| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
|
First published online September 22, 2006; 10.1104/pp.106.084053 Plant Physiology 142:1087-1101 (2006) © 2006 American Society of Plant Biologists An Investigation of Boron Toxicity in Barley Using Metabolomics1,[W]Australian Centre for Plant Functional Genomics, School of Botany, University of Melbourne, Victoria 3010, Australia (U.R., J.H.P., M.G.F., P.L., A.B.); and School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, South Australia 5064, Australia
Boron (B) is an essential micronutrient that affects plant growth at either deficient or toxic concentrations in soil. The aim of this work was to investigate the adaptation of barley (Hordeum vulgare) plants to toxic B levels and to increase our understanding of B toxicity tolerance mechanisms. We used a metabolomics approach to compare metabolite profiles in root and leaf tissues of an intolerant, commercial cultivar (cv Clipper) and a B-tolerant Algerian landrace (cv Sahara). After exposure to elevated B (200 and 1,000 µM), the number and amplitude of metabolite changes in roots was greater in Clipper than in Sahara. In contrast, leaf metabolites of both cultivars only responded following 1,000 µM treatment, at which B toxicity symptoms (necrosis) were visible. In addition, metabolite levels were dramatically altered in the tips of leaves of the sensitive cultivar Clipper after growth in 1,000 µM B compared to those of Sahara. This correlates with a gradual accumulation of B from leaf base to tip in B-intolerant cultivars. Overall, there were always greater differences between tissue types (roots and leaves) than between the two cultivars. This work has provided insights into metabolic differences of two genetically distinct barley cultivars and information about how they respond metabolically to increasing B levels.
Boron (B) is an essential micronutrient for vascular plants. However, when B is present at high concentrations in the soil or ground water, plant growth and reproduction can be affected by B toxicity. B toxicity has been recognized as an important problem limiting crop production in the low rainfall and on highly alkaline and saline soils in regions of Australia, West Asia, and North Africa. Because soil amelioration is impractical, the development of B-tolerant cultivars is a rational solution to the problem.
B freely diffuses into the roots as boric acid [B(OH)3; pKa = 9.25] and accumulates in the cytoplasm as the borate anion [
B is also an essential nutrient, although its role in plant growth, development, and metabolism remains to be clarified. Originally, B was thought to be essentially immobile in the plant and fixed in the apoplast, but recent evidence has shown that, in some species, B is present as soluble bis-diester complexes with sorbitol or mannitol, which are phloem mobile (Hu et al., 1997
Investigation of the genetic control of B tolerance mechanisms in several species has so far allowed efficient approaches for breeding of B-tolerant varieties. In particular, certain wild species are a potential source of B tolerance genes for breeding into related cultivated crops. A RFLP linkage map of a doubled haploid population generated from a cross between an intolerant Australian cultivar (cv Clipper) and the B toxicity-tolerant landrace (cv Sahara 3771) was used to identify chromosomal regions associated with B tolerance in barley. Interval regression mapping allowed the identification of four regions on chromosomes 2H, 3H, 4H, and 6H containing the B tolerance traits that control B uptake, root-length response, dry-matter production, and leaf symptom expression (Jefferies et al., 1999
Two models for this mechanism of active efflux of the borate anion have been proposed, involving either anion exchange or an anion channel (Hayes and Reid, 2004
Morphological Differences between cv Clipper and cv Sahara and the Effect of High B Levels on the Phenotype of Both Cultivars When two barley cultivars, the B-intolerant cv Clipper and the B-tolerant landrace Sahara, were grown in hydroponic growth solution with normal (5 µM) B levels, obvious morphological differences were apparent (Fig. 1 ). In general, the leaf blades of Sahara were longer and wider, resulting in a greater leaf area compared to Clipper (Fig. 1, A and C). Sahara plants also produced more tillers. Comparison of the roots showed that those of Sahara were much thicker and shorter than those of Clipper (Fig. 1, B and D). Following exposure to 200 and 1,000 µM B, Clipper plants begin to develop leaf symptoms (necrotic lesions at the leaf tip; Fig. 1, E and G) in the youngest leaves after a very short time (2–3 d) in a concentration-dependent manner. After 2 weeks, almost all the leaves of Clipper plants were affected. In addition, overall shoot growth was retarded, fewer leaves developed, and roots were less branched and browner compared to those of untreated plants (compare Fig. 1, A, C, and E). In contrast, exposure to 200 µM B in Sahara showed no visible morphological effects, but, with exposure to 1,000 µM B, the tips of younger leaves show yellowing, indicating the beginning of necrosis. However, this was after longer treatment than Clipper (Fig. 1E). The growth of the shoots was not retarded, as it was for Clipper plants, but the roots of Sahara seemed to produce more lateral branching compared to untreated roots (Fig. 1F).
To quantify the effect of elevated B on both varieties in more detail, plants were grown in different concentrations of B, and B levels in the oldest leaf of each plant were determined using inductively coupled plasma-optical emission spectrometry (ICP-OES). During the course of 2 weeks, samples were taken until symptoms first became visible (Fig. 2 ). When Clipper and Sahara plants were grown in low B concentrations (50 µM B; control), the B levels in the oldest leaf were similar throughout the course of growth with no apparent symptoms of B phytotoxicity. When Clipper plants were grown in 1,000 µM B, symptoms appeared after 5 d, when leaf B concentrations reached 178 mg kg–1 dry weight (Fig. 2). In contrast, leaf symptoms did not appear in Sahara plants until 10 d after exposure to 5,000 µM B, when the concentration of leaf B was 191 mg kg–1 dry weight. These results indicated that Sahara could tolerate similar levels of leaf B for a longer period before visible symptoms appeared, which suggested a greater tissue tolerance to B than in Clipper. These data are consistent with previous studies (Jefferies et al., 1999  ; Hayes and Reid, 2004), and we therefore undertook a metabolomic examination of these two cultivars.
