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Gene expression microarrays and their application in drought stress research
Field Crops Research 97 (2006) 101–110 www.elsevier.com/locate/fcr
Gene expression microarrays and their application in drought stress research Arumugam Kathiresan a, H.R. Lafitte a, Junxing Chen a,b, Locedie Mansueto a,c, Richard Bruskiewich a, John Bennett a,* a
International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines College of Agronomy, Nanjing Agricultural University, Jiangsu Province, PR China c Department of Chemical Engineering, University of the Philippines-Diliman, Diliman, Quezon City, Philippines b
Abstract Initial physiological adjustments in response to drought stress lead to drastic changes in gene expression. The traditional approaches of assessing such drought-induced changes in gene expression involve measuring the differences in mRNA levels of one or few genes at a time. DNA expression microarray technology is a powerful tool that can monitor changes in expression of a large number of genes simultaneously. Expression microarrays also provide new insights into physiological and biochemical pathways of drought tolerance, and thus can lead to identification of novel candidate genes that can rapidly advance breeding for drought tolerance. This review describes the basic principles and potential applications of gene expression microarrays in understanding and improving drought tolerance in plants. A case study is presented involving hybridization of field-grown panicle samples from drought tolerant and susceptible rice germplasm targets with probes from a normalized panicle cDNA library. Results indicate contrasting drought responses among both upland versus lowland-adapted cultivars and also between traditional and improved upland types. # 2005 Elsevier B.V. All rights reserved. Keywords: Gene expression; Candidate gene; Panicle; Pathways
1. Introduction Water is a major limiting factor in agricultural production systems in several parts of the world. Due to their sessile nature, plants have had to develop efficient strategies to cope with limited water. Tolerant plants adapt to drought by invoking a battery of changes in their physiological and metabolic activities that lead to sustenance of essential developmental activities such as reproduction. Given a myriad of biochemical processes involved in sensing stress, transducing stress signals and prompting developmentspecific biological responses (Kreps et al., 2002), it is conceivable that plants have evolved a diverse range of cellular adaptive mechanisms. It is however difficult to measure and compare such micro changes at the physiological or biochemical levels with precision. Since alterations in * Corresponding author. E-mail address: [email protected] (J. Bennett). 0378-4290/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2005.08.021
growth and development are often associated with radical changes in gene expression, it is feasible to assess changes at the molecular level. Our ability to assess large-scale changes in gene expression in a comprehensive and unbiased way has hitherto been limiting. The advent of microarray technology (Schena et al., 1995) heralds a significant shift from the traditional single gene approach to monitoring changes in expression of thousands of genes at once. In this review, we focus on the principles and variations of DNA-based gene expression microarray techniques and their potential applications in drought stress research. Because mRNA is directly transcribed from DNA, plant molecular biologists often measure mRNA levels to assess stress-induced gene expression. Although conventional tools such as Northern blotting (Alwine et al., 1977) and reverse transcription-polymerase chain reaction (RT-PCR; Lee et al., 1989) of mRNAs are quite sensitive, they are capable of assessing only one or few genes in an experiment. Other laborious techniques such as dot blot analysis, differential
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display (Liang and Pardee, 1992) and serial analysis of gene expression (SAGE; Velculescu et al., 1995) enable detection of multiple differences in gene expression; they are, however, not quantitative and can be used to compare only a limited number of conditions. In principle, most of the above techniques involve immobilization of RNA or DNA ‘target’ molecules onto a membrane (blot) and hybridization between radioactive ‘probe’ and complementary target molecules on the blot. Besides the limited availability of DNA/RNA on their porous surface for hybridization, the translucent nature of membranes does not allow the use of fluorescent probes the binding of which can easily be quantified using laser detectors. The recent technological advances in immobilization and hybridization of DNA molecules on glass slides have lead to the development of microarray technology.
2. Materials and methods 2.1. Plant materials 2.1.1. Plants used to construct the cDNA library IR64 plants were grown in the field under lowland irrigated (flooded), upland drip-irrigated and upland dripstress. Stress was applied by withdrawing water from the plots for 9 days. Panicles from the primary tillers of these plants were harvested at flowering and at 4 days after flowering. Sampling was done in the morning (07:30 to 08:30 h) and in the afternoon (15:30 to 17:00 h). IR64 plants were also grown in pots under glass-house conditions. Water stress was applied to these potted plants at various developmental stages by not watering the pots for 4 consecutive days. At the end of stress period, panicles from both well-watered and water stressed pots were collected 2 days before heading, heading, 50% flowering and 4 days after 50% flowering. Total RNA from these various panicles was bulked to generate the cDNA library, which is expected to contain a range of genes that is expressed near flowering in drought-stressed and wellwatered IR64. 2.1.2. Plants sampled for expression analysis Three cultivars, Azucena, Apo and IR64, were sown in field plots at the International Rice Research Institue in Los Banos, Philippines (1218150 E, 148130 N). The seeding rate was 80 kg/ha. The crop was established by sprinkler irrigation, after which it was irrigated through a drip system that applied approximately 1.6 times the pan evaporation rate, 3 times each week. This maintained the soil water potential above 20 kPa at both 15 and 30 cm depths. In stress treatments, water was withheld for 18 days (Apo and IR64) or 20 days (Azucena) starting about 7 day prior to the onset of flowering in control plots, after which irrigation was resumed. Shoot water potentials were measured using a pressure chamber (SoilMoisture Corp.).
Panicles for RNA extraction were selected at heading in both stress and control plots. Additional panicles flowering at the same time were tagged for subsequent analysis of grain formation. 2.2. Construction of cDNA library Total RNA from the above-mentioned panicle tissues was extracted individually using TRIzolTM (Invitrogen) as described by the manufacturers. From a total RNA pool containing about 250 mg each, a normalized cDNA library was made at BioCat GmbH, Heidelberg, Germany, as described here. Poly A tailed mRNAs were isolated from the pool using oligo(dT)-cellulose chromatography. About 500 ng of mRNAs were reverse transcribed using oligo(dT)–NotI primers. Linker primers were then ligated to the ends of ss-cDNAs. To normalize the cDNA population, two rounds of denaturation and reassociation were performed. A Cot value of approximately 90 was attained. The remaining ss-cDNAs were amplified using 15 PCR cycles. Normalization of cDNAs was verified by testing the abundance of a highly expressed gene (GAPDH; glyceraldehyde-3-phoshpate dehydrogenase) and that of a poorly expressed gene (DMC1A) in rice panicles. After digesting the cDNAs with NotI, those cDNAs that were greater than 0.75 kbp in size were ligated with EcoRV–NotI digested pBluescript II (SK+) vector. Competent E. coli cells (Invitrogen) were transformed with the ligation reaction. From an estimated titre of 60,000 cfus, 9984 colonies were randomly picked and stocked in 384-well plates for further characterization. The 50 or 30 sequences of 8071 non-redundant clones were submitted to GenBank. 2.3. Array fabrication and hybridization DNA probes prepared by amplifying the inserts of 1536 cDNA clones were amplified using M13F (50 -GTAAAACGACGGCCAGT-30 ) and M13R (50 -AACAGCTATGACCATG-30 ) primers in a polymerase chain reaction (PCR). The PCR products were purified by precipitating with 2 volumes of absolute ethanol and 1/10 volume of 3 M sodium acetate and suspended in 50% DEPC. DNA probes were arrayed using GeneTACTM G3 arrayer (Genomic Solutions Inc.) in 8 4 patches with 10 10 spots per patch on aminosilane slides (GAPS IITM, Corning Inc.). Targets were prepared by reverse transcribing total RNAs (100 mg) from well-watered (Cy3) and water stressed (Cy5) plants using MicromaxTM direct labeling kit (Perkin-Elmer Life Sciences Inc.) as described by the manufacturers. The targets were pooled and hybridized with DNA probes on the array at 60 8C overnight and washed in 0.1 SSC, 0.2% SDS at 60 8C on GeneTACTM hybridization station. The slides were scanned using GeneTACTM LSIV laser scanner and the image was processed using Integrator Analyzer 3.3 software (Genomic Solutions Inc.).
