stomatal development during leaf

formation.

Much is known about stomatal development in Arabidopsis thaliana. Stomatal development

proceeds from the asymmetric division of epidermal meristemoid mother cells to the final terminal

differentiation of the guard cells that will form the stomate early in leaf development. Many of

the components of the regulatory network underlying this terminal differentiation pathway have

been characterized (for a review see Bergmann and Sack (2007), Casson and Hetherington (2010).

Intracellular signalling peptides belonging to the EPIDERMAL PATTERNING FACTOR-LIKE

(EPFL) family, such as, EPF-1 and EPF-2, enforce correct stomatal patterning by acting as negative

regulators of stomatal development (Hara et al., 2007; 2009). By contrast, STOMAGEN acts as

a positive signalling factor in stomatal patterning (Kondo et al., 2010; Sugano et al., 2010). The

positive and negative signalling ligands act antagonistically with cell surface receptors, including

members of the ERECTA family of leucine rich repeat (LRR) -receptor like kinases (ER, ERL-1,

ERL-2) to regulate asymmetric divisions at the onset of stomatal development and spacing divisions

(Nadeau and Sack, 2002; Shpak et al., 2005). TOO MANY MOUTHS (TMM) is another LRR-

receptor-like protein involved in the modulation of stomatal patterning, which acts synergistically

as a signal modulator through interactions with the ER-family of receptors (Lee et al., 2012). In

addition, the subtilisin-like protease, STOMATAL DENSITY AND DISTRIBUTION-1 (SDD-

1) acts independently of EPF-1 and EPF-2, but also negatively regulates asymmetric cell division

(Berger and Altmann, 2000; von Groll et al., 2002). Downstream of the aforementioned receptors,

a mitogen activated protein (MAP) kinase signalling cascade is implicated. Activation of the TMM-

ER family complex leads to the stimulation of the MAP kinase signalling cascade starting with

YODA (YDA), a MAP kinase kinase kinase (Bergmann et al., 2004), which in turn activates MKK4

and MKK5, and finally, MK3 and MK6 (Wang et al., 2007). This signalling cascade negatively

regulates stomatal development through three important basic-helix-loop-helix transcription factors,

SPEECHLESS (SPCH), MUTE, and FAMA (Figure 4.1).

65

Figure 4.1 The stomatal development signalling network, based on current literature. Arrows

represent positive regulation; whereas, blocked lines represent negative regulation. Question marks

represent unknown interactions.

TMM ERECTA-family

STOMAGENEPF1 or 2

YDA

kina

se

Plasma Membrane

Apoplast

MAPK Cascade

Nucleus

SPCHMUTEMYB 88

Stomatal Development

SDD1?

MKK 4/5MPK 3/6

FAMA

66The commitment to stomatal development begins with asymmetric cell division of the meristemoid

mother cell regulated by a basic-helix-loop-helix transcription factor, SPEECHLESS (MacAlister

et al., 2007). Following asymmetric division, cells destined to become guard cells change into a

guard mother cell under the control of MUTE (Pillitteri et al., 2007). Each additional amplifying

asymmetric division results in the creation of a new meristemoid cell and a larger neighbouring

cell. These additional divisions result in the formation of more pavement and stomatal cells. Final

differentiation of the stomatal lineage is controlled by another basic-helix-loop-helix transcription

factor, FAMA (Pillitteri et al., 2007). Furthermore, an additional class of bHLH transcription

factors, SCREAM/ICE1 and SCREAM2 that interact directly with SPCH, MUTE, and FAMA, act

to promote the sequential steps in stomatal differentiation (Kanaoka et al., 2008).

In response to environmental change, plants can modulate stomatal development in new leaves

(Casson and Hetherington, 2010). As mature leaves sense environmental conditions, stomatal

density is adjusted in developing leaves (Lake et al., 2001; Miyazawa et al., 2006). An increase in

light quantity positively influences stomatal numbers through the action of PHYTOCHROME

B (PHYB) and the downstream transcription factor phytochrome-interacting FACTOR 4 (PIF4).

Elevated concentration of carbon dioxide leads to a decline in stomatal density, a phenomenon that

has been observed over geological time (Woodward, 1987). In response to CO2, the gene HIGH

CARBON DIOXIDE (HIC) modulates stomatal development in Arabidopsis (Gray et al., 2000).

Loss-of-function hic mutants exhibit elevated stomatal numbers when grown under elevated CO2

(Gray et al., 2000).

Modification of stomatal density in response to drought varies between plant species, and is

contingent on the severity of water deficit. For example, a drought-induced reduction in stomatal

numbers was observed in wheat (Quarrie and Jones, 1977), squash cotyledons (Sakurai et al., 1986),

and umbu trees (Silva et al., 2009). By contrast, increased stomatal density was observed in grass

with moderate drought stress; although, this increase was reversed under conditions of more severe

drought stress (Xu and Zhou, 2008). Variation in stomatal density was observed in response to

drought in Mediterranean plants (Galmés et al., 2007). No significant alteration to stomatal density

in groundnut was observed under drought (Clifford et al., 1995).

The impact of drought on stomatal density in the ecologically and economically important genus

Populus is examined here. Focusing on Populus balsamifera, the aim was to determine the impact

67of drought on stomatal development during leaf formation by testing the hypothesis that drought-

induced modification of the transcription of genes implicated in the stomatal development

regulatory network are linked to changes in stomatal density. More specifically, we set out to test

the hypothesis that the transcript accumulation of positive regulators of stomatal development will

be lower in the developing foliar tissue of water-deficit-treated trees and, conversely, that transcript

accumulation of negative regulators will be higher in the developing foliar tissue of water-deficit-

treated trees. Making use of a transcriptome database for leaf development, and a time-course

series during leaf formation in the presence and absence of drought, genes involved in the Populus

stomatal development network were identified and a subset shown to show a pattern of transcript

abundance in keeping with a role in modifying stomatal numbers in response to drought.


4.3.1 Plant material

Two Populus balsamifera genotypes (AP-1005 and AP-1006) were propagated from unrooted

cuttings (Alberta Pacific, Boyle, Alberta, Canada) in Sunshine mix-1 (Sun Gro Horticulture

Inc, Bellevue, WA, USA). The cuttings used in this experiment were obtained from the research

stoolbeds at the Alberta-Pacific mill site (Alberta, Canada); however, genotype AP-1005 historically

originates from Slave Lake, Alberta, Canada whereas, genotype AP-1006 originates from Smith,

Alberta, Canada. A more detailed description of the two P. balsamifera genotypes can be found in

Hamanishi et al. (2010; Chapter 3, this volume). Trees were grown in a climate-controlled growth

chamber at the University of Toronto (Toronto, Ontario, Canada) with conditions described by

Hamanishi et al. (2010; Chapter 3, this volume). After nine weeks of growth under well-watered

conditions, half of the trees were placed under water-deficit conditions by withholding water, while

temperature and light conditions remained constant. At the onset of the water-deficit experiment,

the first fully expanded leaf on day 0 of the experiment was marked with a red thread, and the

position of the first expanding leaf relative to the first fully expanded leaf on day 0 was recorded.

Fully expanded P. balsamifera leaves were at leaf plastochron index (LPI) 7–8 (Larson and Isebrands,

1971); whereas the developing leaves on day 5 were often at LPI = 2 and at LPI = 4–5 on day 15

after the onset of the water-deficit experiment.

Plant material was harvested at day 0, and every 5 d thereafter until the completion of the 30

68d experiment (days 0, 5, 10, 15, 20, 25, and 30; see Supplementary Figure S4.1). Using three

replicates from each treatment–genotype combination, at the harvesting time-point, the first fully

expanded leaf marked on day 0 from two trees was collected, pooled, and flash-frozen using liquid

nitrogen. This represented a single sample from a single genotype, treatment, and time-point

combination. Similarly, the first expanding leaf from day 0 was collected from two trees, pooled,

and flash-frozen for future analysis. For each sample collection, only two leaves were removed from

each tree: the first fully expanded leaf, and the first expanding leaf from day 0. Once leaves were

sampled from a given tree, the tree was no longer included in the experiment.

4.3.2 Physiological measurements and stomatal quantification

For each genotype, physiological responses to drought conditions were monitored every 2 d starting

from the onset of the water-withholding experiment. Stomatal conductance (gs) measurements

were taken using an infrared gas analyser (LI-6400XT Portable Photosynthesis System, Li-Cor

Biosciences Inc., Lincoln, NE, USA). Measurements of gs were taken on the mature, fully expanded

leaves at the experimental midday time point (n = 3–5 per genotype–treatment group). Temperature

and relative humidity were maintained at 21.3 ± 0.6°C and 62.6 ± 2.17%, respectively, for gas

exchange measurements. Productivity and relative water content (RWC) was assessed periodically

throughout the 30 d experiment. Height and stem diameter were recorded 5, 10, 20, and 30 d after

the onset of the water-withholding experiment. Above-ground biomass was determined at the end

of the experiment (day 30); plants (n=10) were harvested and above-ground biomass (fresh weight

and dry weight) was measured. Leaf RWC was determined using methods described by Hamanishi

et al. (Hamanishi et al., 2010) 15 and 30 d after the onset of the water-withholding experiment.

Impressions of the abaxial epidermis were taken 30 d after the onset of the water withholding

experiment for two classes of leaves. The two classes of leaves included (a) leaves that were fully

developed prior to the onset of the experiment (the first fully expanded leaves at day 0) and (b)

leaves that expanded during the water-withholding experiment (leaves that were marked as the

first emerging leaf at day 0). Impressions of 10 leaves from each class, genotype, and treatment

combination were assessed. Abaxial impressions were taken using clear nail polish and cellophane

tape, as described by Ceulemans et al. (1995) at the widest point of the leaf (approximately 3cm

wide). A minimum of 5 microscopic fields were randomly selected per sample leaf impression, and

the epidermal cell density and stomatal density were calculated. Stomatal index was defined as:

69Stomatal Index = (s / (s+e)) x 100 (Equation 4.1)

where s is the number of stomata and e is the number of epidermal cells per unit area (Radoglou

and Jarvis, 1990).

4.3.3 Gene selection

Putative homologues of genes known to be involved in stomatal development were identified

from Populus using P. trichocarpa sequence data available on phytozome v7.0 [http://phytozome.

net; Tuskan et al. (2006)]. The protein sequences fromArabidopsis were used as a query for BLAST

(BLASTp/PtPEPv2.0) searches against the databases. FAMA (MacAlister and Bergmann, 2011)

and STOMAGEN (Kondo et al., 2010) orthologues in Populus have previously been reported.

Poplar GeneChip (Affymetrix, Santa Clara, CA, USA) probe sets were identified using the NetAffx

resource (http://www.affymetrix.com/analysis/index.affx). Transcript abundance for homologues of

genes implicated in stomatal development was assessed through interrogation of Populus balsamifera

transcript abundance data available in the PopGenExpress compendium of the Bio-Array Resource

(BAR; http://bar.utoronto.ca/; Wilkins et al. 2009a).

4.3.4 Targeted transcript abundance analysis

Three samples were collected from each genotype (AP-1005 and AP-1006), treatment (well-

watered and water-deficit-treated) and developmental stage (first fully expanded leaf from day

0). Flash-frozen plant material collected throughout the experiment (days 5, 10, 15, 20, 25, and

30) was ground to a fine powder under liquid nitrogen. Starting with 1–2g frozen ground leaf

tissue per sample, total RNA was isolated according to Chang et al. (1993). RNA quality was

assessed electrophoretically and spectrophotometrically. 3 µg total RNA was reverse-transcribed

using oligio(dT)18 primers and SuperScript II Reverse Transcriptase (Invitrogen) according to

manufacturer’s instructions. cDNA was diluted 4-fold prior to qRT-PCR analysis. Using iQ SYBR

Green Supermix (Bio-Rad), qRT-PCR was performed for three biological replicates in triplicate

according to published protocols (Raj et al., 2011). Relative transcript abundance was determined

using the method described by Pfaffl (2001). Primer sequences can be found in Supplementary

Table S4.1.

http://phytozome.net/

http://phytozome.net/

70

4.3.5 Statistical analysis

Significant variation in relative transcript abundance was analysed using a general linear model. The

general linear model for the 2×2×6 factorial experiment (2 genotypes, 2 treatments, and 6 time-

points) is represented by:

y ijk= u + A i + B j + C k + (AB)ij + (AC)ik + (BC)jk + (ABC)ijk + εijk (Equation 4.2)

where A corresponds to genotype with i levels, B corresponds to treatment with j levels, and C

corresponds to time-point with k levels. Four possible interactions between genotype and treatment

are represented by ij, 12 interactions between genotype and time-point are represented by ik, 12

interactions between treatment and time-point are represented by jk, 24 three-way interactions

are represented by ijk, and the random error is εijk. The α-level was set to 0.05 for all analyses.

Analysis of variance (ANOVA) was determined for all relative transcript abundance profiles for each

gene. All analyses were performed using R (R Development Core Team, 2011).

Correlation between relative transcript abundance profiles was calculated using Pearson correlation

coefficient analysis in R (R Development Core Team, 2009). Pair-wise analysis of transcript profiles

across all samples was compared for each given transcript.

4.4 Results

4.4.1 Stomatal conductance (gs) and relative water content (RWC) in response to

water-deficit stress

In the two Populus balsamifera genotypes, AP-1005 and AP-1006, gs was significantly lower after

30 d of water withdrawal (t test; P <0.05; Figure 4.2 a, b). Genotype AP-1006 experienced the

largest gs decline in water-deficit-treated plants relative to well-watered samples by day 30 (88%

decrease; Figure 4.2b). A gs decline in water-deficit-treated samples was observed throughout the

drought period for both genotypes; however, AP-1006 exhibited the earliest significant differences

between water-deficit and well-watered samples. A significant difference in gs was observed between

water-deficit and well-watered samples as early as 7 d after water-withdrawal in AP-1006; whereas,

AP-1005 did not show any significant differences in gs until 15 d after the onset of the drought

experiment (P <0.05, Figure 4.2a, b). In AP-1005 and AP-1006, RWC was significantly lower in

71

Figure 4.2 Variation in the physiological response to drought stress in genotype AP-1005 and AP-

1006. Box plot of the variation in midday stomatal conductance for (a) AP-1005 and (b) AP-1006

for well-watered (blue boxes) and water-deficit-treated (orange boxes) samples. Response of intrinsic

water use efficiency (WUEi; A/gs) across well-watered and water-deficit-treated samples for (c) AP-

1005 and (d) AP-1006 and photosynthesis (A) for (e) AP-1005 and (f ) AP-1006 at days 0, 5, and

15 after the onset of water withdrawal. Error bars represent the standard error of the mean.

0.0

0.1

0.2

0.3

0.4

0.5 AP-1005

Con

duct

ance

(mol

H2O

m-2 s

-1)

Days0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

(a)

AP-1006(b)

0.0

0.1

0.2

0.3

0.4

0.5

0 5 15

WUE i

(µm

ol C

O2 m

mol

−1 H

2O)

WetDrought

(c) AP-1005 (d) AP-1006

0 5 15DaysDays

050

100

150

050

100

150

02

46

810

1412

02

46

810

1412

0 5 15Days

0 5 15Days

A (µ

mol

CO

2 m-2

s-1) (e) AP-1005 (f) AP-1006

72water-deficit-treated plants after 30 d of water withdrawal when compared with well-watered trees

(see Supplementary Table S4.2). Similar to the declines observed in gs, genotype AP-1006 had a

more severe reduction in RWC when compared with AP-1005. Overall, there was an increase in the

intrinsic water use efficiency [WUE = A/gs; (Seibt et al., 2008)] in drought-treated samples on day 15

(Figure 4.2c, d), where a larger increase in intrinsic WUE was observed in genotype AP-1006.

4.4.2 Stomatal quantification following water-deficit stress

Abaxial leaf stomatal density and index was lower in leaves that developed under water-deficit

conditions when compared with leaves that developed under well-watered conditions for both

genotypes (Figure 4.3). Leaves that were fully developed prior to the onset of the drought

experiment had no significant variation between treatments in their stomatal indices. Under well-

watered conditions, AP-1006 had the highest stomatal index; however, the leaves of genotype

AP-1006 that developed under water-deficit conditions had the lowest stomatal indices. The

significant reductions in stomatal index were observed for AP-1005 and AP-1006 at 12% and 25%

respectively. Notably, significant variation between treatment and genotype were observed with

respect to stomatal index (P <0.05). No significant difference in photosynthesis (A) was observed at

days 0, 5 or 15 after onset of water withdrawal between well-watered and water-deficit-treated plants

for genotype AP-1005 or AP-1006. Photosynthetic rates throughout the experimental period for

genotype AP-1005 and AP-1006 were significantly lower than previously observed in field grown

P. balsamifera (Silim et al. 2010). The lower photosynthetic rates observed in the chamber-grown

seedlings may be attributable to the lower light levels in the growth-chamber.

4.4.3 Populus homologues of genes implicated in stomatal development

Stomatal development is a function of the integration of many different endogenous and exogenous

signals. Many of the genes involved in the underlying pathways regulating stomatal development

in Arabidopsis have been identified [for a review see Bergmann and Sack (2007)]. Homologues

of genes underlying stomatal development in Arabidopsis thaliana are found in Populus. The

PopGenExpress transcript abundance compendium (Wilkins et al., 2009a) shows that many of these

homologues have relatively greater transcript abundance in young leaves compared with other plant

organs, consistent with their role in modulating stomatal development (Figure 4.4).

73

AP-1005 AP-1006

Wel

l-Wat

ered

Wat

er-D

efic

it

WetDry

**

0

5

10

15

20

25

Stom

atal

Inde

x

Leaf A Leaf B

Wet Dry Wet Dry

Leaf A Leaf B

Wet Dry Wet Dry

(a) (b)

(c) (d)

(e) (f)

Stom

atal

Den

sity

(no.

mm

-2)

Leaf A Leaf B

Wet Dry Wet Dry

Leaf A Leaf B

Wet Dry Wet Dry

WetDry

0

100

200

300

400

500

600

700 (g) (h)

74Figure 4.3 Variation in leaf epidermis between genotype AP-1005 and AP-1006 under (a, b)

well-watered and (c, d) water-deficit conditions, on day 30. White scale bar=50 µm. (e, f ) Box

plot of variation in stomatal index for well-watered (blue box) and water-deficit-treated (orange

box) samples for leaves that were fully developed prior to the onset of the drought experiment (leaf

A) and for those that developed during the drought experiment (leaf B). A significant reduction

in stomatal index is observed in leaves that developed during the experimental period (leaf B) for

each genotype (e) AP-1005 and (f ) AP-1006; however, no significant variation in stomatal index

is observed for leaves that developed prior to the experiment (leaf A), and the onset of water-

deficit conditions. The midline of the box represents the median value for stomatal index (e-f ) or

stomatal density (g-h), the upper and lower bounds of the box represent the interquartile range,

and the whiskers extend to the most extreme values that are not outliers. No signficant change in

stomatal density in response to water-deficit conditions for genotype (g) AP-1005 and (h) AP-1006.

Asterisks represent significant variation between well-watered and water-deficit-treated plants. *P

<0.05

75

Figure 4.4 Heat map of transcript abundance across a range of tissues for Populus homologues of

genes implicated in stomatal differentiation and patterning. Transcript accumulation for the 14

Populus homologues that had differential transcript abundance across the dataset, was derived from

the PopGenExpress microarray compendium made available via http://bar.utoronto.ca (Wilkins

et al., 2009a). As per the scale provided, elevated transcript abundance is represented by red and

diminished transcript abundance is represented by green. The highest levels of transcript abundance

for this group of genes are in young leaves in contrast to other tissue types. Each column represents

a discrete biological sample, and data are represented as biological triplicate replicates for each tissue

type. Data are row normalized.

Figure 5

−4 0 4Row Z−Score

Color Key

Young Leaf

MatureLeaf

Roots Xylem FemaleCatkin

MaleCatkin

SPCHMUTEFAMASCRMCDKB1PIF4YDAEPF1SDD1ERL1TMMCHALERSTOMAGEN

http://bar.utoronto.ca/

76

Figure 4.5 Variation in transcript abundance between well-watered and water-deficit-treated trees

at six time points (days 5, 10, 15, 20, 25, and 30) for genotype AP-1005 (yellow) and AP-1006

(green) represented by the log2(fold change transcript abundance) for genes involved in stomatal

development. A positive log2(fold change transcript abundance) value is an indicator of higher

transcript abundance in water-deficit-treated samples, whereas a negative log2(fold change transcript

abundance) value is an indicator of lower transcript abundance in water-deficit-treated samples.

Figure 5

5 10 15 20 25 30

Days of water withdrawal

Log 2(f

old

chan

ge tr

ansc

ript a

bund

ance

)

5 10 15 20 25 30


Log 2(f

old

chan

ge tr

ansc

ript a

bund

ance

) AP−1005AP−1006

5 10 15 20 25 30


Log 2(f

old

chan

ge tr

ansc

ript a

bund

ance

)

−3−1

12

3

5 10 15 20 25 30


Log 2(f

old

chan

ge tr

ansc

ript a

bund

ance

)

5 10 15 20 25 30


Log 2(f

old

chan

ge tr

ansc

ript a

bund

ance

)

5 10 15 20 25 30


Fold

cha

nge

trans

crip

t abu

ndan

ce

−20

−3−1

12

3−2

0

−3−1

12

3−2

0−3

−11

23

−20

−3−1

12

3−2

0

−3−1

12

3−2

0(a) ERECTA (b) FAMA

(c) SDD1 (d) STOMAGEN

(d) TMM (e) YDA

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

77

4.4.4 Developmental variation in transcript abundance of stomatal development genes after water deficit

The transcript abundance profiles of six genes with putative roles modulating stomatal development

in Populus were examined through development under well-watered and water-deficit conditions

using qPCR. Based on previous studies (Bergmann and Sack, 2007; Casson and Hetherington,

2010), the genes selected are believed to play roles in the development pathway, ranging from

receptors in the signalling cascade to transcription factors regulating the final differentiation step

in the stomatal lineage. The fold change variation in transcript abundance between well-watered

and water-deficit-treated samples revealed variation between time-points, treatments or genotypes

(Figure 4.5). Total cumulative transcript abundance after 30 d of water-withdrawal varied

considerably among genotypes for all genes analysed in this experiment (Table 4.1). A factorial

ANOVA analysis revealed significant variation among days for all genes analyzed, with peaks in

transcript abundance observed early in the experiment, corresponding to earlier in leaf development

(see Supplementary Table S4.3).

TMM (see Supplementary Figure S4.2c ) and YDA (see Supplementary Figure S4.3c) only had

significant differential transcript abundance among days (ANOVA; P <0.05). The highest transcript

levels of TMM were observed on day 10 in the experimental period; whereas, YDA had peak

transcript accumulation on day 5, with no significant difference in transcript accumulation among

the first three time points (see Supplementary Figure S4.3c). Both TMM and YDA exhibited

gradual declines in transcript abundance after the peak in transcript abundance was observed,

with mean transcript levels declining below a 1-log2(fold-change) by the end of the experimental

timeframe (see Supplementary Figures S4.2 and S4.3).

The transcript abundance profiles for ERECTA (ER), FAMA, STOMAGEN, and STOMATAL

DENSITY AND DISTRIBUTION 1 (SDD1) exhibited significant genotype × day interactions

(ANOVA; P <0.05). Transcript abundance profiles of ER for both genotypes had many differences

(see Supplementary Figure S4.4). The highest levels of ER transcript abundance for well-watered

samples in AP-1005 were observed later in the experimental period (day 15 and day 20) when

compared with water-deficit-treated samples; however, the shift in transcript abundance between

treatments was not as evident in AP-1006. For ER (see Supplementary Figure S4.4c) and SDD1

(see Supplementary Figure S4.5c), a more rapid and severe decline in transcript abundance was

78Table 4.1 Mean cumulative transcript abundance across all time-points for genotype AP-1005 and

AP-1006 in well-watered and water-deficit-treated samples.

1005 1006Wet Dry Wet Dry

ER 13 .48 ± 1 .66 10 .21 ± 1 .07 6 .46 ± 1 .18 8 .90 ± 1 .64FAMA 8 .04 ± 0 .81 5 .81 ± 0 .27 10 .82 ± 1 .67 6 .55 ± 0 .43SDD1 7 .67 ± 0 .29 8 .34 ± 0 .19 8 .39 ± 1 .61 6 .57 ± 0 .65STOMAGEN 11 .84 ± 1 .94 9 .55 ± 1 .03 14 .78 ± 2 .25 9 .03 ± 1 .57TMM 7 .67 ± 0 .58 5 .22 ± 0 .34 8 .21 ± 0 .67 8 .91 ± 0 .31YDA 7 .47 ± 0 .38 7 .12 ± 0 .34 10 .39 ± 1 .99 7 .04 ± 0 .53

79observed in AP-1006; however, the rapid decline in transcript abundance was not as evident in AP-

1005.

Significant variation in ER and SDD1 transcript abundance was observed between genotypes

(ANOVA; P <0.05). An overall reduction in transcript abundance for ER and SDD1 was observed

in AP-1006 (see Supplementary Figures S4.5d and S4.6d). ER had a 55% reduction in total

transcript abundance in AP-1006 relative to AP-1005. The reduction observed in genotype AP-

1006 for SDD1 was not as severe.

FAMA and STOMAGEN also had significant treatment × day interactions (ANOVA; P <0.05).

Genotypic influences on transcript abundance variation were less evident for FAMA and

STOMAGEN. Significantly higher transcript abundance was found on days 5 and 10 for FAMA

in well-watered samples (see Supplementary Figure S4.6). Although there was reduced variation

in FAMA transcript abundance in the later part of the experiment (days 15 through 30), FAMA

had a significant treatment main effect. Like FAMA, STOMAGEN had significantly higher

transcript abundance in well-watered samples on day 10 compared with the low variation observed

between treatments for all other days examined (see Supplementary Figure S4.7c). Peak transcript

abundance of STOMAGEN on day 10 is observed for both genotypes; however, the highest

STOMAGEN transcript abundance for water-deficit-treated samples was day 10 for AP-1005 and

day 15 for AP-1006 (see Supplementary Figure S4.7). Significant variation between treatments was

also observed for STOMAGEN ( ; P <0.1; see Supplementary Figure S4.4).

4.4.5 Genes acting as positive regulators in stomatal development have

correlated transcript profiles

The transcript abundance profiles of FAMA and STOMAGEN are more highly correlated (r=0.62;

Figure 4.6) than the negative regulators of stomatal development. The homologuous FAMA and

STOMAGEN show inverse correlation with other negative regulators of stomatal development

analysed in this experiment.

4.5 Discussion

Optimization of carbon uptake and water loss, regulated through the modulation of stomatal

function and development, is critical for plant survival against a background of fluctuating

80

Figure 4.6 Pearson correlation coefficient (PCC) heat map representing the transcript abundance

profiles across AP-1005 and AP-1006 and six time-points. The PCC was determined for each pair-

wise comparison (gene–gene), and is represented by the colour in the corresponding cell. All genes

are represented in the same order on the x- and y-axes.

Figure 6

YDA

EREC

TA

TMM

SDD1

STOMAG

EN

FAMA

YDA

ERECTA

TMM

SDD1

STOMAGEN

FAMA

0 0.5 1

Color Key

Correlation coefficient0.5

1.00

1.00

1.00

1.00

1.00

1.00

0.62

0.440.35

-0.02 0.620.21

0.02 -0.16-0.24

-0.31 -0.15-0.49

0.01

0.42 -0.32

81environmental conditions. Environmental cues such as light and CO2 concentrations have been

shown to modulate stomatal development in Arabidopsis thaliana (Casson and Hetherington, 2010).

There is relatively scant information about the role that water availability plays in the control of

stomatal development in terrestrial plants (Casson and Hetherington, 2010). The modulation

of stomatal development in response to water deficit in Populus was explored here and the role of

candidate genes in the regulation of this process was examined.

4.5.1 Drought response varied between Populus balsamifera genotypes over time

An increase in intrinsic WUE was observed in water-deficit-treated trees, is attributable to the

decline in stomatal conductance observed in the water-deficit-treated plants (Figure 4.2). No

significant difference in photosynthesis was observed between well-watered and water-deficit-

treated plants at day 0, 5 or 15 after the onset of water withdrawal (Figure 4.2 e, f ). After 30 d of

water-deficit, AP-1006 had the most severe reduction in stomatal conductance, yet it also had the

largest reduction in RWC. Variation between poplar genotypes in their physiological response to

drought stress is often observed, and hypothesized to be a result of various strategies to contend with

drought-stress (Marron et al., 2002; Zhang et al., 2004). The changes in physiological status of the

trees in response to water-deficit stress, including an increase in intrinsic WUE, and the decline in

RWC and stomatal conductance in both genotypes after the imposition of water-deficit conditions

may have been responsible for drought-induced modifications to leaf development.

