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Keywords:

  • Arabidopsis thaliana accessions;
  • mineral concentration;
  • quantitative trait locus (QTL) analysis;
  • water stress

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • • 
    Rosettes of 25 Arabidopsis thaliana accessions and an Antwerp-1 (An-1) × Landsberg erecta (Ler) population of recombinant inbred lines (RILs) grown in optimal watering conditions (OWC) and water deficit conditions (WDC) were analysed for mineral concentrations to identify genetic loci involved in adaptation of mineral homeostasis to drought stress.
  • • 
    Correlations between mineral concentrations were determined for accessions and a quantitative trait locus (QTL) analysis was performed for the RIL population.
  • • 
    Plant growth and rosette mineral contents strongly decreased in WDC compared with OWC. Mineral concentrations also generally decreased, except for phosphorus (P), which remained constant, and potassium (K), which increased. Large variations in mineral concentrations were observed among accessions, mostly correlated with total rosette leaf area. Mineral concentration QTLs were identified in the RIL population, but only a few were common for both conditions. Clusters of mineral concentration QTLs often cosegregated with dry weight QTLs.
  • • 
    Water deficit has a strong effect on rosette mineral status. This is genetically determined and seems largely a pleiotropic effect of the reduction in growth. The low number of common mineral concentration QTLs, shared among different RIL populations, tissues and conditions in Arabidopsis, suggests that breeding for robust, mineral biofortified crops will be complex.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Variations in micronutrient concentrations in plants depend on both genetic and nongenetic factors such as environmental conditions and developmental stages, and on the interactions between them. Assessment of mineral concentrations at different developmental stages of Silene vulgaris plants exposed to soils differing in metal content, illustrates the effect of nongenetic factors (Ernst & Nelissen, 2000). A reduction in water supply is another of such nongenetic factor. The increased occurrence and duration of dry periods in many regions of the world frequently results in the consecutive occurrences of drought stress on cultivated crops (Hu & Schmidhalter, 2005). Drought can affect nutrient uptake and impair acropetal translocation of nutrients. The effect of a combination of drought and nutrient stresses on plant growth is complex. For example, when nutrients are already present in sufficient amounts in the soil but when the drought is severe, an increased nutrient supply will not improve plant growth (Hu & Schmidhalter, 2005), as nutrient supply is no longer limiting. However, with less severe drought it is not clear if nutrient supply or water supply becomes limiting. At low water supply, the diffusion rate of nutrients in the soil to the absorbing root surface will decrease, transpiration rates will be restricted and active transport and membrane permeability will be impaired. Because of this complexity, the interaction between mineral homeostasis and water supply is not much studied.

Plants differ in their tolerance to water deficit because of differences in phenological, morphological, physiological, biochemical and molecular adaptive mechanisms (Perez-Molphe-Balch et al., 1996). Genetic differences in drought tolerance might offer an opportunity to study the interaction between mineral homeostasis and water supply. There is substantial genetic variation for growth traits (Cross et al., 2006; El-Lithy et al., 2006) and for mineral content (Rus et al., 2004; Vreugdenhil et al., 2004; Harada & Leigh, 2006; Rus et al., 2006; Baxter et al., 2008; Waters & Grusak, 2008; Ghandilyan et al., 2009) among natural accessions of Arabidopsis thaliana (Arabidopsis). The response of Arabidopsis rosette development to water deficit and indicators of drought stress tolerance have been analysed in 25 natural accessions collected at different locations around the world (Aguirrezabal et al., 2006; Bouchabke et al., 2008). These accessions showed interesting phenotypic variations in response to mild water deficit. For example, the An-1 (Antwerp-1; originating form Belgium) accession showed a clearly different response among the others, as plants of this particular accession did not show much difference between the final leaf area of plants subjected to water deficit compared with plants grown in well-watered conditions, whereas the differences were considerable for other accessions. An-1 was exceptional as the decrease in maximal absolute leaf expansion rate was compensated by an increase in the duration of individual leaf expansion (Aguirrezabal et al., 2006).

The objective of this study was to analyse natural variation for the effect of water supply on the mineral homeostasis in Arabidopsis. This analysis can help to unravel the genetics of plant responses to environmental changes, including the genetic networks involved in plant mineral homeostasis at different water availabilities. As a first step, a collection of 25 accessions, including An-1, previously tested for their response to water deficit conditions (Granier et al., 2006) was analysed to determine the natural variation in the effect of a water deficit on micronutrient homeostasis.

