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First published online July 1, 2009; 10.1104/pp.109.142026 Plant Physiology 151:306-322 (2009) © 2009 American Society of Plant Biologists OPEN ACCESS ARTICLE
Defining Core Metabolic and Transcriptomic Responses to Oxygen Availability in Rice Embryos and Young Seedlings1,[W],[OA]Australian Research Council Centre of Excellence in Plant Energy Biology, University of Western Australia, Crawley, Western Australia 6009, Australia
Analysis reveals that there is limited overlap in the sets of transcripts that show significant changes in abundance during anaerobiosis in different plant species. This may be due to the fact that a combination of primary effects, changes due to the presence or absence of oxygen, and secondary effects, responses to primary changes or tissue and developmental responses, are measured together and not differentiated from each other. In order to dissect out these responses, the effect of the presence or absence of oxygen was investigated using three different experimental designs using rice (Oryza sativa) as a model system. A total of 110 metabolites and 9,596 transcripts were found to change significantly in response to oxygen availability in at least one experiment. However, only one-quarter of these showed complementary responses to oxygen in all three experiments, allowing the core response to oxygen availability to be defined. A total of 10 metabolites and 1,136 genes could be defined as aerobic responders (up-regulated in the presence of oxygen and down-regulated in its absence), and 13 metabolites and 730 genes could be defined as anaerobic responders (up-regulated in the absence of oxygen and down-regulated in its presence). Defining core sets of transcripts that were sensitive to oxygen provided insights into alterations in metabolism, specifically carbohydrate and lipid metabolism and the putative regulatory mechanisms that allow rice to grow under anaerobic conditions. Transcript abundance of a specific set of transcription factors was sensitive to oxygen availability during all of the different experiments conducted, putatively identifying primary regulators of gene expression under anaerobic conditions. Combined with the possibility of selective transcript degradation, these transcriptional processes are involved in the core response of rice to anaerobiosis.
Various species, including some bacteria, yeasts, and plants, have the ability to survive and grow in the absence of oxygen (Bunn and Poyton, 1996  ; Kwast et al., 1998; Bailey-Serres and Voesenek, 2008). Although the ability of plants to germinate and survive under anaerobic conditions varies greatly, one common response is the synthesis of a number of anaerobic proteins, such as alcohol dehydrogenase, which was identified almost 30 years ago (Sachs et al., 1980) and has been extensively studied at transcript, protein, activity, and regulatory levels (Dolferus et al., 2003; Bailey-Serres and Voesenek, 2008). Analysis of the effects of anaerobic conditions on various plant species reveals that widespread changes limiting energy-demanding processes is a common response (Bailey-Serres and Voesenek, 2008). Overall, under conditions where oxygen is limited, an increase in glycolytic flux, modified Suc degradation, and a decrease in processes such as transport and various biosynthetic pathways occur. More specifically, a switch to the pyrophosphate (PPi)-linked enzymes, such as PPi-dependent phosphofructokinase and pyruvate orthophosphate dikinase, as well as the breakdown of Suc via Suc synthase conserves ATP, a response reported in a variety of studies (Huang et al., 2008; Magneschi and Perata, 2009).
