An initial application of plant genomics has been to monitor gene expression on a scale much larger than previously possible. Although multiplexed assays of RNA abundance have developed more quickly than those for protein and metabolite levels, some combination of these approaches will soon be providing our best views yet into plant molecular biology. Three techniques that have made contributions to the RNA transcript portion of this combination are reviewed. Currently, each can produce a profile of expression levels for a large but
incomplete set of plant genes, at reproducibly high levels of accuracy and over a range of labor and financial expenses.
Addresses
Disease Resistance Group, Trait and Technology Development Pioneer Hi-Bred International Inc., 7300 NW 62nd Ave., Johnston, IA 50131-1004, USA
*e-mail: [email protected]
†e-mail: [email protected] ‡e-mail: [email protected]
Current Opinion in Plant Biology1999, 2:96–103 http://biomednet.com/elecref/1369526600200096 © Elsevier Science Ltd ISSN 1369-5266
Abbreviations
AFLP amplified fragment length polymorphism
AP-PCR arbitrarily primed PCR
cDNA complementary deoxyribonucleic acid
cRNA complementary ribonucleic acid
EST expressed sequence tag
HD high-density
MTAP Microarray Technology Access Program
PCR polymerase chain reaction
Introduction
The ability to generate profiles of the abundance of RNA transcripts has numerous applications in plant biology including identification of tissue-specific or organ-specific transcripts, developmental stage-specific transcripts, tran-scripts induced by environmental stresses, and trantran-scripts induced or repressed upon pathogen infection. Generally, biologists are interested in those genes expressed differen-tially in two or more populations of RNA transcripts. In the past, differentially expressed genes were usually identified by subtractive hybridizations, differential plaque hybridiza-tions, or protein gel differences, followed by micro-sequencing or using antibodies to clone cDNAs. As large sequence databases become available for plants, the number of genes we would like to monitor becomes too large for traditional analyses such as Northern blots. Ideally, an expression assay covering all genes in the plant cell will reveal how patterns change during differentiation, growth, response to stress, etc. In the past few years, several tech-niques have become available to monitor the expression of large numbers of genes. Most of these techniques are based
on gel fractionation of cDNAs or hybridization to DNAs immobilized on a solid support. Another approach, not dis-cussed further in this review, is sequencing-intensive and includes the serial analysis of gene expression [1] and ‘elec-tronic Northerns’ in which the representation of sequences in a database is used to measure the abundance of a partic-ular transcript. The following comparison of methods for detecting differential RNA expression includes gel-based AFLP assays of cDNA, nylon filter arrays and two types of microarrays. Examples using each of these three methods in functional genomics research are referenced, and the next step up in scale for the plant portion of this field is illustrated by a brief look at some of the projects from the NSF Plant Genome Research Program.
Gel-based transcript profiles
The first RNA profiling techniques developed were ‘dif-ferential display’ by Liang and Pardee [2] and arbitrarily primed (AP) PCR by Welsh et al. [3], both of which use arbitrary primers to amplify portions of cDNAs which are then fractionated on a polyacrylamide sequencing gel. The main difference between the two methods is that differen-tial display uses an anchored oligo-dT primer plus one arbitrary primer, whereas AP-PCR is not anchored to the 3′-end. Differential display has been used widely because it is fast, inexpensive, sensitive and simple to perform. The two techniques that it uses, PCR and denaturing polyacry-lamide gel electrophoresis, are routine in molecular biology labs. If a sufficient number of primers are tested, it should be possible to identify most transcripts in an mRNA sam-ple. A protocol is given in [4]. Differential display has been used to isolate a large number of plant genes differentially expressed during development [5,6], hormone response [7,8], environmental stresses [9,10], defense responses [11,12,13•] and nodule formation [14,15].
