Global genome expression analysis of rice in response to drought and high-salinity stresses in shoot, flag leaf, and panicle

To elucidate genome-level responses to drought and high-salinity stress in rice, a 70mer oligomer microarray covering 36,926 unique genes or gene models was used to profile genome expression changes in rice shoot, flag leaf and panicle under drought or high-salinity conditions. While patterns of gene expression in response to drought or high-salinity stress within a particular organ type showed significant overlap, comparison of expression profiles among different organs showed largely organ-specific patterns of regulation. Moreover, both stresses appear to alter the expression patterns of a significant number of genes involved in transcription and cell signaling in a largely organ-specific manner. The promoter regions of genes induced by both stresses or induced by one stress in more than one organ types possess relative enrichment of two cis-elements (ABRE core and DRE core) known to be associated with water stress. An initial computational analysis indicated that novel promoter motifs are present in the promoters of genes involved in rehydration after drought. This analysis suggested that rice might possess a mechanism that actively detects rehydration and facilitates rapid recovery. Overall, our data supports a notion that organ-specific gene regulation in response to the two abiotic stresses may primarily be mediated by organ-specific transcription responses.
Introduction
Drought and highly saline soils are among the most serious challenges to crop production in the world today. This is particularly the case in developing countries, where these abiotic stresses severely limit crop growth and productivity. Both traditional breeding and genetic engineering of crop plants have been utilized to improve drought and high-salinity tolerance or resistance with the goal of increasing agricultural productivity in affected regions. Understanding plant responses to abiotic stresses at the genomic level provides an essential foundation for future breeding and genetic engineering efforts.
Recent research on drought and high-salinity responses in Arabidopsis implied that a large proportion of the genome is involved in drought (Shinozaki et al., 2000, 2003) or high-salinity stress responses (Xiong et al., 2002; Zhu, 2001, 2002). In several cases, it has been shown that alteration of individual gene expression level can significantly impact responses to drought (Garg et al., 2002; Haake et al., 2002) or high-salinity stresses in plants (Kasuga et al., 1999; Shi et al., 2003; Xu et al., 1996; Zhang et al., 2004). Genome-wide identification of genes regulated by drought or high-salinity conditions has manifold significance. First, it provides a more comprehensive understanding of the transcriptional responses to those stresses. Second, it provides a starting point for further elucidating the role of individual genes in stress responses, which will be of great value in crop engineering. Third, it aids in the identification of stress responsive promoters and responsible cis-elements within them that are important both for basic study and crop engineering applications.
DNA microarrays provide a high throughput means of analyzing genome expression, which has been used to study patterns of gene expression in response to drought or high-salinity stresses in several plant species (Seki et al., 2003, 2004). Initially, a microarray containing ∼1,300 full-length cDNA clones from Arabidopsis was used to study gene expression under drought and cold stresses. This study resulted in the identification of 44 and 19 cDNA clones as drought and cold-inducible genes, respectively (Seki et al., 2001). Other studies employed an improved microarray containing around 7,000 Arabidopsis full-length cDNA clones to profile gene expression in response to abscisic acid (ABA) treatment (Seki et al., 2002a) as well as cold, drought, and high-salinity stresses (Seki et al., 2002b). Another study employed an Affymetrix GeneChip covering approximately 8,100 genes from Arabidopsis to monitor changes in gene expression under salt, osmotic, and cold stresses. This study revealed that resulting expression changes varied significantly between root and leaf, with only minor overlap (Kreps et al., 2002). Similar studies have also been performed in barley to assess the drought and high-salinity gene expression responses using a microarray containing 1,463 DNA elements (Ozturk et al., 2002).
Rice (Oryza sativa) is a model plant for cereal crops and has perhaps the richest set of resources available for plant genomic studies (Feng et al., 2002; Goff et al., 2002; RCSC, 2003; Sasaki et al., 2002; Yu et al., 2002). In rice, the high-salinity stress response has been analyzed with a microarray containing 1,728 cDNA clones from a root cDNA library of salt-tolerant rice (var Pokkali) (Kawasaki et al., 2001). Another study with a microarray containing 8,987 DNA elements detected 509 possible abscisic acid (ABA) or gibberellin-responsive genes (Yazaki et al., 2003). Later, the same group used a 22,000 rice cDNA based oligonucleotide array to identify ABA and GA responsive genes, made comparison of ABA-responsive genes and their putative responsible promoter elements between rice and Arabidopsis (Yazaki et al., 2004). This analysis gave an initial global view of the ABA- and GA-responsive genes in rice. A total of 73 genes induced by cold, drought, high-salinity and ABA treatment were further identified using a microarray containing 1,700 full-length cDNA clones (Rabbani et al., 2003). Recently, an Affymetrix rice genome array containing 55,515 probe sets was used to profile the transcriptomes of rice strains with salt-tolerant and salt-sensitive genotypes, revealing genome-wide differential transcription under salinity treatments during vegetative growth (Walia et al., 2005). Taken together, previous studies in rice have identified various genes regulated by different stress conditions. However, a systematic comparison of whole-genome expression responses to drought and high-salinity stresses in various organs has not yet been performed.
The availability of the complete genome sequences of two rice sub-species (Yu et al., 2005) makes the construction of a whole-genome microarray possible. A whole-genome 70mer oligomer microarray for rice was developed and successfully employed to obtain genome expression profiles (Ma et al., 2005b; Jiao et al., 2005). Here, we use this whole-genome microarray to monitor expression changes for a total of 36,926 genes in response to drought and high-salinity stresses in shoots at the four-tiller stage and in flag leaves and panicles at 1 week prior to heading. This analysis revealed the extent of reprograming and alteration of cellular pathways in response to drought and high salinity stresses. The possible underlying mechanism for the observed reprograming of the genome expression was examined.
Materials and methods
Plant material and stress treatments
Plant material used in this study was O. Sativa L. ssp. indica (cv. Minghui 63). Shoot samples were selected at the four-tiller stage (vegetative stage) and flag leaf and panicle samples were selected at one-week-before-heading (reproductive stage). The plants used for shoot samples were grown in hydropolic half-strength Hoagland solution up to the four-tiller stage, and then plants allowed to reach the reproductive stages were grown in soil.
For drought treatment samples, plants were taken out from the Hoagland solution directly and their roots were put on filter paper for drought treatment. Under this condition, the phenotype of shoot was observed. Materials at the following stages were collected: Stage 1 (D1), when leaves were slightly rolled and leaf relative water content (RWC) was around 90–95%; Stage 2 (D2), when leaves were half-rolled and leaf RWC was around 80–85%; Stage 3 (D3), when leaves were completely rolled and leaf RWC was around 70–75%. For most of the plants, usually it took half to 1 h to reach stage 1, and then after another 2 h or 4 h, they could reach stage 2 or stage 3 respectively. Rehydration samples were prepared by first subjecting the rice plants to drought treatment until leaves were completely rolled (D3), then supplying enough water and samples were collected after 48 h. For each treatment stage, three independent replicates were collected. For sample collection under high-salinity treatment (200 mM NaCl), rice plants were collected when plants reached stages 1, 2, or 3 as outlined above for drought treatment. Rice plants were taken out from Hoagland solution and were directly put into Hoagland solution with 200 mM NaCl. For most of plants, usually it took 5 h to reach stage 1, and then after another 12 and 24 h, they could reach stage 2 and stage 3 respectively. For each stage, three independent replicates were collected. Samples from normal grown plants corresponding to each drought and high-salinity stages and rehydration stage were also collected at the same time as respective controls. Upon collection, samples were frozen immediately in liquid nitrogen and stored in the –80°C freezer. The estimation of RWC in rice plants was prior reported method (Bare and Weatherley, 1962).
RNA isolation, probe labeling, and hybridization
RNA preparation, fluorescent labeling of probes, slide hybridization, washing and scanning were performed as described previously (Ma et al., 2005b). Total RNA was prepared from frozen samples using the RNAwiz reagent (Ambion). Each RNA preparation was used to generate a labeled cDNA probe for hybridization. There were at least three high-quality replicate data sets for each experiment, with each data set obtained from an independent biological sample.
The cDNA synthesis, probe labeling, and microarray hybridization, microarray slide washing, and array scanning were performed following previously described protocols (Jiao et al., 2005). Hybridized microarray slides were scanned with an Axon GenePix 4000B scanner, and independent TIFF images for both Cy3 and Cy5 channels were used for subsequent analysis.
Microarray and initial data analysis
The microarray construction was described previously (Ma et al., 2005b, Jiao et al., 2005). Microarray data were preprocessed with Limma R Package (Smyth, 2005). We first performed Loess normalization to balance the dye-bias, and to remove the print-tip effect within each array. Quantile normalization was performed to remove the experimental variances across replicate arrays. Based on the distribution of negative control oligos within each slide, we determined an experimental threshold for minimum non-specific hybridization intensity, and the gene oligos above 90-percentile of negative oligos were set as to be detected in each experiment.
To identify the genes that respond to drought and salinity stress, we used the Limma R implementation to perform hypothesis tests by fitting a linear model to the expression data. During pair-wise comparison between each treated sample versus control, a moderated t-statistics were computed, by using an empirical Bayes method to shrink the gene-wise sample variances towards a common value (Smyth, 2004), and the differential level was represented by a log (2) of intensity ratio. The P value adjustment used in false discovery rate control for multiple testing is Benjamini and Hochberg method (Benjamini and Hochberg, 2000; Reiner et al., 2003). The genes with significantly differential expression were selected by the adjusted P values less than 0.05.
RT-PCR analysis of genes
The expression profiles were further quantified by RT-PCR and compared to results obtained by chip hybridization. The first strand of cDNA was generated from 1 μg of total RNA isolated independently from each sample in a 100 μl volume and 1 μl was used as template in each PCR reaction (25 cycles of 1 min at 94°C, 1 min at 58°C, 1 min at 72°C). A total of eight drought-induced genes were selected for RT-PCR analysis (the primers of these genes are listed in Table S18). The Actin1 gene of rice was used as a control for RT-PCR experiments (forward primer, 5′-cgcagtccaagaggggtatc-3′; reverse primer, 5′-tcctggtcatagtccagggc-3′).
