| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
|
First published online October 1, 2008; 10.1104/pp.108.128488 Plant Physiology 148:2050-2058 (2008) © 2008 American Society of Plant Biologists OPEN ACCESS ARTICLE
Arabidopsis Transcriptome Reveals Control Circuits Regulating Redox Homeostasis and the Role of an AP2 Transcription Factor1,[W],[OA]Department of Biology (A.K., R.Q.), and Department of Electrical and Systems Engineering (T.E.), Washington University, St. Louis, Missouri 63130; and Department of Mathematics and Statistics, Texas Tech University, Lubbock, Texas 79409 (B.G.)
Sensors and regulatory circuits that maintain redox homeostasis play a central role in adjusting plant metabolism and development to changing environmental conditions. We report here control networks in Arabidopsis (Arabidopsis thaliana) that respond to photosynthetic stress. We independently subjected Arabidopsis leaves to two commonly used photosystem II inhibitors: high light (HL) and 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU). Microarray analysis of expression patterns during the period of redox adjustment to these inhibitors reveals that 20% and 8% of the transcriptome are under HL and DCMU regulation, respectively. Approximately 6% comprise a subset of genes common to both perturbations, the redox responsive genes (RRGs). A redox network was generated in an attempt to identify genes whose expression is tightly coordinated during adjustment to homeostasis, using expression of these RRGs under HL conditions. Ten subnetworks were identified from the network. Hierarchal subclustering of subnetworks responding to the DCMU stress identified novel groups of genes that were tightly controlled while adjusting to homeostasis. Upstream analysis of the promoters of the genes in these clusters revealed different motifs for each subnetwork, including motifs that were previously identified with responses to other stresses, such as light, dehydration, or abscisic acid. Functional categorization of RRGs demonstrated involvement of genes in many metabolic pathways, including several families of transcription factors, especially those in the AP2 family. Using a T-DNA insertion in one AP2 transcription factor (redox-responsive transcription factor 1 [RRTF1]) from the RRGs, we showed that the genes predicted to be within the subnetwork containing RRTF1 were changed in this insertion line (  rrtf1). Furthermore, rrtf1 showed greater sensitivity to photosynthetic stress compared to the wild type.
Redox (oxidation-reduction) reactions are basic to all cellular processes. The redox environment of the cell governs the activity of metabolic processes by regulating protein function that in turn regulates key processes in growth and development (Sakuma et al., 2002  ; Wu et al., 2007). Buffering capacity of the cellular redox environment is thus very critical, and, as a result, elaborate mechanisms are in place in plants to keep control of redox levels (e.g. by the action of redox pairs [ferredoxin, thioredoxin, etc.], antioxidant systems [superoxide dismutase, glutathione, etc.], and other secondary metabolites [flavonoids, alkaloids, and carotenoids]; Dietz, 2003).
In plants, the major redox determinant is the photosynthetic electron transport chain. During the day, plants utilize light energy to assimilate carbon using reducing power generated by the photosynthetic electron transport chain and concomitantly generate oxidizing power in the form of molecular oxygen. The enzymes in the chloroplast cycle between their oxidized and reduced forms to regulate metabolic processes during the day (Scheibe, 1991
In response to external stimuli, such as abiotic/biotic stresses, plants modify their normal metabolic responses and alter their physiological and developmental programs. The nature and extent of modification is highly dependent on the nature of the stimulus itself, the dose, and exposure time to the tissue in question. The cross talk between responses to different stresses may involve common intermediates, as has been suggested by identifying common genes (Seki et al., 2002
Previous studies have focused on identification and characterization of individual redox sensors and modifiers. This includes the retrograde signaling pathways between chloroplast and nucleus (Ankele et al., 2007 In our study, we used Arabidopsis (Arabidopsis thaliana) leaves and perturbed the cellular redox status by targeting a photosynthetic reaction center (PSII) using high light (HL) stress and the inhibitor 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU). We generated gene expression networks from control and treated leaves to identify genes that were highly connected to specific networks and possibly to phenotypes. We validated our findings in one subnetwork by demonstrating that when one novel transcription factor (redox-responsive transcription factor 1 [RRTF1]) was made nonfunctional, not only did the mutant plant alter the expression of genes associated in the network, but it lacked the ability to adjust to redox changes.