In spite of its popularity, differential display has several draw-backs. First, the number of false positives generated by this technique can be unacceptable. The annealing of the arbi-trary primers to the cDNA is done at relatively low temperatures (e.g. 40°C), reducing some priming specificity and producing autoradiograph band differences that do not reflect real differences in gene expression. Recognized by many as a major problem with conventional differential dis-play, nonspecific priming can be reduced through modifications to both the arbitrary and oligo-dT primers [16]. This problem can also be minimized by performing a num-ber of replicate experiments, preferably using different RNA preparations and PCR reactions, and only isolating bands that are consistently, differentially represented. A second dif-ficulty is that the fragments generated include only several hundred bases from the 3′-end. Sequence from this region is often insufficient to identify a gene, especially when using a
A comparison of gel-based, nylon filter and microarray
techniques to detect differential RNA expression in plants
database from an unrelated organism. As a result, one must often isolate a longer cDNA clone to identify the differen-tially expressed gene. Recent modifications to address this problem include the development of long-distance differen-tial display PCR, using hot start and rTthDNA polymerase [17]. Finally, a third problem arises during cloning of the identified fragment. Although the band can be excised from the sequencing gel with surprising accuracy, there are usual-ly several species of cDNA present in a band, leading to a mixed population of candidates after reamplification and cloning. We generally sequence six clones per band; we usu-ally see a predominant species represented but sometimes multiple Northern blots are required to identify the differ-entially expressed clone. A recent report comparing expression in normal and mammoplastic epithelial tissues describes dramatic reduction in the number of false positives using gene-specific primers to reamplify differential display products (bands were sequenced directly from the gel). Of 104 differentially displayed bands analyzed, 86% provided readable sequencing runs [18]. This allowed identification of 62 differentially expressed genes, 32 of which matched human ESTs of unknown function. This type of analysis shows the value of RNA profiling for placing uncharacterized ESTs into functional context. More often, high-throughput screens such as dot-blot arrays are now being used in con-junction with more sensitive probes (e.g. riboprobes) to increase the success rate of differential display [19,20].
Some of the above drawbacks of differential display have been overcome with amplified restriction fragment length polymorphism (AFLP) of cDNA, as shown by Bachem et al. [21] in the first application of this method for plant biology. In this technique, the cDNA population is cut with two restriction enzymes and adapters are ligated onto the result-ing cohesive ends. Selective PCR primers that extend past the adapters into the cDNA are used in the subsequent amplification to reduce the number of bands present on the denaturing gel. Again, the procedure is simple and rapidly performed, and the number of mRNA species visualized is limited only by how many pairs of restriction enzymes are tried. The main advantage is stringent primer hybridization to the adapters, thus reducing the variability of traditional differential display. Also, because amplification can origi-nate from any region of the cDNA there is a higher chance of detecting homology to related genes in EST databases. The problem of heterogeneity in the reamplified bands must still be addressed when performing AFLP of cDNA.
Both gel-based methods of transcript profiling are very useful and are easily and inexpensively performed. They do not rely on EST databases or existing cDNA libraries, allow detection of rare transcripts, and require relatively small amounts of mRNA. The main disadvantages include heterogeneity of final products, the need to clone and sequence the product for identification, and the need to isolate a full-length cDNA after obtaining the PCR product. At Pioneer Hi-Bred, we are fortunate to have access to the RNA profiling performed by the CuraGen
Corporation. PCR-amplified cDNA fragments are labeled with fluorescent probes, run on a denaturing gel, and detected by a fluorescence gel scanner. The resulting electropherograms (Figure 1) look similar to a trace from an automated DNA sequencer. Control and test RNA samples are aligned and band differences are easily visu-alized. The reproducibility of bands is excellent. Bands of interest must usually be cloned and sequenced for identi-fication, although CuraGen has a computer program which predicts band identity on the basis of fragment length and known end sequences. A high quality and rel-atively complete EST database is required to make best use of the CuraGen program. CuraGen estimates that their standard protocol for generating cDNA fragments produces 12,000 to 14,000 assayed bands per sample, with an average coverage of three bands per gene.
Transcript profiles using arrays
The second major type of RNA profiling is based on hybridization of transcripts to arrays of DNA molecules bound to a solid support. In these systems the support-bound DNA is in excess, so that the amount of probe hybridized to a particular DNA spot is a measure of the abundance of that transcript in the mRNA population. In general, the advantage of arrays is that they give quantita-tive information on the abundance of hundreds or thousands (depending on the array design) of specific Figure 1
Fluorescence electropherograms from a CuraGen analysis. Disease-resistant maize leaves were infected with a fungal pathogen, Cochliobolus carbonum, for six hours. Replicate profiles of
RNA-derived fragments expressed during the resulting defense response are compared to gel traces from control plants. The 47 base-pair band was induced 3.1-fold.
