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Characterising the response of rice and wheat coleoptiles to anoxia and
re-oxygenation
Rachel Shingaki-Wells
This thesis was submitted as part of the requirement for the degree of Doctor of Philosophy at The University of Western Australia
February 2014
Australian Research Council Centre of Excellence in Plant Energy Biology School of Chemistry and Biochemistry
II
Declaration
The examination of this thesis is an examination of the work of Rachel
Shingaki-Wells. The work has been substantially conducted by Rachel
Shingaki-Wells during enrolment in the degree of Doctor of Philosophy at
The University of Western Australia.
This thesis contains published work and/or work prepared for publication,
some of which has been co- authored. The bibliographical details of the
work and where it appears in the thesis are outlined on the next page. A
description for each publication that clarifies the contribution of Rachel
Shingaki-Wells follows. Signed consent from each co-author are provided
at the back of this thesis.
Rachel Shingaki-Wells
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Publications
Thesis Chapter 1: Shingaki-Wells RN,, Millar AH, Whelan J, Narsai R (2013) What
happens to plant mitochondria under low oxygen? An omics review of the responses to low oxygen and re-oxygenation. Plant Cell and Environment. Reviewed and returned with minor corrections.
Chapter 2: Shingaki-Wells RN, Huang S, Taylor NL, Carroll AJ, Zhou W,
Millar AH (2011) Differential molecular responses of rice and wheat coleoptiles to anoxia reveal novel metabolic adaptations in amino acid metabolism for tissue tolerance. Plant Physiology 156:1706-24
Chapter 3: Shingaki-Wells RN, Huang S, Alexova R, Millar AH (2014) Wheat
genotype responses to anoxia are temperature and tissue dependent. Unpublished.
Chapter 4: Shingaki-Wells RN, Huang S, Millar AH (2014) Proteome and
metabolome responses in cereals subjected to short-term anoxia followed by re-oxygenation. Unpublished.
Additional Addendum: Shingaki-Wells RN, Huang S, Taylor NL, Millar AH (2011)
Pursuing the identification of O2 deprivation survival mechanisms in plants related to selective mRNA translation, hormone-independent cellular elongation and preparation for the arrival of oxygen. Plant Signaling and Behavior 6: 1612-5
Research: Huang S, Jacoby RP, Shingaki-Wells RN, Li L, Millar AH (2013)
Differential induction of mitochondrial photorespiratory machinery by light intensity is linked to changes in respiratory metabolism in rice leaves. New Phytologist 198: 103- 115
Review: Huang S, Shingaki-Wells RN, Taylor NL, Millar AH (2013) The rice
mitochondria proteome and its response during development and to the environment. Frontiers in Plant Proteomics 4:16-21
IV
Author contributions This thesis contains publications/manuscripts as outlined on page IV. The contributions of each co-author are as follows: Introduction 1. Shingaki-Wells RN: writing, figure preparation, editing. 2. Millar AH: writing, editing. 3. Whelan J: editing. 4. Narsai R: writing, figure preparation, editing. Study I 1. Shingaki-Wells RN: experimental work, data analysis, writing, figure
preparation, editing. 2. Huang S: lab guidance, experimental design, writing, editing. 3. Taylor NL: iTRAQ runs and analysis, editing. 4. Carroll AJ: Metabolite computational analysis, metabolite table preparation,
writing, editing. 5. Zhou W: Metabolite analysis. 6. Millar AH: concept, experimental design, writing, editing. Study II 1. Shingaki-Wells RN: experimental work, experimental design, data analysis,
writing, figure preparation. 2. Huang S: lab guidance, experimental design, writing, editing. 3. Alexova R: metabolite analysis guidance. 4. Millar AH: concept, experimental design, editing. Study III 1. Shingaki-Wells RN: experimental work, data analysis, writing, figure
preparation. 2. Huang S: lab guidance, experimental design, figure guidance, editing. 3. Millar AH: concept, experimental design, editing.
The consent of each co-author is provided on page 199 of this thesis.
V
Acknowledgements I would like to take this opportunity to sincerely thank my supervisors Harvey
Millar and Shaobai Huang for their invaluable encouragement and advice. I
appreciate your patience and calming words when I made the silliest of mistakes.
Even though both of you had many more things on your plate than I ever did, I
thank you for always making time for me without hesitation.
Thanks to other members of Plant Energy Biology for your advice, assistance
and friendship: Owen Duncan, Holger Eubel, Julia Grassl, Cristian Holzmann,
Connie Hooper, Sandi Kerbler, Szymon Kubiswevski-Jakubiak, Simon Law, Alex
Lee, Lei Li, Josh Linn, Reena Narsai, Ellen Paynter, Yan Peng, Hafiz Che
Othman, Adriana Pruzinska, Jordan Radomiljac, Michelle Sew, Cory Solheim,
Elke Stroher, Yew-Foon Tan, Tiago Tomaz and Aaron Yap.
Thank you to DAFWA for the wheat seed donations. Special mention to Richard
Jacoby for facilitating seed collection and for your valuable advice over the years.
I would like to thank Wenxu Zhou for running my first samples on the GC-MS. I
am grateful to Adam Carroll for analysing my metabolite samples in the early
days and for explaining program-related details with me. Thanks to Nic Taylor for
his work on the iTRAQ experiments and helpful discussions over the years.
Thanks to Rali Alexova for always taking to time to listen and help. Thanks to
Clark Nelson for your helpful discussions on statistics. Thank you to Jenny Gillett,
Jude Moyle, Deb Yeoman, Allan McKinley, Hayden Walker, Rosie Farthing and
Geetha Shute for making administrative life pleasant and smooth. Thanks to Alice
Trend for all of your support, laughs, encouragement and science outreach
opportunities. Thanks to Ben Gully for your patience, presence and for getting
me through.
I am grateful to the Australian government and the GRDC (GRS183) for providing
me scholarships. Thank you to the Australian Research Council via the Centre of
Excellence in Plant Energy Biology for the positive environment and financial
support for this project. …
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Abbreviations
1,3-PGA 1,3-bisphosphoglycerate 2-PGA 2-phosphoglycerate 2D- PAGE two-dimensional polyacrylamide gel electrophoresis 3-PGA 3-phosphoglycerate 3-PGDH D-3-phosphoglycerate dehydrogenase ACC oxidase 1-aminocyclopropane carboxylic acid ACN acetonitrile ADH alcohol dehydrogenase AdoMet S-adenosylmethionine ADP adenosine diphosphate AK adenylate kinase AlaAT alanine aminotransferase ALDH aldehyde dehydrogenase AMP adenosine monophosphate AMPS ammonium persulfate ANOVA analysis of variance ANPs anaerobic proteins AOX alternative oxidase Apx ascorbate peroxidase Arabidopsis Arabidopsis thaliana ATE Arg-tRNA protein transferases
ATP adensosine triphosphate BAC basic amino acid carrier BBTI Bowman-Birk-type trypsin inhibitor CHAPS 3-[(3-Cholamidopropyl)dimethylammonio]-1-propanesulfonate CoA coenzyme A CHCA α-cyano-4-hydroxycinnamic acid Da dalton ddH2O double-distilled water DHAP dihydroxyacetone phosphate DHAP dihydroxyacetonephosphate DiGE differential in gel electrophoresis DNA deoxyribonucleic acid
DTT DL-dithiothreitol EDTA ethylenediaminetetraacetic acid ERF ethylene response factor ESI electrospray ionization ETC electron transport chain
VII
Fig. figure FK fructokinase Fru-1,6-B2 fructose-1,6-bisphosphate Fru-6-P fructose-6-phosphate
FW fresh weight
G-3-P glyceraldehyde-3-phosphate GABA gamma-aminobutyric acid GAPDH glyceraldehyde-3-phosphate dehydrogenase GC gas chromatography GlnSyn glutamine synthetase Glu-1-P glucose-1-phosphate Glu-6-P glucose-6-phosphate GSH reduced glutathione
GSSG oxidised glutathione H+-ATPase ATP hydrolysing proton pump H+-PPiase pyrophosphtase proton pump HK hexokinase HPLC high performance liquid chromatography HRE hypoxia responsive ERF (ethylene response factor) IAA indole-acetic acid IEF isoelectric focussing ILR1 indole-acetic acid amino acid hydrolase 1 iPGAM 2,3-bisphosphoglycerate-independent phosphoglycerate mutase kDa kilo Dalton Km Michaelis constant
LDH lactate dehydrogenase LEA late embryonogenesis abundant m/z mass to charge MALDI-TOF matrix assisted laser desorption time of flight MDH malate dehydrogenase MDHA monodehydroascorbic acid MES 2-(N-morpholino)ethanesulfonic acid MetSyn cobalamin-indepdendent methionine synthase MM molecular mass MOWSE molecular weight search mRNA messenger RNA
MS mass spectrometry mt mitochondrial n number of biological replicates n/s not significant
VIII
NAD+ nicotinamide adenine dinucleotide (oxidized form) NADH nicotinamide adenine dinucleotide (reduced form) NDPK nucleotide diphosphate kinase NERP N-end rule pathway
NiR nitrite reductase NL non linear NO nitric oxide NR nitrate reductase NTP nucleotide triphosphate OAA oxaloacetate Os Oryza sativa (prefix for rice gene) PDC pyruvate decarboxylase PDH pyruvate dehydrogenase PDLP plasmodesmata-located protein PEP phosphoenolpyruvate PFK-ATP ATP-dependent phosphofructokinase PFK-PPi PPi-dependent phosphofructokinase PGI phosphoglucoseisomerase PGK phosphoglyceratekinase PGM phosphoglyceratemutase Pi inorganic phosphate pI isoelectric point PK pyruvate kinase PPDK pyruvate phosphate dikinase PPi pyrophosphate Prx peroxiredoxin PSAT phosphoserine aminotransferase Put putrescine rad reduced alcohol dehydrogenase activity mutant line RAP related to AP2 RNA ribonucleic acid ROS reactive oxygen species rpm rotations per minute S significant SDH succinate dehydrogenase SDS sodium dodecyl sulphate SHMT serine hydroxymethyltransferase Sig. significant SMM saturated matrix mix SOD superoxide dismutase
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SS sucrose synthase SSA succinic semialdehyde Sub1A-1 Submergence1-A allele SUS sucrose synthase gene TCA tricarboxylic acid TEMED tetramethylethylenediamine TES N-Tris(hydroxymethyl)methyl-2-aminoethanesulfonic acid TFA trifluoroacetic acid TGA transformed geometric mean TPI triosephosphate isomerase TPP thiamine pyrophosphate UDP uridine diphosphate UDP-Gluc-PPiase UDP-glucose pyrophosphorylase V-PPase vacuolar pyrophosphatase v/v volume to volume w/v weight to volume WT wild type x g times the force of gravity
X
Abstract
Floods are estimated to affect 17 million km2 of land worldwide, threatening the
productivity of several major crops. A key consequence of this is the creation of a
physical barrier that causes oxygen deprivation in plants and thus inhibition of
aerobic respiration, the most efficient means of ATP production. As a result,
plants must rely on glycolysis, a less efficient means of ATP synthesis, in order to
survive. Crop anoxia tolerance is highly variable; on one end of the spectrum is
rice (Oryza sativa), which has been known to survive for weeks without oxygen,
showing exceptional abilities to germinate, develop a coleoptile and grow under
strict anoxia. In contrast, species such as wheat (Triticum aestivum), a dry-land
winter crop, can endure anoxia for only a short period of time, and fails to
germinate or grow when oxygen deprived.
While biochemical comparisons between rice and wheat responses to anoxia
exist, little information was available at the proteomic and metabolomic level.
Furthermore, research on the consequences of re-oxygenation, an inevitable
event for plant survival, is scarcely described. Thus, the research presented in
this thesis aimed to characterise the molecular responses of rice and wheat
coleoptiles to anoxia and re-oxygenation.
In Study I, rice (cv. Amaroo) and wheat (cv. Calingiri) coleoptile responses to
anoxia were compared at the physiological, proteome and metabolome level.
This analysis revealed a large proteomic response to anoxia in rice, which
contrasted to that of wheat. For example, rice showed large increases in proteins
involved in glycolysis, a response likely to improve ATP production under
anaerobic conditions. In general, amino acids rapidly accumulated in anoxic rice,
but not in wheat. Most surprisingly, wheat failed to accumulate alanine, a
standard plant response to anoxia. Supplementation of key amino acids,
including alanine, appeared to reduce electrolyte leakage in anoxic wheat. This
result was not apparent in rice.
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Study II took a multi-genotype approach to understanding wheat responses to
anoxia. Four genotypes with purported variation in anoxia tolerance were
compared to the original genotype of interest, Calingiri. Growth recovery, alcohol
dehydrogenase induction, electrolyte leakage and metabolomes were compared
between the five genotypes. Temperature and tissue variations revealed the
sensitive dependence that anoxic responses have on these factors. Thus, the
reported inconsistencies relating to the anoxia tolerance of certain wheat
genotypes appears to be strongly linked with experimental differences.
In Study III, the responses of rice and wheat (cv. Calingiri) to re-oxygenation were
examined. Proteins involved in cell wall re-modelling, oxidative stress and
fermentation were shown to change under these stresses. The large amino acid
accumulation observed in rice was rapidly reversed upon re-oxygenation. This
was not the case in wheat.
Overall, these studies show the highly variable responses that these species
exhibit during anoxia and re-oxygenation. In contrast to wheat, the ability of rice
to tolerate anoxia appears to be linked with its rapid response to this stress.
Amino acid metabolism appears to play an important, but as yet poorly defined
role in both anoxia and re-oxygenation. While wheat is generally anoxia
intolerant, inter-genotype responses to anoxia are variable, and highly dependent
on the tissue and temperature.
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Contents
Declaration........................................................................................ II Publications....................................................................................... III Author contributions......................................................................... IV Acknowledgements.......................................................................... V Abbreviations.................................................................................... VI Abstract ............................................................................................ X Contents............................................................................................ XII Chapter 1: Introduction..................................................................... 1 Foreword to literature review............................................................... 2 Literature review.................................................................................. 4 Chapter 2: Rice and wheat responses to anoxia............................ 49 Foreword to Study I............................................................................ 50 Study I................................................................................................ 51 Supplemental data for Study I............................................................. 70 Chapter 3: Wheat genotype responses to anoxia 75 Foreword to Study II........................................................................... 76 Study II............................................................................................... 77 Figures………………………............................................................... 106 Tables................................................................................................. 114 Supplemental data for Study II............................................................ 117 Chapter 4: Rice and wheat responses to re-oxygenation.............. 136 Foreword to Study III........................................................................... 137 Study IIII.............................................................................................. 138 Figures………………………............................................................... 166 Supplemental data for Study III........................................................... 186 Chapter 5: General Discussion....................................................... 191 Co-author consent........................................................................... 199
1
Chapter 1 Introduction
2
Foreword to literature review The following literature review has been submitted as an invited review of
the ‘omics’ literature that relates to how plants respond to anoxia as well
as re-oxygenation (Shingaki-Wells et al., 2014). This review contains
information reported in the first results chapter, which is published
research (Shingaki-Wells et al., 2011).
Plants that are flooded can be rapidly deprived of oxygen, resulting in
hypoxia or anoxia. The purpose of this project is to determine how two
cereals, Oryza sativa (rice) and Triticum aestivum (wheat), respond to low
oxygen at the physiological and molecular level. Since rice is typically a
flood-tolerant crop, and wheat a flood-intolerant crop, it was suspected
that the molecular responses of these species would be highly divergent.
Rice is especially interesting in that it can germinate under anoxia, a
feature that is absent in wheat. The growth of rice when germinated under
anoxia is aberrant however, with the coleoptile as the only tissue to
develop in young seedlings. This tissue has therefore been the subject of
much research, since it is an example of a tissue with exceptional anoxia
tolerance.
For plants to survive a low-oxygen event, they must also endure the
added consequences that come with re-oxygenation. Typically, oxidative
stress becomes a factor, and plants must appropriately deal with the
molecular damage that ensues.
This project explored three major questions:
A. How do rice and wheat coleoptiles respond to short-term anoxia at
the physiological, proteomic and metabolomic level? Do these
differences underlie the contrasting tolerance of these species to
anoxia?
3
B. How great is the variation in anoxia tolerance between different
wheat genotypes? How do other environmental factors affect
anoxia tolerance?
C. How do rice and wheat respond to re-oxygenation after short-term
anoxia? Do the differences seen between these species, in terms
of their response to anoxia, affect metabolism post-anoxia?
Point A, B and C are discussed in Chapters 2, 3 and 4, respectively. They
are formatted as published, or as manuscripts to be submitted to a
scientific journal. The last section of this thesis, Chapter 5, will draw
general conclusions about this project.
References
Shingaki-Wells RN, Huang S, Taylor NL, Carroll AJ, Zhou W, Millar AH (2011) Differential molecular responses of rice and wheat coleoptiles to anoxia reveal novel metabolic adaptations in amino acid metabolism for tissue tolerance. Plant Physiology 1156: 1706-1724
Shingaki-Wells RN, Millar AH, Whelan J, Narsai R (2014) What happens to plants under low oxygen? An omics review of the responses to low oxygen and re-oxygenation. Plant, Cell & Environment Submitted.
4
What happens to plants under low oxygen? An omics
review of the responses to low oxygen and re-
oxygenation
Rachel Shingaki-Wells1, A. Harvey Millar1, James Whelan1,2, Reena Narsai1,3
1ARC Centre of Excellence in Plant Energy Biology, MCS Building M316
University of Western Australia, 35 Stirling Highway, Crawley 6009, Western
Australia, Australia. 2Department of Botany, School of Life Science, La Trobe
University, Bundoora, Victoria 3086, Australia. 3Centre for Computational
Systems Biology, MCS Building M316 University of Western Australia, 35
Stirling Highway, Crawley 6009, Western Australia, Australia.
Abstract
Floods can rapidly submerge plants, limiting oxygen to the extent that oxidative
phosphorylation no longer generates adequate ATP supplies. Low oxygen
tolerant plants, such as rice, are able to adequately respond to low oxygen by
successfully re-modelling primary and mitochondrial metabolism to partially
counteract the energy crisis that ensues. In this review, we discuss how plants
respond to low oxygen stress at the transcriptomic, proteomic, metabolomic
and enzyme activity level, particularly focussing on mitochondria and interacting
pathways. The role of reactive oxygen species and nitrite as an alternative
electron acceptor as well as their links to respiratory chain components is
discussed. By making intra-kingdom as well as cross-kingdom comparisons,
conserved mechanisms of anoxia tolerance are highlighted as well as tolerance
mechanisms that are specific to anoxia-tolerant rice. We discuss re-
oxygenation as an often overlooked, yet essential stage of this environmental
stress and consider the possibility that changes occurring during low oxygen
may also provide benefits upon re-aeration. Finally, we consider what it takes to
be low-oxygen tolerant and argue that alternative mechanisms of ATP
5
production, glucose signalling as well as reverse-metabolism of fermentation
end-products promote the survival of rice after this debilitating stress.
Introduction
Flooding events deprive plants of oxygen, posing a considerable threat to crop
productivity. The volume of research that focuses its attention on the molecular
consequences of hypoxia or anoxia is both vast and diverse. At the centre of
such study lies a commonality; plants that are deprived of oxygen need to rely
on anaerobic metabolism to maintain adequate ATP production. Different
plants can have significantly different levels of tolerance to low oxygen, with
some plants able to tolerate only hours, while others, such as rice, can even
survive weeks under flooding conditions. As a result, it is not surprising that
different survival tactics have evolved, with some plants adopting rapid-growth
avoidance strategies and others only involving significant metabolic shifts to
ensure survival (Voesenek et al., 2006; Salavati et al., 2012).
Whether in plants or animals, it can be seen that many molecular responses to
low oxygen are conserved, including the induction of fermentation and
glycolysis (Mustroph et al., 2010). Several studies in recent years have even
shown how altering components directly or indirectly involved in these
pathways alters low oxygen tolerance. For example in the last decade, studies
have shown a role for group VII ethylene responsive factors, namely
RAP2.12/RAP2.2, HRE1/HRE2, SNORKEL1/2 and SUB1A-1, in regulating
sugar metabolism, fermentation and/or growth in plants under low oxygen
conditions (Xu et al., 2006; Hattori et al., 2009; Hinz et al., 2010; Licausi et al.,
2010). Furthermore, substantial evidence towards important roles for nitric
oxide (NO) and reactive oxygen species (ROS) signalling have also been
presented in the last decade (Igamberdiev et al., 2010; Gupta and Igamberdiev,
2011) with studies revealing an important role of the mitochondrial respiratory
components facilitating this signalling (Blokhina and Fagerstedt, 2010).
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Given that the genes encoding the mitochondrial respiratory components are
largely conserved across different plants, with the known exception of complex
II in rice (Huang et al., 2010), it is probable that it is not the presence of unique
genes in tolerant species that facilitates survival. Instead, it is more likely that
specific regulation at the transcriptomic, proteomic and metabolomic levels
occurs in low-oxygen tolerant species, which allows sufficient continuation of
respiratory metabolism and ATP production. Additionally, unlike mammals,
plants contain chloroplasts, which also produce energy, facilitating low-oxygen
tolerance. For example, it has been shown that light exposure reduces the
need for fermentation and extends survival during anaerobiosis in rice and
wheat seedlings (Mustroph et al., 2006). Hence, a common thread that
underlies the degree of low-oxygen tolerance in plants is the ability to
successfully shift metabolism in favour of energy production under these
conditions.
It is important to note that while different plant species show significant
variation in their level of tolerance to low-oxygen stress, many plant species can
survive a short period, as may occur during brief water-logging periods.
Adapting to post-anoxic stress is integral to surviving a flood event, yet this
stage is rarely considered. The few studies that have examined this stage
reveal rapid changes in transcript and metabolite abundances, shifting
metabolism back towards aerobic respiration and increased energy production
(Branco-Price et al., 2008; Narsai et al., 2009). Thus, when studying anoxia it is
useful to consider that changes made under low oxygen are not only relevant
for anoxic survival but can also be essential for the survival success of plants
upon return to aerobic conditions. In mammals for example, studies looking at
hypoxia responses in the human heart not only examine the ischemic episode,
leading to the reduced oxygen supply, but also examine the substantial
damage invoked during rapid re-oxygenation (Schaub et al., 2009). Similarly,
for flood-prone farmland to remain economically productive, crop plants must
be able to maintain seed production when floodwaters recede. The question is
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how or even whether plants prepare for this second round of stress, which
often includes a ROS challenge as well as dehydration (Fukao et al., 2011).
In this review, we will take a systems biology approach to discuss the
responses of plants to low oxygen, from organelle morphology to the molecular
response, with a focus on central and mitochondrial metabolism at the
transcript and protein levels, to help answer the question: What happens to
mitochondria under low oxygen conditions? Specifically, we discuss the
interactions of ROS and NO signalling with the mitochondrial respiratory
pathways and reveal differences between low-oxygen tolerant and non-tolerant
plant species. We also discuss the effects of re-oxygenation on plants and
distinguish between the different ways recovery has been examined, i.e.
oxygenation shock and re-oxygenation. Metabolic trends of recovery in
different plant species are also highlighted, and compared across kingdoms.
DNA, RNA and proteins under low oxygen
Before even considering in depth the significant mitochondrial and metabolic
changes known to occur under low oxygen in different species (Gibbs and
Greenway, 2003; Greenway and Gibbs, 2003; Magneschi and Perata, 2009;
Narsai et al., 2011; Narsai and Whelan, 2013), one of the most obvious
differences between low-oxygen tolerant and intolerant species is the ability to
survive and therefore sustain the machinery to carry out DNA replication,
transcription and protein synthesis under low oxygen. Without at least some
function of these, low oxygen survival would not be possible.
The DNA level
Under low oxygen, ATP synthesis is substantially lower than in aerobic
conditions in rice (Mustroph and Albrecht, 2003). However, it has been
proposed that energy “budgeting” occurs under low oxygen in rice, dedicating
specific amounts of ATP to different cellular functions (Edwards et al., 2012).
When the rates of DNA synthesis and energy expenses related to this were
measured under hypoxia and anoxia in rice, it was observed that DNA
synthesis still occurred, but at half the rate of that occurring under normoxia
8
(Edwards et al., 2012). This is further supported by the continued, but slower,
rate of cell division and elongation that occurs under low oxygen in rice
(Takahashi et al., 2011). This is in contrast to intolerant species where these
mechanisms are not able to be sustained.
The RNA level - transcription under low oxygen
Due to the ‘energy crisis’ that occurs when oxygen is limited (Huang et al.,
2008), it is plausible that energy usage for regulating relatively large numbers of
transcripts could be restricted. However, in anoxia-tolerant rice or even anoxia-
intolerant species such as Arabidopsis, transcriptional processes do not
appear to be limited (Narsai and Whelan, 2013). In fact, the transcriptomic
responses to low oxygen are within the range of other abiotic stress responses,
with thousands of genes showing differential expression (Lasanthi-Kudahettige
et al., 2007; Branco-Price et al., 2008; Narsai et al., 2009; Narsai and Whelan,
2013). Selective polysome loading is a factor that regulates low oxygen gene
expression (Branco-Price et al., 2008). For example, hypoxic stress in
Arabidopsis resulted in a reduction of polysome content by approximately
50 %, a decrease that was almost entirely reversed upon re-oxygenation
(Branco-Price et al., 2008). Additionally, it is clear that regulation at the
transcript level is extremely crucial under low oxygen, with many ethylene-
responsive transcription factors (ERFs) showing important function under
hypoxia (Bailey-Serres et al., 2012). The critical role of the gaseous hormone
ethylene in low oxygen signalling was first implied based on the observed
increase in its levels under low oxygen (reviewed in (Voesenek and Sasidharan,
2013)). The synthesis of ethylene requires oxygen at the level of ACC oxidase
(Kende, 1993). However, achieving anoxia requires a transition through
hypoxia, meaning functional ethylene concentrations may exist even in anoxic
cells. Among the most well-known ERFs is the group VII ERF, which is thought
to be involved in regulating the expression of genes encoding enzymes involved
in carbohydrate consumption, ethanolic fermentation and cell expansion in
anoxia-tolerant rice (Fukao et al., 2006). Ethylene signalling and the role of
group VII ERFs under low oxygen also appears to be conserved in several
9
species, with studies in the last couple of years demonstrating their role in
affecting the low-oxygen tolerance in the anoxia-intolerant Arabidopsis thaliana
(Arabidopsis).
The protein level - protein synthesis under low oxygen
Protein synthesis is energetically costly, especially in light of the reduced
capability of plants to produce ATP when oxygen is unavailable (Mustroph and
Albrecht, 2003). Expending energy on protein synthesis should therefore be an
investment with considerable return. Interestingly, the decreased rates of
protein synthesis observed in anoxic rice coleoptiles is less than the decrease
in calculated ATP synthesis rates (Edwards et al., 2012). In fact, the proportion
of ATP used for protein synthesis was highest in anoxic coleoptiles (52%),
followed by normoxic and hypoxic coleoptiles at 19% and 14%, respectively
(Edwards et al., 2012). This suggests the importance of a responsive - or at
least maintained - proteome during adaptation to anoxia. A high-return
investment could include catabolic proteins that increase glycolytic ATP
production, contributing to the observed Pasteur effect during anoxia (Gibbs
and Greenway, 2003). This is also supported by the significant changes in the
proteomes observed under anoxia, including the increased protein abundance
observed for a number of glycolysis and fermentation-associated proteins
(Millar et al., 2004; Howell et al., 2007; Shingaki-Wells et al., 2011). Thus,
despite decreases in the rates of ATP, DNA and protein synthesis (Mustroph
and Albrecht, 2003; Edwards et al., 2012) continued function of these is what
facilitates survival under low oxygen.
Mitochondrial morphology under low oxygen
Survival under low-oxygen conditions is directly linked with the ability to
produce energy (ATP) under these circumstances. The double membrane
structure of mitochondria contains the crucial components of the respiratory
pathway and facilitates the required membrane potential for effective energy
production. Some of the earliest studies examining mitochondria under
anaerobic conditions have been performed in yeast (Nagata et al., 1975). It was
revealed that under anaerobic conditions, there are a significantly smaller
10
number of mitochondria present and these also show differences in their
ultrastructure compared with mitochondria in aerobically grown yeast (Nagata
et al., 1975). Specifically, it was seen that under anaerobic conditions,
mitochondria fail to develop cristae (Figure 1) (Nagata et al., 1975). Similarly,
studies in human cells have also revealed that under hypoxia, significant
changes in mitochondrial shape and ultrastructure occur (Figure 1). When
H9C2 cells were examined during hypoxia under glucose-free conditions, it
was seen that mitochondria swell and become donut-shaped (toroidal) (Figure
1) (Liu and Hajnoczky, 2011). This was also seen upon re-oxygenation in the
presence of glucose (Liu and Hajnoczky, 2011). It is thought that this occurs in
order to better tolerate matrix volume increases and produce mitochondria that
can regain mitochondrial membrane potential (Liu and Hajnoczky, 2011). In
addition to these, a recent study in protists, looking at the mitochondrial
structure of hypoxically-grown choanoflagellates also revealed several
peculiarities under these conditions, including mitochondria showing tubular
cristae, which has never been seen before in choanoflagellates (Figure 1)
(Wylezich et al., 2012).
Ultrastructure under continuous anoxia and in transition to or from
anoxia
In plants, low oxygen studies have involved different experimental designs to
reflect the circumstances faced specifically by plants. Early studies examining
semi-aquatic plants under anoxia revealed mitochondrial degeneration and
developmental failure in the absence of oxygen (Ueda and Tsuji, 1971;
Vartapetian et al., 1976; Oliveira, 1977; Fox and Kennedy, 1991). However,
plants can often undergo brief periods of anaerobic conditions, such as those
that may occur during a brief flood. One study examined 4-day old (anoxia
intolerant) wheat seedlings that were subjected to 90 min of anoxia, before
mitochondrial ultrastructure was examined in the coleoptiles (Vartapetian et al.,
1985). Mitochondrial deterioration was revealed under these conditions, in that
11
Figure 1. Mitochondrial morphology under anoxia.
Structural changes under low-oxygen in yeast (Saccharomyces cerevisiae –
JYD 56-G strain), human (Homo sapiens – H9C2 cells), protists (Codosiga
balthica n. sp. strain IOW94), plants; anoxia tolerant (tol) barnyard grass
(Echinochloa phyllopogon) and rice (Oryza sativa) as well as anoxia intolerant
wheat (Triticum aestivum) are shown.
12
the matrix appeared less dense and cristae, less dilated (Figure 1) (Vartapetian
et al., 1985). Interestingly, the damage was seen to be reversed upon transfer
back to aerated conditions provided the duration of anoxia was relatively small
(Vartapetian et al., 1985). Such flexibility would be advantageous if stress
conditions are transient in nature. Mitochondrial ultrastructure has been linked
to supercomplex formation in the electron transport chain (Dudkina et al.,
2006). The relevance of this to low oxygen is currently unknown, but it is
certainly interesting that ATP synthase dimerization and subsequent
oligermerisation is hypothesised to contribute to cristae formation and
mitochondrial morphology (Dudkina et al., 2006). Whether or not ATP synthase
interactions are responsible for these changes in mitochondrial ultrastructure
under low oxygen is yet to be investigated.
For some plant species, the anaerobic conditions offered by their habitat can
last for extended periods of time and these species have evolved mechanisms
that allow mitochondrial survival even under these conditions. When anoxia-
tolerant plant species including Echinochloa phyllopogon and Echinochloa
crus-galli (barnyard grasses) were examined, it was seen that mitochondrial
size, shape and ultrastructure remained unchanged even under anaerobic
conditions (Kennedy et al., 1980). Similarly, embryos of anoxia-tolerant rice
develop mature mitochondria from promitochondria under both aerobic or
anaerobic conditions (Howell et al., 2007), which further supported the relatively
high level of conservation seen in the transcriptomic responses during both
aerobic and anaerobic germination in rice (Narsai et al., 2009; Narsai and
Whelan, 2013). However, this type of maintenance of mitochondrial structure or
ultrastructure is not consistent throughout rice development or in all anoxia-
tolerant plant species. For example, there does appear to be tissue-specific
differences in mitochondrial morphology under anoxia at the later stages of rice
development.
Specifically, in anaerobic-tolerant rice shoots, anaerobic mitochondria have less
dense matrices but more cristae (Figure 1) (Couee et al., 1992). It has also
13
been shown that excised rice coleoptiles (that no longer receive the
endosperm’s sugar supply) are more sensitive to anoxia, with abnormal
mitochondria observed after 1 day of anoxia, and after 2 days rupturing can
occur (Vartapetian et al., 1976). This effect was lost when glucose was
externally supplied, suggesting that mitochondria are sensitive to low oxygen in
a sugar-dependent manner. It is well known that starch mobilisation occurs via
alpha-amylase induction in anoxic rice and that this response is somewhat
unique to anaerobic tolerance (Guglielminetti et al., 1995; Guglielminetti et al.,
1995), whereby this response is absent in anoxic wheat, which fails to
germinate under anoxia. Thus it appears that metabolic adaptation, rather than
quiescence, is important for the upkeep of anoxic mitochondria in these rice
coleoptiles. Nitrite supplementation under low oxygen also appears to benefit
rice in that mitochondria show increased ATP synthesis (Stoimenova et al.,
2007), a phenomenon discussed in greater detail below. Rice seedlings
germinated and grown under anoxia appear to retain mitochondrial function in
coleoptiles, as these consume oxygen rapidly upon the transfer to aerated
conditions, albeit less efficiently compared to aerated controls (Millar et al.,
2004; Shingaki-Wells et al., 2011). Again, respiratory capacity is restored after
just 1 day of re-oxygenation following 6 days of anoxia (Millar et al., 2004).
Responses to low oxygen
Responses to low oxygen are being increasingly characterised, with
technological advances improving quality and quantity of data from DNA and
RNA to protein and metabolite analysis. It is the combined regulation and
control at each of these levels, which contributes to differences in low oxygen
survival across different plant species. Figure 2 visualises the known molecular
responses, to date, summarising the systems-level responses to low oxygen in
anaerobic tolerant rice, focussing on mitochondrial functions and interacting
pathways. The responses in Figure 2 reflect findings from previous
transcriptomic studies (Lasanthi-Kudahettige et al., 2007; Narsai et al., 2009;
Mustroph et al., 2010; Narsai and Whelan, 2013), proteomic studies (Millar et
-
+
+
+
+
+
--
+ -
+
+
-
-
+
Figure 2. The rice mitochondrial system under anoxia. Full caption follows on the next page.
14
15
Figure 2. The rice mitochondrial system under anoxia. Schematic
diagram visualising transcriptomic, proteomic, metabolomic and enzymatic
activities known to date (to our knowledge, through a systematic pubmed
search), and coloured based on their response to anoxia in rice. Note that for
some pathways shown, not all components and their responses have been
experimentally verified in terms of sub-cellular location or function under anoxia
(in black font). Figure on the previous page.
16
al., 2004; Huang et al., 2005; Howell et al., 2007; Sadiq et al., 2011; Shingaki-
Wells et al., 2011), metabolite studies (Mustroph et al., 2006; Narsai et al.,
2009; Shingaki-Wells et al., 2011) and enzyme activity studies (Fox and
Kennedy, 1991; Couee et al., 1992; Mujer et al., 1993; Carystinos et al., 1995;
Gibbs et al., 2000; Mustroph and Albrecht, 2003).
Fermentation
One of the earliest and most well-characterised responses to low oxygen is the
up-regulation of fermentation pathways as well as changes to the glycolytic
pathway, which were first observed in anaerobically grown yeast (Barnett,
2003). Cross-kingdom analysis reveals that not only are these pathways
conserved across different species, but that there are several commonalities in
their responses to low oxygen. In yeast as well as both tolerant and intolerant
plant species, an increase in alcohol dehydrogenase activity and subsequent
ethanol accumulation is observed (Waters et al., 1991; Guglielminetti et al.,
2001; Barnett, 2003; Branco-Price et al., 2008; Shingaki-Wells et al., 2011). In
the anoxia-intolerant species, Arabidopsis, alcohol dehydrogenase (ADH) is
significantly up-regulated at the transcript level in response to low oxygen
(Branco-Price et al., 2008; Narsai et al., 2011). In contrast, several genes
encoding alcohol dehydrogenase are down-regulated in rice, with the
exception of LOC_Os11g10510.1, which is induced in response to low oxygen
(Lasanthi-Kudahettige et al., 2007; Narsai et al., 2009) and shows significantly
higher expression during submergence in the SUB1A-containing cultivar
(M202(Sub1)) compared to the submergence-intolerant japonica M202 line
(Fukao et al., 2006; Mustroph et al., 2010). SUB1A has been shown to
regulate genes encoding enzymes involved in carbohydrate metabolism (Fukao
et al., 2006).
The comparison of tolerant and intolerant plant species does reveal strong
inter-species conservation in the up-regulation of pyruvate decarboxylase
(PDC) under low oxygen at the transcript level (Mustroph et al., 2010; Narsai
and Whelan, 2013). This is further supported by the finding that while over-
17
expression of ADH1 does not increase flood tolerance in Arabidopsis, the over-
expression of PDC1 and PDC2 does increase survival, supporting a more
active role for PDC in flood tolerance or supporting the notion that PDC activity
is the limiting factor for ethanol production (Ismond et al., 2003). In rice, it can
be seen that PDC is induced, at both the transcript and protein levels (Figure 2)
(Neljubov, 1901; Mustroph et al., 2010; Sadiq et al., 2011; Shingaki-Wells et
al., 2011; Narsai and Whelan, 2013). Interestingly, PDC is also expressed at
significantly higher levels in the SUB1A anoxia-tolerant rice line compared to
the intolerant M2O2 line (Mustroph et al., 2010). In terms of enzyme activity, it
has also been shown that ATP-dependent phosphofructokinase (PFK),
pyrophosphate-dependent phosphofructokinase (PFP), PDC, and ADH are all
increased in response to anoxia in rice coleoptiles (Figure 2) (Gibbs et al.,
2000). However, given that many of these inductions are conserved in both
tolerant and intolerant species, the up-regulation of fermentation alone cannot
confer tolerance.
Glycolysis
Across low oxygen studies in different species, it has been observed how the
presence of an energy source affects the low oxygen response (Nagata et al.,
1975; Liu and Hajnoczky, 2011). In plants specifically, it has even been shown
that anoxic survival is improved upon exposure to light (Mustroph et al., 2006).
Despite lower levels of ATP under anaerobic compared to aerobic conditions,
overall, it was shown that ATP levels were more stably maintained during
anaerobiosis in the light compared to the dark in both rice and wheat
seedlings, and light also had the effect of reducing the rate of fermentation in
these plants (Mustroph and Albrecht, 2003; Mustroph et al., 2006). The
combined effect of light inducing photosynthesis as a source of ATP and as a
localised source of oxygen to fuel respiration is likely to underlie these changes.
Nevertheless, survival was better in rice compared to the anaerobic intolerant
wheat. Thus, maintaining the rates of energy production and flux, via regulated
changes to the glycolytic and fermentation pathways is crucial for anaerobic
tolerance.
18
In rice, it is well known that energy stores, such as those in the form of starch
allow increased anoxia survival as rice maintains a functioning starch
metabolism, via active alpha-amylase induction and function under low oxygen
conditions (Figure 2) (Guglielminetti et al., 1995; Guglielminetti et al., 1995). It
has been shown that many genes encoding proteins involved in starch
metabolism and glycolysis are induced in rice, with sucrose synthase, aldolase,
triosephosphate isomerase and pyruvate phosphate dikinase (PPDK) also
significantly induced at the protein level (Figure 2) (Ricard et al., 1991; Lasanthi-
Kudahettige et al., 2007; Narsai et al., 2009; Shingaki-Wells et al., 2011). In
anaerobic tolerant species like rice, it has been observed that a pyrophosphate
(PPi)-dependent step in glycolysis is induced under low oxygen conditions
(Huang et al., 2005; Huang et al., 2008). Many PPi-utilising enzymes use PPi
bound to Mg2+, rather than free PPi (Horder, 1974). With decreasing ATP under
low oxygen, Mg2+ concentrations rise through liberation from ATP, and this
increases the activity of PPi-dependent enzymes, whose abundances are also
elevated under low oxygen (Horder, 1974; Lasanthi-Kudahettige et al., 2007;
Igamberdiev and Kleczkowski, 2011). PPDK and pyruvate kinase (PK) reactions
may work in a cyclical manner, with PK working in the direction of pyruvate
formation and PPDK working in the direction of pyruvate consumption
(Igamberdiev and Kleczkowski, 2011). Operation of this cycle would result in
PPi production, aiding conservation of ATP via the use of PPi-dependent
glycolytic reactions involving PPi-phosphofructokinase (PFK) and UDP-glucose
pyrophosphorylase as opposed to those that are ATP dependent (Igamberdiev
and Kleczkowski, 2011).
Thus, the induction of PPDK and increased activity of pyrophosphate-
dependent phosphofructokinase (PFK-PPi) is seen under anoxia in rice (Figure
2). Additionally, it is thought that the tonoplast H+-PPiase consumes PPi under
anaerobic conditions, as part of the “switch” to PPi as an alternate to ATP, and
that this activity contributes to cytosolic pH regulation (Huang et al., 2008). This
is also supported by the significant transcript induction of this gene (Lasanthi-
19
Kudahettige et al., 2007) and increased enzymatic activity (Carystinos et al.,
1995) observed under anoxia (Figure 2).
While many other genes encoding glycolytic enzymes are induced under anoxia
in rice, phosphoglycerate mutase, enolase and lactate dehydrogenase are not
induced at the transcript level (Figure 2). Interestingly, despite this,
phosphoglycerate mutase and enolase protein abundance increases under
anoxia (Shingaki-Wells et al., 2011) and lactate dehydrogenase activity is also
known to slightly increase under anoxia (Mustroph and Albrecht, 2003). This
suggests that regulation occurs at the post-transcriptional, post-translational
and enzymatic activity level during anoxia (Figure 2). It is likely that transcript,
protein and enzymatic activity induction (Figure 2) contribute to the 1.4-1.7 fold
faster rate of glycolysis (“Pasteur effect”) seen under anoxia in rice, compared
to under aerobic conditions (Gibbs et al., 2000). It is important to point out here
that many of these enzymes were also activated under low oxygen in anoxia-
intolerant species, such as maize and wheat. However, sucrose accumulation
still occurred and viability was lost, an observation likely to be caused by the
lack of a functional alpha-amylase under anoxia (Perata et al., 1992; Mustroph
and Albrecht, 2003). Similarly, whilst many of these glycolytic enzymes are also
induced at the transcript level in anoxia-intolerant Arabidopsis (Narsai et al.,
2011), this alone, clearly does not lead to survival under anoxia.
Mitochondrial responses
Towards the end of the glycolytic pathways, pyruvate and acetaldehyde are
produced and can be imported into the mitochondria, for use as substrates in
the Tricarboxylic acid (TCA) cycle. It has been observed that the response to
low oxygen involves changes at the mRNA, protein, and enzyme activity levels
for these components, with several studies reporting significant changes under
anoxia (Couee et al., 1992; Howell et al., 2007; Narsai et al., 2011), and this
facilitates oxidation of these products during re-aeration (Tsuji et al., 2003).
20
The TCA cycle
At the transcript level, studies have shown that there are several common
expression responses to low oxygen in different plant species for genes
encoding components of the TCA cycle (Mustroph et al., 2010; Narsai et al.,
2011). Overall, transcripts encoding several TCA cycle enzymes are down-
regulated in response to anoxia, in poplar, rice and Arabidopsis (Narsai et al.,
2011). Upon entry into the mitochondria, pyruvate can be metabolised by
pyruvate dehydrogenase to form acetyl CoA (Figure 2). Under anoxia, pyruvate
dehydrogenase and malate dehydrogenase are down-regulated both at the
transcript and protein levels in coleoptiles from rice seedlings germinated and
grown under anoxia, relative to aeration (Figure 2) (Millar et al., 2004; Howell et
al., 2007; Lasanthi-Kudahettige et al., 2007; Narsai et al., 2009; Shingaki-Wells
et al., 2011). This is not surprising, given that other TCA cycle enzymes and
their corresponding metabolites decrease in abundance under anoxia in rice
(Figure 2). Among these is the TCA cycle enzyme 2-oxoglutarate
dehydrogenase, which can metabolise 2-oxoglutarate to form succinyl-CoA
through reduction of NAD+. The well-recognized accumulation of alanine under
low oxygen (Narsai et al., 2011) when catalysed by alanine aminotransferase
can also result in the co-production of 2-oxoglutarate, providing substrate for
2-oxoglutarate dehydrogenase (Rocha et al., 2010). Rocha and colleagues
(2010a) propose that 2-oxoglutarate can be converted to Succinyl CoA, which
can be further metabolised to generate ATP and succinate. The NAD+ required
for 2-oxoglutarate production could be provided by via malate dehydrogenase
activity in reverse direction (Rocha et al., 2010). However, malate
dehydrogenase protein levels are significantly reduced in anoxic rice coleoptiles
relative to aeration (Shingaki-Wells et al., 2011). In Lotus japonicus, malate
dehydrogenase activity in the OAA-utilising direction during waterlogging is
about a third of that in control conditions, which could be a limiting factor in
sustaining the 2-oxoglutarate dehydrogenase reaction (Rocha et al., 2010).
This does not necessarily rule out the mechanism proposed by Rocha and
colleagues, but could suggest a lower level of malate dehydrogenase activity
during waterlogging, or higher level of ethanolic fermentation, is sufficient to
21
sustain these reactions. Overall, the TCA cycle, from the lowered fumarase
activity (Couee et al., 1992) to the lowered succinyl CoA ligase protein
abundance (Howell et al., 2007) is repressed under anoxia in rice (Figure 2).
However, not all components of the TCA cycle are repressed under anoxia.
Barnyard grass seedlings (Echinochloa phyllopogon (Stapf.) Koss), are an
extremely successful anoxia-tolerant weed of cultivated rice, having anaerobic
mitochondria that outperform those of rice in that TCA cycle enzyme activities
approached their maxima earlier and remain higher for longer during anaerobic
germination, characteristics that also correlate with the unaffected
mitochondrial ultrastructure under anoxia (Figure 1) and the weed-like ability of
Echinochloa to germinate more rapidly under anoxia (Fox and Kennedy, 1991).
Exposure of both rice and Echinochloa to anoxia resulted in most TCA cycle
enzyme activities approaching those of air (Fox and Kennedy, 1991). However,
interestingly, the activity of fumarate reductase, which converts fumarate to
succinate, is almost negligible in aerobic and anaerobic Echinochloa compared
to the 20-fold higher activity found in rice (Figure 2) (Fox and Kennedy, 1991).