Comparison of the Metabolic Profiles of Clipper and Sahara Leaves and Roots under Control B Concentration (5 µM)
In an initial experiment, we compared the metabolic complements of roots and leaves of Clipper and Sahara plants grown for 3 weeks in control (5 µM) B concentration. Resulting metabolite profiles showed that both the roots and leaves of the two cultivars were different (Supplemental Tables S1 and S2). A number of amino acids, namely, Ala (1.6-fold), Pro (3-fold), Thr (1.4-fold), When the metabolic profiles in leaves of Sahara were compared to those in Clipper, only a small number of metabolites (Gly and putrescine [2.0-fold], Asp [0.9-fold], 5-oxoproline [1.2-fold], Asn and Gln [0.5-fold], Lys and saccharic acid [0.6-fold], maleic acid [1.6-fold], fumaric acid [1.5-fold], erythronic acid [0.3-fold], threonic acid and maltose [0.7-fold], Xyl, gulonic acid and raffinose [0.4-fold], and Rib [1.4-fold]) were significantly different between the cultivars (Supplemental Table S2). Most notable, however, was a dramatic increase of 6 kestose of up to 12.8-fold, although the high variation resulted in statistical insignificance due to quantification problems arising from incomplete peak resolution with raffinose. When the resulting metabolic profiles from each replicate were subjected to hierarchical cluster analysis (HCA), the greatest distance was observed between the roots and leaves of each cultivar by formation of two major clusters (data not shown). There was no clear separation of subclusters for each of the cultivars within the tissue clusters. Using principal component analysis (PCA), a similar picture emerged (Fig. 3 ), showing separation of the different tissues by the first component, which represented about 81% of the variation between the samples. The second component differentiates the roots of the two cultivars with about 6% variability, whereas the leaves do not fall into separate clusters. A second important feature of PCA is the ability to assess the importance of each metabolite (eigenvectors or loadings) for cluster formation. The most important metabolites contributing to the variation were Glu, maltose, erythronic acid, trehalose, allantoin, galactonic acid, Glc, glyceric acid, and Trp (data not shown).
Comparison of the Metabolic Responses of Clipper and Sahara Roots to 200 and 1,000 µM B Concentrations Both the tolerant and intolerant barley cultivars were characterized at the metabolite level when grown in control B concentration (5 µM). Plants of both the tolerant landrace Sahara and the intolerant cv Clipper were also grown at two toxic B concentrations: 200 µM, representing a medium level of stress, and 1,000 µM B. Tissue samples of roots and leaves were taken from 3-week-old seedlings after 2 weeks of B treatment. Resulting metabolic profiles were analyzed in two ways, the first being a comparison of metabolic responses due to increasing B within each cultivar and tissue (Supplemental Tables S1 and S2), and the second being a comparison between the two cultivars within a single treatment type (Supplemental Tables S3 and S4). After exposure to 200 µM B, the concentrations of a large number of metabolites decreased in the roots of the intolerant cv Clipper compared to the control condition (Fig. 4 ; Supplemental Table S3). After the 1,000 µM B treatment, only Glu (0.2-fold), glutaric acid (0.4-fold), and all the phosphorylated sugars decreased, but coumaric acid (1.7-fold), saccharic acid (1.6-fold), and Suc (1.2-fold) increased (Fig. 4; Supplemental Table S3). Sahara roots exhibited the opposite pattern, with many metabolite levels having increased after treatment with 200 and 1,000 µM B (Fig. 4; Supplemental Table S3). Only glutaric acid decreased (0.6-fold) at both 200 and 1,000 µM B and 6-phosphogluconic acid decreased (0.6-fold) only in 1,000 µM B.
Another way of interpreting the metabolic profiles is through metabolite differences between the two cultivars for each B treatment (Supplemental Table S1). The levels of the metabolites of the intolerant cv Clipper served as the reference to which the x-fold levels of metabolites in Sahara are compared. When grown in 200 µM B, the levels of almost all amino acids in Sahara roots were significantly higher (between 2- to 8-fold; Supplemental Table S1) when compared to treated Clipper roots. There were only two exceptions that showed a decrease, urea (0.8-fold) and Gly (0.6-fold). A similar pattern was observed when the levels of organic acids were compared between the roots of both cultivars at 200 µM B. Almost all the metabolites were significantly increased with the highest increase (5.8-fold) for  -ketoglutaric acid. The only exception was erythronic acid, which was dramatically decreased (0.1-fold). Similarly, the levels of almost all sugars were significantly increased. The most pronounced differences were found to be in phosphorylated compounds, such glycerol-3-P (8.5-fold), Fru-6-P (6.5-fold), Glc-6-P (6.3), 6-phosphogluconic acid (only detectable in Sahara roots), and in the trisaccharides raffinose (4.9-fold) and 6 kestose (6-fold). After treatment with 1,000 µM B, similar differences were observed to those described for the 200 µM B treatment, but in slightly fewer metabolites, when Sahara roots were compared to Clipper roots (Supplemental Table S1). Again, the majority of amino acids significantly increased with the largest increase in Glu (8-fold). The levels of urea significantly decreased (0.3-fold) and asparargine decreased (0.5-fold) after increasing (2.4-fold) following treatment with 200 µM B. As with the 200 µM B treatment, the differences in the levels of organic acids in Sahara roots compared to those of Clipper increased in a comparable way. Fewer and weaker responses in the levels of metabolites following treatment with 1,000 µM B were observed. Erythronic acid again decreased (0.1-fold). The same responses were seen in the levels of sugars and sugar acids of Sahara roots compared to Clipper roots after growing in 1,000 µM B, but not as dramatically as in 200 µM.