Two commonly used inhibitors of photosynthetic electron transport, HL and DCMU, were used to study redox regulation in Arabidopsis. To follow the redox state of control and treated Arabidopsis leaves, chlorophyll fluorescence was measured as an indicator of photosynthetic efficiency. Leaves that received HL showed a decline in PSII efficiency for the first 1.5 h of HL exposure, and maintained a PSII efficiency of approximately 70% for the next 4.5 h. (Fig. 1 ). DCMU had a more severe effect on PSII efficiency; a continuous decrease in PSII efficiency was observed for the first 3 h of treatment, reaching a steady state of only approximately 20% PSII efficiency by 6 h (Fig. 1). There was no detectable change in the PSII efficiency when leaves received regular light or in the absence of DCMU.
RNA was extracted at each of the time points for both treatments (Fig. 1) and analyzed by microarray analysis. The total number of genes altered by the perturbations are given in Table I (for gene lists, see Supplemental Tables S1–S7), whereas Table II shows the number of genes that are differentially expressed at any one time point or at all time points for HL (htx) and DCMU (dtx) treatments (for gene lists, see Supplemental Tables S8 and S9). From all of the genes that are differentially expressed at all time points, about 20% of the Arabidopsis transcriptome is altered under HL, whereas only 8% of the transcriptome is affected by DCMU treatment.
The set of differentially expressed genes in common to both HL- and DCMU-treated tissue includes the redox responsive genes (RRGs; Fig. 2 ). A redox network in response to HL for the RRG1 set was generated (Fig. 3 ). In the network of 1,201 genes in RRG1 (200 genes did not meet the statistical criteria for coexpression), more than 8,000 connections were demonstrated, indicating that they are coexpressed as a set. Based on their expression profile at different time points under HL, RRG1 were segregated into 10 subnetworks. Genes in subnetworks 1 to 5 have lower transcript expression following HL exposure, whereas genes in subnetworks 6 to 10 have higher expression of transcripts. Each subnetwork has transcripts representing genes from various metabolic pathways, but some subnetworks have a significant number of transcripts whose gene products belong predominantly to one physiological function (Supplemental Table S10). For example, ribosomal gene transcripts are preferentially increased in subnetworks 6 and 7, whereas transcripts from genes that function in energy metabolism are decreased in subnetwork 3. Subnetwork 1 is the largest and has 299 transcripts representing genes involved in several metabolic pathways and whose products are located in different subcellular compartments.
In each of the 10 subnetworks generated under HL, we further investigated how these groups of genes behaved under HL and DCMU treatment. Hierarchal subclustering of genes in each of the 10 subnetworks identified under HL using expression data from the DCMU experiment was performed. The sets of genes that had similar expression profiles under HL and DCMU were obtained (Fig. 4 ). Hence, these new subgroups represent redox subnetworks represented in both HL and DCMU treatments.
To further investigate whether there were common motifs in promoters of each redox subnetwork, as well as whether each redox subnetwork had a defining motif, we examined sequences within 500 bp upstream from the Met start site to identify the most common six-base element within the promoters in each cluster (Fig. 4). It is clear from the P value of the motifs identified for each cluster that they are significant and different from each other, except perhaps clusters 4 and 5, which have the (C)CACGT(G) motif associated with the ABA response element (Zhang et al., 2005 ).
In an attempt to reduce further the core RRGs, the list of genes in RRG1 was narrowed by applying a more stringent criterion. We identified those genes that were differentially expressed (increased or decreased with a fold change
Functional categorization of RRG2 based on the Gene> Ontology (GO) annotation (Fig. 5B) revealed that about one-half (72) of RRG2 were novel with no previously identified function. Because we are interested in defining the redox regulatory network, we observed that RRG2 contained 16 transcription factors, including overrepresentation from AP2 domain-containing transcription factors (Table III ). To validate our prediction that any one of these transcription factors are controlling a set of genes that relate to the stress response, one of the AP2 domain-containing transcription factors was selected for further analysis. We chose At4g34410 (RRTF1), a member of subnetwork 1, whose previous link to the control of redox homeostasis and any other phenotype was unknown.