As the substrate of complex II in the mETC, succinate probably accumulates
when the ubiquinone pool is saturated with electrons due to the absence of the
terminal electron acceptor O2 (Rocha et al., 2010) and despite being lower,
succinate dehydrogenase activity is present in anoxic rice shoots (Couee et al.,
1992). Thus, as well as being a product of ETC blockage, this increased
fumarate reductase activity may also help to explain why anoxic succinate
accumulation in rice is so rapid (Shingaki-Wells et al., 2011). The accumulation
of succinate is a known response to anoxia in rice (Menegus et al., 1989;
Narsai et al., 2009; Shingaki-Wells et al., 2011). In rice, succinate accumulates
3.5 fold in rice coleoptiles after just one day of anoxia (Shingaki-Wells et al.,
2011). Similarly, fumarate significantly increased during anoxic germination,
with higher levels observed from as early as just 3 h of imbibition to 2-d
germinated seeds (Figure 2) (Narsai et al., 2009). Interestingly, fumarate did not
increase in abundance after 1 d anoxia in coleoptiles (Shingaki-Wells et al.,
2011), suggesting that this response may only occur in the early anoxic growth
22
stages. Using 14C labelling, it has been demonstrated that succinate is
metabolised under anoxia (Rumpho and Kennedy, 1983). The exact pathway(s)
responsible for anoxic succinate accumulation is as yet unclear, but it has been
suggested that GABA shunt is partially responsible. A clearer answer on the
mechanism of anoxic succinate production awaits further flux analysis
(Lakshmanan et al., 2013). Overall, these observations highlight the presence of
at least a partially functional anaerobic TCA cycle in both Echinochloa and rice,
with subtle differences that might correlate with Echinochloa’s increased
success in an oxygen-deprived environment.
A special role for alanine during anoxia
In addition to succinate, alanine accumulation has also been observed under
anoxia (Menegus et al., 1989; Narsai et al., 2009; Shingaki-Wells et al., 2011).
In Arabidopsis, alanine accumulates during oxygen deprivation, even in alanine
aminotransferase (AlaAT) mutants (Miyashita et al., 2007) or GABA-T knockouts
(Miyashita and Good, 2008) and even despite nitrogen starvation (Rocha et al.,
2010). It has been proposed that the production of Ala is beneficial by
consuming the excess pyruvate under anaerobic conditions to prevent
respiratory oxygen consumption (Zabalza et al., 2009). Diversion of pyruvate
into the Ala synthesis pathway could be beneficial as the alternatives, lactate
and ethanol, can be toxic or rapidly diffuse out of the cell, respectively (Figure
2).
In addition to alanine, the AlaAT reaction produces 2-oxoglutarate, which is a
substrate for the GABA shunt pathway (Figure 2). Interestingly, metabolite
analysis has shown that although 2-oxoglutarate abundance decreases under
anoxia, GABA and succinate both increase in abundance under anoxia in rice
(Narsai et al., 2009). In Arabidopsis, when glutamate decarboxylase and GABA
transaminase mutants were examined, the initial rapid alanine accumulation
was not significantly affected (Miyashita and Good, 2008). However, the later,
slower phase of alanine accumulation was partially inhibited in these mutants
suggesting that the GABA shunt, or more specifically, the conversion of GABA
23
and pyruvate to alanine and succinic semialdehyde, does contribute to alanine
accumulation under low oxygen conditions (Miyashita and Good, 2008).
It has also been suggested that Ala synthesis could prevent cytoplasmic
acidification that would otherwise occur if all pyruvate was metabolised into
lactate (Reggiani et al., 1988). Interestingly, it has been observed that the
supplementation of Medicago root hairs with alanine (and serine; both of which
are glucogenic amino acids) leads to alkalinisation of the cytoplasm (Felle,
1996). Alanine is a neutral amino acid, with a methyl group side chain. It is
possible that this increase in pH is caused by alanine metabolism, possibly
through conversion to other amino acids or via proton-consuming
gluconeogenesis. However, it was shown that the rapid Ala synthesis that
occurs during the first 90 minutes of oxygen deprivation does not significantly
regulate cytoplasmic pH in maize root tips (Roberts et al., 1992). Whether or
not Ala metabolism contributes to pH regulation during long-term hypoxia is yet
to be determined.
Basic amino acid metabolism in plant mitochondria under anoxia
Mitochondria in rice are also involved in specific aspects of branch chain amino
acid metabolism in anaerobic conditions. An anaerobic-enhanced basic amino
acid carrier (BAC) has been identified in rice and shows a concomitant increase
with mitochondrial arginase and elevation of Arginine and Ornithine in anaerobic
rice tissues, consistent with an anaerobic role of mtBAC (Taylor et al., 2010).
The role for these components in facilitating mitochondrial arginine metabolism
and the plant urea cycle during anoxic growth of rice and in nitrate assimilation
under anaerobic conditions has been presented (Taylor et al., 2010).
Putrescine, synthesised from Arg, is also more abundant in rice during
anaerobic growth (Reggiani et al., 1989). Its synthesis is linked to the ethylene-
enhanced elongation of rice cells (Lee and Chu, 1992). Notably, Pro, which is a
product of Orn, is significantly higher in abundance in anaerobic than aerobic-
germinated rice embryos (Narsai et al., 2009). Pro synthesis occurs very early in
the germination of rice embryos under anaerobic conditions as a major product
24
of seed nitrate assimilation (Reggiani et al., 1993; Reggiani et al., 2000), prior to
the breakdown and degradation of amino acids in storage proteins. Ornithine-
Arginine-Citrate metabolism by mitochondria could therefore be enhanced
during anoxia leading to putrescine-stimulated cell elongation and anaerobic
proline synthesis in rice. This pathway is not common to all plants, for example,
there is no report of hypoxic or anaerobic metabolism using BACs in
Arabidopsis, and AtmBAC1 or AtmBAC2 are not enriched on polysomes
during hypoxia in Arabidopsis (Branco-Price et al., 2008).
Respiratory chain
Overall, the mitochondrial proteomes of aerated, anoxic and post-anoxic rice
coleoptiles were similar (Millar et al., 2004). However, closer examination
revealed that fewer respiratory complexes containing cytochrome were found in
anoxic rice coleoptiles, which likely explains the reduced capacity to respire
under these conditions (Millar et al., 2004). Monitoring respiratory capacity of
coleoptiles from rice seedlings germinated and grown under anoxia shows that
mitochondria are equipped to respire should oxygen become available (Millar et
al., 2004; Shingaki-Wells et al., 2011) and also in the anoxia-tolerant barnyard
grass (Kennedy et al., 1987). Thus, while the respiratory chain is generally
down-regulated under anoxia, there is likely to still be some function (Figure 2).
A previous study comparing the transcript responses to low oxygen in
germinating rice, Arabidopsis and poplar revealed several divergences in the
responses of genes encoding components of the respiratory and alternative
pathways (Narsai et al., 2011). Specifically, it was shown that while AOX and
several components of the electron transport chain are induced under low
oxygen in Arabidopsis, this is not seen in rice and the flood-tolerant poplar
(Narsai et al., 2011). It is possible that the up-regulation of these components in
Arabidopsis suggests that this is a response that aims to counteract the energy
crisis during oxygen deprivation. In the case of AOX, it has been shown that
NO production, which occurs under low oxygen, can result in the inhibition of
acontiase activity and subsequent citrate accumulation. Citrate causes an
increase in AOX protein abundance and capacity (Gupta et al., 2012).
25
Expression of these genes would be futile and energetically burdensome if
oxygen remains a limiting factor. Thus, the inability of Arabidopsis to prevent
expression of certain genes that do not confer tolerance might underlie its
intolerance to anaerobic conditions.
However, the induction of Complex II components was seen in both
Arabidopsis and rice (Narsai et al., 2011). As a component of both the electron
transport chain and TCA cycle, complex II has a vital role in mitochondrial
metabolism. It has previously been shown that there are significant functional
and compositional differences between mitochondrial complex II between
Arabidopsis and rice (Huang et al., 2010), although it is not yet fully understood
exactly how this may contribute to anaerobic tolerance in rice. Additionally,
recent studies are now revealing a link between the respiratory components
and reactive oxygen species (ROS) as well as nitric oxide (NO) metabolism
(Gupta et al., 2009; Gleason et al., 2010; Gupta and Igamberdiev, 2011)
uncovering new involvement of respiratory components in these pathways
under anoxia.
Roles for NO and ROS in anoxia
Nitrite-dependent ATP production and regulation of nitric oxide (NO)
In recent years, the evidence supporting an important role(s) for NO under low
oxygen has been building (Gupta et al., 2011; Stoimenova et al. 2007). NO
production can occur enzymatically via the activity of nitrate reductase (NR) or
through deoxyhemeprotein-catalysed nitrite reduction (Figure 2) (Huang et al.,
2005). Under anoxia, the NR transcript is significantly induced in rice coleoptiles
(Figure 2) (Lasanthi-Kudahettige et al., 2007). Several studies have tried to
elucidate the role of NO under low oxygen (Planchet et al., 2005; Gupta &
Igamberdiev 2011; Horchani 2011; Stoimenova et al. 2007). Nitrate-
supplemented NR-free tobacco mutants do not produce NO, but when
supplied with nitrite under anoxia, substantial amounts of NO can be produced
(Planchet et al., 2005). This suggests that NO production is nitrite-dependent
under anoxia. In contrast, nitrite reductase-deficient tobacco mutants still show
26
NO production (Planchet et al., 2005). It has also been shown that when root
mitochondria from tobacco NR double mutants are supplemented with NADH
and nitrite, NO is generated, and this is exacerbated by falling oxygen
concentrations, supporting a strong link between oxygen and NO (Gupta and
Igamberdiev, 2011). This link is further supported by the finding that electron
transport chain inhibitors inhibit NO production and nitrite supplementation
increases the ATP/ADP ratio in legume nodules (Horchani, 2011).
The addition of nitrite to anaerobic NADH-supplemented barley and rice
mitochondria has also been shown to result in increased ATP synthesis, relative
to nitrite-free anaerobic samples, whilst the same increase in response to nitrite
was not seen in the aerobic counterparts (Stoimenova et al., 2007). Inhibitor
experiments also revealed that nitrite-driven ATP synthesis is sensitive to
uncouplers, myxothiazol (Qo site of complex III) and KCN (complex IV),
suggesting the involvement of proton translocation (Gupta and Igamberdiev,
2011). Stoimenova and colleagues (2007) also reported that nitrite-dependent
ATP production under oxygen deprivation was sustained for longer periods of
time in anoxia-tolerant rice when compared to the less tolerant barley
mitochondria. This may be one factor that underlies the exceptional tolerance
of rice seedlings to anoxia.
As a free radical, NO levels must be regulated to prevent excessive cellular
damage. NO can diffuse out of the mitochondrion into the cytosol to be
converted to nitrate by oxygenated class 1 hemoglobins (Figure 2), which are
up-regulated at the transcript level under low oxygen in Arabidopsis and rice
coleoptiles (Taylor et al., 1994; Lasanthi-Kudahettige et al., 2007; Branco-Price
et al., 2008). This entire cycle oxidizes NADH/NAD(P)H to promote the
maintenance of NAD+/NADP+ levels, which is likely to be important for glycolytic
energy production (Igamberdiev and Hill, 2004). Non-symbiotic hemoglobins
are down-regulated under anoxia during germination, suggesting that this
mechanism of NO scavenging may not come into play until the later stages of
anoxic rice development (Narsai et al., 2011). These non-symbiotic
27
hemoglobins are able to stay oxygenated due to their high affinity for oxygen,
even when oxygen falls below the concentration required for complex IV
saturation (Igamberdiev et al., 2010). Oxygenated hemoglobin can oxidise NO
to form nitrate (Figure 2). The resulting met-hemoglobin is then reduced to form
hemoglobin, which can then be re-oxygenated (Igamberdiev et al., 2010). The
nitrate formed can be reduced by NR to nitrite to start the cycle again and
promote ATP synthesis under oxygen deprivation. The pathways relating to NO
that are shown in Figure 2 reflect the suggested pathway from Gupta and
colleagues under low oxygen (Gupta et al., 2011).
Reactive Oxygen Species metabolism and the Ascorbate Glutathione
cycle
In addition to NO, there is also a role for the established links between ROS
and the mitochondrial electron transport chain (mETC). Hydrogen peroxide
(H2O2) production during anoxia has been documented in rice, wheat and
garden iris, but with the plasma membrane NADPH oxidase activity responsible
for a substantial amount of its production (Blokhina et al., 2001). Hypoxia-
induced H2O2 production has also been reported in wheat (Biemelt et al.,
2000). Superoxide production at the mETC also occurs under low oxygen
(Blokhina et al., 2003). In rice coleoptiles, a mitochondrial Mn superoxide
dismutase (MnSOD) has been observed to accumulate under anoxia at the
protein level (Figure 2) (Shingaki-Wells et al., 2011). This MnSOD may act to
detoxify O2- into H2O2 (Figure 2), which can be subsequently reduced to form
water by the mitochondrial ascorbate peroxidase (Xu et al., 2011). This latter
reaction is linked to the oxidation of ascorbic acid to the radical
monodehydroascorbic acid (MDHA), which can disproportionate to
dehydroascorbic acid (DHA) or ascorbic acid (Noctor and Foyer, 1998).
Reduction of MDHA can also be catalysed by MDHA reductase. Reduced
glutathione (GSH) is involved in the next step in the pathway resulting in its
oxidation (GSSG) (Foyer and Halliwell, 1976; Noctor and Foyer, 1998).
Glutathione reductase completes the ascorbate/glutathione cycle by reducing
GSSG to GSH and oxidizing NADPH to NADP+ (Figure 2). H2O2 may also leave
28
mitochondria to be metabolized in the cytosol or peroxisome. Notably,
cytosolic peroxiredoxin protein abundance is also increased in anoxic rice
coleoptiles that have never been exposed to oxygen post-imbibition (Figure 2)
(Shingaki-Wells et al., 2011).
While the role of the ascorbate glutathione cycle and ROS metabolism is not
fully characterised under low oxygen conditions in rice, there is some evidence
suggesting involvement of these enzymes during oxygen limitation and
recovery in other plant species. For example, flood-tolerant species such as
Spartina anglica, Menyanthes trifoliata and Phragmites australis show increases
in the activities of dehydroascorbate reductase (DHAR) during post-anoxia
(Wollenweber-Ratzer and Crawford, 1994). This observation is reversed in
flood-intolerant Iris germanica and the anoxia tolerant Acorus calamus. For
MDHA reductase, Acorus calamus, Spartina anglica and Menyanthes trifoliate
showed higher activity post-anoxia relative to a pre-anoxic control
(Wollenweber-Ratzer and Crawford, 1994). Iris germanica showed a reduction
in MDHA reductase activity. This suggests that anoxia-tolerant plants had at
least one enzyme whose activity increased during post-anoxia (Wollenweber-
Ratzer and Crawford, 1994). For glutathione, the data were less clear, with
tolerant plants showing lower levels of GSH and GSSG, and an intolerant plant
only showing decreases in GSH post-anoxia (Wollenweber-Ratzer and
Crawford, 1994). Anoxia-tolerant A. calamus showed an increase in GSSG, but
there was little change in the pool size of total glutathione. Ascorbic acid, on
the other hand, did increase in abundance in this anoxia tolerant species
(Wollenweber-Ratzer and Crawford, 1994). However, as Blokhina and
colleagues (2003) discuss, the overall literature correlating antioxidant status
with tolerance is often internally contradictory (Blokhina et al., 2003).
Lastly, although the link between ROS and the ETC is not fully understood in
rice, it is known that transcripts encoding complex II, succinate
dehydrogenase, are induced under low oxygen (Narsai et al., 2011).
Interestingly, recent studies have shown a link between mitochondrial complex
29
II and ROS in both humans (Moreno-Sánchez et al., 2013) and plants (Gleason
et al., 2011). Specifically, in Arabidopsis, it was shown that SDH1-1 mutants
had phenotypes associated with lowered mitochondrial ROS production,
providing evidence that the ETC contributes to localized mitochondrial ROS
production (Gleason et al., 2011). If this link is conserved in rice, a functional
complex II under anoxia may contribute to specific ROS-related signalling or
gene expression (Fukao and Bailey-Serres, 2004), both while under anoxia and
possibly even in preparation for re-oxygenation. Alternatively, a role for reverse
electron flow through succinate dehydrogenase is worthy of consideration
(Hohl et al., 1987; Igamberdiev and Hill, 2009), but this still requires more
evidence that it occurs and is physiologically relevant in plants.
Re-oxygenation in plants
It is important to note that different plant species have adapted to different
levels of tolerance to anaerobic conditions, whereby several species are able to
survive relatively short bursts of hypoxia and recover. For example, despite
significant mitochondrial damage observed in wheat seedlings upon exposure
to low oxygen (Figure 1), mitochondrial recovery does occur when seedlings
are re-oxygenated, provided the duration of low oxygen treatment is brief
(Vartapetian et al., 1985). While this is the case for most species, rice can
survive extended periods of anoxia as a result of specific adaptations. One of
these includes coleoptile elongation, which in some cases is more rapid under
anoxia when compared to aerobically-germinated seedlings (Alpi and Beevers,
1983). Additionally, primary leaves fail to grow and root growth is hindered
under anaerobic conditions. This response of trying to grow in order to reach
the aerobic surface is called the Snorkel effect (Kordan, 1974), reflecting how
rice is re-introduced to oxygen, even after flooded germination. However,
literature searches reveal that the molecular responses to re-oxygenation are
not as well characterised as the response to low oxygen alone.
Re-oxygenated samples can reflect plants germinated and grown under anoxia
that are then switched to an aerated environment. This provides the
30
opportunity to study oxygen-independent development and the capacity of a
naïve plant to adapt to oxygenation shock. However, it is limited by the fact that
relatively few plants can germinate under oxygen deprivation. For example, rice
but not wheat, will germinate in the complete absence of oxygen, so
comparing these species will require aerated conditions at the germination
stage for wheat. Hence, post-anoxic plants can also reflect plants that were
grown under aerated conditions, are subsequently switched to either a
complete or near anaerobic environment to then be re-exposed to oxygen for
‘recovery’, as may occur under a period of brief flood.
Both approaches can use two types of control treatments including (1)
continuously aerated samples as well as (2) samples that are either
continuously anoxic or where the last treatment was anoxia. Using both types
of control treatments could be useful in defining re-oxygenation specific
responses as opposed to those responses that are simply oxygen-dependent.
For example, a transcript or protein that is detected only during recovery, but
not in continuously anoxic or aerated samples could be considered specific to
post-anoxia.
Transcriptomic responses to re-oxygenation
Post-anoxic transcriptome data, although limited in the published literature,
could provide clues into how, and the degree to which, metabolism is re-
modelled when oxygen becomes available. Using a three-way experimental
design including 1) aerobic v anaerobic germination, 2) 24 h aerobic
germination switched to anaerobic conditions for up to 6 h, and 3) 24 h
anaerobic germination switched to aerobic condition for up to 6 h, it was
possible to identify core aerobic and anaerobically responsive transcripts
(Narsai et al., 2009). When rice was germinated for 24 h under anoxia and then
switched to aerobic conditions, significant transcriptome reprogramming was
observed (Narsai et al., 2009). Over 4000 genes were induced within only 6 h
of exposure to air, and over 50% of those had higher expression under aerobic
germination compared to anaerobic germination, indicating a rapid shift
31
towards aerobic growth (Narsai et al., 2009). Another 15% represented genes
specifically responsive to re-oxygenation, whereby these were not higher during
aerobic germination, but were down-regulated in response to switching from
aerobic to anaerobic conditions, indicating oxygen-treatment specific regulation
(Narsai et al., 2009).
In anoxia-intolerant Arabidopsis, selective mRNA translation was examined in
seedlings subjected to hypoxia and subsequently returned to aeration (Branco-
Price et al., 2008). As discussed above, Arabidopsis seedlings subjected to
hypoxia had a 50% reduction in polysome content and increases in 80S
monosomes and ribosome subunits, observations that were reversed upon re-
oxygenation for 1 hour (Branco-Price et al., 2008). Transcripts encoding
proteins involved in cell wall formation, transcription, signalling, cell division,
hormone metabolism and lipid metabolism were translationally repressed under
hypoxia, whereas after 1 hour of re-oxygenation, translational repression was
relieved (cluster 4 (Branco-Price et al., 2008)). A specific example of this is a
protein annotated as a eukaryotic translation initiation factor 4F (At5g57870).
Polysomal mRNA was isolated from Arabidopsis seedlings treated with
hypoxia, re-oxygenation or no stress at all to deduce a putative oxygen
responsive translatome. Indeed, many polysome-associated mRNAs were
induced during hypoxic treatment. Interestingly, 80% of the polysomal mRNAs
that were highly induced after 9 h hypoxia did not significantly decrease after
1h re-oxygenation, an observation in-line with translational repression under
hypoxia (Branco-Price et al., 2008). It is unclear what exactly the biological
significance of this is, but it is possible that some hypoxia-induced transcripts
are important for re-oxygenation, or that 1 h is too short a time for dissociation
of ribosomes and mRNAs in Arabidopsis. Alternatively, there may have been an
evolutionary benefit in delaying polysome dissociation if hypoxia was a frequent
and recurring threat. This is further supported by the identification of a cluster
of genes that were induced during hypoxia, but only associating with
ribosomes during re-oxygenation (cluster 3; (Branco-Price et al., 2008)).
Nevertheless, it is also possible that the observed low oxygen intolerance in
32
Arabidopsis may be contributed to by its delayed response to re-oxygenation.
Re-oxygenation effects on fermentation, TCA cycle and glycolysis
It is very well known that under low-oxygen conditions, fermentation is induced
in many plant species, where pyruvate is first converted to acetaldehyde, which
can enter the mitochondria or remain in the cytosol where it is converted to
ethanol (Figure 2) (Davies et al., 1974; Guglielminetti et al., 2001; Narsai et al.,
2009; Mustroph et al., 2010). However, overall, anaerobic activities result in
decreased ATP and increased cytoplasmic acidity, which can hinder recovery
upon re-oxygenation (Menegus et al., 1991; Felle, 2005). In rice, an aldehyde
dehydrogenase (Aldehyde dehydrogenase 2a - LOC_Os02g49720.1) is
induced at the transcript level under anoxia in coleoptiles and during
germination (Lasanthi-Kudahettige et al., 2007; Narsai et al., 2009). However,
at the protein level, it is reduced in rice embryo mitochondria as well as
coleoptiles under anoxia (Howell et al., 2007; Sadiq et al., 2011). Thus, the
regulation of ALDH2b is contradictory at the transcript and protein levels, with
further investigation necessary to deduce the reasons for this.
Mitochondrial aldehyde dehydrogenases oxidise aldehydes to form acetate. In
rice leaves ALDH2a protein increases slightly after 24 h submergence, despite
the large mRNA induction in the same tissue (Tsuji et al., 2003). ALDH2a then
continues to accumulate post-submergence, reaching its peak at 4 h and then
decreasing by 24 h, an endpoint protein abundance that is higher than before
or immediately after submergence (Tsuji et al., 2003). ALDH2b decreases after
submergence and during re-oxygenation levels reach a minimum at 1.5 h but
show consistent increases up to 24 h (Tsuji et al., 2003). Even though levels of
acetaldehyde and ethanol were high after 24 h submergence, ALDH activity
was only induced during the re-oxygenation phase with concomitant decreases
in acetaldehyde levels (Tsuji et al., 2003). Taken together with the shown
reverse activity of ADH and catalases in ethanol metabolism in young poplar
plants (Kreuzwieser et al., 2001), it has been proposed that upon re-
oxygenation, ethanol produced during anaerobiosis may converted back to
acetaldehyde by peroxidation through catalases or reverse ADH activity
33
(Kreuzwieser et al., 2001; Tsuji et al., 2003). Thus, acetaldehyde oxidation
could aid the post-anoxic re-generation of acetyl-CoA, a TCA cycle substrate,
as well as the complex I substrate NADH, both of which are integral to
respiratory metabolism. Further supporting this is the finding that within only 3 h
of exposure to air after 24 h of anaerobic germination, genes encoding TCA
cycle components are significantly induced and subsequent metabolites
including citrate and 2-oxoglutarate, are also increased in abundance, whilst
succinate and fumarate are depleted, suggesting a rapid response to reinstate
the aerobic TCA cycle (Narsai et al., 2009).
In addition to the TCA cycle, significant changes to the expression of glycolytic
enzymes were also observed upon 3 h of aeration after 24 h of anaerobic
germination, including the induction of phosphoglycerate mutase and enolase
encoding genes (down-regulated under anoxia; Figure 2), as well as increases
in fructose and glucose metabolite content (Narsai et al., 2009). Other
metabolites identified to accumulate under re-oxygenation included
carbohydrates arabinose and trehalose, suggesting a restoration of
carbohydrate pools when the energy crisis presented by anoxia is relieved.
Alanine and post-anoxia
As discussed above, alanine accumulation is seen under anaerobic conditions
(Kato-Noguchi, 2006; Kato-Noguchi and Ohashi, 2006; Shingaki-Wells et al.,
2011). Additionally, this accumulation is thought to also be beneficial during re-
oxygenation (Rocha et al., 2010). The Arabidopsis knockout AlaAT1 sustains
hypoxic accumulation of alanine, however upon the return of oxygen, these
plants show defective rates of Ala consumption relative to WT plants (Miyashita
et al., 2007). This could result in glutamate and pyruvate synthesis, the latter of
which could be metabolised into Acetyl CoA for TCA cycle/ETC operation.
Interestingly, when tracking alanine and AlaAT levels in waterlogged plants,
increases in AlaAT continued to occur beyond the point at which levels of
alanine reached a plateau (de Sousa and Sodek, 2003). Support for a vital role
of AlaATs is demonstrated by the steep declines in Ala during re-oxygenation
(de Sousa and Sodek, 2003). Alanine may be thought of as a transportable
34
energy source, much like lactic acid in the Cori cycle of animals or sucrose of
plants, as its transport through xylem has been shown previously (de Sousa
and Sodek, 2003). Like lactic acid, alanine can be metabolised into pyruvate,
which can subsequently enter the gluconeogenesis pathway or be metabolised
into Acetyl CoA.
Interestingly, the coleoptiles of anoxia-intolerant wheat seedlings subjected to
1d of anoxia did not accumulate alanine, unlike that of anoxia-tolerant rice
(Shingaki-Wells et al., 2011). However, upon supplementation of anoxic
seedlings with alanine, electrolyte leakage was reduced in wheat but not in rice,
whose leakage was already low relative to wheat. Thus, it appears, that alanine
has a significantly beneficial role under anoxia itself and thus the production of
a rice plant defective in alanine synthesis could reveal the regulation and
pathways responsible for the steep alanine accumulation that occurs, even
when plants are deficient of nitrogen (Rocha et al., 2010).
Vacuolar H+-pyrophosphatase (V-PPase)
Anoxically-treated rice seedlings show increased V-PPase protein activity,
which is thought to hydrolyse PPi, as opposed to scarcely abundant ATP, to
pump protons from the cytosol to the vacuole (Figure 2) (Carystinos et al.,
1995). This induction would be useful in the context of anoxia, where Mg2+
concentrations rise as a result of falling ATP, to promote Mg-PPi complex
formation (Igamberdiev and Kleczkowski, 2011). As discussed in detail by
Igamberdiev and Kleczkowski (2011) these conditions would promote the
function of PPi-dependent enzymes. The induction of V-PPase is also seen in
cold-treated seedlings (Carystinos et al., 1995). Re-oxygenation for two days
results in a decrease of V-PPase to levels that are barely detectable and
comparable to pre-anoxic seedlings. The activity of V-PPase also mirrored
these protein abundance changes (Carystinos et al., 1995). Relative to V-
PPase, the activity of the ATP-dependent proton pump increased slightly
during anoxia. Similarly, this activity returned to pre-anoxic levels upon re-
oxygenation. Whether or not V-PPase proton pumping counteracts anoxic
35
cytoplasmic acidification is debatable, since anoxic rice vacuoles show
increases in their pH (Menegus et al., 1991), with alternative purposes thought
to include the maintenance of tonoplast energisation as a preventative cell
death measure (Carystinos et al., 1995).
Post-anoxic decreases in the activities of V-PPase and V-ATPase suggest a
reduction in the need for proton transport across the vacuolar membrane. In
another study, rice seedlings overexpressing V-PPase (OVP1) show increased
survival after cold treatment compared to wild type seedlings, and this
correlates with a decrease in markers for membrane damage as well as higher
proline levels in cold-treated transgenic seedlings (Zhang et al., 2011). The
significance of anoxic V-PPase induction in rice is yet to be fully elucidated.
However, it is worth noting that anoxic rice seedlings rapidly accumulate proline
(Figure 2) and show no significant increase in electrolyte leakage, a marker for
membrane damage, when transferred to anoxia. Anoxic wheat, which is
relatively intolerant to anoxia, accumulates proline, albeit to a lesser extent, and
shows large and significant increases in electrolyte leakage (Shingaki-Wells et
al., 2011). The level of anoxic V-PPase induction in wheat is yet to be
determined.
Respiratory components
Mitochondria isolated from the coleoptiles of rice seedlings grown under anoxia
for 7 days show significantly lower rates of oxygen consumption than their
aerobically-grown counterparts (Millar et al., 2004). When 6 day old anoxic
seedlings are switched to air for 1 day, the rate of oxygen consumption
recovers to levels comparable to those seen in continuous aeration (Millar et al.,
2004). This is also supported by the rapid and significant induction of genes
encoding respiratory chain components, and alternative pathway components
within 3 h of aerobic treatment after 24 h of anaerobic germination (Narsai et
al., 2009). Re-oxygenated coleoptile samples were unique, in that they were
able to respire in a KCN-insensitive manner, attributed to AOX protein induction
also observed upon re-oxygenation (Millar et al., 2004). Soluble mitochondrial
protein profiles of continuously anoxic and re-oxygenated rice coleoptiles are
36
remarkably similar, indicating that the synthesis and maintenance of many
mitochondrial components can occur in an oxygen-independent manner.
Analysis of whole mitochondrial proteomes revealed an enrichment of
chaperones and proteins involved in carbon metabolism during re-oxygenation
of coleoptiles, but on the whole, anoxic and post-anoxic proteomes were
largely similar (Millar et al., 2004).
Complex I and ATP synthase protein abundances did not differ significantly
when comparing anoxic and re-oxygenated samples. Other membrane
proteins that were part of the b/c1 complex (III) and cytochrome c oxidase (IV)
complex were clearly more abundant during the re-oxygenation phase (Millar et
al., 2004). These observations are consistent with the higher cytochrome
content, heme and cytochrome c during air adaptation (Millar et al., 2004).
Post-anoxic AOX induction is seen both at the transcript and protein levels
upon re-oxygenation (Millar et al., 2004; Narsai et al., 2009). This induction may
prevent over-reduction of the respiratory chain in a situation where heme
abundance was previously limited. It is also likely that the high level of
mitochondrial biogenesis that takes place in anoxic rice seedlings aids a rapid
response to re-oxygenation shock (Millar et al., 2004; Howell et al., 2007).
Soybean re-oxygenation responses
The first large-scale re-oxygenation proteomics study focused on the roots of
soybean seedlings (Salavati et al., 2012). Two-day-old seedlings subjected to
one day of flooding were sampled four days after de-submergence and
compared to seven-day-old seedlings kept under continuous aeration.
Seedlings were also sampled immediately after flooding and compared to 3-
day-old continuously aerated controls. Of the seven proteins whose
abundances did not return to control levels during re-oxygenation, six
increased and one decreased during recovery. These six were also significantly
more abundant immediately after the flooding stress. Of interest is 1-Cys
peroxiredoxin (Prx), a protein that detoxifies H2O2, and whose abundance
immediately after flooding was 3 fold higher than that of seedlings grown under
continuous aeration. Prx remained significantly more abundant in de-
37
submerged seedlings compared to its control. Sustained elevation during re-
oxygenation could lend support to the hypothesis that anoxic induction of Prx
is a preparatory measure for the threat of oxidative stress posed during post-
anoxia. Interestingly, Prx induction is also conserved in anoxic rice coleoptiles
and fresh water low oxygen tolerant turtles (Krivoruchko and Storey, 2010;
Shingaki-Wells et al., 2011).
It is important to note that the submerged soybean seedlings were grown
under a light regime (Salavati et al., 2012). Thus, it is likely that the conditions
were not fully anoxic, facilitating ROS production and therefore hypoxic Prx
function is a possibility. However, the conserved response between flooded
soybean, anoxic rice and even anoxic freshwater turtles (Krivoruchko and
Storey, 2010; Shingaki-Wells et al., 2011), in addition to the sustained elevation
of Prx during de-submergence, makes it tempting to speculate that Prx
functions broadly in both plants and animals to detoxify ROS during re-
oxygenation. It is possible that the high abundance of Prx under low oxygen
may also be a result of delayed protein degradation, which would otherwise
occur in aeration, as has been shown in flooded soybean seedlings (Nishizawa
and Komatsu, 2011). The lack of flood-induced Prx mRNA induction also
supports this notion (Nishizawa and Komatsu, 2011).
The classical anaerobic proteins alcohol dehydrogenase and pyruvate
decarboxylase are elevated in re-oxygenated samples compared to aerated
controls (Salavati et al., 2012). This could be a result of a delay in return to
metabolic equilibrium post-hypoxia (i.e. a remnant hypoxic response) or could
indicate the necessity of these proteins after a flood, especially if this event is
likely to re-occur. An overview of re-oxygenated samples treated with 1, 2 or 3
days of flooding also reveals an up-regulation of proteins involved in protein
folding, cell wall biosynthesis, cell expansion and cytoskeletal organization.
Down-regulated proteins were involved in amino acid metabolism, proteolysis
and glycolysis (Salavati et al., 2012). Notably, these proteins are up-regulated in
anoxic rice coleoptiles (Shingaki-Wells et al., 2011), thus the down-regulation of
these during re-oxygenation may represent an attempt to return to equilibrium.
38
Such observations highlight the importance of considering multiple stages of
this plant stress to identify re-oxygenation-specific responses.
Which mitochondrial functions help to make rice an
anoxia survivor?
As an anoxia-tolerant species, rice has many adaptations that allow survival
under both relatively long periods of continuous anoxia, as well as during re-
oxygenation stress, which is known to further hinder survival in other plant
species. Based on the literature we propose some prominent mitochondrial
adaptations and/or responses in this species that may be important survival
traits, and these are summarised below.
• Nitrite-dependent ATP production is sustained for longer
Roles for NO production and scavenging in plant mitochondria under anoxia
have been proposed based on the NO production observed under low oxygen
(Benamar et al., 2008); reviewed in (Gupta et al., 2011)). Using nitrite as an
electron acceptor, it has been shown that anaerobic ATP production in this
manner is sustained for almost twice as long in anoxia-tolerant rice, compared
to anoxia-intolerant barley (Stoimenova et al., 2007). While ATP production
under anaerobic conditions is less than 25% of what is seen in aerobic
conditions, this is still significantly higher than in anoxia-intolerant species such
as maize and wheat under anoxia (Mustroph and Albrecht, 2003). Therefore,
between activating PPi-dependent pathways (Huang et al., 2008) and having
increased nitrite-dependent ATP production (Stoimenova et al., 2007) more
energy is produced under anoxia in rice compared to anoxia-intolerant species.
• Anaerobic arginine metabolism for cell elongation and amino
acid synthesis
The anaerobic enhancement of BAC and arginase in rice mitochondria (Taylor
et al., 2010) provides a role of mitochondria during anoxia in altering amino acid
content of anoxic tissues and even contributing to cell elongation processes in
rice coleoptiles. There are potential links between this process and the N-
39
based electron transport chain metabolism, noted above, that remain to be
explored.
• Aldehyde dehydrogenase functions in low oxygen and re-
oxygenation
The rice mitochondrial ALDH2 oxidises aldehyde to form acetate and is
induced under anoxia in coleoptiles and during anaerobic germination in rice
(Tsuji et al., 2003; Lasanthi-Kudahettige et al., 2007; Narsai et al., 2009). In
contrast, its Arabidopsis orthologue is not induced under low oxygen
(Kursteiner et al., 2003). Given that ALDH2 does not decrease in abundance for
a few hours following re-oxygenation in flood tolerant rice and poplar, it has
been proposed that the ethanol produced during anaerobiosis may be
converted back to acetaldehyde by peroxidation through catalases or ADH
activity (Kreuzwieser et al., 2001; Tsuji et al., 2003). Acetaldehyde may then be
entering the mitochondria for conversion into acetate by ALDH2 (Tsuji et al.,
2003). Thus, this modified fermentative metabolism may contribute to re-
oxygenation survival in low oxygen tolerant species such as rice and poplar.
• Separate response to anoxia from those to other abiotic stresses
A recent study compared low oxygen stress to other abiotic stress (drought,
salt, cold and heat) responses in rice and Arabidopsis, in order to gain insight
into the uniqueness of the low oxygen response (Narsai and Whelan, 2013).
This study showed that several genes associated with oxidative stress (as
identified in (Gadjev et al., 2006) are down-regulated under heat stress but
induced under anoxia in Arabidopsis, whereas their rice orthologues are down-
regulated under anoxia (Banti et al., 2008; Narsai and Whelan, 2013). This
supports the observations that pre-treating Arabidopsis seedlings with heat,
improves low oxygen survival (Banti et al., 2008). For example, the
mitochondrial dicarboxylate carrier 2 (AtDIC2 - AT4G24570) is induced under
anoxia and abiotic stress in Arabidopsis, whereas its rice orthologue
(LOC_Os08g37370.1), annotated as a mitochondrial 2-oxoglutarate/malate
carrier, showed repressed gene expression under anoxia and unchanging
expression under abiotic stress (Narsai et al., 2013b). Similarly, several WRKY
transcription factor encoding genes that were induced under low oxygen in
40
Arabidopsis, were also repressed or unchanging under anoxia in rice,
suggesting that for some stress-responsive pathways, Arabidopsis may
perceive low oxygen stress similarly to other abiotic stresses, whereas, this
does not appear to be the case in rice (Narsai et al., 2013b).
Conclusions
As a major site for energy production, regulation of the mitochondrial system is
essential for survival, from the level of maintaining ultrastructure for membrane
potential (Figure 1) to the transcript, protein, enzyme activity and metabolite
levels that need to be intricately regulated (Figure 2). This functionality is even
more crucial under low oxygen conditions when there is an energy deficit.
Hence, not all species can survive under low oxygen. Anoxia-tolerant rice
represents a relativity unique and increasingly characterised model for low-
oxygen tolerance. In the last decade, much more knowledge has been gained
pertaining to the mechanisms relating to low oxygen survival. In terms of
mitochondria, a complex picture is emerging showing important functional links
between the respiratory pathway and sugar/starch signalling in anoxic rice (Lu
et al., 2007; Cho et al., 2009; Park et al., 2010). Additionally, there is growing
evidence for respiratory chain components being involved in NO and ROS
signalling under low oxygen (Blokhina et al., 2003; Gupta et al., 2011). Thus, in
addition to being the powerhouses for oxidative phosphorylation, mitochondria
are also important organelles in helping to ensure survival under anoxia.
41
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Chapter 2 Rice and wheat responses to anoxia
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Foreword to Study I
The first study of this thesis aimed to characterise the physiological, proteomic
and metabolomic responses of rice and wheat coleoptiles to anoxia. Since
rice has historically been cultivated in flooded, anaerobic soils, many rice
cultivars are anoxia tolerant to the extent that they can germinate under
complete oxygen deprivation. In contrast, wheat is typically a dry-land, winter
crop, rendering it relatively anoxia intolerant.
Coleoptiles were chosen as the sample of interest, since anaerobically
germinated rice fails to develop any other tissue. Since wheat does not
germinate without oxygen, we used several treatment regimes. For rice, we
used anoxic germination, as well as aerobic germination and growth, followed
by a one-day anoxic shock. For wheat, only the latter treatment was used.
Study I provided a multi-faceted understanding of the molecular responses
these species exhibit when treated with anoxia. At the protein level, rice had a
strong response to anoxia, in stark contrast to wheat. At the metabolite level,
anoxic amino acid metabolism appeared to strongly contrast between the two
species, corroborating the response differences seen in the proteomics
analysis. Consequently, it was hypothesised that interfering with wheat amino
acid metabolism would affect fitness outcomes. In the absence of genetic
mutants, seedlings were supplemented with a few key amino acids that
showed inter-species response differences. Using a measure for cell integrity,
wheat coleoptiles, unlike rice coleoptiles, appeared to benefit from amino acid
supplementation. Thus, the results of Study I provide a basis for further
investigation of the benefits of amino acid accumulation under anoxia.
Differential Molecular Responses of Rice and WheatColeoptiles to Anoxia Reveal Novel MetabolicAdaptations in Amino Acid Metabolism forTissue Tolerance1[W][OA]
Rachel N. Shingaki-Wells, Shaobai Huang, Nicolas L. Taylor, Adam J. Carroll,Wenxu Zhou, and A. Harvey Millar*
Australian Research Council Centre of Excellence for Plant Energy Biology (R.N.S.-W., S.H., N.L.T., W.Z.,A.H.M.), Centre for Comparative Analysis of Biomolecular Networks (R.N.S.-W., S.H., N.L.T., A.H.M.), andCentre of Excellence for Plant Metabolomics (W.Z.), University of Western Australia, Crawley, WesternAustralia 6009, Australia; and Australian Research Council Centre of Excellence for Plant Energy Biology,Australian National University, Canberra, Australian Capital Territory 2601, Australia (A.J.C.)
Rice (Oryza sativa) and wheat (Triticum aestivum) are the most important starch crops in world agriculture. While bothgerminate with an anatomically similar coleoptile, this tissue defines the early anoxia tolerance of rice and the anoxiaintolerance of wheat seedlings. We combined protein and metabolite profiling analysis to compare the differences in responseto anoxia between the rice and wheat coleoptiles. Rice coleoptiles responded to anoxia dramatically, not only at the level ofprotein synthesis but also at the level of altered metabolite pools, while the wheat response to anoxia was slight in comparison.We found significant increases in the abundance of proteins in rice coleoptiles related to protein translation and antioxidantdefense and an accumulation of a set of enzymes involved in serine, glycine, and alanine biosynthesis from glyceraldehyde-3-phosphate or pyruvate, which correlates with an observed accumulation of these amino acids in anoxic rice. We show a positiveeffect on wheat root anoxia tolerance by exogenous addition of these amino acids, indicating that their synthesis could be linkedto rice anoxia tolerance. The potential role of amino acid biosynthesis contributing to anoxia tolerance in cells is discussed.
Rice (Oryza sativa) and wheat (Triticum aestivum) areeconomically important crops that are adversely af-fected by multiple environmental stresses. Both thesemonocotyledon grasses operate similar central meta-bolic processes yet notably differ in aspects of theirdevelopment and anatomy as well as in their optimalgrowth conditions: rice is typically cultivated in trop-ical regions on flooded/anaerobic soils, whereas wheatis almost exclusively a dry-land winter crop (Nagai andMakino, 2009). Despite these differences, their criticalrole as the main source of nutrition for humanity
makes the comparative study of these crops underyield-reducing stresses important. Furthermore, thestudy of two species, as opposed to two cultivars of thesame species, may be useful in highlighting mecha-nisms of anoxia adaptation in plants differing in thecontexts of their domestication. Rice is an ideal modelspecies for elucidating the mechanisms of anoxia toler-ance in plants; its full genome sequence is available (Yuet al., 2002), it can survive under prolonged anoxia, andit can even display elongation of its coleoptiles underanoxic conditions (Menegus et al., 1991; Perata et al.,1997). A critical aspect of rice anoxia tolerance is theinduction of the starch-degrading enzyme a-amylaseunder anoxia, providing a continuing supply of sub-strates for metabolism (Perata et al., 1992). Rice growthunder anoxia is largely restricted to the coleoptile,with root and leaf development halted in the absenceof oxygen (Opik, 1973). From an evolutionary perspec-tive, successful coleoptile growth under anoxia pro-vides rice seedlings with the opportunity to reach moreair-saturated conditions above anaerobic mud or stand-ing water (Kordan, 1974), thus increasing the chance ofsurvival. In some regions of the world, wheat also en-counters waterlogging, causing oxygen deficiency, butunlike rice, this normally leads to major or even totalyield losses (Setter and Waters, 2003). Compared withrice seedlings, wheat seedlings are widely consideredto be anoxia intolerant, despite possessing an anatom-
1 This work was supported by the Grains Research and Devel-opment Corporation and an Australian Postgraduate Award Ph.D.scholarship (to R.N.S.-W.), by a University of Western AustraliaResearch Development Award (to S.H.), by the Australian ResearchCouncil through the Australian Research Council Centre of Excel-lence in Plant Energy Biology (grant no. CE0561495), and by anAustralian Research Council Australian Professorial Fellowship (toA.H.M.).
* Corresponding author; e-mail [email protected] author responsible for distribution of materials integral to the
findings presented in this article in accordance with the policydescribed in the Instructions for Authors (www.plantphysiol.org) is:A. Harvey Millar ([email protected]).
[W] The online version of this article contains Web-only data.[OA] Open Access articles can be viewed online without a sub-
scription.www.plantphysiol.org/cgi/doi/10.1104/pp.111.175570
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ically similar coleoptile tissue under normal growthconditions (Menegus et al., 1991). Although wheatseeds are starchy like rice, they cannot germinate andgrow a coleoptile under anoxia due to an absence ofthe starch-degrading enzyme a-amylase in anaerobicseeds (Perata et al., 1992).Without oxygen, the glycolytic pathway that is linked
with ethanolic fermentation is the predominant mech-anism of energy production in plants (Gibbs et al., 2000;Bailey-Serres and Voesenek, 2008). However, there isstill much less energy production during anoxia thanin aeration per unit of carbohydrate metabolized. As aconsequence, the synthesis rate of macromoleculessuch as proteins decreases well below that seen inaerated tissues (Alpi and Beevers, 1983). Even so, ricecoleoptiles still exhibit a complex pattern of newlysynthesized proteins under anoxia (Mocquot et al.,1981; Ricard and Pradet, 1989; Huang et al., 2005).Along with the classical anaerobic proteins first re-ported in maize (Zea mays; Sachs et al., 1996), anoxicrice coleoptiles also synthesize a range of proteins withunknown functions (Huang et al., 2005). To date, theidentified anoxically synthesized protein data set in ricedoes not form complete biochemical pathways (Ricardet al., 1991; Huang et al., 2005). Evidently missing fromthis set are a range of enzymes in glycolysis and theenzymes that could explain the observed amino acidaccumulation in rice coleoptiles under anoxia (Fanet al., 1997; Kato-Noguchi and Ohashi, 2006). It remainsunclear whether these dramatically induced amino acidpools are derived from specific protein degradationunder anoxia or de novo amino acid synthesis. Withthe improved techniques in protein identification us-ing peptide mass spectrometry, it is feasible to analyzeproteins on a large scale using combinations of gel-based or non-gel-basedmethods to address these issuesand provide an in-depth understanding of the mech-anism(s) of anoxia tolerance. Direct comparisons ofproteome responses that occur during anoxia in toler-ant rice coleoptiles and intolerant wheat coleoptilesalso provide an opportunity to differentiate proteomechanges under anoxia associated with cellular stressand damage from those associated with continuedgrowth and adaptation.At the metabolite level, the accumulation of fermen-
tation end products such as ethanol, lactate, and Alahas been extensively studied in plants responding tooxygen deprivation (Raymond et al., 1985; Meneguset al., 1989, 1991; Gibbs and Greenway, 2003). A recentmetabolomic analysis of Lotus japonicus suggests thatthe accumulation of succinate and Ala under lowoxygen might function to generate ATP that is addi-tional to what the glycolytic pathway offers (Rochaet al., 2010). In rice coleoptiles, the anaerobic assimila-tion of inorganic nitrogen into amino acids, particularlyAla and g-aminobutyrate/Glu, may serve to supple-ment ethanolic fermentation in sustaining glycolyticenergy production (Fan et al., 1997). There are alsoseveral studies that report changes of carbohydrates(Suc, Glc, Fru) and sugar phosphates in coleoptiles and
shoots (composed of both leaves and coleoptiles) ofrice seedlings in response to anoxia (Menegus et al.,1991; Guglielminetti et al., 1995; Huang et al., 2003). Ananalysis of the early germination stages of rice embryosat the metabolite level highlighted sets of 10 and 13metabolites, respectively, as aerobic and anaerobic re-sponders (Narsai et al., 2009). However, a broad pic-ture of the changes of metabolites in the anoxic ricecoleoptile itself remains unclear, and measuring only afew compounds, as has been reported in most of theearlier studies (Menegus et al., 1989, 1991), makes it hardto understand the flow of both carbon and nitrogen be-tweenmetabolic pools under anoxia. Furthermore, thereare no reported studies on how wheat coleoptiles re-spond to anoxia across a broad set of metabolite pools.