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3. Principles of expression microarrays The principles and terms used in microarray technology are the reverse of those used in traditional blot techniques. Thousands of gene-specific DNA ‘probe’ molecules are immobilized on the surface of a non-porous glass slide and hybridized against fluorescent-labeled ‘target’ molecules that represent mRNAs in the tissue being examined. By virtue of sequence complementarities, the target molecules bind to those DNA probes that are the same as the genes that they represent in the tissue. The amount of fluorescent labels bound to each DNA probe on the array provides a direct estimate of gene expression in the tissue of interest (Fig. 1). 3.1. Arraying DNA probes The term microarray refers to an orderly arrangement of DNA probes, generally on a microscopic glass slide. Since
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DNA molecules can also be arrayed on a silicon or plastic surface, they are sometimes called ‘chips’. Probes ranging from 0.4 to 2.0 kilo base pairs (kbp) are produced typically by amplifying the desired region of DNA in a polymerase chain reaction (PCR). After amplification, the DNA samples are purified and suspended to a final concentration of 100– 500 ng/mL (Cheung et al., 1999) in 50% diethyl pyrocarbonate (DEPC) that maintains DNA in a readily hybridizable single stranded state. A robot (arrayer) with metallic pins is engaged to print DNA samples at defined locations on the surface of glass slide (Fig. 1). The robotic deposition of probes, known as ‘printing’, usually adopts a user-defined pattern that also establishes the identity and spot replications of each probe. Depending on the manufacturing design of pins, printing is done by either touching or dispensing 0.3– 1 nL of DNA probes onto the glass surface. To enhance the hydrophobicity and retention of DNA, the glass surface is usually coated with poly-lysine or aldehyde- or amino
Fig. 1. Process of gene expression profiling using cDNA microarray. DNA probes amplified through PCR are spotted on coated glass microscopic slides using robot. Targets are prepared by reverse transcribing total RNAs from reference (well-watered) and test (stressed) samples in the presence of cy3-dUTP (green fluor) or cy5-dUTP (red fluor). The targets are combined and hybridized to the array. Relative abundance of transcripts of 96 cDNA probes (spotted in duplicates) in stressed as compared to well-watered panicles of Azucena, Apo and IR64 plants are shown at the bottom.
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reactive silanes. Unlike the aldehyde groups that bind to DNA covalently upon Schiff’s base reduction, the polylysine or amino reactive silanes establish only non-covalent bonding to the printed DNA. Hence, DNA probes printed on such surfaces need to be cross-linked by UV irradiation or baking (Deyholos and Galbraith, 2001). 3.2. Probe formats Designing the probes for an expression microarray largely depends on the research questions being asked. For a broad exploration of genes induced by drought stress in various plant tissues, a whole genome array would be desirable. In model species, such as rice and Arabidopsis, the entire genomic sequences can be scanned for putative genes and sequences corresponding to coding and/or the unique 30 untranslated regions (30 -UTR) of all the genes in the genome can be spotted on the array. Such high-density arrays representing unique genes in a non-redundant fashion are referred to as ‘unigene arrays’ (Boguski and Schuler, 1995). Since most of the physiologically important genes in crop plants exist as ‘gene families’ that comprise two or more variants of the same gene, arrays carrying 30 -UTR probes have an advantage of distinguishing the expression of family members. However, comprehensive genome wide unigene arrays are not yet widely available for agricultural crops due to difficulties in characterizing their complex genomic sequences. This constraint necessitates the use of smaller arrays carrying complementary DNAs (cDNAs) derived from mRNAs in tissue(s) of interest. The complexity of cDNA molecules forms an important basis of microarray and depends on how the cDNA collection (cDNA library) was made. A cDNA library can be generated through ‘subtraction’ by which the cDNA molecules derived from the mRNAs of reference tissues are removed by hybridizing with excess amount of cDNAs derived from test samples. Such subtracted libraries often represent the differentially expressed genes in the test samples. An alternate method of cDNA library production involves ‘normalization’ where the discrepancy in cellular mRNA abundance between different genes is corrected. A tobacco leaf cell, for example, contains about 12,000 distinct mRNA species, of which about 700 most abundant species represent 52% of the total mRNA mass (Goldberg et al., 1978). Normalization of cDNAs is carried out by two or more cycles of denaturation and reassociation among double stranded (ds) cDNAs or between ss-cDNAs and the initial mRNAs. Because reassociation kinetics depends on concentration of molecules and duration, only ss-cDNAs from rare mRNAs and relatively fewer copies of abundantly expressed mRNAs do not reassociate. The ss-cDNAs are then selectively purified and enriched in the cDNA library. Recent studies show that pooling of mRNAs from multiple tissues increases the efficiency of subtraction and normalization of cDNAs and greatly enhances the number of unique genes represented in
the library (Smith et al., 1997). However, since cDNAs in a library represent only the steady-state mRNAs of the tissue(s) from which the cDNA library was made, microarrays carrying cDNA probes are less versatile than whole-genome arrays. Furthermore, cDNA probes do not effectively distinguish the expression of members of a gene family and those mRNA variants that are spliced alternately from the same gene. Such shortcomings are overcome by the development of oligonucleotide-based arrays. Oligonucleotide arrays, also known as ‘oligo arrays’ or ‘DNA chips’, carry short (60 bases) synthetic DNA probes. In a sequential manner, the oligo nucleotides are directly attached to a glass surface that is derivatized with linker molecules carrying a photo-labile group. Using a photolithographic mask, specific areas of the glass slide are illuminated so as to remove the photo-labile caps (deprotection). Then the surface is flooded with selected nucleotides (coupling), which also carries photolabile groups. After washing the excess uncoupled nucleotides, the cycle of deprotection and coupling is repeated with the next nucleotide until completion (Lipshutz et al., 1999). Recent introduction of mask-less array synthesizer (MAS) has significantly reduced the time and cost of synthesizing oligo arrays by replacing photolithographic masks with computer generated virtual masks (Singh-Gasson et al., 1999). An important advantage of oligo arrays is that each gene shall be represented by several different oligos spanning the entire length of the gene. In addition, each of these oligonucleotides shall be accompanied by a mismatch oligonucleotide in which the central base has been changed. Such a combination of probe redundancy and mismatch controls greatly enhances the accuracy in detecting the expression of family members and mRNA variants of genes. 3.3. Hybridization and scanning Expression array hybridizations can be treated in a similar manner to classical crop field experiments, by decomposing the experiment into various samples, treatments and treatment interactions (Bartolome, 2002). Typical treatments might consist of stressed and unstressed germplasm, across various germplasm targets. In the hybridizations, the mRNAs of ‘reference’ (unstressed) and ‘test’ (stressed) sample tissues represent the target molecules. But since mRNAs are highly unstable and often form secondary structures that hinder their hybridization with DNA probes on the glass surface, they are often converted to single stranded cDNA molecules in a single round of reverse transcription in the presence of fluorescent nucleotide labels. Cyanine 3 (cy3) and cyanine 5 (cy5) dUTPs are the two most commonly used fluorescent labels because of their high incorporation efficiencies during reverse transcription and wide separation of their excitation and emission spectra (Carlsson and Ulfhake, 1995). To distinguish their mRNAs, the test and reference samples are
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distinctly labeled with cy3 or cy5 dUTPs (Fig. 1). The labeled cDNA molecules are then combined and added to the surface of a single array in a competitive hybridization reaction. After hybridization, the bound target molecules are quantified by detecting their emission following laser excitation. Green spots represent expression in targets labeled with cy3, red spots correspond to targets labeled with cy5, and those that are yellow represent genes that are expressed in both the targets (Fig. 1). Since the amount of DNA probes on the array is in excess of mRNA levels (Bartosiewicz et al., 2000), the differences in cy3 and cy5 emissions after hybridization represent the differences in transcript abundance between test and reference tissues. It should be noted, however, that intrinsic differences in labeling and/or laser detection efficiencies between cy3 and cy5, and qualitative and quantitative differences between test and reference mRNAs in the labeling reaction could lead to differences in signal intensities. To overcome the intrinsic differences in labeling and detection efficiencies between cy3 and cy5, some researchers label both test and reference targets using the same fluor and hybridize each labeled target individually to two identical arrays (Yazaki et al., 2000). The ability to identify changes in transcript levels of genes, such as transcription factors that are expressed in low abundance, is determined by signal-to-noise ratio (S/N) observed on the slide. S/N represents ratio of specific signals from the array spots and the non-specific signals (noise) from the background. Since transcription factors play a major role in drought stress responses (YamaguchiShinozaki and Shinozaki, 2001), a high S/N ratio is needed to ensure that up- or down regulation of these lowabundance transcripts are also quantified. The sensitivity of detection and hence S/N can be enhanced by labeling the target molecules indirectly. Indirect labeling methods incorporate modified nucleotides such as 5-(3-aminoallyl)-20 -dUTPs during reverse transcription. After hybridization, cDNAs are treated with antibody–enzyme conjugate that recognizes the modified nucleotides and catalyzes the deposition of fluorescent dyes, which result in amplification of hybridization signals (Karsten et al., 2002). Besides enhancing the sensitivity, such methods also reduce the need for large amounts of initial mRNAs for hybridization and thus might enable large-scale gene expression studies in specific tissues such as anthers and ovaries.