A T-DNA insertion line of RRTF1 ( rrtf1) was obtained and confirmed that the corresponding transcript was absent (data not shown). When we compared the set of differentially expressed genes between wild type and rrtf1 after a 1.5-h exposure to HL, 30 of 297 genes present in subnetwork 1 were differentially expressed in the absence of RRTF1. These 30 genes are represented as a coexpression network (within subnetwork 1; Fig. 6
) that is affected by the absence of RRTF1. These included transcripts whose genes are involved in signaling pathways and whose subcellular localizations were located in different compartments, including the nucleus, chloroplast, mitochondrion, and cell wall (Supplemental Fig. S1). These data validate the coexpression predictions based on the network analysis.
To further validate whether these 30 genes are under the control of RRTF1, HL, or both, semiquantitative reverse transcription (RT)-PCR analysis was performed (Fig. 7 ). Compared to normal light, all 30 genes are down-regulated under HL (wild type-HL) in wild type and are up-regulated in rrtf1 (Fig. 7). Compared to wild type-HL, 11 of 30 genes in the subnetwork were not changed significantly in rrtf1-HL (i.e. they showed the same expression response [up-regulated] as they did in rrtf1). This appears to indicate that these genes are normally under the control of RRTF1 during HL stress. However, the remaining 19 genes did change their expression pattern when compared to rrtf1 kept at normal light and are apparently not dependent on RRTF1 in response to HL.
The RRTF1 knockout plants did not exhibit any phenotypic differences under normal growth conditions (data not shown). However, when exposed to HL, we observed a greater sensitivity to this stress as evidenced by bleaching of leaves compared to controls (Fig. 8 ), a phenotype consistent with the inability to reach homeostasis under HL.
Our purpose was to identify global redox response networks by utilizing the photosynthesis perturbants HL and DCMU. Photosynthesis plays a crucial role in determining redox status of the cell as evidenced by the production of a strong oxidant (molecular oxygen at a redox potential of 0.8 V) and a strong reductant, for example, NADPH (redox potential of –0.34 V). We measured chlorophyll fluorescence, a fast, simple, and noninvasive way of detecting changes in PSII efficiency as a measure of redox changes. Following exposure to HL under our experimental condition (750 µE/mol–1), Arabidopsis leaves immediately experience a redox imbalance, but by 90 min reach homeostasis at about 70% of controls. Because DCMU binds strongly to the Qb site of the PSII complex, resulting in the inhibition of electron flow (Russell et al., 1995 ), this treatment also leads to a drastic change in the reducing environment, but by 3 h leaves reach homeostasis at about 20% of controls. Although Arabidopsis leaves attain a new redox homeostasis following HL or DCMU perturbation of PSII (Fig. 1), the time required to reach steady state was different for each stress, as was the magnitude of the decrease in photosynthetic efficiency.
This comprehensive study was used to gain insight into the global response of wild-type Arabidopsis to achieve redox homeostasis following exposure to photosynthetic perturbations using a systems approach. Our microarray analysis revealed that a major part of the transcriptome (approximately 20%) is responsive to HL. In addition to a large set of novel genes, most of the genes previously characterized to be redox responsive (for review, see Oelze et al., 2008
During the period of redox adjustment during the first 3 h of either treatment, we were interested in the gene regulatory networks operating during these PSII stresses. By using two different inhibitors of PSII, we reasoned that the genes differentially expressed by both perturbations would more likely be directly identified with the network regulating PSII efficiency. As a first step, we identified 1,401 genes that showed a change of expression (>2x) during either treatment at any one of the seven time points. This set was called RRGs. It remains to be established for all RRGs that they indeed respond to redox status and not specifically to the treatment. However, a comparison between RRG1 and the genes observed to be responsive to the redox status of the plastoquinone (PQ) pool by Adamiec et al. (2008) We focused our efforts on the RRG1 set of genes that were altered in their expression during HL stress to generate a coexpression network. This analysis resulted in a network containing 1,201 genes with 8,000 connections and clearly revealed 10 distinct subnetworks, indicative of the complexity of the regulatory networks involved in the adjustment of homeostasis.