In response to changes in water availability, leaf morphology can vary considerably (Pena-Rojas

et al., 2005). Specifically, with respect to stomatal numbers, modification to environmental

factors that influence mature leaf gs will have lasting effects on the stomatal differentiation of new

leaves (Miyazawa et al., 2006). Two P. balsamifera genotypes, genotype AP-1005 and AP-1006,

had a reduction in stomatal index in response to drought stress, indicating a potential impact on

leaf development by a reduction in water availability. A greater reduction in stomatal index was

observed in AP-1006 (Figure 4.3). The anatomical variation observed between these two genotypes

with respect to stomatal numbers supports the greater reduction in gs observed in AP-1006 at the

end of the experiment (Figure 4.2). The greater reduction in stomatal index in AP-1006 may

be a result of the reduction in water availability and its influence on whole plant water status. A

reduction in stomatal index would result in a long-term strategy to regulate water loss during

82prolonged periods of drought stress, which would ultimately be reflected in larger declines in gs

between well-watered and water-deficit-treated samples. As integral regulators of carbon uptake and

plant water relations, modulation of stomatal differentiation will influence long-term plant WUE

and productivity (Casson and Hetherington, 2010).

4.5.2 Transcript abundance of the Populus homologues of key stomatal

development regulatory genes varied through leaf development in a manner consistent with their proposed molecular functions

Transcript abundance for the Populus TMM orthologue was highly variable between days (Figure

4.5d; see Supplementary Figure S4.2). TMM, a LRR receptor like protein, functions primarily in

the modulation of stomatal number and regulation of stomatal patterning in A. thaliana (Nadeau

and Sack, 2002). In A. thaliana, a single loss of function mutation in TMM results in an excess

of stomata arranged in clusters (Nadeau and Sack, 2002). Although limited variation in TMM

transcript abundance was observed in P. balsamifera, peak transcript abundance was observed on day

10 of the experimental period, early in leaf development (see Supplementary Figure S4.2c). In A.

thaliana, the transcript abundance of TMM is highest in the early stages of the stomatal lineage, in

the young meristemoid mother cell; whereas transcripts were absent in mature guard cells (Nadeau

and Sack, 2002). Transcript abundance for the P. balsamifera TMM homologue peaked at the early

stages of leaf development, and may be congruent with its role previously described in A. thaliana.

Like TMM, there was significant variation in Populus YDA transcript abundance throughout

leaf development; however, peak transcript abundance for P. balsamifera YDA occurred on day

5, presaging the TMM peak (see Supplementary Figure S4.3). YDA encodes a mitogen-activated

protein (MAP) kinase signalling cascade that is involved in the regulation of stomatal differentiation

downstream of the TMM–ERF receptors. In A. thaliana, loss-of-function mutations in the YDA-

encoded MAP kinase kinase kinase result in excess stomatal proliferation (Bergmann et al., 2004).

Although transcript abundance for the Populus YDA homologue did not have a significant treatment

or genotype effect in the experiments described here, it had the highest transcript abundance early in

the experiment, and development, congruent with its early role in stomatal development.

4.5.3 Elevated Populus ERECTA (ER) transcript abundance early in development

83

corresponded with decreased stomatal indices

Transcript abundance analysis revealed significant genotype, day, treatment × day and genotype

× day effects for a Populus ER homologue (see Supplementary Figure S4.4) over the course of the

experiment. In A. thaliana, ER appears to regulate the initial decision of cells to enter the stomatal

lineage and is important for correct stomatal differentiation. Consistent with this, a single loss-

of-function mutation, er, in A. thaliana results in a higher number of stomatal-lineage ground

cells as well as guard cells (Shpak et al., 2005). In the experiment described here, higher transcript

abundance in water-deficit-treated samples, relative to well-watered samples, was observed 5 d

after the onset of the drought experiment for both poplar genotypes (Figure 4.5a). In both poplar

genotypes, a negative relationship between the stomatal indices of leaves that developed under

water-deficit stress and ER transcript abundance (see Supplementary Figure S4.8a). Declines in

stomatal numbers have been observed in response to increased ER transcript abundance in A.

thaliana (Masle et al., 2005). Similarly, over-expression in Arabidopsis of a Populus ER orthologue

(PdERECTA) conferred decreased stomatal numbers. Thus, transcript abundance patterns early in

the experiments are consistent with ER playing a role in the suppression of stomatal numbers in

Populus under water withdrawal conditions.

Although declines in stomatal index were observed in samples with high ER transcript abundance

on day 5, this pattern was not consistent throughout the developmental period. Increased

variability in transcript abundance patterns between well-watered and water-deficit samples

were observed after day 10 for both genotypes. The variation observed for the day × genotype

interaction (see Supplementary Figure S4.4c) highlights the variation observed between AP-1005

and AP-1006. A gradual decline in ER transcript abundance was observed in AP-1006, earlier in

development; whereas a decline in transcript abundance was not observed until after day 15 in AP-

1005. This could be a result of genotypic plasticity or the influence of the redundancy of ER and

functional paralogues, ERECTA-LIKE 1 (ERL1) and ERECTA-LIKE 2 (ERL2), that are known to

act together in the negative regulation of stomatal development (Shpak et al., 2005). However, the

significant decline in overall ER transcript abundance as determined by the genotypic variation (see

Supplementary Figure S4.4c, d) may indicate a more fundamental role of ER in plant development.

ER plays an important role in regulating leaf and whole plant development, and is not restricted to

its involvement in stomatal development (Tisne et al., 2011). Elevated ER transcript abundance

was observed in AP-1005 that demonstrated significantly more height growth than AP-1006 (Table

84Table 4.2 Mean plant height (in cm) on day 30 ±standard error of the mean, n ≥6

AP-1005 AP-1006 P-valueWell-watered 78 .81 ± 2 .73 67 .70 ± 3 .03 0 .0083 *Water-deficit 69 .00 ± 3 .81 63 .06 ± 2 .98 0 .1811

854.2).

4.5.4 STOMATAL DENSITY AND DISTRIBUTION 1 (SDD1) and genotype-specific

control of stomatal development

Loss of SDD1 function in A. thaliana leads to significant increases in stomatal density. SDD1 is a

subtilisin-like Ser protease that is predominantly expressed in stomatal precursor cells, and activates

ER and TMM to repress stomatal development (von Groll et al., 2002). Despite its prominent

role in A. thaliana stomatal development, no significant variation with respect to treatment was

observed in the transcript abundance pattern of the Populus SDD1 homologue over the course of

the experiment in either genotype (see Supplementary Figure S4.5); however, significant variation

between genotypes was observed. SDD1 transcript abundance in AP-1005 was significantly higher

than in AP-1006 (see Supplementary Figure S4.5c). The reduction in transcript abundance in

AP-1006 may reflect an alteration in signalling mechanisms to the underlying signalling cascade

regulating stomatal development in this genotype.

4.5.5 Stomatal development and the regulation of Populus STOMAGEN and

FAMA transcript abundance in response to water deficit

STOMAGEN encodes a peptide that positively regulates stomatal density. The STOMAGEN

peptide is thought to act through antagonistic competition with other peptide signalling molecules,

EPIDERMAL PATTERNING FACTOR 1 and 2 (EPF1 and EPF2), through the LRR-receptor

like protein, TMM (Kondo et al., 2010; Sugano et al., 2010). In A. thaliana, STOMAGEN, is

derived from the mesophyll-tissue, unlike EPF1 and EPF2 that are primarily expressed in the leaf

epidermis, specifically the early meristemoid cells, guard mother cells and guard cells (Kondo et

al., 2010; Sugano et al., 2010). In A. thaliana, over-expression of STOMAGEN increased stomatal

density; whereas loss of STOMAGEN function decreased stomatal density (Kondo et al., 2010;

Sugano et al., 2010).

In the P. balsamifera drought experiment described here, there was significant variation in

STOMAGEN transcript abundance among days and between treatments (see Supplementary Figure

S4.6 and Table S4.4). ANOVA revealed significant day × treatment and day × genotype interactions

for STOMAGEN transcript abundance, suggesting a role for changes in STOMAGEN transcript

86abundance in genotype- and treatment-dependent differences in the regulation of stomatal

development. The most severe log2(fold-change) reduction in STOMAGEN transcript abundance

between well-watered and water-deficit-treated samples were observed in AP-1006 on day 10

(Figure 5d). Notably, the genotype with the lowest stomatal index in the water-deficit-treated

samples was AP-1006. A positive relationship between STOMAGEN transcript abundance on day

10 and stomatal index was observed (see Supplementary Figure S4.8b), consistent with the Populus

homologue of STOMAGEN playing a role in the control of stomatal density.

In Populus hybrids, the stomatal index of new leaves is highly correlated with stomatal conductance

and the physiological status of fully developed leaves suggesting that a long-distance signalling

mechanism is used to regulate stomatal development (Miyazawa et al., 2006). STOMAGEN is

expressed in the mesophyll tissue of immature Arabidopsis leaves (Sugano et al., 2010); however,

stomata are derived from cells on the epidermal layer of leaves. STOMAGEN may play a role in this

long-distance signalling mechanism by acting as a signalling molecule between the mesophyll and

the epidermal layer in leaves. In the Populus drought experiment, reduced stomatal conductance

was observed in response to water-deficit conditions and, similarly, plants exposed to water-deficit

conditions had reduced transcript accumulation of the STOMAGEN homologue together with

reduced stomatal indices. It may be that, in response to water-deficit stress in Populus, STOMAGEN

optimizes long-term carbon uptake and water loss through its role as a mesophyll-derived signalling

factor modulating stomatal development.

Similar to STOMAGEN, FAMA transcript accumulation was highest in the early stages of

development, with peak transcript abundance at day 5 and 10 (see Supplementary Figure S4.6).

In A. thaliana, FAMA is required for the final stages of stomatal differentiation, exhibiting the

greatest transcript abundance in differentiating guard cells, with declining transcript accumulation

as guard cells mature (Ohashi-Ito and Bergmann, 2006). In the Populus drought experiment,

significant variation in FAMA transcript accumulation was observed between days and treatment

(see Supplementary Figure S4.6). The lower levels of transcript accumulation in water-deficit-

treated samples on days 5 and 10 for both genotypes (Figure 5b) and the lower stomatal numbers

observed in water-deficit-treated P. balsamifera samples (see Supplementary Figure S4.8a, b) are

consistent with the role described for FAMA in A. thaliana. Variation among water-deficit-treated

samples throughout the experimental period is considerably less than in the well-watered samples.

87As a transcription factor that is both required and sufficient for the final stages of differentiation,

a minimum accumulation of FAMA transcript may be required for correct stomatal development.

Regardless, the elevated FAMA transcript abundance suggests a role for modulation of this gene to

control stomatal development in Populus under drought conditions.

Despite our knowledge of other genes that influence stomatal development, it is not yet known how

their expression is influenced by water-deficit stress or how they may influence the stomatal index

in Populus. Further exploration of these known players in the stomatal development pathway may

provide an increased insight into the long-term modulation of stomatal development, including

genotypic variation, in response to water-deficit stress. Although it is evident that some players

in the basal stomatal development pathway show altered transcript abundance under water-deficit

conditions, an important question to consider is how water-deficit cues are perceived and integrated

into the stomatal developmental pathway. Such foci will undoubtedly provide fertile grounds for

future research.

4.6 Conclusion

In response to water-deficit stress, P. balsamifera demonstrated significant declines in stomatal

conductance and RWC. Intraspecific variation in physiological responses between two P. balsamifera

genotypes was observed. The largest declines in physiological status were observed in AP-1006 in

response to the imposition of water-deficit conditions. Reductions in stomatal indices were also

observed in both genotypes; however, declines in stomatal index in AP-1006 were markedly larger

than AP-1005. Quantification of transcript abundance profiles of a subset of genes involved in

stomatal development under well-watered and water-deficit-treated conditions revealed variation

between genotypes, as well as between treatments. Notably, STOMAGEN, a mesophyll-derived

signalling peptide, had significantly higher transcript abundance in well-watered samples on days 5

and 10 for both genotypes that corresponded with higher stomatal indices, congruent with its role

as a positive regulator in stomatal development. ERECTA transcript abundance was reduced in

well-watered samples on day 5 for both genotypes; however, variation in transcript abundance later

in development may be a result of the other roles of ERECTA in plant development. Variation in

transcript accumulation of ER and SDD1 between genotypes may indicate variation in drought-

response strategies, specifically with respect to the modulation of development in response to

water-deficit stress. TMM and YDA may not have notable roles in the regulation of stomatal

88differentiation in response to drought.


We are most grateful to Bruce Hall and Andrew Petrie for excellent greenhouse assistance, John

McCarron for the experimental set-up, Joan Ouellette for technical assistance, and Dave Kamelchuk

(Al-Pac) for collecting all the plant materials. We would also like to extend our gratitude for

helpful comments and feedback received by two anonymous reviewers. Research infrastructure and

technical support was generously provided by the Centre for Analysis of Genome Evolution and

Function at the University of Toronto. ETH was supported by an Ontario Graduate Scholarship in

Science and Technology. This work was supported by generous funding from the Natural Sciences

and Engineering Research Council of Canada (NSERC) and the University of Toronto to MMC.

89

4.8 Supplementary Figures

Supplementary Figure S4.1 Experimental design to test the change in transcript abundance

through time and across a developmental series in Populus balsamifera. The first fully expanded

leaf (red circle) and first expanding leaf (red arrow) was marked at the onset of the water-deficit

experiment (day 0), these leaves were subsequently followed throughout the experimental period

(30 days). This enabled collection of leaf tissue that developed throughout the water-deficit

experimental at day 5, 10, 15, 20, 25 and 30. The first fully expanded leaf at day 0 represented a

leaf that was fully developed prior to the onset of water-deficit treatment.

First expanding leaf at day zero

First fully expanded leaf at

day zero

Day 0 Day 30

Time

90

Supplementary Figure S4.2 Variation in the relative transcript accumulation of aPopulus TMM


transcript abundance levels.


(a)

(b)

5 10 15 20 25 30

0.0

0.5

1.0

1.5

2..5(c)


AP-1005

AP-1006

5 10 15 20 25 30

01

23

4

5 10 15 20 25 30

01

23

4


TMM

: Rel

ativ

e tra

nscr

ipt a

bund

ance

well-wateredwater-deficit

TMM

: Rel

ativ

e tra

nscr

ipt a

bund

ance

TMM

: Rel

ativ

e tra

nscr

ipt a

bund

ance

91

Supplementary Figure S4.3 Variation in the relative transcript accumulation of a Populus YODA



(a)

(b)

(c)


AP-1005

AP-1006

5 10 15 20 25 30

Days of water withdrawal0.

00.

51.

01.

52.

02.

53.

0

5 10 15 20 25 30


0.0

0.5

1.0

1.5

2.0

2.5

3.0

5 10 15 20 25 30

0.0

0.5

1.0

1.5

2.0

2.5


YDA:

Rel

ativ

e tra

nscr

ipt a

bund

ance

YDA:

Rel

ativ

e tra

nscr

ipt a

bund

ance

YDA:

Rel

ativ

e tra

nscr

ipt a

bund

ance

92

Supplementary Figure S4.4 Variation in the relative transcript accumulation of aPopulus ERECTA



5 10 15 20 25 30


01

23

45

6

5 10 15 20 25 30


01

23

45

6

AP−1005 AP−1006

0.0

0.5

1.0

1.5

2.0

2.5

5 10 15 20 25 30

01

23

45

AP−1005AP−1006

Days of water withdrawal Genotype

(d)

AP-1005 AP-1006 well-wateredwater-deficit

Genotype x Day Interaction Genotype main-effect

ER: R

elat

ive

trans

crip

t abu

ndan

ceER

: Rel

ativ

e tra

nscr

ipt a

bund

ance

ER: R

elat

ive

trans

crip

t abu

ndan

ceER

: Rel

ativ

e tra

nscr

ipt a

bund

ance

(c)

(a) (b)

93


STOMATAL DENSITY AND DISTRIBUTION-1 homologue as determined by qRT-PCR.

Transcript abundance calculated relative to ACT-7 transcript abundance levels.

(a) (b)

(c)

0.0

0.5

1.0

1.5

2.0

2.5

Days of water withdrawal Genotype

(d)

AP-1005 AP-1006

5 10 15 20 25 30


01

23

4

5 10 15 20 25 30


01

23

4

AP−1005 AP−10065 10 15 20 25 30

0.0

0.5

1.0

1.5

2.0

2.5

3.0

AP−1005AP−1006

Genotype x Day InteractionGenotype main-effect


SDD

1: R

elat

ive

trans

crip

t abu

ndan

ceSD

D1:

Rel

ativ

e tra

nscr

ipt a

bund

ance

SDD

1: R

elat

ive

trans

crip

t abu

ndan

ceSD

D1:

Rel

ativ

e tra

nscr

ipt a

bund

ance

94

Supplementary Figure S4.6 Variation in the relative transcript accumulation of aPopulus FAMA


transcript abundance levels

5 10 15 20 25 30


01

23

4

5 10 15 20 25 30


01

23

4

(a)

(b)

5 10 15 20 25 30

01

23

4


(c)

AP-1005

AP-1006


Treatment x Day Interaction

FAM

A: R

elat

ive

trans

crip

t abu

ndan

ceFA

MA:

Rel

ativ

e tra

nscr

ipt a

bund

ance

FAM

A: R

elat

ive

trans

crip

t abu

ndan

ce

95


STOMAGEN homologue as determined by qRT-PCR. Transcript abundance calculated relative to

ACT-7 transcript abundance levels


5 10 15 20 25 30


STO

MAG

EN: F

old

Indu

ctio

n0

12

34

56

5 10 15 20 25 30


STO

MAG

EN: F

old

Indu

ctio

n0

12

34

56

(a)

(b)

(c)

05 10 15 20 25 3

01

23

45

67


STO

MAG

EN: F

old

Indu

ctio

n

AP-1005

AP-1006

Treatment x Day Interaction

96

ER

TMM

Stom

atal

Inde

x

FMA

STO

MAG

EN

SDD

1

YDA

ER

TMM

Stomatal Index

FMA

STOMAGEN

SDD1

YDA

-1 0 0.5 1

Color Key

-0.5Correlation coefficient

0.2750.725

0.4360.564

0.4740.526

0.8870.113

0.9890.011

0.9990.001

0.2880.712

0.4170.583

0.4960.531

0.8820.118

0.9870.013

0.1300.870

0.4980.502

0.5950.405

0.9460.054

-0.1930.807

0.5930.407

0.8100.190

-0.6310.369

0.3920.608

-0.4310.569

ER

SDD

1

YDA

TMM

FMA

Stom

atal

Inde

x

STO

MAG

EN

ER

SDD1

YDA

TMM

FMA

Stomatal Index

STOMAGEN0.1390.861

-0.2370.763

0.0370.963

0.5390.461

0.7470.253

0.9780.02

0.0150.985

-0.1200.880

0.0820.918

0.5640.436

0.5920.408

0.5090.491

-0.5170.483

-0.0960.904

0.2650.735

-0.6880.312

-0.6970.303

-0.7440.256

0.6580.342

0.8970.103

0.3000.700

ER

Stom

atal

Inde

x

TMM

SDD

1

YDA

FMA

STO

MAG

EN

ER

Stomatal Index

TMM

SDD1

YDA

FMA

STOMAGEN0.4430.557

-0.2770.723

0.1670.833

0.5560.444

0.6840.316

0.9310.069

-0.7080.292

-0.2720.728

-0.4890.511

0.3580.642

0.8980.102

-0.8560.144

-0.0770.923

0.7320.268

-0.0350.965

-0.0800.920

-0.8170.183

-0.6950.305

-0.4890.511

0.5640.436

0.4430.557

(a) Day 5

(b) Day 10

(c) Day 15

97

Supplementary Figure S4.8 Pearson correlation coefficient (PCC) heat map representing the

transcript accumulation profiles and stomatal indices in P. balsamifera at (a) 5 d, (b) 10 d, and (c)

15 d after the imposition of water-deficit stress. The Pearson correlation coefficient (r; top) and

P-value (bottom) are indicated within each square.

98

Supplementary Table S4.1 Primers used for qRT-PCR analysis.

Forward Primer Reverse PrimerSTOMAGEN TTGTTATTCAAGGATCCAGA CTGTGGAGCCAATCATCAATERECTA ATCCAGGGCTGATGACAACA ACAGTAATGCAAGTTGGAAAFAMA ATCAGTGCCAAGCTTGAAGA AACACAGGGCAGTTGCTTCCSDD1 AATATCATGTCAGGTACATC TTGTCACTAACATAGGCAGTTMM CCTGATGGCGAAGAAAAGGC CTGGGAGCGTTAGGCGAGTGYODA CAAGCGTGATGCGACAGGATC AAATTGCCCGTTTTGGTGGTGACT7 GCATCACACCTTCTACAATGAGC CCTGGATAGCGACATACATTGC

4.9 Supplementary Tables

99Supplementary Table S4.2 Relative water content (RWC) on day 30.

AP-1005 AP-1006Well-watered 93 .13 ± 0 .51 93 .98 ± 0 .76Water-deficit 82 .88 ± 1 .92 78 .57 ± 2 .33

100Supplementary Table S4.3 ANOVA results: transcript abundance.

ERECTA Df SS MS F-value p-valueTreatment 1 0 .219 0 .219 0 .171 0 .68Genotype 1 13 .98 13 .98 10 .892 0 .001 *Day 5 55 .99 11 .198 8 .725 6 .57E-6 *Treatment: Genotype 1 7 .213 7 .213 7 .213 0 .021 *Treatment:Day 5 24 .125 4 .825 4 .825 0 .006 *Genotype:Day 5 7 .42 1 .484 1 .156 0 .344Treatment:Genotype:Day 5 14 .38 2 .876 2 .876 0 .65*data shown in supplemental figure 4

FAMA Df SS MS F-value p-valueTreatment 1 1 .217 1 .217 3 .998 0 .049 *Genotype 1 0 .002 0 .002 0 .007 0 .936Day 5 9 .838 1 .968 25 .792 3 .53E-6 *Treatment: Genotype 1 0 .229 0 .229 0 .828 0 .367Treatment:Day 5 3 .653 0 .731 2 .642 0 .035 *Genotype:Day 5 3 .533 0 .707 2 .555 0 .060 .Treatment:Genotype:Day 5 1 .015 0 .203 0 .877 0 .601*data shown in supplemental figure 6

SDD1 Df SS MS F-value p-valueTreatment 1 0 .469 0 .469 1 .414 0 .240Genotype 1 2 .153 2 .153 6 .495 0 .014 *Day 5 3 .753 0 .751 2 .226 6 .29E-2 *Treatment: Genotype 1 0 .046 0 .046 0 .137 0 .713Treatment:Day 5 0 .502 0 .100 0 .303 0 .909Genotype:Day 5 4 .909 0 .982 2 .98 0 .021 *Treatment:Genotype:Day 5 1 .629 0 .326 0 .983 0 .438*data shown in supplemental figure 5

101

STOMAGEN Df SS MS F-value p-valueTreatment 1 2 .689 2 .689 3 .084 0 .086 .Genotype 1 0 .301 0 .301 0 .345 0 .560Day 5 130 .33 26 .066 29 .897 2 .16E-13 *Treatment: Genotype 1 0 .188 0 .188 0 .216 0 .644Treatment:Day 5 14 .03 2 .806 3 .218 0 .014 *Genotype:Day 5 9 .875 1 .975 2 .265 0 .064 .Treatment:Genotype:Day 5 3 .965 0 .793 0 .91 0 .483*data shown in supplemental figure 7

TMM Df SS MS F-value p-valueTreatment 1 0 .031 0 .031 0 .066 0 .798Genotype 1 1 .163 1 .163 2 .451 0 .123Day 5 14 .085 2 .817 5 .938 1 .77E-2 *Treatment: Genotype 1 2 .131 2 .131 4 .491 0 .038 *Treatment:Day 5 0 .79 0 .158 0 .333 0 .566Genotype:Day 5 1 .23 0 .246 0 .519 0 .474Treatment:Genotype:Day 5 2 .04 0 .408 0 .86 0 .357*data shown in supplemental figure 2

YDA Df SS MS F-value p-valueTreatment 1 0 .001 0 .001 0 .002 0 .961Genotype 1 0 .115 0 .115 0 .296 0 .588Day 5 34 .09 6 .818 17 .535 8 .80E-5 *Treatment: Genotype 1 0 .081 0 .081 0 .209 0 .649Treatment:Day 5 0 .015 0 .003 0 .007 0 .932Genotype:Day 5 4 .64 0 .928 2 .387 0 .127Treatment:Genotype:Day 5 0 0 0 0 .992*data shown in supplemental figure 3

* p-value <0 .05 . p-value <0 .1

102

Chapter 5: Integrated analysis of the drought metabolome and transcriptome in Populus balsamifera

Erin T. Hamanishi, Genoa Barchet, Shawn D. Mansfield and Malcolm M. Campbell.

Contributions: ETH, GB, SDM and MMC designed research and organized experimental

logistics; ETH established biological materials, collected samples and analyzed data; GB and SDM

performed metabolic profiling by GC-MS; ETH wrote chapter with editorial assistance from SDM

and MMC.

103

Chapter 5: Integrated analysis of the drought metabolome and transcriptome in Populus balsamifera

5.1 Abstract

Drought has a major impact on tree growth and survival. Understanding tree responses to this

stress can have important application in both conservation of forest health, and in production

forestry. Trees of the genus Populus provide an excellent opportunity to explore the mechanistic

underpinnings of forest tree drought responses, given the growing molecular resources that are

available for this taxon. Here, foliar tissue of six water-deficit stressed P. balsamifera genotypes

was analysed for variation in the metabolome in response to drought and time of day by using

an untargeted metabolite profiling technique, gas chromatography/mass-spectrometry (GC/

MS). Significant variation in the metabolome was observed in response the imposition of water-

deficit stress. Notably, organic acid intermediates such as succinic and malic acid decreased in

accumulation in response to drought, whereas galactinol and raffinose increased in accumulation.

A significant proportion of metabolites with significant difference in accumulation under water-

deficit conditions exhibited intraspecific variation in metabolite accumulation. Larger magnitude

fold-change accumulation was observed in genotype AP- 947, AP-1005 and AP-2278. In order

to understand the interaction between the transcriptome and metabolome, an integrated analysis

of the drought responsive transcriptome and the metabolome was performed. Genotype AP-1006

demonstrated a lack of congruence between the magnitude of the drought transcriptome response

and the magnitude of the metabolome response. More specifically, metabolite profiles in AP-1006

demonstrated the smallest changes in response to water-deficit conditions. Pathway analysis of the

transcriptome and metabolome revealed specific genotypic responses with respect to primary sugar

accumulation, citric acid metabolism and raffinose family oligosaccharide biosynthesis.

5.2 Introduction

Water limitation, particularly periods of drought, impinge on plant growth and survival. There is a

wealth of evidence underscoring the importance of changes in plant biochemistry as a mechanism

to contend with drought. For example, amino acids including proline (Pro), valine (Val) and

isoleucine (Ile)], carbohydrates [such as sucrose, raffinose family oligosaccharides (RFO) and

sorbitol], polyols, and organic acids vary in abundance in response to drought (Krasensky & Jonak

1042012). Elevated levels of sucrose were observed in leaf tissue of water-stressed Populus tomentosa

(Nishizawa et al. 2008); whereas a combination of glucose, fructose and sucrose accumulated in

Populus hybrids in response to drought (Kozlowski & Pallardy 2002). Some of these compounds

are thought to function as osmolytes, maintaining cell turgor and stabilisation of cellular proteins

(Seki et al. 2007). Raffinose, and other raffinose-type oligosaccharides, accumulate in response

to water-stress, and are hypothesised to be osmoprotectants, with the capacity for membrane and

enzyme stability (Taji et al. 2002; Krasensky & Jonak 2012), along with a putative role as hydroxyl

radical scavengers.

Proline accumulation has long been associated with stress tolerance in plants, and is likely one of the

most widely distributed osmolytes among plants and animals (Bartels & Sunkar 2005; Nishizawa

et al. 2008). Similar to carbohydrates, proline is hypothesised to aid in the osmotic adjustment in

response to drought; however, proline is also hypothesised to have roles in reactive oxygen species

(ROS) scavenging and membrane stability. Proline accumulated in severely water-stressed mature

Populus nigra leaves (Kozlowski & Pallardy 2002; Cocozza et al. 2010); whereas no significant

increase in proline accumulation was observed in field-grown, drought treated Populus hybrids .