The second objective of this study was to determine the genetic contribution to the effect of water deficit on micronutrient homeostasis. For this purpose, this effect was quantified in a population of recombinant inbred lines (RILs) derived from a cross between the laboratory strain Ler (Landsberg erecta; originally from Poland) and the An-1 accession. Ler was previously found to deviate from most other accessions including An-1 in terms of seed mineral concentrations (Vreugdenhil et al., 2004). Quantitative trait loci (QTLs) involved in the variation of mineral homeostasis in water deficit (WDC) and optimal watering (OWC) conditions were detected to provide insight into common or specific genetic loci involved in the control of mineral homeostasis in contrasted watering conditions in Arabidopsis.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Plant material and growing conditions

The following previously described genotypes were used for the experiment: 25 natural Arabidopsis accessions including An-1 (Aguirrezabal et al., 2006), the Ler laboratory strain and 119 Ler × An-1 RILs (El-Lithy et al., 2006). The previously constructed genetic map for the Ler × An-1 population (El-Lithy et al., 2006) was updated with SSLP markers in order to increase the density of markers on the genetic map (Tisné et al., 2008).

Plants were grown in three independent experiments in the PHENOPSIS automated phenotyping platform (Granier et al., 2006). Soil water content was determined before planting. Subsequent changes in individual pot weight were attributed to a change in soil water status and this allowed calculation and adjustment of daily soil water content in each individual pot. This was done automatically by the PHENOPSIS automated platform as described by Granier et al. (2006). In experiment 1, the 25 accessions were tested in eight replicates, in both OWC and WDC. In experiment 2 all RILs were grown in four replicates and the two parental lines in grown eight replicates, in OWC, while in experiment 3 the same plant lines were grown in WDC. All micrometeorological conditions were controlled to remain constant and homogeneous within the whole growth chamber during all three experiments, as described by Granier et al. (2006) (see the Supporting Information, Table S1). Seeds were sown in cylindrical pots (9 cm high, 4.5 cm wide) filled with a 1 : 1 mixture (v : v) of a loamy soil and organic compost. For plants grown in OWC in experiments 1 and 2, soil water content was adjusted daily to 0.40 g H2O g−1 dry soil, as described by Granier et al. (2006) (Table S1) from germination to the stage 6.00 ‘first flower open’ (according to Boyes et al., 2001) by applying a nutrient solution (Table S2). For plants grown in WDC in experiments 1 and 3, soil water content was adjusted daily to 0.40 g H2O g−1 dry soil during a first phase from germination to stage 1.02 ‘two visible leaves’ (according to (Boyes et al., 2001)) and adjusted to 0.23 or 0.20 g H2O g−1 dry soil afterwards until stage 6.00 (Table S1).

Phenotypic analyses

In all experiments, plants were harvested at stage 6.00 for phenotypic analyses. Each individual plant was cut from the soil surface and the rosette leaves were isolated from the rest of the plant. Fresh weight of the rosette (FW) was measured and then each individual leaf was detached and stuck with double-sided adhesive tape to a sheet of paper. The sheet of paper was scanned. Leaves were then placed in a bag and their individual dry weight (DW) was measured after drying for 6 d at 60°C in an oven. Total rosette leaf area (TRLA) and the area of the largest leaf (LLA) were measured from the scans with image analysis software (Bioscan-Optimas V 4.10; Bioscan, Inc., Edmonds, WA, USA). The total rosette leaf number (TRLN) and the largest leaf position (LLP) were also determined by counting leaves on the scans.

Rosette mineral concentrations were measured using Atomic Absorption Spectrometry (AAS) (AAS 1100; Perkin–Elmer, Rodgau-Judesheim, Germany). For each line, four replicate samples were measured. Each sample consisted of c. 50 mg of oven-dried rosettes from the bulk harvest of two to three plants per replicate. Tissues were put in a Teflon cylinder together with 2 ml acid mix (HNO3 : HCl, 4 : 1 v : v), closed tightly and mineralized for 7 h at 140°C. After cooling, each digest was diluted with 3 ml demineralized water and transferred to a sterile 15 ml tube. Different dilutions were made before measuring the mineral concentrations, depending on their expected concentrations. These dilutions were further used to measure the P concentration by colorimetric spectrophotometry, largely according to (Chen et al., 1956). First, 13.33 ml sulphuric acid (95–97%) was diluted by demineralized water to 600 ml. Then 100 ml 10 mm ammonium heptamolybdate, 100 ml 1% ascorbic acid and 100 ml 0.78 mm potassium antimony (III) oxide tartrate were prepared. Finally, all four solutions were combined and supplemented with 100 ml of demineralized water to obtain 1 l of colorimetric solution. The diluted samples were mixed in 1 : 80 (v : v) ratios with the colorimetric solution and incubated for 30 min at room temperature, before measuring P concentrations using a spectrophotometer (Pharmacia–LKB, Ultraspec III) at 875 nm. KRAT values were determined using the following equation: KRAT = [K]/([Ca] + [Mg]) (Larson & Mayland, 2007). All zinc (Zn), manganese (Mn), iron (Fe), potassium (K), calcium (Ca), magnesium (Mg) and phosphorus (P) mineral concentrations are presented in µmol g−1 DW units. These convert to µg g−1 DW units, as follows: 1 µmol g−1 is 65.4 µg g−1 for Zn, 54.9 µg g−1 for Mn, 55.8 µg g−1 for Fe, 39.1 µg g−1 for K, 40.1 µg g−1 for Ca, 24.3 µg g−1 for Mg and 31 µg g−1 for P.

