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© 2004 American Society of Plant Biologists
Cutting Edge Transcriptome Analysis: It's All about Designneckardt{at}aspb.org
Since DNA microarrays came on the scene nearly 10 years ago, the scientific community has struggled to come to grips with this powerful technology. We have seen an expanding range of chip design and improvements in signal detection and data analysis, the creation of public databases and the Microarray Gene Expression Data Society (http://www.mged.org/), and the promotion of community standards for conducting microarray experiments (Brazma et al., 2002
Small scale microarray experiments with little or no biological replication or sophisticated statistical analysis have been used successfully to generate hypotheses about gene function that are then tested and verified by independent means (a good example of this is provided in Mele et al., 2003
Results from microarray experiments have the typical characteristics of data that calls for statistically based experimental design and analysis; namely, huge amounts of data associated with potentially large amounts of variation from multiple and diverse sources (i.e., there are numerous sources of both technical and biological variation). However, microarray data differs from data associated with most other types of conventional statistical experiments in a critical respect that has challenged statisticians to develop new analytical tools (or to impose new twists on old analytical tools). That is, conventional statistics typically deals with many observations (e.g., thousands) made on relatively few parameters (e.g., several), whereas microarray data deals with relatively few observations (in many cases, very few) made on many thousands of parameters (e.g., thousands of genes per chip). Happily, many in the growing fields of biostatistics and bioinformatics have stepped forward to meet this challenge, and there are now numerous analytical methods for the treatment of microarray data (Wolfinger et al., 2001
Still, it is important to realize that there is no single best way of designing and analyzing genome-wide expression experiments. What is important is that researchers provide clear descriptions of their experimental design, methodology, and analysis. A clear distinction should be made between technical and biological replication, and the best analyses strive to account for the major sources of both. In this issue of The Plant Cell, Caldo et al. (pages 2514–2528) provide an excellent example of a well designed and analyzed set of transcript profiling experiments that give us valuable new resources as well as novel insights into the expression of plant defense responses. The authors made use of the Affymetrix Barley1 GeneChip containing probes sets corresponding to
The biotrophic fungus Blumeria graminis f. sp hordei (Bgh) is the causal agent of powdery mildew disease in barley (Hordeum vulgare). Compatible (the successful establishment of the fungus and development of disease) versus incompatible (ultimately leading to the termination of fungal growth and disease resistance) interactions are specified by a classic gene-for-gene mechanism involving genes designated Ml (Collins et al., 2002
Caldo et al. used a 3 x 2 matrix design consisting of three near-isogenic barley lines, each containing an introgressed Mla1, Mla6, or Mla13 allele, challenged separately with two Bgh isolates: Bgh 5874 (containing AvrMla1 and AvrMla6) and Bgh K1 (containing AvrMla1 and AvrMla13). This provided for six different host–pathogen combinations that encompassed two different Rar1-dependent incompatible versus compatible interactions (determined by the presence or absence of Mla6 or Mla13) and two Rar1-independent incompatible interactions (specified by Mla1). For each of the six host–pathogen combinations, 10 to 15 barley seedlings were harvested and pooled for RNA isolation at six time points (0, 8, 16, 20, 24, and 32 h after inoculation). The entire experiment was then repeated three times in a standard split-split-plot design (Kuehl, 2000 The strength of this work comes not solely from the amount of replication and total number of hybridizations performed, but even more importantly, from the careful experimental design that accounted for changes in gene expression over time and differences in host–pathogen interactions depending on plant and fungal genotype. The authors offer clear descriptions of the design, analysis, and the rationale behind their approach. They point out that this particular analysis may have missed some biologically relevant genes (i.e., that are differentially expressed only at one or two time points or only in association with specific host–fungal isolate combinations) but hypothesize that genes showing differential expression patterns across time (i.e., within the 32 h after inoculation) and across different host–isolate combinations offer the most potential for differentiating between compatible and incompatible interactions and determining key components of pathways underlying the molecular basis of these interactions. In one of several different analyses reported in the article, the authors compared gene expression in the Rar1-dependent incompatible and compatible interactions and identified a set of 22 genes whose average 0 to 32 h time-course expression patterns differed significantly. Examination of the individual gene expression profiles showed that expression of these genes was induced in a similar or identical manner in both compatible and incompatible interactions up to 16 h after inoculation and diverged significantly in compatible relative to incompatible interactions after 16 h (see figure). This time point closely coincides with the time that Bgh haustoria make contact with the host cell plasma membrane after initial infection and thus is consistent with the kinetics of Bgh infection. Expression of this set of genes then decreased steadily from 16 to 32 h after inoculation in the compatible interactions, whereas it generally increased or remained steady in the incompatible reactions. Of the 22 genes in this set, seven were predicted to encode components of the shikimate pathway leading to biosynthesis of phenylpropanoid phytoalexins and lignin, 10 had predicted functions in cellular metabolism, oxidative stress, and ethylene biosynthesis, and five corresponded to proteins of unknown function.
