Patterns of gene expression during Arabidopsis flower development from the time of initiation to maturation
- Patrick T. Ryan†1,
- Diarmuid S. Ó’Maoiléidigh†1, 5,
- Hajk-Georg Drost2,
- Kamila Kwaśniewska1,
- Alexander Gabel2,
- Ivo Grosse2,
- Emmanuelle Graciet1, 3,
- Marcel Quint4Email author and
- Frank Wellmer1Email author
© Ryan et al. 2015
Received: 7 February 2015
Accepted: 15 June 2015
Published: 1 July 2015
The formation of flowers is one of the main model systems to elucidate the molecular mechanisms that control developmental processes in plants. Although several studies have explored gene expression during flower development in the model plant Arabidopsis thaliana on a genome-wide scale, a continuous series of expression data from the earliest floral stages until maturation has been lacking. Here, we used a floral induction system to close this information gap and to generate a reference dataset for stage-specific gene expression during flower formation.
Using a floral induction system, we collected floral buds at 14 different stages from the time of initiation until maturation. Using whole-genome microarray analysis, we identified 7,405 genes that exhibit rapid expression changes during flower development. These genes comprise many known floral regulators and we found that the expression profiles for these regulators match their known expression patterns, thus validating the dataset. We analyzed groups of co-expressed genes for over-represented cellular and developmental functions through Gene Ontology analysis and found that they could be assigned specific patterns of activities, which are in agreement with the progression of flower development. Furthermore, by mapping binding sites of floral organ identity factors onto our dataset, we were able to identify gene groups that are likely predominantly under control of these transcriptional regulators. We further found that the distribution of paralogs among groups of co-expressed genes varies considerably, with genes expressed predominantly at early and intermediate stages of flower development showing the highest proportion of such genes.
Our results highlight and describe the dynamic expression changes undergone by a large number of genes during flower development. They further provide a comprehensive reference dataset for temporal gene expression during flower formation and we demonstrate that it can be used to integrate data from other genomics approaches such as genome-wide localization studies of transcription factor binding sites.
KeywordsArabidopsis thaliana Flower development Organ specification Transcriptomics Temporal gene expression Paralog Gene expression atlas
The formation of flowers is one of the main models for studying the molecular mechanisms underlying the control of plant development. Over the past three decades, a large number of regulatory genes, which control a multitude of different processes during flower morphogenesis, have been identified mainly through a combination of forward and reverse genetics approaches [1–3]. Work in Arabidopsis thaliana in particular has led to an understanding of the molecular mechanisms underlying the functions of many of these regulatory genes . Furthermore, it has yielded detailed insights into the regulatory hierarchies among genes that play roles in the control of floral organ formation [5, 6].
With the advent of the genomics era, genetic approaches employed to elucidate the regulation of flower development have been complemented by methods such as global transcript profiling and genome-wide localization studies of transcription factor binding sites. Unfortunately, this work has been hampered in Arabidopsis by the fact that flowers of this model plant are small and early-stage floral buds are too minute to be dissected reliably through conventional approaches. Also, Arabidopsis flowers are initiated sequentially so that all flowers in an inflorescence are at distinct developmental stages . As a consequence, the collection of sufficient numbers of flowers at particular stages for analysis by genomic technologies is challenging especially for early flower development. To circumvent this problem, a number of approaches have been employed: recently, laser capture microdissection has been used to generate transcriptional profiles of early-stage floral buds . An alternative and largely complementary approach has been the use of floral induction systems, which allow the collection of hundreds of synchronized floral buds from a single plant (see below). These systems have been employed to study both temporal and spatial gene expression during the early stages of flower development [9–14]. Other studies have analyzed gene expression in whole inflorescences of wild-type and mutant plants and in some cases relied on the removal of older (and relatively large) buds that may unduly contribute to RNA preparations from these tissues [15–19]. Moreover, transcript profiling was done with wild-type flowers at individual stages and with distinct floral organ types, but this work has been limited to older flowers, as they can be collected with relative ease . Specific developmental processes such as male-gametophyte/pollen and female gametophyte/ ovule development have also been studied through transcriptomics experiments, providing detailed information for individual cell and tissue types [20–23].