In this study, we combined protein and metabolite-profiling analyses to compare the differences in responseto anoxia between anoxia-tolerant rice coleoptiles andanoxia-intolerant wheat coleoptiles. Rice coleoptiles,but not wheat coleoptiles, responded to anoxia dra-matically, not only at the level of new protein synthesisbut also at the level of altered metabolite pools. Wealso found significant increases in anoxic rice coleop-tile proteins related to protein translation, such as 40Sribosomal proteins and elongation factors. The possi-bility of selected mRNA translation and protein turn-over in anoxic rice coleoptiles, but not in anoxic wheatcoleoptiles, which are remarkably unchanged, is dis-cussed in light of the observed low correlation betweenprotein abundance and reported gene expression data.A set of enzymes that increased in abundance in anoxicrice, a change that was not apparent in wheat, are in-volved in Ser, Gly, and Ala biosynthesis from glyco-lytic metabolites. This correlates with the observedaccumulation of these amino acids in anoxic rice cole-optiles. The potential role of amino acid biosynthesiscontributing to anoxia tolerance is discussed, and weshow a positive effect on tolerance upon exogenoussupplementation of these amino acids in wheat butnot in rice.
RESULTS
Physiological Analyses Highlight Differences betweenResponses of Rice and Wheat Seedlings to Anoxia
Germination of the rice and wheat seeds used in thisstudy under anoxic conditions replicated widely re-ported differences that rice can germinate and grow itscoleoptile under anoxia while wheat seeds fail to ger-minate under similar conditions (Alpi and Beevers,1983; Supplemental Fig. S1A). We compared rice cole-optiles from seedlings germinated and grown underanoxia for 6 d with those under aeration for 4 d tocharacterize rice metabolism under prolonged anoxia.Rice coleoptiles from seedlings grown under aerationfor 4 d followed by a 1-d switch to anoxia were alsostudied. This allowed a comparison to be made be-tween prolonged protein changes from germination
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and more rapid changes associated with the loss ofoxygen from aerobic tissue. The treatment involving aswitch to anoxia also generated a data set comparableto publicly available microarray data characterizingcoleoptiles from 4-d-old anoxically germinated riceseedlings (Lasanthi-Kudahettige et al., 2007). As theyare unable to germinate under anoxia, we were con-strained to studying wheat seedlings that were germi-nated and grown under aeration for 4 d and thenswitched to 1 d of anoxia. By using the Evans blue vi-ability stain, a distinction in the anoxia tolerance of riceand wheat was confirmed in that the viability of ricerootswasmuch greater than that of wheat after a switchfrom aeration to anoxia (Supplemental Fig. S1B).
Detailed study of the 24-h anoxic response in riceandwheat coleoptiles was performed tracking growth,sugar content, and metabolic activities (Table I). Thisshowed that aerobically grown rice coleoptiles, but notwheat coleoptiles, displayed significant elongation after1 d of anoxia (Table I). Sugars are the primary carbonsource for energy production via glycolysis and etha-nolic fermentation. The sugar content of rice coleoptilesunder anoxia for 6 d was considerably lower than thatmeasured in 4-d-old aerobic coleoptiles (Table I). Thesugar content of coleoptiles, leaves, and roots of bothrice and wheat seedlings was also significantly lowereven after a 1-d switch from aeration to anoxia (Table I).Induction of alcohol dehydrogenase (ADH) is a keystep in the switch to anaerobic energy production, forit is ethanolic fermentation that regenerates NAD+, anoxidant necessary for the continuation of glycolysis. Inboth anoxic rice and wheat coleoptiles, ADH activitywas induced compared with the aerated control (Table
I). However the apparent inducibility of ADH activityduring anoxia was greater in coleoptiles of rice (3.4-fold) than those of wheat (2.4-fold; Table I). The highestADH activity recorded was observed in rice coleoptilesafter 6 d under anoxia (Table I). In the leaves and rootsof both rice and wheat seedlings, ADH activities werealso induced by anoxia, but the final specific activity ofADH was 5- to 10-fold higher in rice than in wheat(Table I).
The 6-d-old anoxic rice coleoptiles were shorter thanthose from seedlings germinated and grown underaeration for 4 d (Table I). This contrasts with previousreports that rice coleoptiles grown under anoxia weremuch longer than aerated ones (Atwell et al., 1982; Alpiand Beevers, 1983). The explanation for this differencecould be the different cultivars used or the use of N2bubbling as a means to achieve anoxia, rather than thestagnant conditions used in other studies (Magneschiet al., 2009). Bubbling removes other gases such as CO2and even ethylene produced by coleoptiles if traceamounts of oxygen are available, reducing the com-plexity of comparing anoxic and aerated conditions.
We also monitored the capacity for mitochondrialrespiratory function by measuring tissue oxygen con-sumption rate. The rate of oxygen consumption incoleoptiles from seedlings continuously grown underanoxia, but returned to aeration for the measurements,was significantly less than that of continuously aeratedcoleoptiles (Table I). This is consistent with the needfor oxygen for the biosynthesis of heme groups for thecytochromes of the plant respiratory chain (Millaret al., 2004). The oxygen consumption rate capacityin roots and leaves from rice and wheat seedlings was
Table I. Growth, sugar concentration, oxygen consumption rate, and ADH activity of coleoptiles from rice and wheat seedlings exposed toaeration or anoxia
Rice seeds were germinated and grown under aeration for 4 d (control), anoxia for 6 d (anoxically germinated; 6 d N2), or subsequently switchedto 1 d of anoxia (anoxically switched; 4 d air + 1 d N2) or 1 d of air (aerobically switched; 6 d N2 + 1 d air). Wheat seeds were treated withcontrol conditions or were anoxically switched.
Parameters TreatmentsRice Wheat
Coleoptiles Leaves Roots Coleoptiles Leaves Roots
Growth (mm) 4 d air 11.9 6 0.9 – – 28.1 6 2.7 – –4 d air + 1 d N2 14.2 6 1.0a – – 26.2 6 1.5 – –
6 d N2 7.1 6 0.7a – – – – –6 d N2 + 1 d air 9.7 6 0.7b – – – – –
Sugar concentration(mg hexose g21
4 d air 44.8 6 1.4 55.8 6 2.1 28.0 6 2.8 37.0 6 0.7 64.9 6 2.5 12.4 6 1.24 d air + 1 d N2 21.6 6 0.9a 31.3 6 0.7a 13.5 6 0.7a 26.4 6 1.2a 40.4 6 1.0a 3.4 6 0.3a
fresh wt) 6 d N2 3.5 6 0.2a – – – – –6 d N2 + 1 d air 7.7 6 1.1b – – – – –
Oxygen consumptionrate (nmol min21
4 d air 126.3 6 5.1 302.2 6 10.8 398.8 6 20.2 140.6 6 6.0 478.8 6 36.2 436.8 6 14.44 d air + 1 d N2 104.7 6 2.8a 132.7 6 20.0a 82.1 6 1.2a 146.1 6 8.9 336.7 6 10.1 177.3 6 1.0a
g21 fresh wt) 6 d N2 67.4 6 6.0a – – – – –6 d N2 + 1 d air 134.0 6 11.8b – – – – –
ADH activity (unitsmg21 protein)
4 d air 0.75 6 0.06 0.94 6 0.07 0.94 6 0.05 0.25 6 0.01 0.09 6 0.01 0.32 6 0.024 d air + 1 d N2 2.68 6 0.13a 3.42 6 0.04a 5.22 6 0.70a 0.59 6 0.01a 0.28 6 0.01a 1.08 6 0.01a
6 d N2 6.61 6 0.48a – – – – –6 d N2 + 1 d air 4.38 6 0.24b – – – – –
aThe value of continuously anoxic/anoxically switched samples is P , 0.05 when compared with continuously aerated samples. bThe value ofaerobically switched samples is P , 0.05 when compared with continuously anoxic samples.
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significantly lowered by 1 d of anoxia (Table I), indi-cating tissue adaptation to anoxia and/or damage orloss of mitochondrial function. Interestingly, this effectwas more dramatic in rice than in wheat tissues. Incomparison, the respiratory capacity of wheat coleop-tiles was not significantly affected by 1 d of anoxia andin rice the reduction was significant yet slight (Table I),suggesting minimal mitochondrial damage occurredduring this period of anoxia and the immediate abilityof mitochondrial function to return upon the aerationrequired for the measurements to be carried out. Inconclusion, rice coleoptiles grown continuously underanoxia or aeration had significant differences in all theparameters investigated. When switched from aeratedto anoxic conditions, coleoptiles of rice responded toanoxia to a greater degree than those of wheat. Incomparison, leaves and roots of rice and wheat seed-lings responded to anoxia similarly in all parametersinvestigated, distinct from the coleoptile response inboth species, and despite the longer term differencesnoted from Evans blue viability staining of roots (Sup-plemental Fig. S1B).
Quantitative Proteomic Analysis of Coleoptiles Shows a
Significant Rice Response But a Minimal WheatResponse to Anoxia
A number of molecular responses that underlie thedifferences noted in Table I have been investigated inpublished reports (Menegus et al., 1991; Perata et al.,1992). There are also a number of studies on global geneexpression in response to anoxia or oxygen deficiencyin plants including rice (Lasanthi-Kudahettige et al.,2007; Narsai et al., 2009) and Arabidopsis (Arabidopsisthaliana; Klok et al., 2002; Branco-Price et al., 2005; Liuet al., 2005; Loreti et al., 2005; Mustroph et al., 2010).But so far, information on the changes of protein abun-dance in response to anoxia is limited (Mocquot et al.,1981; Ricard and Pradet, 1989; Huang et al., 2005). Be-cause there were such dramatic differences at thephysiological level between 4-d aerated and 6-d anoxiccoleoptiles (Table I), we started analyzing differencesin protein profiles using differential in-gel electropho-resis (DIGE), which is based on staining protein sam-ples with different fluorescent dyes (Fig. 1A). Out of1,259 protein spots detected on isoelectric focusing(IEF)/SDS-PAGE gels (pI range, 3–10), 164 (13%) pro-tein spots met the criteria of significant 2-fold differ-ences in protein abundance on three replicate gels (P,0.05). A total of 107 of these protein spots were moreabundant under anoxia and 57 were more abundantunder aeration (Fig. 1A). This suggested a significantdifference in the protein profiles of samples from 6-danoxic and 4-d aerated rice coleoptiles. We also com-pared rice coleoptiles from 4-d aerated seedlings withthose grown in the same control conditions butswitched to an additional 1 d of anoxia (Fig. 1B). Therewere 1,245 protein spots detected, and 67 (5%) of thesemet the above-mentioned significance criteria (Fig. 1B).Eighty-five percent of these changing protein spots
were more abundant in coleoptiles subjected to theanoxic switch (Fig. 1B), suggesting a rapid 24-h re-sponse to anoxia at the protein level. This is in agree-ment with the results of van Dongen and colleagues(2009), who reported a tendency for gene expression toincrease in response to 0.5 to 48 h of hypoxia as opposedto cessation of transcription in roots of Arabidopsisseedlings. We then compared coleoptile proteomes ofwheat using the same anoxic-switch experimental setupused in rice (Fig. 1C). According to the same significancecriteria, only five (0.4%) of the 1,245 protein spots de-tected differed in abundance between the two treat-ments (Fig. 1C), suggesting a very limited response toanoxia at the protein level in wheat coleoptiles.
We then conducted two additional independentanalyses to further quantify the difference between4-d aerated and 6-d anoxic coleoptiles in order toovercome limitations of the pH 3 to 10 nonlinear (3-10NL) DIGE analysis. We used a broader pH range toshow more basic protein spots in a DIGE analysis(using pI 3–11 gels) and a non-gel-based iTRAQ (forisobaric tag for relative and absolute quantitation)experiment to remove the bias against protein size andsolubility that is inherent to IEF-based analysis. Out ofthe 1,007 spots detected in the pI 3 to 11 DIGE, 140(13.9%) spots met the criteria of significance. Forty-sixof these prospective proteins were more abundantunder anoxia and 94 were more abundant under aer-ation (Supplemental Figs. S2 and S4). Using iTRAQanalysis, we identified 142 proteins, 126 of whichcould be quantified in a three-biological-replicate ex-periment comparing coleoptile proteomes extractedfrom 6-d anoxic and 4-d aerated rice seedlings (Sup-plemental Table S1). Among them, 34 were signifi-cantly more abundant under anoxia and 29 weresignificantly more abundant under aeration (Supple-mental Fig. S2). The fold differences in protein abun-dance as revealed by iTRAQ were proportional tothose revealed by the DIGE analysis, with the r2 being0.61 (Supplemental Fig. S3A). However, the DIGE anal-ysis resolved much larger fold differences between thetwo treatments than those calculated in the iTRAQanalysis (Supplemental Fig. S3A). For example, accord-ing to the DIGE analysis, peroxiredoxin (Os07g44430.1)was reported to be 3.38- to 22.69-fold more abundantin 6-d anoxic rice coleoptiles than in the 4-d aeratedcontrol, whereas this difference was only 2.48-foldaccording to the iTRAQ analysis (Table II). Similardiscrepancies between the linearity of responses by thetwomethods have been previously reported (Wu et al.,2006).
Identified Proteins in Rice Coleoptiles with Changes inAbundance under Anoxia
The identified proteins with significant changes inabundance between aerated and anoxic treatments arelisted in Table II. The protein identification evidencefrom all protein analysis is shown in SupplementalTable S1, and further details for iTRAQ data analysis
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are provided in Supplemental Table S2. We have incor-porated microarray data (4-d-old anoxic and 4-d-oldaerobic rice coleoptiles from Lasanthi-Kudahettigeet al. [2007]) into Table II and Supplemental Table S1for further comparison. The enzymes detected, whichare involved in glycolysis, fermentation, and aminoacid biosynthesis, were also incorporated into a metab-olite pathway map in Figure 2. We could not identifythe five protein spots that were significantly differentbetween treatments in wheat coleoptiles due to theirvery low abundance on gels.
Enzymes Involved in Glycolysis andEthanolic Fermentation
We identified the accumulation of enzymes involvedin ethanolic fermentation such as ADH1 (Os11g10480)and pyruvate decarboxylase 1 (Os05g39310) in anoxic
rice coleoptiles (Table II; Fig. 2), consistent with pub-lished reports (Mocquot et al., 1981; Ricard and Pradet,1989; Bailey-Serres and Chang, 2005; Huang et al., 2005;van Dongen et al., 2009). Proteins significantly in-creasing in abundance and involved in multiple stepsof glycolysis were also identified and listed in Table IIand Figure 2. Those enzymes were pyrophosphate (PPi)-Fru-6-P 1-phosphotransferase b-subunit (Os06g13810),Fru-bisP aldolase (Os05g33380, Os01g67860, Os10g08022),triosephosphate isomerase (Os01g05490), glyceraldehyde-3-phosphate dehydrogenase (Os04g40950, Os02g38920,Os08g03290), phosphoglycerate kinase (Os02g07260),phosphoglucomutase (Os03g50480), 2,3-bisphospho-glycerate-independent phosphoglycerate mutase(Os01g60190), and enolase (Os10g08550; Table II; Fig.2). Exceptions to these anoxia increases were fructoki-nase 2 (Os08g02120), which was observed to decreasein abundance under anoxia, as well as discrepancies in
Figure 1. DIGE on two-dimensional IEF/SDS gels. A and B, Comparisons were made between coleoptile proteomes of riceseedlings treated with 4 d of aeration versus 6 d of anoxia (A) as well as 4 d of aeration with an additional 1 d under anoxia (B). C,Wheat responses to anoxia were also analyzed by comparing coleoptiles from seedlings treated in the same way as in B. The toppanels are gel images of each fluorescence signal, and the bottom panel is a combined image electronically overlaid usingImageQuant TL software (GE Healthcare). Yellowish spots represent proteins of equal abundance between the two samples. Thenumbered arrows indicate proteins identified by MS (listed in Table II) with abundances that were significantly different betweentreatments (identified in all nine gel images; P , 0.05; abundance difference . 1.5). Below the DIGE image is a Venn diagramrepresenting the percentage of protein spots significantly changing in abundance between the two treatments. The percentage ofprotein spots significantly more abundant under anoxia or aeration is shown on the left or right side in each Venn diagram,respectively. The percentage of proteins that did not significantly differ in abundance is shown in the middle.
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Table II. Proteomic analysis of rice coleoptiles in response to anoxia or switch from air to anoxia
Proteins selected from DIGE corresponding to Figure 1, A and B, and iTRAQ (Supplemental Table S2) were identified by MS/MS (Protein ID) withcorresponding rice gene numbers (Os Gene No.). Details of the matched protein size, number of peptides identified, and percentage coverage areshown in Supplemental Table S1. Transcript abundance differences between anoxic (4 d) and aerated (4 d) rice coleoptiles derived from independentmicroarray data (Lasanthi-Kudahettige et al., 2007) have been incorporated (significant increase [positive, boldface], significant decrease [negative,boldface italic]). Protein spots chosen for MS/MS analysis met the following criteria in at least one analysis: a protein abundance difference of 1.5 orgreater where proteins were higher in anoxic samples (positive, boldface) and those higher in aeration (negative, boldface italic), P , 0.05, and anabundance high enough on preparative gels for subsequent MS identification. FAD, Fold abundance difference; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; iPGAM, 2,3-bisphosphoglycerate-independent phosphoglycerate mutase; MetSyn, cobalamin-independent Met syn-thase; N/S, not significant; 3-PGDH, D-3-phosphoglycerate dehydrogenase; PSAT, phospho-Ser aminotransferase; S, significant; SHMT, Serhydroxymethyltransferase; Sig., significantly different from 1 or not; TGM, transformed geometric mean; X, no data.
Functional
CategoryOs Gene No. Protein ID
DIGE: 3-10NL DIGE: 3-11NL DIGE: 3-10NL iTRAQ Array
6 d N2/4 d Air 6 d N2/4 d Air 4 d Air 1 d N2/4 d Air 6 d N2/4 d Air 4 d N2/4 d Air
Spot FAD P Spot FAD P Spot FAD P TGM Sig. FAD
Sugar metabolism, Os03g28330.1 Suc synthase – X X – X X – X X 1.6 S 2
glycolysis, Os06g09450.1 Suc synthase – X X – X X – X X 1.7 S 2
fermentation, Os01g60190.1 iPGAM 31 3.3 4.E-04 20 5.7 5.E-04 27 1.3 1.E-01 21.0 N/S 2
and TCA cycle Os01g60190.1 iPGAM 32 4.3 4.E-06 21 6.1 3.E-05 26 1.5 9.E-03 21.0 N/S 2
Os10g08550.1 Enolase 33 7.5 5.E-09 18 5.7 9.E-04 28 2.4 7.E-04 1.2 S 2
Os10g08550.1 Enolase 34 2.2 3.E-05 19 2.8 2.E-03 29 1.5 8.E-04 1.2 S 2
Os08g02120.1 Fructokinase-2 – X X – X X – X X 22.2 S 22
Os05g33380.1 Aldolase 16 4.7 5.E-06 – X X 5 2.1 3.E-04 2.0 S N/S
Os01g67860.1 Aldolase – X X – X X – X X 2.2 S N/S
Os10g08022.1 Aldolase – X X – X X – X X 2.3 S X
Os04g40950.1 GAPDH 6 2.2 7.E-04 6 2.4 2.E-03 4 1.1 8.E-02 1.4 S N/S
Os04g40950.1 GAPDH 10 3.6 7.E-05 – X X 11 1.0 7.E-01 1.4 S N/S
Os04g40950.1 GAPDH – X X 8 2.6 1.E-03 – X X 1.4 S N/S
Os02g38920.1 GAPDH 18 13.2 5.E-06 – X X 9 1.7 2.E-03 1.8 S 3
Os02g38920.1 GAPDH 17 6.7 4.E-08 – X X 8 1.7 1.E-02 1.8 S 3
Os08g03290.1 GAPDH 15 2.5 6.E-04 – X X 6 1.2 4.E-02 X X N/S
Os08g03290.2 GAPDH – X X – X X – X X 1.3 S N/S
Os03g50480.1 Phosphoglucomutase 14 6.3 2.E-05 – X X 25 2.7 2.E-03 21.1 S N/S
Os02g07260.1 Phosphoglycerate kinase 28 5.3 1.E-04 – X X 31 2.4 1.E-02 1.2 S 2
Os01g05490.1 Triosephosphate isomerase 40 2.9 5.E-05 – X X 36 1.2 9.E-03 1.4 S 4 and N/S
Os06g13810.1 PPi-phosphofructokinase 23 2.8 2.E-02 – X X 19 2.1 9.E-04 1.4 S 2
Os05g39310.1 Pyruvate decarboxylase 1 – X X – X X – X X 2.0 S 594
Os11g10480.1 ADH1 12 6.0 2.E-05 – X X 12 3.6 4.E-05 2.2 S 4
Os01g46070.1 Malate dehydrogenase – X X – X X – X X 21.1 S N/S
Amino acid
synthesis
Os02g50240.1 Gln synthetase 38 22.3 7.E-05 – X X 34 21.3 3.E-04 22.3 N/S 22
Os12g42884.1 MetSyn – X X – X X – X X 21.7 S 22 and N/S
Os12g42876.1 MetSyn 25 2.3 2.E-03 – X X 17 3.3 2.E-03 21.7 S X
Os12g42876.1 MetSyn 26 1.1 6.E-01 – X X 18 2.7 3.E-03 21.7 S X
Os03g06200.1 PSAT 5 4.7 9.E-07 5 6.5 1.E-03 – X X 1.7 N/S 5
Os03g06200.1 PSAT – X X 9 5.6 1.E-03 – X X 1.7 N/S 5
Os12g22030.1 SHMT 19 1.6 9.E-02 – X X 14 2.0 4.E-04 X X N/S
Os04g55720.1 3-PGDH 24 5.8 2.E-05 – X X 20 2.0 1.E-03 1.9 S 4
Os10g25130.1 Ala aminotransferase – X X 15 2.5 4.E-03 – X X X X N/S
Stress responsive Os07g44430.1 Peroxiredoxin 4 5.5 6.E-05 4 11.0 5.E-05 – X X 2.5 S 32
Os07g44430.1 Peroxiredoxin 3 3.4 2.E-03 3 22.7 1.E-06 – X X 2.5 S 32
Os05g25850.1 Manganese-superoxide dismutase 1 1.6 1.E-02 1 1.9 1.E-02 – X X 1.4 N/S N/S
Os03g17690.1 Ascorbate peroxidase 39 24.6 3.E-06 – X X 13 22.3 1.E-04 21.8 S N/S
Os07g49400.1 Ascorbate peroxidase – X X – X X – X X 21.9 S N/S
Os03g07180.1 Embryotic protein DC-8 – X X 10 3.5 4.E-05 – X X X X X
Os03g07180.1 Embryotic protein DC-8 – X X 11 3.9 3.E-05 – X X X X X
Os03g07180.1 Embryotic protein DC-8 – X X 12 3.6 4.E-05 – X X X X X
Os03g07180.1 Embryotic protein DC-8 – X X 13 4.7 1.E-04 – X X X X X
Os05g46480.1 LEA group 3 2 4.1 7.E-07 2 7.1 2.E-04 – X X X X 22
Os02g15250.1 LEA domain containing – X X 14 5.9 8.E-05 – X X – X X
Translation Os03g14530.1 S10/S20 ribosomal protein – X X – X X – X X 1.3 S 2
Os11g29190.1 40S ribosomal protein S5 – X X – X X – X X 21.3 S 2
Os03g08010.1 Elongation factor 1-a – X X – X X – X X 1.3 S N/S
Os01g52470.1 Elongation factor – X X – X X – X X 1.4 S X
Os02g32030.1 Elongation factor – X X – X X – X X 21.1 S N/S
Miscellaneous Os08g04210.1 Protein kinase – X X – X X – X X 3.2 S 1,007
Os08g04250.1 Protein kinase – X X – X X – X X 2.3 S 248
Os08g04240.1 Protein kinase – X X – X X – X X 2.8 S 525
Os04g56430.1 CRK5 – X X – X X – X X 21.9 S 3
Os01g03340.1 BBTI4 11 26.0 2.E-06 – X X – X X 21.9 S 23 and 22
Os01g03360.1 BBTI5 – X X – X X – X X 1.1 S 22
Os03g62060.1 IAA-amino acid hydrolase 36 25.3 3.E-04 17 24.6 6.E-03 32 21.0 4.E-01 23.0 S 2271
Os05g04510.1 S-Adenosyl-Met synthetase – X X – X X – X X 21.6 S N/S
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the direction of change for phosphoglucomutase(Os03g50480) between the DIGE and iTRAQ analyses.
We also identified two Suc synthase enzymes(Os03g28330, Os06g09450) that were significantly moreabundant in rice coleoptiles of anoxically germinatedseedlings (Table II). Our data agree with reports thatunder anoxia, there is a switch from invertase to Sucsynthase as a means of degrading Suc into sugars thatcan then enter the glycolytic pathway (Guglielminettiet al., 1995).
These observations reinforced the evidence for gen-eral enhancement of glycolysis and ethanolic fermen-tation for rice coleoptiles during adaptation to anoxiaand highlight the gene-specific identification of chang-ing proteins that would promote a Pasteur effect. Onlyabout two-thirds of the transcripts for these glycolyticproteins are transcriptionally more abundant underanoxia in rice (Lasanthi-Kudahettige et al., 2007). Butnotably, the decreased abundance of fructokinase 2 wasalso seen at the transcript level under anoxia (Table II).
Figure 2. Effect of prolonged anoxia on carbohydrate metabolism, glycolysis, fermentation, amino acid metabolism, and theTCA cycle in rice coleoptiles. Rice seeds were germinated and grown under anoxia for 6 d or aeration for 4 d. The green and redboxes represent metabolites significantly more abundant under aeration and anoxia, respectively (P , 0.05). The yellow boxesrepresent metabolites whose abundances are unchanged. Enzyme names on arrows are also colored in this fashion. The numberson the top left side of each box represent the response value (RV) of the corresponding metabolite (anoxic/aerated) in ricecoleoptiles. All data were extracted from Tables II and III. Metabolite abbreviations are as follows: DHAP, dihydroxyacetone-phosphate; GABA, g-aminobutyrate; G-3-P, glyceraldehyde-3-phosphate; PEP, phosphoenolpyruvate; 1,3-PGA, 1,3-bisphos-phoglycerate; 2-PGA, 2-phosphoglycerate; 3-PGA, 3-phosphoglycerate; SSA, succinic semialdehyde. Protein abbreviations areas follows: ADH, alcohol dehydrogenase; AlaAT, Ala aminotransferase; aldolase, Fru-bisP aldolase; FK, fructokinase; GAPDH,glyceraldehyde-3-phosphate dehydrogenase; GlnSyn, Gln synthetase; iPGAM, 2,3-bisphosphoglycerate-independent phos-phoglycerate mutase; MDH, malate dehydrogenase; MetSyn, 5-methyltetrahydropteroyltri-Glu-homo-Cys methyltransferase(cobalamin-independent Met synthase); PDC, pyruvate decarboxylase; PFK-PPi, PPi-Fru-6-P 1-phosphotransferase; 3-PGDH,D-3-phosphoglycerate dehydrogenase; PGK, phosphoglycerate kinase; PGM, phosphoglucomutase; PSAT, phospho-Ser amino-transferase; SHMT, Ser hydroxymethyltransferase; SS, Suc synthase; TPI, triosephosphate isomerase.
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Enzymes Involved in Amino Acid Metabolism
We identified several enzymes involved in the syn-thesis of Ser and Gly that increased in abundance underanoxic stress, notably, D-3-phosphoglycerate dehydro-genase (Os04g55720), phospho-Ser aminotransferase(Os03g06200), and Ser hydroxymethyltransferase(Os12g22030; Table II; Fig. 2). Ala aminotransferase 2(Os01g25130) was also more abundant in anoxic ricecoleoptiles. Gln synthetase root isozyme 3 (Os02g50240)was less abundant under anoxia when compared withaeration, and different isoforms of 5-methyltetrahy-dropteroyltri-Glu-homo-Cys methyltransferase (Metsynthase; Os12g42876 up in DIGE, Os12g42876/Os12g42884 down in iTRAQ) showed discrepancies intheir direction of change. Met generation is particu-larly interesting, as this amino acid is involved in thesynthesis of ethylene, a plant hormone involved insubmergence-induced gene expression (Fukao et al.,2006; Xu et al., 2006). With the exception of 6-d anoxiasamples that showed anoxic accumulation, Met tendedtoward a higher abundance under aerobic conditionsin rice (Table III). The enzyme involved in the firststep of ethylene production, S-adenosyl-Met synthase(Os05g04510), was also detected in our iTRAQ analy-sis (Table II). However, it was significantly less abun-dant in coleoptiles of anoxically germinated rice, anobservation in line with the oxygen dependence of theethylene biosynthetic pathway.
Enzymes or Proteins Involved in Reactive OxygenSpecies Detoxification
Reactive oxygen species (ROS) play an important rolein signaling under oxygen deficiency (Baxter-Burrellet al., 2002; Bailey-Serres and Chang, 2005). We iden-tified several proteins involved in ROS degradationthat changed in abundance. As examples, peroxire-doxin (Os07g44430) was significantly more abundantin rice coleoptiles derived from 6-d-old anoxic seed-lings than in 4-d-old aerated seedlings (Table II). Inconcordance, the transcript for this gene was 32-foldhigher in abundance in anoxic rice coleoptiles (Lasanthi-Kudahettige et al., 2007). Peroxiredoxin is an antioxidantenzyme that can reduce both hydrogen peroxide (H2O2)and alkyl hydroperoxides. In contrast, we identifiedanother H2O2-decomposing enzyme, ascorbate peroxi-dase (Os03g17690, Os07g49400), that was less abundantin anoxic coleoptiles (Table II). The Bowman-Birk-typetrypsin inhibitor (BBTI; Os01g03340) found to decreasein abundance under anoxia may have other functionsbeyond its role in proteolysis. BBTIs have been re-ported to act as monodehydroascorbate reductasesand dehydroascorbate reductases in etiolated mungbean (Vigna radiata) seedlings (Hou et al., 2000) androots of sweet potato (Ipomoea batatas; Hou and Lin,1997) and thus can be involved in the regeneration ofascorbate. These results suggest that rice coleoptilesmay use different detoxification systems under anoxia/hypoxia and reaeration from those used during con-tinual aeration. It has been reported that anoxia can
cause an increase in H2O2 in the rice root apoplast andplasma membrane (Blokhina et al., 2001), suggesting aprotective function of these antioxidant defense en-zymes in anoxic rice coleoptiles. However, we havepreviously measured markers for lipid oxidation anddemonstrated that damage was lower in anoxic cole-optiles relative to aerobic or reoxygenated coleoptiles(Millar et al., 2004), suggesting that either (1) oxidativestress under anoxia is minimal and that peroxiredoxinhas a protective role upon the return of oxygen or (2)peroxiredoxin has a function under anoxia that wehave not anticipated. The former seems more likely,given that oxygen is necessary for the formation ofROS. Interestingly, manganese-superoxide dismutasewas detected in two DIGE analyses, and althoughstatistically significant in its accumulation under 6 d ofanoxia (P , 0.05), manganese-superoxide dismutasedid not meet all of the criteria set for significance;specifically, the abundance change did not exceed 2(Table II), indicating the quantitatively differential roleof peroxiredoxin in responding to the availability ofoxygen. What is clear from the literature is that oxygendeprivation perturbs the redox status of cells, whetherit be ROS levels (Blokhina et al., 2001), oxidative damage(Blokhina et al., 1999; Millar et al., 2004), or regulationof genes and small-molecule antioxidants involved inredox regulation (Yan et al., 1996; Biemelt et al., 1998;Blokhina et al., 2000, 2003; van Dongen et al., 2009).
Proteins Involved in the Process of Translation
Selective translation of cytoplasmic mRNAs in plantsunder oxygen deficiency has been discussed (Bailey-Serres, 1999).We identified several proteins in the iTRAQanalysis involved in translation processes, which weremore abundant in 6-d-old anoxic coleoptiles relativeto the control. Those proteins were elongation factor1-a (Os03g08010), elongation factor 2 (Os01g52470),and S10/S20 domain-containing ribosomal protein(Os03g14530; Table II). Others involved in translationthat were less abundant under anoxia were 40S ribo-somal protein S5 (Os11g29190) and elongation factor(Os02g32030). Whether these abundance changes inthe translational machinery are responsible for theperturbations in the levels of glycolytic, amino acidbiosynthetic, and ROS-defense proteins is currentlyunknown.
Other Proteins of Interest
The lower abundance of indole-3-acetic acid (IAA)-amino acid hydrolase precursor (ILR1; Os03g62060)we report under anoxia can be related to a long historyof research on auxin-regulated coleoptile elongationdating back to the famous experiment of Went (1942).The IAA-amino acid hydrolase is involved in thecleavage of conjugates between IAA and amino acids(Bartel and Fink, 1995). The dramatic decrease of ILR1(Table II) could indicate that IAA is maintained in itsconjugate form under anoxia. Microarray data also
Rice and Wheat Anoxia Responses
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Table III. Metabolomic analysis of rice and wheat coleoptiles in response to anoxia
Rice plants were grown under (1) 4 d of aeration, (2) 6 d of anoxia, (3) 4 d of aeration and then 4 h of anoxia, or (4) 4 d of aeration and then 1 d ofanoxia. Wheat plants were grown under all treatments except number 2. Coleoptile tissues were separated from leaves for analysis. Complex polarmetabolite extracts were taken from all tissues and analyzed by GC-MS. Raw GC-MS data processing and statistical analyses were then carried outusing MetabolomeExpress software (version 1.0; http://www.metabolome-express.org). Metabolite signal intensity ratios were calculated by dividingthe mean tissue mass- and internal standard-normalized signal intensity for each metabolite in treated samples by its corresponding value in controlsamples (RV columns). The statistical significance of each ratio was tested by Welch’s t test (P , 0.05; n = 5). Values highlighted in boldface aresignificantly more abundant in anoxic coleoptiles, whereas negative boldface italic indicates a metabolite that is more abundant under aeration, withvalues that meet a threshold for significance of P , 0.05. N/D, No data; RV, response value.
Sample Metabolite
6 d N2/4 d air 4 d Air + 1 d N2/4 d Air 4 d Air + 4 h N2/4 d Air
Rice Rice Wheat Rice Wheat
RV P RV P RV P RV P RV P
Amino acids
and polyamines
L-Pro 98.6 5.8E-06 8.7 2.5E-02 2.6 1.7E-03 0.5 4.9E-01 0.5 1.4E-01
L-Leu 43.8 1.1E-05 11.6 1.7E-03 0.6 6.6E-02 0.6 4.6E-01 0.1 6.0E-01
b-Ala 43.4 1.4E-08 0.7 2.6E-01 1.0 3.3E-03 0.9 4.4E-01 0.9 4.9E-04
L-Ala 42.6 2.3E-06 43.2 8.1E-04 0.1 6.3E-01 6.9 1.5E-03 0.5 3.5E-01
L-Glu 36.5 1.7E-06 211.7 6.9E-02 20.9 5.8E-02 239.2 7.7E-02 20.4 2.8E-01
L-Tyr 22.5 4.4E-06 21.2 4.3E-03 0.4 2.7E-01 22.3 9.6E-02 0.2 3.0E-01
2-Aminoadipic acid 17.3 6.6E-07 21.0 8.1E-02 20.1 7.4E-01 0.1 7.4E-01 20.4 1.6E-01
L-Arg 15.6 1.2E-05 20.7 2.7E-03 0.7 1.0E-01 0.0 9.6E-01 0.1 5.7E-01
L-Homo-Ser 15.5 1.9E-02 20.9 1.0E-02 0.6 3.2E-01 20.9 9.6E-02 20.2 3.2E-01
L-Ile 12.0 3.2E-05 5.2 6.6E-04 0.7 1.1E-01 0.3 4.7E-01 0.3 3.3E-01L-Lys 10.9 3.6E-05 22.0 3.1E-03 1.5 3.5E-02 21.8 1.1E-01 0.5 1.0E-01
L-Val 10.2 5.8E-07 5.5 3.4E-07 20.1 6.6E-01 20.1 8.8E-01 0.3 4.2E-01
L-Ser 9.0 5.9E-12 2.0 2.3E-03 20.8 4.4E-03 0.1 8.3E-01 20.4 4.2E-01
g-Aminobutyric acid 8.9 1.5E-08 8.1 1.2E-03 0.4 3.5E-03 2.3 1.4E-01 0.6 8.6E-04
L-Met 6.0 2.8E-02 21.9 3.9E-02 0.6 1.4E-01 2123.6 5.4E-02 20.2 4.4E-01
L-Trp 5.2 1.8E-05 4.6 1.7E-02 0.6 1.9E-01 20.4 3.5E-01 0.5 8.8E-02
L-Thr 2.4 1.1E-06 2.1 6.4E-06 0.1 4.3E-01 0.6 1.4E-01 0.5 1.2E-01
Gly 1.9 2.5E-03 3.7 1.2E-03 1.4 4.4E-03 0.6 1.9E-01 0.4 2.0E-01
L-Gln 1.8 4.6E-04 20.5 4.4E-03 21.4 9.0E-02 20.2 2.3E-01 21.5 2.0E-03
4-Hyp 1.4 1.1E-06 20.1 4.9E-01 20.1 4.9E-01 0.1 5.0E-01 20.9 2.2E-02
Orn 0.6 3.5E-03 21.3 2.8E-04 0.4 1.8E-01 210.0 4.3E-02 20.9 1.2E-01
L-Asn 20.6 4.5E-01 0.0 9.7E-01 20.2 5.9E-01 240.0 4.7E-02 0.1 6.1E-01
L-Asp 21.5 1.5E-01 216.6 2.2E-01 21.7 3.7E-02 2448.4 4.7E-02 23.2 1.2E-03
L-Phe N/D N/D 22.1 1.5E-01 0.9 5.3E-03 20.2 2.1E-01 0.4 6.6E-02
L-a-Aminobutyric acid N/D N/D 2.5 1.5E-04 22.7 2.7E-01 0.2 4.6E-01 21.2 2.4E-03
Putrescine 52.4 6.9E-08 7.9 1.1E-04 1.7 4.2E-03 0.2 5.9E-01 20.2 1.5E-01
Sugars and
glycolytic substrates
6-Phosphogluconate 9.7 1.0E-03 20.8 3.6E-01 21.6 1.1E-01 21.4 3.9E-02 21.0 1.0E-02
D-Rib 20.8 9.2E-05 21.2 9.2E-04 21.3 3.1E-03 20.5 2.9E-04 20.6 1.3E-02
Trehalose 21.0 2.3E-02 22.1 3.1E-02 21.6 3.3E-04 20.3 1.8E-01 20.9 1.9E-03D-Xyl 22.7 5.8E-06 20.5 1.4E-01 0.1 6.4E-01 0.1 2.2E-01 0.1 4.0E-01
3-Phosphoglyceric acid 18.0 1.3E-04 20.5 5.5E-01 20.2 7.6E-01 20.3 5.6E-01 20.2 1.2E-01
Suc 4.8 3.4E-04 0.7 9.3E-02 25.6 3.6E-05 20.3 1.3E-01 211.5 1.5E-03
Fru-6-P 2.9 2.9E-04 0.1 8.7E-01 20.3 2.8E-01 20.2 3.4E-01 21.2 8.8E-03
Glc-6-P 2.8 4.4E-05 20.2 6.9E-01 20.5 1.0E-01 20.1 1.7E-01 21.4 2.2E-03
D-Fru 22.4 9.9E-07 20.3 1.7E-05 20.4 3.9E-01 0.0 8.1E-01 20.4 3.8E-02
D-Glc 24.2 2.1E-05 20.4 8.8E-05 20.3 6.3E-03 20.1 2.0E-01 0.1 4.9E-01
TCA cycle substrates
and other acids
Succinate 116.5 1.8E-08 3.5 4.2E-03 1.0 1.1E-03 6.5 1.5E-03 21.1 2.7E-03
Fumarate 7.0 2.9E-07 20.7 2.7E-01 20.2 1.6E-01 1.1 5.1E-02 20.4 1.7E-01
Aconitate 4.3 5.3E-04 22.8 3.2E-02 20.5 1.5E-01 21.6 1.1E-02 20.2 2.4E-01
Citrate 2.5 4.0E-05 211.7 5.5E-02 21.5 8.3E-03 23.2 1.3E-02 20.7 1.4E-04
Malate 0.0 1.5E-01 22.2 8.8E-05 21.0 3.1E-05 20.1 9.2E-02 0.0 7.5E-01
Isocitrate 20.1 1.9E-01 22.1 8.7E-02 20.5 3.7E-02 25.3 6.0E-03 20.5 1.1E-03
2-Oxoglutarate 22.2 1.3E-03 23.8 2.6E-02 20.3 1.8E-01 224.5 6.4E-04 20.8 2.8E-03
Glycerate 21.8 1.3E-04 21.5 2.6E-06 20.9 7.3E-03 20.5 1.6E-02 20.2 8.9E-03
Threonate 2118.6 1.0E-05 21.1 7.0E-04 20.2 4.3E-03 0.3 8.7E-03 20.2 2.0E-01
Ascorbate N/D N/D 21.8 3.8E-04 1.2 2.2E-01 20.1 2.0E-01 20.1 2.7E-01
Glucarate 0.9 1.1E-04 20.3 3.0E-02 20.4 3.9E-03 20.2 1.9E-01 20.2 2.1E-01Citramalate 20.5 5.7E-02 20.8 1.9E-02 20.2 6.8E-02 0.2 6.1E-02 21.4 4.9E-03
4-Hydroxycinnamate N/D N/D 20.1 3.0E-01 0.5 1.2E-03 0.2 2.2E-02 0.8 8.0E-04
Urate 561 2.1E-06 0.2 5.8E-01 15.2 2.9E-03 21.4 3.3E-01 15.4 3.4E-04
Other Shikimic acid 20.7 1.0E-03 20.1 4.1E-01 4.5 6.2E-02 0.0 9.3E-01 3.4 1.5E-01
Phosphate 1.6 5.5E-08 0.3 2.9E-02 20.1 6.0E-01 20.1 6.2E-01 20.2 2.4E-01
Cytosine N/D N/D 21.6 2.0E-04 20.6 1.5E-05 20.2 4.0E-01 20.3 3.0E-01
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suggest that the transcript of this protein was dramat-ically down-regulated (271-fold) in anoxic rice coleop-tiles (Lasanthi-Kudahettige et al., 2007). This supportsthe observation that auxin-binding activities were de-creased in anoxic coleoptiles (Mapelli and Locatelli,1995) and that there was no synergistic effect of IAA andanaerobiosis on rice coleoptile elongation (Pegoraroet al., 1988). The repressive effect of anoxia on auxin-related genes has also been observed in Arabidopsisthrough a global gene expression analysis (Loreti et al.,2005).Also of interest was the finding that several proteins
with unknown functions accumulated under anoxia.These proteins are annotated as protein kinases thatcontain the domain of unknown function 26. Theirtranscript fold increases under anoxia range from 248-to 1,007-fold (Os08g04250, Os08g04210, Os08g04240;Lasanthi-Kudahettige et al., 2007). In addition, theArabidopsis ortholog (At5g48540; Supplemental TableS3) is up-regulated in response to 2 and 9 h of hypoxiaboth within the total and polysomal mRNA pools ofArabidopsis seedlings (Branco-Price et al., 2008). Nota-bly, we found anoxic accumulation of two group 3 lateembryogenesis abundant proteins (LEA; Os05g46480,Os02g15250) as well as embryonic protein DC-8(Os03g07180). LEAs are hydrophilic unstructured pro-teins rich in Gly, Ala, and Ser (Baker et al., 1988;Campos et al., 2006). It has been reported that a LEAprotein (Os04g52110) accumulates in anoxic rice em-bryos (Howell et al., 2007), and other groups havedemonstrated the inducibility of GUS reporters whenfused to a carrot (Daucus carota) group 3 LEA promoterunder hypoxia, salinity, and dehydration (Siddiquiet al., 1998). Recently, it was shown that SUB1A in-creased the accumulation of transcripts encoding forproteins involved in dehydration tolerance. Most in-terestingly, the LEA3 transcript level increased to agreater degree during desubmergence in M202(Sub1)rice relative to wild-type M202 (Fukao et al., 2011).This is especially interesting given that dehydration is astress inherent to desubmergence. This LEA3 transcriptactually showed a decrease during submergence, achange in opposition to what we have found. This maybe attributable to the use of different cultivars, themeasurement of transcript levels and not proteinabundance levels, and that submergence and anoxiaare not perfectly comparable. Despite these differences,in our experimental system, it is tempting to speculatethat LEA up-regulation is a response that provides aprotective and anticipatory function for when plantsreturn to air. Clearly, LEAs are stress responsive; how-ever, the role of these proteins in anoxic environmentsawaits further insights into their molecular function.
Metabolomic Analysis Reveals a Greater Response toAnoxia in Rice Than in Wheat Coleoptiles
To investigate the impact of the changes in primarymetabolism on metabolite pools, we considered theoverall changes in the gas chromatography-mass spec-
trometry (GC-MS) profiles of primary metabolites inwheat and rice coleoptiles exposed to anoxia. Consis-tent with the physiological and proteomic data, therewere dramatic differences in metabolite profiles be-tween rice coleoptiles derived from 4-d-old aeratedand 6-d-old anoxic seedlings, with very high accumu-lation of amino acids under anoxia (Table III; Fig. 2).Many of these responses were also observed in ricecoleoptiles that were switched to anoxia for 1 d, al-though these tended to be considerably more subtle(Table III; Fig. 3).