Defense response
Control
Band length (bp)
B
and intensity
genes simultaneously. Limiting the assay to a defined set of genes reduces the value of arrays as a gene discovery tool compared to the gel-based methods. They are, how-ever, invaluable at providing a global view of gene expression changes.
In practice, one can immobilize plasmid DNA, PCR prod-ucts or oligonucleotides to the support, which may be glass, nylon, nitrocellulose or silicon. The probe is usually cDNA derived from polyA+ RNA and labeled with radioactive or fluorescent nucleotides. The methods used and number of clones analyzed depend on the needs and budget of the researcher. Large-scale experiments done by outside com-panies can be extremely expensive. Smaller experiments, such as spotting clones onto nylon membranes, can be per-formed in any laboratory. Array technologies have been refined and combined with methods that enrich complex probe pools [22,23,24•,25•,26], allowing identification of rare but differentially expressed messages. We at Pioneer Hi-Bred have experience using three array technologies: spotting onto nylon membranes, Affymetrix GeneChips, and the Molecular Dynamics/Amersham glass slide Microarray Technology Access Program (MTAP).
Nylon filter arrays
Arrays of many cDNAs spotted or grown on nylon filters have been developed by a number of groups for RNA expression analyses [27–34] and Piétu et al. provide a very nice example of the statistical treatment of resulting array data [30]. The variety of gridding technologies in use illus-trates how filter arrays can be modified to fit research needs. These range from hand-held pinning devices which, when patterns are offset, can make 1536 spots on a 7×12 cm rectangle [35•] to Qbots (Genomix Ltd., Christchurch UK) capable of delivering nearly 60,000 spots to a 22×22 cm square. Robotics has made possible the generation of HD (high-density) arrays on filters as well as on other sup-ports [36•,37,38]. DNA or aliquots of bacterial colonies are removed from 96- or 384-well plates and arranged in repro-ducible fashion, in patterns determined by the user. One interesting recent report describes the application of old computer printer parts (an Apple StyleWriterTMII thermal jet printer) to this end [39]. Human cDNA arrays resulting from collaborations within the IMAGE consortium are available, along with detailed instructions for use [40,41]. Clontech markets ATLASTM arrays containing human, mouse and rat cDNAs involved in, among other pathways, apoptosis, stress response and cell-cycle regulation [42•]. Both Clontech and GenomeSystems offer custom clone picking and array construction.
The in-house filter-based arrays developed by the Pioneer Disease Resistance group provide a highly flexible and less expensive means (about $50 per array, not including cost of robotics) to follow expression of a reasonably large set of interesting genes under many conditions. We made arrays of cDNAs as part of our effort to characterize the defense response in maize, and so chose about 850 ESTs from our
collection that were known or hypothesized to be differen-tially regulated during plant/pathogen interactions. We also included control cDNAs (e.g. maize actins, histones, and ubiquitins, as well as human integrin). PCR-amplified inserts or plasmid DNAs are transferred from 96-well plates to an 864-dot format with a Biomek1000 robot (Beckman) onto 8×12 cm nylon filters. Blots are hybridized with 33P-labeled first-strand cDNA made from polyA+ RNA isolated from test and control tissues. Hybridizations are carried out in duplicate for each sample. Data is captured on PhosphorImager screens and analyzed with ImageQuant software (Molecular Dynamics). Although labor intensive, this approach yields highly reproducible results in our hands. Northern blots or other profiling experiments are used to confirm candidates iden-tified on filter arrays. These arrays rely on known EST sequences and thus cannot directly identify new genes, but new clones from our cDNA sequencing program can be quickly added to the array and assayed under many dis-ease and defence conditions. It should also be noted that arrays can be an indirect discovery tool because the pro-moters of arrayed co-regulated genes serve as probes for unknown regulatory factors.
