RNA-sequencing reveals early, dynamic transcriptome changes in the corollas of pollinated petunias
© Broderick et al.; licensee BioMed Central Ltd. 2014
Received: 18 July 2014
Accepted: 27 October 2014
Published: 18 November 2014
Pollination reduces flower longevity in many angiosperms by accelerating corolla senescence. This response requires hormone signaling between the floral organs and results in the degradation of macromolecules and organelles within the petals to allow for nutrient remobilization to developing seeds. To investigate early pollination-induced changes in petal gene expression, we utilized high-throughput sequencing to identify transcripts that were differentially expressed between corollas of pollinated Petunia × hybrida flowers and their unpollinated controls at 12, 18, and 24 hours after opening.
In total, close to 0.5 billion Illumina 101 bp reads were generated, de novo assembled, and annotated, resulting in an EST library of approximately 33 K genes. Over 4,700 unique, differentially expressed genes were identified using comparisons between the pollinated and unpollinated libraries followed by pairwise comparisons of pollinated libraries to unpollinated libraries from the same time point (i.e. 12-P/U, 18-P/U, and 24-P/U) in the Bioconductor R package DESeq2. Over 500 gene ontology terms were enriched. The response to auxin stimulus and response to 1-aminocyclopropane-1-carboxylic acid terms were enriched by 12 hours after pollination (hap). Using weighted gene correlation network analysis (WGCNA), three pollination-specific modules were identified. Module I had increased expression across pollinated corollas at 12, 18, and 24 h, and modules II and III had a peak of expression in pollinated corollas at 18 h. A total of 15 enriched KEGG pathways were identified. Many of the genes from these pathways were involved in metabolic processes or signaling. More than 300 differentially expressed transcription factors were identified.
Gene expression changes in corollas were detected within 12 hap, well before fertilization and corolla wilting or ethylene evolution. Significant changes in gene expression occurred at 18 hap, including the up-regulation of autophagy and down-regulation of ribosomal genes and genes involved in carbon fixation. This transcriptomic database will greatly expand the genetic resources available in petunia. Additionally, it will guide future research aimed at identifying the best targets for increasing flower longevity by delaying corolla senescence.
The longevity of individual flowers is genetically programmed to allow for efficient reproduction while limiting energy costs associated with maintaining the petals ,. In many angiosperms, pollination reduces flower longevity and initiates global gene expression changes that lead to flower senescence ,. Pollination-induced senescence of the corolla allows for nutrients to be recycled from the petals to the developing ovary ,. In petunias, ethylene biosynthesis is induced by pollination, and the application of exogenous ethylene accelerates senescence . Ethylene in wild type petunias can be measured from pollinated styles within an hour after pollination. This initial ethylene production is not sufficient to induce corolla senescence, but is followed by ethylene biosynthesis in the corolla, which then induces petal wilting ,,. In an effort to extend flower longevity, transgenic approaches have been utilized to alter ethylene perception in petunia. These experiments have created ethylene insensitive petunia flowers that last approximately twice as long as wild type flowers and do not undergo accelerated senescence after pollination ,,,.
Pollen is thought to contain a signaling factor(s) that triggers petal senescence in ethylene-sensitive species . Relatively large amounts of 1-aminocyclopropane-1-carboxylic acid (ACC) and auxin are found in petunia pollen, but experimental evidence has shown that only excessive amounts of these substances are able to increase ethylene production and accelerate flower senescence ,. Other factors such as short-chain fatty acids and changes in electrical potential may play a larger role in pollination-induced petal senescence, either by acting as a signaling factor or by increasing ethylene sensitivity ,. While pollination induces ethylene production and leads to senescence in ethylene-sensitive flowers, it remains unclear how pollination is linked to ethylene biosynthesis. Rather than blocking downstream ethylene-induced responses to delay flower senescence, inhibiting pollination signals that lead to ethylene biosynthesis may provide an alternative means of extending flower longevity.