We further focused our attention on the largest network (i.e. subnetwork 1), which contained 299 genes, and attempted to identify a key regulator based on the network analysis. Within subnetwork 1, about 80 genes were up- or down-regulated by
Because removal of a transcription factor within this network would be the most efficient manner to remove a set of linked regulatory transcripts, we noted that there was an overrepresentation of AP2 domain-containing transcription factors in RRG2. There are 145 DREB/ERF family proteins in Arabidopsis, classified into 12 groups (Nakano et al., 2006
In the redox subnetwork 1, RRTF1 has 13 direct connections, which are regulated at all time points in both treatments. When the transcriptome analysis after 1.5-h exposure to HL between wild-type and a T-DNA insertion into At4g34410 ( Network analysis revealed that 30 genes that are differentially expressed in rrtf1 are in the vicinity of the RRTF1in subnetwork 1. This smaller subnetwork of 30 genes was defined as a core redox network (Fig. 6). In the core redox network, 70% of genes are well documented in literature to have a role during stress responses. If this core network is vital for maintenance of redox homeostasis, perturbation to this regulatory network will lead to a susceptible state.
This led us to the prediction that
Plant Material and Growth Condition
A Salk T-DNA insertion line (SALK_150614; containing insertion in exon of At4g34410) was obtained from the Arabidopsis Biological Resource Center (ABRC). For SALK_150614, the gene-specific primers LP (5'-CGCGATGCTTTGTAGGAGTAG-3') and RP (5'-GATCTCAGGGGAAAACGAAAC-3') were used in conjunction with the T-DNA left-border primer LBb1 (5'-GCGTGGACCGCTTGCTGCAACT-3'; Alonso and Stepanova, 2003
Arabidopsis (Arabidopsis thaliana ecotype Columbia [Col]) wild-type and
Two redox perturbations were chosen that affect redox status of the PQ pool, namely, HL and DCMU. For HL treatment, light intensity of 750 µmol/m2 and 5 µM DCMU were used. Leaves from 4-week-old plants were floated in water, adaxial side up, and used for microarray analysis after 0.75, 1.5, 3, and 6 h of HL or 1.5, 3, and 6 h of DCMU treatment. For each time point of a given treatment, three petri plates with floating leaves were used. Three such independent sets were pooled as one sample and RNA was extracted. At each time point of the treatment, RNA extracted from three samples was in turn pooled and hybridized to the reference RNA pool prepared in a similar manner from control leaves. Supplemental Figure S2 shows the flow chart of sample preparation and design for microarray experiments.
Leaves from the control sets and treatments were frozen in liquid nitrogen at indicated time points. RNA was extracted using Agilent's mini plant RNA isolation kit as per the manufacturer's instructions. A Nanodrop ND-1000 spectrophotometer was used to measure total RNA concentration. Quality of RNA was determined using RNA 6000 Nano Assay (2100 Bioanalyzer; Agilent Technologies). Good-quality RNA samples were pooled for microarray analysis (Supplemental Fig. S2).
RNA samples were given to MOgene for microarray analysis (You et al., 2006
Euclidean distance is calculated between each pair of genes. Low value of Euclidean distance indicates that the two genes have a high degree of correlation and are very close in their expression profile over time. An edge is drawn between two genes if Euclidean distance between them is below the arbitrary cutoff value 0.15. The results are then visualized using Cytoscape 2.3. A connectivity graph was generated using an organic model.
Subclusters obtained using gene expression value similarity are candidates for coregulated genes. Such coregulated genes usually share common binding-site motifs in their upstream regions. A widely used motif identification algorithm known as CONSENSUS developed by the Gary Stormo group at Washington University was used to identify the possible conserved sequences within subclusters. The first 500 bp of the upstream regions of genes belonging to each subcluster were obtained from The Arabidopsis Information Resource (TAIR; http://www.arabidopsis.org) database. Patterns with the length of six were searched without considering the complements of the sequences because the sequences obtained from TAIR had correct orientation of the genes. Each sequence contributed exactly one motif for the final results.
RNA samples were isolated from leaves of wild type and
Supplemental Data The following materials are available in the online version of this article.