4. Data analyses The fluorescent signals captured by the laser detector after hybridization represents the raw data of gene expression. Further analyses of gene expression data involve several steps. First the spots representing each DNA probe on the array must be identified and integrated with the arraying pattern that was generated during printing. Since the background noise could vary across the slide due to
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precipitates, contaminants and dust at various stages of handling, background values are calculated locally for each spot and used to subtract from the total spot intensity. The spot intensities of all the replicated spots are then used to calculate the median or mean for each DNA probe on the array. Once the data for all the DNA probes on the array are obtained for cy3 and cy5, the two expression data sets need to be normalized (corrected) so that the differences in labeling efficiencies between the two fluors and/or the two mRNA populations are taken into account (Tseng et al., 2001). The normalization factor is computed from the intensities of either all the probes on the array (global) or a subset of genes that are not expected to change (internal) or a set of non-plant DNA (external) controls. A linear regression analysis is usually applied in calculating the ‘normalization factor’ based on the assumption that if the labeling efficiencies are identical for the two targets being compared, the scatter plot of measured intensities of either all or control spots should have a slope of one. Any deviation is then calculated as normalization factor and used to rescale the data and correct the slope to one. Following normalization, the data are transformed to logarithmic scale, which has the advantages of treating upand down regulated genes in a similar fashion. When gene expression in the normal and stressed plants are to be expressed as simple arithmetic ratios of cy3 and cy5 signals, then a gene that is up regulated by two-fold has an expression ratio of 2, where as a gene that is down regulated by two-fold has an expression ratio of 0.5. The transformed data will show a gene that is up regulated by two-fold as having a log2 (ratio) of 1 and a gene that is down regulated by two-fold as having log2 (ratio) of 1 (Quackenbush, 2001). In general, genes showing at least two fold up- or down regulation are potentially considered as differentially expressed; however, it is well advised to undertake microarray analyses of variance (MAANOVA; Wu et al., 2003) or similar analyses of multiple mRNA samples extracted from various tissue replicates collected in an experiment (biological replication), to assess whether or not the level of expression of the gene is truly statistically significant. Repeat hybridizations on multiple arrays using the mRNA samples extracted from the same tissue source (technical replication) help define the bounds of experimental error (Bartolome, 2002). Statistically significant differential gene expression data may subsequently be analyzed using a variety of techniques developed over the past several years (for a well written review of many of these approaches, consult Quackenbush, 2001). It is important to critically assess the underlying assumptions of the various approaches. For example, expression profiles may be assembled from the results of multiple experiments across multiple treatments. Such profiles may be subjected to various non-supervised algorithms such as hierarchical clustering based on arbitrary distance measurements between expression profiles. Such techniques can hint at associations between genes but they
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are generally exploratory and do not confirm the underlying genetics of regulation. That is, genes sharing similar profiles may potentially, though not inevitably, be subjected to similar regulation. Analytical methodology, such as latent class modeling, stands upon firmer statistical grounds and therefore should be considered in any full treatment of microarray data (Gieser et al., 2002).
5. Drought-induced changes in gene expression in rice panicles—a case study In the recent years, several research groups have reported drought-mediated changes in gene expression using microarrays. Ozturk et al. (2002) reported that transcripts encoding jasmonate-responsive, late-embryogenesis abundant and ABA-responsive proteins were up regulated by more than 2.5-fold in drought-stressed barley seedlings. Zinselmeier et al. (2002) showed coordinate regulation of starch biosynthesis under drought stress in elite maize hybrids during flowering. Rabbani et al. (2003) identified 62 genes out of 1700 genes as drought inducible in 2-week-old rice seedlings. In drought-stressed Arabidopsis seedlings, Seki et al. (2002) identified 277 drought responsive genes from arrays containing about 7000 genes. During the reproductive phase, drought affects rice yield most when it occurs near flowering (O’Toole and Moya, 1981). To study the drought-induced changes in gene expression patterns in panicles using microarrays, we recently generated a normalized panicle cDNA library (see Section 2). We arrayed the cDNA inserts of 1536 randomly selected clones from this library on glass slides, and examined droughtinduced changes in their expression in panicles at heading. We compared the expression profiles in panicles of three varieties, viz., Apo (a drought-tolerant upland indica cultivar), Azucena (a reportedly drought-tolerant traditional upland japonica cultivar) and IR64 (a drought-susceptible semi-dwarf lowland indica cultivar) grown under wellwatered and stressed conditions in the field. These cultivars showed dramatic decreases in yield when water was withheld from plots for about 18 days prior to heading (Table 1). The midday leaf water potentials of all varieties were decreased significantly by the stress episode,
indicating that all cultivars experienced significant stress (Table 1). The main yield component responsible for the reduction in yield under stress was spikelet fertility, which declined to 33% in Azucena, 38% in Apo and 7% in IR64. The panicles sampled for gene expression studies represented those plants that were able to flower under stress, which generally flowered somewhat earlier than the bulk of the plot. Genes that were up- or down regulated by two-fold in the three varieties were selected and clustered based on their expression pattern (Fig. 2). Drought stress up regulated the transcript levels of dehydrin in the two drought tolerant varieties under drought stress, but not in drought susceptible IR64 (Fig. 2) suggesting that dehydrin may have contributed to the drought tolerance (Xu et al., 1996) of Apo and Azucena. On the other hand, genes such as GTP binding protein 3, GTP binding regulatory protein, S-receptor kinase, cellulose synthase-6, tubulin beta chain, subtilase, peroxidase and four other unknown proteins showed differential regulation only in IR64 (Fig. 2) indicating that transcriptional regulation of these genes may not be of any adaptive value under drought, or indicate damage due to stress. Out of 1536 EST clones, only genes encoding 20S proteasome beta subunit C, pectin methyltransferase and two other unknown proteins showed similar expression pattern in all the three varieties. Despite their divergence, the overall expression patterns of Azucena and IR64 displayed high similarity. Transcripts for 70 kDa heat shock cognate protein, DNA repair protein, reductase, zinc finger protein, histidine transporter, pollen allergen protein, 20S proteasome beta type 3, regulatory protein for Psr1 and 4 unknown proteins showed differential regulation in both Azucena and IR64. On the other hand, Apo and IR64 displayed similar expression pattern only for genes encoding actin depolymerizing factor, pectin esterase and five unknown proteins. Apo and Azucena regulated different sets of genes under stress, suggesting that these two cultivars may possess distinct tolerant mechanisms. For example, up regulation of storage proteins such as glutelin type B1, glutelin type AIII, prolamin 17 and prolamin 14 found in Azucena panicles reflects its ability to maintain grain development under water stress. In Apo, down regulation of genes encoding auxin response factor 7A and NPK1, a mitogen-activated protein
Table 1 Measurements of plant water status, yield and spikelet fertility under stress in three contrasting rice cultivars Parameter
Shoot water potential (midday; MPa) Transpiration (mmol cm2 s1) Grain yield (g m2) Spikelet fertility (%) Thousand grain weight (g)
Azucena
Apo
IR64
L.S.D.0.05
Well-watered
Stress
Well-watered
Stress
Well-watered
Stress
2.0 18 159 58 26.8
2.5 6 7 33 21.4
2.0 19 250 69 19.0
3.0 9 70 38 16.1
2.0 25 129 69 20.1
3.3 7 2 7 15.8
0.38 7 86 20 2.1
Plants were grown in the field in aerobic conditions, with or without an 18-day period of water exclusion that coincided with flowering. Plants were grown at the experiment station of the International Rice Research Institute, Los Ban˜os, Philippines, in the 2002 dry season.