Transcriptomic approaches, including microarrays and RNA-sequencing (RNA-seq), have been used to profile gene expression changes during flower petal development and senescence in multiple species -. A large percentage of the genes that are up-regulated during senescence encode enzymes involved in degradation and transport. The systematic degradation of proteins, nucleic acids, lipids, and cell wall components allows for the remobilization of sugars and other nutrients before the death of the petal cells . A suppressive subtractive hybridization experiment in Alstroemeria flowers showed that genes involved in cell wall synthesis, protein synthesis, metabolism, and signaling were most abundant in the petals of younger flowers, while those involved in macromolecule breakdown were highest at the later stages . Pollination-induced senescence involves similar processes and can reduce flower longevity of Ophrys (orchid) to five or six days. In orchid labella, genes involved in macromolecular breakdown, stress and defense, and nutrient remobilization are differentially expressed after pollination. Floral scent and pigment genes are down-regulated by two days after pollination .
While microarrays have been utilized to study gene expression changes in petunia ,, to our knowledge, genome-wide expression profiling using RNA-sequencing (RNA-seq) has not been performed in petunia flowers. Microarrays are able to measure gene expression changes, but are limited by the availability of Expressed Sequence Tags (ESTs). Additionally, highly expressed genes can saturate the microarrays and reduce the accuracy of gene expression data, especially for lower expressed genes. RNA-seq experiments can provide a global overview of gene expression during corolla senescence without any a priori genetic data. The recent reductions in sequencing costs have made this technology more readily accessible to researchers. RNA-seq is particularly useful for identifying genes and their isoforms, and it can measure gene expression levels that have more than an 8,000-fold difference ,.
This experiment was designed to profile early gene expression changes in petunia corollas following pollination, with the goal of identifying the signaling pathways that are involved in initiating corolla senescence. Another objective was to generate an assembled and annotated RNA-seq transcriptome for petunia corollas. Data from this experiment will provide a valuable addition to the molecular resources available for petunia. This research will guide the future selection of promising candidate genes for extending flower longevity by delaying corolla senescence.
Results and discussion
Pollen tube growth and ethylene biosynthesis of post-anthesis petunia flowers
Pollination accelerates the senescence of petunia flowers. Inducing flower senescence by pollination synchronizes the senescence program and allows for the collection of corollas that are at a very similar stage of senescence . A characterization of pollen tube growth, ethylene production, and visual senescence symptoms in Petunia × hybrida `Mitchell Diploid’ was conducted to identify the best time points for RNA-seq library construction. The goal was to identify genes and pathways involved in early senescence signaling within the corolla, so time points before fertilization, climacteric ethylene production from the petals, and visual corolla wilting were desired.
Petunia corolla EST library construction and evaluation
Illumina HiSeq read processing and mapping results from RNA-seq petunia corolla libraries
To evaluate the accuracy of the assembly, the contigs were compared to 404 complete Petunia × hybrida coding sequences (CDS) available in GenBank (www.ncbi.nlm.nih.gov). From the GenBank-obtained sequences, 164 (41%) were 90-100% identical to the de novo assembled contigs (Figure 2B). The ortholog-hit ratio (OHR)  was calculated using the Solanum lycopersicum ITAG2.3 protein database, and 44% of the contigs had an OHR between 0.8 and 1.2 (Figure 2C). Together, these comparisons indicate that the de novo assembly was robust and accurate.
To generate an EST library, the 162 K contigs were screened for ORFs using TransDecoder, and 37,939 contigs contained putative ORFs larger than 100 amino acids. Additionally, we added 619 contigs that had an OHR greater than 0.8 and did not share the same component identification number that was assigned by Trinity. This was done to prevent removal of contigs that had a putative S. lycopersicum ortholog. Finally, contigs of high similarity to each other (threshold of 90%) were removed using CD-HIT-EST. This threshold was selected to increase the number of uniquely mapped reads during expression analysis, and resulted in an expressed sequenced tagged (EST) library of 33,292. A total of 26,006 genes met specific annotation thresholds and were successfully annotated using Blast2GO. Our data represents the first RNA-seq generated transcriptome from petunia corollas.