Although Arabidopsis flower development has been studied extensively over the past ten years through the genomics approaches described above, a continuous series of gene expression from the time of initiation to maturation has been lacking. Obtaining this information could be highly informative as it would provide a comprehensive view of stage-specific gene expression activities over the entire course of development and would constitute an important component of a gene expression map. Furthermore, such a dataset could be used in analyses, in which, for example, data from transcript profiling and genome-wide localization studies are integrated to obtain a better understanding of the gene network that controls flower formation.
In this study, we employed a floral induction system to close this knowledge gap and to monitor temporal gene expression during flower development from the time of initiation to maturation. We validated the resulting dataset and used it to obtain novel insights into the processes underlying the formation of flowers on a global scale through computational approaches.
Results and discussion
Temporal gene expression during flower development
To validate the results of the microarray experiments, we assessed the expression profiles of genes with known roles in different processes during flower development (Fig. 2 and Figure S2 in Additional file 1) and found that they were in concurrence with their published expression patterns. For example, expression of the floral homeotic genes APETALA3 (AP3) and AGAMOUS (AG) (Fig. 2a-b) strongly increased in early time-points and then remained high throughout most of flower development in agreement with the activation of these genes at stage 3 and their continued expression in developing floral organs [30, 31]. Down-regulation of the floral repressor SHORT VEGETATIVE PHASE (SVP) (Fig. 2c) at early floral stages has been described previously and is dependent on AP1 activity [29, 32]. Expression of the stem cell regulator CLAVATA3 (CLV3) was high at early stages and then rapidly decreased in intermediate-stage flowers (Fig. 2d) likely as a consequence of the loss of floral stem cells around stage 6 of development . This termination of floral meristems is at least in part due to the activity of KNUCKLES (KNU), which we detected to be expressed at intermediate stages (Fig. 2e), in agreement with its known expression pattern at the base of developing carpels and in stamen primordia [34, 35]. Genes with bimodal expression profiles included SUPERMAN (SUP) (Fig. 2f), which is initially expressed in young floral meristems and at later floral stages during ovule development . Strong up-regulation of the regulator of ovule and seed development SEEDSTICK (STK) between days 7 and 9 in our experiment (Fig 2g) corresponds to its expression in developing carpels from stage 8 onward . DUO POLLEN1 (DUO1), a regulator of male germline development, was found to be expressed in late flower development (Fig. 2h) in agreement with its specific expression in pollen . ABORTED MICROSPORES (AMS), which encodes a master regulator of pollen wall formation, was strongly expressed at intermediate stages and reached a maximum around stages 9-10 (9 d after dexamethasone treatment) (Fig. 2i) as previously described . Genes such as NOZZLE/SPOROCYTELESS (NZZ/SPL) (Fig. 2j), EXTRA MICROSPOROCYTES1/ EXTRA SPOROGENOUS CELLS (EMS1/EXS) (Fig. 2k), and DYSFUNCTIONAL TAPETUM1 (DYT1) (Fig. 2l) were expressed during intermediate stages in agreement with their function in early anther development [40–44]. Activation of NZZ/SPL was detected in our experiment around stage 5 and thus earlier than what has been reported previously (i.e. stage 6; ). This difference might stem from initially low mRNA levels, which might hamper a reliable detection in in situ hybridization or reporter gene essays.
We also compared our dataset to those from several previous studies in which temporal [8–10, 14] and spatial [11, 16] gene expression during flower development had been analyzed either in early or in late-stage flowers using different floral induction systems, laser capture microdissection of wild-type flowers, or through a comparison of the gene expression profiles of inflorescences of floral mutants and of the wild type, respectively. For each pair-wise comparison, we found a significant overlap between the datasets and the one described in this study (Table S2 in Additional file 1 and Additional file 3), further validating the results of our time-course experiment.