Statistical tests and QTL mapping

For all statistical analyses, the statistical package SPSS version 15.0 (SPSS Inc., Chicago, IL, USA) was used. Trait data for QTL mapping were tested for normality (with a Kolmogorov–Smirnov test). If the data were not normal, they were transformed with a log(10) function. Differences in mean trait values of the genotypes were analysed by univariate analysis of variance using the Dunnett's pairwise multiple comparison t-tests in the general linear model module of the package. For each analysis, trait values were used as dependent variables and genotypes were used as fixed factors. Tests were performed two-sided with a significance threshold level of 0.05. Independent samples t-test of the package was used to determine mean differences between two individual lines or bulks of lines. Correlation analyses were performed by calculating the Pearson correlation coefficients using the package. Partial correlation analyses among plant mineral concentrations were performed by correcting for dry weight. Broad-sense heritabilities (H2) for traits were calculated using H2 = VG/(VG + VE), where VG is the among-genotype variance component and VE is the residual error variance component of the analysis of variance. The VG and VE were treated as the genetic and environmental variances, respectively.

The QTL mapping was performed using the computer program mapqtl version 5.0 (http://www.kyazma.nl). The residuals for mineral concentrations obtained after regression of mineral concentrations and dry weight were further used to identify QTLs that are not affected by plant dry weight. Epistatic or QTL × QTL interactions occur when either the effect of one QTL is dependent on the presence of an allele at another locus (conditional QTL) or when each locus by itself appears to have no effect on the trait, yet when two loci are considered together there is an effect (coadaptive QTL; Chase et al., 1997). A complete pairwise search for epistatic interactions for each trait (P < 0.001, determined by Monte Carlo simulations) was done using the epistat statistical package (Chase et al., 1997).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Water deficiency affects mineral concentrations in a genotype-dependent manner

To study the natural variation for plant mineral concentration, 25 accessions of Arabidopsis were grown in WDC and OWC (control) (Aguirrezabal et al., 2006). The rosettes were analysed to determine the concentration of seven minerals: Zn, Fe, Mn, K, Ca, Mg and P. Large variations in rosette mineral concentrations were observed between accessions and between watering conditions (Fig. 1). Differences in mineral concentrations between water conditions were mostly observed in rosette K and Ca concentrations, which were significantly different in most accessions. Potassium concentrations were increased in all the accessions grown in WDC except for Ct-1. Calcium concentrations were decreased in all the accessions grown in WDC except for An-1, Mt-0 and Tsu-0, for which no significant difference was observed. Zinc concentrations were significantly decreased in WDC only in the An-1 and Edi-0 accessions. Iron concentrations were significantly increased only in the Shahdara accession. These results suggest that responses of mineral concentrations to water deficit are genotype dependent. A principal component analysis (PCA) for all mineral levels showed that three principal components explain 77.8% of the observed phenotypic variation (Table S3). Iron and Mn concentrations mainly contributed to PC1, while the Ca and Zn concentrations mainly contributed to PC2 and K concentrations mainly to PC3. For PC1 and PC2 especially, An-1 deviated from all others. For PC3 no obvious clustering was observed (Fig. 2, Fig. S1).

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Figure 1. Rosette mineral concentrations (mean ± SE) of 25 Arabidopsis thaliana accessions (arranged according to leaf area in optimal watering conditions (OWC) as found by (Aguirrezabal et al., 2006) grown in water deficit conditions (WDC; closed bars) and OWC (tinted bars). *, Significantly different between WDC and OWC. (a) Zinc (Zn) concentrations; (b) iron (Fe) concentrations; (c) manganese (Mn) concentrations; (d) potassium (K) concentrations; (e) calcium (Ca) concentrations; (f) magnesium (Mg) concentrations; (g) phosphorus (P) concentrations.

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Figure 2. Principal components analysis (PCA) of 25 Arabidopsis thaliana accessions based on the combined data on concentrations of seven minerals in rosettes of plants grown in water deficit conditions and optimal conditions.

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There was no obvious relationship of mineral concentration data with geographical origin, longitude and latitude of the accessions. Significant correlations among rosette mineral concentrations and growth-related traits for accessions were observed (Table S4). For both conditions, negative correlations were generally observed between mineral concentrations and growth traits, whereas only K concentration correlated positively with largest leaf area in WDC and largest leaf position in OWC.