Functional classification
GO terms used in rice gene functional annotations were downloaded from the BGI-RIS database at http://rise.genomics.org.cn (Zhao et al., 2004). For biochemical pathway analysis, we classified genes by associated biochemical pathway(s) using the AraCyc database (http://www.arabidopsis.org/tools/aracyc) for Arabidopsis, which is based on MetaCyc pathway collections (Mueller et al., 2003). A rice gene was considered to be associated with a biochemical pathway if it had an Arabidopsis homolog in that pathway. To compare the expression profiles of conserved genes between rice and Arabidopsis, we selected reciprocal best-matched gene pairs between rice and Arabidopsis, which are based on a TBLASTN search of both rice and Arabidopsis gene sequences. This method used to identify highly homologous genes between rice and Arabidopsis has been described in previous reports (Jiao et al., 2005; Ma et al., 2005b). For the diagrams in Fig. S3, the expression ratios of the selected rice genes at D3 and S3 stage only were used.
Analysis of cis-acting regulatory elements
Differentially expressed genes supported by available full-length cDNA sequences were used to further elucidate regulatory cis-elements in the putative promoter regions. After mapping the FL-cDNAs on indica rice 12 pseudo-molecules, 2 kb DNA sequences upstream from the 5' end of the available cDNA sequences were extracted and searched against a plant cis-regulatory database (Higo et al., 1999) (http://www.dna.affrc.go.jp/htdocs/PLACE/signalscan.html). The abundance of known ABRE core and DRE core elements in each query set were counted.
Identification of novel cis-regulatory elements was performed using the Improbizer tool (cis-Site Seeker) by Jim Kent (http://www.cse.ucsc.edu/∼kent/improbizer/), which is based on ab-initio prediction of consensus binding sites among each co-regulated gene set. The upstream 2 kb regions of co-regulated FL-cDNA genes were used for common motif searching. To evaluate the significance for each putative novel motif, we generated an equal number of 2 kb contrast sequences in each query set by randomly arranging the four nucleotides. We performed the standard Student’s t-test on motif score tables, comparing query set versus contrast set. The known ABRE motif predicted by Improbizer has a pretty high confidence level (above 0.999), and the other two novel motifs were calculated to above a 0.95 confidence level. Motif consensus sequences were displayed by WEBLOGO program at http://weblogo.berkeley.edu/ by Crooks et al. (2004).
Gel shift assays
Nuclear protein extracts and gel mobility shifts were performed as described previously (Gupta et al., 1998). Oligomers were synthesized using the following sequences: OsJRFA070715: GCAGATACTGCAGCCAACCTCTCT, OsJRFA070715M: GCAGATACTGAAGCCAACCTCTCT. The complement sequence for each oligo was also synthesized. After denaturing and annealing, double-stranded probes were used for labeling.
Clustering and chromosomal location analysis of genes involved in stress responses
Unsupervised classification of stress-response genes was based on log-transformed ratios of all up- or down-regulated genes detected in at least one stage of drought treatment. We performed hierarchical clustering analysis to display the expression pattern and tree diagram at different stress stages by employing Cluster and TreeView (Eisen et al., 1998). Classification of rehydration-induced genes was based on the log-transformed ratios of 48-h rehydration and three stages of drought treatment in the three organs, and 807 genes in flag leaf, 281 genes in shoot, and 224 genes in panicle were grouped with the above method.
To analyze the genome-wide distribution of rehydration-induced genes in the three organs and drought and high-salinity induced genes in shoot, as well as identify potential clusters of co-regulated genes, we mapped these genes to the indica 12 chromosomes using the BLAT program. The expression profiles in Fig. 9 were drawn using a customized Perl script plus a scalable vector graphics (SVG) bundle.
Results
Identification of drought and high-salinity stress responsive genes using a rice whole genome oligomer microarray
A previously reported rice genome microarray containing 70mer oligomer probes for 36,926 unique genes or gene models (Ma et al., 2005b; Jiao et al., 2005) was used for genome-wide profiling of gene expression responses to drought and high-salinity stresses. Total RNAs were isolated from four-tiller stage shoots and from flag leaves and panicles (as described in Experimental procedures) at three different time points (or sampling stages) after initiation of drought or high-salinity stress treatments (Fig. S1). The sampling stages for both drought and high-salinity treatments were based on morphological phenotypes (leaf rolling state) and physiological states (relative water content, RWC, see Bare and Weatherley, 1962). Stage one is defined as exhibiting slightly rolled leaves with 90–95% RWC. Stage two is defined as exhibiting halfway rolled leaves with 80–85% RWC. Stage three is defined as exhibiting completely rolled leaves and with 70–75% RWC. For drought treatment, the three stages were designated as D1, D2, and D3, while the three stages for the high-salinity treatment were designated as S1, S2, and S3 (see Fig. S1). The cDNA probes produced from the stress-treated and corresponding untreated control sample pairs were hybridized to microarray slides. For each sample point, three replicates were performed with dye-swap to correct for uneven dye effects.
The microarray data processing includes LOESS normalization within slide and quantile normalization between slides to remove the systematic errors (see Experimental procedures for details). To identify the differentially expressed genes, Limma R implementation was used to perform pair-wise comparison, and a moderated t-statistics was computed for each gene of treated sample versus control sample. Genes with FDR-adjusted P value less than 0.05 were considered to indicate significant differences (see Experimental procedures for details). Numbers of genes up- or down-regulated by drought or by high-salinity stress at each stage in each organ were shown in Fig. 1. In total, 582, 1,257 and 614 drought up-regulated genes and 795, 646 and 1,305 drought down-regulated genes were identified in flag leaf, shoot and panicle, respectively; and 1,676, 817 and 1,310 high-salinity up-regulated genes and 1,270, 1,323 and 2,284 high-salinity down-regulated genes were identified in flag leaf, shoot and panicle, respectively (Fig. 2A, B). The complete lists of these genes are available in the supplemental data (Tables S2–S13).
Fig. 1The number of differentially expressed genes at each stage of drought and high-salinity treatments in three organs. Differentially expressed genes (both induced or repressed) are defined as those with P < 0.05. D1, D2 and D3: three stages of drought stress, S1, S2, and S3: three stages of high-salinity stress. y-axis shows the gene number. (A) Number of genes induced or repressed in flag leaf. (B) Number of genes induced or repressed in panicle. (C) Number of genes induced or repressed in shoot.Fig. 2Comparison of drought and high-salinity genome expression responses in the three rice organs. (A) Total number of drought and high-salinity stress inducible genes and the number of genes induced in response to both stresses in each of the three rice organs. Genes induced at least at one stage under drought or high-salinity treatments are included in the analysis. y-axis shows the gene number. (B) The total number of drought and high-salinity stress repressed genes and the number of genes repressed in response to both stresses in each of the three rice organs. Genes repressed at least at one stage under drought or high-salinity treatments are included in the analysis. y-axis shows the gene number. (C) Clustering analysis of all drought and high salinity responsive genes in each of the three rice organs. The differentially expressed genes (both induced or repressed) with P < 0.05 at least at one stage of drought treatment are included. The median ratio (treated/untreated sample) is log2 transformed and subject to complete linkage hierarchical clustering. D1, D2, and D3: three stages of drought treatment; D3R: 48-h water recovery after drought; S1, S2, and S3: three stages of high-salinity treatment. In total, 1,377, 1,903, and 1,919 genes from flag leaf, shoot, and panicle, respectively, were included in this analysis. (D) Relatedness of the genome expression patterns across selected stress-treated rice organs. A complete-linkage hierarchical clustering analysis of overall relatedness for expression ratios from selected organs at the stage 3 of both abiotic stress treatments. The abbreviations for different sample types are the same as in panel C
Most of the previously known genes responsive to both drought and high-salinity stress have been recovered from our microarray analysis. Those include the LEA protein (OsJRFA063984), aquaporin (OsIRUA001311), OsNAC1 (OsJRFA108080), dehydrin rab 16b (OsIFCC035025) and DREB1 (OsJRFA067313). Many other responsive genes have been identified for the first time in this study (Table S1C). Among those newly revealed genes, some are expected to be involved in general cell function or known to be involved in other stress responses, while others have putative or unknown function. The annotation for some of those genes suggested that they include transcription factors from multiple families, heat shock proteins, various stress (drought, high-salinity, disease, cold, and ABA) responsive genes, protein kinases, transporters, photosynthesis enzymes, and other metabolic pathways (Table S1C). The diversity of affected processes suggests a high level of complexity in regulation.
There is a significant overlap between genome expression profiles in response to drought and high-salinity stress
To compare the gene expression profiles of the drought and high-salinity stress responses, genes commonly regulated by both stresses at each organ type were examined. A total of 322, 415, and 174 genes were up-regulated and 215, 173, and 372 genes were down-regulated by both stresses in flag leaves, shoots and panicles, respectively (Fig. 2A, B). The common genes represent 55, 33 and 28% (induced) and 27, 27 and 29% (repressed) of all drought-responsive genes in flag leaf, shoot and panicle, respectively. These results indicate that about one-third, even half in flag leaf, of the drought responsive genes in each organ are also regulated by high salinity stress.
To further compare gene expression under the two abiotic stresses, all drought regulated genes were selected for cluster analysis (see Experimental procedures). As shown in Fig. 2C, up to half of the genes up- or down-regulated by drought stress also exhibit a similar expression pattern under high-salinity stress. The remainder of the drought responsive genes exhibits minimal expression changes or distinct expression patterns in response to high-salinity stress.
Among genes specifically induced by drought stress, some have known functions while others were previously predicted to have functions related to drought stress or the ABA response. For example, our experiments showed that genes with putative functions including NAM, HLH, G-box binding, Zn-finger, AP2 transcription factors, and protein kinases (including MAPK family genes) were all affected by drought stress. Quite a few known drought-responsive genes such as DREB1A, LEA protein, WSI76 protein, MAP65 (microtubule associated protein), and ubiquitin were also included. These genes were specifically induced by drought and may function exclusively in the response to drought stress in rice. To gain an overall view of the effects of drought and high-salinity stress on various functional gene groups, gene ontology (GO) categories for drought and high-salinity stress induced genes in three organs were examined (Fig. S2). For the majority of these functional categories, the relative numbers of genes responsive to the two stresses were similar.