A slight, but significant, increase was found in some of the amino acids (Asp [1.2-fold], Gly [1.4-fold], N-acetyl-glutamate [2.7-fold], 5-oxoproline [1.5-fold], putrescine [1.6-fold], and Val [1.4-fold]) and organic acids (erythronic acid [1.2-fold], fumaric [1.6-fold], glyceric acid [1.6-fold], maleic acid [1.4-fold]) in Clipper leaves following treatment with 200 µM B when compared to control Clipper leaves (Fig. 4; Supplemental Table S4). For all analyzed sugars, only Suc increased, up to 1.2-fold. When treated with 1,000 µM B, about one-half of the amino acids and organic acids showed significant changes in Clipper leaves when compared to the control leaves (Fig. 4; Supplemental Table S4). The leaves of Clipper plants grown at 1,000 µM B had approximately one-half of the sugar metabolites increased, including Xyl (1.6-fold), Rib (2.0-fold), Fru (3.8-fold), Glc (2.6-fold), GlcUA (1.8-fold), saccharic acid (1.3-fold), maltose (1.4-fold), and raffinose (1.5-fold) when compared to those grown in 200 µM B. Sahara leaves responded in a similar way to 1,000 µM B as Clipper leaves, which is in contrast to the result seen in roots, where Sahara had the opposite metabolic response (Fig. 4; Supplemental Table S4). Only a small number of amino acids and organic acids in Sahara leaves were increased following treatment with 200 µM B compared to control Sahara leaves (Fig. 4; Supplemental Table S4). There was a stronger response in levels of sugars in Sahara leaves after treatment with 200 µM B (Xyl [2.0-fold], Fru [2.3-fold], Glc [5.1-fold], GlcUA [1.9-fold], and galactinol [1.5-fold]), when compared to 1,000 µM treatment where only Fru (1.9-fold), Glc (3.3-fold), and GlcUA (1.6-fold) increased. There was an apparent dramatic decrease (0.2-fold) in the level of 6 kestose, but this was not statistically significant. In contrast, when grown in 1,000 µM B, more amino acids were significantly affected than when grown in 200 µM B (Fig. 4; Supplemental Table S4). The most dramatic decrease was in Glu, to almost undetectable levels, a similar trend as observed in 1,000 µM B Clipper leaves. Only a small number of organic acids changed with 1,000 µM B treatment. When the metabolite levels between the leaves of both cultivars following treatment with 200 µM B in the nutrient solution were compared, there were no differences in the levels of amino acids, only a single organic acid decreased in Sahara leaves (erythronic acid [0.4-fold]) and a number of sugars (Rib [1.7-fold], Fru [2.0-fold], inositol [0.8-fold], Glc [6-fold], and 6 kestose [12.4-fold]) were different in Sahara leaves (Supplemental Table S2). In contrast, many metabolites differed between leaves of both varieties following treatment with 1,000 µM B. Almost all of the amino acids were significantly decreased, with the exception of Gly, which increased (3.1-fold) in Sahara leaves compared to Clipper leaves. A similar trend was observed in the levels of organic acids and sugars. More than one-half of these decreased quite dramatically, with the most pronounced decrease (0.2-fold) both in erythronic acid and raffinose. There were only a few exceptions, maleic acid (2.2-fold), Glc (2.8-fold), and galactinol (1.5-fold), which were found to be increased.
The resulting metabolic profiles were then subjected to both HCA and PCA for easier comparison of similarities and differences. HCA of the metabolomes of Clipper and Sahara roots and leaves grown under control (5 µM), medium-stress (200 µM), and high-stress (1,000 µM) B concentrations again clearly delineated two major clusters; those representing either all root (root cluster) or all leaf (leaf cluster) samples (Fig. 5A ). Within the root cluster, two subclusters were evident; the first consisting of all Sahara samples both from control and treated samples, with an independent subcluster of the control Clipper roots; the second representing both Clipper roots treated with 200 or 1,000 µM B. The leaf cluster did not show strong separation between either the two cultivars or three treatments; the only obvious formations were small subclusters of either Sahara or Clipper leaves treated with 1,000 µM B. PCA of this dataset revealed a similar picture (Fig. 5B). Both root and leaf samples were clearly separated by the first principal component, which represented 70.4% of the variability between the samples. Sahara and Clipper root metabolic profiles were further separated by the second principal component (12.8% variability). Within the two cultivars, there is a distance between the control and treated roots, but the 200 and 1,000 µM B treatments did not resolve into distinct subclusters. The leaf cluster did not show any further separation for either of the cultivars or treatments. The following metabolites had the highest impact on cluster formation: maltose, erythronic acid, trehalose, galactonic acid, allantoin, 6-phosphogluconic acid, Glu, Trp, glycerol-3-P, glyceric acid, gulonic acid, urea, and tyramine.