Variation in rosette mineral concentrations of the Ler × An-1 RIL population

The mineral analysis showed that An-1 is phenotypically very different from most other accessions in its plasticity to soil water deficit, as was previously also concluded on the basis of plant morphology (Granier et al., 2006). Therefore, the Ler × An-1 RIL population (El-Lithy et al., 2006) was used to identify QTLs controlling mineral composition and concentration of rosette leaves for WDC compared with OWC. At both growing conditions, the parental accession An-1 had higher mineral concentrations than parent Ler, except for rosette K concentrations, which were higher in Ler (Fig. 3). For all mineral concentrations, considerable phenotypic variation was observed within the RIL population, even for mineral concentrations that hardly differed between parents (Fig. 3). The maximum/minimum value ratios for the minerals were between 3-fold (many) and 14-fold (Fe) when plants were grown in OWC and 3-fold (Mg) to 10-fold (Fe) when grown in WDC. For rosette DW the variation was much larger: c. 100-fold difference between the highest and lowest DW for both conditions. Considering the strong reduction of DW in WDC compared with OWC, an overall treatment effect on mineral concentrations was expected and indeed the average rosette mineral concentrations (except for P) of the RILs were significantly different when comparing WDC and OWC (Fig. 4). These differences were largely in line with the results obtained for the 25 accessions. The means of the rosette Fe and K concentrations were higher, and means of the rosette Zn, Mn, Ca and Mg concentrations and DW were lower in RILs grown on WDC. This shows that the change in plant growth caused by water deficit significantly and differentially affected rosette mineral concentrations. As the DW was so much reduced by WDC compared with OWC, the rosette mineral contents also strongly decreased in WDC vs OWC. This was also the case for Fe and K contents, despite the increase in the concentration of these minerals (Fig. 4).

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Figure 3. Frequency distributions of the rosette dry weight (DW) and the concentration (µmol g−1 DW) of zinc (Zn), iron (Fe), manganese (Mn), potassium (K), calcium (Ca), magnesium (Mg) and phosphorus (P) in rosettes of the Arabidopsis Ler × An-1 recombinant inbred line (RIL) population grown in water deficit conditions (WDC; dark-tinted bars) and optimal watering conditions (OWC; light-tinted bars). Arrows indicate the values in the parental lines Ler (thin arrows) and An-1 (thick arrows).

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Figure 4. Average rosette zinc (Zn), iron (Fe), manganese (Mn), potassium (K), calcium (Ca), magnesium (Mg) and phosphorus (P) concentrations and content (nmol per rosette) and dry weight (DW) (± SE) of the Arabidopsis Ler × An-1 recombinant inbred lines (RILs) grown in optimal watering conditions (OWC) and water deficit conditions (WDC). Differences between the means for mineral concentrations and contents were all significant (P < 0.001), except for rosette P concentrations (P < 0.056).

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Correlations among rosette mineral concentration traits and DW in the Ler × An-1 RIL population

The relation between mineral homeostasis and DW was further explored (Table S5). The DW was positively correlated (P < 0.01) with K concentrations and negatively correlated with all the other mineral concentrations in the population grown in OWC. The same was found when the population was grown in WDC, although not all correlations were significant. Thus, in general, plants with higher DW had lower mineral concentrations, probably owing to a dilution effect. Zinc and Fe concentrations in OWC positively correlated with other mineral concentrations, except for K and DW. However, when the population was grown in WDC, Fe concentrations were not significantly correlated with Zn concentrations and DW, but were positively correlated with K concentrations. When the concentrations of the same line in the two growth conditions were compared the Zn, Mn, Ca and P concentrations and DW were significantly correlated, but Fe, K and Mg were not, which suggested that Fe, K and Mg concentrations were strongly affected by different genotype × environment interactions. Figure 5 summarizes the network of correlations that was observed between mineral concentrations and DW in both growing conditions. We also removed the effect of plant dry weight on plant mineral concentrations and determined correlations among plant mineral concentrations which are not be affected by plant dry weight (Table S6). There were differences in correlations compared with determined correlations when plant DW effect on mineral concentrations was included. For example, when corrected for plant DW effects, a significant correlation was detected between Zn and Fe concentrations in WDC while no such correlation was found in OWC.

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Figure 5. Correlations between rosette mineral (zinc (Zn), manganese (Mn), iron (Fe), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P)) concentrations and dry weight (DW) in Arabidopsis Ler × An-1 recombinant inbred lines (RILs): (a) with optimal watering conditions (OWC), (b) with water deficit conditions (WDC) and (c) between OWC (light-tinted grey circles) and WDC (dark-tinted grey circles). Lines connecting the traits present the correlation: positive (dark) or negative (light).

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In addition to mineral concentrations, we also examined correlations between rosette mineral contents for both growing conditions (data not shown). In this case all the correlations were positive and highly significant, implying that DW is the main determinant of plant mineral content, as was expected based on the strong decrease in DW in WDC and the concomitant decrease in mineral contents (Fig. 4).