We also examined the effects of drought and high-salinity on rice genes coding enzymes in known metabolic pathways using shoot as a model (Fig. S3). Some pathways are similarly regulated by the two stresses, including the Calvin cycle, TCA cycle variation I, brassinosteroid biosynthesis II, gibberellin biosynthesis, and IAA biosynthesis I, and the de novo biosynthesis of pyrimidine ribnucleotides/pyrimidine deoxyribonucleotides/purine nucleotides (Fig. S3 and data not shown). While other pathways respond differentially to drought or high-salinity stresses, the representative pathways include sterol biosynthesis, sugars and polysaccharides, ABA biosynthesis, lignin biosynthesis, octane oxidation, cutin biosynthesis, and starch degradation (Fig. S3). For example, some steps in these pathways were repressed or invariable under high-salinity stress, but were induced by drought stress. It is noticeable that one step in the ABA-biosynthesis pathway (corresponding to Arabidopsis NCED3) showed slightly inhibited in gene expression under high-salinity stress (only at stage 3), but was induced under drought treatment (Fig. S3c). Our informatic analysis suggested that OsJRFA107649 is the closest Arabidopsis NCED3 homolog, with 66% identity (E = 1.8E–195). From our analysis, OsJRFA107649 expression ratios (log2 transformed) were 0.278 (D1), 3.461 (D2) and 2.53(D3) under drought treatment and 3.626 (S1), 2.283 (S2) and –1.958 (S3) under high salinity treatment. The expression ratios of rice gene at D3 and S3 stage were used for the diagram in Fig. S3. It is interesting to note that under high salinity this gene was initially induced and then inhibited at stage 3 (S3 ratio: –1.958), while under drought treatment this gene was induced starting at stage 2 and maintained after stage 3 (D3 ratio: 2.53). This example illustrated a distinct response kinetics for different responsive genes in response to drought and high-salinity stresses.
By comparing transcriptomes under drought and high-salinity stresses in three distinct organs, we found that drought responsive genome expression in flag leaf is closer to that for high salinity stress than to the drought expression response observed in any other organ (Fig. 2C, D). In general, there seems to be a closer relationship between the transcriptome-level responses to the two abiotic stresses in the same organ than between or among transcriptomes in distinct organs in response to the same stress. However, the extent of overlap in the responses to drought or high-salinity stresses varies for the different rice organs. For example, it is more divergent in the responses to the two abiotic stresses in panicle, while flag leaves show the greatest overlap.
Distinct groups of genes are induced during rehydration after drought stress
In its natural environment, a plant’s ability to respond to rehydration after drought stress is important for its survival, although little is known about the gene expression changes that occur during rehydration. Our experiments showed that after a period of drought (third stage), all the rice leaves rolled and turned yellow, and the RWC dropped to 70–75%. When we applied water to those severely stressed plants, rice leaves started to unroll after 5 h, and exhibited normal flatness within 24-48 hours of rehydration. We collected tissue samples 48 h after rehydration and extracted total RNA for gene expression analysis (Fig. 2C).
Comparing to samples from normal grown rice plants, in flag leaf, shoot and panicle, 807, 281 and 224 genes were up-regulated after 48 h of rehydration following drought stress, respectively (Fig. 3, Table S14–S16). We further compared gene expression profiles during drought treatment with those after rehydration (Fig. 3A–C). The expression patterns of those genes seem to belong to three groups, with flag leaf exhibiting the greatest number of gene expression changes following rehydration. Group I, which included 71, 98 and 68 genes from flag leaf, shoot and panicle, respectively, was induced during both drought treatment and rehydration. Group II, which included 71, 8 and 21 genes in flag leaf, shoot and panicle, respectively, was repressed during drought stress but induced by rehydration (for shoot and panicle, see Table 1). Group III, which included all remaining 665 genes in flag leaf, 175 genes in shoot, and 135 genes in panicle, showed no significant expression changes under drought stress but were up-regulated during rehydration. We suspected that group I genes must be induced by drought stress, but have yet to return to normal levels after 48-h rehydration. Those genes in groups II and III were specifically induced by rehydration.
Fig. 3Cluster analysis of genes exhibiting elevated expression after 48-h rehydration following drought. (A) (B) and (C) Cluster analysis of the genes exhibiting significant elevation of expression (with P < 0.05 as threshold) after 48 h of rehydration treatment for flag leaf, shoot, and panicle. The median ratio is log2 transformed and subject to complete linkage hierarchical clustering. A total of 807 genes in flag leaf (A), 281 genes in shoot (B), and 224 genes in panicle (C) are included in this analysis. D1, D2, and D3 represent three stages of drought treatment, and D3R represents 48-h rehydration of the D3 stage. (D) The histogram shows the number of genes in each of the three rice organs induced under drought treatment at least at one drought stage, after 48-h rehydration following the D3 stage, and the number of genes with overlapping expression in various pairs of organs. y-axis shows the gene numberTable 1Genes induced by 48-h rehydration and inhibited by drought stress in shoot and panicleGene namePutative functionsD1D2D3D3RShootOsIFCC006273Unknown0.596−1.611−3.4971.920OsIFCC021002Glycosyl hydrolases family 170.440−1.105−1.7752.470OsJRFA107373Unknown−1.521−0.487−4.3482.470OsJRFA101949AP2 domain0.216−0.166−2.4562.026OsJRFA106016Kelch motif−0.315−0.582−2.6041.865OsJRFA070715Peroxidase−0.189−0.499−2.4562.215OsJRFA065471Folate/biopterin transporter0.331−0.533−2.5953.095OsIFCC041459Chitinase−0.460−0.136−2.1251.972PanicleOsIFCC023423Photosystem I reaction center subunit IV−0.067−0.293−2.6102.206OsIFCC032344Photosystem I reaction center subunit n−0.141−0.626−1.7002.390OsIFCC001915Photosystem II polypeptide−0.119−0.934−1.7741.787OsJRFA061606Pathogenesis–related protein PR-10a−1.811−0.007−1.8414.117OsJRFA107373Unknown−3.6930.395−1.9785.605OsJRFA109398Chlorophyll a/b binding protein−1.867−1.348−1.8991.905OsIFCC025509Photosystem II 10 kDa polypeptide PsbR−1.077−0.966−1.7351.831OsIFCC038501Chloroplast precursor−1.087−0.966−1.8051.837OsIFCC022062Photosystem I reaction center subunit VI−0.540−0.206−1.9961.811OsIFCC024081Chlorophyll a/b-binding protein−0.595−0.685−1.8142.298OsIFCC014461Unknown−0.794−0.660−1.7221.790OsIFCC017469Unknown−0.879−1.619−1.7642.417OsIFCC012276Catalase−1.567−0.594−1.8932.592OsJRFA060135Unknown−0.922−0.491−2.5401.793OsIFCC033400Pathogenesis-related protein PR-10a−0.115−0.032−2.5502.008OsJRFA059435Magnesium-protoporphyrin IX monomethyl esteraerobic oxidative cyclase−0.344−0.348−1.8372.851OsIFCC022709Protease inhibitor/seed storage/LTP family−0.091−0.300−1.6841.842OsJRFA061968Geranylreductase−0.334−0.244−1.8042.001OsJRFA071762Aluminium-induced protein0.217−0.594−1.8491.752OsIFCC029079Blight-associated protein p12 precursor−1.1100.379−1.8482.980OsJRFA106991Unknown−0.899−0.142−1.8741.715Log2 transformed ratios of all genes at three stages of drought stress and rehydration stage were listed
The down-regulation of group II genes in response to drought led us to speculate that they may play an important role in conferring drought stress tolerance, whereas the up-regulation of group II and group III genes during rehydration could be important for recovery. In both group II and III genes, we found that two classes of genes were over-represented among the rehydration-inducible genes in shoot and panicle: transporter genes and photosynthesis-related genes. These transporter genes included: OsJRFA065471 (folate/biopterin transporter), OsJRFA066919 (putative potassium transporter), OsJRFA067899 and OsIFCC019970 (ABC transporter, putative), OsJRFA068003 and OsJRFA106202 (Transmembrane amino acid transporter protein), OsJRFA068765 (H-ATPase), OsIFCC040482 (phosphate:H+ symporter), OsJRFA072183 (Sodium:sulfate symporter transmembrane region), OsJRFA102086 (putative lipid transfer protein) and OsJRFA103807 (aquaporin). The photosynthesis related genes cover most of the gene components of the two photosystems, such as genes for putative chlorophyll a/b-binding protein, Photosystem I or II reaction center subunits, and plastocyanin. These photosystem genes and transporter genes represent a large portion of all genes induced by rehydration, and their physiological roles in plants fit well with a potential contribution toward plant recovery from drought stress.
Limited overlap of stress responsive genes among rice organs
We also examined the degree of overlap in expression of stress responsive genes in two or more rice organs under drought or high-salinity stress. Venn diagrams revealed that only a small portion of genes was shared between each pair of organs (Fig. 4). The greatest overlap occurred between shoot and flag leaf, which shared more inducible genes than shoot and panicle or flag leaf and panicle under both drought and high-salinity conditions. Under drought stress, one-third of the genes up-regulated in flag leaf (197/582) were also induced in shoot (Fig. 4A), while under high salinity stress, over half of the induced genes (494/817) in shoot (mostly young leaves) were also up-regulated in flag leaf (Fig. 4B). Only 21 and 23% of genes up-regulated in panicle were also induced in flag leaf under drought and high salinity stresses respectively. The percentage of genes shared between shoot and panicle is similar to that between flag leaf and panicle (Fig. 4A, B).