We wish to thank the members of the FIBR groups at Washington University, St. Louis University, and Colgate University (http://fibr.wustl.edu/index.php), as well as the Quatrano lab, for their technical assistance, helpful discussions, and encouragement, especially Dr. David Cove. Special thanks also to the undergraduate students (S. Han, B. Israelow) and postdoctoral fellow (Y. Zhao) for their assistance. Received August 28, 2008; accepted September 26, 2008; published October 1, 2008.
1 This work was supported by the National Science Foundation (FIBR grant no. EF–0425749–1 to A.K. and R.S.Q.).  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: Ralph S. Quatrano (rsq{at}wustl.edu).
[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.108.128488 * Corresponding author; e-mail rsq{at}wustl.edu.
Adamiec M, Drath M, Jackowski G (2008) Redox state of plastoquinone pool regulates expression of Arabidopsis thaliana genes in response to elevated irradiance. Acta Biochim Pol 55: 161–173[Medline] Alonso JM, Stepanova AN (2003) T-DNA mutagenesis in Arabidopsis. Methods Mol Biol 236: 177–188[Medline] Alonso JM, Stepanova AN, Leisse TJ, Kim CJ, Chen H, Shinn P, Stevenson DK, Zimmerman J, Barajas P, Cheuk R, et al (2003) Genome-wide insertional mutagenesis of Arabidopsis thaliana. Science 301: 653–657 Ankele E, Kindgren P, Pesquet E, Strand A (2007) In vivo visualization of Mg-protoporphyrin IX, a coordinator of photosynthetic gene expression in the nucleus and the chloroplast. Plant Cell 19: 1964–1979 Bartsch M, Gobbato E, Bednarek P, Debey S, Schultze JL, Bautor J, Parker JE (2006) Salicylic acid-independent ENHANCED DISEASE SUSCEPTIBILITY1 signaling in Arabidopsis immunity and cell death is regulated by the monooxygenase FMO1 and the Nudix hydrolase NUDT7. Plant Cell 18: 1038–1051 Biehl A, Richly E, Noutsos C, Salamini F, Leister D (2005) Analysis of 101 nuclear transcriptomes reveals 23 distinct regulons and their relationship to metabolism, chromosomal gene distribution and co-ordination of nuclear and plastid gene expression. Gene 344: 33–41[CrossRef][Web of Science][Medline] Dietz KJ (2003) Redox control, redox signaling, and redox homeostasis in plant cells. Int Rev Cytol 228: 141–193[Web of Science][Medline] Fey V, Wagner R, Brautigam K, Wirtz M, Hell R, Dietzmann A, Leister D, Oelmuller R, Pfannschmidt T (2005) Retrograde plastid redox signals in the expression of nuclear genes for chloroplast proteins of Arabidopsis thaliana. J Biol Chem 280: 5318–5328 Gadjev I, Vanderauwera S, Gechev TS, Laloi C, Minkov IN, Shulaev V, Apel K, Inze D, Mittler R, Van Breusegem F (2006) Transcriptomic footprints disclose specificity of reactive oxygen species signaling in Arabidopsis. Plant Physiol 141: 436–445 Karpinski S, Escobar C, Karpinska B, Creissen G, Mullineaux PM (1997) Photosynthetic electron transport regulates the expression of cytosolic ascorbate peroxidase genes in Arabidopsis during excess light stress. Plant Cell 9: 627–640[Abstract] Karpinski S, Gabrys H, Mateo A, Karpinska B, Mullineaux PM (2003) Light perception in plant disease defence signalling. Curr Opin Plant Biol 6: 390–396[CrossRef][Web of Science][Medline] Koussevitzky S, Nott A, Mockler TC, Hong F, Sachetto-Martins G, Surpin M, Lim J, Mittler R, Chory J (2007) Signals from chloroplasts converge to regulate nuclear gene expression. Science 316: 715–719 Mateo A, Funck D, Muhlenbock P, Kular B, Mullineaux PM, Karpinski S (2006) Controlled levels of salicylic acid are required for optimal photosynthesis and redox homeostasis. J Exp Bot 57: 1795–1807 Mishra G, Zhang