QTL analysis of rosette mineral concentrations and DW in the Arabidopsis Ler × An-1 RIL population

Based on the mineral concentration data, broad sense heritability values were calculated for the population grown in both OWC and WDC and (Fig. S2). The heritability of the rosette Fe concentration in WDC could not be determined, but for the other minerals it ranged from 44% (Mn) to 91% (Zn), and for OWC from 27% (Fe) to 81% (Mn). Heritability values for DW were also high at 82% and 70%, respectively, for OWC and WDC. The mineral concentration data were subsequently used to identify QTLs controlling the variation in mineral concentrations. The QTLs affecting rosette mineral concentrations and DW were mapped for both conditions on four out of five chromosomes (Fig. 6, Table S7). More QTLs were mapped for OWC than for WDC, in line with the lower heritability of traits in WDC. Many QTLs for different minerals were found to map to the same locus. This colocalization suggests the presence of common loci with pleiotropic affects and agrees well with the many significant correlations found between mineral concentrations within the population. Most of the colocating QTLs included a QTL for DW. Often the phenotypic effect of the DW QTL allele (An-1 or Ler) was opposite to the allelic effect of the mineral QTL. This corresponds well with the negative correlations observed between DW and mineral concentrations. In addition, specific mineral QTLs were detected depending on the watering condition: K and Mn concentration QTLs on chromosome 2, Mg and Mn concentration QTLs on chromosome 3 and a K/Mg/Ca concentration QTL cluster on chromosome 4. A PCA was performed separately for the two conditions, and for both simultaneously, to identify any common factors responsible for the observed variations in mineral concentrations and DW (Table S8). Although QTLs were identified for many PCs, all but one colocated with a previously determined single-mineral QTL to which the particular PC was best correlated, and therefore they are not included in Fig. 6. Only for PC2 we could identify a QTL not present for any of the single traits. This QTL is closely linked to the ERECTA gene marker. Finally, we examined the data set for epistatic interactions between loci. Several of such interactions were found, identifying QTLs additional to the single QTL identified previously, both for WDC and OWC, respectively. Many of those did not colocate with any of the previously identified main effect QTLs, but still explain significant percentages of the phenotypic variance (Table 1). Since several mineral QTLs colocalized with QTLs for dry weight, we also reanalysed the data after removing the effect of plant dry weight on plant mineral concentrations. Thus we identified several mineral QTLs that are not affected by plant DW (Table 2). It was found that QTLs for Zn and Mg concentration in OWC located on the top of chromosome 3 and QTLs for K concentration in OWC located on chromosome 5 are not controlled by the DW QTLs mapped in the same region (Fig. 6). In addition we identified QTLs for mineral concentrations, which were not identified when plant DW effect on mineral concentrations was included, simply because of increased statistical power.

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Figure 6. Linkage map of the Arabidopsis Ler × An-1 recombinant inbred line (RIL) population showing the locations of quantitative trait loci (QTLs) identified for rosette zinc (Zn), iron (Fe), manganese (Mn), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P) concentrations and dry weight (DW) per plant and for one common principle component not colocating with individual mineral QTLs (pc2all). The population was grown on soil in optimal watering conditions (tinted boxes) and water deficit conditions (closed boxes). The 1-LOD interval of each QTL is indicated with a box at the QTL position, while lines flanking the box represent the 2-LOD interval.

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Table 1.  Epistatic interaction quantitative trait loci (QTLs) affecting rosette iron (Fe), manganese (Mn), potassium (K), calcium (Ca), magnesium (Mg) and phosphorus (P) concentrations and Principle Components (PC) 1, 2 and 3 (see the Supporting Information, Table S7) of the Arabidopsis Ler × An-1 recombinant inbred line (RIL) population grown in water deficit conditions (WDC) and optimal watering conditions (OWC)
 Mineral/PCM 1Chr # M 1Position (cM)M 2Chr # M 2Position (cM)% Exp. var.
  1. For every interaction two loci are identified by closest markers 1 (M 1) and 2 (M 2). For each of these markers the chromosome number (Chr #) and genetic position is indicated. Loci are listed such that the effect of locus M 1 is conditional upon the allele at locus M 2. The percentage of variance that is explained by the interaction (% Exp. var.) is indicated for each interaction.