Comparison of Metabolic Responses of Clipper and Sahara Leaves to Different B Concentrations during Seedling Development In this experiment, Clipper and Sahara plants were grown in parallel for 1 week in control B concentrations (5 µM) and samples from the youngest fully developed leaf were harvested (T0). One-half of the plants were subsequently grown in 1,000 µM B and the other one-half maintained at 5 µM B. Leaf samples were taken after 1 (T1-treated and control) and 2 (T2-treated and control) weeks of growth. Resulting metabolic profiles were compared (1) within each variety through development, with T0 as the reference (Supplemental Table S5); and (2) between the two cultivars for each time point with and without treatment, where the respective Clipper samples were used as the reference data (Supplemental Table S6). For leaves of control Clipper plants, only a small number of metabolites decreased after 2 (T1, five metabolites changed) and 3 (T2, four metabolites changed) weeks of growth when compared to T0 (Supplemental Table S5). But a clear metabolic response was observed in Clipper leaves treated with 1,000 µM B for both 1 and 2 weeks (T1-treated and T2-treated). A range of metabolites increased significantly in both time points, including four amino acids, nine organic acids, and four sugars. Notably, putrescine was only detectable in leaves of Clipper plants after 3 weeks of growth following treatment with high B. In comparison, after 1 week of growth in control 5 µM B (T1), only four metabolites were changed in Sahara leaves when compared to T0 (Supplemental Table S5). However, this result was amplified at T2, where 12 metabolites of all classes were significantly different compared to T0. Sahara plants grown in 1,000 µM B had fewer metabolites responding than Clipper leaves at both T1-treated (eight metabolites) and T2-treated (11 metabolites) compared to T0 (Supplemental Table S5).
Another way of presenting the resulting metabolic profiles from this experiment is a direct comparison of metabolite levels in Sahara leaves of each time point to the metabolite levels in the respective Clipper leaves (Supplemental Table S6). Surprisingly, when considering the results from the above-described experiments in which both cultivars were found to be metabolically distinct from each other (Supplemental Table S5), there were few significant differences found between Clipper and Sahara leaves grown in control 5 µM B for only 1 week (T0). An obvious difference was that putrescine was only detected in Sahara leaves. At T1, at control (5 µM B), both cultivars are distinguishable as nine of the metabolites differ significantly. Again, putrescine was only detected in Sahara leaves. At T2, the trend was similar to that at T1, but, due to relatively high variation, many differences were not statistically significant based on Student's t test. As before, putrescine was only detected in Sahara leaves. In addition, the levels of HCA of these data showed that there were two major clusters formed, one including four subclusters of Clipper leaves harvested at T0, control Clipper leaves at T1, Sahara leaves at T0, and Clipper leaves at T2 treated with 1,000 µM B (Fig. 6A ). The second major cluster had three subclusters, one representing the metabolic profiles of control Sahara leaves at T2, the second treated Sahara leaves at T2, and the largest subcluster represented the control Clipper leaves at T2, treated Clipper leaves at T1, control Sahara leaves at T1, and treated Sahara leaves at T2. In contrast, when PCA was applied, the first principal component (34.0% variability) separated Clipper leaves from Sahara leaves regardless of the treatments, with one exception: Sahara leaves harvested at a very early stage of development (T0) clustered with Clipper at T0, indicating that, at this stage, the metabolic profiles of leaves from Clipper and Sahara are very similar (Fig. 6B). Within the cluster for Sahara leaves from plants older than 1 week, individual clusters for each developmental stage with and without treatment could be assigned. The most important metabolites for cluster separation were putrescine, melibiose, Glc, Fru, 6 kestose, Asp, and erythronic acid (data not shown).
Correlation of Metabolic Profiles on Clipper and Sahara Leaves with the Gradient of B within a Leaf Blade following Treatment with High B Concentration
It is known that following growth in toxic B concentrations, B accumulates in the leaf blades, coinciding with a tissue age-associated gradient with highest concentrations in the leaf tip correlating with B toxicity symptoms (Reid et al., 2004
When metabolite levels were compared along the leaf blade of plants grown in control conditions, three typical metabolite abundance patterns appear. The first is an increase in metabolite abundance from the base to the tip of the leaf (pattern 1). The second is uniform abundance along the length of the leaf blade (pattern 2). The third is an increase in abundance in the base compared to the other parts of the leaf (pattern 3; Supplemental Table S7). In leaves from Clipper, the general trend is an increase in sugar abundance from the base to the tip (pattern 1), with the exception of xylitol, galactinol, and Suc, which displayed pattern 3 distribution. In contrast, sugar distribution along the leaves of Sahara was closer to the uniform, pattern 2-type distribution, again with xylitol as an obvious exception. Pattern 1 distribution was also observed for some amino acids in Clipper leaves, including Following PCA analysis, two distinct clusters divided by the first component (59.1% of the variability) were formed, one representing the metabolite profiles of 1,000 µM B-treated base, middle, and tip portions of the leaves of the intolerant cv Clipper (Fig. 7 ) and the other major cluster representing all other samples (Clipper base, middle, tip control; Sahara base, middle part, tip treated, and control). The second component (15.8% of the variability) separated the former cluster into the tip portions of the Clipper leaf in a subcluster and the base and middle portion of the same leaves into a second subcluster. The latter major cluster was separated by the second component into control Clipper leaf segments, on one side, and treated and control Sahara leaf segments on the other. There was some degree of overlap of the Sahara cluster with the control Clipper cluster. Metabolites with the highest impact on cluster formation (loadings) were mainly those only detected in either one of the cultivars, segments, or treatments (Supplemental Table S7). In addition, Pro, Glu, GABA, homoserine, Met, Gly, and shikimic acid played a major role in cluster formation (data not shown).