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Fig. 2. Expression pattern of differentially regulated genes in Azucena, Apo and IR64 under drought stress. Hierarchial algorithm was used to cluster genes according to their average distance in expression values across all the three genotypes. The brightness of color is proportional to the level of expression.
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kinase, suggests a role for negative regulation of auxin signal transduction pathways in maintaining embryo and endosperm development under stress (Kovtun et al., 1998). In addition, the down regulation of mRNA levels of trehalose phosphatase and triose phosphate translocator in Apo implies that carbon partitioning in panicles might play a role in drought tolerance. Although not elaborate, these results show how microarrays can be used as diagnostic tools to identify the variations in expression pattern in panicles under drought stress. Our findings also demonstrate the utility of expression data in generating hypotheses related to physiological and metabolic pathways of drought tolerance.
6. Applications of expression microarrays in drought research Gene expression microarrays help explain the molecular basis of how different plants respond to a particular stimulus. Unlike traditional techniques, microarrays allow simultaneous analyses of a large number of genes in an unbiased manner. Hence, the microarray is recognized as a powerful tool that can rapidly advance our efforts on improving drought tolerance in agricultural crops. Expression microarrays are being used to study cellular responses to various other stresses in plants, as well (Kawasaki et al., 2001; Seki et al., 2002; Kreps et al., 2002). One of the many potential applications is to identify drought tolerance mechanisms by comparing changes in gene expression in tolerant and susceptible phenotypes. Elucidation of complex gene expression patterns serve as ‘molecular fingerprints’, which can facilitate our understanding and screening for physiological processes involved in drought tolerance. Because gene expression patterns vary between physiological states of different plant tissues such as panicles, leaves and roots, microarrays can generate comparative multidimensional maps of gene expression for normal and stressed tissues. Besides providing insights into physiological processes, a broad the approach to in transcriptional profiling can help dissect complex developmental aspects of composite tissues such as panicles. Drought stress triggers a cascade of biological responses in plants starting from signaling to physiological responses. The physiological responses are prompted by gene expression patterns that generally change in a complex but orchestrated fashion (Kawasaki et al., 2001; Kreps et al., 2002). The coordination relies mainly on the activity of regulatory compounds and/or proteins which themselves are products of genes. When such indirect gene–gene interactions are functionally impaired, they affect the expression of other dependent (downstream) genes. Hence, genes that are significantly up- or down regulated in a drought tolerant genotype when compared to a susceptible genotype, can serve as ‘transcriptional candidates’ for drought tolerance. Correlation of chromosomal positions of transcriptional
candidates with additional data sets such as QTL or, physiological features of drought tolerant genotypes might add significance to these candidate genes. Such analysis holds promise to discover new and specific markers for drought tolerance that may be used in conjunction with classical breeding techniques. Another effective way of using candidate genes is to represent all the drought responsive transcriptional candidates from large-scale expression profiles in smaller ‘sub arrays’. Molecular phenotyping of germplasm using sub-arrays could, after appropriate validation, supplement or replace extensive screening procedures for drought tolerance traits in the field. Gene-to-gene interactions have also led to the concept of ‘gene networks’. Expression microarrays can be used to discover sets of genes that are coordinately expressed in temporal or spatial fashion under drought-stressed conditions. Clustering of such co-expressed genes can be very useful in discovering the regulatory mechanisms of genes. In plants, gene expression is regulated by the binding of specific proteins called ‘transcription factors’ to sequencespecific sites located on the upstream promoter regions of the gene. Genes that are co-expressed often share a similar pattern of regulatory sequences that are responsible for binding of such transcription factors. Using a range of pattern detection algorithms, one can search and score discrete sequence motifs that are statistically over represented in a given cluster of genes (Vilo and Kivinen, 2001). Identification of such regulatory motifs might help us discover potential signaling intermediates of drought stress. Often the regulatory factor(s) responsible for coordinate expression can be identified from the expression profile itself. But the functional roles of such factors under drought stress need to be verified through mutation or transformation studies. By performing expression microarray analyses on mutants or transgenic plants in which such factors are altered, one can assess their controls on the expression of downstream drought-responsive genes. Expression microarrays thus indirectly help in understanding gene functions by providing decisive hints. Besides helping in identification and validation of novel drought tolerance genes, gene expression microarrays can also provide insights into previously unknown physiological consequences of genetic modification in plants. The biological insights gained from microarray studies can thus facilitate finding new and effective transgenic strategies for drought tolerance. There are some limitations to gene discovery using expression microarrays. One is that these arrays, being based on RNA, produce results that are highly stage and tissuedependent. It is possible that a key gene that underpins the difference between drought-tolerant and drought-susceptible cultivars is expressed at a time or in a tissue that is not tested in the experiment, meaning that the gene product is not detected. Other genetic differences between tolerant and susceptible types may be due to post-translational modification, which would not be detected by expression arrays, or may simply be due to differences in plant type or maturity.
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Microarray analysis should be complemented by other techniques that can detect non-expression based differences between lines. In summary, we described the basic principles of microarrays in studying the changes in gene expression. Using rice as a model, we demonstrated how microarrays can be used to compare genotypes for their gene expression profiles in response to drought stress. Although there are other DNA-based microarray techniques that can be used to identify mutations, polymorphisms, and comparative genomic analysis (Solinas-Toldo et al., 1997; Jaccoud et al., 2001), here we discussed the applications of RNA-based gene expression microarrays in understanding and improving drought tolerance. Future applications of expression microarrays will be aided by robust protocols that require less experimental tissues or RNA, techniques that can assess the concurrent changes at protein and metabolite levels, and our ability to identify significant and biologically relevant alterations in gene expression within large-scale data sets.
Acknowledgments We acknowledge the technical support from Evelyn Liwanag and Wen Jiang. This research was supported by a grant from Bundesministerium fu¨r Wirtschaftliche Zusammenarbeitund Entwicklung (BMZ, Germany). We would like to thank Dr. Volker Stiller, University of South Eastern Louisiana, for providing water potential measurements reported in this study.