Proline accumulation in response to drought is variable in among the long-lived, woody Populus

trees and is hypothesised to be the result of the varied role of amino acids in the osmotic adjustment

process, whereby carbohydrates may play a more predominant role in osmotic adjustment in Populus

than amino acids.

Organic acids have also been implicated in the biochemical response to drought. For example,

malic acid increased in abundance under mild periods of water-stress (Tschaplinski et al. 1994;

Escobar-Gutiérrez et al. 1998; Seki et al. 2007). Unlike carbohydrate and amino acid accumulation,

malic acid accumulation may be a function of the stomatal system in plants rather than being

osmotically active (Wilkinson & Davies 2002).

Unsurprisingly, changes in metabolites in response to drought appear to be underpinned by changes

in transcript abundance of specific genes. Large-scale microarray experiments studying water-

deficit stress have identified many transcripts involved in stress tolerance, including key enzymes

involved in the biosynthesis of osmotically-important metabolites in grape (Cramer et al. 2006),

Arabidopsis (Seki et al. 2002; Kreps et al. 2002; Harb et al. 2010), rice (Rabbani et al. 2003) and

poplar (Brosche et al. 2005; Wilkins et al. 2009; Hamanishi et al. 2010; Krasensky & Jonak 2012;

105Yan et al. 2012). For example, galactinol synthase frequently has increased transcript abundance

in response to drought in plants, including Arabidopsis (Taji et al. 2002; Nishizawa et al. 2008) and

Populus (Kozlowski & Pallardy 2002; Wilkins et al. 2009; Hamanishi et al. 2010; Yan et al. 2012).

As the response to drought-stress is not simply the product of the drought-responsive transcriptome,

complexity in the whole plant response to drought is the result of the interactions between genes,

transcripts, proteins, metabolites and the environment. The model plant genus Populus provides

an opportunity to explore the relationship between the drought transcriptome and the drought

metabolome. In keeping with this, the relationship between the transcriptome and metabolome for

specific metabolic pathways in Populus has also been characterised in response to salt-stress, revealing

the importance of control mechanisms for osmotic adjustment (Seki et al. 2007; Janz et al. 2010).

In order to test hypotheses related to intra-specific variation in drought responses in Populus, the

transcriptomes and metabolomes of six genotypes of P. balsamifera were examined. Shared versus

genotype-specific P. balsamifera drought transcriptomes were identified (Hamanishi et al. 2010)

and superimposed onto metabolome variation. This approach identified important pathways in the

drought response, and highlighted genotypic-specific responses that provide insight into different

mechanisms of acclimation to water-limiting conditions.


5.3.1 Plant material and experimental design

Populus balsamifera ramets were grown in a climate controlled growth chamber at the University

of Toronto using conditions as described by Hamanishi et al. (2010). Un-rooted cuttings of six

P. balsamifera genotypes (Alberta Pacific, Boyle, Alberta) were propagated and grown under well-

watered conditions for 9 weeks, at which point, water-deficit stress was imposed on half the trees by

withholding water, while temperature, light and relative humidity remained constant.

Foliar tissue was harvested for metabolite and transcriptome analysis 15 days after the onset of the

water-withdrawal experiment. For the transcriptome analysis, the first fully-expanded, mature

leaf was collected from each tree; three leaves were pooled to create a single replicate. Triplicate

replicates were collected for each genotype and treatment combination at pre-dawn (PD; 1 hour

before the light period)) and mid-day (MD; middle of the light period). Leaves were immediately

106flash frozen, and then ground to a fine powder in preparation for RNA isolation, as described by

Hamanishi et al (2010). For the metabolite analysis, a single mature, fully-expanded leaf (leaf

plastochron index (LPI) = 5-7) was collected from each tree (n=10 per genotype per treatment at

MD and PD). Harvested foliar tissue was weighed to determine fresh weight (FW), subsequently

freeze-dried, and weighed again to determine dry weight (DW). Foliar tissue harvested and

prepared for metabolite analysis was shipped to the laboratory of Professor Shawn Mansfield in the

Faculty of Forestry at the University of British Columbia for analysis.

5.3.2 Non-targeted metabolic profiling by gas chromatography/mass spectrometry

Metabolite extraction was performed using a methanol/chloroform-based extraction protocol as

described by Robinson et al. (2005) and Barchet (2010) at the University of British Columbia.

Approximately 0.5mL of sample was extracted in 1300 uL 97% methanol with the internal standard

ortho-anisic acid (0.62mg mL-1) for 15 minutes at 70°C prior to centrifugation at 17,000 g for 10

minutes. The supernatant was transferred to a new 1.5 mL tube. 130 uL chloroform and 270 uL

distilled, deonized water was added and the tube was gently shaken prior to centrifugation at 17,000

g for 5 minutes. A 400 uL aliquot of the upper polar phase was transferred to a new 1.5mL tube

and dried overnight at 30°C in a Vacufuge (Eppendorf ).

Samples were then derivatized for GC/MS analysis by resuspension in 50uL methoxyamine

hydrochloride solution (20 mg mL-1 in pyridine) and incubated at 37°C for two hours. 10 uL of

n-alkane standard and 70 uL of N-methyl-N-trimethylsilytriflouroacetamide (MSTFA) was added,

and incubated at 37°C for 30 minutes with constant agitation. Samples were then filtered through

filter paper and allowed to rest at room temperature until GC/MS analysis.

GC/MS analysis was conducted on a ThermoFinnigan Trace GC-PolarisQ ion trap MS, fitted with

an AS2000 auto-sampler and a split-injector (Thermo Electron Co., Waltham, MA, USA). The GC

was equipped with a Restek Rtx-5MS column (fused silica, 30m, 0.25 mm ID, stationary phase:

5% diphenyl 95% dimethyl polysiloxane). The GC conditions were set with an inlet temperature

of 250 °C, helium carrier gas at a constant flow rate of 1 mL min-1, injector split ratio 10:1, resting

oven temperature at 70°C and a GC/MS transfer line temperature of 300°C. After a sample

injection of 1 uL, the oven temperature was held at 70°C for two minutes prior to ramping to

107325°C at a rate of 8°C min-1. The temperature was held at 325°C for six minutes before cooling to

the initial resting oven temperature, prior to the next run.

For MS analysis in the positive electron ionization mode an ionization potential of 70eV was used

and the foreline was evacuated to 40 mTorr with helium gas flow in to the chamber set at a rate of

0.3 mL min-1 and the source temperature was held at 230°C. Detector signal was recorded from

3.35-35.5 minutes after the injection, and, with a total scan time 0f 0.58 s, ions were scanned across

the range of 50-650 mass units.

5.3.3 Metabolome: data processing and statistics

The raw metabolite data generated by GC/MS for each metabolite was normalised through

comparison to internal standards and normalised to freeze-dried DW for each tissue sample.

Raw-data was processed using XCMS as described by Barchet (2010; Krasensky & Jonak 2012).

Descriptive statistics were calculated using R 2.14.1 (R Development Core Team 2009). For

subsequent analyses, the metabolite data were log10 transformed. The dataset comprised 87

metabolites, 181 samples (n=4-10 per genotype per treatment per time of day).

Metabolic profiles for all samples were subjected to hierarchical cluster analysis (HCA) using

Pearson correlation coefficient (Eisen et al. 1998; Rabbani et al. 2003) to search for metabolic

similarities and differences among samples and metabolites. The uncertainty associated with HCA

was assessed generating a consensus dendrogram on 1000 bootstrap replicates using the R package

pvclust (Suzuki & Shimodaira 2006). Over-representation of a given metabolite class within a

cluster was determined using Fisher’s exact test in R (R Development Core Team 2009). Statistical

significance was calculated using a three-way ANOVA. The P-values were corrected for multiple

hypothesis testing using the false discovery rate (FDR) procedure of Benjamini and Hochberg

(Benjamini & Hochberg 1995). A P-value of < 0.05 was considered statistically significant.

5.3.4 RNA isolation and transcriptome analysis

RNA isolation and microarray analysis was performed as described by Hamanishi et al. (2010;

Chapter 3 this volume); however, the global drought transcriptome was considered to include

all transcripts significant for a treatment-main effect (P<0.05) with no log2(fold-change) cutoff.

Weighted co-expression network analysis (WGCNA) was performed using the R statistical package

108WGCNA with a power of 7 (Langfelder & Horvath 2008). Functional annotations were assigned

based on the most recent version probe-set annotations from Affymetrix (NetAffx build 32).

Networks generated with WGCNA were plotted using Cytoscape (Lopes et al. 2010). Analysis of

GO term enrichment was calculated by comparing the number of annotations within the list of

query transcripts to all annotated transcripts on the Poplar Affymetrix Genome Array. Statistical

significance was calculated using Fisher’s exact test in R (R Development Core Team 2009), and

applying the Benjamini-Hochberg correction to adjust for FDR . Overrepresentation of GO Slim

terms was confirmed and plotted using AgriGO (Du et al. 2010). Molecular pathways relevant to

the drought transcriptome/metabolome were previously characterised in Kyoto Encyclopedia of

Genes and Genomes (KEGG: Kanehisa & Goto 2000; Masoudi-Nejad et al. 2007; Kanehisa et al.

2011).

5.4 Results and Discussion

5.4.1 Populus balsamifera genotypes were subjected to water withdrawal to induce a drought response

To investigate the impact of drought-like conditions on the abundance of Populus balsamifera

metabolites, six genotypes (AP-947, AP-1005, AP-1006, AP-2278, AP-2298 and AP-2300)

were exposed to a prolonged period of water-withdrawal. All plants were grown under the same

controlled growth conditions for 9 weeks, after which half of the plants continued to receive water

(well-watered) and the other half received no water (water deficit). This divergence in treatment

continued for15 days, at which point plant physiology was recorded, and foliar tissue for metabolic

and transcriptome analysis was collected at pre-dawn (PD) and mid day (MD).

Under conditions of water deficit, declines in above-ground biomass and relative water content

(RWC) were observed in all genotypes (Seki et al. 2002; Kreps et al. 2002; Hamanishi et al. 2010;

Harb et al. 2010). Stomatal conductance significantly decreased in all genotypes, with the greatest

decline observed in AP-1006; whereas genotype AP-2278 had the smallest reduction in stomatal

conductance in response to the imposition of water-deficit conditions (Hamanishi et al. 2010,

Chapter 3, this volume). Net photosynthetic rate also decreased in response to water-deficit

conditions after 15 days of water-withdrawal (treatment main effect; ANOVA, P < 0.05); however, a

significant decline only occurred in genotype AP-1005, AP-1006 and AP-2298 (Welch’s two-sample

109t-test, P < 0.05; Figure 5.1). The net photosynthetic rates were lower in all six genotypes 15 days

after onset of water withdrawal than net photosynthetic rates observed in field-grown P. balsamifera

(Silim et al. 2010). Reduced photosynthetic rates observed in the chamber grown seedlings may

be attributable to the lower light levels in the growth-chamber at the University of Toronto as

compared to ambient levels in field-grown seedlings or trees.

5.4.2 Variation in Populus balsamifera metabolite profiles was evident

To differentiate between genotypic (G), treatment (T) and time-of-day (D) effects, metabolic

profiles of P. balsamifera were analysed using gas chromatography/mass spectrometry (GC/MS).

Trend analysis was restricted to 87 metabolites that were identified across all samples (n=4-10 per

genotype per treatment per time of day), which represented both known and unknown metabolites

(Table 5.1). Hierarchical clustering analysis using the Pearson correlation coefficient revealed the

relative changes in metabolite abundance across samples (Figure 5.2). The dendrogram suggested

that there was a large degree of variation in the metabolite abundance for a given metabolite among

samples, yet both genotype and treatment appeared to play an important role in the segregation

of samples. Notably, the metabolite profiles from water-deficit samples of AP-1005 and AP-2278

appeared most different from the other metabolomes, whereas samples of genotype AP-947 and AP-

2300 clustered in a genotype-wise fashion regardless of treatment or time of day.

Although the metabolomes were highly variable among samples, further investigation of the

relationship among metabolites revealed 13 significant clusters of metabolites that had a high degree

of similarity in their abundance profiles across all samples (Figure 5.3) as determined by hierarchical

cluster analysis using Pearson correlation coefficient. These clusters may be indicative of different

mechanisms of regulation for these metabolites. For example, three of the 13 clusters had significant

over-representation of a given metabolite class (Fisher’s exact test; Padj < 0.05). Specifically, cluster

II comprised predominantly carbohydrates (Padj = 0.00366), cluster IX was all organic acids (Padj =

0.00245) and cluster XII was primarily amino acids (Padj = 0.000251; Figure 5.2).

A three-way factorial analysis of variance (ANOVA) identified metabolites that had significantly

different abundance in response to drought [Treatment (T) main effect], genotype (G main effect),

time of day (D main effect), as well as any interaction between the three experimental factors (Table

5.2). Similar to HCA results, significant variation in the metabolic profiles was attributable to

110

Figure 5.1 Box-plot representing net photosynthetic rate (µmol CO2 m-2 s-1) for genotype AP-947,

AP-1005, AP-1006, AP-2278, AP-2298 and AP-2300. Well-watered samples represented by blue

boxes; water-deficit-treated samples represented by orange boxes (n=3 per treatment per genotype).

The midline of the box represents the median value for photosynthesis, the upper and lower bounds

of the box represent the interquartile range, and the whiskers extend to the most extreme values that

are not outliers.

45

67

89

AP-947 AP-1005 AP-1006 AP-2278 AP-2298 AP-2300

TreatmentWell wateredWater-deficit

Phot

osyn

thes

is (µ

mol

CO

2 m-2 s

-1)

111

Number of

Metabolites

Percent (%) of

MetabolitesGenotype 79 91 .95%Treatment 40 45 .98%Time of Day 11 12 .64%Genotype:Treatment 41 47 .13%Genotype:Time of Day 6 5 .75%Treatment:Time of Day 15 17 .24%Genotype:Treatment:Time of Day 0 0 .00%

Table 5.1 Number of metabolites with significant main effects or interactions (n=87 metabolites).

Padj-value cutoff = 0.05 (Benjamini-Hochberg).

112

Figure 5.2 Dendrogram obtained after hierarchal clustering analysis (HCA) of the metabolic

profiles of the six P. balsamifera genotypes under well-watered and water-deficit conditions at mid-

day and pre-dawn time point. Rows represent specific metabolites (n=87). Columns represent

mean intensity of all replicates for each genotype, treatment and time of day sample. Plotted values

are the mean of n = 4-10 replicates for each sample. Metabolite classes: AA = Amino Acid; C =

Carbohydrate; P = Phenolic, SA = Sugar Alcohol. NI = Not Identified.

−2 0Row Z-Score

Colour Key

AP947AP1005AP1006AP2278AP2298AP2300

GenotypeMid-dayPre-dawn

Time of Day

WetDry

Treatment

2

GenotypeTreatment

Time of Day

NI (8)MaltoseAdenosineSalicinNI (22)NI (Amino Acid; 1)NI (28)NI (3)Shikimic AcidQuercitinKaempferolNI (24)NI (18)L-Ascorbic AcidPyroglutamic AcidL-AlanineL-TheronineL-SerineL-GlutamateButyric_acid-4-amino-n- GlycineL-Proline (1)NI (Amino Acid; 3)L-Proline (2)NI (19)L-Phenylalanine SucroseMyo-inositolQuinic AcidFumaric AcidAspartic AcidMalonic AcidNI (5C Sugar)NI (5C Sugar; 4)NI (2)NI (5C Sugar; 2)NI (Amino Acid; 2)GlycerolThreonic acid 1,4-lactoneThreonic acidGlycolic AcidSuccinic AcidMalic AcidNI (25)NI (7)Thymidine-5'-monophophateNI (17)NI (29)NI (12)Digalactosyl glycerolNI (4)NI (20)NI (11)NI (13)Citric AcidRaffinoseGalactinolNI (27)NI (10)NI (5)NI (15)Ni (9)MelibioseNI(1)NI (5C Sugar; 1)NI (Sugar Alcohol)L-IsoleucineBenzoic AcidCatecholNI (6C Sugar; 2)NI (16)NI (Organic Acid)Fructose (2)NI (6C Sugar; 1)Glucose (2)Glucose (1) Glucose (3)Fructose (1) NI (26)NI (5C Sugar; 3)L-TyrosineSalicyl_alcoholNI (23)NI (6)NI (21)NI (14)Catechin

113

Figure 5.3 HCA reveals 13 significant clusters (P < 0.05). Significant clusters are labeled with

unique colours and numbered (I through XIII) for identification. Hierarchical clustering was done

using pvclust (Suzuki & Shimodaira 2006), with a correlation distance measure and a complete

agglomerative clustering method.

0.0

0.5

1.0

1.5

Cat

echi

nN

I (14

)N

I (21

)N

I (6)

NI (

23)

Salic

yl A

lcoh

olL-

Tyro

sine

NI (

5C S

ugar

; 3)

NI (

26)

Fruc

tose

(1)

Glu

cose

(3)

Glu

cose

(1)

Glu

cose

(2)

NI (

6C S

ugar

; 1)

Fruc

tose

(2)

NI (

Org

anic

Aci

d)N

I (16

)N

I (6C

Sug

ar; 2

)C

atec

hol

Benz

oic

Acid

L−Is

oleu

cine

NI (

Suga

r Alc

ohol

)N

I (5C

Sug

ar; 1

)N

I(1)

Mel

ibio

seN

I (9)

NI (

15)

NI (

5)N

I (10

)N

I (27

)G

alac

tinol

Raf

finos

eC

itric

Aci

dN

I (13

)N

I (11

)N

I (20

)N

I (4)

Dig

alac

tosy

l gly

cero

lN

I (12

)N

I (29

)N

I (17

)Th

ymid

ine−

5’−m

onop

hoph

ate

NI (

7)N

I (25

)M

alic

Aci

dSu

ccin

ic A

cid

Gly

colic

Aci

dTh

reon

ic a

cid

Thre

onic

aci

d 1,

4-la

cton

eG

lyce

rol

NI (

Amin

o Ac

id; 2

)N

I (5C

Sug

ar; 2

)N

I (2)

NI (

5C S

ugar

; 4)

NI (

5C S

ugar

; 5)

Mal

onic

Aci

dAs

parti

c Ac

idFu

mar

ic A

cid

Qui

nic

Acid

Myo

−ino

sito

lSu

cros

eL−

Phen

ylal

anin

eN

I (19

)L−

Prol

ine

(2)

NI (

Amin

o Ac

id; 3

)L−

Prol

ine

(1)

Gly

cine

Buty

ric_a

cid−

4−am

ino−

n−L−

Glu

tam

ate

L-Se

rine

L−Th

eron

ine

L−Al

anin

ePy

rogl

utam

ic A

cid

L−As

corb

ic A

cid

NI (

18)

NI (

24)

Kaem

pfer

olQ

uerc

itin

Shik

imic

Aci

dN

I (3)

NI (

28)

NI (

Amin

o Ac

id; 1

)N

I (22

)Sa

licin

Aden

osin

eM

alto

seN

I (8)

[I] [II] [III] [IV] [V] [VI] [VII] [VIII] [IX] [X] [XI] [XII] [XIII]

Hei

ght

Metabolite Clusters

114genotype. A large proportion of metabolites had differential abundance among genotypes (n=79;

P < 0.05; Table 5.2). Of these 79 metabolites, 38 had no significant two- (i.e., TxG- or DxG-

interaction) or three-way interactions.

ANOVA analysis also revealed a small subset (n=11) of metabolites that varied significantly in

abundance in response to time of day (main effect; Figure 5.4); however, a larger number of

metabolites (n = 15) had abundance that varied significantly in response to water-deficit treatment

in a time-of-day dependent fashion (TxD interaction; Table 5.2; Supplementary Figure 5.4).

Notably, proline had a significant increase at the PD relative to MD (Figure 5.4a). Conversely,

sucrose had increased abundance at the MD time point (Figure 5.4b). Sucrose is a diurnally

regulated metabolite that has increased abundance during light conditions, as seen in potato

(Urbanczyk-Wochniak et al. 2005) and in Populus (Hoffman et al. 2010).

5.4.3 A Populus balsamifera drought metabolome was identifiable

Water withdrawal induced significant changes in metabolite abundance. Forty metabolites had

different abundance levels in response to drought, regardless of genotype or time of day. Twenty-

one metabolites increased in abundance and 19 decreased in abundance (ANOVA; P < 0.05; Table

5.3; Figure 5.5a). No general class of metabolites responded to drought. For example, the amino

acid (AA) class had variable response to drought. The contribution of amino acids in Populus clones

is thought to be small relative to the effect of carbohydrates and other osmolytes (Tschaplinski &

Tuskan 1994); however, isoleucine had the largest fold increase in abundance in response to drought

of any metabolite assessed, but was the only branched chain amino acid (BCAA) to be analysed,

whereas aspartic acid and threonine decreased in abundance in the drought-treated samples.

Increased accumulation of BCAAs has been observed in other organisms including Arabidopsis

(Joshi & Jander 2009) and various wheat cultivars (Bowne et al. 2012). Although increased

accumulation of BCAAs has frequently been observed in response to abiotic stress, little is known

about their role in stress tolerance; however, accumulated BCAAs may serve as a substrate for the

synthesis of other stress-induced proteins and may act as signalling molecules in response to drought

stress (Nambara et al. 1998).

Two organic acids, representative of TCA cycle intermediates, succinic and malic acid, had a

general decline in abundance; whereas raffinose and galactinol, a trisaccharide and sugar alcohol

115Table 5.2 Metabolites with significantly different abundance levels in response to drought

(ANOVA, Padj-value < 0.05).

Group Metabolite Fold-changeAmino Acid Aspartic Acid -0 .68

L-Isoleucine 3 .32L-Threonine -0 .39NI (Amino Acid; 2) -0 .26

Carbohydrate Fructose (2) -0 .16Glycerol -0 .58Melibiose 0 .30NI (5C Sugar; 2) -1 .18NI (6C Sugar; 1) -0 .16Raffinose 1 .13Sucrose 0 .28

Organic Acid Benzoic Acid 1 .13Citric Acid 1 .07Fumaric Acid -1 .30Glycolic Acid -0 .39Malic Acid -0 .12Malonic Acid -1 .58Quinic Acid -0 .37Shikimic Acid -0 .55Succinic Acid -0 .97Threonic acid -0 .25Threonic acid 1,4-lactone -0 .57

Phenolic Catechol 0 .45Quercitin -0 .24Salicin 0 .83Salicyl_alcohol 1 .56

Sugar Alcohol Galactinol 0 .52Myo-inositol 0 .15

Not Identified NI (2) -0 .14NI (3) -0 .43NI (4) 0 .36NI (5) 0 .69NI (7) 0 .15Ni (9) 0 .24NI (10) 1 .36NI (11) 0 .53NI (15) 0 .97NI (18) 0 .39NI (20) 0 .55NI (25) 0 .53

116

Figure 5.4 Time of Day (D) main effect observed for (a) proline (2) and (b) sucrose between mid-

day (MD) and pre-dawn (PD).

MD PD

1.0

1.5

2.0

2.5

Proline (2)

MD PD

5.7

5.9

6.1

6.3

Sucrose

Abun

danc

e

Abun

danc

e

117

MD

AP-947 AP-1005 AP-1006 AP-2278 AP-2298 AP-2300

PDM

DPD

MD

PDM

DPD

MD

PDM

DPD

MD

AP-947 AP-1005 AP-1006 AP-2278 AP-2298 AP-2300

PDM

DPD

MD

PDM

DPD

MD

PDM

DPD

15

(a)

(b) (c

)

Trea

tmen

t m

ain

effe

ctTr

eatm

ent:G

enot

ype

Inte

ract

ion

1517

55

4

1

Trea

tmen

t:Tim

e of

Day

Inte

ract

ion

-0.2

-0.1

5-0

.1-0

.05

00.

050.

10.

15

Rel

ativ

e Ab

unda

nce

[log 2(F

old

Cha

nge)

]

NI

NI

NI

NI

NI

Mel

ibio

seSu

cros

eM

yo-in

osito

lN

IN

I(Am

ino

Acid

)L-

Thre

onin

e NI

Thre

onic

Aci

dG

lyce

rol

Que

rciti

n

−20

12

Row

Z−S

core

Col

or K

ey

−1

118Figure 5.5 Metabolite accumulation levels for treatment main effect and treatment x genotype

interaction. (a) Hierarchally clustered metabolites that are significant for treatment main effect

across all genotypes at two different time-points [pre-dawn (PD) and mid-day (MD)]. (b) Venn

diagram demonstrating the number of metabolites that are significant for treatment main effect or

a 2-way interaction. (c) Mean log2(fold-change) of metabolite abundance for metabolites that are

significant for treatment main effect only.

119Table 5.3 Module membership in the drought transcriptome network of AP-1006 and preservation

of drought modules among the other genotypes.

120

Col

our

Mod

ule

AP-

1006

Num

ber

of m

odul

e m

embe

rs

(gen

es)

Mod

ule

AP-

947

Mod

ule

AP-

1005

Mod

ule

AP-

2278

Mod

ule

AP-

2298

Mod

ule

AP-

2300

Ove

rlap

P-va

lue

Ove

rlap

P-va

lue

Ove

rlap

P-va

lue

Ove

rlap

P-va

lue

Ove

rlap

P-va

lue

yello

wM

1_10

0619

2-

6812

1-

-bl

ueM

2_10

0635

549

387

242

--

blac

kM

3_10

0693

--

58-

-tu

rquo

ise

M4_

1006

399

4030

914

044

244

brow

nM

5_10

0619

948

283

183

-38

gree

nM

6_10

0618

8-

140

149

9751

pink

M7_

1006

72-

201

81-

-re

dM

8_10

0611

174

128

53-

-m

agen

ta-

M9-

947

(27)

--

--

purp

le-

--

M10

_227

8 (5

9)-

-

121respectively, were some of the most highly accumulated metabolites in response to water-deficit

conditions (Table 5.3). Although a general decline was observed in abundance of malic acid,

patterns of accumulation in response to drought in Populus are often varied; both increased and

decreased levels of accumulation in response to drought have been observed (Tschaplinski & Tuskan

1994; Koussevitzky et al. 2008). Malic acid is a very abundant organic acid in plants, and its role

is likely not restricted to the citric acid cycle (Maclennan et al. 1963). Sugars have previously

been shown to increase in abundance in response to water-stress, having an important role in

the osmotic adjustment (Chaves et al. 2003; Regier et al. 2009). Raffinose and galactinol have

been hypothesised to be osmoprotectants in drought-stress conditions, and have frequently been

implicated in the drought response in plants (Taji et al. 2002; Nishizawa et al. 2008).

Notably, of the 40 metabolites that were significant for T main effect, only 15 did not show any

significant interactions (i.e., TxG or TxD; Figure 5.5b). Carbohydrates, a sugar alcohol, and some

unknown metabolites had increased abundance in water-deficit conditions, whereas decreased

abundance was exhibited by a variety of metabolites representative of different metabolite classes

(Figure 5.5c). As indicated by the large proportion of metabolites significant for TxG or TxD

interactions, the accumulation of metabolites was not simply due to the imposition water-deficit

stress, rather, metabolite accumulation was a complex response shaped by genotype and time of day.

The variation in metabolite accumulation across genotypes and at different time-points could be

exploited to further investigate the unique responses of P. balsamifera genotypes.

5.4.4 The drought metabolome varied among P. balsamifera genotypes

While a large proportion of metabolites had significant response to water-deficit treatment,

many of these varied in a genotype- (G) or a time-of-day- (D) dependent manner (Figure

5.5b). The abundance of 41 metabolites was significantly impacted by TxG interaction (Table

5.2; Supplemental Figure A.2). Certain metabolites had opposite patterns of accumulation in

response to drought (i.e., higher abundance in one genotype and lower abundance in another

genotype). Of note, glucose had elevated abundance levels in AP-947 and AP-1006, but decreased

abundance levels in the remaining four genotypes in response to water-deficit conditions. Similarly,

galactinol was significant for a G x T interaction (P = 0.0259), but the highest level of galactinol

accumulation was observed in drought treated samples of genotype AP-947 and AP-2278. Other

metabolites that had a significant TxG interaction also had consistent response to water-deficit stress

122among the six genotypes. Notably, considerable variation in the magnitude of change was observed.

