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Copyright © 2005, American Society of Plant Biologists Peking-Yale Joint Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing 100871, People's Republic of China (L.M., X.W.D.); Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut 06520–8104 (L.M., Y.J., X.W.D.); Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut 06520 (N.S., H.Z.); and Laboratory of Molecular Cell Biology, Hebei Normal University, Shijiazhuang, Hebei 050016, People's Republic of China (X.L.) *Corresponding author; e-mail xingwang.deng/at/yale.edu; fax 203–432–3854. Received October 12, 2004; Revised February 14, 2005; Accepted February 14, 2005. This article has been cited by other articles in PMC.Abstract The development of complex eukaryotic organisms can be viewed as the selective expression of distinct fractions of the genome in different organs or tissue types in response to developmental and environmental cues. Here, we generated a genome expression atlas of 18 organ or tissue types representing the life cycle of Arabidopsis (Arabidopsis thaliana). We showed that each organ or tissue type had a defining genome expression pattern and that the degree to which organs share expression profiles is highly correlated with the biological relationship of organ types. Further, distinct fractions of the genome exhibited expression changes in response to environmental light among the three seedling organs, despite the fact that they share the same photoperception and transduction systems. A significant fraction of the genes in the Arabidopsis genome is organized into chromatin domains exhibiting coregulated expression patterns in response to developmental or environmental signals. The knowledge of organ-specific expression patterns and their response to the changing environment provides a foundation for dissecting the molecular processes underlying development. All complex eukaryotic organisms, including mammals and higher plants, consist of multiple organ and tissue types. The organ and tissue types for a given organism are generated during its life cycle through a temporally and spatially regulated process of selective expression of specific fractions of the same genome in different cells (Meyerowitz, 2002). Therefore, a long-sought objective of developmental biology has been to define the subset of genes expressed and their relative abundance for each organ or tissue type. Higher plants possess a relatively simple developmental process, with only three nonreproductive organ systems and fewer than 25 major tissue and cell types (Eeau, 1977), thereby providing a good model for defining the organ- and tissue-specific genome expression patterns during development. Early studies using an RNA-excess/single-copy DNA hybridization strategy have revealed much about the mRNA complexity in six selected organ types of a tetraploid tobacco (Nicotiana tabacum) strain (Goldberg, 1988; Goldberg and Barker, 1989). These studies suggested that about 24,000 to 27,000 average-sized mRNA species were present in leaf, root, stem, petal, anther, and ovary (Goldberg et al., 1978; Kamalay and Goldberg, 1980). However, it provided little information toward the identities and relative abundance of the individual mRNA species expressed in each organ type. Other approaches to determining the gene expression pattern include RNA blot and in situ hybridization. These later approaches can give detailed information regarding when and where the detected gene is expressed. However, due to the labor intensive nature of these approaches, they have been applied to only a small number of genes (Barker et al., 1988; Yanofsky et al., 1990). DNA microarrays can measure the individual transcript level of tens of thousands of genes simultaneously, thus providing a high throughput means to analyze gene expression levels on a larger scale (Schena et al., 1995; Chu et al., 1998). For instance, Zhu et al. (2001) used a partial-genome array to study the gene expression profiles from six organs and identified some interesting features. Wellmer et al. (2004) analyzed the gene expression profiles of inflorescences from wild type and several floral-homeotic mutants using a whole-genome oligo array and identified genes specifically or predominantly expressed in one type of floral organ within flowers. The complete sequence of the Arabidopsis (Arabidopsis thaliana) genome provides the means to design a microarray with essentially all known and predicted genes in the genome, which can be used to assay the expression of all the genes at once. For those genes that have been defined solely by prediction, a whole-genome expression analysis will provide a confirmation of expression as well. In recent microarray analyses, the expression profiles of the Arabidopsis genome from seedling, flower, root, and cultured cells were compared, which provided confirmation for many predicted genes, as well as led to discovery of new genes (Birnbaum et al., 2003; Yamada et al., 2003). In this study, a 70-mer oligo microarray that covers 25,676 unique known and predicted genes of Arabidopsis (Fig. 1A