Distribution of functional terms among groups of co-expressed genes
Distribution of target genes of floral organ identity factors
Floral organ identity factors are necessary and sufficient for the specification and development of the different types of floral organs [5, 6]. They act in a combinatorial manner as predicted by the well-supported (A)BCE model of floral organ identity specification [50–52]. Insights into the functions of these master regulators, which (with the exception of APETALA2) all belong to the family of MADS-domain proteins and are components of higher-order regulatory protein complexes , have been obtained in recent years through a combination of genome-wide localization studies and gene perturbation experiments [5, 6]. This work has resulted in the identification of some of their direct target genes and of the cellular and developmental processes they control. Furthermore, it has been shown that the floral organ identity factors bind to many of the same sites in the Arabidopsis genome  and that their global binding patterns undergo changes as flower development progresses, at least in part as a consequence of stage-specific alterations in chromatin accessibility . Also, the majority of genes bound by these transcription factors at early floral stages do not respond transcriptionally when the activities of the floral homeotic genes are perturbed [12, 13]. While the molecular mechanisms underlying these observations are currently not well understood, it is clear that from binding data alone it is difficult to identify their bona fide target genes.
In addition to clusters with binding site enrichments, we also found clusters that are significantly depleted for binding sites of the floral organ identity factors. These included especially clusters 2, 3, and 14, which contain genes with predominant expression in the time-course experiment at 9, 13, and 11 d, respectively (Fig. 3). As described above, these clusters comprise in all probability many genes involved in microsporogenesis and pollen development, a process that can progress without the direct involvement of the floral organ identity factors . Taken together, this analysis shows that the results of our transcriptomics study can be used as a reference to integrate different genome-wide datasets and to identify candidates for transcription factor target genes.
Distribution of paralogs within groups of co-expressed genes
The results of our transcriptomics analysis of flower development, which covered most stages from the time of initiation until maturation, shows that the formation of flowers involves the differential expression of at least a quarter of the genes in the Arabidopsis genome. While many gene expression changes occur late in development and are likely due to the activation of specific gene sets in developing pollen and - to a lesser extent - ovules, genes with regulatory functions often exhibit intermittent expression during early and late floral stages. Through computational analyses, we have been able to assign functions to groups of co-expressed genes and to provide temporal information on when these processes likely occur during the almost two weeks during which flowers develop from a small number of meristematic cells into a highly complex structure with different organs, tissues and cell types. Using binding data for selected floral organ identity factors, we have further demonstrated that the results of our transcriptomics experiment can help to interpret and mine datasets from genome-wide localization studies. Our data also provide an important component of a gene expression map for flower development. Through the use of techniques such as Translating Ribosome Affinity Purification (TRAP)  or Isolation of Nuclei Tagged in specific Cell Types (INTACT) , it should be possible to extend this map by introducing detailed spatial information on gene expression for all floral stages.
Plant material, plant growth, treatment conditions and tissue collection
Plants of genotype AP1pro:AP1-GR ap1-1 cal-1  were grown on a soil:vermiculite:perlite (3:1:1) mixture at 20 °C under constant illumination with cool white fluorescent light. Flower development was induced in ~four week-old plants as described in , using a solution containing 10 μM dexamethasone (Sigma-Aldrich), 0.01 % (v/v) ethanol and 0.015 % (v/v) Silwet L-77 (De Sangosse). Floral buds were harvested at different time-points after dexamethasone treatment as described in Fig. 1. Three sets of biologically independent samples were collected for microarray analysis.
Microarray experiments were performed using Agilent whole-genome Arabidopsis microarrays. For each microarray hybridization, amplified and dye-labeled RNA samples from a given time-point was co-hybridized with dye-labeled RNA from a common reference sample. This common reference was generated by pooling equal amounts of RNA from the individual time-points from 2 of the 3 sets of independent samples. RNA extractions, amplification and labelling of RNA preparations, microarray hybridizations, as well as washing and scanning of microarrays were done as previously described [12, 13].