For example, glycolic and threonic acids, two metabolites belonging to cluster IX (Figure 5.3)

decreased significantly in abundance in response to water-deficit conditions, with notable reductions

observed in genotype AP-1005 and AP-2278. Moreover, half of the metabolites that had significant

differences in abundance between treatments (T main effect) also varied in response to genotype

(n=20; Figure 5.5b) confirming the importance of genotype in defining the drought response

observed among samples.

Ten drought-responsive metabolites also had significant differences in abundance for a TxD

interaction, indicative of the variation in metabolite level observed between pre-dawn and mid day.

Raffinose abundance was significant for a TxD interaction, having ~2-fold increase in accumulation

in response to water-deficit at MD (P = 0.0122), but no significant change in abundance at PD

(Supplemental Figure 1).

A notable feature of the P. balsamifera drought metabolome was the magnitude of variation observed

between samples. On average, peak signal intensity (non-transformed data) varied ~3000-fold

between minimum and maximum peak intensity for any given metabolite (Supplementary Figure

5). Similarly, the magnitude of variation in metabolite accumulation between water-deficit and

well-watered samples varied considerably. Among the metabolites whose accumulation had a

significant T main effect, the fold-change variation ranged from ~3 fold decrease in malonic acid

accumulation to ~10 fold increase in isoleucine accumulation. Overall variation in the drought

metabolome was examined by Pearson correlation comparison of the log2(fold-change) of the

water-deficit metabolome of the six P. balsamifera genotypes. This analysis revealed which genotypes

had metabolome responses that were more equivalent to others (Figure 5.6). Notably, genotypes

AP-1005 and AP-2278 had not only the most similar metabolomes (Figure 5.2) but also the most

similar drought metabolomes (r = 0.845; P < 0.05), whereas genotypes AP-2300 (r < 0.550) and

AP-2298 (r < 0.606) were most divergent when compared to all other genotypes (Figure 5.6).

The magnitude of drought-induced changes in metabolite abundance among the six P. balsamifera

genotypes had a high degree of variation (Figure 5.4a). Notably, the largest absolute magnitude

change in drought responsive metabolites occurred in genotype AP-1005 (mean = 0.306, standard

deviation = 0.229) and AP-2278 (mean = 0.290; standard deviation = 0.234); whereas the smallest

magnitude change was observed in genotype AP-1006 (mean = 0.149; standard deviation = 0.150).

123

AP-2298

AP-2300

AP-1006

AP-947

AP-1005

AP-2278

0.4 0.6 0.8 1

Color Key

AP-2

298

AP-2

300

AP-1

006

AP-9

47

AP-1

005

AP-2

278

Pearson correlation coefficient, r

Figure 5.6 Variation in the drought metabolome among six genotypes of P. balsamifera represented

by a Pearson correlation coefficient (PCC) heatmap. Differential abundance [log2(fold-change)]

for metabolites significant for treatment main effect (ANOVA, P < 0.05) are represented. The PCC

value was calculated for each pair-wise comparison among genotypes, and is represented by the

colour in the given cell. All genotypes are represented on both the x- and y-axis in the same order.

124

5.4.5 There were correlations between drought-responsive metabolites and specific components of transcriptome remodelling

To assess relationships between drought-responsive metabolites and transcripts, the metabolomes

and transcriptomes of P. balsamifera were compared. These analyses made use of previously-reported

drought-responsive transcriptome data for P. balsamifera (Hamanishi et al. 2010). Quantitatively,

there was a high level of congruence between the metabolome and the transcriptome, where larger

magnitude changes in the transcriptome corresponded with larger magnitude changes in the

metabolome, with the notable exception for genotype AP-1006 (Figure 5.7). More specifically,

genotype AP-1006 and AP-2278 had significantly larger magnitude change in the drought

transcriptome relative to all other genotypes (Bonferroni’s P < 0.001; Supplementary Figure 7;

Figure 5.7b); whereas the absolute magnitude change observed in the metabolome for AP-1006 and

AP-2278 was among the smallest and largest, respectively. This suggests that coordination of the

transcriptome and metabolome is variable among genotypes, and that the overall magnitude change

in metabolite abundance does not necessarily reflect the magnitude of transcriptome variation

resulting from water-deficit treatment.

A correlation matrix of all pair-wise comparisons among drought responsive metabolites and

transcripts revealed 747 transcripts that were significantly correlated with at least one metabolite

(Pearson correlation coefficient, |r| > 0.60, P < 0.05) based on the similarity of abundance profiles

across all samples (Figure 5.8; Supplementary Figure 8). Correlation patterns between metabolites

and transcripts were similar among a majority of the organic acids with the exception of citric,

benzoic and shikimic acid. A significant proportion of organic acids were previously shown

(5.4.2 Behavior of metabolites in Populus balsamifera) to share similar patterns of abundance

across samples; however, citric, benzoic and shikimic acid did not. Similarly, three amino acids

(aspartic acid, threonine and an unidentified amino acid) had similar correlation patterns; whereas

the correlation pattern for isoleucine was distinct. Unlike the other three amino acids, isoleucine

increased significantly in abundance in response to water-deficit with a more pronounced increase at

the mid-day time point (Supplemental Figure A.1). These results suggest that the regulatory control

of the metabolites with similar patterns of expression may be shared; whereas the metabolites with

distinct correlation patterns are likely influenced by distinct molecular mechanisms.

Among the transcripts significantly correlated with at least one metabolite, enrichment for GO

125

Figure 5.7 Box-plot illustrating the interplay of genotype and treatment in shaping the drought

metabolome and transcriptome of six P. balsamifera genotypes. The average absolute log2(fold-

change) between well-watered and water-deficit-treated samples for all (a) metabolites (n=40; P <

0.05) and (b) transcripts (n = 2636; P < 0.05) with significant variation in their abundance between

treatment conditions at the mid-day (MD) time point.

01

23

AP-947 AP-1005 AP-1006 AP-2278 AP-2298 AP-2300

Log 2(f

old-

chan

ge) t

rans

crip

t acc

umul

atio

n

Transcriptome MD

Metabolome MD

0.0

0.2

0.4

0.6

Log 2(f

old-

chan

ge) m

etab

olite

acc

umul

atio

n

0.8 (a)

(b)

126

−0.5

00.

5Pe

arso

n C

orre

latio

n C

oeffi

cien

t, r

Col

or K

ey

NI

NI

NI

NI

NI

NI

NI

NI

NI

NI

NI

NI

Aspa

rtic

Acid

NI (

Amin

o Ac

id; 2

)L-

Ther

onin

eL-

Isol

euci

neG

lyce

rol

NI (

5C S

ugar

; 2)

NI (

6C S

ugar

; 1)

Fruc

tose

(2)

Mel

ibio

seSu

cros

eR

affin

ose

Gly

colic

Aci

dFu

mar

ic A

cid

Thre

onic

aci

d 1,

4-la

cton

eM

alic

Aci

dTh

reon

ic a

cid

Mal

onic

Aci

dSu

ccin

ic A

cid

Qui

nic

Acid

Shik

imic

Aci

dC

itric

Aci

dBe

nzoi

c Ac

idSa

licyl

alc

ohol

Que

rciti

nSa

licin

Cat

echo

lM

yo-in

osito

lG

alac

tinol

127

Figure 5.8 Heatmap of drought responsive transcript and metabolite correlations. Of all the

drought responsive transcripts, 747 unique transcripts were correlated with at least one metabolite

(|r| > 0.6; P<0.05). The rows in the heatmap represent metabolites, and the columns represent

transcripts. The columns are clustered based on their expression across samples, and the metabolites

are grouped according to functional categories. Pearson correlation coefficient (r) are represented

for each pair-wise M:T comparison, and were calculated using R.

128terms among transcripts was determined. For transcripts with increased transcript abundance in

response to drought and correlated with at least one metabolite (n=404), four significant enriched

GO biological process terms were identified: ‘proline metabolic process’ (GO:0006560), ‘arginine

metabolic process’ (GO:0006525), ‘galactose metabolic process’ (GO: 0006012) and ‘serine family

amino acid metabolic process’ (GO:0009069). A total of 13 significant GO terms were identified

(for complete list see Supplementary Figure 9a). Among transcripts that had decreased transcript

abundance in response to drought and were correlated with at least one metabolite (n=343),

15 significantly enriched GO terms were identified (Supplementary Figure 9b). For GO terms

associated with biological process, ‘serine family amino acid metabolic process’ (GO:0009069),

‘tyrosine metabolic process’ (GO:0006570) and ‘aromatic amino acid family metabolic process’

(GO:0009072) were significantly enriched.

Functional annotation of the correlated transcripts and metabolites revealed pathways that were

perturbed by water withdrawal (Figure 5.9). A functional class related to starch and sucrose

metabolism (pop00500) was overrepresented among the transcripts that are correlated with two

identified 5C sugars and glucose (Figure 5.9). Photosynthesis-related categories (pop00195 and

pop 00196) were highly associated with malic acid, raffinose and galactinol (Figure 5.9).

In spinach, raffinose accumulation reduced electron and cyclic photophosphorylation in

photosynthesis (Santarius 1973), and it has been hypothesised that raffinose and other RFOs play

an important role in the protection of cellular metabolism, especially with respect to photosynthesis

in chloroplasts in Arabidopsis (Nishizawa et al. 2008). Evidence herein suggests there may be a

functional relationship in P. balsamifera between raffinose accumulation and transcripts associated

with photosynthesis. An association between photosynthetic metabolic processes and RFO

accumulation may highlight unique relationships that can be garnered from transcriptome-

metabolome relationships in Populus.

5.4.6 Energy metabolism and secondary metabolite biosynthesis varied in a

genotypic-dependant manner in response to drought

Galactinol accumulation varied in response to water-deficit stress in genotype AP-1006 [log2(fold-

change) = -0.4526]; whereas galactinol accumulated consistently in the other genotypes. Raffinose

accumulation was significant in water-deficit-treated plants, with the exception of trees of the

129

Phenylalanine, tyrosine and tryptophan biosynthesis

Amino sugar and nucleotide sugar metabolism

Steroid biosynthesis

Glycerolipid metabolism

Photosynthesis ~ antenna proteins

Plant hormone signal transduction

Starch and sucrose metabolism

Photosynthesis

Porphyrin and chlorophyll metabolism

Oxidative phosphorylation

biquinone and other terpenoid~quinone biosynthesis

Glycolysis / Gluconeogenesis

Phenylalanine metabolism

Ribosome

Ubiquitin mediated proteolysis

Arginine and proline metabolism

Galactose metabolism

Valine, leucine and isoleucine degradation

RNA transport

Cysteine and methionine metabolism

Purine metabolism

NI

Salic

inN

IN

I_Su

gar_

alco

hol

NI

NI

NI

Cat

echo

lN

IN

IN

IL.

Isol

euci

neC

atec

hin

Benz

oic

Acid

Mel

ibio

seN

I_5C

_sug

arAd

enos

ine

NI

Mal

tose

NI

NI

Sucr

ose

Succ

inic

_aci

dN

I_Am

ino_

acid

NI_

5C_s

ugar

Thre

onic

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d.1.

4.la

cton

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ic_a

cid

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nic_

acid

Fum

aric

Aci

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arN

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Thre

onin

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ymid

ine

5 m

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ate

NI_

Org

anic

_aci

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sine

L.As

parti

c Ac

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ine

Gly

cero

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IM

alic

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lyco

lic_a

cid

Thre

onic

_aci

dM

yo in

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lL.

Prol

ine_

2L.

Prol

ine_

1N

IN

IC

itric

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dN

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_sug

arN

IN

IN

IN

IN

IN

ISa

licyl

_alc

ohol

Glu

cose

_2Ka

empf

erol

Que

rciti

nN

I_Am

ino_

acid

NI_

6C_s

ugar

NI

L.Ph

enyl

alan

ine

NI

Dig

alac

tosy

lgly

cero

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neL.

Glu

tam

ate

L.Se

rine

NI

L.As

corb

ic_a

cid

Buty

ric a

cid

4 am

ino

n-N

IN

IPy

rogl

utam

ic_a

cid

NI

NI

Fruc

tose

_1G

luco

se_1

Glu

cose

_3Sh

ikim

ic_a

cid

NI_

6C_s

ugar

Fruc

tose

2R

affin

ose

Gal

actin

ol

−0.5

00.

5Sp

earm

an R

ank

Cor

rela

tion

Col

or K

ey

130

Figure 5.9 A heatmap of representative functional classes (transcripts) from the correlation data.

The averaged Spearman correlation value is represented for significant functional class: metabolite

comparisons (coloured squares). Red indicates positive correlation, whereas blue indicates negative

correlation.

131genotype AP-2300. There was drought-responsive variation in transcript accumulation of genes

hypothesised to be involved in the galactose metabolism pathway. All genotypes showed increased

abundance of transcripts corresponding to galactinol synthase [EC:3.4.1.123], raffinose synthase

[EC:2.4.1.82] and stachyose synthase [EC: 2.4.1.67] (Figure 5.10). Galactinol synthase transcript

accumulation varied in magnitude in response to water-deficit conditions among the six genotypes,

with the largest increase in transcript accumulation observed in genotypes AP-2278 and AP-1006.

Elevated levels of RFOs in Arabidopsis plants increased drought tolerance, highlighting the

importance of these oligosaccharides in the response to osmotic-stress (Taji et al. 2002). Increased

accumulation of raffinose has been observed in desiccation tolerant seeds (Castillo et al. 1990),

chloroplasts of frost-hardy Brassica oleracea leaves (Santarius 1973), and in Populus tremula leaves

exposed to osmotic stress (Janz et al. 2010). Increased transcript abundance of galactinol synthase

and raffinose synthase has been observed in response to drought in Arabidopsis (Taji et al. 2002;

2004) and Populus (Janz et al. 2010; Hamanishi et al. 2010).

Mounting evidence suggests that the role of raffinose and other RFOs is consistent across species;

however, the magnitude of change is variable, as was observed among the six P. balsamifera

genotypes reported here. This suggests the existence of genotypic specific metabolite profile related

to these oligosaccharides, and that the level of accumulation may influence the overall drought

response. Moreover, the data suggest that AP-1006 may not produce elevated levels of galactinol

in response to drought, galactinol may be metabolised more quickly in AP-1006, or that the RFO

metabolites may not be as important for osmotic protection in certain genotypes.

Unique relationships were also observed in the citrate cycle (TCA) pathway (KEGG, pop00020).

TCA cycle intermediates, such as succinic and malic acid, were significant for T and TxG or

TxD interactions. The metabolic rate of the TCA cycle is known to be influenced by drought

(Rodríguez-Calcerrada et al. 2010). Notably, similar variations among genes involved in the TCA

metabolic pathway were observed in genotype AP-1006 (Figure 5.11a; for other genotypes, see

Supplemental Figure A.4). Pair-wise comparisons within the TCA cycle for select transcripts and

metabolites found weak relationships among transcripts and malic and citric acid accumulation

profiles for AP-1006 (Figure 5.11b); however, succinic acid and malate dehydrogenase

(EC:1.1.1.37) were significantly negatively correlated (r = -0.67, P = 0.0204) in genotype AP-1006

(Figure 5.11b).

132

EC 2.4.1.123

EC 2.4.1.82

EC 2.4.1.67

UDP-Galactose

Galactinol

Raffinose

Stachyose

Sucrose

myo-inositol

UDP

sucrose

myo-inositol

galactinol

myo-inositol

AP-9

47AP

-100

5AP

-100

6AP

-227

8AP

-229

8AP

-230

0

AP-9

47AP

-100

5AP

-100

6AP

-227

8AP

-229

8AP

-230

0

AP-9

47AP

-100

5AP

-100

6AP

-227

8AP

-229

8AP

-230

0

AP-9

47AP

-100

5AP

-100

6AP

-227

8AP

-229

8AP

-230

0

AP-9

47AP

-100

5AP

-100

6AP

-227

8AP

-229

8AP

-230

0

Figure 5.10 Pathway analysis related to the galactose metabolism. (a) Pathway map displays

selected steps from galactose metabolism pathway. Colours indicate fold-change in transcript

or metabolite abundance between water-deficit and well-watered treated samples for all six

genotypes; red indicates higher abundance in water-deficit-treated samples and blue indicates lower

abundance in water-deficit-treated samples. Enzymes are given as EC numbers. EC 2.4.1.123,

galactinol synthase; EC:2.4.1.82, raffinose synthase; EC:2.4.1.67, stachyose synthase. (b) Heatmap

representing Spearman correlation values among transcripts related to galactose metabolism and

raffinose or galactinol.

Raf

finos

e

Gal

actin

ol

EC:2.4.1.67

EC:3.4.1.123

EC:2.4.1.82

Spearman Correlation

Color Key

0.1 0.3 0.5 0.7

(a)

(b)

133

Citrate

Acetyl CoA

cis-Aconitate

Isocitrate

2-ketoglutarateSuccinyl-CoA

Succinate

Fumarate

Malate

Pyruvate

Oxaloacetate

4.2.1.2

1.3.5.1

glycolysis IV

1.1.1.37

6.2.1.5

1.1.1.41

2.3.3.1

4.2.1.3

−2 −1 0 1 2Fold Ratio [Log2(Fold-change)]

Color K ey Populus balsamifera, genotype AP-1006

Malic AcidCitric Acid Succinic Acid

EC

:1.3

.5.1

EC

:1.1

.1.3

7

EC

:4.2

.1.3

EC

:2.3

.3.1

EC

:1.1

.1.4

1

EC

:6.2

.1.5

EC

:4.2

.1.2

(a)

(b)

−1 −0.5 0 0.5 1Pearson Correlation Coefficient

Color Key

Figure 5.11 Pathway analysis related to the citric cycle (TCA cycle). (a) Correlation among select

transcripts and metabolites from the KEGG pathway pop00020 ‘Citrate cycle (TCA cycle)’ for

genotype AP-1006. Colors represent Pearson correlation value. Red indicates positive correlation

and blue represents negative correlation values. (b) Map displays selected steps from citrate cycle

pathway. Colours indicate fold-change in transcript or metabolite abundance between water-

deficit and well-watered treated samples for genotype AP-1006; red indicates higher abundance in

water-deficit-treated samples and blue indicates lower abundance in water-deficit-treated samples.

Enzymes are given as EC numbers. EC 1.1.1.37, malate dehydrogenase; EC:1.1.1.41, isocitrate

dehydrogenase (NAD+); EC:1.3.5.1, succinate dehydrogenase; EC:2.3.3.1, citrate synthase;

EC:5.2.1.2, fumarate hydratase, EC: 5.2.1.3, aconitate hydratase, EC: 6.2.1.5, succinate-CoA ligase,

beta subunit. Pearson correlation and pathway maps for other genotypes can be found in Appendix

Figure A.4.

134The magnitude change between well-watered and water-deficit-treated samples for transcripts

associated with the TCA cycle varied among genotypes. Citrate synthase (EC:2.3.3.1) had increased

transcript accumulation in water-deficit-treated samples of AP-947, AP-1006, AP-2278 and AP-

2300; however, decreased transcript accumulation was observed in the other genotypes. Similarly,

malate dehydrogenase (EC:1.1.1.37), had <1 log2(fold-change) in response to drought in AP-1006

and AP-2298, whereas >1 log2(fold-change) increase was observed in AP-947 and AP-2278. In

Arabidopsis, malate dehydrogenase demonstrated increased transcript accumulation in response

to drought, cold or high-salinity stress (Seki et al. 2007); however, the variation in the genotypic

response in P. balsamifera highlights the complexity in this response. This highlights the influence of

genotype on the drought-induced modifications to the TCA cycle in P. balsamifera.

Comparative pathway analysis among genotypes has proved useful in Populus. In two different

genotypes of Populus with varying salt-tolerance, pathway analysis revealed different mechanisms

of tolerance between the two genotypes. Janz et al. (2010) found that the salt-tolerant Populus

eupharatica demonstrated moderate transcriptome changes in response to stress when compared

to a salt-sensitive Populus hybrid. However, stress tolerance in P. eupharatica was not dependant

on transcriptome modification under conditions of stress; instead, it was linked to greater energy

requirements for cellular metabolism (Janz et al. 2010). In P. balsamifera it is evident that there are

varying degrees of transcriptional remodeling in response to drought among genotypes; however,

further analysis is required to understand the subtleties in these differences.

5.4.7 Network analysis illuminated the nature of genotype-specific responses to

drought

Some drought transcriptome alterations were specific to given genotype. To identify genotype-

specific transcriptome alterations, a network was created including all genes that were deemed

significantly expressed in a T-main effect manner for each genotype using weighted gene co-

expression network analysis (WGCNA; Langfelder & Horvath 2008). Weighted Pearson

correlation matrices were calculated and used to determine topological overlap (TO) among genes.

The TO calculated in WGCNA measured connectivity of a gene within a network relative to its

neighbours. HCA based on the TO scores for all genes in the drought transcriptome grouped genes

with equivalent transcript abundance profiles across all samples (Langfelder & Horvath 2008).

135Table 5.4 Module-treatment or -time of day relationships of the P. balsamifera (AP-1006) drought

transcriptome. Columns 2 and 3 represent the correlation between the mean expression of the

module and the experimental factor (T or D). Significant values are in bold (P-value < 0.05).

Module AP-1006 Treatment (T) Time of Day (D)M1_1006 -0 .833 -0 .484M2_1006 -0 .662 0 .709M3_1006 -0 .77 0 .432M4_1006 -0 .952 0 .164M5_1006 0 .759 -0 .638M6_1006 0 .983 -0 .038M7_1006 0 .928 0 .342M8_1006 0 .699 0 .708M9_947 0 .87 0 .025M10_2278 -0 .85 0 .145

136

(A) AP-947

(B) AP-1005

(C) AP-1006

(D) AP-2278

(E) AP-2298

(F) AP-2300

Figure 5.12 Transcript correlation networks obtained from WGCNA for (a) AP-947, (b) AP-1005,

(c) AP-1006, (d) AP-2278, (e) AP-2298 and (f ) AP-2300. The top 1000 interactions for each

genotype are represented. Nodes in the graphs represent individual transcripts that connect via

edges to other transcripts. Each node is colored according to the modules defined in Table 5.3.

Colour Module yellow M1_1006blue M2_1006black M3_1006turquoise M4_1006brown M5_1006green M6_1006pink M7_1006red M8_1006magenta M9-947purple M10_2278

137Overall, 10 network modules with equivalent transcript abundance patterns were identified.

Many network modules were similar across genotypes - 80% of the network modules significantly

overlapped, with respect to their gene membership, in at least two genotypes (Table 5.4). Not

surprisingly, all of the modules were highly correlated with treatment; whereas only three were

significantly correlated with time of day (M2, M5 and M8; Table 5.4). Notably, M3 was only

shared between AP-1006 and AP-2278, whereas M9_947 and M10_2278 were unique to AP-947

and AP-2278, respectively. M3 (black) had a 62% overlap with respect to gene membership, and

among those transcripts, there was an overrepresentation of transcripts involved in ‘intracellular

signalling cascade (GO: 0007242)’. M5 (brown) demonstrated a high degree of overlap among

genotypes, with the exception of AP-2298. M5 was functionally characterised by a general drought

response that is similar to the overall drought response, with overrepresented GO terms which

include: ‘response to abiotic stimulus (GO:0009628)’, ‘cellular catabolic process (GO: 0044248)’

and ‘response to water deprivation (GO: 0009414)’. The high degree of overlap between modules

identified in each genotype validated the presence of a highly conserved drought transcriptome in P.

balsamifera.

Although there was a high degree of network module preservation among genotypes (Table 5.4),

organisation within modules varied among genotypes. When visualising the top (n=1000) network

connections of each genotype, and labeling the nodes according to their module membership within

the drought transcriptome, two general observations could be made (Figure 5.12). First, transcript

connectivity varied among genotypes. In certain genotypes, there was a higher degree of topological

overlap between individual genes (nodes), as indicated by the colour of the edges (higher TO

indicated with a red/purple colour, whereas lower TO is indicated with blue). For example, a higher

degree of TO was observed in genotype AP-1005 as compared to genotype AP-2278. Second, the

importance of any given module varied among genotypes. For example, the nodes of top network

connections in genotype AP-1005 were from module M4_1006 and M6_1006, whereas genotype

AP-947 had nodes that belonged to many other modules. More specifically, genes that played a

more central (“hub”) role in the drought transcriptome networks varied among genotypes.

5.4.8 AP-1006 had a genotype-specific transcriptome response to drought

To interrogate the uniqueness of the transcriptome of AP-1006, genes central to the network

modules in this genotype were identified. Transcript abundance of hub genes in AP-1006 revealed

138

(a)

GO:0044042 (0.00523)glucan metabolic

proces s8/149 | 329/36285

GO:0006073 (0.00314)cellular glucan

metabolic proces s8/149 | 298/36285

GO:0044238 primary metabolic

proces s

GO:0005975 carbohydrate metabolic

proces s

GO:0044264 (0.0357)cellular polys accharide

metabolic proces s8/149 | 457/36285

GO:0005976 (0.0234)polys accharide metabolic

proces s10/149 | 639/36285

GO:0009311 (0.00314)oligos accharide metabolic

proces s6/149 | 151/36285

GO:0044262 cellular carbohydrate

metabolic proces s

GO:0005982 (0.000229)s tarch metabolic

proces s6/149 | 79/36285

GO:0044260 cellular macromolecule

metabolic proces s

GO:0005984 (0.000582)dis accharide metabolic

proces s6/149 | 101/36285

GO:0005985 (5.82e-05)s ucros e metabolic

proces s6/149 | 56/36285

GO:0009987 cellular proces s

GO:0044237 cellular metabolic

proces s

GO:0043170 macromolecule metabolic

proces s

GO:0008150 biological_proces s

GO:0008152 metabolic proces s

GO:0016137 (0.000582)glycos ide metabolic

proces s7/149 | 159/36285

139

Figure 5.13 Overrepresentation of GO terms associated with transcripts that have (a) decreased

transcript abundance in AP-1006 and (b) increased transcript abundance in AP-1006. Figures

generated using AgriGO (http://bioinfo.cau.edu.cn/agriGO). Significant overrepresentation is

represented by darker coloured boxes (P < 0.05).

GO

:001

6137

(0.0

0109

)gl

ycos

ide

met

abol

icpr

oces

s8/

218

| 159

/362

85

GO

:000

5985

(3.3

8e-0

5)su

cros

e m

etab

olic

proc

ess

7/21

8 | 5

6/36

285

GO

:000

9311

(0.0

0538

)ol

igos

acch

arid

e m

etab

olic

proc

ess

7/21

8 | 1

51/3

6285

GO

:000

5984

(0.0

0065

5)di

sacc

harid

e m

etab

olic

proc

ess

7/21

8 | 1

01/3

6285

GO

:001

9752

ca

rbox

ylic

aci

dm

etab

olic

pro

cess

GO

:000

6520

ce

llula

r am

ino

acid

met

abol

ic p

roce

ss

GO

:003

4641

ce

llula

r nitr

ogen

com

poun

d m

etab

olic

pro

cess

GO

:004

4106

ce

llula

r am

ine

met

abol

ic p

roce

ss

GO

:000

6807

ni

troge

n co

mpo

und

met

abol

ic p

roce

ss

GO

:000

9308

am

ine

met

abol

icpr

oces

s

GO

:004

4281

sm

all m

olec

ule

met

abol

ic p

roce

ss

GO

:004

2180

ce

llula

r ket

one

met

abol

ic p

roce

ss

GO

:000

6082

or

gani

c ac

idm

etab

olic

pro

cess

GO

:000

6519

ce

llula

r am

ino

acid

and

der

ivat

ive

met

abol

ic p

roce

ss

GO

:000

9069

(0.0

268)

serin

e fa

mily

amin

o ac

id m

etab

olic

pro

cess

5/21

8 | 1

00/3

6285

GO

:004

4264

ce

llula

r pol

ysac

char

ide

met

abol

ic p

roce

ss

GO

:000

6073

(0.0

111)

cellu

lar g

luca

nm

etab

olic

pro

cess

9/21

8 | 2

98/3

6285

GO

:004

4262

ce

llula

r car

bohy

drat

em

etab

olic

pro

cess

GO

:004

4260

ce

llula

r mac

rom

olec

ule

met

abol

ic p

roce

ss

GO

:000

5982

(0.0

0018

6)st

arch

met

abol

icpr

oces

s7/

218

| 79/

3628

5

GO

:000

9072

(0.0

268)

arom

atic

am

ino

acid

fam

ily m

etab

olic

pro

cess

5/21

8 | 1

01/3

6285

GO

:000

9987

ce

llula

r pro

cess

GO

:004

4237

ce

llula

r met

abol

icpr

oces

s

GO

:000

6725

ce

llula

r aro

mat

icco

mpo

und

met

abol

ic p

roce

ss

GO

:000

8150

bi

olog

ical

_pro

cess GO

:000

8152

m

etab

olic

pro

cess

GO

:004

4238

pr

imar

y m

etab

olic

proc

ess

GO

:004

3170

m

acro

mol

ecul

e m

etab

olic

proc

ess

GO

:004

3436

ox

oaci

d m

etab

olic

proc

ess

GO

:004

4042

(0.0

197)

gluc

an m

etab

olic

proc

ess

9/21

8 | 3

29/3

6285

GO

:000

5975

ca

rboh

ydra

te m

etab

olic

proc

ess

GO

:000

5976

po

lysa

ccha

ride

met

abol

icpr

oces

s

(b)

140samples clustered according to treatment; however, greater transcript abundance profiles were more

similar between well-watered samples, regardless of time of day. The absolute magnitude change in

abundance of transcripts central to the network modules in AP-1006 was significantly higher than

the magnitude change of the transcripts in the other genotypes [absolute log2(fold-change) AP-

1006 = 2.36]. Many hub transcripts had significant changes in transcript abundance in response to

drought in AP-1006. There are 195 hub transcripts (TO Network Ratio > 0.5) that have decreased

abundance in response to drought in AP-1006; whereas there are 104 hub transcripts that had

increased abundance in response to drought (Table 5.5).

