Characterization of cultivars by their physiological response to long-term drought stress
Drought treatment significantly reduced total biomass (root plus shoot dry weight) of the four rice (Oryza sativa L.) cultivars Nipponbare (NB), Taipei 309 (TP), IR57311 (IR) and LC-93-4 (LC) by up to 79% (Fig. 1). Shoot:root ratio increased significantly under drought stress but showed no significant cultivar effect (Table 1). Dry weight of both shoots and roots was significantly higher in the cultivars LC and IR than in NB and TP (Table 1) under both control and drought conditions. Likewise, LC and IR scored better in a visual scoring test (Supplemental Table S3). More than 75% of the LC and IR plants were scored 3 or better under drought stress, whereas 75% of NB plants and 50% of TP plants scored 5 or worse. At harvest, control and drought treated plants of all cultivars were still in the vegetative tillering phase (BBCH 20–29). Thus, LC and IR were judged as tolerant and NB and TP as sensitive to long-term drought in the juvenile stage of the plant.
In all cultivars, drought treatment significantly reduced the water content of the shoot at the end of the drought period by about 45% compared to control conditions (Table 1). Interestingly, the water content of the leaf blade was much lower than the total shoot water content and changed less in response to drought (Table 2). Under drought stress, shoot water seemed to be mainly depleted from the tissues of the leaf sheath (data not shown). Shoot water content was higher in the tolerant cultivars IR and LC than in the sensitive cultivars NB and TP. For the cultivar LC, this difference to the sensitive cultivars was significant under control and drought conditions. At harvest, mean water potential of the leaf blades (Table 2) ranged between −0.12 and −0.34 MPa pre-dawn and between −0.96 and −2.42 MPa at mid-day. Pre-dawn and mid-day leaf water potentials were significantly lower under drought than under control conditions. At mid-day, leaf water potential was higher in the tolerant cultivars LC and IR than in the sensitive cultivars NB and TP. Likewise, osmotic potential was significantly lower under drought than under control conditions, with the interesting exception of the tolerant cultivar LC that showed a very negative osmotic potential already under control conditions and no osmotic adjustment under drought stress.
During the first 4 days after plants were removed from the water, cultivars did not differ in the amount of water used per day (Table 3). After day four, when the daily water loss was depleted at the end of the light period, the total amount of water used per gram final plant dry weight was significantly lower in the tolerant cultivars IR and LC than in the sensitive cultivars NB and TP (Table 3). However, both tolerant cultivars depleted the soil water content to significantly lower values than the sensitive cultivars, indicating that the tolerant cultivars were not saving water but were rather using the available water more efficiently. Both cultivar groups differed clearly in their response to drought stress, whereas the cultivars within a group showed similar responses and where thus analyzed together as members of a ‘tolerance group’.
Genotyping of the cultivars
For all four cultivars, genotyping with subspecies specific STS markers was performed for six locations (Fig. 2). TP and NB showed, as expected, the length of the PCR product predicted for ssp. japonica, IR the amplicon length for ssp. indica. The fourth cultivar, LC, for which published pedigree information is missing, showed japonica specific lengths of the PCR products.
Drought and drought × tolerance group effects on gene expression
To reduce the effect of biological variation between parallel plants on the within treatment variance, we pooled samples from four plants per experiment and cultivar. We used material from three independent experiments to allow stringent statistical data analysis. Genes with expression levels that were significantly affected by drought were identified by fitting a GLM. The final GLM included the main effects dye, condition (E-effect), tolerance group (G-effect), and the G × E interaction effect on the normalized expression level as response variables. Both tolerance groups contained two cultivars each. Genes were identified as significantly affected by tolerance group or condition, when the t-test on the normalized expression values had a fdr corrected p-value below 0.05 and the induction or repression factor was at least 1.5.
The number of genes that were significantly differentially expressed between sensitive and tolerant cultivars increased twofold to 225 genes under drought stress compared to 123 genes under control conditions. (Fig. 3a). Fifty genes were differentially expressed under both conditions.
To identify genes that were generally affected by drought stress in our experiments, we compared mean gene expression values of plants from all four cultivars grown under drought conditions with those of plants from all cultivars grown under control conditions. Drought stress significantly induced 413 genes and repressed 245 genes. Among the genes most highly influenced under drought conditions were genes coding for metallothionein like protein (induction factor 35.2), and late embryogenesis abundant protein (induction factor 23.2). Five genes encoding cytochrome P450 family proteins and three genes encoding serine/threonine protein kinases were found to be highly drought induced as well. The genes that were most strongly repressed by drought stress were mostly coding for unknown or hypothetical proteins, among the known gene products were a putative EF hand and SANT/MYB domain containing protein (Supplemental Table S4).