When metabolite profiles resulting from each part of the B-treated leaf were compared to the respective segment of the control leaf for both cultivars, most obvious and, in some cases, very strong differences were always seen for the treated Clipper segments (Supplemental Table S8), whereas in treated leaf segments of Sahara only a small number of metabolites were altered compared to the control segment. After B treatment, 14 of the 20 analyzed amino acids were strongly decreased down to 0.02-fold for Glu, Met, Phe, and Pro. Only Asn increased to about 3-fold. In addition, four organic acids and five sugars increased. The middle portion shows a similar picture, with 11 amino acids decreased, again with Glu and Pro to 0.02-fold compared to the control middle part. Asn was again increased, this time to 4.7-fold. Furthermore, eight organic acids changed significantly, as well as almost all of the analyzed sugars. Interestingly, after B treatment, only seven amino acids were changed in the leaf tip of the treated leaves compared to those of the control. The most pronounced changes were once again Pro, which decreased 0.02-fold, and Asn, which increased 9.5-fold. Again, eight organic acids were altered and most of the analyzed sugars increased dramatically. For example, Ara, Fru, galactinol, Glc, melibiose, and Rib increased more than 5-fold. Notably, 3-phosphoglyceric acid, 6 kestose, raffinose, digalactosylglycerol, and the phosphorylated sugars were only detectable in each of the treated segments of the Clipper leaves. When the metabolite levels in segments of treated Sahara leaves were compared to those of the untreated leaves, only a small number of metabolites were altered (Supplemental Table S8). In the base segment seven and in the middle part only two metabolites were significantly changed, whereas metabolite levels in the tips of treated Sahara leaves were similar to those of control Sahara leaves. In these measurements, there were also 16 major components analyzed that could not be positively identified, but based on their mass spectra were assigned to either the sugars or sugar acids. The results for these compounds follow the same trend as for identified metabolites.
The aim of this study was to investigate the mechanisms of B toxicity tolerance in more detail. Two proposed models for a constitutive tolerance mechanism in barley were the basis for this work: one assumes the existence of compounds allowing complexing of B once it accumulates to toxic concentrations within the cell (Reid et al., 2004 ) and the other describes an active efflux of B by a transporter (Hayes and Reid, 2004). Comparison of the accumulation of B in the oldest leaves between Clipper and Sahara plants and the correlation of these B levels with the appearance of visible necrotic regions (Fig. 2) has clearly demonstrated that Sahara is able to actively exclude B from the tissue. This results in lower B levels in the oldest leaf even when grown at much higher B concentrations than in Clipper. In addition, leaves from Sahara take longer to develop necrotic symptoms when comparable tissue B concentrations, relative to Clipper, are reached. This may indicate that, in addition to an efflux mechanism, Sahara also exhibits a cellular/tissue-based mechanism allowing the plants to tolerate higher tissue concentrations of B before cell metabolism is affected leading to necrosis and cell death. This could be either mediated by an apoplastic or vacuolar sequestration or complex formation with specific metabolites. To examine the latter possibility, we decided to use a recently developed GC-MS-based metabolite profiling technology (Roessner et al., 2001; Roessner-Tunali et al., 2003) for a comprehensive comparison of metabolite levels between the B-intolerant commercial barley cv Clipper and the tolerant Algerian landrace Sahara.
Comparison of the metabolite profiles of the two cultivars has demonstrated that there were more metabolic differences between the roots of both cultivars, but only a few differences between the leaves when they were grown in control (5 µM B; Fig. 3). However, in spite of genetic diversity, the metabolite profiles of the two different tissue types, roots and leaves, exhibited greater differences between each other than between the two cultivars (Fig. 5). These results suggest that there may be a metabolic preadaptation in the roots of tolerant cv Sahara contributing to greater tissue tolerance. There were no sugar alcohols or polyols, such as mannitol, sorbitol, or pinitol detected, which have been shown to play a role in B complex formation (Hu et al., 1997
Clipper and Sahara leaf metabolite profiles were similar in the early stages of development (up to 2 weeks of growth). The only striking difference was that in young leaves the polyamine, putrescine, was only detected in Sahara in unstressed conditions, whereas it was only detected in Clipper leaves after 3 weeks of growth following treatment with 1,000 µM B for 2 weeks. At this stage, it was present in similar quantities in both Clipper and Sahara leaves because it decreased in Sahara leaves following a 1,000 µM B treatment compared to control leaves (Supplemental Tables S5 and S6). This compound was also found in up to 50-fold higher concentrations in Clipper leaf tips, but only 5-fold in Sahara leaf tips treated with 1,000 µM B compared to the respective treated leaf base (Supplemental Table S7) or 6-fold higher concentrations compared to the control Clipper tip (Supplemental Table S8). Putrescine belongs to the class of aliphatic polyamines shown to be involved in both abiotic and biotic stress responses (Walters, 2003 An interesting finding was that the metabolite profiles of young, untreated Sahara leaves were more similar to Clipper leaves throughout development, regardless of treatment (Fig. 6B), whereas older Sahara leaves, both treated and untreated, form an independent cluster indicating stronger metabolite differences. This result suggests that Clipper and Sahara plants may develop at a different rate.