A number of major differences observed in 6-d-oldanoxic seedling coleoptiles relative to 4-d-old aerobicseedling coleoptiles were not observed at all as re-sponses in switched seedling coleoptiles (e.g. 17- to45-fold increases in 3-phosphoglycerate, b-Ala, and2-aminoadipate and a remarkable 560-fold increase inurate; Supplemental Table S7). Moreover, some me-tabolites responded in opposite directions to the twotreatments (e.g. Arg, homo-Ser, Lys, and Tyr; Supple-mental Table S7). These discrepancies are consistentwith oxygen-dependent biogenesis of cellular com-ponents involved in the regulation of thesemetabolitesin rice.
The wheat coleoptile metabolite profile also respondedto anoxia (Table III; Fig. 3; Supplemental Table S4). Certainfeatures were found to be common to responses ofwheat and rice coleoptiles to 1-d anoxic transfer. Theseincluded accumulations of g-aminobutyrate, Gly, Ile,Pro, Thr, succinate, and putrescine and decreases inAsp, Fru, Rib, trehalose, citrate, isocitrate, citramalate,glucarate, malate, glycerate, threonate, and cytosine(Table III; Supplemental Table S5). However, in wheat,these responses tended to be much less pronouncedthan those observed in anoxically switched rice cole-optiles (Table III; Fig. 3).
While some metabolite responses to the 1-d anoxicshift were common to both species (Fig. 3; Table III),we did identify a number of species-specific responsesthat may be linked to the differential anoxia toleranceof these species (Supplemental Tables S5 and S6). Rice-specific responses included moderate to strong in-creases in Ser, Ala, Leu, and Trp and decreases in Arg,Met, Tyr, Orn, 6-phosphogluconate, and aconitate (withthe aconitate response being themost consistently strongbetween experiments). Wheat-specific responses in-cluded moderate increases in b-Ala, 4-hydroxycinna-mate, and shikimate, strong increases in urate, andmoderate decreases in 4-Hyp and Suc. Interestingly, asmall number of metabolites responded moderatelystrongly in opposite directions between the two spe-cies. For example, a-aminobutyrate and phosphate in-creased in rice while decreasing in wheat; conversely,Lys, Phe, Xyl, and ascorbate decreased in rice whileincreasing in wheat. The distinctive and significantaccumulation of Ala and Ser in rice was consistent withour evidence of increased abundance of enzymes inthese pathways in rice (Fig. 3).
Surprisingly, L-Ala did not significantly differ inabundance between control and anoxically switched
Rice and Wheat Anoxia Responses
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wheat coleoptiles (Table III), contradictory to a previ-ous report where Ala levels accumulated in wheatshoots to the same degree as that of rice shoots after 8 hof anoxia (Menegus et al., 1989) as well as a range ofreports from other species (van Dongen et al., 2009;Narsai et al., 2011). Such a difference might be ex-plained by differences in the experimental system, thespecific dissection of the coleoptile tissue used in thisreport, or the timing of the amino acid accumulation.For example, accumulation of Ala in roots of Arabi-dopsis was found by treatment with 48 h of 4% and 8%oxygen but not when the concentration of oxygen wasreduced to 1% (van Dongen et al., 2009). To considerthe last of these, we repeatedmetabolite profiling at 4 hafter the switch to anoxia in both rice and wheat, but
again we saw an increase in L-Ala in rice but not inwheat (Table III).
In addition to changes in amino acids, variations inintermediates in the tricarboxylic acid (TCA) cyclewere also observed. In the TCA cycle, the step con-verting succinate into fumarate by succinate dehydro-genase requires the operation of an electron transportchain and reduction of oxygen to water. Withoutoxygen, the TCA cycle will stop at succinate dehydro-genase and succinate will accumulate, as we observedin both rice and wheat coleoptiles (Table III; Fig. 3) andas other studies have reported (Menegus et al., 1991;Fan et al., 1997; Rocha et al., 2010). This claim was alsosupported by the decrease in other TCA cycle inter-mediates such as malate and citrate in both anoxically
Figure 3. Effect of a 1-d anoxic switch on carbohydrate metabolism, glycolysis, fermentation, amino acid metabolism, and theTCA cycle in rice and wheat coleoptiles. Rice and wheat seeds were germinated and grown under aeration for 4 d or for 4 d witha switch to 1 d of anoxia. Green or red boxes represent metabolites significantly more abundant during aeration or the anoxicswitch, respectively (P, 0.05). The yellow boxes represent metabolites whose abundances are unchanged. Enzyme names thataccompany arrows are also colored in this fashion for the rice response only (anoxia-responsive proteins were not identified inwheat). The numbers on the top left and right side of each square represent the response value (RV) of the correspondingmetabolite (anoxia/aeration) in rice and wheat coleoptiles, respectively. All data were extracted from Tables II and III. (Forabbreviations, see Fig. 2 legend).
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switched cereals, yet it was contradicted when observ-ing 6-d-old anoxic rice coleoptiles (Table III; Figs. 2 and3) and suggests that the other intermediates in theTCA cycle were utilized under anoxia. The advantageof the accumulation of succinate under anoxia hasbeen widely discussed in the context of the extra ATPproduction that can result (Gibbs and Greenway, 2003;Bailey-Serres and Voesenek, 2008; Rocha et al., 2010).Under prolonged anoxia, higher abundances of Suc,
Glc-6-P, and Fru-6-P in rice coleoptiles were observed(Table III; Fig. 3). Rice coleoptiles treated with shorterperiods of anoxia (24 or 4 h) revealed no significantdifferences in these sugars between stress and controltreatments. In wheat, however, a 4-h anoxic treatmentresulted in significant decreases in all three sugars,and the 24-h treatment resulted in a decrease of Suc(Table III; Fig. 3). Decreases in the levels of the trans-portable sugar Suc as well as decreases in glycolyticintermediates might be indicative of a delayed transi-tion to anaerobic metabolism in wheat when com-pared with rice.
Database-Driven Metabolic Phenotype Analysis RevealsConserved and Divergent Responses to Low Oxygen in
Rice and Wheat
Having established that wheat and rice coleoptilesdisplay differential responses to oxygen deprivation, wethought it would be informative to compare these re-sponses with those previously observed in other species.To this end, we used the PhenoMeter tool ofMetabolome-Express (https://www.metabolome-express.org; fordetails, see “Materials and Methods”) to search theMetabolomeExpress database of metabolic pheno-types, MetaPhenDB (see “Materials and Methods”),for previously reported metabolic phenotypes of sta-tistically significant qualitative overlap (codirectionalresponses) or inverse overlap (opposite direction re-sponses) with the rice and wheat responses to anoxiathat we report.As expected, the metabolic responses of rice and
wheat coleoptiles to anoxic transfer retrieved signifi-cant hits (P , 0.05; Fisher’s exact test) to a number ofpreviously reported responses to oxygen deprivation(Gibon et al., 2002; Narsai et al., 2009, 2011; Rocha et al.,2010; summarized in Supplemental Table S9) whileretrieving very few matches to any of the many otherfunctionally less closely related metabolic responses inthe MetaPhenDB database (for detailed results, in-cluding Fisher’s exact test P values, see SupplementalTable S9). In addition, a number of species-specificpositive and negative hits were also observed to di-verse treatments (Supplemental Table S8). For exam-ple, rice gave highly significant positive hits to theresponses of Arabidopsis cell suspensions to inhibi-tion of the mitochondrial electron transport chaincomplex I (Garmier et al., 2008), while wheat did notgive any significant hits to this phenotype. Conversely,only wheat gave significant positive hits to low-oxygenresponses of potato (Solanum tuberosum) tubers
(Geigenberger et al., 2000), castor bean (Ricinus com-munis) phloem (van Dongen et al., 2003), or the sulfurdepletion-mediated hypoxia response of the Chlamy-domonas reinhardtii stm6 mutant (Timmins et al., 2009;Supplemental Table S8). In two cases, waterloggingof Populus 3 canescens roots (Kreuzwieser et al., 2009)and low-oxygen treatment of Arabidopsis roots (vanDongen et al., 2009), rice gave positive hits whilewheat gave inverse hits (Supplemental Table S8), in-dicating significant divergence between rice and wheatin metabolites that define these responses. Given thelarge difference that these two species display in theirresponses to anoxia at the metabolite level, we wantedto consider whether the ability to generate a particularmetabolite pool contributes to anoxia tolerance and weset out to test this hypothesis.
Amino Acid-Induced Improvement of Cell Integrity inWheat under Anoxia
A range of reports in mammalian cells have high-lighted that exogenous addition of Gly, Ser, and/orAla can enhance the survival of cells to oxygen dep-rivation (Brecht and Groot, 1994; Tijsen et al., 1997;Wang et al., 2010). To test whether the differentialaccumulation of these amino acids could be part ofplant anoxia tolerance and to define the functionalimportance of the divergence of rice and wheat met-abolic responses to anoxia, we supplemented themedium used for rice and wheat growth under anoxia.We supplemented with a combination of amino acidsand assessed plant performance with the Evans blueroot cell viability assay after 3 d in anoxia (Fig. 4A).This showed that amino acid supplementation signif-icantly increased cell viability in wheat but not in riceroots, consistent with the differential accumulation ofthese amino acids in rice. To confirm this finding fromwhole wheat seedlings, we used the measurement ofelectrical conductivity as a direct indicator of electro-lyte leakage, and thus cell integrity, from anoxicallytreated seedlings in the presence or absence of thesethree amino acids in several different combinations(Fig. 4B). This showed that the combination of Ser/Ala/Gly significantly lowered electrolyte leakage, asdid Ser/Ala, but the presence of only one of the aminoacids did not protect wheat seedlings from electrolyteleakage (P , 0.01). We also confirmed the absence ofthis positive effect in anoxia-tolerant rice seedlings. Thissuggests that the disparity between rice and wheatmetabolite pool responses (Fig. 3) may contribute to thedegree of anoxia tolerance and that partial generation ofthese pools (via exogenous supplementation) in sensi-tive plants can improve cellular integrity.
DISCUSSION
We have analyzed the differential responses of co-leoptiles from rice and wheat seedlings to anoxia at thephysiological, metabolomic, and proteomic levels. Our
Rice and Wheat Anoxia Responses
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data suggest that despite wheat having an anatomi-cally similar coleoptile, it responds to anoxia to a lesserdegree at the molecular level than the coleoptiles ofrice. Our findings are consistent with previous reportsthat rice seedlings were much more tolerant to anoxiathan wheat seedlings and that this involves an adap-tive response (Menegus et al., 1989, 1991). Our results
also suggest that the changes in the capacity of met-abolic pathways, via alterations in protein synthesis ordegradation rates, are important at least in the ricecoleoptile for anoxia tolerance.
Transcriptional Versus Translational Control of RiceAnoxia Response
There is an increasing body of literature on thetranscriptional response of rice to anoxia that showsgreat complexity in the response (Howell et al., 2007;Lasanthi-Kudahettige et al., 2007; Narsai et al., 2009),and the role of differential translation of only an activepool of RNAs during germination and anoxia furthercomplicates its interpretation (Branco-Price et al., 2008).We have extracted published gene expression data from4-d-old anoxic and aerobic rice seedlings (Lasanthi-Kudahettige et al., 2007) to make comparisons withour protein profiling data here. Overall, the correlationbetween differences in protein abundance and differ-ences in mRNA expression in anoxically germinatedrice versus aerated rice was poor (r2 = 0.39 when com-paring log10 ratios; Supplemental Fig. S3B). However,there were some positive correlations observed wherethe direction of change in response to anoxia or aerationwas the same for both the protein and its transcript(Table II). For example, peroxiredoxin (Os07g44430) andprotein kinases (Os08g04250, Os08g04210, Os08g04240)were highly accumulated under anoxia alongside clearup-regulation of the corresponding transcripts (TableII). The BBTI (Os01g03340) and ILR1 (Os03g62060)were less abundant under anoxic conditions, whichwas in concordance with the extracted transcript dataindicating their strong down-regulation (Table II). Suchresults suggest that these particular proteins might beregulated at the transcriptional level. However, anotherBBTI (Os01g03360) was reported as more abundant in6-d anoxic coleoptiles according to iTRAQ quantitation,despite its transcript showing the opposite direction ofchange under anoxia (Table II).
Selective mRNA translation under oxygen deficiencyhas been previously observed in plants (Bailey-Serres,1999). In Arabidopsis, it was recently reported thatselective mRNA translation coordinates “energetic andmetabolic adjustments” to oxygen deficiency and re-covery (Branco-Price et al., 2008). This claim was alsosupported by our proteomic data for the accumulationof proteins from the glycolytic pathway in anoxic ricecoleoptiles (Table II; Fig. 3). For example, Fru-bisPaldolase (Os05g33380) and glyceraldehyde-3-phosphatedehydrogenase cytosolic 3 (Os04g40950, Os08g03290)were significantly more abundant at the protein level,while the extracted microarray data indicated thatboth genes were not responsive to anoxia (Table II).Other isoforms of glyceraldehyde-3-phosphate dehy-drogenase cytosolic 3 (Os02g38920) showed abundancedifferences that were in agreement at the protein andmRNA levels. This suggests that the selected transla-tion of different mRNAs might require modification ofthe cytosolic ribosome. However, the actual mechanism
Figure 4. The effect of exogenous amino acid feeding on cell integrityafter prolonged anoxia in wheat and rice seedlings. A, Rice and wheatseeds were germinated and grown under 4 d of aeration. Fresh culturemedium in the presence or absence of 10 mM Ala, Ser, and/or Gly wasthen added to seedlings. Seedlings were returned to 3 d of aeration(green) or transferred to 3 d of anoxia (nonsupplemented in red;supplemented in dark red). Roots were then analyzed using the Evansblue viability stain (n = 3). An increase in cell death is proportional toincreasedA600. B, Cell membrane permeability in whole rice andwheatseedlings was also analyzed (n = 10–23). This was done by measuringelectrical conductivity after seedlings were incubated in distilled,deionized water for 1 h (C1). A second measurement was taken aftersample boiling (C2) to obtain the proportion of cell leakage in differentsamples. Larger C1/C2 values indicate higher electrolyte leakage andthus lower cell integrity. *** P , 0.001, ** P , 0.01, * P , 0.05 whencomparedwith anoxic seedlings that were not supplemented (red bars).
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of selective translation in plants remains unknown.Matching of the rice genes studied here to their Arabi-dopsis orthologs showed no apparent correlation be-tween the rice proteins, whose abundance was notreflected in rice transcript data, and the ribosomalloading of orthologous mRNA under anoxia in Arabi-dopsis (Supplemental Table S3). Exceptions includesome proteins with unknown functions as well asclassical anaerobic proteins.Alternatively, difference in protein abundance be-
tween treatments could be accounted for by alterationsof the rate of synthesis and/or degradation of eachprotein. The abundance of cytosolic ascorbate perox-idase (Os03g17690) was significantly decreased with-out any apparent change in gene expression (Table II),suggesting that the translation of this gene was in-hibited by some downstream consequence of anoxia orthat this protein underwent selective degradation. Themechanism of selective protein degradation under an-oxia also deserves further investigation. Because thewheat coleoptile proteome was largely unchangedeven after 24 h of anoxia (Fig. 1C), selective mRNAtranslation or protein degradation might not be occur-ring as frequently in this species as is apparent in ricecoleoptiles. The consequence of a smaller upstreamresponse for the regulation of translation and proteinturnover in wheat coleoptiles under anoxia was alsoreflected in metabolic and physiological responses.
Amino Acid Metabolism Is Perturbed during Anoxia
The accumulation of amino acids in anoxic rice andwheat coleoptiles is consistentwith thewell-documentedobservation of this phenomenon when plants are ex-posed to differing degrees of oxygen deprivation (Fanet al., 1997; Kato-Noguchi and Ohashi, 2006; Narsaiet al., 2009; van Dongen et al., 2009; Rocha et al., 2010).We also detected the accumulation of enzymes involvedin Ala, Ser, and Gly biosynthesis concomitant with theaccumulation of those amino acids (Fig. 2). The pro-posed benefit of the accumulation of Ala under oxygendeprivation in different plant species has been dis-cussed in detail (Gibbs and Greenway, 2003; Bailey-Serres and Voesenek, 2008). Also, Ala synthesis throughAla aminotransferase 2 does not contribute to the oxi-dation of NADH, as does lactate or ethanol produc-tion, but rather serves as a retainable carbon sourceupon return to air (Good and Crosby, 1989; Miyashitaet al., 2007). However, the role of Gly and Ser accu-mulation is less clear. The transcripts for a number ofthese biosynthetic proteins are more abundant underanoxia (Lasanthi-Kudahettige et al., 2007; Table II),indicating that amino acid synthesis rather than proteindegradation is likely to be responsible. But, to ourknowledge, direct evidence for the benefits of feedingexogenous amino acids to seedlings growing underanoxia in an anoxia-intolerant but not an anoxia-tolerantspecies (Fig. 4) has not previously been reported.We initiated these exogenous feeding experiments
on the basis of an intriguing report on the positive
effects of Gly, Ser, and Ala on mammalian cells underhypoxic stress. Of the 23 standard amino acids tested,only Gly, L-Ala, and L-Ser provided significant protec-tion from hypoxic injury of cultured hepatocytes (Brechtand Groot, 1994). For some years, hypoxic or energydeficiency injury to hepatocytes and kidney tubuleshas been treated with Gly as a method of cell preser-vation (Weinberg et al., 1991; Carini et al., 1997; Tijsenet al., 1997). Although the literature agrees that pro-tection by Gly is not simply an enhancement of theenergetic state of the hypoxic cells, the mechanism ofprotection is still unclear. Research favors two differ-ent mechanisms associated with the modification ofthe rise in intracellular Na+ during hypoxia due toenergy-induced loss of Na+-K+-ATPase activity: indi-rectly via the activation of Gly receptor neurotrans-mitters (Carini et al., 1997) or directly by blockingnonselective sodium transport (Frank et al., 2000).
Subsequent literature examination also shows thatwhile the addition of a range of amino acids into ex-ternal medium can result in cytoplasmic acidificationof plant cells (Felle, 1981), the addition of Ala and Serdoes not acidify the cytoplasm but instead results in apH increase of some 0.2 to 0.3 units (Felle, 1996). Thissuggests a selective benefit of these amino acids inavoiding cytoplasmic acidification under anoxia. Addi-tionally, Ser is the entry point for sphingolipid biosyn-thesis in plants. The transcript of the gene controllingthe first step of sphingolipid biosynthesis, the conden-sation of palmitate and Ser to form 3-keto-dihydro-sphingosine (Ser palmitoyltransferase [Os01g70370]),was up-regulated 20-fold in anoxic coleoptiles (Lasanthi-Kudahettige et al., 2007) and was classified as a coreanaerobic responder in germinating rice embryos(Narsai et al., 2009). Recent research in Caenorhabditiselegans (Crowder, 2009; Menuz et al., 2009) suggeststhat ceramides play a critical role in anoxia tolerance.The possible role of Ser in ceramide biosynthesisthrough Ser palmitoyltransferase in plant adaptationto anoxia deserves further investigation to identifynovel mechanisms conferring anoxia tolerance.
Hence, there are a range of possible explanations forthe beneficial effects of combinations of Ala/Ser/Glyon plant cell anoxia tolerance through the retention ofcarbon skeletons, modification of biosynthetic processes,and cellular ion balance.
CONCLUSION
In summary, our study reinforced the importance ofglycolysis and ethanolic fermentation in the adapta-tion to anaerobiosis and suggests that glycolysis mightalso be important in providing substrates for amino acidsynthesis. Rice, but not wheat, coleoptiles respondedto anoxia dramatically at the physiological, proteomic,and metabolomic levels, in concordance with the re-spective tolerance and intolerance of these speciesto anoxia. Further investigation into the role of machin-ery differences in selected mRNA translation and/or
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protein turnover between rice and wheat coleoptiles isneeded based on the targets identified here. We providenovel protein and metabolite evidence of the enhance-ment of Ser/Gly biosynthesis as well as support obser-vations that Ala accumulates in anoxic rice. We alsoshow a benefit for wheat by exogenous application ofthese amino acids and highlight a range of mechanismsthat could be responsible for conferring anoxia toler-ance.
MATERIALS AND METHODS
Plant Material
Dehulled rice (Oryza sativa ‘Amaroo’) and wheat (Triticum aestivum
‘Calingiri’) seeds were surface sterilized for 10 min using 50% (v/v) NaOCl
and then thoroughly rinsed with distilled, deionized water. Fifty to 75 seeds
were placed in conical flasks containing 250 mL of culture medium (0.5 mM
MES, 0.4 mM CaSO4, pH 6.5) and bubbled with air or N2 (6–7 L min21). Plastic
tubing delivered the gas to the seeds, and the system was sealed using
Parafilm and aluminum foil. Seedlings were grown in the dark at 30�C for (1)
4 d under aeration, (2) 4 d under aeration with an additional 1-d switch to
anoxia, or (3) 6 d under anoxia. To ensure that stress conditions were anoxic,
oxygen concentrations were monitored, and after 10 min of N2 bubbling, the
oxygen concentration was below the level of detection. This was measured
using the LabQuest Vernier oxygen meter with a sensitivity of 0.01% oxygen.
Rice seed was kindly provided by the New South Wales Department of
Primary Industries and wheat seed by the Western Australian Department of
Agriculture and Food.
Evans Blue Viability Stain
This protocol was adapted from the method described by Baker and Mock
(1994). Fresh tissues were excised from seedlings, weighed (0.05–0.1 g), and
placed in a 10-mL Falcon tube containing 100 mL of distilled, deionized water.
Two milliliters of 0.25% (w/v) Evans blue was added to each sample, and
horizontally lying tubes were shaken at room temperature for 20 min at 300
rpm. The stain was rinsed from tissues in a sieve until the water ran clear, snap
frozen, and then ground in a 2-mL Eppendorf microfuge tube containing a
carborundum ball for 3min at 17 shakes s21. A total of 0.5 mL of 1% (w/v) SDS
was added, and samples were ground for 3 min again. One milliliter of
distilled, deionized water was added to samples, which were then centrifuged
at 8,800g for 3 min. The absorbance of diluted supernatants (1:3) was measured
at 600 nm. The average masses of tissue samples (0.075 g) were used to nor-
malize absorbance measurements so that different samples could be com-
pared (n = 3).
Measurement of Electrical Conductivity
We measured electrical conductivity as an estimation of membrane integ-
rity (Yan et al., 1996). This was done in whole rice and wheat seedlings
germinated and grown for 4 d in aeration and subsequently transferred to
anoxia or air for 3 d in fresh culture medium (0.5 mM MES, 0.4 mM CaSO4, pH
6.5). Some samples were supplemented with the amino acids L-Ala, L-Ser,
and/or Gly at 10 mM concentrations. Seedlings were then washed with
distilled, deionized water, patted dry with tissue paper, and placed in 15-mL
Falcon tubes containing 10 mL of distilled, deionized water for 1 h at 19.5�C.The electrical conductivity of these solutions was measured (C1; TPS Aqua-C
conductivity TDS temperature meter). Samples were then microwaved for 2
min. Care was taken to ensure boiling of each sample. After 1 h, the electrical
conductivity was measured again (C2) at 19.5�C and used as the denominator
in the calculation of percentage electrolyte leakage.
ADH Activity
Measurement of ADH activity was performed as described byWaters et al.
(1991). Briefly, protein was extracted by grinding approximately 70 mg
of snap-frozen plant tissue with acid-washed sand and 1 mL of extraction
buffer (125 mM MES, 110 mM NaCl, 1 mM EDTA, 0.5 mM thiamine PPi, 2.5 mM
MgSO2, and freshly added dithiothreitol at 2 mM, pH 6.8). After centrifugation
at 10,000g for 4 min at 4�C, the supernatant was removed for analysis. The
ADH activity was measured at 340 nm in a 1-mL cuvette in reaction medium
(10 mM acetaldehyde, 50 mM TES, 0.17 mM NADH, pH 7.5).
Carbohydrate Measurement
Carbohydrate levels were measured using a modified method (Trevelyan
and Harrison, 1952). Tissue extracts were prepared by heating 20-mg samples
submerged in 2 mL of 80% ethanol at 70�C for 20 min in a tightly sealed tube.
Extracts were then removed from tissue, and 100 mL of extract was added into
1 mL of freshly prepared anthrone reagent (mixture of 0.2 g of anthrone to 100
mL of 70% [v/v] sulfuric acid). After vortexing, samples were boiled at 100�Cfor 10 min exactly, promptly placed in an icy-water bath for 5 min, and then
removed and stored at room temperature for 5 min before measuring A627.
Oxygen Uptake Measurements
Oxygen uptake measurements followed a procedure described previously
(Lee et al., 2008) using a computer-controlled Clark-type oxygen electrode
unit. Slight modifications include the use of approximately 90 mg of fresh
tissue and 2 mL of oxygen-saturated buffer composed of 5 mM KH2PO4, 10 mM
TES, 10 mM NaCl, and 2 mM MgSO4, pH 7.2.
Protein Purification
Snap-frozen coleoptiles were ground with acid-washed sand and a solu-
bilizing solution (7% [w/v] SDS, 125 mM Tris-HCl, and 10% [w/v] b-mercap-
toethanol at a 5:8 [w/v] ratio, pH 7). Protein purification was carried out using
the chloroform-methanol method (Wessel and Flugge, 1984). Protein pellets
were then incubated with 80% acetone for 1 h at 220�C. The solution was
centrifuged at 14,000 rpm for 10 min at 4�C, and the pellets were air dried.
Protein was resolubilized with rehydration buffer (6 M urea, 2 M thiourea, 2%
[w/v] CHAPS, 2% [v/v] immobilized pH gradient buffer, and 18 mM
dithiothreitol) for preparative gels or lysis buffer (6 M urea, 2 M thiourea, 2%
[w/v] CHAPS, and 40 mM Tris) for DIGE gels by shaking in an orbital rocker at
1,400 rpm at 25�C for 45 min. Centrifugation at 20,000g for 15 min was then
carried out. Protein was quantified using the 2D Quant Kit (GE Healthcare).
IEF/SDS-PAGE Gel Separations
For preparative gels, 800 mg of protein resolubilized in rehydration buffer
containing equal amounts of both samples was loaded onto IEF strips (3-10NL,
3-11NL, 24 cm; GE Healthcare) and separated for 24 h (up to 50 mA per strip,
5 W, 21�C). Six-step program parameters were as follows: 30 V for 12 h and
hold (step and hold [stp]), 500 V for 1-h stp, 1,000 V for 1-h gradient, 3,000 V
for 2-h gradient, 8,000 V for 2-h gradient, and 8,000 V for 6-h stp). IEF strips
were then dipped in 13 gel buffer, placed on top of a 12% acrylamide gel, and
run at 45 mA per gel for 6 to 7 h. For DIGE gels (Eubel et al., 2007), 50 mg of
treated, control, and a 1:1 internal standard mixture of the above-mentioned
coleoptile proteins was labeled separately with 400 mM fluorescent CyDye. The
labeling procedure was carried out according to the recommendations of GE
Healthcare. Samples were pooled and separated in the same manner that
preparative gels were. DIGE gels were scanned using a Typhoon laser scanner
(GE Healthcare), and quantitative analysis was carried out using the DeCyder
software package (version 6.5; GE Healthcare). Three independent dye-
swapping replicates were carried out. Statistically significant spots were
selected for MS identification according to their appearance in nine of nine
fluorescent images, a ratio of abundance difference of 2 or greater, and a value
of P # 0.05. Both DIGE and preparative gels were Coomassie Brilliant Blue
stained and destained.
Protein Identification Using MS
Selected protein spots were excised from gels and digested in gel as
described previously (Taylor et al., 2005). Vacuum-dried samples were
redissolved in 5% (v/v) acetonitrile and 0.1% (v/v) formic acid for analysis
on an XCT Ultra Ion Trap mass spectrometer (Agilent Technologies), and MS/
MS spectra were exported for data analysis. MS spectra were examined
against an in-house rice database of The Institute for Genomic Research Rice
Pseudomolecules and Genome Annotation and mitochondrial and plastid
Shingaki-Wells et al.
1720 Plant Physiol. Vol. 156, 2011 www.plant.org on December 28, 2013 - Published by www.plantphysiol.orgDownloaded from
Copyright © 2011 American Society of Plant Biologists. All rights reserved.
protein sets (Rice version 6) using version 2.2.03 (Matrix Science) of theMascot
search engine. The following settings were selected for searching: MS error
tolerance of 61.2 D, MS/MS error tolerance of 60.6 D, maximum missed
cleavages tolerated as one, two variable modifications including carbamido-
methyl (C) and oxidation (M), peptide charge as 2+ and 3+, and finally, the
instrument selected as ESI-TRAP. After results were retrieved from theMascot
search engine, “Require bold red” and “Standard scoring” checkboxes were
selected with the ion score cutoff set at 37, the “Significance threshold”
set at P , 0.05, and the “Max. number of hits” set at AUTO. MS spectra
files are available for analysis via the Proteome Commons Tranche Project
under hashWiq7A0erU/p/zv9IJSKf+5pyjbFZXnERbYRQYwrglOB3N/kmT/
Sp9qJ4ksGmr9J76AmipUl3xMLTO1I07LFWa19V0K8AAAAAAAACEg = =.
Metabolite Extraction, GC-MS Sample, andData Analysis
Metabolite extraction from coleoptiles followed a modified procedure
described previously (Howell et al., 2009). Tubes containing ground tissue
samples (100 mg of stressed or nonstressed coleoptiles) and grinding balls
(cooled) were placed in a liquid N2-cooled solid rack. Cold metabolite
extraction medium (0.5 mL; 85% [w/v] HPLC-grade methanol, 15% [w/v]
untreated MilliQ water, and 100 ng mL21 ribitol) was added to each tube,
immediately vortexed, and then shaken at 1,400 rpm for 20 min at 65�C.To pellet cell debris, samples were centrifuged at 20,000g for 10 min. Aliquots
(60 mL) of extract were dried in a vacuum centrifuge for approximately 2 h.
Twenty microliters of 20 mg mL21 methoxylamine-HCl (98% purity; Sigma)
was added to each of the dried samples. Samples were then shaken at 1,400
rpm for 90 min at 30�C. To each sample, 30 mL of N-methyl-N-(trimethylsilyl)-
trifluoroacetamide (derivatization grade; Sigma) was added, followed by
shaking again at 1,400 rpm for 30 min at 37�C. After this, 10 mL of n-alkane
standard mix (0.029% [v/v] n-dodecane, 0.029% [v/v] n-pentadecane, 0.029%
[w/v] n-nonadecane, 0.029% [w/v] n-docosane, 0.029% [w/v] n-octacosane,
0.029% [w/v] n-dotriacontane, and 0.029% [w/v] n-hexatriacontane dissolved
in anhydrous pyridine) was added and vortexed. Samples were transferred to
GC-MS amber vials with screw-top seals and low-volume inserts (Agilent
Technologies). These were then incubated for 4 h at room temperature for
equilibration. Analysis of samples on the GC-MS device followed the proce-
dure described previously (Howell et al., 2009). GC-MS data were collected
using Chemstation GC/MSD Data Analysis Software (Agilent Technologies).
Raw GC-MS data preprocessing and statistical analysis were performed using
MetabolomeExpress software (version 1.0; http://www.metabolome-express.
org). Detailed methods have been reported (Carroll et al., 2010).
Metabolic Phenocopy Analysis Using theMetabolomeExpress PhenoMeter
To systematically characterize relationships between the metabolic phe-
notypes observed in this study and metabolic phenotypes reported in previ-
ous studies, we used the PhenoMeter tool of MetabolomeExpress (https://
www.metabolome-express.org) to search the MetabolomeExpress metabolic
phenotype database, MetaPhenDB, for metabolic phenotypes having statisti-
cally significant qualitative overlap with the responses observed in this study
(submitted as a batch of “bait” responses). The PhenoMeter uses the following
procedure for each bait response. The bait response is compared with each
and every reference response in MetaPhenDB, one at a time. Each comparison
is done by first counting (1) the number of metabolites increased (i.e. having a
greater than 1-fold change) in both bait and reference; (2) the number of
metabolites decreased in both bait and reference; (3) the number of metab-
olites increased in bait but decreased in reference; and (4) the number of
metabolites decreased in bait but increased in reference. These counts were
then used as input in a two-tailed Fisher’s exact test to calculate the P value of
obtaining the observed positive (codirectional) or negative (inverse) response
overlap by chance alone. P. 0.05 was used to indicate statistically significant
overlaps or inverse overlaps. To minimize biases caused by the presence of
different sets of “unknown” metabolites in metabolic phenotypes acquired
from different studies, only metabolites of known structure (and hence having
the same name in each study) were considered in comparisons. Only metab-
olites present in both bait and reference were considered. So that the anoxia
responses observed in this study could be compared with previously
published plant responses to anoxia and hypoxia, we added the complete
set of 36 metabolic phenotypes associated with seven peer-reviewed publica-
tions (Geigenberger et al., 2000; Gibon et al., 2002; van Dongen et al., 2003,
2009; Branco-Price et al., 2008; Timmins et al., 2009; Rocha et al., 2010) from
other groups presented in a recent review (Narsai et al., 2011) of the topic to
MetaPhenDB prior to PhenoMeter analysis. At the time of analysis, Meta-
PhenDB contained 12,379 publicly available metabolite response statistics
representing 116 metabolic phenotypes, including oxygen deprivation-related
metabolic phenotypes for a total of six plant species (Arabidopsis [Arabidopsis
thaliana], potato [Solanum tuberosum], Lotus japonicus, Populus 3 canescens,
castor bean [Ricinus communis], and rice) in addition to metabolic phenotypes
associated with a wide variety of other environmental, developmental, and
genetic perturbations.
iTRAQ Analysis
Proteins were purified and quantified as described above. For each sample,
a total of 100 mg of protein was precipitated by the addition of 4 volumes of
cold acetone and stored in 220�C overnight. The precipitated protein was
then resuspended in dissolution buffer and denatured, and Cys residues were
blocked according to the manufacturer’s instructions (AB Sciex). Each sample
was then digested with 20 mL of 0.25 mg mL21 trypsin (Invitrogen) at 37�Covernight and labeled with the iTRAQ tags in triplicate. iTRAQ reagents were
resuspended in 50 mL of 2-propanol and added to each sample, pH adjusted,
and allowed to incubate at room temperature for 2 h. The labeled samples
were pooled prior to further analysis. To remove excess labeling reactants and
to reduce the interference of salts during liquid chromatography-MS/MS
analysis, the pooled samples were diluted 4-fold with strong cation-exchange
buffer A (10 mM KH2PO4 in 25% acetonitrile, pH 3.0) and subjected to strong
cation-exchange chromatography using an OPTI-LYNX cartridge (Optimize
Technologies). The eluent was dried in a vacuum concentrator and stored at
220�C for liquid chromatography-MS/MS analysis.
Samples were analyzed on an Agilent 6510 quadrupole-time-of-flight
(Q-TOF) mass spectrometer with an HPLC Chip Cube source. The chip
consisted of a 160-nL enrichment column (Zorbax 300SB-C18 5mm) and a
150-mm separation column (Zorbax 300SB-C18 5mm) driven by an Agilent
Technologies 1100 series nano/capillary liquid chromatography system. Pep-
tides were loaded onto the trapping column at 4 mL min21 in 5% (v/v)
acetonitrile and 0.1% (v/v) formic acid with the chip switched to enrichment
and using the capillary pump. The chip was then switched to separation, and
peptides were eluted during a 1-h gradient (5%–60% [v/v] acetonitrile) using
the nano pump at 300 nL min21 directly into the mass spectrometer. The
Q-TOF mass spectrometer was run in positive ion mode, and MS scans were
run over a mass-to-charge ratio range of 275 to 1,500 and at 4 spectra s21.
Precursor ions were selected for auto-MS/MS at an absolute threshold of 500
and a relative threshold of 0.01, with a maximum of three precursors per cycle
and active exclusion set at two spectra, and released after 1 min. Precursor
charge-state selection and preference was set to [M+H]2+ and then [M+H]3+,
and precursors were selected by charge and then abundance. Resulting MS/
MS spectra were searched against The Institute for Genomic Research Rice
Pseudomolecules and Genome Annotation and mitochondrial and plastid
protein sets (Rice_osa6) using version 2.2.03 (Matrix Science) of the Mascot
search engine. The following settings were selected for database searching:MS
error tolerance of 6100 ppm; MS/MS error tolerance of 60.5 D; maximum
missed cleavages tolerated as one; fixed modifications methylthio (C),
iTRAQ8plex (N-term), iTRAQ8plex (K); variable modifications carbamido-
methyl (C) and oxidation (M) iTRAQ8plex (Q), peptide charge of 2+ or greater;
and finally, the instrument selected as ESI-Q-TOF. The resulting searches were
then exported, and all peptides identified (P , 0.05) were extracted to create
an exclusion list for the subsequent run. All five runs were performed
and combined using mzdata Combinator version 1.0.4 (West Australian Centre
of Excellence in Computational Systems Biology) for database searching
as outlined below. MS spectra files are available for analysis via the Prote-
ome Commons Tranche Project under hash 5AJQpzyi1I5adgPNIGdJ+
oQf8nlIoXnjLVhePv9x39srDtRpuZe9gQu9ij62NLKetNEdx6t1MqirlvSglVAA-
UHcQZAYAAAAAAAABcA = =.
Quantitation was carried out using default settings in Mascot version
2.2.03 (Matrix Science) for protein identifications as outlined above and
quantitation on isobaric mass tags (iTRAQ) at the peptide level. In more detail,
ratios for individual peptide matches were obtained from peptides meeting
the minimum criteria outlined above and were then combined to determine
ratios for protein hits using a weighted average. Outlier removal was carried
out by Dixon’s method for up to 25 data points per protein or by Rosner’s
method, where more than 25 data points were present and normalization was
carried out by median ratio. Values are reported as geometric means with SD,
and those significantly different from 1 at a 95% confidence interval are
Rice and Wheat Anoxia Responses
Plant Physiol. Vol. 156, 2011 1721 www.plant.org on December 28, 2013 - Published by www.plantphysiol.orgDownloaded from
Copyright © 2011 American Society of Plant Biologists. All rights reserved.
marked with asterisks (Supplemental Table S1). For proteins reported to have
a nonnormal distribution, the geometric SD was determined manually. Here, a
geometric mean for the individual peptide ratios and a 95% confidence
interval window was calculated as a t test in Analyze-it version 2.21.
Supplemental Data
The following materials are available in the online version of this article.
Supplemental Figure S1. Germination, growth, and cell integrity of rice
and wheat under anoxia.
Supplemental Figure S2. Number of protein spots detected in each
proteomic analysis that significantly differed in abundance between
treatments.
Supplemental Figure S3. Correlations between protein abundance de-
tected from iTRAQ and DIGE and between transcript and protein
abundance differences between continuously anoxic and aerated rice
coleoptiles.
Supplemental Figure S4.DIGE on two-dimensional 3-11NL IEF/SDS gels.
Supplemental Table S1. Entire list of rice coleoptile proteins significantly
changing between treatments that were identified by DIGE and iTRAQ
proteomic analyses.
Supplemental Table S2. Quantitative analysis of protein abundance from
4-d aerated and 6-d anoxic rice coleoptiles using iTRAQ.
Supplemental Table S3. Comparison of steady-state and polysomal
Arabidopsis mRNAs under aeration and hypoxia, whose genes are
orthologous to rice genes encoding proteins significantly changing in
abundance in at least one of our rice proteome analyses.
Supplemental Table S4. Whole set of relative metabolite levels in wheat
and rice coleoptiles under aerated and anoxic conditions.
Supplemental Table S5. Comparison of metabolite responses between
wheat and rice coleoptiles from seedlings switched to 1 d of anoxia in
two independent experiments.
Supplemental Table S6. Grouping of metabolite responses in coleoptiles
of 4-d-old rice and wheat seedlings transferred to anoxia for 1 d (anoxic
switch conditions).
Supplemental Table S7. Comparison of metabolite responses in coleop-
tiles of anoxically switched and anoxically germinated rice seedlings.
Supplemental Table S8.Metabolic phenocopy analysis: comparison of the
low-oxygen responses of rice and wheat coleoptiles with low-oxygen
and respiratory-perturbation responses in other species and tissues.
Supplemental Table S9. Expanded results of MetabolomeExpress Pheno-
Meter analysis.
Received March 2, 2011; accepted May 12, 2011; published May 27, 2011.
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Supplemental Tables
For supplemental tables to Study I, please see the CD fixed to the back page
of this thesis.
Alternatively, visit http://www.plantphysiol.org/content/156/4/1706/suppl/DC1
to download your copy.
75
Chapter 3 Wheat genotype responses to anoxia
76
Foreword to Study II
Having observed large differences in the way that rice and wheat respond to
anoxia (Study I), we were interested in determining whether the responses
seen in wheat were representative of several genotypes or a peculiarity
specific to our original genotype of interest (Calingiri). Of particular interest was
the absence of anoxic alanine accumulation in wheat, a response common to
many species treated with low oxygen.
Literature searches revealed a published report showing differential anoxia
tolerance among 11 wheat genotypes (Goggin and Colmer, 2007). Of those,
four were selected for further analysis under treatment regimes previously
employed in Study I. To allow more direct comparisons to this research,
several variables were modified; a 15˚ C temperature treatment was added to
supplement our original 28˚ C treatment, and roots as well as coleoptiles were
harvested.
Growth, electrolyte leakage, alcohol dehydrogenase activity and metabolite
profiles were analysed in all five genotypes. The results of Study II suggest a
strong dependence of the anoxic response on temperature, genotype and
tissue. The difficulty in assessing relative anoxia tolerance of different
genotypes is also discussed.
References Goggin DE, Colmer TD (2007) Wheat genotypes show contrasting abilities
to recover from anoxia in spite of similar anoxic carbohydrate metabolism. Journal of Plant Physiology 164: 1605-1611
77
Wheat genotype responses to anoxia are tissue and
temperature-dependent
Rachel N. Shingaki-Wells, Shaobai Huang, Ralitza Alexova & A. Harvey Millar.
ARC Centre of Excellence in Plant Energy Biology, Centre for Comparative Analysis of
Biomolecular Networks, Bayliss Building M316 University of Western Australia, 35 Stirling
Highway, Crawley 6009, Western Australia, Australia.
Abstract
Wheat seedlings were compared to assess the variation in responses to anoxia
caused by temperature, tissue and genotype. Growth, electrolyte leakage and
alcohol dehydrogenase activity were measured and compared to metabolome
responses under anoxia. These analyses revealed that responses to anoxia are
highly dependent on context; not only do measurement types affect how anoxia
tolerance is defined, but so too does the tissue, temperature and genotype. In
our previous analysis, we found the absence of alanine accumulation in anoxic
wheat, a surprising result in light of the fact that alanine accumulation is
considered a classical anaerobic response. This study showed that this is not
specific to wheat, but is temperature, tissue and genotype-dependent. The role
of alanine in anoxia tolerance remains unclear. In general, seedlings performed
better at 15˚ C than at 28˚ C, and coleoptiles performed better than roots. This
study highlights the difficulty of assessing anoxia tolerance, partially because no
gold standard measurement of tolerance exists, and any apparent tolerance
appears to be highly dependent on other experimental factors.
Introduction
Environmental oxygen deprivation can result from flooding, water logging, or high
soil microbial activity. Rice (Oryza sativa) is a model crop, and is especially useful
in understanding mechanisms of tolerance to oxygen deprivation. Rice is highly
tolerant to anoxia in that it is able to survive and up-regulate enzymes involved in
anaerobic ATP production, namely glycolysis and ethanolic fermentation (Gibbs
78
and Greenway, 2003). In contrast, wheat (Triticum aestivum) is relatively anoxia
intolerant (Perata et al., 1992; Perata et al., 1997), failing to rapidly adjust its
proteome in order to respond to an anaerobic environment (Shingaki-Wells et al.,
2011). The historical cultivation and environmental adaptation of these crops
might explain these differences; rice frequently grows in flooded and/or
anaerobic lowland soils whereas wheat is a dry-land winter crop. Additionally, the
increased rate of glycolysis known to occur under low oxygen is smaller in wheat
than in rice, suggesting energy metabolism is at least partially to blame for the
intolerance of wheat to O2 deprivation (Waters et al., 1991). Flooding is expected
to increase in frequency as a consequence of climate change (Bailey-Serres and
Voesenek, 2008), and thus it is expected that crops other than rice will be
affected by floods, with economic consequences such as yield loss. In 2011, for
example, floods in Queensland, Australia affected a landmass equal to the size of
both France and Germany (Perata et al., 2011). Other records indicate a
worldwide flood effect on 17 million km2 of land (Perata et al., 2011). Studying
highly utilised crops such as wheat will be useful for future improvement
strategies should they be required when flooding events become more common.
Compared to the model plants rice and Arabidopsis, the genome of wheat is
sizeable, complex and largely un-annotated. In addition, mutants are not easily
obtainable, making genetic research a challenge. Possible alternatives include
the use of pre-existing genetic variants, such as cultivars, which are often
accompanied by purported differences in tolerance to various stresses such as
waterlogging (Setter et al., 2009). Goggin and Colmer (2007) compared eleven
genotypes of wheat, which differed in seminal root elongation and degree of root
tissue K+ concentration recovery during resupply of O2 after 72 h of anoxia.
Despite differences in recovery ability, the genotypes did not differ in starch
content, soluble carbohydrate or activity of alpha-amylase in seeds, nor did they
differ in soluble carbohydrate content in roots (Goggin and Colmer, 2007).
However, it remains unclear how different wheat varieties respond to anoxia at
the metabolomic level. Not only does tolerance to anoxia differ between species
but it also differs between tissues. For example, rice roots are sensitive to anoxia
79
whereas coleoptiles are highly tolerant, being the only tissue to grow in rice
seedlings never before exposed to oxygen. Anoxic rice roots respire at rates far
lower than that of coleoptiles, suggesting mitochondrial dysfunction, which might
be disadvantageous during re-oxygenation (Shingaki-Wells et al., 2011). Rice
root growth is inhibited by lower concentrations of ethanol when compared to
coleoptiles, even though both tissues show rapid ethanol accumulation during
anoxia as well as ADH induction (Kato-Noguchi and Kugimiya, 2001; Shingaki-
Wells et al., 2011). The above contrasts between roots and coleoptiles suggest
that understanding anoxia tolerance mechanisms will be tissue dependent. Due
to the well-documented anoxia tolerance of the rice coleoptile, we focus on
changes of metabolites in wheat coleoptiles.
We have previously investigated the metabolomic response of anoxic coleoptiles
from a wheat variety known as Calingiri. Surprisingly, alanine failed to accumulate
during anoxia, which was in dramatic contrast to rice coleoptiles (Shingaki-Wells
et al., 2011). This is particularly interesting because alanine classically
accumulates in anaerobic plant species (Gibbs and Greenway, 2003). Alanine
production is catalysed by an anoxia-inducible enzyme, Alanine
Aminotransferase, using the glycolytic end-product, pyruvate, as well as
glutamate. It is thought that alanine serves as an alternative end-product to
ethanol, which could easily diffuse out of membranes, to be a lost carbon
skeleton for the cell (Rocha et al., 2010). When oxygen returns, alanine could be
converted back to pyruvate, for eventual assimilation into aerobic metabolic
processes. It is also hypothesised that alanine has an important role in
consumption of pyruvate which would otherwise activate alternative oxidase or
interfere with respiration inhibition and consume what little oxygen is left in the
cell (Gupta et al., 2009; Zabalza et al., 2009; Rocha et al., 2010). This thinking
however, is not applicable when plants are anoxic. Alanine synthesis produces 2-
oxoglutarate, whose metabolism is a result of partial TCA cycle operation, and
this could result in the production of an extra ATP during the succinate synthesis
step (Rocha et al., 2010). We also found that upon supplementation with
alanine, only anoxic wheat showed a significant reduction in cell injury (Shingaki-
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Wells et al., 2011), indicating that alanine itself, not just its production or
metabolism, has some beneficial effect on anoxic cells. These findings prompted
our comparison of several wheat genotypes to see whether the stability of
alanine levels upon transition to anoxia was unique to the genotype we were
studying.