Fig. 4Comparison of gene expression patterns among the three rice organs in response to drought and high-salinity stresses. (A) and (B): Venn diagram of drought (A) and high-salinity (B) induced genes among the three rice organs. (C) and (D): Venn diagram of drought (C) and high-salinity (D) repressed genes among the three rice organs
Interestingly, only a small fraction of stress responsive genes were expressed in all three organs examined. For example, 42 and 151 genes were induced in all three organs under drought and high-salinity stress respectively. In general, most of those genes exhibited high levels of expression as well as strong inducibility. Among them, 27 genes were induced by both drought and high-salinity stress in all three organs. This group of 27 genes includes a protein kinase (OsJRFA058518), chlorophyll a/b binding protein (OsJRFA062972), ABA-responsive protein (OsJRFA063578, OsIFCC018156), LEA protein (OsJRFA063984), dehydrin (OsIFCC035028), CHY zinc finger protein (OsIFCC003263), and homeobox protein (OsIFCC018343) (Table 2).Table 2Genes up- or down-regulated by both drought and high-salinity stresses in all three organsGene namePutative functionsFlag leafShootPanicleD(h)S(h)D(h)S(h)D(h)S(h)Up-regulatedOsJRFA058518Protein kinase domain2.5911.9393.9393.4433.1343.553OsJRFA058851Unknown5.4303.9005.8944.6002.0423.878OsJRFA062356Unknown4.9592.4774.6994.6603.3145.727OsJRFA062972Chlorophyll a/b binding protein4.7582.5421.8952.9312.2912.204OsJRFA063156Unknown1.8371.7434.4053.9212.6062.866OsJRFA063334Unknown3.2082.1404.5213.1881.8092.167OsJRFA063578ABA-responsive protein2.7433.3524.1693.7331.8592.748OsJRFA063889Unknown3.3403.2632.1863.3442.0613.712OsJRFA063984LEA protein5.5134.7303.7634.6293.9716.773OsJRFA068381Unknown2.6481.8303.6302.8331.8201.786OsJRFA070577Unknown3.0362.8773.0652.8991.8832.372OsJRFA070872Unknown8.4857.7333.4466.3463.9634.107OsJRFA071812Unknown2.7482.2683.0152.2441.7672.559OsJRFA072568Unknown3.0013.1212.4113.3261.7142.005OsJRFA106307Unknown4.2843.4934.3992.5112.3162.960OsJRFA106562Unknown3.7974.0092.5443.4672.0103.978OsJRFA107065Unknown2.7701.7514.8593.6482.9842.292OsJRFA108083Unknown2.2742.5974.8534.2413.0774.631OsIFCC031279Alpha-galactosidase3.4073.0873.8193.5382.3892.206OsIFCC036408Unknown3.1373.9873.0383.1114.7424.192OsIFCC035028Dehydrin7.9747.6895.1486.7678.0456.382OsIFCC018156Abscisic acid-induced protein5.1677.2243.2434.9112.3292.327OsIFCC003263CHY zinc finger1.7092.1471.9942.2823.7911.956OsIFCC018343Homeobox domain4.2903.9553.4323.9592.6833.017Down-regulatedOsIFCC033098Similar to Arabidopsis F16L1.3 protein−2.676−4.719−2.542−2.346−1.701−2.519OsIFCC015113Phosphoribulokinase /Uridine kinase family−2.456−2.689−1.863−1.717−1.853−1.899The highest ratio of each gene at three stages under drought (D(h)) or high-salinity (S(h)) were log2 transformed and listed
Analysis of stress down-regulated genes (Fig. 4C, D) revealed a similar small overlap among the three organs. Only 68 and 129 out of 795 total genes down-regulated in flag leaf under drought stress were also inhibited in shoot and panicle, respectively. For high-salinity stress, 135 and 214 out of 1,270 down-regulated genes in flag leaf were also repressed in shoot and panicle respectively. Only 16 and 38 genes were down-regulated in all three organs under drought and high salinity stress respectively, while only two genes were inhibited in all three organs under both drought and high salinity stress (Table 2).
The shoot samples we used consisted largely of young leaves and were thus physiologically closer to flag leaf than panicle. Indeed, shoot and flag leaf generally exhibited similar patterns of gene up-regulation in response to drought and high-salinity stress. However, this does not appear to be the case for repressed genes, where overlap between shoot and flag leaf gene expression is minimal.
To validate the microarray data, RT-PCR analysis was used to verify the transcription response of representative genes from microarray results. For this purpose, we picked a group of eight representative genes and designed primers for RT-PCR analysis (Table S18). The cDNA templates were synthesized from total RNA samples prepared from shoot, flag leaf and panicle under drought and unstressed controls. The semi-quantitative RT-PCR results for the eight representative genes were shown in Fig. 5. The expression patterns of all eight genes reflected changes observed by microarray analysis fairly and accurately for all three organs examined, except in three cases where RT-PCR failed to confirm the microarray results (Fig. 5), indicating that our microarray data is reliable.
Fig. 5RT-PCR analysis of the representative drought-induced genes among the three rice organs. Total RNA samples were prepared from the shoot (S1, S2, and S3), flag leaf (F1, F2, and F3), and panicle (P1, P2, and P3) and taken from plants at three stages of drought treatment and the untreated control plants (S0, F0, and P0). The corresponding log2 transformed median ratio of microarray data is shown at the bottom of each mRNA blot line. N/A: no expression. The * corresponds to cases where the microarray data was not confirmed by the mRNA blot results
Enrichment of ABRE and DRE cis-regulatory elements in abiotic stress-induced gene promoters
Previous reports have suggested that several well-characterized drought-, high-salinity and cold inducible gene promoters contain two common cis-regulatory elements, the ABA-responsive element (ABRE) and the dehydration-responsive element (DRE) (Seki et al., 2001, 2002a, b; Yazaki et al., 2003; Rabbani et al., 2003; Dubouzet et al., 2003; Nakabayashi et al., 2005; Maruyama et al., 2004; Yamaguchi-Shinozaki and Shinozaki, 2005). ABRE and DRE confer ABA-dependent and ABA-independent gene expression in response to water stress. It has been reported that a single copy of ABRE in the promoter region only induces relatively minor elevation of expression level, while multiple copies of ABRE strongly activate expression (Shen and Ho, 1995; Nakabayashi et al., 2005).
To elucidate the relationship between copy numbers of DRE and ABRE cis-regulatory elements in gene promoter regions and abiotic stress-induced gene transcription, we divided the genes into groups based on their expression patterns. Genes induced specifically by drought or high-salinity stress in each of the three organs defined six groups, while genes induced by both drought and high-salinity stress in each organ defined another three groups. Common inducible genes shared by at least two organs under drought or high-salinity stress defined another nine groups. We only selected those genes among all those groups with full-length cDNA sequences available for further promoter analysis. In brief, the 2 kb upstream regions (–1 to –2,000 bp) preceding the ATG start codon of genes were selected for promoter motif analysis (see Experimental procedures). A similar number of rice genes with full length cDNA available that lack stress responsive expression were used as control. Copy number of ABRE and DRE core motifs on each promoter region were counted. The percentages of genes in each group with 1 to 6 copies of ABRE core or DRE core elements were then calculated.
Compared to control genes, copy numbers of the ABRE and DRE core motifs in the promoter regions of genes with organ-specific expression in response to drought or high-salinity stress were not markedly different (Fig. 6A, D). The promoter regions of genes responsive to both drought and high-salinity stress in each organ were enriched for ABRE and DRE core motifs compared to the promoters of control genes (Fig. 6B, E). For example, only 25% of control genes have over four copies of ABRE core motif in their promoter regions, but 48, 44 and 48% of genes induced by both drought and high-salinity stresses in flag leaf, panicle and shoot respectively have over four copies of the ABRE core motif in their promoter regions (Fig. 6B).
Fig. 6Distribution of the ABRE and DRE core motif sequences among different groups of drought and high-salinity stress responsive genes. In all panels, the x-axis shows the copy number of ABRE or DRE core motifs and the y-axis shows the relative number of genes containing different copy numbers of ABRE or DRE core motifs.(A) ABRE core motif distribution among promoters of the genes induced only by drought or only by high-salinity stress in each single rice organ. (B) ABRE core motif distribution among promoters of genes induced by both drought and high-salinity stress in each rice organ. (C) ABRE core motif distribution among promoters of genes induced in two or three organs. (D) DRE core motif distribution among promoters of genes induced only by drought or only by high-salinity stress in each rice organ. (E) DRE core motif distribution among promoters of genes induced by both drought and high-salinity stress in each rice organ. (F) DRE core motif distribution among promoters of genes induced in two or three organs or by both stresses in more than one organ. The following 18 gene groups (with number of genes with full-length cDNA gene number) were analyzed. (1) Genes induced by drought stress only in flag leaf (F-D123, 82). (2) Genes induced by high-salinity stress only in flag leaf (F-S123, 433). (3) Genes induced by drought stress only in shoot (S-D123, 272). (4) Genes induced by high-salinity stress only in shoot (S-S123, 77). (5) Genes induced by drought stress only in panicle (P-D123, 139). (6) Genes induced by high-salinity stress only in panicle (P-S123, 420). (7) Genes induced by both stresses in flag leaf (F-D123S123, 190). (8) Genes induced by both stresses in shoot (S-D123S123, 252). (9) Genes induced by both stresses in panicle (P-D123S123, 77). (10) Genes induced by drought stress in both flag leaf and shoot (F-S-D123, 123). (11) Genes induced by drought stress in all three organs (F-S-P-D123, 59). (12) Genes induced by high-salinity stress in all three organs (F-S-P-S123, 97). (13) Genes induced by high-salinity stress in both flag leaf and shoot (F-S-S123, 309). (14) Genes induced by drought stress in both flag leaf and panicle (F-P-D123, 59). (15) Genes induced by high-salinity stress in both flag leaf and panicle (F-P-S123, 184). (16) Genes induced by drought stress in both shoot and panicle (S-P-D123, 51). (17) Genes induced by high-salinity stress in both shoot and panicle (S-P-S123, 124). (18) Genes induced by both stresses in all three organs (F-S-P-D123S123, 20)
When genes expressed in at least two organs under drought or high-salinity stress were compared to control genes, the ABRE and DRE core motif copy numbers also showed significant differences (Fig. 6C, F). For example, 50% of these genes contain over four copies of the ABRE core motif in their promoter regions, compared with 25% for the control genes (Fig. 6C). A similar pattern was found for the DRE core element (Fig. 6F).
Genes induced in an organ-specific manner by drought or high-salinity stresses did not contain a significantly different number of copies of ABRE or DRE core motifs compared to control genes (Fig. 6A, D). Thus it is plausible that the stress-induced expression in this fraction of genes may rely less on transcriptional activation mediated by ABRE and DRE motifs. Unidentified organ-specific cis-regulatory elements may exist in the promoter regions of these genes and play a more important role in transcription activation under drought or high-salinity stress.