Microarrays
Initial reports utilizing microarrays for differential expres-sion analyses have profiled RNA levels in Arabidopsis [43,44••], mammals [45–47], yeast [48,49••,50••,51] and bacteria [52]. Strawberry and petunia genes have been microarrayed [53•] and there are undoubtedly many more existing or planned applications of this technology for plant research. Several recent reviews are available cover-ing large-scale expression assays [36•], microarray theory and design [54,55] and genomics applications [56,57]. The microarray systems from Affymetrix and MTAP reflect the two main approaches currently available for massively par-allel assays of RNA expression: oligonucleotides on silicon and PCR products on glass microscope slides.
Oligomer microarrays on silicon
Table 1
Researchers in the 1998 NSF plant genome research program who are planning to develop expression profiling systems.
Project Principal investigator Institution Title E-mail address
(contacts)
1 Pamela J Green Michigan State University Functional analysis of the Arabidopsis [email protected] genome via gene disruption and global
gene expression analysis
1 (Shauna Sommerville) Carnegie Institute of Washington Microarray contact Shauna@Andrew2. Stanford.edu 1 (John Ohlrogge) Michigan State University Microarray contact [email protected]
1 (Mike Cherry) Stanford University Bioinformatics contact cherry@genome.
stanford.edu 2 Virginia Walbot Stanford University Maize gene discovery, sequencing [email protected]
and phenotypic analysis
2 (David Galbraith) University of Arizona Microarray contact [email protected] 3 Lila Vodkin University of Illinois Urbana-Champaign Functional genomics program [email protected]
for soybean
3 (Randy C Shoemaker) Iowa State University EST contact [email protected] 4 Steven Tanksley Cornell University Development of tools for tomato functional [email protected]
genomics: application to analysis of fruit development, responses to pathogens and
genome synteny with Arabidopsis
5 Douglas R Cook Texas A and M Medicago truncatulaas the nodal species drc1653@acs. for comparative and functional legume genomics tamu.edu 5 (Katheryn A Van Den Bosch) Texas A and M Microarray contact [email protected] 6 Hans Bohnert University of Arizona Genomics of plant stress tolerance [email protected] 7 Thea A Wilkins University of California at Davis Structure and function of the cotton tawilkins@
genome: an integrated analysis of the genetics, ucdavis.edu development and evolution of the cotton fiber
8 Nina V Federoff Pennsylvania State University New DNA microarray detection techniques [email protected] in the study of stress-induced changes in
plant gene expression
9 Rod A Wing Clemson University A BAC library resource of crop genomics [email protected] 10 Bertrand Lemieux University of Delaware Genomic analysis of seed qualilty traits [email protected]
in corn
Figure 2
Pseudo-color image of a maize GeneChip. Tissue samples were from the same fungal infection experiment used in Figure 1. cRNA derived from control plants was labeled and hybridized to the chip shown on the left. A magnified section is shown adjacent to the corresponding section from a second chip that was hybridized with cRNA from infected leaves. A set of 15 perfect match (PM) and mismatch (MM) probes is indicated for a gene that was induced twofold during the defense response.
Fluorescence
PM MM
Control Defense response
The maize GeneChip contains probes for 1500 ESTs or genes synthesized on a 1.6 cm2array. Most of these genes are represented by twenty 20-nucleotide oligomers con-taining sequences predicted to provide high stringency hybridization without cross-specificity for other cDNA sequences from the Pioneer Hi-Bred EST database. Each set of twenty probes is arrayed adjacent to a set of ‘mis-match’ probes that contain one incorrect nucleotide in the middle of the oligomer — a hybridization signal from a mis-match probe indicates a gene-family member may be contributing non-specific background for that probe. The initial study utilizing this GeneChip was designed to detect differentially expressed genes in leaf tissue infected with a fungal pathogen (Figure 2). Chip-to-chip variation was measured as the number of genes showing a signal differ-ence of 1.5-fold or more between replicates. Comparing 16 replicates from several control and treatment hybridiza-tions, the average chip-to-chip variation was 1.6% (standard deviation 0.9) or about 24 genes in the array. These genes varied by an average of 2.2-fold (standard deviation 1.0), and the identities and distribution of probes contributing to this variation appeared to be random. Monitoring such vari-ation is important with this technique because comparisons between mRNA samples require separate chips and hybridization reactions. The initial data suggest these arrays, and accompanying analysis software, are performing very consistently across chips. Deviation among detected fold-change values for differentially expressed genes has also been reasonably low, especially when the magnitude of change is greater than three fold. Accurate resolution of smaller expression differences is problematic with all the techniques reviewed, yet many of the genes that show changes between RNA populations are in this class (Figure 3, and [44••,46,49••,50••,51]). Consistently detect-ing and sortdetect-ing out biologically relevant changes of 1.5 to 3-fold will continue to be a challenge.