Differential gene expression identifies many pollination-associated gene changes
The total number of gene changes demonstrates the complex, dynamic, and orchestrated processes of initiating petal senescence in petunia. These findings are in line with other flower development studies. For example, RNA-seq data from developing Chimonanthus praecox (wintersweet) flowers had 2,706 differentially expressed genes between bud and open flowers and 1,466 between open and senescent flowers . More than 5,400 differentially expressed genes were identified in Rosa chinensis between open and senesced flowers . A microarray experiment in orchid (Ophrys fusca) compared pollinated and unpollinated labella and found that 8.2% of the printed cDNA clones were differentially expressed within 48 hours after pollination. These gene changes occurred long before visual cues of senescence were visualized at 5 to 6 days after pollination . Together these data demonstrate the highly dynamic nature of transcriptomic data in senescing flowers. Similarly, transcriptomic studies in leaves have identified thousands of genes that show either increased or decreased expression during leaf senescence ,.
Weighted gene correlation network analysis identified three pollination-specific modules
The WGCNA and DESeq2 analyses both identified two main expression patterns (i.e. genes that were differentially expressed in pollinated corollas and genes that were differentially expressed at 18 hap) when comparing corollas from pollinated and unpollinated flowers at the same developmental age. Pollination induced changes in gene expression that occurred prior to fertilization and ethylene biosynthesis in the corollas. After pollination, it took more than 24 h for pollen tubes to reach the bottom of the style (Figure 1A). Therefore, a signal(s) must precede fertilization to elicit the expression changes in the corolla that lead to accelerated petal senescence. Pollination signaling may involve ACC, auxin, ethylene, short-chain fatty acids, or electrical pulses ,,. Although ethylene production did not peak until 36 hap in corollas, the styles produced ethylene within the first hour after pollination and continued for 48 h. Inhibiting ethylene production or perception in the style with aminoethyoxyvinylglycine (AVG) or diazocyclopentadiene (DACP), respectively, prevents pollination-induced corolla senescence ,. These results suggest that ethylene signaling within the gynoecium is required for the corollas to respond to pollination. However, the ethylene from pollinated styles that are immediately severed from the flower, but left in the corolla, is not sufficient to accelerate senescence , suggesting that additional factors must be transmitted to the corolla to induce senescence. Wounding also results in elevated ethylene production from petunia stigmas, and at 17 hours after the stigma wounding, petal wilting can no longer be delayed by removing the damaged stigmas . This suggests that the necessary signals for stigma-induced, flower senescence are in place within the first 17 hours after stigma wounding. Short-chain fatty acids that are produced in the gynoecium and transported to the corolla within 12 h of pollination have been shown to increase ethylene sensitivity in corollas, and this may be a component of the pollination signaling ,.
Validation of RNA-seq data by quantitative PCR
Enriched GO terms suggest involvement of plant hormones within 12 hap
To identify the biological relevance of the pollination-associated gene changes, gene ontology (GO) was used to determine the biological processes, cellular components, and molecular functions of the differentially expressed genes  (Additional file 4). At 12 hap, 35 enriched GO terms were identified (FDR <0.05). Many of these terms involve plant hormones like abscisic acid (ABA), auxin, jasmonic acid (JA), and salicylic acid (SA). Of note are the response to auxin stimulus and response to 1-aminocyclopropane-1-carboxylic acid (ACC) GO terms. Both auxin and ACC are found in relatively high concentrations in pollen , and the corolla may be responding to hormonal signals that are transmitted through the gynoecium. At 18 hours after pollination, 154 enriched GO terms were identified including the ethylene signaling pathway. This coincided with the initiation of ethylene production from the corollas. Three of the molecular function GO terms involve autophagy. Autophagy is a catabolic process that involves transporting cellular components to the vacuole for further degradation and nutrient recycling . No enriched terms were identified at 24 hap, but 368 enriched terms were identified when comparing pollinated to unpollinated (P/U) corollas at any time (12, 18, and 24 h). Enriched terms consisted of sucrose metabolic process, response to chitin, and response to wounding. The number of GO terms (557 in total of which 508 were unique) reflects the breadth of changes that occur between 12 and 24 hap in corolla tissue (Additional file 4).