In this study we demonstrate variation in response to anoxia and post-anoxia in
five wheat genotypes previously studied by Goggin and Colmer (2007). We see
how metabolite profiles correlate with physiological parameters like recovery of
growth, tissue damage and induction of fermentation. We also introduced two
treatment temperatures, 28˚ C as an optimal growth temperature for rice used
previously (Shingaki-Wells et al., 2011) and 15˚ C, which may be more suitable
for wheat growth (Goggin and Colmer, 2007). We demonstrate the strong
influence that temperature has on tissue responses to O2 deprivation, and
discuss how these and other factors complicate the process of ranking genotype
tolerance to anoxia.
Materials and Methods
Plant Material
We obtained wheat varieties of Ducula-4, SARC, Spear, Carnamah, Calingiri
from the Department of Agriculture and Food of Western Australia (DAFWA).
Ducula-4, SARC, Spear, Carnamah have been previously tested by Goggin and
Colmer (2007). Calingiri was used in our previous study to compare with rice
(Shingaki-Wells et al., 2011).
Plant growth
Plants were grown according to previous studies (Shingaki-Wells et al., 2011).
Briefly, approximately fifty seeds of each genotype were placed in a 250 mL
growth vessel. Seeds were sterilised by adding 6 % [w/v] NaOCl for 10 min. After
rinsing three times with ddH2O, 200 mL culture solution was added (0.5 mM 2-
(N-morpholino) ethanesulfonic acid (MES), 0.4 mM CaSO4, pH 6.5). Lids
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containing a gas delivery tube and a gas outlet hole were screwed on tightly.
Compressed air was bubbled throughout each vessel for four days, in a 28˚ C or
15˚ C dark growth chamber. Anoxic treatments lasted for one day, by using high
purity nitrogen gas. Post-anoxic treatments were obtained by bubbling with
compressed air for 1 or 3 days.
Tissue length measurements and calculations
Root, leaf and coleoptile lengths were measured using a ruler at several stages of
development with or without an anoxic stress. Measurements were taken at the
same time every day. There were 5-10 seedlings for each genotype /
temperature / treatment combination; and each experiment was repeated three
times for proportional growth calculations. The observed seedlings were treated
as follows: 4 d air; 4 d air 1 d N2 (Anx5d); 4 d air 1 d N2 1d air (Re-ox6d); 4 d air 1 d
N2 3d air (Re-ox8d); 5 d air (Air5d); 6 d air (Air6d); 8 d air (Air8d). For a 1 d recovery
treatment the proportional growth calculation was: [(Re-ox6d) - (Anx5d)] / (Anx5d)
*100. For a 3 d recovery treatment: [(Re-ox8d) - (Anx5d)]/ (Anx5d) *100. Control
calculations were: [(Air6d) - (Air5d)] / (Air5d) *100 or [(Air8d) - (Air5d)] / (Air5d) *100, for 1
d or 3 d of elongation, respectively.
Metabolite extraction and GCMS analysis
Metabolites were extracted by placing 25 ± 5 mg tissue into 2 mL Eppendorf
tubes containing a stainless steel grinding bead. Samples were snap frozen in
liquid nitrogen. Tubes were placed in a liquid nitrogen-cooled mill rack for
homogenisation twice at 15 shakes sec-1 for 2 minutes. Cold metabolite
extraction medium (500 μL; -20˚ C; 85 % [w/v] HPLC-grade methanol, 15 %
[w/v] untreated MilliQ water, and 100 ng μL-1 ribitol) was added to each sample,
which was then mixed at 1400 rpm for 20 min at 65˚ C using a thermomixer.
Samples were centrifuged at 20, 000 x g for 3 minutes at room temperature. 60
μL of supernatant was transferred to a low-volume insert and this was dried
down in a vacuum centrifuge. Inserts were transferred to 2 mL eppendorf tubes
for storage at -80˚ C. Derivatisation was carried out on samples that were re-
dried for 30 min. 20 μL of 20 mg mL-1 methoxyamine hydrochloride in anhydrous
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pyridine was added to each sample for incubation at 30˚ C for 90 min at 1400
rpm. Then, 30 μL room temperature MSTFA was added and incubated at 37˚ C
for 30 min at 1400 rpm. After this, 10 μL of an n-alkane mix was added (0.029%
[v/v] n-dodecane, 0.029% [v/v] n-pentadecane, 0.029% [w/v] n-nonadecane,
0.029% [w/v] n-docosane, 0.029% [w/v] n-octacosane, 0.029% [w/v] n-
dotriacontane, and 0.029% [w/v] n-hexatriacontane dissolved in anhydrous
pyridine). Samples were incubated at room temperature for 30 minutes prior to
GC-MS analysis, the methods of which have been described previously (Howell
et al., 2009). Briefly, 1 μL of sample was injected into an Agilent 7890 GC fitted
with an Agilent 5975 MSD. The carrier gas, helium, had a constant flow of 1
mL.min-1. The inlet temperature was 300˚ C and the initial oven temperature was
set at 70˚ C for 1 min. The oven temperature was increased to 76˚ C at 1˚ C. min-
1, then to 325˚ C at a rate of 6˚ C.min-1. This temperature was held for 8 minutes.
The capillary column used was a Varian Factor 4 (VF-5ms, 30 m x 0.25 mm,
0.25 μm; 10 m EZ-Guard). The MSD transfer line heater was set at 300˚ C, the
MS quadrupole at 150˚ C and the source at 230˚ C. The mass detection range
was set at 40-600 atomic mass units.
The generated data was collected and analyzed using Chemstation GC/MSD
Data Analysis Software (Agilent Technologies). Peak retention time and mass
spectra were manually inspected and checked against National Institute of
Standards and Technology (NIST) mass spectra library. Peak areas were
normalised to tissue weight and ribitol signal. ANOVAs were performed on
normalised and log-transformed data to give Tukey post-hoc p-values. Ratios
were calculated between anoxic and aerated samples using data normalised to
tissue mass and the peak area of a ribitol quantifier ion.
ADH
Protein extracts from snap frozen tissue were placed in 2 mL tubes with stainless
steel beads for homogenisation in cooled racks at 15 shakes/s for 2 mins. This
was repeated with racks turned around. Extraction buffer was added to tissue at
a 14:1 ratio (125 mM MES, 110 mM NaCl, 1 mM ethylenediaminetetraacetic acid
(EDTA), 0.5 mM thiamine pyrophosphate (TPP), 2.5 mM MgSO4.7H2O and
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freshly added 2 mM dithiothreitol (DTT)). This was mixed for 2 mins at 15
shakes/s with care taken to ensure racks were cold. The homogenate was then
centrifuged at 16, 000 x g for 4 min at 4˚ C and the resulting supernatant
transferred to a new tube and stored on ice. ADH activity was measured at 25˚ C
spectrophotometrically at 340nm in a 1 mL reaction volume.
880 μL of a solution containing N-Tris(hydroxymethyl)methyl-2-
aminoethanesulfonic Acid (TES) and acetaldehyde (pH 7.5) was added so that
final assay concentrations were 50 mM and 10 mM, respectively. Next, 100 μL of
a freshly-made solution containing reduced nicotinamide adenine dinucleotide
(NADH) was added to the cuvette so that the final assay concentration was 0.17
mM. Finally, 20 μL of ADH extract was added and promptly mixed using a
pipette before taking spectrophotometric measurements. To control for ADH-
independent NADH oxidation, absorbance in the absence of acetaldehyde or
ADH extract was measured for six minutes with absorbance changes (if there
were any) subtracted from sample measurements. The spectrophotometer
model used was a U- 2810 spectrophotometer (Hitachi High-Technologies
Corporation, Tokyo, Japan) with the measurements processed by computer
software (Hitachi UV Solutions Application, program no. 1344331-08, build 414).
To calculate the specific activity of ADH, total protein concentration of the tissue
extract was measured using the Bradford method (Bradford, 1976).
Cell leakage assays
Cell leakage assays were adapted from a previous study (Yan et al., 1996).
3 seedlings were submerged in 25 mL ddH2O in a 50 mL falcon tube. Samples
were placed in the dark at room temperature (approximately 19˚ C) for 1 hour.
Samples were gently mixed and electrical conductivities measured and recorded
as C1. Samples were boiled, then placed in an icebox for 20 minutes and allowed
to equilibrate to room temperature. A second measurement of electrical
conductivity was taken (C2). The ratio of C1 to C2 was calculated as a proxy for
cell leakage.
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Results
Assessment of anoxia tolerance using recovery growth
To understand the impact of anoxia and temperature on wheat growth, we
measured length of coleoptiles, leaves, primary roots and seminal roots in
aerated seedlings as well as seedlings subjected to one day of anoxia followed
by one and three days of re-oxygenation. These experiments were performed
three times, allowing the calculation of elongation rates (n=10, n=10, n=5). The
absolute growth data with length at different time points are given in
Supplemental Figure 1. Figure 1 represents relative growth rate, comparing
seedlings recovering from anoxia to those kept under constant aeration. We
calculated proportional growth of seedlings that were 6 or 8 days of age, as a
percentage of tissue length of seedlings that were 5 days of age (4d air 1d N2 for
the treatment; or 5d air for the control). Details on calculations are described in
the methods section.
Temperature has a significant impact on the length of all tissues measured (p-
val<0.001 for all tissues), with seedlings at 15˚ C showing apparent
developmental delays when compared with seedlings at 28˚ C (Supplemental
Figure 1a-d). After three days of re-oxygenation, a 28˚ C anoxic treatment
strongly inhibited the recovery of primary root elongation in Ducula-4, Spear and
Carnamah (Figure 1d), presumably due to the cell death in elongating root tips.
Looking from a more immediate perspective (1d re-oxygenation), only Calingiri
failed to show significant anoxic growth inhibition at 28˚ C, although this could be
due to large replicate variation (Figure 1c). In terms of the 3 d recovery of
seminal root elongation at 28˚ C, only Spear showed significant inhibition, with
Ducula-4 showing significant inhibition after 1 d recovery but not at 3 (Figure 1c-
d).
After a 28˚ C anoxia treatment, only the coleoptiles of Calingiri and Spear showed
higher proportional growth than control seedlings (Figure 1c-d). However, these
data should be interpreted with caution because Calingiri is developmentally
delayed compared to the other four genotypes (4d air; p<0.001; Supplemental
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Table 1a), with Carnamah and Ducula-4 coleoptiles at their maximum length at
four days of age (Supplemental Figure 1a; Supplemental Table 1b). We also
measured leaf elongation at a developmental stage where leaves were beginning
to emerge out of coleoptiles. One day of anoxia had less of an impact on the
recovery of leaves, demonstrated by the fact that proportional growth
measurements after 3 days of re-oxygenation are not significantly different
between seedlings kept under control conditions and those treated with anoxia
(Figure 1d). Leaf lengths are considerably larger in all genotypes by 3 d re-
oxygenation when compared to leaf lengths immediately after anoxia (4d air 1d
N2; Supplemental Table 2b). However, leaves from 8 d old control seedlings
were still significantly larger than those from seedlings treated with anoxia and re-
oxygenated for 3 days (at both temperatures), with the exception of 15˚ C
Calingiri leaves (Supplemental Figure 1b, Supplemental Table 2a).
At 15˚ C, SARC was the only genotype to show growth rate inhibition of leaves
and primary roots after 1 d re-oxygenation, but by 3 d re-oxygenation this
inhibition was no longer significant (Figure 1a-b). For 15˚ C seminal roots, only
Spear showed growth rate inhibition one day post-anoxia, but again, this lost its
statistical significance after three days of re-oxygenation (Figure 1a-b). 15˚ C
coleoptiles recovering from anoxia for 3 days showed significantly higher
proportional growth compared to aerated controls in all genotypes except SARC
(Figure 1b). Impressively, 15˚ C coleoptiles, which still have growth capacity at 4
days of age (Supplemental Table 1b), were not significantly shorter when
subjected to anoxia/re-oxygenation, compared to continuously aerated controls
(Supplemental Figure 1a; Supplemental Table 2a). The same is true for 28˚ C
coleoptiles (Supplemental Figure 1a; Supplemental Table 2a) but this is
complicated by the fact that at 4 days of age, coleoptiles are close to or at their
maximum length. Coleoptiles at 15˚ C show rapid growth resumption post-
anoxia (Supplemental Figure 1a; Supplemental Table 2b). At both temperatures,
the coleoptile is the only tissue where all genotypes have equivalent lengths
when comparing 8-d-old seedlings subjected to control treatments with those
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under anoxia/re-oxygenation (Supplemental Table 2a; Supplemental Figure 1).
This growth data suggests that the coleoptile is the most anoxia tolerant tissue.
Assessment of anoxia tolerance using electrolyte leakage measurements
We measured electrolyte leakage as an independent measure of anoxia
tolerance in wheat varieties grown at 15˚ C and 28˚ C (Figure 2). The ratio of
electrolyte leakage by whole seedlings after 1 h incubation (C1) and after boiling
(C2) can be used as a proxy for cell damage. At 28˚ C, all genotypes except
Calingiri had significantly higher leakage values after one day of anoxia,
compared to the aerated control (Figure 2b), suggesting that Calingiri is more
anoxia tolerant than the other genotypes. At 15˚ C, anoxia did not increase
electrolyte leakage in any of the genotypes (Figure 2a). These results suggest
that anoxia at higher temperatures is more stressful for wheat seedlings,
however, it must be noted that leakage levels for aerated seedlings at 15˚ C are
higher than seedlings grown at 28˚ C. The electrolyte leakage after boiling (C2) is
much lower at 15˚ C when compared to 28˚ C, which could explain the high
C1/C2 values in 15˚ C seedlings (data not shown). Although speculative, it is
possible that this discrepancy in apparent electrolyte levels is caused by
developmental differences between the smaller seedlings grown at 15˚ C. In
conclusion, results of our cell leakage suggest Calingiri is exceptional in terms of
its anoxia tolerance.
Assessing responses to anoxia by measuring ADH activity in coleoptiles
To check whether our measures for anoxia tolerance were linked to induction of
other classical responses to anoxia, we estimated the rate of activity for alcohol
dehydrogenase (ADH) in wheat coleoptiles. ADH is an important enzyme in the
transition to anaerobic metabolism, since the NAD+ produced could promote
maintenance of NAD+-dependent glycolytic reactions. Overall, ADH activity is 4-
11 fold higher at 28˚ C when compared to 15˚ C (Figure 3). At 28˚ C, Ducula-4
coleoptiles treated with anoxia have almost twice the activity of those kept under
aeration (Figure 3b). All other genotypes do not show significant induction,
although Calingiri is significant at the 94% confidence level (Supplemental Table
87
3). At 15˚ C, only Calingiri shows significant induction (54%) during anoxia
(Figure 3a). These results suggest that anoxic alcohol dehydrogenase induction
is temperature-dependent, and if ADH induction is a measure of anoxia
tolerance, then Calingiri and Ducula-4 stand out as particularly tolerant.
Metabolite profiles of coleoptiles and roots
The shift from air to anoxia causes dramatic changes to the metabolome of rice
and wheat coleoptiles (Shingaki-Wells et al., 2011). We were interested in
investigating whether wheat genotypes, which apparently differ in their sensitivity
to anoxia (Goggin and Colmer, 2007), have distinct metabolite profiles that
underlie or correlate with tolerance to oxygen deprivation. We conducted
metabolomic analysis of coleoptiles and roots (seminal and primary) from all five
wheat varieties in response to anoxia at 15˚ C and 28˚ C. Table 1 shows the fold
changes (anoxia/air) of individual metabolites belonging to different
classifications.
Overall, the depletion of sugars, TCA cycle-related metabolites and accumulation
of amino acids were observed in roots and coleoptiles across varieties after
anoxic treatment (Table 1). This pattern is much more obvious at 15˚ C than at
28˚ C (Table 1), suggesting that such a shift of metabolism is linked to the
improved anoxia tolerance as observed above.
There was no metabolite in this set that showed a consistent direction of change
across tissues, genotypes and temperatures. GABA shows significant and large
increases under anoxia in 15˚ C roots and coleoptiles. Although the ratios range
from 4.76-17.98 at 28˚ C, no ratios at this temperature reached the significance
threshold due to large variation within the five biological replicates (Supplemental
Figure 2). Individual samples could not be considered outliers since the other
metabolite signals in those replicates were typical in their intensities.
At 15˚ C, alanine significantly accumulates under anoxia in the roots and
coleoptiles of all genotypes (Table 1). At 28˚ C, only the coleoptiles of Carnamah
88
showed anoxic alanine accumulation, and in roots, accumulation was seen in
SARC, Spear and Calingiri. These results suggest that our prior study, in which
we reported the surprising observation that 28˚ C Calingiri coleoptiles fail to
accumulate alanine (Shingaki-Wells et al., 2011), is a response that is
temperature and genotype dependent rather than a feature of wheat itself.
Calingiri coleoptiles at 15˚ C are the only samples in this experiment to show
accumulation of fructose under anoxia (Table 1). All other samples show no
significant difference or rapid depletion of this sugar. Roots at 28˚ C, are the
most extreme in their depletion, followed by roots at 15˚ C. Calingiri is the only
genotype to not show significant depletion of fructose in 15˚ C roots. In 28˚ C
roots, fructose decreases by about 2.5 fold in anoxic Calingiri, whereas the other
genotypes show decreases ranging from 12 (Carnamah) to 63 fold (Ducula-4).
Glucose depletion is also most apparent in 28˚ C roots, followed by 15˚ C roots.
Glucose responses in Calingiri fail to stand out from other genotypes.
We also observed genotypic responses to anoxia across tissues and
temperatures. For example, citric acid, 2-oxoglutaric acid, malic acid, 4-
hydroxyproline, aspartic acid, shikimic acid and threitol/erythritol depletion
occurs in all temperature/tissue combinations of Ducula-4 (Supplemental Table
4). The TCA cycle metabolites, with the exception of succinate, appear to show
greater depletion in Ducula-4 than in Calingiri. In Calingiri and Carnamah, there
were no metabolites that showed a consistent response across tissues and
temperatures. Metabolite responses in 28˚ C Calingiri coleoptiles strongly
contrast to 28˚ C roots and 15˚ C coleoptiles/roots of Calingiri in that their
responses were much more subtle. The latter three sample types showed
consistent responses in 2-oxoglutaric acid (down) alanine (up) and proline (up).
Compared to these samples, which had 17-27 metabolites displaying significant
responses under anoxia, 28˚ C Calingiri coleoptiles only had 3 significant
metabolite responses. These metabolites were sucrose (down), aspartic acid
(down) and glycerol (up).
89
Succinate is the only TCA cycle metabolite that shows accumulation, albeit
inconsistently. For example, succinate accumulates in all genotypes at 28˚ C in
roots. It also accumulates in 15˚ C Carnamah roots and the 28˚ C coleoptiles of
Ducula-4 and Carnamah. Surprisingly, anoxic succinate depletion was also
observed. The 15˚ C roots of SARC, Spear and Calingiri, as well as the 15˚ C
coleoptiles of SARC and Spear had up to 2.8 fold less succinate under anoxia
when compared to air.
Amino acid responses to anoxia vary widely among tissues and temperatures
(Table 1). At 15˚ C, coleoptiles show significant increases in many metabolites,
with beta-alanine, alanine, glycine, isoleucine, leucine, lysine, threonine, tyrosine
and GABA showing consistent increases across all five genotypes. Amino acids
decreasing in anoxic coleoptiles at 15˚ C did so inconsistently across genotypes.
Glutamine decreased in all genotypes except Spear. Other depleted amino acids
like 4-hydroxyproline and aspartic acid decreased in Ducula-4/Spear and
Ducula-4/SARC/Carnamah, respectively. 15˚ C roots show the second strongest
amino acid response under anoxia, with alanine and GABA significantly
increasing across all genotypes. Amino acids that decreased under anoxia were
not limited to 4-hydroxyproline and aspartic acid, but also glutamate in Ducula-4,
SARC and Spear as well as phenylalanine in Ducula-4.
In conclusion, metabolite profiles in wheat roots and coleoptiles at low and high
temperature responded differentially to anoxia, presumably contributing to
variation in anoxia tolerance. Higher temperatures appear to dampen the strong
amino acid responses that typically occur during anoxia, and promote the
depletion of sugars and TCA cycle metabolites. When comparing tissues, it
appears that roots show stronger depletion of sugars/TCA cycle intermediates,
with the exception of succinate. It is more difficult to detect patterns of difference
in the responses of amino acids across tissues. From a genotypic perspective,
the metabolite responses to anoxia in Ducula-4 greatly contrast to that of
Calingiri. Ducula-4 shows stronger depletion of sugars/TCA cycle intermediates
as well as fewer significant increases in many amino acids, with the exception of
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4-hydroxyproline and aspartic acid, which deplete more rapidly in anoxic Ducula-
4 when compared to Calingiri.
Discussion
It is a complicated process to assess mechanisms of anoxia tolerance in
plants.
Assessing anoxia tolerance in plants can be simplified to observing how long
plants survive a low-oxygen event and how productivity is affected. Assessing
the molecular mechanisms underlying this success is considerably more
complicated. We set out to look at various factors such as growth resumption,
seedling damage, fermentative capacity and metabolome re-modelling, in an
attempt to define what a successful and unsuccessful response to anoxia entails.
The anoxia tolerance of rice can be attributed to a large list of responses and
factors. Furthermore, there are different rice varieties that vary in their
mechanisms of anoxia tolerance. For instance, some varieties may exercise
metabolic acclimation at the transcript or protein level and others might undergo
rapid shoot growth when submergence threatens contact to air (Voesenek et al.,
2006). The availability of light for photosynthesis might alter O2 availability as well
as other factors like aerenchyma formation (Gibbs and Greenway, 2003). These
factors, as well as variation in treatment regimes and developmental stages,
make assessing the anoxia tolerance of different plants complicated. In the case
of anoxic coleoptile elongation, differences in length could not be explained by
carbohydrate content or the expression of transcripts encoding glycolytic/
fermentation enzymes and expansins (Magneschi et al., 2009; Magneschi et al.,
2009). Ethanol production on the other hand, correlated with anoxic coleoptile
elongation (Magneschi et al., 2009). This apparent contradiction might be
explained by the selective translation that occurs under low oxygen in
Arabidopsis (Branco-Price et al., 2008). Nevertheless, elongation itself might only
be advantageous in genotypes where the benefit outweighs any energetic cost.
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Considerable variation in the anoxia-tolerance of the wheat variety Ducula-4 has
been reported (Setter et al., 2009). Initial trials conducted in Obregon, Mexico,
showed the high tolerance of Ducula-4 to waterlogging (vanGinkel et al., 1992;
Sayre et al., 1994), whereas trials conducted in Australia showed many other
genotypes outperforming Ducula-4 (Setter, 2000; Setter et al., 2009). Personal
observations indicate that the performance of Ducula-4 in Kaithal, India, is very
poor in waterlogged conditions (Setter et al., 2009). Setter and colleagues (2009)
attribute this performance difference to the more optimal conditions found in
Mexico, including optimal temperatures, better nutrition and absence of post-
submergence drought. This hypothesis requires further study. It is also proposed
that waterlogging tolerance is accompanied by a set of consequences that are
rarely considered, that is, the threat posed by element toxicities (e.g. Na, Mn, Fe)
(Setter et al., 2009). Such toxicities can make experiments on different varieties
hard to replicate in the field (Setter et al., 2009). It was recently shown that the
transcription factor SUB1A not only acts to improve submergence tolerance but
also post-submergence dehydration stress (Fukao et al., 2011). Thus, the
discrepancies seen in low oxygen research might be a product of differences in
field conditions that exert large and unintended co-stresses. Furthermore,
practical characteristics of the experiment such as what is measured, stress
duration and developmental stage are also likely to contribute to large variation
between research groups.
A comparison of our data to prior research.
In our current study we used electrolyte leakage, ADH induction, growth recovery
and metabolite profiling to determine the relative anoxia tolerance of five wheat
genotypes. This study was inspired by our previous research, which compared
rice (cv. Amaroo) and wheat coleoptiles (cv. Calingiri) at 28˚ C (Shingaki-Wells et
al., 2011). We were surprised to find that Calingiri coleoptiles did not accumulate
alanine under anoxic stress, as this is a widely-reported response under low
oxygen (Narsai et al., 2011). Furthermore, the shoots of the wheat variety MEK
86, at 25˚ C, showed anoxic accumulation of alanine (Menegus et al., 1989). We
were interested in knowing if this was unique to wheat coleoptiles or to Calingiri
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specifically, and set out to assess the metabolome, among other measures, in
various wheat genotypes.
With a myriad of varieties to choose from, we searched the literature to reduce
our genotype selection down to five. Goggin and Colmer (2007) had published a
comprehensive comparison of 11 wheat genotypes, measuring seminal root
elongation, root recovery of K+ concentration, root ethanol production as well as
seed starch and alpha-amylase activity. We included our prior genotype of
interest, Calingiri, which was not included in Goggin and Colmer’s research, and
chose Ducula-4/SARC as our top performers, Carnamah as our middle, and
Spear as our bottom performer in terms of post-anoxic root elongation and K+
recovery (Goggin and Colmer, 2007).
Although anoxic Ducula-4 seedlings showed some of the highest EL in our
study, this was only significantly different to Calingiri seedlings, which had the
lowest EL, at the 94% confidence level (p-val=0.054). We are able to conclude
that 28˚ C Calingiri seedlings, which was the only genotype to show no significant
increase in EL when transferred to anoxia, is a unique genotype in this
characteristic. Goggin and Colmer (2007) on the other hand, did not study
Calingiri and found 15˚ C Ducula-4 to be a top performer in that after 3 days of
anoxia and subsequent re-aeration, it showed the highest recovery of tissue K+
concentrations in the expanded zone of the roots. The complexity of comparing
our results with those of Goggin and Colmer’s can arise from their use of
different tissues (seedling vs. roots in their study), the presence of a hypoxic pre-
treatment (absent in our study), age (4-5d vs. ~6-7d in their study), duration of
anoxia (1d vs. 3d in their study) as well as measurement types (electrolyte
leakage vs. K+ in their study).
Goggin and Colmer analysed ethanol production in 15˚ C excised anoxic roots
supplemented with glucose and found that there was no difference between
genotypes and over the time spent under anoxia (24-96h). This is consistent with
our work on ADH activity in 15˚ C coleoptiles, where Ducula-4, SARC, Spear and
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Carnamah show no significant difference in ADH activity, independent of
genotype or O2 availability (Figure 3A). Calingiri, which was not studied by Goggin
and Colmer, was an exception showing significant induction under 24h anoxia
(Figure 3A). Although ethanol production may not contribute to the differences in
anoxia tolerance between Ducula-4, SARC, Spear and Carnamah (Goggin and
Colmer, 2007), ADH activity induction could explain the success of 15˚ C
Calingiri, provided pyruvate decarboxylase activity was not limiting. At 28˚ C, the
coleoptiles of Ducula-4 showed significant ADH induction under anoxia (mean
difference -1.24 units; p-val=0.049; Figure 3b, Supplemental Table 3), with
Calingiri not meeting the significance threshold due to replicate variation (mean
difference -0.89 units; p-val=0.308; Supplemental Table 3). In our previous
study, 28˚ C Calingiri coleoptiles, leaves and roots all showed significant ADH
induction after 24 h anoxia (Shingaki-Wells et al., 2011).
Post-anoxic root elongation retention (72h anoxia then 72 h recovery) at 15˚ C
relative to continuously aerated controls was highest in SARC (100 %), followed
by Ducula-4 (72 %), Carnamah (22 %) and Spear (0 %) (Goggin and Colmer,
2007). For 15˚ C seminal root sums, post-anoxic (4d air, 1d anoxia, 3d air)
Ducula-4 seminal roots were 72 % of the length of their aerated controls (8d air).
SARC, Carnamah, Calingiri and Spear followed at 60, 49, 49 and 45 %,
respectively (Supplemental Figure 1C). The order of length retention is consistent
between studies, even though treatments and measurement types (sum vs.
average in their study) differed. Using the measure of proportional growth we see
no significant difference between control and stressed seedlings in any of the
genotypes (Figure 1b).
Goggin and Colmer also compared 15˚ C seedlings treated with a hypoxic pre-
treatment and 72 h anoxia to see whether they differed in root and seed soluble
carbohydrate, seed starch and alpha amylase activity. They found minimal
genotypic differences and concluded that carbohydrate metabolism cannot
explain recovery ability variation between genotypes (Goggin and Colmer, 2007).
Surprisingly, soluble carbohydrate concentrations in roots decreased more
94
during 72 h aeration than during 72 h anoxia. Seed starch was lower under
anoxia, and in concordance, seed soluble carbohydrates were higher, even
though alpha amylase activity was lower under anoxia (Goggin and Colmer,
2007). A lack of alpha-amylase induction in anoxic wheat seedlings is thought to
underlie the failure of wheat to anoxically germinate (Perata et al., 1992). Our
metabolomics analyses showed that the 15˚ C roots (primary and seminal) of
Ducula-4, SARC, Spear and Carnamah showed significant reductions in
fructose, but for glucose, anoxic reductions were limited to Ducula-4, Carnamah
and Calingiri (Table 1). Sucrose was significantly depleted in SARC and Calingiri
only (Table 1). This data suggests that the four genotypes included in Goggin
and Colmer’s study do differ in terms of 15˚ C root carbohydrate metabolism, at
least for the conditions we studied (24 h anoxia). Although, it is important to note
that metabolome analysis may have differed if seminal and primary roots were
separated.
The importance of ADH: is it a predictor of performance under anoxia?
When rice is transformed with an antisense Adh1 construct so that its activity is
only 4-8% of untransformed plants, seedlings show reduced anaerobic ethanol
production and coleoptile elongation (Matsumura et al., 1998; Rahman et al.,
2001). Although we are unaware of any wheat ADH mutants, the adh null
mutants of other intolerant species such as Arabidopsis, barley and maize show
reduced tolerance to low oxygen including increased sensitivity to acetaldehyde
as well as reduced seed viability and germination during submergence
(Schwartz, 1969; Harberd and Edwards, 1982; Jacobs et al., 1988; Johnson et
al., 1994).
It is also believed that a hypoxic pre-treatment of wheat roots, which increases
activity of PDC and ADH, improves tolerance in subsequent anoxia treatments
(Waters et al., 1991). Pyruvate decarboxylase appears to be rate-limiting in
ethanolic fermentation (Waters et al., 1991). For example, Arabidopsis PDC over-
expressers, but not ADH over-expressers, show improved survival under hypoxia
95
(Ismond et al., 2003). All this suggests that low ADH activity under low oxygen is
likely to confer reduced tolerance to anoxia.
In this study, the only genotype to show anaerobic ADH activity induction in 15˚
C coleoptiles was Calingiri. For 28˚ C coleoptiles, only Ducula-4 showed
significant induction. In our previous study, Calingiri did show significant ADH
activity induction (Shingaki-Wells et al., 2011). The question is whether Calingiri
and Ducula-4 coleoptiles at 15˚ C and 28˚ C, respectively, are top performers in
the other measurements that were made to determine anoxia tolerance.
Tissue anoxia tolerance, the influence of temperature and the validity of
the electrolyte leakage assay
Responses to anoxia were conditional on the temperature at which seedlings
were grown in. This has been reported previously, where wheat roots lose
elongation potential at 25˚ C after only 10 h anoxia compared to seedlings at 15˚
C, where after 20 h anoxia, 50-70 % of the elongation potential remained
(Waters et al., 1991). In our study, 15˚ C seminal roots showed no significant
difference in 3 d proportional growth between post-anoxic and aerated controls
(Figure 1b). At 28˚ C, the case was similar, except for Spear, which did show a
significant reduction in 3 d proportional growth post-anoxia (Figure 1d). 28˚ C
primary roots appeared to be more sensitive to anoxia than seminal roots, in that
Ducula-4, Spear and Carnamah showed significant proportional growth inhibition
post-anoxia (Figure 1d). At 15˚ C, primary roots did not show proportional growth
consequences (Figure 1b). Coleoptiles and leaves at 28˚ C appeared better off
than roots, showing no proportional growth consequences across all five
genotypes post-anoxia. In concordance, at 15˚ C, the coleoptiles and leaves
were the only tissues to show higher post-anoxic proportional growth,
suggesting coleoptiles are more tolerant to anoxia than roots. The greater anoxia
tolerance of coleoptiles relative to roots has been documented. For example,
anoxic growth suppression is high in rice roots but not coleoptiles, whereas ATP
levels, ADH activity, ethanol production and sugar concentrations are higher in
anoxic coleoptiles (Kato-Noguchi et al., 2011).
96
While no significant differences were seen between 15˚ C anoxic and aerated
seedlings, the leakage of aerated seedlings was higher at 15˚ C than at 28˚ C
(Figure 2). The growth data of this study and that of older studies (Waters et al.,
1991) indicates that anoxia at 28˚ C is a harsher stress than at 15˚ C (Figure 1).
Thus if electrolyte leakage is indicative of cell damage, it is expected that anoxic
leakage values would be higher in the 28˚ C treatment compared to the 15˚ C
treatment. On the other hand, cell leakage increases in anoxic seedlings are only
evident at 28˚ C, which is consistent with the idea that anoxia at warmer
temperatures is more stressful than at cooler temperatures.
Electrolyte leakage is a standard measure of cell damage in much research
(Patterson et al., 1976; Yan et al., 1996), yet we are left with the question of
whether this assay measures exclusively what it is purported to. Recent research
has revealed that senescing leaves of barley show increased electrolyte leakage
(EL) despite the fact that there was no significant increase in membrane damage.
The lines of evidence that indicate this include (1) selective leakage of ammonium
when K+ was more abundant (2) absent increase in a cell death stain that is
dependent on membrane damage and (3) increased EL in leaves that were
reversing senescence, a response that requires maintenance of cellular
compartmentalisation (Rolny et al., 2011). Since EL does not appear to
exclusively measure membrane damage, this may explain why 15˚ C control
seedlings show higher EL than 28˚ C seedlings. Interestingly, sterol content is
lower in 28˚ C anoxic roots of all five genotypes, compared to aeration (Table 1).
If this is indicative of membrane damage or membrane composition changes,
alterations in EL could result. However, as this study measured EL of whole
seedlings, the individual contribution of each tissue to EL is unknown.
It is known that there is a net K+ and phosphate uptake when coleoptiles
supplemented with exogenous glucose are treated with anoxia for 60 h (Colmer
et al., 2001). K+ is important for maintaining osmotic pressure, which in turn
promotes elongation of anoxic coleoptile cells (Menegus et al., 1984). Growth of
97
shoots and roots also occurs during re-aeration. In contrast, when coleoptiles
are not supplemented with glucose, post-anoxic recovery was reduced and K+ /
phosphate losses were apparent (Colmer et al., 2001). Reduced tolerance to
anoxia in the absence of exogenous glucose is also inferred via pronounced
degeneration of anoxic mitochondria (Vartapetian et al., 1976), reduced ATP
concentrations (Huang et al., 2005) and a reduction in elongation potential
(Waters et al., 1991). Additionally, rates of net uptake of K+ and phosphate are
associated with ethanol production rates by anoxic rice coleoptiles. Conversely,
losses in K+ and Cl- occurred where glucose was not supplied and during re-
aeration, net rates of uptake were stunted in sugar-deprived samples (Huang et
al., 2005). In our previous study, we showed electrolyte leakage of 28˚ C wheat
seedlings subjected to 3 d of anoxia to be far greater than that of rice, which
correlated with the known tolerance of rice to low oxygen (Shingaki-Wells et al.,
2011). All this suggests that ion leakage and uptake is associated with,
respectively, reduced and improved tolerance to anoxic stress.
These measurements are complicated by the fact that the whole seedling was
used, as opposed to excised coleoptiles, as in the ADH assay. The decision to
keep seedlings intact was a precaution that was taken to minimise the
contribution that excision would have to electrolyte leakage.
Anoxia tolerance of Calingiri and Ducula-4
As discussed above, we set out to determine whether Calingiri was a unique
wheat genotype to explain the surprising result that alanine did not accumulate in
28˚ C rice coleoptiles subjected to anoxia (Shingaki-Wells et al., 2011). Calingiri
seedlings at 28˚ C performed well in terms of electrolyte leakage, and its
coleoptiles significantly increased ADH activity under anoxia at 15˚ C, only just
failing to meet the significance threshold at 28˚ C. 15˚ C growth regimes revealed
the coleoptiles of Calingiri as among three other genotypes to show significant
increases in proportional growth (3 d) post-anoxia relative to continuous aeration.
In contrast, Calingiri leaves were alone in maintaining growth rates (Figure 1b). At
28˚ C, Calingiri and Spear showed higher proportional growth rates (3d) than
98
their respective controls and in primary roots, Calingiri was the only genotype to
not show significant losses in proportional growth post-anoxia. These results
suggest relative anoxia tolerance of Calingiri compared to the other genotypes,
although this tolerance was not consistent across all treatment combinations.
Assigning anoxia-intolerance judgements to the other genotypes has proved
more difficult.
Whilst Ducula-4 coleoptiles showed promise at 28˚ C in the ADH assays (Figure
3b), it also showed significant increases in cell leakage at 28˚ C under anoxia
(Figure 2b). Ducula-4 tissues only show anoxia tolerance in 15˚ C coleoptiles in
terms of growth maintenance, but Ducula-4 is among three other genotypes also
showing this response (Figure 1b). This ranking is further complicated by the
apparent high performance of Ducula-4 at 15˚ C in seminal roots, primary roots
and coleoptiles in terms of % elongation retention when comparing the length of
tissues after 3 d re-oxygenation compared to 8 d old aerated controls
(Supplemental Figure 1e). This demonstrates how different ways of looking at
data can have a profound influence on our interpretations. Using % elongation
retention, Goggin and Colmer (2007) reported Ducula-4 to be among their
anoxia-tolerant genotypes.
Our metabolite analysis highlighted some important differences in metabolism
between genotypes, temperatures and tissues. We were interested in the
benefits of carbohydrate preservation as well as the response of succinate and
amino acids since these metabolites have been shown to strongly and
differentially respond to low oxygen in different species (Narsai et al., 2011).
a. Importance of preservation of CHO: some references in rice and
other plant species
Across genotypes, 15˚ C coleoptiles seem to stand out in their maintenance of
sugar supplies under anoxia (Table 1). 15˚ C coleoptiles of Calingiri are the only
samples to show accumulation of fructose under anoxia. Higher fructose levels
might be due to mobilisation of starch reserves or prevention of sugar
99
exhaustion. In an anoxia-intolerant cultivar of rice (IR22), soluble sugar
concentrations were lower after germination and growth under anoxia than in a
more tolerant cultivar (cv. Amaroo) (Huang et al., 2003). Exogenous sugar supply
improved fresh weight and coleoptile extension of the IR22 (Huang et al., 2003).
Interestingly, when seeds were germinated under air and hypoxically pre-treated,
the tolerance differences between cultivars reduced, with IR22 even showing
higher glucose concentrations and faster growth than a cultivar with intermediate
tolerance (cv. Calrose) (Huang et al., 2003). Furthermore, when IR22 and
Amaroo coleoptiles were excised and supplied with glucose, anoxia tolerance
and ethanol production rates were similar, suggesting anoxia intolerance can be
a function of sugar availability (Huang et al., 2003). The importance of sugar
under anoxia, and the uniqueness of 15˚ C Calingiri coleoptiles in terms of its
fructose response and anoxic ADH induction suggests this sample is relatively
anoxia tolerant (Table 1).
b. Anoxic responses of succinate and GABA
In 15˚ C coleoptiles, Calingiri was the only genotype to maintain citric acid and
malic acid levels, with all other genotypes showing depletion under anoxia (Table
1). Steady citric acid and malic acid levels could be useful in maintaining the
synthesis of amino acids that branch off the TCA cycle via 2-oxoglutarate and
oxaloacetate. The depletion of 2-oxoglutarate and accumulation of alanine and
GABA in all genotypes of 15˚ C coleoptiles suggests operation of the GABA
shunt pathway, whereby 2-oxoglutaric acid is converted to glutamate, which
shows no significant change in all genotypes, which is subsequently
decarboxylated to product GABA (Shelp et al., 1995). Transamination of GABA
with pyruvate produces alanine and succinic semialdehyde, which then
generates succinate. The first and last reaction of GABA shunt produce and
consume NAD+, respectively, meaning this pathway is ‘NAD+ neutral’. This is in
contrast to TCA cycle-derived succinate production, which consumes NAD+, a
cofactor necessary to maintain glycolysis under low oxygen. Succinate did not
change in Ducula-4, Carnamah or Calingiri, and even more surprisingly, was
depleted in SARC and Spear. This leads us to ask what the metabolic fate of
100
succinate is under low oxygen or question whether succinate leakage from cells
is a possibility. Succinate leakage, however, would challenge the idea that anoxic
plants synthesise alanine in preference to ethanol, which can be easily lost from
the cell (Rocha et al., 2010).
b. Alanine responses under anoxia
When oxygen returns, alanine could be converted back to pyruvate, for eventual
assimilation into aerobic metabolic processes (Miyashita et al., 2007). Despite the
fact that its production has no role in NAD+ generation, alanine is likely an
important compound since its accumulation does not decline in the event of low
nitrogen availability (Rocha et al., 2010). In anaerobic roots of barley, the activity
of alanine aminotransferase parallels that of alcohol dehydrogenase, that is, their
activities are induced over days under low oxygen (Good and Crosby, 1989). It
was also shown that alanine aminotransferase activity was induced in the
anaerobic roots of maize, rye and wheat, but not leaves (Good and Crosby,
1989). Alanine aminotransferase also increases in abundance in anoxic rice
coleoptiles (Shingaki-Wells et al., 2011).
It is hypothesised that alanine has an important role in consumption of pyruvate
which would otherwise activate alternative oxidase or interfere with respiration
inhibition and consume what little oxygen is left in the cell (Gupta et al., 2009;
Zabalza et al., 2009; Rocha et al., 2010). This thinking however, is not applicable
when plants are truly anoxic. Alanine production may also be useful in diverting
carbon backbones from excessive production of ethanol, which can diffuse out
of the cell or pose a threat of toxicity (Rocha et al., 2010). Alanine production
produces 2-oxoglutarate, whose metabolism as a result of partial TCA cycle
operation, could result in the production of an extra ATP during the succinate
synthesis step (Rocha et al., 2010). Confirming our previous metabolomics
experiments (Shingaki-Wells et al., 2011), Calingiri coleoptiles at 28˚ C did not
accumulate alanine (Table 1). Only Carnamah showed accumulation in 28˚ C
coleoptiles, and in 28˚ C roots, SARC, Spear and Calingiri were the only
genotypes to show increases in alanine. Across all genotypes, 15˚ C roots and
101
coleoptiles accumulated alanine during anoxia (Table 1). ANOVA analyses
indicate that treatment and temperature interact to have a significant impact on
alanine signals, with alanine levels being higher at 15˚ C and under anoxia (p-
val<0.001). Additionally, coleoptiles have higher levels of alanine than roots (p-
val<0.001), and Calingiri has the highest alanine levels, with Ducula-4 having the
lowest alanine levels (genotype p-val<0.001) (Supplemental Table 6).
Alanine levels are higher at 15˚ C than at 28˚ C, and this might be due to the fact
that alcohol dehydrogenase activity levels are much higher at 28˚ C than at 15˚ C
(Figure 3), meaning more pyruvate can be directed towards the ethanol
fermentation pathway rather than the alanine synthesis pathway at higher
temperatures. This could mean that 15˚ C tissues have a higher capacity to
synthesise alanine due to pyruvate availability.
Conclusions
These analyses have revealed the complexity of ranking genotypes for tolerance
to anoxia. Calingiri, however, might have a slight tolerance advantage: it shows
alcohol dehydrogenase activity induction under anoxia, low cell leakage, superior
growth recovery in some respects, and sugar pools that appear more stable than
other genotypes. We found that an absent alanine response under anoxia is not
specific to wheat, but is temperature, tissue and genotype-dependent. The
specific role of alanine under anoxia is unclear, and requires further investigation.
Root sensitivity to anoxia was confirmed, as was the sensitivity of wheat at higher
temperatures. The large influence that temperature and tissue type have on the
metabolic responses of wheat to anoxia are likely to partially contribute to the
tolerance discrepancies reported in the literature.
102
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Figures
Figure 1. Proportional growth of wheat tissues.
Figure 2. Electrolyte leakage of whole wheat seedlings.
Figure 3. Specific ADH activity of wheat coleoptiles.
107
Figure 1. Proportional growth of wheat tissues at 15˚ C (A-B) or 28˚ C (C-D) subjected to one day of anoxia and then one (A, C) or three (B, D) days of re-oxygenation, relative to continuously aerated controls. Full figure caption and Figure 1C-D on following page.
Me
an
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_R
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T issue Treatment
*
*
**
Me
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*
*
*
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108
Figure 1. Proportional growth of wheat tissues at 15˚ C or 28˚ C subjected to one day of anoxia and then one or three days of re-oxygenation, relative to continuously aerated controls. A. 15˚ C, 1 d proportional growth. B. 15˚ C, 3 d proportional growth. C. 28˚ C, 1 d proportional growth. D. 28˚ C, 3 d proportional growth. Briefly, the difference between the length of seedlings immediately after anoxia was subtracted from the length of seedlings after 1 or
Me
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109
3 d re-oxygenation. This difference was calculated as a proportion of the length of seedlings immediately after anoxia. Control values were calculated using continuously aerated samples at the same ages. See methods for full calculations. *** indicates p-val <0.001; ** indicates p-val<0.01 and * indicates a p-val<0.05. Error bars represent +/- 1 standard error. Figure 1A-B on prior page.
110
Figure 2. Electrolyte leakage of whole wheat seedlings after an hour of incubation as a ratio of maximum electrolyte leakage after sample boiling (3 seedings per replicate, n=11-16). Air indicates a treatment of air for 4 days. Anoxia indicates a treatment of four days of air followed by one say of anoxia. A. 15˚ C seedlings. B. 28˚ C seedlings. *** indicates p-val <0.001; ** indicates p-val<0.01 and * indicates a p-val<0.05. Error bars represent +/- 1 standard error. Figure 2B follows on the next page.
Genotype
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111
Figure 2. Electrolyte leakage of whole wheat seedlings at 15˚ C (A) and 28˚ C (B). For full figure caption, see previous page.