Design and synthesis costs are at this time a constraint upon unlimited use of oligomer chips, but these expenses may be reduced as commercial microarray producers expand beyond product development to full-scale production. Incorporation of new array synthesis techniques may also reduce chip design and production costs. Improvements in quality control, hybridization protocols and fluorescence staining are anticipated. Affymetrix, for example, is devel-oping a chip and scanner capable of assaying 400,000 features [58], or 20,000 genes per chip, will allow most maize genes to be arrayed in a manageable set. Improved pho-tolithography methods, such as the use of micromirrors rather than individual masks [59], may even allow researchers to design and produce their own oligomer arrays.
cDNA microarrays on glass slides
Although there are currently no other plant GeneChip arrays, a collaboration between Monsanto Co. and Synteni/Incyte has developed an Arabidopsis glass slide microarray [60]. Differential profiles for 1443 genes were used to compare expression in leaf, root and two floral tissue stages [44••]. Sensitivity was thought to be sufficient to detect transcripts as rare as one copy per cell, and repro-ducibility between slides was measured as 2.8% of the array elements varying by more than two fold. An advan-tage with this and related systems is the use of two fluorescent dyes (Cy3 and Cy5) to separately label the cDNA derived from samples to be compared. The cDNA pools can then be mixed and hybridized to a single array, and the ratio of Cy3 to Cy5 signals reflects the difference in abundance for the targeted transcript. A similar dual-dye strategy has been used in GeneChip experiments [61].
Another microarray option is the MTAP glass-based tech-nique for in-house array production. The program provides a robotic spotter that delivers PCR amplified cDNA to a microscope slide. Fluorescence labeled first-strand probe is produced by reverse transcription from the mRNA sample to be assayed, and hybridization is carried out in 30µl of solu-tion under a coverslip. Hybridizasolu-tion signals are detected using a scanning confocal microscope with a laser excitation source. The MTAP robotic spotter deposits 1536 targets, in duplicate, on each of 24 glass slides in about five hours. Designs for a do-it-yourself glass slide microarray system are available via the Internet from P Brown [62], and standard lab robotics such as the Biomek 2000 [63] can be adapted to produce similar microarrays. Current concerns include non-linearity of Cy3 versus Cy5 responses at low fluorescence intensities, reproducibility between arrays, and variation due to slight differences in hybridization conditions. Slide attach-ment chemistries, hybridization solution reagents, and alternative nucleotide derivatives for improved labeling and hybridization are all active areas of research.
Future plant microarray resources
The 1998 awards in the NSF Plant Genome Research Program reflect the rapid expansion of RNA expression profiling in plant molecular biology [64]. Nine of the 23 Figure 3
Distribution of differentially expressed maize genes during pathogen defense. Affymetrix GeneChip analysis identified 117 genes that consistently showed induced or repressed levels of RNA expression six hours after various treatments with the fungal pathogen C. carbonum.