KEGG enrichment identifies pollination responsive pathways in the corolla
KEGG enrichment hierarchy and mapping results
Mapped to KEGG
Total in KEGG
Global and overview maps
Biosynthesis of secondary metabolites
Pentose and glucuronate interconversions
Starch and sucrose metabolism
Starch and sucrose metabolism
Carbon fixation in photosynthetic organisms
Alpha-Linolenic acid metabolism
Metabolism of terpenoids and polyketides
Limonene and pinene degradation
Biosynthesis of other secondary metabolites
Stilbenoid, diarylheptanoid and gingerol biosynthesis
Genetic Information Processing
Ribosome biosynthesis in eukaryotes
Environmental Information Processing
Plant hormone signal transduction
Transport and catabolism
Regulation of autophagy
Regulation of autophagy
Four enriched KEGG pathways were identified in pollinated corollas
Four unique, enriched KEGG pathways were identified from the P/U genetic changes identified in DESeq2 and the WGCNA Module I (red). They included Plant-pathogen interactions, Starch and sucrose metabolism, Pentose and glucuronate interconversions, and Plant hormone signal transduction (Table 2). The genes within these KEGG pathways are associated with pollination and may contain key signaling components and molecular events that lead to flower senescence.
Pollination and fungal infection share striking similarities in terms of biological responses, and both processes result in cell death ,. X-ray microanalysis revealed that both pollen tubes and fungal hyphae penetration result in the accumulation of Ca2+ at the interaction sites . Two well-known microbe-associated molecular pattern (MAMP) LRR receptor-like serine-threonine protein kinases, flagellin insensitive 2 (FLS2) and EF-Tu receptor (EFR), were both up-regulated following pollination (Figure 8). Activation of these receptors results in changes in ion flux, reactive oxygen species formation, MAP kinase activation, and ethylene production . It has been hypothesized that pathogen-related proteins are up-regulated during senescence to protect the senescing tissue from pathogenic attack , but petunia pollen tubes may contain an elicitor-like motif that activates FLS2 and EFR. Altering or eliminating these elicitors from pollen may prevent or delay pollination-induced senescence. Alternatively, increased expression of these genes may be a result of elevated ethylene levels. EIN3 and EIL have been shown to activate transcription of FLS2 in Arabidopsis .
The Starch and sucrose metabolism pathway involves the catabolism of carbohydrates. The P/U list had 30 genes map to this KEGG pathway and Module I had 19 (Table 2 and Additional file 5). Many of these genes are involved in the conversion of UDP-D-galacturonate to D-galacturonate, which interacts with ascorbate metabolism. There are also many pectinesterase genes involved in the catabolism of pectin (Additional file 5). Soluble carbohydrates move from senescing to non-senescing flowers in gladiolus . Sugars, particularly sucrose, increase in the phloem of Ipomoea and Hemerocallis (daylily) petals as the flowers open, mature, and senesce ,. Labeling studies in carnations demonstrate that sucrose moves in the phloem from the petals to the gynoecium during senescence, and that this remobilization is accelerated by ethylene treatment . The enrichment of this KEGG pathway suggests that a similar process involving the movement of carbohydrates to sinks, like the developing ovules, may also occur following pollination in petunia. Sucrose has profound effects on extending flower longevity, and has been implicated in the stability of EIN3 in Arabidopsis . The application of sucrose to cut carnation flowers delays petal senescence and the up-regulation of genes involved in ethylene signaling . The competition for carbohydrates also regulates the timing of senescence in ethylene-insensitive flowers like lilies (Lilium), where flower senescence is observed once the carbohydrate content of the tepals is reduced by ~50% .