Enrichment of GO terms within the set of central network hub transcripts from genotype AP-

1006 revealed the components of the genotype-specific drought transcriptome. For example,

transcripts implicated in the response to stress and stimulus were enriched. Of the hub transcripts

with significant declines in abundance, genes implicated in carbohydrate metabolism were

enriched, including those with GO terms for: sucrose (GO:0005985), starch (GO: 0005982) and

disaccharide (GO: 0005984) metabolic processes (Figure 5.9a), among others. Conversely, core

hub transcripts with increased accumulation in response to drought in AP1006 were enriched for

biological processes, including response to stimulus (GO:0050896) and stress (GO: 0006950) as

well as transport (GO:0006910) and regulation of cellular processes (GO:0050794; Figure 5.13b);

however, it should be noted that there was a large proportion of transcripts that had unknown

function. The transcripts that played a central role in the network organisation of the drought

transcriptome in AP-1006 were likely important regulators of the drought response, and the analysis

of transcript co-expression relationships may help with functional annotation; albeit, not with

immediate interpretation.

5.4.9 There were strong correlates between specific transcript-metabolite pairs in response to drought in AP-1006

Pathways with significant correlations between metabolites and transcripts that had significant

differences in abundance in response to drought in AP-1006 included ‘plant hormone signal

transduction’, ‘arginine and proline metabolism’, and ‘glycolysis/gluconeogenesis’. As previously

noted, the largest magnitude change in transcript abundance was observed in AP-1006 (Figure

5.7). Transcripts, including those encoding genes homologues to Arabidopsis thaliana RAC-like 2

protein (ARAC2) and IRREGULAR XYLEM 9 (IRX9) had significantly larger fold-change decrease

141in transcript abundance in response to water-deficit conditions as compared to other genotypes.

Conversely, several transcripts annotated as universal stress proteins or those involved in hormone

signalling had significantly higher transcript abundance in AP-1006 in response to water-deficit

stress. Correlation network analysis revealed core transcripts that might have played a role in the

underlying mechanisms regulating metabolite accumulation in AP-1006. Transcripts most strongly

correlated with metabolite levels were identified (Supplementary Figure 10). Although no particular

class of metabolites or transcripts appeared specific to AP-1006, a large number of transcripts highly

correlated with succinic acid, raffinose and galactinol accumulation were observed (Supplementary

Figure 10). For example, strong positive correlations were observed between raffinose, galactinol

and a photosystem II reaction center PsbP family protein (r = 0871 and 0.835, respectively;

Supplemental 5.6). Strong correlations between drought responsive metabolites and transcripts

reveal pathways that may be of importance in the drought tolerance mechanisms in a genotype.

5.5 Conclusion

The complexity of the metabolomic response to drought in Populus balsamifera was highlighted

by variation among genotypes and between time-of-day responses. Although common drought-

responsive metabolites could be identified across all six P. balsamifera genotypes, a significant

proportion of metabolites varied in a genotype or time-of-day dependant manner. Notably, the

magnitude of drought-induced changes in the metabolite abundance among the six P. balsamifera

genotypes varied significantly. The complexity of the genotype-metabolite relationship was notable,

and likely attributable to the function of many genes, the environment and their interaction.

Integrating transcriptome-and metabolome data identified significant metabolite-gene correlation

whereby biologically meaningful correlations were derived. Metabolite-transcript relationships

from the same and different pathways were identified, and may be useful for future elucidation of

important drought response mechanisms. Integration of the transcriptome and metabolome data at

individual pathway levels revealed variation in metabolite flux and transcript accumulation among

genotypes in energy and galactose metabolism.

142

Chapter 6: Conclusion and Future Directions

143

Chapter 6: General Conclusions and Future Directions

This thesis investigated the intra-specific variation in the Populus balsamifera drought response.

Specifically, the research addressed how transcriptome responses varied among P. balsamifera

genotypes, and how growth, development and metabolism were remodeled in response to drought

stress. The results represent significant contributions to our knowledge about intraspecific variation

within the Populus balsamifera drought responses.

1.1 Major Findings and Significance

This thesis comprised a test of three overarching hypotheses. Those hypotheses and the major

findings that were derived in testing them are reported below.

1. There are significant differences in the transcriptomes of Populus balsamifera trees in response

to drought stress.

The Populus drought transcriptome is a highly dynamic and complex system in which genetic and

environmental cues combine, resulting in various tolerance mechanisms and adaptations that allow

tree to contend with drought stress. A common, shared water-deficit induced transcriptome-level

response for Populus balsamifera was identified using the Affymetrix Poplar GeneChip microarray

platform; however, the amplitude of gene expression varied significantly among genotypes. This

highlights the importance of genotype in shaping the drought-response among Populus species

and genotypes. Selection of single genotypes or hybrid clones for gene expression studies should

proceed with caution, as there can be notable difference between genotypes, and a single genotype

or clone may not be representative of the species in question. Future studies could aim to study

extensive cohorts of genotypes in order to grasp the diversity in responses both within a species, and

among individuals of the same genus. Variation in the physiological and morphological responses

to drought among trees of the genus Populus is frequently observed (Yin et al. 2004; Monclus et

al. 2006; Giovannelli et al. 2007), and the variation in gene expression could be exploited to study

the various mechanisms that underpinning responses among individuals of the genus Populus.

Furthermore, there is a long-term goal of determining how these molecular mechanisms underpin

the drought phenotype in Populus. Genome-wide gene expression data can be used to generate

gene networks, with the hopes of understanding the gene regulatory networks that may explain

144the genetic causes of a given phenotype, or in this case, a drought response. The data generate

in chapter 3 can be used to begin the search for these underlying genetic regulatory networks in

P. balsamifera. Future studies may also begin to integrate DNA structural variants (e.g., single

nucleotide polymorphisms, or SNPs) and gene expression data in order to better understand how

SNPs and genes influence one another, as well as predicting phenotype (Chang and McGeachie

2011).

Physiological and phenotypic drought responses in P. balsamifera also varied significantly among

genotypes, where certain genotypes rapidly modified physiological status at the onset of water-

deficit stress compared to other genotypes that had gradual declines in physiological status.

Notably, phenotypic traits, such as growth, correlated with genetic responsiveness to drought.

Among the six genotypes reported in Chapter 3, genotypes that had increased magnitude change

in their drought responsive transcriptome sustained growth under conditions water-limitation.

Although the sample size for this relationship was limited, the evidence presented herein suggests

a relationship that warrants future investigation. Future studies could be directed at sampling

larger numbers of Populus genotypes with variable growth rates in response to drought. Assessing

variation in their drought transcriptomes and comparing responsiveness to the ability to sustain

growth under drought conditions could lead to better insights into the molecular mechanisms that

define growth under stress conditions in Populus. Specifically, future experiments could test whether

a larger magnitude change in the transcriptome buffers the plant against the negative growth and

development impacts of drought-stress. This would be important knowledge for the maintenance

and improvement of productivity of hybrid poplars in bio-fuel crops, for example, under conditions

of changing climate.

In chapter 3, genotypes with a higher degree of similarity among their drought transcriptomes

also had fewer single feature polymorphism (SFP) differences, suggesting that individuals sharing

a greater degree of genotypic congruence may have conserved drought responses. However, there

was no correspondence between SFP differences and geographic origin. This suggests that some

genotype specific responses may be locally adapted, although others may be spread widely across the

landscape. Moreover, the population structure of P. balsamifera is such that there are three distinct

sub-populations, or demes (Keller et al. 2010). The research presented herein was performed on

genotypes that originated from a single deme; future research should consider sampling across the

145entirety of the range. Sampling within multiple sub-populations across the range would provide

further insight into the genetic and transcriptomic variation within and among different demes

across the range of P. balsamifera.

2. Drought-induced modification of the transcription of genes implicated in the stomatal

development regulatory network are linked to changes in stomatal density.

When two genotypes (AP-1005 and AP-1006) of P. balsamifera were grown under water-deficit

conditions, a significant reduction in stomatal index was observed in leaves that developed under

water-deficit stress, as compared to those that developed under well-watered conditions. Alteration

to the stomatal development program in Populus may be indicative of a long-term strategy to

minimize water loss and contend with drought-stress. Reductions in stomatal conductance was

also observed in P. balsamifera, with the greatest reduction observed in AP-1006, which was also the

genotype that had the largest decline in stomatal index in water-deficit treated samples.

Quantification of transcript abundance of genes hypothesised to be involved in the stomatal

development pathway in Populus were interrogated throughout development, and specific genes

demonstrated transcript abundance profiles congruent with their hypothesised role in stomatal

development. For example, STOMAGEN, a positive regulator of stomatal development had

significantly higher transcript abundance in well-watered samples early in development (day 5 and

10). Other genes, such as ERECTA and STOMATAL DENSITY and DISTRIBUTION 1 had

variable transcript accumulation between genotypes. These findings suggest that there may be

variable drought-response strategies amongst genotypes.

Modifications of stomatal development under conditions of changing environments may represent

long-term water-deficit tolerance strategies. Although limiting water loss in conditions of water-

deficit stress would likely be beneficial in the short term, a reversion to non-drought conditions

may result in suboptimal conditions for the plant in question. More specifically, a tree grown under

water deficit stress with fewer stomatal pores and reduced water-loss during drought will also have

limited capacity for gas exchange under well watered conditions as compared to an individual that

did not have reduced stomatal development under conditions of water-deficit stress.

As stomatal development is regulated by both environmental and endogenous factors, such as ABA,

it is also important to consider the former. In Arabidopsis thaliana cotyledons ABA plays a role in

146both plant physiology and development under water-deficit conditions (Tanaka et al. 2013). ABA

regulation of stomatal development is upstream of both SPCH and MUTE; however, the influence

of ABA on stomatal development is dependent on the presence of stomatal lineage cells. More

specifically, ABA limits stomatal initiation and the enlargement of pavement cells (Tanaka et al.

2013). Future studies in Populus should consider the relationship between ABA and genes known

to regulate stomatal development, such as SPCH and MUTE.

Understanding the variation in the developmental responses to drought-stress in Populus may lead

to a better ability to select or breed plants with improved drought tolerance. Future interrogations

of the stomatal development pathway in Populus will likely improve our knowledge surrounding

the evolution of gene networks underpinning development in various plant species, and the role

stomatal development plays in long-term drought tolerance. The molecular underpinnings of

stomatal development in Arabidopsis are relatively well characterised compared to other plant

species, and experimental evidence in Arabidopsis supports that alterations in stomatal density results

in more drought tolerant individuals (Yoo et al. 2010). For example, Yu et al. (2008) overexpressed

the cDNA of a key transcriptional regulator in transgenic tobacco which lead to increased drought

tolerance associated with decreased stomatal density. In Populus, by understanding the molecular

mechanisms and indentifying the key regulators of stomatal development, transgenic Poplars with

increased drought tolerance can be created.

3. A Populus balsamifera drought metabolome can be identified, and there are transcript-

metabolite relationships that vary in a genotype-dependent manner.

In order to understand the changes that occur at the metabolic level in Populus, the perturbation

of the soluble metabolome was assessed in six genotypes of P. balsamifera under well watered and

water-deficit conditions. In chapter 5, metabolites that had significant differences among genotypes,

treatment and time-of-day were identified. Metabolites from a broad range of functional groups

were included, such as: amino acids (e.g., proline and isoleucine), representatives from the citric

acid (TCA) cycle (e.g., succinic and malic acid), photorespiration (e.g., glycolic acid), phenolic

species (e.g., catechin) and diverse metabolites thought to have roles in osmotic adjustment (e.g.,

galactinol and raffinose). Specifically, TCA cycle intermediates malic and succinic acid had reduced

abundance under conditions of water deficit stress and both exhibited variability among genotypes.

147Assessing the impact of water-deficit stress on metabolite levels is complicated by the involvement

of a given metabolite in multiple pathways. For example, succinic acid is a constituent of the

TCA cycle and also plays a role in γ-aminobutyric acid (GABA) metabolism. Under periods of

drought-stress citric acid metabolism is hypothesized to decrease, whereas GABA metabolism is

thought be up regulated (Allan et al. 2008). Raffinose and galactinol had increased abundance in

response to drought. A significantly larger fold-change increase in raffinose accumulation occurred

at mid-day, whereas galactinol accumulation exhibited variability among genotypes. Both raffinose

and galactinol are thought to play important roles as osmoprotectants in plants under drought

stress, and it has been suggested that raffinose plays an important role protecting the membranes of

chloroplasts (Santarius 1973).

The non-targeted metabolome was compared to the microarray-based transcriptome profiles of six

P. balsamifera genotypes. A total of 747 metabolites were significantly correlated with at least one

metabolite. Notably, transcripts functionally annotated to photosynthesis related categories were

highly associated with malic acid, raffinose and galactinol. The correlation between metabolite and

transcript levels identified transcripts that may be influenced by particular metabolites or indentified

future targets for analysis of metabolites that regulate gene expression under conditions of water-

deficit stress.

Pathway analysis comparing transcript and metabolite abundance reveals variation in the metabolite

flux and gene expression among genotypes. Galactose metabolism is hypothesised to be impacted

by water-deficit stress. Genotypic variability in metabolite abundance and gene expression was

observed. Increased transcript abundance for galactinol synthase was not always congruent with

increased galactinol abundance among genotypes. Variation in the TCA cycle is also notable

among genotypes. This suggests that the genotypic influence on both metabolite and transcript

accumulation in response to drought is a highly complex and dynamic process. Along these lines,

focus on other specific drought-related metabolic pathways may provide insight into the drought

responses among genotyeps of Populus. For example, ABA is known to play an important role in

signalling in response to drought in plants (Shinozaki & Yamaguchi-Shinozaki 1996). Focused

analysis of metabolites involved in the ABA biosynthetic pathways, including precursors such as

zeaxanthin, may identify differences in the biosynthesis and metabolism of an important drought-

related plant horomone.

148Although the number of metabolites analysed in this chapter were limited, they provided insight

into the variation of the drought metabolome in P. balsamifera and provided evidence for treatment,

genotype and time-of day variations in the metabolome. Intraspecific variation at the metabolite-

and transcript-level was underscored by diverse patterns of accumulation. Interestingly, genotype

AP-1006 demonstrated notable differences between the magnitude changes observed at the

transcriptome as compared to its metabolome. To this end, genotype AP-1006 may represent a case

study for diverse metabolite-transcript relationships as compared to the other five genotypes studied

in chapter 5.

Integration of multiple ‘omics platforms is an incredibly powerful tool to assess the interactions

between metabolites and gene expression. A systems biology approach allows a better view of

the dynamics and the complex relationships that occur under drought-stress. However, future

studies in Populus would be aided by the inclusions of an increased number of metabolites, and

the identification of unknown metabolites. Alternative methods of metabolite profiling could be

deployed in order to broaden the characterization of the drought metabolome.

The complexities of the drought response in Populus is tremendous. In order to gain a better

understanding of the dynamics of the response at the molecular level, future studies may wish to

include comprehensive time-series experimental set-up. As previously shown, the Populus drought

transcriptome is shaped by time-of day (Wilkins et al. 2009a), and many metabolites demonstrated

time-of-day variation. Both the metabolome and transcriptome analyses capture the abundance

levels at a specific time; these analyses provide no insight into the flux of metabolites or transcripts

that may occur before or after sampling. A time-series experimental set-up may begin to shed

light into these dynamic fluxes, and highlight relationships between changes in metabolite or gene

expression levels, rather than simply analyzing abundance levels at a given time-point.

The results presented herein were collected from potted grown grown seedlings in a climate-

controlled environment. Extrapolating results to mature, field-grown trees is difficult due to many

confounding factors. These include the impacts of age-dependant variation in physiology; variable

biotic and abiotic stimuli; and, tree-to-tree competition. Although the experiments presented in

this thesis do not take into consideration the combined effects of the various stimuli that a tree

could encounter in its natural environment, the experiments enabled the controlled manipulation of

water-deficit stress for a large number of poplar seedlings. Future experiments could be established

149to explore the intraspecific variation in the molecular underpinnings of the drought responses in P.

balsamifera under field-grown conditions in both young and mature trees in order to understand the

differences attributable to tree age, as well as growth conditions.

The impacts of environmental stress on forest health and productivity are becoming of increasing

concern. The results presented herein demonstrate that future experiments aimed at understanding

the complexities of the responses of forest trees to environmental stimuli must take into

consideration the intraspecific variation in these responses. Although common drought responses

among genotypes of P. balsamifera could be identified, significant intraspecific variation was

observed. Genotype shapes the genome-wide transcriptome and metabolome responses to water-

deficit stress, as well as the modulation of stomatal development in P. balsamifera. The intraspecific

variation in the molecular strategies that underpin the responses to drought among genotypes

may have an important role in the maintenance of forest health and productivity amidst future

predictions of changing climate.

150

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179

Appendix

180Appendix A.1 Metabolites that are significant for a treatment (T):time-of-day (D) interaction (P

<0.05, n=15)

1.52.02.53.03.54.0

M91T611_Benzoic_acid

Abun

danc

e

1.52.02.53.03.54.0


1.52.02.53.03.54.0

M147T645_Thymidine.5..monophoph

Abun

danc

e

1.52.02.53.03.54.0


−0.50.00.51.01.52.02.53.0

M256T666_L.Isoleucine

Abun

danc

e

−0.50.00.51.01.52.02.53.0

M256T666_L.Isoleucine

2

3

4

5

M147T704_Glycolic_acid

Abun

danc

e

2

3

4

5


0

1

2

3

M179T804_Salicyl_alcoholAb

unda

nce

0

1

2

3

M179T804_Salicyl_alcohol

3.03.54.04.55.0

M147T928_Threonic_acid

Abun

danc

e

2.53.03.54.04.55.0


1.52.02.53.03.54.0

M217T1015_NI_5C_sugar

Abun

danc

e

1.52.02.53.03.54.0


0.51.01.52.02.53.0

M333T1095_NI

Abun

danc

e

0.51.01.52.02.53.0

M333T1095_NI

3.5

4.0

4.5

5.0

M204T1132_Shikimic_acidAb

unda

nce

3.5

4.0

4.5

5.0

M204T1132_Shikimic_acid

3.0

3.5

4.0

4.5


Abun

danc

e

3.0

3.5

4.0

4.5


4.5

5.0

5.5

M147T1210_Glucose_1

Abun

danc

e

4.0

4.5

5.0

5.5

M147T1210_Glucose_1

3.5

4.0

4.5

5.0

M147T1224_Glucose_2

Abun

danc

e

3.5

4.0

4.5

5.0

M147T1224_Glucose_2

2.02.53.03.54.0

M204T1305_Glucose_3

Abun

danc

e

2.02.53.03.54.0

M204T1305_Glucose_3

3.03.54.04.55.05.5

M361T1617_Salicin

Abun

danc

e

3.03.54.04.55.05.5

M361T1617_Salicin

2

3

4

5

M361T2065_Raffinose

Abun

danc

e

2

3

4

5

M361T2065_Raffinose

1.51.5

2.5

4.0

2.52.5

Pre-dawn Mid-day

Time of Day

WetDry

Treatment

181

2.02.53.03.54.04.5

M147T569_Malonic_acid

Abun

danc

e

2.02.53.03.54.04.5

M147T569_Malonic_acid

1.52.02.53.03.54.0


Abun

danc

e

1.52.02.53.03.54.0


0.00.51.01.52.02.53.03.5

M142T667_L.Proline_1

Abun

danc

e

0.00.51.01.52.02.53.03.5

M142T667_L.Proline_1

0.51.01.52.02.53.03.5

M174T677_Glycine

Abun

danc

e

0.00.51.01.52.02.53.03.5

M174T677_Glycine

3.03.54.04.55.0

M147T682_Succinic_acidAb

unda

nce

2.53.03.54.04.55.0

M147T682_Succinic_acid

2.0

2.5

3.0

3.5

M254T688_Catechol

Abun

danc

e

2.0

2.5

3.0

3.5

M254T688_Catechol

2

3

4

5


Abun

danc

e

2

3

4

5


3.03.54.04.5

M245T714_Fumaric_acid

Abun

danc

e

2.53.03.54.04.5

M245T714_Fumaric_acid

2.02.53.03.54.0

M188T733_L.AlanineAb

unda

nce

2.02.53.03.54.0

M188T733_L.Alanine

1234

M204T734_L.Serine

Abun

danc

e

1234

M204T734_L.Serine

4.5

5.0

5.5

M147T857_Malic_acid

Abun

danc

e

4.0

4.5

5.0

5.5

M147T857_Malic_acid

0.51.01.52.02.53.03.5

M233T886_L.Aspartic_acid

Abun

danc

e

0.51.01.52.02.53.03.5

M233T886_L.Aspartic_acid

2.53.03.54.04.5

M156T888_Pyroglutamic_acid

Abun

danc

e

2.53.03.54.04.5

M156T888_Pyroglutamic_acid

0.00.51.01.52.02.53.0

M174T892_Butyric_acid.4.amino.n

Abun

danc

e

0.00.51.01.52.02.53.0

M174T892_Butyric_acid.4.amino.n

2.53.03.54.04.55.0


Abun

danc

e

2.53.03.54.04.55.0


AP947AP1005AP1006AP2278AP2298AP2300

Genotype

WetDry

Treatment

Appendix A.2 Metabolites that are significant for a treatment (T):genotype (G) interaction (P

<0.05, n=41)

182

0.00.51.01.52.02.53.0

M306T949_NI

Abun

danc

e

0.00.51.01.52.02.53.0

M306T949_NI

1

2

3

M192T980_L.Phenylalanine

Abun

danc

e

1

2

3

M192T980_L.Phenylalanine

2.0

2.5

3.0

M221T995_NI

Abun

danc

e

2.0

2.5

3.0

M221T995_NI

1.52.02.53.03.54.0


Abun

danc

e

1.52.02.53.03.54.0


0.51.01.52.02.53.0

M200T1035_NI_5C_sugarAb

unda

nce

0.51.01.52.02.53.0


1.52.02.53.03.54.04.5

M273T1143_Citric_acid

Abun

danc

e

1.52.02.53.03.54.04.5

M273T1143_Citric_acid

4.85.05.25.45.65.86.06.2

M345T1180_Quinic_acid

Abun

danc

e

4.85.05.25.45.65.86.06.2

M345T1180_Quinic_acid

1.52.02.53.03.54.0

M201T1190_Fructose_1

Abun

danc

e

1.52.02.53.03.54.0


3.0

3.5

4.0

4.5

M364T1195_NI_6C_sugarAb

unda

nce

3.0

3.5

4.0

4.5


3.63.84.04.24.44.64.85.0


Abun

danc

e

3.63.84.04.24.44.64.85.0


4.5

5.0

5.5

M147T1210_Glucose_1

Abun

danc

e

4.5

5.0

5.5

M147T1210_Glucose_1

3.5

4.0

4.5

5.0

M147T1224_Glucose_2

Abun

danc

e

3.5

4.0

4.5

5.0

M147T1224_Glucose_2

1.52.02.53.03.54.0

M217T1234_NI_Sugar_alcohol

Abun

danc

e

1.52.02.53.03.54.0

M217T1234_NI_Sugar_alcohol

2.02.53.03.54.0

M204T1305_Glucose_3

Abun

danc

e

2.02.53.03.54.0

M204T1305_Glucose_3

2.5

3.0

3.5

4.0


Abun

danc

e

2.5

3.0

3.5

4.0


1.51.5

2.5

2.5

4.0

2.52.5

AP947AP1005AP1006AP2278AP2298AP2300

Genotype

WetDry

Treatment

183

2.53.03.54.0

M204T1411_NI

Abun

danc

e

2.02.53.03.54.0

M204T1411_NI

1.01.52.02.53.03.5

M91T1539_NI

Abun

danc

e

1.01.52.02.53.03.5

M91T1539_NI

2.02.53.03.54.04.5

M204T1542_NI

Abun

danc

e

2.02.53.03.54.04.5

M204T1542_NI

2.53.03.54.04.55.05.5

M361T1617_Salicin

Abun

danc

e

2.53.03.54.04.55.05.5

M361T1617_Salicin

2.0

2.5

3.0

3.5

M219T1659_NI

Abun

danc

e

2.0

2.5

3.0

3.5

M219T1659_NI

1.01.52.02.53.03.5

M236T1674_Adenosine

Abun

danc

e1.01.52.02.53.03.5

M236T1674_Adenosine

2.02.53.03.54.04.5

M368T1807_Catechin

Abun

danc

e

2.02.53.03.54.04.5

M368T1807_Catechin

0.00.51.01.52.02.53.0

M461T1841_NI

Abun

danc

e

0.00.51.01.52.02.53.0

M461T1841_NI

−10123

M456T1869_NI

Abun

danc

e

−10123

M456T1869_NI

1.01.52.02.53.03.54.04.5

M204T1876_Galactinol

Abun

danc

e

1.01.52.02.53.03.54.04.5


2.53.03.54.04.5

M204T1938_Digalactosylglycerol

Abun

danc

e

2.02.53.03.54.04.5

M204T1938_Digalactosylglycerol

1.51.5

2.5

2.5

4.0

AP947AP1005AP1006AP2278AP2298AP2300

Genotype

WetDry

Treatment

184Appendix A.3 Metabolites that are significant for a genotype (G):time of day (D) interaction(P

<0.05, n=6)

1.52.02.53.03.54.0


Abun

danc

e

1.52.02.53.03.54.0


2.0

2.5

3.0

M172T844_NI_Amino_acid

Abun

danc

e

1.5

2.0

2.5

3.0

M172T844_NI_Amino_acid

1.52.02.53.03.54.0


Abun

danc

e1.01.52.02.53.03.54.0


3.5

4.0

4.5

5.0

M147T1224_Glucose_2

Abun

danc

e

3.5

4.0

4.5

5.0

M147T1224_Glucose_2

Abun

danc

e

0.51.01.52.02.53.03.5

M294T1683_NI

0.00.51.01.52.02.53.03.5

M294T1683_NI

1.51.5

AP947AP1005AP1006AP2278AP2298AP2300

Genotype

Mid-dayPre-dawn

Time of Day


01

23

4

185

Citrate

Acetyl CoA

cis-Aconitate

Isocitrate


Succinate

Fumarate

Malate

Pyruvate

Oxaloacetate

4.2.1.2

1.3.5.1

glycolysis IV

1.1.1.37

6.2.1.5

1.1.1.41

2.3.3.1

4.2.1.3




EC:1

.3.5

.1

EC:1

.1.1

.37

EC:4

.2.1

.3

EC:2

.3.3

.1

EC:1

.1.1

.41

EC

:6.2

.1.5

EC:4

.2.1

.2

(a)

(b)


Color Key

AP-947

Appendix A.4 Analysis of the citrate cycle (TCA; pop00020) pathway in genotype AP-947, AP-

1005, AP-2278, AP-2298 and AP-2300. (a) Correlation among select transcripts and metabolites

from the KEGG pathway pop00020 ‘Citrate cycle (TCA cycle)’ for genotype AP-1006. Colors

represent Pearson correlation value. Red indicates positive correlation and blue represents negative

correlation values. (b) Map displays selected steps from citrate cycle pathway. Colours indicate

fold-change in transcript or metabolite abundance between water-deficit and well-watered treated

samples; red indicates higher abundance in water-deficit treated samples and blue indicates lower

abundance in water-deficit treated samples. Enzymes are given as EC numbers. EC 1.1.1.37,

malate dehydrogenase; EC:1.1.1.41, isocitrate dehydrogenase (NAD+); EC:1.3.5.1, succinate

dehydrogenase; EC:2.3.3.1, citrate synthase; EC:5.2.1.2, fumarate hydratase, EC: 5.2.1.3, aconitate

hydratase, EC: 6.2.1.5, succinate-CoA ligase, beta subunit. Pearson correlation and pathway maps

for AP-1006 can be found in Figure 5.11.