BB
Genotype
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Figure 3. Specific Alcohol Dehydrogenase (ADH) activity of wheat coleoptiles before and after anoxia. Air indicates a treatment of air for 4 days. Anoxia indicates a treatment of four days of air followed by one say of anoxia. A. 15˚ C coleoptiles. B. 28˚ C coleoptiles. *** indicates p-val <0.001; ** indicates p-val<0.01 and * indicates a p-val<0.05. Error bars represent +/- 1 standard error. Figure 3B follows on the next page.
Genotype
Sp
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Figure 3. Specific ADH activity of wheat coleoptiles before and after anoxia. A. 15˚ C coleoptiles. B. 28˚ C coleoptiles. Figure 3A on previous page.
Genotype
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114
Tables Table 1. Metabolite profiling of anoxic tissues.
115
Tab
le 1
. Met
abol
ite p
rofil
ing
of a
noxi
c tis
sues
. Ful
l tab
le c
aptio
n an
d se
cond
par
t of t
able
follo
ws
on th
e ne
xt p
age.
116
Tab
le 1
. Con
tinue
d. M
etab
olite
pro
filin
g of
roo
ts (p
rimar
y /s
emin
al) a
nd c
oleo
ptile
s fro
m a
ll fiv
e ge
noty
pes
at 1
5˚ C
and
28˚
C. N
umbe
rs
repr
esen
t m
etab
olite
res
pons
e va
lue
ratio
s w
ith a
noxi
cally
-tre
ated
sam
ples
as
the
num
erat
or, a
nd a
erat
ed s
ampl
es a
s th
e de
nom
inat
or
(4 d
air
1 d
anox
ia d
ivid
ed b
y 4
d ai
r). B
lue
colo
rs in
dica
te m
etab
olite
s th
at s
igni
fican
tly d
eple
te d
urin
g an
oxia
, an
d gr
een
repr
esen
ts
met
abol
ites
that
sig
nific
antly
acc
umul
ate
unde
r an
oxia
. Firs
t par
t of t
his
tabl
e is
on
the
prev
ious
pag
e.
117
Supplemental Figures Supplemental Figure 1. Growth profiles of tissues from seedlings treated with anoxia/re-oxygenation compared to continuous aeration. Supplemental Figure 2. GABA signal normalised to tissue mass and ribitol in 28˚ C coleoptiles and roots.
118
S
upp
lem
enta
l Fig
ure
1. G
row
th p
rofil
es o
f tis
sues
from
see
dlin
gs tr
eate
d w
ith a
noxi
a/re
-oxy
gena
tion
com
pare
d to
con
tinuo
us
aera
tion.
A. C
oleo
ptile
leng
th. B
. Lea
f len
gth.
C. L
engt
h of
sum
of s
emin
al r
oots
. D. P
rimar
y ro
ot le
ngth
. E. %
ret
entio
n gr
owth
of
anox
ic v
s. c
ontin
uous
ly a
erat
ed s
ampl
es a
t 8 d
old
. Sup
plem
enta
l Fig
ures
1B
-E fo
llow
on
the
next
pag
es.
0 10
20
30
40
50
4d air (for air)
5d air
6d air
8d air
4d air (for anoxia)
4d air 1d N2
4d air 1d N2 1d air
4d air 1d N2 3d air
4d air (for air)
5d air
6d air
8d air
4d air (for anoxia)
4d air 1d N2
4d air 1d N2 1d air
4d air 1d N2 3d air
15˚ C
15
˚ C
28˚ C
28
˚ C
Length (mm) C
oleo
ptile
Duc
ula
SA
RC
Spe
ar
Car
nam
ah
Cal
ingi
ri
SF
1a
119
S
upp
lem
enta
l Fig
ure
1. G
row
th p
rofil
es o
f tis
sues
from
see
dlin
gs tr
eate
d w
ith a
noxi
a/re
-oxy
gena
tion
com
pare
d to
con
tinuo
usae
ratio
n. A
. Col
eopt
ile. B
. Lea
f. C
. Sum
of s
emin
al r
oots
. D. P
rimar
y ro
ots.
E. %
ret
entio
n gr
owth
of a
noxi
c vs
. con
tinuo
usly
aer
ated
sa
mpl
es a
t 8 d
old
.
0 20
40
60
80
100
120
140
160
4d air (for air)
5d air
6d air
8d air
4d air (for anoxia)
4d air 1d N2
4d air 1d N2 1d air
4d air 1d N2 3d air
4d air (for air)
5d air
6d air
8d air
4d air (for anoxia)
4d air 1d N2
4d air 1d N2 1d air
4d air 1d N2 3d air
15˚ C
15
˚ C
28˚ C
28
˚ C
Length (mm)
Leaf
Duc
ula
SA
RC
Spe
ar
Car
nam
ah
Cal
ingi
ri
SF
1b
120
Sup
ple
men
tal F
igur
e 1.
Gro
wth
pro
files
of t
issu
es fr
om s
eedl
ings
trea
ted
with
ano
xia/
re-o
xyge
natio
n co
mpa
red
to c
ontin
uous
ae
ratio
n. A
. Col
eopt
ile. B
. Lea
f. C
. Sum
of s
emin
al r
oots
. D. P
rimar
y ro
ots.
E. %
ret
entio
n gr
owth
of a
noxi
c vs
. con
tinuo
usly
aer
ated
sa
mpl
es a
t 8 d
old
. 0 50
100
150
200
250
4d air (for air)
5d air
6d air
8d air
4d air (for anoxia)
4d air 1d N2
4d air 1d N2 1d air
4d air 1d N2 3d air
4d air (for air)
5d air
6d air
8d air
4d air (for anoxia)
4d air 1d N2
4d air 1d N2 1d air
4d air 1d N2 3d air
15˚ C
15
˚ C
28˚ C
28
˚ C
Length (mm) S
um o
f Sem
inal
Roo
ts
Duc
ula
SA
RC
Spe
ar
Car
nam
ah
Cal
ingi
ri
SF
1c
121
Sup
ple
men
tal F
igur
e 1.
Gro
wth
pro
files
of t
issu
es fr
om s
eedl
ings
trea
ted
with
ano
xia/
re-o
xyge
natio
n co
mpa
red
to c
ontin
uous
ae
ratio
n. A
. Col
eopt
ile. B
. Lea
f. C
. Sum
of s
emin
al r
oots
. D. P
rimar
y ro
ots.
E. %
ret
entio
n gr
owth
of a
noxi
c vs
. con
tinuo
usly
aer
ated
sa
mpl
es a
t 8 d
old
. 0 20
40
60
80
100
120
4d air (for air)
5d air
6d air
8d air
4d air (for anoxia)
4d air 1d N2
4d air 1d N2 1d air
4d air 1d N2 3d air
4d air (for air)
5d air
6d air
8d air
4d air (for anoxia)
4d air 1d N2
4d air 1d N2 1d air
4d air 1d N2 3d air
15˚ C
15
˚ C
28˚ C
28
˚ C
Length (mm) P
rimar
y R
oot
Duc
ula
SA
RC
Spe
ar
Car
nam
ah
Cal
ingi
ri
SF
1d
122
S
upp
lem
enta
l Fig
ure
1. G
row
th p
rofil
es o
f tis
sues
from
see
dlin
gs tr
eate
d w
ith a
noxi
a/re
-oxy
gena
tion
com
pare
d to
con
tinuo
usly
ae
ratio
n. A
. Col
eopt
ile. B
. Lea
f. C
. Sum
of s
emin
al r
oots
. D. P
rimar
y ro
ots.
E. %
ret
entio
n gr
owth
of a
noxi
c vs
. con
tinuo
usly
aer
ated
sa
mpl
es a
t 8 d
old
. 0 20
40
60
80
100
120
15˚ C
28
˚ C
15˚ C
28
˚ C
15˚ C
28
˚ C
15˚ C
28
˚ C
Sum
Sem
inal
Roo
ts
Prim
ary
Roo
t C
oleo
ptile
Le
af
% Retention of growth at 8 days of age (post-anoxia/air)
Duc
ula
SA
RC
Spe
ar
Car
nam
ah
Cal
ingi
ri
120
SF
1e
123
S
upp
lem
enta
l Fig
ure
2. G
AB
A s
igna
l nor
mal
ised
to ti
ssue
mas
s an
d rib
itol i
n 28
˚ C c
oleo
ptile
s an
d ro
ots.
124
Supplemental Tables Supplemental Table 1. P-values and mean differences associated with length comparisons between different tissues at the beginning of the experiment (4 d air) and at other time points. Supplemental Table 2. P-values and mean differences associated with length comparisons made between seedlings treated anoxia and samples at other time points. Supplemental Table 3. Alcohol dehydrogenase assay p-values. Supplemental Table 4. Genotype-centric representation of Table 1 (metabolite data). Supplemental Table 5A. Average length of tissues at different time points and % growth retention calculations. Supplemental Table 6. ANOVA interaction data for alanine signals.
125
Sup
ple
men
tal T
able
1A
. P-v
alue
s an
d m
ean
diffe
renc
es a
ssoc
iate
d w
ith le
ngth
com
paris
ons
of c
oleo
ptile
s at
the
begi
nnin
g of
the
expe
rimen
t (4
d ai
r) be
twee
n di
ffere
nt g
enot
ypes
. Num
bers
in b
old
indi
cate
sig
nific
ance
.
Tis
sue�
Tem
p_G
eno
_Tre
at (a
)�vs
. Tem
p_G
eno
_Tre
at (b
)�
Mea
n d
iffer
ence
(a-
b)�
AN
OVA
p-v
al�
cole
optil
e 28
_Cal
ingi
ri_4d
air
(for
air)
28_D
ucul
a_4d
air
(for
air)
-20.1200*
3.33E-09
cole
optil
e 28
_Cal
ingi
ri_4d
air
(for
air)
28_S
AR
C_4
d ai
r (fo
r ai
r) -9.6000*
1.35E-04
cole
optil
e 28
_Cal
ingi
ri_4d
air
(for
air)
28_S
pear
_4d
air
(for
air)
-13.2800*
3.47E-09
cole
optil
e 28
_Cal
ingi
ri_4d
air
(for
air)
28_C
arna
mah
_4d
air
(for
air)
-17.2400*
3.33E-09
cole
optil
e 28
_SA
RC
_4d
air
(for
air)
28_D
ucul
a_4d
air
(for
air)
-10.5200*
6.61E-06
cole
optil
e 28
_SA
RC
_4d
air
(for
air)
28_S
pear
_4d
air
(for
air)
-3.6
8 1.
00E
+00
cole
optil
e 28
_SA
RC
_4d
air
(for
air)
28_C
arna
mah
_4d
air
(for
air)
-7.6400*
2.76E-02
cole
optil
e 28
_SA
RC
_4d
air
(for
air)
28_C
alin
giri_
4d a
ir (fo
r ai
r) 9.6000*
1.35E-04
cole
optil
e 28
_Spe
ar_4
d ai
r (fo
r ai
r) 28
_Duc
ula_
4d a
ir (fo
r ai
r) -6
.84
1.40
E-0
1
cole
optil
e 28
_Spe
ar_4
d ai
r (fo
r ai
r) 28
_SA
RC
_4d
air
(for
air)
3.68
1.
00E
+00
cole
optil
e 28
_Spe
ar_4
d ai
r (fo
r ai
r) 28
_Car
nam
ah_4
d ai
r (fo
r ai
r) -3
.96
1.00
E+
00
cole
optil
e 28
_Spe
ar_4
d ai
r (fo
r ai
r) 28
_Cal
ingi
ri_4d
air
(for
air)
13.2800*
3.47E-09
cole
optil
e 28
_Car
nam
ah_4
d ai
r (fo
r ai
r) 28
_Duc
ula_
4d a
ir (fo
r ai
r) -2
.88
1.00
E+
00
cole
optil
e 28
_Car
nam
ah_4
d ai
r (fo
r ai
r) 28
_SA
RC
_4d
air
(for
air)
7.6400*
2.76E-02
cole
optil
e 28
_Car
nam
ah_4
d ai
r (fo
r ai
r) 28
_Spe
ar_4
d ai
r (fo
r ai
r) 3.
96
1.00
E+
00
cole
optil
e 28
_Car
nam
ah_4
d ai
r (fo
r ai
r) 28
_Cal
ingi
ri_4d
air
(for
air)
17.2400*
3.33E-09
cole
optil
e 28
_Duc
ula_
4d a
ir (fo
r ai
r) 28
_SA
RC
_4d
air
(for
air)
10.5200*
6.61E-06
cole
optil
e 28
_Duc
ula_
4d a
ir (fo
r ai
r) 28
_Spe
ar_4
d ai
r (fo
r ai
r) 6.
84
1.40
E-0
1
cole
optil
e 28
_Duc
ula_
4d a
ir (fo
r ai
r) 28
_Car
nam
ah_4
d ai
r (fo
r ai
r) 2.
88
1.00
E+
00
cole
optil
e 28
_Duc
ula_
4d a
ir (fo
r ai
r) 28
_Cal
ingi
ri_4d
air
(for
air)
20.1200*
3.33E-09
126
Sup
ple
men
tal T
able
1B
. P-v
alue
s an
d m
ean
diffe
renc
es a
ssoc
iate
d w
ith le
ngth
com
paris
ons
mad
e be
twee
n ae
rate
d se
edlin
gs a
t the
be
ginn
ing
of th
e ex
perim
ent (
4 d)
and
tim
e po
ints
late
r in
the
expe
rimen
t. N
umbe
rs in
bol
d in
dica
te s
igni
fican
ce.
C
ole
op
tile�
Leaf�
Sem
inal
Ro
ot�
Pri
mar
y R
oo
t�
Tem
p_G
eno
_Tre
at (a
)�vs
. Tem
p_G
eno
_Tre
at (b
)�
Mea
n D
iffer
ence
(a
-b)�
AN
OVA
p-
val�
Mea
n D
iffer
ence
(a
-b)�
AN
OVA
p-
val�
Mea
n D
iffer
ence
(a
-b)�
AN
OVA
p-
val�
Mea
n D
iffer
ence
(a
-b)�
AN
OVA
p-
val�
15_D
ucul
a_4d
air
(for
air)
15_D
ucul
a_5d
air
-7.9200*
1.44E-02
-7.8
9.98
E-0
1 -2
1.44
1.
00E
+00
-1
4.76
9.
98E
-01
15
_Duc
ula_
6d a
ir -17.2800*
3.33E-09
-18.4000*
6.81E-05
-43.0400*
2.36E-02
-29.2400*
6.72E-03
15
_Duc
ula_
8d a
ir -20.7200*
3.33E-09
-44.1600*
3.33E-09
-85.2400*
3.33E-09
-56.8400*
3.33E-09
15_S
AR
C_4
d ai
r (fo
r ai
r) 15
_SA
RC
_5d
air
-6.9
6 1.
12E
-01
-6.9
2 1.
00E
+00
-2
0.24
1.
00E
+00
-1
3.32
1.
00E
+00
15_S
AR
C_6
d ai
r -14.1600*
3.33E-09
-16.3600*
1.76E-03
-44.2800*
1.40E-02
-24.
84
1.07
E-0
1
15_S
AR
C_8
d ai
r -19.0400*
3.33E-09
-35.6800*
3.33E-09
-100.8400*
3.33E-09
-49.0000*
3.34E-09
15_S
pear
_4d
air
(for
air)
15_S
pear
_5d
air
-7.9600*
1.30E-02
-7.9
6 9.
97E
-01
-19.
8 1.
00E
+00
-1
3.64
1.
00E
+00
15_S
pear
_6d
air
-21.0000*
3.33E-09
-20.7200*
1.01E-06
-52.4400*
2.55E-04
-27.9200*
1.68E-02
15
_Spe
ar_8
d ai
r -28.2000*
3.33E-09
-42.2000*
3.33E-09
-108.9200*
3.33E-09
-56.1600*
3.33E-09
15_C
arna
mah
_4d
air
(for
air)
15_C
arna
mah
_5d
air
-6.8
4 1.
40E
-01
-6.8
4 1.
00E
+00
-1
7.72
1.
00E
+00
-1
3.12
1.
00E
+00
15_C
arna
mah
_6d
air
-19.2000*
3.33E-09
-15.7200*
4.43E-03
-35.
28
3.18
E-0
1 -27.2000*
2.69E-02
15
_Car
nam
ah_8
d ai
r -25.3617*
3.33E-09
-37.3617*
3.33E-09
-65.9017*
1.01E-07
-52.7283*
3.33E-09
15_C
alin
giri_
4d a
ir (fo
r ai
r) 15
_Cal
ingi
ri_5d
air
-5.7
6 5.
99E
-01
-5.5
2 1.
00E
+00
-1
6.32
1.
00E
+00
-7
.44
1.00
E+
00
15
_Cal
ingi
ri_6d
air
-16.0800*
3.33E-09
-13.9600*
4.22E-02
-36.
72
2.17
E-0
1 -1
7.84
9.
12E
-01
15
_Cal
ingi
ri_8d
air
-24.5600*
3.33E-09
-26.7200*
3.33E-09
-85.0000*
3.33E-09
-36.8000*
1.17E-05
28_D
ucul
a_4d
air
(for
air)
28_D
ucul
a_5d
air
-0.2
4 1.
00E
+00
-27.7200*
3.33E-09
-43.5600*
1.90E-02
-19.
8 6.
97E
-01
28
_Duc
ula_
6d a
ir -0
.32
1.00
E+
00
-53.4800*
3.33E-09
-72.7200*
3.76E-09
-34.1600*
1.30E-04
28
_Duc
ula_
8d a
ir 0.
2 1.
00E
+00
-93.8800*
3.33E-09
-102.2800*
3.33E-09
-48.5200*
3.35E-09
28_S
AR
C_4
d ai
r (fo
r ai
r) 28
_SA
RC
_5d
air
-5.2
8.57
E-0
1 -20.7600*
9.35E-07
-56.3200*
2.83E-05
-14.
52
9.99
E-0
1
28_S
AR
C_6
d ai
r -8.4000*
4.28E-03
-38.0400*
3.33E-09
-88.8000*
3.33E-09
-24.
04
1.60
E-0
1
28_S
AR
C_8
d ai
r -4
.76
9.63
E-0
1 -65.2000*
3.33E-09
-120.8400*
3.33E-09
-38.4800*
2.28E-06
28_S
pear
_4d
air
(for
air)
28_S
pear
_5d
air
-7.4800*
3.94E-02
-19.4000*
1.18E-05
-43.6000*
1.87E-02
-18.
16
8.87
E-0
1
28_S
pear
_6d
air
-8.0000*
1.18E-02
-38.8400*
3.33E-09
-77.8800*
3.33E-09
-37.1200*
8.59E-06
28
_Spe
ar_8
d ai
r -5
.96
4.93
E-0
1 -76.3200*
3.33E-09
-109.2000*
3.33E-09
-51.0000*
3.33E-09
28_C
arna
mah
_4d
air
(for
air)
28_C
arna
mah
_5d
air
-1.8
1.00
E+
00
-20.8800*
7.42E-07
-29.
04
8.57
E-0
1 -1
9.64
7.
19E
-01
28
_Car
nam
ah_6
d ai
r -2
.24
1.00
E+
00
-41.2400*
3.33E-09
-57.7600*
1.20E-05
-36.6400*
1.36E-05
28
_Car
nam
ah_8
d ai
r -2
1.00
E+
00
-74.4400*
3.33E-09
-92.2400*
3.33E-09
-56.5200*
3.33E-09
28_C
alin
giri_
4d a
ir (fo
r ai
r) 28
_Cal
ingi
ri_5d
air
-11.7200*
8.79E-08
-18.1200*
1.09E-04
-37.
8 1.
58E
-01
-17.
64
9.26
E-0
1
28_C
alin
giri_
6d a
ir -13.3600*
3.43E-09
-38.4400*
3.33E-09
-68.7600*
1.15E-08
-33.2800*
2.79E-04
28
_Cal
ingi
ri_8d
air
-13.5200*
3.38E-09
-73.1600*
3.33E-09
-108.6400*
3.33E-09
-47.4000*
3.44E-09
127
Sup
ple
men
tal T
able
2A
. P-v
alue
s an
d m
ean
diffe
renc
es a
ssoc
iate
d w
ith le
ngth
com
paris
ons
mad
e be
twee
n se
edlin
gs tr
eate
d w
ith
cont
inuo
us a
erat
ion
and
thos
e tr
eate
d w
ith o
ne d
ay o
f ano
xia
follo
wed
by
thre
e da
ys o
f re-
oxyg
enat
ion.
Co
leo
pti
le�
Leaf�
Sem
inal
Ro
ot�
Pri
mar
y R
oo
t�
Tem
p_G
eno
_Tre
at (a
)V
s. T
emp
_Gen
o_T
reat
(b)
Mea
n D
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ence
(a-
b)�
AN
OVA
p-v
al�
Mea
n D
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ence
(a-
b)�
AN
OVA
p-v
al�
Mea
n D
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ence
(a-
b)�
AN
OVA
p-v
al�
Mea
n D
iffer
ence
(a-
b)�
AN
OVA
p-v
al�
15_D
ucul
a_8d
air
15_D
ucul
a_4d
air
1d N
2 3d
air
0.52
1.
00E
+00
24
.200
0*4.
03E
-09
34.5
6 3.
77E
-01
21
5.20
E-0
1
15_S
AR
C_8
d ai
r 15
_SA
RC
_4d
air
1d N
2 3d
air
2.88
1.
00E
+00
16
.560
0*0.
0013
0751
.760
0*0.
0003
685
33.9
200*
0.00
0160
7
15_S
pear
_8d
air
15_S
pear
_4d
air
1d N
2 3d
air
4.92
9.
35E
-01
19.2
800*
1.46
E-0
573
.880
0*3.
50E
-09
39.8
400*
5.77
E-0
7
15_C
arna
mah
_8d
air
15_C
arna
mah
_4d
air
1d N
2 3d
air
4.64
17
9.81
E-0
1 16
.561
7*1.
68E
-03
46.3
017*
7.04
E-0
330
.968
3*2.
35E
-03
15_C
alin
giri_
8d a
ir 15
_Cal
ingi
ri_4d
air
1d N
2 3d
air
2.88
1.
00E
+00
8.
8 9.
71E
-01
48.7
200*
1.77
E-0
314
.04
1.00
E+
00
28_D
ucul
a_8d
air
28_D
ucul
a_4d
air
1d N
2 3d
air
-3.0
4 1.
00E
+00
48
.160
0*3.
33E
-09
73.8
000*
3.51
E-0
940
.920
0*1.
89E
-07
28_S
AR
C_8
d ai
r 28
_SA
RC
_4d
air
1d N
2 3d
air
-2.6
8 1.
00E
+00
16
.360
0*1.
76E
-03
83.6
000*
3.33
E-0
917
.4
9.40
E-0
1
28_S
pear
_8d
air
28_S
pear
_4d
air
1d N
2 3d
air
1.4
1.00
E+
00
27.8
400*
3.33
E-0
910
4.80
00*
3.33
E-0
948
.840
0*3.
34E
-09
28_C
arna
mah
_8d
air
28_C
arna
mah
_4d
air
1d N
2 3d
air
-2.5
6 1.
00E
+00
35
.480
0*3.
33E
-09
68.9
600*
1.04
E-0
854
.520
0*3.
33E
-09
28_C
alin
giri_
8d a
ir 28
_Cal
ingi
ri_4d
air
1d N
2 3d
air
-1.7
6 1.
00E
+00
24
.720
0*3.
54E
-09
58.1
600*
9.41
E-0
633
.760
0*1.
85E
-04
128
Sup
ple
men
tal T
able
2B
. P-v
alue
s an
d m
ean
diffe
renc
es a
ssoc
iate
d w
ith le
ngth
com
paris
ons
mad
e be
twee
n an
oxic
ally
-str
esse
d an
d re
-oxy
gena
ted
seed
lings
.
Co
leo
pti
le�
Leaf�
Sem
inal
Ro
ot�
Pri
mar
y R
oo
t�
Tem
p_G
eno
_Tre
at (a
)V
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emp
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reat
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-b)�
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15_D
ucul
a_4d
air
1d N
2 15
_Duc
ula_
4d a
ir 1d
N2
1d a
ir -6
.56
2.23
E-0
1 -3
.72
1.00
E+
00
-10.
64
1.00
E+
00
-9.2
1.00
E+
00
15_D
ucul
a_4d
air
1d N
2 15
_Duc
ula_
4d a
ir 1d
N2
3d a
ir -1
8.16
00*
3.33
E-0
9-1
8.64
00*
4.51
E-0
5-4
4.52
00*
1.26
E-0
2-3
0.44
00*
2.76
E-0
315
_SA
RC
_4d
air
1d N
2 15
_SA
RC
_4d
air
1d N
2 1d
air
-5.6
4 6.
62E
-01
-4
1.00
E+
00
-6.4
8 1.
00E
+00
-1
.72
1.00
E+
00
15_S
AR
C_4
d ai
r 1d
N2
15_S
AR
C_4
d ai
r 1d
N2
3d a
ir -1
4.68
00*
3.33
E-0
9-1
7.80
00*
1.86
E-0
4-4
6.04
00*
6.39
E-0
3-1
1.12
1.
00E
+00
15
_Spe
ar_4
d ai
r 1d
N2
15_S
pear
_4d
air
1d N
2 1d
air
-6.4
4 2.
68E
-01
-5.1
6 1.
00E
+00
-4
.64
1.00
E+
00
-4.1
6 1.
00E
+00
15
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ar_4
d ai
r 1d
N2
15_S
pear
_4d
air
1d N
2 3d
air
-22.
0800
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33E
-09
-21.
8800
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05E
-07
-31.
28
6.81
E-0
1 -1
3.76
1.
00E
+00
15
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nam
ah_4
d ai
r 1d
N2
15_C
arna
mah
_4d
air
1d N
2 1d
air
-6.5
2 2.
37E
-01
-4.4
8 1.
00E
+00
-7
.48
1.00
E+
00
-7.6
4 1.
00E
+00
15
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nam
ah_4
d ai
r 1d
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15_C
arna
mah
_4d
air
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air
-20.
8400
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33E
-09
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9200
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86E
-07
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92
9.99
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1 -2
2.8
2.78
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1 15
_Cal
ingi
ri_4d
air
1d N
2 15
_Cal
ingi
ri_4d
air
1d N
2 1d
air
-5.6
4 6.
62E
-01
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8 1.
00E
+00
-9
.64
1.00
E+
00
-6.1
2 1.
00E
+00
15
_Cal
ingi
ri_4d
air
1d N
2 15
_Cal
ingi
ri_4d
air
1d N
2 3d
air
-21.
0400
*3.
33E
-09
-17.
6800
*2.
26E
-04
-34.
32
3.98
E-0
1 -2
0.04
6.
63E
-01
28_D
ucul
a_4d
air
1d N
2 28
_Duc
ula_
4d a
ir 1d
N2
1d a
ir -0
.44
1.00
E+
00
-7.0
4 1.
00E
+00
-3
.88
1.00
E+
00
0.36
1.
00E
+00
28
_Duc
ula_
4d a
ir 1d
N2
28_D
ucul
a_4d
air
1d N
2 3d
air
-1.2
1.00
E+
00
-41.
8800
*3.
33E
-09
-21.
08
1.00
E+
00
0.2
1.00
E+
00
28_S
AR
C_4
d ai
r 1d
N2
28_S
AR
C_4
d ai
r 1d
N2
1d a
ir -3
.96
1.00
E+
00
-10.
88
5.75
E-0
1 -8
.44
1.00
E+
00
-0.0
4 1.
00E
+00
28
_SA
RC
_4d
air
1d N
2 28
_SA
RC
_4d
air
1d N
2 3d
air
-4.0
8 9.
99E
-01
-43.
4400
*3.
33E
-09
-19.
2 1.
00E
+00
-5
.52
1.00
E+
00
28_S
pear
_4d
air
1d N
2 28
_Spe
ar_4
d ai
r 1d
N2
1d a
ir -4
.32
9.95
E-0
1 -1
0.36
7.
18E
-01
-1
1.00
E+
00
0.64
1.
00E
+00
28
_Spe
ar_4
d ai
r 1d
N2
28_S
pear
_4d
air
1d N
2 3d
air
-4.0
4 9.
99E
-01
-46.
7200
*3.
33E
-09
-5.2
1.00
E+
00
-0.6
1.00
E+
00
28_C
arna
mah
_4d
air
1d N
2 28
_Car
nam
ah_4
d ai
r 1d
N2
1d a
ir -1
.44
1.00
E+
00
-5.4
1.00
E+
00
-2.6
8 1.
00E
+00
0.
24
1.00
E+
00
28_C
arna
mah
_4d
air
1d N
2 28
_Car
nam
ah_4
d ai
r 1d
N2
3d a
ir -1
.84
1.00
E+
00
-34.
2400
*3.
33E
-09
-21.
12
1.00
E+
00
0.2
1.00
E+
00
28_C
alin
giri_
4d a
ir 1d
N2
28_C
alin
giri_
4d a
ir 1d
N2
1d a
ir -9
.080
0*6.
47E
-04
-11.
6 3.
77E
-01
-11.
68
1.00
E+
00
-4.2
1.00
E+
00
28_C
alin
giri_
4d a
ir 1d
N2
28_C
alin
giri_
4d a
ir 1d
N2
3d a
ir -1
0.00
00*
3.77
E-0
5-4
3.32
00*
3.33
E-0
9-4
2.28
00*
3.21
E-0
2-9
.76
1.00
E+
00
129
Sup
ple
men
tal T
able
3. A
lcoh
ol d
ehyd
roge
nase
ass
ay p
-val
ues
(AN
OV
A).
AD
H p
-val
ues�
Tem
per
atur
e ( ˚
C
)�G
eno
typ
e_ai
r (a
)�G
eno
typ
e_an
oxi
a (b
)�
Mea
n D
iffer
ence
(a-
b)�
Sig
.�28
Duc
ula_
Air
Duc
ula_
Anx
-1.2405*
0.049
28
SARC_A
ir SARC_A
nx
0.36
25
0.98
6 28
Spe
ar_A
ir Spe
ar_A
nx
-0.305
9 0.99
6 28
Carna
mah
_Air
Carna
mah
_Anx
-0.445
7 0.94
8 28
Calingiri_
Air
Calingiri_
Anx
-0.885
3 0.30
8 15
Duc
ula_
Air
Duc
ula_
Anx
0.01
11
1 1.00
0 15
SARC_A
ir SARC_A
nx
-0.042
7 0.79
6 15
Spe
ar_A
ir Spe
ar_A
nx
-0.049
4 0.64
6 15
Carna
mah
_Air
Carna
mah
_Anx
-0.010
5 1.00
0 15
Calingiri_
Air
Calingiri_
Anx
-.1607*
0.00013
130
Supplemental Table 4. Genotype-centric representation of Table 1. Metabolite profiling of roots (primary /seminal) and coleoptiles from all five genotypes at 15˚ C and 28˚ C. Numbers represent metabolite response values with anoxically-treated samples as the numerator, and aerated samples as the denominator (4 d air 1 d anoxia divided by 4 d air). Blue colours indicate metabolites that significantly deplete during anoxia, and green represents metabolites that significantly accumulate under anoxia.
Ducula SARC Spear Carnamah Calingiri
Root Coleoptile Root Coleoptile Root Coleoptile Root Coleoptile Root Coleoptile 15˚ C 28˚ C 15˚ C 28˚ C 15˚ C 28˚ C 15˚ C 28˚ C 15˚ C 28˚ C 15˚ C 28˚ C 15˚ C 28˚ C 15˚ C 28˚ C 15˚ C 28˚ C 15˚ C 28˚ C
Sugars / Glycolysis
Sucrose 0.93 0.01 0.73 0.21 0.69 0.16 0.83 0.17 0.76 0.11 1.09 0.10 0.80 0.17 0.79 0.90 0.61 0.35 0.88 0.26 D-Fructose 1 0.29 0.02 0.74 0.44 0.54 0.03 1.18 0.52 0.49 0.07 1.12 0.28 0.36 0.08 1.35 0.80 0.76 0.43 2.19 0.54 D-Fructose 2 0.26 0.02 0.70 0.40 0.51 0.03 1.17 0.45 0.47 0.07 1.13 0.25 0.33 0.07 1.37 0.74 0.76 0.39 2.23 0.53 D-Glucose 0.66 0.05 0.53 0.71 0.87 0.26 0.78 0.64 0.75 0.10 0.53 0.39 0.72 0.12 0.77 0.86 0.62 0.13 1.47 0.61 D-Glucose or Galactose 0.53 0.05 0.43 0.54 0.77 0.23 0.74 0.37 0.70 0.08 0.53 0.26 0.65 0.10 0.75 0.66 0.58 0.08 1.61 0.54 D-Fructose-6-Phosphate 0.26 1.58 0.58 0.06 0.85 1.47 0.62 0.29 0.70 0.47 0.76 0.26 0.79 0.09 0.79 3.51 1.53 0.27 0.90 1.19 D-Glucose-6-Phosphate 0.27 0.82 0.52 0.05 0.86 1.48 0.51 0.34 0.72 0.22 0.73 0.28 0.74 0.21 0.69 3.38 1.16 0.25 0.81 1.06 Trehalose 0.73 0.14 0.64 0.88 0.81 0.21 0.85 0.79 0.74 0.75 0.60 0.37 0.93 0.55 0.59 0.91 0.45 0.57 0.62 0.58 Galactose 0.66 0.05 0.53 0.71 0.87 0.26 0.78 0.64 0.75 0.10 0.53 0.39 0.72 0.12 0.77 0.86 0.62 0.13 1.47 0.61
TCA Cycle
Citric Acid 0.25 0.02 0.30 0.19 1.12 0.06 0.43 0.23 1.61 0.28 0.50 0.30 0.52 0.29 0.38 0.71 0.94 0.33 0.62 0.63 Isocitric Acid 0.29 0.10 0.42 0.16 0.77 0.08 0.43 0.28 1.10 0.33 0.86 0.74 0.70 0.27 0.62 0.96 0.64 0.26 0.50 0.56 Fumaric acid 0.91 0.37 0.36 0.71 0.86 0.46 0.59 0.11 0.75 0.50 0.39 0.66 1.18 0.55 0.55 0.58 0.79 1.32 0.70 0.79 2-oxoglutaric acid 0.19 0.01 0.29 0.04 0.33 0.07 0.20 0.12 0.55 0.10 0.27 0.32 0.51 0.10 0.18 1.49 0.44 0.18 0.21 1.08 Succinate 0.96 2.21 0.68 3.66 0.61 2.59 0.56 0.64 0.58 3.39 0.36 1.80 1.52 3.08 0.64 2.40 0.74 4.41 1.16 1.92 Malic Acid 0.39 0.07 0.16 0.11 0.44 0.08 0.34 0.03 0.61 0.12 0.32 0.37 0.50 0.14 0.33 0.34 0.44 0.33 0.74 0.53
Amino Acids
4-Hydroxyproline 0.34 0.03 0.48 0.31 0.86 0.05 0.65 0.23 0.75 0.15 0.55 0.28 0.62 0.23 0.80 0.84 0.81 0.47 1.06 1.01 Aspartic Acid 0.21 0.00 0.22 0.07 0.39 0.01 0.27 0.04 0.58 0.11 0.44 0.12 0.35 0.28 0.38 0.29 0.28 0.18 0.80 0.20 L-Glutamate 0.16 0.62 0.44 0.04 0.45 1.10 0.45 0.06 0.73 0.41 1.05 0.10 0.37 0.29 0.66 1.32 0.80 0.97 1.01 0.61 L-Glutamine 0.58 0.18 0.12 0.06 1.01 0.17 0.05 0.01 0.70 0.29 0.20 0.03 1.11 0.19 0.15 0.75 1.09 0.93 0.15 0.58 beta-Alanine 1.38 0.20 5.19 1.38 4.30 0.72 9.32 1.23 4.72 0.74 6.49 1.61 3.35 1.82 5.70 2.37 5.15 2.94 7.93 2.22 Glycine 1.68 1.18 3.62 1.39 2.58 3.49 5.92 1.76 3.11 5.01 5.39 1.76 4.25 3.06 5.73 3.63 3.77 4.38 5.87 1.40 L-Alanine 2.83 0.82 7.52 1.09 9.13 4.14 6.00 1.22 7.00 9.47 13.18 1.90 9.89 1.98 5.77 6.48 9.55 9.41 4.23 1.71 L-Asparagine 0.85 0.01 0.31 0.15 1.43 0.08 0.24 0.06 1.67 0.04 0.52 0.53 1.87 0.32 1.07 0.56 2.97 0.43 1.04 0.72 L-Isoleucine 0.93 0.07 2.08 0.56 1.35 0.70 3.00 0.51 1.35 1.05 5.72 1.02 2.33 1.10 4.21 1.33 2.04 2.10 4.15 1.04 L-Leucine 0.88 0.08 2.49 1.54 1.34 0.85 4.81 0.94 1.36 1.17 8.08 1.35 2.34 1.04 6.48 3.93 2.25 2.30 6.36 1.36 L-Lysine 2.29 0.26 4.53 1.12 4.21 1.70 5.37 0.69 4.65 0.38 9.53 1.37 7.70 1.63 6.51 2.43 21.20 8.77 6.31 2.53 L-Methionine 0.70 0.00 1.17 0.19 0.88 0.05 0.97 0.20 1.25 0.71 5.40 0.93 1.02 1.90 1.67 1.31 1.30 1.29 1.93 0.62 L-Phenylalanine 0.48 0.05 1.42 1.04 1.17 0.08 2.22 0.59 2.65 1.36 6.54 0.70 1.46 1.52 4.46 4.34 2.70 2.50 3.21 1.46 L-Proline 1.79 0.13 6.12 4.23 6.35 3.93 6.57 1.64 4.62 8.82 36.85 3.76 7.32 7.88 5.65 11.42 13.11 8.18 5.48 1.87 L-Serine 0.61 0.01 1.00 0.48 1.56 0.46 0.59 0.26 2.26 0.90 1.64 0.34 1.58 0.84 1.33 1.18 2.16 1.43 1.68 0.54 L-Threonine (3 TMS) 0.98 0.03 2.55 0.42 1.85 0.62 2.64 0.29 2.28 1.20 5.73 0.92 2.25 1.14 3.37 1.34 2.93 1.77 3.19 0.84 L-Threonine (2 TMS) 0.97 0.08 2.38 0.46 1.55 0.36 2.96 0.56 1.59 1.00 6.68 1.30 1.28 0.77 3.03 1.36 2.24 1.14 2.61 1.21 L-Tryptophan 0.61 0.84 1.16 0.24 0.84 0.91 1.00 0.29 1.17 0.73 1.16 0.60 0.90 0.77 1.05 1.34 0.94 0.78 1.30 2.90 L-Tyrosine 0.53 1.33 2.51 0.68 1.15 1.11 4.12 0.33 1.32 0.52 10.84 0.70 3.28 1.11 6.69 3.43 6.99 1.07 6.74 1.50 L-Valine 0.90 0.14 1.90 0.47 1.54 0.68 2.69 0.47 1.22 1.06 4.99 1.05 1.96 1.03 2.82 1.33 2.07 2.10 3.22 0.97 Ornithine 1.00 0.15 0.70 0.77 1.03 0.32 0.61 0.60 1.58 0.52 0.79 0.64 2.06 0.45 0.95 1.29 1.82 0.64 1.10 0.95 GABA 18.01 4.76 19.80 6.17 25.45 12.64 30.31 12.61 50.89 12.19 13.11 6.94 40.45 14.22 44.11 11.88 34.59 17.98 40.60 9.54 2-Aminobutyric acid 0.68 0.57 0.62 0.52 1.38 0.56 0.49 0.29 0.96 0.86 0.65 0.20 0.56 0.54 0.68 1.76 1.30 1.61 0.63 0.42
Polyamine Putrescine 1.41 0.20 2.34 2.04 1.64 0.65 2.32 1.84 2.77 1.86 2.05 1.27 2.03 2.32 3.09 5.57 1.64 3.51 2.21 1.15
Sugar acid D-Gluconic acid 0.71 0.20 1.02 0.38 1.11 0.37 1.10 0.47 0.75 0.57 1.06 0.55 0.97 0.62 1.09 1.05 1.06 0.74 4.99 5.53 L-Threonic acid 0.51 0.09 0.56 0.40 0.61 0.19 0.83 0.61 0.66 0.39 0.45 0.80 0.70 0.51 0.98 1.56 0.61 0.51 1.29 1.19 Glyceric acid 0.73 0.08 0.83 0.39 1.24 0.10 0.85 0.40 1.12 0.34 0.60 0.75 1.00 0.37 1.04 0.72 1.05 0.55 0.95 0.59
Polyols
Mannitol/Sorbitol 2.89 0.61 5.34 0.57 3.04 1.12 0.81 0.62 0.67 1.24 0.35 0.60 0.42 0.73 0.46 0.83 0.79 0.61 1.65 1.10 Threitol/Erythritol 0.40 0.07 0.16 0.10 0.44 0.08 0.38 0.03 0.61 0.12 0.33 0.36 0.51 0.14 0.33 0.33 0.44 0.33 0.74 0.51 meso-Erythritol 0.40 0.08 0.17 0.11 0.45 0.08 0.36 0.04 0.61 0.13 0.33 0.37 0.51 0.15 0.33 0.35 0.45 0.34 0.74 0.53 Myo-Inositol 1.30 0.57 3.70 1.36 2.97 1.13 2.35 0.76 2.41 1.59 3.44 1.15 2.58 1.89 2.48 3.86 2.38 1.74 1.62 0.99 Glycerol 1.56 0.67 1.45 3.31 1.77 0.91 2.37 1.51 0.89 1.72 1.34 1.07 0.60 1.01 1.54 1.93 0.85 0.53 2.28 2.35 Shikimic acid 0.20 0.18 0.06 0.10 0.39 0.29 0.26 0.05 0.36 0.36 0.87 2.07 0.38 0.57 0.37 0.52 0.38 0.41 0.42 2.51
Sterol Campesterol 0.62 0.54 1.23 1.04 1.12 0.42 0.94 1.45 1.04 0.68 1.19 1.29 0.97 0.60 1.02 0.94 1.03 0.81 0.97 1.16 beta-Sitosterol 0.57 0.39 1.17 0.60 1.15 0.37 0.90 0.87 1.01 0.70 1.18 0.63 0.66 0.45 0.98 0.94 1.04 0.80 0.92 0.72
Misc.
Urea 0.73 0.08 1.27 1.64 1.76 0.17 0.82 1.97 0.86 0.66 1.37 0.53 1.24 0.29 1.06 1.12 1.77 1.07 1.00 0.49 Cinnamic acid 0.43 0.03 0.55 0.58 0.97 0.08 0.91 0.34 1.16 0.25 0.74 0.41 0.60 0.60 0.98 1.07 0.91 0.28 1.27 0.77 Nicotinic acid 0.70 0.07 1.06 1.14 1.08 0.32 1.11 0.56 0.97 0.90 0.80 1.06 0.87 0.81 1.02 0.95 1.10 1.40 0.98 1.03 Phosphoric acid 0.83 0.51 0.94 0.49 1.37 0.32 1.27 0.34 1.37 0.83 0.88 0.81 1.13 0.73 1.09 0.99 1.18 0.84 1.25 0.78
131
Sup
ple
men
tal T
able
5A
. Ave
rage
leng
th o
f col
eopt
iles
at 1
5˚ C
and
28˚
C u
nder
con
trol
, ano
xic
and
post
-ano
xic
cond
ition
s. N
=25
.
Mea
n le
ngth
s (m
m) a
nd %
elo
ngat
ion
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ntio
n �
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Car
nam
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Cal
ing
iri�
cole
op
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15˚ C
�4d a
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or
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9.24
6.96
7.6
7.68
5.2
cole
op
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5d a
ir�
17.16
13.92
15.56
14.52
10.96
cole
op
tile�
6d a
ir�
26.52
21.12
28.6
26.88
21.28
cole
op
tile�
8d a
ir�
29.96
26
35.8
33.041
7 29
.76
�
co
leo
pti
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15˚ C
�4d a
ir (f
or
ano
xia)�
10.04
7.92
7.8
7.24
5.2
cole
op
tile�
4d a
ir 1
d N
2�11
.28
8.44
8.8
7.56
5.84
co
leo
pti
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4d a
ir 1
d N
2 1d
air�
17.84
14.08
15.24
14.08
11.48
cole
op
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4d a
ir 1
d N
2 3d
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29.44
23.12
30.88
28.4
26.88
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Ret
enti
on (3
d)�
98.3
88.9
86.3
86.0
90.3
�
co
leo
pti
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28˚ C
�4d a
ir (f
or
air)�
41.72
31.2
34.88
38.84
21.6
cole
op
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5d a
ir�
41.96
36.4
42.36
40.64
33.32
cole
op
tile�
6d a
ir�
42.04
39.6
42.88
41.08
34.96
cole
op
tile�
8d a
ir�
41.52
35.96
40.84
40.84
35.12
�
co
leo
pti
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28˚ C
�4d a
ir (f
or
ano
xia)�
43.12
34.16
35.2
41.2
25.76
cole
op
tile�
4d a
ir 1
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2�43
.36
34.56
35.4
41.56
26.88
cole
op
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4d a
ir 1
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2 1d
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43.8
38.52
39.72
43
35.96
cole
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4d a
ir 1
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2 3d
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44.56
38.64
39.44
43.4
36.88
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% R
eten
tion
(3d
)�107.3
107.5
96.6
106.3
105.0
132
Sup
ple
men
tal T
able
5B
. Ave
rage
leng
th o
f lea
ves
at 1
5˚ C
and
28˚
C u
nder
con
trol
, ano
xic
and
post
-ano
xic
cond
ition
s. N
=25
.
Mea
n le
ngth
s (m
m) a
nd %
elo
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Car
nam
ah�
Cal
ing
iri�
leaf�
15˚ C
�4d a
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or
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9.12
6.96
7.6
7.68
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le
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5d a
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16.92
13.88
15.56
14.52
10.68
leaf�
6d a
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27.52
23.32
28.32
23.4
19.12
leaf�
8d a
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53.28
42.64
49.8
45.041
7 31
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le
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15˚ C
�4d a
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or
ano
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10
7.72
7.8
7.24
4.88
le
af�
4d a
ir 1
d N
2�10
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8.28
8.64
7.56
5.4
leaf�
4d a
ir 1
d N
2 1d
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14.16
12.28
13.8
12.04
10.08
leaf�
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29.08
26.08
30.52
28.48
23.08
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Ret
enti
on (3
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54.6
61.2
61.3
63.2
72.4
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le
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28˚ C
�4d a
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or
air)�
49.64
32.48
35.56
39.6
17.4
leaf�
5d a
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77.36
53.24
54.96
60.48
35.52
leaf�
6d a
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103.12
70
.52
74.4
80.84
55.84
leaf�
8d a
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143.52
97
.68
111.88
11
4.04
90
.56
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le
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28˚ C
�4d a
ir (f
or
ano
xia)�
51.88
37.4
36.24
43.76
21.16
leaf�
4d a
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d N
2�53
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37.88
37.32
44.32
22.52
leaf�
4d a
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d N
2 1d
air�
60.52
48.76
47.68
49.72
34.12
leaf�
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2 3d
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95.36
81.32
84.04
78.56
65.84
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% R
eten
tion
(3d
)�66.4
83.3
75.1
68.9
72.7
133
Sup
ple
men
tal T
able
5C
. Ave
rage
leng
th o
f sem
inal
roo
t sum
s at
15˚
C a
nd 2
8˚ C
und
er c
ontr
ol, a
noxi
c an
d po
st-a
noxi
c co
nditi
ons.