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Gene expression microarrays and their application in drought stress research Arumugam Kathiresan a, H.R. Lafitte a, Junxing Chen a,b, Locedie Mansueto a,c, Richard Bruskiewich a, John Bennett a,* a
International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines College of Agronomy, Nanjing Agricultural University, Jiangsu Province, PR China c Department of Chemical Engineering, University of the Philippines-Diliman, Diliman, Quezon City, Philippines b
Abstract Initial physiological adjustments in response to drought stress lead to drastic changes in gene expression. The traditional approaches of assessing such drought-induced changes in gene expression involve measuring the differences in mRNA levels of one or few genes at a time. DNA expression microarray technology is a powerful tool that can monitor changes in expression of a large number of genes simultaneously. Expression microarrays also provide new insights into physiological and biochemical pathways of drought tolerance, and thus can lead to identification of novel candidate genes that can rapidly advance breeding for drought tolerance. This review describes the basic principles and potential applications of gene expression microarrays in understanding and improving drought tolerance in plants. A case study is presented involving hybridization of field-grown panicle samples from drought tolerant and susceptible rice germplasm targets with probes from a normalized panicle cDNA library. Results indicate contrasting drought responses among both upland versus lowland-adapted cultivars and also between traditional and improved upland types. # 2005 Elsevier B.V. All rights reserved. Keywords: Gene expression; Candidate gene; Panicle; Pathways
1. Introduction Water is a major limiting factor in agricultural production systems in several parts of the world. Due to their sessile nature, plants have had to develop efficient strategies to cope with limited water. Tolerant plants adapt to drought by invoking a battery of changes in their physiological and metabolic activities that lead to sustenance of essential developmental activities such as reproduction. Given a myriad of biochemical processes involved in sensing stress, transducing stress signals and prompting developmentspecific biological responses (Kreps et al., 2002), it is conceivable that plants have evolved a diverse range of cellular adaptive mechanisms. It is however difficult to measure and compare such micro changes at the physiological or biochemical levels with precision. Since alterations in * Corresponding author. E-mail address: [email protected] (J. Bennett). 0378-4290/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2005.08.021
growth and development are often associated with radical changes in gene expression, it is feasible to assess changes at the molecular level. Our ability to assess large-scale changes in gene expression in a comprehensive and unbiased way has hitherto been limiting. The advent of microarray technology (Schena et al., 1995) heralds a significant shift from the traditional single gene approach to monitoring changes in expression of thousands of genes at once. In this review, we focus on the principles and variations of DNA-based gene expression microarray techniques and their potential applications in drought stress research. Because mRNA is directly transcribed from DNA, plant molecular biologists often measure mRNA levels to assess stress-induced gene expression. Although conventional tools such as Northern blotting (Alwine et al., 1977) and reverse transcription-polymerase chain reaction (RT-PCR; Lee et al., 1989) of mRNAs are quite sensitive, they are capable of assessing only one or few genes in an experiment. Other laborious techniques such as dot blot analysis, differential
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display (Liang and Pardee, 1992) and serial analysis of gene expression (SAGE; Velculescu et al., 1995) enable detection of multiple differences in gene expression; they are, however, not quantitative and can be used to compare only a limited number of conditions. In principle, most of the above techniques involve immobilization of RNA or DNA ‘target’ molecules onto a membrane (blot) and hybridization between radioactive ‘probe’ and complementary target molecules on the blot. Besides the limited availability of DNA/RNA on their porous surface for hybridization, the translucent nature of membranes does not allow the use of fluorescent probes the binding of which can easily be quantified using laser detectors. The recent technological advances in immobilization and hybridization of DNA molecules on glass slides have lead to the development of microarray technology.
2. Materials and methods 2.1. Plant materials 2.1.1. Plants used to construct the cDNA library IR64 plants were grown in the field under lowland irrigated (flooded), upland drip-irrigated and upland dripstress. Stress was applied by withdrawing water from the plots for 9 days. Panicles from the primary tillers of these plants were harvested at flowering and at 4 days after flowering. Sampling was done in the morning (07:30 to 08:30 h) and in the afternoon (15:30 to 17:00 h). IR64 plants were also grown in pots under glass-house conditions. Water stress was applied to these potted plants at various developmental stages by not watering the pots for 4 consecutive days. At the end of stress period, panicles from both well-watered and water stressed pots were collected 2 days before heading, heading, 50% flowering and 4 days after 50% flowering. Total RNA from these various panicles was bulked to generate the cDNA library, which is expected to contain a range of genes that is expressed near flowering in drought-stressed and wellwatered IR64. 2.1.2. Plants sampled for expression analysis Three cultivars, Azucena, Apo and IR64, were sown in field plots at the International Rice Research Institue in Los Banos, Philippines (1218150 E, 148130 N). The seeding rate was 80 kg/ha. The crop was established by sprinkler irrigation, after which it was irrigated through a drip system that applied approximately 1.6 times the pan evaporation rate, 3 times each week. This maintained the soil water potential above 20 kPa at both 15 and 30 cm depths. In stress treatments, water was withheld for 18 days (Apo and IR64) or 20 days (Azucena) starting about 7 day prior to the onset of flowering in control plots, after which irrigation was resumed. Shoot water potentials were measured using a pressure chamber (SoilMoisture Corp.).
Panicles for RNA extraction were selected at heading in both stress and control plots. Additional panicles flowering at the same time were tagged for subsequent analysis of grain formation. 2.2. Construction of cDNA library Total RNA from the above-mentioned panicle tissues was extracted individually using TRIzolTM (Invitrogen) as described by the manufacturers. From a total RNA pool containing about 250 mg each, a normalized cDNA library was made at BioCat GmbH, Heidelberg, Germany, as described here. Poly A tailed mRNAs were isolated from the pool using oligo(dT)-cellulose chromatography. About 500 ng of mRNAs were reverse transcribed using oligo(dT)–NotI primers. Linker primers were then ligated to the ends of ss-cDNAs. To normalize the cDNA population, two rounds of denaturation and reassociation were performed. A Cot value of approximately 90 was attained. The remaining ss-cDNAs were amplified using 15 PCR cycles. Normalization of cDNAs was verified by testing the abundance of a highly expressed gene (GAPDH; glyceraldehyde-3-phoshpate dehydrogenase) and that of a poorly expressed gene (DMC1A) in rice panicles. After digesting the cDNAs with NotI, those cDNAs that were greater than 0.75 kbp in size were ligated with EcoRV–NotI digested pBluescript II (SK+) vector. Competent E. coli cells (Invitrogen) were transformed with the ligation reaction. From an estimated titre of 60,000 cfus, 9984 colonies were randomly picked and stocked in 384-well plates for further characterization. The 50 or 30 sequences of 8071 non-redundant clones were submitted to GenBank. 2.3. Array fabrication and hybridization DNA probes prepared by amplifying the inserts of 1536 cDNA clones were amplified using M13F (50 -GTAAAACGACGGCCAGT-30 ) and M13R (50 -AACAGCTATGACCATG-30 ) primers in a polymerase chain reaction (PCR). The PCR products were purified by precipitating with 2 volumes of absolute ethanol and 1/10 volume of 3 M sodium acetate and suspended in 50% DEPC. DNA probes were arrayed using GeneTACTM G3 arrayer (Genomic Solutions Inc.) in 8 4 patches with 10 10 spots per patch on aminosilane slides (GAPS IITM, Corning Inc.). Targets were prepared by reverse transcribing total RNAs (100 mg) from well-watered (Cy3) and water stressed (Cy5) plants using MicromaxTM direct labeling kit (Perkin-Elmer Life Sciences Inc.) as described by the manufacturers. The targets were pooled and hybridized with DNA probes on the array at 60 8C overnight and washed in 0.1 SSC, 0.2% SDS at 60 8C on GeneTACTM hybridization station. The slides were scanned using GeneTACTM LSIV laser scanner and the image was processed using Integrator Analyzer 3.3 software (Genomic Solutions Inc.).