To identify those genes that may be relevant for the differences in drought stress tolerance between cultivars, we compared the responses of the two tolerant and the two sensitive cultivars. Strikingly, the number of genes that were significantly up- or down-regulated under drought stress was much higher in the sensitive than in the tolerant cultivars (Fig. 3). The number of genes exclusively drought-repressed in the sensitive cultivars was twice the number of genes exclusively repressed in the tolerant cultivars (Fig. 3b). For the induced genes, almost six times as many were exclusively induced in the sensitive compared to the tolerant cultivars (Fig. 3c). To find genes that differed in their treatment-depended expression between cultivars of contrasting tolerance (genes with G × E effect), we singled out those genes that showed a significant t-test for the condition × tolerance group term contrast and an interaction factor (compare Materials and Methods) higher than 1.5. A significant G × E effect was found for 236 genes. Within this group, 78 genes were also significantly affected by drought when all cultivars were compared (Fig. 3d). Among the genes with a significant condition × tolerance group effect, almost three times as many were drought regulated in both sensitive cultivars than in both tolerant cultivars. This difference resulted from a much higher number of drought induced genes in the drought sensitive than in the drought tolerant cultivars.
Functional testing of selected gene lists
To identify the parts of metabolism mostly affected by drought stress in rice and responding differently in sensitive compared to tolerant cultivars, we used the published gene ontology annotation (http://rice.tigr.org) to sort the genes into metabolic groups. The physiological role of the products of those genes that were significantly induced or repressed in all cultivars under drought stress compared to control conditions was visualized with the software MapMan (Thimm et al. 2004) (Fig. 4).
For assignment of rice transcripts to MapMan bins, the already established Arabidopsis bin classification was used as a basis. In total, the translated sequences of 11,208 rice transcripts were compared (BLASTX; Altschul et al. 1990) to the TAIR Arabidopsis peptide database version 6. For the majority of transcripts (83%), an Arabidopsis hit with a blast E-value of less than 10E−10 was found, for 39% the E-value was even lower than 10E−100. Sixteen percent of the best blast hits had a rather poor similarity, with an E-value higher than 10E−10. The proportion of blasted sequences, for which no hit in the Arabidopsis peptide database was found, was very low (0.6%). For transcripts with a blast result with an E-value lower than 10E−10, the MapMan bin classification of the best Arabidopsis sequence homolog was used to assign those rice genes to a MapMan bin. Afterwards, classification was curated manually with the help of the rice annotation and gene ontology data, if available. Bin 35 “not assigned” that contains all genes with unknown function and restricted gene ontology information contained a higher percentage (47.5%) of the genes in the rice classification than in the Arabidopsis classification (38.5%). The second highest number of genes was classified to the bin “protein” (bin 29), followed by “RNA” (bin 27). Overall, distribution of known and expressed genes from the 20 K NSF array to bins is similar to the distribution for Arabidopsis MapMan bin classifications (Supplemental Table S5).
To identify those bins that were significantly affected by drought stress, we used two approaches. In the first approach, we calculated the induction factor of all genes in a bin and compared the average induction factor of a bin to that of all other bins by Wilcoxon rank sum test. In a second approach, we counted the genes whose expression was significantly influenced by the condition and the condition x tolerance interaction and used the Fisher exact test to determine whether induced or repressed genes are overrepresented in a bin compared to all other bins (Table 4). The average change in gene expression under drought stress compared to control conditions for all four cultivars is depicted in a MapMan graph (Fig. 4) to give an overview of the general regulation pattern of genes encoding enzymes involved in major biochemical pathways.
Under drought stress, we found a highly significant down-regulation of genes that code for proteins involved in the photosynthetic light reactions, especially those of photosystem II, both at the level of average induction factors as well as the number of repressed genes. Gene repression was furthermore found for isoprenoid metabolism and a number of protein synthesis bins, especially amino acid activation and synthesis of ribosomal proteins (Table 4). Concordantly, genes for amino acid and lipid degradation were up-regulated.