Despite differences in tolerance to anaerobic conditions, plant size, and life cycle, the use of transcriptomic approaches to study the response to anaerobic conditions in Arabidopsis (Arabidopsis thaliana; Liu et al., 2005
Rice plants can survive periods of anaerobiosis, and rice seeds also have the ability to germinate in an environment completely lacking in oxygen. The identification of the SUB1A locus provides an important breakthrough for understanding molecular responses of rice under submergence (Xu et al., 2006
In order to gain greater insight into the regulatory processes that modulate responses to anaerobic conditions, three experimental strategies were used to define oxygen-responsive genes in rice. It was hypothesized that many of the changes that occur are not primarily due to the effects of a lack of oxygen but instead are dependent on prior growth conditions or secondary responses. We have previously characterized changes in water content and metabolic activity in rice embryos during germination up to 48 h after imbibition (HAI) under aerobic and anaerobic conditions (Howell et al., 2006
Changes in Transcript and Metabolite Abundance during Germination in Aerobic or Anaerobic Conditions
An analysis of changes in transcript abundance during germination between aerobic and anaerobic conditions revealed that there was little difference up to 3 HAI; as a result, transcriptomic data for aerobic and anaerobic germinated seeds at this time point were not differentiated by principal component analysis (PCA) analysis (Fig. 1B; Supplemental Fig. S1). As approximately 2,000 genes had significantly altered transcript abundances 3 HAI under both growth conditions when compared with 1 HAI (Fig. 1B, 1A v 3A and 1N v 3N; Supplemental Tables S1 and S2) and greater than half of these changes were significant increases in abundance, this indicated that initial changes in transcript abundance occurring 3 HAI were independent of oxygen levels. In contrast, after 3 HAI, 2,628 (1,148 up-regulated, 1,480 down-regulated) and 4,892 (2,195 up-regulated, 2,697 down-regulated) transcripts were observed to have significantly different levels at 12 and 24 HAI, respectively (Fig. 1B, A v N), with the different samples forming distinct groups as visualized by PCA analysis (Supplemental Fig. S1). In total, there were 5,948 nonredundant probe sets that showed significant differences in transcript abundance between the two growth conditions up to 24 HAI, compared with approximately 14,000 or approximately 12,000 that were observed to change between 0 and 24 HAI under aerobic (Howell et al., 2009 Analysis of the functional categorization of the genes for the 5,948 transcripts that were higher in abundance in anaerobic germinated seeds (A v N) revealed that lipid metabolism and transcription factor categories were overrepresented based on z-scores where P < 0.01 (Fig. 1C, i; Supplemental Table S3). In contrast, functional categorization of the genes for transcripts that were lower in abundance under anaerobic conditions was enriched in transcripts encoding metabolism, carbohydrate and energy metabolism, and membrane transport functions, with transcripts encoding transcription factors and associated with translation and metabolic/genetic information processing being underrepresented (Fig. 1C, ii). An analysis of changes in metabolites during germination under aerobic and anaerobic conditions revealed that the number of metabolites that changed was similar up to 12 HAI (Fig. 1B, 3A v 12A and 3N v 12N). However, comparing metabolite profiles from aerobic and anaerobic growth conditions showed a number of significant changes, particularly after the 3-h time point, similar to the pattern seen for the transcripts. Specifically, more than 20% of all detected metabolites showed significant differences when 12-HAI samples were compared (Fig. 1B, 12A v 12N), and this increased to more than 40% at the 48-h time point (48A v 48N). Examples included several amino acids such as Gly and Tyr that had significantly (P < 0.05) higher levels under anaerobic germination, while the several metabolites associated with the tricarboxylic acid (TCA) cycle, such as isocitrate, 2-oxoglutarate, and citrate, had significantly (P < 0.05) lower levels under anaerobiosis (Supplemental Table S5).
As there are developmental differences between aerobic and anaerobic germinated seeds (Howell et al., 2007
Applying this principle of seeking complementary responses between three experiments to the transcriptomic analysis defined 1,136 and 730 transcripts as aerobic and anaerobic, respectively (Fig. 3 ). Functional analysis of the aerobic set (1,136) revealed significant enrichment of metabolism and membrane transport functions and underrepresentation of transcription factors, translation, folding, sorting, degradation, and replication and repair functions (Fig. 3). For the anaerobic set, there was significant enrichment of carbohydrate and lipid metabolism and underrepresentation of translation functions (Fig. 3).