N
=25
.
Mea
n le
ngth
s (m
m) a
nd %
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ngat
ion
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ntio
n �
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nam
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ing
iri�
sum
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inal
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˚ C�4d
air
(fo
r ai
r)�
37.64
28.24
26.52
24.64
10.12
sum
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inal
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5d a
ir�
59.08
48.48
46.32
42.36
26.44
sum
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inal
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6d a
ir�
80.68
72.52
78.96
59.92
46.84
sum
_sem
inal
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8d a
ir�
122.88
12
9.08
13
5.44
90
.541
7 95
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su
m_s
emin
al_r
oo
ts� 15
˚ C�4d
air
(fo
r an
oxi
a)�
40.44
30.36
28.92
19.68
11.52
sum
_sem
inal
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4d a
ir 1
d N
2�43
.8
31.28
30.28
21.32
12.08
sum
_sem
inal
_ro
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4d a
ir 1
d N
2 1d
air�
54.44
37.76
34.92
28.8
21.72
sum
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inal
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4d a
ir 1
d N
2 3d
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88.32
77.32
61.56
44.24
46.4
�%
Ret
enti
on (3
d)�
71.9
59.9
45.5
48.9
48.8
�
su
m_s
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al_r
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ts� 28
˚ C�4d
air
(fo
r ai
r)�
87.2
86.2
83.6
62.44
35.4
sum
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inal
_ro
ots�
5d a
ir�
130.76
14
2.52
12
7.2
91.48
73.2
sum
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inal
_ro
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6d a
ir�
159.92
17
5 16
1.48
12
0.2
104.16
su
m_s
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al_r
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ts�
8d a
ir�
189.48
20
7.04
19
2.8
154.68
14
4.04
�
sum
_sem
inal
_ro
ots� 28
˚ C�4d
air
(fo
r an
oxi
a)�
95.32
107.92
83
.96
66.08
42.96
sum
_sem
inal
_ro
ots�
4d a
ir 1
d N
2�94
.6
104.24
82
.8
64.6
43.6
sum
_sem
inal
_ro
ots�
4d a
ir 1
d N
2 1d
air�
98.48
112.68
83
.8
67.28
55.28
sum
_sem
inal
_ro
ots�
4d a
ir 1
d N
2 3d
air�
115.68
12
3.44
88
85
.72
85.88
��
% R
eten
tion
(3d
)�61.1
59.6
45.6
55.4
59.6
134
Sup
ple
men
tal T
able
5D
. A
vera
ge le
ngth
of p
rimar
y ro
ots
at 1
5˚ C
and
28˚
C u
nder
con
trol
, ano
xic
and
post
-ano
xic
cond
ition
s. N
=25
.
Mea
n le
ngth
s (m
m) a
nd %
elo
ngat
ion
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ntio
n �
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nam
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ing
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pri
mar
y_ro
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15˚ C
�4d a
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or
air)�
31.4
24.4
23.24
23.48
6.96
p
rim
ary_
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t�5d
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46.16
37.72
36.88
36.6
14.4
pri
mar
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6d a
ir�
60.64
49.24
51.16
50.68
24.8
pri
mar
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8d a
ir�
88.24
73.4
79.4
76.208
3 43
.76
�
p
rim
ary_
roo
t�
15˚ C
�4d a
ir (f
or
ano
xia)�
34.52
27.8
25.16
21.48
8.6
pri
mar
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4d a
ir 1
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2�36
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28.36
25.8
22.44
9.68
p
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1d
N2
1d a
ir�
46
30.08
29.96
30.08
15.8
pri
mar
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4d a
ir 1
d N
2 3d
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67.24
39.48
39.56
45.24
29.72
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Ret
enti
on (3
d)�
76.2
53.8
49.8
59.4
67.9
�
p
rim
ary_
roo
t�
28˚ C
�4d a
ir (f
or
air)�
56
30.36
57.72
48.48
25.8
pri
mar
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5d a
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75.8
44.88
75.88
68.12
43.44
pri
mar
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6d a
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90.16
54.4
94.84
85.12
59.08
pri
mar
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8d a
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104.52
68
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108.72
10
5 73
.2
�
p
rim
ary_
roo
t�
28˚ C
�4d a
ir (f
or
ano
xia)�
61.72
47.56
59.68
50.88
29.28
pri
mar
y_ro
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4d a
ir 1
d N
2�63
.8
45.92
59.28
50.68
29.68
pri
mar
y_ro
ot�
4d a
ir 1
d N
2 1d
air�
63.44
45.96
58.64
50.44
33.88
pri
mar
y_ro
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4d a
ir 1
d N
2 3d
air�
63.6
51.44
59.88
50.48
39.44
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% R
eten
tion
(3d
)�60.8
74.7
55.1
48.1
53.9
135
Sup
ple
men
tal T
able
6.
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tera
ctio
n da
ta fo
r al
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Tes
ts o
f B
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ject
s Eff
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of
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ted
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4770
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5a
39
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30.7
34.3
1.00E-13
Int
erce
pt�
4402
618.9
1 44
0261
8.9
1235
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1.00E-13
tis
sue�
3088
73.6
1 30
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Chapter 4 Rice and wheat responses to re-oxygenation
137
Foreword to Study III
Since Study I reported rapid amino acid accumulation in anoxic rice, the fate
of these pools upon re-oxygenation was also questioned. The comparatively
subtle response of wheat to anoxia is also interesting, in light of the fact that
sucrose rapidly depletes in anoxic wheat. This suggests that anoxic wheat is
assimilating the carbon from sucrose into glycolysis, but in contrast to rice, is
unable to replenish it from endosperm starch reserves. Despite this, wheat
amino acid metabolism remains relatively stable.
In Study III, aerobically germinated rice and wheat seedlings were treated with
anoxia for one day, at 28˚ C. Re-oxygenation treatments followed and lasted
for one day. Proteome and metabolome changes in coleoptiles were analysed
in an attempt to understand whether these inter-species differences during
anoxia would continue upon re-oxygenation.
Indeed, the proteomic and metabolomic responses to anoxia and re-
oxygenation were generally subtler in wheat. The rapid accumulation of amino
acids seen in rice (Study I) was confirmed in Study III. Furthermore, rapid
responses of the rice metabolome were observed during re-oxygenation.
Specifically, amino acids were consumed post-anoxia. This was not the case
for wheat, which failed to anaerobically accumulate amino acids to the extent
seen in rice. In Study III, the role of amino acid consumption during re-
oxygenation is discussed.
Proteomics analysis revealed several inter-species similarities. For example,
the abundance of several antioxidant proteins was affected by anoxia/re-
oxygenation. Finally, the activity of two classes of antioxidant enzymes was
measured in an attempt to clarify the somewhat contradictory proteomics
data.
138
Differential recovery of rice and wheat during re-
oxygenation after short-term anoxic stress
Rachel N. Shingaki-Wells, Shaobai Huang & A. Harvey Millar.
Australian Research Council Centre of Excellence in Plant Energy Biology, Centre for
Comparative Analysis of Biomolecular Networks, Bayliss Building M316 University of Western
Australia, 35 Stirling Highway, Crawley 6009, Western Australia, Australia.
Abstract
The metabolic responses of plants to anoxia are well documented, however,
comparatively little is known about the effects of re-oxygenation. This study
aimed to understand how two cereal crops, rice and wheat (Oryza sativa and
Triticum aestivum, respectively), respond to post-anoxia at the metabolomic
and proteomic level. We found considerable divergence in the metabolism of
these two species. Specifically, highly accumulating amino acids were rapidly
consumed during re-oxygenation in rice, but not in wheat. At the protein level,
we saw several similarities between these species. Both species exhibited
changes in proteins involved in cell structure re-modelling, translation and
oxidative stress defence. Overall, however, the response of rice to these
stresses appeared to be more pronounced than what was observed in wheat.
We discuss how this could be a result of the ability of rice to synthesise
sucrose during anoxia, to improve anaerobic ATP production, and indirectly
increase amino acid production, for improved fitness during re-oxygenation.
Introduction
Floods are expected to increase in frequency as a result of anthropological
climate change (Arnell and Liu, 2001; Bailey-Serres and Voesenek, 2008).
Submerged plants are quickly deprived of oxygen (Armstrong, 1979),
especially if microbial activity is high and light penetration is limited (Bailey-
Serres et al., 2012). Floods in Pakistan resulted in $4.45 billion worth of
139
damage to rice, wheat and cotton, with rice being highly flood-threatened in
many parts of the world (Arshad and Shafi, 2010; Bailey-Serres et al., 2012).
The threat that floods pose to food security has been a motivator for much
research on crops that are both tolerant and intolerant to low oxygen. Plants
that survive floods are eventually returned to an oxygenated environment. Yet
most studies focus on the low-oxygen event alone, with little attention paid to
the stresses imposed when floodwaters recede. De-submergence can cause
drought stress (Branco-Price et al., 2008) as well as oxidative stress (Blokhina
et al., 1999) and thus the transcriptomes, proteomes and metabolomes of an
anoxic tissue are likely to influence the degree of tolerance a plant exhibits
when oxygen returns.
A study of polysomal mRNA populations in Arabidopsis seedlings treated with
hypoxia followed by re-oxygenation revealed that more than half of the mRNA
population was prevented from complexing with ribosomes during low oxygen,
without a change in the steady state abundance (Branco-Price et al., 2008).
This strongly suggests selective translation is involved in the resulting
proteome, a process that would promote ATP conservation under low oxygen
(Branco-Price et al., 2008). During re-oxygenation, repression of translation
was rapidly reversed, with 88 % recovery of polysome levels within 1 hour,
which could allow rapid proteome adjustment when O2 returns (Branco-Price
et al., 2008). Plants may also prepare for re-oxygenation, as evidenced by a
subset of transcripts that increase under hypoxia, but are only translated
when oxygen returns (Branco-Price et al., 2008).
More recently, the first plant proteomics study focussing on re-oxygenation
showed that soybean roots adjust their proteomes during a flooding event,
and that these changes remain present even during de-submergence (Salavati
et al., 2012). Our interests lie in what happens to rice and wheat during re-
oxygenation, since we previously showed the subtle response of the wheat
coleoptile proteome and metabolome relative to rice under anoxia (Shingaki-
Wells et al., 2011). The availability of oxygen determines the mode of energy
140
metabolism and thus we expect many changes that occur under anoxia to be
reversed when oxygen is no longer limiting. Specifically, we are interested in
the fate of amino acids which so strongly accumulate in anoxic tissues (Kato-
Noguchi and Ohashi, 2006; Shingaki-Wells et al., 2011). In this study, we
analysed the proteomes and metabolomes of rice and wheat coleoptiles
subjected to air, anoxia and re-oxygenation to understand how anoxia-tolerant
and -intolerant species metabolically adjust to re-oxygenation. We also
studied the activity of two oxidative stress defence proteins, peroxidase and
catalase, to understand the role of these systems during the different phases
of anoxic stress.
Methods
Plant Growth Rice and wheat seedlings were grown at 28˚ C as described previously
(Shingaki-Wells et al., 2011). Four-day-old seedlings were germinated and
grown in the dark in a hydroponic-like system, and bubbled with air. The
coleoptiles of some seedlings were harvested at this stage and snap frozen in
liquid nitrogen. Other seedlings went on for a one-day anoxic treatment,
delivered by infusion with 99.9 % nitrogen gas. Re-oxygenation treatments
were given to some of these anoxic seedlings for one day. The details of this
growth regime are described by Shingaki-Wells and colleagues (2011).
Differential in Gel Electrophoresis (DIGE)
Protein isolation from rice and wheat coleoptiles was performed using the
chloroform/ methanol method as described previously (Wessel and Flügge,
1984; Shingaki-Wells et al., 2011). To compare air, anoxia and post-anoxia
treatments, six DIGE gels were run for both species, resulting in the
representation of each treatment by four biological replicates. Samples were
labelled with CyDyes as described previously (GE Healthcare) (Shingaki-Wells
et al., 2011) except one gel was loaded with 500 μg extra unlabelled protein
for downstream mass spectrometry analysis.
141
Quantitative gel analysis
Fluorescent gels were scanned using a Typhoon™ laser scanner (GE
Healthcare) using three filter/laser combinations: 580 BP 30, Green (532 nm);
670 BP 30, Red (633 nm); and 520 BP 40, Blue (488 nm). The PMT was set
at 520 V, sensitivity set at normal and pixel size at 100 microns. Cy2 was
selected as the internal standard. Gels were cropped using ImageQuant TL to
exclude outer gel areas where proteins did not migrate. Then .gel files were
imported into Decodon Delta2D (v 4.3), black speckles removed at a setting of
2, and arranged in their respective treatment groups. The In-Gel Standard
Warping Strategy was chosen with ‘warp mode within gels’ set as identical
and ‘warp mode between gels’ set as automatic. The master gel is by default
the first gel image added to the project. Master gels were changed if the
default gel was not representative of other gels (i.e. it had smearing, or ran
differently). After automatic warpings were performed, match vectors were
reviewed for every automatically warped gel combination. If automatic warping
was unsatisfactory in a portion of the gel, all match vectors were removed in
that section for manual matching instead (exact warp mode). After warping, a
fusion image was generated. Problematic gels or gels with obvious distortion
were excluded from the image fusion. The chosen fusion type was ‘union’.
Internal standard gels were excluded from the image fusion. Other options
were left as default. Spot detection was then carried out on the fusion image.
Delta2D’s proposed detection parameters were left as default. Spots were
manually inspected on the fusion image. Spot editing tools were used to split
spots if it was clear in 3D images that the spots within the boundary were two
separate spots. 3D inspection also allowed calls to be made on whether two
spots should be joined, or an undetected spot should actually be a spot.
Detected spots in smear regions were cancelled. After spot editing, spots
were transferred from the fusion image. Boundary transfers were adapted to
actual spots on the target images by selecting ‘re-model spots after transfer’.
Transferred spot boundaries were manually inspected using the ‘Gel Image
Regions’ window. If editing was necessary, this was done on the fusion image,
142
requiring re-transfer of spots again. Noisy spots were filtered out if the spot
quality (Q) was less than 0.02.
Statistics
Each spot intensity is calculated as a percentage of the signal intensity of the
entire gel (overall normalisation). This value is then normalised to the
corresponding Cy2 spot (horizontal normalisation). Ratios are calculated by
comparing these twice-normalised values between treatments. Delta2D
produces a ‘Quantitation Table’ listing various spot variables including ratio of
mean normalised volumes of treatment to treatment. For spot changes to be
considered significant, an ANOVA p-value had to be less than 0.05 and a ratio
change greater than 1.5.
MALDI-TOF/TOF analysis of peptides
Plugs were cut out of 2D gels and destained twice on an orbital rocker for 45
min with 50 μL 50 % ACN v/v, 25 mM NH4HCO3. Destaining solution was
removed to allow gel slices to dry at room temperature for 20 min. 12 μL
digestion solution (10 mM NH4HCO3, 3 μg/mL trypsin) was added to each
dried gel slice for incubation at 37˚ C for 16 h. Peptides were extracted from
gel slices using 12 μL 100 % v/v ACN for 15 minutes on an orbital rocker. The
supernatant was stored. Then 12 μL 50 % [v/v] ACN 5 % v/v formic acid was
added twice, for 15 min with all supernatants combined and stored. This was
then dried in a vacuum centrifuge and stored at -20˚ C for later use.
A saturated matrix mix (SMM) was made by adding powdered α-cyano-4-
hydroxycinnamic acid (CHCA) to 90 % ACN [v/v] 0.1 % TFA [v/v], briefly
vortexed and sonicated for 15 min. The solution was centrifuged at 10, 000 x
g for 5 mins to pellet residual matrix. The resulting supernatant was used as
the SMM.
Dried samples were re-constituted in 5 μL 5 % ACN [v/v] 0.1 % TFA [v/v].
After 10 mins, 2.5 μL of the sample was spotted on an MTP 384 MALDI target
plate and allowed to dry until approximately half was left. Then 2 μL spotting
143
matrix (90 % ACN [v/v] 10 % SMM [v/v]) was mixed in with the sample and
allowed to dry completely. Spots were washed with 10 μL cold 10 mM
NH4H2PO4 in 0.1 % TFA [v/v] for 10 sec. The washing solution was then
removed and spots were allowed to dry.
Analysis of peptides was carried out with an UltraFlex III MALDI-TOF/TOF
mass spectrometer (Bruker Daltonics). The laser intensity used ranged from
30 % to 60 % with up to 1200 shots per spot for MS. Selected MS/MS ions
ranged from 700-4000 m/z using 3 % additional laser power. Data were
analysed using Biotools (Bruker Daltronics) and the Rice_6.1 or WheatTC
database. The Mascot search engine v 2.3 was used with error tolerances of
±1.2 Da for MS and ±0.6 Da for MS/MS, “Max Missed Cleavages” set at 1,
variable modifications set as Carbamidomethyl (C) and Oxidation (M). The
significance threshold was set at p<0.05 (score>49).
Enzyme activity assays
Enzyme extract preparation for peroxidase and catalase assays
Tissue was ground in liquid-nitrogen cooled racks with stainless steel beads
for 2 mins at 15 shakes/s. Cooled racks were turned around and
homogenisation repeated. Cooled extraction buffer (0.1 M KH2PO4, pH 7.4)
wad added to ground tissue samples at a 5:1 ratio. This was mixed twice for
2 min at 15 shakes/s ensuring the rack was kept cold but not so cold as to
freeze samples. Samples were centrifuged at 15, 000 x g for 20 min at 4˚ C.
Supernatant was stored on ice. Protein concentration was calculated using
the Bradford assay. Assays were performed at 25˚ C.
Assay for peroxidase activity
Peroxidase activity was measured using a modified method reported
previously (Abeles and Biles, 1991).To a 1 mL cuvette, 350 μL 0.2 M sodium
acetate buffer pH 5, 350 μL 1 % guaiacol and 350 μL 0.08 % H2O2 were
added. After mixing and setting to auto-zero, 17.5 μL enzyme extract was
mixed in, and absorbance at 470 nm is recorded for 110 seconds.
144
Assay for catalase activity
Peroxidase activity was measured using a modified method reported
previously (Chance and Maehly, 1955). The spectrophotometer was auto
zeroed using 75 μM Na phosphate buffer, pH 7. To a 3 mL quartz cuvette, 1.9
mL 75 μM Na phosphate buffer, pH 7 was added followed by 100 μL enzyme
extract and then 1 mL 45 μM H2O2. The absorbance was read at 240 nm for
500 seconds.
Metabolomics
Coleoptiles of rice and wheat seedlings were harvested and rapidly snap
frozen in liquid nitrogen. Metabolites were extracted by placing 25 ± 5 mg
tissue into 2 mL Eppendorf tubes containing a stainless steel grinding bead.
Samples were snap frozen in liquid nitrogen. Metabolites were extracted and
run on a GC-MS as described previously (Howell et al., 2009; Shingaki-Wells
et al., 2011). The generated data were collected and analyzed using
Chemstation GC/MSD Data Analysis Software (Agilent Technologies),
according to earlier analyses (Shingaki-Wells et al., 2011). Data were
processed using MetabolomeExpress software, as described previously
(Carroll et al., 2010).
Results
The effect of anoxia/re-oxygenation on metabolism Four-day-old seedlings germinated and grown under aeration were
transferred to anoxia for one day and subsequently returned to air. The
metabolomic response of rice and wheat coleoptiles to anoxia/post-anoxia
contrast considerably.
During anoxia, rice increased the abundance of many amino acids, including,
Ser, Gly, Phe, Tyr, Ala, Leu, Val, Asn, Lys, Met, Homoserine, Thr, Ile, Glu, Pro,
Orn and GABA (Figure 1A, Supplemental Figure 1A). Notably, of the detected
amino acids, none were depleted in anoxic rice coleoptiles. All of the amino
145
acids that increased under anoxia showed decreases during re-oxygenation,
with the exception of ornithine, whose levels were not significantly different to
that of anoxic samples (Figure 1A, Figure 3A). These amino acid levels
remained significantly higher in re-oxygenated samples compared to pre-
anoxic samples, except for Asn and Lys, the abundances of which did not
significantly differ between post-anoxic and pre-anoxic rice samples (Figure
1A, Supplemental Figure 1B).
Sucrose levels increased in anoxic rice, whereas glucose and fructose
remained unchanged (Figure 1B). During re-oxygenation, sucrose declined but
its levels remained higher than pre-anoxic samples (Figure 1B). Glucose and
fructose showed contrasting responses during post-anoxia; glucose
increased so that levels were comparable to pre-anoxia, and fructose
decreased post-anoxia, with levels lower than pre-anoxic samples (Figure 1B).
Both of the detected glycolytic intermediates, glucose-6-phosphate and 3-
phosphoglyceric acid (3-PGA), accumulated under low oxygen in rice (Figure
1B). Both of these metabolites decreased during re-oxygenation. TCA cycle
intermediates showed contrasting responses to anoxia in rice; citrate,
isocitrate and 2-oxoglutarate were depleted during anoxia whereas succinate
and fumarate accumulated (Figure 1C). Aconitate levels remained stable
during low oxygen. Citrate, aconitate, isocitrate and 2-oxoglutarate
accumulated during re-oxygenation, whereas succinate and fumarate
decreased (Figure 1C).
For wheat, the accumulation of some amino acids during anoxia was
accompanied by the depletion of others. Gly, Trp, Leu, Val, Lys, Ile, GABA,
Pro and Orn accumulated and Ser, Asp, Glu and Gln were consumed under
anoxia (Figure 2A, Supplemental Figure 2A). Ala, Phe, Tyr, Asn, Thr, Met,
Homoserine and Arg levels did not change under anoxia (Figure 2A).
146
The levels of accumulated and depleted amino acids in anoxic wheat were
unchanged when oxygen returned. Of the non-responding amino acids in
wheat, only Ala changed, in the direction of accumulation during re-
oxygenation (Figure 2A). When comparing post-anoxic to pre-anoxic samples,
several amino acids were significantly different in abundance. Gly, Ala, Trp, Tyr,
Lys, Thr, GABA, Orn and Arg are higher whereas Asp, Asn, Glu and Gln are
lower in post-anoxic wheat compared to pre-anoxia (Figure 2A, Supplemental
Figure 2B). An interesting inter-species difference was observed in that post-
anoxic levels of sucrose were depleted in wheat (Figure 2B), whereas in rice,
sucrose levels were elevated during post-anoxia (Figure 1B). Glucose, fructose,
Glu-6-P and Fru-6-P remained stable in wheat, whereas 3-PGA decreased
under anoxia (Figure 2B). When oxygen returned, these metabolites remained
stable, except 3-PGA which accumulated post-anoxia (Figure 2B). However,
when looking at pre-anoxic levels, sucrose and glucose are higher when
compared to post-anoxic samples (Figure 2B).
Just as in rice, succinate accumulated in anoxic wheat, but fumarate
remained stable. Malate, citrate, aconitate and isocitrate decreased whereas
2-oxoglutarate levels did not change (Figure 2C). The abundances of several
TCA cycle metabolites remained perturbed during post-anoxia, compared to
pre-anoxic samples. For example, aconitate and malate were depleted in
post-anoxic samples relative to pre-anoxia (Figure 2C).
Re-oxygenation results in changes to the proteomes of rice and wheat
We analysed changes in the proteome of rice and wheat seedlings
germinated and grown under air for four days. Some seedlings were
subjected to a one-day anoxic switch and others were subsequently re-
oxygenated for one day. Protein spots significantly changing in abundance
can be seen in Figure 4A for rice and 4B for wheat. The abundances of these
proteins are represented in Figure 5 for wheat and Figure 6 for rice. Proteins
were categorised into three groups; those that significantly increased in
abundance under anoxia (Figure 5A, Figure 6A) those that did not change
147
(Figure 5B, Figure 6B) as well as those that significantly decreased in
abundance when transferred to anoxia (Figure 5C, Figure 6C). Within each
group was a set of proteins with a variety of responses to re-oxygenation. We
detected six proteins that significantly increased in wheat coleoptiles under
anoxia (Figure 5A). These include a translation initiation factor 5A (IF; 1.96 X),
pyruvate decarboxylase isozyme 2 (PDC2; 1.82 X), NADP-dependent malic
enzyme (1.55 X), 12-oxophytodienoate reductase (OPR; 1.55) and two
proteins of unknown function (2.32 X, 1.63 X) (Figure 5A). During post-anoxia,
the translation IF (spot 7, Figure 4B; Supplemental table 1) did not significantly
differ in abundance to that of the anoxic treatment, but its levels became
comparable to the pre-anoxic abundance. Pyruvate decarboxylase (Spot 33,
Figure 5A; Supplemental table 1), a classical anoxia-inducible protein,
remained elevated after one day of re-oxygenation. NADP-dependent malic
enzyme, which converts malate to pyruvate, is annotated as chloroplastic
when searching against the rice genome (84 %) and remains elevated in post-
anoxic samples.
Several wheat protein spots decreased under anoxia, eight of which could be
identified by mass spectrometry (Figure 5C). Four are unknown function
proteins (-1.44 X to -2.71 X). The others included phenylalanine ammonia
lyase (-3.72 X), which converts phenylalanine to ammonia and trans-cinnamic
acid, as well as a translation initiation factor 5A (-2.44 X). This translation
initiation factor 5A (spot 24) has a gel pI that is 0.4 units more acidic than the
other initiation factor (spot 7), which showed an increase under anoxia. These
two proteins were identified using mass spectrometry as the same gene
product (CK198613; Supplemental table 1). Phenylalanine ammonia lyase
showed an increase in abundance during re-oxygenation, which suggests this
abundance change was highly dependent on oxygen availability (Figure 5C;
spot 15, Figure 4B). ATP synthase F0 subunit 1 (-2.37 X) and heat shock
protein 20 (-1.71 X) also decreased under anoxia, but their experimental
molecular weights were far smaller than the theoretical molecular weights,
suggesting less accumulation of protein degradation products (Supplemental
148
table 1). Several proteins failed to significantly change in abundance when
transitioned to anoxia, but showed significant differences during re-
oxygenation (Figure 5B). Two of these proteins, beta-glucosidase and Hsp20
(spot 36 and 38, respectively), appeared to be protein degradation products.
A putative glycosyl hydrolase family 16 protein (spot 51, Figure 4B, Figure 5B)
has a post-anoxic abundance that is significantly less than pre-anoxic
samples. These proteins are known for their role in cell wall re-modelling
(Strohmeier et al., 2004). We also detected a putative polygalacturonase (spot
20, Figure 4B; Figure 5B), another protein involved in cell wall remodelling,
which significantly increased during re-oxygenation. A protein annotated as a
universal stress domain containing protein (spot 31, Figure 4B; Figure 5B) also
showed a high post-anoxic abundance, relative to anoxic samples. This
protein is homologous to the Adenine nucleotide alpha hydrolases-like
superfamily protein in Arabidopsis thaliana (AT3G53990) (Supplemental table
1). Notably, we detected a putative peroxidase precursor protein, which was
also significantly higher in abundance during re-oxygenation compared to pre-
anoxic samples (TC429713; Spot 28, Figure 4B; Figure 5B). Peroxidases are
involved in the reduction of a range of peroxides (Welinder et al., 2002).
We detected fifteen proteins that significantly increased during the transition to
anoxia in rice (Figure 6A; Figure 4A; Supplemental table 2). Five pyruvate
phosphate dikinase, chloroplast precursor (PPDK) spots were detected, with
differing isoelectric points (Os03g31750; pI=5.25-5.6; Spots 40-44)
(Supplemental table 2). These proteins are involved in the conversion of
phosphoenolpyruvate to pyruvate, and remain elevated during re-oxygenation
(Figure 6A). Thiamine pyrophosphate-dependent pyruvate decarboxylase in
spots 24-27 and glyceraldehyde-3-phosphate dehydrogenase in spot 20,
showed a similar response to PPDK (Figure 6A, Supplemental table 2). Among
the glycolytic enzymes that increased under anoxia, pyruvate kinase
significantly decreased during re-oxygenation (spot 22, Figure 6A).
149
Pyruvate decarboxylase isozyme 2 (Os03g18220; spot 23, 45) and ascorbate
peroxidase (Os04g35520; spot 8) showed post-anoxic levels that were
comparable to pre-anoxic samples (Figure 6A).
Among the proteins that decreased in abundance in anoxic rice coleoptiles,
six were likely to be degradation products according to their gel molecular
mass (Spots 10, 16, 33-35, 6; Supplemental table 2, Figure 6C). This includes
two ATP synthase F0 subunit 1 spots, three tubulin-related protein spots and
an elongation factor. A peroxidase precursor (Os04g59150, spot 18) that
decreased under anoxia remained low in abundance during re-oxygenation
(Figure 6C). A GDSL-like lipase/acylhydrolase (Os06g06290, spot 5)
responded similarly to the peroxidase precursor (Figure 6C). Lipases are
involved in lipid hydrolysis and the Os06g06290 transcript in particular is
responsive to various a/biotic stresses (Jiang et al., 2012) and is down-
regulated in anoxic rice seedlings (Lasanthi-Kudahettige et al., 2007; Narsai et
al., 2009). Unlike the putative wheat peroxidase precursor protein, the rice
orthologue (Os04g59150) was lower in abundance under anoxia and
remained low during re-oxygenation (Spot 28; Supplemental table 2, Figure
4A, Figure 6C). A vesicle-fusing ATPase (Os05g44310) responded similarly to
the rice peroxidase. We also detected a 40A ribosomal protein S5 that
significantly decreased during anoxia, and appeared to stay low under post-
anoxia, except that the associated p-value did not meet the cut-off (p-
val=0.064).
Several rice proteins that decreased during anoxia then went on to
significantly increase during re-oxygenation (Figure 6C). These include actin
(spot 7, Os05g01600), ribosomal protein L6 (spot 11, Os09g31180), ribulose
bisphosphate carboxylase large chain (spot 57, Os01g58020) and three
phenylalanine ammonia lyase (spots 37-39, Os02g41630, pI 6.4-6.7) (Figure
6C, Supplemental table 2). Actin is a cytoskeletal protein and ribulose
bisphosphate carboxylase large chain is involved in photosynthesis. The
reversible abundance of these proteins suggests these proteins are true
150
aerobic responders, whose functions are unnecessary or redundant enough
to afford O2-dependent degradation.
Lastly, we detected several rice proteins that did not significantly change
during anoxia, and instead showed significant changes during post-anoxia
when compared to anoxia or pre-anoxia (Figure 6B). However, it is suspected
that these proteins trended towards change, despite the fact that they did not
meet significance thresholds. For example, five Cysteine-rich proteins
appeared to increase during anoxia, yet their p-values ranged from 0.066-
0.21. During re-oxygenation, the abundances of these proteins failed to
significantly differ to anoxic samples, but compared to pre-anoxic samples,
their abundances were significantly higher (Spots 47, 51-54; Os08g04210,
Os08g04250, Os04g56430) (Supplemental table 2, Figure 6B). It is suspected
that biological variation was responsible for these observations. Nevertheless,
these proteins appear to show an apparent O2-dependent response in that
post-anoxic abundances are 2.8-4.2 fold higher than pre-anoxic abundances.
The transcripts of these proteins are also up-regulated under anoxia in
similarly aged rice coleoptiles (3-1007 X) (Lasanthi-Kudahettige et al., 2007) as
well as young rice seedlings (Narsai et al., 2009).
Several other proteins, whose apparent decrease in abundance failed to meet
the significance threshold upon the transition of rice to anoxia, showed a
significant increase during re-oxygenation (Figure 6B). This includes another
Cysteine-rich receptor-like protein kinase spot (spot 61, Os04g56430), a
glycolsyl hydrolase family 17 protein (spot 55, Os05g31140), an expansin
precursor (spot 56, Os01g60770) and (Figure 6B). The latter two proteins are
involved in cell re-modelling, and more specifically, expansins are known to be
involved in cell wall extension in O2-deprived rice (Huang et al., 2000; Lee and
Kende, 2001; Strohmeier et al., 2004). Interestingly, the expansin transcript
(Os01g60770) is significantly less abundant in young anoxic rice seedlings,
and also shows significant increases during re-oxygenation (Narsai et al.,
2009).
151
Effect of oxygen availability of the activity of H2O2 –degrading enzymes
Re-oxygenation is likely to be accompanied by oxidative stress, so the activity
of two enzymes involved in antioxidant defence were measured. In wheat
coleoptiles, catalase activity was significantly higher in post-anoxic samples
compared to aerated samples (Figure 7A). The data suggest that an increase
in catalase activity starts to occur during the anoxic phase (p-val=0.12), but
the significance threshold is only met during re-oxygenation (p-val=0.01)
(Figure 7A). For rice, there was no significant difference between aerated,
anoxic and post-anoxic samples (Figure 7A). However, rice seedlings
germinated and grown under anoxia for six days show extremely low levels of
catalase activity (Figure 7A).
Wheat coleoptiles from post-anoxic and anoxic seedlings had significantly
higher peroxidase activity compared to aerated controls (Figure 7B). In rice,
post-anoxic coleoptiles had elevated activity compared to aeration (Figure 7B).
Once again, coleoptiles from seedlings under continuous anoxia had very low
peroxidase activity (Figure 7B).
Discussion
Similarities between the response of rice and wheat proteomes to
anoxia and re-oxygenation
While our previous study (Shingaki-Wells et al., 2011) failed to identify
significantly changing proteins in anoxic wheat coleoptiles, this study identified
several (Figure 6). This previous study set a strict ratio of change of at least
two, with few wheat proteins meeting that cut-off, and none that could be
identified. Here we focussed on significant p-value changes and undertook
more detailed analysis of gels to find any changes that might have gone
undetected previously.
We detected several similarities in the way that rice and wheat coleoptiles
respond to anoxia and re-oxygenation at the proteome level. For example,
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both species showed a decrease in phenylalanine ammonia lyase, a change
that was reversed during re-oxygenation (-3.72 X for anoxic wheat, -2.8 to –
5.05 X for rice) (Figure 6C, Figure 5C). However, Phe levels were only affected
in anoxic rice (Figure 1A, Figure 2A, Figure 3). In rice, anoxic Phe levels were
30 X that of aerated samples, and during re-oxygenation, Phe dropped by 10
X (Figure 1A). Wheat Phe levels remained remarkably stable (Figure 2A). A
decline in phenylalanine ammonia lyase might explain anoxic accumulation of
Phe in rice. An increase in this enzyme also supports declines in Phe during
post-anoxia. If Phe ammonia lyase contributes to the dynamics of
phenylalanine metabolism, it is unclear why Phe remains stable in wheat
coleoptiles. It is possible that Phe ammonia lyase requires post-translational
modification for full functionality, or that Phe metabolism in anoxic wheat is
controlled by other mechanisms.
A decrease in transcripts encoding Phe ammonia lyase under low oxygen
conditions has been reported in rice (Narsai et al., 2009). Potato tubers
accumulate this protein when bruised under aerated conditions but not
hypoxic conditions (Vayda and Schaeffer, 1988). Phe ammonia lyase is
involved in the polyphenolic production pathway, compounds that are
constituents of phytoalexins and lignins (Lamb et al., 1989; Rumeau et al.,
1990). Phytoalexin and lignin accumulation is a common response to
wounding (Rumeau et al., 1990). Hypoxic tubers that are wounded are also
more susceptible to bacterial soft rot (Rumeau et al., 1990). Even though
anaerobiosis interferes with the potato tuber wound response, the expression
of mRNAs encoding glycolytic enzymes is maintained, suggesting that the
response to low oxygen is prioritised over the wounding response (Butler et al.,
1990; Rumeau et al., 1990). The decreased abundance of this protein in rice
and wheat coleoptiles may be a product of inhibited mRNA or protein
synthesis, in combination with maintained degradation rates, since it is known
that selective transcription and translation occur under low oxygen (Branco-
Price et al., 2008; Narsai et al., 2009). Nevertheless, it is intriguing that this
153
response is conserved between two species that differ so greatly in their
anoxia tolerance.
Rice and wheat also showed increases in pyruvate decarboxylase (PDC)
isozyme 2 under anoxia (Figure 6A, Figure 5A), suggesting operation of the
NAD+-producing ethanolic fermentation pathway. Post-anoxia saw a
divergence in response between these two species, with rice PDC trending
towards down-regulation during re-oxygenation so that post-anoxic levels
were comparable to pre-anoxia (spot 23, 45; Figure 6A). In wheat, PDC
remained elevated in post-anoxic coleoptiles (spot 33, Figure 5A). This could
suggest that elevated PDC activity is advantageous in post-anoxic wheat, or
that wheat, in contrast to rice, has an inadequate response to re-oxygenation.
Metabolomic datasets suggest that a subtle response to re-oxygenation is
likely, since metabolites whose abundances are highly responsive to oxygen in
rice do not appear to be so in wheat. In particular, the major trend observed
was a highly dynamic amino acid pool in rice, which in contrast to wheat,
trended towards rapid accumulation during anoxia and consumption during
re-oxygenation (Figure 1A, Figure 2A). Wheat showed many fewer changes in
amino acids during both anoxia and re-oxygenation (Figure 2A), in line with our
previous observation that the response of wheat coleoptiles to anoxia is
subdued and perhaps inadequate (Shingaki-Wells et al., 2011).
Differential regulation of translation-related proteins
The increase in a putative eukaryotic translation initiation factor in anoxic
wheat coleoptiles (spot 7, Figure 5A) is interesting in light of the fact that
overall protein synthesis rates are decreased in anoxic plants (Mocquot et al.,
1981). Rice also showed differential regulation of translation-related proteins,
specifically, a 40S ribosomal protein S5 (-2.02 X, spot 26, Figure 6C) and
elongation factors (Shingaki-Wells et al., 2011). Since selective translation is
known to occur in hypoxic Arabidopsis (Branco-Price et al., 2008), the up-
regulation of this translation initiation factor in anoxia-intolerant wheat
suggests specific factors may be required to direct translation under low
154
oxygen. Although the post-anoxic abundance of this protein was not
significantly lower than that of the anoxic abundance, the final abundance is
not significantly different to pre-anoxic levels, suggesting specificity of this
factor under low oxygen. Although wheat had a weak proteomic response in
our previous study (Shingaki-Wells et al., 2011), protein abundance does
appear to be regulated by oxygen levels. The capacity for synthesis however,
appears to be greater in rice, with rice coleoptiles showing an average fold
change – for proteins significantly increasing in abundance – of 4.8 X, and
wheat showing an average of 1.8 X. Since protein synthesis is energetically
demanding, this may simply be a reflection of the ability of rice to mobilise
endosperm starch reserves under anoxia to enhance glycolytic rates
(Guglielminetti et al., 1995).
Proteins of unknown function respond to anoxia and re-oxygenation
We detected several proteins with unknown function, annotated as Cysteine-
rich repeat secretory proteins in rice (Spot 41, 51-52; Figure 6B; Os08g04210,
Os08g04250). These proteins did not show an increase under anoxia that
was significant, however post-anoxic abundances were significantly higher
than pre-anoxic abundances. In our previous study, these proteins were
significantly higher in the coleoptiles of six-day-old continuously anoxic rice
seedlings relative to four-day-old aerated seedlings (Shingaki-Wells et al.,
2011). The transcripts of these proteins are 248-1007 X higher in continuously
anoxic coleoptiles relative to aeration (Lasanthi-Kudahettige et al., 2007). As
discussed previously (Shingaki-Wells et al., 2011b), the Arabidopsis
orthologue (At5g48540) is also responsive to low oxygen (Branco-Price et al.,
2008). This protein contains two DUF26 domains (domains of unknown
function), common to the plasmodesmata-located protein family (PDLP1)
(Thomas et al., 2008). Knockout analysis revealed that these proteins are
involved in cell-to-cell crosstalk (Thomas et al., 2008). At5g48540, however,
lacks a transmembrane domain and instead forms large bodies in the
apoplast, thus differentiating this protein from the PDLP1 family (Thomas et al.,
155
2008). The function of these low oxygen-responsive proteins awaits further
study.
Oxygen availability affects proteins involved in cell structure
Several proteins involved in cell wall re-modelling were detected in our study.
In rice, a glycosyl hydrolases family 17 protein (Os05g31140; spot 55) and an
expansin precursor (Os01g60770; spot 56, Figure 6B) showed low
abundance during anoxia, and strong increases during post-anoxia. The
corresponding expansin transcript is also lower in anoxic rice coleoptiles
(Lasanthi-Kudahettige et al., 2007). Transcripts of the expansin family vary in
their response to anoxia (Magneschi et al., 2009). Actin, a protein integral to
the actin cytoskeleton, also showed a decrease under anoxia in rice, which
was reversed during re-oxygenation (Os05g01600; spot 7, Figure 6C). In
wheat, a glycosyl hydrolases family 16 protein (spot 51, Figure 5B) showed a
significantly lower abundance during post-anoxia, relative to pre-anoxic
coleoptiles, and a polygalacturonase (spot 20, Figure 5B) showed a decrease
during re-oxygenation, relative to anoxia. In soybean roots, an actin protein
has been reported to increase during re-oxygenation, while others decreased,
which suggests differing and oxygen-dependent roles for these actins in cell
re-modelling (Salavati et al., 2012). Other proteins involved in cell wall
remodelling were also detected in soybean roots during re-oxygenation
(Salavati et al., 2012), indicating a perturbation of cell wall metabolism in
multiple species subjected to low oxygen stress. The proteins found in rice
trended towards a return to pre-anoxic levels. Glycosyl hydrolases family 16
protein in wheat remained significantly lower during re-oxygenation, and while
alternative explanations exist, this might suggest a failure to re-adjust
appropriately to the return of oxygen (Figure 5B).
Sugar metabolism contrasts in anoxic rice and wheat
Metabolite analysis revealed large differences in how rice and wheat re-model
metabolism during anoxia and re-oxygenation (Figure 1, Figure 2, Figure 3).
Sucrose is especially important under anoxia since it is a transportable sugar
156
originating from the starchy endosperm of rice (Perata et al., 1997). The ability
of rice to increase sucrose during anoxia (Figure 1B), a response that was
absent in wheat (Figure 2B), aligns with observations that the Pasteur effect,
an increase in the glycolytic rate, is strong in rice and weak in wheat (Waters
et al., 1991). The consumption of sucrose in anoxic wheat appears to
maintain levels of glucose and fructose, to support glycolysis (Figure 2B).
During re-oxygenation, sucrose decreases in rice, but relative to pre-anoxic
levels, sucrose is still significantly higher (Figure 1B). A decrease in sucrose
post-anoxia is consistent with responses seen in other rice studies (Narsai et
al., 2009). In wheat, the low sucrose levels found under anoxia fail to increase
upon re-aeration, suggesting a failure of wheat coleoptiles to mobilise sugars
from starch within the first day of re-oxygenation (Figure 2B). Since aerobic
respiration is efficient in terms of ATP synthesis per sugar unit consumed
(Gibbs and Greenway, 2003), it is unknown whether a low level of sucrose
would affect energy metabolism in post-anoxic wheat. The decrease of post-
anoxic sucrose in rice suggests that starch metabolism is regulated rapidly by
changes in oxygen concentrations, a characteristic which probably
contributes to one aspect of anoxia tolerance. 3-phosphoglyceric acid, an
intermediate of glycolysis, shows contrasting responses between rice and
wheat (Figure 1B, Figure 2B). In rice, this metabolite increases during anoxia,
and is rapidly consumed during re-oxygenation. In wheat, 3-phosphyglyceric
acid decreases during anoxia, and partially recovers during post-anoxia.
These observations further support the large Pasteur effect in anoxic rice
relative to wheat (Waters et al., 1991). This is also supported by previous our
report where anoxic rice was able to increase the production of several
glycolytic enzymes by over two-fold (Shingaki-Wells et al., 2011).
In wheat, NADP-dependent malic enzyme, which converts malate to pyruvate,
increases during anoxia and stays elevated during re-oxygenation (Figure 5A).
The activity of this enzyme is probably activated by low pH (Edwards et al.,
1998), a well-documented consequence of anoxia in plants (Menegus et al.,
1989; Menegus et al., 1991). During the early stages of hypoxia, a six-fold
157
increase in the activity of this enzyme in maize root tips is seen (Edwards et al.,
1998). Consistent with the hypothesis that malic enzyme acts to synthesise
pyruvate, is the decrease of malate seen in anoxic wheat (Figure 2C). Malate
remains at low levels during re-oxygenation in wheat (Figure 2C). The potential
role or consequence of pyruvate production in anoxic and post-anoxic wheat
is unknown, and awaits further investigation.
Amino acid pools respond strongly to oxygen availability in rice, but
not in wheat
As discussed above, the overall trend for rice was an accumulation of amino
acids under low oxygen (Figure 1A, Supplemental Figure 1A). Fewer amino
acids showed changes in anoxic wheat (Figure 2A, Supplemental Figure 2A).
Since many amino acids are directly or indirectly synthesised from
intermediates of glycolysis or the TCA cycle, it is possible that the high
glycolytic rate, permitted by high sucrose levels in rice (Figure 1B), contributes
to amino acid accumulation in the absence of a mitochondrial terminal
electron acceptor. Wheat, on the other hand, does not appear to have an
abundance of mobilised sugar substrate (Figure 1A), thus minimising the
branching off from glycolysis and/or the TCA cycle to synthesise amino acids.
The accumulation of alanine, for example, is thought of as an alternative, non-
toxic endpoint to ethanol and lactic acid production (Reggiani et al., 1988). In
cases where a limited amount of oxygen is available (hypoxia), production of
alanine may be beneficial in diverting pyruvate from processes involving
respiratory oxygen consumption, preventing a transition from hypoxia to
anoxia (Zabalza et al., 2009). Alternatively, amino acid production may simply
be a product of protein degradation, and amino acid inter-conversion
(Reggiani et al., 1988), although the increase in several enzymes involved in
amino acid synthesis in anoxic rice suggests de novo synthesis is also a
contributor (Shingaki-Wells et al., 2011). The absence of alanine production in
anoxic wheat could be a feature of glycolytic substrate limitation, or a failure to
appropriately regulate enzymes involved in alanine production. It has been
reported that rice accumulates more succinate than lactate under anoxia,
158
whereas wheat accumulates much more lactate, which might contribute to
the cytoplasmic acidification observed in wheat (Menegus et al., 1989;
Menegus et al., 1991). The rapid accumulation of succinate in rice, but not
wheat, is consistent with our results (Figure 1C, Figure 2C). Amino acid
accumulation may also contribute to maintenance of osmotic pressure,
counteracting any decrease caused by rapid sugar consumption (Reggiani et
al., 1988).