These observations are consistent with the notion that this set of genes encodes proteins that are involved in basal nonspecific defense pathways and that recognition of avirulence factors in the incompatible interaction leads to the maintenance of increased expression, whereas the compatible interaction leads to active suppression of these genes and the associated nonspecific defense responses. Based on the coordinated suppression of defense-related transcripts, the authors hypothesize that the targets of virulence factors are possible regulators of basal resistance. Taken together, these principles also are consistent with the guard hypothesis of R gene function, which postulates that R proteins guard other host proteins that are the targets of avirulence factors (which, in the absence of specific recognition by the host, play a role in virulence of the pathogen) (Van der Biezen and Jones, 1998  ; Dangl and Jones, 2001). Although many studies have been conducted on plant perception of pathogen-derived molecules, the connection between the recognition of general and specific elicitors in the expression of compatibility and incompatibility responses are just beginning to be elucidated (Navarro et al., 2004). From these results, the authors propose a model that links the recognition of general elicitors and specific avirulence proteins in the expression of plant defense responses, supporting the hypothesis that host-specific resistance evolved from the recognition and prevention of the pathogen's suppression of plant basal defense. In addition to the specific conclusions derived from this analysis, the complete data set from these experiments is publicly available in the BarleyBase repository for cereal GeneChip data (http://barleybase.org/), as well as in ArrayExpress (http://www.ebi.ac.uk/arrayexpress/), providing an invaluable resource for the scientific community.
Azevedo, C., Sadanandon, A., Kitagawa, K., Freialdenhoven, A., Shirasu, K., and Schulze-Lefert, P. (2002). The RAR1 interactor SGT1, an essential component of R gene-triggered disease resistance. Science 295, 2073–2076. Brazma, A., et al. (2002). Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet. 29, 365–371.
Caldo, R.A., Nettleton, D., and Wise, R.P. (2004). Interaction-dependent gene expression in Mla-specified response to barley powdery mildew. Plant Cell 16, 2514–2528.
Close, T.J., Wanamaker, S., Caldo, R.A., Turner, S.M., Ashlock, D.A., Dickerson, J.A., Wing, R.A., Muehlbauer, G.J., Kleinhofs, A., and Wise, R.P. (2004). A new resource for cereal genomics: 22K barley GeneChip comes of age. Plant Physiol. 134, 960–968. Collins, N.C., Sadanandom, A., and Schulze-Lefert, P. (2002). Genes and molecular mechanisms controlling powdery mildew resistance in barley. The Powdery Mildews: A Comprehensive Treatise, R.R., Bélanger, W.R. Bushnell, A.J. Dik, and T.L.W. Carver, eds (St. Paul, MN: APS Press), pp. 134–145. Craig, B.A., Black, M.A., and Doerge, R.W. (2003). Gene expression data: The technology and statistical analysis. J. Agric. Biol. Environ. Stat. 8, 1–28. Cui, X.Q., and Churchill, G.A. (2003). Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 4, 210. Dangl, J.L., and Jones, J.D.G. (2001). Plant pathogens and integrated defense responses to infection. Nature 411, 826–833.[CrossRef][Medline] Draghici, S. (2002). Statistical intelligence: Effective analysis of high-density microarray data. Drug Discov. Today 7 (suppl.), S55–S63.[CrossRef][ISI][Medline]
Draghici, S., Kulaeva, O., Hoff, B., Petrov, A., Shams, S., and Tainsky, M.A. (2003). Noise sampling method: An ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays. Bioinformatics 19, 1348–1359. Halterman, D., and Wise, R.P. (2004). A single amino acid substitution in the sixth leucine-rich repeat of barley MLA6 and MLA13 alleviates dependence on RAR1 for disease resistance signaling. Plant J. 38, 215–226.[CrossRef][ISI][Medline] Jones, J.D.G. (2001). Putting knowledge of plant disease resistance genes to work. Curr. Opin. Plant Biol. 4, 281–287.[CrossRef][ISI][Medline] Jørgensen, J.H. (1994). Genetics of powdery mildew resistance in barley. Crit. Rev. Plant Sci. 13, 97–119. Kuehl, R.O. (2000). Design of Experiments: Statistical Principles of Research Design and Analysis, 2nd ed. (Pacific Grove, CA: Duxbury Press).
Mele, G., Ori, N., Sato, Y., and Hake, S. (2003). The knotted1-like homeobox gene BREVEPEDICELLUS regulates cell differentiation by modulating metabolic pathways. Genes Dev. 17, 2088–2093.
Meyers, B.C., Galbraith, D.W., Nelson, T., and Agrawal, V. (2004). Methods for transcriptional profiling in plants. Be fruitful and replicate. Plant Physiol. 135, 637–652.
Navarro, L., Zipfel, C., Rowland, O., Keller, I., Robatzek, S., Boller, T., and Jones, J.D.G. (2004). The transcriptional innate immune response to flg22. Interplay and overlap with Avr gene-dependent defense responses and bacterial pathogenesis. Plant Physiol. 135, 1113–1128.
Shen, Q.-H., Zhou, F., Bieri, S., Haizel, T., Shirasu, K., and Shulze-Lefert, P. (2003). Recognition specificity and Rar1/Sgt1 dependence in barley Mla disease resistance genes to the powdery mildew fungus. Plant Cell 15, 732–744.
Storey, J.D., and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445. Van der Biezen, E.A., and Jones, J.D.G. (1998). Plant disease-resistance proteins and the gene-for-gene concept. Trends Biochem. Sci. 23, 454–456.[CrossRef][ISI][Medline] Wolfinger, R.D., Gibson, G., Wolfinger, E.D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C., and Paules, R.S. (2001). Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8, 625–637.[CrossRef][ISI][Medline] Related articles in Plant Cell:
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22,000 barley genes (Close et al., 2004
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