Following treatment with increasing B concentrations, the intolerant cv Clipper showed morphological stress responses, such as decreases in root length or necrotic lesions in leaves, whereas Sahara showed visible responses at much later stages. The leaf symptoms were explained by dramatic increases in B within the leaves of sensitive cultivars in contrast to tolerant cultivars, which are proposed to either reduce their B uptake or actively efflux it from cells (Hayes and Reid, 2004
In addition, 6 kestose was dramatically increased in treated leaves of Sahara. 6 Kestose [O-
Analysis of metabolite levels in different parts of the untreated leaves has shown that a large number of metabolites occur in a gradient, either increasing or decreasing, from the base to the tip. This shows the importance of spatially resolved metabolite profiling rather then analyzing a homogenate of whole leaves. Surprisingly, most metabolites were increased in Clipper leaf tips compared to the base, which is the growing zone of grass leaves (Esau, 1977
This work has provided insight into the metabolic responses of barley plants (tolerant and intolerant) to toxic B levels. Our data suggest that none of the analyzed metabolites seem sufficient to explain the cellular tolerance mechanism in the Sahara cultivar. Therefore, we conclude that engineering a functional efflux mechanism (active transporter) in intolerant cultivars might be a more appropriate strategy for increasing B tolerance. The proposed transporter (Hayes and Reid, 2004
Plant Growth Barley (Hordeum vulgare L. cv Sahara 3771 and cv Clipper) seeds were surface sterilized with 70% ethanol for 5 min, washed with water (three times), incubated for 10 min in 0.5% hypochlorite solution, and then rinsed (10 times) with water. The seeds were then imbibed in water under continuous aeration for 24 h at room temperature. Germinated seeds were placed onto moist filter paper for 2 to 3 d until seedlings were 2 to 3 cm in height. Individual seedlings were placed in a 5-mL plastic pipette tip with a cut end to allow root growth and transferred to plastic growth containers filled with hydroponic solution. The nutrient solution contained the following basal macronutrients: 2 mM Ca(NO3)2, 5 M KNO3, 5 mM NH4NO3, 2 mM MgSO4, 0.1 mM KH2PO4, 0.5 mM Na2SiO3, and 0.05 mM NaFe(III) EDTA, and the following micronutrients: 5 µM MnCl2, 5 µM ZnSO4, 0.5 µM CuSO4, and 0.1 µM NaMoO3 with 5 µM H3BO3. The nutrient solution was aerated continuously and replaced every 3 d. Stress conditions were applied after 1 week of growth in control conditions by adding 200 or 1,000 µM H3BO3. Plants were grown in a controlled environment at 18°C day/13°C night, a photoperiod regime of 14-h d/10-h night at 180 µmol m–2 s–1 photon flux intensity at the plant level. Root and leaf (the youngest fully developed leaf) tissues were harvested in 5 to 6 h of the light period after 3 weeks of growth and for the time course experiment after 1, 2, and 3 weeks of growth. Samples were immediately frozen in liquid nitrogen and stored at –80°C until extraction.
All chemicals were purchased from Sigma-Aldrich. N-methyl-N-(trimethylsilyl)-trifluoroacetamide was purchased from Biolab.
Plants were grown as described above, with a B concentration of 50 µM. After 7 d, plants were transferred to identical solutions supplemented with B as described (Fig. 2). At indicated time points (Fig. 2), the oldest leaf was removed and weighed before and after drying at 70°C for 16 h. The B content of the dried leaf samples was determined using ICP-OES after tissues were digested with concentrated nitric acid. Measurements were normalized on a tissue dry weight basis.
Metabolite analysis was carried out by GC-MS using a modified method of Roessner-Tunali et al. (2003)
The GC-MS system comprised an AS 3000 autosampler, a trace GC Ultra, and a DSQ quadrupole mass spectrometer (Thermo Electron Corporation). The mass spectrometer was tuned according to the manufacturer's recommendations using tris-(perfluorobutyl)-amine (CF43). GC was performed on a 30-m VF-5MS column (with 10-m Integra guard column, i.d. 0.25 µm, 0.25-nm film thickness; Varian). The injection temperature was set at 230°C, the MS transfer line at 280°C, and the ion source at 250°C. Helium was used as carrier gas at a flow rate of 1 mL min–1. The analysis was performed under the following oven temperature program: injection at 70°C followed by 1°C min–1 oven temperature ramp to 76°C, and then by 6°C min–1 to 330°C, and finishing with 10-min isothermal at 330°C. The GC-MS system was then temperature equilibrated for 1 min at 70°C prior to injection of the next sample. Mass spectra were recorded at two scans per second with a mass-to-charge ratio of 70 to 600 atomic mass units scanning range. Both chromatograms and mass spectra were evaluated using the Xcalibur program (ThermoFinnigan) and the resulting data are prepared, normalized, and presented as described by Roessner et al. (2001)
A recently developed method for metabolic profiling using GC-MS (Roessner et al., 2000
The derivatization, GC-MS setup, and chromatogram evaluation procedures were previously described by Roessner-Tunali et al. (2003) Following deconvolution of resulting chromatograms, more than 400 individual compounds were detected in the polar extracts. Of these approximately 130 polar compounds, including amino acids, organic acids, and sugars were identified. Further automatic quantification was conducted using the processing setup method built in the Xcalibur software (ThermoFinnigan) on more than 70 identified and approximately 20 unknown polar metabolites. For each targeted metabolite, a specific trace was selected and used for quantification in each chromatogram. The resulting areas were normalized to the area of a specific trace of the internal standard ribitol, resulting in relative response ratios, which were further normalized by the fresh weight of each sample.
Data were prepared as described in Roessner et al. (2001)
Sequence data from this article can be found in the GenBank/EMBL data libraries under accession numbers
The following materials are available in the online version of this article.
We would like thank Dr. Tim Sutton, Australian Centre for Plant Functional Genomics, University of Adelaide, Australia, for providing us with barley cv Clipper and cv Sahara seeds. We are also grateful to Prof. Mark Tester, Australian Centre for Plant Functional Genomics, University of Adelaide, Australia, for discussing hydroponic growth of barley plants. Special thanks to Dr. Ellen Zuther, Max-Planck-Institute for Plant Molecular Physiology, Golm, Germany, for providing us with purified 6 kestose (Zuther et al., 2004 ) for peak identification. U.R. thanks Suganthi Suren for help in producing Figure 4. Received May 23, 2006; accepted September 13, 2006; published September 22, 2006.