RESULTS Analysis of Arabidopsis Representative Organ Transcriptomes Supports Expression of Most Known and Predicted Genes During the life cycle of Arabidopsis, vegetative (root, stem, and leaf) and reproductive (petal, sepal, stamen, pistil, silique, and seed) organs are formed, and individual organs are specialized to carry out specific biological functions. We selected samples from 17 representative organs throughout the life cycle of Arabidopsis (Fig. 1B We first estimated the number of known and predicted genes for which expression can be detected experimentally. Among the known and predicted genes covered by the array, the expression for 24,733 (96%) out of 25,676 can be detected in at least one of the 17 organs or cultured cells under our experimental conditions (Fig. 1A Annotation of genes using gene ontology (GO) functional categories assigns functions to genes with a dynamic and controlled vocabulary (Gene Ontology Consortium, 2000). We functionally classified all expressed genes using GOslim terms from The Arabidopsis Information Resource (TAIR) annotation (Rhee et al., 2003). The functional classifications for genes that were expressed in either one or more organs, and in all organs are shown in Figure 1, C and D Different Proportions of the Genome Are Expressed in Representative Organs Examination of the fractions of the genome expressed in each organ type revealed that the percentage of expressed genes varies from organ to organ. Over 70% of the total genes examined were expressed in stamen, petal, rosette leaf, and sepal, while about 40% of the total genes were expressed in root, hypocotyl, germinating seed, and late-stage silique (Fig. 2
Relatedness of Genome Expression Correlates with Developmental Relationship of Organs in Arabidopsis We next used the overall genome expression profiles of individual organs relative to cultured cells to examine the relatedness of the genome expression changes across the selected organs based on the average linkage clustering with correlation distance (Eisen et al., 1998). As shown in Figure 3
Organ-Enriched Expression of the Arabidopsis Genome We designated genes as organ enriched if they fulfilled the two criteria: (1) showing differential expression (P < 0.05) based on the F test in a group of samples; and (2) their expression levels were at least 2-fold higher than that in any other nonhomologous organs. With these criteria, the expression of 699 (2.7%), 747 (2.9%), 762 (3.0%), 805 (3.2%), 827 (3.2%), 143 (0.5%), 89 (0.4%), 317 (1.2%), 36 (0.1%), and 187 (0.7%) genes were found to be enriched in germinating seed, rosette leaf, root, stamen, petal, sepal, silique, pistil, hypocotyl, and stem, respectively (Fig. 4A
Distinct Portions of the Genome Respond to Light Regulation in the Three Arabidopsis Seedling Organs Arabidopsis seedling development is dramatically regulated by light. The three seedling organs (root, hypocotyl, and cotyledon) exhibit distinct developmental responses to light (Fig. 6A
Organ-Specific Light Regulation of Metabolic Pathways in Arabidopsis Seedlings In a previous study using whole seedlings, more than 26 metabolic and regulatory pathways were found to be regulated by light in Arabidopsis (Ma et al., 2001). Our genome expression profiles from the three seedling organs showed distinct light regulation patterns, with only a small overlap of light-regulated genes (Fig. 6 Many pathways are subjected to light regulation in only one of the three organs. For example, the starch and Suc biosynthetic pathways were up-regulated by light only in cotyledon (Fig. 7E Many Gene Family Members Exhibit Distinct Light or Organ Regulation Patterns in Arabidopsis Large portions of genes in the Arabidopsis genome are classified into gene families based on their sequence homologies (Arabidopsis Genome Initiative, 2000). Thus, we examined light regulation profiles for the gene family members among the three seedling organs. It is evident that the light regulation and organ-specific expression patterns for gene members in the same family are not identical. To illustrate this point clearly, we chose three medium-size gene families, arabinogalactan protein (AGP), expansin, and xyloglucan endotransglucosylase (XTH) families, for hierarchical clustering analysis using the average linkage method. It should be pointed out that expansin and XTH families are both involved in cell elongation and growth (Campbell and Braam 1999; Li et al., 2003). As shown in Figure 8A
We further examined the gene expression patterns for different gene families among all 17 organs examined. We found that the organ-specific expression pattern for gene members in the same family is not identical in all 17 organs. Again, we used the above-mentioned three gene families to do cluster analyses. Because we compared each organ with cultured cells to obtain the genome expression profile for each organ, we used the expression ratio of organ and cultured cells to show the gene expression patterns among all 17 organs. As shown in Figure 9
Coregulation of Gene Expression Patterns in the Arabidopsis Genome Recent studies from several species suggest that a significant fraction of those organisms' genomes may be organized into chromatin domains that contain a number of adjacent genes whose expressions are coordinately regulated (Cohen et al., 2000; Caron et al., 2001; Lercher et al., 2002; Spellman and Rubin, 2002; Birnbaum et al., 2003). For example, in budding yeast (Saccharomyces cerevisiae), pairs or triplets of adjacent genes displayed similar expression patterns (Cohen et al., 2000), whereas groups of 10 to 30 adjacent genes showed similar expression patterns in Drosophila (Spellman and Rubin, 2002). To further examine whether the Arabidopsis genome also contains these chromatin domains with coregulated adjacent genes, we used the organ expression data sets to calculate the coregulated gene clusters (with a block size of 10 genes) based on their physical positions on the chromosome (Spellman and Rubin, 2002). We also considered the fact that the Arabidopsis genome contains many tandemly repeated genes and therefore excluded these from the fraction of coregulated genes during our calculation. This analysis indicated that the chromatin domains of coregulated genes are evident in the Arabidopsis genome, possibly accounting for over 12% of the Arabidopsis genome in one calculation (Table I). This estimated fraction of the genome organized into these coregulated chromatin domains in the Arabidopsis genome is similar to the reported 20% for Drosophila (Spellman and Rubin, 2002).