W, Deng F, Zhao J, Wang X (2006) A bifurcating pathway directs abscisic acid effects on stomatal closure and opening in Arabidopsis. Science 312: 264–266 Murashige T, Skoog F (1962) A revised medium for rapid growth and bio-assays with tobacco tissue cultures. Physiol Plant 15: 473–497[CrossRef] Nakano T, Suzuki K, Fujimura T, Shinshi H (2006) Genome-wide analysis of the ERF gene family in Arabidopsis and rice. Plant Physiol 140: 411–432 Noctor G, De Paepe R, Foyer CH (2007) Mitochondrial redox biology and homeostasis in plants. Trends Plant Sci 12: 125–134[CrossRef][Web of Science][Medline] Oelze ML, Kandlbinder A, Dietz KJ (2008) Redox regulation and overreduction control in the photosynthesizing cell: complexity in redox regulatory networks. Biochim Biophys Acta 1780: 1261–1272 op den Camp RG, Przybyla D, Ochsenbein C, Laloi C, Kim C, Danon A, Wagner D, Hideg E, Gobel C, Feussner I, et al (2003) Rapid induction of distinct stress responses after the release of singlet oxygen in Arabidopsis. Plant Cell 15: 2320–2332 Pfannschmidt T, Brautigam K, Wagner R, Dietzel L, Schroter Y, Steiner S, Nykytenko A (2008) Potential regulation of gene expression in photosynthetic cells by redox and energy state: approaches towards better understanding. Ann Bot (Lond) (in press) Rhoads DM, Subbaiah CC (2007) Mitochondrial retrograde regulation in plants. Mitochondrion 7: 177–194[CrossRef][Web of Science][Medline] Russell AW, Critchley C, Robinson SA, Franklin LA, Seaton G, Chow WS, Anderson JM, Osmond CB (1995) Photosystem II regulation and dynamics of the chloroplast D1 protein in Arabidopsis leaves during photosynthesis and photoinhibition. Plant Physiol 107: 943–952[Abstract] Sakuma Y, Liu Q, Dubouzet JG, Abe H, Shinozaki K, Yamaguchi-Shinozaki K (2002) DNA-binding specificity of the ERF/AP2 domain of Arabidopsis DREBs, transcription factors involved in dehydration- and cold-inducible gene expression. Biochem Biophys Res Commun 290: 998–1009[CrossRef][Web of Science][Medline] Scheibe R (1991) Redox-modulation of chloroplast enzymes: a common principle for individual control. Plant Physiol 96: 1–3 Seki M, Ishida J, Narusaka M, Fujita M, Nanjo T, Umezawa T, Kamiya A, Nakajima M, Enju A, Sakurai T, et al (2002) Monitoring the expression pattern of around 7,000 Arabidopsis genes under ABA treatments using a full-length cDNA microarray. Funct Integr Genomics 2: 282–291[CrossRef][Medline] Vandenabeele S, Vanderauwera S, Vuylsteke M, Rombauts S, Langebartels C, Seidlitz HK, Zabeau M, Van Montagu M, Inze D, Van Breusegem F (2004) Catalase deficiency drastically affects gene expression induced by high light in Arabidopsis thaliana. Plant J 39: 45–58[CrossRef][Web of Science][Medline] Wu G, Ortiz-Flores G, Ortiz-Lopez A, Ort DR (2007) A point mutation in atpC1 raises the redox potential of the Arabidopsis chloroplast ATP synthase You YS, Marella H, Zentella R, Zhou Y, Ulmasov T, Ho TH, Quatrano RS (2006) Use of bacterial quorum-sensing components to regulate gene expression in plants. Plant Physiol 140: 1205–1212 Zhang W, Ruan J, Ho TH, You Y, Yu T, Quatrano RS (2005) Cis-regulatory element based targeted gene finding: genome-wide identification of abscisic acid- and abiotic stress-responsive genes in Arabidopsis thaliana. Bioinformatics 21: 3074–3081
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| ASPB Publications | PLANT PHYSIOLOGY® | THE PLANT CELL | |
|---|---|---|---|
TOP
ABSTRACT
RESULTS
0.01 and a fold change 
±2 in at least one time point.
-subunit regulatory disulfide above the range of thioredoxin modulation. J Biol Chem 282: 36782–36789