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3. Principles of expression microarrays The principles and terms used in microarray technology are the reverse of those used in traditional blot techniques. Thousands of gene-specific DNA ‘probe’ molecules are immobilized on the surface of a non-porous glass slide and hybridized against fluorescent-labeled ‘target’ molecules that represent mRNAs in the tissue being examined. By virtue of sequence complementarities, the target molecules bind to those DNA probes that are the same as the genes that they represent in the tissue. The amount of fluorescent labels bound to each DNA probe on the array provides a direct estimate of gene expression in the tissue of interest (Fig. 1). 3.1. Arraying DNA probes The term microarray refers to an orderly arrangement of DNA probes, generally on a microscopic glass slide. Since
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DNA molecules can also be arrayed on a silicon or plastic surface, they are sometimes called ‘chips’. Probes ranging from 0.4 to 2.0 kilo base pairs (kbp) are produced typically by amplifying the desired region of DNA in a polymerase chain reaction (PCR). After amplification, the DNA samples are purified and suspended to a final concentration of 100– 500 ng/mL (Cheung et al., 1999) in 50% diethyl pyrocarbonate (DEPC) that maintains DNA in a readily hybridizable single stranded state. A robot (arrayer) with metallic pins is engaged to print DNA samples at defined locations on the surface of glass slide (Fig. 1). The robotic deposition of probes, known as ‘printing’, usually adopts a user-defined pattern that also establishes the identity and spot replications of each probe. Depending on the manufacturing design of pins, printing is done by either touching or dispensing 0.3– 1 nL of DNA probes onto the glass surface. To enhance the hydrophobicity and retention of DNA, the glass surface is usually coated with poly-lysine or aldehyde- or amino
Fig. 1. Process of gene expression profiling using cDNA microarray. DNA probes amplified through PCR are spotted on coated glass microscopic slides using robot. Targets are prepared by reverse transcribing total RNAs from reference (well-watered) and test (stressed) samples in the presence of cy3-dUTP (green fluor) or cy5-dUTP (red fluor). The targets are combined and hybridized to the array. Relative abundance of transcripts of 96 cDNA probes (spotted in duplicates) in stressed as compared to well-watered panicles of Azucena, Apo and IR64 plants are shown at the bottom.
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reactive silanes. Unlike the aldehyde groups that bind to DNA covalently upon Schiff’s base reduction, the polylysine or amino reactive silanes establish only non-covalent bonding to the printed DNA. Hence, DNA probes printed on such surfaces need to be cross-linked by UV irradiation or baking (Deyholos and Galbraith, 2001). 3.2. Probe formats Designing the probes for an expression microarray largely depends on the research questions being asked. For a broad exploration of genes induced by drought stress in various plant tissues, a whole genome array would be desirable. In model species, such as rice and Arabidopsis, the entire genomic sequences can be scanned for putative genes and sequences corresponding to coding and/or the unique 30 untranslated regions (30 -UTR) of all the genes in the genome can be spotted on the array. Such high-density arrays representing unique genes in a non-redundant fashion are referred to as ‘unigene arrays’ (Boguski and Schuler, 1995). Since most of the physiologically important genes in crop plants exist as ‘gene families’ that comprise two or more variants of the same gene, arrays carrying 30 -UTR probes have an advantage of distinguishing the expression of family members. However, comprehensive genome wide unigene arrays are not yet widely available for agricultural crops due to difficulties in characterizing their complex genomic sequences. This constraint necessitates the use of smaller arrays carrying complementary DNAs (cDNAs) derived from mRNAs in tissue(s) of interest. The complexity of cDNA molecules forms an important basis of microarray and depends on how the cDNA collection (cDNA library) was made. A cDNA library can be generated through ‘subtraction’ by which the cDNA molecules derived from the mRNAs of reference tissues are removed by hybridizing with excess amount of cDNAs derived from test samples. Such subtracted libraries often represent the differentially expressed genes in the test samples. An alternate method of cDNA library production involves ‘normalization’ where the discrepancy in cellular mRNA abundance between different genes is corrected. A tobacco leaf cell, for example, contains about 12,000 distinct mRNA species, of which about 700 most abundant species represent 52% of the total mRNA mass (Goldberg et al., 1978). Normalization of cDNAs is carried out by two or more cycles of denaturation and reassociation among double stranded (ds) cDNAs or between ss-cDNAs and the initial mRNAs. Because reassociation kinetics depends on concentration of molecules and duration, only ss-cDNAs from rare mRNAs and relatively fewer copies of abundantly expressed mRNAs do not reassociate. The ss-cDNAs are then selectively purified and enriched in the cDNA library. Recent studies show that pooling of mRNAs from multiple tissues increases the efficiency of subtraction and normalization of cDNAs and greatly enhances the number of unique genes represented in
the library (Smith et al., 1997). However, since cDNAs in a library represent only the steady-state mRNAs of the tissue(s) from which the cDNA library was made, microarrays carrying cDNA probes are less versatile than whole-genome arrays. Furthermore, cDNA probes do not effectively distinguish the expression of members of a gene family and those mRNA variants that are spliced alternately from the same gene. Such shortcomings are overcome by the development of oligonucleotide-based arrays. Oligonucleotide arrays, also known as ‘oligo arrays’ or ‘DNA chips’, carry short (60 bases) synthetic DNA probes. In a sequential manner, the oligo nucleotides are directly attached to a glass surface that is derivatized with linker molecules carrying a photo-labile group. Using a photolithographic mask, specific areas of the glass slide are illuminated so as to remove the photo-labile caps (deprotection). Then the surface is flooded with selected nucleotides (coupling), which also carries photolabile groups. After washing the excess uncoupled nucleotides, the cycle of deprotection and coupling is repeated with the next nucleotide until completion (Lipshutz et al., 1999). Recent introduction of mask-less array synthesizer (MAS) has significantly reduced the time and cost of synthesizing oligo arrays by replacing photolithographic masks with computer generated virtual masks (Singh-Gasson et al., 1999). An important advantage of oligo arrays is that each gene shall be represented by several different oligos spanning the entire length of the gene. In addition, each of these oligonucleotides shall be accompanied by a mismatch oligonucleotide in which the central base has been changed. Such a combination of probe redundancy and mismatch controls greatly enhances the accuracy in detecting the expression of family members and mRNA variants of genes. 3.3. Hybridization and scanning Expression array hybridizations can be treated in a similar manner to classical crop field experiments, by decomposing the experiment into various samples, treatments and treatment interactions (Bartolome, 2002). Typical treatments might consist of stressed and unstressed germplasm, across various germplasm targets. In the hybridizations, the mRNAs of ‘reference’ (unstressed) and ‘test’ (stressed) sample tissues represent the target molecules. But since mRNAs are highly unstable and often form secondary structures that hinder their hybridization with DNA probes on the glass surface, they are often converted to single stranded cDNA molecules in a single round of reverse transcription in the presence of fluorescent nucleotide labels. Cyanine 3 (cy3) and cyanine 5 (cy5) dUTPs are the two most commonly used fluorescent labels because of their high incorporation efficiencies during reverse transcription and wide separation of their excitation and emission spectra (Carlsson and Ulfhake, 1995). To distinguish their mRNAs, the test and reference samples are
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distinctly labeled with cy3 or cy5 dUTPs (Fig. 1). The labeled cDNA molecules are then combined and added to the surface of a single array in a competitive hybridization reaction. After hybridization, the bound target molecules are quantified by detecting their emission following laser excitation. Green spots represent expression in targets labeled with cy3, red spots correspond to targets labeled with cy5, and those that are yellow represent genes that are expressed in both the targets (Fig. 1). Since the amount of DNA probes on the array is in excess of mRNA levels (Bartosiewicz et al., 2000), the differences in cy3 and cy5 emissions after hybridization represent the differences in transcript abundance between test and reference tissues. It should be noted, however, that intrinsic differences in labeling and/or laser detection efficiencies between cy3 and cy5, and qualitative and quantitative differences between test and reference mRNAs in the labeling reaction could lead to differences in signal intensities. To overcome the intrinsic differences in labeling and detection efficiencies between cy3 and cy5, some researchers label both test and reference targets using the same fluor and hybridize each labeled target individually to two identical arrays (Yazaki et al., 2000). The ability to identify changes in transcript levels of genes, such as transcription factors that are expressed in low abundance, is determined by signal-to-noise ratio (S/N) observed on the slide. S/N represents ratio of specific signals from the array spots and the non-specific signals (noise) from the background. Since transcription factors play a major role in drought stress responses (YamaguchiShinozaki and Shinozaki, 2001), a high S/N ratio is needed to ensure that up- or down regulation of these lowabundance transcripts are also quantified. The sensitivity of detection and hence S/N can be enhanced by labeling the target molecules indirectly. Indirect labeling methods incorporate modified nucleotides such as 5-(3-aminoallyl)-20 -dUTPs during reverse transcription. After hybridization, cDNAs are treated with antibody–enzyme conjugate that recognizes the modified nucleotides and catalyzes the deposition of fluorescent dyes, which result in amplification of hybridization signals (Karsten et al., 2002). Besides enhancing the sensitivity, such methods also reduce the need for large amounts of initial mRNAs for hybridization and thus might enable large-scale gene expression studies in specific tissues such as anthers and ovaries.