186

Citrate

Acetyl CoA

cis-Aconitate

Isocitrate


Succinate

Fumarate

Malate

Pyruvate

Oxaloacetate

4.2.1.2

1.3.5.1

glycolysis IV

1.1.1.37

6.2.1.5

1.1.1.41

2.3.3.1

4.2.1.3




EC:1

.3.5

.1

EC:1

.1.1

.37

EC:4

.2.1

.3

EC:2

.3.3

.1

EC:1

.1.1

.41

EC

:6.2

.1.5

EC:4

.2.1

.2

(a)

(b)


Color Key

AP-2278

AP-1005

Citrate

Acetyl CoA

cis-Aconitate

Isocitrate


Succinate

Fumarate

Malate

Pyruvate

Oxaloacetate

4.2.1.2

1.3.5.1

glycolysis IV

1.1.1.37

6.2.1.5

1.1.1.41

2.3.3.1

4.2.1.3




EC:1

.3.5

.1

EC:1

.1.1

.37

EC:4

.2.1

.3

EC:2

.3.3

.1

EC:1

.1.1

.41

EC

:6.2

.1.5

EC:4

.2.1

.2

(a)

(b)


Color Key

187

AP-2298

Citrate

Acetyl CoA

cis-Aconitate

Isocitrate


Succinate

Fumarate

Malate

Pyruvate

Oxaloacetate

4.2.1.2

1.3.5.1

glycolysis IV

1.1.1.37

6.2.1.5

1.1.1.41

2.3.3.1

4.2.1.3




EC:1

.3.5

.1

EC:1

.1.1

.37

EC:4

.2.1

.3

EC:2

.3.3

.1

EC:1

.1.1

.41

EC

:6.2

.1.5

EC:4

.2.1

.2

(a)

(b)


Color Key

AP-2300

Citrate

Acetyl CoA

cis-Aconitate

Isocitrate


Succinate

Fumarate

Malate

Pyruvate

Oxaloacetate

4.2.1.2

1.3.5.1

glycolysis IV

1.1.1.37

6.2.1.5

1.1.1.41

2.3.3.1

4.2.1.3




EC:1

.3.5

.1

EC:1

.1.1

.37

EC:4

.2.1

.3

EC:2

.3.3

.1

EC:1

.1.1

.41

EC

:6.2

.1.5

EC:4

.2.1

.2

(a)

(b)


Color Key

188Appendix A.5 Summary statistics for all metabolites (n=87). Mean peak intensity values

represented for all samples, wet samples and dry samples ± standard deviation. Relative abundance

between water-deficit and well-watered samples represented as log2(fold-ratio); positive values

indicate increased abundance in water-deficit conditions, whereas negative values indicate decreased

abundance in water-deficit conditions relative to control conditions. P-values for treatment (T)

main effect as calculated per the factorial ANOVA; metabolites significant for treatment main

effect denoted with an * (FDR < 0.05). Metabolite classes are as follows: AA = Amino Acid; C =

Carbohydrate; P = Phenolic, SA = Sugar Alcohol, and NI = Not Identified.

189

Peak

IDM

etab

olite

Cla

ssM

ean

Peak

Inte

nsity

Mea

n Pe

ak In

tens

ity

(Wet

)M

ean

Peak

Inte

nsity

(D

ry)

Rel

ativ

e A

bund

ance

[lo

g 2(Dry

/W

et)]

AN

OVA

: T-

mai

n ef

fect

P ad

j-val

ue

M23

6T16

74Ad

enos

ine

N52

6 .56

±39

6 .21

530 .

53±

291 .

5752

2 .80

±47

6 .15

-0 .0

20 .

137

M23

3T88

6As

parti

c Ac

idAA

296 .

28±

294 .

0736

7 .67

±30

7 .16

228 .

74±

265 .

49↓

-0 .6

80 .

031*

M91

T611

Benz

oic

Acid

OA

1504

.38

±15

08 .1

093

3 .98

±47

0 .81

2043

.78

±19

06 .3

2↑

1 .13

<0 .0

01*

M17

4T89

2Bu

tyric

_aci

d-4-

amin

o-n-

OA

259 .

23±

257 .

5327

5 .43

±25

0 .97

243 .

59±

264 .

24-0

.18

0 .74

7M

368T

1807

Cat

echi

nP

3100

.35

±37

08 .6

422

67 .4

5±

1948

.34

3888

.47

±46

93 .4

90 .

780 .

099

M25

4T68

8C

atec

hol

P70

0 .87

±43

6 .49

590 .

17±

324 .

8780

4 .35

±49

9 .59

↑0 .

450 .

004*

M27

3T11

43C

itric

Aci

dO

A31

39 .6

8±

3234

.99

2001

.96

±19

82 .6

442

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204T

1938

Dig

alac

tosy

l gly

cero

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1063

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697

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240 .

054

M20

1T11

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se (1

) C

2337

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723

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2±

1296

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2305

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3-0

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0 .05

4M

218T

1196

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1171

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2823

4 .79

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311 .

75±

1143

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0 .01

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245T

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532

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10G

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1048

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246 .

9438

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Org

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A36

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1982

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I79

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0 .02

0 .60

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1167

NI (

2)N

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1773

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244

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1942

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110

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2572

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1147

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29 .5

710

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8244

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204T

1673

NI (

8)N

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250 .

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M21

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2678

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0 .24

0 .02

3*M

217T

1771

NI (

10)

NI

1616

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56 .7

686

8 .07

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3 .35

2224

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1*M

219T

1659

NI (

11)

NI

469 .

32±

281 .

9138

2 .76

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5 .86

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22±

344 .

35↑

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01*

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7 .48

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2 .53

221 .

02±

167 .

7823

3 .59

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6 .19

0 .08

0 .95

0M

253T

1990

NI (

13)

NI

2769

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70 .8

525

86 .2

3±

1495

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2941

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10 .

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304

M29

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290 .

87±

255 .

1936

5 .97

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8 .44

0 .33

0 .97

1M

306T

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15)

NI

160 .

21±

137 .

2610

7 .17

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210 .

40±

166 .

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0 .97

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9 .64

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6 .85

315 .

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3525

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194

M35

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390 .

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3548

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2826

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4396

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6437

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0 .01

0 .87

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1763

NI (

20)

NI

70 .6

7±

54 .4

057

.00

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.41

83 .4

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63 .5

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0 .55

0 .01

7*M

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1980

NI (

21)

NI

6686

4 .97

±38

927 .

7865

389 .

43±

3511

7 .64

6826

1 .18

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362 .

490 .

060 .

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192

M38

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I (22

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772 .

62±

1538

6 .42

1372

4 .68

±13

532 .

3013

817 .

99±

1702

9 .85

0 .01

0 .97

1M

396T

1886

NI (

23)

NI

744 .

93±

664 .

7366

7 .89

±53

2 .68

817 .

01±

763 .

950 .

290 .

971

M45

6T18

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I (24

)N

I96

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6 .14

89 .8

5±

203 .

3410

3 .05

±14

6 .02

0 .20

0 .17

2M

461T

1841

NI (

25)

NI

163 .

79±

175 .

3413

2 .77

±12

1 .86

191 .

64±

209 .

01↑

0 .53

0 .02

2*M

469T

2109

NI (

26)

NI

596 .

45±

582 .

4457

3 .39

±60

0 .45

618 .

21±

567 .

500 .

110 .

393

M47

6T20

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I (27

)N

I10

64 .4

7±

794 .

6392

9 .97

±59

7 .17

1191

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±92

9 .80

0 .36

0 .24

7M

523T

2058

NI (

28)

NI

147 .

51±

123 .

5514

1 .41

±10

5 .57

152 .

99±

138 .

100 .

110 .

971

M91

T153

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I (29

)N

I89

2 .93

±75

2 .00

790 .

84±

618 .

8698

1 .96

±84

4 .89

0 .31

0 .32

9

193

M147T569_Malonic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 3 .361 0 .672 6 .309 2 .40E-5 ***Treatment 1 9 .325 9 .325 87 .528 2 .20E-16 ***TimePoint 1 0 .341 0 .341 3 .202 0 .076 .Genotype:Treatment 5 1 .881 0 .376 3 .531 0 .005 **Genotype:TimePoint 5 0 .578 0 .116 1 .085 0 .371Treatment:TimePoint 1 0 .300 0 .300 2 .814 0 .096 .Genotype:Treatment:TimePoint 5 0 .659 0 .132 1 .238 0 .294Residuals 151 16 .087 0 .107

M218T582_NI_Amino_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 3 .069 0 .614 3 .400 0 .006 **Treatment 1 0 .678 0 .678 3 .755 0 .054 .TimePoint 1 0 .306 0 .306 1 .694 0 .195Genotype:Treatment 5 1 .250 0 .250 1 .385 0 .233Genotype:TimePoint 5 1 .891 0 .378 2 .095 0 .069 .Treatment:TimePoint 1 0 .344 0 .344 1 .907 0 .169Genotype:Treatment:TimePoint 5 0 .807 0 .161 0 .894 0 .487Residuals 155 27 .983 0 .181

M91T611_Benzoic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 3 .686 0 .737 9 .203 1 .09E-7 ***Treatment 1 2 .290 2 .290 28 .594 3 .15E-7 ***TimePoint 1 0 .752 0 .752 9 .390 0 .003 **Genotype:Treatment 5 1 .016 0 .203 2 .536 0 .031 *Genotype:TimePoint 5 1 .593 0 .319 3 .978 0 .002 **Treatment:TimePoint 1 0 .892 0 .892 11 .132 0 .001 **Genotype:Treatment:TimePoint 5 0 .225 0 .045 0 .561 0 .730Residuals 155 12 .416 0 .080


Genotype 5 27 .431 5 .486 10 .024 3 .01E-8 ***Treatment 1 0 .021 0 .022 0 .039 0 .843TimePoint 1 1 .439 1 .439 2 .630 0 .107Genotype:Treatment 5 5 .537 1 .107 2 .023 0 .079 .

Appendix A.6 ANOVA results: metabolite abundance. Significance: ***P < 0.001; **P < 0.01; * P

< 0.05; . P < 0.1

194

Genotype:TimePoint 5 2 .551 0 .510 0 .932 0 .462Treatment:TimePoint 1 0 .87 0 .870 1 .590 0 .209Genotype:Treatment:TimePoint 5 3 .772 0 .754 1 .378 0 .236Residuals 144 78 .815 0 .547

M147T645_Thymidine .5 . .monophophateDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 5 .762 1 .152 8 .256 1 .12E-6 ***Treatment 1 0 .000 0 .000 0 .001 0 .971TimePoint 1 0 .617 0 .617 4 .424 0 .038 *Genotype:Treatment 5 2 .005 0 .401 2 .872 0 .018 *Genotype:TimePoint 5 1 .171 0 .234 1 .678 0 .146Treatment:TimePoint 1 1 .759 1 .759 12 .604 0 .001 ***Genotype:Treatment:TimePoint 5 0 .546 0 .109 0 .783 0 .564Residuals 113 15 .772 0 .140

M256T666_L .IsoleucineDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 5 .491 1 .098 3 .803 0 .003 **Treatment 1 10 .425 10 .425 36 .094 1 .43E-8 ***TimePoint 1 5 .724 5 .724 19 .819 1 .68E-5 ***Genotype:Treatment 5 2 .504 0 .501 1 .734 0 .130Genotype:TimePoint 5 2 .738 0 .548 1 .896 0 .098 .Treatment:TimePoint 1 4 .456 4 .456 15 .429 0 .000 ***Genotype:Treatment:TimePoint 5 2 .745 0 .549 1 .901 0 .098 .Residuals 146 42 .168 0 .289

M142T667_L .Proline_1Df Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 4 .662 0 .932 2 .111 0 .071 .Treatment 1 0 .085 0 .085 0 .193 0 .661TimePoint 1 2 .912 2 .912 6 .593 0 .012 *Genotype:Treatment 5 7 .004 1 .401 3 .172 0 .011 *Genotype:TimePoint 5 5 .675 1 .135 2 .570 0 .032 *Treatment:TimePoint 1 0 .194 0 .194 0 .439 0 .510Genotype:Treatment:TimePoint 5 1 .375 0 .275 0 .623 0 .683Residuals 89 39 .309 0 .442

M174T677_GlycineDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 3 .874 0 .775 3 .901 0 .003 **Treatment 1 0 .544 0 .544 2 .740 0 .101TimePoint 1 1 .193 1 .193 6 .004 0 .016 *

195

Genotype:Treatment 5 2 .990 0 .598 3 .010 0 .015 *Genotype:TimePoint 5 2 .842 0 .568 2 .862 0 .019 *Treatment:TimePoint 1 0 .417 0 .417 2 .102 0 .151Genotype:Treatment:TimePoint 5 1 .233 0 .247 1 .242 0 .296Residuals 92 18 .274 0 .199

M147T682_Succinic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 5 .080 1 .016 22 .876 2 .20E-16 ***Treatment 1 6 .116 6 .116 137 .722 2 .20E-16 ***TimePoint 1 3 .403 3 .403 76 .616 3 .33E-15 ***Genotype:Treatment 5 1 .609 0 .322 7 .244 4 .02E-6 ***Genotype:TimePoint 5 0 .189 0 .038 0 .850 0 .517Treatment:TimePoint 1 0 .122 0 .122 2 .750 0 .099 .Genotype:Treatment:TimePoint 5 0 .442 0 .088 1 .990 0 .083 .Residuals 155 6 .884 0 .044

M254T688_CatecholDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 1 .052 0 .210 4 .669 0 .001 ***Treatment 1 0 .494 0 .494 10 .952 0 .001 **TimePoint 1 0 .035 0 .035 0 .785 0 .377Genotype:Treatment 5 0 .804 0 .161 3 .568 0 .004 **Genotype:TimePoint 5 0 .416 0 .083 1 .846 0 .107Treatment:TimePoint 1 0 .129 0 .129 2 .851 0 .093 .Genotype:Treatment:TimePoint 5 0 .370 0 .074 1 .642 0 .152Residuals 154 6 .942 0 .045

M147T704_Glycolic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 5 .199 1 .040 9 .964 3 .79E-8 ***Treatment 1 3 .703 3 .703 35 .486 2 .05E-8 ***TimePoint 1 0 .003 0 .003 0 .027 0 .870Genotype:Treatment 5 5 .291 1 .058 10 .141 2 .80E-8 ***Genotype:TimePoint 5 0 .187 0 .037 0 .358 0 .876Treatment:TimePoint 1 1 .014 1 .014 9 .713 0 .002 **Genotype:Treatment:TimePoint 5 0 .690 0 .138 1 .323 0 .258Residuals 137 14 .295 0 .104

M245T714_Fumaric_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 8 .223 1 .645 23 .924 2 .20E-16 ***Treatment 1 7 .873 7 .873 114 .540 2 .20E-16 ***

196

TimePoint 1 0 .248 0 .248 3 .609 0 .059 .Genotype:Treatment 5 2 .300 0 .46 6 .692 1 .14E-5 ***Genotype:TimePoint 5 0 .330 0 .066 0 .961 0 .444Treatment:TimePoint 1 0 .011 0 .011 0 .158 0 .692Genotype:Treatment:TimePoint 5 1 .163 0 .233 3 .385 0 .006 **Residuals 154 10 .586 0 .069

M188T733_L .AlanineDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 6 .420 1 .284 16 .919 2 .33E-13 ***Treatment 1 0 .000 0 .000 0 .001 0 .971TimePoint 1 0 .198 0 .198 2 .609 0 .108Genotype:Treatment 5 1 .597 0 .319 4 .207 0 .001 **Genotype:TimePoint 5 1 .164 0 .233 3 .066 0 .011 *Treatment:TimePoint 1 0 .012 0 .012 0 .153 0 .696Genotype:Treatment:TimePoint 5 0 .433 0 .087 1 .142 0 .341Residuals 157 11 .915 0 .076

M204T734_L .SerineDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 9 .801 1 .960 5 .077 0 .000 ***Treatment 1 0 .115 0 .115 0 .298 0 .586TimePoint 1 0 .004 0 .004 0 .011 0 .916Genotype:Treatment 5 5 .299 1 .060 2 .745 0 .022 *Genotype:TimePoint 5 4 .276 0 .855 2 .215 0 .057 .Treatment:TimePoint 1 0 .264 0 .264 0 .683 0 .410Genotype:Treatment:TimePoint 5 1 .748 0 .350 0 .905 0 .480Residuals 123 47 .494 0 .386

M247T748_Threonic acid 1,4-lactoneDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 0 .248 0 .050 1 .682 0 .142Treatment 1 1 .595 1 .595 54 .058 1 .17E-11 ***TimePoint 1 0 .068 0 .067 2 .287 0 .133Genotype:Treatment 5 0 .253 0 .051 1 .715 0 .135Genotype:TimePoint 5 0 .203 0 .041 1 .376 0 .236Treatment:TimePoint 1 0 .003 0 .003 0 .113 0 .738Genotype:Treatment:TimePoint 5 0 .403 0 .081 2 .733 0 .022 *Residuals 150 4 .425 0 .030

M101T761_L .ThreonineDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 8 .979 1 .796 26 .238 2 .00E-16 ***

197

Treatment 1 0 .434 0 .434 6 .335 0 .013 *TimePoint 1 0 .064 0 .064 0 .940 0 .334Genotype:Treatment 5 0 .428 0 .086 1 .250 0 .291Genotype:TimePoint 5 0 .797 0 .159 2 .328 0 .047 *Treatment:TimePoint 1 0 .017 0 .017 0 .249 0 .619Genotype:Treatment:TimePoint 5 0 .383 0 .077 1 .119 0 .354Residuals 116 7 .939 0 .068

M179T804_Salicyl_alcoholDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 2 .576 0 .515 1 .556 0 .178Treatment 1 1 .915 1 .915 5 .785 0 .018 *TimePoint 1 0 .582 0 .582 1 .758 0 .187Genotype:Treatment 5 3 .543 0 .709 2 .140 0 .065 .Genotype:TimePoint 5 2 .547 0 .509 1 .539 0 .183Treatment:TimePoint 1 5 .18 5 .180 15 .645 0 .000 ***Genotype:Treatment:TimePoint 5 4 .208 0 .842 2 .542 0 .032 *Residuals 116 38 .405 0 .331


Genotype 5 1 .116 0 .223 8 .354 5 .58E-7 ***Treatment 1 0 .365 0 .365 13 .662 0 .000 ***TimePoint 1 0 .000 0 .000 0 .013 0 .908Genotype:Treatment 5 0 .045 0 .009 0 .337 0 .890Genotype:TimePoint 5 0 .523 0 .105 3 .913 0 .002 **Treatment:TimePoint 1 0 .006 0 .006 0 .219 0 .640Genotype:Treatment:TimePoint 5 0 .175 0 .035 1 .310 0 .263Residuals 148 3 .954 0 .027

M147T857_Malic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 1 .807 0 .361 14 .965 5 .28E-12 ***Treatment 1 0 .264 0 .264 10 .936 0 .001 **TimePoint 1 0 .004 0 .004 0 .160 0 .690Genotype:Treatment 5 0 .892 0 .178 7 .389 3 .01E-6 ***Genotype:TimePoint 5 0 .073 0 .015 0 .607 0 .695Treatment:TimePoint 1 0 .005 0 .005 0 .199 0 .656Genotype:Treatment:TimePoint 5 0 .076 0 .015 0 .632 0 .675Residuals 157 3 .791 0 .024

M233T886_L .Aspartic_acidDf Sum Sq Mean Sq F-value Pr(>F)

198

Genotype 5 4 .149 0 .830 3 .361 0 .007 **Treatment 1 1 .561 1 .561 6 .322 0 .013 *TimePoint 1 0 .627 0 .627 2 .540 0 .113Genotype:Treatment 5 3 .549 0 .710 2 .875 0 .016 *Genotype:TimePoint 5 4 .067 0 .813 3 .294 0 .007 **Treatment:TimePoint 1 0 .118 0 .118 0 .479 0 .490Genotype:Treatment:TimePoint 5 1 .65 0 .330 1 .336 0 .252Residuals 157 38 .764 0 .247

M156T888_Pyroglutamic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 6 .202 1 .240 15 .247 3 .34E-12 ***Treatment 1 0 .286 0 .286 3 .511 0 .063 .TimePoint 1 0 .059 0 .059 0 .729 0 .395Genotype:Treatment 5 1 .255 0 .251 3 .086 0 .011 *Genotype:TimePoint 5 1 .195 0 .239 2 .938 0 .015 *Treatment:TimePoint 1 0 .020 0 .020 0 .244 0 .622Genotype:Treatment:TimePoint 5 0 .657 0 .131 1 .615 0 .159Residuals 157 12 .773 0 .081

M174T892_Butyric_acid-4-amino-n- Df Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 20 .773 4 .155 12 .753 2 .97E-10 ***Treatment 1 0 .078 0 .078 0 .238 0 .626TimePoint 1 0 .928 0 .928 2 .848 0 .094 .Genotype:Treatment 5 5 .095 1 .019 3 .128 0 .010 *Genotype:TimePoint 5 1 .045 0 .209 0 .642 0 .668Treatment:TimePoint 1 1 .044 1 .044 3 .204 0 .076 .Genotype:Treatment:TimePoint 5 1 .997 0 .399 1 .226 0 .300Residuals 143 46 .584 0 .326

M292T913_NI_Organic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 0 .714 0 .143 0 .819 0 .538Treatment 1 0 .273 0 .273 1 .567 0 .213TimePoint 1 0 .333 0 .332 1 .906 0 .170Genotype:Treatment 5 0 .740 0 .148 0 .849 0 .518Genotype:TimePoint 5 1 .199 0 .240 1 .374 0 .238Treatment:TimePoint 1 0 .583 0 .583 3 .341 0 .070 .Genotype:Treatment:TimePoint 5 0 .874 0 .175 1 .003 0 .419Residuals 134 23 .371 0 .174


199

Df Sum Sq Mean Sq F-value Pr(>F)Genotype 5 3 .504 0 .701 17 .129 2 .03E-13 ***Treatment 1 1 .255 1 .255 30 .668 1 .31E-7 ***TimePoint 1 0 .010 0 .010 0 .248 0 .619Genotype:Treatment 5 2 .017 0 .403 9 .859 3 .52E-8 ***Genotype:TimePoint 5 0 .077 0 .015 0 .375 0 .865Treatment:TimePoint 1 0 .361 0 .361 8 .825 0 .003 **Genotype:Treatment:TimePoint 5 0 .402 0 .080 1 .966 0 .087 .Residuals 152 6 .219 0 .041

M142T941_L .Proline_2Df Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 1 .572 0 .314 1 .396 0 .229Treatment 1 0 .393 0 .393 1 .743 0 .189TimePoint 1 2 .806 2 .806 12 .462 0 .001 ***Genotype:Treatment 5 2 .245 0 .449 1 .994 0 .083 .Genotype:TimePoint 5 2 .001 0 .400 1 .778 0 .121Treatment:TimePoint 1 0 .398 0 .398 1 .767 0 .186Genotype:Treatment:TimePoint 5 0 .961 0 .192 0 .854 0 .514Residuals 152 34 .228 0 .225

M306T949_NIDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 3 .589 0 .718 5 .288 0 .000 ***Treatment 1 2 .336 2 .336 17 .203 5 .48E-5 ***TimePoint 1 0 .036 0 .036 0 .266 0 .607Genotype:Treatment 5 2 .596 0 .519 3 .824 0 .003 **Genotype:TimePoint 5 1 .107 0 .221 1 .630 0 .155Treatment:TimePoint 1 0 .015 0 .015 0 .112 0 .738Genotype:Treatment:TimePoint 5 1 .591 0 .318 2 .343 0 .044 *Residuals 157 21 .314 0 .136

M249T957_GlycerolDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 0 .613 0 .123 4 .055 0 .002 **Treatment 1 1 .273 1 .273 42 .080 1 .26E-9 ***TimePoint 1 0 .007 0 .007 0 .225 0 .636Genotype:Treatment 5 0 .103 0 .021 0 .683 0 .637Genotype:TimePoint 5 0 .140 0 .028 0 .924 0 .467Treatment:TimePoint 1 0 .135 0 .135 4 .454 0 .037 *Genotype:Treatment:TimePoint 5 0 .167 0 .033 1 .106 0 .360Residuals 147 4 .447 0 .030

200

M246T973_L .GlutamateDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 17 .696 3 .539 7 .860 1 .28E-6 ***Treatment 1 0 .041 0 .041 0 .091 0 .763TimePoint 1 0 .1 0 .1 0 .222 0 .638Genotype:Treatment 5 5 .384 1 .077 2 .391 0 .040 *Genotype:TimePoint 5 1 .932 0 .386 0 .858 0 .511Treatment:TimePoint 1 0 .125 0 .125 0 .278 0 .599Genotype:Treatment:TimePoint 5 2 .009 0 .402 0 .892 0 .488Residuals 155 69 .791 0 .450

M192T980_L .PhenylalanineDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 17 .407 3 .481 7 .192 1 .18E-5 ***Treatment 1 0 .223 0 .223 0 .461 0 .499TimePoint 1 0 .903 0 .904 1 .867 0 .175Genotype:Treatment 5 7 .202 1 .441 2 .976 0 .016 *Genotype:TimePoint 5 3 .413 0 .683 1 .410 0 .229Treatment:TimePoint 1 0 .088 0 .088 0 .183 0 .670Genotype:Treatment:TimePoint 5 2 .737 0 .547 1 .131 0 .350Residuals 85 41 .145 0 .484


Genotype 5 1 .572 0 .314 5 .177 0 .000 ***Treatment 1 0 .002 0 .002 0 .025 0 .874TimePoint 1 0 .004 0 .004 0 .070 0 .791Genotype:Treatment 5 1 .136 0 .227 3 .739 0 .003 **Genotype:TimePoint 5 0 .697 0 .139 2 .294 0 .048 *Treatment:TimePoint 1 0 .001 0 .001 0 .020 0 .888Genotype:Treatment:TimePoint 5 0 .250 0 .050 0 .822 0 .535Residuals 157 9 .537 0 .061

M217T1015_NI_5C_sugarDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 3 .964 0 .793 11 .400 2 .14E-9 ***Treatment 1 6 .660 6 .660 95 .759 2 .20E-16 ***TimePoint 1 0 .154 0 .154 2 .211 0 .139Genotype:Treatment 5 3 .218 0 .644 9 .255 9 .68E-8 ***Genotype:TimePoint 5 0 .104 0 .021 0 .300 0 .912Treatment:TimePoint 1 0 .546 0 .546 7 .852 0 .006 **Genotype:Treatment:TimePoint 5 0 .525 0 .105 1 .510 0 .190Residuals 157 10 .919 0 .070

201


Genotype 5 4 .764 0 .953 5 .591 9 .50E-5 ***Treatment 1 0 .823 0 .823 4 .828 0 .030 *TimePoint 1 0 .031 0 .030 0 .179 0 .673Genotype:Treatment 5 2 .052 0 .410 2 .408 0 .039 *Genotype:TimePoint 5 2 .058 0 .412 2 .415 0 .039 *Treatment:TimePoint 1 0 .073 0 .073 0 .426 0 .515Genotype:Treatment:TimePoint 5 1 .501 0 .300 1 .762 0 .124Residuals 148 25 .223 0 .170


Genotype 5 15 .478 3 .096 17 .498 9 .42E-14 ***Treatment 1 0 .909 0 .909 5 .140 0 .025 *TimePoint 1 0 .010 0 .010 0 .058 0 .811Genotype:Treatment 5 4 .527 0 .905 5 .118 0 .000 ***Genotype:TimePoint 5 2 .504 0 .501 2 .831 0 .018 *Treatment:TimePoint 1 0 .01 0 .010 0 .057 0 .812Genotype:Treatment:TimePoint 5 1 .321 0 .264 1 .493 0 .195Residuals 157 27 .776 0 .177