Linking Metabolite and Transcript Changes to Visualize Modification of Metabolism under Anaerobic Conditions
A comparison of the differences in transcripts after 24 HAI under aerobic or anaerobic conditions, or upon transferring seedlings from growth in anaerobic conditions to aerobic conditions, revealed differences in lipid metabolism, cell wall metabolism, secondary metabolism (particularly flavonoids and phenylpropanoids and phenolics), carbohydrate metabolism, and amino acid metabolism (Supplemental Fig. S3). A variety of metabolite and transcript changes were observed that relate to starch/Suc breakdown via glycolysis, the TCA cycle,
Using Germination and Switch Experiments to Explore the Oxygen Dependence of Specific Processes
Lipid metabolism is known to be sensitive to oxygen at three distinct steps: in β-oxidation of fatty acids at the acyl-CoA oxidase step (Graham, 2008
The core lists of aerobic and anaerobic genes defined above allow a broader analysis of the regulatory processes that may determine transcript abundance under aerobic or anaerobic conditions. Twelve subsets of the genes that were defined as aerobic (1,136) or anaerobic (730) were analyzed for the presence of common regulatory elements in their upstream promoter regions, based on their potential to regulate transcript abundance (transcription factors) or on their relative enrichment in the set of aerobic or anaerobic genes relative to the whole genome (Supplemental Table S7A). The subsets chosen were aerobic and anaerobic sets of genes encoding transcription factors, proteins targeted to mitochondria and plastids, proteins involved in membrane transport and carbohydrate metabolism, and random sets of 50 aerobic or anaerobic genes. The MEME database was used to search for conserved elements in the sequence 1 kb upstream of the translation start site (Supplemental Table S7B). Only elements that occurred in 70% of any subset were used for further analyses (Supplemental Table S7C). These elements were taken and their enrichment in these subsets of genes was compared with the genome (Table I ). Furthermore, 23 elements or variations of elements previously characterized to mediate responses to anaerobic conditions were tested for enrichment in the various subsets of genes (Supplemental Table S7C). Twelve unique elements were found to be significantly enriched in the promoter regions of anaerobic genes (Table I, highlighted in blue), two elements were found to be enriched in the promoter regions of aerobic genes (Table I, highlighted in pink, percentages in red), while four elements were significantly underrepresented in aerobic genes (Table I, highlighted in pink, percentages in blue). Fifteen elements were found to be enriched in both anaerobic and aerobic genes (Table I, highlighted in gray; Supplemental Table S7C).
Under the three experimental conditions, approximately 40% to 50% of transcripts were down-regulated in abundance in response to oxygen availability (Fig. 1B). In order to assess the role of transcript degradation, potential regulatory elements were examined by searching for common sequences within the 3' untranslated regions (UTRs) of genes in the aerobic and anaerobic sets using MEME. It is important to note that the analysis of the 3' UTRs of the aerobic and anaerobic sets was limited by the small number of genes in rice for which this annotation exists. Nevertheless, we did find five enriched elements within the 3' UTRs of the aerobic set (P < 0.01) but none significantly enriched in the anaerobic set.
From a complete list of 3,098 rice transcription factors (described in "Materials and Methods"), it was found that 2,009 transcripts encoding for transcription factors were detected in at least one sample from the germination and switch samples (Supplemental Table S8). Of these, 437 were found to change significantly in abundance in the germination comparison, and 734 displayed significant differences within the switch samples. Four main subsets were selected for analysis: genes that were significantly up-regulated during anaerobic germination (224), genes that were down-regulated in anaerobic germination (212), genes that were present in the aerobic set (63), and genes that were present in the anaerobic set (71; Supplemental Table S8). The distribution of the various transcription factor families within each subset was compared with the genome (2,009) to determine which families were significantly overrepresented or underrepresented in each subset (Supplemental Table S8). Only four transcription families were significantly overrepresented or underrepresented compared with the genome in one or more subset(s) (Fig. 5
). The bZIP family was enriched in the subset of transcripts up-regulated under anaerobic germination and in the core anaerobic subset (Fig. 5). The enrichment of the bZIP family in both of these subsets is in agreement with the previous finding that this family is induced in tomato (Solanum lycopersicum) plants subjected to anaerobic conditions (Sell and Hehl, 2004
Aerobic and Anaerobic Transcription Factors Display Contrasting Expression Patterns in Rice Tissues and in Response to Various Stresses
In an attempt to understand how regulation of responses to anaerobic conditions compares with other stresses or rice development and growth, we investigated the expression of the 63 transcription factors defined as aerobic (Fig. 6A