Identification of two novel cis-regulatory elements that respond to rehydration after drought stress
As shown in Table 1, there are 8 and 21 genes with full-length cDNA sequences repressed by drought stress but induced by rehydration in shoot and panicle, respectively. An analysis of the upstream region of these eight shoot genes identified a novel motif (motif-SP, GGCAGCCG) located near to the translation start codon (–73 to –250) in five of them (Fig. 7A). To investigate the potential functional role of this novel motif, we performed a gel shift mobility assay which involved incubating a 24 bp probe containing motif-SP from one of those five genes (OsJRFA070715) and a control containing a single base mutation at an invariable position with nuclear extracts (see Experimental procedures). Only nuclear extracts from drought-treated shoot (at the third stage), but not from unstressed shoot or shoot under rehydration, showed a sequence specific binding activity with the probe (Fig. 7C). A single base mutation at an invariable position in the motif-SP core of the probe completely abolished protein binding activity, suggesting high specificity of interaction with the core motif included in this 24 bp sequence. This specific binding activity was only observed in drought-treated plants, suggesting that it is involved in transcriptional repression under drought conditions.
Fig. 7A novel promoter motif associated with genes repressed by drought but induced after rehydration in rice shoot. (A) The core sequence of the motif and the position of the motif in each promoter. (B) The expression pattern of a representative gene containing motif-SP. The histogram shows the log2 transformed ratio at three drought stress stages (D1, D2, D3) and 48-hour rehydration (D3R). (C) Gel shift assay of motif-SP. Nuclear proteins were extracted from shoots of untreated plants (C), plants at stage 3 of drought treatment (D3), and plants after 48-h rehydration following stage 3 (D3R). Core sequences of the probe OsJRFA070715: TGCAGCCA, and core sequence of the probe with a single base point mutation OsJRFA070715M: TGAAGCCA
We also searched promoter regions of 21 genes inhibited by drought stress but induced by rehydration in panicle. Three conserved motifs were found through this blind search in 11 of the 21 genes: ABRE motif, the above-mentioned motif-SP, and another novel element, motif-P (Fig. 8). The presence of the ABRE motif suggests that a gene may respond to drought stress by employing an ABA-dependent pathway, while motif-SP may play an important role in drought-stress repression and subsequent activation during rehydration. We failed to detect specific binding activity using similar nuclear extracts (data not shown) and thus the role of motif-P remains to be defined.Fig. 8Three motifs associated with genes repressed during drought and induced by rehydration in panicle. The core sequence logos and the position of the motifs in each of the 11 promoters are listed
Transcription factor genes under drought and high salinity stress are expressed in a largely organ specific manner
We further examined transcription factor genes whose expression are regulated by drought and high-salinity stresses. Among all genes induced by drought or high-salinity treatment in the three organs, a total of 186 genes (Table S17) were predicted to be transcription factors with a DNA-binding domain. These transcription factors belong to various families, including AP2, bHLH, MYB, HB, NAC, zinc finger, MADS, bZIP, WRKY and HSF families. These transcription factors could also be divided into several groups depending on their expression patterns. Among the 186 transcription factor genes, 12 genes were induced in all three organs in at least one stage following either drought or high-salinity stress, whereas the remaining 174 genes were mainly up regulated in one or two organs. Transcription factors induced only in one individual organ were listed in Table 3. It is interesting to note that over half of the transcription factors were expressed in at least two organs, while other transcription factors were activated in an organ-specific manner. This suggests that the expression of different transcription factors may play a key role in common or organ-specific gene expression in response to drought or high-salinity conditions.
Table 3Transcription factor genes induced in each organ by drought or high-salinity stressGene name Putative functionPanicle: induced by high-salinity onlyS(h)D(h)OsIFCC018668bHLH transcription factor1.673−0.850OsIFCC029156Helix-loop-helix DNA-binding domain1.720−0.599OsJRFA110611No apical meristem (NAM) protein2.234−1.500OsJRFA105079CCAAT-box binding factor HAP5 homolog3.521−0.850OsJRFA070817RING zinc finger protein1.708−0.685OsIFCC039583Zinc finger, C3HC4 type (RING finger)2.2780.530OsJRFA108605Helix-loop-helix DNA-binding domain4.7130.240OsJRFA108208AP2 domain2.429N/AOsJRFA101136C3HC4-type zinc finger5.559−0.753OsIFCC008718No apical meristem (NAM) protein2.542N/AOsJRFA066984Dof domain, zinc finger1.8460.168OsJRFA110661Zinc finger C-x8-C-x5-C-x3-H type1.732N/AOsJRFA106969Myb-like DNA-binding domain2.010N/AOsIFCC016263Zinc finger, C2H2 type2.636N/APanicle: induced by drought onlyS(h)D(h)OsIFCC042866AP2 domainN/A3.050Panicle: induced by both drought and high-salinityS(h)D(h)OsJRFA107283NAM-like protein7.7732.080Shoot: induced by drought onlyS(h)D(h)OsJRFA105599DRE-binding protein 1AN/A1.784OsIFCC031932WRKY DNA -binding domain−2.7462.186OsIFCC031182Myb factorN/A2.565OsIFCC042758Helix-loop-helix DNA-binding domain−0.0242.367OsJRFA100208Helix-loop-helix DNA-binding domain−0.8363.132OsJRFA107524Dof domain, zinc fingerN/A2.294OsJRFA106333Helix-loop-helix DNA-binding domain0.4301.762OsJRFA106282WRKY DNA -binding domainN/A2.026OsIFCC043271Helix-loop-helix DNA-binding domain0.2452.259OsIFCC000715Myb-like DNA-binding domain0.9492.617OsJRFA110587Similar to DNA-binding protein WRKY30.9054.075OsJRFA107146AP2 domain0.6252.746OsIFCC038336Zinc finger transcription factor ZF10.5292.407Shoot: induced by both drought and high-salinityS(h)D(h)OsJRFA072192Zinc-finger protein2.0742.059OsIFCC000984WRKY DNA -binding domain1.7442.153Shoot: induced by high-salinity onlyS(h)D(h)OsIFCC017057B3 DNA binding domain2.026N/AOsJRFA067496TRAF-type zinc finger2.1990.801Flag leaf: induced by drought onlyS(h)D(h)OsIFCC029554Zinc finger, C2H2 type−0.5532.032Flag leaf: induced by high-salinity onlyS(h)D(h)OsIFCC001054Zinc finger C-x8-C-x5-C-x3-H type1.7050.582The highest ratio of each gene among three stages of drought (D(h))or high-salinity (S(h)) were log2 transformed and listed
Minimal co-regulation of neighboring genes in drought or high-salinity stress responses
Co-regulation of adjacent genes has been observed in several organisms (Hurst et al., 2004), including human (Caron et al., 2001; Lercher et al., 2002), Drosophila (Spellman and Rubin, 2002), Arabidopsis (Birnbaum et al., 2003, Ma et al., 2005a), and yeast (Cohen et al., 2000). All these studies identified co-regulation of neighboring gene clusters on chromosomes (chromatin domains). In rice, analysis of organ-specific gene expression revealed that about 10% of the genome belongs to chromatin domains that exhibit this co-expression pattern (Ma et al., 2005b). However, similar analysis of light-regulated genes failed to reveal significant co-regulation (Jiao et al., 2005).
To investigate possible co-regulation of neighboring genes in the drought and high-salinity responses, we mapped rehydration induced genes in three organs and drought and high-salinity induced genes in shoot to all 12 indica chromosomes (Figs. 9, S4). This analysis indicated that significant gene clustering among rehydration, drought, and high-salinity induced genes did not occur at the chromosomal level, similar to the pattern characteristic of light-regulated genes (Jiao et al., 2005). Among the 12 rice chromosomes, only three regions showed significant enrichment of 10 or more co-regulated genes within any given 2 Mb domain. For example, the 33–35 Mb region on chromosome 2, the 11–13 Mb region in chromosome 3, and the 31–33 Mb region in chromosome 6 (Fig. 9B) contain significant enrichment of rehydration regulated genes in all three organs, although even in those cases genes are not directly adjacent.
Fig. 9Distribution of drought, high salinity and rehydration regulated genes in a representative rice chromosome. (A) Map of chromosome 3 genes differentially expressed at the first stage of drought, first stage of high-salinity stress, and 48 h after rehydration in shoot. (B) Map of chromosome 3 genes differentially expressed at 48 h after rehydration in the three rice organs
Discussion
This study provides new insight into the rice response to drought and high salinity stresses at the whole genome level. Using a whole genome microarray, we monitored the expression of 36,926 unique or known rice genes or gene models in three different organs under drought and high salinity stress. Our work thus offers the first comprehensive picture of genome expression modulation in response to drought and high salinity stress in three distinct rice organs.
Genome expression reprograming showed significant overlap between drought and high salinity responsive genes
Using the criteria outlined above, a total of 2,957 and 2,090 rice genes showed significant up- or down-regulation in response to high salinity stress and drought stress in at least one of the three organs. Our analysis suggested that 927 out of 2,090 (44%) genes induced by drought were also up regulated by high-salinity stress in the same organs. This number is consistent with previous studies reported for Arabidopsis (Seki et al., 2002a, b; Shinozaki et al., 2003). In rice, an even higher percentage of drought induced genes were also up-regulated under high-salinity stress, as reported previously on a smaller scale (Rabbani et al., 2003), which covered only cDNAs from plants under drought, cold and high-salinity treatment. Thus our analysis should be more representative of the overall rice genome response to drought and high salinity stresses without such bias. However, drought and salt stress conditions in the field may be more subtle and less regular as in our experimental conditions, thus our results are only meant to illustrate some particular situations rice genome expression responses to stresses.
It is interesting to note an overlap of up to about half of the genes induced or inhibited by drought and high-salinity stress in rice. This observation is consistent with current understanding that these two stresses affect plants in overlapping but not identical ways. At a physiological level, both stresses cause water depletion in the above-ground portion of the plants and induce similar morphological responses (Fig. S1). At the molecular level, half of the transcriptional factor genes identified in our results were shared by both stresses in each organ, which is consistent with the observation that about half of the genes that respond to the two stresses were shared at the whole genome level.