OWCFeNGA139530.0FRI43.09.9
FeFRI43.0NGA139530.09.5
FeNGA139530.0NGA17233.79.5
MnSNP77513.3SNP233240.213.8
MnSNP233240.2SNP77513.34.3
CaSNP77513.3NGA17233.718.8
PNGA17233.7F5I14168.015.8
PF5I14168.0NGA17233.75.6
PC1NGA139530.0NGA17233.78.7
PC2NGA17233.7F12A24b217.823.7
WDCFeSNP233240.2M3-32350.94.6
KF8D20455.7SNP132115.011.6
MgF12A24b217.8SNP248362.18.2
PNGA17233.7SNP77513.314.4
PSNP77513.3NGA17233.711.9
PC3SNP233240.2SNP248362.110.9
Table 2.  Quantitative trait loci (QTLs) affecting rosette zinc (Zn), iron (Fe), manganese (Mn), potassium (K), magnesium (Mg) and phosphorus (P) concentrations of the Arabidopsis Ler × An-1 recombinant inbred line (RIL) population grown in water deficit conditions (WDC) and optimal watering conditions (OWC)
 MineralMarkerChr #Position (cM)% Exp. Var.
  1. For each of these markers the chromosome number (Chr #) and genetic position is indicated. The percentage of variance that is explained by the locus (% Exp. var.) is indicated.

  2. The DW effect on mineral concentrations is removed then QTLs are identified.

OWCZnSNP1053 012.5
FeErecta234.811.0
KF12A24b217.811.2
KMBK5584.613.9
MgSNP1053 012.5
MgNGA17233.711.5
MgNGA17233.711.5
WDCZnSNP1053 024.4
ZnSNP225340.615.2
ZnSNP232455.213.9
MnSNP233240.218.6
KSNP232455.217.3
MgNGA1126236.612.9
MgM3-19331.719.0
MgSNP232455.222.4
PSNP1053 018.3
PSNP225340.616.1
OWCZnSNP1053 012.5
FeErecta234.811.0
KF12A24b217.811.2
KMBK5584.613.9
MgSNP1053 012.5
MgNGA17233.711.5
MgNGA17233.711.5

In addition to the traits analysed, we studied the [K+]/([Ca2+] + [Mg2+]) molar charge ratio or KRAT values. For grazing animals, values of KRAT over 2.2 increase the risk of grass tetany or hypomagnesaemia (Sleper et al., 1989; Larson & Mayland, 2007). The KRAT values were higher for plants grown in WDC than in OWC (0.68 for WDC and 0.33 for OWC), as rosette K concentrations were higher and Ca + Mg concentrations were lower in WDC compared with OWC. Thus, growing plants in WDC has a negative effect on plant quality for feeding purposes because of increased KRAT values. The QTLs for the KRAT values were determined but all colocated with QTLs for rosette K, Ca and Mg concentrations and are thus not included in Fig. 6.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

We first examined the rosette mineral concentrations in 25 accessions of Arabidopsis and compared these with their soil water plasticity, as studied previously (Aguirrezabal et al., 2006) to identify any correlations. Growth of 25 Arabidopsis accessions based on leaf area was strongly reduced by soil water deficiency, with a high variability depending on the accession, ranging from little reduction (c. 20%; An-1, Jea) to large reduction (c. 60%; Cvi-0, Di-m, Oy-0) (Aguirrezabal et al., 2006). No correlations were found between rosette mineral concentrations and geographical origin of the accessions, as was previously also the case for seed mineral concentrations (Vreugdenhil et al., 2004), but for most minerals, except for the K concentration, there was a significant negative correlation with TRLA (Table S4). Since TRLA and rosette DW are strongly correlated, there was also a negative correlation between mineral concentrations and rosette DW. As TRLA generally decreased when plants grew in WDC, plants grown in a water deficit also showed a decrease in their mineral concentrations. Drought reduces the rate of diffusion of nutrients in the soil to the absorbing root surface, nutrient uptake by the roots, transport from the roots to the shoots because of restricted transpiration rates and impairs active transport and membrane permeability (for review see Hu & Schmidhalter, 2005). Considering the scarcity of resources for the plants growing under WDC, they are likely to invest more in root growth than in shoot growth (Hermans et al., 2006). This could explain the reduction in the concentration of almost all minerals except for K and P. The growth in WDC took several weeks, thus plants had sufficient time to adapt to the low water supply, including adaptation of mineral homeostasis. There may be several reasons why K concentrations increased, both in the accessions and in the Ler × An-1 RIL population. Potassium is a major osmolyte, accounting for a very significant part of a plant's water potential. Enhanced K concentrations under drought stress can thus help to adjust a plant's water potential and to maintain its water balance. Potassium is also supposed to play an inhibitory role against reactive oxygen species (ROS) production during photosynthesis and NADPH oxidase activity (Cakmak, 2005), and drought stress is likely to enhance the production of ROS. It may also reflect a preference for plants to allow the concentration of K to rise, in order to decrease the concentrations of other, more toxic minerals, such as Na, at decreasing water content and decreased growth. Alterations in K homeostasis are known to affect Na homeostasis (Rus et al., 2004).

Rosette P concentrations remained relatively stable at the two growth conditions. This is different from what was observed for the uptake of P by crop plants in dry-soil conditions, which decreased such that plants became P deficient (Pinkerton & Simpson, 1986). Outside conditions are, however, likely to cause more stress to plants than the controlled conditions used for Arabidopsis. The ability to keep the P concentrations stable may have contributed to the sustained drought tolerance of Arabidopsis, since increasing the P supply to white clover plants improved their water status in dry soil as a result of a higher leaf water potential compared with low-P plants (Singh et al., 1997).