) and the 71 transcription factors defined as anaerobic (Fig. 6B) in all publicly available Affymetrix rice microarray data (68 different conditions). Analysis of the expression pattern of the 63 aerobic transcription factors reveals that young root tissue, stigma, and anther have relatively high expression of approximately 15 of these genes (Fig. 6A; Supplemental Fig. S5A; Supplemental Table S9). This may correspond to energy-demanding processes in these tissues. Expression in response to drought, salt, and cold stress of seedlings was also observed. Expression of these transcription factors was high in salt stress of crown and growing point, and salt-sensitive rice varieties have decreased expression compared with salt-tolerant rice varieties in salt stress experiments (Fig. 6A; Supplemental Fig. S5A; Supplemental Table S9). Leaf material also appears to have high levels of expression for about 10 aerobic genes that may decrease slightly with cytokinin treatment (Fig. 6A; Supplemental Fig. S5A; Supplemental Table S9). Analysis of changes in transcript abundance of the 63 aerobic transcription factors in published studies using quantitative reverse transcription-PCR reveal that one NAC transcription factor (LOC_Os02g13710.1; Fig. 6A, no. 16; Supplemental Table S9) has been shown to increase under salt stress (Fang et al., 2008
For the 71 anaerobic transcription factors, several differences in expression were observed compared with the public array data (Fig. 6B; Supplemental Fig. S5B; Supplemental Table S9). The greatest expression was observed in ovary and suspension cells (Fig. 6B, blue boxes; Supplemental Fig. S5B; Supplemental Table S9). There was little expression during biotic or abiotic stress (Fig. 6B; Supplemental Fig. S5B; Supplemental Table S9). One gene (LOC_Os05g45410.1; Fig. 6B, no. 46; Supplemental Table S9) has previously been identified to encode a heat shock transcription factor (Yamanouchi et al., 2002 ), and another (LOC_Os03g26210.1) increased under dehydration stress (Nijhawan et al., 2008). This group also contains a number of myb-type transcription factors previously associated with stress responses (Yanhui et al., 2006).
Using three different experimental conditions, 10 metabolites could be defined as aerobic across all three conditions, out of a total of 39 that displayed a significant difference in the presence of oxygen in any one condition, and 13 were defined as anaerobic in all three conditions, out of a total of 82 that were up-regulated in response to anaerobiosis in any one condition. For the transcriptome, over 9,000 nonredundant transcripts were observed to change in at least one condition. However, only 1,866 of these transcripts showed complementary changes in all three conditions. This reveals that although rearrangement of the transcriptome underlies tolerance to anaerobic conditions in rice, the majority of these changes depend on the prior growth conditions and/or the stage of development tested. The identification of the common responses in all three experimental conditions provides an insight into the core set of genes that respond to oxygen, aerobic (1,136) and anaerobic (730), and provide a basis for detailed analysis of the oxygen regulatory mechanisms.
A previous study analyzed the differences in transcript abundance in rice grown under aerobic and anaerobic conditions (Lasanthi-Kudahettige et al., 2007
Metabolite and transcript profiles revealed a large number of changes in central carbon and nitrogen metabolism under oxygen-deprived conditions, especially during anaerobic germination. It is generally accepted that anoxia-tolerant plant species such as rice offset losses in aerobic ATP production by increasing fluxes through glycolytic and fermentative pathways, a phenomenon known as the Pasteur effect (Bailey-Serres and Voesenek, 2008
Our nontargeted gas chromatography-mass spectrometry (GC-MS) approach revealed a wide variety of oxygen-dependent metabolic responses that, to our knowledge, have not previously been reported in rice. In addition to the previously reported aerobic rice metabolite 2-oxoglutarate (Reggiani et al., 1988
In our core anaerobiosis-induced metabolite set, only 4-aminobutyrate, succinate, and Phe have previously been reported to accumulate in anaerobic rice tissues (Bertani et al., 1981
In addition to changes in transcripts and metabolites involved in carbon and nitrogen metabolism, 28 transcripts encoding proteins involved in lipid metabolism were present in the core anaerobic set. These included 13 transcripts specifically categorized as lipid metabolism and 15 transcripts encoding proteins associated with lipid metabolism (e.g. signaling/lipid metabolism). To our knowledge, a role for lipid metabolism in response to anaerobic conditions in plants has not been suggested before. These changes in lipid metabolism have the potential to play a central role in sensing and signaling anaerobic conditions. An absence of oxygen will result in an inability to synthesize polyunsaturated fatty acids due to the oxygen-requiring desaturase step, thus affecting membrane composition and fluidity. This has the potential to affect signaling, as has been previously described for stress signaling pathways in plants (Penfield, 2008