Reprograming of genome expression in response to drought and high-salinity stresses is largely organ specific
In this study, we examined whole genome expression profiles under drought and high-salinity conditions in three organs: four-tiller stage shoot, filling stage flag leaf and panicle. Rice plants at these two growth stages, particularly the late one, are sensitive to drought and high-salinity stress. It is well known that drought or high-salinity stress at the heading and early panicle stages can severely compromise rice growth and development and reduce crop yield even with late rehydration. It is evident that the rice genome is subject to significant reprograming with regard to which portion of genome is expressed under drought or high salinity stress.
Our results showed that only a limited number of drought and high salinity responsive genes were shared between any two organs. We found that only 13.5 and 20.5% of drought-induced genes in panicle were shared with those induced in shoot and flag leaf respectively, and 33.8% of drought-induced genes in shoot were also activated in flag leaf (Fig. 4). The percentages of genes shared between any two organs under high-salinity conditions were similar to those under drought stress, suggesting that responses to drought or high-salinity stress in different organs were independently regulated. This observation is similar to the observed small overlap of cold responsive gene expression between root and leaf in rice (Kreps et al., 2002).
Genome expression reprograming under either drought or high salinity stress entails a large number of genes involved in many aspects of cellular function. A GO analysis of stress responsive genes (Fig. S2) indicates that, in most categories, three organs activated similar scale (number) of genes in response to drought or high salinity stress, while only small percentage of responsive genes were overlapped between or among organs (Fig. 4). This suggests that responsive genes under the same category are largely distinct individuals under distinct organ types. It is thus possible that homologous or functionally similar gene family members are responsive to the same stresses in each organ. This is consistent with our observation of organ-specific transcription factor gene expression in response to both drought and high salinity stresses (Table 3). The fact that genes specifically induced in each organ do not exhibit enrichment of DRE and ABRE motifs (Fig. 6) suggests that organ-specific transcriptional factor gene expression may be responsible for activating organ-specific downstream genes in a secondary transcriptional response to stress. For these genes, it is likely that certain organ-specific promoter elements mediate this response pathway. According to the GO analysis, under high salinity stress, about 10% storage proteins were up-regulated in panicle, while in flag leaf and shoot only less than 1% storage protein genes were induced (Fig. S2). This kind of difference among organs may play a role in organ specific response to two stresses; further function analysis of these differentially regulated genes among organs are needed.
Rice may possess specific mechanisms to facilitate plant recovery during rehydration after drought
According to our microarray analysis, rice genes induced by 48-h rehydration were divided into three groups according to their expression patterns (Fig. 3), somewhat similar to a previous report for Arabidopsis (Oono et al., 2003). This observation suggested that monocotyledonous and dicotyledonous plants share similar gene expression responses to rehydration after drought. Analysis of the genome-wide distribution of rehydration-regulated genes in the three organs showed no significant co-regulation of neighboring genes at the chromosomal level. This observation is similar to the case of light-regulated genes (Jiao et al., 2005) but different from general gene transcription from organ samples (Ma et al., 2005b). This distinction in the modulation of genome expression may reflect different mechanisms employed in response to different developmental or environmental signals.
Up to now only one possible cis-element has been reported to be involved in the rehydration process after dehydration in Arabidopsis (Satoh et al., 2002; Oono et al., 2003). In rice, at the whole genome level we identified 807, 281 and 224 genes induced by rehydration in flag leaf, shoot and panicle separately (Fig. 3). These genes provided us an important starting point to study rehydration mechanisms and to search for novel cis-regulatory promoter elements associated with dehydration or rehydration. Two novel cis-regulatory elements (motif-SP and motif-P) were identified (Fig. 8). Motif-SP was also found in shoot-specific genes with similar expression patterns in response to drought and rehydration. Gel shift assays provided evidence that motif-SP may function as a cis-element to mediate the drought-induced repression and late de-repression (activation) during rehydration in rice. Further functional analysis of the promoter elements may substantiate the role of those novel promoter motifs.
Previous studies reported that drought stress suppressed plant photosynthesis systems and significantly modulated the activity of some membrane transporters (Rizhsky et al., 2002; Ramachandra et al., 2004; Johansson et al., 2004; Becker et al., 2003). Here microarray analysis revealed that genes involved in photosynthesis and genes encoding transporters were repressed or maintained at low levels of expression under drought but were then strongly activated after rehydration. The repression of metabolic genes during drought stress allows the plant to conserve energy and subsist on less water, conferring better drought tolerance. When supplied plenty of water upon rehydration, activation of these genes could aid in the recovery of full photosynthesis activity and transmembrane solute/water exchange, thus helping plant resume its normal growth and development quickly.
Electronic supplementary material
Below are the electronic supplementary materials.
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rice drought salinity promoter cis-element rehydration


The Chemistry of the Postpharyngeal Gland of Female European Beewolves

Females of the European beewolf, Philanthus triangulum, possess a large glove-shaped gland in the head, the postpharyngeal gland (PPG). They apply the content of the PPG to their prey, paralyzed honeybees, where it delays fungal infestation. Here, we describe the chemical composition of the gland by using combined GC-MS, GC-FTIR, and derivatization. The PPG of beewolves contains mainly long-chain unsaturated hydrocarbons (C23–C33), lower amounts of saturated hydrocarbons (C14–C33), and minor amounts of methyl-branched hydrocarbons (C17–C31). Additionally, the hexane-soluble gland content is comprised of small amounts of an unsaturated C25 alcohol, an unknown sesquiterpene, an octadecenylmethylester, and several long-chain saturated (C25, C27) and unsaturated (C23–C27) ketones, some of which have not yet been reported as natural products. Surprisingly, we found a dimorphism with regard to the major component of the PPG with some females having (Z)-9-pentacosene, whereas others have (Z)-9-heptacosene as their predominant component. The biological relevance of the compounds for the prevention of fungal growth on the prey and the significance of the chemical dimorphism are discussed.
Introduction
Hymenoptera possess a huge variety of exocrine glands (e.g., Hölldobler and Wilson 1990). The chemistry and function of different types of these have been studied for a number of social species, whereas comparatively little is known from solitary bees and wasps. Recently, a postpharyngeal gland (PPG) has been described from a species of digger wasp (Strohm et al. 2007), the European beewolf, Philanthus triangulum Fabricius 1775 (Hymenoptera: Crabronidae, formerly Sphecidae, Melo 1999). The occurrence of this gland is surprising since the PPG was assumed to be restricted to ants (Hölldobler and Wilson 1990; Schoeters and Billen 1997; Lenoir et al. 1999) where it functions in generating the colony odor (e.g., Hefetz et al. 1992, 1996; Soroker et al. 1994, 1995, 1998; Vienne et al. 1995; Dahbi et al. 1998; Lenoir et al. 1999, 2001; Oldham et al. 1999; Soroker and Hefetz 2000; for a review of other proposed functions, see Eelen et al. 2006).
In beewolves, the PPG has a unique function in protecting the larval provisions from microbial attack (Strohm and Linsenmair 2001; Herzner and Strohm 2007; Herzner et al. 2007a). Female European beewolves hunt and paralyze honeybees, bring them to their nest burrow, and provision one to six bees in a brood cell as larval food for one progeny. Due to the humid and warm conditions in the brood cell, the highly nutritive provisions are prone to detrimental microbial attack (Strohm and Linsenmair 2001). Early fungus infestation inevitably destroys the food resources, and larvae are killed by fungal toxins or starve to death. Observations in special cages (Strohm and Linsenmair 1994–1995) revealed that beewolf females intensively lick the bodies of the paralyzed bees and apply the secretion from the PPG to the bees’ surface (Strohm and Linsenmair 2001; Herzner et al. 2007a). This treatment has the effect of delaying fungus growth for 2 to 3 days, which is a highly relevant effect given the short larval feeding period of only 8 to 11 days. The primary mechanism of this delay is not a direct chemical effect of the secretion on fungi, but the prevention of water condensation on the bees that in turn impairs the germination and growth of fungal spores (Herzner and Strohm 2007). Male European beewolves also have a PPG that is even larger than in females (Herzner et al. 2007b). However, it serves as a reservoir for the scent marking pheromone that males apply to their territories to attract females (Kroiss et al. 2006).
Despite the advanced understanding of the function of the secretion of the PPG of female European beewolves, there has been no detailed investigation of its chemistry. Therefore, we analyzed the chemical composition by using combined gas chromatography-mass spectrometry (GC-MS), gas chromatography-Fourier transform infrared spectroscopy (GC-FTIR), fractionation, and derivatizations.
Materials and Methods
Sampling Beewolf females were taken either from a field population close to the Biocenter of the University of Würzburg, Germany, or from a laboratory population kept at the University of Würzburg (daughters of field caught females). They were all mated with actively provisioned nests with honeybees and were between 1 and 4 weeks old. To identify the chemicals of the PPG, females that were freshly killed by CO2 were decapitated, and their PPGs were removed from the heads by grasping the hypopharynx with tweezers and gently pulling the attached gland out through the mouth (Strohm et al. 2007). The glands were immersed immediately in 0.25 ml n-hexane (Fluka Chemie GmbH, Buchs, Switzerland) that had been distilled prior to use. For the identification of the components in the PPG, the glands of four females were pooled. This enabled us to identify minor components that were not reliably detectable in the extracts of individual PPGs. In order to have an easier method to obtain the content of the PPG, we investigated whether extracts of whole heads differed in composition from dissected glands. There was a large amount of hydrocarbons in the PPG, and extracts of entire heads were identical to extracts of the dissected glands. There were no additional peaks detectable and the proportions were the same (see Herzner et al. 2007b for an analogous procedure for male PPGs). Thus, as an easier alternative to the dissection of the PPG, entire heads were extracted. To obtain data on PPG content variability, we analyzed individual extracts made from the heads of 37 females. Heads of freshly killed females were cut off and extracted in distilled hexane for 4 h. An internal standard (octadecane, Sigma, St Louis, MO, USA) was added to assess the absolute amount of compounds in the PPG (although octadecane could be detected in pooled samples, it was not detectable in individual samples and was therefore employed as an internal standard).