When examining individual accessions for their differences in mineral concentrations at OWC and WDC, several deviating accessions are found, such as Edi-1 for Zn concentration, An-1 and Shahdara for Fe and Mn concentrations and Sakata for Mg and Ca concentrations. Similarly deviating accessions were found by (Rus et al., 2006) after elemental profiling of 12 different Arabidopsis accessions, which enabled them to identify deviating alleles of the HKT1 Na+ transporter gene in two of these accessions. It will therefore be interesting to test for the presence of major loci controlling mineral concentration in the deviating accessions we identified.

Previously, An-1 was found to be an outlier among the 25 screened accessions in its plasticity to soil water deficit (Aguirrezabal et al., 2006; Granier et al., 2006), as its leaf area hardly decreased when comparing plants grown in OWC and WDC. We found it is also an outlier for rosette mineral concentrations, mainly for Fe, Mn, Mg and P concentrations. However, after identifying the QTLs controlling rosette DW and mineral concentration, it is clear that there is no major locus segregating in the Ler × An-1 RIL population that can account for the observed genetic differences. This seems to be more common than finding strong major QTLs (Vreugdenhil et al., 2004; Rus et al., 2006; Waters & Grusak, 2008; Wu et al., 2008; Ghandilyan et al., 2009).

Many of the mineral loci colocalize with DW loci, confirming the strong correlation found between DW and mineral content and indicating that the genetic networks controlling rosette mineral concentrations at two environments overlap, although care must be taken not to automatically assume that colocation actually refers to one locus with pleiotropic effects. There is always the possibility that colocalization refers to two separate loci that are just closely linked. Often the DW QTLs also have the highest LOD scores, suggesting their presence is robust. The LOD scores of mineral concentrations are generally lower, which was also previously observed when analysing the genetics of mineral concentrations in plants (Vreugdenhil et al., 2004; Wu et al., 2008; Ghandilyan et al., 2009). Both positive and negative correlations were found for rosette K and Ca concentrations when comparing RILs grown in OWC and WDC, respectively. This was further supported by QTL colocations. Under WDC a QTL cluster was identified on chromosome 4 (around marker SNP232) for both minerals, with opposite allelic phenotypic effects, supporting the negative correlation between K and Ca. For plants grown in OWC an additional QTL cluster was found on chromosome 4 (between markers NGA111 and SNP295) for K and Ca with same allelic phenotypic effects, supporting a positive correlation between both mineral concentrations. A DW QTL also maps to this locus and Tisnéet al. (2008) map a QTL controlling leaf cell area in OWC to the same locus, indicating that the variation in DW and K/Ca concentrations may have a pleiotropic origin related to leaf cell area.

In another study, based on the Arabidopsis Ler × Cvi RIL population, QTLs were identified for K concentration in fresh and dry leaf matter (Harada & Leigh, 2006). These QTLs do not overlap with any of the rosette K concentration QTLs identified in our study. Also, when comparing rosette mineral QTLs previously identified by A. Ghandilyan et al. (unpublished) in the Ler × Kond and Ler × An-1 RIL populations grown in a temperature-controlled glasshouse, only one QTL (Zn concentration in Ler × An-1 on top chromosome 3) overlapped with QTLs identified in this study. Also, only few of the QTLs for rosette mineral concentrations, as found in this study, colocated with QTLs previously identified for seed mineral concentrations in the Ler × Cvi or Ler × Col RIL populations (Vreugdenhil et al., 2004), and even these colocalization may concern different loci, as the map resolutions and population sizes do not allow an accurate localization. Still, several QTLs for growth related traits, such as flowering time, did colocate when comparing different populations (El-Lithy et al., 2006). Only one mineral QTL has been identified in several populations and tissues (Ler × Cvi, Ler × Kond, Ler × Eri-1 and Ler × An-1) (Bentsink et al., 2003; Vreugdenhil et al., 2004; Harada & Leigh, 2006; Waters & Grusak, 2008; Ghandilyan et al., 2009), which relates to the concentration of P and which maps to the top of chromosome 3. It appears that Ler carries an aberrant and infrequent allele for this locus, which negatively affects the P, phosphate and phytate concentrations in this accession and was thus detected in all of these populations accept for the Ler × Col population (Waters & Grusak, 2008). A QTL for rosette DW was found in the same region, apparently overlapping with the mineral concentration QTLs (Fig. 6). Although there is no evidence of one QTL controlling both DW and mineral concentrations, it may mean that the relation between DW and mineral concentration is a matter of dilution, with higher DW values leading to lower mineral concentrations. Cloning this gene or genes will be needed to verify this.