To identify the metabolic pathways that differed in drought-induced changes between tolerant and sensitive cultivars, we used the parameters induction factor calculated separately for drought tolerant and drought sensitive cultivars, and interaction factor. The absolute interaction factor is high when the compared cultivars show opposite responses, and close to zero when the compared cultivars show concordant responses. Bins were compared for both induction and interaction factors by Wilcoxon rank sum test. Again, the percentages of drought-induced or repressed genes or genes with a significant interaction effect within each bin were compared to the general distribution by Fisher exact test (Table 4). We identified a number of pathways, in which gene expression was differentially affected by drought in sensitive and tolerant cultivars. In the sensitive cultivars, genes for protein synthesis were strongly down-regulated, especially those genes coding for ribosomal proteins of plastids. The effect was not a consequence of the down-regulation of a few genes, but rather a response of all genes in the bin. Concordantly, genes for protein degradation were strongly up-regulated in the sensitive cultivars. The induction factor for cysteine proteases and ubiquitin E3 ligases were significantly higher than average for the sensitive cultivars whereas the tolerant cultivars showed no drought effect on the gene expression of these pathways. In the next steps after protein degradation, namely amino acid degradation and the metabolisation of the carbon bodies by the TCA cycle, up-regulated genes were overrepresented in the sensitive, but not in the tolerant cultivars. Likewise, genes for the lipid degradation pathway, which feeds into the TCA cycle as well, were significantly induced in the sensitive, but not in the tolerant cultivars. The overall picture is that the high number of genes differentially expressed under drought stress in drought sensitive and drought-tolerant cultivars indicates a shift of metabolism towards degradation pathways in sensitive cultivars.
Drought stress strongly down-regulated photosynthesis genes in both sensitive and tolerant cultivars. However, for the polypeptide subunits of photosystem I and photosystem II, the number of down-regulated genes was, surprisingly, higher in the tolerant than in the sensitive cultivars, in spite of the higher growth rate of the tolerant cultivars (Table 4). Drought repressed gene expression specifically in tolerant cultivars for photosystem II protein D2 and a photosystem II 44 kDa protein, two chlorophyll a/b binding proteins, the photosystem I reaction center subunits III and IX, ribulose bisphosphate carboxylase small subunit C and the alpha and beta chains of cytochrome b559 (Supplemental Table S4).
The genes of the cytochrome P450 bin (Table 4, bin 26.10), which contained one of the most highly drought induced genes, were generally up-regulated under drought stress. The induction factor of the entire gene family was significant in the tolerant but not in the sensitive cultivars, which makes this bin another candidate for pathways contributing to drought-tolerance. Five cytochrome P450 genes exhibited a significant G × E interaction effect on their expression level. Two cytochrome P450 cyp86A2 genes were induced under drought in the tolerant but not in the sensitive cultivars resulting in a significant positive G × E interaction. In contrast, cytochrome P450 76C2 was 10-fold induced in the sensitive, but just two-fold in the tolerant cultivars and thus showed a negative G × E interaction.
Among the other bins that contained the most highly drought induced genes, the bin with the LEA proteins (bin 33.2) was represented with too few genes on the slide to allow a general statement. The bin with the metallothionein genes (bin 15.2) contained a significantly higher number of up-regulated genes in the sensitive than in tolerant cultivars.
Thus, the analysis of drought effects on gene expression yielded two candidate bins that may contribute to improved performance of tolerant cultivars, namely the bins containing photosynthesis and cytochrome P450 genes.
Mapping of candidate genes to drought tolerance QTL and confirmation by quantitative RT-PCR (qRT-PCR)
To identify genes that localize to genomic regions contributing to drought tolerance under field conditions, we mapped our candidate genes to drought tolerance QTL available in the Gramene Database. Location of the QTL was estimated with the help of the flanking markers and QTL longer than 5 million bases were excluded.
Of the 236 genes with a significant G × E interaction, 108 (45.8%) fell into a published QTL (Table 5 and Fig. 5). Likewise, 44.5 % of the genes that were significantly affected by drought (E effect) fell into a QTL. Among the genes that had no significant effect of G × E or E, 42.8% fell into published QTL. The hypothesis that there is an overrepresentation of genes with significant effect in QTL is thus to be rejected with an error of p = 0.2. Genes that were drought affected in our climate chamber experiments were thus only slightly and not statistically significantly overrepresented in drought related QTL. However, as many of these QTL have been identified in field trials, the location of a candidate gene within a QTL increases the likelihood that the gene is relevant for drought tolerance under field conditions. We thus used the location within a QTL as an additional filter to narrow down the list of candidate genes gained from our climate chamber experiments.
We chose 45 of the 108 QTL located genes with a significant G × E effect, based on the p-values, for an additional analysis by qRT-PCR (Table 6), using material from an independent experiment (#4). Due to the smaller number of plants sampled (three instead of 12), the test power was lower than in the statistical analysis of the array data. In the qRT-PCR analysis, 22 genes showed a significant G × E interaction at the p = 0.1 level. For 32 genes, the p-value was lower than 0.25 (Table 6). Among the genes with a significant G × E effect in both array and qRT-PCR analyses was a putative LEA protein, a MYB transcription factor and an ethylene responsive transcription factor, but also a number of genes with unknown function that would not have been identified as candidates in a search focused on functional categories.