Identification Identification of the chemicals was accomplished by GC-MS. Head extracts were fractionated by solid phase extraction (SPE, Chromabond, unmodified silica, 3 ml, 500 mg, Macherey-Nagel, Düren, Germany) with hexane as the first and dichloromethane as the second eluent to separate nonpolar and polar fractions. Alkanes were characterized by comparison of mass spectra and retention indices with those of purchased standard alkanes (Aldrich, Deisenhofen, Germany). Corresponding alkenes were tentatively identified by their typical mass spectra, their retention indices, and (depending on availability) with commercially available (Aldrich) and synthesized standards. Dimethyl disulfide (DMDS) derivatization was carried out to determine the position of double bonds according to the method of Dunkelblum et al. (1985). The configurations of the double bonds were determined by using GC-FTIR (Attygalle 1994). Methyl-branched alkanes were identified by using MS databases and diagnostic ions, and by determining retention indices (Carlson et al. 1998). Details on the identification of polar compounds are given in the results section.For the identification of the hydrocarbons, we used a Hewlett Packard HP 6890 Series GC System coupled to a Hewlett Packard HP 5973 Mass Selective Detector (Agilent Technologies, Böblingen, Germany). The GC was equipped with a DB-1 fused silica capillary column (30 m × 0.25 mm i.d., 0.25 μm film thickness; J & W, Folsom, CA, USA). Temperature was programmed from 100°C to 300°C with a 6°C/min heating rate, held for 20 min at 300°C. Helium was used as carrier gas with a constant flow of 1 ml/min. Injection was carried out at 300°C in the splitless mode for 2 min. The electron impact mass spectra (EI-MS) were recorded with an ionization voltage of 70 eV and a source temperature of 230°C. The software ChemStation (Agilent Technologies) for windows was used for data acquisition.GC-FTIR spectra were obtained by using an HP 5890 GC (Agilent Technologies) coupled to an FTS 575C Tracersystem (BioRad, Hercules, CA, USA). The GC was equipped with the same column as described above. Temperature was programmed from 80 to 270°C with a 4°C/min heating rate. Helium was used as carrier gas with a constant flow of 1–2 ml/min. Injections were carried out by using a split/splitless injector at 250°C in the splitless mode for 60 s. Injection volume was 0.1 μl. IR spectra were recorded by scanning 256 times in a frequency range from 4,000 to 700 cm-1 with a resolution of 1 cm-1. Data system was a Dell Optiplex GX110-PC with BioRad WinIR Pro (Version 2.7) Tracer Software and Sadtler IRSearchMaster.
Data Handling and Statistical Analysis Because the relative amounts of compounds constitute compositional data, they were transformed to logcontrasts according to Aitchison (1986): Zij = log10(Yij/g(Yj)) where Zij is the standardized peak area i for individual j, Yij is the peak area i for individual j, and g(Yj) is the geometric mean of all peaks for individual j, prior to statistical analysis. Several peaks had to be combined for the quantitative analysis of individuals because they were not always clearly separated in the chromatograms. To test for differences in the proportions of components between groups, we subjected the transformed data to exact tests for two independent groups (SPSS 13.0, SPSS Inc. 2004).
Results
Identification and Analysis of Hydrocarbons in the PPG The chromatograms of the crude hexane extracts of the PPG showed a total of 53 peaks that represented 62 different compounds (Fig. 1, Table 1). The mean amount of all compounds was 337 ± 292 μg (minimum, 36.7 μg; maximum, 1,410 μg, N = 37). All alkanes as well as (Z)-9-tricosene, (Z)-9-pentacosene, and (Z)-9-heptacosene were identified by comparison of mass spectra and retention indices with those of available standards. The location and configuration of double bonds of other alkenes were determined with DMDS derivatives and GC-FTIR data (band at 721 cm-1: cis configuration of RCH = CHR′; Attygalle 1994). Some components could, however, not be completely characterized (location of methyl groups or double bond, configuration of double bond) due to the small amounts in the extracts. One component could not be identified at all. There were more unsaturated (81.5%) than saturated compounds in the nonpolar fraction of the PPG extract from beewolf females.
Fig. 1Total ion chromatograms of the hexane extract of the PPG from individual female European beewolves, Philanthus triangulum, with a (Z)-9-pentacosene (C25-type) and b (Z)-9-heptacosene (C27-type) as the predominant hydrocarbon peak. Numbers correspond to the numbers in the peak list (Table 1). Some compounds listed in Table 1 were present in quantities too low to be visible in these chromatogramsTable 1List of compounds in the postpharyngeal gland of females of the European beewolf, Philanthus triangulumPeak no.RICompound nameDiagnostic MS ions11400Tetradecanea19821500Pentadecanea21231600Hexadecanea22641700Heptadecanea24051710X-Methylheptadecane–61800Octadecanea25471814X-Methyloctadecane–81900Nonadecanea26891906Sesquiterpene41, 69, 81, 93, 107, 120, 133, 222102000Eicosanea282112080(Z)-9-Octadecenylmethylestera222, 264, 296122100Heneicosanea296132200Docosanea310142267Δ-X-Tricosene322152273(Z)-9-Tricosenea322162286Δ-7-Tricosene322172300Tricosanea32418233811-; 9-Methyltricosane168/169, 196/197; 140/141, 224/2251923437-Methyltricosane112/113, 252/2532023525-Methyltricosane84/85, 280/281212371Δ-X-Tetracosene336222400Tetracosanea338232436Δ-14-Tricosen-6-one99, 115, 127, 181, 265, 336242464Δ,Δ-X,Y-Pentacosadiene348252471(Z)-9-Pentacosenea350262492Δ-Pentacosene350272500Pentacosanea35228253713-; 11-; 9-Methylpentacosane196/197; 168/169, 224/225; 140/141, 252/253292538Δ-15-Tetracosen-7-one94, 113, 141, 181, 195, 265, 3503025437- Methylpentacosane112/113, 280/281312554Δ-16-Pentacosen-8-ol67, 348322572(Z)-9-Hexacosene364332600Hexacosanea366342639Δ-16-Pentacosen-8-one127, 155, 195, 209, 265, 364352668Pentacosan-8-one127, 143, 155, 267, 283, 366362672(Z)-9-Heptacosenea378372700Heptacosanea380382714NI–39273513-; 11-; 9-Methylheptacosane196/197, 224/225; 168/169, 252/253; 140/141, 280/281402740Δ-17-Heptacosen-9-one141, 169, 209, 223, 265, 378412800Octacosanea394422840Δ-18-Heptacosen-10-one155, 171, 183, 223, 237, 265, 392432868Heptacosan-10-one127, 143, 155, 171, 267, 283, 295, 394442872(Z)-9-Nonacosene406452900Nonacosanea40846293315-; 13-; 11-Methylnonacosane224/225; 196/197, 252/253; 168/169, 280/281473000Triacontanea422483076(Z)-9-Hentriacontene434493100Hentriacontanea43650313415-; 13-Methylhentriacontane224/225, 252/253; 196/197, 280/281513200Dotriacontanea450523275(Z)-9-Tritriacontene462533300Tritriacontanea464RI retention index; X, Y position of the methyl-group or double bond not known; Δ configuration of the double-bond not known; NI not identifiedaIdentification with available standards.
Identification of the Constituents in the Polar Fraction The polar fraction contained a group of uncommon constituents. Hydrogenation of double bonds with H2 and palladium on carbon (Attygalle 1998) as well as DMDS adducts were used to obtain sufficient information by mass spectrometry to identify these compounds. 18-Heptacosen-10-one was identified by comparison with an already published mass spectrum (Yasui et al. 2003). The mass spectrum of this compound showed a molecular ion of m/z 392 and two diagnostic fragment ions at m/z 155 for [C9H19–CO]+ and at m/z 265 for [C17H33–CO]+ indicating the carbonyl position at C10. After hydrogenation of this unsaturated ketone, the resulting 10-heptacosanone as well as the 10-heptacosanone of the untreated extract showed identical mass spectra (m/z: 155 [C9H19-CO]+ , 267 [C17H35-CO]+, 394 M+) and identical retention indices (2,868) with that already published by Yasui et al. (2003). 14-Tricosen-6-one was characterized by its diagnostic masses (m/z: 99 [C6H11-CO]+, 265 [C17H33-CO]+, 336 M+). Also, hydrogenated 14-tricosen-6-one was tentatively identified as 6-tricosanone by comparison with a commercially available MS library (NIST 2.0, Stein et al. 2001). The double bond positions in these and additional unsaturated ketones were determined from mass spectra of DMDS adducts. Additionally, three unsaturated and one saturated ketone were tentatively identified as 15-tetracosen-7-one, 16-pentacosen-8-one, 17-hexacosen-9-one, and 8-pentacosanone. The corresponding alcohol 16-pentacosen-8-ol was tentatively identified by its diagnostic masses after hydrogenation of the polar fraction with lithium aluminum hydride (Attygalle 1998; Table 1). (Z)-9-Octadecenylmethylester was characterized by using a commercially available standard (Aldrich). Finally, a compound with a typical sesquiterpene mass spectrum was found in small amounts in the polar fraction of the extract. We could not determine the type of functional group in this sesquiterpene.
Chemical Dimorphism One alkene dominated the composition of the PPG, and surprisingly, this major compound differed among individuals (Fig. 2, Table 2). Among the 37 females whose PPG content was analyzed, 29 had (Z)-9-pentacosene and eight had (Z)-9-heptacosene as their major peak (from here on referred to as the C25- and C27-type, respectively). The frequency of the two types differed significantly from equality (χ2 = 15.4, df = 1, P < 0.001). By using exact tests for independent samples to assess whether the proportions of other components were also affected by the major peak, we revealed that 14 of the 21 peaks differed significantly between the C25- and the C27-type (Table 2). Generally, the C27-type had larger proportions of components with longer chain lengths.