The Pentose and glucuronate interconversions KEGG pathway was enriched in Module I, but not in the P/U gene list. A total of 12 mapped genes were found within Module I, nine of which involve pectin degradation (Table 2). This pathway contained five pectinesterase proteins, a polygalacturonase, and a UDP-glucose 6-dehydrogenase that overlap with the Starch and sucrose metabolism pathways. The other four Pentose and glucuronate interconversion-associated proteins are putative pectin lyase proteins. These enzymes are involved in cell wall loosening and have been shown to increase free Ca2+ levels as the calcium-cross-linked bridges are lysed (Additional file 5) ,,. Galactose loss is the main feature of cell wall changes during the senescence of petunia, Sandersonia and carnation flowers -.
Eleven KEGGs are enriched at 18 hap
Large gene changes were observed specifically at 18 hap, and 11 enriched KEGG pathways were identified (see KEGGs designated with Module I and 18-P/U in Table 2). Most of the genes within the alpha-Linolenic acid metabolism, Endocytosis, Limonene and pinene degradation, Peroxisome, and Regulation of autophagy KEGG pathways were up-regulated following pollination, while the Carbon fixation in photosynthetic organisms, Ribosome, and Ribosome biosynthesis in eukaryotes KEGG pathways were down-regulated (Table 2, Additional file 5, and Additional file 6).
The petunia autophagy genes APG5, APG7, APG8H, ATG1C, ATG13, ATG6, ATG8C, and ATG8F are up-regulated at 18 h in corollas of pollinated flowers (Additional file 5). The 18-hour time point was collected after approximately 5 hours of darkness. This suggests that many pollination-induced autophagy genes may be regulated by darkness or may be functioning during the night. Rubisco degradation via autophagy occurs in early stages of dark-induced senescence . During this process, Rubisco is remobilized to the vacuole, and a decrease in chlorophyll can be measured after just one day of darkness. However, in Arabidopsis atg5 mutants, Rubisco is not remobilized in darkened leaves ,. Changes in the expression of autophagy genes have also been reported during starch degradation in darkened Arabidopsis leaves . Similarly, the remobilization of Rubisco from chloroplasts in the petunia corolla, which are primarily located in the tube, may be occurring via autophagy. Monodansylcadaverine (MDC) staining has been used to visualize the accumulation of acidic bodies during the pollination-induced senescence of petunia petals, but MDC staining is not specific to autophagosomes . While all of these studies have provided compelling evidence for the involvement of autophagy in corolla senescence, additional morphological studies are needed to confirm the accumulation of autophagosomes in senescing petunia corollas .
Five transcription factor families have more than 20 members in pollinated corollas
Uncharacterized genes may play an integral role in pollination and future research
The KEGG enrichment analysis provided a wealth of biological relevance through the identification of 15 uniquely enriched pathways. This provided a meaningful framework for the specific biological activities that are involved in pollination-induced corolla senescence in petunias. These enriched KEGG pathways still only represent a minority of the differentially expressed genes. The remaining differentially expressed genes likely also hold important biological relevance, particularly for those genes and pathways that might be unique to petunia. This analysis demonstrates the power of next generation sequencing to capture a global overview of thousands of gene expression changes in a single experiment. Understanding the relevance of these genes is currently the rate-limiting step. This technology provides fundamental data upon which more hypothesis-driven experiments can be organized and conducted.