Genotype 5 3 .332 0 .666 17 .287 1 .96E-13 ***Treatment 1 0 .042 0 .042 1 .092 0 .298TimePoint 1 0 .057 0 .057 1 .486 0 .225Genotype:Treatment 5 0 .217 0 .043 1 .126 0 .349Genotype:TimePoint 5 0 .148 0 .030 0 .77 0 .573Treatment:TimePoint 1 0 .004 0 .004 0 .112 0 .739Genotype:Treatment:TimePoint 5 0 .097 0 .019 0 .502 0 .774Residuals 147 5 .666 0 .039


Genotype 5 1 .819 0 .364 3 .763 0 .003 **Treatment 1 0 .004 0 .004 0 .039 0 .844TimePoint 1 0 .098 0 .098 1 .017 0 .315Genotype:Treatment 5 0 .409 0 .082 0 .846 0 .520Genotype:TimePoint 5 0 .794 0 .159 1 .642 0 .153Treatment:TimePoint 1 0 .039 0 .039 0 .406 0 .525Genotype:Treatment:TimePoint 5 0 .35 0 .07 0 .724 0 .607

202

Residuals 136 13 .151 0 .097


Genotype 5 2 .515 0 .503 4 .844 0 .000 ***Treatment 1 0 .266 0 .266 2 .561 0 .112TimePoint 1 0 .045 0 .045 0 .433 0 .512Genotype:Treatment 5 1 .306 0 .261 2 .515 0 .034 *Genotype:TimePoint 5 0 .923 0 .185 1 .778 0 .123Treatment:TimePoint 1 0 .798 0 .798 7 .687 0 .007 **Genotype:Treatment:TimePoint 5 1 .132 0 .226 2 .18 0 .061 .Residuals 110 11 .423 0 .104

M204T1132_Shikimic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 3 .412 0 .682 16 .238 6 .81E-13 ***Treatment 1 1 .283 1 .282 30 .517 1 .35E-7 ***TimePoint 1 0 .002 0 .002 0 .042 0 .839Genotype:Treatment 5 0 .436 0 .087 2 .075 0 .071 .Genotype:TimePoint 5 0 .396 0 .079 1 .883 0 .100Treatment:TimePoint 1 0 .325 0 .325 7 .738 0 .006 **Genotype:Treatment:TimePoint 5 0 .583 0 .117 2 .777 0 .020 *Residuals 157 6 .598 0 .042


Genotype 5 4 .449 0 .890 4 .600 0 .001 ***Treatment 1 1 .113 1 .112 5 .750 0 .018 *TimePoint 1 0 .936 0 .936 4 .839 0 .030 *Genotype:Treatment 5 2 .066 0 .413 2 .135 0 .065 .Genotype:TimePoint 5 1 .247 0 .249 1 .289 0 .272Treatment:TimePoint 1 1 .234 1 .234 6 .380 0 .013 *Genotype:Treatment:TimePoint 5 1 .757 0 .351 1 .816 0 .114Residuals 136 26 .311 0 .193

M273T1143_Citric_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 8 .473 1 .695 15 .551 2 .18E-12 ***Treatment 1 3 .785 3 .785 34 .733 2 .28E-8 ***TimePoint 1 0 .738 0 .738 6 .774 0 .010 *Genotype:Treatment 5 2 .881 0 .576 5 .288 0 .000 ***Genotype:TimePoint 5 0 .663 0 .133 1 .216 0 .304Treatment:TimePoint 1 0 .262 0 .262 2 .406 0 .123

203

Genotype:Treatment:TimePoint 5 0 .835 0 .167 1 .532 0 .183Residuals 155 16 .891 0 .109

M173T1167_NI

Df Sum Sq Mean Sq F-value Pr(>F)Genotype 5 1 .663 0 .333 10 .856 5 .54E-9 ***Treatment 1 0 .313 0 .313 10 .231 0 .002 **TimePoint 1 0 .01 0 .01 0 .326 0 .569Genotype:Treatment 5 0 .189 0 .038 1 .232 0 .297Genotype:TimePoint 5 0 .136 0 .027 0 .889 0 .490Treatment:TimePoint 1 0 .019 0 .019 0 .635 0 .427Genotype:Treatment:TimePoint 5 0 .057 0 .011 0 .374 0 .866Residuals 157 4 .809 0 .031

M345T1180_Quinic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 0 .635 0 .127 6 .902 7 .51E-6 ***Treatment 1 0 .739 0 .739 40 .149 2 .37E-9 ***TimePoint 1 0 .003 0 .003 0 .167 0 .683Genotype:Treatment 5 0 .345 0 .069 3 .751 0 .003 **Genotype:TimePoint 5 0 .045 0 .009 0 .488 0 .785Treatment:TimePoint 1 0 .025 0 .025 1 .347 0 .248Genotype:Treatment:TimePoint 5 0 .148 0 .030 1 .604 0 .162Residuals 157 2 .888 0 .018

M201T1190_Fructose_1Df Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 5 .444 1 .089 13 .836 3 .77E-11 ***Treatment 1 0 .390 0 .390 4 .952 0 .028 *TimePoint 1 1 .523 1 .523 19 .358 2 .02E-5 ***Genotype:Treatment 5 2 .802 0 .560 7 .121 5 .14E-6 ***Genotype:TimePoint 5 1 .614 0 .323 4 .103 0 .002 **Treatment:TimePoint 1 0 .033 0 .033 0 .414 0 .521Genotype:Treatment:TimePoint 5 0 .705 0 .141 1 .792 0 .118Residuals 153 12 .040 0 .079


Genotype 5 2 .965 0 .593 18 .198 3 .20E-14 ***Treatment 1 0 .200 0 .200 6 .129 0 .014 *TimePoint 1 0 .033 0 .033 1 .018 0 .314Genotype:Treatment 5 0 .591 0 .118 3 .626 0 .004 **

204

Genotype:TimePoint 5 0 .289 0 .058 1 .772 0 .122Treatment:TimePoint 1 0 .236 0 .236 7 .247 0 .008 **Genotype:Treatment:TimePoint 5 0 .102 0 .020 0 .629 0 .678Residuals 157 5 .115 0 .033

M218T1196_Fructose_2Df Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 2 .608 0 .522 24 .551 2 .20E-16 ***Treatment 1 0 .164 0 .164 7 .699 0 .006 **TimePoint 1 0 .092 0 .092 4 .309 0 .040 *Genotype:Treatment 5 0 .603 0 .121 5 .677 7 .67E-5 ***Genotype:TimePoint 5 0 .222 0 .044 2 .091 0 .069 .Treatment:TimePoint 1 0 .050 0 .050 2 .331 0 .129Genotype:Treatment:TimePoint 5 0 .166 0 .033 1 .563 0 .174Residuals 157 3 .335 0 .021

M147T1210_Glucose_1Df Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 2 .761 0 .552 10 .270 1 .57E-8 ***Treatment 1 0 .269 0 .269 4 .996 0 .027 *TimePoint 1 0 .644 0 .644 11 .982 0 .001 ***Genotype:Treatment 5 3 .890 0 .778 14 .467 1 .19E-11 ***Genotype:TimePoint 5 0 .923 0 .185 3 .435 0 .006 **Treatment:TimePoint 1 0 .407 0 .407 7 .574 0 .007 **Genotype:Treatment:TimePoint 5 0 .519 0 .104 1 .929 0 .092 .Residuals 157 8 .442 0 .054


Genotype 5 3 .769 0 .754 11 .240 2 .88E-9 ***Treatment 1 0 .289 0 .289 4 .311 0 .040 *TimePoint 1 0 .947 0 .947 14 .116 0 .000 ***Genotype:Treatment 5 5 .274 1 .055 15 .729 1 .59E-12 ***Genotype:TimePoint 5 1 .486 0 .297 4 .430 0 .001 ***Treatment:TimePoint 1 0 .659 0 .659 9 .826 0 .002 **Genotype:Treatment:TimePoint 5 0 .835 0 .167 2 .489 0 .034 *Residuals 156 10 .462 0 .067

M280T1229_L .TyrosineDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 2 .12 0 .424 0 .939 0 .458Treatment 1 0 .374 0 .374 0 .829 0 .364TimePoint 1 0 .504 0 .504 1 .116 0 .293

205

Genotype:Treatment 5 2 .427 0 .485 1 .075 0 .377Genotype:TimePoint 5 1 .569 0 .314 0 .695 0 .628Treatment:TimePoint 1 1 .304 1 .304 2 .888 0 .092 .Genotype:Treatment:TimePoint 5 3 .506 0 .701 1 .553 0 .178Residuals 129 58 .244 0 .452

M217T1234_NI_Sugar_alcoholDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 7 .451 1 .490 23 .036 2 .20E-16 ***Treatment 1 0 .004 0 .004 0 .067 0 .797TimePoint 1 0 .101 0 .101 1 .558 0 .214Genotype:Treatment 5 1 .074 0 .215 3 .319 0 .007 **Genotype:TimePoint 5 0 .132 0 .026 0 .408 0 .843Treatment:TimePoint 1 0 .116 0 .116 1 .791 0 .183Genotype:Treatment:TimePoint 5 0 .267 0 .053 0 .826 0 .533Residuals 155 10 .027 0 .065

M332T1243_L .Ascorbic_acidDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 1 .677 0 .335 2 .870 0 .017 *Treatment 1 0 .171 0 .171 1 .464 0 .228TimePoint 1 0 .052 0 .052 0 .443 0 .507Genotype:Treatment 5 0 .784 0 .157 1 .341 0 .250Genotype:TimePoint 5 0 .283 0 .057 0 .485 0 .787Treatment:TimePoint 1 0 .148 0 .148 1 .265 0 .263Genotype:Treatment:TimePoint 5 0 .734 0 .147 1 .256 0 .286Residuals 152 17 .766 0 .117


Genotype 5 6 .779 1 .356 9 .699 6 .12E-8 ***Treatment 1 0 .54 0 .540 3 .863 0 .051 .TimePoint 1 1 .658 1 .658 11 .861 0 .001 ***Genotype:Treatment 5 5 .242 1 .048 7 .500 3 .03E-6 ***Genotype:TimePoint 5 1 .565 0 .313 2 .238 0 .054 .Treatment:TimePoint 1 1 .066 1 .066 7 .625 0 .007 **Genotype:Treatment:TimePoint 5 1 .370 0 .274 1 .960 0 .089 .Residuals 136 19 .012 0 .140

M305T1348_Myo .inositolDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 0 .124 0 .025 2 .618 0 .026 *Treatment 1 0 .084 0 .084 8 .873 0 .003 **

206

TimePoint 1 0 .008 0 .008 0 .806 0 .371Genotype:Treatment 5 0 .012 0 .002 0 .253 0 .938Genotype:TimePoint 5 0 .011 0 .002 0 .242 0 .943Treatment:TimePoint 1 0 .005 0 .005 0 .506 0 .478Genotype:Treatment:TimePoint 5 0 .035 0 .007 0 .737 0 .597Residuals 157 1 .483 0 .009


Genotype 5 0 .576 0 .115 3 .135 0 .010 *Treatment 1 0 .000 0 .000 0 .002 0 .967TimePoint 1 0 .001 0 .001 0 .035 0 .853Genotype:Treatment 5 0 .529 0 .106 2 .876 0 .017 *Genotype:TimePoint 5 0 .065 0 .013 0 .354 0 .879Treatment:TimePoint 1 0 .029 0 .029 0 .777 0 .380Genotype:Treatment:TimePoint 5 0 .046 0 .009 0 .249 0 .940Residuals 148 5 .442 0 .037


Genotype 5 1 .11 0 .222 3 .690 0 .004 **Treatment 1 0 .795 0 .795 13 .218 0 .000 ***TimePoint 1 0 .018 0 .018 0 .304 0 .582Genotype:Treatment 5 1 .033 0 .207 3 .434 0 .006 **Genotype:TimePoint 5 0 .571 0 .114 1 .900 0 .098 .Treatment:TimePoint 1 0 .134 0 .134 2 .220 0 .138Genotype:Treatment:TimePoint 5 0 .134 0 .027 0 .444 0 .817Residuals 152 9 .144 0 .060


Genotype 5 9 .533 1 .907 16 .735 5 .60E-13 ***Treatment 1 1 .373 1 .373 12 .049 0 .001 ***TimePoint 1 0 .165 0 .165 1 .450 0 .230Genotype:Treatment 5 0 .625 0 .125 1 .098 0 .364Genotype:TimePoint 5 0 .644 0 .129 1 .130 0 .347Treatment:TimePoint 1 0 .015 0 .015 0 .129 0 .720Genotype:Treatment:TimePoint 5 0 .241 0 .048 0 .422 0 .833Residuals 142 16 .177 0 .114


Genotype 5 6 .468 1 .294 54 .170 <2e-16 ***

207

Treatment 1 0 .114 0 .114 4 .779 0 .030 *TimePoint 1 0 .041 0 .041 1 .735 0 .190Genotype:Treatment 5 0 .156 0 .031 1 .302 0 .266Genotype:TimePoint 5 0 .043 0 .009 0 .357 0 .877Treatment:TimePoint 1 0 .008 0 .008 0 .319 0 .573Genotype:Treatment:TimePoint 5 0 .083 0 .017 0 .697 0 .627Residuals 157 3 .749 0 .024

M204T1529_MelibioseDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 8 .055 1 .611 61 .058 2 .20E-16 ***Treatment 1 0 .537 0 .537 20 .338 1 .26E-5 ***TimePoint 1 0 .015 0 .015 0 .583 0 .446Genotype:Treatment 5 0 .244 0 .049 1 .851 0 .106Genotype:TimePoint 5 0 .166 0 .033 1 .256 0 .286Treatment:TimePoint 1 0 .012 0 .012 0 .447 0 .505Genotype:Treatment:TimePoint 5 0 .018 0 .004 0 .134 0 .984Residuals 157 4 .142 0 .026


Genotype 5 5 .502 1 .100 13 .725 4 .07E-11 ***Treatment 1 0 .554 0 .554 6 .906 0 .009 **TimePoint 1 0 .139 0 .139 1 .729 0 .190Genotype:Treatment 5 0 .257 0 .051 0 .640 0 .669Genotype:TimePoint 5 0 .273 0 .055 0 .681 0 .639Treatment:TimePoint 1 0 .059 0 .059 0 .739 0 .391Genotype:Treatment:TimePoint 5 0 .217 0 .043 0 .542 0 .744Residuals 157 12 .588 0 .080


Genotype 5 9 .971 1 .994 17 .160 3 .70E-13 ***Treatment 1 0 .160 0 .160 1 .38 0 .242TimePoint 1 0 .025 0 .025 0 .211 0 .647Genotype:Treatment 5 2 .252 0 .450 3 .876 0 .003 **Genotype:TimePoint 5 0 .579 0 .116 0 .997 0 .422Treatment:TimePoint 1 0 .002 0 .002 0 .016 0 .901Genotype:Treatment:TimePoint 5 0 .406 0 .081 0 .698 0 .626Residuals 137 15 .920 0 .116


208

Genotype 5 35 .033 7 .007 119 .323 2 .20E-16 ***Treatment 1 0 .791 0 .791 13 .477 0 .000 ***TimePoint 1 0 .022 0 .022 0 .369 0 .544Genotype:Treatment 5 1 .329 0 .266 4 .527 0 .001 ***Genotype:TimePoint 5 0 .851 0 .170 2 .900 0 .016 *Treatment:TimePoint 1 0 .003 0 .003 0 .044 0 .835Genotype:Treatment:TimePoint 5 0 .103 0 .021 0 .352 0 .880Residuals 142 8 .338 0 .059

M261T1575_MaltoseDf Sum Sq Mean Sq F-value Pr(>F)



Genotype 5 30 .354 6 .071 61 .515 <2e-16 ***Treatment 1 0 .048 0 .048 0 .482 0 .488TimePoint 1 0 .013 0 .013 0 .135 0 .714Genotype:Treatment 5 0 .620 0 .124 1 .256 0 .286Genotype:TimePoint 5 0 .056 0 .011 0 .114 0 .989Treatment:TimePoint 1 0 .040 0 .040 0 .407 0 .524Genotype:Treatment:TimePoint 5 0 .775 0 .155 1 .571 0 .171Residuals 157 15 .494 0 .099

M361T1617_SalicinDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 5 .668 1 .134 11 .340 2 .37E-9 ***Treatment 1 1 .061 1 .061 10 .616 0 .001 **TimePoint 1 0 .421 0 .421 4 .212 0 .042 *Genotype:Treatment 5 1 .695 0 .339 3 .391 0 .006 **Genotype:TimePoint 5 1 .771 0 .354 3 .544 0 .005 **Treatment:TimePoint 1 0 .712 0 .712 7 .123 0 .008 **Genotype:Treatment:TimePoint 5 0 .525 0 .105 1 .050 0 .391Residuals 157 15 .696 0 .100

M219T1659_NI

209

Df Sum Sq Mean Sq F-value Pr(>F)Genotype 5 1 .244 0 .249 6 .514 1 .56E-5 ***Treatment 1 0 .764 0 .764 20 .004 1 .48E-5 ***TimePoint 1 0 0 .000 0 .000 0 .988Genotype:Treatment 5 0 .611 0 .122 3 .201 0 .009 **Genotype:TimePoint 5 0 .330 0 .066 1 .729 0 .131Treatment:TimePoint 1 0 .037 0 .037 0 .964 0 .328Genotype:Treatment:TimePoint 5 0 .078 0 .016 0 .407 0 .843Residuals 157 5 .999 0 .038


Genotype 5 5 .533 1 .106 15 .604 4 .20E-12 ***Treatment 1 0 .220 0 .220 3 .107 0 .080 .TimePoint 1 0 .028 0 .028 0 .394 0 .531Genotype:Treatment 5 0 .153 0 .030 0 .43 0 .827Genotype:TimePoint 5 0 .322 0 .064 0 .908 0 .478Treatment:TimePoint 1 0 .032 0 .032 0 .445 0 .506Genotype:Treatment:TimePoint 5 0 .113 0 .023 0 .319 0 .901Residuals 135 9 .573 0 .071

M236T1674_AdenosineDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 15 .328 3 .066 41 .473 2 .20E-16 ***Treatment 1 0 .222 0 .222 3 .007 0 .085 .TimePoint 1 0 .095 0 .095 1 .289 0 .258Genotype:Treatment 5 1 .549 0 .310 4 .192 0 .001 **Genotype:TimePoint 5 0 .752 0 .150 2 .034 0 .077 .Treatment:TimePoint 1 0 .111 0 .111 1 .503 0 .222Genotype:Treatment:TimePoint 5 0 .385 0 .077 1 .043 0 .395Residuals 157 11 .605 0 .074


Genotype 5 42 .363 8 .473 78 .231 2 .20E-16 ***Treatment 1 0 .001 0 .001 0 .011 0 .916TimePoint 1 0 .18 0 .180 1 .665 0 .199Genotype:Treatment 5 0 .409 0 .082 0 .755 0 .583Genotype:TimePoint 5 2 .315 0 .463 4 .274 0 .001 **Treatment:TimePoint 1 0 .02 0 .020 0 .184 0 .669Genotype:Treatment:TimePoint 5 0 .705 0 .141 1 .302 0 .266Residuals 150 16 .245 0 .108

210

M361T1693_SucroseDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 0 .233 0 .047 3 .38 0 .006 **Treatment 1 0 .321 0 .321 23 .237 3 .36E-6 ***TimePoint 1 0 .128 0 .128 9 .266 0 .003 **Genotype:Treatment 5 0 .069 0 .014 1 .003 0 .418Genotype:TimePoint 5 0 .092 0 .018 1 .335 0 .252Treatment:TimePoint 1 0 .005 0 .005 0 .348 0 .556Genotype:Treatment:TimePoint 5 0 .082 0 .016 1 .187 0 .318Residuals 157 2 .166 0 .014


Genotype 5 6 .154 1 .231 19 .088 8 .27E-15 ***Treatment 1 0 .751 0 .751 11 .646 0 .001 ***TimePoint 1 0 .159 0 .159 2 .458 0 .119Genotype:Treatment 5 0 .323 0 .065 1 .001 0 .419Genotype:TimePoint 5 0 .721 0 .144 2 .235 0 .053 .Treatment:TimePoint 1 0 .064 0 .064 0 .996 0 .320Genotype:Treatment:TimePoint 5 0 .288 0 .058 0 .893 0 .487Residuals 157 10 .124 0 .064


Genotype 5 5 .037 1 .007 7 .899 1 .18E-6 ***Treatment 1 0 .973 0 .973 7 .627 0 .006 **TimePoint 1 0 .005 0 .005 0 .041 0 .840Genotype:Treatment 5 0 .941 0 .188 1 .475 0 .201Genotype:TimePoint 5 1 .267 0 .253 1 .988 0 .083 .Treatment:TimePoint 1 0 .029 0 .029 0 .226 0 .635Genotype:Treatment:TimePoint 5 0 .178 0 .036 0 .279 0 .924Residuals 156 19 .895 0 .128


Genotype 5 3 .814 0 .763 6 .181 4 .87E-5 ***Treatment 1 1 .773 1 .773 14 .371 0 .000 ***TimePoint 1 0 .010 0 .010 0 .083 0 .774Genotype:Treatment 5 0 .995 0 .199 1 .613 0 .163Genotype:TimePoint 5 0 .428 0 .086 0 .694 0 .629Treatment:TimePoint 1 0 .144 0 .144 1 .168 0 .282Genotype:Treatment:TimePoint 5 0 .106 0 .021 0 .172 0 .972Residuals 101 12 .463 0 .123

211



M368T1807_CatechinDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 8 .501 1 .700 16 .144 7 .91E-13 ***Treatment 1 0 .384 0 .383 3 .641 0 .058 .TimePoint 1 0 .013 0 .013 0 .122 0 .728Genotype:Treatment 5 4 .275 0 .855 8 .118 7 .75E-7 ***Genotype:TimePoint 5 1 .473 0 .295 2 .798 0 .019 *Treatment:TimePoint 1 0 .100 0 .100 0 .951 0 .331Genotype:Treatment:TimePoint 5 0 .209 0 .042 0 .397 0 .850Residuals 157 16 .535 0 .105


Genotype 5 22 .781 4 .556 25 .334 2 .20E-16 ***Treatment 1 1 .270 1 .270 7 .059 0 .009 **TimePoint 1 0 .058 0 .058 0 .325 0 .570Genotype:Treatment 5 3 .845 0 .769 4 .276 0 .001 **Genotype:TimePoint 5 1 .441 0 .288 1 .603 0 .163Treatment:TimePoint 1 0 .064 0 .064 0 .355 0 .552Genotype:Treatment:TimePoint 5 0 .578 0 .116 0 .643 0 .667Residuals 143 25 .719 0 .180


Genotype 5 46 .1 9 .220 21 .611 2 .81E-16 ***Treatment 1 1 .096 1 .096 2 .569 0 .111TimePoint 1 0 .276 0 .276 0 .646 0 .423Genotype:Treatment 5 19 .601 3 .920 9 .189 1 .19E-7 ***Genotype:TimePoint 5 3 .682 0 .736 1 .726 0 .132Treatment:TimePoint 1 0 .005 0 .005 0 .012 0 .914Genotype:Treatment:TimePoint 5 1 .094 0 .219 0 .513 0 .766

212

Residuals 151 64 .423 0 .427

M204T1876_GalactinolDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 1 .019 0 .204 1 .650 0 .153Treatment 1 1 .966 1 .966 15 .922 0 .000 ***TimePoint 1 46 .155 46 .155 373 .821 2 .20E-16 ***Genotype:Treatment 5 1 .993 0 .399 3 .228 0 .010 **Genotype:TimePoint 5 6 .732 1 .346 10 .905 2 .02E-8 ***Treatment:TimePoint 1 0 .296 0 .296 2 .401 0 .124Genotype:Treatment:TimePoint 5 2 .763 0 .553 4 .475 0 .001 ***Residuals 102 12 .594 0 .123



M560T1902_KaempferolDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 15 .743 3 .149 9 .183 1 .15E-7 ***Treatment 1 1 .558 1 .558 4 .545 0 .035 *TimePoint 1 0 .015 0 .015 0 .045 0 .833Genotype:Treatment 5 0 .875 0 .175 0 .510 0 .768Genotype:TimePoint 5 2 .682 0 .536 1 .565 0 .173Treatment:TimePoint 1 0 .149 0 .149 0 .435 0 .511Genotype:Treatment:TimePoint 5 1 .59 0 .318 0 .927 0 .465Residuals 154 52 .804 0 .343

M204T1938_DigalactosylglycerolDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 0 .610 0 .122 2 .204 0 .057 .Treatment 1 0 .278 0 .278 5 .020 0 .026 *TimePoint 1 0 0 0 0 .999Genotype:Treatment 5 1 .047 0 .209 3 .784 0 .003 **Genotype:TimePoint 5 0 .336 0 .067 1 .212 0 .306Treatment:TimePoint 1 0 .001 0 .001 0 .013 0 .910

213

Genotype:Treatment:TimePoint 5 0 .137 0 .027 0 .495 0 .780Residuals 157 8 .688 0 .055

M648T1955_Quercitin

Df Sum Sq Mean Sq F-value Pr(>F)Genotype 5 11 .507 2 .301 6 .970 6 .66E-6 ***Treatment 1 3 .384 3 .384 10 .247 0 .002 **TimePoint 1 0 .004 0 .004 0 .013 0 .908Genotype:Treatment 5 1 .03 0 .206 0 .624 0 .682Genotype:TimePoint 5 2 .458 0 .492 1 .489 0 .196Treatment:TimePoint 1 0 .39 0 .39 1 .181 0 .279Genotype:Treatment:TimePoint 5 1 .132 0 .227 0 .686 0 .635Residuals 156 51 .511 0 .330


Genotype 5 8 .983 1 .797 33 .203 2 .00E-16 ***Treatment 1 0 .037 0 .037 0 .683 0 .410TimePoint 1 0 .013 0 .013 0 .241 0 .624Genotype:Treatment 5 0 .092 0 .018 0 .340 0 .888Genotype:TimePoint 5 0 .513 0 .103 1 .897 0 .098 .Treatment:TimePoint 1 0 0 .000 0 .000 0 .989Genotype:Treatment:TimePoint 5 0 .225 0 .045 0 .833 0 .528Residuals 157 8 .495 0 .054


Genotype 5 1 .749 0 .350 2 .898 0 .016 *Treatment 1 0 .186 0 .186 1 .542 0 .216TimePoint 1 0 .404 0 .404 3 .347 0 .069 .Genotype:Treatment 5 0 .279 0 .056 0 .463 0 .803Genotype:TimePoint 5 0 .782 0 .156 1 .296 0 .269Treatment:TimePoint 1 0 .227 0 .227 1 .878 0 .173Genotype:Treatment:TimePoint 5 0 .563 0 .113 0 .933 0 .462Residuals 139 16 .774 0 .121


Genotype 5 22 .555 4 .511 90 .681 2 .00E-16 ***Treatment 1 0 .000 0 .000 0 .004 0 .949TimePoint 1 0 .004 0 .004 0 .088 0 .767Genotype:Treatment 5 0 .564 0 .113 2 .266 0 .051 .