DISCUSSION In this study, we provide an organ-specific genome expression atlas during Arabidopsis development by analyzing the genome expression profile of individual representative organs using a 70-mer oligomer microarray. This analysis provides experimental evidence for a large fraction of those predicted genes in the Arabidopsis genome (Fig. 1 Development in plants is often reprogrammed by environmental signals. For example, light is one of most important environmental signals for controlling plant growth and development (Kendrick and Kronenberg, 1994; Deng and Quail, 1999; Neff et al., 2000). Plants undergo dramatic changes in developmental patterns depending on the presence or absence of light in the growth environment. When grown in the dark, an Arabidopsis seedling develops with a long hypocotyl, unopened small cotyledons with an apical hook, and short root, whereas the light-grown seedling exhibits a photomorphogenic phenotype with a short hypocotyl, open cotyledons without an apical hook, and long strong root (Kendrick and Kronenberg, 1994; Deng and Quail, 1999; Neff et al., 2000; Fig. 6A An interesting feature is that only a small set of light-regulated genes are shared among the three seedling organ types. Some of the genes that differ among the three organ types even show opposite light regulation patterns (e.g. light induces a gene expression in cotyledon, while the same gene is repressed by light in root or hypocotyl; Fig. 6, C–E Recent results from human (Caron et al., 2001; Lercher et al., 2002), Drosophila (Spellman and Rubin, 2002), Arabidopsis (Birnbaum et al., 2003; this study), and yeast (Cohen et al., 2000) suggest that the regulation of genome expression involves coordinated regulation of adjacent genes in chromosomal regions. Our results further suggest that approximately 12% of the Arabidopsis genome shows a coregulation expression pattern (Table I). However, the mechanism for this coregulated expression pattern in the genome is not clear yet. One reasonable possibility is the involvement of chromatin modification mechanism in these coregulated neighbor genes. As histone proteins in the nucleosomes around a given gene are modified (e.g. acetylation) by chromatin remodeling mediators according to a given signal, the chromatin domain is opened not only for the gene it is binding directly, but also for those neighbor genes within the whole chromatin domain. Genome-wide analysis of histone acetylation or methylation patterns in representative Arabidopsis organs or in response to the given signals may provide evidence for this prediction. MATERIALS AND METHODS Plant Materials The wild-type Arabidopsis (Arabidopsis thaliana) used in this study was the Columbia ecotype. Surface sterilization, cold treatment of the seed, and seedling growth were performed as described previously (Ma et al., 2001). The germinating seeds were collected after the seeds were planted on growth medium agar plates containing 1% Suc and grown under continuous white light (150 μmol m−2 s−1) for 48 h at 22°C. Arabidopsis seedlings used in this study were 6 d old. The seedlings were planted on agar plates containing growth medium with 1% Suc and grown at 22°C in continuous white light (150 μmol m−2 s−1) or darkness. The cotyledon, hypocotyl, and root were collected from the same seedling, respectively. The rosette leaf was collected from 3-week-old plants grown at 22°C under continuous white light (150 μmol m−2 s−1), with the root, bolt, and senescing leaves removed. Adult Arabidopsis plants were grown in a walk-in Environmental Growth Chamber (EGC, Chagrin Falls, OH) at 22°C under continuous white light (250 μmol m−2 s−1). The cauline leaf and stem were collected from 4-week-old adult plants, respectively. The floral organs were collected from the mature flowers of adult plants at flower stage 14 (Bowman, 1994). The silique was collected from adult plants 3 or 8 d after pollination. Suspension culture cells were prepared starting from seeds as described by Martinez-Zapater and Salinas (1998). The cultured cells used for RNA isolation were collected at the logarithmic growth phase. Oligo Microarray The 70-mer oligo set for the Arabidopsis genome was designed and synthesized by Qiagen/Operon (http://oligos.qiagen.com/arrays/omad.php) based on the Arabidopsis genome information available on February 20, 2002. The oligos were purchased from Qiagen (Valenica, CA) and printed onto polylysine coated microscope slides in the DNA microarray laboratory at Yale University (http://info.med.yale.edu/wmkeck/dna_arrays.htm). There