Pollination induces many hormonal, physiological, and molecular changes in petunia corollas that lead to senescence. Gene expression in the corollas was already altered by 12 hap, and 618 differentially expressed genes were identified. These changes occurred well before fertilization, ethylene biosynthesis from the corolla, and petal wilting. At 18 hap, large changes in gene expression were measured and an additional 2,137 genes were identified as being differentially expressed. The enriched GO term analysis suggested that at 12 hap, the corollas were responding to auxin and ACC, which are found in high abundance in pollen. KEGG enrichment identified 15 pathways, 11 of which were involved in metabolic pathways and autophagy regulation. The sequence data from this experiment will make a valuable contribution to the genomic resources available in petunia and will enable researchers to identify the genes involved in regulating flower senescence. While senescence studies have demonstrated that the initiation and timely progression of senescence requires transcription, senescence is also controlled post-transcriptionally. Previous studies in petunia have shown that genes expression patterns do not always correlate with protein expression . Combined genomic, proteomic, and metabolomic approaches will be required to gain a comprehensive understanding of petal senescence.
The plants used in this study were Petunia × hybrida (Hook.) Vilm. `Mitchell Diploid' , a doubled haploid derived from a P. axillaris/P.hybrida `Rose of Heaven' hybrid . Seeds were originally obtained from Dr. David Clark (University of Florida). Petunia seeds were sown in plug trays on soil-less media (Pro-mix BX, Premier Horticulture, Quebec, Canada) and grown under fluorescent, full-spectrum lights. After four weeks, plants were transplanted into 16-cm pots and moved to a greenhouse with temperatures set at 24/16°C (day/night) and a 13 h photoperiod. Supplemental lighting was supplied by high pressure sodium and metal halide lights (GLX/GLS e-systems GROW lights, PARSource, Petaluma, CA, USA) to maintain light levels above 300 μmol M-2 s-1.
Pollen tube growth measurements
To prevent self-pollination, anthers were removed 1 d before flower opening. On the following day, emasculated flowers were pollinated between 8:00 and 8:30 AM. Four styles were collected at 0, 12, 18, 24, and 36 hours after pollination (hap) and submerged in a 3:1 ratio of ethanol and acetic acid to fix the tissue overnight. They were then rinsed with 1 M potassium phosphate buffer (pH 7.0) followed by submersion in 1 N sodium hydroxide for 24 h. Finally, the styles were triple rinsed in sterile dH2O and stained with 0.1% aniline blue overnight. Styles were fixed on glass slides and visualized under an inverted epifluorescence Leica DM IRB microscope (Wetzlar, Germany) equipped with a Q Imaging Retiga 2000 cooled digital camera (Burnaby, BC, Canada). The lengths of the pollen tubes were measured using ImageJ .
Three biological replicates of two pollinated and unpollinated flowers were collected and photographed at 0, 12, 18, 24, 36 and 48 h. The corollas and styles from those flowers were collected and sealed in 22 ML and 7 ML glass vials, respectively. After a 30 Minute incubation period, 1 ML of the headspace was withdrawn from each vial through a rubber septum in the lid. The samples were injected into a gas chromatograph equipped with a flame ionization detector and separated on a stainless steel column packed with HayeSep R (Varian 3800, Agilent, Santa Clara, CA, USA). The flow rate of the carrier gas (He) was 30 ML Min-1.
RNA extraction and library preparation
Flowers were emasculated as described previously. Four unpollinated and four pollinated flowers were harvested at 12, 18, and 24 h after flower opening. Three biological replicates were collected for each treatment-time combination. Corollas were detached from the receptacle (which removed the ovary and style), filaments were removed, and corollas were rinsed with sterile dH2O to remove any pollen contamination. Total RNA was extracted from the corollas using Trizol reagent (Invitrogen, Carlsbad, CA, USA) followed by an additional purification step using mini spin columns (Qiagen, Valencia, CA, USA). The quality of the RNA was determined using an Agilent 2100 Bioanalyzer RNA 6000 Nano kit (Agilent, Santa Clara, CA, USA) and it was quantified using a Qubit 2.0 fluorometer RNA Assay Kit (Invitrogen Inc. Carlsbad, CA, USA). A total of 5 μg of RNA was used to create each strand-specific RNA-seq library. Eighteen libraries (3 time points × 2 treatments × 3 biological replications) with six unique barcodes were prepared following the protocol of Zhong et al. , including the modification using the universal adaptor system. The libraries were sequenced at the Genomics Resources Core Facility at Weill Cornell Medical College (New York, NY, USA). Paired-end sequences (101 bp) were generated using three lanes of an Illumina HiSeq2000 flow cell (Ilumina Inc. San Diego, CA, USA). Individual biological replicates for each library were run in separate lanes on the flow cell.