The changes observed in transcript abundance are ultimately mediated by transcription factors binding CAREs in the upstream regions of targeted genes. Twelve CAREs were found to be overrepresented in the promoter regions of anaerobic genes, whereas only two were found to be overrepresented in aerobic genes and four were underrepresented (Table I). Many CAREs enriched in the anaerobic set have been previously associated with hypoxia (refs. in Table I) and shown to be functional in individual genes. However, in addition to CAREs enriched in anaerobic genes, 15 CAREs were found to be enriched in both the anaerobic and aerobic sets. This may appear paradoxical at first; however, it has been proposed that a down-regulation of genes that encode proteins that require oxygen to function may be an energy-saving mechanism under anaerobic conditions, but this requires repression of transcription (Bailey-Serres and Voesenek, 2008
In terms of transcription factors that may bind the CAREs identified above, the bZIP family of transcription factors were overrepresented, both in germination under anaerobic conditions and in the anaerobic core set. bZIP transcription factors are associated with various stress responses in plants (Jakoby et al., 2002
Rice Growth
Dehulled, sterilized rice seeds (Oryza sativa ‘Amaroo’) were grown under aerobic or anaerobic conditions in the dark at 30°C as described previously (Howell et al., 2007
Total RNA was isolated from rice embryos as described previously (Howell et al., 2006
Affymetrix GeneChip Rice Genome Arrays were used for the transcriptomic analysis as described previously (Howell et al., 2009
Following filtration of the data set, all probe intensities were then analyzed using the GC (content) robust multiarray average (GC-RMA) algorithm, and three-dimensional principal component analysis was carried out to visualize the global differences between the arrays. To perform differential expression analysis, the GC-RMA normalized data were then log transformed and false discovery rate correction (Benjamini and Hochberg, 1995
The transcription factor list was generated using three main sources, DRTF (Gao et al., 2006
These were performed exactly as described by Howell et al. (2009)
In order to examine transcript abundance changes across different tissues, under different conditions, and compare these with the transcript abundance profiles generated from this study, rice array data were retrieved from the Gene Expression Omnibus within the National Center for Biotechnology Information database as described previously (Howell et al., 2009
Following expression analysis, the 12 groups of transcripts selected for promoter analysis (Supplemental Table S7A) were analyzed for the presence of overrepresented sequence elements in the 1-kb upstream regions. In order to study these coexpressed transcripts more closely, all 1-kb upstream regions of the 29,087 transcripts were retrieved and the upstream regions of the 12 subgroups were extracted and examined for putative cis-acting elements. Programs designed to detect sequence elements generally have limits of approximately 50 to 60 input sequences; thus, these smaller lists were selected. The MEME Web server (Bailey et al., 2006
The full genome 3' UTR and 5' UTR sequences available from The Institute for Genomic Research were downloaded and filtered to retain only the 3' UTRs. However, this only included a total of 3,027 UTRs available for the "whole genome." Taking this small number into consideration, it was not feasible to look at the 12 subsets, as these lists were too small. Thus, for the 3' UTR, all of the genes in the aerobic and anaerobic responsive subsets were used for the analysis of overrepresented sequence elements in the 3' UTRs. The settings were set to search for five motifs that are 6 to 8 bp (default) in each of the subsets, and the outputs are shown at the bottom of Supplemental Table S7B. It is important to note that setting the output to five motifs can result in false present calls for motifs that are not significant when the input list is small; therefore, only the significantly enriched motifs (present in 60%–70% of all input sequences) were included for further analysis (Supplemental Table S7D). In addition to these putative predicted motifs, other motifs known to be associated with RNA stability/instability (Newman et al., 1993
Metabolite extraction, derivatization, and GC-MS analysis were all carried out as outlined by Howell et al. (2009)
The following materials are available in the online version of this article.
Received May 25, 2009; accepted June 25, 2009; published July 1, 2009.
1 This work was supported by the Australian Research Council Centre of Excellence (grant no. CEO561495) and an Australian Research Council Australian Professorial Fellowship to A.H.M. 
2 These authors contributed equally to the article.
3 Present address: Max-Planck-Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: James Whelan (seamus{at}cyllene.uwa.edu.au).
[W] The online version of this article contains Web-only data.
[OA] Open Access articles can be viewed online without a subscription. www.plantphysiol.org/cgi/doi/10.1104/pp.109.142026 * Corresponding author; e-mail seamus{at}cyllene.uwa.edu.au.
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N), and (3) significantly up-regulated in response to switching to aerobic conditions (N 
-aminobutyrate (GABA) shunt, and amino acid metabolism (