Processing of microarray data
Microarray data were analyzed using the software package limma (Linear Models for Microarray Data)  implemented in R. Background correction was done using the subtract method and within array normalization was performed with the loess method . Between array normalization was done using the Aquantile method. Probes within each array were averaged on a gene-level and filtered to remove entries that had expression values below the median value of negative control probes. Linear models were fitted to the data using the lmscFit function. Correlograms were generated using the R package corrgram. Statistics for differential expression were first calculated using the ebayes function within limma. Genes with a p-value (after false discovery rate adjustment using the Benjamini-Hochberg procedure) below 0.01 were considered as differentially expressed. Because this analysis led to a very large number of differentially expressed genes that may not reflect true gene regulatory events (see Results and Discussion), we next compared gene expression between consecutive or near-by time-points using ebayes. To this end, we conducted all possible contrasts between time-points that lay within a 2-d interval (see Table S1 in Additional file 1). In order to be called as differentially expressed, genes were required to exhibit a p-value below 0.01 after adjustment for false discovery rate across the experiment and a fold-change in expression of 1.7 or greater.
K-means clustering was performed in R using scaled log2-transformed ratios of expression averaged across each replicate across all time-points for each gene, separating differentially expressed genes into 15 clusters on the basis of the similarity of the pattern of their temporal expression. The number of clusters was chosen heuristically based on the elbow method, which aims at maximizing the amount of variance explained while minimizing the number of clusters chosen. To this end, we compared, using the kmeans function implemented in R, the between-cluster sum-of-squares to the total sum-of-squares for different values for k (ranging from 2 to 200). We then plotted the data and selected a value for k in the ‘elbow’ of the plot.
Comparison of expression data with data from an Arabidopsis gene expression atlas
Genes assigned to each k-means cluster were compared to a previously described  Arabidopsis gene expression atlas, which is based on published transcriptomics datasets for floral and non-floral tissues, to identify trends in tissue-specific expression within each cluster. This tissue atlas was also used to identify the tissues where genes within a cluster had their highest and lowest expression levels in order to investigate the correlation of changes in temporal expression within developing tissues.
Gene ontology analysis
Gene Ontology analysis was performed using PlantGSEA . Statistical significance calculations were performed with a Fisher’s exact test using False Discovery Rate adjustment method from Benjamini and Yekutieli  with a p-value cut off of 0.05.
Identification of paralogs
All known protein sequences from Arabidopsis were individually aligned against the sequences from the entire proteome of Arabidopsis using blastp to select alignments with an E-value cut off of 1x10-20 and which covered 80 % of the query sequence . The top 5 non-reciprocal alignments were retained as potential paralogs. Using this information, we determined the percentage of paralogs within each of the 15 clusters of differentially expressed genes described in Fig. 3. To test whether paralogs were significantly enriched in the clusters, we conducted the following background calculation: we first generated, for each cluster, two groups of genes drawn randomly either from the list of 7,405 differentially expressed genes or from genes present on the microarrays used in this study. Both groups contained 100 sets of genes each and the number of genes in a set was identical to the size of a cluster. We then calculated the mean percentage and standard deviations for paralogs in each of the groups and compared them to the percentage of paralogs we had identified in a corresponding cluster. Clusters with percentage values that were beyond three standard deviations from the random gene groups were considered significantly different.
Comparison of expression data with data from genome-wide localization studies
Data from genome-wide localization studies were contrasted with each of the 15 k-means clusters to determine the frequency with which genes identified as being bound by the transcription factors AP1 and SEP3 , as well as by AP3, PI, and AG [12, 13], occurred in each cluster. This was contrasted against the frequencies with which bound genes occurred in randomized but equally-sized clusters of genes drawn from the 7,405 differentially expressed genes identified in the time-course experiment.
Availability of supporting data
The data sets supporting the results of this article are included within the article (and its additional files). Microarray data have been deposited with the Gene Expression Omnibus (GEO) repository (at http://www.ncbi.nlm.nih.gov/) under GSE64581.
This study was supported by grants from Science Foundation Ireland to F.W. (10/IN.1/B2971) and E.G. (09/SIRG/B1600), and the Deutsche Forschungsgemeinschaft to M.Q. (Qu 141/6-1).
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