Sequence quality assessment and de novo assembly
Sequence qualities were assessed before and after trimming using FastQC version 0.10.1 (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). Reads with a Phred quality score less than 20, and sequences shorter than 40 bp, were removed using trim-fastq.pl version 1.2.2 . This resulted in two files that contained proper paired-end sequences and one file that contained sequences that lost the mate due to the preprocessing. Adaptors, barcodes, polyA, and polyT ends were trimmed using cutadapt version 1.2.1 . After trimming, paired-end sequences were normalized to a maximum depth of 1,500 and assembled using Trinity r2012-10-05 . To create an EST database for further analysis, the resulting contigs were screened for putative open reading frames (ORF) using the TransDecoder utility from Trinity. Additionally, contigs that had both an ortholog hit ratio  of more than 80% to the Solanum lycopersicum ITAG2.3 protein database (using BLASTx) and a unique component (comp#) and subcomponent (c#) were added to the EST library. Finally, CD-HIT-EST  was used to remove contigs that had 90% or greater sequence identity to each other.
EST library annotation
The EST library was annotated using Blast2GO version 2.7.0 . The translated Basic Local Alignment Search Tool (BLASTx)  was used to obtain top hits from the SwissProt database  for each contig using a minimum E-value threshold of 1.0 × 10-3. The remaining contigs with no BLASTx hits were aligned against the non-redundant (NR) database from National Center for Biotechnology Information (NCBI). Following the BLASTx step, finalized annotations for each gene were filtered with an E-value of 1.0 × 10-6, gene ontology (GO) terms were added, and conserved domains were identified using the InterPro Scan tool  for each contig. To obtain A. thaliana-specific annotations, the Arabidopsis proteome database was downloaded from UniProt and BLASTx was performed locally.
Expression analysis from read mapping
Burrows-Wheeler Aligner (BWA) version 0.7.5a-r405  was used to align the unprocessed Illumina reads to the EST library using the default alignment stringency. Paired-end and single reads that resulted from the pre-processing step as mentioned above were used to calculate the expression profile of each contig within a library. Sam2counts.py (https://github.com/vsbuffalo/sam2counts/blob/master/sam2counts.py) generated count tables of the reads that aligned to the EST library. Only uniquely-mapped reads were used for differential gene expression analysis. The R package DESeq2 version 1.4.1  was used to determine the significant differentially expressed genes. In this package, principle component analysis (PCA) was used to screen for outliers among the libraries. A base mean threshold of ten was set to eliminate contigs with few counts, since contigs with very low reads typically have inaccurate expression patterns due to sampling error. Comparisons of all pollinated and unpollinated corollas (P/U), 12-h pollinated with 12-h unpollinated (12-P/U), 18-h pollinated and unpollinated (18-P/U), and 24-h pollinated and unpollinated (24-P/U) were made. An adjusted p-value (using the Benjamini & Hockberg adjustment) of 0.05 was used as the statistical cutoff for differentially expressed genes. A Venn diagram was used to visualize overlapping genes between comparisons .