were 26,090 unique oligos, and 12 distinct negative control oligos. Each negative control oligo was printed 16 times at well-spaced locations on each slide. Thus, each slide included a total of 26,090 oligo spots and 192 negative control spots. The negative controls were positioned all over the slide to avoid potential errors caused by spatial effects. These negative controls do not have a match in the genome sequence. RNA Isolation, Probe Labeling, and Hybridization Total RNA was extracted from the above-mentioned organs using the Qiagen RNeasy Plant Mini prep kit. RNA preparations from two to four independent biological samples for each test were made and used for probe synthesis. Thus, each experiment produced two to four biological replicate data sets. Total RNA (50 μg) was first labeled with aminoallyl-dUTP (aa-dUTP; Sigma, St. Louis) by direct incorporation of aa-dUTP during reverse transcription, as described previously (Ma et al., 2002). The purified probe was further labeled with fluorescent dye by conjugating aa-dUTP and monofunctional Cy-3 or Cy-5 (Amersham Pharmacia Biotech, Piscataway, NJ). The dye-labeled probe was purified from the unincorporated dye molecules by washing three times through a Microcon YM-30 filter (Millipore, Bedford, MA). The purified labeled probes from specific organ versus culture cell control pairs or dark versus light organ pairs were combined to hybridize the microarray slide for 12 to 16 h at 42°C (Ma et al., 2001). Except petal (two replicates) and pistil of 1 d postpollination (1DPP, four replicates), there were three biological replicates used for all other organ types, with one quality data set from each replicate. Coverage of Oligo Set for Arabidopsis Genes and EST Clones We first blasted the sequences of 26,090 oligos individually against Munich Information Center for Protein Sequences (MIPS) Arabidopsis gene annotation (March 20, 2003 version) to associate oligos with gene IDs (e.g. AT3G20980) if they matched to the annotated genes. We used the criterion that the matching identity between oligo sequence and genome sequence should be 70% (or at least matching 49 out of 70 nucleotides) or higher, to define the match between oligo and gene locus. In fact, the vast majority of oligos (97%) were mapped to the chromosomal genes with more than 90% identity. For oligos with no match in the MIPS annotated gene sequences, we blasted them against The Institute for Genomic Research (TIGR) annotation (July 31, 2002 version) downloaded from TAIR (ftp://ftp.arabidopsis.org/home/tair/Sequences/blast_datasets/). More corresponding gene loci were obtained, and their locus IDs were assigned to those oligos. In total, we are able to assign 25,822 oligos that represent 25,676 unique locus IDs (genes). Among them, the majority (94%) of the oligos had a single match to a unique gene locus. A small fraction of the oligos (6%, 1,553) fall into the following two categories: one oligo matches two or more unique genes, or one unique gene matches more than one oligo (with the 70% identity cutoff). In the former case, we assigned the oligo with multiple locus IDs to cover all possible genes that may contribute to the detected expression signal. While in the latter case, as multiple oligos assigned the same locus ID, the median intensity of those oligo spots was taken as its expression level. To define the number of genes covered by the oligo array that have EST hits, we joined together the information in ESTtoAT and mRNAtoAT at TAIR (ftp://ftp.arabidopsis.org/home/tair/Sequences/) and ESTmatchingtoAT at MIPS (http://mips.gsf.de/proj/thal/; downloaded on July 8, 2003) to obtain the unique locus numbers with at least one EST or mRNA hit from the above three resources. This analysis resulted in the number (16,998) of the genes from the above 25,676 unique locus IDs that have at least one EST or mRNA hit. GO annotation for all gene models were downloaded from TAIR (Rhee et al., 2003; ftp://ftp.arabidopsis.org/home/tair/Genes/Gene_Ontology/). We functionally classified all genes from GO annotation using GOslim terms. We followed the TAIR April 14, 2003 version GO annotation. We also updated the annotation for (basic helix-hoop-helix) transcription factor family according a recent report (Toledo-Ortiz et al., 2003). Data Normalization and Determination of Expression Spot intensities were quantified using Axon GenePix Pro 3.0 image analysis software. The net intensities for each channel and channel ratios were measured using the GenePix Pro 3.0 median