1 This work was supported by the Australian Centre for Plant Functional Genomics from the Australian Research Council, the Grain Research and Development Council, and State Governments.  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: Ute Roessner (ute.roessner{at}acpfg.com.au).
[W] The online version of this article contains Web-only data. www.plantphysiol.org/cgi/doi/10.1104/pp.106.084053 * Corresponding author; e-mail ute.roessner{at}acpfg.com.au; fax 61–3–9347–1071.
Amiard V, Morvan-Bertrand A, Billard JP, Huault C, Keller F, Prud'homme MP (2003) Fructans, but not the sucrosyl-galactosides, raffinose and loliose, are affected by drought stress in perennial ryegrass. Plant Physiol 132: 2218–2229 Bino RJ, Hall RD, Fiehn O, Kopka J, Saito K, Draper J, Nikolau BJ, Mendes P, Roessner-Tunali U, Beale MH, et al (2004) Opinion: potential of metabolomics as a functional genomics tool. Trends Plant Sci 9: 418–425[CrossRef][Web of Science][Medline] Bolaños L, Lukaszewski K, Bonilla I, Blevins D (2004) Why boron? Plant Physiol Biochem 42: 907–912[CrossRef][Web of Science][Medline] Broeckling CD, Huhman DV, Farag MA, Smith JT, May GD, Mendes P, Dixon RA, Sumner LW (2005) Metabolic profiling of Medicago truncatula cell cultures reveals the effects of biotic and abiotic elicitors on metabolism. J Exp Bot 56: 323–336 Callahan DL, Baker AJM, Kolev SD, Wedd AG (2006) Metal ion ligands in hyperaccumulating plants. J Biol Inorg Chem 11: 2–12[CrossRef][Web of Science][Medline] Camacho-Cristobal JJ, Maldonado JM, Gonzalez-Fontes A (2005) Boron deficiency increases putrescine levels in tobacco plants. J Plant Physiol 162: 921–928[CrossRef][Web of Science][Medline] Capell T, Bassie L, Christou P (2004) Modulation of the polyamine biosynthetic pathway in transgenic rice confers tolerance to drought stress. Proc Natl Acad Sci USA 101: 9909–9914 Darvill AG, McNeil M, Albersheim P (1978) Structure of plant cell walls. Plant Physiol 62: 418–422 Esau K (1977) Anatomy of Seed Plant, Ed 2. John Wiley & Sons, New York Fernie AR, Trethewey RN, Krotzky AJ, Willmitzer L (2004) Metabolic profiling: from diagnostics to systems biology. Nat Rev Mol Cell Biol 9: 763–769 Hayes JE, Reid RJ (2004) Boron tolerance in barley is mediated by efflux of boron from the roots. Plant Physiol 136: 3376–3384 Hu H, Penn SG, Lebrilla CB, Brown PH (1997) Isolation and characterization of soluble boron complexes in higher plants: the mechanism of phloem mobility of boron. Plant Physiol 113: 649–655[Abstract] Ishii T, Matsunaga T, Pellerin P, O'Neill MA, Darvill A, Albersheim P (1999) The plant cell wall polysaccharide rhamnogalacturonan II self-assembles into a covalently cross-linked dimmer. J Biol Chem 274: 13098–13104 Jefferies SP, Barr AR, Karakouis A, Kretschmer JM, Manning S, Chalmers KJ, Nelson JC, Islam AKMR, Langridge P (1999) Mapping of chromosome regions conferring boron toxicity tolerance in barley (Hordeum vulgare L.). Theor Appl Genet 98: 1293–1303[CrossRef][Web of Science] Legocka J, Kluk A (2005) Effect of salt and osmotic stress on changes in polyamine content and arginine decarboxylase activity in Lupinus luteus seedlings. J Plant Physiol 162: 662–668[CrossRef][Web of Science][Medline] Matoh T, Kawaguchi S, Kobayasi M (1996) Ubiquity of a borate-rhamnogalacturonan II complex in the cell walls of higher plants. Plant Cell Physiol 37: 636–640 Nable RO, Bañuelos GS, Paull JG (1997) Boron toxicity. Plant Soil 193: 181–198[CrossRef] Nable RO, Cartwright B, Lance RC (1990) Genotypic differences in boron accumulation in barley: relative susceptibilities to boron deficiency and toxicity. In N El Bassam, M Dambroth, B Laoghman, eds, Genetic Aspects of Plant Mineral Nutrition. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 243–251 Nakagawa-Yokoi Y, Kobayashi M, Takashima K, Shunli Z, Fujiwara T (2005) Expression of rice BOR1 homologs and their boron transport activity. Third International Symposium on All Aspects of Plant and Animal Boron Nutrition, Huazhong Agricultural University, Wuhan, China Noguchi K, Yasumori M, Imai T, Naito S, Matsunaga T, Oda H, Hayashi H, Chino M, Fujiwara T (1997) bor1-1, an Arabidopsis thaliana mutant that requires a high level of boron. Plant Physiol 115: 901–906[Abstract] O'Neill MA, Eberhard S, Albersheim P, Darvill AG (2001) Requirement of borate cross-linking of cell wall rhamnogalacturonan II for Arabidopsis growth. Science 294: 846–849 O'Neill MA, Ishii T, Albersheim P, Darvill AG (2004) Rhamnogalacturonan II: structure and function of a borate cross-linked cell wall pectic polysaccharide. Annu Rev Plant Biol 55: 109–139[CrossRef][Medline] Power PP, Woods WG (1997) The chemistry of boron and its specification in plants. Plant Soil 193: 1–13[CrossRef][Web of Science] Reid RJ, Hayes JE, Post A, Stangoulis JCR, Graham RD (2004) A critical analysis of the causes of boron toxicity in plants. Plant Cell Environ 25: 1405–1414 Roessner U, Luedemann A, Brust D, Fiehn O, Linke T, Willmitzer L, Fernie AR (2001) Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13: 11–29 Roessner