-5
-4.5 -4 -3.5 -3 -2.5 -2 -1.5 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 >10
35
25 30
20
15
10
5
Fold change
Genes
funded abstracts propose development of techniques or databases for large-scale expression analyses. The Arabidopsis Functional Genomics Consortium intends to create service facilities that will provide the plant research community a knockout mutant screening service and glass slide microarrays for dual-dye expression profiling, begin-ning in year two of their grant (Table 1, project 1). Initially 7000–10,000 singleton ESTs from the Michigan State University collection will be arrayed as PCR fragments, and an independent review panel will prioritize proposed experiments that utilize the arrays. Expression profiles will be made publicly available on a database accessible via the World Wide Web. A similar ‘virtual center’ will focus on EST sequencing, mutant generation and glass slide microarrays for maize (project 2). The University of Illinois Biotechnology Center plans to compare nylon filter and glass slide arrays for soybean, using ESTs available from the Soybean Growers Association, and will provide stan-dard expression profiles in a public database (project 3). Additional plant profiling is planned for tomato (project 4), Medicago truncatula, basal, symbiotic and pathogen defense gene expression (project 5), salt and water stress genes (project 6), and cotton (project 7). Also, the Microarray Facility at Pennsylvania State University is developing Arabidopsis arrays with enhanced sensitivity using colloidal gold labeling and surface plasmon resonance for detection (project 8), and the Clemson University Genomics Institute plans to expand its plant bacterial artificial chro-mosome (BAC) and EST library arrays and to prepare microarrays for expression profiling (project 9). A collabo-ration has been formed to array Arabidopsis open reading frames and maize embryo ESTs in a glass slide system with detection of four dyes (project 10), and a microarray of Arabidopsis defense-related genes is in use [65•].
Conclusions
Of the two main RNA profiling approaches — gel-based assays and array hybridization — the gel-based techniques have been used more extensively to isolate many impor-tant differentially expressed genes. Differential display and AFLP of cDNA are inexpensive, rapid and can be per-formed in any laboratory. They are ‘open-ended’, that is to say they are not limited to an existing EST database or library of clones. They are excellent discovery tools to identify specific genes whose expression levels differ in two very similar populations of transcripts.
The solid-support arrays are more useful to give a broad view of gene expression changes between samples, although they also can be important discovery tools if the arrays are large enough. Nylon-based filter arrays are attractive for individual labs or for experiments where a specific set of genes are studied. They allow flexibility for quickly adding or removing clones from the array, and require no special equipment for hybridization. Gridding can be done by a robot or manually with a pinning device.
Large-scale commercial microarrays hold great promise, but are still in the developmental stage, and a limited num-ber of biologically important results have been published so far. Recent investigations of cell cycle-regulated expres-sion in yeast are examples of the scope of interesting questions that can be addressed using arrays with genome wide coverage [66,67••]. Such comprehensive tools will undoubtedly become the most efficient way to monitor gene expression changes in plant tissues. Currently these arrays are limited to a few thousand clones, but within a few years, systems providing nearly complete coverage of the Arabidopsis and maize genomes should be available. GeneChip and microscope slide methods are likely to be used in plant profiling experiments that complement each other. The Affymetrix package of multiple, independent and specific probes and comprehensive analysis software is well suited for initial surveys of gene expression. In-house microarrays provide a highly flexible and less expensive means to follow expression of a large set of interesting genes under many conditions. The immediate capacity to increase glass-slide array densities appears limited, and the investment of time, labor and organization can be signifi-cant for a collection of tens of thousands of purified PCR products. Whole-genome coverage may not always be nec-essary, however, and the ability to quickly add newly identified genes (or probes for newly created transgenic constructs) to a subset array will remain attractive. The degree of detail with which biologists will be able to mon-itor changes in gene expression using any of these approaches will allow huge leaps in our understanding of plant gene regulation.
Note added in proof
The Chipping Forecast is a supplement to Nature Genetics [68] that features fourteen perspectives and reviews on microarray analysis. It is available at http:// genetics.nature.com/chips_interstitial.html or by calling 1-800-524-0384 (US only) or + 1-615-377-3322 (outside the US). Also, two other recent articles featured expression profiling and differential display [69,70]. Additional Internet resources and a discussion of microarray applica-tions are provided by Kehoe, Villand and Somerville [71].
Acknowledgements
We thank our collaborators at CuraGen, Affymetrix, and Molecular Dynamics/Amersham for technical advice and reviews. We appreciate the support provided by our colleagues in genomics research at Pioneer Hi-Bred and DuPont, and thank participants in the Plant Genome Research Program for sharing project details.
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