214

Genotype:TimePoint 5 0 .311 0 .062 1 .251 0 .288Treatment:TimePoint 1 0 .038 0 .038 0 .758 0 .385Genotype:Treatment:TimePoint 5 0 .246 0 .049 0 .988 0 .427Residuals 157 7 .810 0 .050


Genotype 5 0 .615 0 .123 4 .177 0 .001 **Treatment 1 0 .003 0 .003 0 .102 0 .750TimePoint 1 0 .021 0 .021 0 .715 0 .399Genotype:Treatment 5 0 .181 0 .036 1 .232 0 .297Genotype:TimePoint 5 0 .142 0 .028 0 .963 0 .442Treatment:TimePoint 1 0 .025 0 .025 0 .836 0 .362Genotype:Treatment:TimePoint 5 0 .118 0 .024 0 .800 0 .551Residuals 157 4 .621 0 .029




Genotype 5 7 .472 1 .494 5 .552 0 .000 ***Treatment 1 0 .003 0 .003 0 .011 0 .916TimePoint 1 0 .216 0 .216 0 .804 0 .371Genotype:Treatment 5 0 .578 0 .116 0 .429 0 .828Genotype:TimePoint 5 2 .724 0 .545 2 .024 0 .079 .Treatment:TimePoint 1 0 .18 0 .180 0 .667 0 .415Genotype:Treatment:TimePoint 5 1 .082 0 .216 0 .804 0 .548Residuals 145 39 .029 0 .269

M361T2065_RaffinoseDf Sum Sq Mean Sq F-value Pr(>F)

Genotype 5 5 .731 1 .146 4 .762 0 .001 ***Treatment 1 9 .111 9 .111 37 .848 0 .000 ***TimePoint 1 22 .607 22 .607 93 .915 0 .0 ***

215

Genotype:Treatment 5 2 .272 0 .455 1 .888 0 .101Genotype:TimePoint 5 2 .213 0 .443 1 .839 0 .110Treatment:TimePoint 1 3 .076 3 .076 12 .780 0 .001 ***Genotype:Treatment:TimePoint 5 1 .445 0 .289 1 .200 0 .313Residuals 119 28 .646 0 .241


Genotype 5 18 .746 3 .749 29 .105 <2e-16 ***Treatment 1 0 .141 0 .141 1 .095 0 .297TimePoint 1 0 .008 0 .008 0 .065 0 .800Genotype:Treatment 5 0 .265 0 .053 0 .411 0 .840Genotype:TimePoint 5 0 .419 0 .084 0 .651 0 .661Treatment:TimePoint 1 0 .133 0 .133 1 .030 0 .312Genotype:Treatment:TimePoint 5 0 .120 0 .024 0 .186 0 .968Residuals 147 18 .936 0 .129

216Appendix A.7 Pair-wise comparisons among genotypes for absolute magnitude log2(fold-change)

variation for the drought transcriptome. Bolded asterisks indicate significant differences according

to Bonferroni’s test (P < 0.05).

Padj (Bonferroni)

AP-947 AP-1005 <0.001 *AP-947 AP-1006 <0.001 *AP-947 AP-2278 <0.001 *AP-947 AP-2298 <0.001 *AP-947 AP-2300 0.024 *AP-1005 AP-1006 <0.001 *AP-1005 AP-2278 <0.001 *AP-1005 AP-2298 <0.001 *AP-1005 AP-2300 <0.001 *AP-1006 AP-2278 1 .000 AP-1006 AP-2298 <0.001 *AP-1006 AP-2300 <0.001 *AP-2278 AP-2298 <0.001 *AP-2278 AP-2300 <0.001 *AP-2298 AP-2300 1 .000

217

Padj (Bonferroni)

AP-947 AP-1005 1 .000AP-947 AP-1006 0 .510AP-947 AP-2278 1 .000AP-947 AP-2298 1 .000AP-947 AP-2300 1 .000AP-1005 AP-1006 0.007 *AP-1005 AP-2278 1 .000AP-1005 AP-2298 1 .000AP-1005 AP-2300 0.022 *AP-1006 AP-2278 0.024 *AP-1006 AP-2298 0 .778AP-1006 AP-2300 1 .000AP-2278 AP-2298 1 .000AP-2278 AP-2300 0 .069AP-2298 AP-2300 1 .000

Appendix A.8 Pair-wise comparisons among genotypes for absolute magnitude log2(fold-change)

variation for the drought metabolome. Bolded asterisks indicate significant differences according to

Bonferroni’s test (P < 0.05).

218Appendix A.9 GO term enrichment among transcripts that are significantly correlated with at least

one metabolite. (a) For transcripts with increased abundance in water-deficit treated samples and

(b) for transcripts with decreased abundance in water-deficit treated samples.

219

(a)

GO

Ter

mTy

peA

nnot

atio

n

Num

ber i

n R

efer

ence

Li

st

Num

ber i

n B

ackg

roun

d Li

stP-

valu

ePa

dj

GO

:001

6023

Ccy

topl

asm

ic m

embr

ane-

boun

ded

vesi

cle

1241

30 .

0001

30 .

0064

GO

:003

1988

Cm

embr

ane-

boun

ded

vesi

cle

1243

30 .

0002

10 .

0064

GO

:003

1410

Ccy

topl

asm

ic v

esic

le12

482

0 .00

053

0 .01

1G

O:0

0319

82C

vesi

cle

1250

70 .

0008

20 .

013

GO

:000

9536

Cpl

astid

4837

280 .

0012

0 .01

5G

O:0

0056

67C

trans

crip

tion

fact

or c

ompl

ex61

850 .

0035

0 .03

6G

O:0

0057

73C

vacu

ole

1059

70 .

021

0 .18

GO

:000

5739

Cm

itoch

ondr

ion

3025

750 .

025

0 .19

GO

:004

4444

Ccy

topl

asm

ic p

art

1081

1331

0 .03

60 .

25G

O:0

0352

50F

UD

P-ga

lact

osyl

trans

fera

se a

ctiv

ity33

30 .

0021

0 .17

GO

:000

4022

Fal

coho

l deh

ydro

gena

se (N

AD) a

ctiv

ity35

90 .

011

0 .33

GO

:000

3743

Ftra

nsla

tion

initi

atio

n fa

ctor

act

ivity

5176

0 .01

30 .

33G

O:0

0083

78F

gala

ctos

yltra

nsfe

rase

act

ivity

382

0 .02

60 .

51

GO

:001

6667

Fox

idor

educ

tase

act

ivity

, act

ing

on s

ulfu

r gr

oup

of d

onor

s41

940 .

066

0 .99

GO

:000

8194

FU

DP-

glyc

osyl

trans

fera

se a

ctiv

ity74

680 .

076

0 .99

GO

:000

6560

Ppr

olin

e m

etab

olic

pro

cess

526

1 .70

E-06

0 .00

038

GO

:000

6525

Par

gini

ne m

etab

olic

pro

cess

533

5 .70

E-06

0 .00

065

GO

:000

6012

Pga

lact

ose

met

abol

ic p

roce

ss43

20 .

0001

10 .

0082

GO

:000

9069

Pse

rine

fam

ily a

min

o ac

id m

etab

olic

pr

oces

s61

000 .

0001

40 .

0082

GO

:000

9821

Pal

kalo

id b

iosy

nthe

tic p

roce

ss31

50 .

0002

0 .00

91G

O:0

0064

46P

regu

latio

n of

tran

slat

iona

l ini

tiatio

n44

80 .

0005

40 .

021

GO

:000

5985

Psu

cros

e m

etab

olic

pro

cess

456

0 .00

097

0 .03

2G

O:0

0059

82P

star

ch m

etab

olic

pro

cess

479

0 .00

350 .

086

GO

:000

6573

Pva

line

met

abol

ic p

roce

ss34

10 .

004

0 .08

6G

O:0

0424

30P

indo

le a

nd d

eriv

ativ

e m

etab

olic

pro

cess

483

0 .00

410 .

086

220

GO

:004

5426

Pqu

inon

e co

fact

or b

iosy

nthe

tic p

roce

ss34

20 .

0043

0 .08

6G

O:0

0423

75P

quin

one

cofa

ctor

met

abol

ic p

roce

ss34

30 .

0046

0 .08

6

GO

:004

2401

Pce

llula

r bio

geni

c am

ine

bios

ynth

etic

pr

oces

s48

70 .

0049

0 .08

6

GO

:000

6576

Pce

llula

r bio

geni

c am

ine

met

abol

ic

proc

ess

5143

0 .00

550 .

089

GO

:000

9064

Pgl

utam

ine

fam

ily a

min

o ac

id m

etab

olic

pr

oces

s51

490 .

0065

0 .08

9G

O:0

0065

86P

indo

lalk

ylam

ine

met

abol

ic p

roce

ss34

90 .

0066

0 .08

9G

O:0

0065

68P

trypt

opha

n m

etab

olic

pro

cess

349

0 .00

660 .

089

GO

:000

5984

Pdi

sacc

harid

e m

etab

olic

pro

cess

4101

0 .00

820 .

1G

O:0

0193

18P

hexo

se m

etab

olic

pro

cess

8393

0 .01

30 .

16G

O:0

0442

62P

cellu

lar c

arbo

hydr

ate

met

abol

ic p

roce

ss16

1093

0 .01

40 .

16

GO

:003

4641

Pce

llula

r nitr

ogen

com

poun

d m

etab

olic

pr

oces

s15

1031

0 .01

80 .

2G

O:0

0060

90P

pyru

vate

met

abol

ic p

roce

ss37

70 .

022

0 .23

GO

:000

5996

Pm

onos

acch

arid

e m

etab

olic

pro

cess

9526

0 .02

40 .

24

GO

:000

9066

Pas

parta

te fa

mily

am

ino

acid

met

abol

ic

proc

ess

4145

0 .02

70 .

24G

O:0

0424

34P

indo

le d

eriv

ativ

e m

etab

olic

pro

cess

383

0 .02

70 .

24G

O:0

0098

20P

alka

loid

met

abol

ic p

roce

ss41

440 .

027

0 .24

GO

:000

9311

Pol

igos

acch

arid

e m

etab

olic

pro

cess

4151

0 .03

10 .

26

GO

:000

9081

Pbr

anch

ed c

hain

fam

ily a

min

o ac

id

met

abol

ic p

roce

ss38

90 .

032

0 .26

GO

:001

6137

Pgl

ycos

ide

met

abol

ic p

roce

ss41

590 .

036

0 .27

GO

:000

6417

Pre

gula

tion

of tr

ansl

atio

n41

590 .

036

0 .27

GO

:000

6413

Ptra

nsla

tiona

l ini

tiatio

n41

600 .

037

0 .27

GO

:000

9072

Par

omat

ic a

min

o ac

id fa

mily

met

abol

ic

proc

ess

3101

0 .04

40 .

32G

O:0

0421

80P

cellu

lar k

eton

e m

etab

olic

pro

cess

2218

860 .

046

0 .32

GO

:004

4106

Pce

llula

r am

ine

met

abol

ic p

roce

ss12

890

0 .05

0 .33

GO

:000

6520

Pce

llula

r am

ino

acid

met

abol

ic p

roce

ss12

890

0 .05

0 .33

221

GO

:000

6732

Pco

enzy

me

met

abol

ic p

roce

ss74

690 .

076

0 .48

GO

:000

9308

Pam

ine

met

abol

ic p

roce

ss14

1171

0 .08

0 .49

GO

:000

6073

Pce

llula

r glu

can

met

abol

ic p

roce

ss52

980 .

085

0 .51

222

GO

Ter

mTy

peA

nnot

atio

n

Num

ber i

n R

efer

ence

Li

st

Num

ber i

n B

ackg

roun

d Li

stP-

valu

eP ad

j

GO

:001

6023

Ccy

topl

asm

ic m

embr

ane-

boun

ded

vesi

cle

3041

32 .

.00E

-20

1 .50

E-18

GO

:003

1988

Cm

embr

ane-

boun

ded

vesi

cle

3043

37 .

60E-

202 .

80E-

18G

O:0

0314

10C

cyto

plas

mic

ves

icle

3048

21 .

50E-

183 .

60E-

17G

O:0

0319

82C

vesi

cle

3050

75 .

80E-

181 .

10E-

16G

O:0

0095

36C

plas

tid59

3728

9 .80

E-8

1 .40

E-6

GO

:004

4444

Ccy

topl

asm

ic p

art

113

1133

10 .

001

0 .00

9G

O:0

0319

78C

plas

tid th

ylak

oid

lum

en5

110

0 .00

10 .

009

GO

:003

1977

Cth

ylak

oid

lum

en5

110

0 .00

10 .

009

GO

:000

9543

Cch

loro

plas

t thy

lako

id lu

men

511

00 .

001

0 .00

9G

O:0

0095

34C

chlo

ropl

ast t

hyla

koid

1163

30 .

006

0 .04

3G

O:0

0319

76C

plas

tid th

ylak

oid

1165

50 .

008

0 .05

GO

:003

1984

Cor

gane

lle s

ubco

mpa

rtmen

t11

677

0 .00

90 .

057

GO

:000

9579

Cth

ylak

oid

1392

40 .

015

0 .08

1

GO

:000

8287

Cpr

otei

n se

rine/

thre

onin

e ph

osph

atas

e co

mpl

ex4

136

0 .01

60 .

081

GO

:004

4436

Cth

ylak

oid

part

1069

20 .

026

0 .13

GO

:000

9507

Cch

loro

plas

t33

3355

0 .04

0 .17

GO

:000

5737

Ccy

topl

asm

113

1329

30 .

040 .

17G

O:0

0056

67C

trans

crip

tion

fact

or c

ompl

ex4

185

0 .04

10 .

17G

O:0

0198

43F

rRN

A bi

ndin

g5

189

0 .01

10 .

65G

O:0

0161

68F

chlo

roph

yll b

indi

ng3

710 .

014

0 .65

GO

:001

6709

F

oxid

ored

ucta

se a

ctiv

ity, a

ctin

g on

pai

red

dono

rs, w

ith in

corp

orat

ion

or re

duct

ion

of m

olec

ular

oxy

gen,

NAD

H o

r NAD

PH

as o

ne d

onor

, and

inco

rpor

atio

n of

one

at

om o

f oxy

gen

311

20 .

044

1

GO

:000

3755

Fpe

ptid

yl-p

roly

l cis

-tran

s is

omer

ase

activ

ity3

114

0 .04

61

(b)

223

GO

:001

0876

Plip

id lo

caliz

atio

n5

322 .

90E-

60 .

001

GO

:000

9069

Pse

rine

fam

ily a

min

o ac

id m

etab

olic

pr

oces

s6

100

0 .01

60 .

008

GO

:000

6570

Pty

rosi

ne m

etab

olic

pro

cess

317

0 .00

00 .

014

GO

:000

9072

Par

omat

ic a

min

o ac

id fa

mily

met

abol

ic

proc

ess

510

10 .

001

0 .03

8G

O:0

0066

64P

glyc

olip

id m

etab

olic

pro

cess

335

0 .00

20 .

069

GO

:004

4106

Pce

llula

r am

ine

met

abol

ic p

roce

ss15

890

0 .00

20 .

069

GO

:003

4641

Pce

llula

r nitr

ogen

com

poun

d m

etab

olic

pr

oces

s16

1031

0 .00

30 .

082

GO

:000

6694

Pst

eroi

d bi

osyn

thet

ic p

roce

ss5

142

0 .00

30 .

082

GO

:000

6568

Ptry

ptop

han

met

abol

ic p

roce

ss3

490 .

005

0 .08

2G

O:0

0093

08P

amin

e m

etab

olic

pro

cess

1711

710 .

005

0 .08

2G

O:0

0065

86P

indo

lalk

ylam

ine

met

abol

ic p

roce

ss3

490 .

005

0 .08

2G

O:0

0065

20P

cellu

lar a

min

o ac

id m

etab

olic

pro

cess

1489

00 .

005

0 .08

2G

O:0

0161

25P

ster

ol m

etab

olic

pro

cess

499

0 .00

50 .

082

GO

:000

6470

Ppr

otei

n am

ino

acid

dep

hosp

hory

latio

n5

173

0 .00

80 .

11G

O:0

0422

54P

ribos

ome

biog

enes

is9

489

0 .00

90 .

11G

O:0

0068

69P

lipid

tran

spor

t5

188

0 .01

10 .

14G

O:0

0163

11P

deph

osph

oryl

atio

n5

194

0 .01

20 .

14G

O:0

0434

36P

oxoa

cid

met

abol

ic p

roce

ss22

1849

0 .01

40 .

14G

O:0

0060

82P

orga

nic

acid

met

abol

ic p

roce

ss22

1852

0 .01

40 .

14G

O:0

0197

52P

carb

oxyl

ic a

cid

met

abol

ic p

roce

ss22

1849

0 .01

40 .

14G

O:0

0464

17P

chor

ism

ate

met

abol

ic p

roce

ss3

740 .

015

0 .14

GO

:000

9073

Par

omat

ic a

min

o ac

id fa

mily

bio

synt

hetic

pr

oces

s3

730 .

015

0 .14

GO

:004

2180

Pce

llula

r ket

one

met

abol

ic p

roce

ss22

1886

0 .01

70 .

15

GO

:000

6576

Pce

llula

r bio

geni

c am

ine

met

abol

ic p

ro-

cess

414

30 .

018

0 .15

GO

:001

6126

Pst

erol

bio

synt

hetic

pro

cess

382

0 .02

0 .15

GO

:004

2434

Pin

dole

der

ivat

ive

met

abol

ic p

roce

ss3

830 .

021

0 .15

224

GO

:004

2430

Pin

dole

and

der

ivat

ive

met

abol

ic p

roce

ss3

830 .

021

0 .15

GO

:002

2613

Prib

onuc

leop

rote

in c

ompl

ex b

ioge

nesi

s9

583

0 .02

30 .

17G

O:0

0082

02P

ster

oid

met

abol

ic p

roce

ss5

233

0 .02

50 .

17G

O:0

0066

43P

mem

bran

e lip

id m

etab

olic

pro

cess

310

90 .

041

0 .28

GO

:000

8610

Plip

id b

iosy

nthe

tic p

roce

ss12

973

0 .04

50 .

29

225Appendix A.10 Pair-wise M:T Spearman correlation values in AP-1006.

226

Met

abol

iteTr

ansc

ript

Des

crip

tion

p-va

lue

Spea

rman

C

orre

latio

nM

361T

2065

_Raf

finos

ePt

p .14

60 .1

.S1_

a_at

inte

gral

mem

bran

e fa

mily

pro

tein

8 .32

E-5

-0 .8

95M

147T

682_

Succ

inic

_aci

dPt

p .19

69 .1

.A1_

atun

know

n pr

otei

n0 .

000

-0 .8

85M

361T

2065

_Raf

finos

ePt

p .20

97 .1

.S1_

s_at

CKI

1 (C

ASEI

N K

INAS

E I);

kin

ase

0 .00

0-0

.884

M20

4T18

76_G

alac

tinol

Ptp .

1398

.1 .S

1_at

TPI (

TRIO

SEPH

OSP

HAT

E IS

OM

ERAS

E); t

riose

-pho

spha

te

isom

eras

e0 .

000

-0 .8

81M

361T

2065

_Raf

finos

ePt

p .33

3 .1 .

S1_a

tzi

nc fi

nger

(AN

1-lik

e) fa

mily

pro

tein

0 .00

0-0

.876

M91

T611

_Ben

zoic

_aci

dPt

p .13

39 .1

.S1_

s_at

PRA1

.B4

(PR

ENYL

ATED

RAB

AC

CEP

TOR

1 .B

4)0 .

000

-0 .8

74M

204T

1876

_Gal

actin

olPt

p .24

9 .1 .

S1_a

tSE

C (s

ecre

t age

nt);

trans

fera

se, t

rans

ferri

ng g

lyco

syl g

roup

s0 .

000

-0 .8

73M

204T

1876

_Gal

actin

olPt

p .14

60 .1

.S1_

a_at

inte

gral

mem

bran

e fa

mily

pro

tein

0 .00

0-0

.873

M36

1T16

17_S

alic

inPt

p .13

39 .1

.S1_

s_at

PRA1

.B4

(PR

ENYL

ATED

RAB

AC

CEP

TOR

1 .B

4)0 .

000

-0 .8

72M

361T

2065

_Raf

finos

ePt

p .24

9 .1 .

S1_a

tSE

C (s

ecre

t age

nt);

trans

fera

se, t

rans

ferri

ng g

lyco

syl g

roup

s0 .

000

-0 .8

67

M91

T611

_Ben

zoic

_aci

dPt

p .31

63 .1

.A1_

atBC

CP2

(BIO

TIN

CAR

BOXY

L C

ARR

IER

PR

OTE

IN 2

); bi

otin

bi

ndin

g0 .

000

-0 .8

67M

256T

666_

L .Is

oleu

cine

Ptp .

1501

.1 .A

1_s_

atM

YB4;

DN

A bi

ndin

g 0 .

000

-0 .8

63M

204T

1876

_Gal

actin

olPt

p .26

33 .1

.S1_

atAT

GLX

1 (G

LYO

XALA

SE I

HO

MO

LOG

); la

ctoy

lglu

tath

ione

lyas

e0 .

000

-0 .8

54M

179T

804_

Salic

yl_a

lcoh

olPt

p .15

86 .1

.S1_

atLS

H10

(LIG

HT

SEN

SITI

VE H

YPO

CO

TYLS

10)

0 .00

0-0

.850

M20

4T18

76_G

alac

tinol

Ptp .

2097

.1 .S

1_s_

atC

KI1

(CAS

EIN

KIN

ASE

I); k

inas

e0 .

001

-0 .8

45M

204T

1876

_Gal

actin

olPt

p .33

03 .1

.S1_

atC

KB1;

pro

tein

kin

ase

regu

lato

r0 .

001

-0 .8

42M

361T

1617

_Sal

icin

Ptp .

1346

.1 .A

1_at

mev

alon

ate

diph

osph

ate

deca

rbox

ylas

e, p

utat

ive

0 .00

1-0

.842

M14

7T68

2_Su

ccin

ic_a

cid

Ptp .

2056

.1 .S

1_s_

atC

LASP

(CLI

P-AS

SOC

IATE

D P

RO

TEIN

); bi

ndin

g0 .

001

-0 .8

41M

147T

682_

Succ

inic

_aci

dPt

p .10

64 .1

.A1_

atpo

lyga

lact

uron

ase,

put

ativ

e / p

ectin

ase,

put

ativ

e0 .

001

-0 .8

39M

204T

1876

_Gal

actin

olPt

p .33

3 .1 .

S1_a

tzi

nc fi

nger

(AN

1-lik

e) fa

mily

pro

tein

0 .00

1-0

.839

M20

4T18

76_G

alac

tinol

Ptp .

1271

.3 .S

1_a_

atAT

SC35

; RN

A bi

ndin

g 0 .

001

-0 .8

37M

256T

666_

L .Is

oleu

cine

Ptp .

3510

.1 .S

1_at

DN

A bi

ndin

g0 .

001

-0 .8

36M

256T

666_

L .Is

oleu

cine

Ptp .

1339

.1 .S

1_s_

atPR

A1 .B

4 (P

REN

YLAT

ED R

AB A

CC

EPTO

R 1

.B4)

0 .00

1-0

.829

M17

9T80

4_Sa

licyl

_alc

ohol

Ptp .

1404

.1 .S

1_at

NTM

C2T

2 .1

0 .00

1-0

.828

M36

1T20

65_R

affin

ose

Ptp .

3337

.1 .S

1_s_

atun

know

n pr

otei

n0 .

001

-0 .8

28M

91T6

11_B

enzo

ic_a

cid

Ptp .

1346

.1 .A

1_at

mev

alon

ate

diph

osph

ate

deca

rbox

ylas

e, p

utat

ive

0 .00

1-0

.825

227

M20

4T18

76_G

alac

tinol

Ptp .

1489

.2 .S

1_s_

atAt

RAB

A5d

(Ara

bido

psis

Rab

GTP

ase

hom

olog

A5d

); G

TP

bind

ing

0 .00

1-0

.823

M25

6T66

6_L .

Isol

euci

nePt

p .15

88 .1

.S1_

s_at

PIP2

;5 (P

LASM

A M

EMBR

ANE

INTR

INSI

C P

RO

TEIN

2;5

); w

ater

ch

anne

l0 .

001

-0 .8

21M

306T

949_

NI

Ptp .

1501

.1 .A

1_s_

atM

YB4;

DN

A bi

ndin

g 0 .

001

-0 .8

21M

147T

682_

Succ

inic

_aci

dPt

p .11

40 .1

.A1_

atph

otos

yste

m II

reac

tion

cent

er P

sbP

fam

ily p

rote

in0 .

001

-0 .8

20M

91T6

11_B

enzo

ic_a

cid

Ptp .

1416

.1 .A

1_s_

atFK

BP15

-2; F

K506

bin

ding

0 .

001

-0 .8

20

M20

4T18

76_G

alac

tinol

Ptp .

2026

.1 .S

1_s_

atEL

F5A-

1 (E

UKA

RYO

TIC

ELO

NG

ATIO

N F

ACTO

R 5

A-1)

; tra

nsla

tion

initi

atio

n fa

ctor

0 .00

1-0

.818

M91

T611

_Ben

zoic

_aci

dPt

p .35

10 .1

.S1_

atD

NA

bind

ing

0 .00

1-0

.816

M14

7T70

4_G

lyco

lic_a

cid

Ptp .

3022

.1 .A

1_s_

atH

AT22

; tra

nscr

iptio

n fa

ctor

0 .00

1-0

.813

M36

1T20

65_R

affin

ose

Ptp .

2629

.1 .S

1_s_

atse

nesc

ence

-ass

ocia

ted

prot

ein-

rela

ted

0 .00

1-0

.812

M25

6T66

6_L .

Isol

euci

nePt

p .13

23 .1

.S1_

atD

EAD

box

RN

A he

licas

e, p

utat

ive

0 .00

1-0

.809

M20

4T18

76_G

alac

tinol

Ptp .

2716

.1 .S

1_at

glut

ared

oxin

, put

ativ

e0 .

001

-0 .8

09M

91T6

11_B

enzo

ic_a

cid

Ptp .

1510

.1 .S

1_s_

atpr

otea

se in

hibi

tor

0 .00

1-0

.808

M36

1T20

65_R

affin

ose

Ptp .

1549

.1 .S

1_at

UBP

7 (U

BIQ

UIT

IN-S

PEC

IFIC

PR

OTE

ASE

7); u

biqu

itin

thio

lest

eras

e0 .

001

-0 .8

08M

91T6

11_B

enzo

ic_a

cid

Ptp .

2462

.1 .S

1_at

ribos

omal

pro

tein

L5

fam

ily p

rote

in0 .

002

-0 .8

07

M20

4T18

76_G

alac

tinol

Ptp .

2536

.1 .A

1_at

NF-

YB11

(NU

CLE

AR F

ACTO

R Y

, SU

BUN

IT B

11);

trans

crip

tion

fact

or0 .

002

-0 .8

05M

361T

2065

_Raf

finos

ePt

p .12

71 .3

.S1_

a_at

ATSC

35; R

NA

bind

ing

0 .00

2-0

.805

M20

4T18

76_G

alac

tinol

Ptp .

321 .

1 .S1

_a_a

tAT

HVA

22E

0 .00

2-0

.804

M20

4T18

76_G

alac

tinol

Ptp .

1267

.1 .S

1_x_

atid

entic

al p

rote

in b

indi

ng

0 .00

2-0

.803

M20

4T18

76_G

alac

tinol

Ptp .

1886

.1 .S

1_at

CIB

1 (C

RYPT

OC

HR

OM

E-IN

TER

ACTI

NG

BAS

IC-H

ELIX

-LO

OP-

HEL

IX 1

); D

NA

bind

ing

0 .00

20 .

801

M24

9T95

7_G

lyce

rol

Ptp .

1075

.1 .A

1_a_

atun

know

n pr

otei

n0 .

002

0 .80

3M

247T

748_

Thre

onic

.ac

id .1

.4 .la

cton

e . .2

TMS .

. .tra

ns .

Ptp .

1323

.1 .S

1_at

DEA

D b

ox R

NA

helic

ase,

put

ativ

e0 .

002

0 .80

6M

247T

748_

Thre

onic

.ac

id .1

.4 .la

cton

e . .2

TMS .

. .tra

ns .

Ptp .

1075

.1 .A

1_a_

atun

know

n pr

otei

n0 .

001

0 .80

8

228

M14

7T70

4_G

lyco

lic_a

cid

Ptp .

3043

.1 .S

1_s_

at4C

L2 (4

-CO

UM

ARAT

E:C

OA

LIG

ASE

2); 4

-cou

mar

ate-

CoA

lig

ase

0 .00

10 .

809

M14

7T68

2_Su

ccin

ic_a

cid

Ptp .

3348

.2 .A

1_a_

atps

eudo

urid

ine

synt

hase

fam

ily p

rote

in0 .

001

0 .80

9M

345T

1180

_Qui

nic_

acid

Ptp .

1308

.1 .S

1_at

calm

odul

in-re

late

d pr

otei

n, p

utat

ive

0 .00

10 .

812

M14

7T70

4_G

lyco

lic_a

cid

Ptp .

2269

.1 .S

1_s_

atba

nd 7

fam

ily p

rote

in0 .

001

0 .81

4

M36

1T16

93_S

ucro

sePt

p .29

22 .1

.S1_

atAT

CAP

1 (A

RAB

IDO

PSIS

TH

ALIA

NA

CYC

LASE

ASS

OC

IATE

D

PRO

TEIN

1);

actin

bin

ding

0 .00

10 .

819

M14

7T68

2_Su

ccin

ic_a

cid

Ptp .

333 .

1 .S1

_at

zinc

fing

er (A

N1-

like)

fam

ily p

rote

in0 .

001

0 .82

3M

147T

704_

Gly

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894

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