Fig. 2Frequency distribution (population of N = 37) of individual female European beewolves, Philanthus triangulum, based on the proportion of (Z)-9-heptacosene in their PPG secretion (values transformed to logcontrasts (Aitchison 1986): Zij = log10(Yij/g(Yj)) where Zij is the standardized peak area i for individual j, Yij is the peak area i for individual j, and g(Yj) is the geometric mean of all peaks for individual j). There is a clear bimodal distribution, i.e., some individuals have a large proportion of (Z)-9-heptacosene, whereas some have a small proportion; there are no intermediate typesTable 2Mean proportions of selected hydrocarbons and ketones from the postpharyngeal glands of individual female European beewolves, P. triangulum, that had either (Z)-9-pentacosene (C25-type) or (Z)-9-heptacosene (C27-type) as the major componentaCompound nameC25-typeC27-typeDifferencesbP values(Peak no. in Table 1)MeanSDMeanSDPent-HeptHeneicosane (12)0.200.140.180.130.020.086Docosane (13)0.160.090.130.080.040.001Tricosenes (14, 15, 16)0.360.270.080.070.28<0.001Tricosane (17)14.063.7412.933.021.130.003Methyltricosanes (18, 19, 20)0.100.100.050.040.050.003Tetracosene (21)1.190.650.140.081.06<0.001Tetracosane (22)0.170.100.140.070.020.003Pentacosenes (25, 26)77.487.968.295.5069.19<0.001Pentacosane (27)2.050.922.821.20−0.780.335Methylpentacosanes (28)0.060.030.080.03−0.020.704Hexacosene (32)0.130.051.620.72−1.50<0.001Hexacosane (33)0.050.030.050.020.000.06716-Pentacosen-8-one (34)1.130.730.250.150.88<0.001Heptacosene (36)1.000.3969.808.86−68.80<0.001Heptacosane (37)0.380.220.310.120.070.032Octacosane (41)0.030.030.030.020.000.18218-Heptacosen-10-one (42)1.025.181.730.75−0.70<0.001Nonacosene (44)0.040.060.950.43−0.90<0.001Nonacosane (45)0.300.300.320.11−0.020.550Hentriacontene (48)0.010.040.040.02−0.03<0.001Hentriacontane (49)0.070.080.060.020.010.062aTable entries include mean and one standard deviation (SD) of the proportion (%) of the components for females of the C25 (N = 29) and of the C27-type (N = 8), as well as the difference (C25–C27) and the significance level for the difference according to an exact test (P, significant differences in bold).bDifferences were calculated from the original data, rounding of values produced some rounding error.
Discussion
The PPG secretion of beewolf females contains predominantly unbranched unsaturated long chain hydrocarbons (C23–C33, mainly either C25 or C27), smaller amounts of saturated hydrocarbons (C14–C33), and small or trace amounts of methylalkanes (C17–C31), unsaturated ketones (C23–C27), saturated ketones (C25, C27), a sesquiterpene, an unsaturated C18 methylester, and an unsaturated C25 alcohol.
The secretion of the PPG of beewolf females is involved in the preservation of their honeybee prey that serves as larval food (Herzner and Strohm 2007). Females apply large amounts of the PPG secretion to the prey prior to oviposition (Strohm and Linsenmair 2001; Herzner et al. 2007a). The prevalence of unsaturated hydrocarbons in the PPG of beewolf females is probably related to the preservation function. The preservation seems to be mainly accomplished by a physical mechanism (Herzner and Strohm 2007): the secretion prevents the condensation of water on the bees and in this way renders the microclimatic conditions unsuitable for the growth of fungi. Possibly, the PPG secretion covers structures on the paralyzed bee that would otherwise function as effective nuclei for the condensation of water. Scanning electron microscopy revealed that the PPG secretion forms a contiguous layer over the whole surface of the prey (Herzner and Strohm 2007). A hydrocarbon mixture of predominately alkenes might be an ideal means to build up such layers, because at the temperatures that prevail in beewolf brood cells, the unsaturated hydrocarbons might be in a more or less liquid state and can be spread easily over the bee surface.
The composition of hydrocarbons on the cuticle of paralyzed honeybees is dramatically modified due to the treatment by beewolf females (Herzner et al. 2007a, b). Untreated honeybees and most other insects bear predominantly saturated straight or branched hydrocarbons (e.g., Howard and Blomquist 1982, 2005; Schmitt et al. 2007). Less frequently, alkenes constitute large proportions of cuticular hydrocarbons (e.g., on a termite, Howard et al. 1978; on an aphid parasitoid, Liepert and Dettner 1996; on workers of European hornets, Ruther et al. 2002; on stingless bee foragers, Abdalla et al. 2003; on diapausing butterfly pupae, Kaneko and Katagiri 2004; or on nesting females of the burrowing bee, Simmons et al. 2003). The reasons for the prevalence of saturated hydrocarbons in some species and unsaturated hydrocarbons in other species are unknown. Possibly, the physicochemical properties of the surface can be adjusted to specific requirements by a particular mixture of saturated and unsaturated compounds (e.g., Gibbs and Pomonis 1995; Gibbs 1998).
In addition to the ubiquitous alkanes and alkenes that we found in the PPG, we also identified long chain unsaturated ketones, some of which had not yet been described as natural products. Only (Z)-18-heptacosen-10-one and 10-heptacosanone had previously been reported as components of a contact sex pheromone from females of the white-spotted longicorn beetle, Anoplophora malasiaca (Yasui et al. 2003). The function of these compounds in the secretion of beewolf females is unclear. Besides a not-completely identified sesquiterpene, (Z)-9-octadecenylmethylester, and 16-pentacosen-8-ol, these ketones are the only identified components in the PPG of beewolf females that have a functional group. They might be likely candidates to exhibit an antifungal effect. However, in bioassays, no direct antifungal effect of the PPG content could be detected (Herzner et al. 2007a). Male European beewolves have some of the same ketones and a slightly shorter unsaturated ketone in their marking secretion (Schmitt et al. 2003; Kroiss et al. 2006). Their exact function in males is also unknown (see below).
The estimated amount of hydrocarbons in the secretion of the PPG differed considerably among females. This might be due to differences in physiological status, size, and age. Interestingly, the mean (approximately 330 μg) and maximum (approximately 1,400 μg) amounts match the quantities found on honeybees that were embalmed with the PPG secretion by beewolf females. Bees are each embalmed with approximately 110 μg (Herzner et al. 2007a) and females provision on average three bees per day and a maximum of ten bees per day (Strohm and Linsenmair 1997). Thus, an average female has available the necessary amount of PPG secretion for the embalming of the average number of bees caught on 1 day. Likewise, the maximum amount found in some individuals would be sufficient to embalm the maximum number of bees that the most successful females hunt per day. Moreover, according to a three-dimensional reconstruction based on histological sections, the maximum volume of the gland was estimated to be 3–4 μl (Strohm et al. 2007). The comparatively large size of the gland is explained by the need to provide enough secretion for the treatment of several prey items per day. The supply with such large amounts of unsaturated hydrocarbons might be costly and might represent a considerable part of the cost of parental care in this species (Strohm and Linsenmair 1999, 2000; Strohm and Marliani 2002).
Remarkably, beewolf females show a striking dimorphism with regard to the chemical composition of the PPG secretion. The major component is either (Z)-9-pentacosene or (Z)-9-heptacosene, and there are no intermediate individuals. Both compounds are widespread among aculeate Hymenoptera and other insects (e.g., Ruther et al. 2002; Simmons et al. 2003). Most other components of the PPG secretion also differ between the two morphs, in that C27-types tend to have larger proportions of long-chain compounds. Possibly, the whole metabolism of hydrocarbons is adjusted to longer chain lengths in the C27-types. Why females have either (Z)-9-pentacosene or (Z)-9-heptacosene as the major compound is unclear. Preliminary analyses (E. Strohm, G. Herzner, M. Kaltenpoth, unpublished data) suggest that there is no effect of age or physiological status on the expression of the major component. One proximate explanation could be that conditions during development differ between the females and cause differential gene activation [for example, the synthesis of (Z)-9-heptacosene might be induced by high temperatures because of the presumably higher melting point, e.g., Gibbs et al. 1998; Rouault et al. 2000]. However, our study specimens were bred under identical temperature conditions in the same climate chamber with the same diet (honeybees from the same population) and, nevertheless, showed this dimorphism. Furthermore, an analysis of beewolf females from different populations ranging from northern Germany to the southern valleys of the Alps not only showed both types of females, but also revealed that they occurred in similar proportions (Strohm et al. 2008). Together with the lack of intermediate individuals, this might suggest that the dimorphism has a genetic basis. Such a dimorphism would have to be balanced because otherwise one morph would disappear at least from some populations either because it has a selective disadvantage or because of genetic drift. One possible explanation for a balanced dimorphism is a spatial difference in the suitability of the two alleles. Such spatial heterogeneity might either be generated by differences in abiotic or biotic conditions. There are specialized cuckoo wasps that enter the nests and oviposit on the bees (Strohm et al. 2001). These wasps seem to employ chemical mimicry in order not to leave traitorous signs in the nest. Most notably, the chemical mimicry only refers to the C27-type of females (Strohm et al. 2008). Thus, the C27-type might be disadvantaged with regard to the rate of parasitism, but this could be balanced by other advantages. Again, this hypothesis is weakened by the fact that the proportions of both types of individuals are similar over a wide geographical range despite probable differences in the abundance of the cuckoo wasps.
Compared to the content of the PPG of males of the European beewolf (Schmitt et al. 2003; Kroiss et al. 2006), females have fewer components with functional groups and lower proportions thereof, whereas the composition of the alkanes and alkenes is similar. The function of the secretion of the male PPG is also quite different. Males use this secretion to scent mark their territories and to attract receptive females (Simon-Thomas and Poorter 1972; Evans and O’Neill 1988; Strohm 1995; Strohm and Lechner 2000; Schmitt et al. 2003). The marking secretion is stored in and delivered from the PPG (Kroiss et al. 2006). Therefore, the compounds with functional groups may play a role in the attraction of females (Herzner et al. 2005; Kroiss et al. 2006) and may also convey some additional information on male quality and suitability as a mate (Herzner et al. 2006; Kaltenpoth and Strohm 2006).
In summary, females of the European beewolf, P. triangulum, have large amounts of mainly unsaturated hydrocarbons in their PPG. The composition of the secretion is probably shaped by its function as an antifungal coating of the prey, paralyzed honeybees. Thus, the function of the PPG of beewolf females differs entirely from this gland in ants. However, the general chemistry is consistent with that found in ants. This supports earlier arguments based on morphology, ultrastructure, and behavioral context (Strohm et al. 2007) that the PPGs of these two taxa are homologous. Comparison of physiological aspects of the PPGs of ants and beewolves, as well as the investigation of other aculeate Hymenoptera is necessary to obtain further insights into the evolution and function of the PPG in this group of insects.

postpharyngeal gland
philanthus triangulum
ppg
gc-ftir
hymenoptera
crabronidae
sphecidae
antifungal