Re-oxygenation resulted in major changes to the metabolome of rice,
specifically, 16 of the 17 amino acids accumulating under anoxia were
consumed post-anoxia, the exception being Orn (Figure 1A, Figure 3A). In
contrast, of the nine accumulating amino acids in anoxic wheat, none showed
significant decreases during re-oxygenation, relative to anoxia (Figure 2A,
Figure 3B). This might improve the success of rice seedlings recovering from
anoxia if the carbon skeletons of amino acids were directed into primary
energy metabolism. Indeed, citrate, aconitate, isocitrate and 2-oxoglutarate
accumulate post-anoxia, suggesting resumption of TCA cycle activity. Post-
anoxic conversion of Ala to pyruvate could contribute to this flow, as
suggested in Arabidopsis studies (Miyashita et al., 2007). It is worth pointing
out that the accumulation of Ala during anoxia is 98 X in rice, whereas in
wheat, Ala fails to accumulate (Figure 1A, Figure 2A), suggesting a higher
capacity of rice to engage in post-anoxic Ala metabolism. In line with this is
the observation that wheat coleoptiles accumulate Ala during post-anoxia
(Figure 2A).
Succinate accumulates rapidly in anoxic rice (34 X, Figure 1C), and less so in
wheat (1.4 X, Figure 2C). During re-oxygenation, succinate is rapidly
consumed in rice (Figure 1C), which is likely a result of oxygen-dependent
electron transport chain operation. Succinate is also consumed post-anoxia in
wheat (Figure 2C). Presumably, one day of anoxia is not long enough to inhibit
the re-initiation electron transport chain operation in both species when
oxygen returns.
159
Oxidative stress defence and its role during recovery
The detection of multiple peroxidase enzymes changing in abundance under
anoxia and/or re-oxygenation suggests oxygen availability affects oxidative
defence status. In rice, a peroxidase precursor decreased under anoxia and
remained low in abundance after one day of re-oxygenation (Os04g59150;
spot 18, Figure 6C). In contrast, the stromal ascorbate peroxidase increased
in anoxic rice and remained elevated during post-anoxia (Os04g35520; spot 8.
Figure 6A). We previously detected two ascorbate peroxidases originating
from different genes that decreased in anoxic rice coleoptiles (Os03g17690,
Os07g49400) (Shingaki-Wells et al., 2011). Ascorbate peroxidase activity has
been shown to decrease under low oxygen in wheat roots, with a subsequent
return to pre-anoxic levels post-anoxia (Biemelt et al., 1998). In our
experiments however, we detected one peroxidase precursor in wheat,
whose post-anoxic abundance was significantly higher than pre-anoxic levels
(spot 28, Figure 5B).
Since oxidative stress is a known stress during re-oxygenation (Blokhina et al.,
2003), we wanted to measure the activity of two H2O2 decomposing enzymes
to clarify these apparent contradictions in rice. Wheat and rice peroxidase
activity significantly increased during re-oxygenation, relative to anoxia (Figure
7B). This agrees with the direction of change that the wheat peroxidase
precursor showed (spot 28, Figure 5B), but differs from what was seen in rice
(Os04g59150; spot 18, Figure 6C). Os04g59150 has been experimentally
shown to be located in etioplasts (Reiland et al., 2011) and is predicted to be
a secretory or endoplasmic reticulum protein (Narsai et al., 2013).
For stromal ascorbate peroxidase the activity data agrees to some extent,
except that activity did not increase under anoxia, while the abundance of
Os04g33520 did (spot 8, Figure 6A). The location of this protein is
experimentally verified in the chloroplast (Kleffmann et al., 2007; Narsai et al.,
2013; Xu et al., 2013). Overall, it is difficult to correlate protein abundance
data with peroxidase activity, since activity measurements are the sum of
160
multiple peroxidases. It is possible that different peroxidases are regulated at
the post-translational level to affect their activity, and thus the changes will
influence the general peroxidase activity measurement.
A similar change in activity was seen in wheat catalase, but not rice (Figure
7A). The post-anoxic activity of catalase was not significantly different to
anoxic or aerated rice coleoptiles. In rice coleoptiles from seedlings
germinated and grown under continuous anoxia for 6 days, the activity of both
enzymes were extremely low, suggesting a role for oxygen exposure in the
regulation of these enzymes. Indeed, it has been shown that anti-oxidant
levels are affected by both low oxygen and re-aeration in wheat roots (Biemelt
et al., 1998). In summary, anoxia followed by re-oxygenation affects the status
of the anti-oxidant defence system, but the exact proteins responsible for
these changes are unknown.
Conclusion The response of rice and wheat to anoxia and re-oxygenation is highly
divergent, particularly at the metabolome level. While rice accumulates many
amino acids, wheat accumulates fewer amino acids at rates less rapid than
rice. Post-anoxia results in the consumption of amino acids in rice, which is
suspected to improve the retention of carbon skeletons, that would otherwise
be lost if pyruvate was diverted solely to ethanol production. Proteomes are
also affected by oxygen availability, including enzymes involved in glycolysis
and ethanolic fermentation. Generally, the degree of accumulation of proteins
in wheat was subtle, compared to rice. Further proteomic experiments
involving iTRAQ may be required to delve deeper into the post-anoxic
response. We conclude that active metabolic re-modelling in rice plays a role
in defence against anoxia and re-oxygenation, which may underlie its superior
tolerance to this stress.
161
162
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Figures
Figure 1. Relative metabolite signals in rice coleoptiles subjected to air, anoxia
and re-oxygenation.
Figure 2. Relative metabolite in wheat coleoptiles subjected to air, anoxia and
re-oxygenation.
Figure 3. Metabolite response ratios in rice and wheat coleoptiles from aerated
seedlings subjected anoxia and re-oxygenation.
Figure 4. Representative images of Differential in Gel Electrophoresis (DiGE)
using rice and wheat coleoptiles from aerated seedlings subjected anoxia and re-
oxygenation.
Figure 5. Relative protein abundance in wheat coleoptiles subjected to air,
anoxia and re-oxygenation.
Figure 6. Relative protein abundance in rice coleoptiles subjected to air, anoxia
and re-oxygenation.
Figure 7. Catalase and peroxidase activity in rice and wheat coleoptiles.
16
7
Figu
re 1
. Rel
ativ
e m
etab
olite
sig
nals
in r
ice
cole
optil
es s
ubje
cted
to a
ir (d
ark
blue
), an
oxia
(gre
en) a
nd r
e-ox
ygen
atio
n (li
ght b
lue)
. M
etab
olite
pea
k ar
eas
wer
e no
rmal
ised
to r
ibito
l and
tiss
ue fr
esh
wei
ght.
Aer
ated
sam
ples
wer
e no
rmal
ised
to 1
and
com
pare
d to
an
oxic
and
pos
t-an
oxic
sam
ples
. A. A
min
o ac
ids;
B. S
ugar
s an
d gl
ycol
ytic
inte
rmed
iate
s; C
. TC
A c
ycle
inte
rmed
iate
s. T
hree
p-v
alue
s ar
e as
sign
ed to
eac
h m
etab
olite
. The
firs
t pos
ition
indi
cate
s th
e p-
val a
ssoc
iate
d w
ith th
e ch
ange
from
air
to a
noxi
a, th
e se
cond
po
sitio
n re
late
s to
the
anox
ia a
nd p
ost-
anox
ia c
ompa
rison
, and
the
third
pos
ition
rel
ates
to th
e po
st-a
noxi
c an
d pr
e-an
oxic
com
paris
on.
* in
dica
tes
the
diffe
renc
e is
sig
nific
ant (
p-va
l<0.
05);
- in
dica
tes
the
diffe
renc
e is
not
sig
nific
ant.
N=
5. F
igur
e 1B
and
1C
on
next
pag
e.
0 10
20
30
40
50
60 L-S
erine
Glyc
ine
L-Tryp
topha
n
L-Phe
nylal
anine
L-Tyro
sine L-A
lanine
L-Leu
cine
L-Vali
ne
L-Glut
amic
acid L-G
lutam
ine L-P
roline
Ornithi
ne Putr
escin
e L-Argi
nine
Gamma-A
minobu
tyric
acid
L-Asp
artic
acid L-A
spara
gine
L-Lys
ine
L-2-A
minoad
ipic a
cid
L-Hom
oseri
ne
L-Meth
ionine
L-Thre
onine
L-Iso
leucin
e
Ric
e
100
..
..
Normalised signal
A
16
8
Figu
re 1
. Rel
ativ
e m
etab
olite
sig
nals
in r
ice
cole
optil
es s
ubje
cted
to a
ir (d
ark
blue
), an
oxia
(gre
en) a
nd r
e-ox
ygen
atio
n (li
ght b
lue)
. See
fu
ll fig
ure
capt
ion
on p
revi
ous
page
.
0 1 2 3 4 5 6 7
Sucros
e
D-Gluc
ose
D-Fruc
tose
Glucos
e 6-ph
osph
ate
Fructos
e 6-ph
osph
ate
3-Pho
spho
glyce
ric ac
id
Ric
e
Normalised signal B
0 1 2 3 4 5 6 7 8 9 10 Citri
c acid
cis-A
conit
ic ac
id Iso
citric
acid Oxo
glutar
ic ac
id Suc
cinic
acid
Fumari
c acid
L-M
alic a
cid
Ric
e 34
Normalised signal
C
16
9
Fig
ure
2. R
elat
ive
met
abol
ite in
whe
at c
oleo
ptile
s su
bjec
ted
to a
ir (d
ark
blue
), an
oxia
(gre
en) a
nd r
e-ox
ygen
atio
n (li
ght b
lue)
. Met
abol
ite
peak
are
as w
ere
norm
alis
ed to
rib
itol a
nd ti
ssue
fres
h w
eigh
t. A
erat
ed s
ampl
es w
ere
norm
alis
ed to
1 a
nd c
ompa
red
to a
noxi
c an
d po
st-a
noxi
c sa
mpl
es. A
. Am
ino
acid
s; B
. Sug
ars
and
glyc
olyt
ic in
term
edia
tes;
C. T
CA
cyc
le in
term
edia
tes.
Thr
ee p
-val
ues
are
assi
gned
to
eac
h m
etab
olite
. The
firs
t pos
ition
indi
cate
s th
e p-
val a
ssoc
iate
d w
ith th
e ch
ange
from
air
to a
noxi
a, th
e se
cond
pos
ition
rel
ates
to
the
anox
ia a
nd p
ost-
anox
ia c
ompa
rison
, and
the
third
pos
ition
rel
ates
to th
e po
st-a
noxi
c an
d ai
r co
mpa
rison
. * in
dica
tes
the
diffe
renc
e is
sig
nific
ant (
p-va
l<0.
05);
- in
dica
tes
the
diffe
renc
e is
not
sig
nific
ant.
N=
5. F
igur
e 2B
and
Fig
ure
2C o
n ne
xt p
age.
0 1 2 3 4 5 6 7 8 9 10 L-S
erine
Glyc
ine
L-Tryp
topha
n
L-Phe
nylal
anine
L-Tyro
sine L-A
lanine
L-Leu
cine
L-Vali
ne
L-Glut
amic
acid L-G
lutam
ine L-P
roline
Ornithi
ne Putr
escin
e L-Argi
nine
Gamma-A
minobu
tyric
acid
L-Asp
artic
acid L-A
spara
gine
L-Lys
ine
L-2-A
minoad
ipic a
cid
L-Hom
oseri
ne
L-Meth
ionine
L-Thre
onine
L-Iso
leucin
e W
heat
Normalised signal A
17
0
Figu
re 2
. Rel
ativ
e m
etab
olite
sig
nals
in w
heat
col
eopt
iles
subj
ecte
d to
air
(dar
k bl
ue),
anox
ia (g
reen
) and
re-
oxyg
enat
ion
(ligh
t blu
e).
See
full
figur
e ca
ptio
n on
pre
viou
s pa
ge.
0 0.
2 0.
4 0.
6 0.
8 1 1.
2 1.
4 1.
6 1.
8
Sucros
e
D-Gluc
ose
D-Fruc
tose
Glucos
e 6-ph
osph
ate
Fructos
e 6-ph
osph
ate
3-Pho
spho
glyce
ric ac
id
Whe
at
Normalised signal
0 0.
5 1 1.
5 2 2.
5 3 3.
5 4 4.
5
Citric a
cid cis
-Aco
nitic
acid
Isocit
ric ac
id Oxoglu
taric
acid
Succin
ic ac
id Fum
aric a
cid
L-Mali
c acid
W
heat
Normalised signal
C
B
17
1
.
Fig
ure
3. P
ost-
anox
ic m
etab
olom
es o
f ric
e (A
) and
whe
at (B
) col
eopt
iles.
Ful
l fig
ure
capt
ion
on fo
llow
ing
page
s.
Met
abol
ite R
espo
nse
Valu
es
Hig
her u
nder
ano
xia
Hig
her u
nder
pos
t-ano
xia
Unc
hang
ed (n
/s)
Not
det
ecte
d
glyc
olys
is TC
A cy
cle
sucr
ose
gluc
ose
fruct
ose
gluc
ose-
6-P
fruct
ose-
6-P
fruct
ose-
1,6-
bis-
P
G-3
-P
Cys
S
er
Gly
3-P
GA
PE
P
pyru
vate
acet
yl C
oA
citra
te
acon
itate
isoc
ritra
te
2-ox
oglu
tara
te
succ
inyl
-CoA
succ
inat
e
fum
arat
e
mal
ate
oxal
oace
tate
acet
alde
hyde
et
hano
l
Ala
Leu
Val
Asn
Asp
Lys
hom
oser
ine
Thr
Met
Ile
Glu
P
ro
Gln
H
is
GA
BA
SS
A
orni
thin
e
Arg
Phe
Tyr
Trp
shik
imat
e
2-am
inoa
dipi
c ac
id
putre
scin
e
DH
AP
1,3-
PG
A
2-P
GA
3-ph
osph
oser
ine
3-ph
osph
ohyd
ro
-pyr
uvat
e
UD
P-g
luco
se
chor
ism
ate
0.
4
0.1
0.
2
6.3
2.9
8.
2
12.6
0
.03
0
.2
1.
3
0
.3
1.2
0.2
0.
2
0.
6
0
.5
0.
4
0.3
0.
5
0.1
0.
3
0.
2
1.5
0.5
0.
3
0.
6
0.3
0.4
0.1
0.1
0
.6
1.7
0
.6
A
17
2
Fig
ure
3.P
ost-
anox
ic m
etab
olom
es o
f ric
e (A
) and
whe
at (B
) col
eopt
iles.
Ful
lfig
ure
capt
ion
on fo
llow
ing
page
.
glyc
olys
is TC
A cy
cle
sucr
ose
gluc
ose
fruct
ose
gluc
ose-
6-P
fruct
ose-
6-P
fruct
ose-
1,6-
bis-
P
G-3
-P
Cys
S
er
Gly
3-P
GA
PE
P
pyru
vate
acet
yl C
oA
citra
te
acon
itate
isoc
ritra
te
2-ox
oglu
tara
te
succ
inyl
-CoA
succ
inat
e
fum
arat
e
mal
ate
oxal
oace
tate
acet
alde
hyde
et
hano
l
Ala
Leu
Val
Asn
Asp
Lys
hom
oser
ine
Thr
Met
Ile
Glu
P
ro
Gln
H
is
GA
BA
SS
A
orni
thin
e
Arg
Phe
Tyr
Trp
shik
imat
e
2-am
inoa
dipi
c ac
id
putre
scin
e
DH
AP
1,3-
PG
A
2-P
GA
3-ph
osph
oser
ine
3-ph
osph
ohyd
ro
-pyr
uvat
e
UD
P-g
luco
se
chor
ism
ate
1.2
1.3
2.6
1.
1
5.0
0.6
1.9
4.7
0
.6
0
.7
1.0
1
.7
1
.6
1.1
3.
6
0.
7
0.9
1
.1
1.
4
1
.4
1.1
0
.7
1.6
1.
1
1.
1
0.9
1.4
1.
3
1.
5
1.9
0.6
1.0
1.1
1.3
1
.3
0.8
0
.8
Met
abol
ite R
espo
nse
Valu
es
Hig
her u
nder
ano
xia
Hig
her u
nder
pos
t-ano
xia
Unc
hang
ed (n
/s)
Not
det
ecte
d
B
17
3
Fig
ure
3. M
etab
olite
res
pons
e ra
tios
in r
ice
(A) a
nd w
heat
(B) c
oleo
ptile
s fro
m a
erat
ed s
eedl
ings
sub
ject
ed a
noxi
a an
d re
-oxy
gena
tion.
Met
abol
ites
in g
reen
are
mor
e ab
unda
nt u
nder
ano
xia
and
thos
e in
blu
e ar
e m
ore
abun
dant
dur
ing
post
-ano
xia.
Dar
k gr
ey in
dica
tes
no
sign
ifica
nt d
iffer
ence
bet
wee
n th
e si
gnal
s in
the
two
trea
tmen
ts a
nd li
ght g
rey
indi
cate
s th
at th
e m
etab
olite
was
not
det
ecte
d. N
umbe
rs
indi
cate
the
ratio
of c
hang
e th
at o
ccur
s du
ring
re-o
xyge
natio
n, r
elat
ive
to a
noxi
a. F
igur
es o
n pr
evio
us p
ages
.
174
Figure 4. Representative images of Differential in Gel Electrophoresis (DiGE) using rice (A) and wheat (B) coleoptiles from aerated seedlings subjected anoxia and re-oxygenation. Full figure caption follows on next page.
A
175
Figure 4. Representative images of Differential in Gel Electrophoresis (DiGE) using rice (A) and wheat (B) coleoptiles from aerated seedlings subjected anoxia and re-oxygenation. In this image, anoxic samples were labeled with Cy 5 (red) and post-anoxic samples with Cy 3 (green). An overlay of the image produces spots of differing colours, including red for proteins more abundant under anoxia, green for proteins more abundant under post-anoxia, and yellow for proteins that have the same abundance under both treatments. For details on DIGE, refer to the methods section. Delta2D (Decodon) was used to analyse gel images, and significantly changing spots (p-val<0.05) were selected for MALDI-TOF MS/MS analysis. Caption continued on next page.
B
176
Figure 4. Caption continued. To see detail on spot identification, see Supplemental table 1 and 2 for wheat and rice, respectively, as well as Figures 5-6. Figures on previous pages.
177
Figure 5. Relative protein abundance in wheat coleoptiles subjected to air (dark blue), anoxia (green) and re-oxygenation (light blue). Aerated protein abundances were normalised to 1 and compared to anoxic and post-anoxic samples. The numbers that precede protein identities indicate the spot numbers shown in Figure 4B. Proteins were split into three categories depending on the response during anoxia. A. Proteins that significantly increased from air to anoxia; B. Proteins that did not significantly change in abundance from air to anoxia; C. Proteins that significantly decreased under anoxia. Above each protein is a significance indicator; the first position indicates the p-val associated with the change from air to anoxia, the second position relates to the anoxia and post-anoxia comparison, and the third position relates to the post-anoxia and air comparison. * indicates the difference is significant (p-val<0.05); - indicates the difference is not significant; # indicates this is probably a protein degradation product. Figure 5B and Figure 5C on following pages.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
7. Euk
ar. tra
nslat
ion IF
5A
43. e
xpres
sed p
rotein
18. 1
2-oxo
phyto
dieno
ate re
ducta
se
33. P
yr de
carbo
xylas
e iso
zyme 2
34. N
ADP-depe
nden
t mali
c enz
yme,
12. e
xpres
sed p
rotein
Nor
mal
ised
sig
nal
*/-/- */-/* */*/-
A
17
8
Fig
ure
5. R
elat
ive
prot
ein
abun
danc
e in
whe
at c
oleo
ptile
s su
bjec
ted
to a
ir (d
ark
blue
), an
oxia
(gr
een)
and
re-
oxyg
enat
ion
(ligh
t bl
ue).
Full
figur
e ca
ptio
n on
pre
viou
s pa
ge.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
26. e
xpres
sed p
rotein
29
. exp
resse
d prot
ein
32. N
t PPas
e/pho
spho
dieste
rase
36. b
-gluc
osida
se, e
xo-b-
gluca
nase
37
. exp
resse
d prot
ein
51. g
lycos
yl hy
drolas
es fa
mily 16
25. D
NA-bind
ing pr
otein
28. p
eroxid
ase p
recurs
or 20. p
olyga
lactur
onas
e
38. H
sp20
/a-cry
stallin
family
31. u
nivers
al str
ess p
rotein
domain
-/*/*
-/-/*
-/*/-
Normalised signal
#
#
B
17
9
Fig
ure
5. R
elat
ive
prot
ein
abun
danc
e in
whe
at c
oleo
ptile
s su
bjec
ted
to a
ir (d
ark
blue
), an
oxia
(gr
een)
and
re-
oxyg
enat
ion
(ligh
t bl
ue).
Full
figur
e ca
ptio
n on
pre
viou
s pa
ge.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
47. e
xpres
sed p
rotein
30. e
xpres
sed p
rotein
6.
expre
ssed
prote
in
21. A
TP synth
ase F
0 sub
1
15. P
he am
monia-
lyase
24. E
ukar.
trans
lation
IF 5A
39. H
sp20
/a-cry
stallin
family
prote
in
48. e
xpres
sed p
rotein
*/-/*
*/
-/-
*/*/
-
Normalised signal #
#
C
18
0
Fig
ure
6. R
elat
ive
prot
ein
abun
danc
e in
ric
e co
leop
tiles
sub
ject
ed to
air
(dar
k bl
ue),
anox
ia (g
reen
) and
re-
oxyg
enat
ion
(ligh
t blu
e).
Full
figur
e ca
ptio
n on
nex
t pag
es.
0 2 4 6 8 10
12
14
16
18
19. M
ito pr
oces
sing p
eptid
ase
22. p
yruva
te kin
ase
8. Stro
mal Asc
orbate
Pero
xidas
e
23. P
DC isoz
yme 2
45. P
DC isoz
yme 2
20. G
3PDH
24. th
iamine
PPi e
nzym
e
25. th
iamine
PPi e
nzym
e
26. th
iamine
PPi e
nzym
e
27. th
iamine
PPi e
nzym
e
40. P
PDK, chlo
roplas
t prec
ursor
41. P
PDK, chlo
roplas
t prec
ursor
42. P
PDK, chlo
roplas
t prec
ursor
43. P
PDK, chlo
roplas
t prec
ursor
44. P
PDK, chlo
roplas
t prec
ursor
Normalised signal
*/*/
-
*/-/-
*/-/*
A
18
1
Fig
ure
6. R
elat
ive
prot
ein
abun
danc
e in
ric
e co
leop
tiles
sub
ject
ed to
air
(dar
k bl
ue),
anox
ia (g
reen
) and
re-
oxyg
enat
ion
(ligh
t blu
e).
Full
figur
e ca
ptio
n on
nex
t pag
es.0 1 2 3 4 5 6 7 8 9
47. C
ys-ric
h rep
eat s
ecret
ory pr
otein
51. C
ys-ric
h rep
eat s
ecret
ory pr
otein
52. C
ys-ric
h rep
eat s
ecret
ory pr
otein
53. C
ys-ric
h rec
eptor
-like p
rotein
kina
se
54. C
ys-ric
h rec
eptor
-like p
rotein
kina
se
55. g
lycos
yl hy
drolas
es fa
mily 17
56. e
xpan
sin pr
ecurs
or
61. C
ys-ric
h rec
eptor
-like p
rotein
kina
se
58. ri
bulos
e BisP
carbo
xylas
e small
chain
60. C
ys-ric
h rec
eptor
-like p
rotein
kina
se
62. P
DI
12
Normalised signal
-/-/*
-/*/-
-/*/*
B
18
2
Fig
ure
6. R
elat
ive
prot
ein
abun
danc
e in
ric
e co
leop
tiles
sub
ject
ed to
air
(dar
k bl
ue),
anox
ia (g
reen
) and
re-
oxyg
enat
ion
(ligh
t blu
e).
Full
figur
e ca
ptio
n on
nex
t pag
e.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
5. GDSL-l
ike lip
ase/a
cylhy
drolas
e
18. p
eroxid
ase p
recurs
or
30. v
esicl
e-fus
ing ATPas
e
36. 4
0S rib
osom
al pro
tein S
5
10. A
TP synth
ase F
0 sub
unit 1
13. C
ys-ric
h rep
eat s
ecret
ory pr
otein
55
16. tu
bulin
/FtsZ do
main co
ntaini
ng pr
otein
33. tu
bulin
/FtsZ do
main co
ntaini
ng pr
otein
34. tu
bulin
/FtsZ do
main co
ntaini
ng pr
otein
35. A
TP synth
ase F
0 sub
unit 1
6. elo
ngati
on fa
ctor
7. ac
tin
11. ri
boso
mal pro
tein L
6
37. P
he am
monia-
lyase
38. P
he am
monia-
lyase
39. P
he am
monia-
lyase
57. ri
bulos
e BisP
carbo
xylas
e larg
e cha
in
Normalised signal
*/-/*
*/
-/-
*/*/
-
# #
# #
# #
C
18
3
Fig
ure
6. R
elat
ive
prot
ein
abun
danc
e in
ric
e co
leop
tiles
sub
ject
ed t
o ai
r (d
ark
blue
), an
oxia
(gr
een)
and
re-
oxyg
enat
ion
(ligh
t bl
ue).
Aer
ated
pro
tein
abu
ndan
ces
wer
e no
rmal
ised
to
1 an
d co
mpa
red
to a
noxi
c an
d po
st-a
noxi
c sa
mpl
es.
The
num
bers
tha
t pr
eced
e pr
otei
n id
entit
ies
indi
cate
the
spo
t nu
mbe
rs s
how
n in
Fig
ure
4A.
Pro
tein
s w
ere
split
into
thr
ee c
ateg
orie
s de
pend
ing
on t
he r
espo
nse
durin
g an
oxia
. A. P
rote
ins
that
sig
nific
antly
incr
ease
d fro
m a
ir to
ano
xia;
B. P
rote
ins
that
did
not
sig
nific
antly
cha
nge
in a
bund
ance
from
ai
r to
ano
xia;
C.
Pro
tein
s th
at s
igni
fican
tly d
ecre
ased
und
er a
noxi
a. A
bove
eac
h pr
otei
n is
a s
igni
fican
ce i
ndic
ator
; th
e fir
st p
ositi
on
indi
cate
s th
e p-
val a
ssoc
iate
d w
ith th
e ch
ange
from
air
to a
noxi
a, th
e se
cond
pos
ition
rel
ates
to th
e an
oxia
and
pos
t-an
oxia
com
paris
on,
and
the
third
pos
ition
rel
ates
to
the
post
-ano
xia
and
air
com
paris
on.
* in
dica
tes
the
diffe
renc
e is
sig
nific
ant
(p-v
al<
0.05
); -
indi
cate
s th
e di
ffere
nce
is
not
sign
ifica
nt;
# in
dica
tes
this
is
pr
obab
ly
a pr
otei
n de
grad
atio
n pr
oduc
t. Fi
gure
s on
pr
evio
us
page
s.
184
Figure 7. Catalase (A) and peroxidase (B) activity in rice and wheat coleoptiles. Seedlings were germinated and grown under aeration for 4 days and subsequently transferred to anoxia and re-oxygenation for 1 day. Alternatively, rice seedlings were germinated and grown under anoxia for 6 days. Figure 7B on following page.
0
5
10
15
20
25
30
35
40
45
50
Air Anoxia Post-anoxia Air Anoxia Post-anoxia Continuous anoxia
Wheat Rice
Cat
alas
e ac
tivity
(mM
H2 O
2.min
-1.m
g-1 p
rote
in)
p=0.01 p=0.0009
p=0.02
(1 d) (1 d) (6 d)
A
185
Figure 7. Catalase (A) and peroxidase (B) activity in rice and wheat coleoptiles. Seedlings were germinated and grown under aeration for 4 days and subsequently transferred to anoxia and re-oxygenation for 1 day. Alternatively, rice seedlings were germinated and grown under anoxia for 6 days. Figure 7B on following page. Figure 7A on previous page.
0
50
100
150
200
250
300
350
400
450
500
Air Anoxia Post-anoxia Air Anoxia Post-anoxia Continuous anoxia
Wheat Rice
Per
oxid
ase
activ
ity (m
M g
uaia
col .
min
-1.m
g-1 p
rote
in)
p=0.005
p=0.006
p=0.02
p=0.02
p=0.009
p=0.04
(1 d) (1 d)
(6 d)
B
186
Supplemental Figures
Supplemental Figure 1 (SF 1). Metabolite response ratios in rice coleoptiles
when comparing anoxia to air (A) and post-anoxia to air (B).
Supplemental Figure 2 (SF 2). Metabolite response ratios in wheat coleoptiles
when compared anoxia to air (A) and post-anoxia to air (B).
18
7
Sup
ple
men
tal F
igur
e 1
(SF1
). Met
abol
ite r
espo
nse
ratio
s in
ric
e co
leop
tiles
whe
n co
mpa
ring
anox
ia to
air
(A) a
nd p
ost-
anox
ia to
air
(B).
Sup
plem
enta
l Fig
ure
1B o
n fo
llow
ing
page
.
Met
abol
ite R
espo
nse
Valu
es
Hig
her u
nder
ano
xia
Hig
her u
nder
air
Unc
hang
ed (n
/s)
Not
det
ecte
d
glyc
olys
is TC
A cy
cle
sucr
ose
gluc
ose
fruct
ose
gluc
ose-
6-P
fruct
ose-
6-P
fruct
ose-
1,6-
bis-
P
G-3
-P
Cys
S
er
Gly
3-P
GA
PE
P
pyru
vate
acet
yl C
oA
citra
te
acon
itate
isoc
ritra
te
2-ox
oglu
tara
te
succ
inyl
-CoA
succ
inat
e
fum
arat
e
mal
ate
oxal
oace
tate
acet
alde
hyde
et
hano
l
Ala
Leu
Val
Asn
Asp
Lys
hom
oser
ine
Thr
Met
Ile
Glu
P
ro
Gln
H
is
GA
BA
SS
A
orni
thin
e
Arg
Phe
Tyr
Trp
shik
imat
e
2-am
inoa
dipi
c ac
id
putre
scin
e
DH
AP
1,3-
PG
A
2-P
GA
3-ph
osph
oser
ine
3-ph
osph
ohyd
ro
-pyr
uvat
e
UD
P-g
luco
se
chor
ism
ate
2.
2
97.6
30
.9
0.6
0.8
0.
1
0.1
3
4.1
6.
7
0.
7
1
2.7
1.7
4
0.4
28
.6
4.1
10.3
13.2
5.6
2.
7
17.2
9.
1
13
.1
1.1
10.4
7.
1
5.
9
5.7
2.0
28.2
28.
9
2
.6
0.7
1
.2
SF
1A
18
8
Sup
ple
men
tal F
igur
e 1
(SF1
). Met
abol
ite r
espo
nse
ratio
s in
ric
e co
leop
tiles
whe
n co
mpa
ring
anox
ia to
air
(A) a
nd p
ost-
anox
ia to
air
(B).
Sup
plem
enta
l Fig
ure
1A o
n pr
ior
page
.
Met
abol
ite R
espo
nse
Valu
es
Hig
her u
nder
air
Hig
her u
nder
pos
t-ano
xia
Unc
hang
ed (n
/s)
Not
det
ecte
d
glyc
olys
is TC
A cy
cle
sucr
ose
gluc
ose
fruct
ose
gluc
ose-
6-P
fruct
ose-
6-P
fruct
ose-
1,6-
bis-
P
G-3
-P
Cys
S
er
Gly
3-P
GA
PE
P
pyru
vate
acet
yl C
oA
citra
te
acon
itate
isoc
ritra
te
2-ox
oglu
tara
te
succ
inyl
-CoA
succ
inat
e
fum
arat
e
mal
ate
oxal
oace
tate
acet
alde
hyde
et
hano
l
Ala
Leu
Val
Asn
Asp
Lys
hom
oser
ine
Thr
Met
Ile
Glu
P
ro
Gln
H
is
GA
BA
SS
A
orni
thin
e
Arg
Phe
Tyr
Trp
shik
imat
e
2-am
inoa
dipi
c ac
id
putre
scin
e
DH
AP
1,3-
PG
A
2-P
GA
3-ph
osph
oser
ine
3-ph
osph
ohyd
ro
-pyr
uvat
e
UD
P-g
luco
se
chor
ism
ate
0.
9
7.1
5.
4
3.6
2.2
1.
1
1.0
0
.9
1
.4
0.
9
3
.4
1.9
9.1
7.
0
2.
4
4
.7
5.
5
1.4
1.
3
1.7
2.4
2.
5
1.8
5.2
2.
4
3.
3
1.9
0.9
3.6
2.0
1
.6
1.3
0
.7
SF
1B
18
9
Sup
ple
men
tal F
igur
e 2
(SF2
). M
etab
olite
res
pons
e ra
tios
in w
heat
col
eopt
iles
whe
n co
mpa
ring
anox
ia to
air
(A) a
nd p
ost-
anox
ia to
air
(B).
Sup
plem
enta
l Fig
ure
2B o
n fo
llow
ing
page
.
Met
abol
ite R
espo
nse
Valu
es
Hig
her u
nder
ano
xia
Hig
her u
nder
air
Unc
hang
ed (n
/s)
Not
det
ecte
d
glyc
olys
is TC
A cy
cle
sucr
ose
gluc
ose
fruct
ose
gluc
ose-
6-P
fruct
ose-
6-P
fruct
ose-
1,6-
bis-
P
G-3
-P
Cys
S
er
Gly
3-P
GA
PE
P
pyru
vate
acet
yl C
oA
citra
te
acon
itate
isoc
ritra
te
2-ox
oglu
tara
te
succ
inyl
-CoA
succ
inat
e
fum
arat
e
mal
ate
oxal
oace
tate
acet
alde
hyde
et
hano
l
Ala
Leu
Val
Asn
Asp
Lys
hom
oser
ine
Thr
Met
Ile
Glu
P
ro
Gln
H
is
GA
BA
SS
A
orni
thin
e
Arg
Phe
Tyr
Trp
shik
imat
e
2-am
inoa
dipi
c ac
id
putre
scin
e
DH
AP
1,3-
PG
A
2-P
GA
3-ph
osph
oser
ine
3-ph
osph
ohyd
ro
-pyr
uvat
e
UD
P-g
luco
se
chor
ism
ate
1.2
1.1
1
.5
1.
9
0
.4
0.7
0.5
0.7
1.4
0
.9
0.6
0.
4
0
.4
3.3
1.
7
2.
0
2.3
1
.5
1.
3
1
.7
2
.5
0
.9
0.7
1.
1
0.
9
0.4
1.5
2.
1
0.
6
0.3
0.6
1.0
2.0
1.4
0
.3
1
.0
1
.0
SF
2A
19
0
Sup
ple
men
tal F
igur
e 2
(SF2
). M
etab
olite
res
pons
e ra
tios
in w
heat
col
eopt
iles
whe
n co
mpa
ring
anox
ia to
air
(A) a
nd p
ost-
anox
ia to
air
(B).
Sup
plem
enta
l Fig
ure
2A o
n pr
ior
page
.
Met
abol
ite R
espo
nse
Valu
es
Hig
her u
nder
air
Hig
her u
nder
pos
t-ano
xia
Unc
hang
ed (n
/s)
Not
det
ecte
d
glyc
olys
is TC
A cy
cle
sucr
ose
gluc
ose
fruct
ose
gluc
ose-
6-P
fruct
ose-
6-P
fruct
ose-
1,6-
bis-
P
G-3
-P
Cys
S
er
Gly
3-P
GA
PE
P
pyru
vate
acet
yl C
oA
citra
te
acon
itate
isoc
ritra
te
2-ox
oglu
tara
te
succ
inyl
-CoA
succ
inat
e
fum
arat
e
mal
ate
oxal
oace
tate
acet
alde
hyde
et
hano
l
Ala
Leu
Val
Asn
Asp
Lys
hom
oser
ine
Thr
Met
Ile
Glu
P
ro
Gln
H
is
GA
BA
SS
A
orni
thin
e
Arg
Phe
Tyr
Trp
shik
imat
e
2-am
inoa
dipi
c ac
id
putre
scin
e
DH
AP
1,3-
PG
A
2-P
GA
3-ph
osph
oser
ine
3-ph
osph
ohyd
ro
-pyr
uvat
e
UD
P-g
luco
se
chor
ism
ate
1.4
1.4
3.8
2.
0
1
.8
0.4
1.0
3.3
0
.9
0
.7
0.6
0
.7
0
.6
3.6
6.
2
1.
4
2.0
1
.7
1.
9
2
.3
2.8
0
.6
1.1
1.
1
1.
0
0.3
2.0
2.
8
0.
9
0.6
0.4
1.0
2.2
1.9
0
.4
0.8
0
.9
SF
2B
191
Chapter 5 General Discussion
192
General Discussion The overall aim of this thesis was to understand and identify key responses of
rice (cv. Amaroo) and wheat (cv. Calingiri) seedlings to anoxia as well as re-
oxygenation. In Study I, four-day-old aerated rice and wheat seedlings were
treated with anoxia for one day (Shingaki-Wells et al., 2011). Initial
experiments set out to confirm a physiological effect of anoxia on these
seedlings. Rice coleoptiles showed significant growth during anoxia, whereas
those of wheat did not. Both species showed significant decreases in
coleoptile sugar content, as well as significant increases in ADH activity after
one day of anoxia. Curiously, wheat coleoptiles did not show a significant loss
of respiratory capacity after one day of anoxia, despite evidence to the
contrary, where damage to wheat coleoptile mitochondria was shown after
only 1.5 hours of anoxia (Vartapetian et al., 1985). However, mitochondrial
ultrastructure was promptly repaired during re-aeration, but not if anoxia
lasted 36 hours (Vartapetian et al., 1985).
Since a physiological response to anoxia was observed in both species,
proteomics analysis was carried out to further explore the molecular
adaptations of these seedlings. Immediately evident was the comparatively
subtle response of wheat coleoptiles to anoxia. Whereas 4.6 % of the
detected proteins increased after one day of anoxia in rice, only 0.08 % did so
in wheat. Rice coleoptiles from seedlings germinated and grown under
continuous anoxia for six days had even larger differences to those of aerated
controls, with 8.5 % of the detected proteins being significantly higher in
abundance under anoxia, and 4.5 % being significantly lower, meaning 13 %
of the protein spots detected were affected by oxygen availability. Proteins
involved in glycolysis, fermentation, amino acid metabolism, oxidative stress
and translation were differentially regulated in anoxic rice, changes which were
mostly corroborated by other proteomic and transcriptomic studies (Lasanthi-
Kudahettige et al., 2007; Narsai et al., 2009; Sadiq et al., 2011). In remarkable
193
similarity, Sadiq and colleagues (2011) found that 14 % of both transcripts
and proteins were significantly different in abundance between aerated and
continuously anoxic seedlings.
Metabolite analysis largely supported the idea that rice responses to anoxia
are strong, and those of wheat are weak. While many amino acids
accumulated during anoxia in rice, which was later confirmed by Study III,
comparatively few did so in wheat. The accumulation of free amino acids in
anoxic rice has been documented before, using three-day-old aerated
seedlings transferred to anoxia for up to 48 hours (Kato-Noguchi and Ohashi,
2006). It is interesting that amino acids continue to accumulate past the point
of our measurements, 24 hours, suggesting this response might not be
transient. Alanine and GABA made up 19 and 23 % of the amino acid pool,
respectively, in anoxic rice coleoptiles (Kato-Noguchi and Ohashi, 2006). While
anoxic alanine accumulation is considered a classical anaerobic response
(Narsai et al., 2011), the analyses of Study I showed no significant
accumulation of alanine in anoxic wheat coleoptiles. This contrasts to an
earlier study, which showed wheat shoots accumulating alanine faster than
rice at initial time points, and equally at later time points (8 h) (Menegus et al.,
1989).
While the benefits of anaerobic alanine accumulation are unknown, several
hypotheses have surfaced. As discussed more thoroughly in Chapter 1,
potential benefits include hypoxic pyruvate consumption to prevent anoxia
(Zabalza et al., 2009), carbon skeleton retention for post-anoxia (Miyashita et
al., 2007) or prevention of excessive cytoplasmic acidification through
diverting carbon skeletons away from lactic acid production (Reggiani et al.,
1988). None of these hypotheses, however, are able to explain why alanine
supplementation of anoxic wheat seedlings, but not rice seedlings, reduces
cell leakage (Study I) (Shingaki-Wells et al., 2011). It is important to point out,
however, that a combination of serine, glycine and alanine also had this effect,
but not serine or glycine alone (Shingaki-Wells et al., 2011). It is possible that
194
the benefit of this supplementation is related to osmotic pressure, but it is also
possible that the inter-conversion of amino acids provides some benefit in
anoxic wheat.
Intrigued by these results, four more wheat genotypes were selected for
further analysis (Study II) and compared to our primary genotype of interest,
Calingiri. These four genotypes, SARC1, Ducula4, Carnamah and Spear, were
selected on the basis that the anoxia tolerance of their roots was highly
variable (Goggin and Colmer, 2007). For a more direct comparison to this
research, two variables were modified. This included the addition of a 15˚ C
temperature treatment to our original 28˚ C treatment used for Study I and III,
and analysis of roots as well as coleoptiles. An absent alanine response in 28˚
C anoxic coleoptiles of Calingiri seedlings confirmed the results of Study I.
However, it appeared that an anaerobic alanine response in Calingiri was
tissue dependent, with 28˚ C Calingiri roots showing significant accumulation
under anoxia. Additionally, all genotypes accumulated alanine in 15˚ C tissues
(coleoptiles and roots), indicating that temperature has a strong effect on
anoxic alanine accumulation, a finding that was confirmed by ANOVA
interaction analyses.
The focus on alanine was an attempt to identify whether its accumulation
could be used as a marker for anoxia tolerance, but this study, which
suggests Calingiri coleoptiles are among the top performers, challenges this
hypothesis. This analysis was further complicated by the difficulty in finding a
gold standard measure of anoxia tolerance. For example, there are several
ways to measure what could be considered the simplest of variables, growth,
which can lead to different conclusions. The vast differences identified in the
metabolomics analysis suggests that anoxia tolerance is likely to be sensitively
dependent on even subtle environmental or experimental differences, as
exemplified by the inconsistencies of the Ducula-4 literature (Setter et al.,
2009). Study II, therefore, contributes the finding that anoxia tolerance is
temperature and tissue dependent, and complicated by the fact that plants
195
may achieve tolerance by different means. Employing other measurements of
cell integrity as alternatives to the electrolyte leakage assay could be useful in
clarifying the damage caused by anoxia in different wheat genotypes. This
includes cell stains such as propidium iodide (Rolny et al., 2011).
In Study III, our interest shifted to the consequences of re-oxygenation on the
coleoptiles of rice and wheat (Calingiri) seedlings, at 28˚ C. In relaxing our
significance thresholds for the proteomics analysis, we were able to detect
several wheat proteins that changed during anoxia, as well as re-oxygenation.
Proteins changing in anoxic wheat were involved in translation and pyruvate
metabolism. Confirming our results in Study I, these changes were mostly
below 2-fold, suggesting a relatively restrained response to anoxia. Re-
oxygenation resulted in changes common to both species, including a
decrease in phenylalanine ammonia lyase under anoxia, which was reversed
upon re-oxygenation. This protein is suspected to be involved in cell wall re-
modelling (Rumeau et al., 1990). In addition, post-anoxic changes in other
proteins involved in cell structure re-modelling were seen in rice and wheat.
This aligns with the findings reported in the first study to document proteomic
changes to re-oxygenation in plants (Salavati et al., 2012). Since low oxygen
events reduce the efficiency of ATP production (Gibbs and Greenway, 2003),
it follows that growth, which may lose preference to other critical processes,
would be aberrant under different oxygen concentrations.
Re-oxygenation caused large changes to the metabolome of rice, but less so
in wheat. The rapid accumulation of amino acids in anoxic rice, which was
also observed in Study I, was reversed during post-anoxia (Study III). The
hypothesis that alanine accumulation might be beneficial for post-anoxic
metabolism (Miyashita et al., 2007) may therefore also apply to other amino
acids. It is suspected that the inability of wheat to mobilise starch under
anoxic conditions (Guglielminetti et al., 1995) contributes to a lower glycolytic
flux (Waters et al., 1991), and thus a reduced capacity to accumulate amino
acids (Study I and III). This could negatively affect post-anoxic recovery in
196
wheat, since the amylotic enzymes required for starch mobilisation would not
be immediately available during re-oxygenation, nor the amino acids whose
inter-conversion to pyruvate could allow prompt resumption of aerobic
respiration. As seen in Study II, anoxic amino acid accumulation is relatively
strong and sucrose levels relatively stable in 15˚ C coleoptiles, suggesting a
strong dependence of anoxic metabolism on temperature.
Future work
While general trends have been identified, the complex experimental design
outlined in Study II requires the development of a computational method to
systematically compare physiological measurements with metabolomics
analyses. Incorporating rice data into this analysis has the potential to identify
metabolic biomarkers of tolerance. This analysis will be carried out in the near
future. Proteomics analysis in re-oxygenated seedlings would be well
complemented by translatome or shotgun proteomics analysis to overcome
the shortfalls of gel analysis. Incorporating more treatment regimes, including
anoxically-germinated rice seedlings transferred to air for the first time, could
be useful in isolating post-anoxic shock responses. Such analysis could also
be useful in imitating the conditions often faced by lowland rice, which often
germinate under anaerobic conditions. While this has been done using anoxic
rice coleoptile mitochondrial samples (Millar et al., 2004), this analysis has not
been performed at the whole-cell level. Integration of this data with that of the
anoxia literature would provide a solid basis for identifying key responses to
low oxygen and re-aeration, two stresses that are often intertwined. Follow up
of these changes, through the development of genetic mutants would be
useful in understanding the role these genes play in low oxygen survival. For
example, targeting a set of enzymes involved in cell structure re-modelling
could clarify the importance of their role during re-oxygenation. Alternatively,
disrupting several reactions of amino acid metabolism in tolerant and
intolerant crop species could clarify any speculations made on the functional
importance of these pathways.
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Shingaki-Wells RN, Huang S, Taylor NL, Carroll AJ, Zhou W, Millar AH (2011) Differential molecular responses of rice and wheat coleoptiles to anoxia reveal novel metabolic adaptations in amino acid metabolism for tissue tolerance. Plant Physiology 156: 1706-1724
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Co-author consent This thesis contains publications/manuscripts as outlined on page III. The
consent of each co-author is provided on the following pages.
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I, Adam Carroll , approve the u se of the following publication, of which I am
a co -author, to be included in the thesis of Rachel Shingaki -Wells for the
degree of Doctor of Philosophy at The University of Western Australia.
1. Shingaki -Wells RN , H uang S, Taylor NL, Carro ll AJ, Zhou W, Millar AH. (2011) Differential molecular responses of rice and wheat coleoptiles to anoxia reveal novel metabolic adaptations in amino acid metabolism for tissue tolerance. Plant Physiology 156(4):1706 -24.
Signed __ _______________
Date ___________________________ 06/01/2014
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I, Wenxu Zhou, approve the use of the following publication, of which I am a
co-author, to be included in the thesis of Rachel Shingaki-Wells for the
degree of Doctor of Philosophy at The University of Western Australia.
1. Shingaki-Wells RN,, Huang S, Taylor NL, Carroll AJ, Zhou W, Millar AH. (2011) Differential molecular responses of rice and wheat coleoptiles to anoxia reveal novel metabolic adaptations in amino acid metabolism for tissue tolerance. Plant Physiology 156(4):1706-24.
Signed ________________________
Date ___________________________
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