4. Data analyses The fluorescent signals captured by the laser detector after hybridization represents the raw data of gene expression. Further analyses of gene expression data involve several steps. First the spots representing each DNA probe on the array must be identified and integrated with the arraying pattern that was generated during printing. Since the background noise could vary across the slide due to
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precipitates, contaminants and dust at various stages of handling, background values are calculated locally for each spot and used to subtract from the total spot intensity. The spot intensities of all the replicated spots are then used to calculate the median or mean for each DNA probe on the array. Once the data for all the DNA probes on the array are obtained for cy3 and cy5, the two expression data sets need to be normalized (corrected) so that the differences in labeling efficiencies between the two fluors and/or the two mRNA populations are taken into account (Tseng et al., 2001). The normalization factor is computed from the intensities of either all the probes on the array (global) or a subset of genes that are not expected to change (internal) or a set of non-plant DNA (external) controls. A linear regression analysis is usually applied in calculating the ‘normalization factor’ based on the assumption that if the labeling efficiencies are identical for the two targets being compared, the scatter plot of measured intensities of either all or control spots should have a slope of one. Any deviation is then calculated as normalization factor and used to rescale the data and correct the slope to one. Following normalization, the data are transformed to logarithmic scale, which has the advantages of treating upand down regulated genes in a similar fashion. When gene expression in the normal and stressed plants are to be expressed as simple arithmetic ratios of cy3 and cy5 signals, then a gene that is up regulated by two-fold has an expression ratio of 2, where as a gene that is down regulated by two-fold has an expression ratio of 0.5. The transformed data will show a gene that is up regulated by two-fold as having a log2 (ratio) of 1 and a gene that is down regulated by two-fold as having log2 (ratio) of 1 (Quackenbush, 2001). In general, genes showing at least two fold up- or down regulation are potentially considered as differentially expressed; however, it is well advised to undertake microarray analyses of variance (MAANOVA; Wu et al., 2003) or similar analyses of multiple mRNA samples extracted from various tissue replicates collected in an experiment (biological replication), to assess whether or not the level of expression of the gene is truly statistically significant. Repeat hybridizations on multiple arrays using the mRNA samples extracted from the same tissue source (technical replication) help define the bounds of experimental error (Bartolome, 2002). Statistically significant differential gene expression data may subsequently be analyzed using a variety of techniques developed over the past several years (for a well written review of many of these approaches, consult Quackenbush, 2001). It is important to critically assess the underlying assumptions of the various approaches. For example, expression profiles may be assembled from the results of multiple experiments across multiple treatments. Such profiles may be subjected to various non-supervised algorithms such as hierarchical clustering based on arbitrary distance measurements between expression profiles. Such techniques can hint at associations between genes but they
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are generally exploratory and do not confirm the underlying genetics of regulation. That is, genes sharing similar profiles may potentially, though not inevitably, be subjected to similar regulation. Analytical methodology, such as latent class modeling, stands upon firmer statistical grounds and therefore should be considered in any full treatment of microarray data (Gieser et al., 2002).
5. Drought-induced changes in gene expression in rice panicles—a case study In the recent years, several research groups have reported drought-mediated changes in gene expression using microarrays. Ozturk et al. (2002) reported that transcripts encoding jasmonate-responsive, late-embryogenesis abundant and ABA-responsive proteins were up regulated by more than 2.5-fold in drought-stressed barley seedlings. Zinselmeier et al. (2002) showed coordinate regulation of starch biosynthesis under drought stress in elite maize hybrids during flowering. Rabbani et al. (2003) identified 62 genes out of 1700 genes as drought inducible in 2-week-old rice seedlings. In drought-stressed Arabidopsis seedlings, Seki et al. (2002) identified 277 drought responsive genes from arrays containing about 7000 genes. During the reproductive phase, drought affects rice yield most when it occurs near flowering (O’Toole and Moya, 1981). To study the drought-induced changes in gene expression patterns in panicles using microarrays, we recently generated a normalized panicle cDNA library (see Section 2). We arrayed the cDNA inserts of 1536 randomly selected clones from this library on glass slides, and examined droughtinduced changes in their expression in panicles at heading. We compared the expression profiles in panicles of three varieties, viz., Apo (a drought-tolerant upland indica cultivar), Azucena (a reportedly drought-tolerant traditional upland japonica cultivar) and IR64 (a drought-susceptible semi-dwarf lowland indica cultivar) grown under wellwatered and stressed conditions in the field. These cultivars showed dramatic decreases in yield when water was withheld from plots for about 18 days prior to heading (Table 1). The midday leaf water potentials of all varieties were decreased significantly by the stress episode,
indicating that all cultivars experienced significant stress (Table 1). The main yield component responsible for the reduction in yield under stress was spikelet fertility, which declined to 33% in Azucena, 38% in Apo and 7% in IR64. The panicles sampled for gene expression studies represented those plants that were able to flower under stress, which generally flowered somewhat earlier than the bulk of the plot. Genes that were up- or down regulated by two-fold in the three varieties were selected and clustered based on their expression pattern (Fig. 2). Drought stress up regulated the transcript levels of dehydrin in the two drought tolerant varieties under drought stress, but not in drought susceptible IR64 (Fig. 2) suggesting that dehydrin may have contributed to the drought tolerance (Xu et al., 1996) of Apo and Azucena. On the other hand, genes such as GTP binding protein 3, GTP binding regulatory protein, S-receptor kinase, cellulose synthase-6, tubulin beta chain, subtilase, peroxidase and four other unknown proteins showed differential regulation only in IR64 (Fig. 2) indicating that transcriptional regulation of these genes may not be of any adaptive value under drought, or indicate damage due to stress. Out of 1536 EST clones, only genes encoding 20S proteasome beta subunit C, pectin methyltransferase and two other unknown proteins showed similar expression pattern in all the three varieties. Despite their divergence, the overall expression patterns of Azucena and IR64 displayed high similarity. Transcripts for 70 kDa heat shock cognate protein, DNA repair protein, reductase, zinc finger protein, histidine transporter, pollen allergen protein, 20S proteasome beta type 3, regulatory protein for Psr1 and 4 unknown proteins showed differential regulation in both Azucena and IR64. On the other hand, Apo and IR64 displayed similar expression pattern only for genes encoding actin depolymerizing factor, pectin esterase and five unknown proteins. Apo and Azucena regulated different sets of genes under stress, suggesting that these two cultivars may possess distinct tolerant mechanisms. For example, up regulation of storage proteins such as glutelin type B1, glutelin type AIII, prolamin 17 and prolamin 14 found in Azucena panicles reflects its ability to maintain grain development under water stress. In Apo, down regulation of genes encoding auxin response factor 7A and NPK1, a mitogen-activated protein
Table 1 Measurements of plant water status, yield and spikelet fertility under stress in three contrasting rice cultivars Parameter
Shoot water potential (midday; MPa) Transpiration (mmol cm2 s1) Grain yield (g m2) Spikelet fertility (%) Thousand grain weight (g)
Azucena
Apo
IR64
L.S.D.0.05
Well-watered
Stress
Well-watered
Stress
Well-watered
Stress
2.0 18 159 58 26.8
2.5 6 7 33 21.4
2.0 19 250 69 19.0
3.0 9 70 38 16.1
2.0 25 129 69 20.1
3.3 7 2 7 15.8
0.38 7 86 20 2.1
Plants were grown in the field in aerobic conditions, with or without an 18-day period of water exclusion that coincided with flowering. Plants were grown at the experiment station of the International Rice Research Institute, Los Ban˜os, Philippines, in the 2002 dry season.