of intensity or ratio method. Replicates were normalized first to remove artifacts due to experimental variations using custom-designed programs (http://bioinformatics.med.yale.edu/software.html). Then normalization based on median of intensities was performed among all the experiments. We followed a commonly used strategy (Rinn et al., 2003) to define whether a gene is expressed or not with minor adjustment. First, we stipulated that the normalized intensity of an expressed gene (spot) has to be higher than the intensity value at the 90% of the normalized median intensities of 192 negative control oligos. Second, we consider that the expression of a gene is detectable only if the majority (two out of two, at least two out of three, and at least three out of four) of the corresponding spots from multiple experiments showed experimentally detectable expression as defined in the first criterion. Third, those spots that exhibited a large difference between replicates were defined as outliers and eliminated from further analysis. Identification of Differential Expression To identify differentially expressed genes among organ groups (Fig. 4 ijkl, where yijkl denotes the logarithm transformed signal for gene l on slide i labeled with dye j of sample k. The overall mean effect was represented by μ; A, D, and G represented main effects from array, dye, and gene. The interaction terms AD, VG, DG, and AG represented array by dye, sample (variation) by gene, dye by gene, and array by gene. The random error was denoted by ijkl. We were interested in the term VG. The above ANOVA was performed on each spot using MAANOVA for R with F statistics computed on the James-Stein shrinkage estimates of the error variance (Cui and Churchill, 2003; Wu et al., 2003). We selected organ-enriched genes with a P value <0.05 in the above F test, and if the expression intensities in one organ were 2-fold, or more, higher than in other organs in the same comparison group.Similar statistical analyses were performed for the identification of light regulated genes. A student's t test was performed for each gene between light-growth condition and dark-growth condition. We consider genes with a P value <0.05 as light regulated. To reduce the occurrence of false positives, we applied an additional 2-fold expression changed for some subsequent analysis. Estimation of Average Intensities for Each Gene (Spot) For other analyses in this work, normalized intensities were averaged among all replicates of the same sample to obtain a single statistic, which is considered a relative expression for a gene. Similarly, we also used a single statistic for ratios for each gene. We calculated the expression ratio for a sample pair only when at least one channel showed experimentally detectable expression as defined above. Calculation of Chromatin Domains with Coregulated Adjacent Genes We used the method reported by Spellman and Rubin (2002) to calculate the coregulated adjacent genes. For each given block size, we calculated the average correlation coefficient among the gene expression data. The tandem repeat genes were excluded from our analysis. The value was compared to the values from 10,000 sets of randomly selected genes of the same number of genes to calculate the P value. We carried out such analysis for the block sizes of 2, 4, 6, 8, 10, 15, 20, 25, and 30 genes and found that the block size of 10 represented the major block size. We then calculated the number of genes showing coexpression at P values 0.001, 0.005, and 0.01 with the block size of 10 genes. All the microarray data described in this study were deposited into the NCBI GEO database (accession no. GSE 1599). Acknowledgments We thank Mr. Matthew Holford for assisting with the Java programming, Elizabeth Strickland, Jessica Habashi, and Lei Li for reading and commenting on this manuscript, and the Yale DNA microarray laboratory of the Keck Biological Resource Center for the production of the microarray used in this study (http://info.med.yale.edu/wmkeck/dna_arrays.htm). Notes 1This work was supported by the National Science Foundation of China (strategic international corporation project grant no. 30221120261), by the National Institutes of Health (grant nos. GM–47850 to X.W.D. and GM59507 to H.Z.), and by the National Science Foundation (grant no. DMS 0241160). L.M. is a long-term postdoctoral fellow of the Human Frontier Science Program. [w]The online version of this article contains Web-only data. References
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