Quantitative PCR validation of gene expression patterns
The expression patterns of five genes (comp31514_c0_seq2, comp39985_c0_seq4, comp18014_c0_seq1, comp40361_c0_seq2, and comp47181_c0_seq6) were analyzed using quantitative real-time PCR (qPCR). RNA from four biological replicates of each treatment and time point were included in the qPCR analysis. cDNA was synthesized from 2 μg total RNA using Omniscript RT Kit (Qiagen, Valencia, CA). Primers were designed to amplify the specific transcripts using IDT Primer Quest (Additional file 7). Quantitative PCR was performed in a 20 μl reaction volume using iQ SYBR Green Supermix (Bio-Rad, Hercules, CA) with 1 μl cDNA as template as described previously . Each reaction was performed in triplicate. Amplification specificity was determined by melt curve analysis. Amplification efficiencies of the target genes and reference genes were similar. Fold change for each target gene, normalized to PhACTIN, was calculated relative to expression in the U12 sample using the 2-ΔΔ Cq method.
Weighted gene correlation network analysis (WGCNA)
The R package WGCNA version 1.36 , was used to identify modules within the data set and to create dendrograms and heatmaps. A soft threshold value, power of 16, was used to transform the adjacency matrix to meet the scale-free topology criteria for optimal clustering. The libraries were clustered to identify outlier libraries using an average linkage hierarchical cluster tree based on Euclidean distance. Modules were grouped using a stringency threshold of 0.75. The code for the WGCNA analysis is available at the GetHub repository (https://github.com/wijerasa/WGCNA_Analysis). The pollination-specific modules were identified as Module I (red), Module II (cyan), and Module III (grey60) (Figure 5 and Additional file 3).
GO and KEGG enrichment
An enriched GO term analysis was conducted using a Fisher's Exact Test on the differentially expressed genes in Blast2GO. This test includes a correction for multiple testing  to reduce the false discovery rate (FDR). GO terms with a Term Filter Value of above 0.05 were excluded. TAIR codes for the EST library were obtained from a BLASTx that was restricted to A. thaliana. DESeq2 and WGCNA module contigs were mapped directly in KEGG mapper (http://www.genome.jp/kegg/mapper.html). The hypergeometric function in R was used to test for enriched pathways, and P-values were adjusted using an FDR correction.
Protein-protein interactions visualized in STRING
To visualize the plant hormone protein interactions, TAIR codes for the P/U genes were uploaded into STRING to identify the genes belonging to the Plant hormone signal transduction KEGG pathway and their nearest interacting partner (stringency of interactions set at high confidence level of 0.70) . The genes and their interacting partners were then uploaded into STRING and a network image was generated. The confidence view, which displays edges as blue lines, was selected and the image was exported. Colors of the circles were altered in Photoshop CS6 (Adobe, San Jose, CA, USA).
Transcription factor analysis
Transcription factors within the 4,746 unique DESeq2 differentially expressed genes were identified by matching TAIR codes to the A. thaliana transcription factor database (Plant TFDB v3.0 ).
Availability of supporting data
RNA-seq data were deposited with the Sequence Read Archive (SRA) database at NCBI (BioProject ID: PRJNA259884). Code for the WGCNA analysis can be accessed at the GitHub repository (https://github.com/wijerasa/WGCNA_Analysis).
SB participated in experimental design, collected tissue samples, participated in library construction and data analysis, and drafted the manuscript. SW participated in all bioinformatics analyses. AW participated in experimental design and bioinformatics analyses. LC conducted the qPCR validation experiments and edited the manuscript. TM participated in experimental design and data analyses. MJ participated in experimental design, data interpretation, manuscript writing and editing. All authors read and approved the final manuscript.
Salaries and research support were provided by State and Federal funds appropriated to the Ohio Agricultural Research and Development Center, The Ohio State University (Journal Article Number HCS 14-09). Research was also funded by the D. C. Kiplinger Endowment, The American Floral Endowment Gus Poesch Fund, and SEEDS: The OARDC Research Enhancement Competitive Grants Program. We thank Jason Van Houten and Esther van der Knaap for help with the library construction and experimental design.
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