U, Wagner C, Kopka J, Trethewey RN, Willmitzer L (2000) Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J 23: 131–142[CrossRef][Web of Science][Medline] Roessner-Tunali U, Hegemann B, Lytovchenko A, Carrari F, Bruedigam C, Granot D, Fernie AR (2003) Metabolic profiling of transgenic tomato plants overexpressing hexokinase reveals that the influence of hexose phosphorylation diminishes during fruit development. Plant Physiol 133: 84–99 Ryan P, Delhaize E, Jones D (2001) Function and mechanism of organic anion exudation from plant roots. Annu Rev Plant Physiol Plant Mol Biol 52: 527–560[CrossRef][Web of Science][Medline] Schauer N, Steinhauser D, Strelkov S, Schomburg D, Allison G, Moritz T, Lundgen K, Roessner-Tunali U, Forbes MG, Willmitzer L, et al (2005) GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett 579: 1332–1337[CrossRef][Web of Science][Medline] Sumner LW, Mendes P, Dixon RA (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62: 817–836[CrossRef][Web of Science][Medline] Takano J, Noguchi K, Yasumori M, Kobayashi M, Gajdos Z, Miwa K, Hayashi H, Yoneyama T, Fujiwara T (2002) Arabidopsis boron transporter for xylem loading. Nature 420: 337–340[CrossRef][Medline] Thomas JR, Darvill AG, Albersheim P (1989) Isolation and structural characterization of the pectic polysaccharide rhamnogalacturonan II from the walls of suspension-cultured rice cells. Carbohydr Res 185: 261–277 Walters DR (2003) Polyamines and plant disease. Phytochemistry 64: 97–107[CrossRef][Web of Science][Medline] Wang C, Van den Ende W, Tillberg JE (2003) Fructan accumulation induced by nitrogen deficiency in barley leaves correlates with the level of sucrose:fructan 6-fructosyltransferase mRNA. Planta 211: 701–707[CrossRef] Weckwerth W, Wenzel K, Fiehn O (2004) Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks. Proteomics 4: 78–83[CrossRef][Web of Science][Medline] Zuther E, Kwart M, Willmitzer L, Heyer A (2004) Expression of a yeast-derived invertase in companion cells results in long-distance transport of a trisaccharide in an apoplastic loader and influences sucrose transport. Planta 218: 759–766[CrossRef][Web of Science][Medline] This article has been cited by other articles:
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| ASPB Publications | PLANT PHYSIOLOGY® | THE PLANT CELL | |
|---|---|---|---|
TOP
ABSTRACT
RESULTS
] due to pH-dependent interconversion. An inability to exclude B from the roots results in high B concentrations in the tissue. B phytotoxicity manifests itself in a broad range of physiological effects, including decreased shoot and root growth, root cell division and RNA content, reduced leaf chlorophyll, lower photosynthetic rates and stomatal conductance, and reduced levels of lignin and suberin (for review, see Nable et al., 1997
-Ala (2.1-fold), Glu (1.9-fold), and tyramine (4.5-fold) were significantly higher in Sahara roots compared to those of Clipper, as were a small number of organic acids (glyceric acid, fumaric acid, and malic acid [each 1.4-fold]). The only exception was erythronic acid, which was dramatically decreased (0.1-fold) in Sahara roots compared to those of Clipper. The differences in the levels of sugars and sugar acids included slight increases in glycerol-3-P and Glc (1.7-fold), Fru (1.4-fold), inositol (2.3-fold), and galactinol (1.5-fold). In addition, the levels of Xyl, inositol-1P (0.7-fold), and gulonic acid (0.2-fold) decreased in the roots of Sahara compared to Clipper. 
-aminobutyrate (GABA), Gln, Ile, Leu, and Pro, whereas for Ala, Asn, Gly, and Phe pattern 3 distribution was displayed. Notably, the levels of Phe were only strongly decreased in the tip compared to the other parts of the leaf. In Sahara leaves, a similar distribution was observed, with GABA, Gln, and Gly following pattern 1 distribution and Ala and Asn following pattern 3 distribution. Most organic acids in Clipper leaf followed pattern 1 and pattern 2 distribution, whereas in Sahara leaves only threonic acid-1,4-lactone displayed pattern 1 distribution and all other measured organic acids displayed pattern 2 distribution. Following treatment with 1,000 µM B, the majority of the distributions in Clipper leaves were similar, but with much greater amplitude. In Clipper leaves, all measured amino acids followed pattern 1 distribution, with the exception of 5-oxoproline, which showed pattern 2 distribution. Similarly, most organic acids displayed pattern 1 distribution, with the exception of ascorbic acid, which displayed pattern 2 distribution, and shikimic acid, which now displayed pattern 3 distribution in contrast to untreated leaves. The same pattern emerged for sugar distribution following pattern 1, with 3-phosphoglyceric acid, digalactosylglycerol, and Suc following pattern 2 distribution. In Sahara leaves, only small changes compared to the patterns of untreated leaves were observed. The only differences were Pro, putrescine, 1,6-anhydro-Glc, Fru, Gal, and Glc, which now displayed pattern 1 distribution compared to pattern 2 distribution in untreated leaves. 