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Fig. 2. Expression pattern of differentially regulated genes in Azucena, Apo and IR64 under drought stress. Hierarchial algorithm was used to cluster genes according to their average distance in expression values across all the three genotypes. The brightness of color is proportional to the level of expression.
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kinase, suggests a role for negative regulation of auxin signal transduction pathways in maintaining embryo and endosperm development under stress (Kovtun et al., 1998). In addition, the down regulation of mRNA levels of trehalose phosphatase and triose phosphate translocator in Apo implies that carbon partitioning in panicles might play a role in drought tolerance. Although not elaborate, these results show how microarrays can be used as diagnostic tools to identify the variations in expression pattern in panicles under drought stress. Our findings also demonstrate the utility of expression data in generating hypotheses related to physiological and metabolic pathways of drought tolerance.
6. Applications of expression microarrays in drought research Gene expression microarrays help explain the molecular basis of how different plants respond to a particular stimulus. Unlike traditional techniques, microarrays allow simultaneous analyses of a large number of genes in an unbiased manner. Hence, the microarray is recognized as a powerful tool that can rapidly advance our efforts on improving drought tolerance in agricultural crops. Expression microarrays are being used to study cellular responses to various other stresses in plants, as well (Kawasaki et al., 2001; Seki et al., 2002; Kreps et al., 2002). One of the many potential applications is to identify drought tolerance mechanisms by comparing changes in gene expression in tolerant and susceptible phenotypes. Elucidation of complex gene expression patterns serve as ‘molecular fingerprints’, which can facilitate our understanding and screening for physiological processes involved in drought tolerance. Because gene expression patterns vary between physiological states of different plant tissues such as panicles, leaves and roots, microarrays can generate comparative multidimensional maps of gene expression for normal and stressed tissues. Besides providing insights into physiological processes, a broad the approach to in transcriptional profiling can help dissect complex developmental aspects of composite tissues such as panicles. Drought stress triggers a cascade of biological responses in plants starting from signaling to physiological responses. The physiological responses are prompted by gene expression patterns that generally change in a complex but orchestrated fashion (Kawasaki et al., 2001; Kreps et al., 2002). The coordination relies mainly on the activity of regulatory compounds and/or proteins which themselves are products of genes. When such indirect gene–gene interactions are functionally impaired, they affect the expression of other dependent (downstream) genes. Hence, genes that are significantly up- or down regulated in a drought tolerant genotype when compared to a susceptible genotype, can serve as ‘transcriptional candidates’ for drought tolerance. Correlation of chromosomal positions of transcriptional
candidates with additional data sets such as QTL or, physiological features of drought tolerant genotypes might add significance to these candidate genes. Such analysis holds promise to discover new and specific markers for drought tolerance that may be used in conjunction with classical breeding techniques. Another effective way of using candidate genes is to represent all the drought responsive transcriptional candidates from large-scale expression profiles in smaller ‘sub arrays’. Molecular phenotyping of germplasm using sub-arrays could, after appropriate validation, supplement or replace extensive screening procedures for drought tolerance traits in the field. Gene-to-gene interactions have also led to the concept of ‘gene networks’. Expression microarrays can be used to discover sets of genes that are coordinately expressed in temporal or spatial fashion under drought-stressed conditions. Clustering of such co-expressed genes can be very useful in discovering the regulatory mechanisms of genes. In plants, gene expression is regulated by the binding of specific proteins called ‘transcription factors’ to sequencespecific sites located on the upstream promoter regions of the gene. Genes that are co-expressed often share a similar pattern of regulatory sequences that are responsible for binding of such transcription factors. Using a range of pattern detection algorithms, one can search and score discrete sequence motifs that are statistically over represented in a given cluster of genes (Vilo and Kivinen, 2001). Identification of such regulatory motifs might help us discover potential signaling intermediates of drought stress. Often the regulatory factor(s) responsible for coordinate expression can be identified from the expression profile itself. But the functional roles of such factors under drought stress need to be verified through mutation or transformation studies. By performing expression microarray analyses on mutants or transgenic plants in which such factors are altered, one can assess their controls on the expression of downstream drought-responsive genes. Expression microarrays thus indirectly help in understanding gene functions by providing decisive hints. Besides helping in identification and validation of novel drought tolerance genes, gene expression microarrays can also provide insights into previously unknown physiological consequences of genetic modification in plants. The biological insights gained from microarray studies can thus facilitate finding new and effective transgenic strategies for drought tolerance. There are some limitations to gene discovery using expression microarrays. One is that these arrays, being based on RNA, produce results that are highly stage and tissuedependent. It is possible that a key gene that underpins the difference between drought-tolerant and drought-susceptible cultivars is expressed at a time or in a tissue that is not tested in the experiment, meaning that the gene product is not detected. Other genetic differences between tolerant and susceptible types may be due to post-translational modification, which would not be detected by expression arrays, or may simply be due to differences in plant type or maturity.
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Microarray analysis should be complemented by other techniques that can detect non-expression based differences between lines. In summary, we described the basic principles of microarrays in studying the changes in gene expression. Using rice as a model, we demonstrated how microarrays can be used to compare genotypes for their gene expression profiles in response to drought stress. Although there are other DNA-based microarray techniques that can be used to identify mutations, polymorphisms, and comparative genomic analysis (Solinas-Toldo et al., 1997; Jaccoud et al., 2001), here we discussed the applications of RNA-based gene expression microarrays in understanding and improving drought tolerance. Future applications of expression microarrays will be aided by robust protocols that require less experimental tissues or RNA, techniques that can assess the concurrent changes at protein and metabolite levels, and our ability to identify significant and biologically relevant alterations in gene expression within large-scale data sets.
Acknowledgments We acknowledge the technical support from Evelyn Liwanag and Wen Jiang. This research was supported by a grant from Bundesministerium fu¨r Wirtschaftliche Zusammenarbeitund Entwicklung (BMZ, Germany). We would like to thank Dr. Volker Stiller, University of South Eastern Louisiana, for providing water potential measurements reported in this study.
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