The general absence of common QTLs between different populations sharing one of the two parents (Ler), or even when comparing the same population under different conditions, is very much in line with earlier observations for Arabidopsis (Waters & Grusak, 2008; Ghandilyan et al., 2009). The general conclusion from such comparative QTL analysis is that there are many QTLs controlling mineral concentrations in different parts of the plant, with relatively small effects, and there is a strong interaction of QTLs with environment. All these aspects contribute to the variation in identified QTLs and indicate considerable difficulties in trying to clone the genes underlying such QTLs.

Using PCA, we tried to identify PCs with a corresponding QTL that could not be identified using single mineral concentration data as variable, for example, because single LOD values did not exceed the threshold value, but when the variances for all the mineral concentrations are jointly taken into account the threshold value is reached. This analysis only yielded one additional QTL, for PC2 (Fig. 6), which is not present for any of the single traits. This QTL is closely associated to the ERECTA gene marker on chromosome 2. Previously, a QTL was identified at approximately the same position for seed Zn and Mn concentrations in the Ler × Cvi RIL population (Vreugdenhil et al., 2004), for seed Fe and K concentrations in the Ler × Kond RIL population and for seed Zn, Fe, Mg and P concentrations in the Ler × An-1 population (Ghandilyan et al., 2009). Also, Waters & Grusak (2008) describe QTLs for seed Ca, Cu, Fe, K, Mg, Mn, P, S and Zn concentrations at this locus in the Ler× Cvi and Ler × Col RIL populations, and they suggest it is the actual polymorphism at the ERECTA gene (causing an aberrant morphology phenotype of the Landsberg erecta accession used in all of these populations as one of the parents) that causes this cluster of colocalizing QTLs.

It is tempting to try and identify other possible candidates underlying the identified QTLs, as was done previously (Vreugdenhil et al., 2004; Waters & Grusak, 2008). However, the added value of this information is limited. The confidence intervals around the QTL peak-LOD positions still comprise several cM, and with each cM covering, on average, 250 kb or c. 50 genes in Arabidopsis, this means that several-hundreds of genes still reside in the QTL regions, with a high likelihood that simply by random distribution of metal homeostasis genes (Mäser et al., 2001) several will reside in the QTL region. After reducing the QTL candidate gene area to < 100 kb (c. 20 genes) by fine-mapping, assigning candidate genes may be helpful for further research. However, complementation or knock-out mutation studies will be needed to convincingly identify the gene underlying the QTL.

When the results presented here and the previous genetic analyses on mineral concentrations in Arabidopsis (Vreugdenhil et al., 2004; Harada & Leigh, 2006; Waters & Grusak, 2008; Ghandilyan et al., 2009) are extrapolated to crop species, for which comparable data were found (Wu et al., 2007; Broadley et al., 2008; Wu et al., 2008; Zhao et al., 2008), it may not be straightforward to improve crop quality for biofortification purposes (Mayer et al., 2008) by breeding for increased Ca, Zn or Fe concentrations in edible parts of crops under a range of environmental conditions. It may be advisable to first screen a large collection of accessions to examine the possibility of identifying rare alleles with major beneficial contributions to mineral concentrations, and use these for cloning or breeding purposes before exploiting the less extreme and more recalcitrant genetic variation that is generally present in the different species.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

This work was supported by grant GPLA-06014G from GENOPLANTE (to S.T.). Technical assistance from R. Vooijs for the mineral analysis and from M. Dauzat and J. J. Thioux during the three growth experiments is appreciated.

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  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Fig. S1 Graphical presentation of principal components analysis of mineral concentrations of 25 Arabidopsis accessions.

Fig. S2 Heritabilities and total explained phenotypic variances for rosette mineral concentrations of the Arabidopsis Ler × An-1 RIL population.

Table S1 Growth conditions for the three experiments with Arabidopsis accessions and the Ler × An-1 recombinant inbred line (RIL) population grown at different soil water contents

Table S2 Composition of the nutrient solution used to daily irrigate each pot

Table S3 Principal component analysis (PCA) of mineral concentrations in 25 Arabidopsis accessions grown in optimal watering conditions (OWC) and water deficit conditions (WDC)

Table S4 Correlation coefficients for correlations between rosette mineral concentrations and rosette morphological traits for 25 Arabidopsis accessions

Table S5 Correlation coefficients for correlations between rosette mineral concentrations and rosette dry weights in Ler × An-1 recombinant inbred lines (RILs)

Table S6 Correlation coefficients for correlations between rosette mineral concentrations in Ler × An-1 RILs after removing the effect of dry weight

Table S7 Positions of quantitative trait loci (QTLs) identified for rosette mineral concentrations and dry weight in the Arabidopsis Ler × An-1 recombinant inbred line (RIL) population

Table S8 Principal component analysis (PCA) of rosette mineral concentrations and dry weight in the Ler × An-1 recombinant inbred line (RIL) population

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